Authors: Elijah Knaap*, University of California Riverside, Gerrit Knaap, University of Maryland, Nicholas Finio, University of Maryland, Sergio Rey, University of California Riverside
Topics: Spatial Analysis & Modeling, Urban Geography, Regional Geography
Keywords: urban data science, spatial analysis, inequality
Session Type: Paper
Start / End Time: 9:55 AM / 11:35 AM
Room: Congressional A, Omni, West
Presentation File: No File Uploaded
In 2015 the United Nations adopted a set of 17 "Sustainable Development Goals" (SDGs) designed to move a globalized world into a new era of social equity, environmental stewardship, and economic prosperity. The SDGs are ambitious and the outcomes they envision are bold in prescription and broad in scope. As a consequence, measuring progress toward their achievement is important but fraught with considerable difficulty. In this article we describe how spatial data science provides avenues for assessing intraurban inequality across a diverse set of international metropolitan contexts. In so doing, we present a novel mode of analysis whose results are simultaneously comparable across global cities and flexible enough to incorporate such diverse datasets. To demonstrate, we use disparate data from nine countries on four continents to analyze, compare, and contrast the spatial structure of social inequality in each metropolitan area. We conduct our analyses using spatial clustering algorithms originally developed for computer science and regional economics that partition the neighborhoods of each metropolitan area into a set of mutually exclusive regions. We repurpose these algorithms to inform questions of urban inequality by incorporating social data pertaining to three constructs: (1) relative deprivation,(2) purchasing power, and (3) human capital. Our results yield a series of urban informatics that reveal a variety of metropolitan dynamics. We discuss how these results can be developed to measure progress toward SDGs and inform more equitable urban policy.