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Investigating the Relationship between Neighborhood Socioeconomic Status and Proximity to Public Services

Description
With growing levels of income inequality in the United States, it remains as important as ever to ensure indispensable public services are readily available to all members of society. This paper investigates four forms of public services (schools, libraries, fire stations, and police stations), first by researching the background of…

With growing levels of income inequality in the United States, it remains as important as ever to ensure indispensable public services are readily available to all members of society. This paper investigates four forms of public services (schools, libraries, fire stations, and police stations), first by researching the background of these services and their relation to poverty, and then by conducting geospatial and regression analysis. The author uses Esri's ArcGIS Pro software to quantify the proximity to public services from urban American neighborhoods (census tracts in the cities of Phoenix and Chicago). Afterwards, the measures indicating proximity are compared to the socioeconomic statuses of neighborhoods using regression analysis. The results indicate that pure proximity to these four services is not necessarily correlated to socioeconomic status. While the paper does uncover some correlations, such as a relationship between school quality and socioeconomic status, the majority of the findings negate the author's hypothesis and show that, in Phoenix and Chicago, there is not much discrepancy between neighborhoods and the extent to which they are able to access vital government-funded services.
ContributorsNorbury, Adam Charles (Author) / Simon, Alan (Thesis director) / Simon, Phil (Committee member) / Department of Information Systems (Contributor) / Department of English (Contributor) / Department of Economics (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
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Developing a Model to Predict Match Outcomes in League of Legends

Description
League of Legends is a popular multiplayer online battle arena (MOBA) developed by Riot Games. Each team consists of 5 players who each control a single character (champion) which they select at the beginning of each game. In order to win the match, a team has to destroy the Nexus…

League of Legends is a popular multiplayer online battle arena (MOBA) developed by Riot Games. Each team consists of 5 players who each control a single character (champion) which they select at the beginning of each game. In order to win the match, a team has to destroy the Nexus (the central structure) in the opponent's base. League of Legends has grown rapidly since its release in 2009 and has over 70 million registered players. Several community websites have been created that track the performance of players and show detailed statistics for just about every aspect of the game. This project focuses on exploring the applicability of predictive analytics within League of Legends, by predicting the outcome of any given ranked match at the start of the game. It resulted in a model with accuracy of 58% using decision trees. An additional contribution of the project is a solution to a data collection anomaly that has biased previous studies.
ContributorsNeumann, Alexander (Author) / Clark, Joseph (Thesis director) / Simon, Alan (Committee member) / Department of Information Systems (Contributor) / Barrett, The Honors College (Contributor)
Created2015-12