Urban Studies &

Agent-Based Modeling

Research

Leveraging Newspapers to Understand Urban Issues:

A Longitudinal Analysis of Urban Shrinkage in Detroit

Jan 2023 - Today we are awash with data, especially when it comes to studying cities from a diverse data ecosystem ranging from demographic to remotely sensed imagery and social media. This has led to the growth of urban analytics providing new ways to conduct quantitative research within cities. One area that has seen significant growth is using natural language processing techniques on text data from social media to explore various issues relating to urban morphology. However, we would argue that social media only provides limited insights when dealing with longer-term urban phenomena, such as the growth and shrinkage of cities. This relates to the fact that social media is a relatively recent phenomenon compared to longer-term urban problems that take decades to emerge. Concerning longer-term coverage, newspapers, which are increasingly becoming digitized, provide the possibility to overcome the limitations of social media and provide insights over a timeframe that social media does not. To demonstrate the \textcolor{red}{utility of newspapers for} urban analytics and to study longer-term urban issues, we utilize an advanced topic modeling technique (i.e., BERTopic) on a large number of newspaper articles from 1975 to 2021 to explore urban shrinkage in Detroit. Our topic modeling results reveal insights related to Detroit’s shrinkage. For example, the 2008 side effects of economic recessions on Detroit’s automobile industry, local employment status, and the housing market” Time period of shrinkage is captured by the model. As such, this work demonstrates the potential of utilizing newspaper articles to study long-term urban issues.

From Agent-Based Model to Digital Twins

Jan 2022-Urban digital twins can be created by utilizing the combination of ABM, synthetic population and other computational methods (e.g., machine learning).

- Various data can be collected to Monitor different phenomena within urban.
- Data can be used to Analyze and Model those phenomena by implementing different computational techniques.
  - A Synthetic population can be created to represent various elements within the urban system
  - NLP can be utilized to Analyze people’s discussions on social media platforms or news articles related to different urban phenomena
  - ABM can be utilized to Model different urban phenomena (e.g., policy change, disaster and pandemic ).

Generation of Reusable Synthetic Population and Social Networks

July 2021Within agent-based models, agents interact with each other (e.g., social networks) and their environment, and it is through such interactions more aggregate patterns emerge (e.g., disease outbreaks, traffic jams). While the popularity of agent-based modeling has grown, one challenge remains, that of creating and sharing realistic synthetic populations which incorporate social networks. To overcome this challenge, this paper introduces a new approach that creates a reusable synthetic population using the New York Metro Area as a study area. Our method directly incorporates social networks (i.e., connections within a family or workplace) when creating a synthetic population. To demonstrate the utility and reusability of the synthetic population and to highlight the role of social networks, we show two example applications: traffic dynamics and the spread of a disease. These applications demonstrate how our synthetic population method can be easily utilized for different modeling problems.

Exploring Urban Shrinkage via computational Approaches

May 2021- I have participated in the ACM SIGSIM PAD conference by introducing my research related to Urban Shrinkage. This presentation is part of my dissertation proposal and mainly focuses on three main questions: 1) To what extent can urban shrinkage be raveled by applying social media analysis (i.e., sentiment analysis)? 2) How urban shrinkage emerges at the macro-level through the simulation of housing trades at the individual level? 3) How can patterns of shrinkage be measured through social media analysis and simulation?