Posts Tagged ‘gis’

AAG Meetings – Visualization of Abstracts

Thursday, March 27th, 2008

Since the annual AAG meeting is coming up in Boston soon I present you this map (from via Will Oscar Larson ever stop sending out email announcements for this event already!?

aag visualization of abstracts

This map is a visualization derived from more than 22,000 abstracts submitted to the Annual Meetings of the Association of American Geographers during a ten-year period from 1993 to 2002. The methodology is centered around the representation of each document as an n-dimensional vector of terms. These vectors are used to construct a neural network model of the geographic knowledge domain using a Self-Organizing Map (SOM). The neural network model is then transformed into two types of information: (1) a landscape in which elevation indicates the degree to which a single, focused topic is addressed; and (2) multilevel text labels associated with regions in the visualization. The final rendering was executed in standard geographic information systems (GIS) software.

Zillow® Labs – Neighborhood Boundaries Shapefiles for Download

Tuesday, March 25th, 2008

zillow logo( via The Zillow data team has created a database of nearly 7,000 neighborhood boundaries in the largest cities in the U.S. And they’d like to share them with you! They’re sharing these neighborhoods under a Creative Commons license to allow people to use and contribute to their growing database.

Now comes the fine print: You are free to use the files in this database in applications as long as you attribute Zillow when you use it. You may also make your own changes to the database files and distribute them, as long as you provide them under the same kind of license and give Zillow attribution. The neighborhood shapes are available below, zipped up in the Arc Shapefile format.

The downloads are by state and available here:

GIS Routing Topology – This Psychologist Might Outsmart the Math Brains Competing for the Netflix Prize (Wired)

Monday, March 24th, 2008

To me the following sounds like a “cost routing” topology problems associated with road travel and travel times from my GIS classes in university. My local movie rental outlet closed this weekend and I’m back to Netflix land. Here’s hoping they can improve their “you’d like this” recommendations.

netflix statsBy Jordan Ellenberg Email 02.25.08 | 6:00 PM

(From Wired) At first, it seemed some geeked-out supercoder was going to make an easy million. 

In October 2006, Netflix announced it would give a cool seven figures to whoever created a movie-recommending algorithm 10 percent better than its own. Within two weeks, the DVD rental company had received 169 submissions, including three that were slightly superior to Cinematch, Netflix’s recommendation software. After a month, more than a thousand programs had been entered, and the top scorers were almost halfway to the goal.

But what started out looking simple suddenly got hard. The rate of improvement began to slow. The same three or four teams clogged the top of the leaderboard, inching forward decimal by agonizing decimal. There was BellKor, a research group from AT&T. There was Dinosaur Planet, a team of Princeton alums. And there were others from the usual math powerhouses like the University of Toronto. After a year, AT&T’s team was in first place, but its engine was only 8.43 percent better than Cinematch. Progress was almost imperceptible, and people began to say a 10 percent improvement might not be possible.

Then, in November 2007, a new entrant suddenly appeared in the top 10: a mystery competitor who went by the name “Just a guy in a garage.” His first entry was 7.15 percent better than Cinematch; BellKor had taken seven months to achieve the same score. On December 20, he passed the team from the University of Toronto. On January 9, with a score 8.00 percent higher than Cinematch, he passed Dinosaur Planet. 

Read more at . . .