Application of Random Forest and SHAP Tree Explainer in Exploring Spatial (In)Justice to Aid Urban Planning

Abstract

In light of recent local, national, and global events, spatial justice provides a potentially powerful lens by which to explore a multitude of spatial inequalities. For more than two decades, scholars have been espousing the power of this concept to help develop more equitable and just communities. However, defining spatial justice and developing a methodology for quantitatively analyzing it is complicated and no agreed upon metric for examining spatial justice has been developed. Instead, individual measures of spatial injustices have been studied. One such individual measure is economic mobility. Recent research on economic mobility has revealed the importance of local geography on upward mobility and may serve as an important keystone in developing a metric for multiple place-based issues of spatial inequality. This paper seeks to explore place-based variables within individual census tracts in an effort to understand their impact on economic mobility and potentially spatial justice. The methodology relies on machine learning techniques and the results show that the best performing model is able to predict economic mobility of a census tract based on its spatial variables with 86% accuracy. The availability and density of jobs, compactness of the area, and the presence of medical facilities and underground storage tanks have the greatest influence, whereas some of the influential features are positively while the others are negatively associated. In the end, this research will allow for comparative analysis between differing geographies and also identify leading variables in the overall quest for spatial justice.