Unit 12 

The Future of Geospatial Information Technology

 

　

 

The Future of GIS

 

Sensor Web: 

http://en.wikipedia.org/wiki/Sensor_Web

　(cited from Wikipedia)  The Sensor Web is a type of sensor network or geographic information system (GIS) that is especially well suited for environmental monitoring and control. In 1997, Kevin Delin of NASA's Jet Propulsion Laboratory used the term to describe a specific type of sensor network: an amorphous network of spatially distributedsensor platforms (pods) that wirelessly communicate with each other. This amorphous architecture is unique since it is both synchronous and router-free, making it distinct from the more typical TCP/IP-like network schemes. The architecture allows every pod to know what is going on with every other pod throughout the Sensor Web at each measurement cycle.

Sensor Web (http://aaaprod.gsfc.nasa.gov/sensorweb/)

 

http://www.technologyreview.com/read_article.aspx?id=16781&ch=infotech

　

http://hpwren.ucsd.edu/news/021115.html

　

Air Quality Sensor https://www.youtube.com/watch?v=rDTAJmoxeXA

EPA Citizen Science: https://www.youtube.com/watch?v=lL5tPNn5X48

Smart Vehicle-to-Vehicle communication:  https://www.youtube.com/watch?v=gLXQaU9MuTM

https://www.youtube.com/watch?v=BXXlodI9gO0 

US DoT:  https://www.youtube.com/watch?v=3z09fCqmILU

 

 

 

Machine Learning  (Artificial Intelligence) and Deep Learning.

 

 

Machine learning is a field of computer science that uses statistical techniques to give computer systems the ability to "learn" (i.e., progressively improve performance on a specific task) with data, without being explicitly programmed.

https://www.youtube.com/watch?v=nKW8Ndu7Mjw (Google)

https://www.coursera.org/learn/machine-learning/lecture/Ujm7v/what-is-machine-learning

Deep learning is the fastest-growing field in machine learning. It uses many-layered Deep Neural Networks (DNNs) to learn levels of representation and abstraction that make sense of data such as images, sound, and text.  (cited from https://developer.nvidia.com/deep-learning )

https://www.youtube.com/watch?v=b99UVkWzYTQ

 

What's NEXT?  Are we ready for this new AI world?  Should we be afraid of these developments?

 

 

https://www.youtube.com/watch?v=Cm_oAaQVWZ8&feature=youtu.be

　

 

 

 

The Future Directions of GIS

Multiple versions from various institues.　

 

 

UCGIS 1996
  • Spatial Data Acquisition and Integration
  • Distribute Computing; Extensions to Geographic Representations
  • Cognition of Geographic Information
  • Interoperability of Geographic Information
  • Scale
  • Spatial Analysis and Modeling in a GIS Environment
  • The Future of the Spatial Information Infrastructure
  • Uncertainty in Geographic Data and GIS-based Analyses; GIS and Society.

 

 

UCGIS 2004
  • Spatial Data Acquisition and Integration
  • Cognition of Geographic Information
  • Scale
  • Extensions to Geographic Representations
  • Spatial Analysis and Modeling in a GIS Environment
  • Research Issues on Uncertainty in Geographic Data and GIS-Based Analysis
  • The Future of the Spatial Information Infrastructure
  • Distributed and Mobile Computing
  • GIS and Society
  • Interrelation, Integration, and Transformation
  • Geographic Visualization
  • Ontological Foundations for Geographic Information Science
  • Remotely-Acquired Data and Information in GIScience
  • Geospatial Data Mining and Knowledge Discovery.

 

 

David Mark (SUNY-Baffalo)

 
  • GIS Data Integration
  • Scale
  • Process Models
  • Usability
  • Representation
  • Uncertainty
  • Cognition
  • Simulation
  • Data Collecting

　

GIS textbook (Longley, Goodchild, Rhind)

Ten Grand Challenges for GIS
  • Global Data Layers;
  • The GIS Profession;
  • The GIS Curriculum;
  • Near-universal Empowerment;
  • Global Population Statistics;
  • Richer Geodemographic Data Infrastructure;
  • Ego-centered Mapping;
  • Comprehensive Data Models;
  • Balancing of Fighting Terrorism and Maintaining Lifestyles;
  • Richer Geo-simulation;

　

National Research Council (2003).  IT Roadmap to a Geospatial Future

　

Location-aware Computing; 

Geospatial Databases and Data Mining;

Human Interaction with Geospatial Information;

High-quality Geospatial Information.

[1] UCGIS (via R. McMaster). (1996). Research Priorities for Geographic Information Science. Cartography and Geographic Information Systems, 23 (3), 115-127, 1996.

[2] Mark, D.M. (2000). Geographic Information Science: Critical Issues in an Emerging Cross-Disciplinary Research Domain. Workshop Report, Workshop on Geographic Information Science and Geospatial Activities at NSF. January 14-15, 1999.URISA Journal 12(1): 45 – 54.

[3] National Research Council (2003).  IT Roadmap to a Geospatial Future. National Academies Press., Washington, DC. (Available athttp://www.nap.edu/catalog/10661.html)

[4] McMaster, R., and User, E. L. (2004). A Research Agenda for Geographic Information Science. Taylor & Francis. http://www.ucgis.org/priorities/research/2006ResearchNextSteps.htm