Mon, Dec 15, 2025
Studying the street: Movement, Measurement, Contestation
Daniel Romm · McGill University
urban planningtransportationmicromobilityinfrastructureresearch
Many cities today are redesigning their streetscapes to redress the historical privilege afforded to the automobile in planning and policy. Much streetscape redesign is around transport infrastructure space, which largely prioritizes car travel and marginalizes other travel modes. Attempts by planners and policy makers to this end often are met with public opposition by advocates of the car, protesting about losing space on the street. This is empirically investigated with the case of Montréal by determining the allocation of street space to transport infrastructures, deriving measures of infrastructure space per traveller, and devising an Equal Infrastructure Allocation score to measure the imbalance between infrastructure provision per travel mode. Per borough, the distribution of transport infrastructure is examined, alongside correlations with demographic, socio-economic, land use, and crash rate variables. Potential scenarios of significant micromobility infrastructure improvement are modelled to test how infrastructure space apportionment per mode changes. This investigation discovers that even large improvements to micromobility infrastructure have a minor effect on space allocated to automobiles. Equal Infrastructure Allocation score and associated indicators are presented as useful tools for planners and policy makers implementing micromobility infrastructure projects, to better communicate with the public and address potential opposition.
Mon, Dec 1, 2025
Targeted urban afforestation can substantially reduce income-based heat disparities in U.S. cities
Lelia Hampton · Massachusetts Institute of Technology
urban planningclimateheat mitigationafforestationresearch
Previous studies on urban heat mitigation, critical for urban planning and public health, have generally focused on a handful of cities, ignored logistical constraints, or insufficiently resolved urban-scale processes. Here, we fuse satellite-derived estimates of urban heat and multiple physical properties to develop a non-parametric machine learning approach to capture non-linearities in thermal anomalies (ΔAT) across 493 U.S. cities. This enables computationally-efficient data-driven assessments of urban heat mitigation strategies, including strategies targeting low-income communities since ~90% of these cities show income-based temperature disparities. All strategies lower daytime ΔAT, with targeted afforestation with (without) albedo management reducing daytime ΔAT for low income groups from 0.56±0.94℃ to 0.22±0.92℃ (0.24±0.93℃) and income-based ΔAT gap from -0.50±0.94℃ to -0.15±91℃ (-0.17±93℃). Our results demonstrate the importance of targeted heat mitigation in low income communities, where residents have less options to adapt to extreme heat.
Mon, Oct 20, 2025
30 Day Map Challenge
WG Activity
mappingactivity30DoM
Presenting fun maps, forming mapping groups, setting up a gameplan for the 30 Day Map Challenge (running during the month of November), and walking members through our GitHub repository for the challenge.
Mon, Oct 6, 2025
PUBLICSPEAK: Hearing the Public with a Probabilistic Framework
Sabina Tompkins
civic engagementframeworksentiment analysisguest speaker
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%.
Mon, Sep 22, 2025
Fall 25 Kickoff
Intros!
kickoff
Member intros and introduction to the data journalism projects we will endeavour upon on later this semester.
Mon, May 19, 2025
Spatial data science for just and sustainable cities
Rafael M. H. Pereira
open-sciencejusticeaccessibilityguest speaker
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.
Mon, May 5, 2025
FloodNet
Charlie Mydlarz
climateurban-techsensingguest speaker
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.
Mon, Mar 10, 2025
Using Administrative Datasets to Identify Landowners and Operationalize their Characteristics
Henry Gomroy
housingrestorative-justiceguest speaker
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.
Mon, Feb 24, 2025
Undermatching Disparities and Portfolio Decisions: Evidence from the New York City High School Match
Kenny Peng
matchingschoolsguest speaker
In the New York City High School Match, applicants rank programs from over 800 options and are placed through a centralized stable matching process. We analyze individual application ranking (portfolio) behaviors that explain undermatching, defined as the difference in selectivity between where the student matched and where they could have matched had they applied. There are substantial disparities: undermatching is over 50% higher for Black and Hispanic applicants than for Asian or white applicants, with further gaps by income and geography. However, while individual student demographic characteristics and grades alone explain only 3.8% of the variation in undermatching, including individual application behaviors explains 40.9%. Black and Hispanic students are more likely to underreach (by only listing unselective programs, or inverting the order of selective and nonselective programs), while Asian and white applicants are more likely to overreach (by applying to only selective programs). Finally, we calculate and interpret ex-ante “theoretically optimal” perturbations of each student’s portfolios, using only program-level offer rate information from the previous year. Recommended portfolio changes from this model decrease undermatching by 24%. Our results suggest the benefit and possibility of personalized feedback, and forecast the effects of different types of interventions: some applicants (disproportionately Asian and white) are more likely to benefit from interventions that encourage listing more non-selective programs and from removing list length restrictions, while others (disproportionately Black and Hispanic) are more likely to benefit from interventions that encourage listing more selective programs and avoiding inverting the ranking order of selective and non-selective programs.
Mon, Feb 10, 2025
Global Rewards in Restless Multi-Armed Bandits: an Application to Urban Food Rescue
Naveen Raman
reinforcement learningurban-techresource allocationguest speaker
Restless multi-armed bandits (RMAB) extend multi-armed bandits so pulling an arm impacts future states. Despite the success of RMABs, a key limiting assumption is the separability of rewards into a sum across arms. We address this deficiency by proposing restless-multi-armed bandit with global rewards (RMAB-G), a generalization of RMABs to global non-separable rewards. To solve RMAB-G, we develop the Linear- and Shapley-Whittle indices, which extend Whittle indices from RMABs to RMAB-Gs. We prove approximation bounds but also point out how these indices could fail when reward functions are highly non-linear. To overcome this, we propose two sets of adaptive policies: the first computes indices iteratively, and the second combines indices with Monte-Carlo Tree Search (MCTS). Empirically, we demonstrate that our proposed policies outperform baselines and index-based policies with synthetic data and real-world data from food rescue.
