Machine learning application to spatio-temporal modeling of urban growth

摘要: Understanding the dynamics of urban growth is among the most important tasks in urban planning due to their influence on policy decision-making. Specifically, prediction of urban growth at regional levels is crucial for regional policy makers. Making such predictions is difficult because of the existence of complex topological structures and the high-dimensional nature of data sets related to urban growth. Spatial and temporal auto-correlation and cross-correlations, together with regional social and physical covariates, need to be properly accounted for improving the forecasting power of any statistical or machine learning method. To that end, we develop novel machine learning methodologies to perform predictions of urban growth at regional levels by incorporating lead-lag non-linear relationships among past urban changes in each region and its neighbors. Based on this analysis, machine learning algorithms …