Forecasting Vacancy Dynamics in Growing Versus Shrinking Cities: A Smart City Initiative
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Every city seeks to spur economic development, and land, especially vacant land, plays an important role in these endeavors. Although vacant land exists in every city regardless of whether they are growing or shrinking, the causes and effects of changes in vacant land differ. While large scale annexation can increase vacant land in growing cities, depopulation and economic downturn may increase vacant properties in shrinking cities. However, despite these different characteristics, most cities pursue growth-oriented development strategies due, partially, to their inability to accurately predict future urban growth/decline patterns. Therefore, understanding land use alternation patterns and predicting future possible scenarios is critical when developing more proactive land use policies on urban decline and regeneration. In this study, the city of Chicago, Illinois, was used as a case site to test an urban land use change model predicting future vacant lands in shrinking cities, and the city of Fort Worth, Texas, was selected to forecast vacant land transformation in growing cities. By understanding not only simple decrease or increase of vacant properties but also analyzing historical patterns of vacancy changes and predicting the probability of future transitions with accuracy outputs, this research can be used to improve policies on vacancy. This project employed the Land Transformation Model (LTM) which combines GIS and artificial neural networks to forecast land use change. While this research used causal drivers to predict future vacant land changes in growing and shrinking cities, findings can also be used to simulate land use changes to suggest suitable alternatives for shrinking and growing cities with high risk of vacancy and future infill development plans. Study results indicate that housing market conditions and economic factors are the primary variables contributing to land vacancy decline with mobility and physical conditions being stronger predictors of vacant land specifically in growing cities. In terms of plan quality associated with vacancy-related policies, this study found that Fort Worth is more attentive to socially and physically vulnerable areas, working to revitalize the economy and reduce vacant properties, than healthier communities while Chicago may need to improve their policies regarding the transportation accessibility and physical conditions of their structures.
Land use change model
land use prediction models
Lee, Jaekyung (2017). Forecasting Vacancy Dynamics in Growing Versus Shrinking Cities: A Smart City Initiative. Doctoral dissertation, Texas A & M University. Available electronically from