In the Big Data era, Earth observation is becoming a complex process integrating physical and social sectors. This study presents an approach to generating a 100 m population grid in the Contiguous United States (CONUS) by disaggregating the US census records using 125 million of building footprints released by Microsoft in 2018. Land-use data from the OpenStreetMap (OSM), a crowdsourcing platform, was applied to trim original footprints by removing the non-residential buildings. After trimming, several metrics of building measurements such as building size and building count in a census tract were used as weighting scenarios, with which a dasymetric model was applied to disaggregate the American Community Survey (ACS) 5-year estimates (2013-2017) into a 100 m population grid product. The results confirm that the OSM trimming process removes non-residential buildings and thus provides a better representation of population distribution within complicated urban fabrics. The building size in the census tract is found in the optimal weighting scenario. The product is 2.5Gb in size containing 800 million populated grids and is currently hosted by ESRI (http://arcg.is/19S4qK) for visualization. The data can be accessed via https://doi.org/10.7910/DVN/DLGP7Y. With the accelerated acquisition of high-resolution spatial data, the product could be easily updated for spatial and temporal continuity.
Digital Object Identifier (DOI)
Published in Big Earth Data, Volume 5, Issue 1, 2020, pages 112-133.
© 2020 The Author(s). Published by Taylor & Francis Group and Science Press on behalf of the International Society for Digital Earth, supported by the CASEarth Strategic Priority Research Programme. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Huang, X., Wang, C., Li, Z., & Ning, H. (2020). A 100 m population grid in the CONUS by disaggregating census data with open-source Microsoft Building Footprints. Big Earth Data, 5(1), 112–133. https://doi.org/10.1080/20964471.2020.1776200