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Our project is a customised guide to Barcelona with places to stay, eat, drink and see.
Our dataset is a csv file we make from LE COOL guide of Barcelona.We have already 200 places.

we will upload all the addresses on google maps and define every place according to the following categories:

sleeping, eating, drinking, dancing

currently our impure application is like this:

the following example is a mockup of how we would like to visualise our application

Up to now we have finished our datafile set and we have used the GeocoderMultygooglemaps module to extract the coordinates and the Filewrite to save a new csv file with the coordinates of x, y on google maps, so as to correct possible mistakes of the data.

now we will put the points on the map and color them differently according to the category they fall into.

https://visit.impure.com/space/#/anpistof/guiamaja2

Group Members: Francisco Marmolejo, Maria Margariti, Diego Lopez Ibarra

For our final assignment we decided to create a system where one can select their area of interest (anywhere in the world), which will provide them with the amount of solar hours in that area. Then they are ask to select the solar panel that is suitable for their needs. Thus,  the final outcome would be a calculation of the amount of energy that they will be able to obtain with that specific solar panel in their area.

We are in the process of establishing this system and this is our current progress.

The project started by creating a spreadsheet with the different types of solar panels and their characteristics. This file was then created into a cvs file and was brought into the workspace with a “CsvLoader.” Then we created a “Scatter2” graph that would illustrate some of the important characteristics of the panels. This was done by getting the information from the csv file and applying a “GetSubListFromPositions“, which gathered the information from columns 2,7,8 in the spreadsheet and illustrated the graph with the y-axis = column 2 (Price), the x-axis = column 7 (watts), and the size of circles defined by column 8 (price per watts). This information was accompanied with another component, “GetElementFromList” , which was defined by getting information from column 1 (name of model). Thus, providing additional information when selecting a circle, one would see not only the price, watts, price per watts, but also the model that the other data corresponds to.

Our next step was to illustrate the countries where these solar panels are fabricated. We selected the component “GetElementFromList“, which help us get the data from column 9 (countries where solar panels were fabricated). This information was then linked to “JoinStringList” that combined the information in one string and linked them with the “,”. Next, we linked the component “ReplaceSubString” that allowed us to replaced the “/” with “,” where there was more than one country to a model. Next, we linked the “SplitString” component to separate each word that was separated by “,” therefore having each word be isolated. Next, we decided to eliminate all the repeating elements from the list and find out how many different countries were fabricating the solar panels. We did this by linking the “RemoveRepetitions” component which then gave us the final number of countries. Finally, to visualize the final list we had to link the component “StringListToCountryList” to the “ColorMapVisualizator” illustrating the 12 countries that we have as fabricators of solar panels.

The last part of our process till now was to link the the data achieved in the last step in obtaining the 12 countries and having a way of zooming in. This was accomplished by linking the data from the “StringListToCountryList” to “ListElementSelector“, which gave us a module to select 1 of the 12 countries. Next, we linked it to the “GeocoderGoogleMaps” that produces the coordinates to find the selected country in a map. Next, we applied the “GetElementFromTable” component that gets the coordinates from the table and links this information to the “GoogleMapsVisor” that illustrates the coordinates.

Link to the PDF: The Final Assignment_Review 02/03/2011

Link to High Resolution image: http://legacy.iaacblog.com/visualprogramming/files/2011/03/Workspace_layout.png

Gamze Gündüz & Mathew Owen

Group: Carolina Libardi | Carolina Miro | Erina Filipovska

The idea of this project is to map the biggest/most important Spanish cities using what is being said about them in the internet as our main parameter. We want to develop an interactive visualization that can work with different web sites and different key words.

As an example, we could consider how many times the word “traffic” was used on twitter for the cities of Sevilla, Madrid, Barcelona and Ibiza. The result would change throughout the day, and would give an idea of the actual traffic situation in those cities. The concept can work with many different sources of data, like newspaper websites, as well as whatever cities we want to compare. The main goal is to be able to make comparisons between different urban spaces, and to provide answers for the different behaviours. The concept is to develop a system that can work with many different data sources. The idea is to map the number of occurrences of one specific word in the internet. Initially, we think about starting the project mapping the occurrences on Twitter and national newspapers. The goal is to create a map that responds to real-time data, and changes according to the time of the day, different urban conditions, different seasons of the year. It should be a map that is always evolving, changing, and that works as a tool not only for urban planners, but to whoever wishes to understand the most important subjects that are being discussed about different cities. In a world were information travels almost as fast as electricity, the maps should respond in the same way.

The Mockup.

The link of the workspace:

https://visit.impure.com/space/#/vampirica/project_rough_draft2

The draft of the workspace: