Game Results and City Planner Score
Results update after checking clusters or running an algorithm.
Challenge Setup
Choose a scenario, algorithm, feature weights, difficulty, and game rules.
Feature Selection and Weights
Interactive City Canvas
Drag city cards into cluster zones. Select a city to inspect its full profile.
City Profile
Selected city and game guidance.
Hints and Feedback
Event Log
City Dataset
Use samples, random fictional cities, manual editing, pasted tables, or CSV files.
| City | Region | Population | Climate | Affordability | Transport | Infrastructure | Cluster | Actions |
|---|
Visual Analytics
Explore projections, profiles, distances, correlations, quality, and cluster composition with Plotly.js.
Save, Share, and Export
Keep progress locally or export data and reports.
Challenge codes include city data, weights, scenario, seed, and cluster settings.
Cluster Summary
Representative cities, outliers, and feature strengths.
Formula Used
The game combines weighted normalized features, distance calculations, and cluster-quality metrics.
Weighted vector: x′ᵢ = normalize(xᵢ) × feature weight Euclidean distance: d(x,y) = √Σ(xᵢ − yᵢ)² Manhattan distance: d(x,y) = Σ|xᵢ − yᵢ| Silhouette: s(i) = (b(i) − a(i)) / max(a(i), b(i)) WCSS: Σ clusters Σ points ||x − centroid||² Davies–Bouldin: average maximum cluster similarity Game score: quality + scenario fit + speed + move efficiency − hint penalty
How to Use
- Choose a scenario, difficulty, method, distance measure, and cluster count.
- Select active city characteristics and adjust their influence with weight sliders.
- Load sample data, generate fictional cities, add records, paste tables, or import CSV.
- Start the game, then drag city cards into the most suitable canvas zones.
- Use hints when needed, or run automatic clustering for comparison.
- Check clusters to calculate quality metrics, score, rank, and recommendations.
- Explore Plotly charts and export the results as CSV, JSON, PNG, or PDF.
Example City Data
| City | Population | Climate | Affordability | Transport | Infrastructure |
|---|---|---|---|---|---|
| Nova Harbour | 8.2M | 72 | 39 | 91 | 88 |
| Meadowgate | 1.1M | 84 | 78 | 62 | 68 |
| Ironvale | 3.6M | 48 | 71 | 55 | 81 |
Frequently Asked Questions
What does the game cluster?
It groups cities using selected population, climate, affordability, transport, and infrastructure characteristics.
Why should I normalize the data?
Normalization prevents large numeric scales, especially population, from dominating every distance calculation.
What is a good silhouette score?
Values nearer one indicate clearer separation, while values near zero suggest overlapping clusters.
How are feature weights used?
Each normalized characteristic is multiplied by its selected weight before distances are measured.
Can I import my own cities?
Yes. Import CSV, paste tabular data, or add and edit cities manually.
What does DBSCAN do?
DBSCAN finds dense city groups and can label isolated records as noise or outliers.
Does the game save progress?
Yes. Progress is stored in browser local storage and can be resumed later.
How is the score calculated?
The score combines cluster quality, scenario alignment, move efficiency, speed, and hint usage.
Can I compare manual and automatic groups?
Yes. Run an algorithm after manual grouping, then inspect scores, charts, and cluster summaries.