Using the land transformation model to forecast vacant land

Growing or shrinking cities can experience increases in vacant land. As urban populations and boundaries fluctuate, holes can open in once tight urban areas. Many cities chase growth-oriented approaches to dealing with vacancies. It is critical to understand land-use alteration to accurately predict transformations of physical change in order to make better informed decisions about this phenomenon. This research utilizes the land transformation model (LTM), an artificial neural networking mechanism in Geographic Information Systems, to forecast vacant land. Variable influence on vacant land prediction and accuracy of the LTM is assessed by comparing input factors and patterns, using time-series data from 1990 to 2010 in Fort Worth, Texas, USA. Results indicate that the LTM can be useful in simulating vacant land-use changes but more precise mechanisms are necessary to increase accuracy. This will allow for more proactive decisions to better regulate the process of urban decline and regeneration.