Mapping urban resilience to disasters – A review

The deployment of HSR services in the recent decades has been, arguably, the most significant innovation for intercity travel around the globe. HSR has brought impacts which have been widely studied in different countries in relation to the different socioeconomic, territorial and transport characteristics. This paper analyses the economic growth, the transport accessibility and the social impacts observed in Italy after ten years of HSR operation, as well as the estimated impacts of the system completion. The Italian case study is of particular interest since along the 1,467 km of new high speed line (300 km/h), a combination of major cities distances and a unique HSR competitive market, producing prices reductions and more daily trains, brought a 200% increase of HSR demand (from 15 to 45 millions of passengers/year). Estimations results show that, on average, HSR in Italy contributed to a significant increase in transport accessibility (+32%) for the zones along the HSR network, while only marginal for the others (+6%). Impacts on the economic growth show that HSR has contributed to an extra growth of per capita GDP of + 2.6% in 10 years and would have contributed to a further increase of 3.6% if the final project scenario (HSR_N) would had been completed by 2018. Regional (horizontal) equity impacts were evaluated in terms of the Gini indexes variations with respect to the distribution of the transport accessibility. It results that HSR in Italy has decreased equity in terms of users’ travel time accessibility of 11%, increasing the differences between the zones served by HSR and those not. If the HSR_N scenario would have been completed equity indices would have increased of 29% with respect to the pre-HSR 2008 scenario, thus reducing regional inequalities in the country. Results show that the HSR project was a country-level “game changer” in Italy, suggesting that the wider economic benefits, the assumptions on market regulation, the effects on regional disparities and the compensatory measures should be included in the ex ante and ex post evaluation of similar projects.

Economic growth, transport accessibility and regional equity impacts of high-speed railways in Italy: ten years ex post evaluation and future perspectives.

Big data is the concept of enormous amounts of data being generated daily in different fields due to the increased use of technology and internet sources. Despite the various advancements and the hopes of better understanding, big data management and analysis remain a challenge, calling for more rigorous and detailed research, as well as the identifications of methods and ways in which big data could be tackled and put to good use. The existing research lacks in discussing and evaluating the pertinent tools and technologies to analyze big data in an efficient manner which calls for a comprehensive and holistic analysis of the published articles to summarize the concept of big data and see field-specific applications. To address this gap and keep a recent focus, research articles published in last decade, belonging to top-tier and high-impact journals, were retrieved using the search engines of Google Scholar, Scopus, and Web of Science that were narrowed down to a set of 139 relevant research articles. Different analyses were conducted on the retrieved papers including bibliometric analysis, keywords analysis, big data search trends, and authors’ names, countries, and affiliated institutes contributing the most to the field of big data. The comparative analyses show that, conceptually, big data lies at the intersection of the storage, statistics, technology, and research fields and emerged as an amalgam of these four fields with interlinked aspects such as data hosting and computing, data management, data refining, data patterns, and machine learning. The results further show that major characteristics of big data can be summarized using the seven Vs, which include variety, volume, variability, value, visualization, veracity, and velocity. Furthermore, the existing methods for big data analysis, their shortcomings, and the possible directions were also explored that could be taken for harnessing technology to ensure data analysis tools could be upgraded to be fast and efficient. The major challenges in handling big data include efficient storage, retrieval, analysis, and visualization of the large heterogeneous data, which can be tackled through authentication such as Kerberos and encrypted files, logging of attacks, secure communication through Secure Sockets Layer (SSL) and Transport Layer Security (TLS), data imputation, building learning models, dividing computations into sub-tasks, checkpoint applications for recursive tasks, and using Solid State Drives (SDD) and Phase Change Material (PCM) for storage. In terms of frameworks for big data management, two frameworks exist including Hadoop and Apache Spark, which must be used simultaneously to capture the holistic essence of the data and make the analyses meaningful, swift, and speedy. Further field-specific applications of big data in two promising and integrated fields, i.e., smart real estate and disaster management, were investigated, and a framework for field-specific applications, as well as a merger of the two areas through big data, was highlighted. The proposed frameworks show that big data can tackle the ever-present issues of customer regrets related to poor quality of information or lack of information in smart real estate to increase the customer satisfaction using an intermediate organization that can process and keep a check on the data being provided to the customers by the sellers and real estate managers. Similarly, for disaster and its risk management, data from social media, drones, multimedia, and search engines can be used to tackle natural disasters such as floods, bushfires, and earthquakes, as well as plan emergency responses. In addition, a merger framework for smart real estate and disaster risk management show that big data generated from the smart real estate in the form of occupant data, facilities management, and building integration and maintenance can be shared with the disaster risk management and emergency response teams to help prevent, prepare, respond to, or recover from the disasters.

Big Data and Its Applications in Smart Real Estate and the Disaster Management Life Cycle: A Systematic Analysis

Towards resilient cities: A maturity model for operationalizing resilience

The concept of resilience continues to grow in influence and prominence in national and international programs seeking to improve individual and collective capacity to prepare for and respond to disasters. This review of the literature published in 2018 examines how disaster scholars and professionals are conceptualizing and applying the concept of community disaster resilience. Three trends in the literature on community disaster resilience are observed: (1) advancements in the measurement of resilience continue to refine the concept and its related mechanisms using both primary and secondary data, (2) social capital continues to be a central mechanism through which community resilience reduces disaster impact and enhances recovery, and (3) programs across the globe are advancing the practice of improving resilience through community interventions to enhancing adaptive capacities. Community disaster resilience offers much promise as a guiding paradigm for the promotion of disaster risk reduction and the enabling of disaster recovery through attention to, and investment in, local capacities for adaptation to a changing and uncertain environment. However, there remains work to be done in the clarification of the concept and the operationalization of the mechanisms leading to enhancing community capacity for resilience.

A Review of the Literature on Community Resilience and Disaster Recovery.

The Resilience to Emergencies and Disasters Index: Applying big data to benchmark and validate neighborhood resilience capacity

Digital footprints: Using WiFi probe and locational data to analyze human mobility trajectories in cities

New York City faces the challenge of an ever-increasing homeless population with almost 60,000 people currently living in city shelters. In 2015, approximately 25% of families stayed longer than ni...

Applications of Machine Learning Methods to Predict Readmission and Length-of-Stay for Homeless Families: The Case of Win Shelters in New York City

A data-driven methodology for equitable value-capture financing of public transit operations and maintenance

City governments all over the world face challenges understanding mobility paerns within dense urban environments at high spatial and temporal resolution. While such measures are important to provide insights into the functional paerns of a city, novel quantitative methods, derived from ubiquitous mobile connectivity, are needed, oering decision makers beer insights to improve urban management and planning decisions. In this paper, we propose a model that uses Wi probe request data to model urban mobility in a dense, mixed–use district in New York City. We collect probe request data over 29 access points of a public WiFi network in Lower Manhaan for one day, accounting for more than 1million observations and over 60,000 unique devices. First, we aggregate unique entries per access point and per hour, showing that our method has the potential to collect the same type of data as other common methods, by detecting dierences in usage activity paerns by time of day. We then use a spatial network analysis to identify edge frequencies of journeys between the network nodes, and apply the results to the road and pedestrian sidewalk network to identify usage and trajectories at the street segment level.

Awais Malik

Papers

The Resilience to Emergencies and Disasters Index: Applying big data to benchmark and validate neighborhood resilience capacity

Digital footprints: Using WiFi probe and locational data to analyze human mobility trajectories in cities

Applications of Machine Learning Methods to Predict Readmission and Length-of-Stay for Homeless Families: The Case of Win Shelters in New York City

A data-driven methodology for equitable value-capture financing of public transit operations and maintenance

Digital Traces: Modeling Urban Mobility using Wifi Probe Data