3D Demographic Visualization with Rhino × GIS

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I’ve been exploring how GIS and statistical data can be brought together in Rhino and Grasshopper to create different ways of reading spatial information.

In this example, I created a 3D Demographic visualization of Japan using Rhino, Grasshopper and Heron.
The workflow combines two different types of data:

  • Prefecture boundary and population data from GIS
  • Gini coefficient data from Japanese government statistics

The GIS data is imported into Rhino using Heron. Population is represented by the height of the cylinders, while the Gini coefficient is used to classify and colour them.
Rather than treating the datasets separately, bringing them together in the same 3D environment makes it possible to compare geographic distribution, population scale and statistical differences spatially.
The purpose here is not to perform rigorous statistical analysis or draw definitive conclusions from the data. I’m more interested in how Rhino can be used as an environment for exploring, comparing and visualising different kinds of spatial information, and how bringing them together can reveal new ways of understanding spatial relationships, particularly as an early-stage aid to decision-making.

What I find particularly useful about this approach is that the result does not have to remain inside Rhino. The same spatial workflow can potentially continue towards design studies, further GIS analysis, or web-based geospatial visualisation.

I’d be interested to see how others are combining Rhino / Grasshopper with GIS or statistical data for spatial analysis and design.

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Here is one that I worked on with a friend, based on dataset of the city of Helsinki showing when each of its buildings was erected. Purely 2D, though

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Thanks for sharing — this is a really interesting example of Rhino × GIS.
I like the idea of using the construction date as another layer for understanding the city spatially. Even in 2D, the temporal dimension adds a very different way of reading the urban fabric.
One thing I was wondering: how does the dataset handle buildings that have been demolished? Does it only contain buildings that still exist, or is demolition information available as well?

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As far as I know, it only contains buildings that still exist.

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Thanks, that makes sense. In any case, I found the idea of using temporal attributes to visualise how a city has developed over time useful. It gives me another perspective on how spatial data can be explored and represented!