Remote Sensing and GIS Application in the Detection of Environmental Degradation Indicators

Geo-Spatial Information Science, 2011, Volume 14, Number 1, Pages 39-47

A. S. Hadeel, Mushtak T. Jabbar and Xiaoling Chen

“The main aim of this research is to highlight the environment change indicators during the last 20 years in a representative area of the southern part of Iraq (Basrah Province was taken as a case) to understand the main causes which led to widespread environment degradation phenomena using a 1:250000 mapping scale. Remote sensing and GIS’s software were used to classify Landsat TM in 1990 and Landsat ETM+ in 2003 imagery into five land use and land cover (LULC) classes: vegetation land, sand land, urban area, unused land, and water bodies. Supervised classification and Normalized Difference Vegetation Index (NDVI), Normalized Difference Build-up Index (NDBI), Normalized Difference Water Index (NDWI), Normalized Difference Salinity Index (NDSI), and Topsoil Grain Size Index (GSI) were adopted in this research and used respectively to retrieve its class boundary. The results showed a clear deterioration in vegetative cover (514.9 km2) and an increase of sand dune accumulations (438.6 km2), accounting for 10.1, and 10.6 percent, respectively, of the total study area. In addition, a decrease in the water bodies’ area was detected (228.9 km2). Sand area accumulations had increased in the total study area, with an annual increasing expansion rate of (33.7 km2 · yr−1) during the thirteen years covered by the study. It is therefore imperative that Iraqi government undertake a series of prudent actions now that will enable to be in the best possible position when the current environmental crisis ultimately passes.”

Implementation and Evaluation of a Flow Map Demonstrator for Analyzing Work Commuting Flows between Norway and Sweden

GeoViz: Linking Geovisualization with Spatial Analysis and Modeling, 10-11 March 2011, Hamburg, Germany

Quan Ho, Hai-Phong Nguyen, Mikael Jern

“Statistics visualization facilitating methods from the geovisual analytics research domain has recently gained interest. Global, national and even sub-national statistics foundations have started to migrate from tabular representations to interactive web-enabled visualization that show trends. Flow map statistics, however, visualizing quantities of trade, transport or migration is still rare. This paper covers spatial interactions for a wide variety of realized movements of people such as commuting and migration between an origin and a destination. This type of flow data can be visually expressed by directed weighted arrows over a geographic space. For a small number and properly distributed regions directed arrow symbols can be an attractive means of visualization. Cartographic flow maps showing official statistics related to a larger number of sub-national regions (e.g. counties and municipalities) are still problematic and often skewed and detailed which leads to cluttered flows where important details are obscured. In this paper, we introduce an interactive flow map demonstrator that effectively can explore reasonable large spatio-temporal and multivariate statistical flow datasets using bidirectional flow arrows where both in- and outflows can be clearly shown.”

H.E.L.P: A GIS-based Health Exploratory AnaLysis Tool for Practitioners

Applied Spatial Analysis and Policy, 2011, Volume 4, Number 2, Pages 113-137

Eric Delmelle, Elizabeth Cahill Delmelle, Irene Casas and Thomas Barto

“The last two decades have been characterized by a growing number of Geographical Information System (GIS) applications to the field of health science. From a decision-making and policy perspective, undeniable benefits of GIS include the assessment of health needs and delivery of services, and also the appropriate allocation of workforce and prevention resources. Despite these attractive attributes, the literature suggests that there has been limited GIS uptake among health care decision makers. This paper presents a GIS-based Health Exploratory and anaLysis tool for Practitioners (H.E.L.P.) for the analysis and visualization of space-time point events, applied to hospital patients. H.E.L.P. is viewed as a spatial decision support system which provides a set of powerful analytical tools integrating the computational capabilities of Matlab with the visualization and database functionalities of GIS. The system outputs improve the understanding of disease dynamics and provide resources for decision-makers in allocating appropriate staffing. As an example, H.E.L.P. is applied to a dataset of hospital patients in Cali, Colombia.”

Spatio-temporal Analysis of Malaria Incidence at the Village Level in a Malaria-endemic Area in Hainan, China

Malaria Journal 2011, 10:88, published 14 April 2011

Liang Wen, Chengyi Li, Minghe Lin, Zhengquan Yuan, Donghui Huo, Shenlong Li, Yong Wang, Chenyi Chu, Ruizhong Jia, and Hongbin Song

“Malaria incidence in China’s Hainan province has dropped significantly, since Malaria Programme of China Global Fund Round 1 was launched. To lay a foundation for further studies to evaluate the efficacy of Malaria Programme and to help with public health planning and resource allocation in the future, the temporal and spatial variations of malaria epidemic are analysed and areas and seasons with a higher risk are identified at a fine geographic scale within a malaria endemic county in Hainan.

“Methods: Malaria cases among the residents in each of 37 villages within hyper-endemic areas of Wanning county in southeast Hainan from 2005 to 2009 were geo-coded at village level based on residence once the patients were diagnosed.

“Based on data so obtained, purely temporal, purely spatial and space-time scan statistics and geographic information systems (GIS) were employed to identify clusters of time, space and space-time with elevated proportions of malaria cases.

“Results: Purely temporal scan statistics suggested clusters in 2005,2006 and 2007 and no cluster in 2008 and 2009. Purely spatial clustering analyses pinpointed the most likely cluster as including three villages in 2005 and 2006 respectively, sixteen villages in 2007, nine villages in 2008, and five villages in 2009, and the south area of Nanqiao town as the most likely to have a significantly high occurrence of malaria.

“The space-time clustering analysis found the most likely cluster as including three villages in the south of Nanqiao town with a time frame from January 2005 to May 2007.

“Conclusions: Even in a small traditional malaria endemic area, malaria incidence has a significant spatial and temporal heterogeneity on the finer spatial and temporal scales. The scan statistics enable the description of this spatiotemporal heterogeneity, helping with clarifying the epidemiology of malaria and prioritizing the resource assignment and investigation of malaria on a finer geographical scale in endemic areas.”