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Posts Tagged ‘3d scenario’

Taxing the sun?. Yes, in Spain this seems to be possible.

2015/04/09

Absolutely ashamed by my government’s insane policies on this regards, Spain is now (…) attempting to scale back the use of solar panels – the use of which they have encouraged and subsidized over the last decade – by imposing a tax on those who use the panels. The intention is clearly to scare taxpayers into connecting to the grid in order to be taxed. The tax, however, will make it economically unfeasible for residents to produce their own energy: it will be cheaper to keep buying energy from current providers. And that is exactly the point. (…)

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Source: http://www.forbes.com/sites/kellyphillipserb/2013/08/19/out-of-ideas-and-in-debt-spain-sets-sights-on-taxing-the-sun/

While we see anywhere else in the world this is being encouraged we don’t, we do exactly the opposite… but if you wonder why this could happen in a allegedly developed country like mine i herewith let you know the reason why… not to compete with other energies or more exactly other big companies providing that energy. A shame or even more than that, a fu***** shame.

So you encourage sustainability and now you discourage it?. In a country like Spain with such an incredible unemployment rate, which slowly reduces this figures at the expense of lower wages, winning competitiveness but losing anywhere else!!! (Mostly if we have cities with +3000 hours of sunshine a year!)

Anyway, trying not to get too upset after writing this words and also trying to make this makes sense in a GIS blog i will try to show you how Lon Angeles county in the US is encouraging the installation of solar panels, ranking all 2010 parcels according to wattHours per square meter. Isn’t it a politically and technically state of the art approach? Yes in my opinion it is, indeed.

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http://egis3.lacounty.gov/dataportal/2015/04/07/solar-data-summarized-to-2010-parcels/

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Important Note.  The shapefile includes 4 fields that are summaries of the solar potential:

  1. Rank 1 – Square feet of roof receiving excellent solar input (> 1.4 million wattHours per square meter)
  2. Rank 2 – square feet of roof receiving good solar input (1.15 – 1.4 million wH/meter squared)
  3. Rank 3 – square feet of roof receiving poor solar input (950,000 – 1.15 million wH/meter squared)
  4. Rank 4 – square feet of roof receiving negligible solar input (< 950,000 million wH/meter squared)

Hope you like this post, if so, share it.

Kind Regards,

Alberto C.L.
MSc GIS and worried about “government insanity”.

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Remote Sensing, Photogrammetry, Lidar and Landuse IGN Spain

2014/11/18

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A few more lines for leting you know again that i passed this other course just now in Instituto Geográfico of Spain (IGN).

Remote Sensing, Photogrammetry, Lidar and Landuse, a comprehensive 40h update on relevant information i need tu use on a daily basis. This ‘update’ helps me to better understand what i am working with and this way, being able to properly describe it for my daily analysis,

DTM from SRTM? Let’s compare sources using RMSE (Root Mean Square Error) and a gaussian kernell density map

2014/10/29

I guess we all can make a DTM out of many sources but SRTM is one of the most common ones, right?. Then let’s learn from this very simple approach how close we are from the SRTM raw data.

  1. Selecting a not very big representative area to be able to handle it,
  2. exporting raster to polygon (from SRTM 3 arcsec/90m) dataset 1
  3. exporting raster to polygon 30m (our DTM dataset) dataset 2
  4. exporting to POIs 30m (our DTM dataset) dataset 2b
  5. Spatial join POIs dataset 2b vs dataset 1
  6. RMSE
  7. visualizing delta using a density map/gaussian kernell +appropriate symbolization

In yellow we see theres a full correspondence between SRTM and our DTM dataset and in blue there’s a ‘hole’ and in red there’s a ‘mountain’, this means it’s in here where the shift is more important.

This way we can highlight if sources are OK.

It’s simple but it works. How do you like it?. Please feel free to send some feedbak.
(Software used: ArcGIS 10.1, Global Mapper 13.2)

Cheers,
Alberto Concejal
MSc GIS, QC

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density maps parameters

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Spatial join between both DTM datasets

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Density map for highlighting differences between both datasets (ours and SRTM’s)

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RMSE. It’s not too big so there’s need to visualize to find potential bizarre spots

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bizarre DTM heights

Windmills (wind turbines). Geovisualization live!

2009/08/10

I will do my best to build a 3d scenario incluiding a few wind generators located very close to Pancorbo Cliff, North of Spain, about 320 km from Madrid. As usual, I started getting a model from 3D warehouse and included inside my google sketch up session… after that it was just placing it in my GE scenario… That’s it!. Now a little bit of literature: 

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The most modern generations of windmills are more properly called wind turbines, or wind generators, and are primarily used to generate electricity. Modern windmills are designed to convert the energy of the wind into electricity. The largest wind turbines can generate up to 6MW of power (for comparison a modern fossil fuel power plant generates between 500 and 1,300MW).

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With increasing environmental concern, and approaching limits to fossil fuel consumption, wind power has regained interest as a renewable energy source. It is increasingly becoming more useful and sufficient in providing energy for many areas of the world.

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One area where turbines have become feasible is in the Midwestern United States, due to great amounts of wind.

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Source: Wikipedia

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Hope you liked it.
Alberto
BA Geography
MSc GIS and Remote Sensing
GIS Technician
albertoconcejal -at -gmail.com