In spatial health research, it is necessary to consider not only the spatial-temporal patterns of diseases, but also external environmental factors, such as the effects of climate change on air quality, that may influence the insurgence or progression of diseases (e.g. respiratory and cardiovascular diseases, cancer, male human infertility, etc.). In this paper, we propose a Spatial Data analysis Infrastructure (SDI) for the analysis of health pathologies related to environmental factors and, more specifically, to climate change. The main goal is the development of a new methodology to predict and manage health risks, finding correlations between diseases and air pollution due to climatic factors. The presented SDI consists of different modules. A gynecological-obstetrical clinical folder application has been developed to collect and manage clinical data. Anonymous and geo-referenced patients data are extracted from the clinical folder application and data mining techniques, such as a hot spot analysis based on the Getis-Ord Gi∗ statistics, have been applied to the gathered data by exploiting the Hadoop framework. The results of the analysis are displayed in a web application that provides data visualization through geographical maps, using Geographical Information Systems (GIS) technology. This prototype, combining big data, data mining techniques, and GIS technology, represents an innovative approach to find correlations between spatial environmental factors and the insurgence of health diseases.

A spatial data analysis infrastructure for environmental health research

ALOISIO, Giovanni
2016

Abstract

In spatial health research, it is necessary to consider not only the spatial-temporal patterns of diseases, but also external environmental factors, such as the effects of climate change on air quality, that may influence the insurgence or progression of diseases (e.g. respiratory and cardiovascular diseases, cancer, male human infertility, etc.). In this paper, we propose a Spatial Data analysis Infrastructure (SDI) for the analysis of health pathologies related to environmental factors and, more specifically, to climate change. The main goal is the development of a new methodology to predict and manage health risks, finding correlations between diseases and air pollution due to climatic factors. The presented SDI consists of different modules. A gynecological-obstetrical clinical folder application has been developed to collect and manage clinical data. Anonymous and geo-referenced patients data are extracted from the clinical folder application and data mining techniques, such as a hot spot analysis based on the Getis-Ord Gi∗ statistics, have been applied to the gathered data by exploiting the Hadoop framework. The results of the analysis are displayed in a web application that provides data visualization through geographical maps, using Geographical Information Systems (GIS) technology. This prototype, combining big data, data mining techniques, and GIS technology, represents an innovative approach to find correlations between spatial environmental factors and the insurgence of health diseases.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11587/411824
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