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Urban Land Redevelopment Zoning Variance Assistant

Navigating municipal zoning laws and obtaining variance approvals requires comparing proposed site specs against strict land-use ordinances. Build a automated zoning variance engine using Python, PostGIS, and SQLAI tools to cross-reference municipal code limits against commercial development requests. Achieve 90% accuracy in identifying required zoning variance applications.

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What you are building

Construct a geospatial database querying pipeline using PostGIS and Python to evaluate urban land development zoning variance needs.

How work is evaluated

Evaluation

Evaluates accuracy of zoning variance requirement determinations for commercial site proposals.

Datasets

Public datasets, plus the private datasets you have access to.

How submissions are scored

Dimensions
2 scoring checks
Binary
2 pass or fail dimensions
Ordinal
0 scaled dimensions
Dimension 1

variance_flag_match

Checks correct determination of variance requirement.

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Dimension 2

reason_precision

Precision of identified variance reason codes. • target: 0.92 • range: 0-1

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Learning goals

  • Formulate spatial SQL queries in PostGIS for site parcel boundary intersections

  • Compare requested Floor Area Ratio (FAR) and maximum height against underlying zoning district caps

  • Flag parameters requiring conditional use permits or explicit zoning variance requests

  • Generate automated compliance summaries for urban development teams

Resources

Dataset notes

Dataset of municipal land parcels, underlying zoning classifications, maximum envelope parameters, and project proposals.

Challenge setup

Evaluates accuracy of zoning variance requirement determinations for commercial site proposals.

Challenge input

JSON object with parcel_id, zoning_code, proposed_far, proposed_height_ft, max_allowed_far, max_allowed_height_ft

PostGIS

Standard open-source spatial GIS database tool.

Evaluated output

JSON object with variance_required (boolean), variance_reasons (array of strings)

Evaluation checks
  • Checks correct determination of variance requirement.
  • Precision of identified variance reason codes. • target: 0.92 • range: 0-1
Configured evaluation evidence
  • Benchmark: GISZoningComplianceBench
  • Reason Precision target: 0.92
  • 1 public reference case
Configured runtime
  • Python execution harness
  • Python sandbox (unavailable on Versalist)

Shows the first task, up to three tools, and up to three evidence items.

View technical recipe

Configured tools

Action Space
  • Text Generation Inference · Required
  • AWS · Optional
  • Vijil · Optional

Evaluation contract

  • variance_flag_match · Weight 1
  • reason_precision · Weight 1

Metrics

  • reason_precision · Target 0.92

Benchmarks

  • GISZoningComplianceBench
  • general

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON object with parcel_id, zoning_code, proposed_far, proposed_height_ft, max_allowed_far, max_allowed_height_ft

    Output: JSON object with variance_required (boolean), variance_reasons (array of strings)

    Execution harness: Python

Evaluation tools

  • PostGIS · Spatial Database Extension

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Urban Land Redevelopment Zoning Variance Assistant