phd candidate in spatial analysis and urban modelling

PhD candidate in Spatial Analysis and Urban Modelling

University of luxembourg.

The University of Luxembourg is an international research university with a distinctly multilingual and interdisciplinary character. The University was founded in 2003 and counts more than 6,700 students and more than 2,000 employees from around the world. The University's faculties and interdisciplinary centres focus on research in the areas of Computer Science and ICT Security, Materials Science, European and International Law, Finance and Financial Innovation, Education, Contemporary and Digital History. In addition, the University focuses on cross-disciplinary research in the areas of Data Modelling and Simulation as well as Health and System Biomedicine. Times Higher Education ranks the University of Luxembourg #3 worldwide for its “international outlook,” #20 in the Young University Ranking 2021 and among the top 250 universities worldwide.

The Faculty of Humanities, Education and Social Sciences (FHSE) brings together expertise from the humanities, linguistics, cognitive sciences, social and educational sciences. People from across 20 disciplines are working within the Faculty. Along with the disciplinary approach a very ambitious interdisciplinary research culture has been developed.

The faculty's research and teaching focuses on social, economic, political and educational issues with the common goal of contributing to an inclusive, open and resourceful society. The FHSE offers six Bachelor and twenty Master degrees and a doctoral school providing students with the necessary knowledge and high-qualified skills to succeed in their future career.

The Department of Geography, Spatial Planning and Architecture (DGEO) is composed of around 50 staff members and researchers from various disciplines. They work together to examine spatial development processes on a local, regional and international level. Further information at: https: // wwwen.uni.lu/recherche/fhse/dgeo

The University of Luxembourg invites application for a PhD candidate in Spatial Analysis and Urban Modelling within its Department of Geography and Spatial Planning (DGEO), Faculty of Humanities, Social Science and Education (FHSE).

The candidate will prepare a doctoral thesis in Geography under the supervision of Prof. Geoffrey Caruso.

The research goals are open but to be developed in order to contribute an original generic understanding of urban processes and patterns at the scale of several urban regions or through formalised theoretical models, i.e. not a unique case study. The exact topic can relate to land use patterns and change especially the interaction between the natural/green and the built environment, to the climate or environmental impacts of urban structures (pollution, noise, carbon balance), to linking socio-demographic patterns related to environmental externalities (segregation, justice) or to the production of the built space itself (housing in particular) and land markets.

The candidate will employ advanced spatial analysis and modelling techniques such as a combination of spatial econometrics and high-resolution image or vector data analysis over a large set of cities, and/or spatial simulation techniques (agent-based) across applied heterogeneous environments or theoretical, fully controlled, environments. Conceptual frameworks will typically relate to formalised theories in urban geography, urban economics or urban scaling.

Applications will also be considered in the field of urban experimentation in the case the candidate can demonstrate a very strong background and experience with creating virtual reality environments or immersive videos and a capacity to develop online tools to scale up experimentation beyond a single lab.

In addition to preparing a doctoral thesis, the candidate will also contribute teaching with R, including basic statistical analysis and econometrics and the use of spatial packages (sf, terra), and will contribute to tutoring Master students.

• Master in geographical science, planning, economics or social science, environmental science, mathematics, physics, or computational science.
• Demonstrate some experience with geographic thinking and proficiency with mathematical, statistical, or computational tools.
• Mathematical and computer literacy (programming, parallel computing, prior simulation work) are important.
• Experience with open-source GIS and/or R is particularly valued.
• Interest for interdisciplinary work
• Curiosity and open mindedness are fundamental.
• Good command of English
• Contract Type: Fixed Term Contract 36 Month
• Employee and student status
• Work Hours: Full Time 40.0 Hours per Week
• Location: Belval
• Internal Title: Doctoral Researcher
• Job Reference: UOL05918

The yearly gross salary for every PhD at the UL is EUR 39953 (full time)

Applications should be submitted online and include:

• Curriculum Vitae
• A cover letter including a motivation for the job (1 page max) and a project draft (around 2 pages)
• Any publications to date
• A reference letter from a recent supervisor or at least two reference names

We ensure a full consideration for applications received by 17th August 2023.

Early application is highly encouraged, as the applications will be processed upon reception. Please apply formally through the HR system. Applications by email will not be considered.

The University of Luxembourg embraces inclusion and diversity as key values. We are fully committed to removing any discriminatory barrier related to gender, and not only, in recruitment and career progression of our staff.

• Multilingual and international character . Modern institution with a personal atmosphere. Staff coming from 90 countries. Member of the “University of the Greater Region” (UniGR).
• A modern and dynamic university. High-quality equipment. Close ties to the business world and to the Luxembourg labour market. A unique urban site with excellent infrastructure.

A partner for society and industry . Cooperation with European institutions, innovative companies, the Financial Centre and with numerous non-academic partners such as ministries, local governments, associations, NGOs …

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Urban Systems, Ph.D.

The NYU Doctoral program in Urban Systems offers an interdisciplinary learning and research environment designed to meet the needs of students pursuing careers in academia, research organizations, local and national government and public service agencies. This Ph.D. program expands upon the unique legacy of decades of collaboration in education and research, development and training between NYU faculty, city agencies, and industry. The program is administered by NYU Tandon in partnership with other NYU schools including: the Stern School of Business ,  Langone Health ,  Wagner Graduate School of Public Service , and NYU research centers including the  Center for Urban Science and Progress and the Center for Connected Mobility  C2SMART . 

This program is aligned with the vision and commitment of the university to work within the ‘city as a lab’ to accelerate the field deployment of innovative solutions to emerging urban needs. Areas of study include sustainability and climate action, infrastructure and resilience, public health and equity. This interdisciplinary laboratory of urban research and innovation brings together expertise and the research excellence of NYU faculty in New York as well as our global campuses in Abu Dhabi and Shanghai, and study abroad sites in London, Paris, Berlin, Madrid, Florence, and Prague. Drawing from the expertise of the Urban Faculty across the university, we have built a unique and competitive interdisciplinary educational environment based on the following disciplinary pillars:

• Data Science & Informatics
• Communication and Information Science
• Social Studies, Health, and Policy
• Systems Engineering
• Economics, Finance, and Planning                                 

The program is available to students with diverse educational backgrounds pursuing their studies across disciplines, including Engineering, Environmental Science, Architecture, Urban Planning, Computing, Data Science, Systems Science, Economics, Finance, Public Health, Public Policy, and Law. Development of skills in mathematics, statistics, and programming is included in the course of study, depending on the need associated with the student’s dissertation topic.

View Urban Faculty

• Master or bachelor degree from an accredited program in physical and mathematical sciences, social sciences, or engineering (other fields upon approval of program administrator)
• Minimum master and bachelor degree GPA of 3.5/4.0, and 3.0/4.0, respectively.
• Submission of GRE and English language proficiency test scores

An applicant who has not yet earned a master’s degree may be directly admitted into the Ph.D. program with the written approval of the program director. Applicants with a master’s degree in any discipline other than Master of Science may be required to have prerequisites in the subjects listed below:

• Calculus with analytic geometry,
• Statistics and Probability,
• Introduction to computer programming.

In addition to these degree requirements and the NYU Tandon general admission requirements , acceptance to the program will depend on (1) academic excellence, (2) research interests congruent with areas of urban scholarship and faculty research at NYU including the global campuses, and (3) positive recommendations (e.g., from former employers or research advisors).

Note: Application and/or admission to the program does not require prior identification of a research advisor. Acceptance to the program is not automatically accompanied with financial support.

The Ph.D. curriculum includes 54 credits of graduate coursework beyond the Bachelor’s degree and 21 credits of dissertation research, totaling 75 credits. The student may use 30 credits from an approved Master’s degree completed within 10 years of admission to the program, as well as 18 additional graduate credits subject to approval of the program director. The program also includes a qualifying exam, a research proposal exam, and the dissertation defense.

To earn a doctoral degree in Urban Systems, the candidate must meet the following requirements:

54 credits of graduate coursework beyond the Bachelor’s degree (not including the Ph.D. dissertation), with cumulative average of 3.5 or better on a 4.0 scale. Up to 6 credits of the 54 credits may be satisfied by individual guided studies, readings, and projects.

Successful completion of the qualifying examination. The qualifying examination has a written section and an oral section. The written exam is based on the program’s three core courses, while the oral exam is designed to judge the students' critical thinking.

The qualifying exam is administered only once, regularly scheduled at the end of spring semester of the first year of the program

Passing of the dissertation proposal exam. This exam should be administered on or before the spring midterm of year two of the program, and signed off by the dissertation/guidance committee and submitted for the record within a week of the exam. Meeting this deadline is a requirement of the program.

Completion and successful defense of 21-credits of dissertation. Dissertations must consist of original research that advances the state of the art in the research subject area and should result in the publication of at least three papers in a peer-reviewed journal (2 published by time of defense, another under review). It is expected that the student is the first author; it is also encouraged to engage the dissertation guidance committee members to the extent that they may be justified as co-authors.

Submission of the Ph.D. dissertation following the University’s  Guidelines for Dissertations . It is encouraged that the student’s publication be planned in advance such that they may be used as the backbone of the dissertation content.

The program includes fifty-four (54) credits of graduate coursework beyond the Bachelor’s degree and twenty-one (21) credits of dissertation research. A total of 15 of the 54 credits are based on required courses, while the remainder are electives. The required courses include three Core courses (9 credits), an Urban Systems Studio (1.5 credit), and a Community Impact Project (1.5 credits) which takes place as an immersion, outside of the classroom/campus (see section on required courses). The program’s elective coursework is designed to be flexible in order to support the student’s research interests, educational background and career goals, offering an integrated education program that blends urban domains with supporting informatics content. Below are details on dissertation credits and minimum credit requirements.

a. CE-GY 998X: Dissertation level research, up to 6 credits can be taken before passing the qualifying exam.

b. CE-GY 999X: Dissertation level research. A minimum of 15 credits of CE-GY 999X must be taken after passing the qualifying examination.

c. Registration for CE-GY 998X is optional before registering for CE-GY 999x.

d. After the qualifying exam, registration for a minimum of 3 credits per term of dissertation work is required, and must be continuous (excluding summer semesters), unless a formal leave of absence is requested and approved.

e. Ph.D. candidates who have completed the 75-credit requirement, including all dissertation credits, will be permitted to maintain their student status by registering for Maintenance of Studies (MOS) every fall, spring and summer, up to the program completion (limited to total of 5 years from start of the program).

Core Courses (9 credits)

Urban Infrastructure Systems; CE-GY 7843

Monitoring Cities; CE-GY 6053

Introduction to Applied Data Science; CUSP-GX 7013

Other required courses (6 credits)

Urban Systems Studio CE-GY; 7815

Urban Systems Immersion for Social Good; CE-GY 7915 (Alternative: CP-GY 9941)

Writing and Communication for Engineers and Scientists; GA-GY 9993

Below are only selected options, other courses are permissible; please consult the program director for feedback.

Urban Systems:

Building Information Modeling: (BIM) CE-GY 8383

Disaster Risk Analysis: CE-GY 7993

Selected Topic - Climate and Energy; CE-GY 7713 / TECH-GB 2384

Urban Ecology; ENYC-GE 2070

Water, Waste and Urban Environment; FOOD-GE 2036

Data-driven Mobility Modeling and Simulation; TR-GY 7353

Forecasting Urban Travel Demand; TR-GY 6113 / CE-GY 804

Statistics and Data Science:

Artificial Intelligence I; CS-GY 6613

Algorithmic Machine Learning and Data Science; CS-GY 6763

Introduction to Data Science; DS-GA 1001

Probability and Statistics for Data Science; DS-GA 1002

Programming for Data Science; DS-GA 1007

System Optimization Methods; ECE-GY 6233

Statistics for Data Analysts; MG-GY 6193

Robotic Perception; ROB-GY 6203

Regression and Multivariate Data Analysis; STAT-GB 2301

Practicum in Applied Statistics: Applied Probability; APSTA-GE 2351

Urban Informatics:

Machine Learning for Cities; CUSP-GX 5003

Big Data Management and Analysis; CUSP-GX 6002

Applied Data Science; CUSP-GX 6001

Urban Spatial Analytics; CUSP-GX 7002

Big Data Analytics for Public Policy; CUSP-GX 2505/PADM-GP 2505

Urban Decision Models; CUSP-GX 7004

Large-scale Visual Analytics; CS-GY 6323

Geographic Information Systems and Analysis; URPL-GP 2618

Advanced GIS: Interactive Web Mapping and Spatial Data Visualization; URPL-GP 4650

Finance, Governance, Society:

Financing Urban Government; PADM-GP 4443

Project Finance and Investment; FINC-GB 3186

Citizenship Culture: Art, Urban Governance; ELEC-GG 2840

Adapting the Physical City; URPL-GP 2612

Planning for Emergencies and Disasters; URPL-GP 2645

Environmental Infrastructure for Sustainable Cities; URPL-GP 2625

History and Theory of Planning; URPL-GP 2660

Research Methods; PHD-GP 5902

Qualifying Exam

The qualifying exam will be administered shortly after the completion of the second semester of the programs first year. The exam will be in two parts, written and oral. The written portion will be based on the program core courses, while the oral portion is meant to judge student’s skills in critical thinking and to assess the student’s ability to carry out independent research. This exam has a pass/fail grade, and may not be retaken.

Dissertation

Students declare a dissertation/research advisor during the fall semester of year two, shortly after passing the Ph.D. qualifying exam. The student and the advisor will subsequently select a dissertation guidance committee by start of the spring semester of the same academic year. The guidance committee will be composed of the research advisor and three other faculty members including one external advisor (from another institution or from an NYU school other than the primary advisor). The function of the dissertation guidance committee is to monitor and support the student’s progress on an ongoing basis, starting from the dissertation proposal planning. Declaration of the primary advisor and the dissertation committee is done by submitting the designated forms according to the timeline described above.

The research proposal examination, overseen by the dissertation guidance committee, must be passed by the spring midterm of program’s second year. The objective of this exam is to ensure the student has chosen an appropriate Ph.D. research topic and that the research plan is rigorous with a high likelihood of success. The results of each student’s proposal examination must be submitted by the primary advisor no later than one week following the exam, along with the proposed scope of work, the student and the dissertation committee copied. A memo on passing of this exam and the committee composition will be documented at the NYU Tandon graduate affairs office. Failing to pass this exam in a timely fashion may result in the student being placed under probation.

At end of each term, the student submits a progress report outlining the term’s academic progress. Subsequent to passing the proposal examination, the progress report should be signed off by the dissertation guidance committee prior to submission. 

With the dissertation research advisor and the dissertation guidance committee’s approval, the student will submit a written dissertation, in compliance with all requirements of NYU Tandon. It is expected that the student has published at least three articles in a reputable peer reviewed journal (two accepted and one under review). The dissertation must be provided to the guidance committee members who also serve as the examination committee, at least two weeks prior to the defense. The defense includes a public presentation by the student and with questions from the audience. Following the public presentation, the student meets privately with the committee members for comments and/or further questions. The committee makes a decision that is then transmitted, in writing, to the program director and there from to the registrar.

Registration and Graduation Information

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Program Director

Masoud Ghandehari

PhD Research

The Urbanism Research Programme provides a lively stimulating environment for PhD research. Candidates also participate in the TU Delft Graduate School which provides doctoral education in research skills and personal and career development. PhD work forms a major part of Urbanism research. The research programme is organised in research themes, and all proposals must demonstrate how they will contribute to the themes. The overall Urbanism Research Programme can be viewed here . PhD candidates participate in a four-year programme. In the first year, the candidate prepares a comprehensive research proposal and a long paper, which is assessed by a professorial panel following a public presentation. PhD opportunities may arise for specific projects funded by the Dutch National Research Organisation (NWO) or other funding bodies. In these cases the PhD candidate will be employed by the TU Delft to undertake a specific project. These opportunities will be advertised at ‘ Working at TU Delft’ . However, most PhD candidates find their own funding. In this case TU Delft offers supervision and an academic environment and the candidate performs research in the university on a daily basis based on hospitality to enable him/her to write a thesis within four years. The PhD candidate may be granted time or financing by a) a foreign university; b) personal grant-issuing organisations such as national research councils, or c) his/her employer. If the Urbanism Programme decides to accept an applicant on a preliminary basis who has not already secured funding, supervisors are able to assist with the preparation of applications for grant funding, but the responsibility for finding funding rests with the applicant. Applicants will need to explain on their application where they expect to gain funding. We do not accept applicants who are self-financed from personal funds.

Call for applications

New topics will be posted periodically on this website.

Topic 1: Cross-national comparison of territorial governance, spatial planning and regional design.

The Urbanism Research Programme conducts research on the theme of international comparative planning and regional design. We aim to take forward comparative research that builds knowledge of the key factors that determine the performance of 

regional and urban planning and design, and which supports more responsive and progressive planning that can influence spatial development in more sustainable directions. We are keen to develop our comparative knowledge of spatial planning in China in collaboration with colleagues at South China University of Technology through the joint Centre on Urban Systems and Environment. The theme will continue to build on previous work, for example:

Nadin, V. and Stead, D. (2008) European spatial planning systems, social models and learning, DISP 172, January, 35-47;

Nadin, V. (2013), and International comparative planning methodology: introduction to the theme, Planning Practice and Research, 27(1): 1-5. 

Potential supervisors include Vincent Nadin, Dominic Stead, Wil Zonneveld and Marcin Dabrowski.

Topic 2: The meaning and role of ‘spatial planning’ and ‘territorial governance’ under difficult and adverse conditions

Many recent spatial planning reforms across the world have led to shifts in planning regimes: often from statutory plan-led to development-led approaches. In various countries regional design and the ‘art’ of making spatial representations and the imagination of spatial metaphors has emerged as a powerful tool in capacity- and consensus building in multi-actor settings. It is often used as a

 way of overcoming conflicting rationales and images of desired spatial development and spatial futures. In practice, regional design fulfils different roles in different situations, depending on the actor settings and the nature of the issues at hand. We would like to develop research that focus on the performance of regional design in various institutional settings in different countries and (urban) regions. This research will continue current research including:

Balz, V. & Zonneveld, W. (2014) Regional Design in the Context of Fragmented Territorial Governance: South Wing Studio, European Planning Studies, OnlineFirst doi 10.1080/09654313.2014.889662.

Potential supervisors include: Wil Zonneveld, Vincent Nadin, Dominic Stead

Topic 3: The performance of regional design in complex governance settings.

Many recent spatial planning reforms across the world have led to shifts in planning regimes: often from statutory plan-led to development-led approaches. In various countries regional design and the ‘art’ of making spatial representations and the imagination of spatial metaphors has emerged as a powerful tool in capacity- and consensus 

building in multi-actor settings. It is often used as a way of overcoming conflicting rationales and images of desired spatial development and spatial futures. In practice, regional design fulfils different roles in different situations, depending on the actor settings and the nature of the issues at hand. We would like to develop research that focus on the performance of regional design in various institutional settings in different countries and (urban) regions. This research will continue current research including:

Topic 4: The process of metropolisation in polycentric metropolitan regions.

Metropolisation is understood as the process through which a loose collection of proximally located cities starts to become more functionally, culturally and institutionally integrated. It can be assumed that in theory metropolisation enhances 

performance, and indeed this conviction underlies many European metropolitan development strategies. Yet little is known about how this potential is realised in practice, nor has the relationship between the level of metropolisation and performance of polycentric metropolitan areas been explored. Issues that can be addressed include the development of regional identity alongside urban identities, overcoming governmental fragmentation in polycentric metropolitan regions and the development of functional relationships within such regions. Also, from an economics perspective, the concepts of ‘borrowed size’ and ‘agglomeration shadows’ within polycentric metropolitan regions deserve further exploration. This research builds for instance on:

Meijers, E., Hoogerbrugge, M & K. Hollander (2014) Twin Cities in the Process of Metropolisation, Urban Research & Practice, 7(1), 35-55

Burger, M., Meijers, E., Hoogerbrugge, M & J. Masip Tresserra (2014) Borrowed Size, Agglomeration Shadows and Cultural Amenities in North-West Europe – European Planning Studies; available online first DOI:  dx.doi.org/10.1080/09654313.2014.905002 .

Potential supervisors include: Evert Meijers, Wil Zonneveld. 

Topic 5: The relationship between the spatial and socio-economic performances of built environments.

Urbanism is concerned with the relationship between the spatial structure of the physical built environment and social and economic life. However, there is a great uncertainty about the effect of the 

physical world on society and how it varies according to the local cultures and planning laws or regulations. This hinders effective intervention though planning and urban design.

Our research aims to improve understandings of the variable performance of the physical arrangement of the built environment, the spatial structure of the transport networks at varying scales from neighbourhood to regional levels, in terms of economic vitality, social cohesion and environmental sustainability (Van Nes, Akkelies, 2011, “Measuring spatial visibility, adjacency, permeability and degrees of street life in urban areas. The one- and two-dimensional isovists analyses in Space Syntax”, in: S. Nijhuis, R. van Lammeren, F. van der Hoeven (eds) ”Exploring the visual landscape; Advances in Landscape physiognomic Research in the Netherlands”, IOS press, Amsterdam, Ye, Yu, and Van Nes, Akkelies 2014, “Quantitative tools in urban morphology: Combining space syntax, spacematrix and mixed-use index in a GIS framework” in: Journal of Urban Morphology (forthcoming); Van Nes, Akkelies and Lopez, Manuel, 2010, “Macro and micro scale spatial variables and the distribution of residential burglaries and theft from cars: an investigation of space and crime in the Dutch cities of Alkmaar and Gouda”, in: Journal of Space Syntax, no 2.). We examine the extent to which planning and other urban interventions take account of knowledge of these relationships and with what effects.

Future research will explore the relationship between physical layout, building functions and social life, comparing historic districts with contemporary developments in different cultural (national) contexts. We are particularly interested in the effects of the spatial morphology of built environments on society in terms of the incidence of crime, vitality of shopping areas, livability of housing areas and land values. This requires developing or improving existing analyses tools, to test combination of existing tools, and to deal with the possibilities of improved computer capacities and software development useful for handling research on built environments. Other methods for analyzing the spatial properties of built environments needs improvement, from a phenomenological as well as from a morphological and topological/configurationally approach.

Potential supervisors include Akkelies van Nes.

Topic 6: Planning and Designing for Development: Spatial Strategies for Urban Development in Rapidly Growing Economies.

The Urbanism programme undertakes critical analyses of urbanization processes in the developing world, including the comparative study of planning frameworks, tools and cultures, governance structures and the dynamics of spatial form, as well as issues arising from the interactions between planned and unplanned, formal and informal and legal and extra-legal urban development. We examine the interactions between spatial planning, political structures, social struggles and the built environment. This research cluster relies on expertise of several staff members and builds on previous research and education programs carried out in the Department of Urbanism of TU Delft. Prospective PhD candidates must be able to conduct research aimed to inform the preparation of plans and strategies that tackle issues associated with international urban development. Research in this area must also underpin education offered at Master level in the Department of Urbanism. Our recent work includes:

Ballegoijen, J. V. & Rocco, R. 2013. The ideologies of informality: Informal urbanization in the architectural and planning discourses. Third World Quarterly, 34, 1794-1810;

Fernández-Maldonado, A.M. (2014) Incremental housing in Peru and the role of the social housing sector, in: van Lindert, P., Smets, P. & Bredenoord, J. (eds) Affordable Housing in the Urban Global South, London and New York: Earthscan;

Fernández-Maldonado, A. M. (2011) Trends toward Urbanization in the Americas, in: H. M. Tarver (ed.), World History Encyclopedia, Era 8: Crisis and Achievement, 1900-1945, Santa Barbara, CA: ABC-CLIO;

Pojani, D. 2013. “From Squatter Settlement to Suburb: The Transformation of Bathore, Albania.” Housing Studies 28 (6): 805-821. 

Potential supervisors include: Ana María Fernández Maldonado, Roberto Rocco.

Topic 7: Doing a PhD at the research group 3D GeoInformation

The 3D Geoinformation research theme studies the technologies underpinning geographical information systems (GIS), and aims at designing, developing, and implementing better systems to model 3D cities, buildings and landscapes. The research focuses on spatial data, and specifically the modelling, structuring, maintenance, analysis and dissemination of large amounts of (3D) geoinformation about urban areas.   

3D geoinformation can make a key contribution to the design and planning of interventions in the urban environment. Thus, serving the needs of practice is extremely important and we develop solutions in close collaboration with users such as experts in noise, wind and emergency evacuation simulations.  We have a history of successful collaborations with the industry and the government: our research has led to open source software, standards, and patents for the management of 3D geographic information.

Example research questions 

• 3D geoinformation infrastructure: how to collect, model, maintain or disseminate 3D information about urban and rural areas and use it for many different applications?
• How to reconstruct semantically rich 3D city models?
• What data structures and algorithms are needed for 3D modelling?
• How to deal with different levels of detail of 3D geoinformation?
• How to connect information from the design&construction world (BIM) with geo-information applications? 

Interested in doing PhD in our group?

If you are interested doing a PhD in our group there are two possibilities:

(1) Apply for a paid position Sometimes paid positions become available for a postdoc or PhD candidate, when a research project has been granted funding. Whenever we have such a position, you can find it at our home page: https://3d.bk.tudelft.nl

(2) Open application with own funding If you have found own financial support, you can apply for a PhD position in our group to either work on your project full-time as a contract PhD candidate or keep your current job and/or stay where you live and work on your project part-time as an external PhD candidate. Please contact the chair of the group for more information and the conditions for such a type of PhD research: Prof dr Jantien Stoter, [email protected]

Process of consideration of applications

• Applications must be made through the Graduate School AB+E. See the application & selection process on the Graduate School website .
• Please state clearly at the top of your proposal which topic your proposal addresses.
• A panel will assess the applications and create a shortlist. The criteria are: a) the scientific quality of the research proposal; b) the societal relevance of the research proposal; c) the quality of the curriculum vitae including academic qualifications; d) other considerations like evidence of writing skills, funding and English language competence.
• The panel will pay particular attention to the ability of the candidate to develop knowledge in the relevant topic.
• Shortlisted applicants may be interviewed, if necessary by video link or telephone.
• Subject to funding, the successful applicants will be given hospitality for 4 years, during which they will undertake a formal progress review (a go,no-go). Following a successful review the candidate will register with the University for the PhD. 

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Our innovative Ph.D. program brings together researchers from across NC State University to train a new generation of interdisciplinary data scientists skilled in developing novel understanding of spatial phenomena and in applying new knowledge to grand challenges.

This one-of-a-kind degree focuses on integrative thinking and experiential learning:

• Collaborative, cross-disciplinary teamwork  unites students and faculty from many research fields
• Guaranteed funding  for four years includes a competitive minimum stipend of $30,000, health insurance, and tuition
• Professional seminar  supports student success through training in science communication, proposal writing and geospatial data ethics
• Travel funding is available for students to attend scientific conferences
• Program values include prioritizing student mental health and work/life balance, open data, environmental and social justice, and a commitment to collaboration, community and equity

If your research goals intersect geospatial problem-solving from any number of fields, you will find your fit here.  Our  Faculty Fellows  advise students interested in a range of disciplines––from design, to social and behavioral sciences, natural resources and the environment, computer science, engineering and more––and approach their work in a range of  geospatial research areas . Students with strong backgrounds in quantitative methods in geography, data science, remote sensing and earth sciences are strongly encouraged to apply. We are especially committed to increasing the representation of students that have been historically excluded from participation in U.S. higher education.

Find recent publications by our students and faculty through NC State’s  Libraries Citation Index and learn more about the achievements of our students and alumni.

Program news

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Center Ph.D. Students Volunteer at STEM Resource Fair for K-12 Students with Disabilities

Titilayo Tajudeen, Emma Butzler, Pratikshya Regmi and Rebecca Composto taught students and their families about satellite imagery at the event hosted by NC State’s Science House.

March 26, 2024

5 Questions with Geospatial Software Engineer Vinicius Perin

Perin graduated in 2022 with a Ph.D. in geospatial analytics from our Center. He now works as a geospatial software engineer for Planet Labs in San Francisco, California.

February 15, 2024

Using Written Records – and Tweets – as a Roadmap for Plant Disease Spread

New research led by Geospatial Analytics Ph.D. student Ariel Saffer and co-authored by Faculty Fellow Laura Tateosian and MGIST alum Yi-Peng Yang shows how analyzing historic and modern texts can help researchers track and visualize plant diseases like late blight.

Apply for a Ph.D. in Geospatial Analytics

Ten fully funded Ph.D.  graduate assistantships  with $30,000 salary, benefits, and tuition waiver are available for Fall 2024 through the Center for Geospatial Analytics.

Applications for Fall 2024 admissions are now open. The application deadline is February 1, 2024 – all recommendations and test scores must be received by this date.

There are several opportunities for students to receive a stipend above the base rate of $30,000. These fellowships do not require an additional application:

• Goodnight Doctoral Fellowship. One to two incoming students with a track record of exceptional achievement in the sciences will earn an additional $10,000 per year + all student fees waived for four years
• University Graduate Fellowship. Five incoming students will receive an additional $4,000 in their first year
• Diversity Enhancement Fellowship. Two incoming students will receive an additional $2,000 in their first year
• Mansour Doctoral Fellowship. One incoming international student will be nominated to receive an additional $10,000 in their first year

Admission Requirements

Our most competitive applicants will have

• Significant quantitative research experience outside of the classroom, beyond basic data collection or data entry
• Computational/quantitative background, including a combination of the following coursework or demonstrated skills: statistics, advanced mathematics, quantitative research methods, R, Python
• Prior coursework, background and/or research interests in the area of geospatial analytics
• For international applicants: IBT TOEFL score ≥ 80 overall (18 in each section), IELTS score ≥ 6.5 on each section, Duolingo English ≥ 110. Scores are not required for citizens of  these countries  or who have completed at least one year of full time study at U.S. college or university

Supporting Documents

• Official NC State Graduate School  application.
• Unofficial transcripts  from all colleges/universities attended (official transcripts are only required if admitted to the program).
• Your academic and career goals as well as your motivation in pursuing a Ph.D.
• Research experiences and background/skills that would make you a successful Ph.D. student in geospatial analytics
• Relevant research interests
• Your specific interest in the Ph.D. in Geospatial Analytics at NC State
• 3 letters of recommendation.  Submit the names and contact information for your recommenders through the online application, and they will receive an email with instructions for submitting their letters online. Please select recommenders who can speak to your academic and/or research potential.
• Curriculum vitae/resume.
• Optional GRE scores. Taking the GRE is strongly recommended for international students who have not previously studied in the U.S.

If you have questions about the application process, please contact  Rachel Kasten , Graduate Services Coordinator ([email protected], 919-515-2800). Please note that there is a required application fee of $75 for domestic applicants and $85 for international applicants. McNair Scholars will have the application fee waived. This fee cannot be waived or reduced for international students.

More information for prospective international students can be  found here .

Degree Requirements

The Ph.D. program consists of

• 72 credit hours beyond the Bachelor’s degree .  The core required courses comprise 18 credit hours. The remaining 54 credit hours are devoted to an individually tailored selection of electives and research.
• an off-campus professional experience.  By the beginning of their third year in the program, students participate in an experiential learning activity within government (local, state, federal), industry, private and academic research institutions, or other organizations in the geospatial arena. Students consult with their advisors to identify specific opportunities that will enhance their doctoral program.
• a comprehensive exam.  Students will complete both written and oral exams by the end of their fifth semester in order to be admitted to candidacy.
• a   written dissertation  and  final dissertation oral defense  required to complete the degree.

Core Curriculum

The core curriculum includes the following courses; click course names to view descriptions. Students are required to take GIS 710 and any three additional core courses, as well as six elective credits:

GIS 710: Geospatial Analytics for Grand Challenges

Students examine why sustainable solutions to grand societal challenges need geospatial analytics. Emphasis is placed on the roles that location, spatial interaction and multi-scale processes play in scientific discovery and communication. Discussion of seminal and leading-edge approaches to problem-solving is motivated by grand challenges such as controlling the spread of emerging infectious disease, providing access to clean water and creating smart and connected cities. Students also engage in several written and oral presentation activities focused on data science communication skills and professionalization.

GIS 711: Geospatial Data Management

Applied experience in the architecture of geospatial data management, including open source options. The course introduces students to: (i) spatial and temporal data types (OGC specification, GPS and accelerometer matching), (ii) spatial predicates, (iii) spatial indices and (iv) spatial query processing. In addition, students will be exposed to modern spatial data management systems like NoSQL and graph databases, and data integration principles including protected health information (PHI/HIPAA).

GIS 712: Environmental Earth Observation and Remote Sensing

Advanced understanding of physical principles of remote sensing, image processing and applications from earth observations. Awareness of tradeoffs between earth observing sensors, platforms and analysis techniques will help prepare the students to critically assess remote sensing products and devise solutions to environmental problems. Students will be able to communicate the complexities of image analysis and will be better prepared to integrate earth observations into their areas of expertise. Topics include electromagnetic energy and radiative transfer; US and international orbital and suborbital data acquisition platforms; passive and active imaging and scanning sensors; spatial, spectral, radiometric, and temporal resolutions; geometric corrections and radiometric calibrations; preprocessing of digital remotely sensed data; advanced image analysis including enhancement, enhancement, classification, geophysical variable retrieval, error and sensitivity analysis; data fusion; data assimilation; and integration of remotely  sensed data with other data types in a geospatial modeling context.

GIS 713: Geospatial Data Mining and Analysis

Spatial data mining is the process of discovering interesting and previously unknown, but potentially useful, patterns from spatial and spatiotemporal data. However, explosive growth in the spatial and spatiotemporal data (~70% of all digital data), and the emergence of geosocial media and location sensing technologies has transformed the field in recent years. This course reviews the current state of the art in spatial, temporal and spatiotemporal data mining and looks at real-world applications ranging from geosocial networks to climate change impacts. Course introduces various spatial and temporal pattern families and teaches how to incorporate spatial relationships and constraints into data mining approaches like clustering, classification, anomalies and colocations.

GIS 714: Geospatial Computation and Simulation

Methods, algorithms and tools for geospatial modeling and predicting spatio-temporal dimensions of environmental systems. The course covers the physical, biological, and social processes that drive dynamics of landscape change. Deterministic, stochastic, and multi-agent simulations are explained, with emphasis on coupling empirical and process based models, techniques for model calibration and validation and sensitivity analysis. Applications to real-world problems are explored, such as modeling multi-scale flow and mass transport, spread of wildfire, biological invasions and urbanization.

GIS 715: Geovisualization

Principles of visualization design and scripting for geospatial visualization. This course provides a systematic framework of visualization design principles based on the human visual system and explores open-source geospatial data visualization tools. Topics include challenges and techniques for visualizing large multivariate dataset, spatio-temporal data and landscape changes over time. Students have the opportunity to work with broad range of visualization technologies, including frontiers in immersive visualization, tangible interaction with geospatial data and eye tracking.

Frequently Asked Questions

Below are some of the most frequently asked questions we have received about the Ph.D. program in Geospatial Analytics. If your questions are still not answered here, please feel free to contact us through the form below.

Can the program be completed online or part-time?

No, the Ph.D. in Geospatial Analytics is a traditional full-time on-campus program.

I am currently in a master’s degree program and will complete my degree in the spring. Can I still apply now to start the Ph.D. program in the fall?

Yes. We accept unofficial transcripts with your application. Official transcripts will be requested if you are admitted to the program.

Do I need to have been a geography major to apply?

No, we welcome applications from students with strong computational skills from diverse backgrounds, including computer science, data science, environmental science, ecology, engineering, and more.

Do I need a master’s degree to apply?

No, students may enroll without a master’s degree. Successful applicants, however, will have had previous academic research experience.

Do you offer application fee waivers?

Application fee waivers are offered only for domestic students who have participated in specific research programs (i.e. McNair Scholars).

Is financial assistance available?

Incoming doctoral students receive a tuition waiver, health insurance benefits, and a $30,000 stipend.

Do I need to secure an advisor before applying?

While you are encouraged to connect with faculty who share your interests prior to applying (the application will ask you to name a preferred advisor), students can be admitted on program funding without a specific advisor/position.

What kinds of projects might I work on?

Students in the Geospatial Analytics doctoral program work on a diverse range of data science frontiers intersecting multiple disciplines, with funding available from the Ph.D. program as well as from external grants secured by faculty. Assistantships are each fully funded for four years. Below are a sample of the opportunities that were available in previous years. For more details about each opportunity, and to learn about past projects, visit our Graduate Assistantships page .

• Landscape Connectivity Dynamics in Surface Water Networks — Join the Geospatial Analysis for Environmental Change Lab to investigate climate and land-use change effects on landscape connectivity dynamics.
• Seasonality from Space — Join the Spatial Ecosystem Analytics Lab on a NASA-funded project investigating satellite data fusion and time series analysis.
• Winter Weather — Join the Environment Analytics group to study the complex interactions within snow storms and wintery mix storms.
• Modeling Forest and Water Resources under Changing Conditions — Join the Watershed Ecology lab group and combine various data sources to create projections of future landscape conditions.
• Modeling Agricultural and Water Resource Dynamics — Join the Biosystems Analytics Lab to study the effects of global and local change on fresh and estuarine water quality, land-sea connectivity and agroecosystem productivity.
• Surface Water Dynamics from Space — Join the Geospatial Analysis for Environmental Change Lab to investigate hydroclimatic drivers of surface water extent dynamics and advance quantification of water extent and volume.
• Remote Sensing Forest Gap Dynamics — Join the Applied Remote Sensing and Analysis lab group to examine the role and influence of forest gaps in relation to localized large-scale disturbances.

Funding is available for additional projects, and in all cases students are encouraged to develop research questions and methods that suit their interests and career goals.

We’re here to help! Contact us for more information about the Ph.D. in Geospatial Analytics.

Explore Opportunities

Our graduate assistantships are fully funded with a yearly stipend, tuition support, and benefits. Learn more about opportunities at NC State and the Research Triangle to enrich your graduate experience.

• About NC State
• Campus Life
• Geospatial Grad Student Organization
• NC State Graduate School
• The Research Triangle

Centre for Advanced Spatial Analysis MPhil/PhD

London, Bloomsbury

This programme is likely to appeal to students interested in modelling aspects of cities, social or physical, or in understanding the impact of new technologies on an evolving society. CASA gives students the opportunity to explore a wide range of subjects from complexity to smart cities, from a theoretical or applied perspective pertinent to contemporary problems.

UK tuition fees (2024/25)

Overseas tuition fees (2024/25), programme starts, applications accepted.

• Entry requirements

A minimum of an upper second-class UK Bachelor's degree and a Master's degree, or an overseas qualification of an equivalent standard, in a relevant subject, is essential. Exceptionally: where applicants have other suitable research or professional experience, they may be admitted without a Master's degree; or where applicants have a lower second-class UK Honours Bachelor's degree (2:2) (or equivalent) they must possess a relevant Master's degree to be admitted. We expect any successful application to include a sufficiently strong and convincing proposal, and those holding a Master's degree are typically well prepared to provide one. Relevant work experience is highly desirable.

The English language level for this programme is: Level 1

UCL Pre-Master's and Pre-sessional English courses are for international students who are aiming to study for a postgraduate degree at UCL. The courses will develop your academic English and academic skills required to succeed at postgraduate level.

Further information can be found on our English language requirements page.

Equivalent qualifications

Country-specific information, including details of when UCL representatives are visiting your part of the world, can be obtained from the International Students website .

International applicants can find out the equivalent qualification for their country by selecting from the list below. Please note that the equivalency will correspond to the broad UK degree classification stated on this page (e.g. upper second-class). Where a specific overall percentage is required in the UK qualification, the international equivalency will be higher than that stated below. Please contact Graduate Admissions should you require further advice.

About this degree

Studying at CASA gives students a unique opportunity to develop research within a strong academic centre, linking different areas, such as geography, mathematics, physics, urban planning and architecture, in collaboration with different national and international universities. CASA also has partnerships with industry and the government, facilitating insertion within these areas.

Who this course is for

Applicants should be: Curious and imaginative; Academically excellent; Self-motivated and organised; Resilient and adaptable; Precise and attentive to detail; Capable of producing high quality written work.

What this course will give you

The UCL Centre for Advanced Spatial Analysis (CASA) is a leading research centre within the UCL Bartlett Faculty of the Built Environment. It has a world-leading PhD programme which has welcomed over 80 students since 2007.

Its multidisciplinary perspective, ranging from urban planning and policy, to complexity theory, gives the student a wider overview than more traditional programmes with a restricted focus. As a result, many CASA alumni now have successful academic careers, or have become key advisers to government and industry. The programme is intensive, as students are encouraged to contribute to CASA’s research community alongside taking any taught courses they may wish to.

In REF2021 , 51% of The Bartlett's research was awarded the maximum rating of 4* (world-leading), with a further 40% recognized as 3* (internationally excellent). The Bartlett submitted the largest quantity of 4* world-leading research outputs of any university submitting to the Architecture, Built Environment and Planning sub-panel. Furthermore, 67% of our impact case studies were rated 4*, and the Bartlett received the highest score for the vitality and sustainability of its research environment- of any institution submitting to the Architecture, Built Environment and Planning sub-panel.

The foundation of your career

Studying for a CASA PhD offers unrivalled opportunities to interact with world-leading researchers in a truly interdisciplinary environment. We frequently host visitors from around the world and encourage attendance at leading conferences across a range of disciplines. There are also opportunities to present and network as part of our seminar series as well as to hear about the research of others. Much of our research is applied in the commercial and policy context, so our graduates develop the expertise to secure demanding roles at top-level organisations. Many often return to CASA as visiting researchers to further strengthen our links with the wider academic, commercial and government sectors.

Employability

CASA graduates have gone on to work in world-leading universities (in the US at Berkeley, University of California, George Mason, University of Maryland, Johns Hopkins University; and in the UK at King’s College London, the Universities of Bristol and Liverpool), where they lecture and set up their own research centres. Graduates moving into the private sector have become key players in geospatial organisations (Transport for London, Dr Foster and AGI) and advisers to large government departments including the Ministries of Defence and Transport, and the Department for Communities and Local Government.

As a part of London's "Global University" there are a huge number of networking opportunities for CASA students to benefit from. We have a weekly seminar series and social event that attracts people from across London to hear about the latest cities research. This provides a relaxed atmosphere in which CASA students can get to know one another and the CASA research network. In addition, our students are encouraged to represent CASA at key events, such as academic conferences, where they become known to the broader community. Finally we are keen to attract funding and students who have commercial or government partners.

Teaching and learning

A doctoral degree is a self-driven qualification. Students are allocated two research supervisors, with whom they will meet regularly to discuss their work. However, they are expected to define their own research directions and questions, to teach themselves the necessary skills and tools to address these questions, and to manage their own time, both over the long and short term.

More formal instruction is also available, both through the extensive catalogue of doctoral skills training courses offered by the university, and through the opportunity to request to audit (sit in on) any lecture course offered across the university, space and resources permitting.

The Doctor of Philosophy (PhD) consists of a piece of independent research, normally undertaken over a period of three years full-time. It is an exciting opportunity to nurture and channel your imagination, to dive deep into the details of the subjects that most interest you, to take responsibility for your own learning and development, and to discover new knowledge and new approaches to difficult and meaningful problems.

Formal assessment for this doctoral programme is in two parts.

Firstly, between 9 and 18 months (or between 15 and 30 months, if part-time), students are required to pass an "upgrade" assessment. This involves the submission of documents on your progress and research plans, the delivery of a departmental seminar, and an interview, in which you set out your research before a panel of experts. Success in the upgrade results in transfer from the MPhil degree to the full PhD programme.

The main assessment of the PhD, at the end of the course, is by means of a written thesis (of a maximum of 100,000 words), and a formal "viva" examination - an oral defence of your research before two expert examiners.

This is a full-time doctoral research position (36.5 hours per week). Students will be expected to meet with their supervisors regularly throughout the year.

Research areas and structure

• Complexity: spatial network analysis, scaling and explanatory mechanisms
• Design and visualisation: GIS and datavis; CAD, multimedia and 3D models; virtual cities; virtual and augmented realities
• Geodemographics: neighbourhood profiling; health; crime; public service delivery
• ICT in society: web-based cities; the spatial organisation of the internet; social media; the Internet of Things
• Simulation: agent-based models, spatial models, land use transportation models.

Research environment

The UCL Centre for Advanced Spatial Analysis (CASA) is a research centre within the Bartlett, UCL's Faculty of the Built Environment. We aim to provide you with opportunities to interact with researchers in an interdisciplinary environment. We frequently host visitors from around the world and encourage attendance at leading conferences across a range of disciplines. There are also opportunities to present and network as part of our seminar series as well as to hear about the research of others.

Our main areas of research are: complexity (spatial network analysis, scaling and explanatory mechanisms); design and visualisation (GIS and datavis, 3D models, virtual cities, virtual and augmented realities); geodemographics; ICT in society; simulation (agent-based models, spatial models, land use transportation models).

The length of registration for the research degree programmes is normally 3 years for full-time students. You will register initially for the MPhil degree with the expectation of transfer to PhD after successful completion of an 'upgrade' assessment, typically between 9-18 months after initial registration.

Primarily, you are expected to conduct independent research, with guidance and supervision. The programme places emphasis on a close one-to-one working relationship between you and your supervisor. Your supervisor may suggest that you enrol in, or audit, an additional taught module. Taught modules do not form part of your MPhil/PhD programme and so are not formally assessed. 

Between 15 and 30 months following enrolment, part-time students “upgrade” from MPhil to PhD status.

The upgrade process requires a presentation to peers and the creation of a detailed written report which demonstrates the importance of the planned research and the key milestones required to achieve it.

MPhil/PhD students studying part-time should aim to complete their research, submit their thesis and take the final examination within a period of 5 years. The minimum period of registration is three years for part-time students.

Accessibility

Details of the accessibility of UCL buildings can be obtained from AccessAble accessable.co.uk . Further information can also be obtained from the UCL Student Support and Wellbeing team .

Fees and funding

Fees for this course.

The tuition fees shown are for the year indicated above. Fees for subsequent years may increase or otherwise vary. Where the programme is offered on a flexible/modular basis, fees are charged pro-rata to the appropriate full-time Master's fee taken in an academic session. Further information on fee status, fee increases and the fee schedule can be viewed on the UCL Students website: ucl.ac.uk/students/fees .

Additional costs

As a research student, your additional costs may include expenses such as books, conference attendance and field research, in the UK or overseas.

The Built Environment Faculty Office provides financial support to students through the Bartlett Student Conference Fund, Bartlett Doctoral Initiative Fund, Bartlett External Training Fund and Bartlett Extenuating Circumstances Fund. However, please note that these funds are limited and available through competition. 

For more information on additional costs for prospective students please go to our estimated cost of essential expenditure at Accommodation and living costs .

Funding your studies

UCL offers a range of  financial awards  aimed at assisting both prospective and current students with their studies.

Any additional funding available from the Bartlett Centre for Advanced Spatial Analysis and the Built Environment Faculty Office are advertised on the respective websites.

For a comprehensive list of the funding opportunities available at UCL, including funding relevant to your nationality, please visit the Scholarships and Funding website .

Bartlett Promise PhD Scholarship

Deadline: 19 May 2024 Value: Full fees, plus £19,668 maintenance (Normal duration of programme) Criteria Based on financial need Eligibility: UK

UCL Research Opportunity Scholarship (ROS)

Deadline: 12 January 2024 Value: UK rate fees, a maintenance stipend, conference costs and professional development package (3 years) Criteria Based on both academic merit and financial need Eligibility: UK

Prospective MPhil/PhD applicants are encouraged to send an informal research enquiry before applying. This should be sent directly to the academic you would like to supervise you. Please refer to the staff list on the  department website  and see UCL's  Institutional Research Information Service  (IRIS) for staff profiles. Please attach to your e-mail a referenced research proposal of around 1,000 to 2,000 words and your curriculum vitae (CV).

Further details on how to apply to an MPhil/PhD can be found on the  UCL Graduate Admissions  website.

Please note that you may submit applications for a maximum of two graduate programmes (or one application for the Law LLM) in any application cycle.

Choose your programme

Please read the Application Guidance before proceeding with your application.

Year of entry: 2024-2025

Got questions get in touch.

Centre for Advanced Spatial Analysis

[email protected]

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Spatial Planning Urban Modelling Data Analytics Digital Cities

An interdisciplinary research group at department of land economy, university of cambridge.

Rent Analysis and Prediction (Chengdu)

We are a modelling research group focusing on understanding urban land and transport development processes and policy mechanisms through applied urban modelling and data analytics.

The research group is led by Dr. Li WAN .

New Book in 2023: Digital Twins for Smart Cities: Conceptualisation, challenges and practices .

GROUP MEMBERS

#urban planner #group lead.

Dr. Jerry Chen

#Economist #Public Health

Qiancheng wang, #urban energy #built environment.

Ilaï Bendavid

#Economist #Urban-Environmental Policy

#economist #modeller, shantong wang, #gis&rs #urban planning, emily tianyuan wang, #data scientist #urban economics, #architect #urban designer, #land finance #urban landscape, zengquan li, #geographer #remote sensing, enjia zhang, #urban analytics #urban planning.

Dr. Donggyun Ku

#Transportation Planning #Optimisation Research

A city-level digital twin prototype for Cambridge

Causality Between the Built Environment and Subjective Wellbeing: Applying Difference-in-Differences and Synthetic Control Methods to Longitudinal Data From England

Causality between the built environment and subjective wellbeing has thus far been segmentally explored and partially quantified. We identify two unresolved challenges in the literature. Firstly, a reliance on cross-sectional data produces associative findings. Secondly, a reductive approach to regress aggregate subjective wellbeing on limited and disparate built environment measurements risks significant confounding effects. We address the research gaps by leveraging residential relocation as a natural experiment to investigate the causality between built environment change and subjective wellbeing. Two causal inference methods (difference-in-differences and synthetic control) are applied and compared. Our research design incorporates novel extensions to the canonical forms of both causal inference methods – staggered difference-in-differences and generalised synthetic control methods – to accommodate individual-level data with multiple relocation timepoints.

Complements or Substitues: Greenbelt and Congestion Charge Policy For Post-pandemic City Regions - Insights From A New Spatial Equilibrium Model

This research switches the policy assessment from the single city and one policy analysis into a multi-policy city-regional scale assessment by considering the city network, different hierarchical city levels, and policy scenario comparison. The changing preferences for city-center and suburban amenities and the popularity of remote work in the post-pandemic are two new challenges to existing SCGE models. Those two new challenges on existing SCGE models have been considered in this research to model the public location choice and urban spatial structure.

Developing a New Spatial Equilibrium Model for quantifying the Energy Impacts of Alternative Urban Spatial Planning Strategies for Fast-growing City Regions: A Case Study of Greater Beijing, China

This study builds on the activity-based approach in transport modelling and expands it as a unified analytical framework for quantifying the effects of the land-use and transport planning on long-term city-scale energy intensity in fast-growing city regions. The new modelling framework features a consistent activity-based choice model linking the building and transport sector, subject to explicit time and budget constraints and spatial equilibrium condition in the housing market. The study demonstrates the policy use of the model through an empirical model application for the Greater Beijing in China and estimates the carbon/energy intensity elasticities with respect to planning policy variables with the base-year model after calibration. The calibrated model will then be used to estimate the emission/energy outlook based on a series of urban spatial development scenarios and to test the magnitude and rate of technological and behavioural change required for achieving local sustainability goals.

Examine the Spatial Economic Impact of Singapore’s Ethnicity Integration Policy

The study aims to quantitatively assess the spatial and economic impact of Ethnic Integration Policy (EIP) on the development of housing market and urban spatial structure in Singapore. Specifically, two main research questions are proposed. (1) Whether EIP would have an impact on the spatial pattern and time-series changes of housing price in Singapore? If so, how would EIP affect the development of urban spatial structure in Singapore through its impact on the housing market? (2) How to mitigate/enhance the negative/positive impact of EIP in relation to the 2030 planning goals in Singapore? The research design includes three main work packages, population synthesis, modelling residential location choice under housing market equilibrium and policy scenario analysis.

Using Natural Language Processing to Identify Key Planning Strategies from Government Documents – A Case Study of 117 Chinese Cities from 2011 to 2019

Inter-city development disparity is a salient issue for both developed and developing countries. A causal determination of whether and to what extent variations in planning policies lead to different outcomes remains an onerous analytical challenge. Quantitative modelling of such causality requires the identification of a bundle of planning strategies from text-based planning documents in a holistic and temporally consistent manner such that the evolution of development outcomes could be investigated in relation to planning policy changes over time. Enabled by recent progress in natural language processing (NLP), this paper presents a novel NLP application for identifying key planning strategies from a large amount of text-based government documents through a case study of 117 prefecture-level cities in China. Based on official, city-level government reports from 2011 to 2019, the evolving policy strategies are identified and linked with the change of development outcome. Policy implications and directions for future research are discussed.

An interdisciplinary method for generating large-scale longitudinal urban expansion data using night-time light data – A case study of 600+ Chinese cities

Night-time light (NTL) data provide a novel and accessible source for monitoring the Spatio-temporal dynamics of urban expansion. The static thresholds ignore the path-dependent nature of urban development. Using NTL data for 2012-2018, this study proposes a new method using dynamic threshold (DT) for extracting UBA using NTL data for 600+ Chinese cities. The dynamic thresholds explicitly address the temporal continuity of urban physical development and further consider intra-city heterogeneity in terms of NTL brightness change pattern. Through a comparison with official statistics in China and UN-Habitat Sentinel-2 Human Settlement data, it is demonstrated that the overall accuracy of the DT method exceeds 85%, and the Kappa index exceeds 0.45.

Understanding the Implications of Electric Vehicles on Car Dependence and Public Transport - an Inter-Sectoral Approach

The Charging Infrastructure (CI) enables large uptakes of Electric Vehicle (EV) for transport decarbonisation. However, the large adoption of EVs may cause congestions. For sustainable mobility, it is timely important to incorporate congestion control when promoting EVs. Smart placement and operation of CIs should be applied to tackle congestion issues by proactively intervening traffic and driving behaviours for the more EV transport system. CI planning should be considered with the public transport while car dependency can be influenced by economic factors such as charging prices. The research question is how adaptive and inter-sectoral EV charging policies may help mitigate car dependence and complement public transport by spatial and technical configuration of CIs and the associated pricing mechanism. Parameters of traffic, parking, demography and public transport will be incorporated with spatial analysis, while the feasibility of employing dynamic and adaptive charging pricing to influence travel behaviour will be explored.

The Reshaped Urban Recreational Space in the Digital Era: Trends, Characteristics, and Responses

The growing penetration of ICT has redefined recreational activities and changed the demand, choice, and location of urban recreational services. As the Chinese proverb goes, “fragrant wine would not be restricted by deep alleys,” ICT has alleviated many restrictions imposed by conventional locations on people’s spatial preference for leisure activities, enabling new forms and levels of functional integration. Under the influence of ICT, urban recreational service space has been spreading to the urban suburbs and further penetrating the urban two-dimensional and three-dimensional spaces such as residential and office areas. In this context, urban mixed-use development evolves from block scale to the building scale and from ground level to ground above level, resulting in the need for new policies and regulations. Therefore, this study aims to measure the penetration degree of recreational service, identify the characteristics of the built environment elements, and explore urban planning and real estate policy by referring to international case studies. The findings could enhance the theory of mixed-use development and compact city in the digital era, and contribute to policymaking for managing and re-purposing urban spaces, especially for high-density cities.

PUBLICATIONS

City-level digital twins, digital twins for smart cities: conceptualisation, challenges and practices.

ICE Publishing，2023

Conceptualising city digital twins (CDTs)

Digital Twins for Smart Cities: Conceptualisation, challenges and practices，2023

Case studies: City-scale digital twins

A social construction of technology view for understanding the delivery of city-scale digital twins.

Published on ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences，2022

Digital twin implementations at the regional and city levels

Digital Twins in the Built Environment: Fundamentals, principles and applications，2022

City-level digital twin for strategic planning – Learnings from Cambridge

Presentation for the Data for Policy 2021 Conference, 2021

A socio-technical perspective on urban analytics: The case of city-scale digital twins

Published on Journal of Urban Technology, 2020

Exploring resilient observability in traffic-monitoring sensor networks: A Study of spatial–temporal vehicle patterns

Published on ISPRS International Journal of Geo-Information, 2020

Digital twins give urban planners virtual edge

Financial Times article, 2020

Developing a city-level digital twin - Propositions and a case study

Published on International Conference on Smart Infrastructure and Construction, 2019

DIGITAL AND SMART CITIES

Leadership of urban digital innovation for public value: a competency framework.

IET Smart Cities, 2023

Leadership for responsible digital innovation in the built environment: A socio-technical review for re-establishing competencies

Published on Journal of Urban Management, 2023

The conundrum in smart city governance: Interoperability and compatibility in an ever-growing ecosystem of digital twins

Published online by Cambridge University Press, 2023

Automatic number plate recognition (ANPR) in smart cities: A systematic review on technological advancements and application cases

Published on Cities, 2022

Confusion of goals — Rethinking the implication of data analytics and modelling for urban planning and design industry

Published on Landscape Architecture Frontiers, 2020

Digitalisation for smarter cities: moving from a static to a dynamic view

Published on Proceedings of the Institution of Civil Engineers - Smart Infrastructure and Construction, 2019

UNDERSTANDING JOURNEYS TO WORK

Inferring trip purposes and mode substitution effect of rental e-scooters in london.

Published on Transportation Research Part D: Transport and Environment, 2024

Modeling emission reduction benefits of the premium point-to-point bus service in Metropolitan Manila, Philippines–a scenario analysis

Published on International Journal of Sustainable Transportation, 2023

Estimating commuting matrix and error mitigation – A complementary use of aggregate travel survey, location-based big data and discrete choice models

Published on Travel Behaviour and Society, 2021

Understanding non-commuting travel demand of car commuters – Insights from ANPR trip chain data in Cambridge

Published on Transport Policy, 2021

Big data and urban system model – substitutes or complements? A case study of modelling commuting patterns in Beijing

Published on Computers, Environment and Urban Systems, 2017

SPATIAL EQUILIBRIUM MODELLING

Understanding the spatial impact of covid-19: new insights from beijing after one year into post-lockdown recovery.

Lincoln Institute Working Paper, 2022

UK2070 Futures: Post-covid scenario modelling

For the UK2070 Commission, 2020

Assessment of model validation outcomes of a new recursive spatial equilibrium model for the Greater Beijing

Published on Environment and Planning B: Urban Analytics and City Science, 2019

Environmental impacts of transformative land use and transport developments in the Greater Beijing Region: Insights from a new dynamic spatial equilibrium model.

Published on Transportation Research Part D: Transport and Environment, 2017

ECONOMIC GEOGRAPHY

The post-covid city.

Published on Cambridge Journal of Regions, Economy and Society, 2022

Monitoring spatial changes in manufacturing firms in Seoul metropolitan area using firm life cycle and locational factors

Published on Sustainability, 2019

Identifying the growth patterns of high-tech manufacturing industry across Seoul metropolitan area using latent class analysis

Published on Journal of Urban Planning and Development, ASCE, 2017

WORKING PAPERS

Li wan and ilaï bendavid (2023).

The lack of information on trip purpose and alternative mode in micromobility service usage data remains a major analytical challenge. Conventional survey method is subject to significant sampling and stated preference biases. To overcome this challenge, this paper presents a new inference method through a case study of rental e-scooters in London. The inference method features a rule-based algorithm for matching observed rental e-scooter trips with filtered trip samples in the English National Travel Survey (NTS) series. Probability distribution of trip purposes and alternative modes are then retrieved from NTS. Inference results are validated using official data. Discrepancies, sources of biases and correction measures are investigated. Based on the inferred mode substitution pattern, we estimate greenhouse gas emissions reduction of selected rental e-scooter trips in London (36-103g CO2e per mile). It is expected that the proposed method is applicable to a wide range of micromobility studies using service usage data.

Jerry Chen and Li Wan (2023)

Causality between the built environment and subjective wellbeing has thus far been segmentally explored and partially quantified. We identify two unresolved challenges in the literature. Firstly, a reliance on cross-sectional data produces associative findings. Secondly, a reductive approach to regress aggregate subjective wellbeing on limited and disparate built environment measurements risks significant confounding effects. We address the research gaps by leveraging residential relocation as a natural experiment to investigate the causality between built environment change and subjective wellbeing (measured with composite score of negatively phrased General Health Questionnaire-12 items). Two causal inference methods (difference-in-differences and synthetic control) are applied and compared. The use of the ‘Understanding Society’ dataset (The UK Household Longitudinal Study, 2009-2019), combined with holistic locational attributes (Area Classification at the Lower Super Output Area level as per the UK Census) for exploring such causality is novel in literature. Specifically, to estimate the wellbeing effects of residential relocation, we compare movers (treatment n=773) to non-movers (control n=4,619). To estimate the effects of built environment change, we compare movers with a change of built environment type (n=506) to those moving into the same built environment type (n=267). Our research design incorporates novel extensions to the canonical forms of both causal inference methods – staggered difference-in-differences and generalised synthetic control methods – to accommodate individual-level data with multiple relocation timepoints.

Workplace and lifestyle heterogeneity in subjective wellbeing: Latent class analysis of UK time use survey before and during COVID-19

Jerry chen and li wan (2022).

Mental health in the UK had deteriorated compared with pre-pandemic trends. The impact of COVID-19 on the subjective wellbeing of working populations with distinct lifestyles is not yet studied. Methods: Combining time use surveys collected pre- and during COVID-19, latent class analysis was used to identify distinct lifestyles based on aggregated daily activity patterns and reported working modes. We provide qualitative pen portraits alongside pre-versus-during pandemic comparisons of intraday time use and wellbeing patterns. Lifestyle heterogeneity in wellbeing was quantified in relation to aggregated activity types. Results: COVID-19 impact on wellbeing varied significantly between usual working hours (6am-6pm) and rest of the day. The decline in wellbeing outside of usual working hours was significant and consistent across lifestyles. During usual working hours, the direction of impact varied in line with working modes: wellbeing of homeworkers decreased, remained relatively stable for commuters, and increased for certain hybrid workers. Magnitude of impact correlates strongly with lifestyle: those working long and dispersed hours are more sensitive, whereas non-work dominated lifestyles are more resilient. Conclusion: The direction and magnitude of impact from COVID-19 were not uniformly manifested across activity types, time of day, and latent lifestyles. Blurring work-life boundaries and general anxiety about the pandemic may be key determinants of the decline outside of usual working hours. During usual working hours, strong yet complex correlations between wellbeing and time-use changes suggested that policies aiming to enhance wellbeing of workers need to consider not only spatial flexibility but also provide wider support for temporal flexibility.

Ling Li and Li Wan (2021)

This paper studies the spatial impact of COVID-19 pandemic through the lens of intra-city population and house rent changes in Beijing, China. Drawing on multiple geospatial data sets, we find that the pandemic has flattened the housing bid-rent curve in Beijing, which corroborates existing literature mainly based on cities in developed countries. Through regression analysis and spatial equilibrium modelling, we identify key mechanisms of the flattened bid-rent curve and the accompanying decentralisation of residents. First, workplace population change, particularly in central Beijing, seems to be the main factor contributing to the resident population and house rent changes. Second, we find no significant evidence on the spatial impact from remote working, as the share of remote working in Beijing appears low after about one year recovery. This finding contrasts to existing studies where remote working has been perceived as the main driver for urban spatial structure change in a developed country context. Third, through a novel method for quantifying locational preference changes, it is found that the observed decentralisation trend in Beijing, ceteris paribus, may also be associated with increased (decreased) preference for living in suburban (central) locations. However, the preference change for central locations is marginal, hence providing an early rebuttal of the ‘demise of centres’ proposition.

Understanding the role of land finance in economic and infrastructure development in Chinese cities: new evidence from a novel structural equation modelling approach

Li wan, tianyuan wang, and helen x. h. bao (2021).

The rapid urbanisation in Chinese cities features a distinctive land finance model, where land market, local economy, government revenue, and urban development are intertwined. Quantifying the interdependence between land market and other parts of the social, economic, and political systems has been a challenging undertaking, and the task is further complicated by the great cross-city heterogeneity in natural endowment and local socio-economic conditions. Few existing studies succeeded in capturing both the complexity of the system and the nuance of cross-city variations at the same time. We propose a novel structural equation modelling (SEM) method, integrated with the latent class analysis (LCA), to address this challenge. The LCA is used to identify distinct city groups based on two purposely constructed land-use efficiency measurements. The categorical latent classes of cities are then incorporated in a series of structural equation models, capturing the non-linear heterogeneity across cities. Based on data for 272 prefecture-level Chinese cities between 2012 and 2017, we found quantified evidence on both the direct channel (i.e., one-off revenue from land conveyance fee) and indirect channel (e.g., sustainable tax revenue from the business and employment growth enabled by land development) through which land supply drives urban development. The study also quantifies the significant gap among Chinese cities in terms of land-use efficiency. Our findings highlight the importance of developing and implementing reliable land-use efficiency measurements, the need to shift policy focus from one-off income to long-term sustainable revenue, and the potential of lower-tier cities in the next stage of urbanisation in China.

Our project secures funding from AI@Cam

Jerry Chen and Dr. Li Wan are one of the winning teams for the AI-deas Challenge , one of five to be selected from over 70 submissions. They will investigate how local authorities in England are using AI to make decisions about issues such as placemaking, land use and mobility, and sustainable water supply systems to create public value.

This interdisciplinary project is in collaboration with the Department of Engineering and the Department of Computer Science and Technology at Cambridge, led by Dr. Kwadwo Oti-Sarpong at the Centre for Smart Infrastructure and Construction, who was previously a Research Associate in Land Economy.   [Read more on Land Economy's website]

A New Book Published! Digital Twins for Smart Cities: Conceptualisation, Challenges and Practices

2023 highlight.

Dr. Li Wan has recently got this new book published: Originated from manufacturing and aerospace engineering, the concept of ‘digital twin’ has been celebrated as the next-generation smart city technology, with a number of high-profile applications emerging across the globe. By employing a socio-technical framework, this book critically examines the limitations and potential risks of deploying ‘digital twins’ as a generic technology to cities, and calls for a socio-technical perspective for conceptualising, developing, evaluating and governing city digital twins. The book provides both conceptual clarity and practical guidance for supporting the development of city digital twins...   [Read more]

Jerry Chen Presented His Research Work at 40th ICML Conference in Hawaii, United States

Our PhD Candidate Jerry Chen joined the 40th International Conference on Machine Learning (ICML) from 23-29 July at Hawaii Convention Center. He provided poster presentation highlighting his recent work titled "Counterfactuals for subjective wellbeing panel data: Integrated application of statistical ensemble and causal forest methods".

Our Team Presented at the 18th CUPUM Conference at McGill Uni, Canada

Five members of our group, Qiancheng Wang (PhD Student), Emily Tianyuan Wang (PhD Student), Enjia Zhang (Visitng Student), Dr. Donggyun Ku (Visting Academic Researcher), and Shan Yu (MPhil Student), recently joined the 18th Computational Urban Planning and Urban Management (CUPUM) Conference at McGill Uni from 20-22 June 2023. They delivered five presentations in total for each of their recent research work.

Emily Tianyuan Wang Presented Her Recent Work at 7th SDSC Conference in UNSW, Australia

PhD student Emily Tianyuan Wang recently participated in the 7th Smart Data Smart City (SDSC) Conference, hosted by University of New South Wales (UNSW) in Australia. She presented her recent research work titled "A novel use of latent class modelling to understand the heterogeneity of urban land use efficiency"...   [Read more]

Jerry Chen Provided Keynotes Speech at OECD International Conference on SMEs and the Urban Fabric in Trento, Italy

Hybrid working – the truly flexible working arrangement   [Watch the replay]

https://oecd-events.org/smes-cities/onlinesession/fedb6e76-368b-ec11-a507-a04a5e7d20d9

[Watch the replay]

Dr. Li Wan Posted a Blog Article on New Civil Engineer: Government levelling up strategy is a wake-up call for planners

Earlier this month, the UK Government unveiled an overdue yet ambitious 'levelling-up’ plan that aims to spread opportunity and prosperity to all parts of the UK. A quick word search through the Executive Summary reveals that the word ‘planning’ appears only twice, one referring to the protection of Green Belts and another alluding to the seemingly stalled planning reform...   [Read more]

Dr. Li Wan and Zengquan Li Provided an Invited Talk at The Hong Kong Polytechnic University

A new method for identifying built-up areas using night-time light data – A case study of 600+ Chinese cities: Night-time light (NTL) data provide a novel and accessible source for monitoring the spatio-temporal dynamics of urban expansion. Existing methods tend to use national/regional and temporally static thresholds for separating urban built-up areas (UBA) and non-UBA...   [Read more]

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Home > Books > Spatial Analysis, Modelling and Planning

Introductory Chapter: Spatial Analysis, Modelling, and Planning

Submitted: 06 June 2018 Published: 05 November 2018

DOI: 10.5772/intechopen.81049

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From the Edited Volume

Spatial Analysis, Modelling and Planning

Edited by Jorge Rocha and José António Tenedório

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Author Information

José antónio tenedório.

• Interdisciplinary Centre of Social Sciences (CICS.NOVA), Faculty of Social Sciences and Humanities (NOVA FCSH), Universidade NOVA de Lisboa, Portugal

Jorge Rocha *

• Universidade de Lisboa, Portugal

*Address all correspondence to: [email protected]

1. Introduction

It can be difficult to separate spatial analysis from other fields of interest such as geography, location analysis, geographic information science, etc. Yet, its beginnings are to some extent easy to identify. It started with both the “spatial thinking” paradigm and the geography quantitative revolution in the 1950s–1960s [ 1 , 2 ].

The first promoters of this paradigm shift (Brian Berry, Waldo Tobler, Art Getis, etc.) had a geography background and, as such, conducted their work in a multidisciplinary crossroad approach, allowing crossing ideas and spatial analysis approaches from substantially different disciplines (e.g., statistics and computer science). One of the most marking add-ins to spatial analysis was Peter Haggett’s [ 3 ] work, which remains as a reference for spatial analysis researchers and scholars.

Spatial analysis stands over the principle that there is some spatial component—absolute, relative, or both—in data. Indeed, in the beginning of the twentieth century, 80% of all data have already some kind of spatial explanation [ 4 ]. Spatial analysis comprehends numerous representational models of reality based on the spatial properties of the data features [ 5 ].

The importance gathered by spatial analysis within geography, its achievement in getting into the analytical framework of several sciences (e.g., natural, social, and physical), and its prominence as a pillar of geographic information science [ 6 ] reflects that geographic space does matter and greatly influences the way natural, social, and physical processes evolve.

Spatial patterns and processes have idiosyncratic properties [ 7 ] that establish the core of the spatial analysis paradigm. One is spatial dependence, which postulates that the spatially located semantic information gives some insights about the existent information in nearby locations. This is known as spatial autocorrelation (i.e., a kind of statistical dependence relationship) and when it applies to univariate analysis is often understood as some kind of spatial expansion process. Here it is impossible not to mention Tobler’s (1970) First Law of Geography, stipulating that all things are related, but near things are more related than distant things [ 8 ].

Another basic principle of spatial analysis is known as spatial heterogeneity. Here univariate/multivariate analysis is possibly not static throughout the geographic space, that is, anisotropy. Thus, one may find local hot and cold spots [ 9 ], because the parameter calibration of these models may vary athwart the study area, mirroring local variations of the global model adjusted for the study area as an all [ 10 ].

In a narrow view, one can consider that spatial data is special [ 11 ]. Yet, a rigid interpretation has often resulted in the postulate that the geographic space exists objectively and independently of the social and natural processes that operate across the spatial extension and in the conceptual and operational separation of spatial and semantic information in spatial models. Most recently this idea has been considered dogmatic and detached from the authenticity of the geographic space [ 12 ].

Geography has a history of a, sometimes crispy, relation between law-seeking (nomothetic) and description-seeking (idiographic) knowledge [ 13 ]. Wisely, physical geographers get away from these debates, but the nomothetic-idiographic tension keeps on in human geography [ 13 , 14 , 15 ]. Possibly without surprise, geography has been censured for invalidated theories, results that cannot be reproduced, and a division among practice and science [ 16 ].

Goodchild and Li [ 17 ] debate that old synthesis process, which is usually hidden from general view and not easily related to the final result, will be more explicit and of serious importance in the new big data era. Much of the geographic knowledge is made of formal theories, models, and equations that need to be processed in an informal manner. By the contrary, data mining techniques require explicit representations, for example, rules and hierarchies, with straight access deprived of processing [ 18 ].

One can state that multiple regression is undoubtedly the most widely used statistical approach in geography. This model assumes that the model that is being used in the analysis is the most correct [ 19 ]. Regrettably, the background theories are hardly ever satisfactorily developed to include even the most pertinent variables. Also, often many researchers do not even know what can be those missing variables. Hence, one often finds us testing a limited set of variables, and the incapacity to include crucial mislaid variables can have severe implications in the model’s accuracy and thus in the conclusions drawn from the data [ 20 ].

In opposition to this traditional methodology, the data analytic approach trusts on multiple models or a group of models. Instead of selecting the only best model and accepting that it properly defines the data generation process, a group of models analyze all the possible models to be resultant from the existing variables set and combine the results through a multiplicity of techniques, for example, bootstrap aggregation, bagging, boosting, support vector machines, neural networks, genetic algorithms, and Bayesian model averaging [ 21 , 22 ]. The subsequent group of models constantly achieves better results than the designated best model making higher accurate predictions across a wide group of domains and techniques [ 23 , 24 ]. However, one should note that the new advanced data analytic techniques do not always outperform the more traditional techniques [ 25 ].

This book is a gathering of original research contributions focusing on recent developments in spatial analysis and modelling with implications to—spatial—planning. The book is organized in three parts that make use of spatial analytic approaches in a progressively integrated and systemic way. It pretends to show how computational methods of spatial analysis and modelling in a geographic information system (GIS) environment can be applied on systems comprehension and allow a more informed spatial planning and, thus, theoretically improved and more effective. The 12 topics comprise new types of data, analysis to distinguish the importance of data in structures, functions and processes, and the use of approaches to backing decision-making.

2. Spatial analysis

The emergence of critical geography (mainly physical), critical GIS, and radical approaches to quantitative geography fostered the idea that geographers are well prepared to combine quantitative methods with technical practice and critical analysis [ 26 ]. This proved to be not quite true, but presently big data opens, specially through data mining, new possibilities for spatial analysis research [ 27 ] and can extend the limits of quantitative approaches to a wide array of problems usually addressed qualitatively [ 27 , 28 ].

Despite that big data puts challenges to conventional concepts and practices of “hard” sciences, where geographic information science is included [ 29 , 30 ], the predominance of big data will undoubtedly lead to a new quantitative turn in geography [ 31 ]. This is clearly a new paradigm shift in geography research methodologies: a fourth—data-intensive—paradigm [ 32 ].

The alleged spatially integrated social sciences intend to influence GIS in order to analyze the enormous amounts of available geocoded data [ 33 ]. Making sense of these data requires both computationally based analysis methods and the ability to situate the results [ 34 ] and brings together the risk of plunging traditional interpretative approaches [ 35 ]. The big data era calls for new capacities of synthesis and synergies between qualitative and quantitative approaches [ 36 ].

This paradox alliance between “poets and geeks” [ 37 ] can be a unique opportunity for geography, stimulating wider efforts to create a bridge over the qualitative-quantitative crater [ 15 ] and enabling smart combinations of quantitative and qualitative methodologies [ 38 , 39 , 40 ].

It is a similar case to the rebirth of social network theory and analysis where due to the growing availability of relational datasets covering human interactions and relationships, network researchers manage to implement a new set of theoretical techniques and concepts [ 41 ].

Surveys are an example of this new paradigm. This methodology is at a crisis because of the decline of response rates, sampling frames, and the narrow ability to record certain variables that are the core of geographical analysis, for example, accurate geographical location [ 42 ]. Gradually, self-reported surveys quantifying human motivations and behaviors are being studied and compared with more “biological” data sources [ 43 ].

These limitations are still more pronounced if one considers two additional features: (i) the majority of social survey data is cross-sectionally deprived of a longitudinal temporal facet [ 44 ] and (ii) most social datasets are rough clusters of variables due to the limitations of what can be asked in self-reported approaches.

Big data is leading to advances on both aspects, shifting from static snapshots to dynamic recounting and from rough aggregations to high resolution, spatiotemporal, data. Here, what matters the most is the likelihood of an increased emphasis of geography on processes rather than structures. Again, network analysis works as a good example as the availability of longitudinal relational data generated the latest procedural and theoretic advances on network dynamics [ 41 ].

Big data and its influence on geographic research have to be interpreted in the context of the computational and algorithmic shift that is progressively influencing geography research methods. To fully understand such shift, one can make the distinction between two modelling approaches [ 45 ]: (i) the data modelling approach which assumes a stochastic data model and (ii) the algorithmic modelling approach that considers the data as complex and unknown. The first evaluates the parameter values from the data and then uses the model for information and/or prediction, and in the second, there is a move from data models to algorithms properties.

This is precisely the type of data created from immense complex systems simulations [ 46 ], but a big percentage of it is provided by sensors and/or software that collect a wide range of social and environmental patterns and processes [ 47 , 48 ]. The geographic sources of this spatial and temporal data embrace location-aware tools such as mobile phones, airborne (e.g., unmanned aerial vehicles) and satellite remote sensors, other sensors attached to infrastructures or vehicles, and georeferenced social media, among others [ 18 , 49 , 50 ].

There is in big data an enormous potential for innovative statistics [ 51 ]. Perhaps the upmost importance is the necessity for a distinct mind-set because big data points toward a paradigm shift, comprising an increased and improved use of modelling practices [ 52 , 53 ]. Taking in consideration the growing importance of location, it is fundamental for geographers to stop just questioning “where?” but also start to enquire “why?” and “how?” [ 47 ].

Spatial analysis is defined as a way of looking at the geographical patterns of data and analyzes the relationships between the entities. In spatial analysis, the tendency in the direction of local statistics, for example, geographically weighted regression [ 54 ] and (local) indicators of spatial association [ 9 ], characterizes a concession where the main rules of nomothetic geography can evolve in their own way across the geographic space. Goodchild [ 55 ] sees GIS as a mix of both the nomothetic and idiographic characteristics, retained, respectively, on the software and algorithms, and within the databases.

Hence, spatial analysis is some sort of modelling procedure that relates data features over a geographic space (2D), across several spaces (3D), and along time dimension (4D).

3. Spatial modelling

What is a model? Well, in a broad sense, a model is a simplification of the reality: thus, all models are wrong [ 56 ]. As one can understand, it is impractical or even functionally impossible to collect cartographic information using an exact match between the representation and the real objects; the elements generated would be a replica of the studied area and not a model. The acquisition of information is therefore a numerical relationship between reality and the cartographic representation and, therefore, requires a semantic transfer, inseparable from the graphic and thematic generalization processes.

Lewis Carroll, the world-renowned writer for his book Alice in Wonderland , in his poetic tale The Hunting of the Snark (An Agony in 8 Fits) [ 57 ], presents a very particular vision of the relation: greater abstraction versus less information versus more extensive understanding, by proposing an empty map (the Bellman’s Map, Figure 1). This blank sheet of paper, with suggestions for navigation (North, South, etc.) and very mysterious, can represent the total ignorance of humans in relation to their location but at the same time was a map that everyone understood. The point is only the simplification/selection, since in the middle of the ocean, this map can be quite accurate, if there is nothing else to consider than water itself.

Chorley and Haggett [ 58 ] mention that one of the approaches to model building can start with the simplification of a system to its essential and then start building an increasingly complex structure, by induction, a priori reasoning, and so on. Hardly there may be a standard procedure for the construction of a system model never before modeled, but the suggestion of ways to address the problem given by the authors can help in a first approach to the problem. The original thought processes are difficult to understand and explain, and the solutions of the problems auto-suggest in strange shapes and times. It is not expected that two researchers working on the same subject address two models in the same way. What is expected is that they start with a topic of interest and then try to model it their own way.

All information is gathered at a certain range. This can be set, in a somewhat crude manner, as the number of real-world metrics units that correspond to a same unit in the spatial model. As one reduces the operating scale, the level of detail decreases according to the implicit generalization. However, before doing it, this option should be weighted because, in practice, it is not always possible to reduce and then enlarge a map, without such procedures will lead to a loss of information.

According to a story collected by Jorge Luís Borges from “Travels of Praiseworthy Men” written by Suárez Miranda (1658), and published in the chapter “Of Exactitude in Science” of the book named A Universal History of Infamy [ 59 ], this would not have been the understanding of a group of cartographers who, perhaps compelled by the thirst for the power of an empire, intended to make a map of their country. More driven by the greatness than by the desire to better understand that territory, these cartographers endeavored to design or, rather, copy the shape of their territory in increasing scales—1:10,000, 1:1000, 1:100, and 1:10—until 1 day they reached what they considered the perfect representation, that is, a map with a 1:1 scale. Inevitably, “less attentive to the Study of Cartography, succeeding generations came to judge a map of such magnitude cumbersome, and, not without irreverence, they abandoned it to the rigors of sun and rain” [ 59 ].

The Empire’s cartographers had copied the territory in an obsessive way as if it were a text. It is possible that, except if the Empire comes to decline, the next step would have been to represent each of the transformations of any details of that territory to the extent that it would be impossible to distinguish the importance between the representation and the object represented. These cartographers though believed achieving an increasingly better representation, through a perfect copy of the geometry of place, distorted, in inverse proportion, the ability of these maps to explain the territory of the Empire. Today we can associate modernity, to which Marc Augé refers [ 60 ], to the excess of information that submerges us with spatial data; but at the same time reduces the distances due to information and communication technologies (ICT).

Nowadays, spatial modelling and in a broad sense geography have shifted from a data-scarce to a data-rich environment. Contrary to the generalized idea, the critical change is not about the data volume, but relatively to the variety and the velocity at which georeferenced data can be taken. Data-driven geography is (re)emerging due to a massive georeferenced dataflow coming from sensors and people.

The notion of data-driven science defends that the generation of hypothesis and theory creation is up to date by an iterative process where data is used inductively. Hence, it is possible to name a new category of big data research that leads to the creation of new knowledge [ 61 ]. One should note that the inductive process should not start in a theory-less void. Preexisting knowledge is used to outline the analytic engine in order to inform the knowledge discovery process, to originate valuable conclusions instead of detecting any-and-all possible relations [ 62 ].

Data-driven geography raises some issues that in fact have been long-lasting problems debated within the geographic community. Just to name a few, one can point dealing with large data volumes the problem of samples versus populations, the data fuzziness, and the frictions between idiographic and nomothetic approaches. Yet, the conviction that location matters (i.e., spatial context) is intrinsic to geography and acts as a strong motivation to approaches such as spatial statistics, time geography, and geographic information science as an all.

Models can have very distinct applications, from the conception of suitability, vulnerability, or risk indicators, to simulation to the assessment of planning scenarios. In a GIS framework, modelling can provide insights about the way real systems work with enough precision and accuracy to permit prediction and assertive decision-making.

Nowadays, two distinct cultures of modelling coexist [ 45 , 63 ]. By one side, one can start imaging a stochastic data model in what can be called a data modelling culture. The other one, the algorithmic modelling culture, assumes that the core of the model is complex and unidentified. The former uses the model for both information and prediction after retrieving the parameter values from the data. In the latter, a shift exists from the data models to the algorithms properties.

Putka and Oswald [ 64 ] indicate how geography could benefit by implementing the data algorithmic philosophy. They claim that the actual data modelling philosophy prevents the ability to predict results more accurately, generates models that do not integrate a phenomenon’s key drivers, and cannot incorporate models’ uncertainty and complexity in a satisfactory manner.

4. Modelling and planning

The history of territories reveals cycles, both of progress and decline, if we consider only the opposites. Each cycle mirrors, in scales, dimensions, and variable rhythms, the importance of political decisions. Planning the territory constitutes an instituted praxis from which the models of the desired evolution are derived. As a general rule, the models are drawn up on the basis of essentially qualitative assumptions. They establish themselves as models that transpose the dominant ideas resulting from the interpretation of the spirit of the laws and regulations, from the debate of the technical solutions, and from public participation.

However, in the light of the recent theories on the territorial dynamics, there is the possibility to resort to quantitative models that reveal the self-organizing systems of the territories (e.g., cellular automata, multi-agent systems, fractal analysis, etc.). These models use intensively spatial modelling in a GIS environment and future scenarios simulation based on historical information of geographical changes. When, for instance, one quantifies the land use/cover changes and relates them with what is predicted in the plans, the conditions for quantitative modelling are created, favoring the dialectic between models. Therefore, it is not a question of using the confrontation between qualitative and quantitative models to nullify the relevance and/or excessive valorization of one of them. On the contrary, it is to evaluate the potential of each other and make use of it to improve the technical efficiency in the moment of preparation, monitoring, and evaluation of the territorial management instruments.

The legal systems and regulations of each country can be an opportunity to use geosimulation models, of quantitative root, to enrich the political and technical debate, about the planning of the territories in the future. It was in this context that we captured the questions relating to the analysis and spatial modelling as fundamentals of urban planning and regional planning, which, as we know, are complex processes of geographic space organization.

5. Conclusions

The difficulties to interpret and understand the territory, particularly with regard to the mixing of subsystems, inevitably require using the notion of complexity. Thus, it is essential to provide tools that could address complexity, linking both spatial organizations and the system of actors who make them evolve. Therefore, the systems approach presents itself as a paradigm capable of guiding the use and understanding of complex systems and as a prerequisite for more advanced modelling approaches.

Understanding social complexity requires the use of a large variety of computational approaches. For instance, the multiscale nature of social clusters comprises a countless diversity of organizational, temporal, and spatial dimensions, occasionally at once. Moreover, computation denotes several computer-based tools, as well as essential concepts and theories, varying from information extraction algorithms to simulation models [ 65 , 66 ].

Location analysis and modelling as an integrating part of spatial analysis [ 67 ] come out from Weber’s industrial location theory. Location models might embrace a descriptive methodology, but they can also be very operative as normative environments. Hence, spatial analysis overlaps typical data analytic methods such as statistics, network analysis, and several data science viewpoints, such as data mining and machine learning.

Whereas there is an interesting discussion between statistics and machine learning researchers about the advantages and disadvantages of each method, it is unmistakable that the huge mainstream of quantitative analytical methods falls inside the concept of data modelling culture. This enables a profounder knowledge about the importance of spatial values in shaping the geographic space.

The spatial analysis overlapping with numerous fields of application leads to the coin of the designation spatial science [ 68 ], which seems to better represent its singularities. In addition to geography, spatial analysis has a clear linkage to regional science.

Ever since its beginning, regional science has dealt with knowledge discovery adopting a neopositivism approach. It embraces the emerging architype of geospatial data integration rooted in geographic information science [ 69 , 70 , 71 ] to analyze the complex systems and the spatiotemporal processes that make them. It also extended the procedural boundary of spatial analysis, through both exploratory spatial data analysis [ 72 ] and confirmatory spatial data analysis [ 73 ].

Thus, spatial analysis and modelling is an interesting area of application within geographic information science, directing analysis, modelling, and improving the comprehension of spatiotemporal processes. It comprises a group of narrowly connected subareas, for example, geographic knowledge discovery, data analytics, spatiotemporal statistics, social network analysis, spatiotemporal modelling, and agent-based simulation.

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Research opportunities

Spatial modelling and dynamics.

Expertise of research area accessibility; autonomous driving; cities; cloud computing; connected vehicle; decarbonisation; driver behaviour variability; human behaviour modelling and simulation; Low-emission oriented driving; policy; portable emissions measuring systems (PEMS); Smart objects and interaction design; sustainability; transport & mobility

We develop and validate mathematical and statistical models and simulation tools for the representation, analysis and optimisation of traffic and transportation systems, with a particular focus on the use of big data in new modelling approaches.

<p>Research in&nbsp;<a href="https://environment.leeds.ac.uk/transport-spatial-modelling-dynamics">Spatial Modelling and Dynamics</a> is divided into five different areas:</p> <ul> <li><strong>Connected transport with the Internet of Things (IoT)</strong> &ndash;&nbsp;addresses how the Internet of Things solutions can be used in connected transport and automated driving to increase safety, provide more comfort and create many new business opportunities for mobility services. We will carry out the multi-criteria evaluations (Technical, user, business, legal) of the IoT impact on pushing the level of autonomous driving, using data from real-world pilots.</li> <li><strong>User acceptance of connected and autonomous vehicles &ndash;&nbsp;</strong>addresses all issues raised by the majority (if not all) of the general public that hinder the wide market uptake of Connected and Autonomous Vehicles (CAV). We not only focus on the interaction of the &ldquo;users&rdquo; in or near CAV, but also assess the impact of connected transport on people&rsquo;s wellbeing, quality of life, and equity. We aim to capture the public&rsquo;s acceptance, attitude and concerns, model/simulate scenarios for hand-on practices and validate the innovation in real-world trials.</li> <li><strong>Optimal fuel consumption with predictive vehicle control &ndash;&nbsp;</strong>aims to bring together the most advanced technologies from powertrain control and intelligent transport systems to achieve a global optimum for fuel consumption. It works towards the creation of a global optimiser which consists of a set of dynamic, intelligent control and prediction components designed for effective powertrain management, utilizing real-time environment data, road topography, traffic and weather conditions. It also studies key societal challenges such as social equity.</li> <li><strong>Low-emission driving, management and assistance &ndash;&nbsp;</strong>addresses a major policy concern about the impact of road traffic on local air quality and assesses innovations for improving underlying vehicle and fuel technologies, traffic management and enforcement. It aims to develop solutions to substantially reduce vehicle emissions from not only powertrain, but also brake wear and tyre wear. It will provide evidence to guide the derivation of effective driving practices and training courses for different user groups.</li> <li><strong>Modelling of innovative urban mobility management and policy</strong> &ndash; addresses urban public administration and innovational services development with respect to innovative urban mobility management, development and implementation. It encompasses modelling approaches and experiments to be developed for and carried out in various cities Europe and beyond, notably projects involving the use of innovative instruments, eg&nbsp;automatic/electric driving, shared mobility, and stakeholders coordination mechanisms.</li> </ul> <p>We currently&nbsp;have opportunities for prospective postgraduate researchers.&nbsp; Previous topics have included:</p> <ul> <li>train timetable rescheduling</li> <li>tools for participation in transport planning</li> <li>international transport policy in case of landlocked countries</li> <li>airport-driven development, transport planning and sustainable mobility</li> <li>towards comprehensive measures of performance and reliability for London&rsquo;s multi-modal public transport networks</li> </ul> <p>In addition to research study associated with a specific project, prospective students can also suggest their own topic. In this case, we ask prospective students to contact us for an informal discussion, before submitting a research proposal. Search <a href="https://phd.leeds.ac.uk/search?clive=leeds-pgr-web-supervisors&amp;query=&amp;f.school%7Cschools%5B%5D=Institute+for+Transport+Studies">PhD supervisors</a> in the Institute for Transport Studies.</p> <h5>Why do your PhD at Leeds?</h5> <p><strong>Study in an active research environment&nbsp;</strong><br /> Studying your PhD with us means you&rsquo;ll be working in a professional research environment, using UK-leading facilities to bring your project to life &ndash; alongside active researchers who are at the forefront of their area.&nbsp;<br /> <strong>A strong network of support &nbsp;</strong><br /> The Leeds Doctoral College connects our community of researchers and can offer you the guidance, services and opportunities you&rsquo;ll need to get the most out of your PhD.&nbsp;<br /> <strong>Close industry links&nbsp;</strong><br /> Our partnerships and links to companies and academic institutions give you the opportunity to network at industry talks, seminars and conferences, building connections that&#39;ll benefit your next steps after you complete your PhD.&nbsp;<br /> <strong>Professional skills development &nbsp;</strong><br /> We think of the whole picture at Leeds. That&rsquo;s why we offer a range of workshops and courses that&#39;ll enhance your skillset further and transfer into your professional career.&nbsp;<br /> <strong>Personal and wellbeing services&nbsp;</strong><br /> Mental health and wellbeing support are integral to who we are at Leeds and you&rsquo;ll have access to the full range of services we offer to ensure you&rsquo;re feeling your best &ndash; and reaching your potential in your studies.&nbsp;<br /> <strong>Join our global community&nbsp;</strong><br /> We welcome students, researchers, academics, partners and alumni from more than 140 countries, all over the world. This means, as a university, we&rsquo;re bringing together different cultures and perspectives which helps strengthen our research &ndash; and societal impact.</p> <h3>Useful links and further reading:</h3> <ul> <li><a href="https://environment.leeds.ac.uk/transport-research-degrees">Research degrees within the Institute of Transport Studies</a></li> <li><a href="https://environment.leeds.ac.uk/transport-spatial-modelling-dynamics">Spatial Modelling and Dynamics</a></li> <li><a href="https://environment.leeds.ac.uk/transport-research">Institute of Transport Studies,&nbsp;Research&nbsp;and Innovation</a></li> </ul> <h3>Leeds Doctoral College</h3> <p>Our <a href="https://www.leeds.ac.uk/research-leeds-doctoral-college">Doctoral College</a> supports you throughout your postgraduate research journey. It brings together all the support services and opportunities to enhance your research, development and overall experience.</p>

<p>Formal applications for research degree study should be made online through the <a href="https://www.leeds.ac.uk/research-applying/doc/applying-research-degrees">University&#39;s website</a>.</p>

<p>For general enquiries and details regarding the application process, please contact the Graduate School Office:<br /> e:&nbsp;<a href="mailto:[email protected]">[email protected]</a>, t: +44 (0)113 343 5326.</p>

PhD Candidate: GIS Analysis and Spatial Modelling of Urban Heat, Health and Ecosystem Disservices

Radboud university , netherlands.

Are you an aspiring researcher looking for a new opportunity in the field of urban planning, climate adaptation and GIS? Would you like to join a friendly and open environment where you can develop your skills and learn new things? Then you have a part to play as a PhD Candidate within the interdisciplinary BENIGN research project funded by the Climate Adaptation and Health programme of the Dutch Research Agenda .

The BENIGN (BluE and greeN Infrastructure desiGned to beat the urbaN heat) project aims to investigate how blue (lakes, canals) and green infrastructure (trees, other plants) can be employed in urban areas to create healthy living conditions.

As a successful PhD candidate, you will be expected to analyse and model the effect of built environment characteristics on the indoor and outdoor climate in relation to heat stress for vulnerable groups, water quality and plant pollen diversity using vulnerability mapping and spatial modelling. This will enable you to monitor and predict the positive (i.e. services) and negative (i.e. disservices) effects of blue and green interventions. You will then integrate the resulting models into a decision support system co-designed with end users and other experts.

In short, your core duties may include (1) the construction of GIS models of natural and built environments (2) (rudimentary) segmentation and classification of remote sensing images and Google Street View images of built environments where existing datasets are not sufficient, (3) qualitative validation of GIS models using questionnaires and/or interviewing techniques, and (4) using statistical analysis to explore whether specific elements of the environment correlate with aspects of public health. In general, we do not advocate a technology-driven research approach to spatial analysis. Instead, we promote a critical perspective on GIS, and look for new ways of complementing qualitative approaches with quantitative GIS and statistical analysis.

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Urban Mobility Consultancy

EIT Urban Mobility's Urban Mobility Consultancy supports organizations, businesses, and start-ups looking to develop or transition to sustainable urban mobility practices.

With specialized knowledge and expertise from a diverse network of talents, we offer student consultants in urban mobility to established businesses and organizations in the mobility landscape, as well as to new non-mobility businesses entering the mobility ecosystem. By helping our clients pioneer sustainable mobility transitions, we contribute to the creation of more livable urban spaces.

We provide industrial training and exposure to students and recent graduates in the early stages of their professional development. Our consultants work with external clients, our network of partners, and portfolio start-ups, and can also join the ranks of one of our clients or partners through our recruitment service.

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Areas of Expertise: Mobility infrastructure, Active mobility, Mobility for all

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Sustainable urbanism specialist 

Warsaw, Poland

Annika is a sustainable urbanism specialist with a Master's in Spatial Planning & Built Environment. Approaching issues of urbanism, mobility, environment and spatial development from a transdisciplinary perspective, she is always enthusiastic to employ a holistic approach as well as learn from and amplify best practices globally. Her mobility work highlights the many dimensions of enhancing walkability in communities, including the importance of quality, accessible and affordable public transportation, the myriad public health benefits and relationship to climate action. Central in her approach is collaborating with various actors across communities, including municipal agencies, stakeholders, community organizations, institutions and the public.

Areas of Expertise: Sustainable Urbanism, Civic Engagement & Outreach, Walkability, Active Mobility

PhD Research: Warsaw as a Laboratory for Walkability: A Life Quality Component in the Built Environment

Masters: Spatial Planning with major in the Built Environment from KTH, Stockholm, Historic Preservation Planning from Cornell University

Filippo Tassinari (PhD)

Applied microeconomist

Filippo is an applied microeconomist, with a strong quantitative background and a proven ability and interest in spatial analysis and public policy evaluation. He has a proven ability in defining research projects, handling big dataset, and communicate results and policy recommendations. More information can be found on his personal website: https://sites.google.com/view/filippotassinari

Areas of Expertise: Mobility infrastructure, Future mobility, Pollution reduction

PhD Research: Essays on Urban Economics

Masters: Economics and Economic Policy from University of Bologna

Francisco Macedo (PhD Candidate)

Spatial planning specialist

Utrecht, The Netherlands

Francisco is a consultant with 5 years of experience in urban design, master planning, policy design, and the utilization of geospatial technologies and models to support infrastructure investments and policy decisions. Francisco has delivered advice to governments (mostly municipalities and provinces) in Brazil and in the Netherlands in the form of Master Plans, technical drawings, data insights, workshops and dashboards.

Areas of Expertise: Shared (micro)mobility; Geospatial Data Analytics; Urban Design; Transport Planning

PhD Research: Causal Effects of the Built Environment on Active Travel and Physical Activity

Masters: Spatial Planning from Radboud University, Nijmegen; Transport Engineering from Universidade Federal do Ceará ,Brazil

Gabriel Ogunkunbi (PhD Candidate)

Sustainable urban transport specialist

Budapest, Hungary

Gabriel is a result-driven professional with a master's degree in civil engineering, focusing on sustainable urban transport. With varied experience in transport projects and emerging expertise in project management, he is keen on projects dedicated to reducing the environmental impact of transport, exploring alternative transport energy sources, and reallocating public space.

Areas of Expertise: Pollution reduction, Creating public realm, Mobility and energy

PhD Research: Criteria for effective urban vehicle access regulations.

Masters: Civil Engineering – Transportation Engineering and Highway Materials from University of Ilorin

Hannah Hook (PhD)

Human geographer 

Ghent, Belgium

Hannah is a human geographer with a background in research regarding travel behaviour, active travel, travel satisfaction, and well-being. She continues research on these topics, while expanding her interests to include themes such as gender and mobility, placemaking and ‘streets for people’, and mobility justice. Her work strives to find the subjective value of mobility through holistic approaches to travel behaviour research. 

Areas of Expertise: Travel behaviour, active travel, mobility and well-being, positive utility of travel

PhD Research: The Happy Traveler: Connecting undirected trips to travel satisfaction, positive utility, and subjective well-being.

Masters: Sustainable Cities from King’s College London; GIS Technology from University of Arizona

Iaroslav Kriuchkov (PhD Candidate)

Data analyst

Helsinki, Finland

Iaroslav is a data analyst, conducting his research at the intersection of management science, computer science and traffic modelling. His background comes from the business analytics side, and he strives to make insights from data. His current research work examines changes in traffic behaviour related to introduction of driver assistance systems and future introduction of autonomous vehicles Level 3 and further. ​

Areas of expertise: Future mobility, Mobility infrastructure, Mobility and energy​

PhD Research: Exploring the Variability of Road Capacity and its Impact on Traffic Congestion​

Masters: International Management from CEMS (WU Wien/GSOM SPbU)​

Iman Baratvakili (PhD Candidate)

Resilient urban mobility researcher 

Karlsruhe, Germany

Iman is an Urban Planner focusing on Resilient smart mobility in his Ph.D. thesis at KIT in Germany. Based on the citizens' Happiness study in his master period, he found security an essential factor and mobility a critical platform. He always seeks solutions to secure future mobility, especially for emerging cyber-attacks, through real-time responses and citizens' awareness. Besides, he has seven years of experience in Marketing consultancy and real estate. He also teaches at universities and provides online courses about urban mobility, urban resiliency, and built heritage.

Areas of Expertise: Future mobility, Mobility infrastructure, Sustainable city logistics

PhD Research: Exploring the Impact of Cyber-attacks on Smart Mobility in Smart Cities

Masters: Urban Design, Shahid Beheshti University, Tehran

Lakshya Pandit (PhD)

Urban mobility researcher

Darmstadt, Germany

Lakshya is an urban mobility researcher, focusing on multimodal accessibility. Previously, he has been associated with projects involving traffic calming, audits, urban health studies, master planning, slum upgradation, barrier-free accessibility and more. Having worked with different firms, city and government authorities, he continues to search for opportunities to grow through an eclectic way of learning.​

Areas of Expertise: Urban Mobility, Accessibility, Spatial Analysis​

PhD Research: Measuring Multimodal Accessibility through Urban Spatial Configurations: Case studies of three cities in the Rhein-Main Agglomeration.​

Masters: Infrastructure Systems Indian Institute of Technology, Roorkee​

Mahsa Movaghar (PhD Candidate)

Transport and geoinformation technology specialist

Delft, The Netherlands

Mahsa is a Ph.D. candidate in the department of Transport and Planning at the faculty of Civil Engineering and Geosciences (CEG) at TU Delft. Mahsa obtained her M.Sc. in Transport and Geoinformation Technology at KTH Royal Institute of Technology, Stockholm. Collaborating with SWECO, AI and Automation group, her research focused on applying Machine Learning and data analysis techniques to predict ridership in public transport. ​Mahsa has a keen interest in converting data-based insights into automated, robust, and sustainable decisions for transportation planning and infrastructure. Now her research focuses on developing eXplainable Artificial Intelligence (XAI) approaches to evaluate the impacts of road disruptions on traffic and environment.​

Areas of Expertise: Mobility infrastructure, Future Mobility, Transportation Data Analysis, Pollution Reduction​

PhD Research: Explainable Artificial Intelligent approaches to evaluate the effects of road disruptions on traffic and environment by incorporating multi-source datasets.​

Masters: Transport and Geoinformation Technology from KTH Stockholm; Railway Tracks Engineering from Iran University of Science and Technology, Tehran​

Mauricio Orozco Fontalvo (PhD Candidate)

Civil engineer

Lisbon, Portugal

Mauricio is a civil engineer with a master’s in transportation from the Universidad del Norte in Colombia and is currently a Ph.D. researcher in Transportation Systems at Lisbon University. Before his Ph.D studies., he was an assistant professor at the Nueva Granada Military University in Bogota and at Universidad de La Costa in Barranquilla, where he conducted several studies on travel behaviour, sustainable transportation, crime in public transport, micromobility and road safety. He has also worked in urban transport consultancy projects in Colombia and Mexico.​

Areas of Expertise: Mobility for all, Active Mobility, Intermodality​

PhD Research: Mobility as a Service governance and user's acceptance.​

Masters: Civil Engineering from Universidad del Norte, Colombia​

Misa Bakajic (PhD Candidate)

Information and service management specilist

Misa’s background includes working within academia, industry, and startups. His current PhD research is at the intersection of sustainable supply chains and innovation management in both city and enterprise contexts. He is adept at working within multidisciplinary teams to tackle complex problems and deliver results. In addition, he has knowledge and experience developing business models for innovative products and services. 

Areas of Expertise: Sustainable city logistics, Pollution reduction, Mobility Infrastructure 

PhD Research: Supply chains as mechanisms for sustainable value creation 

Masters: MSc in Information and Service Management from Aalto University

Modupe Osunkoya (PhD Candidate)

Urban spatial analyst 

Tallinn, Estonia

Modupe is an Urban spatial analyst with vast experience in planning, designing, and executing mobility solutions to improve urban transportation, urban planning, and urbanism. She is adept at planning for sustainable urban mobility and measuring vital urban areas, especially for cities in the digital transition and for the overall mobility experience for city residents.

Areas of Expertise: Active mobility, Creating public realm, Mobility Infrastructure

PhD Research: Smart Urban Futures: (Re)discovering urban vitality measurement for cities in the digital transition.

Masters: Urbanism and Strategic Planning from Katholieke Universiteit Leuven Belgium; Transportation Science from Hasselt University Belgium.

Mohd Aiman Khan (PhD Candidate)

Electrical engineer 

Stockholm, Sweden

Mohd Aiman is an electrical engineer with a Master's degree in Energy for Smart Cities from KU Leuven and KTH Sweden. He has several papers published on the topic of optimization of electric vehicle charging infrastructure and has won multiple awards for his start-up project, E-Connect, which focuses on autonomous charging solutions. Aiman is passionate about working in emerging technologies such as autonomous public transportation, electric vehicles, charging infrastructure deployment and operation.

Areas of Expertise: Future Mobility, Mobility and Energy, Mobility Infrastructure

PhD Research: Modelling of autonomous public transportation

Masters: Energy for Smart Cities from KU Leuven, and KTH, Stockholm

Nishad Malik (MSc)

Consultancy coordinator

Sustainable mobility transition specialist

Nishad is in charge of overseeing the Urban Mobility Consultancy as the Consultancy & Talent Pool coordinator at EIT Urban Mobility. He obtained a dual master's degree from KTH in Stockholm and UPC in Barcelona through EIT Urban Mobility's Sustainable Urban Mobility Transitions program, which has equipped him with technical expertise, as well as training and practical experience in innovation and entrepreneurship. In addition, he participated in EITUM’s in-house entrepreneurship training program. Prior to earning his master's degrees, Nishad worked in Project Management for nearly five years in the Oil & Gas and Real Estate industries in Saudi Arabia and India.

Area of Expertise: Innovation, Project Management

Location: Barcelona, Spain

Masters: Transport, Mobility & Innovation from KTH, Sweden; Urban Mobility from UPC, Barcelona

Siyu Li (PhD Candidate)

Transport engineer 

Siyu is a PhD researcher with both Bachelor's and Master’s degrees in transportation engineering, focusing on transportation planning, management and policies. He has rich professional experiences in transportation and mobility consulting. He wants to help cities achieve people-centred mobility systems with novel concepts and technologies, making mobility sustainable and enjoyable.

Areas of Expertise: Sustainable urban logistics, Intermodality, Future mobility

PhD Research: Urban Freight Distribution Management and Pricing with Tradable Mobility Credit

Masters: Transportation Engineering from Tongji University

Tjark Gall (PhD Candidate)

Urban systems researcher  

Paris, France

Tjark is an urban systems researcher working on mobility futures. He has extensive international experience in urban development, climate action, and urban analytics for decision- and policy-making. He currently works on designing system transitions via future scenarios of urban mobility. Tjark is always available to discuss systemic impact analysis of urban solutions and the integration of future uncertainties and trends in simulation and design processes of urban systems.

Areas of Expertise: Mobility infrastructure, Creating public realm, Future mobility

PhD Research: Uncertainty in urban system design: A scenario-based method applied to mobility in Paris and Cairo

Masters: Urban Management and Development from IHS Erasmus University, Rotterdam; Architecture focussing on Urban Design from Technical University of Brunswick 

Zaher Akkad (PhD Candidate)

Logistics and supply chains engineer

Zaher is an enthusiastic young researcher in the areas of logistics and supply chains in developed industry 4.0 environments to reach optimized sustainable results. While he has an impressive research record, he also has deep practice in the real life to identify and analyse the causes root to crystallize meaningful outcomes. His research and professional experiences make him a perfect fit and an added value to any project.

Areas of Expertise: Sustainable city logistics, Pollution reduction, Future mobility

PhD Research: Optimization of supply chain and logistics systems in Industry 4.0 Environment

Masters: Mechanical Engineering from University of Miskolc

Ya'ara Tsairi (PhD Candidate)

Ya'ara is a PhD candidate in Urban Planning at the Technion. She has been active in the field of sustainable transportation and environment advocacy for more than six years now. She gained her experience by working for a leading local non-profit public transportation consumer alliance and as a parliamentary advisor to a coalition parliament member and the head of the Environment, Climate and Health parliament subcommittee. 

Areas of Expertise: Active Mobility, Intermodally, Pollution reduction  

PhD Research: Socially desired role of employer policy toward employee transport 

Master: Environmental Studies from Tel Aviv University (summa cum laude) 

Yunfei Zhang (PhD Candidate)

Traffic analyst 

Munich, Germany

Yunfei is a traffic analyst with a Master's degree in transportation systems, focusing on traffic data analysis. Possessing rich experiences in traffic modelling and simulation, he is always enthusiastic about quantifying the impacts of traffic, especially for emerging technologies and modes such as autonomous driving, electric vehicles and Mobility-on-Demand services.

Areas of Expertise: Future Mobility, Mobility and energy, Intermodality

PhD Research: Automated Vehicle as a Sensor: Traffic State Estimation in the Operation of Mobility on Demand Services

Masters: Transportation Systems from Technical University of Munich

Enrique Jiménez Meroño (PhD Candidate)

Mobility system specialist

Enrique has a Civil Engineering background, and towards the end of his degree he specialized in transportation and mobility systems. He is an expert in analytical modelling, optimization, and transport simulation, having worked with both commercial planning software or self-developed tools. During his PhD he has participated in several consulting and development projects, in which he applied that knowledge for the development of innovative transportation modes, in particular car-sharing systems.​

Areas of Expertise: Future mobility, Intermodality, Mobility infrastructure​

PhD Research: Design, management, and simulation of vehicle-sharing systems​

Masters: Civil Engineering from Universitat Politécnica de Catalunya, Barcelona​

Luis Leal Pinho De Morais (PhD Candidate)

Mechanical engineer 

Porto, Portugal​

Luis has a diverse background being a PhD candidate of Urban Planning and Management Engineering and having a Master’s degree on Mechanical engineering. During his PhD he analysed mobility data and EV registrations to estimate charging demand and assessed the sufficiency of a charging network both in terms of capacity and placement. He is passionate about electric mobility, infrastructure and sustainable transport systems.​

Areas of Expertise: Mobility and energy, Pollution reduction, Mobility infrastructure​

PhD Research: Urban planning of electric mobility in Milan​

Masters: Mechanical Engineering from Politècnico di Milano​

Maria Savall​ (PhD Candidate)

Aerospace engineer  

Maria is an aerospace engineer and has a Master's degree in Industrial Organisation. She has experience as a continuous improvement consultant and works on different mobility projects for Barcelona City Council. Holding a challenge in her hands and creating a conducive group environment is a rewarding day for her.​

Areas of Expertise: Sustainable urban logistics, Freight distribution, Pollution reduction​

PhD Research: Development of a sustainable urban freight distribution model for the city of Barcelona​

Masters: Aerospace Engineering and Industrial Organisation from Universitat Politècnica de Catalunya, Barcelona​

Sana Iqbal (PhD)

Transport geography specialist

Sana Iqbal is a research professional who has a PhD in Transport Geography. Her PhD research was focused on understanding the features of sustainable transport planning and operation faced by disentangling the impacts of urban poverty and transport inequities, including those related to the accessibility for women, people with disabilities, and other disadvantaged groups. There was a particular focus on the United Nations’ Sustainable Development Goals (SDGs) and civic engagement work across the sectors related to climate resilience, gender equality, circular economy and community engagement for social and environmental projects. She is specially interested in unpacking the socio-economic and environmental impact of changes in policy-making related to sustainable transport. Her research has been published widely in renowned journals including the Journal of Transport Geography. ​

Areas of Expertise: Future mobility, Mobility for all, Creating public realm​

PhD Research: Gender and social inclusion; public transport and active modes of transport; passenger wellbeing and transport justice/poverty​

Masters: Architecture from NED University of Engineering and Technology, Karachi.​

Thomas Rochow (PhD Candidate)

Public and urban policy specialist

Thomas is a researcher in the Urban Studies department at the University of Glasgow. His PhD research is focused on exploring young people’s employment and housing transitions during a period of heightened insecurity, economic recessions, and intensified active labour market policies. ​His has interest in the lived experiences of social policies and in local mobility projects which seek to develop a safer, more sustainable, and equitable urban environment. Also he has experience in engaging with local residents to understand and disseminate the qualitative longitudinal impacts of increasing walkability and cyclability within urban neighbourhoods. ​

Areas of Expertise: Mobility  for all, Creating public realm, Future mobility​

PhD Research: Young People and Welfare Conditionality Over Time.​

Masters: Public and Urban Policy from University of Glasgow​

Yu Liu (PhD Candidate)

Urban planner  

Copenhagen, Denmark

Yu is an urban planner, and he is interested in integrated urban planning, especially how built environment can generate liveability, public realm, and simultaneously improve human health and wellbeing. Yu sees cycling and walking as the future of mobility, and he is passionate about promoting active mobility for the benefits of active living and a resilient urban environment. Now, Yu's PhD project is intersected within urban green space management and active mobility planning – nudging everyday walking and cycling trips to urban green spaces in response to physical inactivity challenge. The project will support rethinking of public spaces for active mobility trips and foster behaviour change. ​

Areas of Expertise: Active mobility, Creating public realm, Mobility for all ​

PhD Research: From able to invited, promotion of active mobility with urban green spaces​

Masters: Landscape architecture and planning from University of Copenhagen, Denmark​

Gabriel Dell’Orto (PhD Candidate)

Ground vehicles specialist

Delft, The Netherlands) and Milan, Italy

Gabriele obtained the MSc degree in Mechanical Engineering from Politecnico di Milano. His expertise is focused on Ground Vehicles. He started his PhD in 2020 focusing on vehicles for micro-mobility. He studied the tyre mechanics of such vehicles, by means of a testing machine specifically designed for bicycle tyres, now certified ISO 9001. He is currently investigating bicycle dynamics, and the effect of tyres characteristics on bicycle stability. Of course, in free time he is also a really passionate cyclist!​

Areas of Expertise: Future mobility, Micromobility vehicles​

PhD Research: Bicycle dynamics, bicycle-type tyre testing, tyre and bicycle models.​

Masters: Ground Vehicles (Mechanical Engineering degree) from Politecnico di Milano​

Seshadri Naik (PhD Candidate)

Transportation engineer

Seshadri Naik is a transportation professional specializing in connected and autonomous vehicle (CAV) traffic. He holds a master's degree in Transportation Engineering from the National Institute of Technology Warangal, India. Seshadri has a wide range of experience, from road safety to autonomous navigation testbed planning and construction. His experience includes working with academia. and industry partners. In his PhD, he works on the CAV platoon, modelling at a microscopic level and exploring the management strategies to improve the overall traffic flow and efficiency in the presence of CAVs.​

Areas of Expertise: Future Mobility, Mobility Infrastructure, Active Mobility​

PhD Research: Platooning of Connected Autonomous Vehicles: Microscopic modelling and management strategies.​

Masters: Transportation Engineering from National Institute of Technology, Warangal, India.​

Marwa Ben Ali (PhD) 

Electric mobility researcher

Sfax, Tunisia

Marwa is a doctor of electrical engineering with a background in research regarding electric mobility, optimization, management, and analysis. She is passionate about sustainable systems, sources, and innovations that could accelerate the transition process. Now her research focuses on project and risk management, perspectives of the decarbonization project, and the necessity to accelerate the transition to more sustainable urban mobility solutions to create a cleaner and more reliable environment for worldwide citizens. 

Area of Expertise: Future mobility, Mobility and energy, and Mobility for all.

PhD Research: Electric mobility, optimization, management, and analysis in Tunisia.

Masters: Electrical Engineering from National Engineering School of Gabes, Tunisia.

Avital Angel (PhD Candidate)

Landscape architect

Haifa, Israel

Avital is a Landscape Architect, urban planner and PhD candidate at the Technion – Israel Institute of Technology, who specializes in walkability studies using big data technologies. She nurtures a keen interest in designing healthier, walkable urban environments and in implementing knowledge from research in practice. With experience in environmental planning, neighbourhood planning, landscape infrastructure and urban design in the public sector, Avital is investigating tempo-spatial dynamics of walking behaviour in the urban environment. More specifically, she is interested in the relationship between walking and attributes of the built environment at street-level, using big data technologies. Her research fields of interest also include urban /regional structure and the environment, active mobility, creating public realm, health promoting environments and smart cities.

Areas of Expertise: Walkability, pedestrian movement monitoring using big data technologies, tempo-spatial dynamics of walking behaviour, Bluetooth sensor technology, Mobile app data.

PhD Research: Street-level walkability and its influencing factors – A big data based research.

Masters: Urban and Regional Planning from Technion, Israel

Ravid Luria (PhD Candidate)

Urban and regional planner

Jerusalem, Israel

Ravid is doing his PhD research that deals with the factors of the digital mobility divide and its effect on accessibility and socio-spatial integration at the Technion Israel Institute of Technology. His academic work involves both quantitative and qualitative methods in the fields of urban planning, mobility, geography and sociology. Over the past seven years, he has been working as an urban planner in the municipality of Jerusalem. And recently he has been managing a team responsible for all plans in the quarters which he is in charge of, and that includes initiating plans or regulating plans, instructing planners, reviewing plans and writing decision drafts for plans for the local planning committee.

Areas of Expertise: Urban planning and the spatial and sociological aspects of mobility

PhD Research: Urban management instruments and strategies in facing Urban Heat Islands

Masters: Urban and regional planning focusing on active transportation from Hebrew University in Jerusalem

Sandra Karina Meza Parra (PhD Candidate) 

Urban management and valuation specialist

Sandra is an Architect with Master’s Degree in Urban Management and Valuation at Polytechnic University of Catalonia. Wide experience in proposing urban and rural strategies to improve the accessibility to educational services in Peru, as well as leading interdisciplinary work teams. Interested in the dynamics happening in the city and how these design the environment. Currently doing research about Urban Heat Islands, Cool Islands and Climate Shelters.

Areas of Expertise: Urban planning, urban and territorial management

Masters: Urban Management and Valuation from Universitat Politécnica de Catalunya, Barcelona

Shadi Haj Yahia (PhD Candidate)

Transportation, mapping, and geo-information engineer 

Shadi is a transportation, mapping, and geo-information engineer with a Master's degree in Transportation and Highways Engineering from the Technion. He is currently pursuing a PhD with a focus on utilizing emerging technologies, developing machine learning and data analysis methodologies for choice analysis, and understanding travel behaviour. Shadi is passionate about improving transportation infrastructure and mobility by applying innovative technologies to transportation systems.

Areas of Expertise:  Transportation Data Analysis, Future mobility, Intermodality, Sustainable city logistics

PhD Research: Integration of machine learning methods in travel demand models

Masters: Civil Engineering – Transportation and Highways Engineering (Cum Laude) from Technion, Israel

Anteneh Afework (PhD Candidate)

Sustainable mobility and safety specialist

Anteneh is a data-driven sustainable mobility and transport safety professional, with a master’s degree in civil engineering – specialized in transportation engineering. His endeavors are yielding substantial advances in scientific comprehension in the areas of sustainable urban mobility and transport safety. With over a decade of experience spanning academia and industry, he has successfully carried out several projects in various cities across three continents, enhancing their safety and livability. Anteneh is committed to accomplishing impactful research projects from ideation and design to implementation, reporting, and policy recommendations.

Areas of Expertise: Active mobility, Intermodality, Mobility for all, Mobility infrastructure, Safe mobility.

PhD Research: Modelling Road Safety in a Heterogeneous System as a Pillar of Sustainable Urban Mobility.

Masters: Transportation Engineering from the Indian Institute of Technology, MBA from Addis Ababa University.

Miklós Radics (PhD Candidate)

Seville, Spain

Miklós has an academic background in transportation engineering and urban planning. He is a doctoral student focusing on the potential adaptability and implications of the x-minute city concept. Miklós is an enthusiastic advocate of active mobility and liveable urban spaces. He has worked various years within these domains in the non-governmental, private, public, and academic sectors. His main interests and strengths lay in strategic planning and policy making. As a researcher he uses various tools for (spatial) data analysis and visualization, lately he is specialized in accessibility analysis. 

Areas of Expertise: Active Mobility, Mobility infrastructure, Pollution reduction

PhD Research: The Adaptability and Potentials of the 15-minute City Concept In Spain

Masters: Transportation Engineering from the Budapest University of Technology and Economics, Urban Studies from the Budapest University of Technology and Economics

Isabel Cunha (PhD Candidate)

Urban Planner

Porto, Portugal.

Isabel Cunha is an architect and urban planner with several years of professional and research experience on active mobility, GIS-based planning support tools, and mixed-method assessments. As a PhD candidate in Spatial Planning, Isabel is currently investigating how transport planners address equity issues in Bicycle Master Plans, measuring the effects of cycling infrastructure provision on the accessibility levels of distinct socioeconomic groups. Driven by the commitment to developing zero-carbon and fair transportation systems, her research aims to create an evidence base to promote a shift towards equitable planning practices, targeting planners, politicians, researchers, and practitioners engaged in sustainable mobility.

Areas of Expertise: Active Mobility, Mobility for all, Creating public realm

PhD Research: Bicycle Accessibility Planning Towards an Equitable Approach

Masters: Spatial Planning and Urban Project from University of Porto

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Director Impact Ventures

Daniela Rodrigues

Advisory Board coordinator

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prof. dr. ir. Peter H. Verburg

• Full Professor , Faculty of Science , Environmental Geography
• Phone +31 (0)20 59 83594

Room 8A-61 , NU-building

Institute for Environmental Studies (IVM) , De Boelelaan 1111

1081 HV Amsterdam

Personal profile

Personal information.

Peter Verburg is professor Environmental Spatial Analysis and leads the Environmental Geography group that is part of the Institute for Environmental Studies, Vrije Universiteit Amsterdam.

Peter is named a Highly Cited Researcher, his publications being amongst the top 1% most cited in the Web of Science. Peter has established a leading position in the field of land use analysis and modelling. He has developed and applied a wide range of methods to analyze spatial patterns of land use at scales from local to global. Methods used in his research originate from different disciplines, including social sciences (interviews, participatory workshops, multi-level statistics, foresight and scenario studies), econometrics (efficiency analysis, spatial econometrics), geography (accessibility analysis, spatial modelling, remote sensing) and earth sciences (biogeochemical modelling).

Having worked with collaborators from this wide variety of fields Peter has established insight in the different disciplinary traditions and vocabularies which facilitates interdisciplinary collaboration. His work on regional and global scale land use modeling has resulted in one of the most frequently used land use models worldwide (CLUE). Peter is the former chair of the  Global Land Programme of Future Earth  and is actively involved in several EU-level research projects in the field of land use, sustainable city planning, climate change adaptation, rural development and ecosystem services. He has published over 300 peer-reviewed articles and several book chapters. He has organized workshops and post-graduate courses on the topic of land use modelling and ecosystem services.

Currently he is a member of the Earth Commission (www.earthcommission.org), a member of the Science-Policy Interface of UNCCD, and co-Editor-in-Chief of the journal Landscape and Urban Planning.

Land change analysis, spatial analysis and modelling, landscape ecology, ecosystem services, interdisciplinary analysis of human-environment interactions, environmental decision making, scenarios, multi-functional land use, sustainable intensification, landscape aestetics.

2000: PhD. Land Use Modelling, Wageningen University.

1996: MSc. Physical Geography, Wageningen University.

Professional employment history

• 2010-present: Professor Environmental Spatial Analysis, Institute for Environmental Studies, VU University Amsterdam
• 2009-2010: Senior Researcher at Alterra, WUR
• 2003-2010: Assistant professor, Land dynamics group, Wageningen University
• 2001-2003: Post-doc, Faculty of Geographical Sciences, Utrecht University
• 2000-2003:  Post-doc, Laboratory of Soil Science and Geology, Wageningen University
• 1996-2000: Junior researcher, Department of Agronomy, Wageningen University

Publications

For an overview of my publications please visit my  ResearchGate  profile.

Ancillary activities

• Elsevier publisher | Amsterdam | co-editor-in-chief | 2019-01-01 - 2024-03-31
• Swiss Federal Research Institute WSL | Birmensdorf | Medewerker | 2020-03-01 - 2024-03-31

Ancillary activities are updated daily

Expertise related to UN Sustainable Development Goals

In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):

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• 1 Similar Profiles
• Land Earth and Planetary Sciences 100%
• Land Use Earth and Planetary Sciences 98%
• Model Earth and Planetary Sciences 83%
• Investigation Earth and Planetary Sciences 81%
• Area Earth and Planetary Sciences 73%
• Landscape Earth and Planetary Sciences 67%
• Ecosystem Service Earth and Planetary Sciences 66%
• Europe Earth and Planetary Sciences 61%

Collaborations and top research areas from the last five years

Dive into details.

Select a country/territory to view shared publications and projects

Research output

• 326 Article
• 13 Review article
• 6 Comment / Letter to the editor
• 4 Conference contribution
• 4 Editorial
• 2 Working paper
• 1 Inaugural speech
• 1 Erratum / Corrigendum

Research output per year

Living within the safe and just Earth system boundaries for blue water

Research output : Contribution to Journal › Article › Academic › peer-review

• Boundary 100%
• Surface Water 85%

Navigating tensions in inclusive conservation: Learning from the Utrechtse Heuvelrug National Park in the Netherlands

• Conservation 100%
• National Parks 100%
• Learning 100%
• Stakeholder 57%
• Approach 42%

Perceptions of ecosystem services and knowledge of sustainable development goals around community and private wetlands users in a rapidly growing city

• Knowledge 100%
• Consumers 100%
• Communities 100%
• Sustainable Development Goals 100%
• Urban Areas 100%

Securing Nature's Contributions to People requires at least 20%–25% (semi-)natural habitat in human-modified landscapes

• People 100%
• Humans 100%
• Landscape 100%
• Habitat 100%
• Biodiversity 100%

Supporting spatial planning with a novel method based on participatory Bayesian networks: An application in Curaçao

• Land Use 100%
• Spatial Planning 100%
• Utilization 100%
• Suitability 80%

Causes and Consequences of Environmental Change

Food systems & sustainability, grand challenges for sustainability, human-environment systems and sustainability transformations, land use change and ecosystems.

Projects per year

BrightspotsCSA: Bright spots of climate smart agriculture – learning from high-performing farming systems (BrightspotsCSA)

Schulp, N. , Levers, C., Verkuil, L. , Nooij, J. & Verburg, P. H.

1/04/22 → 31/03/27

Project : Research

• Farming System 100%
• Climate-Smart Agriculture 100%
• Climate 100%
• Agriculture 100%

CONtract SOLutions for Effective and lasting delivery of agri-environmental-climate public goods by EU agriculture and forestry

Schulp, N. , Harmanny, K., Verburg, P. H. , Hackbarth, T. X. , Farokhi, A. & van Rosmalen, R.

1/05/19 → 31/10/22

Monitoring tools

Schulp, N. & Verburg, P. H.

1/01/18 → 1/01/19

Land-use change modelling capacity building for the Mekong region

Verburg, P. H. & van Vliet, J.

1/04/14 → 1/07/17

TURAS: Urban Resilience and Sustainability

Schrammeijer, B. , Koks, E. , de Moel, H. , Schulze, K. , Derkzen, M. & Verburg, P. H.

1/10/11 → 1/10/16

• 9 Editorial work
• 7 Membership
• 5 Lecture / Presentation
• 4 Consultancy
• 1 Conference

Activities per year

Advocacy work on fossil fuel collaborations in academia

Petra Verdonk (Participant), Julia Schaumburg (Participant), C Kaupa (Participant), Niels Debonne (Participant), Edina Doci (Participant), Koen Lemaire (Participant), I. Maas (Participant), Marthe Wens (Participant), Hans Ossebaard (Participant), J. Leggett (Participant), Peter H. Verburg (Participant), PH Pattberg (Participant), M. Holland (Participant), D Iannuzzi (Participant), Pim Klaassen (Participant), Remco Kort (Participant) & Mathieu Blondeel (Participant)

Activity : Other

Land use impacts of the Chinese 2035 biodiversity strategy

Žiga Malek (Consultant), J van Vliet (Consultant), Peter H. Verburg (Consultant) & Elizaveta Khazieva (Consultant)

Activity : Consultancy

Land use modeling for the Integrated economic-environmental modeling + Ecosystem Services Modeling Platform

Žiga Malek (Consultant), Cecilia Zagaria (Consultant), Sean Goodwin (Consultant) & Peter H. Verburg (Consultant)

Ecosystem accounting geospatial data support

Žiga Malek (Consultant), Peter H. Verburg (Consultant) & Katharina Schulze (Consultant)

Climate change impacts on Fairtrade farmers

Žiga Malek (Consultant), Claudia Parra Paitan (Consultant) & Peter H. Verburg (Consultant)

Pantropical distribution of short-rotation woody plantations under current and future climate

Schulze, K. (Creator), Malek, Ž. (Creator), Schepaschenko, D. (Creator), Lesiv, M. (Creator), Fritz, S. (Creator) & Verburg, P. H. (Creator), DataverseNL, 18 May 2023

DOI : 10.34894/T3A3RM

Land degradation data for Central Asia

Khazieva, E. (Creator), Malek, Ž. (Creator) & Verburg, P. H. (Creator), DataverseNL, 26 Jun 2023

DOI : https://doi.org/10.34894/E3YYWC

Data and model for simulating land system changes in China (2015-2050)

Wang, Y. (Creator), van Vliet, J. (Creator), Debonne, N. (Creator), Pu, L. (Creator) & Verburg, P. H. (Creator), DataverseNL, 2021

DOI : 10.34894/O8ZHGT , https://doi.org/10.34894/O8ZHGT

Data from: Quantifying urban ecosystem services based on high-resolution data of urban green space: an assessment for Rotterdam, the Netherlands

Derkzen, M. L. (Contributor), van Teeffelen, A. J. A. (Contributor) & Verburg, P. H. (Contributor), Zenodo, 2016

DOI : 10.5061/dryad.kk504 , https://zenodo.org/record/4963697

CLUE model, CLUMondo model and various European and global datasets

Verburg, P. H. (Creator), VU University Amsterdam, 2017

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22 PhD jobs in Luxembourg

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• PhD positions in Esch-sur-Alzette (11)

Other countries

• PhD positions in Belgium (134)
• PhD positions in Netherlands (104)
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• PhD positions in Sweden (43)
• PhD positions in France (42)

Search results (22)

Doctoral researcher (PhD) in Cyber Policy

The Department of Law of the Faculty of Law, Economics and Finance of the University of Luxembourg is looking for a PhD candidate to conduct research in cyber policy.Your Role...The doctoral resear...

Doctoral candidates in Statistics and Probability

The University | About us...The MATHCODA Doctoral Training Unit is a doctoral program covering a coherent set of themes around the ideation and study of novel mathematical tools for dealing with hi...

Doctoral researcher (PhD) in Private International Law

The University of Luxembourg aspires to be one of Europe's most highly regarded universities with a distinctly international and interdisciplinary character. It fosters the cross-fertilisation of r...

Doctoral researcher in Psychology

The University | About us...The University of Luxembourg is an international research university with a distinctly multilingual and interdisciplinary character. The University was founded in 2003 a...

Doctoral researcher (PhD candidate) in Computer Science

The University of Luxembourg is an international research university with a distinctly multilingual and interdisciplinary character. The University was founded in 2003 and counts more than 6,700 students and more than 2,000 employees from around t...

Doctoral researcher (PhD) in Financial Law

The Department of law of the Faculty of Law, Economics and Finance of the University of Luxembourg is looking for a PhD candidate to conduct research in Financial Law with a specialization in Investment Fund Law.Your Role...The doctoral researcher...

PhD-positions in Finance

The Department of Finance at the University of Luxembourg invites students to apply for PhD-student positions in Finance.Your Role... The program follows the standard international format with rigorous coursework in the first year and for the rema...

PhD Candidate in Forming Effective Code Representations

(Valid from 25/11/2023 to 30/06/2024)   Language: English (UK) Country: Luxembourg   Organisation data: Interdisciplinary Centre of Security, Reliability and Trust Job Number: UOL06110 Contract Type: Fixed Term Contract Duration 36 Month Schedule ...

PhD candidates - Spontaneous Applications for Next Generation Radar Sensing Networks

(Valid from 22/01/2024 to 31/12/2024)   Language: English (UK) Country: Luxembourg   Organisation data: Interdisciplinary Centre of Security, Reliability and Trust Job Number: UOL05451 Contract Type: Fixed Term Contract Duration 36 Month Schedule ...

PhD Candidate in Artificial Intelligence and Software Engineering

(Valid from 09/02/2024 to 31/07/2024)   Language: English (UK) Country: Luxembourg   Organisation data: Interdisciplinary Centre of Security, Reliability and Trust Job Number: UOL06246 Contract Type: Fixed Term Contract Duration 36 Month Schedule ...

Doctoral candidate (PhD student) in ultra-reliable and energy-efficient intra-avionic wireless communication systems

(Valid from 15/01/2024 to 30/06/2024)   Language: English (UK) Country: Luxembourg   Organisation data: Interdisciplinary Centre of Security, Reliability and Trust Job Number: UOL06196 Contract Type: Fixed Term Contract Duration 36 Month Schedule ...

PhD position in Quantum Information Theory

The University of Luxembourg is an international research university with a distinctly multilingual and interdisciplinary character. The University was founded in 2003 and counts more than 6,700 students and more than 2,000 employees from around t...

PhD thesis in Statistical Physics of Active Matter

(Valid from 16/01/2024 to 31/12/2024)   Language: English (UK) Country: Luxembourg   Organisation data: Department of Physics and Materials Science (DPHYMS) Job Number: UOL06198 Contract Type: Fixed Term Contract Duration 36 Month Schedule Type: F...

Doctoral Researcher in Contemporary History

(Valid from 20/12/2023 to 31/05/2024)   Language: English (UK) Location Belval Country: Luxembourg   Organisation data: Luxembourg Center for Contemporary and Digital History Job Number: UOL06174 Contract Type: Fixed Term Contract Duration 36 Mont...

Doctoral researcher (PhD candidate) in Social Sciences

Doctoral researcher (phd) in law.

The Department of Law of the Faculty of Law, Economics and Finance of the University of Luxembourg is looking for a PhD candidate to conduct research in Law.Your Role...The doctoral researcher will...

PhD Position in Civil Engineering

Phd candidate for deployment and control of a distributed telescope as a formation of nanosatellites.

About the SnTSnT is a leading international research and innovation centre in secure, reliable and trustworthy ICT systems and services. We play an instrumental role in Luxembourg by fueling innova...

Doctoral candidate (PhD student) in the field of Spaceborne Global Navigation Satellite System Reflectometry

The University | About us...HORIZON EUROPE - MARIE SKŁODOWSKA-CURIE ACTIONS - DOCTORAL NETWORKSGNSS-R SATELLITE EARTH OBSERVATION (GLITTER)The Geophysics and remote Sensing (GRS) Laboratory is dedi...

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• © 2018

Trends in Spatial Analysis and Modelling

Decision-Support and Planning Strategies

• Martin Behnisch 0 ,
• Gotthard Meinel 1

Leibniz Inst of Eco Urb & Reg Dev (IOER), Dresden, Germany

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• Presents contributions from leading academics in the fields of spatial sciences, environmental studies, geography, cartography, GIS, urban planning, architecture, with a focus in investigations of settlements and infrastructure
• Multidisciplinary approach to studying recent developments in spatial analysis and modelling which contributes to a better understanding of built-up areas, sustainable resource management, planning and regional development, and spatial information knowledge
• Cooperation between the disciplines spatial planning, geography and computer science is useful for the complementary linkage of data and methods in the context of supporting spatial planning relevant impulses

Part of the book series: Geotechnologies and the Environment (GEOTECH, volume 19)

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36 Citations

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• Table of contents

About this book

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Table of contents (11 chapters)

Front matter, towards a better understanding of settlements and infrastructure, reverse engineering of land cover data: machine learning for data replication in the spatial and temporal domains.

• Galen Maclaurin, Stefan Leyk

Geospatial Analysis Requires a Different Way of Thinking: The Problem of Spatial Heterogeneity

Geographic data mining, a survey on spatiotemporal and semantic data mining.

• Quan Yuan, Chao Zhang, Jiawei Han

Contribution Towards Smart Cities: Exploring Block Level Census Data for the Characterization of Change in Lisbon

• Fernando José Ferreira Lucas Bação, Roberto Henriques, Jorge Antunes

The Application of the SPAWNN Toolkit to the Socioeconomic Analysis of Chicago, Illinois

• Julian Hagenauer, Marco Helbich

Spatial Modelling, System Dynamics and Geosimulation

The evolution of the land development industry: an agent-based simulation model.

• Jonatan Almagor, Itzhak Benenson, Daniel Czamanski

Dynamic Relationships Between Human Decision Making and Socio-natural Systems

• Andreas Koch

Lessons and Challenges in Land Change Modeling Derived from Synthesis of Cross-Case Comparisons

• Robert Gilmore Pontius Jr., Jean-Christophe Castella, Ton de Nijs, Zengqiang Duan, Eric Fotsing, Noah Goldstein et al.

Multi-scale Representation and Analysis

Applications of 3d city models for a better understanding of the built environment.

• Bruno Willenborg, Maximilian Sindram, Thomas H. Kolbe

An Automatic Approach for Generalization of Land-Cover Data from Topographic Data

• Frank Thiemann, Monika Sester
• Denise Pumain

Back Matter

• Developments in Spatial Analysis
• Settlements and Infrastructure
• Spatial Modeling
• Spatial Planning
• Spatial Planning Data
• landscape/regional and urban planning

Leibniz Inst of Eco Urb & Reg Dev (IOER), Dresden, Germany

Martin Behnisch, Gotthard Meinel

Martin Behnisch received his diploma and doctoral degrees at the Department of Architecture, Karlsruhe Institute of Technology. He also received a degree in Wood Processing Technologies (University of Cooperative Education, Dresden, Germany) and a master’s degree in Geographical Information Science (University of Salzburg, Austria) with distinction. He worked in Switzerland as a post-doctoral researcher (2007-2011) at the Institute of Historic Building Research (ETH Zurich). He is currently a senior scientist at the Leibniz Institute of Ecological Urban and Regional Development. His research interests are in spatial analysis and modeling, urban data mining, spatial monitoring, land use science as well as building stock research. He has published numerous refereed articles in international journals, scientific books and conference proceedings in his discipline.

Dr. Gotthard Meinel is specialist in the field of monitoring of land use development. His research interests are indicator development, automated spatial analysis of large datasets and visualization technologies. Since 1992 he has been acting as a project leader in the field of informatics, GIS and remote sensing at Leibniz Institute of Ecological Urban and Regional Development (IOER). Since 2009 he has been head of the research area “Monitoring of settlement and open space development” at IOER in Dresden. He received an M.S. in Information Technology in 1981 and a Ph.D. degree in Image Processing at Dresden University of Technology in 1987. Later he was a postdoctoral researcher in biomathematics and technical mathematics. He has published more than 100 research articles in international journals and refereed conference proceedings.

Book Title : Trends in Spatial Analysis and Modelling

Book Subtitle : Decision-Support and Planning Strategies

Editors : Martin Behnisch, Gotthard Meinel

Series Title : Geotechnologies and the Environment

DOI : https://doi.org/10.1007/978-3-319-52522-8

Publisher : Springer Cham

eBook Packages : Earth and Environmental Science , Earth and Environmental Science (R0)

Copyright Information : Springer International Publishing AG 2018

Hardcover ISBN : 978-3-319-52520-4 Published: 09 November 2017

Softcover ISBN : 978-3-319-84923-2 Published: 05 September 2018

eBook ISBN : 978-3-319-52522-8 Published: 24 October 2017

Series ISSN : 2365-0575

Series E-ISSN : 2365-0583

Edition Number : 1

Number of Pages : XIII, 214

Number of Illustrations : 30 b/w illustrations, 46 illustrations in colour

Topics : Geographical Information Systems/Cartography , Landscape/Regional and Urban Planning , Data Mining and Knowledge Discovery

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PhD Candidate in ‘Water flow modelling and flooding hotspot analysis in a changing climate’

The Water Resources and Ecosystems Department in IHE Delft conducts research and education activities that aim to support society in decisions and policy that sustain freshwater resources and ecosystems by balancing their protection, restoration, use, and development. We conduct research and provide post-graduate education and training to professionals in the fields of hydraulic engineering, hydrology/hydrogeology, aquatic ecosystems, while maintaining inter-disciplinary collaborations with other departments in IHE Delft and with international partners.

Description of the project and the role of the PhD candidate

IHE Delft Institute for Water Education seeks a PhD research candidate to conduct research related to water flow modelling and flooding hotspot analysis, as part of EU project SPRINGS “Supporting Policy Regulations and Interventions to Negate aggravated Global diarrheal disease due to future climate Shocks”. SPRINGS aims to better understand the impact of climate change on the burden of water-borne infectious disease. The PhD project will specifically focus on modelling of water flows in catchments and floodplains to map and analyse flooding hotspots in the present and future climates. You will also collaborate with SPRINGS partners, in particular with researchers at Vrije University Amsterdam and National Institute for Public Health and the Environment (RIVM).

The PhD candidate will be hosted by the Water and Ecosystems Department of IHE Delft, and supervised by Professor Dr. Ioana Popescu, Associate Professor Dr. Shreedhar Maskey and Dr. Arlex Sanchez. The PhD candidate will be also registered at the Technical University of Delft (TUD).

We are looking for a highly motivated and creative candidate with a strong computational background and an education in hydrology/hydraulics, water science and engineering, physics or a similar field. You will develop comprehensive (distributed/semi-distributed) hydrological models for two basins in Ghana and Romania using open source data, and develop an innovative methodology for flood inundation modelling in data-scarce regions. You will apply the developed methodology to the two case study areas for flooding hotspots mapping under past, current and future climate conditions.

Responsibilities

• Develop and apply distributed/semi-distributed hydrological models, primarily using open source data, for two case study basins in Ghana and Romania.
• Develop an innovative methodology for inundation modelling and flooding hotspot mapping for data-scarce region and apply the methodology in the two case study areas.
• Travel to the case-study regions for data collection and consultations.
• Develop and successfully defend a PhD research proposal.
• Publish the obtained results in peer reviewed journals, which will contribute to the final PhD thesis.
• Contribute to the preparation and reporting of deliverables of the SPRINGS project related to the PhD research.
• Contribute and participate in the project (SPRINGS) activities where necessary.
• Present the project results at national and international conferences and symposia.
• Contribute to IHE's education programme including mentoring MSc theses which are linked to this PhD research.
• Write and successfully defend a PhD thesis.

Requirements

• Master degree in hydrology, hydraulics, water science and engineering, physics, or closely related natural science or engineering disciplines.
• Proven experience of catchment - river system modelling (hydrological, hydrodynamic).
• Basic knowledge of chemistry and biology and affinity with water quality and related health risks.
• Proven skills of Python programming and GIS software (e.g. QGIS).
• Proven skill of handling hydro-meteorological time series and spatial data. Working experience with Google Earth Engine is an advantage.
• Knowledge of remote sensing-based and other open source data products for hydrological applications.
• Have affinity with and knowledge of climate change and impacts on water sector.
• Excellent communication skill in English and proven skill in scientific writing.
• Willingness to mentor master theses of MSc students and have a collaborative attitude.
• Willingness to work in and value a multi-cultural work environment and diversity.

Terms of employment

This is a position for 48 months (4 years), full time, with the expectation that the candidate will submit and successfully defend the PhD thesis within this period. The candidate will be stationed in Delft, the Netherlands. Employment at IHE Delft is according to the Collective Labour Agreement Dutch Universities (scale P). The appointment implies entry into the Netherlands' Civil Service Pension Fund (ABP).

The initial contract is for 18 months. Within the first year a go/no-go decision will be made based on a detailed PhD research proposal to be developed by the candidate, which will determine whether or not the contract will be extended.

The expected start date of the position is 1 July 2024 or as soon as possible thereafter.

Information and application

Additional information about the scope of the work and scientific content can be obtained from Dr. Shreedhar Maskey ([email protected]).

Applications (in English) should respond specifically to the ‘requirements’ mentioned above, and can be sent by 8 May 2024  (end of day CET) including 1) a motivation letter, 2) curriculum vitae, 3) abstract of your MSc thesis (1 page), 4) names and contact details of two contactable referees, and 5) a copy of the transcripts of your MSc degree. 

Interviews with short listed candidates will likely take place in the third week of May 2024.

IHE Delft follows an open procedure of recruitment, which respects diversity and provides equal opportunity to applicants of all backgrounds.

Please see for more information about the PhD programme and requirements: PhD Programme | IHE Delft Institute for Water Education (un-ihe.org).

Please use the ''apply'' button on the bottom of the page, applications through email will not be taken into consideration. 

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