Geography Department Theses and Dissertations

Permanent URI for this collection

https://hdl.handle.net/1805/681

About the MS GIS Program

Rapid growth has occurred in the field of geographic information during the last two decades. Stimulated by advances in the collection, storage and analysis of data, a new discipline has emerged - Geographic Information Science. Geographic Information Science involves research both “on” and “with” spatial technologies including geographic information systems (GIS), remote sensing, and the global positioning system (GPS). At the core of Geographic Information Science is the integration of these technologies and their application to problems of spatial analysis. The foundational theory and principles of Geographic Information Science are based in the discipline of Geography. However, virtually all fields (from engineering, to medicine, science, business, social sciences and humanities) are now embracing Geographic Information Science in both theoretical and applied research.

Program Objectives

The Masters of Science in Geographic Information Science (MS GIS) integrates a suite of core courses with internship and independent research experiences to provide advanced training in the field of Geographic Information Science and its applications. Students completing the MS GIS will:

develop, manage and analyze spatial data using field and laboratory techniques
engage the rapidly expanding body of literature devoted to spatial data analysis and the application of geographic technologies
apply geographic technologies creatively in real-world settings to answer questions about spatial patterns and processes

More on Masters in GIS requirements: http://www.iupui.edu/~geogdept/graduate/msgis.html

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Now showing 1 - 10 of 13

Augmenting Indiana's groundwater level monitoring network: optimal siting of additional wells to address spatial and categorical sampling gaps
(2014-11-21) Sperl, Benjamin J.; Banerjee, Aniruddha; Lulla, Vijay O.; Bein, Frederick L. (Frederick Louis), 1943-
Groundwater monitoring networks are subject to change by budgetary actions and stakeholder initiatives that result in wells being abandoned or added. A strategy for network design is presented that addresses the latter situation. It was developed in response to consensus in the state of Indiana that additional monitoring wells are needed to effectively characterize water availability in aquifer systems throughout the state. The strategic methodology has two primary objectives that guide decision making for new installations: (1) purposive sampling of a diversity of environmental variables having relevance to groundwater recharge, and (2) spatial optimization by means of maximizing geographic distances that separate monitoring wells. Design objectives are integrated in a discrete facility location model known as the p-median problem, and solved to optimality using a mathematical programming package.
Availability of Supermarkets in Marion County
(2010-07-20T15:32:53Z) Heintzelman, Asrah; Banerjee, Aniruddha; Wilson, Jeffrey S. (Jeffrey Scott), 1967-; Ottensmann, John R.
Concern over significant increase in obesity has prompted interdisciplinary research to address the physical food environment in various regions. Empirical studies analyze units of geography independently of each other in studying the impact of the built environment in the health of a region. However, we know that geographical spaces have neighbors and these adjacent areas should be considered in analytical analysis that attempt to determine the effects present. This research incorporates the first neighbor influences by developing a refined hierarchical regression model that takes spatial autocorrelation and associated problems into account, based on Relative Risk of corporate supermarkets, to identify clustering of corporate supermarkets in Marion County. Using block groups as the unit of analysis, 3 models are run respectively incorporating population effect, environment effect, and interaction effects: interaction between population and environmental variables.Lastly, based on network distance to corporate supermarkets as a cost matrix, this work provides a solution to increase supermarkets in an optimal way and reduce access issues associated with these facilities. Ten new sites are identified where policy should be directed towards subsidizing entry of corporate supermarkets. These new sites are over and above the existing block groups that house corporate supermarkets. This solution is implemented using TransCAD™
Comparing Methods for Estimation of Daytime Population in Downtown Indianapolis, Indiana
(2011-08-23) Bell, Karen Denise; Wilson, Jeffrey S. (Jeffrey Scott), 1967-; Banerjee, Aniruddha; Johnson, Daniel P. (Daniel Patrick), 1971-
This paper compares two new methods for estimating daytime population against two existing models within downtown Indianapolis in Marion County, Indiana. The two existing models consist of the 2009 USA Daytime Population model created by ESRI and the LandScan Global Population Project developed by the Oak Ridge National Laboratory. A parking study of downtown Indianapolis, as prepared by the City of Indianapolis, Division of Metropolitan Development, is the basis of the first new method of estimating daytime population. The second method is a direct count of the daytime population using a methodology previously developed. Additionally, these four population estimates will be compared when applied to a scenario involving a hypothetical toxic gas plume.
Comparison of Sky View Factor Estimates using Digital Surface Models
(2022-02) Adhikari, Bikalpa; Wilson, Jeffrey S.; Dwyer, Owen J.; Banerjee, Aniruddha; Thapa, Bhuwan
Better comprehension of the Urban Heat Island study requires information on the natural as well as built characteristics of the environment at high spatial resolution. Sky View Factor (SVF) has been distinguished as a significant parameter for Local Climate Zone (LCZ) classification based on environmental characteristics that influence the urban climate at finer spatial scales. The purpose of this thesis was to evaluate currently available data sources and methods for deriving continuous SVF estimates. The specific objectives were to summarize the characteristics of currently available digital surface models (DSMs) of the study region and to compare the results of using these models to estimate SVF with three different raster-based algorithms: Horizon Search Algorithm in R-programming (Doninck, 2018), Relief Visualization Toolbox (RVT) (Žiga et al., 2016), and the Urban Multi-scale Environmental Predictor (UMEP) plugin in QGIS (Lindberg, et al., 2018).
Exploring spatial allocation techniques for the placement of food pantries: Madison County, Indiana
(2017-02-01) Ashraf, Maria; Banerjee, Aniruddha; Wilson, Jeffery S.; Dwyer, Owen J.; Lulla, Vijay
The thesis highlights the effectiveness of using location allocation model to find the optimum location of food pantries such that it serves maximum food insecure households. Since most households do not have personal means of transportation, the model makes sure that the food pantries are accessible at a walkable distance from the households. To reduce the shortage of food that the food pantries often face, the pantries are located near the food rescue sites like grocery stores, restaurants and institutional canteens. In this way, extra edible food with myriad choices can be diverted to the food insecure population at a walkable distance . Reducing food loss and food insecurity helps us move towards a better , more sustainable future.
Exploring Spatial Optimization Techniques for the Placement of Flow Monitors Utilized in RDII Studies
(2010-08-31T14:31:55Z) Skehan, Christopher A.; Banerjee, Aniruddha; Johnson, Daniel P. (Daniel Patrick), 1971-; Wilson, Jeffrey S. (Jeffrey Scott), 1967-
The aging infrastructure of a wastewater collection system can leak, capture ground water, and capture precipitation runoff. These are some of the most common problems in many of today’s US collection systems and are often collectively referred to as Rain Derived Inflow and Infiltration (RDII or I/I). The goal of this study is to investigate such optimized methods and their potential to improve flow monitor placement, especially for RDII studies, and to improve upon Stevens (2005) methodology. This project adopts a methodology from the “facility location problem”, a branch of operations research and graph theory. Solutions to a facility location problem will be adapted and utilized within a transportation GIS application to determine optimal placement.
EXTREME HEAT EVENT RISK MAP CREATION USING A RULE-BASED CLASSIFICATION APPROACH
(2012-03-19) Simmons, Kenneth Rulon; Johnson, Daniel P. (Daniel Patrick), 1971-; Banerjee, Aniruddha; Wilson, Jeffrey S. (Jeffrey Scott), 1967-
During a 2011 summer dominated by headlines about an earthquake and a hurricane along the East Coast, extreme heat that silently killed scores of Americans largely went unnoticed by the media and public. However, despite a violent spasm of tornadic activity that claimed over 500 lives during the spring of the same year, heat-related mortality annually ranks as the top cause of death incident to weather. Two major data groups used in researching vulnerability to extreme heat events (EHE) include socioeconomic indicators of risk and factors incident to urban living environments. Socioeconomic determinants such as household income levels, age, race, and others can be analyzed in a geographic information system (GIS) when formatted as vector data, while environmental factors such as land surface temperature are often measured via raster data retrieved from satellite sensors. The current research sought to combine the insights of both types of data in a comprehensive examination of heat susceptibility using knowledge-based classification. The use of knowledge classifiers is a non-parametric approach to research involving the creation of decision trees that seek to classify units of analysis by whether they meet specific rules defining the phenomenon being studied. In this extreme heat vulnerability study, data relevant to the deadly July 1995 heat wave in Chicago’s Cook County was incorporated into decision trees for 13 different experimental conditions. Populations vulnerable to heat were identified in five of the 13 conditions, with predominantly low-income African-American communities being particularly at-risk. Implications for the results of this study are given, along with direction for future research in the area of extreme heat event vulnerability.
Geography: its place in higher education enrollment
(2011-03-16) McDonald, Joseph P.; Banerjee, Aniruddha; Dwyer, Owen J.; Ward, Richard E.
The fundamental principles colleges and universities use to recruit students have remained largely unchanged for decades. Traditionally, admissions professionals visit high schools and attend college fairs, while colleges and universities hold preview days and publish viewbooks all in the interest of attracting a high-quality and diverse student population. The recruiting process has been greatly improved through the application of modern technology. The analytic abilities of technologies such as geographic information systems (GISystems), which allow for the visualization and analysis of spatial data, presents previously underutilized strategies for higher education recruiting methods. In addition, the incorporation of a Hierarchical Bayesian model will effectively model influential enrollment factors, which successful students possess. Hierarchical Bayesian models use the prior distribution, and likelihood of an events occurrence to create the posterior distribution or Bayesian inference. The intelligence created by combining traditional recruiting techniques with GISystems and Hierarchical Bayesian modeling will allow admissions professionals to improve the success rate of enrollment efforts and expenditures. This paper will explore the application of Hierarchical Bayesian models and GISystems within higher education recruiting.
A Habitat Suitability Model for Ricord’s Iguana in the Dominican Republic
(2009-06-23T20:28:12Z) Dine, James; Wilson, Jeffrey S. (Jeffrey Scott), 1967-; Banerjee, Aniruddha; Ramer, Jan
The West Indian iguanas of the genus Cyclura are the most endangered group of lizards in the world (Burton & Bloxam, 2002). The Ricord’s iguana, Cyclura ricordii, is listed as critically endangered by the International Union for Conservation of Nature (IUCN) (Ramer, 2004). This species is endemic to the island of Hispaniola (Figure 1), and can only be found in limited geographic areas (Burton & Bloxam, 2002). The range of this species is estimated to be only 60% of historical levels, with most areas being affected by some level of disturbance (Ottenwalder, 1996). The most recent population estimation is between 2,000 and 4,000 individuals (Burton & Bloxam, 2002). Information on potentially suitable habitat can help the conservation efforts for Ricord’s iguana. However, intensive ground surveys are not always feasible or cost effective, and cannot easily provide continuous coverage over a large area. This paper presents results from a pilot study that evaluated variables extracted from satellite imagery and digitally mapped data layers to map the probability of suitable Ricord’s iguana habitat. Bayesian methods were used to determine the probability that each pixel in the study areas is suitable habitat for Ricord’s iguanas by evaluating relevant environmental attributes. This model predicts the probability that an area is suitable habitat based on the values of the environmental attributes including landscape biophysical characteristics, terrain data, and bioclimatic variables.
Medical Imaging Centers in Central Indiana: Optimal Location Allocation Analyses
(2016-01) Seger, Mandi J.; Banerjee, Aniruddha; Wilson, Jeffrey S.; Lulla, Vijay O.; Wiehe, Sarah Elizabeth
While optimization techniques have been studied since 300 B.C. when Euclid first considered the minimal distance between a point and a line, it wasn’t until 1966 that location optimization was first applied to a problem in healthcare. Location optimization techniques are capable of increasing efficiency and equity in the placement of many types of services, including those within the healthcare industry, thus enhancing quality of life. Medical imaging is a healthcare service which helps to determine medical diagnoses in acute and preventive care settings. It provides physicians with information guiding treatment and returning a patient back to optimal health. In this study, a retrospective analysis of the locations of current medical imaging centers in central Indiana is performed, and alternate placement as determined using optimization techniques is considered and compared. This study focuses on reducing the drive time experienced by the population within the study area to their nearest imaging facility. Location optimization models such as the P-Median model, the Maximum Covering model, and Clustering and Partitioning are often used in the field of operations research to solve location problems, but are lesser known within the discipline of Geographic Information Science. This study was intended to demonstrate the capabilities of these powerful algorithms and to increase understanding of how they may be applied to problems within healthcare. While the P-Median model is effective at reducing the overall drive time for a given network set, individuals within the network may experience lengthy drive times. The results further indicate that while the Maximum Covering model is more equitable than the P-Median model, it produces large sets of assigned individuals overwhelming the capacity of one imaging center. Finally, the Clustering and Partitioning method is effective at limiting the number of individuals assigned to a given imaging center, but it does not provide information regarding average drive time for those individuals. In the end, it is determined that a capacitated Maximal Covering model would be the preferred method for solving this particular location problem.

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Browsing Geography Department Theses and Dissertations by Author "Banerjee, Aniruddha"

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