The goal of this project is to look at dairy farms and their spatial relationships to water, water-supply, and cryptosporidium in humans in the overlying or nearby towns.
List of steps:
1. Begin with a spatial join layer of dairy farms to towns.
2. Determine the possibility of also including number of cattle in each town (by farm). I would like to create a table with this information in excel and then join it to the shapefile.
3. Add aggregate annual precipitation data from PRISM (1993-2003). Clip the layer to the state of Massachusetts, spatially join to previous layers.
4. Add elevation layer for Massachusetts, spatially join to previous layers.
5. Add 1:25 Hydrology layer for Massachusetts, spatially join to previous layers
6. Add Public Water Supply Point layer, spatially join to previous layers.
7. Add preordered NRCS soils layer for Massachusetts, spatially join to previous layers.
8. Add a land-use layer and spatially join to other layers.
9. Create a cryptosporidiosis rate spreadsheet by town and join it to the dairy farm layers.
10. On top of these joined layers I will overlay an 800 meter grid of Massachusetts.
Using these joined layers, I will perform the following analysis operations:
1. With density tools I will determine the average number of dairy farms in each cell.
2. Additionally, I will determine the average slope, soil type, vegetation, and precipitation in each cell.
3. I will determine the average distance of dairy farms to surface waters and to public water supply intakes in each cell.
4. I will determine the average slope and elevation in each cell.
5. I will determine the average precipitation in each cell.
6. I would like to combine the above averages to find the highest ranking cells, in terms of shortest distance to surface waters and/or public water supply intakes, steepest slope, most easily erodible soil, highest precipitation, and presence of dairy farms. Using kernel might be useful in this analysis.
7. If this is possible, I will display these rankings by color gradation.
Present 2 maps: One of crytosporidiosis rate by town, and the second map of the information in steps 6 and 7.
In the future I would like to create a cost distance of dairy farms to surface waters and water supply. The above variables would need to be transformed into a run-off potential index.