Posts Tagged ‘arcmap’

Allocation analysis: Attaching customers to facilities


Allocates a set of demand points (Customers) to user specified number of supply points (Facilities) out of a Facilities point dataset based on the Euclidian distance between the Customers and Facilities.


100 customers anywhere in the World

In other words the function selects N Facilities out of K candidates to service a set of M Customer locations in such a way that each Customer is allocated to a single Facility (based on Euclidean distance) and the total distance between the Customers and selected Facilities is minimized.


Customers attached to 3 pre-defined facilities

In a more simple way: take a bunch of customers and assign them the closest facility (using euclidean distance, the “ordinary” (i.e. straight-line) distance between two points)). In this particular theoretical analysis I have also selected a maximum range of 5000 meters so anything beyond won’t be taken into consideration.

Questions:  Am i giving a proper service with those facilities i have already deployed?. Is there any of them way too far away so we cannot service at all?. Is there any of them over populated and in the end we cannot provide a proper service?. If you happen to come across any other question, please add it to comments so i can modify the post.

Result table:

FID Shape Id FacilityID Facility Type Num_Alloc Max_Dist Total_Dist
0 Point 0 2 2 Selected 4 4852.68 15362.93
1 Point 0 1 1 Selected 11 4110.57 37839.93
2 Point 0 0 0 Selected 18 4991.27 73591.27

This ArcGIS video shows some light over these type of analysis:

This links shows how to create a network dataset


Software used: ArcGIS 10.3; ET Geowizards 11.1

Hope you guys have liked it, if so, share or let me know about it.

Geographer and MSc GIS


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


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. (…)



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.



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”.

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


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)

Alberto Concejal


density maps parameters


Spatial join between both DTM datasets


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


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


bizarre DTM heights