HETEROGENEOUS DATA AND PROBABILISTIC SYSTEM MODEL ANALYSES FOR ENHANCED SITUATIONAL AWARENESS AND RESILIENCE OF CRITICAL INFRASTRUCTURE SYSTEMS

The protection and resilience of critical infrastructure systems (CIS) are essential for public safety in daily operations and times of crisis and for community preparedness to hazard events. Increasing situational awareness and resilience of CIS includes both comprehensive monitoring of CIS and their surroundings, as well as evaluating CIS behaviors in changing conditions and with different system configurations. Two frameworks for increasing the monitoring capabilities of CIS are presented. The proposed frameworks are (1) a process for classifying social media big data for monitoring CIS and hazard events and (2) a framework for integrating heterogeneous data sources, including social media, using Bayesian inference to update prior probabilities of event occurrence. Applications of both frameworks are presented, including building and evaluating text-based machine learning classifiers for identifying CIS damages and integrating disparate data sources to estimate hazards and CIS damages. Probabilistic analyses of CIS vulnerabilities with varying system parameters and topologies are also presented. In a water network, the impact of varying parameters on component performance is evaluated. In multiple, small-size water networks, the impacts of system topology are assessed to identify characteristics of more resilient networks. This body of work contributes insights and methods for monitoring CIS and assessing their performance. Integrating heterogeneous data sources increases situational awareness of CIS, especially during or after failure events, and evaluating the sensitivity of CIS outcomes to changes in the network facilitates decisions for CIS investments and emergency response.Ph.D

Human dynamics in the age of big data: a theory-data-driven approach

The revolution of information and communication technology (ICT) in the past two decades have transformed the world and people’s lives with the ways that knowledge is produced. With the advancements in location-aware technologies, a large volume of data so-called “big data” is now available through various sources to explore the world. This dissertation examines the potential use of such data in understanding human dynamics by focusing on both theory- and data-driven approaches. Specifically, human dynamics represented by communication and activities is linked to geographic concepts of space and place through social media data to set a research platform for effective use of social media as an information system. Three case studies covering these conceptual linkages are presented to (1) identify communication patterns on social media; (2) identify spatial patterns of activities in urban areas and detect events; and (3) explore urban mobility patterns. The first case study examines the use of and communication dynamics on Twitter during Hurricane Sandy utilizing survey and data analytics techniques. Twitter was identified as a valuable source of disaster-related information. Additionally, the results shed lights on the most significant information that can be derived from Twitter during disasters and the need for establishing bi-directional communications during such events to achieve an effective communication. The second case study examines the potential of Twitter in identifying activities and events and exploring movements during Hurricane Sandy utilizing both time-geographic information and qualitative social media text data. The study provides insights for enhancing situational awareness during natural disasters. The third case study examines the potential of Twitter in modeling commuting trip distribution in New York City. By integrating both traditional and social media data and utilizing machine learning techniques, the study identified Twitter as a valuable source for transportation modeling. Despite the limitations of social media such as the accuracy issue, there is tremendous opportunity for geographers to enrich their understanding of human dynamics in the world. However, we will need new research frameworks, which integrate geographic concepts with information systems theories to theorize the process. Furthermore, integrating various data sources is the key to future research and will need new computational approaches. Addressing these computational challenges, therefore, will be a crucial step to extend the frontier of big data knowledge from a geographic perspective. KEYWORDS: Big data, social media, Twitter, human dynamics, VGI, natural disasters, Hurricane Sandy, transportation modeling, machine learning, situational awareness, NYC, GI

Evaluating network criticality of interdependent infrastructure systems: applications for electrical power distribution and rail transport

Critical infrastructure provides essential services of economic and social value. However, the pressures of demand growth, congestion, capacity constraints and hazards such as extreme weather increase the need for infrastructure resilience. The increasingly interdependent nature of infrastructure also heightens the risk of cascading failure between connected systems. Infrastructure companies must meet the twin-challenge of day-to-day operations and long-term planning with increasingly constrained budgets and resources. With a need for an effective process of resource allocation, this thesis presents a network criticality assessment methodology for prioritising locations across interdependent infrastructure systems, using metrics of the expected consequence of an asset failure for operational service performance. Existing literature is focused mainly upon simulating the vulnerability of national-scale infrastructure, with assumptions of both system dynamics and dependencies for simplicity. This thesis takes a data-driven and evidence-based approach, using historical performance databases to inherently capture system behaviour, whilst network diagrams are used to directly identify asset dependencies. Network criticality assessments are produced for three applications of increasing complexity from (i) electricity distribution, to (ii) railway transport, to (iii) electrified railway dependencies on external power supplies, using case studies of contrasting infrastructure management regions. This thesis demonstrates how network criticality assessments can add value to subjective tacit knowledge and high-level priorities both within and between infrastructure systems. The spatial distribution of criticality is highlighted, whilst the key contribution of the research is the identification of high-resolution single points of failure and their spatial correlation across systems, particularly within urban areas. Service-level metrics also have a broad applicability for a range of functions, including incident response, maintenance and long-term investment. The role of network criticality within a holistic and systemic decision-making process is explored, for risk assessment and resilience interventions. The limitations of the research, regarding sample-size caveats and the definition of system boundaries within performance databases, lead to recommendations on cross-system fault reporting and the improvement of information systems

Protecting Electricity Networks from Natural Hazards

This handbook supports OSCE participating States in better protecting critical electrical energy infrastructure from natural hazards. By providing risk management options, tools and case studies, it is designed as a guide for policy-makers, state authorities, transmission networks operators and regulators in charge of protecting energy networks. In recent years the risk of supra-national power blackouts in the OSCE area causing significant economic losses has increased. One contributing factor is that extreme weather conditions occur more frequently. Another is an increased connectivity of power and telecommunication infrastructures and a higher technical complexity of the grid due to a changing energy mix, leaving industrial and commercial companies, the public and the private sector at risk

Impact of Braess's Paradox and Simultaneous Imposition of Non-Coincidental Transmission Outages on FTR Auctions

This thesis identifies and resolves an issue caused by Braess's paradox in Financial Transmission Right (FTR) auctions. Braess's paradox in power systems is the situation where adding a new transmission line can reduce the transmission system capacity, and vice-versa. FTRs are auctioned by Regional Transmission Organizations (RTOs) to market parties who wish to hedge uncertain transmission costs. The issue can cause the RTO to over-allocate FTRs and become revenue inadequate which leaves the RTO the dilemma of how to recover the deficit. An auction process called the simultaneous feasibility test (SFT) limits the FTR awarded to ensure that sufficient congestion rents are collected by the RTO to pay the FTR holders. The problem stems from an SFT approximation coined in this thesis the Simultaneous Imposition of Non-coincidental Transmission Outages (SINTO) that models planned transmission outages concurrently rather than as scheduled. When Braess's paradox applies to FTR auctions, the SFT approximation defies the intuitive assumption that removing transmission lines will reduce transmission system capacity. Thus, two methods are proposed to mitigate the effects of Braess's paradox in FTR auctions. The first is the Chronological Imposition of Planned Transmission Outages (CHIMPO), which ideally models the transmission outages as scheduled but also considerably increases the auction's computational cost. The second method, called the Normally-Operated – SINTO (NO-SINTO), is a robust and computationally inexpensive approximation that adds a single set of transmission constraints to the SINTO model. The five contributions of this thesis are described through simple examples and case study simulation using actual historical FTR auction data. The first establishes, using the SINTO SFT approximation, that the existence of Braess's paradox can lead to revenue inadequacy in FTR auctions. The second demonstrates that modeling SINTO in FTR auctions may aggravate the impact of the paradox. The third offers two alternative FTR auction models (CHIMPO, NO-SINTO) to reduce the risk of revenue inadequacy from Braess's paradox. The fourth demonstrates that the ideal CHIMPO allocation of FTRs is better approximated by the NO-SINTO model than the SINTO model. The fifth indicates that RTOs may practically implement the NO-SINTO approximation on a realistically sized power networks

Urban Informatics

This open access book is the first to systematically introduce the principles of urban informatics and its application to every aspect of the city that involves its functioning, control, management, and future planning. It introduces new models and tools being developed to understand and implement these technologies that enable cities to function more efficiently – to become ‘smart’ and ‘sustainable’. The smart city has quickly emerged as computers have become ever smaller to the point where they can be embedded into the very fabric of the city, as well as being central to new ways in which the population can communicate and act. When cities are wired in this way, they have the potential to become sentient and responsive, generating massive streams of ‘big’ data in real time as well as providing immense opportunities for extracting new forms of urban data through crowdsourcing. This book offers a comprehensive review of the methods that form the core of urban informatics from various kinds of urban remote sensing to new approaches to machine learning and statistical modelling. It provides a detailed technical introduction to the wide array of tools information scientists need to develop the key urban analytics that are fundamental to learning about the smart city, and it outlines ways in which these tools can be used to inform design and policy so that cities can become more efficient with a greater concern for environment and equity