Posts Tagged ‘open source’

Solar Radiation Analysis test over Madrid using Open Data


Incoming solar radiation (insolation) received from the sun is the primary energy source that drives many of the earth’s physical and biological processes. Understanding its importance to landscape scales is key to understanding a broad range of natural processes and human activities.

Taking advantage of official Open Data sources (Cadastre footprints and CNIG mainly) I haver performed a preliminary solar radiation analysis test over Madrid.

With landscape scales, topography is a major factor that determines the spatial variability of insolation. Variation in elevation, orientation (slope and aspect), and shadows cast by topographic features all affect the amount of insolation received at different locations. This variability also changes with time of day and time of year and in turn contributes to variability of microclimate including factors such as air and soil temperature regimes, evapotranspiration, snow melt patterns, soil moisture, and light available for photosynthesis.

Global radiation (Globaltot) is calculated as the sum of direct (Dirtot) and diffuse (Diftot) radiation of all sun map and sky map sectors, respectively.

Globaltot = Dirtot + Diftot

This raster results below shows WH/m2 over certain period of time and can be esaily spatially joined to a vector geometry.

It makes more sense the higher the resolution.

These analysis of Solar Radiation are fashionable due to the raise of Smart Cities. Do you want me to perform a little test over?. I herewith copy a very interesting Solar analysis over a single block of buildings using Autodesk software.

Also please take a look at some of my old links on these regards.

I guess you have more than enough to start with. Right?. Let me know if you guys have some doubts!

MSc GIS and Remore Sensing

Sources: Own elaboration,,

Visualizar mapas animados en el tiempo: Seguimiento de aves en CARTO [ENG]


SuperinteresantE demo para ‘jugar’con datos reales georeferenciados desde la aplicación CARTO. Tres aves migrando desde El Norte de Europa hasta el África subsahariana.


bird-tracking-20170123.pngThis guide describes how to visualize point data over time, by applying the ANIMATED aggregation style to animate your map. This feature requires a map layer containing point geometries with a timestamp, or numeric field.

  1. Select the bird_trackinglayer
  2. Click STYLE to apply styling options for the map layer
  3. Choose ANIMATED as the aggregation option
  4. Ensure the column time_date is selected

To gain better understanding from our bird tracking data, color the paths of each of the three birds separately, by using the bird_name column to style the points by value.

After animating your data, click the FILL color and select BY VALUE. Choose the column bird_name to style your markers by the birds’ names. Edit the stroke to 0, change the blending to source-over, and set the resolution to 1.


You  can download the datasource here: bird_tracking

Risk exposure. Geoprocessing using Open Source Data!! Next steps!!


Now that we have completed a first example, let’s continue with a real-world one. Its important working on a Data Model to define what we understand as a Risk and how important this is. Meaning. High voltage power lines are an actual risk but the closer we are, i guess the bigger the risk is, meaning i.e 3 if we are within 50m and 1 if we are 150m away… It’s only a guess.


Same thing related to antennas, Petrol stations, etc.

This is my Data Model defined over the city of Madrid, Spain.

1 LINES- Roads speed >50 km/h within 100m risk=3
2 LINES- Power lines within 100m risk=3


High voltage towers,
Petrol stations:

risk if within 50m=3; risk if within 100m=2; risk if within 150m=1; 


Cement factories,
Electric Sub-stations,
Waste storage facilities:

risk if within 50m=3; risk if within 100m=2; risk if within 150m=1; 

(NOTE: You can choose your own risk thresholds and importance. Also note these information downloaded from Open Source data (Cartociudad, CNIG) has not been double checked and it has been used as is).


How is this risk, or these combination of risks impacting in the population of Madrid?


Can we extrapolate these patterns to other cities in the world?
We will definitely continue  this analysis shortly.

You can also visuallize this analysis using CartoDB, the field regarding “risk exposure level” is called ALL2, and ranges from 2 to 12:

Software: ArcGIS 10.3, Global Mapper 17, CartoDB

Please share if you enjoyed it… or just to say hello!

Alberto C
MSc GIS and remote sensing UAH