Tillyer, Marie Skubak; Walter, Rebecca J. (2019). Low-income Housing And Crime: The Influence Of Housing Development And Neighborhood Characteristics. Crime & Delinquency, 65(7), 969 – 993.
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Abstract
This study examines the distribution of crime across various types of low-income housing developments and estimates the main and interactive effects of housing development and neighborhood characteristics on crime. Negative binomial regression models were estimated to observe the influence of security and design features, neighborhood concentrated disadvantage, residential stability, and nearby nonresidential land use on crime at the housing developments. The findings suggest that low-income housing developments are not uniformly criminogenic, and both development characteristics and neighborhood conditions are relevant for understanding crime in low-income housing developments. Implications for prevention are discussed.
Keywords
Violent Crime; Micro Places; Guardianship; Criminology; Multilevel; Proximity; Patterns; Context; Trends; Impact; Low-income Housing; Criminal Opportunity; Concentrated Disadvantage
Zou, Tianqi; Aemmer, Zack; Mackenzie, Don; Laberteaux, Ken. (2022). A Framework for Estimating Commute Accessibility and Adoption of Ridehailing Services Under Functional Improvements from Vehicle Automation. Journal Of Transport Geography, 102.
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Abstract
This paper develops an analytical framework to estimate commute accessibility and adoption of various ridehailing service concepts across the US by synthesizing individual commute trips using national Longitudinal Employer-Household Dynamics Origin-Destination Employment Statistics (LODES) data. Focusing on potential improvements in cost and time that could be enabled by vehicle automation, we use this modeling framework to simulate a lower-price autonomous service (e.g., 50% or 75% lower) with variable wait times and implementation levels (solo, pooled, and first/last mile transit connections services, alone or in combination) to determine how they might affect adoption rates. These results are compared across metrics of accessibility and trip density, as well as socioeconomic factors such as household income. We find - unsurprisingly - that major cities (e.g. New York, Los Angeles, and Chicago) support the highest adoption rates for ridehailing services. Decreases in price tend to increase market share and accessibility. The effect of a decrease in price is more drastic for lower income groups. The proposed method for synthesizing trips using the LODES contributes to current travel demand forecasting methods and the proposed analytic framework can be flexibly implemented with any other mode choice model, extended to non-commute trips, or applied to different levels of geographic aggregation.
Keywords
Choice Of Transportation; Demand Forecasting; Poor People; Adoption; Price Cutting; Metropolis; Employment Statistics; Los Angeles (calif.); New York (state); Chicago (ill.); Accessibility; Autonomous Vehicles; New Mobility Services; Ridehailing; Travel Demand; Preferences
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