GeoComputation in Disease Mapping

A High-Throughput Computational Approach to Environmental Health Study Based on CyberGISXun Shi1, Anand Padmanabhan2, and Shaowen Wang2

1Department of Geography, Dartmouth College2Department of Geography and Geographic Information Science, National Center for Supercomputing Applications (NCSA), University of Illinois at Urbana Champaign

September, 2013Basic functionality of CyberGISAccessibility: Making GIS capabilities accessible to a large of number of users for research and education, through online cyberGIS Gateway;

Computational Capability: Embedding geospatial software capabilities into advanced cyberinfrastructure environments;

Interoperability: Managing heterogeneous and distributed resources and services through GISolve middleware. First of all, it is GIS. Brings advantages to GIS-based capability.Loose or break the limitation orEnable new methodology.

2Basic functionality of CyberGISAccessibility: Making GIS capabilities accessible to a large of number of users for research and education, through online cyberGIS Gateway;

Computational Capability: Embedding geospatial software capabilities into advanced cyberinfrastructure environments;

Interoperability: Managing heterogeneous and distributed resources and services through GISolve middleware. First of all, it is GIS. Brings advantages to GIS-based capability.Loose or break the limitation orEnable new methodology.

3Disaggregate polygon-level location data using restricted and controlled Monte Carlo (RCMC).

Calculate local statistics, e.g., calculate intensity of disease occurrence using kernel ratio estimation (KRE).

Estimate statistical significance of the intensity using unrestricted and controlled Monte Carlo (UCMC).A computational approach to spatial epidemiologyThere is no sophisticated parametrical statistical modeling involved. The whole idea is to use computation as an alternative to statistical modeling. 4Disaggregate polygon-level location data 23 births with defects1202 births

Birth with defect(s)Normal birthPopulationHighLow 5Restricted and Controlled Monte Carlo (RCMC) for DisaggregationAssign polygon-level addresses to random locations.

The randomization is restricted by the smallest polygon to which a polygon-level address belongs.

The randomization is controlled by the detailed background data.

The randomization is repeated many times (Monte Carlo).6Advantages of RCMCAllows analyses designed for individual/precise locations to be conducted.

Maximize the utilization of available spatial information.

Explicitly evaluate the spatial uncertainty caused by the imprecision in the data. 7Kernel ratio estimation (KRE) for Estimating Local Disease Intensity

Birth with defect(s)Normal birthEssentially, calculate the ratio between cases and cohort for each and every location.8Setting of KREfixed bandwidthvs. adaptive bandwidthsite-side kernel vs. case-side kernel9Types of KRE

Site-side fixed bandwidthCase-side fixed bandwidthSite-side adaptive bandwidthCase-side adaptive bandwidth10Unrestricted and Controlled Monte Carlo (UCMC) for Estimating Statistical Significance RCMC

KREUCMCKRE

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