Encyclopedia of GIS
Description
The first two editions of the encyclopedia are comprehensive in the coverage of established GIS concepts, applications, and platforms. However, the field is growing quickly and there is a need to cover recent trends and developments. The goal of the third edition is to cover the recent trends since the second edition
(2017). It will divide the trends into three areas namely GIS concepts, methods and theories (e.g., Geographic Knowledge graphs, convolutional and spatial variability aware neural networks), GIS use-cases and applications (e.g., Geo-AI, Contact Tracing, spatial pathology), and GIS platforms (e.g., nano-satellites, UAVs, microscopes) and datasets (e.g., Multiplexed immunofluorescence imagery and cell level maps).
The new edition will cover novel and interesting GIS use-cases and applications which are being developed in the area of pandemic management, smart cities, electric vehicles, and so on to address emerging societal needs. For example, there has been a rapid development of contact-tracing apps to assess COVID-19 spread across geographic regions. Similarly, the development of digital twins allows a virtual look ahead into future city infrastructure, operational management,
etc. Further, GIS support is critical for optimal placement of charging stations and real-time determination of optimal routing operations amongst others. Beyond these there are other relevant applications being developed in the field of autonomous vehicles, spatial pathology, etc.
The new edition also covers novel emerging applications which support infrastructure such as IoT and embedded spatial computing platforms, unmanned aerial vehicles (UAVs), nano-satellite constellations, edge computing, and so on. For example, devices like OAK-D can perform real-time collision detection of bicycle riders. Similarly, rapid development of software libraries helps UAV devices in real-time imagery collection, etc and perform other operations such as geo-fencing, etc. Further, with a significant increase in the number of space-borne nano-satellites, commercial access to large-scale geospatial imagery is increasing rapidly. This has led to the emergence of multiple commercial geospatial cloud computing platforms (e.g., AWS Earth, Google Earth Engines, NEX). Besides these, trends such as location-aware edge computing, geo-collaboration via volunteered geographic information systems are also emerging.
Finally, this edition will cover novel GIS and spatial computing trends which advance fundamental GIS. For example, with an increased reach of spatial technology and computational tools across society there are calls for responsible spatial data science to develop methods, processes, and algorithms which do "no harm" to
society. Further, deep-spatial aims at developing spatially-explicit deep learning based models. To enable efficient and accurate spatial data search and management the semantic relationship between different spatial entities (e.g., point of interests) are being modeled as spatial knowledge graphs. Besides these, fundamental GIS developments are taking place in 3D positioning and orientation (for VR, UAV/aircraft navigation, brain surgery), differential privacy and quantum spatial computing.
Scope: The new edition will not cover topics where we may not find field editors. Moreover, we will not cover GNSS and satellite systems from countries other than the US. Finally, we will not cover controversial geo-political topics (e.g., gerrymandering, role of maps in warfare).
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Other editions
Previous edition
Persons
Shashi Shekhar is a McKnight Distinguished University Professor at the University of Minnesota (Computer Science faculty). For contributions to geographic information systems (GIS), spatial databases and spatial data mining, he received the IEEE-CS Technical Achievement Award and was elected an IEEE Fellow and an AAAS Fellow. He was also named a key difference-maker for the field of GIS by the most popular GIS textbook. He has a distinguished academic record that includes 280+ refereed papers, a popular textbook on Spatial Databases (Prentice Hall, 2003) and an authoritative Encyclopedia of GIS (Springer, 2008). He is serving as a member of the Computing Community Consortium Council (2012-15), a co-Editor-in-Chief of Geo-Informatica, a series editor for the Springer Briefs on GIS and as a member of the National Research Council (NRC) committee on Geo-targeted Disaster Alerts and Warning (2013). Previously, he served on multiple NRC committees including Future Workforce for Geospatial Intelligence (2011), Mapping Sciences (2004-2009) and Priorities for GEOINT Research (2004-2005). He also served as a general or program co-chair for the International Conference on Geographic Information Science (2012), the International Symposium on Spatial and Temporal Databases (2011) and ACM International Conference on Geographic Information Systems (1996). He also served on the Board of Directors of University Consortium on GIS (2003-4), as well as the editorial boards of IEEE Transactions on Knowledge and Data Engineering and IEEE-CS Computer Science and Engineering Practice Board. In early 1990s, his research developed core technologies behind in-vehicle navigation devices as well as web-based routing services, which revolutionized outdoor navigation in urban environments. His recent research played a critical role in evacuation route planning for homeland security and received multiple recognitions including the CTS Partnership Award for significant impact on transportation.He pioneered the research area of spatial data mining via pattern families (e.g. collocation, mixed-drove co-occurrence, cascade), keynote speeches, survey papers and workshop organization. He received a Ph.D. in Computer Science from the University of California, Berkeley.
Dr. Hui Xiong is an Associate Professor and Vice Chair of the Management Science and Information Systems Department, and the Director of the Rutgers Center for Information Assurance, at Rutgers, the State University of New Jersey, where he received a two-year early promotion/tenure (2009), the Rutgers University Board of Trustees Research Fellowship for Scholarly Excellence (2009) and the ICDM-2011 Best Research Paper Award (2011).
Dr. Xiong received his Ph.D. in Computer Science from the University of Minnesota, in 2005, a B.E. in Automation from the University of Science and Technology of China and an M.S. in Computer Science from the National University of Singapore. His general area of research is data and knowledge engineering, with a focus on developing effective and efficient data analysis techniques for emerging data intensive applications. He has published prolifically in refereed journals and conference proceedings (3 books, 40+ journal papers and 60+ conference papers). He is a co-Editor-in-Chief of the Encyclopedia of GIS, an Associate Editor of IEEE Transactions on Data and Knowledge Engineering (TKDE) and the Knowledge and Information Systems (KAIS) journal. He has served on the organization and program committees of numerous conferences, including as a Program Co-Chair of the Industrial and Government Track for the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining and a Program Co-Chair for the 2013 IEEE International Conference on Data Mining.
Content
Basic Concepts.- Basic Storage and Retrieval Structure.- Cartography and Visualization.- Commercial GIS.- Commercial Spatial Databases.- Critical Evaluation of Standard Proposals.- Data Exchange and Interoperability.- Digital Road Map.- Emergency Evacuations.- Evacuation Planning and Operations.- GeoSensor Networks.- Geospatial Semantic Web.- GIS in Business Intelligence.- Routing.- GIS Issues and Applications.- Indoor Positioning.- Information Collection Using Sensor Network.- Open Source GIS Software.- Photogrammetry.- Representation of Inexact Spatial Information.- Road Network Databases.- Security and Privacy in Geospatial Information Systems.- Spatial Analysis.- Spatial Aspects of Bioinformatics.- Spatial Aspects of Distributed Computing.- Spatial Aspects of Mobile Computing.- Spatial Association Discovery.- Spatial Colocation Rule Mining.- Spatial Constraint Databases.- Spatial Data Warehousing and Decision Support.- Spatial Database Modeling for Applications.- Spatial Indexing.- Spatial Outlier Detection.- Spatial Prediction.- Spatial Thinking.- Spatial Time Series.- Spatial Uncertainty and Imprecision.- Spatio-Temporal Data Modeling.- Spatio-Temporal Databases.- Statistical Modeling for Spatial Data.- Tesselation Data Models.- Use of Spatial Data for Simulation.- Qualitative Volunteered Data and Next-Generation Sensor Measurement.- Spatio-Temporal Prediction.- Synthesizing Multiple Viewpoints of Past, Present, and Future.- Spatial and Spatio-Temporal Computing Standards.- Spatial Computing Infrastructure.- Augmented Reality.- Collection, Fusion and Curation of Sensing Data.- Computational issues for Spatial Big Data.- Spatial Cognitive Assistance.- Spatial Computing for Human-Human Interaction/Collaboration.- Context-aware Spatial Computing.- Improving Spatial Abilities and Skills Developing Spatial Abilities and Talent in US Students?- Ubiquitous Computing.- Persistent Sensing and Monitoring.- Trustworthy Localization and Transportation Systems.- Understanding Geo-Privacy Concerns.