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Al igual que otras plataformas de datos geográficos, y debido a un problema de vulnerabilidad del software GeoServer, la información contenida en http://datos.cedeus.cl no está disponible por el momento. Estamos investigando posibles soluciones desde el 22 de octubre de 2024. Dada la gravedad del problema, es posible que sea necesario rearmar el portal de geodatos CEDEUS.

Cualquier consulta, la puede realizar al correo observatorio.cedeus@gmail.com (y si no hay respuesta: se puede escribir a Stefan.Steiniger-arroba-PUCV.cl 

De momento, facilitamos enlaces para acceder a nuestro copia en Google Drive.

Por su comprensión, muchas gracias.

Equipo Observatorio

El Dron DJI Phantom 3 Pro puede volar hasta 17min con una batería y sobre una altura mayor a 130m según la DGAC:

A continuación, el primer vuelo del equipo CEDEUS en San Joaquín (sin nuestro piloto Cristian):

La cámara (Sony EXMOR) tiene una resolución de 4k. La siguiente foto es capturada desde una altura 130m y muestra una parte del campus PUC San Joaquín (haga clic aquí para ver imagen con alta resolución):

También adjuntamos una fotografía de Santiago y los Andes con contaminación del aire 5. Julio 2016. Esta foto es parte de un video grabado con el Dron (haga clic aquí para ver imagen con alta resolución):

• http://cedeusdata.geosteiniger.cl/layers/geonode:elementos_tome
• http://cedeusdata.geosteiniger.cl/layers/geonode:elementos_viaje

Top 10 - Datos/Capas:

1.  Rios y Esteros de Chile : 210 Vistas
2. Modelo de Elevaciones - SRTM 250m - Chile : 171 Vistas
3. Regiones de Chile : 134 Vistas
4. Hidrografia Region Metropolitana : 121 Vistas
5. Paises Latin Americanos : 109 vistas
6. Red hidrografica de Chile : 93 Vistas
7.  Comunas de Santiago : 92 Vistas
8. Areas Urbanas Chile : 90 Vistas
9. Clasificacion de Pisos de Vegetación Luebert Pliscoff : 88 Vistas
10. Zonas EOD Santiago 2012 : 131 Vistas

Top 11 - Documentos

¿Once? Porque Once? Bien... para romper el patrón...

1. PLADECO Lo Espejo : 1504 Vistas
2. PLADECO La Florida : 1483 Vistas
3. PLADECO Independencia : 499 Vistas
4. PLADECO Ñuñoa : 486 Vistas
5. PLADECO El Bosque : 339 Vistas
6. PLADECO La Cisterna : 247 Vistas
7. PRC Quilicura : 234 Vistas
8. Mapa UC - Campus San Joaquin - Escuela de Ingeniería - 2013 : 152 Vistas
9. PLADECO Calera de Tango : 162 Vistas
10. PLADECO Conchalí : 137 Vistas
11. EOD Santiago 2012 : 129 Vistas

Las vistas de PLADECO de Lo Espejo y La Florida son impresionante. Seria interesante saber porque hay tantas vistas.

1. un tabla CSV/XLS con los datos de uso de suelo del Servicio Impuestos Internos para el año 2014, 
2. adjunto con un archivo shape con los roles del SII, y
3. un archivo de polígonos de pre-censo,

todos seleccionados por el Chef Sebastian Tamblay.

Después de unos días de inspiración en la receta, el Chef Stefan Steiniger cocina los ingredientes unos días (a temperatura baja = máx 1 fuego/núcleo) hasta conseguir los sabores deseados: uso de suelo en unidades, m2, y valores por cada unidad del área de precenso; todo separado en tipos de uso de la A-Z. Los datos se combinan bien con información de transporte (transacciones BIP) y actividades del mercado inmobiliario. Los invitados a probar este plato serán todos aquellos miembros de la cocina:

http://cedeusdata.geosteiniger.cl/layers/geonode:result_precenso_polys_with_siicode_and_landuse_utm19s

Alguien lo quiere ver o tiene documentos para subir? Visita http://146.155.17.18:20080/omeka/ y/o mandanos un email.

PS: ahora se puede llegar a Omeka usando http://cedeuspubs.geosteiniger.cl/pubs/

For those who don't know OpenStreetMap yet I like to summarize it in three points:

• OpenStreetMap is a volunteer mapping project with more than 10.000 active mappers that aim to create a detailed map of the world.
• The OpenStreetMappers use  simple tools, such as the iD Broswer Editor, and expert tools, such as JOSM Editor, to create a map and topographic database.
• The geographic data created by the OpenStreetMappers can be downloaded and used freely, because they are distributed under the free "Open Database Licence" license (ODbL).

You want to know more about how to get the data and how to use OpenStreetMap data in your project? Then talk to us at the CEDEUS Observatory (to Stefan or Helen), or head over to http://wiki.openstreetmap.org/es for more information.

Below an image showing the new UOCT webpage that uses OpenStreetMap data.

Esta aplicación nació en Israel y su creador Uri Levine cuenta que la idea surgió luego de observar los problemas de tráfico en su país causados por accidentes, calles en mantención, congestión entre múltiples eventos que pueden suceder en la ruta. Es así como comenzó con la venta de información sobre el tráfico en diferentes ciudades pero tiempo después observó que este problema es global y transformó esta idea en una plataforma de interacción en el año 2008.

Dentro de sus principales funciones está: ayudar a evitar la frustración de estar detenidos en el tráfico, alertar sobre el control policial o indicar  rutas más cortas todo esto sobre el mapa de Waze. En el inicio estos mapas eran exactos en su geometría pero desactualizados en algunos casos y además carecían de información que caracteriza las vías como puentes, líneas de tren y sentido de las calles. Su evolución se centra en crear principalmente dos herramientas (waze.com): 1) Edición de mapas en la web para que los usuarios de Waze puedan editar sus barrios, ciudades u otros lugares con los que estén familiarizados. 2) La obtención de datos del GPS de los usuarios de Waze para modificar los mapas y asignar el sentido de la circulación y los permisos de giro en las intersecciones.

Estas herramientas le dieron un gran impulso a WAZE y en Chile, para el año 2013 ya existían 1.900.000 usuarios siendo el segundo país de latinoamérica que más utiliza esta aplicación según afirma el fundador y creador.

A partir de Waze se plantea un gran desafío para el transporte y la planificación en las ciudades ya que reúne información de caracter público y participativo que puede ser de gran ayuda para futuras intervenciones que se realicen en ellas.

PD: En el wiki de waze existe una guía completa e instructivo de uso para sus herramientas.

-La dirección debe estar escrita de esta forma: Calle N°xxx, comuna, país, todo en una misma celda. Ejemplo: Anibal Pinto 45, Talcahuano, Chile.

-Estar en un formato XLS para posteriormente subir como un google spreadsheets (planilla de google drive) https://www.google.com/fusiontables/data?dsrcid=implicit

-Seleccionar la columna que se ocupará para la geocodificación

-Presionar la pestaña "Map" donde se visualizarán todas las direcciones como puntos

-Luego en file colocar guardar como kml y listo.

Acá es importante mencionar que la calidad de esta geocodificación no es de un 100% pero es una herramienta de gran utilidad para obtener una ubicación espacial aproximada.

Cualquier comentario no duden en escribir.

Saludos.

In a report of the newspaper La Segunda from 2013, titled "Louis de Grange, políticamente incorrecto: Sus argumentos para decir que la bicicleta NO es una opción al transporte público", it is written that Santiago has so few street space, that there is no space available to accommodate for the surge of bicycle use. Dr. de Grange has, however, his numbers** from a publication by Vasconcelos (2001) - which contains the following graphics:

As we see it is estimated by De Grange that only 8 percent of Santiago's area is dedicated to streets. And even more so, it is suggested that this is significantly less than for other cities in the world. The number of only 8% seemed to us pretty low and we therefore wanted to do an analysis of Santiago's street space ourselves. For a better comparison, and to exclude gross errors in the estimation, we will also evaluate how much street space is allocated to Sao Paulo.

Data needed for the analysis

To estimate the space used by streets we need two basic datasets:

• a street dataset, and
• a dataset outlining the municipal limits, or something similar.

A street dataset we can obtain from OpenStreetMap, e.g. the ready country shapefiles from here:  Chile + Brazil. To not bother with the huge amount of street data for the whole country, its better to actually use the metro extracts from Mapzen.

If we overlay the street data with the municipal boundaries we may be aware that the comuna contains also areas that are not urban areas, such as mountainous areas, forests or agricultural areas. Including such areas in our analysis would greatly influence, i.e. lower, the percentage of city space designated to streets. For example in the figure below we display Sao Paulo's administrative boundaries in green (obtained from OpenStreetMap as well) and the road dataset. As it becomes clear we should select for Sao Paulo a representative, central region to calculate the percentage of street space. For our analysis this will be the area within the yellow box.

A bit different situation we encounter for Santiago: If we want to perform the street space analysis for Santiago, then we actually do not want to do the analysis for the fairly small "comuna" Santiago, which has only 200k inhabitants. Instead we want to perform the analysis for Gran Santiago, which consisting of many more comunas and has about 5.1 Million inhabitants.

The limits of the urban area for Gran Santiago, and other urban areas of Chile, are available in a dataset from the Biblioteca Nacional de Chile (BNC). This dataset is not the newest (its from 2009), but as the population of Santiago grows, it is a good estimate. This means, to calculate the percentage of street area for Gran Santiago we will not select a representative area, as done for Sao Paulo, but will use the urban limits provided in the BNC dataset. The figure below shows the urban area for Gran Santiago from the BNC datasaet in yellow and the road network obtained from OpenStreetMap.

From road centre lines to street surface area

The street network data downloaded from OpenStreetMap contain only the center lines for each street or path. Hence, if we would calculate the street area from the lines, then we would got a big fat zero. So how to get the street area? Fairly easy from a GIS perspective: (1) first we should check what road classification was used in the OpenStreetMap dataset (e.g. residential, motorway, etc.) (2) then we measure in an aerial image the width for each street type, and (3) we apply a GIS buffer operation to each road, in which the buffer size will correspond to the street width with respect to the road type. Below I will outline the steps a bit more detailed:

1. Road types in the OpenStreetMap dataset

For all urban areas of Chile I analyzed the road type tag for each street segment. Then I calculated the segment length and ordered the types by length to see which seem to be most important. I got then the following list:

• residential - 21'319 km*
• tertiary - 3'066 km
• living_street - 2'610km
• primary - 2'500 km
• secondary - 2'235 km
• service - 836 km
• ...

* Note, the length in km is calculated for all way segment of this type within urban areas of Chile, taken from BNC dataset. In total these were 165'433 street segments in 400 urban areas.

As we can see, the most important road types for our analysis are "residential", "tertiary" and "living_street". We should therefore measure the width of these type more carefully. This means, we should actually measure the road width of these frequent street types (a) repeatedly, and (b) perhaps also perform an error analysis later on.

Since the OpenStreetMap (OSM) data are a live snapshot from the current OSM database, there may exist also some road type tags that appear non-standard or represent street types that we are not interested in. For these cases the road segments that have such unwanted or non-standard tags are to be removed from the dataset. Examples for removed road types are "bridleway", "raceway", "construction", and "steps". However, "footway" and "pedestrian" types are included.

As a further note I want to add that before I started analyzing the road data, I filtered the road network dataset so that only those line segments within urban areas are retained. This will later save a significant amount of processing time.

2. Measuring street type widths.

To measure the street widths there are three options, (a) going out and measuring it like a surveyor, (b) consult road construction manuals, or (c) measure the width in aerial images. Now, option (a) is quite time consuming and rather difficult if we want to measure the width of highways and motorways, as we cannot stop traffic. (b) is in our case also not so recommendable, since the average data creator of OpenStreetMap does not consult any road design and construction manual when assigning the road type tags. Hence, we are left with option (c).

So, fortunately we had some high resolution aerial images available (16cm/pixel) from Sectra for Santiago, because the satellite images from Google Maps, Here and Bing Maps do not seem to be sufficient for measuring road widths. The street furniture details and even sidewalks are hardly to see in image data from these providers. However, this also means that the widths measured are more or less all from Santiago. Hence, when we apply the same street widths to data from other cities, e.g. Sao Paulo, then we obtain some larger error, since cultural background (i.e city planning history) and street construction manuals will be different from country to country, region to region, and climate zone to climate zone.

For almost all street types I measured the street width in the aerial images in a repeated fashion. For some road types, such as links, e.g. "secondary_link" and "trunk_link", etc., I defined a default width after measuring only once. The street width measurements do include the sidewalks, and if existing, also the green spaces between road and sidewalk.

As I measured the widths repeatedly I am able to calculate some measurement statistics. In particular I calculated the mean and the standard deviation (std). With the help of the standard deviation I later on calculated a lower bound for the street width, i.e. width_min = mean - 1*std , and a upper bound: width_max = mean + 1*std. Both bounds enable to calculate an lower and a upper limit for the total street area, which helps to get an idea about the effect of variation in width measurements.

In the listing below we summarize the mean, min, and max width of the measurements, and indicate the number of repeated measurements in brackets:

road type (#)       min (m)    mean (m)    max (m)

• default:                6m         6m         6m
• primary (15):        10.8        15.6        20.5
• secondary (11):    10.0        12.5        15
• residential (15):     7.1        10.3        13.6
• tertiary (15):          8.3        12        15.7
• motorway (3):      12.4        13.5        14.7
• path (7):               2.7        4.5        6.4
• service (8):            6.1        6.9        7.8
• cycleway (2):         2.9        3.3        3.6
• pedestrian (10):     4.8        9.8        14.8
• footway (6):           2.1        4.4        6.7
• living-street (10):    5.2        6.6        7.9
• motorway-link (6):  4.8        5.6        6.4
• track (11):             4.5        6.5        8.4
• unclassified (9):     6.8        9.2        11.6
• trunk (6):               8.0        8.9        9.8

(3) Buffering of road center lines, and union of street segment

Having obtained the mean, min, and maximum street type widths, I added the information as an attribute to each street segment. With the help of a GIS buffer operation, where the widths are converted into a radius value, the area for each street segment is then obtained from its given center line. In the figure below we can see, overlaid on an aerial image, the street segment center lines in cyan and the obtained buffered areas in blue. Additionally the road type of each street segment is displayed, e.g. footway and primary road, so one can see that different road types will result in different buffer sizes.

Now that we have the area for each road segment one more GIS operation is necessary before we can get the street area: All street segments need to be union-ed, i.e. merged. This is necessary since the resulting buffer areas may overlap, as can be seen in the figure above on street crossings (emerging circle forms). If one would calculate the area with the overlaps, then the street area would be over-estimated.  Important to note is, that the union operation is a very costly operation, with the number of operations dependent on the square of the number of vertices / street segments in the dataset. Hence, calculation can take a few hours for a dataset that contains all urban areas of Chile.

The final step for each city is to calculate (a) the size of the areas for the buffered streets, and (b) the size of the urban area; or for Sao Paulo the size of the study area bounding box (in yellow above).

Result of estimation of urban area dedicated to street

Below are listed in detail the different estimates for the areas dedicated to streets: For Santiago I obtain an average street area of 19.7% from the total urban area, and for Sao Paulo I obtain that only 17.5 % of the urban area is dedicated to streets. As outlined above we applied here the same street type width, measured in Santiago, to the street center lines that we have for Sao Paulo. Hence, we need to account for a certain amount of error when we compare the street density of both cities. However, it needs also be added that these estimates are fairly conservative since the OpenStreetMap dataset that we use does not contain parking lots, e.g. parking areas of shopping malls (they would be modeled as polygons and are therefore not part of the center line dataset), and the dataset does only in a few cases contain private roads.

1. Area of Santiago dedicated to streets:

• urban area of Santiago (from BNC dataset) : 582.1 km²
• street area estimates:
  • mean: 114.5 km² => 19.7%
  • lower bound: 83.7 km2 => 14.4%
  • upper bound: 143.6 km2 => 24.7%

2. Area of Sao Paulo dedicated to streets

• study area in bounding box: 443.3 km²
• street area estimate within bounding box:
  • mean: 77.5 km² => 17.5%
  • lower bound: 57.4 km2 => 12.9%
  • upper bound: 97.2 km2 => 21.9%

More than 8% of Santiago's urban space is street space

Given the numbers above, we can clearly say that for Santiago more than 8 percent of its urban area is used for streets. I am even quite convinced that we can safely say that Santiago's urban streets area occupies by more than double of that, i.e. 19 percent of the area is occupied by streets. If one has followed how we calculated the street area, then one will also notice that this includes space for sidewalks too - and not only space for cars (and bicycles).

Looking at the estimate for Sao Paulo, then we see that our value of 17.5% is much closer to the 21%  presented by Vasconcellos (2001) than for De Granges Santiago's street value (19% vs. 8%). This seems to indicate to me that there is something wrong in the calculation of Santiago's estimate. Perhaps one included the huge municipal area of Colina in the north of Santiago, which contains large areas of agronomic uses, into the calculation? Or he added the eastern parts of the comunas in the "oriente", that cover mountainous areas too? However, one may also need to account for the fact that since 2000 there has been quite a bit of road construction activity in Santiago. But this should not be so much that street surface goes up by more than 2 percent, perhaps.

When we compare the calculated street surface estimate for Santiago with the numbers for the other metropolitan cities in the first figure (20-25%), then with the 19% Santiago seems to align more or less well with them - but still taking the same spot in the density ranking. If we instead chose our optimistic upper bound value of 24.7 % for the comparison, then Santiago seems to have more space available for cars and pedestrians than cities such as London and New York. Calcutta claims the last rank in the original listing from 2001 with a surprising 7% of street space. Now with the availability of a global road network dataset from OpenStreetMap.org, a map project that gained momentum in 2007, it should be not so difficult to re-evaluate the street density for Calcutta. I assume a strong corrections will be necessary here.

But returning to the original question of "Is there space available for cycling infrastructure in Santiago?" - Well, we don't even need to look at the numbers. Just walk around in Santiago and you will see that there is plenty of space; especially when one sees all the roads with cars parked on the sides taking up a full lane. Such space could easily be converted into cycling infrastructure if the residents can be convinced (which seems to be a problem in the comuna Providencia. Also, if there seems to be street space available for conversion to BRT lanes, then there is also space that can be converted for use by bicycle.

References:

[1] Vasconcellos, Eduardo A. (2001), Urban Transport, Environment and Equity - the case for developing countries, Earthscan, UK, table 2.1, página 12.

Les cuento que actualmente contamos con un total de 55 capas de información disponibles en la plataforma del Observatorio Cedeus. Hay capas a nivel nacional como red vial, red ferroviaria, divisiones administrativas entre otras, que son de gran utilidad para confeccionar mapas de áreas de estudio o para mostrar información base. También existe información más específica sobre las 6 ciudades del centro y algunos documentos sobre instrumentos de planificación territorial, CENSO, Encuesta Origen Destino, que puedes revisar.

Las capas de información son de tipo vectorial, es decir shapefiles, en el próximo post estaremos comentando sobre los distintos tipos de formatos y cómo subir o descargar estos datos. Cualquier duda pueden escribir a Stefan o a mi.

Y por último, ¿Qué información les gustaría ver en la página?

http://cedeusdata.geosteiniger.cl/

ahora están visitando la nueva versión de pagina web del observatorio. No parece muy distinto? Si, tiene razón, porque el diseño es mas o menos lo mismo. Pero, este pagina es basado en el red social Elgg. Y esto permite agregar muchos cosas mas a la pagina, como noticias sobre datos, un foro para preguntas, perfiles de investigadores, etc.

Para algunas de estas funciones tenemos que desarrollar un poco, pero... ahora tenemos la base hecho.

hasta pronto - con noticias sobre nuevas datos añadios,

Stefan