The method is based on the computation of spatial indicators using a Geographical Information System (GIS) to predict and quantify critical impacts, such as ecosystem loss and fragmentation, soil erosion, geomorphologic hazards, interference with flora and fauna, and visibility.
When environmental levels of the agent (contaminant) of interest in the environment of the study population cannot be measured or accurately predicted, GIS provide the optimal technology for using proximity to contaminant source in an environmental epidemiology study.
This GI prediction method is noted for having more sensitivity/recall in predicting GIs versus other methods studied in an evaluation of GI predictors [18].
Ultimately, however, this approach can be extended to include geographical and ecological factors using a geographic information systems (GIS) approach to predict how the distribution of schistosomiasis will change in response to increased temperatures.
Miranda and colleagues have previously used GIS modeling to predict lead exposure risk levels in North Carolina children (Miranda et al. 2002) and to identify first Gulf War veterans at risk for amyotrophic lateral sclerosis based on toxic exposures in Iraq (Miranda et al. 2008).
General growth patterns predicted by the GIS overlay model resembled those generated by the CA, but the CA model produced more patches and edge in the landscape.
The alignment of Hel1-331 (gi: 192757708; B73) with maize ESTs, (gis: 71331232, 71324104, 71331231, and 78110425) predicted a gene structure of eight exons and seven introns embedded within the element (data not presented).
This GIS-based software predicts the risks associated with contaminated water entering water distribution systems from surrounding foul water bodies such as sewers, drains and ditches.
