TB care and prevention is led by the public sector, but many TB symptomatics first seek care in the private sector, highlighting the importance of engaging these facilities. This report examines basic TB epidemiology (such as the gap in case finding that could be addressed in part via private sector activities), health-seeking behaviors (HSB) and diagnostic delays, the size of the private sector, activities of professional societies, and financing. It examines the potential utility of integrating Voronoi tessellations as a GIS-based method for informing and improving Public-Private Mix (PPM) efforts throughout six states in Nigeria. Further, it explores a potential methodology for quantifying the theoretical number of individuals served by each private facility in order to better guide funding allocation to private providers who need it most. Standardized searches of academic papers obtained from PubMed, Google, and of key planning documents from National Tuberculosis Programs (NTPs) were conducted. In Nigeria, PPM is quickly becoming entrenched in healthcare, and Voronoi tessellations supplement these efforts by dividing private provider location data into meaningful and clear partitions. These tessellations were deficient in areas with high levels of clustering (dense urban sprawl), but are suited for examining private provider distribution in rural locations. The methodology of estimating populations through the polygons was too inaccurate to yield meaningful conclusions. Themes for PPM implementation include effective consolidation and representation of private sector providers, structured leadership from the NTP, and an understanding of HSBs.

There is a growing need to address the effects of roadway presence on wildlife. Not only do roads directly impact gene dispersal from a movement perspective, but they limit movement of the individual animal from a habitat perspective by presenting an artificial barrier between one area of viable habitat and another. For this reason it is becoming increasingly important to quantify contact between humans and wildlife and to develop better methods for mitigating these types of conflicts. Studying habitat connectivity and animal mobility in the context of roads can provide actionable information on how, where, and when these encounters might occur in order to minimize the effects transportation networks have on wildlife. This study uses two different approaches for studying wildlife-road interactions: (1) quantifying habitat fragmentation caused by roads and (2) directly quantifying wildlife interaction with roadways. This was achieved through the development and extension of methods found in the fields of landscape ecology and time geography. First, this study demonstrates the utility of one newly created road-based landscape metric through a detailed case study via the creation of an original ArcGIS toolbox. Second, this study develops a new time-geographic methodology to probabilistically measure and predict where wildlife interactions are most likely to occur on road networks. Additionally, it is important to ensure these methods not only quantify effects of roads from habitat and movement perspectives but can be used to mitigate these conflicts in real world conservation settings. Each of these approaches individually leverages techniques found in the field of spatial optimization to strategically locate wildlife crossing structures. This study developed two new methodologies to quantify where, when, and how wildlife interactions with roads are most likely to occur: the first using road-based landscape metrics and the second using a probabilistic voxel-based time-geographic approach. To address habitat connectivity issues, one road-based landscape metric was validated on a real world data set and further advanced by developing a GIS-based tool for real world applications. Utilizing landuse and roadway layers in combination with user specified parameters, the script tools developed here readily calculate this road-based landscape metric for a given study area. To address wildlife mobility issues, probabilistic space-time prisms were used to quantify interaction probabilities between wildlife and roads. These prisms were generated for a given set of tracking points and overlaid with an intersecting roads layer in GIS. Summing the probabilities at prism-roadway intersections revealed a pattern in the likelihood of animal-roadway interactions. Finally, each method was expanded to capture habitat fragmentation and animal movement in the presence of roads over large spatial scales using location analysis techniques. This research also develops and implements new methods that explicitly address wildlife-road interactions and aid in siting potential wildlife crossing structures. Since this study directly addresses effects of roadway presence on wildlife, the techniques developed here offer an alternative approach versus existing methods from a habitat and wildlife movement perspective. These methods can aid planners in the conservation of wildlife whose habitat has been impacted by road development by identifying and targeting areas of high impact.

The purpose of this thesis was to identify the different patterns of food consumption across space and time at Liangchengzhen, a Longshan (ca. 2600-1900 B.C.) site located in Shandong Province, China. The primary hypothesis of the research contended that evidence of increasing social inequality with respect to food consumption would be found from early to late phases at Liangchengzhen. In addition, rice and meat from mammals, especially pigs, were hypothesized as the most likely types of prestigious foods for daily and ritual activities. Fish and marine foods in general were hypothesized to be foods that average households could obtain since Liangchengzhen was close to the sea and would not have as high a value as mammal meat. Pottery was sampled from Early Phase storage/trash and ritual pits as well as Late Phase storage/trash and ritual pits located in Excavation Area One. Pottery types included ding and guan, hypothesized for cooking meat, and yan, hypothesized for steaming vegetables and grains. Lipid residue analysis was performed using gas chromatography/mass spectrometry (GCMS) to quantify the amount of C15 and C17 alkane peaks in the pottery and compare these quantities to the amount of C15 and C17 alkane peaks in terrestrial and marine food reference sources. Results indicated that socially valued food consumption transitioned from marine food sources in the early phase ritual pits to rice and pig in the late phase ritual pits. Millet and plant residues were consistently present in storage/trash pits from both early and late phases. Findings also indicated that the use of pottery types for cooking were not limited to one source, i.e., marine, rice, millet and plant residues were found in all pottery types while pig residues were found in ding and yan pottery. Results of the lipid residue analysis provide partial support of increasing social inequality with respect to food consumption from early to late phases at Liangchengzhen, The findings from the lipid residue analysis in this thesis more closely resemble the distribution of integrative, communal consumption pattern in the early phase and a hierarchical consumption pattern during the late phase. Fish, more abundant in the early phase, was almost non-existent by the late phase. Pig and rice, hypothesized as preferred foods, were found only during the late phase, primarily in the ritual pit, H31. Millet and plant were conspicuously present during both phases, but had greater separation from ritual pits during the late phase. However, these findings are surprising since it does not match the material remains of rice and pig found in early phase pits or late phase storage/trash pits from Excavation Area One. It can be concluded that patterns of consumption at Liangchengzhen changed substantially from the early phase to the late phase with regards to food residues found in hypothesized ritual pits. Considering these data with the understanding that food in China has historically been used as a tool to wield influence and power, it can be hypothesized that a social hierarchy may have developed by the late phase that was not present during the early phase. However, participation in the activities held in late phase ritual pits may have been inclusive for all Liangchangzhen residents rather than exclusive for higher status individuals. The current research provides a starting point for further investigation into the foodways at Liangchengzhen. This thesis is the first systematic study of food residues from the interior of Neolithic vessels from ancient China that relates the results of the residue analysis to patterns of food consumption and social change.

The Nubia plate is normally considered to be a rigid plate and as such used in the realization of terrestrial reference frame. Gondwana breakup plate reconstruction, the Cameroon volcanic line, seismicity, and the morphology of the Okavango rift zone (ORZ) suggest the presence of internal deformation within the Nubia plate. To test this hypothesis, six different reference frames were developed from the velocity field of three individual regions (West, Central and South), and of different combinations of them (West+Central, South+Central, and Nubia as a whole). The residual velocities with respect to these references frame help us understand the presence of the relative motion between the different regions thus the stability of the plate. To realize the reference frames, all the publicly available global positioning system (GPS) data within the “stable” Nubia plate was processed. Given the small relative velocity, it is important to eliminate eventual biases in the analysis and to have good estimates of uncertainty of the observed velocities. For this reason, velocities were analyzed, and rate uncertainties computed using the Allan variance of rate (AVR) technique, accounting for colored noise. Although geological and geophysical studies indicate the possibility of internal deformation within Nubia, the results of this study shows that the current GPS network is not capable to identify intraplate deformation and within uncertainties Nubia is a single plate. As final note, both the color of the noise and the amplitude of the annual signal of each time series as function of latitude and climatic region were analyzed. The study shows that the noise is approximately flicker for all the good stations independently of the location. On the contrary, the amplitude of the annual signal is strongly dependent on the climate of the regions.

This study investigates a way to characterize the geology and biology of the seafloor in two Marine Protected Areas on the West Florida Shelf. Characterization of benthic habitats needs to include sufficient detail to represent the complex and heterogeneous bottom types. Characterizations can be interpreted from multiple data sets and displayed as benthic habitat maps. Multibeam sonar bathymetry and backscatter provide full spatial data coverage, but interpretation of such data requires some form of ground truth (to characterize the habitat). Imagery from towed underwater video provides continuous transects of seafloor data, which provide a more efficient method than data from sediment grabs, stationary cameras, or video from slow-moving remotely-operated vehicles while a ship is on station. Two Marine Protected Areas, Steamboat Lumps and Madison-Swanson, were previously mapped by the USGS using a 95 kHz multibeam sonar system. Researchers at the University of South Florida, using a 300 kHz high-resolution multibeam sonar in 2002 and a 400 kHz high-resolution multibeam sonar in 2016, filled in the northeast triangular portion of Madison-Swanson. Bathymetry and backscatter data were compared to towed underwater-video observations. A modified version of the Coastal and Marine Ecological Classification Standard (CMECS), utilizing a scale-based hierarchy, was used for habitat characterization of video images. Identifiers from the geoform and substrate components of CMECS, as well as substrate-influencing biologic components, were characterized using still images at 15-second intervals from towed underwater video collected using the Camera-Based Assessment Survey System (C-BASS). These characterizations were then georeferenced (located in three-dimensional space) for comparison with bathymetry and backscatter data. In Steamboat Lumps, eight substrate variations were identified from video, while in Madison-Swanson 27 substrate variations were identified, including many combinations of hard and soft substrate types. Four new hard-bottom textures are identified from video in Madison-Swanson: exposed high-relief, moderate-relief, and low-relief hard bottom, as well as covered low-relief hard bottom identified by the presence of attached biota. Hard- and mixed-bottom substrate types identified from video are more heterogeneous than can be resolved from 95 kHz Kongsberg EM 1002 multibeam sonar bathymetry and beam-averaged backscatter. However, in soft bottom areas, more changes are evident in beam-averaged backscatter than are visible in video, though this may be attributed to changes in sonar settings. This does not appear to be the case with high-resolution and ultra-high resolution multibeam sonars, such as the 300 kHz Kongsberg EM 3000 and the 400 kHz Reson SeaBat 7125, which can use time-series rather than beam-averaged backscatter. Analyses of the multibeam bathymetry data indicate that 94.5% of Steamboat Lumps is “flat” (slope < 5°) versus “sloping” for the remaining area (5° < slope < 30°). Only 87% of Madison-Swanson is “flat” versus “sloping”. Both marine protected areas have very low rugosity, i.e., the surface of the seafloor is nearly planar.

Sea levels have risen approximately 20 cm since the beginning of the 20th century and more than 3 cm in the past 20 years, suggesting that global sea level rise is accelerating. As sea levels continue to rise and storms become more intense, coastal property and populations become more susceptible to damage. Florida is especially vulnerable to hurricane-induced storm surge (HSS) and the onset of accelerated sea-level rise (ASLR) due to its extensive coastline and high population density along the coast. The main purpose of this research is to assess the potential economic impacts of ASLR and HSS for two of western Pasco County’s municipalities, Port Richey and New Port Richey. A Geographic Information System is used to determine the spatial extent at a high-resolution of coastal inundation, the economic loss based on property value and road expenditure due to this inundation, and its impact on critical infrastructure. The results from this study showed coastal flooding generated by 0.5m SLR amounted to 48.8% land loss and $217,108,692 of property loss. Monetary losses from inundated properties shifted dramatically from 1.0m to 1.5m SLR, from $295 million to $417 million, suggesting that the tipping point could only be a half-meter SLR. Based on the 2.0m SLR results, most of major highway US-19 was completely flooded, property tax losses amounted to approximately $7.1 million, and road expenditure was approximately $158 million. Data provided in this study can be useful for coastal management and planning in Port Richey and New Port Richey.

The spread of infectious diseases can be described in terms of three interrelated components: interaction, movement, and scale. Transmission between individuals requires some form of interaction, which is dependent on the pathogen, to occur. Diseases spread through the movement of their hosts; they spread across many spatial scales from local neighborhoods to countries, or temporal scales from days to years, or periodic intervals. Prior research into the spread of disease have examined diffusion processes retrospectively at regional or country levels, or developed differential equation or simulation models of the dynamics of disease transmission. While some of the more recent models incorporate all three components, they are limited in the way they understand where interactions occur. The focus has been on home or work, including contact with family or coworkers. The models reflect a lack of knowledge about how transmissions are made at specific locations in time, so-called nodes of transmission. That is, how individuals’ intersections in time and space function in disease transmission. This project sought to use the three factors of interaction, movement, and scale to better understand the spread of disease in terms of the place of interaction called the node of transmission. The overarching objective of this research was: how can nodes of transmission be identified through individual activity spaces incorporating the three factors of infectious disease spread: interaction, movement, and scale? This objective fed into three main sub-objectives: defining nodes of transmission, developing an appropriate methodology to identifying nodes of transmission, and applying it using geotagged social media data from Twitter. To develop an appropriate framework, this research relied on time geography, and traditional disease. This particularly relied on the idea of bundling to create the nodes, and a nesting effect that integrated scale. The data source used to identify nodes of transmission was collected from Twitter for the Los Angeles County, USA, area from October 2015 to February 2016. Automated text classification was used to identify messages where users self-reported an influenza-like-illness. Different groupings were created that combined both the syndrome and the symptoms of influenza, and applied to the automated classification. The use of Twitter for small-area health analysis was evaluated along with different text classification methodologies. A space-time hierarchical clustering technique was adapted to be applied towards the twitter data in both identifying nodes of transmission and identifying spatiotemporal contact networks. This clustering data was applied to the classified Twitter data to look at where interaction between the classified users were occurring. This pointed to six nodes that were typically densely populated areas that saw the merging of large groups of people in Los Angeles (e.g. Disneyland and Hollywood Boulevard).The movement of these individuals were also examined by using a edit distance to compare their visits to different clusters and nodes.

The term “home range” refers to the area in which an animal spends most of its time during everyday activities. This study examined the effects of four different home range estimation techniques on the proportions of habitats located therein. The study utilized a point dataset collected for twenty individual Florida Black Bears (Ursus americanus floridanus), occurring in five different areas throughout the state of Florida. Each dataset was used to create home ranges using the following techniques: (1) Minimum Convex Polygon, (2) Kernel Density Estimation, (3) Characteristic Hull Polygon, and (4) Time-Geographic Density Estimation, a new home range estimator which has not been thoroughly tested prior to this study. A dataset of land cover types was clipped with each home range and the areas of habitats were recalculated. The proportion of each land cover type was evaluated and the results compared first within each dataset, then between all datasets used. Repeated measures analysis of variance (ANOVA) was used to determine whether the four home range estimation techniques produced significantly different areas and proportions of each habitat type. These results were then evaluated to determine whether the method of home range estimation has an effect on which land cover types are most utilized by a species and, therefore, which habitats are considered preferable. While the choice of home range estimation did not have an effect on which habitats were determined to be most frequently visited, it did affect the amount of each habitat found within each home range. Furthermore, there was a statistically significant change in the amount of developed areas, specifically between the characteristic hull polygon and kernel density estimation methods. These results suggest the choice of home range estimator affects habitat analysis and that researchers should use the method best suited for the dataset.

The Green Swamp Preserve is a large geographic area that has sustained many changes since Europeans settled in Florida. There has been little published research on the impacts of anthropogenic activity on this system. This thesis research seeks to document more recent changes in the Green Swamp and to evaluate the effects of various human activities on the system. The study period is from 1985 to 2015. For this time period changes in land use and landcover were examined using neural network classifications. Changes in vegetation health were evaluated by examining Normalized Difference Vegetation Index (NDVI) and Green Vegetation Index differences. Field site visits were made to document current conditions at thirty sample locations within the study area. Changes in land use and landcover and vegetation health were evaluated in relation to anthropogenic activities such as proximity to pollution sources, conservation lands and restoration sites. Statistical analysis was conducted to determine if statistically significant clustering occurred in these changes and if present geographically weighted regressions were performed to determine if a significant spatial relationship existed between the clustering and the various human activities. WAP data showed an overall decline in wetland health at the assessment sites and showed a trend of lower wetland health at sites within 2 Km of pollution sources, specifically petroleum tank contamination sites and state roads. The statistically significant clustering identified in land use landcover changes from 1985 to 2015 were in relation to changes from field, forested and wetland landcover types to built environments. Spatial relationships were identified between the proximity of petroleum tank contamination sites, state roads and solid waste facilities and clustering of NDVI decreases from 1985 to 2015. NDVI increases in the study area from 1985 to 2015 also showed statistically significant clustering in relation to conservation lands and lands purchased by the Southwest Florida Water Management District for environmental protection. These preliminary findings suggest that human activities may have influenced changes in the health of the Green Swamp. Further, more extensive research is suggested to confirm these findings.

This dissertation introduced a method to construct a new measure for trade flows within a region using nighttime lights. After analyzing the relation between lights data and other proxies of economic human activity, I employed light data and econometric techniques to estimate the bilateral trade between any two regions around the world. Using these estimations, I estimated the overall internal trade volume for all countries. Moreover, I estimated the effect of internal trade within a state of the United States on the state’s income. The first essay proposed nighttime lights as an alternative proxy for economic activity to be used in gravity regressions. Due to the well-known problems in the measurement of gross domestic or regional products, gravity regressions based on both international and intranational trade data suffer from potential biases. At both international and intranational levels, log nighttime lights positively and significantly enter the gravity regressions (with a coefficient of roughly one) that explain at least about half of the variance in exports. The results were shown to be robust to the inclusion of several control variables and the consideration of predicted trade flows. Trade within a nation and internal distance are variables known to play key roles in explaining home bias and the distance puzzle in international trade literature, but data on these measures are limited to only a few countries. To address this problem, in the second essay, I constructed micro-founded measures of internal trade and internal distances from satellite data on nighttime lights. By estimating the gravity equation coefficients using the simulated method of distance estimation, I constructed the bilateral trade flow at subnational scale and aggregated it to overall internal trade. I found my internal trade measure is highly correlated with its benchmark, the difference between GDP and total exports; however, I showed it has more information and is a more precise measure for developed countries, which have a large amount of non-tradable services included in their national income account data. The internal distance measure is generated as the lights-weighted average distances between the states within a country. While my internal distance measure is largely correlated with its alternative, which is constructed based on city-level population data, it does not suffer from the uncertainty surrounding population data. Correlation between trade and income cannot identify the effect of trade because of the endogeneity problem. The third essay examined this relationship at subnational level and by focusing on instrumenting trade via time varying geographic factors. Proximity and economic size are determinants of trade that are uncorrelated with other income determinants. This experiment not only confirmed the effect of interregional trade, but also provided evidence that intraregional trade has a large and statistically significant impact on income. I found, however, that the effect of both trade measures is statistically similar; a one percentage point increase in the interregional and intraregional trade ratio increases income per person by 2 to 4 percent.