Spatial analyses in the fields of urban and regional planning, transport planning, environment, climate or geology often require high resolution socio-economic data. Such analyses work with raster data to calculate indicators such as exposure to air pollutants or to noise. In many cases available socio-economic data do not have the necessary spatial resolution. Usually, data on population, employment or housing are available only for larger areas such as provinces, districts, municipalities or other statistical entities, i.e. units that might be too coarse to be used in such spatial models. This is where DisAgg extension for ArcGIS® might be instrumental. Its main function is to spatially disaggregate zonal data to raster level. Unlike other approaches which distribute zonal data equally to raster cells, assuming same density in all parts of a zone, DisAgg takes account of actual land use schemes, assuming that areas of different density within a zone correspond to different land-use categories. Data are then disaggregated by complex Monte-Carlo simulations.