Emergency Management Information Systems (EMIS) are defined as a set of tools that aid decision-makers in risk assessment and response for significant multi-hazard threats and disasters. Over the past three decades, EMIS have grown in importance as a major component for understanding, managing, and governing transportation-related systems. To increase resilience against potential threats, the main goal of EMIS is to timely utilize spatial and network datasets about (1) locations of hazard areas (2) shelters and resources, (3) and how to respond to emergencies. The main concern about these datasets has always been the very large size, variety, and update rate required to ensure the timely delivery of useful emergency information and response for disastrous events. Another key issue is that the information should be concise and easy to understand, but at the same time very descriptive and useful in the case of emergency or disaster. Advancement in EMIS is urgently needed to develop fundamental data processing components for advanced spatial network queries that clearly and succinctly deliver critical information in emergencies. To address these challenges, we investigate Spatial Network Database Systems and study three challenging Transportation Resilience problems: producing large scale evacuation plans, identifying major traffic patterns during emergency evacuations, and identifying the highest areas in need of resources. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2020. / FAU Electronic Theses and Dissertations Collection

The emerging global middle class, which is expected to double by 2050 desires more walkable, liveable neighbourhoods, and as distances between work and other amenities increases, cities are becoming less monocentric and becoming more polycentric. African cities could be described as walking cities, based on the number of people that walk to their destinations as opposed to other means of mobility but are often not walkable. Walking is by far the most popular form of transportation in Africa’s rapidly urbanising cities, although it is not often by choice rather a necessity. Facilitating this primary mode, while curbing the growth of less sustainable mobility uses requires special attention for the safety and convenience of walking in view of a Global South context. In this regard, to further promote walking as a sustainable mobility option, there is a need to assess the current state of its supporting infrastructure and begin giving it higher priority, focus and emphasis. Mobile phones have emerged as a useful alternative tool to collect this data and audit the state of walkability in cities. They eliminate the inaccuracies and inefficiencies of human memories because smartphone sensors such as GPS provides information with accuracies within 5m, providing superior accuracy and precision compared to other traditional methods. The data is also spatial in nature, allowing for a range of possible applications and use cases. Traditional inventory approaches in walkability often only revealed the perceived walkability and accessibility for only a subset of journeys. Crowdsourcing the perceived walkability and accessibility of points of interest in African cities could address this, albeit aspects such as ease-of-use and road safety should also be considered. A tool that crowdsources individual pedestrian experiences; availability and state of pedestrian infrastructure and amenities, using state-of-the-art smartphone technology, would over time also result in complete surveys of the walking environment provided such a tool is popular and safe. This research will illustrate how mobile phone applications currently in the market can be improved to offer more functionality that factors in multiple sensory modalities for enhanced visual appeal, ease of use, and aesthetics. The overarching aim of this research is, therefore, to develop the framework for and test a pilot-version mobile phone-based data collection tool that incorporates emerging technologies in collecting data on walkability. This research project will assess the effectiveness of the mobile application and test the technical capabilities of the system to experience how it operates within an existing infrastructure. It will continue to investigate the use of mobile phone technology in the collection of user perceptions of walkability, and the limitations of current transportation-based mobile applications, with the aim of developing an application that is an improvement to current offerings in the market. The prototype application will be tested and later piloted in different locations around the globe. Past studies are primarily focused on the development of transport-based mobile phone applications with basic features and limited functionality. Although limited progress has been made in integrating emerging advanced technologies such as Augmented Reality (AR), Machine Learning (ML), Big Data analytics, amongst others into mobile phone applications; what is missing from these past examples is a comprehensive and structured application in the transportation sphere. In turn, the full research will offer a broader understanding of the iii information gathered from these smart devices, and how that large volume of varied data can be better and more quickly interpreted to discover trends, patterns, and aid in decision making and planning. This research project attempts to fill this gap and also bring new insights, thus promote the research field of transportation data collection audits, with particular emphasis on walkability audits. In this regard, this research seeks to provide insights into how such a tool could be applied in assessing and promoting walkability as a sustainable and equitable mobility option. In order to get policy-makers, analysts, and practitioners in urban transport planning and provision in cities to pay closer attention to making better, more walkable places, appealing to them from an efficiency and business perspective is vital. This crowdsourced data is of great interest to industry practitioners, local governments and research communities as Big Data, and to urban communities and civil society as an input in their advocacy activities. The general findings from the results of this research show clear evidence that transport-based mobile phone applications currently available in the market are increasingly getting outdated and are not keeping up with new and emerging technologies and innovations. It is also evident from the results that mobile smartphones have revolutionised the collection of transport-related information hence the need for new initiatives to help take advantage of this emerging opportunity. The implications of these findings are that more attention needs to be paid to this niche going forward. This research project recommends that more studies, particularly on what technologies and functionalities can realistically be incorporated into mobile phone applications in the near future be done as well as on improving the hardware specifications of mobile phone devices to facilitate and support these emerging technologies whilst keeping the cost of mobile devices as low as possible.

Today's society has grown to be highly digitalized where technologies are playing a large role in everyone's lives. Moreover, society is still developing at a rapid pace, with a new innovation around every corner and with more and more business conducted online. By using this as a starting point, the authors of this thesis choose to examine how blockchain can influence the use of big data in digital marketing. Moreover, as previous articles have perceived GDPR as an obstacle for implementing blockchain within this context, the authors perceived it as necessary to examine blockchains’ relation to GDPR. Thus, this thesis has set out to both identify challenges and opportunities that exist when applying blockchain within the context of digital marketing. This exploratory thesis applies a qualitative data collection method in order to fulfill the purpose of the study and to answer the following components of the research question; (1) How will blockchain influence the use of big data within digital marketing? (2) How will blockchain in a digital marketing context cope with the regulations of GDPR? (3) How will blockchain influence the future of digital marketing? The qualitative collection method chosen is semi-structured interviews and has included six participants from Europe.  The findings of this thesis suggest that blockchain will influence big data and thus, digital marketing will be influenced because it is heavily data-driven. Furthermore, blockchain will influence the companies’ knowledge of the customer, allow more reliable data to be obtained, and allow consumers to retain ownership over their data. Thus, blockchain places a higher demand on companies to deliver relevant information and match the interest of customers in order for the companies to gain access to customers’ data. Additionally, the findings suggest that blockchain has the ability to remove intermediaries and eliminate fraudulent activities, such as deep fakes, illegitimate reviews, click-fraud, within digital marketing. Despite that blockchain has a lot of potentials, it also faces a lot of challenges and obstacles. For instance, large companies such as Google may feel threatened by the blockchain and thus, these companies may have incentives for disrupting the implementation of the technology. Moreover, practical challenges have been identified. These were related to non-scalable algorithms and limited capacities to store large amounts of data.

Britain’s Royal Navy of the nineteenth century was the unquestioned master of the world’s oceans, having won such standing after over a century of near-uninterrupted warfare. However, while the strategies, tactics and technology of the navy evolved dramatically during this period, the laws that governed its many thousands of sailors and officers remained virtually unchanged from the original 1661 Articles of War. Despite minor amendments throughout the eighteenth century and a major reworking in 1749, both capital and corporal punishments were frequently employed as punishment for minor offences in a system that made England’s “Bloody Code” look positively humane. The 1860 Naval Discipline Act provided the first substantive overhaul of the original Articles of War, but historians have generally lamented this act as providing little comprehensive change to the governance of the navy. Using statistical data collected from thousands of courts martial records, this thesis takes a broad look at trends in naval courts martial, studying how these courts interacted with the legislative changes of the 1860s. Viewing how charges and sentences changed on the global scale, it becomes clear that the “arbitrary and cruel punishments” of the previous century had at last given way to a centralized, formal expression of discipline. / Graduate / 2021-07-21

No description available.

<div>Cities face significant challenges in developing urban infrastructure systems in an inclusive, resilient, and sustainable manner, with rapid urbanization and increasing frequency of shocks (e.g., climate hazards, epidemics). The complex and dynamic interdependencies among urban social, technical, institutional, and natural components could cause disruptions to cascade across systems, and lead to heterogeneous recovery outcomes across communities and regions. Large scale data collected from mobile devices, including mobile phone GPS data, web search data, and social media data, allow us to observe urban dynamics before, during, and after disaster events in an unprecedented spatial-temporal granularity and scale. Despite these opportunities, we lack data-driven methods to understand the underlying mechanisms that govern the recovery and resilience of cities to shocks.</div><div>Such dynamical models, in contrast to static index based metrics of resilience, will allow us to test the effects of policies on the heterogeneous post-disaster recovery trajectories across space and time. </div><div><br></div><div>In this dissertation, I studied the recovery dynamics and resilience of urban systems to disasters using a large-scale human-centered data-driven modeling approach, with particular emphasis on the complex interdependencies among social, economic, and infrastructure systems. First, statistical analysis of large-scale human mobility data collected from over 1 million mobile phone devices in five major disaster events across the globe, revealed universal population recovery processes across regions and disasters, including disproportionate disaster effects based on income inequalities and urban-rural divide. Second, human mobility data are used to infer the recovery of various socio-economic systems after disasters. Using Bayesian causal inference models, regional and business sectoral inequalities in disaster recovery are quantified. Finally, the analysis on social, economic, and physical recovery were integrated into a dynamical model of coupled urban systems, which captures the bi-directional interdependencies among socio-economic and physical infrastructure systems during disaster recovery. Using the model and data collected from Puerto Rico during Hurricane Maria, a trade-off relationship in urban development is revealed, where developed cities with robust centralized infrastructure systems have higher recovery efficiency of critical services, however, have socio-economic networks with lower self-reliance during crises, which lead to loss of community resilience. Managing and balancing the socio-economic self-reliance alongside physical infrastructure robustness is key to resilience. </div><div><br></div><div>The proposed models and results presented in this dissertation lay the scientific foundations of urban complexity and resilience, encouraging us to move towards dynamical and complex systems modeling approaches, from conventional static index-based resilience metrics. Big data-driven, dynamical complex systems modeling approaches enable quantitative understanding of the underlying disaster recovery process (e.g., interdependencies, feedbacks, cascading effects) across large spatial and temporal time scales. The approach is capable of proposing community-based policies for urban resilience via cross-regional comparisons and counterfactual scenario testing of various policy levers. </div>

En el presente documento de tesis, podremos explicar la problemática y propuesta de solución a nivel diseño del proceso la emisión de pólizas para el Seguro Vida Inversión. Con fines de confidencialidad mantendremos en reserva el nombre de la empresa y utilizaremos el seudónimo de “Seguros VERDE” cuando queramos hacer referencia a la misma. .En primer lugar se analizó el contexto de la empresa, logrando identificar los macroprocesos, visualizar sus objetivos y conocer a los interesados .Realizado ello, identificamos que el macroproceso de gestión de operaciones tiene mayor injerencia sobre los objetivos de la empresa y los interesados, por lo cual decidimos proponer una solución sobre el proceso emisión del Seguro Vida Inversión , ya que se realiza desde su inicio a fin de forma manual e impacta en la gestión de Operaciones, partiendo por la búsqueda de la información del cliente ya que se tiene que buscar de forma manual en 5 diferentes bases de datos que posee la empresa, cálculos erráticos de las primas y proyecciones en las cotizaciones, verificaciones de forma manual de las solicitudes, emisiones. Todo ello genera una demora en la entrega de una solicitud de cotización hacia los clientes, muchas veces el cliente termina desistiendo de la solicitud y/o se logran emitir pólizas con primas de asegurabilidad no favorables para la empresa. Para poder resolver con eficacia los problemas descritos anteriormente, identificaremos las reglas de negocio , los requerimientos funcionales y no funcionales del proyecto , permitiéndonos escoger los drivers arquitectónicos del proyecto y así poder ir justificando nuestra propuesta de solución .Se propone un diseño de un sistema el cual permita automatizar los procesos de la emisión de pólizas del Seguro Vida Inversión , con el fin de optimizar los tiempos de entrega de la solicitudes hacia los clientes, cálculos correctos de primas, verificaciones automáticas de emisiones, emisión y cálculos de fondos de pólizas automatizados. Asimismo, mediante un entorno big data integraremos las 5 base de datos de los clientes de la empresa, obteniendo así una base única de clientes, el cual permitirá a los agentes de ventas realizar una búsqueda de manera eficaz y eficiente. Finalmente, nos apoyaremos del diagrama de 4c para poder visualizar nuestra solución arquitectónica desde un alto nivel de contexto hasta realizar acercamiento cada vez más acotados bajo diagramas de contenedores, componentes y diagramas de código, los cual permitirán explicar gráficamente nuestro diseño arquitectónico. / In this thesis document, we will be able to explain the problems and proposed solutions at the design level of the issuance of policies for Vida Inversion. For confidentiality purposes, we will keep the name of the company in reserve and we will use the pseudonym "Seguros VERDE" when we want to refer to it. In the first place, the context of the company was analyzed, managing to identify the macroprocesses, visualize their objectives and meet the stakeholders. Once this was done, we identified that the operations management macroprocess has a greater influence on the objectives of the company and the stakeholders, For this reason, we decided to propose a solution on the process of issuing policies for Vida Inversion, since it is carried out from the beginning to the end manually and has an impact on the management of Operations, starting with the search for customer information since it has to Search manually in 5 different databases that the company owns, erratic calculations of premiums and projections in quotes, manual verification of applications, emissions. All this generates a delay in the delivery of a request for a quote to the clients, many times the client ends up giving up the request and / or policies are issued with insurability premiums not favorable to the company. In order to effectively solve the problems described above, we will identify the business rules, the functional and non-functional requirements of the project, allowing us to choose the architectural drivers of the project and thus be able to justify our solution proposal. which allows to automate the processes of the issuance, in order to optimize the delivery times of the requests to the clients, correct calculations of premiums, automatic verifications of emissions, issuance and calculations of automated policy funds. Likewise, through a big data environment we will integrate the 5 databases of the company's customers, thus obtaining a unique customer base, which will allow sales agents to carry out a search effectively and efficiently. Finally, we will lean on the 4c diagram to be able to visualize our architectural solution from a high level of context to make increasingly narrow approaches under container diagrams, components and code diagrams, which will allow us to graphically explain our architectural design. / Tesis

<p>Data and information have increased exponentially in recent years. The promising era of big data is advancing many new practices. One of the emerging big data applications is healthcare. Large quantities of data with varying complexities have been leading to a great need in smart and secure big data systems. </p> <p>Mobile edge, more specifically the smart phone, is a natural source of big data and is ubiquitous in our daily lives. Smartphones offer a variety of sensors, which make them a very valuable source of data that can be used for analysis. Since this data is coming directly from personal phones, that means the generated data is sensitive and must be handled in a smart and secure way. In addition to generating data, it is also important to interact with the big data. Therefore, it is critical to create edge systems that enable users to access their data and ensure that these applications are smart and secure. As the first major contribution of this thesis, we have implemented a mobile edge system, called s²Edge. This edge system leverages Amazon Web Service (AWS) security features and is backed by an AWS cloud system. The implemented mobile application securely logs in, signs up, and signs out users, as well as connects users to the vast amounts of data they generate. With a high interactive capability, the system allows users (like patients) to retrieve and view their data and records, as well as communicate with the cloud users (like physicians). The resulting mobile edge system is promising and is expected to demonstrate the potential of smart and secure big data interaction.</p> <p>The smart and secure transmission and management of the big data on the cloud is essential for healthcare big data, including both patient information and patient measurements. The second major contribution of this thesis is to demonstrate a novel big data cloud system, s²Cloud, which can help enhance healthcare systems to better monitor patients and give doctors critical insights into their patients' health. s²Cloud achieves big data security through secure sign up and log in for the doctors, as well as data transmission protection. The system allows the doctors to manage both patients and their records effectively. The doctors can add and edit the patient and record information through the interactive website. Furthermore, the system supports both real-time and historical modes for big data management. Therefore, the patient measurement information can, not only be visualized and demonstrated in real-time, but also be retrieved for further analysis. The smart website also allows doctors and patients to interact with each other effectively through instantaneous chat. Overall, the proposed s²Cloud system, empowered by smart secure design innovations, has demonstrated the feasibility and potential for healthcare big data applications. This study will further broadly benefit and advance other smart home and world big data applications. </p>

Governments and municipalities need to understand their citizens’ psychological needs in critical times and dangerous situations. COVID-19 brings lots of challenges to deal with. We propose NeedFull, an interactive and scalable tweet analysis platform, to help governments and municipalities to understand residents’ real psychological needs during those periods. The platform mainly consists of four parts: data collection module, data storage module, data analysis module and data visualization module. The whole process of how data flows in the system is illustrated as follows: Our crawlers in the data collection module gather raw data from a popular social network website Twitter. Then the data is fed into our human need detection model in the data analysis module before stored into the database. When a user enters a query through the user interface, they will get all the related items in the database by the index system of the data storage module and a comprehensive human needs analysis of these items is then presented and depicted in the data visualization module. We employed the proposed platform to investigate the reaction of people in four big regions including New York, Ottawa, Toronto and Montreal to the ongoing worldwide COVID-19 pandemic by collecting tweets posted during this period. The results show that the most pronounced human need in these tweets is relatedness with 51.32%, followed by autonomy with 22.56% and competence with 18.82%. And the percentages of tweets expressing frustration are larger than those of tweets expressing satisfaction for each psychological need in general.

Indiana University-Purdue University Indianapolis (IUPUI) / A significant hurdle for analyzing big data is the lack of effective technology and statistical inference methods. A popular approach for analyzing data with large sample is subsampling. Many subsampling probabilities have been introduced in literature (Ma, \emph{et al.}, 2015) for linear model. In this dissertation, we focus on generalized estimating equations (GEE) with big data and derive the asymptotic normality for the estimator without resampling and estimator with resampling. We also give the asymptotic representation of the bias of estimator without resampling and estimator with resampling. we show that bias becomes significant when the data is of high-dimensional. We also present a novel subsampling method called A-optimal which is derived by minimizing the trace of some dispersion matrices (Peng and Tan, 2018). We derive the asymptotic normality of the estimator based on A-optimal subsampling methods. We conduct extensive simulations on large sample data with high dimension to evaluate the performance of our proposed methods using MSE as a criterion. High dimensional data are further investigated and we show through simulations that minimizing the asymptotic variance does not imply minimizing the MSE as bias not negligible. We apply our proposed subsampling method to analyze a real data set, gas sensor data which has more than four millions data points. In both simulations and real data analysis, our A-optimal method outperform the traditional uniform subsampling method.