Posts Tagged ‘arcgis’

Running in Madrid. A GIS approach!


Love running. I have been running without stop (well, three months after my Marathon in Valencia i had to stop due to a minor surgery) for the last -almost- four years. I love running and and love GIS and statistics. This is a bomb combination.

I wanted to figure out visually where i normally run. Well, i know it, that’s true, but its just what i remember, i go here and there. I love running in Casa de Campo and in Parque Lineal del Manzanares mostly, but not only. Also i have run in every country i have the chance to visit, normally for business purposes.


I have run in South Africa (Cape Town), in Tunis (Tunis), in Santiago (Chile), in Lima (Peru), in Cannes (France) and i dont even remember where else… running5

Anyway, these density maps performed in Global Mapper overlay the geometry lines saved out my running application (Garmin Connect). and once they are exported to points, i can generate a density map, chosing a legend easy to understand and i overlay to Google Earth so its also easy to be sent if needed be (why for? i don’t know!).





Anyway, hope you guys like it.


2018-05-13 09.03.18.jpg

Euclidean allocation analysis II


Imagine you need to promote recycling. Imagine you have 1000 inhabitants from a small village and you need to provide proper colored plastic bags for each and every one of the categories you need to disaggregate: organic, plastic and paper.

You need to service them all properly but you can only choose 5 shops (out of the 10 available)  where you will distribute the bags for free.

In green all 1000 inhabitants (houses) and in orange the placement of all ten shops.


In green the final result: the 5 shops chosen out of the 10 potential available due to having checked they are the most optimal (the shortest euclidean distance).


Software used:

ArcGIS 10.3
ET Geowizards 11.3

Hope you guys liked it,

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”.

Remote Sensing, Photogrammetry, Lidar and Landuse IGN Spain



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


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

Analyse du Localisation des équipements publics relevant du thème ‘Sports, loisirs’ de Nantes Métropole


La première chose a dire, comme toujours est: Désolé pour mon français, je fais mon mieux:-)

Aujourd’hui je voudrais faire l’analyse sur la localisation des équipements publics de la thématique ‘Sports, loisirs’ de Nantes Métropole.

Les données donnent les coordonnées et la catégorie (centre sportif, circuit de plein air, stade, gymnase, espace sportif de proximité, piscine, plaine de jeux, tennis, autre équipement).
Ils sont où les équipements publics de la thématique ‘Sports, loisirs’ a Nantes ?. Est-ce qu’il y a un patron de localisation ?. Où est-ce qu’on peut trouver plus grandes concentrations ?. Si on parle de quelque endroit spécifique, il y a des équipements  ?. On va voir.

Voici l’structure des données, nom, type, statu, adresse, téléphone, etc.


Voici on peut visualiser les données sur la couche de quartiers (c’était vraiment difficile de trouver chez data.paysdelaloire mais finalement ça va),


Voici on peut remarquer où il y a des concentrations (on n’a pas fait distinctions qualitatives mais on pourrais le faire, selon l’statu privé ou publique, par exemple),



On peut montrer l’image. En ce cas ci je montre l’orthoimage 2005 (resolution 0.20m, precision 0,40m),


Et finalement on remarque qu’il y a pas assez d’des équipements publics (sports, loisir) sur l’île de Nantes (j’habite ici, c’est pour ça que parfois  j’aime bien me concentrer ici),


Bon, j’espère que vous trouvez ça intéressant et s’il vous plait, n’hésitez pas a me contacter pour résoudre des doutes.
Merci bien de me donner votre avis si vous le souhaitez.

Logiciel ArcGIS 10.0/ Spatial Analyst Tools