Diagnostic mapping of yellow, green, gray, red percentages

A technique to evaluate neighborhood health by measuring the area percentages of pedestrian, garden, building, and car space.

Neighborhood — ranked by edge-count

framework

Four-fold pattern of yellow, green, gray, and red
implements
A conceptual scheme for analyzing and redesigning neighborhoods by balancing pedestrian space (yellow), gardens (green), buildings (gray), and car space (red).

chapter

Chapter 9: The Way That Living Processes Can Guide The Reconstruction Of An Urban Neighborhood
introduces
The working unit that describes the four-fold pattern process for transforming blighted neighborhoods into living structures.

cosine ≥ 0.65 · no typed edge

Entities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.

Table 2: Healthy percentages target: Yellow 25%, Green 25%, Gray 25%, Red 25%finding0.795
Ideal balance of the four colors for a living neighborhood derived from the model.
Genotype-phenotype mapping problemquestion0.754
Green areas will be mixed in size, and all positive; gray areas partly surround green areas.claim0.721
Description of the topological invariants produced by the process.
The relative percentages of the four colors largely encapsulates what is wrong with the neighborhood.claim0.720
Claim that the area proportions alone diagnose neighborhood health.
Table 1: Unhealthy present-day percentages (Berkeley): Yellow 2%, Green 28%, Gray 23%, Red 47%finding0.714
Quantitative analysis of a typical American neighborhood showing extreme imbalance, especially minimal pedestrian space.
Diagnostic Probingmethod0.709
Earlier interpretability method applying classifiers to DNN hidden representations; shares complexity-accuracy dilemma with causal abstraction
Color distances learned from language cooccurrence statistics closely mirror those learned from image cooccurrence statistics and human perceptual distances (CIELAB)finding0.693
Case study confirming that PMI-based learning in different modalities recovers the same perceptual representation
Voltage imaging dyesmethod0.690