I am working with two global raster maps using the R Software (original file: netCDF for map 1 and GeoTIFF for map 2), with a resolution of 5 min of arc.
- Map 1 contains data on the share of the land in each grid-cell which is cultivated -i.e. cropland area in each grid-cell. Values range from 0 to 1.
- Map 2 contains data on the livestock density in each grid-cell -i.e. number of animals in each grid-cell. values range from 0 to thousands.
My objective is to calculate/obtain an indicator of segregation of livestock production in comparison of crop production -i.e. an indicator of the spatial co-presence of livestock and crop production. My final aim could be to get a numerical indicator of e.g. how much crop production and livestock are co-present within a certain territory, as instance in each grid-cell. In other words, I am interested in getting a sort of indicator estimating the spatial distance between livestock and crop at a certain spatial scale. Finally, I would be interested in get a sort of "average" value of such indicator at Country level. My idea is to use this indicator in estimating the amount of livestock excreta that could be available at a Country scale (i.e. if the there is a high co-presence of livestock and crop production within a certain Country, this is reflected in a high availability of excreta for such crop production).
Which kind of statistical method or set of tools could be suitable to reach such result?
Here some information on how the two dataset were generated, which I retrieved from the original sources:
- Map 1: The author used two different high-resolution (1-km) satellite based, global land cover classification data sets that are available for circa 2000, and applied a simple set of climatic parameters to mask obviously non-agriculture areas within the satellite datasets. Additional information can be found here http://www.earthstat.org/wp-content/uploads/METADATA_CroplandPastureArea2000.pdf
- Map 2: The datased is an improvement of a previous dataset (Gridded Livestock of the World - GLW). The estimated densities were based on statistical relationships between observed densities within administrative units derived from survey and census data, and several explanatory variables, including a time-series of remotely sensed satellite data relating to climate and the environment, and other spatial data relating to demography, land cover and terrain. Reported sub-national statistics were thus spatially disaggregated and gaps where none were available filled with predictions to provide a complete global distribution map for each species. More information are available here: http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0096084