Le mie letture per TANTO

Esta dica vem do blog do Edmar Moretti, que é o curso online de gvSig que foi montado pelos alunos do CEFET de Goiás, sob orientação do Prof. Nilson Ferreira.

O gvSig pra quem não conhece é um software para Sistemas de Informações Geográficas que dispõe de funções para aquisição, armazenamento, gerenciamento, manipulação, processamento, exibição e publicação de dados e informações geográficas.

Quem estiver interesse no curso sobre o gvSig, segue o link: http://www.lapig.iesa.ufg.br/lapig/cursos_online/gvsig/index.html

Fonte: Blog do Edmar Moretti

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We just released QGIS 1.3 ‘Mimas’. Download your copy http://qgis.org/en/download/current-software.html and read the official announcement over on the QGIS blog here

I did some revisions to the Windows all in one installer for this release and was able to shrink it down in size quite a bit – always good news for those of us living in hte ‘internet badlands’.

The vector editing features in QGIS are now becoming a serious contender with commercial applications in my opinion, the new goodies in QGIS 1.3 just go even further to qualify that statement.

Hope you all enjoy using the work of a very dedicated QGIS developer team (these guys always blow my mind with their work!).

I will probably plan the next release for after the QGIS Hackfest in Vienna in November. Really looking forward to that!

It has been way to long since the initial release, but it’s finally there: a new release of GeoCouch. For all first time visitors, GeoCouch is an extension for CouchDB to support geo-spatial queries like bounding box or polygon searches.

I keep this blog entry relatively short and only outline the highlights and requirements for the new release as GeoCouch finally has a real home at http://gitorious.org/geocouch/. Feel free to contribute to the wiki or fork the source.

Highlights

• Many geometries are supported: points, lines, polygons (using Shapely).
• Queries are largely along the lines of the OpenSearch-Geo extension draft. Currently supported are bounding box and polygon searches.
• Adding new backends (in addition to SpatiaLite) is easily possible.

Requirements

• Linux 2.6.26
• CouchDB 0.10.0
• Python 2.6.0
• couchdb-python 0.6.x (0.6.0 doesn't work)
• Shapely 1.0.12
• APSW - Another Python SQLite Wrapper 3.5.9-r2
• SpatiaLite 2.3.1

Other versions might work.

Download

If you don’t like Git, you can download GeoCouch 0.10.0 here.

Todd came into town for the Gov2.0 Summit last week, and in additional to dropping off a terabyte worth of data from Afghanistan, he talked a bit about what has made the “beer for data” program work at the Taj. Outside the universal thirst for beer data sharing success boiled down to three basic principles:

1) Create immediate value for anyone contributing data: when users contribute data they should get an immediate return on that investment. In the case of the Afghan pilot that meant getting to see your contributed data on a map of high resolution satellite imagery as soon as you uploaded it. The imagery for Afghanistan was made available by NGA, then tiled and served up by a Fusion Server, graciously donated by Google.

2) Make contributor’s data available back to them with improvements: any data that goes in should be available to download back out again. Further, the data should come back better than when it went in. In the Afghan pilot this meant if you shared data in a spreadsheet format into the platform you could get it back out as KML, shapefile, Atom, JSON, spatialite etc. (Addendum to principle 2 - PDF’s are evil, and make parsing and extracting data into a sharable format complete misery.)

3) Share derivative works back with the data sharing community: urge users who create derivative works, with shared data, to contribute their data products back to the group. In the case of the Afghan pilot researchers were taking the detailed data from the field and feeding it into their sophisticated models and simulations. Researchers would then upload the results into GeoIQ to share the derivative works back with the data sharing community. This meant that agencies and individuals that shared data again got a better product back by contributing. The researchers get better data to feed their models, and a self perpetuating feedback loop is created that sustains increasing data sharing.

While these sound like simple principles, it is amazing how often they are not followed and effective data sharing is blunted. Too often data sharing - especially with government and corporations - is a black hole. Data goes in but never comes back out. Also it is rare to see the positive feedback loops of researchers sharing their work products back with the data sharing community. Too often researchers get wrapped around the axle on their products being proprietary or sensitive. While this can be the case their is huge benefit in feeding results back to gauge their veracity and accuracy. I’ve definitely seen way too many models that look great in the lab and completely fall apart in reality because researchers would not feed results back to the field for verification and error bounding. I’m hoping we’ll have more opportunities to implement these principles in future projects and we can see the success of Todd’s work in Jalalabad duplicated hundreds of times over.