Data science has recently gained much attention for a number of reasons, and among them is Big Data. Scientists (from almost all disciplines including physics, chemistry, biology, sociology, among others) and engineers (from all fields including civil, environmental, chemical, mechanical, among others) are faced with challenges posed by data volume, variety, and velocity, or Big Data. This book is designed to highlight the unique characteristics of geospatial data, demonstrate the need to different approaches and techniques for obtaining new knowledge from raw geospatial data, and present select state-of-the-art geospatial data science techniques and how they are applied to various geoscience problems.
Geospatial Data Science: A Transdisciplinary Approach. Geocoding Fundamentals and Associated Challenges. Deep Learning with Satellite Images and Volunteered Geographic Information. Visual Analysis of Floating Car Data. Recognizing Patterns in Geospatial Data Using Persistent Homology: A Study of Geologic Fractures. LiDAR for Marine Applications. Spatiotemporal Point Pattern Analysis Using Ripley’s K Function. Geospatial Data Science Approaches for Transport Demand Modeling. Geography of Social Media in Public Response to Policy-Based Topics. Geospatial Data Streams.