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This study is a spatiotemporal analysis of the COVID-19 pandemic in school-age children (5-18 years) in Tennessee, from 2020-03-19 to 2022-02-12. Trend Analysis, Emerging Hot Spot Analysis, and a time series revealed three significant waves in both age groups. Therefore, Change Point Detection at the county level was completed using six defined change points to identify the wax and wane of the three COVID-19 waves. Hierarchical Cluster Analysis grouped counties with similar change points into six clusters. No spatial pattern was observed in distribution of the six clusters, however, when each change point was evaluated separately, spatial autocorrelation was present, showing that timing of the individual waves was clustered in space. This research describes appropriate spatioanalytical methods useful at different stages of a pandemic and could inform policymaking by public health officials.

Harmful algal blooms (HABs) and nuisance algal blooms (NABs) are a worldwide phenomenon with implications for human health and safety. HABs occur when algae or bacteria grow in high enough densities to harm animals and humans. A primary component of harmful algal blooms is cyanobacteria, which are aquatic, photosynthesizing microorganisms that produce toxins at high concentrations. Cyanobacterial biomass has increased worldwide in recent decades, raising concern about the future of fresh- and marine-water systems in a changing climate. Understanding the patterns and conditions of past algal blooms can provide useful insights for managing future blooms. Remote sensing can enhance our understanding of the spatiotemporal distribution of HABs and NABs. We used radiometrically corrected images from the USGS Landsat Collections available in the Google Earth Engine for cloud processing. In 2016, the USGS calibrated the sensors of Landsat 4, 5, 7, and 8 to create a continuous collection of satellite images from 1984 to present. We use this 34-year dataset to expand the historical record of algal blooms at our study site and to understand factors relating to the spatiotemporal patterns of these blooms. We applied three models, including the Floating Algae Index (FAI), the Normalized Difference Vegetation Index (NDVI), and one developed with in situ chlorophyll-a (chl-a) data, to 398 images masked for cloud cover and lake elevation taken from 34 growing seasons (April – October). We found that the Normalized Difference Water Index (NDWI) used to separate water and land pixels fails under algal bloom conditions, whereas a modified NDWI does not. We also performed an emerging hot spot analysis in ArcGIS using the chlorophyll-a, NDVI, and FAI predictions from the surface reflectance values of the images. Our analysis indicates that the Provo Bay and parts of the eastern shoreline of Utah Lake have had algal blooms for 30 out of the 34 years included in this study, rendering them enduring hot spots. The remainder of the lake is a cold spot, showing clusters of low mean chl-a, NDVI, and FAI values over time. The overall trend of mean NDVI and lake surface area over this 34-year dataset is decreasing, whereas lake water temperature is increasing. This study develops a method for analyzing algal blooms over multiple decades and provides useful information for the management and prediction of future blooms.

This study uncovered the pattern and spatial relationships between socio-economic factors and aggregated COVID-19 rates in Ottawa, Canada, from July 2020 to December 2021 at the neighbourhood scale. Both top-down and bottom-up data mining approaches were used to predict COVID-19 rates. The top-down approach employed ordinary least squares regression (OLS), spatial error model (SEM), geographically weighted regression (GWR) and multi-scale geographically weighted regression (MGWR). Model intercomparison was also undertaken. The pattern of COVID-19 in Ottawa exhibited a significant moderately positive spatial structure among neighbourhoods (Moran's I = 0.39; p = 0.0001). Local Moran's analysis identified areas of low and high COVID-19 clustering, interspersed with cold spots. The OLS model used determinants based on a literature review. Determinants were tested for normality using the Shapiro-Wilks test with those that failed the test had transformatoins to normality applied. Next, an OLS-based backward stepwise approach was used to select the optimal set of determinants based on goodness of fit, selecting the model with the lowest Akaike Information Criterion (AIC). The percentage of people who take public transit to work, percentage of people with no high school diploma, percentage of people over 65 years old, and percentage of people with a Bachelor level degree or above comprised the final set of determinants. A SEM model was created to account for residual spatial autocorrelation in the OLS model's residuals and yielded an adjusted R² = 0.63. Based on the SEM, a one-unit increase in the square root of the percentage of people with a bachelor's degree or above was associated with a 3.2% increase in COVID-19 rates, while the same unit increase in the square root of the percentage of people with no high school diploma was associated with a 10.6% increase in COVID-19 rates. Conversely, a one percent increase in the percentage of people aged 65 and older was linked to a 34.6% decrease in COVID-19 rates. To examine local variations in the relationships between the determinants and COVID-19, a MGWR with a Bisquare kernel and an adaptive bandwidth was used to improve upon the overall explained variance of the SEM model. The residuals of the MGWR model exhibited no significant spatial autocorrelation (Moran's I = -0.04; p = 0.62) and residuals were approximately normal (W = 0.98; p > 0.25). The MGWR model yielded an adjusted R² = 0.75. Taking a data mining and bottom-up approach, an optimized Random Forest model provided a very different set of determinants as important when compared to the top-down regression approaches and accounted for 47.34% of the COVID-19 variance.

No description available.

No description available.

No description available.

This work investigates births to white, African American and Hispanic adolescents in Hillsborough and Pinellas Counties, Florida, from 1992 to 1997 in two age groups - 13 to 17 year-olds and 18 to 19 year-olds - using spatial statistical techniques along with key informant interviews to provide insights into the utility of the research findings. The research developed a method for estimating the adolescent population in inter-census years, which was used to determine denominators for calculating teen birth rates. It also developed a composite deprivation index using socioeconomic indicators at the census block group level. The index provided context for hot and cold spot analysis, areas where expected teen birth rates were statistically higher or lower than expected. The association between socioeconomic deprivation in a neighborhood and rates of teen births was inconclusive, indicating a need for further research. Next steps include investigating individual-level risk and protective factors using multi-level modeling and cluster analysis as alternate analytic methods, and conducting ethnographic investigation to help provide context to the neighborhoods.

Traffic accidents is one of the major causes of fatalities and economic loss around the world. Thus, there is an urgent need for a better understanding about the factors that contribute to accidents so that the accidents can be prevented in the future. The research objective of this thesis is to analyze the traffic accidents between vehicles and unprotected road users (pedestrians and bicycles) in Stockholm, finding spatial distribution patterns, related attributes and examining relationships between accidents and a number of vehicle flows. The data is first analyzed with general statistical analysis to examine the basic characteristics. There is no apparent trend of change among the number of accidents per year, while the numbers of accidents happening from May to October is higher than the rest of the year except for July due to less traffic during holiday period. Most traffic accidents occur in overcast weather, on a dry road surface, or during the day. In the spatial analysis part of the thesis, Global Moran’s I is used to detect whether there is an attribute-related spatial distribution pattern. Hot spot analysis is then applied on the clustered attributes to find significant hot and cold spots over the study area. The conclusions are that road surface conditions and occurrence time during day/night are two related factors that influence traffic accidents while weather is not considered a related attribute since the accidents distribute randomly in terms of weather, of which it is difficult to obtain temporally-aligned, detailed local information for further analysis. Different parameters are selected and discussed during the process. When calculating the distance between two accidents in traffic accident analysis, Manhattan distance is more appropriate than Euclidean distance since traffic accidents are restricted to the road network. The distance band determines scales of analysis tools, with 50 meters on an intersection and 500 meters for a larger region in Stockholm. Most hot spots arise at intersections and roundabouts where different types of traffic flows meet each other. The result of the relationships between traffic accidents and different types of vehicle flows shows that the correlation coefficients between number of traffic accidents and traffic flows are low, meaning that there is no obvious correlation between them, which is also proved by the scatter plots. Poisson regression model is applied on the traffic accident data. As a result, high-risk and low-risk areas in Stockholm are pointed out. Some are consistent with the hot-spot analysis result.