Archives for posts with tag: grasshopper

While my project will focus more on what is indicated in orange  as the approximate area of the interchange, there will be a secondary component of the thesis at a larger urban scale. This component will begin to illustrate the beginnings of an emergent system of networks.  For this portion, I plan to use the abandoned subway tunnel [seen as red dotted line] as a distribution spine from the transit interchange.

I have been experimenting with Grasshopper to help demonstrate some distance mappings of possible routes to and from random points.  My second attempt to use the GH component “Shortest Walk” yielded some helpful results.  The idea is to visualize the existing subway tunnel as a spine that extends eastward from the interchange and towards Rochester’s downtown.  I used a measurement of 1/2 mile as the walking tolerance so see all the possible routes one would take from the tunnel path outward.  This GH definition is a simplified version of what may come in the near future.  I intend to incorporate other disruptions or attractions within the city’s downtown to model the more complex nuances of movement.

Screen shot | green = tunnel, red = suggested route, yellow = line connecting start and end points, red “x” = traveler, grey “x” = all possible intersection points [destinations] within 1/2 mile radius along tunnel

Grasshopper definition

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The problem of the traveling salesman is a useful approach to determine the most efficient [shortest length] routes to take within a given transportation network.  In this exploration, I used the streets of downtown Rochester as the network and all the intersections as start and end points of a desired route.

Giulio Piacentino with McNeel Europe created a Grasshopper component that attempts to solve this problem.  Here is the aerial image of downtown Rochester:

Below are all the intersections:

…and from the intersections is the network of street.  This network includes all the possible routes one could take  from any intersection to another.

The simple grasshopper definition below allows testing of almost an endless number of paths.

This animation demonstrates a selection of random start and end points [connected by a yellow line], and draws the shortest route from one to the next by following the streets [red lines].

Rochester | shortest street routes from 2 points from Jie Huang on Vimeo.

Below is the PDF presentation I used for my mid-review.  It went well and was very helpful for me.  Next steps include investigating larger infrastructural systems and potential impacts to vacancy rates in Rochester [about 10%], making and testing a proposition for the city and a few other key goals.  There will be a lot to consider and much to investigate/explore, but that’s the fun part!

The full PDF can be downloaded here:  MIDREVIEW-presentation-sm72

As parking areas begin to cluster based on proximity to each other, the shortest distance is mapped to the subway tunnel. This process starts to reveal potential hotspots for interventions.

tunnel intervention locations from Jie Huang on Vimeo.

I spent a little time trying to create a 3D diagram of my thesis through Grasshopper.  It’s in its early stages and should evolve into something more complex.

Thesis Web from Jie Huang on Vimeo.

I’ve combined the 3 animations from my previous post, but they are a little hard to read as separate networks. Perhaps we want to read the three layers as one system?

1982 Diagram-RiverCanalRR from Jie Huang on Vimeo.

Rochester’s layers of infrastructural networks illustrate a complex history.  As I dissect the layers both in time and by system, I hope to reveal an underlying structure significant to Rochester’s current and future development.

1892 Diagram-Genesee River from Jie Huang on Vimeo.

1892 Diagram-Genesee River from Jie Huang on Vimeo.

1892 Diagram-Genesee River from Jie Huang on Vimeo.

These Grasshopper animations begin with tracing the 1892 paths of the river, canal and railroad.  Each system is then  divided into equal segments with a voronoi diagram attached to each division point.  As the number of divisions increase, the amount of system’s influence also became more clear.  The next step is to merge the 3 networks and study their interactivity.

To get a general sense of pattern in Rochester, I began by using the Google aerial below to generate some urban graining through Grasshopper [GH].

By using a bitmap image, GH assigns values to points based on black and white values.  From those points, a Voronoi diagram [red] is created to get the following [thin green curve = approximate subway line]:

With further GH mappings, a series of estimated influence points began to populate the subway line, creating an interesting pattern of impact: