A python script for longitudinally measuring the duration of vacant land uses.

Populating and depopulating cities have some degree of underutilised land. The duration of vacancy, or length of time a property remains unused, more strongly influences urban decline than the amount of vacant land. Assessment of the duration of vacancy is seldom conducted, due to a lack of linking longitudinal data. This research creates and applies a Python script to track the duration of vacancy in Minneapolis, MN, U.S.A, to create a tool that can be utilised by cities with vacant land inventories. The tool can be used globally to prioritise treatment areas for urban regeneration plans.

Neighborhood decline and mixed land uses: mitigating housing abandonment in shrinking cities.

Neighborhood decline is a critical issue in shrinking cities. Components of sustainable urbanism such as mixed land uses have risen as possible urban planning-based approaches to help mitigate urban and neighborhood decline. This research identifies examines if mixed land uses can help mitigate urban decline by using the tax delinquent status of single family houses as a proxy for decline in Dayton, Ohio, USA. Logistic regression models are utilized to estimate the probability of tax delinquency. The results suggest that the proximity to mixed land uses is associated with increasing or decreasing the probability of tax delinquent for single family lots. The number of commercial and industrial lots in a neighborhood also has effects on the probability of a lot becoming tax delinquent, but the specific types of commercial and industrial lots dictate the direction of effects. The existence of commercial apartment lots, retail lots, and industrial food and drink plant lots were shown to help decrease the probability of tax delinquent lots. Also, decreasing the amount of property tax applied to parcels can help to limit distress in neighborhoods. This research contributes to the ongoing efforts to stymie the amount of residential abandonment in depopulating and declining cities.

Evaluating drivers of housing vacancy: a longitudinal analysis of large U.S. cities from 1960 to 2010.

Housing vacancies have become a major issue in depopulating, or shrinking, cities. All urban areas, however, are subject to some degree of vacant housing. A small percentage is necessary to allow mobility and sufficient space for growth, and is an indicator of healthy urbanization. Conversely, widespread housing vacancies may indicate structural crisis due to property abandonment. Land area and population changes, shifts in employment, demographic trends, development intensity, and economic conditions are primary drivers of housing vacancies. The degree to which these interrelated factors contribute can fluctuate by city. This paper explores relationships between factors contributing to housing vacancies over time to identify changes in underlying factors. The research examines U.S. cities of over 100,000 population over the period of 1960-2010, conducting multivariate regression analyses in 10-year periods and performing longitudinal panel analyses. The regressions examine changes in urban housing vacancy factors over time while the panel models assess which factors have remained consistent. The panel model results indicate that population change, percent nonwhite populations, unemployment and density are consistent, significant predictors of housing vacancies, The incremental regression models suggest that unemployment and regional location have also been strong indicators of housing vacancies. These results, while somewhat exploratory, provide insight into long-term data that cities should track over time to determine the optimal policy approaches to offset housing vacancies.

Neighborhood Landscape Spatial Patterns and Land Surface Temperature: An Empirical Study on Single-Family Residential Areas in Austin, Texas.

Rapid urbanization has accelerated land use and land cover changes, and generated the urban heat island effect (UHI). Previous studies have reported positive effects of neighborhood landscapes on mitigating urban surface temperatures. However, the influence of neighborhood landscape spatial patterns on enhancing cooling effects has not yet been fully investigated. The main objective of this study was to assess the relationships between neighborhood landscape spatial patterns and land surface temperatures (LST) by using multi-regression models considering spatial autocorrelation issues. To measure the influence of neighborhood landscape spatial patterns on LST, this study analyzed neighborhood environments of 15,862 single-family houses in Austin, Texas, USA. Using aerial photos, geographic information systems (GIS), and remote sensing, FRAGSTATS was employed to calculate values of several landscape indices used to measure neighborhood landscape spatial patterns. After controlling for the spatial autocorrelation effect, results showed that larger and better-connected landscape spatial patterns were positively correlated with lower LST values in neighborhoods, while more fragmented and isolated neighborhood landscape patterns were negatively related to the reduction of LST.