Abstract
In the past two centuries, many American urban areas have experienced significant expansion in both populating and depopulating cities. The pursuit of bigger, faster, and more growth-oriented planning parallels a situation where municipal decline has also been recognized as a global epidemic. In recent decades many older industrial cities have experienced significant depopulation, job loss, economic decline, and massive increases in vacant and abandoned properties due primarily to losses in industry and relocating populations. Despite continuous economic decline and depopulation, many of these so-called ‘shrinking cities’ still chase growth-oriented planning policies, due partially to inabilities to accurately predict future urban growth/decline patterns. This capability is critical to understanding land use alternation patterns and predicting future possible scenarios for the development of more proactive land use policies dealing with urban decline and regeneration. In this research, the city of Chicago, Illinois, USA is used as a case site to test an urban land use change model that predicts urban decline in a shrinking city, using vacant land as a proxy. Our approach employs the Land Transformation Model (LTM), which combines Geographic Information Systems and artificial neural networks to forecast land use change. Results indicate that the LTM is a good resource to simulate urban vacant land changes. Mobility and housing market conditions seem to be the primary variables contributing to decline.
Highlights
Today, the world is undergoing the largest wave of urban growth in history
The most visible byproduct of urban shrinkage is vacant and abandoned property; vacant land represents the physical manifestation of economic decline, and, the growing amount of these properties has emerged as a critical theme to measure the effects of urban shrinkage [8,9,10]
Urban shrinkage is a multi-faceted process created by the interaction of many physical, social, economic, and environmental factors
Summary
The world is undergoing the largest wave of urban growth in history. Global populations are projected to increase by 2.3 billion by 2050, with urban populations rising from 3.6 billion to 6.3 billion (nearly 67% of the world’s population) [1,2]. Most of this research has identified the number of vacant properties by cities or by region, but failed to do longitudinal assessments or develop comprehensive factors to predict spatial and temporal changes simultaneously. This was primarily due to insufficient databases and a lack of development in computer technology. In most land use prediction studies, there is a lack of explanation of the influences of predictor variables and insufficient testing of the accuracy assessment of the prediction output To fill this gap and increase model output validity and reliability, this research performs four different accuracy assessment processes: kappa coefficients, percent correct metric (PCM), agreement/disagreement measures, and the relative operating characteristic (ROC)
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