https://visit.impure.com/space/#/caroliagui/Aguirre_Caltabiano_Iqbal_FinalProjectEmbed

As data source we used the Official information given by each one of the Airports, in which it’s clearly detailed each one of the flights. We gathered information about all the arrivals and departures in order to have a complete schedule that is representative for the general activity of the airport. We processed that information in order to have CSV usable files for the Impure Workspace.

To begin with the Workspace, we downloaded all the information from the Barcelona Airport webpage, the arrivals and departures real schedules. Then we had to organize it as CSV files in order to load them into the workspace.

After filtering the tables, with getElementfromList we got the Airport codes and names. We connect them to the GeocoderMultiGoogleMaps to get their real coordinates(x,y) with getPropertiesValuesfromList and then we used ListAssembler5 and save a file with the data.

We load the files using the Switch5 module to be able to change between the different individual Airports’ information and the General information.

We used the getSubTable and getElementFromList to get the lists of the Destination and Origin Airports to use them in the Visualizators.

Then we used the TransformationGeo modules to transform the data into the Polygon2DSimpleVisualizator2 where it is projected using also the GoogleMapsVisor as the base for it.

Then we added the Network2D Visualizator to combine the coordinates we had and build a Network between the Origin and Destiny Cities.

We used ‘Polygon2Dsimple visualizator’ to feed the data to another visualizator called ‘BitmapDataSimpleVisualizator’ . We connected ‘Polygon2Dsimple visualizator’ with the operator ‘BitmapDataGaussianBlur’ along with ‘ApplyColorScaleOnChannel’ and ‘CreateColorScale’ (having the color scheme of temperature).

Then we connected the network information through the operator ‘ApplyColorScaleOnChannel’ with the ‘BitmapDataSimpleVisualizator’. This is actually giving a more clear image of the flight intensity among the concerned places.

So we got the data on 3 types of visualizators. The ‘polygon2Dsimple visualizator’ is giving us the locations where we can choose the places by clicking each point. The ‘Network2D’ is giving us the networks of lines with varying thicknesses responding to the flight frequencies between the two concerned places. The ‘GoogleMapVisor’ is giving us the image of world map.

The maps were adjusted to the same size to overlap them to have all the visualizations into one single image which is actually representing the real time data.

The Workspace.

The idea of the project is to analyze cities/countries throughout what is being posted about them in the virtual world. To demonstrate the concept, we chose the Flickr website as the data source, and started mapping the tags that were related to the images. As an example, we chose the word “Park” and filtered the last 500 pictures that included that specific tag. After creating a table with the relevant information about those selected images, we geolocated each image and overlapped that information with a map. In that way, it is possible to visualize in witch cities the word is most recurrent. It is also possible to link the map with the data source website, so that the user can click on the chosen city and access the image related to the word he is searching for.

The workspace, as it is now, gives us two different possibilities for a final presentation. The first is to predefine a specific country, allowing the user to input only the word he’s interested in researching. The other option is to allow the user to choose freely the word and the country. The down point of the second option is the zoom. Although we connected a scroll to the map, every time you change the zoom, the data has to be reloaded, and that can be very time consuming. Besides that, the user would have to find in the map the chosen country by himself. The first option is more user friendly, considering we can chose a center point for the predefined country, like we did in the shown example. In this workspace we defined Madrid as the center of the map, so whenever the user zooms in or out, the country in the center of the map is always Spain.

The Interface - Option 01.

The Interface - Option 02.

Group: Carolina Libardi | Carolina Miro | Erina Filipovska

The URL link to the workspace:

https://visit.impure.com/space/#/carolalibardi/GroupWork_Brazil_CarolinaLibardi

The main tools used in the project:

[ Structure]

Recent Earthquakes

GeocoderGoogleMaps

Table Visualizator

GetElementfromList

CreatesTransformationGeo

Addition5

Polygon2dSimpleVisualizator (for the interactivity)

Different types of BitmapDataVisualizators (as backgrounds)

[Current status of the project]

[Current Achievements]

Till now we reached all the topics we proposed to do:

Visualizing the earthquakes status by using the Impure APIS Application, with the same name.

Extraction of information related to the geoposition, the Universal Projection on Transformation Geo was applied.

Interactivity of the data – Polygon2dSimpleVisualizator

GoogleMap Visual to show the World Map and to overlap the different worldwide locations

P.S : The information used in the project were subtracted from CSV files to show the amount of people killed during earthquakes in different parts of the World and also the total population per country.

[Last Achievement to do]

Is to change the colour of interactivity data on the Polygon2dVisualizator,with the purpose to highlight different zones on the Globe where the Magnitude or the Number of Deaths are bigger.

Team : Andres Briceno  - Diana Nitreanu

[Definition of the Project]

Image 1.Possible green technologies

The goal of the project is to create a tool for all the IaaC’s Self Sufficient Building Studio to visualize the best option of energy to be implemented into the projects.

The idea is that anyone can put their own data (constructed area and energy consumed) and know how efficient are the new technologies proposed (image 1).

In this example we try to visualize the amount of energy consumed by each existing building in the block located in between the streets of Pujades and Llull (image 2) and how much new energy off grid it can produce.

In order to achieve this we must have calculate the current amount of energy consumed per building by area and zoning.

Afterwards set every technology connected to an interval selector. Each intejrval selector would set the amount of energy each technology can produce per meter squared or units. In other words, it would set a range of efficiency of energy production.

Image 2. Location of the Case Study

At the end we want to visualize if the amount of energy consumed and produced can be balanced according to the strategies that we are using or not in our new design.  When you set all the parameters of the green technologies, you can see if the amount of energy consumed and produced can reach a balance between them.

Image 3. Energy of each building (Kwh)

All the amount of energy that the technologies produce are going to be gathered and sum in one entity and the amount of energy consumed is going to be another separate entity and the subtraction of both is going to be the result of the efficiency or the performance of the block. To visualize this amount of energy (Kwh) we want to do it with Histograms and combine all the information in a 3D visualizator such as we can see in the image 3.

[Description of the dataset]

1.Height of the buildings in the block.

Gather the number of floors of each building in the webiste of the Ayuntamiento of Barcelona through the Catastro of each parcela. Assuming the average height of one floor is 3.5m, create a CSV file that demonstrates the height in meters of each building in the existing block.

2.Areas of the buildings in the block.

Gather the information in the webiste of the Ayuntamiento of Barcelona through the Catastro of each parcela, create a CSV file that demonstrates the constructed area of each building in the existing block.

3.Amount of Energy Consumed by each existing building.

Create a CSV file that demonstrates the amount of kw/hr/yr that each building consumes  according to its use and its constructed area.

4.Amount of Energy Produced by each alternative technology.

Create a CSV file that demonstrates the amount of kw/hr/yr that each alternative technology produces. The technologies being studied are: photovoltaic panels, wind mills, geothermal and hydroelectricity.

Once we have this data visualized we want to put some interval selectors  that represent “green strategies” or “alternative technologies” such as photovoltaic panels, green roofs, wind mills, etc. that could be implemented into the design of the building.

For example assuming that each square meter of photovoltaic panel produces X amount of  energy (Kwh), when we move the interval selector the 3D amount of energy already represented in the building, would be reduced, since now its consumption of energy is produced off-grid and no longer from the existent infrastructure of Barcelona as represented in the first graph.

[Structure]

String

CSV Loader

Table Visualizator

Get Element from List

Get Sub List

Number

Histogram

Color Picker

[Mockup]

Image 4.Mock up Histograms

Image 5.Data into map

[Achievements]

So far we have achieved to visualize the amount of energy each technology can produce by each m². We were able to combine all the information into a single CSV so we can select each information by using GetElementfromList in order to do the calculations of which technologies to select to balance with the amount of energy the block already consumes.

[Workspace]

The constructed area demonstrated in a Pie Graph.

The height of the buildings demonstrated by a histogram set into temperature colors.

Technologies in separate CSVs

Each technology separated into a CSV and the Histogram representing the amount of energy produced by m².

Green technologies gathered in one Csv

Each technology gathered into a unique CSV, selecting each column (technology) using an ElementfromList  connecting to individual Histograms representing the amount of energy produced by m².

The Team Members: Duygu Kirisoglu, Antigoni Anastasopoulou, Andreana Papantoniou

The Pdf of  the  final assigment draft: finalAssignmentDraft_duygu_antigoni_andreana

Here are images of our impure workspace and the source of the data:

By Panagiota Banioti, Jose A.Guerrero Mora, Katerina Inepologlou