Grant Number:	1R43CA092807-01 Interpret this number
Primary Investigator:	Jacquez, Geoffrey
Organization:	Biomedware
Project Title:	Simulation Algorithms for Spatial Pattern Recognition
Fiscal Year:	2001

DESCRIPTION (provided by applicant): A new generation of satellites is imaging the earth's surface with unprecedented spatial and spectral resolution. With the ability to identify local features related to environmental exposures, this high-resolution imagery is gong to revolutionize health risk assessment. The realization of this potential depends critically on our ability to recognize spatial patterns on these large images. This project will develop fast spatial null models for use in statistical pattern recognition, and will accomplish 4 aims. (1) Implement fast simulation algorithms conditioned on properties of the data, and on spatial functions; (2) Assess project feasibility by evaluating the performance of these algorithms on existing high-resolution, hyperspectral imagery; (3) Implement the simulation algorithms in 2 commercial spatial analysis software packages; (4) Apply the software and methods to demonstrate the approach and unique benefits for risk assessment. The phase 1 research will address the first two aims; aims three and four will be accomplished in phase 2 once feasibility is demonstrated. The technologic and scientific innovations from this project are expected to greatly enhance our ability to extract knowledge from high resolution imagery. PROPOSED COMMERCIAL APPLICATION: The imminent launch of over a dozen satellites capable of high-resolution imagery is giving health researchers powerful new data for relating environmental features to health outcomes, but existing software packages cannot undertake spatial analysis of these extraordinarly large data sets. The fast simulation algorithms from this research will be incorporated into 2 commercial software packages, providing advanced spatial analysis for large imagery.




Accounting For Rate Instability And Spatial Patterns In The Boundary Analysis Of Cancer Mortality Maps
Authors: Goovaerts P. .
Source: Environmental And Ecological Statistics, 2008 Dec; 15(4), p. 421-446.
PMID: 19023455
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Space-time clustering of case-control data with residential histories: insights into empirical induction periods, age-specific susceptibility, and calendar year-specific effects.
Authors: Meliker J.R. , Jacquez G.M. .
Source: Stochastic Environmental Research And Risk Assessment : Research Journal, 2007 Aug; 21(5), p. 625-634.
PMID: 18560470
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Geostatistical analysis of disease data: visualization and propagation of spatial uncertainty in cancer mortality risk using Poisson kriging and p-field simulation.
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Source: International Journal Of Health Geographics, 2006-02-09 00:00:00.0; 5, p. 7.
EPub date: 2006-02-09 00:00:00.0.
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Case-control Geographic Clustering For Residential Histories Accounting For Risk Factors And Covariates
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Source: International Journal Of Health Geographics, 2006; 5, p. 32.
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Geostatistical analysis of disease data: estimation of cancer mortality risk from empirical frequencies using Poisson kriging.
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Detection of temporal changes in the spatial distribution of cancer rates using local Moran's I and geostatistically simulated spatial neutral models.
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Source: Journal Of Geographical Systems, 2005 May; 7(1), p. 137-159.
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Accounting for regional background and population size in the detection of spatial clusters and outliers using geostatistical filtering and spatial neutral models: the case of lung cancer in Long Island, New York.
Authors: Goovaerts P. , Jacquez G.M. .
Source: International Journal Of Health Geographics, 2004-07-23 00:00:00.0; 3(1), p. 14.
EPub date: 2004-07-23 00:00:00.0.
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