As a spatial data scientist and urban geographer, my research is focused broadly on better understanding how urban spatial structure and transportation systems influence economic, environmental, and social outcomes in order to help solve the complex ongoing crises facing our interconnected global society in the 21st century: economic inequality, climate change, widespread health disparities, and other forms of social, racial, and environmental injustice. Cities are complex systems, and their spatial organisation has a direct impact on basically every feature of human life: how our economy functions, how new ideas are generated, how people have access to jobs, how they interact with their friends and strangers, how they get exercise, how they get their food, how they vote, and how much they pollute – and the decisions that city governments make (or don’t make) thus have far-reaching impacts on people’s health, economic behaviour, and relative social advantage or disadvantage.

I study these topics mostly using quantitative, data-driven approaches because I believe that “data is power,” and although all policy decisions inherently occur in a political framework (and structural political change is certainly a prerequisite for solving these complex problems), quantitative data and statistical analysis – when properly applied and understood – can provide powerful evidence in favour of particular policies that can foster beneficial change in the world. To make change, we always need to know the empirical situation: the drivers, consequences, and outcomes of urban spatial structure and planning policies. And as the data and methods used to characterise the empirical situation become more complex, we need to likewise sharpen our understanding and interpretation of these data and methods, which includes understanding how spatial ways of thinking and explicitly spatial methodological approaches can be used to analyse large datasets and can be better integrated into conventional statistical and newer machine learning methods.

Specifically, my recent research focuses on understanding how machine learning (ML) and artificial intelligence (AI) approaches can be designed to: 1) more explicitly integrate spatial information and spatial ways of thinking, 2) assess problems of causal inference, and 3) provide better insight into the explanatory relationships driving model results. I am currently working on a range of projects in this area, including: developing causal machine learning models for spatial data, designing a community-focused health + environment spatial data dashboard for the Dublin 8 neighbourhood, developing an AI tool to help increase building energy retrofit uptake, and analysing non-auto commuting patterns in Dublin. I am an Affiliate Member of the Maynooth University Hamilton Institute (2022), an Academic Collaborator at the ADAPT Centre for Digital Media Technology in the Digital Content Transformation (DCT) Strand (2022), a Fellow of the Center for Spatial Data Science at the University of Chicago (2021), and received my PhD in Geography from Michigan State University in 2018. 

Follow me on Twitter @KevinCredit for periodic research updates, and check out my profiles on Planetizen (where I have taught courses on location optimisation), Google ScholarResearchGate, and LinkedIn

Research Interests

Spatial data science, machine learning, non-auto transportation, entrepreneurship, health geography, retail location, energy, and greenhouse gas emissions

Research Projects

Title Role Description Start date End date Amount
D8 Health + Environment Dashboard PI The goal of this project is to create a community-focused, interactive, and open source data dashboard to help inform local planning, decision-making, and investment. Through a partnership with An Taisce, Smart D8, and other community stakeholders, we will accomplish this by first engaging with the public to understand the specific health and environment data needs of the community, then collecting and combining data on key metrics, creating new data through citizen science projects where needed. Importantly, we plan to make these metrics available in a straightforward, easy-to-use, online spatial data platform that provides local area and neighbourhood-wide reports and maps. 16/12/2022 15/09/2023 11099
Exploring realistic pathways to the decarbonization of buildings at urban scale: a case study of Dublin city PI 02/01/2023 30/06/2024 71093
ADDITIONAL FUNDING - Exploring realistic pathways to the decarbonization of buildings at urban scale: a case study of Dublin city PI 01/07/2023 30/06/2024 71093
Promoting the active mobility of children from disadvantaged backgrounds: a comparative approach between Dublin and Strasbourg Co-I This project investigates the active mobility of children in Ireland and France, with a particular focus on disadvantaged urban areas in Dublin and Strasbourg. The aim of this project is to better understand potential structural barriers to child active mobility in urban areas of disadvantage. How are children’s opportunities for active mobility in Dublin and Strasbourg shaped by the built environment in each city, and what are the main similarities and differences between the two contexts? How does the provision of infrastructure for children compare in the two cities, and what is the potential impact on children’s mobility? 01/01/2024 31/12/2025 2685
A Just Transition for Housing in Ireland Co-I The overall concept of the project is about building on cutting edge international research on the social justice implications of low-carbon and climate resilient development, applying innovative methodological approaches in the context of an urgent environmental and social challenge, and ensuring that findings are translated into actionable policy recommendations and communicated effectively to key stakeholders and decision-makers. 01/01/2024 31/12/2025 506202

Peer Reviewed Journal

Year Publication
2024 Credit, K.; Kekezi, O.; Mellander, C.; Florida, R. (2024) 'Third places, the connective fibre of cities and high-tech entrepreneurship'. Regional Studies, . [Link] [DOI]
2023 Credit, K.; Lehnert, M. (2023) 'A structured comparison of causal machine learning methods to assess heterogeneous treatment effects in spatial data'. Journal of Geographical Systems, . [Link] [DOI]
2022 Credit, K; Arnao, Z (2022) 'A method to derive small area estimates of linked commuting trips by mode from open source LODES and ACS data'. Environment And Planning B: Urban Analytics And City Science, . [DOI] [Full-Text]
2021 Credit, K (2021) 'Spatial Models or Random Forest? Evaluating the Use of Spatially Explicit Machine Learning Methods to Predict Employment Density around New Transit Stations in Los Angeles'. Geographical Analysis, . [DOI] [Full-Text]
2021 Credit K.; van Lieshout E. (2021) 'The pandemic economy: Exploring the change in new business license activity in chicago, usa from march – september, 2020'. Region, 8 (2):29-56. [DOI] [Full-Text]
2021 Credit K.; Dias G.; Li B. (2021) 'Exploring neighbourhood-level mobility inequity in Chicago using dynamic transportation mode choice profiles'. Transportation Research Interdisciplinary Perspectives, 12 . [DOI] [Full-Text]
2021 Ballantyne P.; Singleton A.; Dolega L.; Credit K. (2021) 'A framework for delineating the scale, extent and characteristics of American retail centre agglomerations'. Environment And Planning B: Urban Analytics And City Science, . [Full-Text]
2020 Credit K. (2020) 'Neighbourhood inequity: Exploring the factors underlying racial and ethnic disparities in COVID-19 testing and infection rates using ZIP code data in Chicago and New York'. Regional Science Policy And Practice, 12 (6):1249-1271. [DOI]
2019 Credit K. (2019) 'Transitive properties: a spatial econometric analysis of new business creation around transit'. Spatial Economic Analysis, 14 (1):26-52. [DOI] [Full-Text]
2019 Credit K. (2019) 'Accessibility and agglomeration: A theoretical framework for understanding the connection between transportation modes, agglomeration benefits, and types of businesses'. Geography Compass, 13 (4). [DOI] [Full-Text]
2019 Mack E.; Credit K. (2019) 'New Business Activity and Employment Dynamics in the Inner City: The Case of Phoenix, Arizona'. Urban Affairs Review, 55 (2):530-557. [DOI] [Full-Text]
2019 Credit K.; Mack E. (2019) 'Place-making and performance: The impact of walkable built environments on business performance in Phoenix and Boston'. Environment And Planning B: Urban Analytics And City Science, 46 (2):264-285. [DOI] [Full-Text]
2019 Kevin Credit, Elizabeth Mack, and Sarah Wrase (2019) 'A Multi-Regional Input-Output (MRIO) Analytical Framework for Assessing the Regional Economic Impacts of Rising Water Prices'. Review of Regional Studies, 49 (2). [Link] [Full-Text]
2018 Mack E.A.; Credit K.; Suandi M. (2018) 'A comparative analysis of firm co-location behaviour in the Detroit metropolitan area'. Industry and Innovation, 25 (3):264-281. [DOI] [Full-Text]
2018 Credit K. (2018) 'Transit-oriented economic development: The impact of light rail on new business starts in the Phoenix, AZ Region, USA'. Urban Studies, 55 (13):2838-2862. [DOI] [Full-Text]
2018 Credit, K; Mack, EA; Mayer, H (2018) 'State of the field: Data and metrics for geographic analyses of entrepreneurial ecosystems'. Geography Compass, 12 . [DOI]
2017 Mack E.A.; Tong D.; Credit K. (2017) 'Gardening in the desert: A spatial optimization approach to locating gardens in rapidly expanding urban environments'. International Journal of Health Geographics, 16 (1). [DOI] [Full-Text]

Book Chapter

Year Publication
2023 Kevin Credit; Irene Farah; Luc Anselin (2023) 'The Ups and Downs of Retail: 2000 – 2015' In: Streetlife: The Future of Urban Retail. Toronto, Canada : University of Toronto Press.
2018 Kevin Credit and Elizabeth Mack (2018) 'The Intra-Metropolitan Geography of Entrepreneurship: a Spatial, Temporal, and Industrial Analysis (1989-2010)' In: Geographies of Entrepreneurship. New York : Taylor and Francis.

Conference Contribution

Year Publication
2024 Xiao, Q.; Liu, D.; Credit, K. (2024) International Conference on Learning Representations (ICLR) 2024 Model Failure or Data Corruption? Exploring Inconsistencies in Building Energy Ratings with Self-Supervised Contrastive Learning Austria, . [Link]
2024 Credit, K.; Xiao, Q.; Lehane, J.; Vazquez, J.; Liu, D.; de Figueiredo, L. (2024) PLEA 2024 LuminLab: An AI-Powered Building Retrofit and Energy Modelling Platform Poland, . [Link]
2024 Credit, K.; Farah, I.; Talen, E.; Anselin, L.; Ghomrawi, H. (2024) Proceedings of the 32nd GISRUK Conference The Walkable Accessibility Score (WAS): A spatially-granular open-source measure of walkability for the continental US from 1997-2019 UK, . [Link]
Certain data included herein are derived from the © Web of Science (2024) of Clarivate. All rights reserved.

Professional Associations

Description Function From / To
Center for Spatial Data Science Fellow 01/01/2021 -
Geography Society of Ireland Member 01/08/2021 -
American Collegiate Schools of Planning Member 01/01/2014 -
Association of American Geographers Member 01/08/2014 -
Regional Science Association International Member 01/11/2015 -
ADAPT Centre for Digital Media Technology Academic Collaborator 01/01/2022 -

Honors and Awards

Date Title Awarding Body
01/01/2022 Best Paper in Regional Science Policy and Practice Regional Science Policy and Practice


Employer Position From / To
Maynooth University Assistant Professor 01/02/2021 -
University of Chicago Assistant Director for Urban Informatics, Assistant Instructional Professor in GIScience 01/07/2018 - 31/01/2021


Start date Institution Qualification Subject
01/09/2006 University of Kansas BA English
01/09/2015 Michigan State University PhD Geography
01/09/2010 Kansas State University Masters Regional and Community Planning

Other Activities


Teaching Interests

Maynooth University
Data Analytics Project (Spring 2021-current)
GY638: Geographic Information Systems in Practice (Spring 2022-current)
Geography Research Workshops - Geographies of Entrepreneurship and 'Churn' (Spring 2023)
GY208: Field Methods and Data Analysis (Spring 2023-current)

University of Warsaw
Short course on Causal Machine Learning for Spatial Data (Fall 2023)

University of Chicago
Introduction to GIS and Spatial Analysis for Social Scientists
Geographic Information Science I
GIScience Practicum
Social Science Inquiry: Spatial Analysis III
Introduction to Location Analysis
Transportation Geography
Introduction to Urban Planning

Michigan State University
Introduction to Physical Geography
Introduction to Economic Geography
Location Theory and Land Use Analysis