A significant increase in campus-based emergencies warrants the investigation into emergency management information systems that serve a novice crisis decision-maker. Institutions of higher education that are not large enough to have dedicated emergency management offices generally press novice decision-makers into emergency management roles. An investigation was conducted to assess the impact of an emergency management geospatial information system on the decision performance of novice crisis managers through the use of a scenario-based simulation. A mixed method sequential explanatory method was used to collect quasi-experimental data on decision time, decision accuracy and situational awareness. Qualitative analysis was conducted through interviews with participants. Statistical results indicate the decision accuracy is positively affected by the use of an emergency management geospatial information system. Data Envelopment Analysis (DEA) is non-parametric linear programming method used to identify decision-making units in a data set that are optimal in their use of single or a set of resources (inputs) in delivering a set of expected results (outputs). DEA indicated that efficiency ratios from the geospatial information system group outperform the traditional group. Geospatial information systems hold much promise in providing systems that are easy to use, promote heightened levels of situational awareness and decision support.

Indiana University-Purdue University Indianapolis (IUPUI) / The distribution of the Common Mudpuppy (Necturus maculosus maculosus) is widespread but greatly understood. It is assumed that mudpuppy populations are declining due to poor habitat quality. However, there is not enough data to support this claim. The distribution of the mudpuppy is throughout the entire state, but only 43 of the 92 counties in Indiana have records. This project utilized habitat suitability modeling, focused on Indiana, to gain a better understanding of their distribution within the state. Data from Ohio and the Salamander Mussel (Simpsonais ambigua) were included to bolster the dataset. Environmental DNA was included to validate the model. Variables used in this analysis were Strahler Stream Order, distance to forest, percent agriculture, and tree canopy cover. Results showed that stream orders 4 to 6, a shorter distance to forest, less agriculture, and 30 to 40% of tree canopy cover was what contributed to suitable habitat. Stream order was the variable that contributed to the model the most. The areas of suitable habitat found were the HUC08 sub-watersheds in the northeastern and southwestern corners of the state. These areas included 19 counties were there were no previous records of mudpuppies. Environmental DNA showed that the negative samples were not found in suitable habitat. Further supporting the predicted area of suitable habitat. It is recommended that conservation efforts focus on the northeastern and southwestern regions. Interpreting this data to align with the regions set by the Indiana State Wildlife Action Plan shows that conservation should focus in the Great Lakes, Interior Plateau, and Valley and Hills area. It is recommended that more environmental data be conducted and that proactive conservation measures are implemented.

Several hundred thousand miles of energy pipelines span the whole of North America -- responsible for carrying the natural gas and liquid petroleum that power the continent's homes and economies. These pipelines, so crucial to everyday goings-on, are closely monitored by various operating companies to ensure they perform safely and smoothly. Happenings like earthquakes, erosion, and extreme weather, however -- and human factors like vehicle traffic and construction -- all pose threats to pipeline integrity. As such, there is a tremendous need to measure and indicate useful, actionable data for each region of interest, and operators often use computer-based decision support systems (DSS) to analyze and allocate resources for active and potential hazards. We designed and implemented a geospatial data service, REST API for Pipeline Integrity Data (RAPID) to improve the amount and quality of data available to DSS. More specifically, RAPID -- built with a spatial database and the Django web framework -- allows third-party software to manage and query an arbitrary number of geographic data sources through one centralized REST API. Here, we focus on the process and peculiarities of creating RAPID's model and query interface for pipeline integrity management; this contribution describes the design, implementation, and validation of that model, which builds on existing geospatial standards.

The aim of this degree project is to investigate alternatives to the relational database paradigm when storing hierarchical geospatial data. The document paradigm is found suitable and is therefore further examined. A benchmark suite is developed in order to test the relative performance of the paradigms for the relevant type of data. MongoDB and Microsoft SQL Server are chosen to represent the two paradigms in the benchmark. The results indicate that the document paradigm has potential when working with hierarchical structures. When adding geospatial elements to the data, the results are inconclusive. / Det här examensarbetet ämnar undersöka alternativ till den relationella databasparadigmen för lagring av hierarkisk geospatial data. Dokumentparadigmen identiferas som särskilt lämplig och undersöks därför vidare. En benchmark-svit utvecklas för att undersöka de två paradigmens relativa prestanda vid lagring av den undersökta typen av data. MongoDB och Microsoft SQL Server väljs som representanter för de två paradigmen i benchmark-sviten. Resultaten indikerar att dokumentparadigmen har god potential för hierarkisk data. Inga tydliga slutsatser kan dock dras gällande den geospatiala aspekten.

Today’s economy and infrastructure is dependent on raw natural resources, like crude oil and natural gases, that are optimally transported through a net- work of hundreds of thousands of miles of pipelines throughout America[28]. A damaged pipe can negatively a↵ect thousands of homes and businesses so it is vital that they are monitored and quickly repaired[1]. Ideally, pipeline operators are able to detect damages before they occur, but ensuring the in- tegrity of the vast amount of pipes is unrealistic and would take an impractical amount of time and manpower[1]. Natural disasters, like earthquakes, as well as construction are just two of the events that could potentially threaten the integrity of pipelines. Due to the diverse collection of data sources, the necessary geospatial data is scat- tered across di↵erent physical locations, stored in di↵erent formats, and owned by di↵erent organizations. Pipeline companies do not have the resources to manually gather all input factors to make a meaningful analysis of the land surrounding a pipe. Our solution to this problem involves creating a single, centralized system that can be queried to get all necessary geospatial data and related informa- tion in a standardized and desirable format. The service simplifies client-side computation time by allowing our system to find, ingest, parse, and store the data from potentially hundreds of repositories in varying formats. An online web service fulfills all of the requirements and allows for easy remote access to do critical analysis of the data through computer based decision support systems (DSS). Our system, REST API for Pipeline Integrity Data (RAPID), is a multi- tenant REST API that utilizes HTTP protocol to provide a online and intuitive set of functions for DSS. RAPID’s API allows DSS to access and manage data stored in a geospatial database with a supported Django web framework. Full documentation of the design and implementation of RAPID’s API are detailed in this thesis document, supplemented with some background and validation of the completed system.

This research relates Hg levels in the Common Loon (Gavia immer) to a variety of physical factors. Constructed within the framework of a GIS system, this model analyzes the spatial relationships and the influence of physical land cover factors as a function of distance from the individual loon territories. Thiessan polygons were used to generate the territory for each loon. Buffering of the thiessan polygons was done to establish the boundaries of the individual distance classes and to gather information on the percentage of individual land cover classes within each distance class. Information on precipitation was also gathered. Results from the regression analysis (R2 = 57.3% at the 150m distance class) performed on the variables suggest that the proximity of certain land use types such as cropland, shrub land, and wetlands influence the rates at which Hg is available within an individual territory. Within the 150m and 300m buffers, crop land, shrub land, and wetland exhibited the strongest relationship with the Hg levels in the common loon, with cropland exhibiting a negative relationship suggesting that the proximity of cultivated lands plays a role in decreasing the amount of available Hg in a territory. / Master of Science

In Ontario, supporting "a system of care that provides women and their families with equitable choice in birth environment and provider," (PCMCH & MOHLTC, 2017, p.33) has been identified as one of the central objectives of the Provincial Council for Maternal and Child Health's Low Risk Maternal Newborn Strategy. In theory, pregnant people in Ontario can choose to seek maternity care from a midwife, a family physician, or an obstetrician; however, in practice, pregnant people's choice of provider remains constrained. Extant literature suggests that in order to afford pregnant people the opportunity to exercise autonomous choice of provider, health systems must ensure that an acceptable range of provider options is available and accessible within the local organization of maternity care, that pregnant people are made aware of and knowledgeable about the available provider options, and that pregnant people have the ability and resources to navigate access to their provider of choice (Mackenzie, 2014; Sutherns, 2004). As a result, I designed this thesis to fill a gap in the evidence base to determine whether or not provincial policies had translated into the levels of access, awareness, and resourcing required to afford pregnant people the opportunity, ability and propensity to exercise autonomous choice of provider within the local maternity care system in Ontario's Champlain Region. I sought to elicit the structural conditions that would be necessary to equitably support pregnant people's access to and choice of a maternity care provider. In the pursuit of these objectives, I adopted an integrated knowledge translation approach (Bowen & Graham, 2013), using an explanatory sequential mixed methods design (Creswell, 2014), which encompassed two complementary stages: (1) quantitative geospatial mapping to assess pregnant people's access to the full range of maternity care providers across the Champlain Region; and (2) qualitative focus groups and individual interviews with parents, providers, and policy-makers to explore the individual, organizational, and system-level factors that are enabling or restricting access and autonomy. Using a systems approach to the investigation of this locally-identified issue, I demonstrate in this thesis that pregnant people within the Champlain Region have inequitable opportunities to exercise autonomous choice of maternity care provider due to (1) system and organizational-level factors that are creating imbalances in the supply, distribution and mix of maternity care provider options, and (2) pregnant people's differential access to the enabling information and resources required to exercise autonomous choice of provider and to navigate access to their services.

Background and Purpose: Colorectal cancer (CRC) is the third most common cancer, the second leading cause of cancer death, with lower survival rates at later stages. Adherence to CRC screening can prevent the development of cancerous polyps and reduce incidence. Area-level characteristics, such as access to healthcare and deprivation, can create barriers to timely screening, increasing the risk of developing CRC. The degree to which area-level characteristics versus individual-level characteristics are responsible for CRC outcomes, including incidence and stage at diagnosis, are not well-understood. Specifically, deficits in the use of spatial statistical techniques has led to a lack of clarity in the current literature. This study aimed to overcome these deficiencies by identifying and utilizing the optimal measurement for area-level access to healthcare and deprivation, employing robust spatiotemporal and multilevel analytic methods to assess their effects on CRC incidence and late-stage diagnosis in Pennsylvania (PA) at the block group-level from 2008 to 2017. The results of this research will more accurately map areas of high predicted CRC relative risk for targeted public health interventions to reduce the burden of CRC over time. The following three study aims were used to address the research problem: Aim 1: Identify the best predictive measure of access to healthcare for estimating CRC incidence risk at the block group-level in PA from 2008 to 2017. Q1: What is the best measure of access to care for estimating risk of CRC incidence? H1.1: The most comprehensive measurement, Multi-Modal 2SFCA, is optimal for predicting CRC incidence compared to unidimensional distance, availability, and other 2SFCA measures. H1.2: Weighting access to healthcare measures for individual insurance coverage improves predictive performance of CRC incidence. Aim 2: Ascertain the relative risk from area-level deprivation on CRC incidence at the block group-level in PA from 2008 to 2017.Q2: How does area-level deprivation affect CRC incidence? H2.1: WQS will demonstrate the relative importance of an extensive array of SES variables for CRC incidence. H2.2: Higher deprivation will be positively associated with risk of CRC incidence. Aim 3: Determine the individual-level likelihood of being diagnosed with late-stage CRC based on place of residence across PA from 2008 to 2017.Q3: How does place of residence affect the likelihood of developing late-stage CRC incidence after adjusting for individual-level characteristics and covariates? H3.1: PA residents living in areas of worse deprivation and low access to care have a higher likelihood of being diagnosed with late-stage CRC. H3.2: The likelihood of late-stage CRC varies significantly by individual characteristics. Methods: This research used ecologic and cross-sectional study designs to perform secondary data analysis of the cancer registry and publicly available data. The geographic units were block groups in PA (N = 9,740), accessed from the US Census Bureau. The sample included screening age-eligible PA residents, 45-75 years, diagnosed with a primary incident case of CRC from 2008 to 2017 (N=34,250), identified via the PA Cancer Registry. Out-of-state residents at diagnosis and high-risk individuals were excluded. Nine block groups were uninhabitable with no population and thus excluded. Primary exposure variables (i.e., area-level access to healthcare and deprivation) were calculated using the PA Cancer Registry, a provider database, the US Census Bureau’s polygon and network shapefiles, and American Community Survey. Ecologic covariates (see below) were derived from the American Community Survey, the Behavioral Risk Factor Surveillance System, and the USDA’s Rural-Urban Commuting Areas. The PA Cancer Registry provided individual data for patient demographics, tumor characteristics, and insurance coverage. Exploratory spatial, temporal, and spatiotemporal analyses of the CRC data were performed before Aims 1 to 3. Aim 1: CRC cases were aggregated by block group to represent a count of CRC incidence. Area-level access to healthcare measures was calculated using providers’ addresses, population-weighted block group centroids, and road/rail networks (i.e., driving, walking, and public transit). Measures included great-circle distance, driving distance to the nearest provider by miles/time, physician-to-population ratio, enhanced two-step floating catchment area (2SFCA), variable 2SFCA, and multi-modal 2SFCA. Four 15-minute catchment sizes were tested (range = 15-60-minutes). A weighted version of each 2SFCA measure for insurance coverage was calculated. Predictive performance was assessed with model fit statistics from 29 hierarchical Bayesian spatiotemporal Poisson regression models. All models included CRC screening adherence, rurality, age, race, education level, unemployment, and poverty level. Aim 2: CRC cases were aggregated by block group to represent a count of CRC incidence. Area-level deprivation indicators (n=39) were calculated from the American Community Survey’s five-year pooled estimates for demographic, social, economic, and housing characteristics and represented at the census tract or block group-level. Weighted Quantile Sum regression generated an area-level deprivation index, weighting each indicator by its relative relationship with CRC incidence. A hierarchical Bayesian spatiotemporal Poisson regression with conditional autoregressive priors and a first-order autoregressive time series process was used to estimate the relative risk of CRC. The ecologic covariates included in the model were area-level access to healthcare from Aim 1, CRC screening adherence, rurality, age, and sex. Aim 3: Three binary outcome variables represented localized vs. regional, distant, and regional and distant CRC at diagnosis. Aim 1 and 2’s area-level access to healthcare and deprivation measurements were used for this study’s primary exposure variables. The data was split into three time periods (2008-2009, 2010-2013, and 2014-2017) to analyze CRCS coverage mandates from the Affordable Care Act for private insurers in 2010 and Medicare in 2014. Using binomial distributed outcomes, three two-level generalized linear mixed models using hierarchical Bayesian methods with conditional autoregressive priors were run for each time period. Results: There were 34,250 eligible incident cases with 0-6 cases per block group (N=9,731) each year and an average of 3.5 cases per block group for the pooled study period. From 2008 to 2017, the pooled CRC incidence rate was 7.45 cases per 1,000 for 45 to 75 year olds in PA. Scan statistics found the highest CRC burden was in Philadelphia (northeast, west, and south), Pittsburgh, and rural areas in southwest PA (e.g., Westmoreland County and Fayette County) and northcentral PA (e.g., Lycoming County, Clinton County, and Centre County). In PA, yearly crude CRC rates decreased slightly over the ten years (0.80 to 0.72, Δ =-.08), though not empirically tested. Aim 1: The best fitting model used the Multi-Modal 2SFCA, which included aggregated physician-to-population ratios within 45-minutes from the provider facility for population-weighted block group centroids via driving, walking, and public transit of the same distance. Access was generally worst in rural areas and best in urban/suburban areas. Block groups with access one standard deviation above the state median had 27% decreased CRC risk. Weighting for insurance coverage improved a measure’s predictive ability for shorter travel times (i.e., 15-minutes and 30-minutes). Aim 2: Of a 39 indicator deprivation index, nine were statistically significant and three were related to SES (i.e., median household income, the percent of the block group without a high school degree, or living in a house without heating). However, the most important significant indicators belonged to geography and income domains, collectively representing 71% of the relative influence of the index. The area-level deprivation index was significant and positively associated with CRC incidence at the block group-level in PA from 2008 to 2017 (RR: 1.33, 95% CI: 1.32–1.34). Aim 3: After accounting for individual age, race, and insurance coverage, the relationship between area-level access to healthcare and deprivation and late-stage CRC became non-significant. While no area-level effects were significant, several individual-level features had consistent significant findings across outcomes and time periods. At the individual-level, having government insurance and being uninsured had significant positive relationships for all outomes and time periods. Age, and race had significant inverse relationships with late-stage CRC diagnosis. Conclusions: In summary, this study addressed the limitations of previous research by employing innovative measurement techniques, such as the Multi-Modal 2SFCA and Weighted Quantile Sum regression, and rigorous spatiotemporal methods to assess the impact of area-level access to healthcare and deprivation on CRC incidence and late-stage diagnosis. The findings highlight the importance of considering walking and public transit access to healthcare in relation to CRC incidence. Additionally, the study demonstrated the effectiveness of the WQS method in calculating an accurate area-level deprivation index, which enhanced the prediction of CRC incidence and identified high-risk areas for targeted interventions. However, individual-level characteristics, particularly insurance coverage, were found to be more influential in predicting the stage at which CRC was diagnosed than area-level effects. Regardless, using inferences and similar methods from this dissertation improves disease mapping and resource allocation for CRCS outreach, supports evidence for policy, and helps guide the development of tailored public health interventions to ultimately reduce the burden of CRC. / Epidemiology

Seafloor properties, including total organic carbon (TOC) and the vertical thickness (isochores) of geological units, are sparsely measured on a global scale and spatial interpolation (prediction) techniques are often used as a proxy for observations. Previous geospatial interpolations of seafloor TOC exhibit gaps where little to no observed data exists. Recent machine learning techniques, based upon a suite of geophysical and geochemical properties (e.g., seafloor biomass, porosity, distance from coast) show promise in making globally complete, comprehensive, and statistically robust geospatial seafloor predictions. Here I apply a non-parametric (i.e., data-driven) machine learning (ML) algorithm, specifically k-nearest neighbors (kNN), to estimate the global distribution of seafloor TOC and marine isochores. This machine learning approach shows major advantages relative to geospatial interpolation, including results that are quantitative, easily updatable, accompanied with uncertainty estimation, and agnostic to spatial gaps in observations. Additionally. analysis of parameter space sample density provides a guide for future sampling. Resulting predictions of the global distribution of seafloor TOC and marine isochore thicknesses were used with ML workflow to predict other seafloor parameters (e.g., heat flow, temperature, salinity) in order to constrain the global distribution of the base of hydrate stability zone and methane generation for all sub-seafloor sediments. Estimating global carbon budgets is first-order dependent on accurate model input, therefore our estimate of the base of hydrate stability zone, and subsequent carbon and methane accumulation in the subseafloor yields improvement over the standard interpolation techniques used in previous global modeling analyses. By using these globally updateable machine learning parameters as the input to predictions, results provide easily updated global budgets of total carbon and methane generated. This dissertation presents valuable new global distributions of seafloor geological properties including total organic carbon, sediment isochores, and subsequently the global distribution of carbon and methane. These estimates should be used in further analysis to understand how carbon is cycled and sequestered in the marine environment. Further, this document is well-suited to serve as a guide for geospatially predicting globally complete seafloor and subseafloor properties.

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