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.
Highlights
Spatial justice as a theoretical concept holds much promise for exploring, understanding, and solving issues of spatial inequality in a wide variety of landscapes [1]
Inspired by the sporadic connections made by the previous research between locationbased factors and economic mobility, the research presented in this paper aims to explore this relevancy in detail and utilizes a data science and machine learning-based approach to empirically evaluate the impact of place-based variables mentioned in Rocco’s framework on economic mobility and potentially spatial justice
These results suggest that the number and density of jobs available at a particular location, area covered by that location, number of available medical facilities, and the existence and frequency of underground storage tanks storing either
Summary
Spatial justice as a theoretical concept holds much promise for exploring, understanding, and solving issues of spatial inequality in a wide variety of landscapes [1]. From questions about the definition of the term, to issues of tackling past, current, and/or future spatial injustices, to making the larger public aware of the concept and its potential, spatial justice as a working concept is still in its infancy With this in mind, the goal of this paper is to advance our collective understanding of spatial justice as it relates to measuring geographic inequalities that could potentially aid urban planners and policy makes. Harvey builds upon Lefebvre’s “right to the city” and believes that geography cannot remain disengaged, impartial and objective, when many ills confront cities across the planet As a result, he calls on geographers and others to bring together spatial and social analysis to improve urban spaces [23]. “Surely it would be another string in their bow if geographers could answer questions such as these: is a person’s living at place x just? Is the spatial distribution of grocery stores just? Is the siting of some new airport just? Is the re-siting of the hospital just? Is the removal and rehousing of squatters just? These questions range over the justness of absolute and relative location as well as over the justness of processes of siting and relocation” [24]
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