Master of Science in Geographic Information Science and Technology

Series Type
Degree Series
Associated Organization(s)
Associated Organization(s)

Publication Search Results

Now showing 1 - 10 of 16
  • Item
    Site Suitability Analysis of Mobility Hubs: Determining Suitable Locations for Transit Center Enhancement in Metro Atlanta
    (Georgia Institute of Technology, 2023-08) Murali, Malavika
    Mobility hubs are an efficient and effective transportation solution that promotes connectivity and last-mile modal options for commuters and residents that integrate multi-modal transportation infrastructure. In addition to encouraging place-making efforts, mobility hubs transform cities with innovative approaches to seamless modal transitions and integrating smart technologies for wayfinding, safety, and accessibility. This study identified three existing transit stations within Fulton, Clayton, and DeKalb counties in Georgia that can be turned into regional shared mobility hubs through analyzing origin-destination data of these stations, the surrounding land uses, and the population demographics of these areas. These three counties were selected as they make up the existing Metropolitan Atlanta Regional Transportation Authority (MARTA) passenger rail network. Based on research on the benefits of mobility hubs, the factors of close distance, added connectivity, and proximity to activity centers are proven to improve the implementation of mobility hubs. Thus, the purpose of this study is to determine the potential for the tri-county area to implement mobility hubs at regional scales to expand the reach of alternative modes of transportation, and to address the issues of inaccessible transportation networks. After analyzing the transit stations using demographic data of the study area and a multi-criteria analysis (MCA), three locations were found to be ideal sites for developing into mobility hubs: the Midtown, Decatur, and Dome/GWCC/Philips/CNN MARTA stations.
  • Item
    Geographies of Fear: The Spatial Relationship between Human-Environement Interactions and Reported Sasquatch Sightings
    (Georgia Institute of Technology, 2022-07) Wells, K.P.
    This paper explores the potential spatial relationship(s) the physical landscape, cultural or demographical characteristics, and the incidence of reported Sasquatch sightings in the continental United States.
  • Item
    A Text-Mining and GIS Approach to Understanding Transit Customer Satisfaction
    (Georgia Institute of Technology, 2020-07-24) Yap, Soo Huey
    Performance evaluation is a concept that most can understand. Examples of performance evaluation include evaluating the performance of students in schools via assignments and exams, and corporations and boards evaluating departmental and corporation-wide performance. In many of these instances, the objectives of performance evaluation are clear. In our first example, the aim of schools may be the education of students, and therefore performance evaluation is conducted to measure students’ understanding and learning of the syllabi. In our second example, the aim of corporations may be to improve efficiency (reduce costs) and increase income. Performance measures used by private sector corporations may include number of sales, customer satisfaction ratings, and number of clicks on advertisements.
  • Item
    Covid-19 Vulnerability Index for United States Counties
    (Georgia Institute of Technology, 2020) Nair, Shruthy
    COVID-19 is a highly infective virus with a rapid transmission rate. It has led to a pandemic that has impacted millions of people all around the world. In the United States alone, over 3 million people have being directly affected by COVID-19 as they tested positive and millions more have been affected indirectly due to the virus. The purpose of this study is to determine if a COVID-19 Vulnerability Index can be created using GIS, that would enable one to identify high risk counties within the United States. A Vulnerability Index measures how vulnerable a population or region is to a particular illness. Multiple socio-economic, demographic, transportation and health related factors were utilized in the development of the Vulnerability Index. Principal Component Analysis were applied to analyze the distribution and correlation in the factors and create the index values. The COVID-19 case rates, death rates and the COVID-19 Vulnerability Index values were compared using spatial clustering and then their actual results were compared to see if the Vulnerability Index is a good measure for COVID-19 case rates and death rates. Results indicated that the COVID-19 Vulnerability Index is a good measure to identify counties that are at risk of increasing their case rate, but not death rates. Furthermore, ordinary least squares regression and spatial lag model were run to evaluate the effectivity of the COVID-19 Vulnerability Index in identifying counties with increasing risk of COVID-19 cases. The regression models indicated that the Vulnerability Index is a relatively good measure determining high risk counties.
  • Item
    Quantifying Impact of Weather Condition on Travel Time
    (Georgia Institute of Technology, 2020) Joshi, Sambhavi
    Most transportation systems operate at capacity. Minor changes in the system could result in congestion and delays. One of the many impacting factors of transportation is weather condition. Weather conditions might lead to a totally different setting for management of transportation systems. Since weather is predictable, being able to measure the impact of weather conditions on transportation systems would help in better transportation management. Estimating dependency of travel time on weather condition will enable us to predict more accurate travel time. But it is possible that not all components of weather impact travel time equally. There are several other factors associated with travel time that interact with weather conditions to affect travel time. Other questions raising from this are: 1. Which weather component impacts travel time the most? 2. Is the impact of weather on travel time a function of time? The exercise investigates regression models to understand the effect of weather condition, accidents, and time on travel duration. Based on the identified factors parametric and non-parametric classifiers are implemented to provide class-based predictions. Lastly, the machine learning models are the rated based on accuracy, precision, recall, and Cohen Kappa score, and envisioned for various use cases.
  • Item
    Modeling Transit Dependency Index and the Analysis on the Intersecting Transit-Dependent Groups: A Spatial Microsimulation Approach
    (Georgia Institute of Technology, 2018-12) Pang, Jian
    This research is primarily focused on building a methodology framework to model a Composite Transit Dependency Index (CTDI) that incorporates various Transit-Dependent groups. The application of Spatial Microsimulation in this research helps better identify intersecting demographic groups that contribute to the overall Transit Dependency of an area. By performing Multivariate Linear Regression, the TDIs are also found to be able to predict the number of outbound trips of a census tract to some level of extent. And the results of the regression can be used into forming the Composite Transit Dependency Index.
  • Item
    What statistical and spatial relationships exist between health insurance, race, income, and education in the state of Georgia immediately before and after the implementation of the Affordable Care Act?
    (Georgia Institute of Technology, 2018-08) Walker, Evan
    What statistical and spatial relationships exist between health insurance, race, income, and education in the state of Georgia immediately before and after the implementation of the Affordable Care Act?”. To answer this question, two datasets were used. They were both five-year estimates from the American Community sSurvey. The first range was for 2009-2013, and the second was an estimate from 2012-2016. The data obtained was for the 1959 census tracts in the state of Georgia. These years were chosen because the ACA was implemented in 2014, therefore the first dataset would not be affected by the ACA and the second would what largely be after its implementation. This study combined both linear statistical analysis as well as spatial statistical analysis. The variables chosen were income, race, education level, and health insurance. More specifically: average income for each tract, percent non-white/minority population, percent of individuals over 25 years-old with less than a high school diploma or GED equivalent, and the percentage of the population that in uninsured. These were chosen because I felt that they are all suitable metrics for examining these complex socio-economic factors. In the linear regression analysis health insurance was the dependent variable (DV) in all the regressions. For each dataset several combinations of the independent variables (IV) were used, in addition the difference between variables in the two time periods was regressed, and finally a logistic regression was performed on the differences between the two time periods. Unfortunately, the regression produced very little correlation amongst any of the variables. (This will be discussed more thoroughly in the results section). The next part of the analysis was the spatial analysis for each variable a get-is Ord hotspot analysis was performed, a Moran’s I test for spatial autocorrelation, and then individual choropleths were generated for each variable as well.
  • Item
    Highways, Urban renewal, and patterns in the Built Environment: Exploring Impacts on Atlanta Neighborhoods
    (Georgia Institute of Technology, 2018-08) Leonard, Matthew
    During the mid-twentieth century, cities across the United States underwent drastic changes known broadly at the time as “urban renewal.” In many cases, these changes included widespread demolition of varied neighborhoods in the established urban core to make way for uses deemed more appropriate, such as Interstate highways, public housing projects, and other large-scale public developments or private developments with public backing. Atlanta, Georgia serves as a prime example of this trend, as large swathes abutting its historic downtown were leveled in the 1950s and 1960s for construction of Interstate 75-85 (the Downtown Connector), Interstate 20, and Atlanta Stadium (later known as Atlanta-Fulton County Stadium and subsequently demolished). Significant additional parts of Atlanta’s inner city were similarly cleared later in the twentieth century for construction of landmarks such as Freedom Parkway, the Georgia World Congress Center, Turner Field, and various other projects. Such changes obviously had a profound disruptive impact on neighborhoods that existed previously.
  • Item
    Calculating Change in Regional Accessibility Due to Autonomous Vehicles
    (Georgia Institute of Technology, 2018-08) Anand, Spandana
    The following project tries to answer the question “How will autonomous vehicles affect growth in the Metro Atlanta region?” We attempt to do this through calculating how accessibility will change based on traffic conditions. We also determine how it compares to the predicted population/employment growth by the Atlanta Regional Commission and the kind of land use patterns that are present in those regions.
  • Item
    Economic Shifts Along the US-Mexico Border: Investigating the Changes in Location Quotient at the Block Level in Four US Border
    (Georgia Institute of Technology, 2018-08) Cunningham, James
    US border cities are often considered “city-pairs” with coinciding Mexican industrial cities. Current literature suggests that the export economy of these Mexican cities increases employment in US border cities for the transport/warehousing, retail trade, and manufacturing sectors from the years 1976 to 2006. Focusing on Douglas, AZ, Nogales, AZ, Calexico, CA and San Diego, CA, this study uses LODES WAC census block level data and a location quotient analysis to (1) determine if these three industries have continued to grow from 2004 to 2015 using summary statistics, still maps, and animated maps (2) determine if these shifts are related to US/MX border proximity using regression techniques. It was found that the location quotient for manufacturing decreased in all cities but San Diego, with location quotient values being strongly related to border proximity. Similarly, all cities but San Diego showed a decrease in retail trade location quotient, although this trend was not always related to border proximity. California border cities showed a decrease, but Arizona cities showed a continued increase in transport/warehousing location quotient with most cases related to border proximity. These results suggest that while spillover effects continue to exist in these US/MX city pairs, they are largely concentrated in the transport/warehousing sector, with the maturation and continued development of Mexican industrial cities likely leading to less manufacturing needs in US border cities across the study period.