Scoring conventions

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H3 cell index

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Resolution

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Per-metro resolution overrides

• Phoenix
• Houston
• Las Vegas
• Dallas
• San Antonio
• Nashville
• Jacksonville
• Oklahoma City
• El Paso
• Fort Worth

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Cell IDs in API responses

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Scoring profiles

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general profile weights

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Development pipeline sub-scores

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Permit Scope Quality (AXL-108)

{
  "developmentPipeline": {
    "score": 71.4,
    "subScores": [
      { "name": "Permit Activity", "score": 78, "weight": 0.40, "source": "Building Permits" },
      { "name": "Permit Scope Quality", "score": 64, "weight": 0.10, "source": "AXL-108" }
    ],
    "sourcesUsed": ["Building Permits", "AXL-108"],
    "sourcesMissing": []
  }
}

​

Safety & environment sub-scores

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Flood Loss History (AXL-109)

• Flood Risk — What does the regulatory map say this cell is? Forward-looking, redrawn on a multi-year cycle.
• Flood Loss History — What has actually happened here? Backward-looking, derived from FEMA National Flood Insurance Program (NFIP) claims.

1. Finding the nearest US zip centroid to the cell (single PostGIS nearest-neighbor lookup).
2. Joining that zip to the aggregated NFIP claims table (677k+ raw claims rolled up to ~17.8k zip-level records).
3. Returning a normalized exposure score in [0, 1] derived from claim count, repeat-loss policy share, and the most recent claim year.
4. Inverting to a 0–100 safety contribution: a zip with no historic claims scores 100, a maximum-exposure zip scores 0.

{
  "safetyEnvironment": {
    "score": 64.2,
    "confidence": 0.83,
    "subScores": [
      { "name": "Crime Rate", "score": 72, "weight": 0.30, "source": "Local Police" },
      { "name": "Flood Risk", "score": 80, "weight": 0.20, "source": "FEMA" },
      { "name": "Flood Loss History", "score": 78, "weight": 0.15, "source": "AXL-109 (FEMA NFIP)" },
      { "name": "Environmental Risk", "score": 61, "weight": 0.15, "source": "EPA EJScreen" }
    ],
    "sourcesUsed": ["Local Police", "FEMA", "AXL-109 (FEMA NFIP)", "EPA EJScreen"],
    "sourcesMissing": ["FEMA NRI", "EPA AirNow", "311 Service Requests"]
  }
}

​

Complaint Trend YoY (AXL-107)

1. Count 311 complaints in the trailing 30 days (density_30d).
2. Count 311 complaints in the matching 30-day window one year prior (the 335-to-395-day window).
3. Compute the YoY delta as (current − prior) / max(prior, 1), capped at +5× to bound runaway ratios in cells with a near-zero prior baseline.
4. Map the delta onto a 0–100 safety contribution: −1 (complaints down 100%) → 100, +3 (complaints up 3×) → 0, with values above +3× saturating at 0.

{
  "safetyEnvironment": {
    "score": 67.1,
    "subScores": [
      { "name": "Crime Rate", "score": 72, "weight": 0.30, "source": "Local Police" },
      { "name": "Complaint Density", "score": 64, "weight": 0.05, "source": "311 Service Requests" },
      { "name": "Complaint Velocity", "score": 58, "weight": 0.05, "source": "311 Service Requests Velocity" },
      { "name": "Complaint Trend YoY", "score": 81, "weight": 0.05, "source": "AXL-107 (311 YoY)" }
    ],
    "sourcesUsed": ["Local Police", "311 Service Requests", "311 Service Requests Velocity", "AXL-107 (311 YoY)"]
  }
}

​

Confidence semantics

{
  "composite": 78.4,
  "confidence": 0.92,
  "sourcesUsed": 36,
  "sourcesTotal": 43
}

• 0.9+ (high) — almost all data sources contributed; treat as authoritative.
• 0.7–0.9 (medium) — significant signals present; treat as directional, not exact.
• 0.5–0.7 (low) — sparse coverage; useful for filtering but not for ranking.
• <0.5 (very low) — Locus returns the score for transparency but it should not be used for production decisions.

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Coverage guards on signal groups

{
  "safetyEnvironment": {
    "score": 50,
    "confidence": 0,
    "subScores": [],
    "sourcesUsed": [],
    "sourcesMissing": ["Local Police", "AXL-109 (FEMA NFIP)", "EPA EJScreen", "FEMA NRI", "EPA AirNow", "311 Service Requests"]
  }
}

​

Pioneer Signal and early-stage gentrification indicator

1. Pioneer businesses — new specialty coffee shops, art galleries, or co-working spaces appear in a previously underserved area.
2. Council language shift — city council meeting minutes begin referencing “revitalization,” “mixed-use,” or “transit-oriented development” for the area.
3. Permit acceleration — building permit velocity increases, particularly renovation and change-of-use permits.
4. Rezoning activity — formal rezoning applications or variance requests are filed.

{
  "composite": 78.4,
  "esgi": {
    "score": 0.72,
    "stage": "permit_acceleration",
    "upzoning_probability": 0.45,
    "litigation_risk": 0.12,
    "spatial_spillover": 0.31
  }
}

​

Uncertainty quantification

• Confidence intervals — geo-conformal prediction intervals at 80% and 90% coverage.
• Sobol sensitivity indices — first-order and total-order indices showing which signal groups contribute most to score variance.
• Epistemic flags — signals where data is sparse or conflicting, flagged for transparency.
• Spatial Sharpe ratio — a risk-adjusted score metric analogous to a financial Sharpe ratio, indicating score stability relative to spatial neighbors.

curl "https://axiomlocus.io/api/score?lat=37.7749&lng=-122.4194&include=uncertainty"

{
  "composite": 78.4,
  "confidence": 0.92,
  "uncertainty": {
    "ci_80": [72.1, 84.7],
    "ci_90": [69.5, 87.3],
    "spatial_sharpe": 1.42,
    "sobol_first_order": {
      "businessVitality": 0.28,
      "developmentPipeline": 0.22,
      "demographics": 0.15,
      "accessibility": 0.12,
      "economicStrength": 0.10,
      "safetyEnvironment": 0.07,
      "populationMomentum": 0.04,
      "amenityDemand": 0.02
    },
    "epistemic_flags": ["populationMomentum"]
  }
}

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Per-cell consensus class

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Class definitions

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Example response

{
  "h3_index": "882a100d63fffff",
  "composite_score": 78.4,
  "support_stability": 0.92,
  "consensus_class": "stable_core",
  "consensus_computed_at": "2026-04-29T20:14:00Z"
}

​

When to use it

• Ranking and shortlisting. Filter to consensus_class = stable_core when you need the most defensible top-N list — those cells survive a grid change.
• Risk-flagging the long tail. Cells in ambiguity_shell are the cells most worth a human review before commitment. They typically sit on tier boundaries where small grid choices flip rankings.
• Suppressing false-positive low scores. A stable_non_signal cell is genuinely low across grids — a stable_non_signal paired with a composite_score of 35 means the cell is reliably quiet, not under-sampled.

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Data freshness

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API stability commitments

• Cell IDs — frozen across major API versions
• Scoring profile names — frozen
• Composite + sub-score scales (0–100) — frozen
• Confidence semantics — frozen
• Sub-score names within a group — may change with 90-day notice
• Underlying data sources — may change without notice (we pick the best available)
• Weight tuning per profile — adjusted quarterly based on backtesting; minor adjustments not announced, major rebalances get a 30-day blog post

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What Locus doesn’t include

• Individual person data (names, addresses, phone numbers)
• Mobile-device location traces (Locus uses aggregated patterns, not raw movements)
• Customer-specific data unless you explicitly upload it via /api/data/upload