Spatiotemporal aggregation for temporally extensive international microdata

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.

[1]  Tom Kauko,et al.  A Comparative Perspective on Urban Spatial Housing Market Structure: Some More Evidence of Local Sub-markets Based on a Neural Network Classification of Amsterdam , 2004 .

[2]  Katherine J Curtis,et al.  A Note on the Identification of Common Geographies , 2017, Sociological methods & research.

[3]  Steven M. Manson,et al.  Terra Populus: Workflows for Integrating and Harmonizing Geospatial Population and Environmental Data , 2015 .

[4]  D. Phaneuf,et al.  An integrated model of regional and local residential sorting with application to air quality , 2015 .

[5]  M. Bailey,et al.  Did Improvements in Household Technology Cause the Baby Boom? Evidence from Electrification, Appliance Diffusion, and the Amish , 2009 .

[6]  S. Ruggles.  Integrated Public Use Microdata Series , 2021, Encyclopedia of Gerontology and Population Aging.

[7]  Luc Anselin,et al.  Spatial Effects and Ecological Inference , 2002, Political Analysis.

[8]  T. DiPrete,et al.  The Black Gender Gap in Educational Attainment: Historical Trends and Racial Comparisons , 2010, Demography.

[9]  David Martin,et al.  Zone design for environment and health studies using pre-aggregated data. , 2005, Social science & medicine.

[10]  E. Ostrom,et al.  The concept of scale and the human dimensions of global change: a survey , 2000, Ecological Economics.

[11]  S. Piantadosi,et al.  The ecological fallacy. , 1988, American journal of epidemiology.

[12]  Hoyt Bleakley,et al.  Malaria Eradication in the Americas: A Retrospective Analysis of Childhood Exposure. , 2010, American economic journal. Applied economics.

[13]  Diansheng Guo,et al.  Regionalization with dynamically constrained agglomerative clustering and partitioning (REDCAP) , 2008, Int. J. Geogr. Inf. Sci..

[14]  R. Nawrotzki,et al.  International Climate Migration: Evidence for the Climate Inhibitor Mechanism and the Agricultural Pathway. , 2017, Population, space and place.

[15]  Michael R Kramer,et al.  International Journal of Health Geographics Open Access Methodology Methodology Do Measures Matter? Comparing Surface-density-derived and Census-tract-derived Measures of Racial Residential Segregation , 2022 .

[16]  Stephen E. Fienberg,et al.  Confidentiality and Disclosure Limitation , 2005 .

[17]  R. Nawrotzki,et al.  Do Rainfall Deficits Predict U.S.-Bound Migration from Rural Mexico? Evidence from the Mexican Census , 2013, Population research and policy review.

[18]  Michael R. Kramer.  Race, Place, and Space: Ecosocial Theory and Spatiotemporal Patterns of Pregnancy Outcomes , 2016 .

[19]  Gary King,et al.  A Solution to the Ecological Inference Problem: Reconstructing Individual Behavior from Aggregate Data , 1998 .

[20]  J. Spijker,et al.  Changing household patterns of young couples in low- and middle-income countries , 2011 .

[21]  David J. Martin,et al.  Getting the Foundations Right: Spatial Building Blocks for Official Population Statistics , 2013 .

[22]  Robert J. Gitter,et al.  The Effect of Rainfall on Migration from Mexico to the United States 1 , 2016 .

[23]  J. Hox,et al.  Sufficient Sample Sizes for Multilevel Modeling , 2005 .

[24]  Robert McCaa,et al.  IPUMS-Europe : Condifentiality measures for licensing and disseminating restricted-access census microdata extracts to academic users , 2006 .

[25]  S. Matthews,et al.  Recapturing space : new middle-range theory in spatial demography , 2016 .

[26]  L. Zayatz.  Disclosure avoidance practices and research at the U.S. Census Bureau: an update , 2007 .

[27]  Stefan Leyk,et al.  Spatially and Temporally Varying Associations between Temporary Outmigration and Natural Resource Availability in Resource-Dependent Rural Communities in South Africa: A Modeling Framework. , 2012, Applied geography.

[28]  Stan Openshaw,et al.  A geographical solution to scale and aggregation problems in region-building, partitioning and spatial modelling , 1977 .

[29]  Diansheng Guo,et al.  Automatic Region Building for Spatial Analysis , 2011 .

[30]  David J. Martin.  Extending the automated zoning procedure to reconcile incompatible zoning systems , 2003, Int. J. Geogr. Inf. Sci..

[31]  Stefan Leyk,et al.  Rural Outmigration, Natural Capital, and Livelihoods in South Africa. , 2014, Population, space and place.

[32]  David O'Sullivan,et al.  Beyond the Census Tract: Patterns and Determinants of Racial Segregation at Multiple Geographic Scales , 2008, American sociological review.

[33]  Otis Dudley Duncan,et al.  An Alternative to Ecological Correlation , 1953 .