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Social Sensing and Big Data Computing for Disaster Management captures recent advancements in leveraging social sensing and big data computing for supporting disaster management. Specifically, analysed within this book are some of the promises and pitfalls of social sensing data for disaster relevant information extraction, impact area assessment, population mapping, occurrence patterns, geographical disparities in social media use, and inclusion in larger decision support systems. Traditional data collection methods such as remote sensing and field surveying often fail to offer timely information during or immediately following disaster events. Social sensing enables all citizens to become part of a large sensor network which is low cost, more comprehensive, and always broadcasting situational awareness information. However, data collected with social sensing is often massive, heterogeneous, noisy, and unreliable in some aspects. It comes in continuous streams, and often lacks geospatial reference information. Together, these issues represent a grand challenge toward fully leveraging social sensing for emergency management decision making under extreme duress. Meanwhile, big data computing methods and technologies such as high-performance computing, deep learning, and multi-source data fusion become critical components of using social sensing to understand the impact of and response to the disaster events in a timely fashion. This book was originally published as a special issue of the International Journal of Digital Earth.

Emergency management --- Online social networks --- Big data. --- Gestion des situations d'urgence --- Réseaux sociaux (Internet) --- Données massives --- Data processing. --- Informatique.

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The convergence of big data and geospatial computing has brought forth challenges and opportunities to Geographic Information Science with regard to geospatial data management, processing, analysis, modeling, and visualization. This book highlights recent advancements in integrating new computing approaches, spatial methods, and data management strategies to tackle geospatial big data challenges and meanwhile demonstrates opportunities for using big data for geospatial applications. Crucial to the advancements highlighted in this book is the integration of computational thinking and spatial thinking and the transformation of abstract ideas and models to concrete data structures and algorithms.

Research & information: general --- Geography --- task --- workflow --- geospatial problem-solving --- knowledge base --- social media --- big data --- fine-grained emotion classification --- spatio-temporal analysis --- hazard mitigation --- missing road --- city blocks --- topology --- big mobile navigation trajectory data --- geographic knowledge representation --- geographic knowledge graph --- formalization --- GeoKG --- overlay analysis --- shape complexity --- massive data --- cloud --- parallel computing --- geovisual analytics --- machine learning --- smart card data --- transit corridor --- mobility community --- trip --- CA Markov --- land-use change prediction --- Hadoop --- MapReduce --- cloud computing --- ETL --- ELT --- sensor data --- IoT --- geospatial big data --- climate science --- metadata --- web cataloging service --- big geospatial data --- geospatial cyberinfrastructure --- topographic surface --- terrain modeling --- global terrain dataset --- geospatial computing --- cyberGIS --- GeoAI --- spatial thinking