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Do residential locations of Housing Choice Voucher (HCV) households reflect tenants' preferences for neighborhood quality? Study results come from a three-part methodology: (1) survey of voucher holders to find neighborhood preferences and other factors in the consumer decision-making model, (2) geographic information system (GIS) analysis of actual locational outcomes in terms of neighborhood opportunity and transportation accessibility, and (3) quantitative analysis of the strength of preference-outcome relationships. The results reveal that survey participants placed high priority on neighborhoods that were safe and clean, and with quality schools. Despite this, higher priority on quality housing and search barriers affected the housing choice. As such, to a large extent, the residents did not live in places that met their location preferences. The study calls for an expansion of location assessment measures in the current policy framework and the provision of more information about housing and neighborhood options to voucher recipients.

Motorization and increased levels of car ownership have partly contributed to traffic congestion and air pollution, which is a prime concern in the era of climate change. Therefore, vehicle ownership-related topics have been extensively explored by transportation scholars, economists, and planning researchers. However, relatively fewer scientists have investigated the spatial patterns and socioeconomic factors of car ownership simultaneously within a large geographic scale. Thus, the goal of this article is to illuminate how high levels of auto ownership may cluster spatially and what factors relate to such phenomena by developing an integrative framework and applying it in three counties in South Florida (US). Specifically, this study first evaluated whether vehicle ownership is spatially autocorrelated using Global and Local Moran's I statistics. It then justified significant factors associated with car ownership by employing Poisson and Corrected Poisson regression models. The findings, using raw data, show that there exist locally spatial clusters of the households with high levels of automobile ownership, while globally the patterns of auto ownership are statistically random. Furthermore, the results suggest that the number of drivers, the number of workers, household income level, housing tenure, the proximity to schools, and net house density significantly influence car ownership levels. The results can assist urban planners and local governments in developing planning schemes that aim at transit, cycling, walking, and other non-motorized travel modes, thereby furthering environmentally friendly communities.