Presentations at the AAG

At the recent American Association of Geographers (AAG) Annual Meeting in Honolulu, Hawaii, our group had several presentations showcasing some of the research we are doing here at the University at Buffalo with respect to agent-based modeling, social media analysis and machine learning. If these sound of interest feel free to reach out to us to find out more. 

First up was Na (Richard) Jiang who presented a paper entitled "Populating Digital Twins with Humans: A Framework Utilizing Artificial Agents". In this presentation he showcase our workflow of embedding agents in models of cities using examples from simple commuting models (like that shown below) to the spread of diseases.

Abstract

Over the last few decades, considerable efforts have been placed in creating digital virtual worlds. Ranging in applications from engineering, geography, industry, and translation. More recently, with the growth of computational resources and the explosion of spatial data sources (e.g., satellite imagery, aerial photos, and 3-dimensional urban data), creating detailed virtual urban environments or urban digital twins has become more widespread. However, these works emphasize on the physical infrastructure and built environment of the urban areas instead of considering the key element acting within the urban system, which are the humans. In this paper, we would like to remedy this by introducing a framework that utilizes agent-based modeling to add humans to such urban digital twins. Specifically, this framework consists of two major components: 1) synthetic population datasets generated with 2020 Census Data; and 2) pipeline of using the population datasets for agent-based modeling applications. To demonstrate the utility of this framework, we have chosen representative applications that showcase how digital twins can be created for study various urban phenomena. These include building evacuations, traffic congestion and disease transmission. By doing so, we believe this framework will benefit any modeler wishing to build an urban digital twin to explore complex urban issues with realistic populations.

Keywords: agent-based model, geosimulation, urban digital twins

Following this talk, I presented work on behalf of  Fuzhen Yin and Na (Richard) Jiang  entitled "Modeling Covid Vaccination uptake in New York State: An agent-based modeling perspective". In this paper we utilize Richards workflow and add social media networks to it in order to explore vaccination uptake for the whole of New York state. We do this through the lens of opinion dynamics and agent-based modeling, in the sense how people may change their opinions about wether or not to get vaccinated based on information from different sources (e.g. family, friends or online).   

Abstract

The effect of the recent COVID pandemic has been significantly curtailed with the introduction of vaccinations. However, not everyone has been vaccinated for a multitude of reasons. For example, people might be influenced by what they read online or the opinions of others. To explore the changes in people’s views on vaccination, we have developed a geographically explicit agent-based model utilizing opinion dynamics. The model captures people’s opinions on COVID vaccination and how this relates to actual vaccination trends. Using the entire state of New York with a population of over 22 million agents, we model vaccination uptake from January 1, 2021, until May 15, 2022. Agents within the model synthesize information from the other agents they are connected with either in physical or cyberspace and decide whether to vaccinate or not. We compare these vaccination statuses among different age groups with actual vaccination rates provided by New York State. Our results suggest that there is an interplay between different spaces and ages when it comes to agents making a decision to vaccinate or not. As such the model offers a novel way to explore vacation decisions from the bottom up.

Keywords: Agent-based modeling, Covid, Vaccine, Geosimulation, Social Networks

Ofter this,  Qingqing Chen presented work with Ate Poorthuis and myself entitled "Mapping the Invisible: Decoding Perceived Urban Smells through Geosocial Media in New York City" in which we explore how social media can be used to map smells in large metropolitan areas. 

Abstract

Smells can shape people’s perceptions of urban spaces, influencing how individuals relate themselves to the environment both physically and emotionally. Although the urban environment has long been conceived as a multisensory experience, research has mainly focused on the visual dimension, leaving smell largely understudied. This paper aims to construct a flexible and efficient bottom-up framework for capturing and classifying perceived urban smells from individuals based on geosocial media data. Thus, increasing our understanding of this relatively neglected sensory dimension in urban studies. We take New York City as a case study and decode perceived smells by teasing out specific smell-related indicator words through text mining and network analysis techniques from a historical set of geosocial media data (i.e., Twitter). The dataset consists of over 56 million data points sent by more than 3.2 million users. The results demonstrate that this approach, which combines quantitative analysis with qualitative insights, can not only reveal “hidden” places with clear spatial smell patterns, but also capture elusive smells that may otherwise be overlooked. By making perceived smells measurable and visible, we can gain a more nuanced understanding of smellscapes and people’s sensory experiences within the urban environment. Overall, we hope our study opens up new possibilities for understanding urban spaces through an olfactory lens and, more broadly, multi-sensory urban experience research.

Keywords: Smellscape, Urban smells, Geosocial media, Text mining, Network analysis, Multi-sensory urban experiences.

Last but not least,  Boyu Wang presented his work entitled "Simulating urban flows with geographically explicit synthetic populations".  In this talk, Boyu showed how a deep learning spatial-temporal urban flow model is trained to predict the aggregated inflows and outflows within regions and feed directly into an agent-based model.

Abstract

Urban human mobility is an active research field that studies movement patterns in urban areas at both the individual and aggregated population levels. Through individual’s movement, higher level phenomena such as traffic congestion and disease outbreaks emerge. Understanding how and why people move around a city plays an important role in urban planning, traffic control, and public health. An abundance of agent-based models have been built by researchers to simulate human movements in cities and are often integrated with a GIS component to realistically represent the study area. In this work we build a geographically explicit agent-based model where agents move between their home and workplaces, to simulate people’s daily commuting patterns within a city. In order to build this model, we develop a geographically explicit synthetic population based on census data. A deep learning spatial-temporal urban flow model is trained to predict the aggregated inflows and outflows within regions of the study area, which are subsequently used to drive individual agents’ movements. To validate results from the agent-based model, agents’ movements are aggregated and evaluated along with the urban flow model. Commuting statistics are also collected and compared to existing travel surveys. As such we aim to demonstrate how urban simulation models can be complemented by recent advancements in GeoAI techniques. Conversely, the aggregated deep learning model predictions can be investigated at a fine-grained individual level. This extends traffic patterns forecasting from just looking at the patterns to the processes that lead to these patterns emerging.

Keywords: Agent-Based Modeling, Urban Flow, GeoAI, Urban Simulation, Synthetic Populations 

References: 

Yin, F., Jiang., N. and Crooks, A.T. (2024), Modeling Covid Vaccination uptake in New York State: An Agent-based Modeling Perspective, The Association of American Geographers (AAG) Annual Meeting, 23rd –27th April, Honolulu, HI. (pdf)

Jiang., N. Crooks, A.T., Wang, B. and Yin (2024), Populating Digital Twins with Humans: A Framework Utilizing Artificial Agents, The Association of American Geographers (AAG) Annual Meeting, 23rd –27th April, Honolulu, HI. (pdf)

Chen, C., Poorthuis, A. and Crooks, A.T. (2024), Mapping the Invisible: Decoding Perceived Urban Smells through Geosocial Media in New York City, The Association of American Geographers (AAG) Annual Meeting, 23rd –27th April, Honolulu, HI. (pdf)

Wang, B. and Crooks, A.T. (2024), Simulating Urban Flows with Geographically Explicit Synthetic Populations, The Association of American Geographers (AAG) Annual Meeting, 23rd –27th April, Honolulu, HI. (pdf)