Titlebook: Geoinformatics in Health Facility Analysis; Praveen Kumar Rai,Mahendra Singh Nathawat Book 2017 Springer International Publishing Switzerl

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书目名称Geoinformatics in Health Facility Analysis影响因子(影响力)




书目名称Geoinformatics in Health Facility Analysis影响因子(影响力)学科排名




书目名称Geoinformatics in Health Facility Analysis网络公开度




书目名称Geoinformatics in Health Facility Analysis网络公开度学科排名




书目名称Geoinformatics in Health Facility Analysis被引频次




书目名称Geoinformatics in Health Facility Analysis被引频次学科排名




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书目名称Geoinformatics in Health Facility Analysis年度引用学科排名




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书目名称Geoinformatics in Health Facility Analysis读者反馈学科排名

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Integration of Census Data with GIS for Analysis of Population Characteristics, city is 876, which is lower than the state urban average of 885 and national urban average of 901. Such rural-urban differentials in sex-ratio are the product of sex selective migration from rural areas to urban areas. Occupational composition is an important index to through light upon the economi

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Analysis of Health Care Facility Using GIS and GPS,ulate the number of persons live within a 10 km. radius from a particular primary health centre’s (PHCs) or community health centres (CHCs) or from the other governmental hospitals etc. of the district. By applying the proximity analysis of health centres, it is found that maximum rural population a

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GIS in Vector Born Disease Mapping,la-azar cases are identified through GPS. Important favorable indicators i.e. stream, ponds/water tanks, nalas, sewage zone, overhead tanks and slum areas in the Varanasi city also are very helpful malaria breeding sources and these indicators are extracted from remote sensing satellite data for the

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GIS Initiatives in Health Care Planning, be improved in Varanasi district. When the outcomes are compared with Multiple Linear Regressions and Information Value method then it is found that the malaria model developed with Information Value method is an optimum model which is selected for the measurement of hospital requirement index usin

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https://doi.org/10.1007/978-3-322-92563-3sing remote sensing data (IRS-1C/1D-LISS-III) of 2008, in which five important classes are delineated i.e. agricultural field, fallow land, vegetation, built-up area, and water bodies. In Varanasi district there are 85 Ganga ghats with typical structure on the river Ganga which are frequented by a l

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