“There are good things to see in the tide pools and there are exciting and interesting thoughts to be generated from the seeing.”
–Ed Ricketts
Day: October 19, 2009
Modelling Individual Space–time Exposure Opportunities: A Novel Approach to Unravelling the Genetic or Environment Disease Causation Debate
…in Spatial and Spatio-Temporal Epidemiology, Volume 1, Issue 1…
Clive E. Sabel, Paul Boyle, Gillian Raab, Markku Löytönen, Paula Maasilta
“The aetiology of Amyotrophic Lateral Sclerosis (ALS) is uncertain. While around 10% is assumed to be inherited, the relative influence of genetic versus physical or social environmental factors (or some combination of the two) has yet to be determined.
“A previous study identified significant clustering of ALS at the time of birth in south-east Finland and this could support either a genetic or an environmental hypothesis. We know that south-east Finland is an environmentally degraded area, but the population in this region may also be genetically susceptible to this condition.
“We therefore extend this research by comparing the lifetime residential histories of 1000 ALS cases and 1000 controls matched by birth date, sex and municipality of birth. By focusing on those who originated in the south-east, and comparing the subsequent residential mobility of these two groups, we test whether remaining in south-east Finland is more common among cases than controls and, hence, whether there may be an environmental or genetic influence on ALS associated with that region. Our results indeed suggest that the cases were more likely to remain in south-east Finland after birth, compared to the geographically matched controls. This suggests that moving away is protective, and points towards a risk factor after birth being implicated in the aetiology of the disease.”
Map of the Day: National Commodity Crop Productivity Index
…from the ESRI Map Book, Volume 24…
“The National Commodity Crop Productivity Index (NCCPI) is a model that uses inherent soil properties, landscape features, and climatic characteristics to assign ratings for dry-land commodity crops such as wheat, cotton, sorghum, corn, soybeans, and barley. The model arrays Soil Survey Geographic Database map unit components from 0.01 to 1.0; components with the most desirable soil properties, landscape features, and climatic characteristics will display larger NCCPI values than soils with less desirable traits. The maps presented above are part of the Detailed Soil Survey Atlas, a national collection of state-centered maps prepared at a scale of 1:500,000 derived from U.S. Department of Agriculture soil geographic databases.
“Courtesy of U.S. Department of Agriculture, Natural Resources Conservation Service.”
GIS and Science 