Browsing by Author "Wilson, Jeffrey S. (Jeffrey Scott), 1967-"

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    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™
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    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.
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    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.
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    A comparison of geocoding baselayers for electronic medical record data analysis
    (2014-01-16) Severns, Christopher Ray; Wilson, Jeffrey S. (Jeffrey Scott), 1967-; Johnson, Daniel P. (Daniel Patrick), 1971-; Martin, Pamela A.
    Identifying spatial and temporal patterns of disease occurrence by mapping the residential locations of affected people can provide information that informs response by public health practitioners and improves understanding in epidemiological research. A common method of locating patients at the individual level is geocoding residential addresses stored in electronic medical records (EMRs) using address matching procedures in a geographic information system (GIS). While the process of geocoding is becoming more common in public health studies, few researchers take the time to examine the effects of using different address databases on match rate and positional accuracy of the geocoded results. This research examined and compared accuracy and match rate resulting from four commonly-used geocoding databases applied to sample of 59,341 subjects residing in and around Marion County/ Indianapolis, IN. The results are intended to inform researchers on the benefits and downsides to their selection of a database to geocode patient addresses in EMRs.
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    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%.
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    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).
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    The correlation of sea surface temperatures, sea level pressure and vertical wind shear with ten tropical cyclones between 1981-2010
    (2013-11-12) Compton, Andrea Jean; Martin, Pamela A.; Johnson, Daniel P. (Daniel Patrick), 1971-; Wilson, Jeffrey S. (Jeffrey Scott), 1967-
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    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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    Exploring the Utility of High Resolution Imagery for Determining Wetland Signatures
    (2012-07-03) DeLury, Judith Ann; Wilson, Jeffrey S. (Jeffrey Scott), 1967-; Ottensmann, John R.; Tedesco, Lenore P.
    Wetland habitats are characterized by periodic inundation and saturation by water creating anaerobic conditions that generate hydric soils and support hydrophytic vegetation. Wetland habitats provide important ecological functions including breeding grounds for fish, other wildlife, water purification, reduction in flooding, species diversity, recreation, food production, aesthetic value, and transformation of nutrients (Tiner, 1999). The multiple benefits of wetlands make them an important resource to monitor. A literature review suggests a combination of geospatial variables and methods should be tested for appropriateness in wetland delineation within local settings. Advancements in geospatial data technology and ease of accessing new, higher resolution geospatial data make study at local levels easier and more feasible (Barrette et al, 2000). The purpose of the current study is to evaluate new sources of geospatial data as potential variables to improve wetland identification and delineation. High resolution multispectral digital imagery, topographic data, and soils information are used to derive and evaluate independent variables. Regression analysis was used to analyze the data.
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    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.