Kala Abhishek K, Tiwari Chetan, Mikler Armin R, Atkinson Samuel F
Advanced Environmental Research Institute and Department of Biological Sciences, University of North Texas , Denton , TX , United States.
Advanced Environmental Research Institute and Department of Geography and the Environment, University of North Texas , Denton , TX , United States.
PeerJ. 2017 Mar 28;5:e3070. doi: 10.7717/peerj.3070. eCollection 2017.
The primary aim of the study reported here was to determine the effectiveness of utilizing local spatial variations in environmental data to uncover the statistical relationships between West Nile Virus (WNV) risk and environmental factors. Because least squares regression methods do not account for spatial autocorrelation and non-stationarity of the type of spatial data analyzed for studies that explore the relationship between WNV and environmental determinants, we hypothesized that a geographically weighted regression model would help us better understand how environmental factors are related to WNV risk patterns without the confounding effects of spatial non-stationarity.
We examined commonly mapped environmental factors using both ordinary least squares regression (LSR) and geographically weighted regression (GWR). Both types of models were applied to examine the relationship between WNV-infected dead bird counts and various environmental factors for those locations. The goal was to determine which approach yielded a better predictive model.
LSR efforts lead to identifying three environmental variables that were statistically significantly related to WNV infected dead birds (adjusted = 0.61): stream density, road density, and land surface temperature. GWR efforts increased the explanatory value of these three environmental variables with better spatial precision (adjusted = 0.71).
The spatial granularity resulting from the geographically weighted approach provides a better understanding of how environmental spatial heterogeneity is related to WNV risk as implied by WNV infected dead birds, which should allow improved planning of public health management strategies.
本文所报告研究的主要目的是确定利用环境数据中的局部空间变化来揭示西尼罗河病毒（WNV）风险与环境因素之间统计关系的有效性。由于最小二乘回归方法没有考虑用于探索WNV与环境决定因素之间关系的空间数据类型的空间自相关和非平稳性，我们假设地理加权回归模型将有助于我们更好地理解环境因素如何与WNV风险模式相关，而不受空间非平稳性的混杂影响。
我们使用普通最小二乘回归（LSR）和地理加权回归（GWR）来研究常见的地图环境因素。两种模型都用于检验WNV感染死鸟数量与这些地点各种环境因素之间的关系。目标是确定哪种方法能产生更好的预测模型。
LSR分析确定了三个与WNV感染死鸟有统计学显著关系的环境变量（调整后 = 0.61）：溪流密度、道路密度和地表温度。GWR分析提高了这三个环境变量的解释价值，且具有更好的空间精度（调整后 = 0.71）。
地理加权方法产生的空间粒度能更好地理解环境空间异质性如何与WNV感染死鸟所暗示的WNV风险相关，这应有助于改进公共卫生管理策略的规划。
