Use of Machine Learning in Exploring Spatial (In)Justices1

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 spatial justice to help develop more equitable and just communities. However, defining spatial justice and developing a methodology for quantitatively analyzing spatial justice 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 of spatial justice 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 injustice. As a result, 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 data science and machine learning techniques and the results show that the deep leaning model is able to predict economic mobility of a census tract based on its spatial variables with 89% accuracy. 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.