Kathleen Ski

My Geography/GIS Experience at OSU

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      I completed the Spatial Analysis Methods (SAM) program within the in Geography Major at OSU, which specializes in the use of Geographic Information Systems. My program included a total of 60 quarter hours. My final GPA was 3.97 within the SAM/GIS major. To view all my classes and grades click here. Here is the complete listing of my Geography-SAM/GIS classes:

  • Geog 210: Physical Geography and Environmental Issues
  • Geog 580: Elements of Cartography                                      Special Project
  • Geog 630: Conservation of Natural Resources                     Report
  • Geog 680: Numerical Cartography                                         Maps
  • Geog 683: Introduction to Geographic Analysis
  • Geog 685: Intermediate GIS                                                    Special Project
  • Geog 687: Design & Implementation of GIS                          Special Project
  • Geog 689: Student Internship Program in Geography          At BPRC
  • Geog 689: Student Internship Program in Geography          At CRP
  • Geog 693: Individual Studies - Cartograms                           Cartogram Lab
  • Geog 693: Individual Studies - Natural Resources                Soil Science Lab
  • Geog 693: Individual Studies - New Visualizations                Maps
  • Geog 787: Advanced Applications in GIS: Web-GIS            Special Project
  • CIS 201: Elementary Computer Programming - Java
  • CIS 230: Intro to C++ Programming
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Class Details, Special Projects and Accomplishments

Special Project for Geog 580: Elements of Cartography
      The students in this class, taught by Dr. Ola Ahlqvist, covered the basics of cartography, including map projections, symbolization, color, media, working with digital elevation models, isolines, geodata exploration, drawing setup and creation of shape files (points,line, polygons). Creating maps maps from scratch was the big challenge and the Autrailia map at the right was my first map without using existing shape files. For our final project, we were required to find a client and work with this client to develop a map. This map had to meet needs and specifications supplied by the client. I was also taking an environmental science class in the department of Natural Resources at the time, so I began seeking a client that would allow me to combine my mapping project in an interesting way with environmental science. I learned that Dr. Brian Slater taught GIS to students in the Natural Resources department and introduced myself. I offered to use my GIS skills to develop a map, and he took me up on my offer. The resulting map project for soil science students was designed to show an example of the map elements required in the final results of their lab class. The soil science lab map was a success.
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Research Paper for Geog 630: Conservation of Natural Resources  
      This class was assigned a group term paper on a topic selected by our instructor Dr. Joel Wainwright. I took on the leadership role for the team and consolidated the other students research into our final report on Global Warming. This report includes two maps that I created displaying CO2 data by country. An excerpt of the report that represents only my portion is available for download by clicking on the map to the right. Other topics covered in this class included colonialism, biodiversity and genetic modification of food, development and globalization, the WTO and the IMF, urbanization and energy consumption, the oceans, coral reefs and wetlands.
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Maps for Geog 680: Numerical Cartography  
      While this class, taught by Dr. Ola Ahlqvist, did not have a final project or research paper, it did leave more time to explore a great number of mapping techniques. Our topics included visualizations using dot density, proportional symbols, bivariate and multivariate, cartograms, dasymetric, isarithmic, interpolation, Kriging, uncertainty and animation. Each pair of maps below represents two different ways of visualizing the same data. Click on any map to view the larger image.
Percent Female combined with Income
Maps 1 and 2 are multivariate visualizations showing how two different variables combine together using a complementary color scheme. Map 1 is relatively simple to construct because it simply lays two semi-transparent maps over top of each other. Map 2 is created by manual classification and produces a map with a legend that is easier to match to the map.
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Map 1
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Map 2
Population Density
Map 3 and 4 are visualizations showing population density. Map 3 is a standard choropleth map with five categories at the census block group level. Map 4 uses a dasymetric technique, combining land use data with the census data of the population. It produces a more realistic population density map, because it places the population where it is most likely to be found.
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Map 3
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Map 4
Income Distribution
Map 5 and 6 are visualizations showing income distribution. Map 5 is a standard choropleth map. The median family income classified into five categories. Map 6 is created using a surfacing technique called ordinary Kriging. This process produces an estimated value over the surface area. It is helpful when additional analysis with another surface variable is going to be performed.
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Map 5
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Map 6

Special Project for Geog 685: Intermediate GIS
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Bivariate Analysis Raster Overlay                                      Combined Raster Analysis Focus on the OSU Campus Area

      The Intermediate GIS class, taught by Dr. Darla Munroe is designed to explore spatial analysis methods. It provided in depth problem solving assignments using the ArcMAP spatial analysis modeling techniques. The class project was a group assignment. My team explored the relationship between income and grocery pricing in Franklin County, OH. We developed a standard student grocery shopping list. Over a 4-day period the five team members surveyed 45 grocery stores for pricing of the selected items, capturing both the regular price and discounted price. I collated all of the data from the surveys and geocoded the site locations. I supplied each team member with the point data and attached prices. Each team member was required to develop his or her own visualization of the results. The three maps above are the visualizations I prepared for our report using the spatial analysis modeling methods emphasized in our class work. The results of this analysis indicated that the low-income areas of the county were experiencing higher grocery prices than the higher income areas. Our full report titled "Grocery Gouging of Students (and Other Vulnerable Populations)" is available for download by clicking on the picture to the right.
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Special Project for Geog 687: Design & Implementation of GIS pitsmall.jpg
I-70 Collapse
      This class focused on relational databases, Universal Modeling Language, ArcObject programming and project management for software development. Our instructor Dr. Ningchuan Xiao broke the class up into two software development teams, assigning each team a client. My team was assigned to work with Jim McDonald of the Ohio Department of Natural Resources, (ODNR) Geological Survey Division. Our class project was to use everything we focused on in class to develop a custom software solution for the ODNR.
      Jim McDonald did a presentation to the class, which filled us in on a bit of Ohio history that clearly showed why the ODNR need our help. On March 5, 1995, a 10 ft deep sinkhole opened on Interstate 70 near Cambridge, OH. Several cars and a truck partly entered the sinkhole and others managed to miss it by swerving off the road. I-70 was closed for months to repair the sinkhole, which was caused by the collapse of an abandoned underground mine. Avoiding Voids explains more about the hazards posed by abandoned underground mines. The Mine Subsidence report from ODNR contains more information on this issue. DataMiningInterface.jpg
Data Mining Interface
     The ODNR Geological Survey Division has been mapping coal bed seams and coalmine locations from historical data. What they needed was a tool to explore the data they had accumulated. By taking into account mine depth and comparing overlaid bedrock thickness and sediment thickness, they hoped to develop a means to predict locations at high risk for subsidence. This was the tool we were to develop for the ODNR. We completed:
  • The project plan
  • The needs assessment
  • The design specifications
  • A UML model for proposed software
  • The custom software application
  • The software documentation manual and tutorial
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Data Mining Results
     I took on the leadership role for the software development phase of the project for the team because I had the most programming experience among the team members. I assigned a section of the coding to each team member, while I took on the actual data calculations and result reporting section of the code. I consolidated all of the Visual Basic code from the other team members into our final product. I also wrote the most of the software manual and tutorial. The project was a big success! Working as a team on a complete software development project for a client was a wonderful learning experience. My training in geology classes provided me the under lying science I needed to fully understand the issues and terminology need to perform this job. The bonus of this project is that this application helps Ohio avoid potential hazards of mine subsidence and may even save lives.
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Data Mining Report
Special Project for Geog 787: Advanced Applications in GIS: Web-GIS COpp.jpg
Geography of Opportunity: Austin
     This class taught by Dr. Ningchuan Xiao concentrated on Web-GIS, using open source tools for creating maps accessible on the internet. Throughout the class we learned to use variety of Web-GIS software tools: Google Maps, Google Earth, uDig, Geoserver and MapServer. We also explored a wide range of other GIS tools such as NASA World Wind, Yahoo Maps and Yahoo Pipes. In addition, we discussed topics including search engines in respect to locations, Web-GIS in City Planning and the Semantic Web.
      We broke ourselves down into small groups and created a proposal and development schedule for our final project. We proposed to create an online resource, which would feature the Geography of Opportunity: Austin Region from the work being done by the Kirwan Institute for the Study of Race and Ethnicity at OSU. My partner for the project, Samir Gambhir, works for the Kirwan Institute and participated in this research project. The Opportunity Model research is an ongoing project for the institute. So far, they have completed this research for:
  • Austin, TX
  • Atlanta, GA
  • Baltimore, MD
  • Boston, MA
  • Columbus, OH
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Opportunity Interface
      Our goal was to create a prototype of the work for one city, which could then be expanded to include the research results of four other cities. We chose to start with the data from the Austin, TX region. This work is important because it aids city planners in identifying regions of low opportunity. They can then target these areas with specific development programs to help the population find greater opportunities, making their lives better. Housing.jpg
Neighborhood Quality with Highways
      We combined our Web-GIS knowledge and training with the latest AJAX programming style to create interactive web maps for the project. We developed the web site using HTML, XML, PHP, CSS and JavaScript languages in combination with MapServer, an open source GIS program. In addition, we found the uDig tool for visualization helpful in selecting color schemes for each of the maps. ArcMap was used to for editing the shape files, building the layers and linking the data to the maps included in the design.  
      Each map features a reference map, a population density map, zoom and pan controls, browse or query options and five layers: The Opportunity map, Highways, Roads, Lakes and Rivers. Both the roads and rivers layers are scale dependent, displaying only when they will not completely crowd the other feature layers. A selection box is provided for switching between any of the following opportunity maps:
  • Economic Opportunity
  • Educational Opportunity
  • Health Opportunity
  • Mobility Opportunity
  • Neighborhood Quality Opportunity
  • Comprehensive Opportunity
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Scale dependent layers active
      The query map feature allows users to get additional demographic data about any census tract by simply clicking on it. The query result displays the following additional data:
  • Z-score for the Opportunity variable
  • Total Population
  • Whites
  • Hispanics
  • African Americans
  • Seniors (65+)
  • Children
  • Child Poverty
  • Minority Children
  • Low Income Households
  • Middle Income Households
  • High Income Households
  • Comparative Analysis
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Map Query Results Interface
      I have to say that this is one of the best classes I have ever taken. Dr. Xiao provided us with a very comprehensive course. I learned so many new and useful skills. Our project demonstration was extremely well received by both our class and members of the Kirwan Institute. Since we developed this as a prototype, we hope that this project will continue to grow and include the Opportunity Model results of the other cities that have been completed to date. To compare all of the projects completed by the students in this class click here.
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Comparative Analysis

Geog 693: Individual Studies - Cartograms CartogramDiversitys.jpg
Diversity Cartogram

      I was intrigued with cartogram visualizations I saw in my Geog 580 Cartography class; however, we did not get a chance to create a cartogram map. I approached Dr. Ola Ahlqvist and proposed an independent study class on this topic. We settled on developing a cartogram lab for the Geog 580 students. I researched the design methods for cartogram creation and searched for a software tool for creating cartograms. Dr. Frank Hardisty, formally of OSU and now at the e-Education Institute and GeoVISTA Center at Penn State developed a cartogram program. Using both ArcMAP and the Hardisty cartogram software, I designed a lab project for the for Cartography students, which explores both continuous and non-continuous cartograms. To download the lab instructions and shape file, click here (file size is about 6 mg).
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Geog 693: Individual Studies - Soil Science GIS Analysis in Natural Resources NBassMap.jpg
      Having worked with Dr. Brian Slater in the Department of Natural Resources on a project for my Cartography class, I approached him about an independent studies project. Since the students in his advanced soil science lab had difficulty completing the GIS Analysis Lab, he asked me to rewrite the instructions for the upcoming Fall quarter class, updating it for the new version of ArcGIS. As I had already made the example map (at the right), I was familiar with the process. I revised the lab instructions and served as an assistant for the lab class the first time it was used in the Fall quarter of 2006. It was a big success! Each student completed 3 maps during the lab. The students had a chance to see what a valuable tool GIS can be for environmental problem solving.
      Since the students in the advanced class did such a great job with this lab, Dr Slater and I discussed getting his introductory soil science class introduced to online GIS tools available for analysis. I revised a lab project featuring the Web Resources for Soil Analysis and Land Use Planning. The students viewed online maps available and learn the use of the USDA Natural Resources Conservation Service application - Web Soil Survey for land use planning. The lab also provides an ArcMap example of how this problem could be approached using GIS software. Dr. Slater encourages students to take a minor in GIS to complement their natural resources program, especially if they plan to continue into a masters program.
     Since both of these labs are lengthy, they have not been posted to this web site. However if you would like a copy of one or both, just drop me an email (see the contact me page).
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Geog 693: Individual Studies - Exploring New Visualizations  
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   Male Low Income                                        Female Low Income
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Low Income Dot

      I worked with Dr. Ola Ahlqvist on this independent studies project. The purpose of this class was to find a better visualization of low- income distribution. A standard choropleth map shown at the right is a poor representation because it leads one to think that there are no low-income earners anywhere in the county except in the census tracts colored light yellow. Theses tracts of the lowest 20% (quintile classification) indicate a median household income range of $6136-$28431. In addition, this map does not provide us with any idea of how many households there are in these areas. In reality, the largest tracts is the airport and the second largest tract is the university campus both of whom have little population.
     Compare the Household Income map at the right with the Low Income Dot map above it. The dot density technique uses a color scheme of dots (from red to green) for the range of household income and it focuses specifically on the low-income range. This technique overcomes the issues of the location of the low-income earners by showing them even in high-income areas. It also provides greater clarity of the income level using the dot color scheme. It overcomes the issue of the unpoplualted areas around the airport and university locations. Moreover, it provides a feel for how many low-income earners there are.
     The Male and Female Low Income maps, which show only income below the $30,000 level, provide even a greater resolution of the low-income distribution. In comparison, the income gap between male and female wage earners is evident. These three dot maps work best when produced for a large format printer (higher resolution large format maps are available just send me an email using the contact me link at the bottom of the page). This same technique can be used to look at poverty levels. It would provide an even better understanding of the location of people living in poverty, how many are in poverty and the depth of poverty. This technique reduces the uncertainty found in a standard choropleth map and produces a better visualization. Dr. Ahlqvist and I are working on a paper for publication discussing this technique.
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Household Income
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