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. 2017 Feb 10:2016:391-400.
eCollection 2016.

Integrative Spatial Data Analytics for Public Health Studies of New York State

Xin Chen  1 , Fusheng Wang  1
Affiliations

Integrative Spatial Data Analytics for Public Health Studies of New York State

Xin Chen et al. AMIA Annu Symp Proc. .

Abstract

Increased accessibility of health data made available by the government provides unique opportunity for spatial analytics with much higher resolution to discover patterns of diseases, and their correlation with spatial impact indicators. This paper demonstrated our vision of integrative spatial analytics for public health by linking the New York Cancer Mapping Dataset with datasets containing potential spatial impact indicators. We performed spatial based discovery of disease patterns and variations across New York State, and identify potential correlations between diseases and demographic, socio-economic and environmental indicators. Our methods were validated by three correlation studies: the correlation between stomach cancer and Asian race, the correlation between breast cancer and high education population, and the correlation between lung cancer and air toxics. Our work will allow public health researchers, government officials or other practitioners to adequately identify, analyze, and monitor health problems at the community or neighborhood level for New York State.

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Figures

Figure 1.
Figure 1.
The choropleth map that visualizes Geographically Weighted Regression (GWR) local coefficient of Asian population for stomach cancer Relative Risk (RR).
Figure 2.
Figure 2.
The choropleth map that visualizes Geographically Weighted Regression (GWR) local coefficient of low education population for breast cancer Relative Risk (RR).
Figure 3.
Figure 3.
The choropleth map that visualizes Geographically Weighted Regression (GWR) local coefficient of PAHPOM for lung cancer Relative Risk (RR).
See this image and copyright information in PMC

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