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
CompareP-value11MalesFemalesAGEAGEcountrateAGEAGEcountrate>0029293939494954545959646469697474