Browsing by Author "Hu, Yujie"

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    Accessing Opportunity: Employment and Community Patterns among Low-, Medium-, and High-Wage Workers in Houston
    (Kinder Institute for Urban Research, 2018) Wu, Jie; Hu, Yujie; Zhang, Mingming; Patterson, Grant
    Proximity to jobs is important for all residents as it can affect employment outcomes, but it is especially crucial for low-income households whose budgets can be disproportionately impacted by transportation costs and long commutes. This report uses data from the Longitudinal Employer-Household Dynamics (LEHD) program, the American Community Surveys and other survey data to explore the geographical movement of workers in an urban setting. The purpose of the work presented here is to document differences in commuting patterns among different income groups and to inform the development of programs designed to enhance the physical and economic mobility of Houston’s labor force.
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    Dangerous Crossings: The Links Between Intersections and Crashes in Houston
    (Kinder Institute for Urban Research, 2017) Hu, Yujie; Shelton, Kyle
    Traffic accidents involving pedestrians and bicyclists have increased both in Houston and nationally in recent years. Coverage of this situation relies mainly on statistics that list the number and possibly the location of crashes. However, this information alone is not enough to help policymakers address this deadly problem. This report identifies how the attributes of intersections in Houston correlate with higher crash risks.This report uses a technique called colocation, which identifies spatial patterns — such as the physical distance between two objects or events — in order to analyze the impacts of the built environment on collisions between bicycles or pedestrians and automobiles in Houston. The colocation analysis identifies both the physical characteristics that make an intersection likely to attract future collisions and specific intersections that require attention in Houston. The analysis does not provide a prediction of the number of incidents that will occur at a given intersection; instead it identifies intersections that will likely attract incidents in the future. This work should help policymakers and engineers identify troublesome areas and improve street design to promote greater safety for all users.