Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-8, 205-211, 2014
http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-8/205/2014/
doi:10.5194/isprsarchives-XL-8-205-2014
© Author(s) 2014. This work is distributed
under the Creative Commons Attribution 3.0 License.
 
27 Nov 2014
Geomatics Approach for Assessment of respiratory disease Mapping
M. Pandey, V. Singh, and R. C. Vaishya GIS Cell,Motilal Nehru National Institute of Technology, Allahabad, India
Keywords: Air Quality Index, Fuzzy Inference System, Geostatistical Analysis, Respiratory risk map, IDW Technique, Gettis Statistical Technique Abstract. Air quality is an important subject of relevance in the context of present times because air is the prime resource for sustenance of life especially human health position. Then with the aid of vast sums of data about ambient air quality is generated to know the character of air environment by utilizing technological advancements to know how well or bad the air is. This report supplies a reliable method in assessing the Air Quality Index (AQI) by using fuzzy logic. The fuzzy logic model is designed to predict Air Quality Index (AQI) that report monthly air qualities. With the aid of air quality index we can evaluate the condition of the environment of that area suitability regarding human health position. For appraisal of human health status in industrial area, utilizing information from health survey questionnaire for obtaining a respiratory risk map by applying IDW and Gettis Statistical Techniques. Gettis Statistical Techniques identifies different spatial clustering patterns like hot spots, high risk and cold spots over the entire work area with statistical significance.
Conference paper (PDF, 864 KB)


Citation: Pandey, M., Singh, V., and Vaishya, R. C.: Geomatics Approach for Assessment of respiratory disease Mapping, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-8, 205-211, doi:10.5194/isprsarchives-XL-8-205-2014, 2014.

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