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.
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
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DOI: 10.5814/j.issn.1674-764x.2012.03.004
Balk D L, U Deichmann, G Yetman, et al. 2006. Determining global population distribution:Methods, applications and data. Advances in Parasitology, 62:119-156.
Benavides R, F Montes,ARubio, et al. 2007. Geostatistical modelling of air temperature in a mountainous region of Northern Spain. Agricultural and Forest Meteorology, 146:173-88.
Boer E P J, K M de Beurs, A D Hartkamp. 2001. Kriging and thin plate splines for mapping climate variables. International Journal of Applied Earth Observation and Geoinformation, 3:146-54.
Bolstad P V, L Swift, F Collins, et al. 1998. Measured and predicted air temperature at basin to regional scales in the southern Appalachian Mountains. Agricultural and Forest Meteorology, 91:161-76.
Brown D P and A C Comrie. 2002. Spatial modeling of winter temperature and precipitation in Arizona and New Mexico, USA. Climate Research,22:115-28.
Calder C A, N Cressie, K Rob, et al. 2009. Kriging and variogram models. International Encyclopedia of Human Geography. Oxford:Elsevier.
Centers for Disease Control and Prevention[CDC]. 2009. Estimated countylevel prevalence of diabetes and obesity-United States, 2007. MMWR Morb Mortal Wkly Rep, 58:1259-63.
Cressie N (Ed. ). 1993. Statistics for spatial data. New York:John Wiley and Sons.
Croner C M, J Sperling, F R Broome. 1996. Geographic information systems (GIS):New perspectives in understanding human health and environmental relationships. Statistics in Medicine, 15:1961-77.
Dobson J E, E A Bright, P R Coleman, et al. 2000. LandScan:A global population database for estimating populations at risk. Photogrammetric Engineering & Remote Sensing, 66:849-57.
ESRI. 2001. ArcGIS geostatistical analyst:Statistical tools for data exploration, modeling, and advanced surface generation. An ESRI White Paper.
Goovaerts P. 2000. Geostatistical approaches for incorporating elevation into the spatial interpolation of rainfall. Journal of Hydrology, 228:113-29.
He H Y, Guo Z H, Xiao W F. 2005. Review on spatial interpolation techniques of rainfall. Chinese Journal of Ecology, 23:6-11.
Hudson G and H Wackernagel. 1994. Mapping temperature using kriging with external drift:theory and an example from scotland. International Journal of Climatology, 14:77-91.
Ishida T and S Kawashima. 1993. Use of cokriging to estimate surface air temperature from elevation. Theoretical and Applied Climatology, 47:147-57.
Jiang X, Liu X, Huang F, et al. 2010. Comparison of spatial interpolation methods for daily meteorological elements. Chinese Journal of Applied Ecology, 21:624-30.
Johnston K, J M V Hoef, K Krivoruchko, et al. 2003. ArcGIS 9 using ArcGIS geostatistical analyst. ESRI.
Juan P, J Mateu, M M Jordan, et al. 2010. Geostatistical methods to identify and map spatial variations of soil salinity. Journal of Geochemical Exploration, 108:62-72.
Lapen D R and H N Hayhoe. 2003. Spatial analysis of seasonal and annual temperature and precipitation normals in Southern Ontario, Canada. Journal of Great Lakes Research, 29:529-44.
Lee H J, M R Rubio, I T Elo, et al. 2005. Factors associated with intention to breastfeed among low-income, inner-city pregnant women. Maternal and Child Health Journal, 9:253-61.
Li J L, Zhang J, Zhang C, et al. 2006. Analyze and compare the Spatial Interpolation Methods for climate factor. Pratacultural Science, 23:6-11.
Li J, You S, Huang J. 2004. Spatial interpolation method and spatial distribution characteristics of monthly mean temperature in China during 1961-2000. Ecology and Environment, 15:109-14.
Littell R C, G A Milliken, W W Stroup, et al. 2006. SAS for mixed models. Cary, NC:SAS Institute Inc. .
Li X, Cheng G, Lu L. 2005. Spatial analysis of air temperature in the Qinghai-Tibet Plateau. Arctic, Antarctic, and Alpine Research, 37:246-52.
Ludington-Hoe S M, P E McDonald, R Satyshur. 2002. Breastfeeding in African-American women. Journal of National Black Nurses' Association, 13:56-64.
Mahdian M H, S R Bandarabady, E Sokouti, et al. 2009. Appraisal of the geostatistical methods to estimate monthly and annual temperature. Journal of Applied Sciences, 9:128-34.
Ninyerola M, X Pons, J M Roure. 2000. A methodological approach of climatological modelling of air temperature and precipitation through GIS techniques. International Journal of Climatology, 20:1823-41.
Nommsen-Rivers L A, C J Chantry, R J Cohen, et al. 2010. Comfort with the idea of formula feeding helps explain ethnic disparity in breastfeeding intentions among expectant first-time mothers. Breastfeeding Medicine, 5:25-33.
Parry M L, C Rosenzweig,ALglesias, et al. 2004. Effects of climate change on global food production under SRES emissions and socio-economic scenarios. Global Environmental Change, 14:53-67.
Stahl K, R D Moore, J A Floyer, et al. 2006. Comparison of approaches for spatial interpolation of daily air temperature in a large region with complex topography and highly variable station density. Agricultural and Forest Meteorology, 139:224-36.
Tian Y, Yue T, Zhu L, et al. 2005. Modeling population density using land cover data. Ecological Modelling, 189:72-88.
Vaaler M L, J Stagg, S E Parks, et al. 2010. Breast-feeding attitudes and behavior among WIC mothers in Texas. Journal of Nutrition Education and Behavior, 42:S30-S38.
Wu C and A T Murray. 2005. A cokriging method for estimating population density in urban areas. Computers, Environment and Urban Systems, 29:558-79.
Yang J, Wang Y, P V August. 2004. Estimation of land surface temperature using spatial interpolation and satellite-derived surface emissivity. Journal of Environmental Informatics, 4:37-44.
Yue T X, Wang Y A, Liu J Y, et al. 2005. Surface modelling of human population distribution in China. Ecological Modelling, 181:461-78.
Zhao C Y, Nan Z R, Cheng G D. 2005. Methods for modelling of temporal and spatial distribution of air temperature at landscape scale in the southern Qilian Mountains, China. Ecological Modelling, 189:209-20.
