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The COVID-19 pandemic has heightened the need for fine-scale analysis of the clustering of cases of infectious disease in order to better understand and prevent the localized spread of infection. The students living on the University of Massachusetts, Amherst campus provided a unique opportunity to do so, due to frequent mandatory testing during the 2020-2021 academic year, and dense living conditions. The South-West dormitory area is of particular interest due to its extremely high population density, housing around half of students living on campus during normal conditions. Using data gathered by the Public Health Promotion Center (PHPC), we analyzed the clustering of SARS-CoV 2 cases in three-dimensional space as well as time within and between the three tallest occupied buildings in the Southwest dormitory area, John Quincy Adams, Kennedy, and Coolidge. We used the SaTScan program and its Space-Time Permutation Model, which searches for areas with a greater than expected number of cases. Analysis was done at various levels of spacial detail. Additionally, this analysis was compared to the purely temporal surveillance method, CDC’s Early Aberration Reporting System (EARS). Analysis with SaTScan at the room and floor level showed multiple significant clusters within the Coolidge dormitory building. Floor-level analysis was found to be as sensitive as and less burdensome than room-level analysis. We recommend using scan statistics in conjunction with other methods such as purely temporal scans and wastewater analysis to detect and respond to outbreaks on campus.

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With the advent of smart sensor devices and Internet of Things (IoT) in the rapid urbanizing cities, data is being generated, collected and analyzed to solve urban problems in the areas of transportation, epidemiology, emergency management, economics, and sustainability etc. The work in this area basically involves analyzing one or more types of data to identify and characterize their impact on other urban phenomena like traffic speed and ride-sharing, spread of diseases, emergency evacuation, share market and electricity demand etc. In this work, we perform spatio-temporal analysis of various urban datasets collected from different urban application areas. We start with presenting a framework for predicting traffic demand around a location of interest and explain how it can be used to analyze other urban activities. We use a similar method to characterize and analyze spatio-temporal criminal activity in an urban city. At the end, we analyze the impact of nearby traffic volume on the electric vehicle charging demand at a charging station. / Master of Science / Because of the ubiquity of the Internet and smart devices, a tremendous amount of data has been collected from multiple sources like vehicles, purchasing details, online searches etc., which is being used to develop innovative applications. These applications aim to improve economic, social and personal lives of people through new start-of-the-art techniques like machine learning and data analytics. With this motivation in mind, we present three applications leveraging the data collected from urban cities to improve the life of people living in such cities. First, we start by using taxi trip data, collected around a given location, and use it to develop a model that can predict taxi demand for next half hour. This model can be used to schedule advertisements or dispatch taxis depending upon the demand. Second, using a similar mathematical approach, we propose a strategy to predict the number of crimes that can happen at a given location on the next day. This helps in maintaining law and order in the city. As our third and last application, we use the traffic and historical charging data to predict electric vehicle charging demand for the next day. Electricity generating power plants can use this model to prepare themselves for the higher demand emerged because of the increasing use of electric vehicles.

Economic input-output (I-O) and watershed models provide useful results but when seeking to integrate these systems, the structural, spatial, and temporal differences between these models must be carefully considered. To reconcile these differences, a hydrologic-economic modeling framework is designed to couple an economic model with a watershed model. A physically constrained, I-O model, RCOT, is used to represent the economic system in this framework because it provides sectoral detail for a regional economy and calculates physical resource quantities used by these sectors. Uniquely, it also allows for technology options for all sectors and minimizes the resource use based on environmental constraints imposed by the watershed, which adds complexity to the representation of the economic system and its interactions with the watershed system. To represent the watershed system in this framework, the Hydrological Simulation Program-Fortran (HSPF) is used. An HSPF model has been calibrated to represent the hydrological processes of Cedar Run Watershed by the Occoquan Watershed Monitoring Laboratory (OWML). Thus, the capabilities of this framework are demonstrated using strategic scenarios developed to examine future development patterns that may occur within Fauquier County, northern Virginia, and its local basin, Cedar Run Watershed. The scenarios evaluate both the downstream and seasonal impacts on water flow and nitrogen concentration within the watershed, and the changes made within the economic system in response to these impacts. For these scenarios, the most efficient solution is the one that minimizes the use of resource inputs within the economic sectors, including developed land, water withdrawn, and applied nitrogen, which in turn inform watershed health. The scenario results demonstrate that this coupled hydrologic-economic modeling framework can overcome the spatial differences of the individual models and can capture the interactions between watershed and economic systems at a temporal resolution that expands the types of questions one can address beyond those that can be analyzed using these models separately. / Doctor of Philosophy / Water is an essential commodity for human survival, a necessary resource for many industries, and a crucial indicator of environmental health. Rising human populations have created stress on the natural supply of water resources while corresponding economic activities have contributed to the deterioration in water quality. Therefore, it is essential to identify pathways for addressing water use and contamination while also supporting economic progress to achieve sustainable development. The region of study is Fauquier County, located in northern Virginia, USA. This county has a long association with agricultural production, but it has been experiencing development pressure due to its proximity to Washington DC (50 km southwest). Within Fauquier County lies Cedar Run Watershed (498 km2), a sub-basin of Occoquan Watershed (1,515 km2). Occoquan Watershed drains into the Occoquan Reservoir, which is a drinking water source for close to two million residents in northern Virginia. The motivation of this research is to design a coupled modeling framework that allows insight to be gained into the interactions that occur between watershed and economic systems. This framework is then used to evaluate how changes in economic activities will cause changes in water use and contamination levels within Cedar Run Watershed and vice versa. By designing strategic scenarios to provide implications about future development patterns that may occur in the region, changes can be anticipated, and conclusions can be reached.

Natural disasters have become more severe and frequent than previous assessments with global warming. The increasing risk of natural disasters presents different groups of populations with diverse vulnerabilities, particularly those underrepresented social groups which need specific support before, during, and after extreme disasters. Hence, it is highly desired to examine vulnerability quantitatively and qualitatively across different social groups in risk to natural disasters. This dissertation study aims to investigate the measure of social vulnerability to two types of climate change-related natural disasters: sea-level-rise floodings and wildfires. In the study of sea-level-rise floodings, high-risk flooding areas are first identified for a coastal city. Then, we measure social vulnerability index (SVI) using a new SVI metric to identify vulnerable social groups which should be paid more attention for coastal flooding disaster mitigation. Compared to existing SVI methods, the new SVI leverages principal component analysis and analytic hierarchy process to achieve a better social vulnerability analysis. In the study of wildfires, we focus on the understanding of minority vulnerabilities and their disparities to wildfires over time and space. Minority vulnerabilities are analyzed with spatial clustering methods including Local Moran’s I and Getis-Ord Gi*. The vulnerability disparity is measured based on a reference point from which the quantity separates a minority group on a particular place. Both location quotient and location amplitude index are used to quantitively measure the vulnerability disparity among different minorities. Lastly, in addition to the “direct” impact of disasters on vulnerable population, this dissertation study also conducts vulnerability analysis to failed infrastructure (e.g., power systems) due to disasters, i.e., the “indirect” impact of disasters on different social groups. Recently, scheduled power outages known as Public Safety Power Shutoff (PSPS) are becoming increasingly common to mitigate threats of wildfires to power systems. However, current PSPS decision making processes do not consider the unequal distribution of various social groups, particularly those who are more vulnerable to the power outage. This study investigates the measure of social vulnerability in high-risk fire areas to PSPS, which will help decision makers to better determine the efficiency of a PSPS event for wildfire mitigation.

Fluxos de dados são usualmente caracterizados por grandes quantidades de dados gerados continuamente em processos síncronos ou assíncronos potencialmente infinitos, em aplicações como: sistemas meteorológicos, processos industriais, tráfego de veículos, transações financeiras, redes de sensores, entre outras. Além disso, o comportamento dos dados tende a sofrer alterações significativas ao longo do tempo, definindo data streams evolutivos. Estas alterações podem significar eventos temporários (como anomalias ou eventos extremos) ou mudanças relevantes no processo de geração da stream (que resultam em alterações na distribuição dos dados). Além disso, esses conjuntos de dados podem possuir características espaciais, como a localização geográfica de sensores, que podem ser úteis no processo de análise. A detecção dessas variações de comportamento que considere os aspectos da evolução temporal, assim como as características espaciais dos dados, é relevante em alguns tipos de aplicação, como o monitoramento de eventos climáticos extremos em pesquisas na área de Agrometeorologia. Nesse contexto, esse projeto de mestrado propõe uma técnica para auxiliar a análise espaço-temporal em data streams multidimensionais que contenham informações espaciais e não espaciais. A abordagem adotada é baseada em conceitos da Teoria de Fractais, utilizados para análise de comportamento temporal, assim como técnicas para manipulação de data streams e estruturas de dados hierárquicas, visando permitir uma análise que leve em consideração os aspectos espaciais e não espaciais simultaneamente. A técnica desenvolvida foi aplicada a dados agrometeorológicos, visando identificar comportamentos distintos considerando diferentes sub-regiões definidas pelas características espaciais dos dados. Portanto, os resultados deste trabalho incluem contribuições para a área de mineração de dados e de apoio a pesquisas em Agrometeorologia. / Data streams are usually characterized by large amounts of data generated continuously in synchronous or asynchronous potentially infinite processes, in applications such as: meteorological systems, industrial processes, vehicle traffic, financial transactions, sensor networks, among others. In addition, the behavior of the data tends to change significantly over time, defining evolutionary data streams. These changes may mean temporary events (such as anomalies or extreme events) or relevant changes in the process of generating the stream (that result in changes in the distribution of the data). Furthermore, these data sets can have spatial characteristics such as geographic location of sensors, which can be useful in the analysis process. The detection of these behavioral changes considering aspects of evolution, as well as the spatial characteristics of the data, is relevant for some types of applications, such as monitoring of extreme weather events in Agrometeorology researches. In this context, this project proposes a technique to help spatio-temporal analysis in multidimensional data streams containing spatial and non-spatial information. The adopted approach is based on concepts of the Fractal Theory, used for temporal behavior analysis, as well as techniques for data streams handling also hierarchical data structures, allowing analysis tasks that take into account the spatial and non-spatial aspects simultaneously. The developed technique has been applied to agro-meteorological data to identify different behaviors considering different sub-regions defined by the spatial characteristics of the data. Therefore, results from this work include contribution to data mining area and support research in Agrometeorology.

A cidade que mais cresce no mundo. São Paulo não pode parar... São Paulo deve parar! Mais do que slogans que marcam a história da maior metrópole brasileira, essas frases revelam uma capital marcada por mudanças e manifestações sociais típicas de grandes centros urbanos. O presente trabalho visa compreender essas mudanças por intermédio de uma das manifestações que melhor distingue o território paulistano: os homicídios dolosos, um dos principais problemas sociais desta metrópole. Para tanto, conceitos e concepções presentes na literatura que aborda o movimento da criminalidade urbana paulista são debatidos, utilizados como fundamento teórico e hipóteses a serem testadas. Além disso, técnicas estatísticas e geoestatísticas são empregadas como ferramentas analíticas do material empírico, obtido de diversas fontes. O trabalho é composto por estudos descritivo-exploratórios e análises em escala intraurbana. Congregando um amplo número de pesquisas cientificas, esses estudos buscam esclarecer por que, em um determinado período e local, as taxas de homicídios dolosos apresentam estabilidade, crescimento ou retração. Já as análises avaliam esse fenômeno do ponto de vista dos diversos padrões de urbanização e de homicídios que configuram a cidade de São Paulo. De modo geral, as investigações são consideradas em perspectiva longitudinal, o que possibilita uma observação mais adequada das nuances e variações dos homicídios, assim como melhor contextualização das matrizes teóricas que sustentam ou contestam os resultados obtidos. Entretanto, mais do que considerar o movimento dos homicídios dolosos por intermédio de suas taxas e das condicionantes que explicam sua variabilidade no tempo e no espaço, avalia-se o efeito destas entre si, sob um ponto de vista sócio-histórico e, em sentido amplo, dialético e plural. Considerando o desenvolvimento da cidade, buscou-se apresentar as transformações ocorridas na urbe e como elas se associam às taxas de homicídios. Essas transformações são vistas tanto em nível macrossociológico como microssocial. No primeiro, focaliza-se o movimento dos homicídios dolosos tendo em vista fatores históricos, econômicos, políticos e sociais que aparecem direta ou indiretamente associados com esse movimento. No segundo nível, em cada uma das milhares de partes em que a cidade é dividida são verificadas características econômicas, infraestruturais, demográficas e sociais, entre outras, que se mostraram significativas para explicar a variabilidade dos homicídios. Ambos os níveis são articulados durante todo o trabalho, sendo as conclusões alcançadas oriundas desta articulação condutoras da reflexão acerca das principais conclusões deste estudo. Essas conclusões contrariam a noção de que o movimento dos homicídios pode ser explicado por teorias universais e atemporais, apontando para a importância de uma avaliação científica da área e do período de estudo, das teorias sobre crimes urbanos e das mudanças sociais capazes de alterar esse movimento. / The world´s fastest growing city. Sao Paulo cant stop Sao Paulo must stop! More than slogans that marked the history of the biggest Brazilian metropolis; these sentences reveal a city characterized by change and social manifestations distinctive of large urban centers. The present work aims to comprehend these changes through one of the manifestations that best distinguishes the city area; homicides, one of the metropolis main social problems. For this purpose, the concepts and conceptions present in the literature that approaches the citys urban criminality movement are addressed, and used as theory basis and hypothesis to be tried. Also, the statistical and geostatistical techniques are employed as analytical tools to study the empirical data obtained through several sources. This work is composed of descriptive exploratory studies and assessments on intraurban scales. The studies gathered a wide number of scientific researches that seek to clarify why, in a certain period and location, the homicide rates present stability, growth or retraction. On the other hand the analysis approaches this phenomenon through the point of view of several urbanization and homicidal patterns that characterize Sao Paulo city. Overall, the research is taken into consideration on a longitudinal perspective, what enables a more suitable observation of homicide shades and variations, as a better contextualization of the theoretical matrices that support or challenge the obtained results. However, more than considering the movement of homicide by its rates and conditionings, that explain their variations in time and space, it was also assessed the effects among each other, under a sociohistorical point of view and, in a wider perspective, dialectical and plural as well. Taken into account the citys development, it was soughed to present the transformations that occur in the city and how they are related to the homicide rates. Those transformations can be seen in a macrosociological and microsocial levels. At the first, it was focused on the movement of homicide taken into historic, economic, politic and social account factors that show directly or indirectly associated with this movement. At the second level, in each of the thousand parts that composed the city it is verified economic, infrastructural, demographical and social characteristics, among others, that were found to be significant to explain the variability of homicide. Both levels are articulated throughout this work, and these conclusions reached arising of this articulation lead to reflection on the main conclusions of this work. These conclusions contradict the notion that the homicide movement can be explained by universal and timeless theories that show the importance of scientific assessments over place and time the study took place, of urban crime theories and the social changes capable of altering that movement.

D'après le rapport de l'OMS en 2015, près de la moitié de la population mondiale est exposée au risque du paludisme et le plus grand nombre des cas recensés se trouve en Afrique subsaharienne. Madagascar fait partie des pays où le paludisme est encore endémique. La géographie et le climat de l'île se traduisent par une répartition assez particulière du paludisme. Les plus fortes incidences sont observées sur les littoraux alors que les plus faibles le sont sur les Hautes Terres Centrales. Cinq zones épidémiologiques et opérationnelles ont été définies par les services de lutte contre le paludisme : Est, Ouest, Sud, Hautes Terres et les Marges. Cette étude vise à apporter une meilleure compréhension de l'épidémiologie du paludisme et de mesurer l'impact des mobilités humaines sur la transmission des zones à forte transmission vers les zones à faible transmission afin de contribuer à mieux cibler les actions de contrôle par les acteurs de la santé publique. Elle offre une nouvelle approche permettant d'évaluer la dynamique spatio-temporelle du paludisme, de quantifier la circulation de l'infection palustre en tenant compte de la mobilité de la population par l’utilisation des données de téléphonie mobile et d'identifier les principales zones exportatrices et importatrices de la maladie. En premier lieu, à travers une analyse rétrospective des données d'incidence, ce travail a montré une hétérogénéité évidente dans chaque stratification épidémiologique et la recrudescence de la maladie sur les Hautes Terres et les Marges. En second lieu, indépendamment de la densité de la population, nous avons montré que les Hautes Terres et surtout la capitale Antananarivo sont une zone à fort risque d'importation de paludisme et que les zones exportatrices sont surtout situées à l'Est et à l'Ouest. Enfin, notre enquête de terrain a souligné l'importance d'une mobilité inter district faible et d'une mobilité intra district voire intra communale importante qui mériterait d'être prise en compte dans la mesure de la circulation de l'infection palustre. Cette étude a permis de mettre en lumière que le paludisme est très dynamique à Madagascar avec un degré d'intensité différent même si les zones appartiennent à la même stratification. Ce constat devrait se traduire par une adaptation des stratégies de lutte. Enfin, la mobilité humaine joue un rôle important dans la transmission. A l'heure où la téléphonie mobile s'est largement diffusée à Madagascar, son utilisation pour estimer le déplacement des populations devient un outil pertinent pour contribuer à orienter le contrôle des maladies. / According to the 2015 WHO report, almost half of the world population is exposed to malaria, with the largest number of reported cases in sub-Saharan Africa. Madagascar is one of the countries where malaria is still endemic because of its geographical location. As a matter of fact, the geography and climate of the island gives a specific epidemiological stratification of malaria. There are five malaria epidemiological zones: East, West, South, Highlands and Fringe. The highest incidence is observed on coastal areas, while the lowest incidence is observed on the Central Highlands. This study aims to provide a better understanding of the epidemiology of malaria and to measure the impact of human mobility on transmission from high transmission areas to low transmission areas in order to help better target control actions by public health actors. This study proposes an alternative approach to assess the spatiotemporal dynamics of malaria, quantify the circulation of malaria infection and take into account the mobility of the population to identify the main source and sink areas of malaria. Firstly, through a retrospective analysis of incidence data, this work showed a clear heterogeneity in each stratum, and an increase the Highlands and the Fringe areas. Secondly, regardless of the population density, we have shown that the Highlands and especially the capital of Madagascar, Antananarivo, was a zone at high risk of the importation of malaria. The source areas were mainly in the eastern and the western part of the country. Finally, our field survey highlighted the importance of low inter-district mobility and high intra-district or even intra-communal mobility which should be taken into consideration when assessing the spreading of malaria infection. This study revealed that malaria is very dynamic in Madagascar with a different degree of intensity even if such areas belong to the same stratum. This observation should translate into an adaptation of control strategies. Finally, human mobility plays a leading part in the transmission. At a time when mobile telephony has spread widely in Madagascar, its use to estimate the mobility of populations is becoming a relevant tool to help guide disease control.