Urban Data Science & Equitable Cities

Local governments around the world are making consequential decisions on behalf of their constituents, and these constituents are responding with requests, advice, and assessments of their officials at public meetings. So many small meetings cannot be covered by traditional newsrooms at scale. We propose PublicSpeak, a probabilistic framework which can utilize meeting structure, domain knowledge, and linguistic information to discover public remarks in local government meetings. We then use our approach to inspect the issues raised by constituents in 7 cities across the United States. We evaluate our approach on a novel dataset of local government meetings and find that PublicSpeak improves over state-of-the-art by 10% on average, and by up to 40%.

Member intros and introduction to the data journalism projects we will endeavour upon on later this semester.

In this presentation, I will give an overview of my research at the intersection of spatial data science, urban analytics and accessibility, and sustainable mobility. Specifically, I will showcase work related to the development of open data science tools and methods for transportation network modeling used to examine spatial accessibility, energy use and the environmental performance of urban mobility systems. These tools contribute to research and planning by aiding researchers, students, and practitioners in effectively handling large-scale geospatial data for the examination of urban transportation networks and mobility futures. I will give particular attention to two projects related to: (1) a new scalable computational model to estimate public transport emissions at high spatial and temporal resolutions; and (2) recent developments of powerful multimodal routing models and their contribution to the analysis of socioeconomic and spatial inequalities in access to opportunities. At the end, I will discuss some of the advantages and limitations of these tools and models, reflecting on new research avenues for using spatial data science for sustainable and inclusive cities.

FloodNet NYC is a sensor network for real-time urban flood monitoring and community flood resilience. Our team develops tools for real-time urban flood monitoring, implement these tools to measure flooding in New York City, and make flood data and monitoring tools available in a manner that is accessible and useful to stakeholders including residents, community-based organizations, government agencies, and researchers.

Landowners play central roles in many urban sociological theories, but empirical analysis of these actors has frequently been stymied by insufficient data. Few surveys collect detailed information on landowners and administrative data present multiple challenges, most importantly, that property owners frequently obscure their identities through corporate structures. This paper presents a data construction pipeline for creating linked, longitudinal datasets describing urban properties and the people and companies that own them using widely available tax assessment records and business filings. The author implements this approach in four metropolitan areas — Boston, Massachusetts, Baltimore Maryland, Miami, Florida, and Houston, Texas — between 2005 and 2020, demonstrating the adaptability of the method to areas with different levels of data quality. The pipeline draws on four methodological innovations. First, it uses internal validation and external harmonization to address biases and inaccuracies within tax assessment records. Second, it presents a network-based entity reconciliation methodology better suited than existing methods to the sparse but linked data contained in the source records. Third, it presents a flexible and comprehensive method for operationalizing landowners’ corporate networks. Finally, it operationalizes multiple sociological characteristics of landowners and estimates their potential bias. The paper concludes by demonstrating several empirical analyses this methodology opens.