Source of Sustainable Architecture in Europe

Group members: Antigoni Anna Anastasopoulou, Duygu Kirisoglu, Andreana Papantoniou

The visualization:

https://visit.impure.com/space/#/antigonianna/SourceOfSustainableArchitectureInEurope_

Visualiza

The workspace:

https://visit.impure.com/space/#/antigonianna/SourceOfSustainableArchitectureInEurope_fi

nal

Our objective is to create a “source” of Sustainable Architecture for Europe where someone

can see information for the systems and techniques applied on the most well-known

contemporary examples of sustainable buildings and in the same time the monthly weather

conditions for the city where they are located. The main goal of this data-set is to

understand in which way and level weather conditions affect the design of buildings in

terms of lighting, ventilation, cooling, heating, shading etc. At the visualization every point on

the map represents one building. So, when one of them is selected, the systems applied to it

can be seen as well as the photos of it in flickr. The weather conditions of the specific city

can be also seen.

We started our project doing a research on Sustainable Architecture and we created a list of

the most important self-sufficient buildings in different cities of Europe. Then, we had to

identify the energy systems and design strategies that have been applied to each building.

We also had to confirm that the examples of the buildings we chose could be found in flickr

and we should find the exact key words to limit the photos unrelated with it. We found the

exact location of each building and we took its coordinates from Google Earth. Finally, we

took the monthly values of the average temperature and wind for each city for one year

from the following site: http://www.wunderground.com/.

Therefore, we organized our data by creating one csv file. This file contains the Selected

Buildings, the Country, the City, the Architect, the Flickr Tags, the Coordinates of each

location on Google map and the Systems of Sustainability and the weather data; twelve

measurements of temperature and wind for every month for each city.

This file was brought into our workspace with a “FileLoader” control. Then, we used a

“Polygon2dSimpleVisualizator2” in order to locate the points-buildings that should appear on

the map, getting the coordinates from the appropriate columns. This was done by using the

“getSublist” with defined intervals 5 and 6, the “pairsNumberTableToPolygon” and the

“universalProjectionOnTransformationGeo” operators.Then, we used a “GoogleMapsVisor”

visualizator which has been connected to the “GeocoderGooglemaps” defining at the

string the world “Europe”. The two vizualizators have been finally connected using the

“CreatesTransformationGeo” and the “universalProjectionOnTransformationGeo” operators.

Our next step was to connect the “Polygon2dSimpleVisualizator2” with the

“ImageTransition” visualizator in order to see images of the selected building. For this, we

used “FlickrImageMultiSearch” api and the “MultiImageLoader” control to search and load

images on Flickr. By using the “getElementFromList” and defined the number equal to 4 the

list of „Flickr Tags‟ is given to “FlickrImageMultiSearch” which allows the search to be started.

By using a second “getElementFromList” for connecting the “Polygon2dSimpleVisualizator2”

with the “MultiImageLoader”, the image loading can be achieved by selecting the building

from the map. Next, we had to connect the “Polygon2dSimpleVisualizator2” to the

“HTMLVisualizator” in order to show the different systems of sustainability in a text. This was

done by using two “getElementFromList” to take both the list 7 from the central csv file which

corresponds to the systems and from that list the element from the selected building.

Therefore, by selecting the building you can see at the same time images from Flickr and the

systems that have been used.

The last part of our project was to visualize the weather data; average temperature and wind of the city where the selected building is located and at the same time to visualize the average of the weather conditions in all the European cities of our project in order to make the comparison between them easier. To manage that, we used two “curvesGraph2” visualizators which we combined with the “getRow”, the “getSubList” and the “listAssembler5” operators and two more “curvesGraph2” visualizators which we combined with the “getSubListFromPosition”, the “averages” and the “listAssembler5” operators. Finally, we overlapped all these visualizators so that they can be seen together.