Urbanization poses both challenges and opportunities for sustainable development and environmental management. Improved data on patterns of human settlement and trends in population can help researchers and policy makers better understand differences between urban and rural areas in terms of their impacts on the environment and vulnerability to environmental variability and change. The newly released Global Rural-Urban Mapping Project, Version 1 (GRUMPv1) data collection is a valuable resource both for researchers studying human-environment interactions and for applied users working to address critical environmental and societal issues.
Developed by the NASA Socioeconomic Data and Applications Center (SEDAC) operated by CIESIN, GRUMPv1 is actually a collection of three global data sets:
1) Population density and population count grids build on SEDAC’s Gridded Population of the World, Version 3 data set (GPWv3), which does not distinguish between urban and rural areas. GRUMPv1 identifies urban areas based in part on observations of lights at night collected by a series of Department of Defense meteorological satellites over several decades.
2) A geo-referenced database of urban settlements with populations greater than 5,000 persons, which may be downloaded in both tabular and shapefile formats.
3) An urban-rural delineation (urban extents grid) which identifies those areas of the Earth’s land surface that appear to be urbanized. This classification is based on a combination of night-time lights, and, where there are no lights, settlement points with a buffer representing the population size.
GRUMPv1 also includes four ancillary data sets: land/geographic unit area grids, national boundaries, national identifier grids, and coastlines. All grids are provided at a resolution of 30 arc-seconds (~1km), with population estimates normalized to the years 1990, 1995, and 2000. All data sets are available for download as global products, and several are available as continental, regional, and national subsets.
Nick“One thing to ciodnser is that for a lot of these sites, there will also be wind data. You could also extract wind data from the weather maps at the time on a large scale.So now you could test a site that is to the east of an urban area. You also need a site to the west of an urban area. Depending on the wind direction you either get no difference, or a bias one way or the other.”################## the wind data would be more interesting to explain why there is no UHI in the city. A 7m/sec wind will eliminate most UHI. The more interesting effect would be changes to precipitation downwind.