The Nature of Neighborhoods: Using Big Data to Rethink the Geographies of Atlanta’s Neighborhood Planning Unit System

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Abstract

Neighborhoods have long held a central place in the analysis and planning of urban spaces. Despite this centrality, the exact definition of a neighborhood, as well as how and where to draw its boundaries, has remained unclear. Although these questions have generated significant academic debate, they have arguably little effect on people’s everyday lives or decision-making processes. Atlanta, Georgia, represents a different case, however, because of the city’s system of neighborhood planning units (NPUs), which has shaped the city’s planning and political processes since the mid-1970s. Despite significant changes within the city’s intraurban geography since the 1970s, however, the geography of the NPU system has remained largely unchanged. As such, this article attempts to harness the potentials of big data to rethink the nature and geographies of Atlanta’s neighborhoods. Drawing on archival documents, historic maps of the city’s neighborhoods, and new sources of big data, this article explores the evolution of Atlanta’s neighborhoods from the 1960s up to the present day. In particular, our analysis highlights the fundamentally fuzzy, fluid, and relational nature of neighborhoods, both in the ways in which neighborhoods are defined by political and administrative entities and through the everyday lives of residents. Ultimately, this work calls into question the idea of the neighborhood as a natural, preexisting, discrete, and static spatial unit and points to the utility of using new sources of data and analytical techniques for revealing this underlying relationality. Key Words: big data, neighborhoods, regionalization, sociospatial theory, urban geography.

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Shelton, T., & Poorthuis, A. (2019). The Nature of Neighborhoods: Using Big Data to Rethink the Geographies of Atlanta’s Neighborhood Planning Unit System. Annals of the American Association of Geographers, 109(5), 1341–1361. https://doi.org/10.1080/24694452.2019.1571895

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