Incorporating Data Mining into Cloud Computing

Data mining plays an essential role in uncovering new, reliable, valuable, and comprehensible patterns in data. When combined with cloud computing, it forms a flexible and scalable system, allowing for the effective extraction of large datasets from interconnected virtual repositories. The primary objective is to produce meaningful information that supports informed decision-making. This paper discusses the necessity of incorporating data mining into cloud computing frameworks, focusing on providing efficient and secure services to users while also reducing infrastructure and storage expenses

Cluster Analysis of Twitter Data: A Review of Algorithms

Twitter, a microblogging online social network (OSN), has quickly gained prominence as it provides people with the opportunity to communicate and share posts and topics. Tremendous value lies in automated analysing and reasoning about such data in order to derive meaningful insights, which carries potential opportunities for businesses, users, and consumers. However, the sheer volume, noise, and dynamism of Twitter, imposes challenges that hinder the efficacy of observing clusters with high intra-cluster (i.e. minimum variance) and low inter-cluster similarities. This review focuses on research that has used various clustering algorithms to analyse Twitter data streams and identify hidden patterns in tweets where text is highly unstructured. This paper performs a comparative analysis on approaches of unsupervised learning in order to determine whether empirical findings support the enhancement of decision support and pattern recognition applications. A review of the literature identified 13 studies that implemented different clustering methods. A comparison including clustering methods, algorithms, number of clusters, dataset(s) size, distance measure, clustering features, evaluation methods, and results was conducted. The conclusion reports that the use of unsupervised learning in mining social media data has several weaknesses. Success criteria and future directions for research and practice to the research community are discussed

Social Space and Social Media: Analyzing Urban Space with Big Data

This dissertation focuses on the key role that big data can play in minimizing the perceived disconnect between social theory and quantitative methods in the discipline of geography. It takes as its starting point the geographic concept of space, which is conceptualized very differently in social theory versus quantitative methodology. Contrary to this disparity, an examination of the disciplinary history reveals a number of historic precedents and potential pathways for a rapprochement, especially when combined with some of the new possibilities of big data. This dissertation also proposes solutions to two common barriers to the adoption of big data in the social sciences: accessing and collecting such data and, subsequently, meaningful analysis. These methods and the theoretical foundation are combined in three case studies that show the successful integration of a quantitative research methodology with social theories on space. The case studies demonstrate how such an approach can create new and alternative understandings of urban space. In doing so it answers three specific research questions: (1) How can big data facilitate the integration of social theory on space with quantitative research methodology? (2) What are the practical challenges and solutions to moving “beyond the geotag” when utilizing big data in geographical research? (3) How can the quantitative analysis of big data provide new and useful insight in the complex character of social space? More specifically, what insights does such an analysis of relational social space provide about urban mobility and cognitive neighborhoods

An investigation into the role of crowdsourcing in generating information for flood risk management

Flooding is a major global hazard whose management relies on an accurate understanding of its risks. Crowdsourcing represents a major opportunity for supporting flood risk management as members of the public are highly capable of producing useful flood information. This thesis explores a wide range of issues related to flood crowdsourcing using an interdisciplinary approach. Through an examination of 31 different projects a flood crowdsourcing typology was developed. This identified five key types of flood crowdsourcing: i) Incident Reporting, ii) Media Engagement, iii) Collaborative Mapping, iv) Online Volunteering and v) Passive VGI. These represent a wide range of initiatives with radically different aims, objectives, datasets and relationships with volunteers. Online Volunteering was explored in greater detail using Tomnod as a case study. This is a micro-tasking platform in which volunteers analyse satellite imagery to support disaster response. Volunteer motivations for participating on Tomnod were found to be largely altruistic. Demographics of participants were significant, with retirement, disability or long-term health problems identified as major drivers for participation. Many participants emphasised that effective communication between volunteers and the site owner is strongly linked to their appreciation of the platform. In addition, the feedback on the quality and impact of their contributions was found to be crucial in maintaining interest. Through an examination of their contributions, volunteers were found to be able to ascertain with a higher degree of accuracy, many features in satellite imagery which supervised image classification struggled to identify. This was more pronounced in poorer quality imagery where image classification had a very low accuracy. However, supervised classification was found to be far more systematic and succeeded in identifying impacts in many regions which were missed by volunteers. The efficacy of using crowdsourcing for flood risk management was explored further through the iterative development of a Collaborative Mapping web-platform called Floodcrowd. Through interviews and focus groups, stakeholders from the public and private sector expressed an interest in crowdsourcing as a tool for supporting flood risk management. Types of data which stakeholders are particularly interested in with regards to crowdsourcing differ between organisations. Yet, they typically include flood depths, photos, timeframes of events and historical background information. Through engagement activities, many citizens were found to be able and motivated to share such observations. Yet, motivations were strongly affected by the level of attention their contributions receive from authorities. This presents many opportunities as well as challenges for ensuring that the future of flood crowdsourcing improves flood risk management and does not damage stakeholder relationships with participants

A collaborative approach for disaster risk reduction: mapping social learning with Mistawasis Nêhiyawak

Social learning and its relation to disaster risk reduction (DRR) have been increasingly highlighted in the literature. Yet, limited empirical research has hampered practical DRR applications. This thesis demonstrated social learning loops and their outcomes by reflecting on the case of 2011 flooding in Mistawasis Nêhiyawak. Using a mixed-methods research design, I explored the role of participatory processes, including communication of scientific knowledge to lay-experts, in social learning. First, I created flood extent maps for the community using spatial data and modeling techniques. In the second phase, I presented the maps in a workshop held at the community center to understand their value in regard to what people learn from them. This included deliberating not only about physical parameters of the flood but also exploring the social (and human) parameters. Hence, I used fuzzy cognitive mapping (FCM) as a novel method to represent the human perception of flood risk and to measure social learning. In the workshop, FCM was complemented by focus group discussions and participatory mapping. From the results, it was found that i) social learning can be measured using social sciences tools, ii) sharing experiences and stories from past events augmented learning, and iii) awareness on the role of emergency planning in DRR was found to be a significant outcome of social learning. In the growing urgency of climate uncertainties, social learning theory will be critical in helping design practical and ethical research approaches to DRR that emphasize knowledge sharing, two-way communication, and reflexivity. These will ultimately have enhanced emphasis on behavioral responses to disasters that are complementary to the investments in structural responses

A COMPREHENSIVE DISASTER RISK INDEX FOR THE UNITED STATES

Risks to life, property, infrastructure and even environmental security emanate from a variety of hazard sources. Key to reducing this risk is the ability to measure it and present it decision-makers and stakeholders in a meaningful and understandable way. Currently, there exist no comprehensive hazard risk indices for the United States that have the ability to capture and convey a contemporary conceptualization of risk to hazards. Such an index, the World Risk Index, exists at the global level. The World Risk Index serves as an analog for further research on risk at various scales. The purpose of this dissertation is to facilitate an increased awareness of risk and the different factors that contribute to it and to provide a method for easily assessing risk at subnational scales. The following broad research questions frame this work: a) Can the World Risk Index be customized to a subnational scale in the United States? Which indicators are appropriate for use at the state and county level in the United States? b) Does the disaggregation of disaster risk to state and county scales provide more detailed understanding of the spatial distribution of risks and the components of risk? c) How does the risk assessment produced by a top-down approach compare to other US risk assessments at the county scale? To answer these questions, this dissertation is focused on the development of a risk index, the United States Disaster Risk Index (USDRI), tailored to assess risk at various scales. The USDRI is a proof of concept, and uses the methodology and indicators of the aforementioned World Risk Index to establish a baseline for evaluating risk at the state and county level. The validity of the index is examined through exploratory spatial statistical analysis. The results are also compared to loss data in order to assess whether the USDRI explains variability in loss. In addition, the USDRI and its components are compared to existing indices to determine similarities and differences. The results indicate that the USDRI provides new insight into risk at the state and county scale in the US. The ability to quickly tailor the index to various hazards of interest – to include potential hazards such as sea-level rise - proves to be one of its strongpoints. The USDRI, with some modification to the exposure component, shows the ability to explain variation in loss, especially at the state level. When compared to existing indices, USDRI risk and vulnerability show many similarities but also some important differences. For example, both the USDRI vulnerability component and the established Social Vulnerability Index show clusters of lower vulnerability in the Northeast US, but the USDRI shows large clusters of vulnerability in the Midwest that the Social Vulnerability Index does not. When the lessons learned are taken into consideration, the USDRI is successful in providing a baseline for the future evaluation of risk at the subnational level

Past, Present and Future of a Habitable Earth

This perspective of this book views Earth's various layers as a whole system, and tries to understand how to achieve harmony and sustainable development between human society and nature, with the theme of " habitability of the Earth." This book is one effort at providing an overview of some of the recent exciting advances Chinese geoscientists have made. It is the concerted team effort of a group of researchers from diverse backgrounds to generalize their vision for Earth science in the next 10 years. The book is intended for scholars, administrators of the Science and Technology policy department, and science research funding agencies. This is an open access book