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

  • 1 Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China;
    2 National Center for Public Health Surveillance and Information Services, Chinese Center for Disease Contral and Prevention (China CDC), Beijing 102206, China;
    3 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;
    4 Division of Oral Health, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention (CDC),Atlanta, Georgia 30333, USA;
    5 Division ofAdult and Community Health, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention (CDC),Atlanta, Georgia 30333, USA

Received date: 2012-05-14

  Revised date: 2012-08-27

  Online published: 2012-09-27

Supported by

the findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the Centers for Disease Control and Prevention.


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 v9.2 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.

Cite this article

QI Xiaopeng, WEI Liang, Laurie BARKER, Akaki LEKIACHVILI, ZHANG Xingyou . Comparison of ArcGIS and SAS Geostatistical Analyst to Estimate Population-Weighted Monthly Temperature for US Counties[J]. Journal of Resources and Ecology, 2012 , 3(3) : 220 -229 . DOI: 10.5814/j.issn.1674-764x.2012.03.004


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