A TWITTER-INTEGRATED WEB SYSTEM TO AGGREGATE AND PROCESS EMERGENCY-RELATED DATA

A major challenge when encountering time-sensitive, information critical emergencies is to source raw volunteered data from on-site public sources and extract information which can enhance awareness on the emergency itself from a geographical context. This research explores the use of Twitter in the emergency domain by developing a Twitter-integrated web system capable of aggregating and processing emergency-related tweet data. The objectives of the project are to collect volunteered tweet data on emergencies by public citizen sources via the Twitter API, process the data based on geo-location information and syntax into organized informational entities relevant to an emergency, and subsequently deliver the information on a map-like interface. The web system framework is targeted for use by organizations which seek to transform volunteered emergency-related data available on the Twitter platform into timely, useful emergency alerts which can enhance situational awareness, and is intended to be accessible to the public through a user-friendly web interface. Rapid Application Development (RAD) is the methodology of choice for project development. The developed system has a system usability scale score of 84.25, after results were tabulated from a usability survey on 20 respondents. Said system is best for use in emergencies where the transmission timely, quantitative data is of paramount importance, and is a useful framework on extracting and displaying useful emergency alerts with a geographical perspective based on volunteered citizen Tweets. It is hoped that the project can ultimately contribute to the existing domain of knowledge on social media-assisted emergency applications

Convergence Behaviour of Bystanders: An Analysis of 2016 Munich Shooting Twitter Crisis Communication

Educational theory has purported the notion that student-centric modes of learning are more effective in enhancing student engagement and by extension, learning outcomes. However, the translation of this theoretical pedagogy of learning into an applied model for medical training has been wrought with difficulty due to the structural complexity of creating a classroom environment that enables students to exercise full autonomy. In this paper, we propose an intelligent computational e-learning platform for case-based learning (CBL) in Medicine that enriches and enhances the learning experiences of medical students by exposing them to simulated real-world clinical contexts. We argue that computational systems in Medicine should not merely provide a passive outlay of information, but instead promote active engagement through an immersive learning experience. This is achieved through a digital platform that renders a virtual patient simulation, which allows students to assess, diagnose, treat and test patients as they would in the real-world

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

Crowdsourcing geospatial data for Earth and human observations: a review

The transformation from authoritative to user-generated data landscapes has garnered considerable attention, notably with the proliferation of crowdsourced geospatial data. Facilitated by advancements in digital technology and high-speed communication, this paradigm shift has democratized data collection, obliterating traditional barriers between data producers and users. While previous literature has compartmentalized this subject into distinct platforms and application domains, this review offers a holistic examination of crowdsourced geospatial data. Employing a narrative review approach due to the interdisciplinary nature of the topic, we investigate both human and Earth observations through crowdsourced initiatives. This review categorizes the diverse applications of these data and rigorously examines specific platforms and paradigms pertinent to data collection. Furthermore, it addresses salient challenges, encompassing data quality, inherent biases, and ethical dimensions. We contend that this thorough analysis will serve as an invaluable scholarly resource, encapsulating the current state-of-the-art in crowdsourced geospatial data, and offering strategic directions for future interdisciplinary research and applications across various sectors

Geographic Information Systems (GIS) in Humanitarian Assistance: A Meta-Analysis

Every year natural and man-made disasters cause mass population displacement, loss of lives, and human suffering. On a given disaster several international or non-profit organizations will respond depending on the region in need as well as media and donor attention Olsen, Gorm Rye, et al (2003). Because of the extreme unique difficulties found in each disaster zone such as infrastructural damages, uncertain demand and supply, geographical challenges and time pressures, it is imperative that humanitarian organizations have readily available and applicable response methodologies as well as information technologies to increase their relief impact. In regards to the latter Geographic Information Systems (GIS) has proven to be an indispensable tool in the humanitarian sector. However, despite there being great recognition in regards to the importance of geospatial information in relief operations there is still a knowledge gap in regards to all the different tasks and uses of GIS in the humanitarian sector. For example, Espindola et al (2016) lament that despite the recent increase in literature which utilizes GIS for humanitarian logistics most of the research is limited to net-work analysis and also that GIS’s full potential for disaster relief has not been fully tapped. This meta-analysis, for the first time, seeks to address such gap of knowledge by achieving two main goals: (1) To better understand the various ways in which Geographic Information System (GIS) can be applied in humanitarian settings by revealing how the academic community is utilizing such technology in their research, and (2) to point out strengths and areas that have been overlooked as well as help guide future research in this field

A study of volunteered geographic information (VGI) assessment methods for flood hazard mapping: A review

Floods are known as frequent and destructive global events that are caused by natural and human factors. Beside traditional methods, flood hazard mapping has been empowered by spatially enabled cell phones and web mapping technology which are feed by user generated data. This user generated information or Volunteered Geographic Information (VGI), becomes the first point of response during any natural disaster. Since this information is created by volunteers, its reliability and credibility issues bring restriction on use of them as main source of information. The available methods of VGI credibility assessment mainly focus on meta data analysis, VGI spatial pattern analysis and comparison of VGI data with reference data. This paper thoroughly discusses recent development in these three groups of VGI assessment methods. At the end we highlighted several research gaps and potentials of combining and improving these methods to support flood hazard mapping

BIG DATA APPLICATIONS AND CHALLENGES IN GISCIENCE (CASE STUDIES: NATURAL DISASTER AND PUBLIC HEALTH CRISIS MANAGEMENT)

This dissertation examines the application and significance of user-generated big data in Geographic Information Science (GIScience), with a focus on managing natural disasters and public health crises. It explores the role of social media data in understanding human-environment interactions and in informing disaster management and public health strategies. A scalable computational framework will be developed to model extensive unstructured geotagged data from social media, facilitating systematic spatiotemporal data analysis.The research investigates how individuals and communities respond to high-impact events like natural disasters and public health emergencies, employing both qualitative and quantitative methods. In particular, it assesses the impact of socio-economic-demographic characteristics and the digital divide on social media engagement during such crises. In addressing the opioid crisis, the dissertation delves into the spatial dynamics of opioid overdose deaths, utilizing Multiscale Geographically Weighted Regression to discern local versus broader-scale determinants. This analysis foregrounds the necessity for targeted public health responses and the importance of localized data in crafting effective interventions, especially within communities that are ethnically diverse and economically disparate. Using Hurricane Irma as a case study, this dissertation analyzes social media activity in Florida in September 2017, leveraging Multiscale Geographically Weighted Regression to explore spatial variations in social media discourse, its correlation with damage severity, and the disproportionate impact on racialized communities. It integrates social media data analysis with political-ecological perspectives and spatial analytical techniques to reveal structural inequalities and political power differentials. The dissertation also tackles the dissemination of false information during the COVID-19 pandemic, examining Twitter activity in the United States from April to July 2020. It identifies misinformation patterns, their origins, and their association with the pandemic\u27s incidence rates. Discourse analysis pinpoints tweets that downplay the pandemic\u27s severity or spread disinformation, while spatial modeling investigates the relationship between social media discourse and disease spread. By concentrating on the experiences of racialized communities, this research aims to highlight and address the environmental and social injustices they face. It contributes empirical and methodological insights into effective policy formulation, with an emphasis on equitable responses to public health emergencies and natural disasters. This dissertation not only provides a nuanced understanding of crisis responses but also advances GIScience research by incorporating social media data into both traditional and critical analytical frameworks

Exploring the data needs and sources for severe weather impact forecasts and warnings : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Emergency Management at Massey University, Wellington, New Zealand

Figures 2.4 & 2.5 are re-used with permission.The journal articles in Appendices J, L & M are republished under respective Creative Commons licenses. Appendix K has been removed from the thesis until 1 July 2022 in accordance with the American Meteorological Society Copyright Policy, but is available open access at https://doi.org/10.1175/WCAS-D-21-0093.1Early warning systems offer an essential, timely, and cost-effective approach for mitigating the impacts of severe weather hazards. Yet, notable historic severe weather events have exposed major communication gaps between warning services and target audiences, resulting in widespread losses. The World Meteorological Organization (WMO) has proposed Impact Forecasts and Warnings (IFW) to address these communication gaps by bringing in knowledge of exposure, vulnerability, and impacts; thus, leading to warnings that may better align with the position, needs, and capabilities of target audiences. A gap was identified in the literature around implementing IFWs: that of accessing the required knowledge and data around impacts, vulnerability, and exposure. This research aims to address this gap by exploring the data needs of IFWs and identifying existing and potential data sources to support those needs. Using Grounded Theory (GT), 39 interviews were conducted with users and creators of hazard, impact, vulnerability, and exposure (HIVE) data within and outside of Aotearoa New Zealand. Additionally, three virtual workshops provided triangulation with practitioners. In total, 59 people participated in this research. Resulting qualitative data were analysed using GT coding techniques, memo-writing, and diagramming. Findings indicate a growing need for gathering and using impact, vulnerability, and exposure data for IFWs. New insight highlights a growing need to model and warn for social and health impacts. Findings further show that plenty of sources for HIVE data are collected for emergency response and other uses with relevant application to IFWs. Partnerships and collaboration lie at the heart of using HIVE data both for IFWs and for disaster risk reduction. This thesis contributes to the global understanding of how hydrometeorological and emergency management services can implement IFWs, by advancing the discussion around implementing IFWs as per the WMO’s guidelines, and around building up disaster risk data in accordance with the Sendai Framework Priorities. An important outcome of this research is the provision of a pathway for stakeholders to identify data sources and partnerships required for implementing a hydrometeorological IFW system