Michael Meinild Nielsen

A Multiscalar Approach for Identifying Clusters and Segregation Patterns That Avoids the Modifiable Areal Unit Problem

One problem encountered in analyses based on data aggregated into areal units is that the results can depend on the delineation of the areal units. Therefore, a particular aggregation at a specific scale can yield an arbitrary result that is valid only for that specific delineation. This problem is called the modifiable areal unit problem (MAUP), and it has previously been shown to create issues in analyses of clusters and segregation patterns. Many analyses of segregation and clustering use the ratio or difference between a value for an areal unit and the corresponding value for a larger area of reference. We argue that the results of such an analysis can also be rendered arbitrary if one does not examine the effects of varying the geographical extent of the area of reference to test whether the analysis results are valid for more than a specific areal delineation. We call this the part of the MAUP that is related to the area of reference. In this article, we present and demonstrate a multiscalar approach for studying segregation and clustering that avoids the MAUP, including the part of the problem related to the area of reference. The proposed methods rely on multiscalar aggregation of the k nearest neighbors of a location in a statistical comparison with a larger area of reference consisting of the K nearest neighbors. The methods are exemplified by identifying clusters and segregation patterns of the Hispanic population in the contiguous United States.

Classification of different urban categories corresponding to the strategic spatial level of urban planning and management using a SPOT4 scene

Information about the spatial and structural properties as well as different indicators of social and economic functions cannot be easily extracted from remote-sensing data in an urban milieu. This paper focuses on the extraction of information that is relevant to the strategic spatial level of urban planning management, i.e., more general land-use descriptions, using the window-independent context segmentation method to extract urban area categories from a SPOT4 satellite scene. In this study, we were able to extract three different urban categories, industrial/commercial, and two residential categories that belong to different suburbanisation phases.

Remote sensing for urban planning and management: The use of window-independent context segmentation to extract urban features in Stockholm

Abstract The strategic scale of urban planning and management is concerned with the planning and monitoring of general land use in a city, such as different types of residential, industrial and commercial areas. Because of the poor results of standard per-pixel-based classification methods in urban areas, visual interpretation of remote sensing data is often preferred. This paper empirically tests the ability of a novel method, called window-independent context segmentation, to extract information that is useful at the strategic scale of urban planning and management. The method is implemented in a theoretical framework that is a response to Bibby and Shepherd’s call for a new ontology in the application of geographic information systems and remote sensing to land use issues. In a case study using a SPOT5 satellite image of central Stockholm, the window-independent context segmentation method extracts urban features that correspond to the strategic scale of urban-planning and management and that differ in function and underlying planning theory and practice.

Extraction of different urban area categories from satellite images using Window Independent Context Segmentation

Per-pixel based methods for spectral classification of remote-sensed images in urban areas are problematic because of the rather high spectral variability in urban materials and the fact that a specific spectral signature might appear in a number of different contexts in the urban landscape. It is by and large the specific contextual arrangement that defines the urban features, not the individual pixels spectral characteristics. Consequently the classification reliability has been lower in urban areas than in rural settings. In this paper a novel method called Window Independent Context Segmentation (WICS) is used to show that it is possible to extract categories of urban areas that differ in both function and underlying planning ideology from a SPOT5 satellite image covering the central parts of Stockholm, Sweden.

Formalized interpretation of compound land use objects – Mapping historical summer farms from a single satellite image

Notions of land cover relating to physical landscape characters are readily captured by satellite imagery. Land use on the other hand relates more to the societal aspects of a landscape. We argue that much of the spatial configuration of landscape characters is related to land use and that satellite data can be used to represent and investigate interpretations of land use. We propose and demonstrate the joint use of a novel SRPC procedure for satellite imagery together with an explicit representation of category semantics. We use these two mechanisms to identify a collection of conceptual spaces related to land use on Swedish historic summer farms. We also outline a framework for analysis of the relations between two separate ways of knowing: the machine-based knowledge and the human, mental knowledge. An evaluation demonstrates that satellite images can be used to identify land use processes as a mixture of land cover objects occurring in particular spatial contextual relationships closely tied to the land use category semantics. This opens up an unexplored possibility for research on vague spatial ontologies and questions on how to formally articulate different interpretations of space, land use, and other branches of spatial social science.

Residential Segregation of European and Non-European Migrants in Sweden: 1990–2012

In this paper, we analyse how a migrant population that is both expanding and changing in composition has affected the composition of Swedish neighbourhoods at different scales. The analysis is based on Swedish geocoded individual-level register data for the years 1990, 1997, 2005, and 2012. This allows us to compute and analyse the demographic composition of neighbourhoods that range in size from encompassing the nearest 100 individuals to the nearest 409,600 individuals. First, the results confirm earlier findings that migrants, especially those from non-European countries, face high levels of segregation in Sweden. Second, large increases in the non-European populations in combination with high levels of segregation have increased the proportion of non-European migrants living in neighbourhoods that already have high proportions of non-European migrants. Third, in contrast to what has been the established image of segregation trends in Sweden, and in an apparent contrast to the finding that non-European migrants increasingly live in migrant-dense neighbourhoods, our results show that segregation, when defined as an uneven distribution of different populations across residential contexts, is not increasing. On the contrary, for both European migrants from 1990 and non-European migrants from 1997, there is a downward trend in unevenness as measured by the dissimilarity index at all scale levels. However, if segregation is measured as differences in the neighbourhood concentration of migrants, segregation has increased.