Geography Department Theses and Dissertations

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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 44

Remote Sensing of Cyanobacteria in Case II Waters Using Optically Active Pigments, Chlorophyll a and Phycocyanin
(2007-03-27T16:29:12Z) Randolph, Kaylan Lee; Wilson, Jeffrey S. (Jeffrey Scott), 1967-; Tedesco, Lenore P.; Li, Lin
Nuisance blue-green algal blooms contribute to aesthetic degradation of water resources and produce toxins that can have serious adverse human health effects. Current field-based methods for detecting blooms are costly and time consuming, delaying management decisions. Remote sensing techniques which utilize the optical properties of blue-green algal pigments (chlorophyll a and phycocyanin) can provide rapid detection of blue-green algal distribution. Coupled with physical and chemical data from lakes, remote sensing can provide an efficient method for tracking cyanobacteria bloom occurrence and toxin production potential to inform long-term management strategies. In-situ field reflectance spectra were collected at 54 sampling sites on two turbid, productive Indianapolis reservoirs using ASD Fieldspec (UV/VNIR) spectroradiometers. Groundtruth samples were analyzed for in-vitro pigment concentrations and other physical and chemical water quality parameters. Empirical algorithms by Gitelson et al. (1986, 1994), Mittenzwey et al. (1991), Dekker (1993), and Schalles et al. (1998), were applied using a combined dataset divided into a calibration and validation set. Modified semi-empirical algorithms by Simis et al. (2005) were applied to all field spectra to predict phycocyanin concentrations. Algorithm accuracy was tested through a least-squares regression and residual analysis. Results show that for prediction of chlorophyll a concentrations within the range of 18 to 170 ppb, empirical algorithms yielded coefficients of determination as high as 0.71, RMSE 17.59 ppb, for an aggregated dataset (n=54, p<0.0001). The Schalles et al. (2000) empirical algorithm for estimation of phycocyanin concentrations within the range of 2 to 160 ppb resulted in an r2 value of 0.70, RMSE 23.97 ppb (n=48, p<0.0001). The Simis et al. (2005) semi-empirical algorithm for estimation of chlorophyll a and phycocyanin concentrations yielded coefficients of determination of 0.69, RMSE 20.51 ppb (n=54, p<0.0001) and 0.85, RMSE 24.61 pbb (n=49, p<0.0001), respectively. Results suggest the Simis et al. (2005) algorithm is robust, where error is highest in water with phycocyanin concentrations of less than 10 ppb and in water where chlorophyll a dominates (Chl:PC>2). A strong correlation between measured phycocyanin concentrations and blue-green algal biovolume measurements was also observed (r2=0.95, p<0.0001).
Comparing Spatial Measures of the Built Environment for Health Research
(2008-03-07T13:35:29Z) Hoch, Shawn C.; Wilson, Jeffrey S. (Jeffrey Scott), 1967-; Liu, Gilbert; Wiehe, Sarah
Research on the association between health and the built environment often delineates environmental exposure using different spatial forms and distances surrounding points of interest, such as residences or schools. Examples from the literature include Euclidian and network buffers, administrative and census boundaries, and other arbitrary geographies, such as grid cells. There is a lack, however, of reports that describe the justifications or implications for using different methods. This research compares different forms and distances for measuring environmental variables surrounding residential locations in the context of adult walking behavior in Marion County, Indiana. Walkability index and vegetation greenness variables were evaluated within 400-meter, 1-kilometer, and 2-kilometer Euclidian and network buffers, census block groups and tracts, and 805- X 805-meter grid cells. Results of analyses using each of these methods to test walkability and greenness as correlates of self-reported walking behavior were compared. Significant differences were observed in measurements of environmental variables as a function of both size and form. There were also significant differences between spatial measure methods when measuring components of walkability and NDVI. Census geographies, widely used in the public health literature, yielded environmental variable measurements differently than did similarly-sized residence-based measure methods. In logistic regressions, the walkability index did not exhibit a significant relationship with self-reported walking behavior. NDVI exhibited a negative relationship with self-reported walking, although the relationship was reversed and significant when stratifying by residential density.
CONFOUNDING CONSTITUENTS IN REMOTE SENSING OF PHYCOCYANIN
(2008-08-22T13:53:38Z) Vallely, Lara Anne; Wilson, Jeffrey S. (Jeffrey Scott), 1967-; Tedesco, Lenore P.; Li, Lin
This project examines the impact of confounding variables that have limited the accuracy of remotely predicting phycocyanin in three Indiana drinking and recreational water reservoirs. In-situ field reflectance spectra were collected from June to November 2006 over a wide range of algal bloom conditions using an ASD Fieldspec (UV/VNIR) spectroradiometer. Groundtruth samples were analyzed for chlorophyll a, phycocyanin, total suspended matter, and other water quality constituents. Previously published spectral algorithms for the detection of phycocyanin were evaluated against lab measured pigment concentrations using linear least squares regression. Algorithm performance varied across study sites (best performing models by reservoir resulted in r2 values of 0.32 to 0.84). Residuals of predicted versus measured pigment concentrations were analyzed against concentration of potential confounding water constituents. Residual analysis revealed optically active constituents contributed between 25% and 95% of original phycocyanin model errors. Inclusion of spectral variables into models to account for significant confounders resulted in improved spectral estimates of phycocyanin (r2 = 0.56 to 0.93).
Comparison of Urban Tree Canopy Classification With High Resolution Satellite Imagery and Three Dimensional Data Derived From LIDAR and Stereoscopic Sensors
(2008-08-22T13:59:51Z) Baller, Matthew Lee; Wilson, Jeffrey S. (Jeffrey Scott), 1967-; Tedesco, Lenore P.; Li, Lin
Despite growing recognition as a significant natural resource, methods for accurately estimating urban tree canopy cover extent and change over time are not well-established. This study evaluates new methods and data sources for mapping urban tree canopy cover, assessing the potential for increased accuracy by integrating high-resolution satellite imagery and 3D imagery derived from LIDAR and stereoscopic sensors. The results of urban tree canopy classifications derived from imagery, 3D data, and vegetation index data are compared across multiple urban land use types in the City of Indianapolis, Indiana. Results indicate that incorporation of 3D data and vegetation index data with high resolution satellite imagery does not significantly improve overall classification accuracy. Overall classification accuracies range from 88.34% to 89.66%, with resulting overall Kappa statistics ranging from 75.08% to 78.03%, respectively. Statistically significant differences in accuracy occurred only when high resolution satellite imagery was not included in the classification treatment and only the vegetation index data or 3D data were evaluated. Overall classification accuracy for these treatment methods were 78.33% for both treatments, with resulting overall Kappa statistics of 51.36% and 52.59%.
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.
Predictors of Primary Care Physicians Practicing in Medically Underserved and Rural Areas of Indiana
(2009-10-01T16:53:26Z) Bellinger, Nathan; Zollinger, Terrell W.; Dwyer, Owen J.; Brokaw, James J.; Wilson, Jeffrey S. (Jeffrey Scott), 1967-
Purpose: This study examines whether Indiana physicians’ choices to practice in medically underserved and rural areas of Indiana are associated with select physician characteristics. Methods: Physician data were gathered from the American Medical Association Physician Masterfile. Analysis was limited to primary care physicians currently practicing, whose birth city and/or state were known (if American born) and whose current practice location could be matched to an Indiana ZIP Code. The underserved and rural areas and physician data were mapped using ArcGIS. Chi square and logistic regression analyses were performed to identify significant associations between the physician characteristics and choice of practice location. Results: In instances where a physician was born in a county that fell below its state’s median income level in the decade of birth, there is a significant likelihood of future choice to practice in underserved and rural areas. Attending a medical school in the Midwest and region of birth (subdivided by state) were proven to have no predictive value. Conclusions: This result, when compared with other studies that have found physician hometown to be a predictive factor, seems to confirm and strengthen the argument that factors in a physician’s past, including social and economic setting of his or her upbringing, influence choice to practice in underserved and/or rural areas.
Parallel worlds: attribute-defined regions in global human geography
(2009-11-13T14:02:28Z) Ford, Of The; Dwyer, Owen J.; Wilson, Jeffrey S.; Pegg, Scott M.
Global human regionalization often depends heavily on conventions, especially the country model. Standardized “countries” are used as default regions, and influence other regionalizations as well. Proposed here is the preference for multiple independent systems of regions based on empirical criteria specific to each field of inquiry. These regions, defined by attributes of the landscape, would subsume formal and functional regions alike, as well as the very similar “trait geographies” and “process geographies”. Two specific inquiries are studied, politics and language; in both cases, existing data tend towards the conventional. A primary empirical regionalization for politics can be based on effective government control. A primary empirical regionalization for language can be based on mutual intelligibility of vernacular dialects. Examined in political geography are concepts of juridical and empirical statehood and the question of state territoriality; examined in linguistic geography are the question of language versus dialect and the standard reference ‘Ethnologue’.
Using high resolution satellite imagery to map aquatic macropyhtes on multiple lakes in northern Indiana
(2009-12-08T21:34:32Z) Gidley, Susan; Wilson, Jeffrey S. (Jeffrey Scott), 1967-; Tedesco, Lenore P.; Johnson, Daniel P.
Resource managers need to be able to quickly and accurately map aquatic plants in freshwater lakes and ponds for regulatory purposes, to monitor the health of native species and to monitor the spread of invasive species. Site surveys and transects are expensive and time consuming, and low resolution imagery is not detailed enough to map multiple, small lakes spread out over large areas. This study evaluated methods for mapping aquatic plants using high resolution Quickbird satellite imagery obtained in 2007 and 2008. The study area included nine lakes in northern Indiana chosen because they are used for recreation, have residential development along their shorelines, support a diverse wildlife population, and are susceptible to invasive species. An unsupervised classification was used to develop two levels of classification. The Level I classification divided the vegetation into detailed classes of emergent and submerged vegetation based on plant structure. In the Level II classification, these classes were combined into more general categories. Overall accuracy of the Level I classification was 68% for the 2007 imagery and 58% for the 2008 imagery. The overall accuracy of the Level II classification was higher for both the 2007 and 2008 imagery at 75% and 74%, respectively. Classes containing bulrushes were the least accurately mapped in the Level I classification. In the Level II classification, the least accurately mapped class was submerged vegetation. Water and man-made surfaces were mapped with the highest degree of accuracy in both classification schemes. Overhanging trees and shore vegetation contributed to classification error. Overall, results of this research suggest that high resolution imagery provides useful information for natural resource managers. It is most applicable to mapping general aquatic vegetation categories, such as submerged and emergent vegetation, and providing general estimates of plant coverage in lakes. Better methods for mapping individual species, species assemblages, and submerged vegetation constitute areas for further research.
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™
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.

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