Using Emerging Hot Spot Analysis to Explore Spatiotemporal Patterns of Housing Vacancy in Ohio Metropolitan Statistical Areas

《Using Emerging Hot Spot Analysis to Explore Spatiotemporal Patterns of Housing Vacancy in Ohio Metropolitan Statistical Areas》

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作者: Victoria Morckel

来源: URBAN AFFAIRS REVIEW,Vol.59,Issue1,P.

语言: 英文

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摘要: IntroductionSophisticated methods for studying changes in the physical forms of cities that are losing population (i.e. “shrinking cities”) are lacking in the literature. In their review of spatial metrics, Reis, Silva and Pinho (2016) found 123 methods for studying urban growth, but only 15 for studying shrinkage. In this research note, we highlight the use of a newer method—emerging hot spot analysis of space-time cubes from defined locations—to examine the spread of housing vacancy, a common indicator of shrinkage (Reis, Silva and Pinho 2016; Couch and Cocks 2013). The method has existed in ESRI's ArcGIS Pro since 2017 (Bennett, D; costa and Vale 2017), but has not been previously applied to the study of vacancy.1 This method differs from traditional hot spot methodologies in that it identifies statistically significant spatiotemporal relationships (i.e. spatial change over time).We begin with a description of why vacancy is a salient issue and how researchers have studied its spatial patterns. We then describe the aforementioned method in detail, and investigate how the spread of vacancy relates to population change—another indicator of shrinkage—at various geographic scales. Then, we present the results of our analyses, as well as the urban planning and policy implications of our methodology and findings.Why Vacancy Matters: Vacant properties reduce property values and tax revenues (Whitaker and Fitzpatrick IV 2013), increase a community's service costs and need for demolitions (Morckel 2017; Schilling 2009), and discourage private investment (Mallach 2012). Moreover, there is increasing evidence that vacant properties adversely affect the health of the people who live near them due to conditions like trash build up, overgrown vegetation, vermin, and background lead exposure (Castro et al. 2019; Garvin et al. 2013; Teixeira and Wallace 2013). In line with Wilson and Kelling’s (1982) “broken windows” hypothesis, vacancies correlate with elevated property and violent crime rates (Roth 2019; Boessen and Chamberlain 2017; Cui and Walsh 2015; Spelman 1993), and contribute to residents’ poor mental health by reducing “cues to care” and increasing social disorder at the neighborhood-level (Wang and Immergluck 2018; Cagney et al. 2014; Nassauer and Raskin 2014). Because vacancies tend to cluster in the most distressed neighborhoods (Morckel 2014b), they have a disproportionate impact on vulnerable populations like minorities, the poor, and recipients of rental vouchers (Silverman, Yin and Patterson 2013), making the problem of property vacancy a social and environmental justice concern (Lord 1995). For these reasons, it is important for urban planners and policy makers to understand how vacancy proliferates.Prior Research on Housing Vacancy: Many studies demonstrate that levels of vacancy vary from neighborhood to neighborhood and/or city to city. The majority of these studies explore changes in vacancy rates within fixed political boundaries (without consideration for neighboring units), or they predict vacancy levels in different places (e.g. neighborhoods) at one point in time, using cross-sectional measures [e.g. Immergluck (2016) for census tracts in 50 metropolitan areas; Morckel (2014a) for block groups in Columbus, OH; Silverman, Yin and Patterson (2013) for census tracts in Buffalo, NY; Wilson, Margulis and Ketchum (1994) for census tracts in Cleveland, OH].Prior studies of changes in vacancy have several limitations. First, they usually do not consider whether changes or differences in vacancy rates represent real statistically significant changes. Second, they do not examine whether vacancy within a geographic unit of interest (e.g. a neighborhood or tract) is statistically significantly different relative to other units in the sampling frame (e.g. the other neighborhoods or tracts). Third, they do not use spatial statistics and thus cannot clearly identify if or how vacancy is concentrating or spreading. Fourth, to the extent that these studies examine the spatial clustering of vacancies, they do not examine changes in these patterns of clustering over time. Vacancy may increase in a small geographic area like a census block group or tract for example, but this does not necessarily mean it is moving outward into adjacent geographic units. The methodology we present herein addresses these limitations. Moreover, Table 1 provides a brief summary of other methods that could be used to examine spatial and/or temporal changes in vacancy, and why our method is preferable.Table 1. Other Common Methods to Examine Spatial and/or Temporal Change.MethodDisadvantage Compared to Emerging Hot Spot AnalysisTraditional Panel RegressionDoes not consider spatial relationships. Also, the focus is different. Regression is generally about understanding variables that relate to, predict, or influence a phenomenon; emerging hot spot analysis is more about identifying patterns to understand the distribution or nature of a phenomenon over space and time.Spatial Autoregressive ModelsDoes not consider temporal relationships in the basic, cross-sectional version of the model. To mirror our emerging hotspot analysis in a regression framework, one would need to examine spatial and temporal autocorrelation, both in the dependent variable and the error term. There are therefore four potential types of models to be run (temporal lag, temporal error, spatial lag, and spatial error), instead of just one emerging hot spot analysis.Calculate Increase/Decrease Over Time, Then Create a Categorical Variable (such as high increase in vacancy, moderate increase, low increase, and so forth)The cut off points between the categories and/or time periods may be subjective or arbitrary. Does not identify trends as accurately as emerging hot spot analysis, since it uses fewer time periods (starting and ending points, as opposed to the 30 time periods in our study).Panel Regression with Getis-Ord Gi* VariableOne would need to run the Getis-Ord Gi* analysis individually for every time period in the study (in our case, 30 periods) to create the longitudinal, spatial variable. Unlike emerging hot spot analysis, this method does not use inferential statistics to categorize tracts (or other geographies of interest) in the final outputs.Population Change & Regional Context: Because many studies have shown an association between property vacancy and population change, we also consider the effects of local and regional population dynamics on vacancy spread. Ribant and Chen (2020) found that shrinking cities have higher levels of vacant housing than growing cities, and the extent of vacancy depends on regional context. Of the 367 U.S. shrinking cities they identified, 60.5 percent were large central cities (N = 81) or shrinking suburbs of those central cities (N = 141). Earlier work by Morckel (2013) also supports the importance of regional population dynamics when measuring or predicting vacancy. She found that the odds of a house being abandoned in Youngstown, Ohio—a shrinking city located in a shrinking MSA—were significantly greater than the odds of a similar house in a similar neighborhood being abandoned in Columbus, Ohio—a growing city located in a growing MSA. However, neither of these works [Ribant and Chen (2020) nor Morckel (2013)] were longitudinal—and thus, they do not identify how the spread of vacancy varies at different scales. The new method outlined herein allows for such an examination.The potential importance of geographic scale is further emphasized by Segers et al. (2020) who argue that more diverse meanings for shrinkage are found by connecting shrinkage on the local level to growth on larger planning and policy levels—a concept they call “shrinkage in growth” (p. 8). If we find that spatial patterns of vacancy differ by place and/or scale, it would emphasize how vacancy reduction initiatives (which are usually implemented at the neighborhood-level) might be more effective if paired with interventions that account for the broader context of population change.