Abstract
Information on the distribution and dynamics of dwellings and their inhabitants is essential to support decision-making in various fields such as energy provision, land use planning, risk assessment and disaster management. However, as various different of approaches to estimate the current distribution of population and dwellings exists, further evidence on past dynamics is needed for a better understanding of urban processes. This article therefore addresses the question of whether and how accurately historical distributions of dwellings and inhabitants can be reconstructed with commonly available geodata from national mapping and cadastral agencies. For this purpose, an approach for the automatic derivation of such information is presented. The data basis is constituted by a current digital landscape model and a 3D building model combined with historical land use information automatically extracted from historical topographic maps. For this purpose, methods of image processing, machine learning, change detection and dasymetric mapping are applied. The results for a study area in Germany show that it is possible to automatically derive decadal historical patterns of population and dwellings from 1950 to 2011 at the level of a 100 m grid with slight underestimations and acceptable standard deviations. By a differentiated analysis we were able to quantify the errors for different urban structure types.
Highlights
Information on the distribution and dynamics of dwellings and their inhabitants is essential to support decision-making in various fields such as energy provision, land use planning, risk assessment and disaster management
As various different of approaches to estimate the current distribution of population and dwellings exists, further evidence on past dynamics is needed for a better understanding of urban processes
The data basis is constituted by a current digital landscape model and a 3D building model combined with historical land use information automatically extracted from historical topographic maps
Summary
The spatial distribution of dwelling units and population is highly relevant for addressing general questions of resource management and spatial planning [1,2,3]. Hussain and Shan [24] later modified the approach by introducing a weighting scheme for different housing types such as “houses” and “apartments.” Meinel, Hecht and Herold [21] differentiate between eight residential building types and apply empirically determined average dwelling and population densities and non-residential uses within buildings are integrated to provide for more accurate estimations [23,25,26] Such models open a way for multi-temporal estimations of populations as information on buildings and functions allows respective changes in population distributions to be estimated during the day and night time [27,28] or different seasons due to tourism [29].
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