Urban Data Science & Equitable Cities

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.

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.

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.

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.