Sunday, December 3, 2017
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
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 FGDL.org. 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.
Here is a link to my webmap: http://students.uwf.edu/klc106/SpecialTopics/Weston.html
Monday, November 27, 2017
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.
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
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
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
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.
Friday, November 3, 2017
This portion of the lab was about using the Ordinary Least Squares Regression tool. The dependent variable for this model was the Meth Density field, and the explanatory variables were 29 census group fields. After running the OLS tool once with all 29 census fields, I looked at the OLS Table and re-ran the tool again, removing any variables that had high VIF values (greater than 7.5), high probablity values (greater than 0.4), and a coefficient that was close to 0.
After removing a majority of the variables, this is my final OLS tables, showing which variables were kept and the final Jerque-Berc Statistic value and the Adjusted R-Squared value. I also provided my final map showing the dependent variable and these 11 variables.
http://students.uwf.edu/klc106/Internship/Portfolio_KCaporrino.pdf This is my GIS Portfolio. It's about 18 pages long and includes my...
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