New issue of Geografia Slovaca about spatial disaggregation

Rosina, K. Hurbánek, P.
SPATIAL DISAGGREGATION OF POPULATION DENSITY USING LAND COVER AND REMOTE SENSING DATA.

Institute of Geography SAS, 2016, 81 P.
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Researchers in geography and other fields of research and practice (decision making, spatial planning, and emergency response among others) often need to analyze population data using areal units that are incompatible with available reporting areal units, or finer spatial detail of information on population distribution is required. Areal interpolation methods can be employed to estimate the data for target zones or to disaggregate the available data into a regular grid with fine spatial resolution. Such grids can be in turn used to approximate arbitrary polygons, thus being useful to a wide range of users. The monograph describes the production of a gridded model of population distribution with a spatial resolution of 100 m by disaggregation of municipality-level population counts using open-access ancillary spatial data (CORINE land cover, high-resolution imperviousness layer, road and rail networks from OpenStreetMap project). The aim of the work was to improve an existing method of intelligent spatial disaggregation and to investigate how certain parameters of the method influence the estimation accuracy.