Sergio Rey
Center for Geospatial Sciences, University of California, Riverside

Elijah Knaap
Center for Geospatial Sciences, University of California, Riverside

Su Han
Center for Geospatial Sciences, University of California, Riverside

Levi Wolf
School of Geographical Sciences, University of Bristol

Wei Kang
Center for Geospatial Sciences, University of California, Riverside

Video: https://youtu.be/VWMj_rNb0io

Abstract

The neighborhood effects literature represents a wide span of the social sciences broadly concerned with the influence of spatial context on social processes. From the study of segregation dynamics, the relationships between the built environment and health outcomes, to the impact of concentrated poverty on social efficacy, neighborhoods are a central construct in empirical work. From a dynamic lens, neighborhoods experience changes not only in their socioeconomic composition, but also in spatial extent; however, the literature has ignored the latter source of change. In this paper, we discuss the development of a novel, spatially explicit tool: the Open Source Longitudinal Neighborhood Analysis Package (OSLNAP) using the scientific Python ecosystem.

Keywords

neighborhoods, GIS, clustering, dynamics

DOI

10.25080/Majora-4af1f417-012

Bibtex entry

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Copyright © 2018 Sergio Rey et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.