Introduction to Big Data Computing for Geospatial Applications

The convergence of big data and geospatial computing has brought challenges and opportunities to GIScience with regards to geospatial data management, processing, analysis, modeling, and visualization. This special issue highlights recent advancements in integrating new computing approaches, spatial methods, and data management strategies to tackle geospatial big data challenges and meanwhile demonstrates the opportunities for using big data for geospatial applications. Crucial to the advancements highlighted here is the integration of computational thinking and spatial thinking and the transformation of abstract ideas and models to concrete data structures and algorithms. This editorial first introduces the background and motivation of this special issue followed by an overview of the ten included articles. Conclusion and future research directions are provided in the last section

Big Data Analytics in Humanitarian and Disaster Operations: A Systematic Review

By the outset of this review, 168 million people needed humanitarian aid, and the number grew to 235 million by the end of the completion of this review. There is no time to lose, definitely no data to lose. Humanitarian relief is crucial not just to contend with a pandemic once a century but also to provide help during civil conflicts, ever-increasing natural disasters, and other forms of crisis. Reliance on technology has never been so relevant and critical than now. The creation of more data and advancements in data analytics provides an opportunity to the humanitarian field. This review aimed at providing a holistic understanding of big data analytics in a humanitarian and disaster setting. A systematic literature review method is used to examine the field and the results of this review explain research gaps, and opportunities available for future research. This study has shown a significant research imbalance in the disaster phase, highlighting how the emphasis is on responsive measures than preventive measures. Such reactionary measures would only exacerbate the disaster, as is the case in many nations with COVID-19. Overall this research details the current state of big data analytics in a humanitarian and disaster setting

Social Media Big Data Mining and Spatio-Temporal Analysis on Public Emotions for Disaster Mitigation

Social media contains a lot of geographic information and has been one of the more important data sources for hazard mitigation. Compared with the traditional means of disaster-related geographic information collection methods, social media has the characteristics of real-time information provision and low cost. Due to the development of big data mining technologies, it is now easier to extract useful disaster-related geographic information from social media big data. Additionally, many researchers have used related technology to study social media for disaster mitigation. However, few researchers have considered the extraction of public emotions (especially fine-grained emotions) as an attribute of disaster-related geographic information to aid in disaster mitigation. Combined with the powerful spatio-temporal analysis capabilities of geographical information systems (GISs), the public emotional information contained in social media could help us to understand disasters in more detail than can be obtained from traditional methods. However, the social media data is quite complex and fragmented, both in terms of format and semantics, especially for Chinese social media. Therefore, a more efficient algorithm is needed. In this paper, we consider the earthquake that happened in Ya’an, China in 2013 as a case study and introduce the deep learning method to extract fine-grained public emotional information from Chinese social media big data to assist in disaster analysis. By combining this with other geographic information data (such population density distribution data, POI (point of interest) data, etc.), we can further assist in the assessment of affected populations, explore emotional movement law, and optimize disaster mitigation strategies

“Envisioning Digital Sanctuaries”: An Exploration of Virtual Collectives for Nurturing Professional Development of Women in Technical Domains

Work and learning are essential facets of our existence, yet sociocultural barriers have historically limited access and opportunity for women in multiple contexts, including their professional pursuits. Such sociocultural barriers are particularly pronounced in technical domains and have relegated minoritized voices to the margins. As a result of these barriers, those affected have suffered strife, turmoil, and subjugation. Hence, it is important to investigate how women can subvert such structural limitations and find channels through which they can seek support and guidance to navigate their careers. With the proliferation of modern communication infrastructure, virtual forums of conversation such as Reddit have emerged as key spaces that allow knowledge-sharing, provide opportunities for mobilizing collective action, and constitute sanctuaries of support and companionship. Yet, recent scholarship points to the negative ramifications of such channels in perpetuating social prejudice, directed particularly at members from historically underrepresented communities. Using a novel comparative muti-method, multi-level empirical approach comprising content analysis, social network analysis, and psycholinguistic analysis, I explore the way in which virtual forums engender community and foster avenues for everyday resilience and collective care through the analysis of 400,267 conversational traces collected from three subreddits (r/cscareerquestions, r/girlsgonewired & r/careerwoman). Blending the empirical analysis with a novel theoretical apparatus that integrates insights from social constructivist frameworks, feminist data studies, computer-supported collaborative work, and computer-mediated communication, I highlight how gender, care, and community building intertwine and collectively impact the emergent conversational habits of these online enclaves. Key results indicate six content themes ranging from discussions on knowledge advancement to scintillating ethical probes regarding disparities manifesting in the technical workplace. Further, psycholinguistic and network insights reveal four pivotal roles that support and enrich the communities in different ways. Taken together, these insights help to postulate an emergent spectrum of relationality ranging from a more agentic to a more communal pattern of affinity building. Network insights also yield valuable inferences regarding the role of automated agents in community dynamics across the forums. A discussion is presented regarding the emergent routines of care, collective empowerment, empathy-building tactics, community sustenance initiatives, and ethical perspectives in relation to the involvement of automated agents. This dissertation contributes to the theory and practice of how virtual collectives can be designed and sustained to offer spaces for enrichment, empowerment, and advocacy, focusing on the professional development of historically underrepresented voices such as women

Assessing the social impacts of extreme weather events using social media

The frequency and severity of extreme weather events such as flooding, hurricanes/storms and heatwaves are increasing as a result of climate change. There is a need for information to better understand when, where and how these events are impacting people. However, there are currently limited sources of impact information beyond traditional meteorological observations. Social sensing, which is the use of unsolicited social media data to better understand real world events, is one method that may provide such information. Social sensing has successfully been used to detect earthquakes, floods, hurricanes, wildfires, heatwaves and other weather hazards. Here social sensing methods are adapted to explore potential for collecting impact information for meteorologists and decision makers concerned with extreme weather events. After a review of the literature, three experimental studies are presented. Social sensing is shown to be effective for detection of impacts of named storms in the UK and Ireland. Topics of discussion and sentiment are explored in the period before, during and after a storm event. Social sensing is also shown able to detect high-impact rainfall events worldwide, validating results against a manually curated database. Additional events which were not known to this database were found by social sensing. Finally, social sensing was applied to heatwaves in three European cities. Building on previous work on heatwaves in the UK, USA and Australia, the methods were extended to include impact phrases alongside hazard-related phrases, in three different languages (English, Dutch and Greek). Overall, social sensing is found to be a good source of impact information for organisations that need to better understand the impacts of extreme weather. The research described in this project has been commercialised for operational use by meteorological agencies in the UK, including the Met Office, Environment Agency and Natural Resources Wales.Engineering and Physical Sciences Research Council (EPSRC