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.
Against the backdrop of rapidly expanding urban structures, land policies in many countries have been adapted to contain and redirect growth to existing urban structures. However, obstacles remain to measure the effects of policies. In the meantime, geoinformation technologies have given rise to a wide range of approaches to measure and describe urban form. Nevertheless, its application for the assessment of land policy has a high, but not yet fully exploited, potential. It is thus the aim of this research to address and investigate the options of spatial analysis and machine learning in particular to analyse urban form from a land policy perspective. To do so, we develop urban metrics informed by urban planning and land readjustment policies of two countries describing urban form on different spatial levels. We therefore formulate hypotheses on causal relations between policy and form. Based on the metrics, we apply the random forest algorithm to classify the building stock of the region. We then extract the residential areas, those with single-family houses, as this is where the effects of the policy are considered most visible. In a next step, we use random forest to predict the nationality of a building. Through variable importance measures, we identify and discuss urban morphological differences between the two countries and test the hypotheses on effects of land policies. We develop and test the approach for the French-German city-region of Strasbourg using OpenStreetMap data. We identify significant differences in the building coverage ratios, which tend to be higher in Germany. This can be linked to differences in planning regulations. Furthermore, German residential areas appear to be more diverse in urban form. Differences in land readjustment policies have proven to be plausible here, as French policies favour strong actors that develop residential areas more uniformly. In Germany, policies favour fragmented ownership-oriented development of residential areas. The metrics and the applied algorithm for building classification have proven to be robust in terms of data heterogeneity and have shown high levels of accuracy. They could also be successfully used for tracing causal relations.
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