Archives for posts with tag: mapping

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

The US Census Bureau’s website is a labyrinth of useful data and is unnecessarily difficult to navigate.  Fortunately for us, NYTimes decided to help us out by visualizing the mystery information from the American Community Survey.  They’ve translated data into easily comprehensible maps and diagrams like many of their past data representations.  Take a look here and have fun exploring:

Here is a closer look at Rochester [click on the image to enlarge]:

Racial distribution | racially segregated [blue dots=black; green dots=white]

Income distribution | the lighter the blue, the lower the income

Change in Median Income | the darker the blue, the higher the decline

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.

These animations show further development on the Grasshopper models seen from a previous post.   By using the proximity component [in green], I am able to create an animation mapping distances among the various parking areas to each other.  This allows us to see where clusters begin to appear/form and perhaps become potential opportunities for interventions.

top view

1892 Diagram-Genesee River from Jie Huang on Vimeo.

perspective view

1892 Diagram-Genesee River from Jie Huang on Vimeo.

The field of parking lots [outlined in red and green] spread throughout downtown Rochester prompted a Grasshopper [GH] exploration on their relationships to two corridors: the abandoned subway tunnel [shorter segment] and the Genesee River [oriented north-south].

Based on the size of each parking lot [green outlines are parking garages], points are positioned from the center of each shape while its Z-value is a factor of each individual footprint.

A modified voronoi surface creates a terrain based on parking lot sizes in top and perspective views.

After creating the 3D landform, I took perpendicular sections along each corridor to describe its relationship to the distribution of parking area.  The first set of 3 images describes the conditions along the abandoned subway tunnel.

Flattening these sections would probably help illustrate more clearly the variation in area distribution or parking [potential usable] availability along the corridor.  That will be for another time.

The following 3 images are of sections along the Genesee River.

Just when you think there are too many parking lots in downtown Rochester, you find even more parking space with stacked lots.  Here is an updated diagram with the addition of parking garages [in red].  Could we use existing networks [i.e. subway, rail, river, etc] to connect these patches?

1| Clinton + Woodbury

2| Court + South

3| South btwn Main + Broad

4| Clinton + Mortimer

5| Scio + Main

6| Andrews by the Genesee River

7| Fitzhugh

8| State + Commercial

I attempted to map all the parking lots [in orange] found on Rochester’s aerial image but soon came to realize its immense scope.  I think I covered all if not most of the parking areas inside the loop and some outside.  It is a bad sign when parking dominates so much of downtown.

As a point of reference, the image below includes the same diagram from above but layered over the aerial map with highlighted vegetated [green] spaces.  What would happen if a portion of the orange becomes green?  How could we use the abandoned subway line to enliven the sea of parking?

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.