Designing Associativity - Assignment 01: Data Visualization

Liliana Viveros Diaz, Luz Michelle Lavayen and Renata Stefanelli

[Speculation]

Grasshopper can be used to visualize data when you combine external information from internet and other programs such as Excel. We decided to

look into the total amount of energy generated (kilowatts/hour) by each country during the year 2008.

This research is very important and useful to our group because we are working with this type of data in the Self Sufficient Studio. This gives us the

chance to visualize the countries with the highest amount of energy production, since it relates directly to how much energy these countries consume.

We can also compare Spain to the other leading countries of the world to see which will be a good target for the amount of energy that has to be

produced or replaced by sustainable methods and systems for clean generation of energy.

In the next assignment we will be developing a definition to figure out the best orientation for solar radiation capture/absorbance of façade devices

according to its shape and surface. This will help calculate which is the best possibility for greater amount of energy production (kwh.) These could be a

good reference point to measure energy by numbers, and understanding much better our goals for the self sufficient block.

[Data Mining]

The data was gathered from the website Gapminder where we found a spreadsheet with the values of kw/hour/year for several countries in the world.

We cleared the data separating in two rows: Countries and Electricity.

Gapminder – Data Source www.gapminder.org/data

In order to organize the data in Rhino, we downloaded a .3dm file of the world map, making sure each country was one single Closed Curve.

Data in Spreadsheet

Rhino file – World Map

Source

[Data Visualizing]

We uploaded the spreadsheet in .xlsx format to grasshopper using Spreadsheet In, then in order to organize the information we made two lists, one List

Item for the headers and one Shift List for the information itself, but first we pass this information trough a Tree Branch for putting just a part of the

entire spreadsheet.

Grasshopper – linking spreadsheet

After this we put another List Item in order to take the amount of energy produced, but the list appears from the lowest level to the highest, so in

order to change the list from highest to lowest we put a Reverse List, and again a Tree Branch to get the Numeric Information (amount of energy

produced) with the names of every country.

Grasshopper – sorting information

Again we have two List Items, one with the information of the names and the other one with the list of values, but this time we have the list in a

descendent order. After this we connect the Item List correspondent to the names of the countries with a Text tag 3D that serves to visualize the names

of the countries in the rhino viewport. At the same time we have also connected to this Text Tag 3D the curves of every country and this to the Brep

Area.

Grasshopper – Curves to be extruded

The other list (the one with all the numeric values) is connected to a domain bounds that give us the range of values that we want to work with, then

this is connected to a remap numbers that convert them into values. Afterwards, we connected this to a Unit Z so it can extrude those values into the Z

axis, then we connected it into a cap holes in order to close the extrusion curves.

The domain bounds are also connected to a Domain Components to decompose a numeric domain into its component parts and finally connect this

into a gradient to set the colors.

Grasshopper – Data Visualization

The variables go from green to yellow, orange and then red, in accordance to highest amount of energy consumed to the lowest.

Data visualization – energy produced by day in the world – leading countries