Project 4 - Analysis 2

Tilemill and Mapbox

GIS 4930

This week we used what we learned previously in Tilemill and integrated it into Mapbox.  WOW! What a great little program that is completely free!  It holds up to 100MB in layers/space and allows anyone with the know how the ability to produce visually stunning digital maps.

For this map, I was just getting comfortable with the tools.  The real work begins with the Report week.  Here I wanted to just simply show the data that I produce and put it on the map.  I see that the transparency does not come through, and this does not look well when it comes to the choice of basemap that I chose.  I will no doubt need to work on this for my final report.

My Grocery Store data (seen as small red dots on the map) were collected with local knowledge of Grocery Stores in the area, as well as, the help of Google Earth to nudge my memory.  Using Google Earth I was able to identify and isolate my grocery stores into a single group.  With just those locations selected I was able to export the points as a kml extension file.  I brought this file into ArcGIS where I was then able to convert KML into a shapefile.  I utilized the project tool to convert it to a New Jersey State Plane projection. With my tools from ArcGIS done, I then used QGIS to review all data.

My Food Desert locations were identified by first using the 2010 US Census Block data that I obtained from New Jersey Geographic Information Network (NJGIN) https://njgin.state.nj.us.  This location is the New Jersey GIS data warehouse that the Office of GIS for New Jersey utilizes and updates.  Using QGIS I was able to isolate the blocks that fell within the Municipal Boundary (also obtained from NJGIN).  With the Census Blocks isolated and identified I used the polygon centroid tool within QGIS to locate the centroids of the blocks.  Then using QGIS’s Spatial Selection capability I identified the blocks whom centroid was within 1 mile of a Grocery Store.  These blocks were then exported as a Food Oasis layer.  I then reversed the selection and exported the remaining blocks that were then considered Food Deserts. 

I believe the quality and credibility is quite accurate.  The Grocery Stores that I included were those of major chains and were large enough to support several hundred individuals.  Smaller stores like 7-Eleven or Gas Stations were not cataloged, as were very small stores that did not have the square footage to support mass produce.   The Desert Oasis layer made up of Census Blocks are only as good as the data provided.  Being familiar with the area, the population numbers look accurate, and coming from the US Census the data should be of good quality, even though 4 years has passed since it was last collected. 

The data was not very surprising to me for my local area.  The west and south central part of my town is heavy commercial and industrial buildings. The trends I observed here were that Grocery Stores were more prevalent in areas where residential population were higher in the north side of town.  This makes sense since their customers are predominantly shopping for their home and not their business.  What I did notice was subdivisions located near or around these industrial parks were in Food Desert areas.  In fact, some of the higher population census blocks are locate near these industrial parks in the central part of town.   The construction of a single grocery store in the Central section of town could reduce the food desert by approximately 90%.  What does surprise me is the fact that a local retail store has not identified the market potential here and build a store to capture the market.