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.”