Saturday, November 28, 2015

Open Source tools and Food Deserts

What a week. Happy after Thanksgiving to everyone. This week was about open source GIS tools. I learned about TileMill and Leaflet. Great tools for building web maps. I had a challenge to get all the tools on Leaflet to work properly for me. Tools such as the Layer Control or Geocoder. Leaflet is an html based program that allows you to upload maps using Tiles (Tilemill) and customize them by essentially copying the html code. However, even individuals that have extensive experience with code struggle with getting the correct, code, spacing, punctuation, and the case of letters. Needless to say, this week was a challenge. Given more time and coffee, I am sure I will enjoy using both TileMill and Leaflet in the future.

The objective this week was to develop a map showing Food Desert locations surrounding Pensacola Florida. Below is the link to my Food Desert map. It is a work still under construction. Please check back later to see what features and tools I have added.

Tuesday, November 24, 2015

GIS Day - Brevard County

I have been conducting my internship with Brevard County Survey & Mapping Dept. In past years, the Brevard County Commissioners have passed a decree recognizing GIS Day (here's a link to the 2012 GIS Day Proclamation). So far this year, I have not seen nor heard that a decree was passed. So, with no official activities for GIS Day at Brevard County Government Center, I spent my day discussing how to compute Geometry and input coordinates on my new "Speed Hump" map. I had been working on a project for the Transportation Dept mapping all the Speed Humps in Brevard County - all 459 of them...and Iwas at the point of conclusion so I was working on completing my map. It is a good feeling to actually complete a project for a "customer."

Friday, November 20, 2015

Open Source GIS: Food Deserts

This week we are working with an open source GIS tool called QGIS (Quantum GIS). Open source programs are a great way to work as a community toward a common goal. This week we are preparing data to compile a Food Desert map of Southern Escambia county. The final project will be to create a Food Desert map of an area of my choice. A food desert is defined as an urban area with a grocery store or fresh food source, that is greater than 1 mile away and in a rural area, a grocery store located greater than 10 miles away. I took the data from Escambia County and the US Census data for this area and applied a layer for grocery stores. Below are the Food Desert maps I produced. 

The first QGIS map shows Escambia County in relation to all Counties in Florida. 

  The second map created in QGIS shows the relative  location of Escambia County and the Study Area in the inset, and the two data frames with the study area that depict the Food Desert and Food Oasis locations:

Tuesday, November 10, 2015

Supervised Classification: Germantown, MD

What a week it has been. In Photo Interpretation, we continued learning about classification of aerial images. This week was Supervised Classification. Using ERDAS Imagine, and a raster image of Germantown, MD, I created my own Spectral Signature and Supervised Classification. I used the AOI Seed tool (Drawing tab, Insert Geometry group, press the Grow dropdown arrow, then click Growing Properties) to designate eight areas (pixels) on the image that were of a particular type of land cover, including:

Deciduous Forest
Mixed Forest (Deciduous/Conifer)  
Fallow Field

I used Mean Plots and Histograms to evaluate where there was Spectral Confusion. More accurately, I was looking for areas where the Mean Plot lines did not converge or which bands had the most separation. I discovered that my Plots had the most spectral separation in Bands 1 -2- and 3. Then I used the  Maximum Likelihood tool to classify my image after setting color to "Approximate true color."   

The result of my work including a Distance Map is shown below.

Thursday, November 5, 2015

Statistical Analysis:Ordinary Least Square – Meth Lab Indicators

This week we continue to work on our project of Statistical Analysis. I used ArcMap and the Spatial Statistics tools available to me: ArcToolbox, > Spatial Statistics Tools> Modeling Spatial Relationships> Ordinary Least Squares. Regression analysis is the most commonly used statistic in the social sciences, hence I used the OLS tool for this study. In this lab exercise I am examining the relationship between the existence of Meth Labs (Meth Lab Density), my dependent variable, and certain socio-economic factors. I ran the Ordinary Least Square tool in ArcMap more than 22 times to remove extraneous variables from the 2010 US Census data so that I could finally arrive at the six explanatory (independent variables) that I will use in my study report.

My explanatory variables are listed below:

The objective of running the OLS tool so many times was to create a valid model that would aid in the prediction of the presence of Meth Labs. This involved examining a variety of statistics, listed above, in a methodical way to evaluate six separate Checks:

     1. Are your independent variables helping or hurting?

     2. Are the relationships what I expected?

     3. Are there redundant explanatory variables?

     4. Is your model biased?

     5. Are any important independent variables missing?

     6. How well is the model predicting the dependent variable?

After reviewing each "Check" above and after sifting through approximately 70 US Census attributes, I arrived at my destination with data that still needs to be evaluated. But, I can say at this point that I have six explanatory variables that show some relationship to the prediction of Meth Lab Density.

Monday, November 2, 2015

Unsupervised Classification

This week I learned about the two ways to classify pixels on an image: Supervised and Unsupervised. For a Supervised classification, I would identify "training pixels" to use to identify the rest of the features on the image I was working with. For Unsupervised classification, the pixels are simply divided into a number of classes depending on the number you specify. The tools to accomplish this in ArcMap are: Iso Cluster Tool and the Maximum Likelihood Classification tool
We also used ERDAS Imagine to classify pixels. In ERDAS, the classification process was more tedious but generally worked the same. In the Raster tab, under the Classification group, click the Unsupervised button, then select Unsupervised Classification.

I performed an Unsupervised Classification using an aerial of the UWF Campus. I made five classes, named them and designated colors for each class:

1. Grass – green  
2. Trees – dark green 
3. Buildings/Road – grey 
4. Shadows – black 
5. Mixed - orange

The process was long but I am happy with the results. Additionally, I created another column or field for "Area" and then I totaled the area as "Permeable" or "Impermeable" and listed the percentages of each on my final map.