Urban Transit-Oriented Development Demand & Yield Scoring Engine
High-density land parcels near transit corridors require detailed demand scoring based on pedestrian accessibility and transit frequency. Construct a geospatial demand scoring model using PySal, GeoPandas, and OpenStreetMap data to evaluate site releases in high-density urban nodes like Singapore. Achieve 90% correlation with empirical residential absorption rates.
What you are building
The core problem, expected build, and operating context for this challenge.
Build an urban Transit-Oriented Development (TOD) demand scoring pipeline using GeoPandas and PySal to evaluate parcel site releases.
How work is evaluated
Evaluates accuracy of calculated TOD Walkability Score and demand tier classification.
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.
tier_match
Verifies correct demand tier classification.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
tod_score_mae
Mean Absolute Error in computed TOD score. • target: 1.5 • range: 0-10
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Ingest urban transit network geometries using GeoPandas and OSMnx
Compute walkability Isochrones and transit access frequency scores for target parcels
Build spatial econometrics models using PySal for spatial lag regression
Output standardized TOD Suitability Score (0-100) for land bidding strategies
Reference links and supporting material
Dataset of land parcel spatial coordinates, MRT/transit station locations, train headway frequencies, and retail amenity density.
Challenge setup
Evaluates accuracy of calculated TOD Walkability Score and demand tier classification.
Challenge input
JSON object with parcel_id, transit_distance_meters, peak_train_frequency_per_hr, commercial_amenities_500m
GeoPandas
Standard Python spatial vector data framework.
PySal
Standard spatial econometrics library.
Evaluated output
JSON object with tod_score, demand_tier ('PRIME'|'SECONDARY'|'MARGINAL')
- Verifies correct demand tier classification.
- Mean Absolute Error in computed TOD score. • target: 1.5 • range: 0-10
- Benchmark: UrbanTransitScoringBench
- Tod Score Mae target: 1.5
- 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
- Ellipsis · Required
- Sacred · Optional
- Softgen AI · Optional
Evaluation contract
- tier_match · Weight 1
- tod_score_mae · Weight 1
Metrics
- tod_score_mae · Target 1.5
Benchmarks
- UrbanTransitScoringBench
- general
Reference cases
- 1 public reference case
Workflow tasks
- Task 1
Input: JSON object with parcel_id, transit_distance_meters, peak_train_frequency_per_hr, commercial_amenities_500m
Output: JSON object with tod_score, demand_tier ('PRIME'|'SECONDARY'|'MARGINAL')
Execution harness: Python
Evaluation tools
- GeoPandas · Geospatial Data Framework
- PySal · Spatial Analysis Library
Recipe freeze marker
No recipe freeze time is recorded.