„Environmental justice“ (Umweltgerechtigkeit)

Combined Small- and Large-Scale Geo-Spatial Analysis of the Ruhr Area for an Environmental Justice Assessment

This paper investigates spatial relationships regarding the accessibility of urban green space, the overall yearly vitality of the surrounding vegetation, and additional indicators such as air and noise pollution, in urban areas. The analysis uses socio-economic data sets derived from a sophisticated disaggregation approach. It results from applying a new tool that processes data from coarse and small-scale data sets to smaller spatial units in order to derive more fine-grained insights into the characteristics of the smallest suburb. The consequent data sets are then augmented by comprehensive raster-based accessibility network analysis and the incorporation of measured data on air and noise pollution. Gaining an overview over the whole area on the one hand, and looking at smaller city districts in detail on the other, unveils whether there is an imbalance regarding all combined indicators. After correlating two socio-economic indicators, a spatial comparison of the preliminary results determines whether this approach reveals neighborhoods wherein residents of a lower socio-economic status are exposed to multiple threats at once. As a result, the paper presents a workflow to obtain a broader and, at the same time, more small-scale overview of polycentric agglomeration. Simultaneously, it provides a large-scale insight into single sites, right down to the city block level. Consequently, this study provides a sophisticated approach that helps to assess the quality, quantity and characteristics of the specific spatial distribution of environmental justice in small- to large-scale urban areas at a glance. The results help to identify regions of inequalities and disadvantages. They allow for querying additional values assigned to large-scale spatial units. These versatile variables provide a means to reveal other noticeable indicators. Furthermore, this entails the opportunity to evaluate the distinct living conditions of locally affected demographic groups, and improve them with tailored approaches. Finally, the results can enhance the perception of these living conditions, and be used to promote the capacity for organizing the lives of the respective residents more sustainably, helping the neighborhood to grow accordingly.

Geo-Spatial Analysis of Population Density and Annual Income to Identify Large-Scale Socio-Demographic Disparities

This paper describes a methodological approach that is able to analyse socio-demographic and -economic data in large-scale spatial detail. Based on the two variables, population density and annual income, one investigates the spatial relationship of these variables to identify locations of imbalance or disparities assisted by bivariate choropleth maps. The aim is to gain a deeper insight into spatial components of socioeconomic nexuses, such as the relationships between the two variables, especially for high-resolution spatial units. The used methodology is able to assist political decision-making, target group advertising in the field of geo-marketing and for the site searches of new shop locations, as well as further socioeconomic research and urban planning. The developed methodology was tested in a national case study in Germany and is easily transferrable to other countries with comparable datasets. The analysis was carried out utilising data about population density and average annual income linked to spatially referenced polygons of postal codes. These were disaggregated initially via a readapted three-class dasymetric mapping approach and allocated to large-scale city block polygons. Univariate and bivariate choropleth maps generated from the resulting datasets were then used to identify and compare spatial economic disparities for a study area in North Rhine-Westphalia (NRW), Germany. Subsequently, based on these variables, a multivariate clustering approach was conducted for a demonstration area in Dortmund. In the result, it was obvious that the spatially disaggregated data allow more detailed insight into spatial patterns of socioeconomic attributes than the coarser data related to postal code polygons.