by Klaus Steinnocher, Jürgen Weichselbaum and Mario Köstl
During the last decades urban areas have experienced an enormous growth in terms of human population and physical size. Applying remote sensing techniques has become a standard approach for monitoring the physical growth of urban areas. However, when it comes to social studies statistical data are used for analysis in a first line. Only a few studies have been undertaken that try to combine these complementary data sources. In this paper we will show how land cover information from remote sensing can be combined with population data in order to derive refined information products.
Demographic data is usually derived from census and represented in administrative units such as districts or municipalities. Spatial analysis on a finer level is not possible due to this restriction. Introducing remote sensing can partially overcome this problem by indicating where people actually live within the administrative units. The total population of one unit can then be allocated to the built-up areas within the unit leading to a spatial refinement of the statistical data.
The paper will present a number of examples for spatial disaggregation of population data based on remote sensing derived information on urbanised areas – ranging from binary settlement masks to housing densities – for regional and European applications. A special focus will be set on to the assessment of accuracies of the presented method. Detailed demographic data available for selected regions will be used as reference for the spatial disaggregation results. These comparisons show that the approach is reliable and the quality of the final information products can be controlled via the level of detail of the remote sensing analysis.
Demographic data is usually derived from census and represented in administrative units such as districts or municipalities. Spatial analysis on a finer level is not possible due to this restriction. Introducing remote sensing can partially overcome this problem by indicating where people actually live within the administrative units. The total population of one unit can then be allocated to the built-up areas within the unit leading to a spatial refinement of the statistical data.
The paper will present a number of examples for spatial disaggregation of population data based on remote sensing derived information on urbanised areas – ranging from binary settlement masks to housing densities – for regional and European applications. A special focus will be set on to the assessment of accuracies of the presented method. Detailed demographic data available for selected regions will be used as reference for the spatial disaggregation results. These comparisons show that the approach is reliable and the quality of the final information products can be controlled via the level of detail of the remote sensing analysis.
more about Germany and Austria:
No comments:
Post a Comment