Full Citation

Title: Regional Policy Analysis in the Era of Spatial Big Data

Citation Type: Book, Section

Publication Year: 2020

ISBN: 978-981-15-1434-0

ISSN:

DOI: 10.1007/978-981-15-1435-7_7

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Abstract: New and expanding sources of spatial big data hold tremendous potential for regional policy analysis. Such data enable us to analyze regional policies in ways not possible with traditional sources of data, such as administrative records. At the same time, the use of spatial big data is fraught with issues and challenges that must be addressed. In this paper, we discuss both the opportunities and challenges of using spatial big data for regional policy analysis. We also explore analytical issues tied to the use of regional policy analysis methods in the era of big data, as well as the state of art in applying such methods to spatial big data. Our discussion focuses on three types of methods: (1) statistical and regression modeling, (2) traditional nonparametric modeling, and (3) deep neural learning.

Url: http://link.springer.com/10.1007/978-981-15-1435-7_7

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Authors: Schintler, Laurie A.

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Pages: 93-109

Volume Title: Development Studies in Regional Science. New Frontiers in Regional Science: Asian Perspectives, vol 42

Publisher: Springer, Singapore

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Data Collections: IPUMS Terra

Topics: Other, Population Data Science

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