Comments for Volunteered Geographic Information

Comment on Jenks’ Natural Breaks Algorithm in Python by Dan

Dan — Thu, 08 Jul 2010 17:58:39 +0000

If it is of interest, I think this could be much optimised using Numpy arrays and some more intelligent programming – it would likely use less memory too. Might not get round to it though.

Comment on London Population Cartograms in Processing by comparative population cartogram 1966 to 2001 | Nepal: Lies, truths and statistics!

Thu, 08 Jul 2010 00:24:05 +0000

[...] on Dan J Lewis’s post, I took population data from various sources through the years 1966 (estimate) to 2001 and created [...]

Comment on Jenks’ Natural Breaks Algorithm in Python by Drew

Drew — Thu, 17 Jun 2010 19:50:25 +0000

Good work! I work for ESRI software development on the core geoprocessing team and was investigating goodness of variance fit classification using Python and this is right on the money! Your Python port is much faster than the version I scripted, so congratulations to the original programmer and you for your excellent port!

Comment on Apple’s iPad and Mobile/Field GIS by Howard D

Howard D — Wed, 16 Jun 2010 15:20:40 +0000

I work as a geologist in the Mineral Exploration sector and we use MapInfo and ArcGIS heavily, along with Satellite imagery and also geophysics data. A viewer/simple GIS layering app would be totally awesome for us. Garmin is moving in the right direction with BirdEye imagery available for download on their new GPS62, but it’s still small and fiddly. Scale that up on a user friendly touch screen and I am very excited about the possibilities – farewell to those old clunky handhelds.

Comment on Computers and the Renaissance of Cartography by Computers and the Renaissance of Cartography (1976) « Po Ve Sham – Muki Haklay’s personal blog

Sat, 12 Jun 2010 12:09:14 +0000

[...] in geography, to turn this booklet into PDF format so we can share it. Dan put some of the maps on his blog [...]

Comment on UK OAC map in Python by Dan

Dan — Thu, 03 Jun 2010 17:48:48 +0000

Just tried the GeoJSON method – cuts the computation time by a factor of 3, cheers!

Comment on UK OAC map in Python by Dan

Dan — Thu, 03 Jun 2010 15:02:15 +0000

Thanks for the advice Sean, I’ll have to look into GeoJSON, I’ve always associated it with webmapping applications, and as a result never really invested time in it. It’ll be interesting to see what ArcGIS 10 does, part of the reason I was looking into python was to output a lot of maps for a dataset for exploratory purposes, looking at different variables and classifications; this was something that didn’t seem possible in ArcGIS 9.x, so it will be interesting to see whether such functionality will turn up in ArcGIS 10. We’ve played with a beta of ArcGIS Desktop 9.4 (which became 10) at UCL, I can’t confirm the objects rumour though, if it is true it will make exploratory mapping of large datasets a snap!

Comment on UK OAC map in Python by Sean Gillies

Sean Gillies — Thu, 03 Jun 2010 08:16:47 +0000

Dan, this is great stuff. I appreciate that you’re trying Shapely, but I think you can probably save time (extra data copying) by cutting it out of the process. Use GeoJSON-like dicts instead

{“type”: “Polygon”, “coordinates”: [[[0, 0], …]]}

In fact, descartes asks Shapely to render geometric objects into this form, so the actual Shapely instances are unnecessary unless you’re doing some other analysis.

I’ve heard a rumor that objects in the new ArcPy (10) provide the same interface and you’ll be able to pass them straight to descartes.

Comment on A Thematic Map in Python by Sean Gillies

Sean Gillies — Wed, 26 May 2010 08:28:47 +0000

Nice. You might be interested in my helper package for matplotlib called “descartes”

http://pypi.python.org/pypi/descartes/1.0

It makes proper matplotlib patches out of GeoJSON-like objects (or Shapely objects). Patches are more powerful than “fill”: you can render polygons with holes. There’s an example image on descartes’ PyPI page.

Comment on OAC quality using entropy scores by Multi Dimensional Scaling of Southwark OAC data – Volunteered Geographic Information

Wed, 28 Apr 2010 16:34:50 +0000

[...] entropy scores for each OA in the UK with respect to distance from all supergroup cluster centres (here). Recently, Pete Fischer presented some very clever work in this vein at the recent GISRUK 2010 [...]