This doctoral dissertation research is designed to advance our understanding of human perceptions of places using geospatial data science. Measuring human perceptions of places, such as how safe and lively a neighborhood is, is crucial for investigating the human sense of place and for furthering our knowledge of human-environment relationships. However, previous studies using interpretive approaches (e.g., interviews, questionnaires) to measure human perceptions of places face challenges such as high labor intensity, long update periods, and geographic scale limitations. Geospatial data science, including the usage of multiple sources of urban big data (e.g., street view imagery, human mobility) and the development of advanced geospatial artificial intelligence (GeoAI), provides unprecedented opportunities for researchers to not only model objective geographic phenomenon but also assess subjective human perceptions of places from a variety of dimensions (e.g., lively, safe, wealthy).Here, we first introduce a computational framework to measure human perceptions of places from a data-driven perspective. We employ geocomputational approaches to assess human perceptions of places with large-scale street view images and advanced GeoAI approaches. By comparing with conventional interpretive approaches, we illustrate the effectiveness of geospatial data science for measuring human perceptions of places and acknowledge its potential biases and challenges. We then explore what and how various urban design and environmental factors may affect people's place perceptions to inform city developments. After that, we demonstrate how human subjective perceptions of place might be integrated into place-based spatial analytics and highlight the critical role of human place perception in understanding human-environment interactions. This dissertation is developed based on the author's three peer-reviewed journal articles and has been arranged as chapters 4, 5, and 6. This work makes contributions to the broader fields of GIScience, geography, and urban planning. First, it shows how socioeconomic and environmental factors influence human perceptions of places and reveals the complex interactions between human activities and the physical environment. Second, it provides insights and decision-making suggestions for urban planners and governments toward building safer, livelier, and wealthier communities and cities. Third, it illustrates how humanistic insights can be integrated into geospatial data science and offers insights for addressing ethical issues in the development of GIScience.

This doctoral dissertation research is designed to advance our understanding of human perceptions of places using geospatial data science. Measuring human perceptions of places, such as how safe and lively a neighborhood is, is crucial for investigating the human sense of place and for furthering our knowledge of human-environment relationships. However, previous studies using interpretive approaches (e.g., interviews, questionnaires) to measure human perceptions of places face challenges such as high labor intensity, long update periods, and geographic scale limitations. Geospatial data science, including the usage of multiple sources of urban big data (e.g., street view imagery, human mobility) and the development of advanced geospatial artificial intelligence (GeoAI), provides unprecedented opportunities for researchers to not only model objective geographic phenomenon but also assess subjective human perceptions of places from a variety of dimensions (e.g., lively, safe, wealthy).Here, we first introduce a computational framework to measure human perceptions of places from a data-driven perspective. We employ geocomputational approaches to assess human perceptions of places with large-scale street view images and advanced GeoAI approaches. By comparing with conventional interpretive approaches, we illustrate the effectiveness of geospatial data science for measuring human perceptions of places and acknowledge its potential biases and challenges. We then explore what and how various urban design and environmental factors may affect people's place perceptions to inform city developments. After that, we demonstrate how human subjective perceptions of place might be integrated into place-based spatial analytics and highlight the critical role of human place perception in understanding human-environment interactions. This dissertation is developed based on the author's three peer-reviewed journal articles and has been arranged as chapters 4, 5, and 6. This work makes contributions to the broader fields of GIScience, geography, and urban planning. First, it shows how socioeconomic and environmental factors influence human perceptions of places and reveals the complex interactions between human activities and the physical environment. Second, it provides insights and decision-making suggestions for urban planners and governments toward building safer, livelier, and wealthier communities and cities. Third, it illustrates how humanistic insights can be integrated into geospatial data science and offers insights for addressing ethical issues in the development of GIScience.

This comprehensive handbook covers Geospatial Artificial Intelligence (GeoAI), which is the integration of geospatial studies and AI machine (deep) learning and knowledge graph technologies. It explains key fundamental concepts, methods, models, and technologies of GeoAI, and discusses the recent advances, research tools, and applications that range from environmental observation and social sensing to natural disaster responses. As the first single volume on this fast-emerging domain, Handbook of Geospatial Artificial Intelligence is an excellent resource for educators, students, researchers, and practitioners utilizing GeoAI in fields such as information science, environment and natural resources, geosciences, and geography. Features Provides systematic introductions and discussions of GeoAI theory, methods, technologies, applications, and future perspectives Covers a wide range of GeoAI applications and case studies in practice Offers supplementary materials such as data, programming code, tools, and case studies Discusses the recent developments of GeoAI methods and tools Includes contributions written by top experts in cutting-edge GeoAI topics This book is intended for upper-level undergraduate and graduate students from different disciplines and those taking GIS courses in geography or computer sciences as well as software engineers, geospatial industry engineers, GIS professionals in non-governmental organizations, and federal/state agencies who use GIS and want to learn more about GeoAI advances and applications.

Over the past decade, a new set of interactive, open, participatory and networked spatial media have become widespread. These include mapping platforms, virtual globes, user-generated spatial databases, geodesign and architectural and planning tools, urban dashboards and citizen reporting geo-systems, augmented reality media, and locative media. Collectively these produce and mediate spatial big data and are re-shaping spatial knowledge, spatial behaviour, and spatial politics. Understanding Spatial Media brings together leading scholars from around the globe to examine these new spatial media, their attendant technologies, spatial data, and their social, economic and political effects. The 22 chapters are divided into the following sections: Spatial media technologies Spatial data and spatial media The consequences of spatial media Understanding Spatial Media is the perfect introduction to this fast emerging phenomena for students and practitioners of geography, urban studies, data science, and media and communications.

Humanistic geography is one of the major emerging themes which has recently dominated geographic writing. Anne Buttimer has been one of the leading figures in the rise of humanistic geography, and the research students she collected round her at Clark University in the 1970s constituted something of a ‘school’ of humanistic geographers. This school developed a significantly new style of geographical inquiry, giving special emphasis to people’s experience of place, space and environment and often using philosophical and subjective methodology. This collection of essays, first published in 1980, brings together this school and offers insight into philosophical and practical issues concerning the human experience of environments. An extensive range of topics are discussed, and the aim throughout is to weave analytical and critical thought into a more comprehensive understanding of lived experience. This book will be of interest to students of human geography.

This book discusses theoretical backgrounds, techniques and methodologies, and applications of the current state-of-the-art human dynamics research utilizing social media and geospatial big data. It describes various forms of social media and big data with location information, theory development, data collection and management techniques, and analytical methodologies to conduct human dynamics research including geographic information systems (GIS), spatiotemporal data analytics, text mining and semantic analysis, machine learning, trajectory data analysis, and geovisualization. The book also covers applied interdisciplinary research examples ranging from disaster management, public health, urban geography, and spatiotemporal information diffusion. By providing theoretical foundations, solid empirical research backgrounds, techniques, and methodologies as well as application examples from diverse interdisciplinary fields, this book will be a valuable resource to students, researchers and practitioners who utilize or plan to employ social media and big data in their work.

Exploring human knowledge of geographical space and related behavior not only helps in understanding human-environment interactions and dynamic geographic processes, but also advances Geographic Information Systems (GIS) toward a human-centric paradigm to make daily life more efficient. Today's relatively easy acquisition of various big data provides an unprecedented opportunity for geographers to answer research questions that previously could not be adequately addressed. However, new challenges also arise regarding data quality and bias as well as change in methodology for dealing with big data that are different from traditional data types. Representing people's perception of place and studying driver's route-choice behavior are two of the many applications of big data in answering research questions about human knowledge and behavior in the fields of GIS and transportation. Incorporating three papers, this dissertation focuses on these two different applications to achieve the following objectives: 1) examine the degree to which a geographic place's spatial extent can be estimated from human-generated geotagged photos; 2) address the challenge of geotagged photos' uneven spatial distribution in place estimation and explore an approach that can better derive a place's spatial extent; 3) develop a method that can properly estimate the spatial extent of a place that has multiple disjoint regions while considering geotagged photos' uneven distribution; 4) explore useful spatiotemporal patterns of taxi drivers' route-choice behavior in a dynamic urban environment. This dissertation makes three major contributions to big data applications' systematic theory: 1) proposes an effective approach to handling the uneven spatial distribution problem of geotagged photos as a type of volunteered geographic data by modeling their representativeness; 2) develops methods that can properly derive the vague spatial extent of a place with or without disjoint regions; and 3) explores taxi drivers' route-choice patterns in different situations that can inform future transportation decisions and policy-making processes.