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.
What you are building
The core problem, expected build, and operating context for this challenge.
Construct a geospatial database querying pipeline using PostGIS and Python to evaluate urban land development zoning variance needs.
How work is evaluated
Evaluates accuracy of zoning variance requirement determinations for commercial site proposals.
Challenge datasets
Public datasets, plus the private datasets you have access to.
How submissions are scored
These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.
variance_flag_match
Checks correct determination of variance requirement.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
reason_precision
Precision of identified variance reason codes. • target: 0.92 • range: 0-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
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
Reference links and supporting material
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)
- Checks correct determination of variance requirement.
- Precision of identified variance reason codes. • target: 0.92 • range: 0-1
- Benchmark: GISZoningComplianceBench
- Reason Precision target: 0.92
- 1 public reference case
- Python execution harness
- Python sandbox (unavailable on Versalist)
Summary shows the first task, up to three tools, and up to three evidence items. Open the technical recipe to view all configured items.
View technical recipe
Configured tools
- 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
- 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.