GEOG 583
Geospatial System Analysis and Design

Welcome to GEOG 583 - Geospatial System Analysis and Design

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Geography 583 is a required course in the Penn State Professional Masters in Geographic Information Systems. This course surveys a range of contemporary systems analysis and design methods through case studies, collaborative work, and critical reading/writing. Key topics in the course outline the broad range of current GIS systems, how they are designed and evaluated, and how emerging technologies may impact their design and implementation in the near future.

Want to join us? Students who register for this Penn State course gain access to assignments and instructor feedback and earn academic credit. For more information, visit Penn State's Online Geospatial Education Program website.

Professor Introductions

Alan MacEachren

Alan MacEachren directs the GeoVISTA Center, an interdisciplinary geographical information science center. GeoVISTA conducts and coordinates integrated and innovative research in GIScience, covering a broad range of domains from spatial cognition, through formal geo-information representation, to spatial analysis, cartography and visual analytics.

MacEachren’s own research roots are in cartography and spatial cognition. His current research interests cover a wide spectrum of GIScience topics. These include: geovisual analytics, geovisualization and exploratory spatial data analysis, geosemantics and geographical information retrieval. Applications domains to which his research connects include public health, crisis management, and environmental science.

Click for a transcript of Alan MacEachren's Intro Video.

Hi. Alan MacEachron, professor of geography and information sciences and technology here at Penn State. I've been here a little bit more than 30 years doing research and teaching in cartography, visualization, and various aspects of geographic information science. Last couple of years I've been focusing in quite a bit on place and big data.

Like most of the faculty in our department, I spend most of my time working. But when I do have a chance to get away from work, my main outdoor activity is bird watching. I've been trying figure out over the last several years how to combine that bird watching activity with some of my research.

Finally have an opportunity to do that. I gave a talk in Germany last year on place and big data, and I happened to mention the large citizen science project at Cornell University called eBird. It collects data from bird watchers around the world, and they now have about half a billion bird sighting records in eBird. I put in about 2,400 of those myself. I'm sitting here on my porch doing a little bit of morning bird watching. Here's the bird list for just the birds that I saw from my porch here this morning.

At any rate, I got some colleagues in Germany, some computer scientists, interested in this topic. And they decided to do a visual analytics research project trying to take advantage of these eBird data, but with a particular focus on the many kinds of uncertainty-- spatial, temporal, and attribute uncertainty that exist in these data. The data are really important for modeling migration of various species around the world, but you need to understand the uncertainty in order to do good modeling.

So hopefully, maybe in about six months or nine months, you'll get a chance to see a paper that comes out from this collaborative work where I've been able to attach my birding hobby to my visual analytics research. I hope to see some of you in one of my classes this year or perhaps off at a conference. I'll be heading to Melbourne, Australia for the GIScience conference in a few weeks. Hope to do a little bit of birding there, myself. Or maybe with your binoculars I'll see you out in the field doing some birding with me.