Spatiotemporal aggregation for temporally extensive international microdata. (May 2017)

• Record Type: Journal Article
• Authors: Kugler, Tracy A.; Manson, Steven M.; Donato, Joshua R.
• Is Part Of: Computers, environment and urban systems. Volume 63(2017)
• Page Start: 26
• Page End: 37
• Publication Date: 2017-05
• Subjects: Regionalization -- Cluster analysis -- Census microdata; City planning -- Data processing -- Periodicals; Regional planning -- Data processing -- Periodicals; 303.4834
• DOI: 10.1016/j.compenvurbsys.2016.07.007
• Languages: English
• ISSNs: 0198-9715

Abstract

Abstract: We describe a strategy for regionalizing subnational administrative units in conjunction with harmonizing changes in unit boundaries over time that can be applied to provide small-area geographic identifiers for census microdata. The availability of small-area identifiers blends the flexibility of individual microdata with the spatial specificity of aggregate data. Regionalizing microdata by administrative units poses a number of challenges, such as the need to aggregate individual scale data in a way that ensures confidentiality and issues arising from changing spatial boundaries over time. We describe a regionalization and harmonization strategy that creates units that satisfy spatial and other constraints while maximizing the number of units in a way that supports policy and research use. We describe this regionalization strategy for three test cases of Malawi, Brazil, and the United States. We test different algorithms and develop a semi-automated strategy for regionalization that meets data restrictions, computation, and data demands from end users. Highlights: Small-area geographic identifiers on microdata enhance their spatial utility. We develop a harmonization-regionalization strategy for microdata geographic units. Harmonization is conducted before regionalization to protect confidentiality. Full CLK and Ward regionalization algorithms perform best, depending on context.