Environmental Justice in Berlin: GIS-based Method Determining an Aggregated Index for Urban Planning

REAL CORP 2010 Proceedings/Tagungsband, Vienna, 18-20 May 2010

Gesa Geißler, Birgit Kleinschmit, Robert Ahrberg, Battugs Erdenetsogt, Yvonne Heimann, Lisa Heinsch, Laura Hensel, Thomas Herff, Josephine Janke, Philipp Kaufmann, Miriam Kothe, Hendrikje Leutloff, Anja Manzke, Anke Rehhausen, Sebastian Schramm, Stefanie Töpper, Sebastian Unger, and Bartlomiej Wisniewski

“In the early nineties, a wide discussion about Environmental Justice has started among German health scientists, sociologists, and other academics. In this context, several studies focussing on the disproportional burden of discrete environmental hazards on different socio-economic groups were carried out.

“The present paper reviews a method to index several Environmental Justice factors within planning areas of Berlin. In order to examine the current status, the spatial distribution of thermal comfort, green spaces and emissions of PM10 and NOx has been determined, using a GIS-based analysis. These results were then related to data on the social status. The various outcomes show a complex relation between social status and exposure to environmental quality, but reveal a tendency of disproportional distribution, prejudicing groups of lower social status.

“In order to develop a planning area based measure of Environmental Justice, the analysed factors were aggregated into a single environmental impact factor and combined with the associated social status. Finally, possibilities of integrating this factor into urban planning in Berlin were identified.”

Automatic Cluster Identification for Environmental Applications using the Self-organizing Maps and a New Genetic Algorithm

Geocarto International, Volume 25, Issue 1 February 2010, pages 53 – 69

Tonny J. Oyana and Dajun Dai

“A rapid increase of environmental data dimensionality emphasizes the importance of developing data-driven inductive approaches to geographic analysis. This article uses a loosely coupled strategy to combine the technique of self-organizing maps (SOM) with a new genetic algorithm (GA) for automatic identification of clusters in multidimensional environmental datasets. In the first stage, we employ the well-known classic SOM because it is able to handle the dimensional interactions and capture the number of clusters via visualization; and thus provide extraordinary insights into original data. In the second stage, this new GA rigorously delineates the cluster boundaries using a flexibly oriented elliptical search window. To test this approach, one synthetic and two real-world datasets are employed. The results confirm a more robust and reliable approach that provides a better understanding and interpretation of massive multivariate environmental datasets, thus maximizing our insights. Other key benefits include the fact that it provides a computationally fast and efficient environment to accurately detect clusters, and is highly flexible. In a nutshell, the article presents a computational approach to facilitate knowledge discovery of massive multivariate environmental datasets; as we are too familiar with their accelerating growth rate.”

The Longevity Pattern in Emilia Romagna: A Spatio-temporal Analysis

Paper submitted to SIS 2010

Giulia Roli, Rossella Miglio, Rosella Rettaroli, and Alessandra Samoggia

“In this paper, we investigate the pattern of longevity in Emilia Romagna, a North-Eastern region of Italy, at a municipality level. We consider a modified version of Centenarian Rate in two different periods. Spatio-temporal modeling is used to tackle at both periods the random variations in the occurrence of long-lived individuals, due to the rareness of such events in small areas. This method allows to exploit the spatial proximity smoothing the observed data, as well as to control for the effects of a set of regressors. As a result, clusters of areas characterized by extreme indexes of longevity are well identified and the temporal evolution of the phenomenon can be depicted. In addition, we evaluate the effects of the structure of mortality on the cohort of long-lived subjects in the second period. A spatial analysis is carried out by including the territorial patterns of mortality in a longitudinal perspective. We control for the major causes of death in order to deepen the analysis of the observed geographical differences.”

A Spatial Analysis of Health-related Resources in Three Diverse Metropolitan Areas

Health Place, 2010 May 15

Smiley MJ, Diez Roux AV, Brines SJ, Brown DG, Evenson KR, and Rodriguez DA

“Few studies have investigated the spatial clustering of multiple health-related resources. We constructed 0.5 mile kernel densities of resources for census areas in New York City, NY (n=819 block groups), Baltimore, MD (n=737), and Winston-Salem, NC (n=169). Three of the four resource densities (supermarkets/produce stores, retail areas, and recreational facilities) tended to be correlated with each other, whereas park density was less consistently and sometimes negatively correlated with others. Blacks were more likely to live in block groups with multiple low resource densities. Spatial regression models showed that block groups with higher proportions of black residents tended to have lower supermarket/produce, retail, and recreational facility densities, although these associations did not always achieve statistical significance. A measure that combined local and neighboring block group racial composition was often a stronger predictor of resources than the local measure alone. Overall, our results from three diverse U.S. cities show that health-related resources are not randomly distributed across space and that disadvantage in multiple domains often clusters with residential racial patterning.”