In many countries, densification is a central goal of land policies to promote sustainable development. However, while these policies intervene in existing built-up structures and thus affect diver...

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.

While governments worldwide rely upon compact city policies to reduce land consumption from urban growth, recent studies have addressed the potential trade-off between densification and housing affordability. Concerns have been voiced that densification leads to a one-sided housing supply, structurally excluding low-income households. However, few studies address household income variation across densification projects, leaving us with a limited understanding of the circumstances under which exclusion occurs. To this end, we explore household incomes in densification projects between 2012 and 2020 in the Province of Utrecht, the Netherlands, where urban development is traditionally strongly regulated through active land policy. At the same time, current shifts towards a more deregulated housing market make for an interesting case. Exceptional access to detailed cadastral and census data allows us to identify densification projects and assign them a median household income each. We investigate the influence of location and transformation process on household incomes through regression analysis and conduct qualitative case studies of projects whose median income was highly mispredicted by the regression model. This allows us to integrate non-quantified factors, such as land ownership and public policy interventions, in explaining such interesting cases. For the Province of Utrecht, our study confirms that while households in densification projects earn significantly more than their neighbours, the range of incomes in densification projects is large. Project characteristics such as centrality, neighbourhood status and transformation process explain only a small share of this variance. For cases where median incomes are much lower than predicted by the model, public land ownership, in combination with inclusionary zoning, is essential in ensuring housing affordability. Our approach highlights the necessity of supplementing densification policies with measures that secure affordable housing.

While governments worldwide develop policies to promote urban densification, critics point to possible negative effects of densification on social sustainability. The occurrence and distribution of these negative social effects are strongly influenced by land policies. This makes it crucial to monitor the role of land policies and understand what processes shape urban development in the context of densification. To do so, detailed, large-scale international comparisons of densification patterns, including building and social changes, are needed. We address this issue by introducing a method to measure and compare urban development in two countries with contrasting planning systems: the Netherlands, where public actors play a strong and active role, and Switzerland, where strong private property titles and a highly democratic planning system are prevailing. Our GIS-based method analyses densification processes within their surrounding morphological and socio-demographic context. A k-proto cluster analysis on highly detailed spatial and statistical data based on housing units, covering 2011–2019, results in five densification types. The distribution of these types reveals different patterns in the two city regions of Utrecht (NL) and Bern (CH). Most strikingly, contiguous redevelopments frequently occurred in Utrecht but hardly in Bern, pointing at possible advantages for Dutch municipalities to intervene in property rights. While having developed an empirical basis in this study, future research that refines the analysis of the legal, planning and ownership conditions underlying the identified densification patterns can contribute significantly to policy evaluation.