The Socio-spatial Distribution of Alcohol Outlets in Glasgow City

Health & Place, Volume 16, Issue 1, January 2010, Pages 167-172

Anne Ellaway, Laura Macdonald, Alasdair Forsyth, and Sally Macintyre

“Aims: The aim of this study was to examine the distribution of alcohol outlets by area deprivation across Glasgow, Scotland.

“Methods: All alcohol outlets were mapped and density per 1000 residents and proximity to nearest outlet calculated across quintiles of area deprivation.

“Results: The socio-spatial distribution of alcohol outlets varies by deprivation across Glasgow but not systematically. Some deprived areas contain the highest concentration while others in similar deprivation quintiles contain very few.

“Conclusions: Considerations of the local context are important in examining access to alcohol but more research is also required on purchasing behaviour.”

Using Remote Sensing and GIS for Damage Assessment after Flooding, the Case of Muscat, Oman after Gonu Tropical Cyclone 2007: Urban Planning Perspective

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

Lotfy Kamal Azaz

“Natural Disasters occur frequently around the world, and their incidence and intensity seem to be increasing in recent years. The Disasters such as cyclones and floods often cause significant loss of life, large-scale economic and social impacts, and environmental damage. For example, Cyclone Gonu was the strongest tropical cyclone on record in the Arabian Sea, and tied for the strongest tropical cyclone on record in the northern Indian Ocean and was the strongest named cyclone in this basin. On June 5 2007 it made landfall on the eastern-most tip of Oman with winds of 150 km/h (90 mph). Gonu dropped heavy rainfall near the eastern coastline, reaching up to 610 mm (24 inches), which caused flooding and heavy damage. The cyclone caused about $4 billion in damage and nearly 50 deaths in Oman, where the cyclone was considered the nation’s worst natural disaster. Nowadays, we have access to data and techniques provided by remote sensing and GIS that have proven their usefulness in disaster management plan. Remote Sensing can assists in damage assessment monitoring, providing a quantitative base for relief operations. After that, it can be used to map the new situation and update the database used for the reconstruction of an area. Disaster management plan consists of two phases that takes place before disaster occurs, disaster prevention and disaster preparedness, a three phases that happens after the occurrence of a disaster i.e. disaster relief, rehabilitation and reconstruction. In the disaster rehabilitation phase GIS is used to organize the damage information and the post-disaster census information, and in the evaluation of sites for reconstruction. In this  study, two IKONOS satellite images of Muscat, Oman have been utilized; one image before the cyclone and one after. The two images have been geometrically corrected. Change detection has been applied to identify and assess the damages. The results of this study emphasize the importance of using remote sensing and GIS in damage assessment phase as part of effective Disaster Management Plan.”

Mapping Diversified Peri-urban Agriculture – Potential of Object-based Versus Per-field Land Cover/Land Use Classification

Geocarto International, Volume 25, Issue 3 June 2010 , pages 171 – 186

Dionys Forster; Tobias Walter Kellenberger; Yves Buehler; Bernd Lennartz

“High spatial resolution satellite data contribute to improving land cover/land use (LCLU) classification in agriculture. A classification procedure based on Quickbird satellite image data was developed to map LCLU of diversified agriculture at sub-communal and communal level (7 km2). Segmentation performance of the panchromatic band in combination with high pass filters (HPF) was tested first. Accuracy of field boundary delineation was evaluated by an object-based segmentation, a per-field and a manual classification, along with a quantitative accuracy assessment. Sub-communal classification revealed an overall accuracy of 84% with a κ coefficient of 0.77 for the per-field vector segmentation compared to an overall accuracy of 56-60% and a κ coefficient of 0.37-0.42 for object-based approaches. Per-field vector segmentation was thus superior and used for LCLU classification at communal level. Overall accuracy scored 83% and the κ coefficient 0.7. In diversified agriculture, per-field vector segmentation and classification achieved higher classification results.”

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.”