Posts Tagged ‘density’

Running en Nantes


Cada vez que corría por esta maravillosa ciudad, lo grababa con la aplicación Runkeeper, así que he superpuesto todas las ocasiones para ver dónde exactamente se concentran las rutas que más he usado y las zonas por las que he pasado más veces. Esto es lo mal de concentrar en la misma persona alguien al que le gusta correr, apuntar cosas, visualizarlas, analizarlas…

Creo que hacer tracking de rutas, tiempos, ritmos, etc, me ayuda a enfocar lo importante que es para mí la regularidad y la constancia. No es correr en sí lo que me gusta, que sí, sino demostrarme que soy capaz de hacer algo que me entretiene, me relaja, de forma periódica y con contadísimas excepciones (lo único que me deja en casa es una lesión o un cabreo).


Y se ve claramente en rojo cuáles son esas zonas!!! He exportado las líneas a puntos y he creado un mapa de densidad en Global Mapper 17, al que he superpuesto un layer de Open Street Maps.


Cómo echo de menos correr por el Loira, sus parques, sus puentes, sus riachuelos… ahora corro en Madrid y también me gusta mucho pero me trae muy buenos recuerdos puesto que fue allí donde empecé a hacerlo en serio.


Bueno en serio quiero decir, a hacerlo siempre.


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

RSME comparing LIDAR data with a third party’s 3D dataset


I would like to share with you an easy analysis i have been working in the last days. I had a vector dataset of buildings and i knew how high they were (there was a field called ‘AGL’ or Above Ground Level) and a LIDAR 2m resolution dataset over the city of London. My aim was comparing both sources, understanding LIDAR data was the actual reality (or a closer version to it) and my source of 3D buildings was the dataset i needed to deliver to my customer…  Te actual height of those 3D buildings had been extracted using stereo photogrammetry methods. I also needed to focus on residential data, so heights below 15m… So make it easy. The question was:

How accurate is my dataset of residential buildings over London?. Which is the RMSE measuring them both?

I used Global Mapper v.13.2 (b062012) and ArcGIS 10.0 (b3200)

This is the 2m resolution LIDAR data provided by


I also needed to get a layer of points out of this dataset so i used Global Mapper and went to Files/Export elevation grid format and choose ASCII as the format.LIDAR-06

This is the layer of buildings and their AGL as label

I flagged those residential buildings

and using ArcGIS i performed a Spatial Analysis using Arctoolbox/Spatial analysis to join the Lidar heights in ASCII format and the residential heights… to be able to measure the difference between both datasets

this way i got a new vector layer which table contained both elevation fields (Lidar and my 3D buildings)

As you can see, i added a new field in ArcGis using table/add field and added ‘compare’ and SQL [“AGL”- “ELEVATION”]

then i measured it visually using a density grid in Global Mapper. Create density Grid.

And finally measured the RMSE by opening the table in excell format and usign the actual formula for extracting RSME values:

= SQRT(SUMSQ(M1:Mn)/COUNTA(M1:Mn)) —> Note this formula is only valid for this case. You’d need to update Mx values using yours:-)


Wow! a very high value. Does this value corresponds to our accuracy figures? Yes? No?.

Now it’s the time for decission makers to bring into action!


And what about some geostatistical analysis. I performed this using North East Trends in ArcGis. We can see from West to East there’s no variation  but we can see it increases the error the further the south…


So this is the area concentrating the higher differences comparing both datasets.

Hope you liked the analysis, if so…share!!!!