La geográfica cabecera de ‘Up in the air’ con George Clooney

2015/08/24

Para alguien que maneja mapas a diario es una sorpresa interesante cruzarse con esta cabecera de la película de Jason Reirman protagonizada por George Clooney ‘Up in the air’. Nubes, campos de cultivo, ciudades en 2D, 3D. Qué bien he elegido mi profesión:-)

Para alguien que viaja mucho, salvando las distancias, este vídeo, también del mismo fil ‘Up in the air’ es un simpático recuerdo de lo que es un viaje pasando por uno y mil auropuertos, arcos voltaicos, empaquetando, desempaquetando…

Espero que os guste!

Alberto

(Fuente: Jose Ignacio Sánchez de Nosolosig)

Tipologías usuarios Madrid Río. Estadísticas y tendencias interesantes

2015/08/21

Después de 6 semanas en Madrid, no ha habido un solo día en que no tuviera que meterme en Madrid Rio o bien para ir a trabajar, para llevar a la niña a la guardería, volver a casa o simplemente para pasear… Madrid Río se ha convertido en la espina dorsal de mis comunicaciones por la ciudad. Una obra con la que originalmente estaba en profundo desacuerdo (por su planificación y ejecución) se ha convertido en, de alguna manera en el eje que articula mis movimientos.

2015-08-21 08.38.20

Para la multitud de personas a las que como a mí, les ha sorprendido esta infrastructura en frente de sus casas hay posibilidad de encontrarse en el mismo metro cuadrado a alguien corriendo, alguien andando, una pareja con un carrito de niños, unos amigos en patines, alguna persona en bici… unos más rápido y unos más lento, todos hemos de convivir en un trazado de unos 7,5km de largo y no más unos metros de ancho, con lo que unas mínimas normas de circulación se imponen.

Partiendo de la máxima de que el peatón tiene prioridad, no se debería pensar que este pueda hacer de su capa un sayo y moverse a su antojo por el recorrido. Otra máxima debe ser que las bicicletas respeten un límite máximo de velocidad (así como la gente en patines, patinetes, segways, etc).

Mi punto de partida ha sido medir desde un mismo punto la pasada de los usuarios y tipificarlos de acuerdo a su sexo, edad aproximada, tipo de deporte que practican y si estaban ubicados de manera correcta en el recorrido de manera que pudieran interactuar de manera normal con los otros usuarios, minimizando al posibilidad de encontronazos, golpes, caídas, etc. Entendiendo como ‘correcto’ si los usuarios circulan por su derecha.

51243949b

He tipificado a 100 usuarios en dos momentos diferentes del día y en el mismo lugar, para poder estableceer comparaciones. Ahí van los datos y posteriormente los resultados del análisis y algunas preguntas abiertas para cuando haya más tiempo o más interés.

  • #1 Avenida de Manzanares 204/ Madrid RIO 20 de Agosto 2015 entre las 16:28 y las 17:45. 35º centígrados

estadisticas-01
estadisticas-01B

sexo: 0= varón, 1=mujer
tipo: 0=andando, 1=corriendo, 2=bicicleta, 3=otro (segway, patín, patinete, etc.)
resultado: 0=correcto, 1=incorrecto

edad mediana= 28 años
moda sexo=hombre
moda tipo deporte=bicicleta
densidad usuarios=78 usuarios/hora

porcentaje posición correcta: 72%
CORRECTO

correlación sexo-corrección?= 0.28, débil
correlación edad-corrección?= -0.29, hay correlación negativa (débil)
correlación tipo deporte-corrección?= -0.33, hay correlación negativa (débil)
(ver post sobre correlación de variables)

(…)
r=1, correlation is PERFECT
0.75<r<1, correlation is STRONG
0.5<r<0.75, correlation is MODERATE
0.25<r<0.5, correlation is WEAK
<0.25, almost NO correlation, both variables are hardy related
(…)

Resumiendo, a esta hora de la tarde, las 4 y pico del mes de agosto con unos 35 grados celsius, la densidad es de 78 personas a la hora, de las cuales el 72% circula de manera correcta.

El perfil tipo de usuario a esta hora es el de VARON, CICLISTA, 28 AÑOS, POSICIÓN EN LA VÍA CORRECTA

Hay una correlación débil entre sexo y posición correcta, lo que quiere decir que las mujeres y hombres se ubican de manera incorrecta sin seguir ningún patrón definido o lo que es lo mismo, entre los mal colocados, casi el mismo número eran mujeres que hombres.

tampoco hay una relación clara de correlación de acuerdo a la edad o el tipo con respecto a la corrección o no de la ubicación.

  • #2 Avenida de Manzanares 204/ Madrid RIO 21 de Agosto 2015 entre las 09:20 y las 9:42. 28º centígrados

estadisticas-02
estadisticas-02b

sexo: 0= varón, 1=mujer
tipo: 0=andando, 1=corriendo, 2=bicicleta, 3=otro (segway, patín, patinete, etc.)
resultado: 0=correcto, 1=incorrecto

edad mediana= 35 años
moda sexo=hombre
moda tipo deporte=bicicleta
densidad usuarios=273 usuarios/hora

porcentaje posición correcta: 90% 

CORRECTO

correlación sexo-corrección?= 0.08, débil
correlación edad-corrección?= -0.07, hay correlación negativa (débil)
correlación tipo deporte-corrección?= -0.22, hay correlación negativa (débil)
(ver post sobre correlación de variables)

(…)
r=1, correlation is PERFECT
0.75<r<1, correlation is STRONG
0.5<r<0.75, correlation is MODERATE
0.25<r<0.5, correlation is WEAK
<0.25, almost NO correlation, both variables are hardy related
(…)

Resumiendo, a esta hora de la mañana, las 9 y pico del mes de agosto con unos 28 grados celsius, la densidad es de 273 personas a la hora, de las cuales el 90% circula de manera correcta.

El perfil tipo de usuario a esta hora es el de VARON, CICLISTA, 35 AÑOS, POSICIÓN EN LA VÍA CORRECTA

Hay una correlación casi inexistente entre sexo y posición correcta, entre los mal colocados, hay casi el mismo número eran mujeres que hombres, tampoco hay una correlación de acuerdo a la edad o el tipo con respecto a la corrección o no de la ubicación.

Ahora dejo algunas pregutas en el aire, es siempre el perfil tipo el de varón en bici de mediana edad o por el contrario hay picos horarios o ubicaciones donde este perfil es diferente. Podríamos encontrar alguna correlación mayor entre la posición correcta en el recorrido y alguno de los tipos estudiados?. Hay algún otro tipo (por ejemplo nivel de estudios o algún rango específico de edad) en el que veamos una relación clara con la correcta/incorrecta ubicación?.

El estudio específico de estas correlaciones podría permitir informar adecuadamente a los usuarios a através de paneles informativos y de esta manera reducir los potenciales golpes entre las personas que disfrutan de Madrid Río pero también ayudaría a integrar correctamente a los diferentes grupos de usuarios para que todos disfrutemos más adecuadamente de estas instalaciones.

Espero que te haya parecido interesante, si necesitas o quieres más información o aclaración, no dudes en ponerte en contacto conmigo por email.
Un saludo cordial!.

Alberto Concejal
albertoconcejal [at] gmail.com
MSc GIS

Freelancing is good?. Well, kind of!

2015/07/29

Hi guys… fed up with this never ending heat we have these days in Madrid (almost 4 weeks already with more than 40º celsius max!). Just wanted to share with you one of the reasons why its good freelancing… you can wear hawaian t-shirts without any problem!

freelancing-20150729

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. (…)

OneRoof-Energy11

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.

solar-mapping-data-20150409

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

solar-mapping-data-20150409-03

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 validation using Google Earth (and RMSE extraction)

2015/03/10

Hi guys,

Surfing the internet is great when you need to figure out something. I needed to validate some DTM from unknown sources against an also unknown source (but at least a kind of reliable one, Google Earth).

All we need is

  • Google Earth
  • TCX converter
  • ARcGIS
  • Excel

This is the procedure i have followed:

  1. First of all we draw a path over our AOI using Google Earth, we save this as KML,
  2. This KML is opened by TCX converter, added heights and exported as CSV,
  3. CSV is imported by ArcGIS,
  4. We use the tool ‘extract multi values to points‘ to get in the same table the values of our DTM and the values from Google Earth,
  5. We use Excel to calculate the RMSE and get a quantitative result,

These are the values in our DTM

dtm-validation-02

This is the path we have to draw in Google Earth

dtm-validation-03

Using TCX converter we get the heights out of Google Earth’s DTM

dtm-validation-01

Using the tool ‘extract multi values to points‘ we get the heights out of our DTM

dtm-validation-04

We measure the differences and extract the RMSE.
Are we within our acceptance threshold or expected level of accuracy?.

You guys have to figure this out for yourselves!!!

Lost regarding RMSE calculation?. Think you have to take a look at this other post.

dtm-validation-05

dtm-validation-06

Hope you guys have enjoyed this post, if so, don’t forget sharing it.

Alberto Concejal
MSc GIS and QCQA expert (well this is my post and i say what i want :-))

Pearson correlation and GIS

2014/11/28


pearson-01
Do these two variables have a correlation?. To answer this important question first of all we have to know that only if it’s a linear relationship and there are no outliers we can take advantage of Mr Pearson’s correlation statiscal tool.

If i love chocolate, does this mean i have tendency of being chuby? or on the other hand there’s no relationship at all. Let’s figure it out.

For this particular occasion, input data XY are two DTM heights, my guess is the following: if correlation is too big, i may deduce they’re not independent products and one might been created from the other, in other words, we might have tried to cheat and we are using a different source that the one we have stated… In GIS sometimes things are not exactly as expected and there’s need to be assertive and making a plan for discovering this minor issues.

 

 

 

Let’s start from the beginning, if source 1 is the same as source 2, the correlation would be perfect, is this correct?. The answer is yes. r (Person correlation) would be = 1. So yes, if this was asking about chocolate and fleshiness this would be 100% right but this hardly or never happens in real life (direct and no other explanation or variable interaction… why is always so0o complicated?).

pearson-formula

pearson-04

With real data, you would not expect to get values of r of exactly -1, 0, or 1. For example, the data for spousal ages (white couples) has an r of 0.97. Don’t ask me where i got this weird source (well, just in case: http://onlinestatbook.com/2/describing_bivariate_data/intro.html)

age_scatterplot

If i fill source 2 with a random number, the correlation would be almost none accordingly (in this case r=0.17)

pearson-06

Now if we see the diagram of the first two sources and we get the Pearson correlation coefficient (r=0.24) which means the correlation is very weak.

pearson-03

But that was only a very small part of the table (only 30 iterations), so if i do the same calculation out of the +13,000 iterations i really need, i get these figures (by the way, theres no need to use such a complicated formula above, you can use this one in EXCEL: =PEARSON(A1:An;B1:Bn))

pearson-07

So the correlation now its moderate, which makes me deduct at least the sources seem different and i’d need more clues to think my customer might have tried to actually cheat me using the same source for both datasets.

Summarizing:

r=1, correlation is PERFECT

0.75<r<1, correlation is STRONG

0.5<r<0.75, correlation is MODERATE

0.25<r<0.5, correlation is WEAK

<0.25, almost NO correlation, both variables are hardy related

I hope you guys have found this post interesting,
looking forward to hear where could you use it and/or your feedback,

Regards,

Alberto Concejal
MSc GIS

Comparing France Meteo and Spain Meteo from the visualization point of view

2014/11/24

After living in France for four years i have to tell i am always aware of Meteo information on TV (well, i live in Brittany, i guess this makes sense!). It was the same in Spain or anywhere else in the world where i had lived and the reason why is i have always loved Meteo and statistiques, mostly after working as an aerial surveying photographer in the late nineties… but that’s another history.

Weather forecast its quantitative data that distributes spatially, meaning every single spot will have a different figure, even if it’s separated no more than 1 mm, at least in theory. So the question is: as its impossible showing predictions for every single square mm of the area of interest, we need to estimate them using different models. Still if we point anywhere at the map we should know if the icon or figure applies or not to the spot i want to know about.

Let’s make it easier to understand, lets use images!!!

First of all, i know its difficult but it’s important, please don’t have into account Meteo news are presented (in this particular case) by Anaïs BAYDEMIR, which is a beautiful TV journalist at France 2… Let’s not focus on this (but if you happen to want to know more about her i hereby copy a couple of links to both wikipedia and youtube:

http://fr.wikipedia.org/wiki/Ana%C3%AFs_Baydemir

spain-meteo

Having said that, le’s take a look the way this is shown in Spain (TVE 2014). Well, again let’s not focus on the guy’s grey suit but…

 

 

 

 

 

spain-meteo-02

Information it’s kind of OK but what happens if we want to know about a spot in the middle of two icons?. Is the partly cloud icon which applies to my place or it’s the ‘sun and flies’ one?. How can i be sure of the forecast if i live in this this big region in the SW of Spain?…

 

 

 

 

france-meteo

On the other hand let’s focus on Anaïs_Baydemir, ops, meaning let’s focus on the way France 2 shows this information:

france-meteo-02

Every single square mm is perfectly defined, if we want to know the forecast in a particular place we know the icon that corresponds to the spot and we don’t have to guess…

france-meteo-03

I know it’s kind of nothing too important, mostly if introduced this saucy way but think about it, wouldn’t you prefer to read Meteo this way? (again i’m not asking if you prefer the way the french beauty is showing the info compared to the way the spanish guy does, that’s completely irrelevant… right?)

Regards,
Alberto CONCEJAL
MSc GIS and Meteo fan

Remote Sensing, Photogrammetry, Lidar and Landuse IGN Spain

2014/11/18

teledeteccion-fotogrametria-lidar-usos-del-suelo-ign-20141118b

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,

Advanced Thematic Cartography IGN Spain

2014/11/14

cartografia-tematica-avanzada-20141118b

A few lines for leting you know i passed this course last year 2013 in Instituto Geográfico of Spain (IGN). Spatial analysis, Spatial stats, proper simbolization, data mining and geovisualization. A very interesting 40h online course that helps me on a daily basis to be able to show geodata in a more professional way.

Because we normally need to deepen our geodata without making too complex to understand the result of our analysis.

HTML High resolution DTM visualization using Quantum GIS (Qgis)

2014/11/03

This QGIS Plugin, Qgis2threejs, exports terrain data, map canvas image and vector data to your web browser!!

3dvisualizatio-DTM-QGIS-20141103

All you have to do is opening the DTM in Qgis (2.4.0 Chugiak), go to plugins library and install Qgis2threejs.

3dvisualizatio-DTM-QGIS-20141103-03

Once its installed you will see this icon on screen iconand you will need to clic on it.

3dvisualizatio-DTM-QGIS-20141103-04

Then choosing the parameters of the visualization and voilá!!

I have used a 5m DTM which source was LIDAR so the quality is very good

3dvisualizatio-DTM-QGIS-20141103-05

Hope you guys like it. Feedback would be greatly appreciated.

Alberto Concejal
MSc GIS and Quality Control
albertoconcejal [at] gmail.com


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