Examining spatial expansion and stemming strategies of urban shrinkage

In the past we have written about how one can study urban shrinkage with a specific emphasis on Detroit from both an agent-based modeling perspective and also from analyzing newspapers through natural language processing  Keeping with the theme of Detroit and urban shrinkage we (Xiaoliang Meng, Yichun Xie, Junyi Wu, Heather Khan Welsh,  Shi Zeng and myself) have a new paper entitled "Examining spatial expansion and stemming strategies of urban shrinkage: evidence from Detroit, USA" which was recently published in npj Urban Sustainability. 

In this paper we introduce a method for studying urban shrinkage by constructing multi-scale spatial structures based on urban network connectivity which we call gravity-networked spatial interaction zones-based spatial panel modeling or GSIZs-Spanel for short. We demonstrate this method by exploring the spatial processes and scopes of past urban shrinkage in Detroit between 2000 and 2020.  If this sounds of interest, below you can read the abstract to the paper, along with the conceptual design of GSIZs-Spanel modeling framework and some of our results. While at the bottom of the post you can find the full referece and link to the paper. 

Abstract:

This study introduces a new modeling paradigm called gravity-networked spatial interaction zones-based spatial panel modeling (GSIZs-Spanel). Using Detroit as a case study, this paper investigates urban shrinkage by integrating shrinkage driving factors, their regional interactions, networks of cities, spatial processes, and longitudinal dynamics. Results suggest that high minority population concentration and persistent poverty are the primary factors impacting Detroit’s inner-city shrinkage. Demographics, economics, and development practices affect shrinkage in suburbs and surrounding cities. Shrinkage spreads outwards like waves; different juxtapositions of driving factors affect shrinkage resilience; spillover effects are particularly vibrant at 25–50 GSIZs; rightsizing is a rational strategy, but it failed to work alone. Integrating spatial planning of driving factors, land uses, spillover effects, rightsizing strategy, and regional collaboration among federal, regional, and local organizations could moderate urban decline. GSIZs-Spanel, which was developed here, could be applied in any U.S. city or other global city.

The conceptual design of GSIZs-Spanel modeling framework.

Patterns of spillover effects of the Spanel models at the 5-incremental spatial clusters. (a: Spatial processes of urban shrinkage. b: Spatial patterns of vacancy severity.)

Spillover effects of the Spanel models at the 5-incremental spatial clusters

Full Reference: 

Meng X., Xie, Y., Crooks, A.T., Wu J., Khan-Welsh, H. and Zen, S. (2025), Examining spatial expansion and stemming strategies of urban shrinkage: evidence from Detroit, USA, npj Urban Sustainability, 5: 52. Available at https://doi.org/10.1038/s42949-025-00245-5 (pdf)

New Editorial: Generative AI and Urban Modeling

In the current issue of Environment and Planning B, we (Boyu Wang, Na Jiang and myself) have a new editorial entitled "Generative AI and Urban Modeling". The premise of this editorial is that Generative AI (GenAI) is impacting all aspects of our daily lives and as such has we were wondering how will it impact urban modeling? 

For example, in the editorial we discuss how  GenAI could speed up the overall urban modeling process. To demonstrate this we show how ChatGPT (and its built-in coding interface Canvas) can take published papers and build agent-based models from them (one being of an abstract space and another being spatially explicit). 

However, while model building is time consuming task, another challenge modelers face is how to incorporate decision making within them. To this end we also discuss how large language models (LLMs) have the potential to help with  agent-decision making in the form of generating  agent-personas or scheduling agent activities. 

We conclude the editorial with a series of questions: how will GenAI impact urban modeling? Will it open up the field to more people without the need for strong coding skills? Will we see growth in using LLMs for generating behavior? Will GenAI lead to a new generation of modeling toolkits? While these are only a short list of questions, they also raise concerns that relate back to some of the more thorny issues of urban modeling, that of verification and validation. 

If this sounds of interest you can read the full editorial here. 

Full Referece: 

Crooks, A.T., Jiang, N. and Wang, B. (2025), Generative AI and Urban Modeling, Environment and Planning B, 52(6), 1277-1281. (pdf)

CUPUM 2025

I have just gotten back from attending the 19th International Conference on Computational Urban Planning and Urban Management (CUPUM) in London and thought I would  share the two papers we presented at the conference. 

The first paper was with Qingqing Chen and Linda See and was entitled "Using New Sources of Data for Urban Climate Modeling Generated through MLLMs on Street View Imagery. "As the title might suggest, this paper was about how one can leverage multi-modal large language models (MLLMs) to extract information on building height, age and function from street level photographs. We demonstrate this using street view images from Mapillary and than ask ChatGPT to estimate the building height, age and function and compare the results to authoritative data sources. If this sounds of interest, below you can see the abstract to the paper, some if the figures (i.e., the work flow and prompts) while the results can be seen in the attached paper (see the link below).

Abstract:

Urban climate and energy balance models require data on the form and function of buildings, but high resolution spatially explicit data sets are often lacking. Here we demonstrate how multi-modal large language models (MLLMs) can be used to extract information on building height, age and function from street level photographs for New York City. A workflow is presented that illustrates the approach, with initial results indicating that the building function can be identified with good accuracy while moderate accuracies were obtained for building heights and age. Suggestions for how to improve these accuracies are also provided. 

KEYWORDS: Buildings, ChatGPT, Multi-modal Large Language Models (MLLMs), Mapillary, Street View Images (SVI).

An overview of research workflow.

The detailed description of multi-step prompting and an example of extracted building attributes information.

Full Reference:

Chen, Q., See, L. and Crooks, A.T. (2025), Using New Sources of Data for Urban Climate Modeling Generated through MLLMs on Street View Imagery. In Cramer-Greenbaum, S., Dennett, A., and Zhong, C (eds.), Proceedings of the 19th International Conference on Computational Urban Planning and Urban Management (CUPUM), London, UK. (pdf)

We then moved back to agent-based modeling with a paper with entitled "Enhancing Spatial Reasoning and Behavior in Urban ABMs with Large-Language Models and Geospatial Foundation Models" which brought back together Nick Malleson, Alison Heppenstall, Ed Manley and myself. In this paper we discuss the potential role of LLMs and geospatial foundation models in the context of agent-based modeling. If this sounds of interest, below you can read the abstract to the paper and find a link to it at the bottom of the post. Nick has also shared the slides of this presentation here. 

Abstract: 

Modeling human behavior continues to be a significant challenge for the field of agent-based modeling, and one that prohibits the development of comprehensive empirical ABMs for urban applications, such as Urban Digital Twins. However, two recent methodological advances offer the potential to transform empirical agent-based models.

Early evidence suggests that large-language models (LLMs) can be used to represent a wide range of human behaviors, with models responding in realistic ways to given prompts. Indeed there is already a flurry of activity that focusses on implementing LLM-backed agents -- i.e. agents who are controlled by LLMs. At the same time, the concept of the foundation model is also being applied in domains beyond text analysis. Of particular interest are geospatial foundation models that automatically encode spatial data in such a way as to associate different spatial objects in numerous and nuanced ways that have otherwise alluded manual classification schemes. Taken together, these two technologies offer considerable potential for a new generation of agent-based models that contain agents who can behave in response to spatial and social prompts in a way that is realistic and has so far proven impossible to replicate using manually-programmed behavioral rules.

This paper presents a discussion of the state of the art in both LLMs and geospatial foundation models in the context of their potential role in agent-based modelling. It discusses the transformational potential of these technologies and outlines the critical questions that need to be addressed before they can be used to create robust, reliable and trustworthy models for empirical policy applications that support decision-making.

KEYWORDS: Agent-based Modeling; Large language model; Geospatial foundation model; Urban Modeling.

Full Reference:

Malleson, N., Crooks, A.T., Heppenstall, A. and Manley, E. (2025), Enhancing Spatial Reasoning and Behavior in Urban ABMs with Large-Language Models and Geospatial Foundation Models. In Cramer-Greenbaum, S., Dennett, A., and Zhong, C (eds.), Proceedings of the 19th International Conference on Computational Urban Planning and Urban Management (CUPUM), London, UK. (pdf)

Talks: ABM, AI and other Thoughts

This is a slightly different post to normal, in the sense its not really about papers but my take on agent-based modeling, urban analytics and the growth of Artificial Intelligence impacting both. 

First up, while I was in Santa Fe last October for the 2024 International Conference of the Computational Social Science Society of the Americas  I was interviewed by John Cordier from Epistemix for their Flux Podcast which resulted in this "From Micro-Behaviors to Macro-Patterns: Exploring Agent-Based Models with Andrew Crooks. Rather than me trying to sum it up I will just quote from the podcast episode 

"In this episode of The Flux, host John Cordier sits down with Andrew Crooks ..... They dive into the world of agent-based modeling (ABM) - what it is, why it matters, and how it helps us simulate and better understand human behavior in complex systems. From simulating traffic jams to modeling social influence on vaccine uptake, Andrew shares how data, geography, and synthetic populations are revolutionizing our ability to forecast and inform decisions. They also explore the growing role of AI tools in democratizing modeling, the evolution of computational capabilities, and even ask: what if we had run a simulation before Brexit?"

If this sounds of interest, you can listen to the full podcast here. 

Next up, I was asked to give a talk back in late May to give a seminar talk at the Department of Geography and Spatial Sciences (GSS) at the University at Delaware hosted by Yao Hu. The title of the talk was "Monitoring and Analyzing Cities through the Lens of Urban Analytics" In this talk I reflect what urban analytics means to me and how the field is changing. If this sounds of interest, below you can read the abstract to my talk and also see the recording. However, before ending this I would really like to thank Yao for hosting me, and the others from the GSS and the universty at large for making it a great visit and being an engaged audience. 

Abstract: 

For the first time in human history, more people are living in cities than rural areas and this trend is only expected to grow in the coming decades. This growth will place unprecedented challenges on cites with respect to sustainable development especially in light of climate change and increasing populations. One way to explore and understand cities is through the lens of urban analytics, a set of methods that allow us to monitor, analyze and model urban areas. This talk will explore how urban analytics has changed over time and showcase how our understanding of cities has benefited from it. I will showcase how new sources of data can be used to monitor and analyze cities and how in turn these can be integrated into models to explore various aspects of city life from pedestrian movement to urban growth. The talk will conclude with a discussion and demonstration of how artificial intelligence can be integrated into the urban analytics toolbox and what opportunities and challenges it poses.

Also in late May, Alison Heppenstall, and myself were interviewed by Dr. Andy Collins discussing as part of the Computational Social Science Society of the Americas (CSSSA) webinar series on Agent-based modeling and simulation (ABMS). To quote from CSSSA, the purpose of these webinars is that: 

"Agent-based modeling and simulation (ABMS) has been applied far and wide to better understand our world. Each new application domain brings with it existing cultures of the domain's experts, including expectations and requirements. As such, it is foolhardy to expect agent-based modeling to be standardized across all domains. As practitioners, there is a desire to understand how these domain cultures differ, how they use agent-based modeling, and what the future of agent-based modeling is within those domains. To start to grapple with these grand questions, for the ABMS community, we are proposing to run a series of interviews with experts from different domains to try to map the world of agent-based modeling."

Readers, might not be surprised but we were asked to discuss ABM in the context of geography. So if you want to hear us discuss ABM and geography, you can see the talk below. It should also be noted the CSSSA has a whole host of other webinars on their YouTube Channel. 

Finally, at the start of May, I was invited to give one of the keynotes at the Inaugural AI and Cities: An International Forum for Innovation and Collaboration hosted by University of Florida entitled "Artificial intelligence and Urban Analytics: Opportunities and Challenges."  This talk is slightly different from the others as the focus was more on AI, so if you are wondering what my take on AI is (or my current research), you can read the abstract to the talk below and also find a link to the recording of it. 

Abstract: Urban areas now provide homes for more people than ever before, and with more and more people living in cities achieving sustainable cities is crucial for the betterment of all. Coinciding with the growth of the world’s population is the growth of artificial intelligence (AI) is which is becoming pervasive in all aspects of our daily lives. In this talk I will discuss how AI is offering us new opportunities when it come studying cities, specifically, through the lens of urban analytics. Urban analytics can be broadly defined a set of methods to explore, understand and predict the properties of cities. Through a series of examples, I will highlight how AI especially through the use of multimodal large language models (LLMs) is offering accessible methods for geographic information extraction and modeling of cities. I will showcase how AI can improve the granularity of urban data collection while at the same time provides more advanced GIS tools to practitioners in a more accessible and user-friendly way. However, AI alone is not the panacea when it comes to archiving urban sustainability and many challenges exist and the talk with conclude with these.

If the abstract sounds interesting click here to watch the talk.  Also the other keynotes talks are also available online here. 

Crowdsourcing dust storms utilizing social media data

In the past we have explored how social media can be used to delineate earthquakes, study human-wildlife interactions, understand urban morphology, urban smells or  locating wildfires among many other things. 

Keeping with the last topic (i.e., locating things), in a new paper published in GeoJournal entitled "Crowdsourcing dust storms in the United States utilizing social media data," Stuart Evans, Festus Adegbola and myself explore how we can use X (formerly Twitter) and Flickr  to source observations of windblown dust. 

As such the paper demonstrates how social media data can act as supplementary source for dust events monitoring and captures the seasonal trends of such events. Furthermore, the paper highlights the potential of using crowdsourced data for the often overlooked field of dust monitoring that has substantial health and economic impacts. 

If this sounds of interest, below we provide the abstract to the paper along with some figures which showcase our methodology and comparison with National Weather Service dust advisories and VIIRS satellite data. At the bottom of the post, you can find the full reference to the paper along with a link to it. 

Abstract: 

Dust storms and other dust events are natural phenomena characterized by strong winds carrying large amounts of fine particles which have significant environmental and human impacts. However, capturing the occurrence of such phenomena is a challenge. Previous studies have limitations due to available data, especially regarding short-lived, intense dust storms and events that are not captured by observing stations and satellite instruments. In recent years, the advent of social media platforms has provided a unique opportunity to access vast amounts of crowdsourced data. This paper explores the utilization of Flickr and X (Twitter) data to study dust event occurrences within the United States and their correlation with National Weather Service (NWS) advisories. The work ascertains the reliability of using crowdsourced data as a supplementary source for dust events monitoring. Our analysis of Flickr and X indicates that the Southwest region is most susceptible to dust events, with Arizona leading in the highest number of occurrences. On the other hand, the Great Plains show a scarcity of crowdsourced data related to dust events, which can be attributed to the sparsely populated nature of the region. Furthermore, seasonal analysis reveals that dust events are prevalent during the Summer months followed by Spring. These results are consistent with previous traditional studies that did not use social media of dust occurrences in the U.S., and Flickr-identified images of dust events show substantial co-occurrence with regions of NWS dust warnings. This paper highlights the potential of using crowdsourced data for the often overlooked field of dust monitoring that has substantial health and economic impacts.

Keywords: Dust storms, Crowdsourcing, Social media, Weather. 

 

Flowchart of our workflow

Selected posts retrieved from X showing active dust events.

Selected images retrieved from Flickr showing active dust events.

Map showing the distribution of flickr-identified dust event occurrences, X-identified dust event occurrences, National Weather Service dust advisories, including dust storm (DS) warnings and blowing dust (DU) advisories.

Seasonal cycle of dust events using social media metadata, the National Weather Service advisories, and the VIIRS satellite data.

Examples of social media identified dust events and satellite observations for the same day. Brown shaded pixels indicate locations Suomi-VIIRS observed dust particles. Any VTEC warnings issued by NWS for the location are shown after the date of each dust event, with HWW and DSW indicating High Wind Warning and Dust Storm Warning, respectively.

Full Referece: 

Adegbola, F., Crooks, A.T. and Evans, S.M. (2025). Crowdsourcing dust storms in the United States utilizing social media data. GeoJournal, 90(3), pp.1-18. Available at https://doi.org/10.1007/s10708-025-11359-9 (pdf)