GIS Portfolio

My GIS Portfolio!

Welcome to my digital portfolio! On my home page, you will find a lovely picture of myself, my name in bold, contact information, and links to my LinkedIn and Blogger accounts. A little further down, and you will find a short and sweet bio about my current status and aspirations. Afterwards, there are several sections set up to resemble a resume. All my educational experience is listed in chronological order, as well as my relevant experience to the GIS field. Next I have a few of my main skills listed that I acquired over time in the program. Last, I have a few various samples of projects I have completed over the course of this program. You will find them in the gallery, titled, with a link to each project blog.

Creating this portfolio was a blast, if not confusing and difficult at times. There is so much meticulous work behind every section, that hours could easily be spent behind each one. I really like this kind of format over using LinkedIn, which doesn't really give a person much creative room to express them self.

Final Project - Chaco Canyon: A Site Prediction Model

For my final project, I chose to create a predictive model for Chaco Canyon, located in the San Juan Basin of northwestern New Mexico. This region is characterized by its extensive Puebloan ruins and large Chaco Great Houses. The purpose of this study was to create a predictive model for potential site locations based off generated secondary surfaces from a Digital Elevation Model (DEM) of the study area. These secondary surfaces would then be used as input towards a Weighted Overlay model and an Ordinary Least Squares (OLS) linear regression analysis. These methods are intended to aid in identifying trends, relationships between variables, and possible missing variables for the current predictive model and perhaps future ones.

All analysis was conducted using Esri’s ArcMap program. Data used for the study was gathered from various USGS data download websites and consisted of a DEM, an aerial imagery raster, and a hydrography shapefile. A polygon boundary for the study area and point shapefiles for known site locations were created in ArcCatalog to be used in the study. The DEM, aerial raster, and hydrography data were all clipped to the study area boundary to reduce tool process time and produce results relevant to the study.

Slope was calculated first from the DEM using the Slope (Spatial Analyst) tool. In the Symbology tab of the slope raster’s Properties, the color scheme was set to display the Slope scheme for better visual understanding. Next, the slope raster’s categories were transformed into more meaningful divisions through the Classify button in the Symbology tab. The Classification was set to the Method of Defined Interval, with an Interval Size of 12. After symbolizing the slope raster appropriately, it was then reclassified using the Reclassify (Spatial Analyst) tool. In the dialog box that appears for the tool, the Old Values reflect the category changes I made previously for the raster and are left as is, since they represent the range of slope breaks for the data. Meanwhile, the New Values were changed to give a higher weight to the most desirable slope for habitation.The values were inversely weighted as follows: 3 = 0-12⁰, 2 = 13-24⁰, 1 = 25-36⁰, and 1 = 37-48⁰.

 The next secondary surface to be calculated from the DEM was aspect, which was created using the Aspect (Spatial Analyst) tool. The output aspect raster was also re-symbolized by setting its color scheme to the Aspect scheme in the Symbology tab of its Properties. Next, the Reclassify (Spatial Analyst) tool was used to reclassify the aspect raster into appropriate categories . The Old Values column for this raster display the numbers 0-360 divided into 11-12 categories, which represent the numeric directional facing of the compass. In the New Values column, this was simplified by breaking down the directional facing into three categories, with a higher weight assigned to the most desirable directional facing. The values were inversely weighted as follows: 1 = north, 2 = east/west, and 3 = south.

This area is relatively consistent in terms of elevation, there only being a maximum distance of 300 meters between the highest (~2100m) and lowest elevations (~1800m). In the Classification dialog box of the DEM, the Classification Method was set to Manual, and the Break Values were set at 100 meters apart, creating three categories. The DEM was then ready to be reclassified with the Reclassify (Spatial Analyst) tool . The New Values were then inversely weighted, with the most desirable elevation assigned with a higher weight. The values are as follows: 1 = 2000-2100 meters, 2 = 1900-2000 meters, 3 = 1800-1900 meters.

The last secondary surface to be calculated was hydrography. A 200-meter buffer was created around the streams of the clipped hydrography shapefile in preparation to be converted into a secondary surface. The previously created stream buffer of 200-meters was then converted into a raster using the Feature to Raster tool. The hydrography raster was then reclassified with the Reclassify (Spatial Analyst) tool in order to give the no data value (leftover area with no hydrography data) a new value that would be meaningful for the study . Those values are as follows: 3 = streams, 1 = no data.

Finally, all four of these secondary surfaces were ran through the Weighted Overlay tool in order to create a basic predictive model for potential site locations. In the weighted overlay tool dialog box, the weight of each variable was set to be calculated at: Aspect (40%), Elevation (30%), Hydrography (20%), and Slope (10%) . 

With all known sites accounted for, non-sites were needed in order to conduct the additional predictive model. Non-site data was generated using the Create Random Points tool. A sum of 150 points, with a spacing of 30 meters between all points, was created to compare the sites against. The random points were then merged with known points with the Merge tool and labeled OLS points .

Finally, the Ordinary Least Squares (OLS) (Spatial Statistics) tool was conducted on the OLS points . In the tool dialog box, the Unique ID field was set to the previously calculated ID field. The dependent variables were set to the secondary surfaces slope, elevation, aspect, and hydrography. 

Lastly, the Hotspot Analysis (Getis-OrdGi) (Spatial Statistics) tool was ran to determine if there were any cold (under-predicted) or hot spots (over-predicted) in the data .