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High-resolution population grid in the CONUS using microsoft building footprints: a feasibility study

Published: 05 November 2019 Publication History

Abstract

Better knowledge of where people live is of great importance for a wide range of studies, including disaster responses, public health, resource management, and urban planning. Given the increasing demand for population grid with improved quality, this study explores the feasibility of generating high-resolution (100m) population grids in the Conterminous U.S. (CONUS) using a total of 125 million building footprints recently released by Microsoft. Those building footprints were used to disaggregate census tract population of the latest ACS 5-year estimates (2013-2017). Land use dataset from the OpenStreetMap (OSM) was applied to trim raw buildings footprints by removing those that are not likely residential. Weighting scenarios were designed, with which a dasymetric model was applied to disaggregate the ACS census tract estimates into a 100m population grid product. The results suggest that building footprints as a weighting layer, particularly footprint size after trimming, outperforms other commonly used weighting layers and is able to capture the great heterogeneity of population distribution at the micro-level. This study provides valuable experience in developing high-resolution population grid products that can benefit a wide range of studies in need of spatially explicit population data.

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      cover image ACM Conferences
      GeoHumanities '19: Proceedings of the 3rd ACM SIGSPATIAL International Workshop on Geospatial Humanities
      November 2019
      45 pages
      ISBN:9781450369602
      DOI:10.1145/3356991
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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      Published: 05 November 2019

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      Author Tags

      1. dasymetric mapping
      2. microsoft building footprints
      3. population disaggregation
      4. population grid

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      Overall Acceptance Rate 15 of 21 submissions, 71%

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      • (2024)Cross-City Building Instance Segmentation: From More Data to Diffusion-Augmentation2024 IEEE International Conference on Big Data (BigData)10.1109/BigData62323.2024.10825702(8502-8511)Online publication date: 15-Dec-2024
      • (2024)Pre-trained regional models for extracting buildings from high resolution satellite imagery to support public health initiativesRemote Sensing Applications: Society and Environment10.1016/j.rsase.2024.101270(101270)Online publication date: Jun-2024
      • (2021)Geospatial Data Disaggregation through Self-Trained Encoder–Decoder Convolutional ModelsISPRS International Journal of Geo-Information10.3390/ijgi1009061910:9(619)Online publication date: 16-Sep-2021
      • (2021)Sensing Population Distribution from Satellite Imagery Via Deep Learning:Model Selection, Neighboring Effects, and Systematic BiasesIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing10.1109/JSTARS.2021.307663014(5137-5151)Online publication date: 2021
      • (2021)Land consumption in cities: A comparative study across the globeCities10.1016/j.cities.2021.103163113(103163)Online publication date: Jun-2021
      • (2020)Estimating building-scale population using multi-source spatial dataCities10.1016/j.cities.2020.103002(103002)Online publication date: Nov-2020

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