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.