Welcome to the continued look at open source GIS with a flavor for fresh food, or lack thereof as is the case for areas identified as food deserts. This is a continuation of the analysis started last week. This week was dedicated to the following objectives:
This is a very rough product just highlighting that I could take a basemap which is the satellite imagery and add the layers over top, showing grocery stores (red dots) and various effects of food desert census tracts. The visible tracts are food deserts, all other areas should be considered not food desert. These deserts are then color coded by population affected, the darker the more people impacted. Once again this is a rough overview of a capability of adding these in Mapbox. The stylized finish product coming next week will involve much more functionality as it transitions to a full functioning web map through the use of Leaflet. This will be like last weeks map example, only tailored with my personal study area and information. So you might ask what has been done so far?
My data is for the Palm Springs city area as defined by the US Census Places layer which is a subset of Riverside County California. My boundary files for this area were all derived from the US Census acquired partnership files. The layers obtained include state outline, Riverside County Boundary, Places within Riverside county, and census tracts within riverside county, to include the US Census population data for 2010. The palm springs boundary was clipped from all of the places within Riverside county, then used as the study area to clip the appropriate study area census tracts. Tabular census data was joined to the census tracts by tract ID.
Locating the pertinent grocery stores was a combination of Google Earth searching, Google searching, and Yellowpages.com to cross reference all available grocery stores including fresh produce and the like. There were 9 of these points that were marked within Google Earth. The KML file was then exported from Google Earth, and within ArcMAP the KML to Layer tool was used. From there I had a problem uploading this point file into Tilemill and having it display appropriately. My workaround was to create a blank point feature class and heads up digitize new points over the existing point using the snap to point feature to ensure that the points were appropriately overlayed. This newly created layer was effectively useable with tilemill for the creation of the mapbox tiles.
This data is already starting to show trends and pertinent information for the area, but I will save those results for next weeks post. Please stay tuned and thank you.
- Tile Shapefiles for internet usage
- Create a custom basemap with the use of Mapbox
- Utilize web mapping to communicate a subject
This is a very rough product just highlighting that I could take a basemap which is the satellite imagery and add the layers over top, showing grocery stores (red dots) and various effects of food desert census tracts. The visible tracts are food deserts, all other areas should be considered not food desert. These deserts are then color coded by population affected, the darker the more people impacted. Once again this is a rough overview of a capability of adding these in Mapbox. The stylized finish product coming next week will involve much more functionality as it transitions to a full functioning web map through the use of Leaflet. This will be like last weeks map example, only tailored with my personal study area and information. So you might ask what has been done so far?
My data is for the Palm Springs city area as defined by the US Census Places layer which is a subset of Riverside County California. My boundary files for this area were all derived from the US Census acquired partnership files. The layers obtained include state outline, Riverside County Boundary, Places within Riverside county, and census tracts within riverside county, to include the US Census population data for 2010. The palm springs boundary was clipped from all of the places within Riverside county, then used as the study area to clip the appropriate study area census tracts. Tabular census data was joined to the census tracts by tract ID.
Locating the pertinent grocery stores was a combination of Google Earth searching, Google searching, and Yellowpages.com to cross reference all available grocery stores including fresh produce and the like. There were 9 of these points that were marked within Google Earth. The KML file was then exported from Google Earth, and within ArcMAP the KML to Layer tool was used. From there I had a problem uploading this point file into Tilemill and having it display appropriately. My workaround was to create a blank point feature class and heads up digitize new points over the existing point using the snap to point feature to ensure that the points were appropriately overlayed. This newly created layer was effectively useable with tilemill for the creation of the mapbox tiles.
This data is already starting to show trends and pertinent information for the area, but I will save those results for next weeks post. Please stay tuned and thank you.
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