Google SketchUp

Google has everything. And I think Google would have hard time to develop new product portfolio. Their first product Google search engine was categorized to "Destructive Innovation"- brutally easy.

So their 3D program follows the policy. East and intuitive. However it takes time to get certain level because you need to get used to their rules. It uses push and pull approach to form shapes. I am not sure how many people are working in 3D industry - from star artists of Pixer to CAD operator in Japan - but I am afraid this would be a switching barrier.

After struggled 1 hour, I gave up and forced to learn in Google's website. An amateur instructor (by the way, it is one of Google ways) was screaming and claimed "it was a easy product" several times.

I know, at least for you right?

Then those are my first pieces. (To be honest I used Bryce - it is free too) for background. Also you can match up with photos.

Actually, you do not have to build everything by yourself. You can find your favorite furniture in Google's gallery like this. You can match up with your a CAD file.

Also you can output sketchy picture to add some scent to your presentation. What you have to do is to find your favorite items. 

The best part is not yet. It is Free of Charge as long as you stick with free version.

As you may know the latest version of Photoshop CS4 Extended can handle SketchUp files.

Social Graph Visualization

Your website is connected with many other web sites. With Google Social Graph API vieweryou can find out how your web site is connected. Also you can visualize this connection. I used PHP + SVG to draw the graph.

However there is a big problem - browser compatibility. Firefox2.0.0, Safari 1.3.2 and Opera 8.15 could not display embedded SVG. Firefox 3, Opera 3 are fine with it. Opera 9 needed viewer software.

Also you can bring this format to Adobe Illustrator to edit.

This can be used to find out people. For example you can go to Linkedin and find out people and connection to find out who is the center of influence.

Decision Tree

Developing a decision tree is not that easy. I thought R might have function to develop a decision tree. However, I could not find that. Then I googled (as usual) and found Weka. 

OK, I found data online and draw a tree like this and now what?

In MBA program you use Excel to develop a decision tree. Weka is much far better with a beautiful visualization. But Weka can't save the image file. So I need to design a program with this.

A decision tree is useful for web designers. If you know a campaign A is much better developing potential customer than campaign B, you can optimize budget allocation for campaign. 

So it is useful only if you know how to apply this decision tree to an application.

Grouping Items

The second move was little bit challenging to me. First of all, I needed to learn how to group things. According to 集合知プログラミング (Programming Collective Intelligence) you may use K-means. 

Then I googled for k-means and struggled. What I learned was Europe is divided to 2 sub-sets and Japan is a part of the second one (in the middle of the chart) while other East Asian countries belong to a different cluster. 

So it might not be a good idea to form "Asia/ Pacific" devision because management style can be different.

Similar analysis might be useful to design localization policy. When distance is far from your form country you may need to consider re-developing your product for your host country.

/// break into cluster
result <- kmeans(pdi_idv,5) 

/// Creat a new dataset dataset_pdi_idv<-data.frame(cluster=result$cluster,PDV=pdi_idv[,1],IDV=pdi_idv[,2]) 

/// Plot a graph plot(subset(dataset_pdi_idv, cluster == 1)[,2],subset(dataset_pdi_idv, cluster == 1)[,3], main="PDI/IDV",xlim=c(0,100),ylim=c(0,100),col="red") 

par (new=T) plot(subset(dataset_pdi_idv, cluster == 2)[,2],subset(dataset_pdi_idv, cluster == 2)[,3], main="PDI/IDV",xlim=c(0,100),ylim=c(0,100),col="blue") 

par (new=T) plot(subset(dataset_pdi_idv, cluster == 3)[,2],subset(dataset_pdi_idv, cluster == 3)[,3], main="PDI/IDV",xlim=c(0,100),ylim=c(0,100),col="purple") 

par (new=T) plot(subset(dataset_pdi_idv, cluster == 4)[,2],subset(dataset_pdi_idv, cluster == 4)[,3], main="PDI/IDV",xlim=c(0,100),ylim=c(0,100),col="green") 

par (new=T) plot(subset(dataset_pdi_idv, cluster == 5)[,2],subset(dataset_pdi_idv, cluster == 5)[,3], main="PDI/IDV",xlim=c(0,100),ylim=c(0,100),col="black") 

R

With "R" (GNU R) I took two hours to reach this chart. You need to learn programming but it is very easy and straight forward. 

plot(hof1$PDI,hof1$IDV,main="PDI/IDV",xlim=c(0,100),ylim=c(0,100),col="red")

par (new=T)

plot(hof8$PDI,hof8$IDV,main="PDI/IDV",xlim=c(0,100),ylim=c(0,100),col="blue")

par (new=T)

plot(hof7$PDI,hof7$IDV,main="PDI/IDV",xlim=c(0,100),ylim=c(0,100),col="green")

par (new=T)

plot(hof5$PDI,hof5$IDV,main="PDI/IDV",xlim=c(0,100),ylim=c(0,100),col="purple")

This is famous Hofstede's indexes. Power Distance Index and Individual Index are plotted. You can easily find out Japan is in unique place (between Asia and Europe). Japan and Spain are similar.

I plotted the chat on R and used Photoshop for final touch.

This is not 3D but still useful.

You can download R here. 

Simulation

I just learned how to cycle objects... So I wanted to simulate how planets move. You can observe how Venus and Mercury retrograde and Mars stays. 

The Earth takes 365.25 days for one cycle. Mercury's cycle is 87 days and one of Venus is 224.70 days. A year of Mars is 687 years. The moon's cycle is 27 days. 

In this movie the moon cycles every 2 seconds. Therefore one year is 27 seconds. Time flies fast!

Disruptive Innovation

Christensen's disruptive innovation. 

Wooden one is for low cost solution and a green cube is for a new market.

I like animation can express dynamism.

But need more refinement for sophistication...