Jeff Wilson

Permanent URI for this collection

https://hdl.handle.net/1805/6824

Jeff Wilson, Department of Geography in the IU School of Liberal Arts, focuses his translational work in environmental planning and management at both the state and local levels. The Greenways research project provides data regarding the use of public trails using infared monitors in the most comprehensive trail monitoring system in the United States. Using models to estimate traffic on proposed trails, practitioners can design cost effective trails and estimate safety improvements such as additional stop lights.

This research has also provided data indicating property values on or near trails actually increases contradicting a publicly held belief that trails are bad for local neighborhoods. This translational research effort has received national attention from the Robert Wood Johnson Foundation which is interested in the correlation between obesity, public health and urban design.

The Greenway project is another example of how IUPUI's faculty are TRANSLATING their RESEARCH INTO PRACTICE.

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Now showing 1 - 10 of 34

Method of modeling the socio-spatial dynamics of extreme urban heat events
(United States Patent Office, 2013-10-22) Johnson, Daniel P.; Wilson, Jeffrey S.
A method of coupling surface urban heat island measures with socio-economic indicators of vulnerability to create improved spatially specific models to assist public health professionals in predicting extreme heat events mortality in urban environments. The method includes utilizing landsat TM imagery for the measuring of the urban heat island intensity levels and a spatial analysis of the variables in question.
Strategic placement of urban agriculture: A spatial optimization approach
(Wiley, 2021) Thapa, Bhuwan; Banerjee, Aniruddha; Wilson, Jeffrey S.; Hamlin, Samantha; Geography, School of Liberal Arts
Strategic placement of urban agriculture such as community gardens can expand alternate food supply, support physical activity, and promote social interactions. While social and health benefits are critical priorities when planning new urban agriculture locations, no widely accepted site selection methods have been established. We developed a spatial optimization model to identify new urban agriculture locations in the City of Indianapolis, Marion County, Indiana. Considering block groups with vacant parcels as potential locations, the study uses p-median optimization to identify the 25 best locations that would minimize travel from any block group in the city to potential garden locations. We weighted each block group based on food access and prevalence of obesity, where food access was characterized on three dimensions: economic, geographical, and informational. The model was simulated for three policy scenarios with equal, stakeholder-driven, and obesity-driven weights, and the results were compared with randomly selected locations. We found that optimally selected locations were 52% more efficient than randomly chosen locations in terms of the average distance traveled by residents based on the p-median solution. However, there was no significant difference in travel distance among the three policy scenarios. The spatial optimization model can help policymakers and practitioners strategically locate urban agriculture sites.
Integrating Disparate Electronic Data Sources to Identify and Analyze Spatial Chlamydia Health Disparities
(2019) Lai, Patrick T.S.; Jones, Josette; Wu, Huanmei; Wilson, Jeffrey S.; Dixon, Brian E.
Many social determinants of health are not routinely captured or are absent from inclusion from these electronic health records inhibiting research being done to analyze vulnerable populations and measuring health disparities within the population. Research in disease health disparities will involve collecting and integrating data from different data sources to identify underlying factors of disease and to utilize data analytics.
Sky View Factor Measurements in Support of Local Climate Zone Classification
(Indiana View, 2020) Adhikari, Bikalpa; Wilson, Jeffrey S.; Geography, School of Liberal Arts
Increasing urbanization coupled with threats from global climate change are driving research innovations that seek to inform sustainability of urban socio-ecological systems. The Local Climate Zone (LCZ) classification system developed by Stewart and Oke (2012) provides a framework for examining relationships between urban morphology and temperature, as well as a standardized approach to facilitate data integration from around the globe. In addition to urban heat island studies, parameters used to define LCZs are increasingly applied in related fields, such as modeling fine-scale variations in urban air quality (Badach et al., 2020).
Community Studies of Antisemitism in Schools (CSAIS) Community Typology Explorer
(2021) Price, Jeremy F.; Wilson, Jeffrey S.; Schall, Carly E.; Snorten, Clifton L.; Hasan, Mohammad A.; Luo, Xiao; Jahin, S. M. Abrar
This is a companion document to the CSAIS (Community Studies of Antisemitism In Schools) Community Typology Explorer which can be found at https://jeremyfprice.github.io/csais-dashboard/. Details about specific incidents, communities, and community types can be found at the CSAIS Community Typology Explorer. This project utilizes data from the ADL H.E.A.T. Map between 2016 and 2019 to identify incidents of antisemitism that specifically took place in schools. These incidents in schools are influenced by demographic, historical, social, and political factors. This project brings this data together to construct a community typology at the national level. This typology will provide insight into the ways that school-based incidents of hate are enacted and reported in context. Developing a community typology will allow providers to better target specific demographic, historical, and political attributes of the communities in which these incidents occur through curriculum and learning experiences.
Identifying risk factors for healthcare-associated infections from electronic medical record home address data
(BMC, 2010-09-17) Wilson, Jeffrey S.; Shepherd, David C.; Rosenman, Marc B.; Kho, Abel N.; Geography, School of Liberal Arts
Background Residential address is a common element in patient electronic medical records. Guidelines from the U.S. Centers for Disease Control and Prevention specify that residence in a nursing home, skilled nursing facility, or hospice within a year prior to a positive culture date is among the criteria for differentiating healthcare-acquired from community-acquired methicillin-resistant Staphylococcus aureus (MRSA) infections. Residential addresses may be useful for identifying patients residing in healthcare-associated settings, but methods for categorizing residence type based on electronic medical records have not been widely documented. The aim of this study was to develop a process to assist in differentiating healthcare-associated from community-associated MRSA infections by analyzing patient addresses to determine if residence reported at the time of positive culture was associated with a healthcare facility or other institutional location. Results We identified 1,232 of the patients (8.24% of the sample) with positive cultures as probable cases of healthcare-associated MRSA based on residential addresses contained in electronic medical records. Combining manual review with linking to institutional address databases improved geocoding rates from 11,870 records (79.37%) to 12,549 records (83.91%). Standardization of patient home address through geocoding increased the number of matches to institutional facilities from 545 (3.64%) to 1,379 (9.22%). Conclusions Linking patient home address data from electronic medical records to institutional residential databases provides useful information for epidemiologic researchers, infection control practitioners, and clinicians. This information, coupled with other clinical and laboratory data, can be used to inform differentiation of healthcare-acquired from community-acquired infections. The process presented should be extensible with little or no added data costs.
Measuring and Visualizing Chlamydia and Gonorrhea Inequality: An Informatics Approach Using Geographical Information Systems
(University of Illinois at Chicago, 2019-09-19) Lai, Patrick T.S.; Wilson, Jeffrey S.; Wu, Huanmei; Jones, Josette; Dixon, Brian E.; Geography, School of Liberal Arts
Background: Health inequality measurements are vital in understanding disease patterns in identifying high-risk patients and implementing effective intervention programs to treat and manage sexually transmitted diseases. Objectives: To measure and identify inequalities among chlamydia and gonorrhea rates using Gini coefficient measurements and spatial visualization mapping from geographical information systems. Additionally, we seek to examine trends of disease rate distribution longitudinally over a ten-year period for an urbanized county. Methods: Chlamydia and gonorrhea data from January 2005 to December 2014 were collected from the Indiana Network for Patient Care, a health information exchange system that gathers patient data from electronic health records. The Gini coefficient was used to calculate the magnitude of inequality in disease rates. Spatial visualization mapping and decile categorization of disease rates were conducted to identify locations where high and low rates of disease persisted and to visualize differences in inequality. A multiple comparisons ANOVA test was conducted to determine if Gini coefficient values were statistically different between townships and time periods during the study. Results: Our analyses show that chlamydia and gonorrhea rates are not evenly distributed. Inequalities in disease rates existed for different areas of the county with higher disease rates occurring near the center of the county. Inequality in gonorrhea rates were higher than chlamydia rates. Disease rates were statistically different when geographical locations or townships were compared to each other (p < 0.0001) but not for different years or time periods (p = 0.5152). Conclusion: The ability to use Gini coefficients combined with spatial visualization techniques presented a valuable opportunity to analyze information from health information systems in investigating health inequalities. Knowledge from this study can benefit and improve health quality, delivery of services, and intervention programs while managing healthcare costs.
Factorial Invariance of the Abbreviated Neighborhood Environment Walkability Scale among Senior Women in the Nurses’ Health Study Cohort
(Taylor & Francis (Routledge): SSH Titles, 2019) Starnes, Heather A.; McDonough, Meghan H.; Wilson, Jeffrey S.; Mroczek, Daniel K.; Laden, Francine; Troped, Philip J.; Geography, School of Liberal Arts
The purpose of this study was to examine the factorial invariance of the Abbreviated Neighborhood Environment Walkability Scale (NEWS-A) across subgroups based on demographic, health-related, behavioral, and environmental characteristics among Nurses’ Health Study participants (N = 2,919; age M = 73.0, SD = 6.9 years) living in California, Massachusetts, and Pennsylvania. A series of multi-group confirmatory factor analyses were conducted to evaluate increasingly restrictive hypotheses of factorial invariance. Factorial invariance was supported across age, walking limitations, and neighborhood walking. Only partial scalar invariance was supported across state residence and neighborhood population density. This evidence provides support for using the NEWS-A with older women of different ages, who have different degrees of walking limitations, and who engage in different amounts of neighborhood walking. Partial scalar invariance suggests that researchers should be cautious when using the NEWS-A to compare older adults living in different states and neighborhoods with different levels of population density.
Accelerometer and GPS Data to Analyze Built Environments and Physical Activity
(Taylor & Francis, 2019-09) Tamura, Kosuke; Wilson, Jeffrey S.; Goldfeld, Keith; Puett, Robin C.; Klenosky, David B.; Harper, William A.; Troped, Philip J.; Geography, School of Liberal Arts
Purpose: Most built environment studies have quantified characteristics of the areas around participants' homes. However, the environmental exposures for physical activity (PA) are spatially dynamic rather than static. Thus, merged accelerometer and global positioning system (GPS) data were utilized to estimate associations between the built environment and PA among adults. Methods: Participants (N = 142) were recruited on trails in Massachusetts and wore an accelerometer and GPS unit for 1-4 days. Two binary outcomes were created: moderate-to-vigorous PA (MVPA vs. light PA-to-sedentary); and light-to-vigorous PA (LVPA vs. sedentary). Five built environment variables were created within 50-meter buffers around GPS points: population density, street density, land use mix (LUM), greenness, and walkability index. Generalized linear mixed models were fit to examine associations between environmental variables and both outcomes, adjusting for demographic covariates. Results: Overall, in the fully adjusted models, greenness was positively associated with MVPA and LVPA (odds ratios [ORs] = 1.15, 95% confidence interval [CI] = 1.03, 1.30 and 1.25, 95% CI = 1.12, 1.41, respectively). In contrast, street density and LUM were negatively associated with MVPA (ORs = 0.69, 95% CI = 0.67, 0.71 and 0.87, 95% CI = 0.78, 0.97, respectively) and LVPA (ORs = 0.79, 95% CI = 0.77, 0.81 and 0.81, 95% CI = 0.74, 0.90, respectively). Negative associations of population density and walkability with both outcomes reached statistical significance, yet the effect sizes were small. Conclusions: Concurrent monitoring of activity with accelerometers and GPS units allowed us to investigate relationships between objectively measured built environment around GPS points and minute-by-minute PA. Negative relationships between street density and LUM and PA contrast evidence from most built environment studies in adults. However, direct comparisons should be made with caution since most previous studies have focused on spatially fixed buffers around home locations, rather than the precise locations where PA occurs.
Association Between Residential Greenness and Cardiovascular Disease Risk
(Wiley, 2018-12-05) Yeager, Ray; Riggs, Daniel W.; DeJarnett, Natasha; Tollerud, David J.; Wilson, Jeffrey S.; Conklin, Daniel J.; O'Toole, Timothy E.; McCracken, James; Lorkiewicz, Pawel; Xie, Zhengzhi; Zafar, Nagma; Krishnasamy, Sathya S.; Srivastava, Sanjay; Finch, Jordan; Keith, Rachel J.; DeFilippis, Andrew; Rai, Shesh N.; Liu, Gilbert; Bhatnagar, Aruni; Department of Geography, School of Liberal Arts
Background Exposure to green vegetation has been linked to positive health, but the pathophysiological processes affected by exposure to vegetation remain unclear. To study the relationship between greenness and cardiovascular disease, we examined the association between residential greenness and biomarkers of cardiovascular injury and disease risk in susceptible individuals. Methods and Results In this cross‐sectional study of 408 individuals recruited from a preventive cardiology clinic, we measured biomarkers of cardiovascular injury and risk in participant blood and urine. We estimated greenness from satellite‐derived normalized difference vegetation index (NDVI) in zones with radii of 250 m and 1 km surrounding the participants’ residences. We used generalized estimating equations to examine associations between greenness and cardiovascular disease biomarkers. We adjusted for residential clustering, demographic, clinical, and environmental variables. In fully adjusted models, contemporaneous NDVI within 250 m of participant residence was inversely associated with urinary levels of epinephrine (−6.9%; 95% confidence interval, −11.5, −2.0/0.1 NDVI) and F2‐isoprostane (−9.0%; 95% confidence interval, −15.1, −2.5/0.1 NDVI). We found stronger associations between NDVI and urinary epinephrine in women, those not on β‐blockers, and those who had not previously experienced a myocardial infarction. Of the 15 subtypes of circulating angiogenic cells examined, 11 were inversely associated (8.0–15.6% decrease/0.1 NDVI), whereas 2 were positively associated (37.6–45.8% increase/0.1 NDVI) with contemporaneous NDVI. Conclusions Independent of age, sex, race, smoking status, neighborhood deprivation, statin use, and roadway exposure, residential greenness is associated with lower levels of sympathetic activation, reduced oxidative stress, and higher angiogenic capacity.

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