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Challenge

Regional Casino Non-Gaming Floor Space Foot-Traffic Optimizer

As digital betting expands, regional casinos are reallocating gaming floor space toward high-margin non-gaming amenities like dining and entertainment. Build a spatial foot-traffic and revenue density optimization model using Python and NetworkX to maximize total facility EBITDA. Achieve an 8% increase in non-gaming revenue density per square foot.

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Challenge brief

What you are building

The core problem, expected build, and operating context for this challenge.

Construct a floor space network optimization engine using NetworkX to reallocate casino square footage toward non-gaming revenue sources.

Delivery guide

How work is evaluated

Evaluation

Evaluates calculated optimal square footage reallocations and projected EBITDA increase.

Datasets

Challenge datasets

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

Evaluation rubric

How submissions are scored

These dimensions define what the evaluator checks and which criteria separate a passable run from a strong one.

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

total_sqft_conserved

Checks that target slot + target dining equals total floor area.

Binary check

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

Dimension 2

ebitda_gain_mae

Mean Absolute Error in projected EBITDA gain. • target: 1000 • range: 0-50000

Binary check

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

Learning goals

What you should walk away with

  • Represent casino floor spatial layouts as weighted network graphs using NetworkX

  • Simulate customer movement vectors between gaming zones and non-gaming amenities

  • Optimize square footage allocation between slot floor, table games, and dining units

  • Maximize total property net EBITDA contribution per square foot

Resources and assets

Reference links and supporting material

Dataset notes

Dataset of regional casino floor footprints, gaming revenue densities, and hospitality customer flow volumes.

Challenge setup

Evaluates calculated optimal square footage reallocations and projected EBITDA increase.

Challenge input

JSON object with total_floor_sqft, current_slot_sqft, slot_rev_per_sqft, dining_rev_per_sqft

NetworkX

Standard graph modeling package.

Evaluated output

JSON object with target_slot_sqft, target_dining_sqft, projected_ebitda_gain

Evaluation checks
  • Checks that target slot + target dining equals total floor area.
  • Mean Absolute Error in projected EBITDA gain. • target: 1000 • range: 0-50000
Configured evaluation evidence
  • Benchmark: SpatialSpaceOptimizationBench
  • Ebitda Gain Mae target: 1000
  • 1 public reference case
Configured runtime
  • 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

Action Space
  • Decipher AI · Required
  • All Hands AI · Optional
  • Google Gemini Code Assist · Optional

Evaluation contract

  • total_sqft_conserved · Weight 1
  • ebitda_gain_mae · Weight 1

Metrics

  • ebitda_gain_mae · Target 1000

Benchmarks

  • SpatialSpaceOptimizationBench
  • general

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON object with total_floor_sqft, current_slot_sqft, slot_rev_per_sqft, dining_rev_per_sqft

    Output: JSON object with target_slot_sqft, target_dining_sqft, projected_ebitda_gain

    Execution harness: Python

Evaluation tools

  • NetworkX · Graph Network Library

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Regional Casino Non-Gaming Floor Space Foot-Traffic Optimizer