At the AAG Annual meeting this year, two of my students gave talks about their ongoing research. Ying Zhou presented her work with a talk entitled "Exploring the Relationship between Urban Morphology and People’s Emotions." In this talk, Ying showed how one could mine social media posts to gain a sense of how different emotions are spatially spread around a city using New York city as a case study. If this sounds of interest, below you can see the abstract of the talk, the research methodology and a sample of the results.
Abstract:
Urban morphology records physical information about spatial patterns (e.g., streets and land use) and their evolution over time, as well as human settlement information. People who live in or visit a city gain experiences through interaction with its spatial patterns, and these experiences influence people’s emotions. Therefore, it is necessary to explore the spatial relationships between urban morphology and people’s emotions. Taking New York City as a case study, this research uses social media data to obtain and locate people's emotions in different parts of the city. To extract the emotion relating to specific space, we use the RoBERTa-based model to label texts in social media with six primary emotions (i.e., happiness, sadness, fear, anger, surprise, and disgust). We then used DBSCAN to identify spatial clustering features of these emotions. Finally, we compared the clustered emotions with urban morphology (both in terms of both its form and function) and how such emotions evolve and change over a span of five years. Such analysis reveals the relationship between people’s emotions and broader setting that they inhabit (i.e., the city). Moreover, these works offer bottom-up insights into how urban morphology shapes people’s feelings, which can serve as feedback for urban planning and management.Keywords: Urban Morphology, Emotion Detection, Spatial Analysis, Urban Studies.
While in another talk, Boyu Wang continues to add new functionality to the Mesa, a python agent-based modeling toolkit, this time in the form of utilizing large language models for agent-based decision making, with a talk entitled "Mesa-LLM: Generative agent-based modeling with large language models empowered agents"
If this sounds of interest, below you can see the abstract of the talk, along with the Mesa-LLM architecture. While further details about Mesa-LLM can be found on Boyu's GitHub page: https://github.com/mesa/mesa-llm.
Abstract
Agent‐based models (ABMs) have long been used to examine how individual behaviors give rise to aggregated social and spatial phenomena. Mesa, an open source ABM library in Python, provides modular components and browser based visualization to create and analyze agent based models in the PyData ecosystem. Agents’ behaviours in these models are often governed by rule-based decisions. The recent advancements of large language models (LLMs) have created a new paradigm, namely generative agent-based modeling, where LLMs are integrated as decision-making engines so that agents can communicate, negotiate, and decide based on natural language. In this paper, we introduce Mesa-LLM, an LLM extension to the Mesa framework. Its modular design allows users to customize reasoning, memory and planning components and plug in different LLMs (e.g., GPT, Gemini, Llama). We demonstrate Mesa-LLM through Epstein’s civil violence model. In contrast to the classical model where agents act based on calculated probabilities and pre-defined thresholds, agents through Mesa-LLM have their decisions articulated in natural language. This demonstration shows how an archetypal ABM can be enriched by language-based decision making to explore complex social dynamics such as protest escalation. Through this simple example, we highlight how incorporating LLMs into ABMs opens new possibilities for geographers to model human behavior from the bottom up by leveraging generative artificial intelligence (GenAI).
Keywords: Agent-Based Modeling, Large Language Model, AI Agent, Python.
References
Wang, B., Frisch, C., Nair, S., Kazil, J. and Crooks, A.T. (2026), Mesa-LLM: Generative Agent-Based Modeling with Large Language Models Empowered Agents, The Association of American Geographers (AAG) Annual Meeting, 17th –21th March, San Francisco, CA. (pdf)
Zhou, Y. and Crooks, A.T. (2026), Exploring the Relationship between Urban Morphology and People’s Emotions, The Association of American Geographers (AAG) Annual Meeting, 17th –21th March, San Francisco, CA. (pdf)
