Geostatistical software for space-time interpolation and uncertainty modeling

DESCRIPTIONprovided by applicantA key component in any investigation of association and or cause effect relationships between the environmente gair pollutionheat wavesand health outcomese gasthmaheart diseasecanceris the availability of accurate models of exposure at the same geographical scale and temporal resolution as the health outcomesThe computation of human exposure is particularly challenging for cancers since they may take years or decades to developespecially in presence of low level of contaminantsIn this situation pollutant levels are rarely available for every location and time interval visited by thesubjectstherefore data gaps need to be filled in through space timeSTinterpolationSurprisinglythere is currently no commercial software for the geostatistical treatment of space time dataincluding the interpolation at unmonitored times and locationsThis SBIR project is developing the first commercial software to offer tools for geostatistical ST interpolation and modeling of uncertaintyThe research product will be a stand alone module into the desktop space time visualization core developed by BioMedwarean Esri partnerThis software package will offer a comprehensive suite forthe computation and advisor guided modeling of ST variogramsthe ST prediction and stochastic modeling of exposure data at the same scale as health outcomei eaggregated or individual leveland using any secondary information availablee gremote sensingland use regression modelair dispersion modelother air pollutantsandthe quantification and Monte Carlo based propagation of uncertainty attached to estimates through exposure reconstructionThese tools will be suited for the analysis of data outside health sciencessuch as in remote sensingnuclear environmental engineering or climate changebroadening significantly the commercial market for the end productThis project will accomplish four aimsExpand the statistical methodology developed in Phase I to tacklethe case where multiple correlated attributese gair pollutantswere measured with different sampling densities and temporal frequencieswhich will require developing ST cokriging and testing its performance over the kriging approach implemented in Phase Iandstochastic modeling and propagation of exposure uncertaintyexposure measurement errorsthrough regression analysisBuild a fully functional and tested ST interpolation and simulation module ready for commercial distributionConduct a usability study to evaluate the design of the prototype based on NIH usability protocolsApply the software to demonstrate the approach and its unique benefits in several epidemiological studiesincluding impact of air pollution on birth outcomes and urban extreme heat on cardiovascular mortalityThese technologicscientific and commercial innovations will revolutionize our ability to model geostatistically space time phenomena and compute estimates and the associated uncertainty at the scalee gpoint locationcensus tract levelthe most relevant for environmental epidemiological studies