Thursday, December 14, 2017

Project 4 OS: Report

This is the final project of the class. This project was about food deserts, but mainly focused on open source GIS software, like QGIS, Webmap, and leaflet. This final report was about creating a food desert presentation about a city of my choosing, so I decided to do Weston, Florida, because that is where I live.

Overall, I did really enjoy this project, but I really liked learning about the different OS softwares. These feel like programs I will definitely use in the future. I provided a link to both my leaflet web map and final presentation below.


PowerPoint Presentation

Sunday, December 3, 2017

GIS Portfolio

This is my GIS Portfolio. It's about 18 pages long and includes my resume, samples of work, my unofficial transcript, and the ESRI virtual courses I took.

Overall, I thought creating my portfolio would be more challenging or a lot of work, but I had a lot of fun going back through all my old assignments and picking which ones I liked best. It was fun going back through my old assignments and seeing how much I improved so far.

Saturday, December 2, 2017

Project 4: Analyze 2

This week was a lot like last week, but I instead had to choose a city of my choice. I chose my hometown, Weston, Florida. I first had to download the needed data, which I did from After downloading, I isolated the features and created shapefiles from my chosen city. I also used Google Earth to find and save grocery stores, and was able to open the KMZ file in QGIS. I created centroids for each census tract and used the Near tool to create a csv file (a table) and joined it with the census tract layer in QGIS. Any feature that had a -1 in Near_dist was a food desert. 

I used Mapbox to create my main map and changed the symbology to reflect which census tracts were food deserts. Food deserts are areas that are 1 mile or more away from a grocery store. Below is a screenshot of my map.

I then used leaflet and an html code to create a webmap. Overall, I am not surprised by this data. Those are the more rural and suburban areas of the city, so these suburban neighborhoods would have vehicles to drive to the grocery stores.

Here is a link to my webmap:

Monday, November 27, 2017

Project 4: Open Source Analyze Week 1

This week for project 4, I learned about a website called Mapbox, which is a site that enables you to create maps while using our own data layers and changing the symbology. I think it was an interesting website to use, and is easy to use. It's a good option if you're just trying to make a fast and simple map. For this portion of the lab, I added grocery store and food desert layers that I created during Prepare week. Data are called tilesets on this website, and they were fairly easy to add to my map. I created duplicate layers of Food Deserts and used filters to separate each layer by classes, and changed the symbology to graduated colors so each layer was easily distinguishable.

I also became familiar with Leaflet, which is a Javascript library that enables you to create web maps. This portion of the lab consisted of using HTML code, which I'm not familiar with at all, but fortunately the code work was pretty simple. Part of this code referenced the map I created in Mapbox, and I used additional coding to create a legend and a geocoded search bar embedded into the actual web map.

Below is the link to my webmap about Food Deserts in Pensacola, Florida. It also shows locations of grocery stores in this area, and different shapes and polygons that show what areas are food deserts.

Overall, this lab wasn't too difficult. The most difficult part was using leaflet, mainly the coding part. I enjoyed learning about new websites and programs, and figuring out how web maps actually work.

Friday, November 17, 2017

Project 4: Open Sourced Prepare

For this project, I was introduced to a new program called QGIS Desktop. It's a lot like ArcMap, except with less statistical analysis tools. But you can add shapefiles and create maps like in ArcMap. After completing the lab, I actually enjoyed using this new program. It acts very similarly to ArcMap, so it wasn't too hard to learn. Most of the learning happened while making the maps.

This project focuses on Food Deserts, which are areas that do not have accessible means to a grocery store or nutritional food. Below is a map I created about food deserts and food oases in Escambia County. The process to create these results will be repeated with a city of my choice. I decided to choose my hometown, Weston, Florida. 

Tuesday, November 14, 2017

Lab 10: Supervised Classification

This lab was based on supervised classification. This process was completed using ERDAS Imagine. First I had to add an AOI layer in order to create signatures of the different features. There were two ways of creating features; using the polygon tool, or using the Grow Properties and changing the spectral euclidean distance value to create its own polygon. After, I then used supervised classification and recoded this supervised output file so there were more organized classes. 

Below is the map I created using this process. 

Saturday, November 4, 2017

Lab 9: Unsupervised Classification

This module was all about Unsupervised Classification, and the lab taught how to perform unsupervised classification in both ArcMap and ERDAS Imagine. In ArcMap you need both the Iso Cluster tool and the Maximum Likelihood Classification tools. 

In ERDAS Imagine, there is a specific tool titled Unsupervised Classification. After running the tool, you open the attribute table and see what colors were chosen for different classes. I then split the classes into 5 categories, and changed the colors one by one for each category. Below is the map I created in ERDAS Imagine, with 50 classes recoded into 5 categories. I then manually calculated the acreage and percentages of permeable and impermeable areas of the UWF Campus.

Project 4 OS: Report

This is the final project of the class. This project was about food deserts, but mainly focused on open source GIS software, like QGIS, Webm...