Despite the unprecedentedly growing discussion of big data generated in urban environments and the widespread use of so-called urban big data (UBD) in recent years, there has been no consensus or widely accepted definition of UBD. Existing UBD studies have been either case-specific or applied in specific planning domains, such as transportation or tourism planning. A comprehensive exploration of the definitions of UBD in urban planning and related fields is timely and vital. This study is a systematic review of recent literature, consolidating 49 UBD definitions from 48 published articles in 39 journals, and classifying them into four themes: characteristics, sources, analytics, and impact. We found that most definitions are not given in an urban context and do not differentiate UBD from big data in a general sense. It is difficult to arrive at a one-size-fits-all definition of what constitutes UBD. Instead, the fourfold classification of UBD definitions allows us to identify three essential qualities of UBD that differentiate UBD from general big data and benefit urban studies: refinement of both spatiotemporal features and individual attributes at the microlevel, and the capacity and impact to depict, predict, and manage cities. We also identified three categories of challenges imposed on urban planning. This study serves as a starting point for a comprehensive understanding of UBD and contributes to expanding the discussion of UBD definitions and opportunities that UBD opens up in urban planning, facilitating better city management in the future.

The U.S. Congress authorized the creation of real estate investment trusts (REITs) in 1960 so companies could develop publically traded real estate investment portfolios. REITs focus on commercial property, retail property, and rental property. During the last decade, REITs became more active in regional housing markets across the U.S. Single-family rental (SFR) REITs have grown tremendously, buying up residential properties across the country. In some regional housing markets, SFR REITs own noticeable shares of single-family homes. In those settings, SFR REITs take large numbers of housing units off of real estate markets where homeownership transactions occur and manage these properties as part of commercial rental inventories. This has resulted in a new category of multiple property owners, composed of institutional investors as opposed to individual investors, which further exacerbates property wealth concentration and polarization. This study examines the socio–spatial distribution of properties in SFR REIT portfolios to determine if SFR REIT properties tend to cluster in distinct areas. This study will focus on the regional housing market in Nashville, TN. Nashville has one of the most active SFR REIT sectors in the country. County tax assessor records were used to identify SFR REIT properties. These data were joined with U.S. Census data to create a profile of communities. The data were analyzed using SPSS statistical software and GIS software. Our analysis suggests that neighborhoods with clusters of SFR REITs fit the SFR REIT business model. Clusters occur in communities with newer homes, residents with higher levels of educational attainment, and middle to upper-middle incomes. The paper concludes with several recommendations for future research on SFR REITs.

Smart City (SC) strategies developed by local governments reflect how governments and planners envision SC and apply smart technologies, and what challenges they face and try to address. Little attention, however, has been given to investigating SC strategies or applications, especially in the U.S. context. Moreover, there is insufficient attention paid to whether SC strategies address social issues such as equity and public participatory opportunities. Based on the documentation from the U.S. Department of Transportation 2015 Smart City Challenge, we developed a framework to evaluate SC strategies on urban transportation systems using six standards: Safety, Mobility, Sustainability, Opportunity, Efficiency, and Equity. In addition, we synthesized the challenges U.S. smart cities encounter, and SC strategies proposed by local municipal governments to tackle them. Our findings show that most SC strategies aimed to improve Efficiency (78%) and Mobility (57%), while less attention has been given to providing Equity (8%) or Opportunity (7%). The most well-acknowledged challenge that the local governments face is the limited data and tools for decision-making, with 416 SC strategies (27%) proposed to address related issues. Our framework and results contribute to the future SC strategy evaluation and inclusive smart city development. Our study also identified a broad spectrum of available SC strategies planners and policymakers can refer to when designing an SC or overcoming SC challenges.

It is essential to understand how the built environment affects transit ridership to prioritize public transit and make it more appealing, particularly in mid-sized cities on the Rust Belt due to the experience of population decrease and urban sprawl in the U.S. Although many studies have looked at factors that influence ridership, there is still a need for a methodological design that considers both route and environment characteristics for bus ridership. This study examined the daily ridership of 3794 bus stops across 57 routes in the Buffalo area of New York State and used random coefficients models to account for different levels of characteristics (bus stop level, route level, and transportation analysis zone (TAZ) level). The study found that bus frequency and bus stop centrality were positively correlated with ridership, while total route stops had a negative effect. By controlling the impact of bus routes, the study showed that the multi-level design using random coefficients models was more effective than traditional OLS and spatial lag models in quantifying the impact of bus routes and TAZs. These findings provide local policy implications for route design, bus operation, and transit resource allocation.