ArcGIS与SAS地统计分析方法应用于美国县区人口加权月温度估算的比较(英文)

气温变化对人群健康有重要的影响。通过对美国县区人口加权的月平均温度的准确估计可以用于气温与人群健康行为以及疾病的关联关系研究,如基于以县区为单位的抽样或者报告数据。针对气温的估计,多数学者都采用ArcGIS软件,很少使用SAS这一统计软件。本文比较了两种地统计模型的性能,并在同一个CITGO平台上采用ArcGIS9.3和SAS9.2工具软件估算全美48个州县区月平均温度。来自全美5435个气温监测站点2007年1-12月的平均温度和站点的海拔高度被用于估算县区人口中心点的温度,其中海拔数据是作为协变量。通过调整决定系数R2、均方误差、均方根误差和处理时间等指标来比较模型的效能。在ArcGIS中独立验证预测准确性在11个月中都达到90%以上,SAS中12个月均达到90%以上。与ArcGIS协同克里格相比,SAS协同克里格插值能获得更高的准确性和较低的偏差。两个软件包对于县区水平的气温估计值呈现正相关(调整R2在0.95-0.99之间);通过引入海拔高度作为协变量,使准确性和精确性都得以改善。两种方法对于美国县区层面的气温估计都是可靠的,但ArcGIS在空间数据前期处理和处理时间上的优势,尤其在涉及多年或者多个州的项目中是软件选择上的重要考虑。

Comparison of ArcGIS and SAS Geostatistical Analyst to Estimate Population-Weighted Monthly Temperature for US Counties

Temperature changes are known to have significant impacts on human health. Accurate estimates of population-weighted average monthly air temperature for US counties are needed to evaluate temperature＇s association with health behaviours and disease, which are sampled or reported at the county level and measured on a monthly--or 30-day--basis. Most reported temperature estimates were calculated using ArcGIS, relatively few used SAS. We compared the performance of geostatistical models to estimate population-weighted average temperature in each month for counties in 48 states using ArcGIS v9.3 and SAS v 9.2 on a CITGO platform. Monthly average temperature for Jan-Dec 2007 and elevation from 5435 weather stations were used to estimate the temperature at county population centroids. County estimates were produced with elevation as a covariate. Performance of models was assessed by comparing adjusted R2, mean squared error, root mean squared error, and processing time. Prediction accuracy for split validation was above 90% for 11 months in ArcGIS and all 12 months in SAS. Cokriging in SAS achieved higher prediction accuracy and lower estimation bias as compared to cokriging in ArcGIS. County-level estimates produced by both packages were positively correlated （adjusted R2 range=0.95 to 0.99）; accuracy and precision improved with elevation as a covariate. Both methods from ArcGIS and SAS are reliable for U.S. county-level temperature estimates; However, ArcGIS＇s merits in spatial data pre-processing and processing time may be important considerations for software selection, especially for multi-year or multi-state projects.