title: Comparison of ArcGIS and SAS Geostatistical Analyst to Estimate Population-Weighted Monthly Temperature for US Counties.
abstract: 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 adjustedR2, 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 (adjustedR2range=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.
authors.affiliation: Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China ; National Center for Public Health Surveillance and Information Services, Chinese Center for Disease Contral and Prevention (China CDC), Beijing 102206, China ; Office of Informatics and Information Resources Management, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention (CDC), Atlanta, Georgia 30333, USA.
authors.forename: Q I
authors.initials: QI
authors.lastname: Xiaopeng
authors.affiliation: Division of Oral Health, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention (CDC), Atlanta, Georgia 30333, USA.
authors.forename: Wei
authors.initials: W
authors.lastname: Liang
authors.affiliation: Division of Oral Health, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention (CDC), Atlanta, Georgia 30333, USA.
authors.forename: Laurie
authors.initials: L
authors.lastname: Barker
authors.affiliation: Office of Informatics and Information Resources Management, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention (CDC), Atlanta, Georgia 30333, USA.
authors.forename: Akaki
authors.initials: A
authors.lastname: Lekiachvili
authors.affiliation: Division of Adult and Community Health, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention (CDC), Atlanta, Georgia 30333, USA.
authors.forename: Zhang
authors.initials: Z
authors.lastname: Xingyou
keywords: ArcGIS; SAS; cokriging; county data; temperature estimation