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Exposing the urban continuum: implications and cross-comparison from an interdisciplinary perspective
Authors:Johannes H. Uhl  Hamidreza Zoraghein  Stefan Leyk  Deborah Balk  Christina Corbane  Vasileios Syrris
Affiliation:1. Department of Geography, University of Colorado Boulder, Boulder, CO, USAJohannes.Uhl@colorado.edu"ORCIDhttps://orcid.org/0000-0002-4861-5915;3. National Center for Atmospheric Research (NCAR), Boulder, CO, USA;4. Department of Geography, University of Colorado Boulder, Boulder, CO, USA"ORCIDhttps://orcid.org/0000-0001-9180-4853;5. CUNY Institute for Demographic Research and Baruch College, City University of New?York, New?York City, NY, USA"ORCIDhttps://orcid.org/0000-0002-9028-7898;6. European Commission, Joint Research Centre (JRC), Ispra, Italy"ORCIDhttps://orcid.org/0000-0002-2670-1302;7. European Commission, Joint Research Centre (JRC), Ispra, Italy
Abstract:ABSTRACT

There is an increasing availability of geospatial data describing patterns of human settlement and population such as various global remote-sensing based built-up land layers, fine-grained census-based population estimates, and publicly available cadastral and building footprint data. This development constitutes new integrative modeling opportunities to characterize the continuum of urban, peri-urban, and rural settlements and populations. However, little research has been done regarding the agreement between such data products in measuring human presence which is measured by different proxy variables (i.e. presence of built-up structures derived from different remote sensors, census-derived population counts, or cadastral land parcels). In this work, we quantitatively evaluate and cross-compare the ability of such data to model the urban continuum, using a unique, integrated validation database of cadastral and building footprint data, U.S. census data, and three different versions of the Global Human Settlement Layer (GHSL) derived from remotely sensed data. We identify advantages and shortcomings of these data types across different geographic settings in the U.S., which will inform future data users on implications of data accuracy and suitability for a given application, even in data-poor regions of the world.
Keywords:Built-up surfaces  Global Human Settlement Layer (GHSL)  Sentinel-1  Landsat  agreement assessment  urban modeling  cadastral parcel data  U.S. census data
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