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ViewBuilder

Demonstration of algorithmic method for automated analysis of viewlines from hypothetical building.

Viewline Evaluation by Ira Winder Video: https://www.dropbox.com/s/dboe8ryp7e7r8jz/viewLines_demo2.mov?dl=0

How to Use

  1. Make sure you have installed the latest version of Java
  2. Download Processing 3
  3. Clone or download this Github repository to your computer
  4. Open and run "Processing/ViewBuilder/Main/Main.pde" with Processing 3

Explanation of Method

The purpose of this algorithmic "sketch" is to demonstrate how one might implement a relatively efficient and straight-forward analysis of a piece of real estate's view quality. For instance, if one were to stand and look out the window of a particular facade, how can we computationally generate a quantified estimate of the quality of that view?

Method:

  1. This demonstration first generates a random cityscape that includes elements of land, water, sky, buildings, and trees rendered in 3D geometry with false colors associated with each element.
  2. A building representing our "development", and consequently the views we would like to analyze, is randomly placed on our cityscape. The building contains a number of viewpoints, one for each side of each floor of the building. Therefore, a 10-story building with a rectangular footprint has 40 viewpoints we wish to analyze.
  3. A virtual camera is placed at each of the viewpoints, pointing perpendicular away from the building's surface. The resulting views are saved to memory as two-dimensional projections (i.e. bitmaps).
  4. Each bitmap is simplified as a low resolution matrix of colors that are sampled directly from the bitmap. The colors in the matrix are cross-referenced with a table of known false colors associated with each element in order to generate a crude map of which elements are being seen in each part of a view. The sampled colors may be slightly different from the reference colors, so a sum-squares method is used to determine the least-different matching color based on hue, saturation, and brightness.
  5. The element table includes a "hard-coded" weight that describes how desirable an element is to have in one's view. for example, a water pixel is weighted as +100 view quality, while the view of another building facade is -50 view quality. These coefficients are placeholders, for now, but could be updated to be more accurate with further statistical analysis.

Note to developers: Those intending to learn the algorithm with the intention of rebuilding the logic in their own environment, the "Model" Tab will likely be most relevant.

Class Structures:

Element() -> View() -> Facade()
               
CityScape()

Example View Element Data Structure

| Element Name  | False Color    | View Quality Weight |
| ------------- | -------------- | ------------------- |
| Sky           | #FFFFFF (White)| +60                 |
| Land          | #FF0000 (Red)  | +50                 |
| Building      | #666666 (Gray) | -50                 |
| Water         | #0000FF (Blue) | +100                |
| Tree          | #00FF00 (Green)| +100                |
| Landmark      | #FF00FF (Pink) | +500                |