HarvestChoice AgMarketFinder is a web-based geoprocessing GIS application that provides a quick-and-easy access to the spatially-explicit agricultural statistics databases, including crop, livestock, and rural/urban human population. No GIS skill necessary!
Need a Market?
In almost any type of business, location is often the key to the success (yes, location, location, location!). If you're in agricultural sector and wanting to open a new business for selling, say, fertilizer and seeds, that targets farmers with particular needs, you'd need to know about the status of agriculture in the area as well as how many potential crop/livestock and/or population you can potentially cover within certain travel time.
AgMarketFinder (http://marketfinder.info) is a web-based geo-processing GIS application that provides spatially-explicit agricultural statistics data (crop, livestock, and rural/urban human population) provided by HarvestChoice. Three types of spatial analysis can be conducted on-the-fly:
- from a user-selected point, four travel time zones (i.e., 0-2, 2-4, 4-6, and 6-8 hours of travel time),
- by sub-national administrative units, or
- by user-drawn polygon boundary.
What's New in v2.0
- Processing speed improvement (no more (or at least much less) time-out)
- Livestock population data layer added (cattle, chickens, goats, pigs, and sheep)
- Two more geo-processing tools are added (by administrative unit and by user-drawn polygon boundary)
How to Use It?
Screencast by Todd Slind (SpatialDev)
- Data layers developed and provided by HarvestChoice
- Prototype developed by ESRI Applications Prototype Lab (APL) in March 2009
- Version 2.0 perfected by Spatial Development International (SpatialDev) in August 2010
This application helps you identify a potential market location by analysing the travel time distances for 0-8 hour in 2 hour increments. The results are shown both on the map and as tabular data which can be downloaded.
Data used in these analyses are from very different scales, and results are to be treated as indicative. We are continuously working to improve the harmonization and reliability of the underlying data layers.