Racial Segregation in Indianapolis, 1990–2010: A Spatial Perspective

The index of dissimilarity is the most widely used method for measuring racial segregation. When applied to Indianapolis, this index has returned results showing the city to be among the most segregated in the country. The resulting measure, however, suffers from two shortcomings. First, the index of dissimilarity is sensitive to the census-defined geographic unit chosen for the analysis; thus, this index returns different (though proportionate) results depending on whether the population data are aggregated to larger or smaller enumeration units. Second, the index of dissimilarity cannot account for the influence of spatial proximity; adjacent census blocks interact regardless of administrative boundaries. In place of the index of dissimilarity, we apply the segregation index in order to treat the phenomena as a surface that is simultaneously smooth and continuous. In this article, we calculate the segregation index for Indianapolis from 1990 to 2010 using the kernel density estimation method. The results of the analysis are presented in three pairs of decennial maps. These maps add to the understanding of residential segregation by resolving in a statistically reliable manner the phenomenon’s geographic component. Our visualization of segregation confirms its presence in distinct clusters, its growth over time, and a strong bias of this growth to be contiguous. In a manner akin to examinations of residential segregation’s impact on education attainment and health outcomes, careful description of segregation’s spatial aspect leads to a more nuanced understanding of phenomenon’s pervasiveness across social life

Visitor bikeshare usage: tracking visitor spatiotemporal behavior using big data

Bikeshare programs are a popular, convenient, and sustainable mode of transportation that provide a range of benefits to urban communities such as reduction in carbon emissions, decreased travel times, financial savings, and heightened physical activity. Although, tourists are especially inclined to use bikeshare to explore a destination as the programs are a convenient, cheap, flexible, and an active alternative to vehicles and mass transit little research or attention has focused on visitor usage. As such the current study investigated the spatial-temporal usage patterns of bikeshare by visitors to an urban community using GPS based big data (N = 353,733). The results revealed differential usage patterns between visitors and local residents based on user provided ZIP Codes using a 50 mile geometric circular buffer around the urban destination. The visitors and residents significantly varied on numerous trip behaviors including route selection, time of rental, checkout/check-in locations, distance, speed, duration, and physical activity intensity. The user patterns uncovered suggest visitors primarily use bikeshare for leisure based urban exploration, compared to residents’ primary use of bikeshare to be public transportation related. Implications for bikeshare, urban planning, and tourism management are provided aimed at delivering a more sustainable and richer visitor experience

Measurement and Modeling of Ground-Level Ozone Concentration in Catania, Italy using Biophysical Remote Sensing and GIS

This experimental study examined spatial variation of ground level ozone (O3) in the city of Catania, Italy using thirty passive samplers deployed in a 500-m grid pattern. Significant spatial variation in ground level O3 concentrations (ranging from 12.8 to 41.7 g/m3) was detected across Catania’s urban core and periphery. Biophysical measures derived from satellite imagery and built environment characteristics from GIS were evaluated as correlates of O3 concentrations. A land use regression model based on four variables (land surface temperature, building area, residential street length, and distance to the coast) explained 74% of the variance (adjusted R2) in measured O3. The results of the study suggest that biophysical remote sensing variables are worth further investigation as predictors of ground level O3 (and potentially other air pollutants) because they provide objective measurements that can be tested across multiple locations and over time

Predicting COVID-19 community infection relative risk with a Dynamic Bayesian Network

As COVID-19 continues to impact the United States and the world at large it is becoming increasingly necessary to develop methods which predict local scale spread of the disease. This is especially important as newer variants of the virus are likely to emerge and threaten community spread. We develop a Dynamic Bayesian Network (DBN) to predict community-level relative risk of COVID-19 infection at the census tract scale in the U.S. state of Indiana. The model incorporates measures of social and environmental vulnerability—including environmental determinants of COVID-19 infection—into a spatial temporal prediction of infection relative risk 1-month into the future. The DBN significantly outperforms five other modeling techniques used for comparison and which are typically applied in spatial epidemiological applications. The logic behind the DBN also makes it very well-suited for spatial-temporal prediction and for “what-if” analysis. The research results also highlight the need for further research using DBN-type approaches that incorporate methods of artificial intelligence into modeling dynamic processes, especially prominent within spatial epidemiologic applications

Substantial Decreases in U.S. Cities’ Ground-Based NO2 Concentrations during COVID-19 from Reduced Transportation

A substantial reduction in global transport and industrial processes stemming from the novel SARS-CoV-2 coronavirus and subsequent pandemic resulted in sharp declines in emissions, including for NO2. This has implications for human health, given the role that this gas plays in pulmonary disease and the findings that past exposure to air pollutants has been linked to the most adverse outcomes from COVID-19 disease, likely via various co-morbidities. To explore how much COVID-19 shutdown policies impacted urban air quality, we examined ground-based NO2 sensor data from 11 U.S. cities from a two-month window (March–April) during shutdown in 2020, controlling for natural seasonal variability by using average changes in NO2 over the previous five years for these cities. Levels of NO2 and VMT reduction in March and April compared to January 2020 ranged between 11–65% and 11–89%, consistent with a sharp drop in vehicular traffic from shutdown-related travel restrictions. To explore this link closely, we gathered detailed traffic count data in one city—Indianapolis, Indiana—and found a strong correlation (0.90) between traffic counts/classification and vehicle miles travelled, a moderate correlation (0.54) between NO2 and traffic related data, and an average reduction of 1.11 ppb of NO2 linked to vehicular data. This finding indicates that targeted reduction in pollutants like NO2 can be made by manipulating traffic patterns, thus potentially leading to more population-level health resilience in the future

Small Unmanned Aerial Systems (sUAS) for environmental remote sensing: challenges and opportunities revisited

Hardin and Jensen (2011) presented six challenges to using small Unmanned Aerial Systems (sUAS) for environmental remote sensing: challenge of the hostile flying environment, challenge of power, challenge of available sensors, challenge of payload weight, challenge of data analysis, and challenge of regulation. Eight years later we revisit each of the challenges in the context of the current sUAS environment. We conclude that technological advances made in the interim (as applied to environmental remote sensing) have either (1) improved practitioner ability to respond to a challenge or (2) decreased the magnitude of the challenge itself. However, relatively short flight time remains a primary challenge to using sUAS in environmental remote sensing

The Indiana Pacers Bikeshare Program – An Opportunity to Explore Research Questions in Geography, Transportation, and Public Health

poster abstractThe Indiana Pacers Bikeshare program is funded by the Herb and Simon Family Foundation and overseen by the Indianapolis Cultural Trail, Inc., (ICT) a non-profit organization which provides access to bikes for a small fee to travel around the Indianapolis downtown area. There are twenty six stations and 250 bikes collated across the Indianapolis downtown area. There are two ways of using the bikeshare: a 24-hour pass or an annual membership. A user is entitled to unlimited 30-minute rides during the duration of the pass or membership. Each bicycle is equipped with a GPS unit. B-Cycle, a company that handles the implementation of the bikeshare system, collects GPS data of trips when the bike is in motion. In collaboration with ICT, our research team has acquired data from initiation of the program in April 2014 to the end of 2015. The dataset is large with slightly over 13 million records. Our research team is planning, at least, four projects based on this dataset: evaluating associations between built environment and bike use patterns, estimating physical activity accumulated during bikeshare use, differences in use by different membership types, and optimal placement of existing stations and planning for expansion of new stations

Terrorism, Counterterrorism and “The Rule of Law”: State Repression and “Shoot-to-Kill” in Northern Ireland

Authors have argued that counterterrorism must be consistent with “the rule of law.” Often associated with this approach is the assumption that plural political structures limit the state’s response to terrorism and that state agents will be held accountable if their response is excessive. Scholars who focus on social movements reject this assumption.. We examine the state’s response to anti-state violence in Northern Ireland between 1969 and 1994. In 1982, Sinn Féin did much better than expected in an election to the Northern Ireland Assembly. Following the election, it is alleged that state agents followed a “shoot-to-kill” policy and shot dead Irish republican paramilitaries instead of arresting them. We find evidence suggesting such a policy and consider the implications

Relocating Bike-Kiosks to Maximize Ridership – A Weighted Matching Optimization Problem

poster abstractBike share infrastructure can benefit from optimized allocation of resources, such as bike share stations or kiosks that have relatively high capital costs. Suboptimal placement of stations increases the cost of service and may impede membership. If impedances like distance to kiosk is high then ridership may decrease thereby not allowing the system to reach its full potential. Currently, the 25 bike-share stations managed by the non-profit Indiana Pacers Bikeshare program are located around the Indianapolis downtown area and the Indianapolis Cultural Trail. We developed a weighted matching solution to minimize the distance potential users must travel to reach a kiosk while taking into account the pairing of existing kiosks with new locations. We also provide a model to introduce several new locations that restricts maximum distance traveled by customers to the nearest kiosk. For both, we apply integer programming heuristics to solve the optimization problem – an NP hard problem. NP hard problems are computationally prohibitive and require specialized mathematical programming for robust solutions. Analyses show that 20 optimally located kiosks will serve the 25 existing kiosks clientele without any increase in impedance to kiosk access – a 20 percent increase in efficiency

Intact landscape promotes gene flow and low genetic structuring in the threatened Eastern Massasauga Rattlesnake

Genetic structuring of wild populations is dependent on environmental, ecological, and life-history factors. The specific role environmental context plays in genetic structuring is important to conservation practitioners working with rare species across areas with varying degrees of fragmentation. We investigated fine-scale genetic patterns of the federally threatened Eastern Massasauga Rattlesnake (Sistrurus catenatus) on a relatively undisturbed island in northern Michigan, USA. This species often persists in habitat islands throughout much of its distribution due to extensive habitat loss and distance-limited dispersal. We found that the entire island population exhibited weak genetic structuring with spatially segregated variation in effective migration and genetic diversity. The low level of genetic structuring contrasts with previous studies in the southern part of the species' range at comparable fine scales (~7 km), in which much higher levels of structuring were documented. The island population's genetic structuring more closely resembles that of populations from Ontario, Canada, that occupy similarly intact habitats. Intrapopulation variation in effective migration and genetic diversity likely corresponds to the presence of large inland lakes acting as barriers and more human activity in the southern portion of the island. The observed genetic structuring in this intact landscape suggests that the Eastern Massasauga is capable of sufficient interpatch movements to reduce overall genetic structuring and colonize new habitats. Landscape mosaics with multiple habitat patches and localized barriers (e.g., large water bodies or roads) will promote gene flow and natural colonization for this declining species