This book addresses how accelerating advances in information and communication technology, mobile technology, and location-aware technology have fundamentally changed the ways how social, political, economic and transportation systems work in today’s globally connected world. It delivers on many exciting research questions related to human dynamics at both disaggregate and aggregate levels that attract the attention of researchers from a wide range of disciplines. Human Dynamics Research involves theoretical perspectives, space-time analytics, modeling human dynamics, urban analytics, social media and big data, travel dynamics, privacy issues, development of smart cities, and problems and prospects of human dynamics research. This book includes contributions on theoretical, technical, or application aspects of human dynamics research from different disciplines. Appealing to researchers, scholars and students across a wide range of topics and disciplines including: urban studies, space-time, mobility and the internet, social media, big data, behavioral geography and spatio-temporal-network visualization, this book offers a glimpse at the cutting edge of research on human dynamics.

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.

This book describes how powerful computing technology, emerging big and open data sources, and theoretical perspectives on spatial synthesis have revolutionized the way in which we investigate social sciences and humanities. It summarizes the principles and applications of human-centered computing and spatial social science and humanities research, thereby providing fundamental information that will help shape future research. The book illustrates how big spatiotemporal socioeconomic data facilitate the modelling of individuals’ economic behavior in space and time and how the outcomes of such models can reveal information about economic trends across spatial scales. It describes how spatial social science and humanities research has shifted from a data-scarce to a data-rich environment. The chapters also describe how a powerful analytical framework for identifying space-time research gaps and frontiers is fundamental to comparative study of spatiotemporal phenomena, and how research topics have evolved from structure and function to dynamic and predictive. As such this book provides an interesting read for researchers, students and all those interested in computational and spatial social sciences and humanities.

Contemporary Approaches Studying Customer Experience in Tourism Research develops approaches and related methods to understand, analyze, and evaluate the tourist consumption experience under its different forms and stages before, during and after.

This book reports on the latest, cutting-edge scholarship on integrating social network and spatial analyses in the built environment. It sheds light on conceptualization and Implementation of such integration, integration for intra-city level analysis, as well as integration for inter-city level analysis. It explores the use of new data sources concerning human and urban dynamics and provides a discussion of how social network and spatial analyses could be synthesized for a more nuanced understanding of the built environment. As such this book will be a valuable resource for scholars focusing on city-related networks in a number of ‘urban’ disciplines, including but not limited to urban geography, urban informatics, urban planning, urban sociology, and urban studies.

A new era of innovation is enabled by the integration of social sciences and information systems research. In this context, the adoption of Big Data and analytics technology brings new insight to the social sciences. It also delivers new, flexible responses to crucial social problems and challenges. We are proud to deliver this edited volume on the social impact of big data research. It is one of the first initiatives worldwide analyzing of the impact of this kind of research on individuals and social issues. The organization of the relevant debate is arranged around three pillars: Section A: Big Data Research for Social Impact: • Big Data and Their Social Impact; • (Smart) Citizens from Data Providers to Decision-Makers; • Towards Sustainable Development of Online Communities; • Sentiment from Online Social Networks; • Big Data for Innovation. Section B. Techniques and Methods for Big Data driven research for Social Sciences and Social Impact: • Opinion Mining on Social Media; • Sentiment Analysis of User Preferences; • Sustainable Urban Communities; • Gender Based Check-In Behavior by Using Social Media Big Data; • Web Data-Mining Techniques; • Semantic Network Analysis of Legacy News Media Perception. Section C. Big Data Research Strategies: • Skill Needs for Early Career Researchers—A Text Mining Approach; • Pattern Recognition through Bibliometric Analysis; • Assessing an Organization’s Readiness to Adopt Big Data; • Machine Learning for Predicting Performance; • Analyzing Online Reviews Using Text Mining; • Context–Problem Network and Quantitative Method of Patent Analysis. Complementary social and technological factors including: • Big Social Networks on Sustainable Economic Development; Business Intelligence.

This book describes the spatial and temporal perspectives on COVID-19 and its impacts and deepens our understanding of human dynamics during and after the global pandemic. It critically examines the role smart city technologies play in shaping our lives in the years to come. The book covers a wide-range of issues related to conceptual, theoretical and data issues, analysis and modeling, and applications and policy implications such as socio-ecological perspectives, geospatial data ethics, mobility and migration during COVID-19, population health resilience and much more. With accelerated pace of technological advances and growing divide on political and policy options, a better understanding of disruptive global events such as COVID-19 with spatial and temporal perspectives is an imperative and will make the ultimate difference in public health and economic decision making. Through in-depth analyses of concepts, data, methods, and policies, this book stimulates future studies on global pandemics and their impacts on society at different levels.

This book includes the original, peer-reviewed research articles from the International Conference on Computational Intelligence and Computing (ICCIC 2020), held in September 2020 on a virtual platform jointly organized by SR Group of Institutions, Jhansi, India, IETE, Kolkata Centre, India, and Eureka Scientech Research Foundation, Kolkata India. It covers the latest research in image processing, computer vision and pattern recognition, machine learning, data mining, big data and analytics, information security and privacy, wireless and sensor networks and IoT applications, artificial intelligence, expert systems, natural language processing, image processing, computer vision, artificial neural networks, fuzzy logic, evolutionary optimization, rough sets, web intelligence, intelligent agent technology, virtual reality, and visualization.

Geographic information science (GISc) and systems (GIS) have grown rapidly in recent decades, increasingly on a separate track from geographic thought. As geography's "big ideas"--such as space, place, boundaries, scale, process, and relationality--have evolved, what does this mean for their computational representation? This book considers how key concepts have developed in geography and are represented (or not) in GISc, with a view to bridging gaps between the two. David O'Sullivan shows how revisiting the theoretical underpinnings of geography offers insights on enduring GIS challenges--including map projections, the modifiable areal unit problem, scale and map generalization, and the nature of space and place--while also enriching geographic thought. The book uses examples from across geography's subdisciplines to promote understanding. Chapters are self-contained essays that can easily form the basis of classroom discussions. The companion website provides the figures, code to produce versions of selected figures, updated web links, and other resources.

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.