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 adjusted R 2 , 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 R 2 range=0.95 to 0.99); accuracy and precision improved with elevation as a covariate. Both methods from ArcGIS and * Corresponding author: WEI Liang. LWei1@cdc.gov.. § These authors contributed equally to this work.Note: 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. HHS Public AccessAuthor manuscript J Resour Ecol. Author manuscript; available in PMC 2015 July 09. Author Manuscript Author ManuscriptAuthor ManuscriptAuthor Manuscript 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.Keywords temperature estimation; county data; ArcGIS; SAS; cokriging BackgroundSpatial data analysis has received considerable attention and played an important role in disciplines of environmental science and socio-economic science due to the rapid development of Geographic Information Systems (GIS) in recent years. The need for reliable environmental geospatial databases is fast-growing (Croner et al. 1996). Ecology is the scientific study of the relations that people have with respect to each other and their natural environment. The environment is dynamically interlinked, imposed upon and constrains people at any time throughout their life. Meteorological measurements such as temperature and precipitation are needed to assess links between the environment and diseases in the population. Various spatial interpolation methods including inverse distance weighting (IDW), multiple regression, thin plate smoothing spline (TPSS), kriging and cokriging...

Evidence for Urban–Rural Disparity in Temperature–Mortality Relationships in Zhejiang Province, China

“…2013). The population-weighted averages of the daily 24-h mean temperature and the daily 24-h mean relative humidity in each county were then estimated in attempt to accurately measure human exposure (Qi et al. 2012).…”

The spatiotemporal distribution of human brucellosis in mainland China from 2007-2016

“…Gravity-center Using spatial statistical tools (such as the mean center) to calculate the incidence gravitycenter of brucellosis each year, we assumed that a region is composed of several sub-regions. Each sub-region has a particle P i (X i , Y i ) and its attribute value is M i , meaning the center coordinate formula of an attribute is [28]:…”

Water Intake by Outdoor Temperature among Children Aged 1–10 Years: Implications for Community Water Fluoridation in the U.S.

“…Outdoor temperatures measured at the county level may not reflect the true outdoor temperature at home or school during the day of water intake. 35 Additionally, outdoor temperatures may not reflect the temperatures to which participants were exposed, because portions of the day were likely spent indoors. Logistics of the NHANES MECs limited the range of temperatures under which water intake could be measured, because examinations were primarily conducted in the coldest areas of the country only in the summer, while the hottest areas of the country were avoided in the summer.…”