printer friendly pageEARTHSYS 144: Fundamentals of Geographic Information Science (GIS) (ESS 164)
Everything is somewhere, and that somewhere matters." The rapid growth and maturity of spatial data technologies over the past decade represent a paradigm shift in the applied use of location data from high-level overviews of administrative interests, to highly personalized location-based services that place the individual at the center of the map, at all times. The use of spatial data and related technology continues to grow in fields ranging from environmental sciences to epidemiology to market prediction. This course will present an overview of current approaches to the use of spatial data and its creation, capture, management, analysis and presentation, in a research context. Topics will include modeling of geographic objects and associated data, modeling of geographic space and the conceptual foundations of "spatial thinking," field data collection, basic spatial statistical analysis, remote sensing & the use of satellite-based imagery, "Big Data" and machine learning approaches t
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Everything is somewhere, and that somewhere matters." The rapid growth and maturity of spatial data technologies over the past decade represent a paradigm shift in the applied use of location data from high-level overviews of administrative interests, to highly personalized location-based services that place the individual at the center of the map, at all times. The use of spatial data and related technology continues to grow in fields ranging from environmental sciences to epidemiology to market prediction. This course will present an overview of current approaches to the use of spatial data and its creation, capture, management, analysis and presentation, in a research context. Topics will include modeling of geographic objects and associated data, modeling of geographic space and the conceptual foundations of "spatial thinking," field data collection, basic spatial statistical analysis, remote sensing & the use of satellite-based imagery, "Big Data" and machine learning approaches to spatial data, and cartographic design and presentation including the use of web-based "Storymap" platforms. The course will consist of weekly lectures, guest speakers, computer lab assignments, midterm and final exams, as well as an individual final project requirement. This course must be taken for a minimum of 3 units and a letter grade to be eligible for Ways credit.
Terms: Aut, Spr
| Units: 3-4
| UG Reqs: GER: DB-NatSci, WAY-AQR
Instructors:
Maples, S. (PI)
;
Moanga, D. (PI)
;
Friedman, K. (TA)
...
more instructors for EARTHSYS 144 »
Instructors:
Maples, S. (PI)
;
Moanga, D. (PI)
;
Friedman, K. (TA)
;
Molbak, L. (TA)
;
Spencer, R. (TA)
;
Zimmerman, M. (TA)
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