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A 100 m population grid in the CONUS by disaggregating census data with open-source Microsoft building footprints

机译:通过将人口普查数据与开源Microsoft构建足迹分解人口普查数据,在康士胡斯中100米人口网格

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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.
机译:在大数据时代,地球观察正成为一系列复杂的整合物理和社会部门的过程。本研究提出了一种通过在2018年使用Microsoft发布的1.5亿建筑占据方式分解美国人口普查记录来在连续的美国(康明斯)中产生100米人口电网的方法。来自OpenStreetMap(OSM)的土地使用数据,a众包平台应用于通过移除非住宅建筑来修剪原始脚印。修剪后,使用诸如人口普查道中的建筑物尺寸和建筑物数量的若干标准测量标准作为加权方案,其中应用了脱稗模型,以分解美国社区调查(ACS)5年估算(2013-2017)进入100米人口网格产品。结果证实,OSM修剪过程消除了非住宅建筑物,从而在复杂的城市织物中提供了更好的人口分布。人口普查道中的建筑物尺寸在最佳加权方案中找到。该产品的大小为2.5GB,填充了8亿个填充网格,目前由ESRI(http://cricg.is/19s4qk)托管进行可视化。可以通过https://doi.org/10.7910/dvn/dlgp7y访问数据。随着加速获取高分辨率空间数据,产品可以很容易地更新空间和时间连续性。

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