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Health sector has emerged a major area where GIS and other geoinformatics tools are being increasingly applied. Health sector and experts of disease control and spatial epidemiology are keen to use GIS to see the geographic extent of their work and disease patterns, trends and risk zones so that they can provide best services in limited resources and can have a bigger picture of the disease spread and outbreak. This module is part of the GeoS4S project co-funded under ERASMUS + by European Union. This module presents the application of GIS for disease surveillance and establishing its' causative factors using various type of strong and rich repertoire of geospatial tools of analysis. This paper presents an overview of the lessons, hands on exercises and assignments that will help the students to attain the objectives of the learning of this new are of application of GIS. The course is prepared considering its scope as online material for slef-learning but it may very well be used direct teaching in traditional class room setting in an institution. [ABSTRACT FROM AUTHOR]

In 2009, the fatal outbreak of H1N1 influenza A became one of the most serious global concerns since mid-April. Within two months, the outbreak spectrum surged to pandemic. After one year, H1N1 caused at least 18,000 deaths in more than 200 countries worldwide. However, with timely collaborative responses among various organizations worldwide, the spread of the disease was finally brought back to normal. Swift action and cooperation through sharing relevant and comprehensive information played a crucial role for decision makers to issue pertinent measures and guidelines for preparedness and response. By deploying web-based technology and geographic information systems (GIS), these planning and intervention processes were achieved more economically and effectively. [ABSTRACT FROM AUTHOR]

Geographic Information System (GIS) techniques are used to develop the risk zone map of Dengue Fever (DF)/Dengue Haemorrhagic Fever (DHF) in Chachoengsao province, Thailand. The risk zonation index is derived using the Analytical Hierarchy Process (AHP). The medical database considered for the study was referenced to the geographical and meteorological data layers. Based on this analysis, potential areas were categorized as very high, high, moderately high, moderately low, low, and very low risk categories. The model used in this study provided valuable information to prepare DF/DHF risk zones for decision support to mitigate the epidemic. Factor weights used in AHP were evaluated and found to be acceptable as the consistency ratio (CR) was 0.031, which was < 0.1. Spatial modelling included a huge database about physical-environment, climatic, and demographic factors. Approximately, 74.96% people lived in very high, high, and moderately high risk zones. The most at risk zone was shown to be Mueang Chachoengsao district with a population of 142,485 or 22.34% of total population. The results from AHP based DF/DHF risk zonation gave useful information on different levels of disease risk areas. The methodology is general and can he applied in other application fields, such as disease outbreak during natural disaster. [ABSTRACT FROM AUTHOR]

The Chi River always faces floods and water shortage problems in the rainy season and subsequent dry season, respectively. The Royal Thai Government emphasizes on construction of floodwater retention reservoirs to store more water along the Chi River. This water is to be used mainly for agriculture in the subsequent dry season. A methodology was developed to identify potential floodwater retention zones using the Analytical Hierarchical Process and GIS-based data. They were surface water, drainage density, landform, land use, soil drainage, salt crust, geological formation, lineament density, vegetation index, and groundwater yield. The result was the potential water retention zones map, which was categorized as excellent, good, moderate, poor and very poor classes. The map was validated with a field survey of floodwater retention site. The results agreed with the field check. The hydrodynamic model (MIKE11) was employed to simulate diversion of discharges from the Chi River into retention reservoirs. The results of simulation revealed that the proposed reservoir (Kud Dok) could retain floodwater at the maximum storage capacity of 8.13 million m³ for return periods of 3, 5, 8 and 25 years. [ABSTRACT FROM AUTHOR]

This investigation is aimed in contribute the concepts and methods of the innovative development and application of Geographical Information System (GIS) and Remote Sensing regarding Malaria prevalence. Kanchanaburi province, Thailand is chosen 0.3 study area. The input data are based on the geospatial factors including climatic aspects, physical environment, and statistical record of Malaria cases. Afterwards data preprocessing needs to be carried out. Malaria cases map and information value (i-value) approach will he used in order to identify the relation between physical and social factors, and also statistical analysis will be carried out so as to define the relation of climatic factor. The final output based on those approaches is a Malaria risk map which is classified into three classes including high risk level, moderate risk level and low risk level. This study also develops the model based on i-values which are formed as thematic layers in GIS database and are computed using different map analysis techniques, for example, map crossing. [ABSTRACT FROM AUTHOR]

Quantitative analysis morphometric parameters such as stream length, drainage density (DD), bifurcation ratio (Rb), slope, shape factors are good indicators for morphological evolution of a basin. Relationships of these parameters with structure, lithology and geomorphology are enhancing the factors which control the erosion behavior of the area and further use for proper management and development programs. In this study LANDSAT ETM+ data is used for DIP techniques such as NDVI and lineaments enhancement, and GIS techniques to extract different models. In this study the Dashtgol basin which is a watershed overlooking the reservoir of the great Shahid Abasspour dam in the south west Iran has selected to evaluate geomorphic factors. This basin is located within the ZFB. It exhibits complex geological and geomorphic configuration. The information from remotely sensed and ground truth data are analysed in GIS environment. Further, the study, area is divided in 10 hydrologic divisions. The results show that the maximum erosion belongs to the sixth hydrologic division (27.5 t/h per year) and the minimum erosion belongs to second hydrologic division (14.8 t/h per year). Geologic interpretation and geomorphic analysis in GIS environment on the Sabzab river profile shows that the tectonic process is a factor to increase the rates of sedimentation and erosion in the study area. [ABSTRACT FROM AUTHOR]

The classification of irrigated crops by remote sensing requires the use of time series data, since the timing. cropping intensity and duration of cropping is quite variable over the course of a year. Rice is the dominant irrigated crop in tropical and sub-tropical Asia, where rainfall is high, but is seasonal and often uni-modal. Existing crop classification methods for rice are not able to distinguish between rainfed and irrigated crops, leading to errors in classification and estimated irrigated area. This paper describes a technique, a 'peak detector algorithm', to successfully discriminate between rainfed and irrigated rice crops in Suphanburi province, Thailand. The methodology uses a three-year time series of Satellite pour l'Observation de Ia Terre (SPOT) VEGETATION S10 Normalized Difference Vegetation Index (NDVI) data (10 day composites) to identify cropping intensity (number, timing and peak values). Peak NDVI is then lag-correlated with long term average rainfall data. There is a high correlation at a 40-50 day lag, between a peak rainfall and a 'single' peak NDVI of rainfed rice. In irrigated areas, there are multiple peaks, and multiple correlations with low values for at least 90 days after peak rainfall. The methodology currently uses a mask to remove un-cropped and non-rice areas, which is derived from existing Geographical Information Systems (GIS). The method achieves a classification accuracy of 89% or better against independent groundtruth data. The procedure is designed as a second level of analysis to refine classifications using other techniques of mapping irrigated area at global and regional scales. [ABSTRACT FROM AUTHOR]

A Geographic Information System based Decision Support System (GIS based DSS) was developed for evaluating the optimal erosion management (which includes soil and water conservation: SWC and crop management system: CMS) alternatives watershed based upon the multi-objectives such as greatest physical effectiveness, cost-benefit return and social benefit acceptance. The system has been developed by integrating the decision element models: erosion model (Universal Soil Loss Equation: USLE, Modified USLE: MUSLE, Potential Nutrient Loss Model), economic model (Cost-Benefit Analysis: CBA), and social benefit model (Net Benefit Index: NBI), with a SWC knowledge based system (SWC-KBS) and multi-objectives optimization approach, into one system, using Arc-View GIS and Visual Basic programming. The application was tested in the Huai Talupkup small watershed, Thailand, an area of 1,550 ha. The selected optimal erosion management for 10 land class units (LCUs), which were based upon the similarity of land management regime which include a variety of terrace practices and suitable crop management systems. The selected optimal erosion management from the system, using a discount rate of 8 % and a project lifetime of 10 years, was found to have a net present value (NPV) of about 9.52 million Thai baht, erosion (soil loss) could be reduced about 78 % while the social net benefit index increased by 47 %from current conditions. The system proposed in this research enables quick review of results for different erosion management alternatives using preference criteria settings, which might not be possible with a dispersed analytical systems computation. It is suggested that this system can be applied to other watersheds for evaluating and enhancing the accuracy in effective decision-making in the optimal erosion management at the small watershed level. [ABSTRACT FROM AUTHOR]