Project 4 - Report Week

Final Presentation - South Plainfield, NJ Food Deserts

GIS 4930

For my Final Project in GIS 4930 I finished out the class with the wrapping up of my project as a whole. This week I was asked to create a PowerPoint Presentation to display the Analysis of Food Deserts in South Plainfield, New Jersey.  I utilized the same concepts and principles we used in the previous weeks to produce not only a static map within QGIS, but also a webmap utilizing Tilemill, Mapbox, and Leaflet.

In addition, I became more familiar with how to add plugins into my leaflet code.  This took a little more time than I anticipated as I had to do quite a bit of research online and working with colleagues to produce the results.  In the end, I was able to add a full screen plugin, along with a Mini Map interface on my webmap.  These new plugins along with the earlier Geo Locator tool plugin allowed the users of my map to work with the map.

As you will see in my results, I found that a large area of South Plainfield, New Jersey is located with a food desert.  This is primarily due to a lack of a quality grocery store on the western side of town. If you look at the map below you can see the area has been identified using a color ramp to highlight the population density affected by this Food Desert phenomenon.  

Please feel free to take a look at my Presentation below.  In it I go into greater detail about the methods used to identify these food deserts as well as highlight what I believe can be done to minimize their existence in the local area.

South Plainfield, New Jersey - Food Desert Analysis Presentation

My webmap can also be accessed at:

http://students.uwf.edu/jbg16/STGIS/Final/JBG16_Final.html

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.

Project 4 - Analysis 1

Intro to Tilemill and Leaflet

GIS4930

This week we jumped into learning Tilemill and Leaflet, both free open source software packages.  In Tilemill we learned how to navigate through the program and added layers, modified symbology, and brushed up on my color brewer skills.  All this while utilizing code to do so.  In Leaflet we learned how to reach out and captivate an audience through the power of web mapping.  By scraping some source data, we modified it and made it our own.  In Image 1 you can see the code utilized to identify a point and modify the text that appears on that points popup window.  Here also you can see how a circle was drawn to identify a food oasis area.  Finally in the code you can see the lat/long points utilized to draw a polygon, and the code that made it possible. 

Image 1 - Code utilized to identify/draw a point, circle, and polygon

Image 2 shows how the coding from Image 1 was displayed on the web map.

Image 2 - Webmap of Pensacola Florida depicting the identifiers build into the code.

Later in the same lab, I was able to add more layers to the webmap and make them intractable.  In Image 3 below you can see discgolf locations found around the Pensacola, Florida.  The dialog box in the upper right shows what layers can be turned on and off with a click of the mouse.

Image 3 - Discgolf locations around Pensacola Florida

This weeks lab was a great lab!  I will definitely put to use what I learned this week in project I have planned for the future.  What great tools, and for the right price too!

Project 4 - QGIS

Prepare week

GIS4930

This week's lab had us begin to utilize an Open Source GIS program called QGIS.  This program does not have all the bells and whistles available in it that ArcGIS desktop has to offer, but it gives the user the basic capabilities of ArcGIS for a price that can't be argued about, FREE.

To begin this lab I first familiarized myself with the inner workings and setup of the layout.  Quickly I discovered that it was similar, with a few minor changes.  Some of these changes were for the better and others just annoyed me.  However the overall application was easy to work with and allowed me to accomplish the task as assigned.

Clipping, Attribute Selection, Mapping Basics, and Analysis tools all were available or integrated into my finished maps below.  I even utilized the Dissolve tool to create a border for my study area in part B.

For the first map, I became familiar with how to do basic map elements and work the Print Composer like an artist would do.

Escambia Florida - University of West Florida - QGIS developed map

The second map, show below, had a basic geoprocessing analysis built into it.  Everything was done withing QGIS with the exception of the NEAR tool, which was run from ArcGIS.  The map shows Food Deserts and Food Oasis locations.  Essentially a food desert is describes as an area that does not have access to quality grocery stores within a mile, while the food oasis is just the opposite.  As you can see a large area of Pensacola Florida is within what could be defined as a Food Desert.

The lab this week was a great look into alternate programs outside of ArcGIS that can help provide quick and cheap map making capabilities.  With a strong background in ArcGIS and Geographic Information Science users can present to the reader the desired results.

Meth Lab Analysis Week

Project 3 - OLS Analysis

GIS 4930

This week we delved into the world of Statistical Analysis in GIS.  Specifically the Ordinary least squares and geographic weighted regression.  Below is the OLS table and the map showing the results of that OLS analysis.  I followed the lab, and feel that my results fell within the directions given.  As this type of analysis is widely uses, I would like to have a more in depth understanding of it.  Statistical analysis is not a strong point of mine, and I felt I need to understand it better.

The map itself turned out quite well and produced the results I theorized it would.  Meth labs would be more likely to appear inside the urban populated area of Charleston WV.  Rather than show the Standard Residual and the decimal breakdown, I took the time to make it more readable to my viewers by describing where there may be trends in the legend.  I felt showing a digital values limited your audience, as most people would not understand what the Standard Residual values mean.  As this map targets law enforcement and not my GIS peers this only makes sense.  Also, I left the Inset to the Charleston area as this is the focus of most of the meth lab increase likeliness.  

Overall, this project was a bit difficult to understand and move forward on.  I would like to have more time to focus on understanding this type of statistical analysis in a more confined study with limited variables so I can see and understand the significance of the statistical changes.  Although advanced, I did understand the basic principles, but not to the level I feel I should. 

Project 3: Statistics - Prepare Week
Meth Labs per Square Mile - Charleston, WV

GIS 4930

For this weeks lab we began the Prepare Week by getting a basemap together that will be utilized in next weeks lab.  I identified the Charleston, West Virginia area as our Study Area for the project.  For this project we are looking into the socio-economic factors for the distribution of methamphetamine labs.  Utilizing the Census Tracts data from the U.S. Census Bureau and merging that data with education and lab locations my hope is to be able to identify areas that may show a greater likelihood of having an illicit meth laboratory.  

Some of the steps to produce my final basemap included calculating multiple percentages.  I utilized Field Calculator to calculate percent population growth, percent white, percent roommates.  Later in the lab we sped up the process of calculating percentages by using a python script.  This script quickly calculated multiple attribute fields and I was tasked with modifying the script to include percentages for age groups 40-49, 50-64, and above 64 years.  I also wrote script to calculate the male to female ratio and percentage of uneducated individuals in the population.

Utilizing a Spatial Join, I was then able to get a total count of meth labs per census tract.  This in turn allowed for me to calculate the number of labs per square mile for any specific census tract.  I used this data to then show a categorized symbology with a color ramp.  This symbology showed areas where there was meth labs and how abundant they were in that specific tract compared to others in the study area.  My final task was to clean up my attribute table, removing unneeded fields and produce a basemap for this weeks project.  

Project 2 - Report Week

Mountain Top Removal Analysis

GIS 4930

Mountain Top Removal (MTR) was the focus of this project as well as the environmental impact it has on the countryside.  Utilizing Satellite Imagery from 2010, I was able to do a multi-band spectral analysis on those areas in our group study area (Group 3) and determine locations that may have MTR projects ongoing.  I also utilized Digital Elevation Models (DEM) to run analysis and determine locations where streams may be located.  Streams locations are important as these are greatly impacted by MTR projects.  Runoff into these streams could have detrimental impacts on the environment and surrounding wildlife.

Starting off the project we identified what groups we would be in, and those groups were assigned specific study areas.  Utilizing DEM’s we not only created a steam layer, but also identified watershed locations.  Later in the lab, the group was tasked with identifying MTR locations using 2010 reclassified satellite imagery.  This was done in ERDAS Imagine program.  The classified locations were then brought into ArcGIS and the data was converted from a raster to a polygon feature.  With the polygon feature identified noise and interference was removed.  This consisted areas around Roadways, Highways, Streams and Major Rivers, all of which shared a similar bands with MTR locations.  Finally with this removed we isolated those polygon features that were larger than 40 acres.  This left us with the most likely polygons that were MTR locations.

Our group consisted of four members.  Each member played a role in the production of the data.  We had two satellite images to analyze so we assigned 2 users to each.  I took on the responsibility of keeping communication open.  I started many of the discussions where we needed to communicate back and forth to share information.  I also was able to return initial results to the group quickly so I could share my results and give help on issues that may have arisen.  The final MTR features were posted to ArcGIS Online to share the results that the group found.  I added another layer to the map that shows coal fields as identified by the USGS Eastern Energy Resources Science Center.

Mountain Top Removal Map

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