In Sudan, the prevalence of cancer cases increased and cancer ranked as the major cause of death. Therefore, forming a cancer control program and putting strategic action plans into practice became an important matter for the health industry. The correlation of variations in different societies and environmental factors should be examined spatially with reliable data. The aim of this study is to produce base maps for implementation of cancer control program and cancer density maps through the utilization of GIS in health work. In this study, a database was built with the use of GIS to examine the distribution of cancer cases and maps relating to cancer events in allocation units were created. Cancer cases data registered from 1999 to 2008, by the Institute of Nuclear Medicine and Molecular Biology and Treatment of Tumors--University of Gezira in El Gezira State, was used as case in this study. Using ArcGIS, the distribution of cancer cases were presented on cancer maps including allocation units and incidence values, which were calculated for each villages and locality region. According to the world standards, cancer rates were determined and examined by the spatial analysis power of GIS. The research concluded that cancer cases were increased, in some localities over the past 10 years (1999-2008). This can be related to many reasons including the existence of the Gezira Scheme were farmers used fertilizers and pesticides, as well as increasing health awareness among the citizens through the establishment of use in the state.

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Drought is a harmful and slow natural phenomenon that has significant effects on the economy, social life,agriculture and environment of the country. Due to its slow process it is difficult to study this phenomenon. RemoteSensing and GIS tools play a key role in studying different hazards like droughts. The main objective of the study wasto investigate drought risk by using GIS and Remote Sensing techniques in district Khushab, Pakistan. Landsat ETMimages for the year 2003, 2009 and 2015 were utilized for spatial and temporal analysis of agricultural andmeteorological drought. Normalized difference vegetation index (NDVI) Standardized Precipitation Index (SPI) andrainfall anomaly indices were calculated to identify the drought prone areas in the study area. To monitormeteorological drought SPI values were used and NDVI was calculated for agricultural drought. These indices wereintegrated to compute the spatial and temporal drought maps. Three zones; no drought, slight drought and moderatedrought were identified. Final drought map shows that 30.21% of the area faces moderate drought, 28.36% faces slightdrought while nearly 41.3% faces no drought situation. Drought prevalence and severity is present more in the southernpart of Khushab district than the northern part. Most of the northern part is not under any type of drought. Thus, anoverall outcome of this study shows that risk areas can be assessed appropriately by integration of various data sourcesand thereby management plans can be prepared to deal with the hazard.

Rift Valley Fever (RVF) is an emerging, mosquito-borne disease with serious economical and negative implications on human and animal health. This study was conducted to verify the factors which influenced the spatial pattern of Rift Valley Fever occurrence and identified the high risk areas for the occurrence of the disease at Sinner State, Sudan. The normalized difference vegetation index (NDVI) derived from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite and rainfall data in addition to the point data of RVF clinical cases in humans were used in this study. In order to identify the RVF high risk areas, remote sensing data and rainfall data were integrated in a GIS with other information including, soil type, water body, DEM (Digital Elevation Model), and animal routes and analyzed using Spatial Analysis tools. The information on clinical cases was used for verification. The Normalized Difference Vegetation Index (NDVI) was used to describe vegetation patterns of the study area by calculating the mean NDVI. The results of the study showed that, RVF risk increased with the increase in vegetation cover (high NDVI values), and increase in rainfall, which both provided suitable conditions for disease vectors breeding and a good indicator for RVF epizootics. The study concluded that, identification of high risk area for RVF disease improved the understanding of the spatial distribution of the disease and helped in locating the areas where disease was likely to be endemic and therefore preparedness measures should be taken. The identification represents the first step of prospective predictions of RVF outbreaks and provides a baseline for improved early warning, control, response planning, and mitigation. Further detailed studies are recommended in this domain. K. M. S. Ahmed et al.

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