Enhancing areal interpolation frameworks through dasymetric refinement to create consistent population estimates across censuses

ABSTRACT To assess micro-scale population dynamics effectively, demographic variables should be available over temporally consistent small area units. However, fine-resolution census boundaries often change between survey years. This research advances areal interpolation methods with dasymetric refinement to create accurate consistent population estimates in 1990 and 2000 (source zones) within tract boundaries of the 2010 census (target zones) for five demographically distinct counties in the US. Three levels of dasymetric refinement of source and target zones are evaluated. First, residential parcels are used as a binary ancillary variable prior to regular areal interpolation methods. Second, Expectation Maximization (EM) and its data-extended version leverage housing types of residential parcels as a related ancillary variable. Finally, a third refinement strategy to mitigate the overestimation effect of large residential parcels in rural areas uses road buffers and developed land cover classes. Results suggest the effectiveness of all three levels of dasymetric refinement in reducing estimation errors. They provide a first insight into the potential accuracy improvement achievable in varying geographic and demographic settings but also through the combination of different refinement strategies in parts of a study area. Such improved consistent population estimates are the basis for advanced spatio-temporal demographic research.

[1]  J. Mennis.  Generating Surface Models of Population Using Dasymetric Mapping , 2003, The Professional Geographer.

[2]  Jonathan P Schroeder,et al.  Because Muncie's Densities Are Not Manhattan's: Using Geographical Weighting in the EM Algorithm for Areal Interpolation. , 2013, Geographical analysis.

[3]  C. Lo,et al.  Dasymetric Estimation of Population Density and Areal Interpolation of Census Data , 2004 .

[4]  Suming Jin,et al.  Completion of the 2011 National Land Cover Database for the Conterminous United States – Representing a Decade of Land Cover Change Information , 2015 .

[5]  Jeremy Mennis,et al.  Dasymetric Spatiotemporal Interpolation , 2015 .

[6]  Michael F. Goodchild,et al.  Areal interpolation: A variant of the traditional spatial problem , 1980 .

[7]  Christopher R. Herrmann,et al.  Mapping Population Distribution in the Urban Environment: The Cadastral-based Expert Dasymetric System (CEDS) , 2007 .

[8]  Peter F. Fisher,et al.  Modelling the Errors in Areal Interpolation between Zonal Systems by Monte Carlo Simulation , 1995 .

[9]  Jeremy Mennis,et al.  Dasymetric Mapping for Estimating Population in Small Areas , 2009 .

[10]  Cynthia A. Brewer,et al.  Dasymetric Mapping and Areal Interpolation: Implementation and Evaluation , 2001 .

[11]  Stefan Leyk,et al.  Assessing the Accuracy of Multi-Temporal Built-Up Land Layers across Rural-Urban Trajectories in the United States. , 2018, Remote sensing of environment.

[12]  Mitchel Langford,et al.  Obtaining population estimates in non-census reporting zones: An evaluation of the 3-class dasymetric method , 2006, Comput. Environ. Urban Syst..

[13]  Barbara P. Buttenfield,et al.  Establishing relationships between parcel data and land cover for demographic small area estimation , 2013 .

[14]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[15]  J. Wickham,et al.  Thematic accuracy of the NLCD 2001 land cover for the conterminous United States , 2010 .

[16]  Jeremy Mennis.  Increasing the Accuracy of Urban Population Analysis with Dasymetric Mapping , 2015 .

[17]  Barbara P. Buttenfield,et al.  Dasymetric Modeling and Uncertainty , 2014, Annals of the Association of American Geographers. Association of American Geographers.

[18]  Barbara P. Buttenfield,et al.  Exploring the impact of dasymetric refinement on spatiotemporal small area estimates , 2015 .

[19]  John K. Wright.  A Method of Mapping Densities of Population: With Cape Cod as an Example , 1936 .

[20]  Barbara P. Buttenfield,et al.  Exploiting temporal information in parcel data to refine small area population estimates , 2016, Comput. Environ. Urban Syst..

[21]  Barbara P. Buttenfield,et al.  Comparing the effects of an NLCD-derived dasymetric refinement on estimation accuracies for multiple areal interpolation methods , 2015 .

[22]  Mark W. Green,et al.  Areal interpolation and types of data , 1994 .

[23]  Barbara P. Buttenfield,et al.  Modeling residential developed land in rural areas: A size-restricted approach using parcel data , 2014 .

[24]  Ferri Stefano,et al.  Operating procedure for the production of the Global Human Settlement Layer from Landsat data of the epochs 1975, 1990, 2000, and 2014 , 2016 .

[25]  Jonathan P. Schroeder.  Target-Density Weighting Interpolation and Uncertainty Evaluation for Temporal Analysis of Census Data , 2007 .

[26]  Jose M. Pavía,et al.  Can Dasymetric Mapping Significantly Improve Population Data Reallocation in a Dense Urban Area , 2017 .