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 . 

Biscayne Shipwrecks - Analyze Week

   In this week's lab, analysis was conducted on the benthic and bathymetric data from last week. Buffers and clipping was used to determine what type of benthic features could be found within 300 meters of the Heritage Trail shipwreck sites. The benthic and bathymetric data was further analyzed through reclassification in order to determine areas that could be potentially dangerous for ships to travel through. Finally, this reclassified data was ran through a weighted overlay analysis, combining both inputs together to create an output that displays potential areas for shipwreck locations.

300 meter buffer displaying benthic features around each site

Reclassified data

Weighted Overlay Model output

Supervised Classification

In this lab, a supervised classification of current land use in Germantown, Maryland was conducted using ERDAS Imagine. AOI signatures were selected by hand using the polygon tool and recorded in the Signature Editor dialog box. All signatures were analyzed through the Mean Plot tool to determine which bands provided the greatest difference between signatures. The bands 3, 4, and 5 provided the greatest difference in signatures and was set as the band combination to reflect the data best. The signatures were then run through the Supervised Classification tool, with an additional Distance File output to show if any signature features are likely to have the wrong classification (symbolized as bright spots). The Distance File is used as a reference for correcting any wrongly classified signatures. Lastly, the supervised image is recoded by consolidating the signatures to eight classes. Those classes are agriculture, deciduous forest, fallow field, grasses, mixed forest, roads, urban/residential, and water. The final output map was created through ArcMap.

Modeling Biscayne Shipwrecks - Prepare Week

   This week, we gathered data on shipwrecks in the Biscayne National Park. This data will be used to generate a weighted overlay model. Data gathered includes a historical nautical chart from 1892, a current ENC, and bathymetric data for the Biscayne Bay. The historical chart was downloaded from the NOAA's Historical Map and Chart Collection and georeferenced to the location. The bathymetric data was downloaded from the NOAA's National Geophysical Data Center and symbolized to reflect depth in meters (shallow in red, deep in blue).

Unsupervised Classification

   In this lab, an unsupervised classification was performed on an aerial image of the UWF campus, in ERDAS Imagine, using the Unsupervised Classification tool in the Raster tab. Afterward, the results of the unsupervised classification was further reclassified by condensing the original output of fifty color categories into just five color categories. These five categories are grass, trees, shadows, roads/buildings, and mixed surfaces. Mixed surfaces is classified as pixels that can be found across multiple surface types and can not be pinpointed to just one category.

   The total area of the campus is 232.26 hectares. Of that total, 142.735 ha (61%) was classified as permeable while 89.5237 ha (39%) was classified as impermeable. Permeable surfaces consisted of the categories grass, trees, and shadows. While some shadows covered impermeable surfaces, the majority covered permeable surfaces. Impermeable surfaces consisted of roads/buildings and mixed. While some mixed surfaces covered permeable areas, the majority covered impermeable surfaces.

Thermal Imagery

The feature I identified for this lab was a large tract of bare soil located at the southern tip of the city, surrounded predominantly by urban area and some vegetation directly to the south of it. I was looking over the stretched symbology (Band 6) of the image in ArcMap when I saw a bright spot in that area, surrounded by grey (urban area) and a darker spot just below it (which looks like vegetation in the natural color image). Further analysis and comparison between the two images (natural color and thermal) determined this was bare soil. I chose to use the band combination Red- 6, Green- 3, Blue- 2. This band combination is used to distinguish between different soils and soil moisture content. The 6, 3, 2 band combination made soils appear in light to dark reds, starkly contrasting it with surrounding colors.