PerryGeo

Some folks have had trouble submitting links so I figured I should post a bit more detail on that. To get articles to show up on the geospatial reddit (not the main reddit), go to http://reddit.com/r/geospatial/submit or click the “Submit a Link” button on the right - from the geospatial page. When you’re submitting the url, you should see “submit to geospatial” as the page header.

I know at least 2 of us have been successful at posting. If this doesn’t work for you, please let me know and I’ll try and figure it out.

All the great GIS news/blog aggregators out there (planetgs, slashgeo, etc) are moderator driven - a few people act as the gatekeepers and inevitably have to decide what information is useful. This is not the ideal way to do things.

There’s a more democratic and distributed way to spread the role - it’s called reddit. More specifically, Geospatial Reddit. For those unfamiliar with reddit (or similar sites like digg), the idea is simple: users submit stories and users vote on stories. The most popular ones rise to the top and, theoretically, the best articles magically appear on the front page. Much like democracy itself, there are flaws in the theory but its the best thing we’ve got.

Geospatial Reddit is public so sign up, submit your favorite stories and vote. Lets see if we can make this work.

Guides like Tomislav Hengl’s Practical Guide to Geostatistical Mapping of Environmental Variables and Rossiter’s Introduction to applied geostatistics do an excellent job of providing a grounded, relatively easy to understand, introduction to geostatical prediction and kriging.

But if you’re an experience learner (like me) you don’t absorb the mathematics fully without doing something with the knowledge; Seeing it in action brings the concepts to life. Unfortunately most geostats/kriging software is either too complex for exploratory learning (not enough immediate feedback) or too simplistic (making too many assumptions, disallowing access to the nitty-gritty details). Either way, you’re bound to produce output with fundamental flaws because you’re not aware of the finer details of variogram modelling. I speak from exerience!

Luckily Dennis J. J. Walvoort of the Wageningen University & Research Center saw the same problem and created an nifty learning to to explore varigoram models and spatial predictions using ordinary kriging - EZ-Kriging. No degree in math or statistical theory required. Just drag the points around, play with the parameters and alter the underlying data as a table and see the results immediately.

Its nothing more than a simulation so don’t expect to load your own datasets or produce any meaningful output with it. But it truly excels as a learning tool to understand the core concepts behind kriging and is a great complement to Hengl and Rossiter’s work. With that knowledge you can do the real deal in Surfer, R, ILWIS or your geostats software of choice.

EDIT: One complaint about this EZ-Kriging that I have: it doesn’t show the observed sample variogram cloud overlayed on the variogram model. Oh well still a nice tool.

EDIT2: It’s a windows .exe but it runs smoothly under wine in linux.

As a followup to my previous post on turning Ubuntu Gutsy into a GIS workstation, Here are the revised instructions for Ubuntu 8.04 (The Hardy Heron).

Note that there are a few additonal apps and changes in here:

• Postgis
• Mapnik
• New version of QGIS installed via repository
• OpenStreetMap tools (JOSM and osm2pgsql)
• Geotiff utilities
• Some nice python spatial libs (shapely, owslib, geopy and pyproj)

Run the following as root on your new Hardy installation, answer a few configuration questions and you’ll be ready to go.

echo 'deb http://ppa.launchpad.net/qgis/ubuntu hardy main' >> /etc/apt/sources.list

apt-get update

apt-get -y --force-yes install grass mapserver-bin \
gdal-bin cgi-mapserver python-qt4 python-sip4 python-gdal \
python-mapscript gmt gmt-coastline-data r-recommended gpsbabel \
shapelib qgis qgis-plugin-grass python-setuptools \
python-mapnik mapnik-plugins mapnik-utils osm2pgsql josm postgresql-8.3-postgis \
python-dev build-essential libgdal-dev geotiff-bin sun-java6-jre

easy_install shapely geopy owslib pyproj

EDIT: If you’re looking for more up to date packages for geos, gdal, etc, try adding deb http://les-ejk.cz/ubuntu/ hardy multiverse to your /etc/apt/sources.list