GIS4930 Project 3 - Statistical Analysis with ArcGIS (Analyze)

The examination of various socio-economic factors in facilitating the ability to predict methamphetamine lab locations began with the Ordinary Least Squares (OLS) regression to help assure the creation of a good model. Initially all independent (explanatory) variables under consideration in this study were applied to the dependent variable (meth lab locations) as a group. The results were examined for redundancy, statistical significance, and a correlation between the independent and dependent variables. Following this critique of the results, the variable was evaluated for consideration of inclusion based on considered importance to the study. The goal of the OLS regression was a return of a high Adjusted R-Square value. Independent variables were removed individually in an effort to maximize the Adjusted R-Square value based on a critique of the results. This was followed by another run of the OLS tool. This process was reiterated numerous times until satisfactory results were achieved. A table of the final OLS results follows:

Final OLS Results Table

To inspect for bias, the OLS regression's Jarque-Bera Statistic Score was checked along with the histograms of a scatter plot matrix. Finally, the Standard Residual values for each census tract were added to the map for visual critique. Most of the two-county area was predicted accurately with this model as shown in the map below. Additional work would include resolving the high and low residuals.

Meth Lab Locations and Standard Residual Values
Charleston, West Virginia, with Kanawha and Putnam Counties

GIS4930 Project 3 - Statistical Analysis with ArcGIS (Prep)

Statistical Analysis is the focus of the third project in Special Topics. This particular study will analyze a variety of socio-economic factors to determine whether there is a connection to the location of illicit methamphetamine production labs in Kanawha and Putnam Counties, West Virginia. With 2013 being a record-setting year for meth lab busts in West Virginia (Eyre), law enforcement officials will appreciate having additional information that may help locate existing meth labs or prevent additional ones from starting business.

Preparation for the statistical analysis included creating new attribute fields for the census data and then using the field calculator to determine percentages of some data. Utilizing percentages rather than raw numbers for the census tracts will provide more equalized comparison of data. Python scripting was also used to add additional attribute fields and perform related calculations in a more efficient manner. Drug Enforcement Administration (DEA) meth lab bust information with geocoded data was joined to the Census data as well. Tidying up the attribute table was done by turning off fields which wouldn't be used for the statistical analysis. The following basemap of the study area was created. It includes cities and towns which have had meth lab busts in the past.

Socio-Economic Factors and Meth Lab Locations

Study Area: Kanawha and Putnam Counties, WV

Resource:
Eyre, E. (2014, February 19). W.Va. Senate OKs anti-meth prescription bill. Charleston Gazette. Retrieved from http://wvpress.org/news/w-va-senate-oks-anti-meth-prescription-bill/

GIS4035 Module 3 - Land Use / Land Cover Classification Mapping

Land Use / Land Cover Classification
(Pascagoula, Mississippi)

Creating a classified land use and land cover (LULC) map from an aerial image was the goal for this lab. After a polygon feature shapefile was created and additional fields were added to the attribute table for LULC code and description, Editor was used to create features by digitizing polygons for different categories of land use and land cover. Vertex and edge snapping were used to digitize adjacent polygons, but eventually some issues involving the Snapping Toolbar were encountered that could not be resolved promptly. The ability to clip out polygons from within other polygons is a skill that will be handy for future projects. The identify tool was very helpful while clipping out polygons.

Although time-consuming, especially because of the scale and zoom level I selected (it seemed like a good choice initially), this was a very enjoyable lab to complete.

GIS4102 Module 3: Python Fundamentals Part 1

After last week's experience with scripting, this lab assignment was a breeze. Practicing within the exercise part of the module helped prepare for the assignment which involved starting with a string of my full name, splitting it into a list of names prior to printing the last name, and then determining the length of the last name. Mine is 13 ~ did anyone top that? Finally the length of the last name was multiplied by three. Trying to remember the functions and methods took some effort, but the book and instructions provided handy reference. I was most nervous about making sure that my name was spelled right, since it would be out there for all to see:

Results: Length of Last Name Times 3

The scripting was so fascinating that I ended up trying it out with the names of several friends and family members. I can tell that this is going to be a lot of fun... 

GIS4048 Module 3: Natural Hazards - Tsunami

The March 2011 tsunami that devastated Japan served as a reminder of the importance of planning for natural and nuclear disasters. GIS provides a comprehensive system for producing maps and graphics to aid in such natural and nuclear disaster planning. This lab assignment tasked students with mapping the evacuation zones around the Fukushima Nuclear Power Point as well as determining the at-risk populations within each of those zones. The lab also included determining evacuation zones based on actual measurements of tsunami "run up" (observed maximum water height above a reference sea level) and identifying at-risk cities, roads, and nuclear power plants in those zones. This information was consolidated in one map:

Evacuation Zones in Vicinity of Fukushima II Nuclear Power Plant

Production of the map began with creating a file geodatabase to keep data organized. Feature datasets were created in the file geodatabase and then populated with imported shapefiles. All of the feature datasets were assigned the same projected coordinate system (WGS 1984 UTM 54N). One Excel file was converted and added to the geodatabase. As it was for several other students, creating raster datasets and mosaicking the DEMs caused plenty of issues. Many thanks go out to Brandon and Aaron for their very helpful discussion board posts which resulted in successful completion of that part of the lab. All of this organized data then was used to complete the second portion of the lab.

The second portion of the lab included creation of another file geodatabase and data creation using selection by location, buffering and clipping, extracting by mask, creating a graph (to determine breakpoints for classes of data), and model building. The model built was very involved and awesome to see in action. The flexibility and ease with which changes could be made to the model were appreciated. Another new technique learned in this lab was how to build label expressions. While many of the processes (especially those used to determine the run-up evacuation zones) are not among those that I use often, with continued practice I expect that they will be easier to use and troubleshoot should the need arise.

Finally, the information was synthesized into one map showing the evacuation zones around the Fukushima Nuclear Power Plant and the evacuation zones for tsunami run-up. This was a lot of information to pack into a single map, but, as the saying goes, "A picture is worth a thousand words". In all honesty, now I am feeling a bit vulnerable about living 75 feet above sea level.