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GIScience 2016 Short Paper Proceedings Spatializing Global Urban Extent: A Source Driven Approach J. J. Moehl 1 , A. N. Rose 1 , E. A. Bright 1 Oak Ridge National Laboratory, Oak Ridge, TN 37831 Email: moehljj;rosean;brightea@ornl.gov Abstract “Urban” is something that intuitively feels very well defined; however, when it comes time to express this idea on a map, things get complicated. Definitions of “urban” vary globally, and as such there is not universal understanding of what makes a given place “urban”. The common approach to spatially defining urban extents is through remotely sensed imagery. The alternative approach presented in this paper uses the percent of population in urban areas, which is a common macroeconomic (country-level) variable with the definition for urban generally defined by each country’s statistical office, along with temporally-aligned high- resolution population data to spatially define urban extents for each country. Because the percent urban number is defined by the same producer as other urban/rural defined statistical data, such as household characteristics or birth rate, understanding the spatial aspect of urban from the same perspective is ideal for high resolution spatial modeling of these other phenomena. 1. Introduction At Oak Ridge National Laboratory, we’ve been modeling population for nearly two decades. Whether using a top down approach such as the LandScan Global model (Dobson et al. 2000), or a hybrid top down/bottom up approach as is used to develop LandScan USA (Bhaduri et al. 2007), spatial data that aligns with tabular datasets is essential. Ensuring that national and subnational boundaries match the data from statistical organizations is extremely important for dasymetric mapping, as these boundaries play a large role in appropriately disaggregating zonal summaries. Many summary data values, such as population counts, birth rates, average household size, and age distributions are listed not only by administrative zone, but often times they are further listed by whether they occur in urban or rural areas. Spatial data for administrative boundaries are widely available, although of varying quality and level of detail. However, very rarely are spatial data available with urban area delineation from the perspective of the statistical organization. Summary data designated as urban or rural for each administrative zone within the country is common. The idea of what is “urban” is defined and implemented by each country’s statistical office. The UN’s World Urbanization Prospects: The 2014 Revision provides an exhaustive comparison of these definitions which vary widely in their considerations (UN 2014). This may be an extremely detailed formal definition like the one provided by the US Census Bureau (US Census 2011) complete with spatial data freely available in multiple formats, or very generally defined with no publicly accessible spatial data. Administrative data and population density are the most common criteria used by countries to fit the urban definition, with 125 and 137 countries respectively using each criterion in part or whole (UN 2014). The HYDE datasets, which provide historical population and land use estimates, combine historical urban population estimates with population density in their models; however, these datasets are historical and at a coarser ~10 sq km resolution (Klein Goldewijk 2001, 2005).