Food security has been an ongoing concern of governments and international organizations. This study aims at ensuring food security using Geospatial technology to find suitable sites for building food industry plants. In this respect, Geospatial technology including remote sensing, Geospatial information system, and global positioning system was implemented to identify suitable sites for the construction of food industry plants in Qaemshahr County, Iran and an up-to-date and efficient geodatabase for this purpose was prepared. Since rice and citrus are the main products of the study area, to ensure food security and income for the people of the region, suitable site selection to build food industry plants to sort, package, store, convert, and process rice and citrus is very important. On the other hand, because Ghaemshahr is the main hub of rice and citrus production in the country as well as export, food security at the national level can also be ensured. To support the achieved result of selected sites for food industry plants, several analyses were carried out. The resulting suitability map was reclassified based on proximity to raw food materials as well as main roads and cities. Consequently, among the obtained suitable sites, the most suitable places based on these factors were identified. In addition, a comparative analysis was performed between the selected sites and the existing industrial zones in the study area. The findings of this study are useful to ensure food security in the region and country. The study also demonstrated the effectiveness of Geospatial technology for this purpose.

This work illustrates the impact of excessive mining on the precipitation trends and ground water condition of the Ramgarh district over a period of 12 years (2007-2018). The Landsat 8 and Landsat TM-5 data is processed under Arc-GIS in order to compare the LULC maps. Out of 7 classified classes, the Results obtained indicate the expansion of the mining area, barren land, settlement, and water body by 10. 95%, 10. 07%, 3. 44%, and 0. 43%, while a reduction in the forest, fallow, and crop land by 11. 24%, 11. 31%, and 2. 34% respectively. The TRMM 3B43 data is used to trace out the annual precipitation values of 5 selected raster location points through Arc GIS. The annual precipitation under the mining regions (lower Mandu, Ramgarh, Bhurkunda) shows a decreasing trend. The Mann-Kendall test and Sen’ s slope estimator method is used in order to evaluate the ground water pattern in the pre-and post-monsoonal conditions. The Mandu block, the densest mining region of the district with the positive Z values of 1. 714 and 0. 137 in the pre-and post-monsoon period shows a decrease in the ground water level at the rates of 0. 103 m/year and 0. 017 m/year, respectively. The continuous rise in the mining activities has created an alarming shift of weather pattern and deteriorated ground water table in Ramgarh.

Traditional leveraging statistical methods for analyzing today’s large volumes of spatial data have high computational burdens. To eliminate the deficiency, relatively modern data mining techniques have been recently applied in different spatial analysis tasks with the purpose of autonomous knowledge extraction from high-volume spatial data. Fortunately, Geospatial data is considered a proper subject for leveraging data mining techniques. The main purpose of this paper is presenting a hybrid Geospatial data clustering mechanism in order to achieve a high performance hotspot analysis method. The method basically works on 2 or 3-dimensional geographic coordinates of different natural and unnatural phenomena. It uses the systematic cooperation of two popular clustering algorithms: the AGlomerative NEStive, as a hierarchical clustering method and k-means, as a partitional clustering method. It is claimed that the hybrid method will inherit the low time complexity of the κ-means algorithm and also relative independency from user’s knowledge of the AGNES algorithm.Thus, the proposed method is expected to be faster than AGNES algorithm and also more accurate thanκ-means algorithm. Finally, the method was evaluated against two popular clustering measurement criteria. The first clustering evaluation criterion is adapted from Fisher’s separability criterion, and the second one is the popular minimum total distance measure. Results of evaluation reveal that the proposed hybrid method results in an acceptable performance. It has a desirable time complexity and also enjoys a higher cluster quality than its parents (AGNES and k-means). Real-time processing of hotspots requires an efficient approach with low time complexity. So, the problem of time complexity has been taken into account in designing the proposed approach.

Flash flood has been increasing in the Khartoum area, Sudan due to geographical conditions and climatic change as heavy rainfall and high temperature, therefore the present work tried to estimate the sensitivity of flash flood. The present work proposes an advanced technique of flood sensitivity mapping using the method of analytical hierarchy process. Ten factors as elevation, slope, distance from the network, land use, density of the drainage, flow accumulation, surface roughness, stream power index, topographic wetness index and curvature of the topography were digitized and then contributed in the mapping of Flash flood. Remote sensing data were integrated with analytical hierarchy process to determine the flood sensitive area in Sudan. The model was applied and completed as the consistency ratio was mostly reasonable (< 0. 1). Based on the proposed model, about 75. 56 Km2 (12. 26 %), 156. 14 Km2 (25. 33%), 169. 89 Km2 (27. 56 %), 141. 40 Km2 (22. 94 %) and 73. 50 Km2 (11. 92 %); were classified as no susceptible, low susceptible, high susceptible, moderate susceptible and very highly susceptible to flooding. The present study showed a high variation in flood sensitivity due to climatic change and geographic condition. This index can be modified and applied in areas of the same characteristics of climatic conditions as one of the main recommendation in the study area. The study showed that poor infrastructure and lack of preparedness were the main causes of the disaster of flood in Sudan. This study merely demonstrated the critical analysis of Geospatial mapping in proper mitigating, sustainable development and great monitoring the negative effects of flooding along the Khartoum region to reduce hazards of flood.

Availability and accessibility to reliable and up-to-date urban data are key components for successful planning and management of the city, as well as providing more efficient services to its citizens. In addition, the availability of reliable and up-to-date data can play a major role in reducing the costs of city management. Urban Services Data (USD) cover the main part of the data needed for efficient management of the city. Urban services data consist of all data about urban entities that must be created, maintained and enhanced by the municipality of Tehran. The USD consists of a large volume of data and posses a high frequency of changes. Additionally, there are many users and decision makers from various disciplines that need data to conduct their daily business. As a result, there is a serious need for the implementation of services for accessing the USD for many distinct kinds of users and their respective preferences. Users! Preferences directly affect the design and development of services. In addition, because various kinds of users (municipality departments, public, private and academic sectors and citizens) utilize different computing platforms and diverse communication technologies to access these services, services should be interoperable. In this context, users come from two broad communities: the Geospatial Information (GI) and Information Technology (IT) communities. There are distinct technologies and concepts for providing interoperability for both communities. In order to provide highest level of interoperability, Open Geospatial Consortium services and Web Services technologies must be integrated. Since these two kinds of services are not fully compatible, various integration issues must be addressed efficiently. In this paper, each implementation issue is identified and evaluated. Then possible solutions are proposed and implemented using state of the art technologies.