What Is the PolygonSensorOptimizer?

The PolygonSensorOptimizer is a modular, feature-rich tool that takes a user-defined polygon (e.g., a city or district) and returns the optimal locations to place air quality sensors based on:

Real-world OpenStreetMap (OSM) data
Environmental, industrial, and transport features
Minimum distance enforcement
KMeans clustering for spatial diversity
Land use-based categorization (Commercial, Urban Background, Background, Rural)

This isn't just a spatial grid; it’s a smart, data-driven recommendation engine.

Input example

{
  "geometry": {
    "coordinates": [[[39.201279,-6.872916],[39.201279,-6.758374],[39.265823,-6.754283],[39.294662,-6.82383],[39.333115,-6.852464],[39.282303,-6.893366],[39.201279,-6.872916]]]
  },
  "config": {
    "distance": {
      "min_sensor_distance": 50
    },
    "sensor_density": {
      "base_density": 0.5,
      "recommended_fraction": 0.3
    }
  }
}

Key Features

1. Adaptive Grid Creation

The optimizer divides the polygon into an adaptive grid using user-configured or auto-sized cells. It ensures that:

Each cell intersects with the polygon,
Water bodies are excluded (using OSM data),
Grid parameters like width, height, and area are recorded.

2. Stratified Sampling

Each valid grid cell gets a random point generated within its boundaries-preferably away from water-and the center is used as a fallback.

3. Context-Aware Scoring

Each grid cell is scored using OSM features and weights:

🚗 Transport: Proximity to roads and intersections
🏢 Urban: Hospitals, schools, markets, building density
🏭 Industrial: Factories, construction zones
🌳 Environment: Parks, forests, and water bodies

Each cell gets a suitability score ranging from 0 to 1.

4. Category-Based Clustering

Using KMeans clustering, the algorithm groups locations to ensure spatial and environmental diversity. Every site is categorized as:

Commercial
Urban Background
Background
Rural

These categories ensure all urban typologies are represented.

Distance Enforcement

Good sensor placement isn't just about good locations—it’s about good spacing.

The tool enforces a minimum distance between sensors using:

KDTree for fast proximity checks,
A hybrid selection algorithm that balances:
- Suitability scores,
- Spatial dispersion.

Intelligent Selection

From hundreds (or thousands) of possible points, the tool:

Picks top candidates from each category,
Fills remaining slots using the hybrid scoring strategy,
Applies post-filtering to maintain the desired sensor count.

The result is a curated list of sites that are:

Well-spaced,
Diverse,
High in suitability,
Fully justified with scores and metadata.

Final Output

The result includes:

Grid and spatial metrics,
Sensor counts (max, min, recommended),
Detailed per-site info including:
- Latitude, longitude,
- Category,
- Suitability and component scores,
- Proximity warnings (e.g., water bodies),
- Cluster ID.

{
            "category_scores": {
                "environment": 0.6173,
                "industry": 0.1843,
                "transport": 0.2281,
                "urban": 0.8516
            },
            "cluster_id": 17,
            "detailed_reasons": [
                "Transport score (traffic pollution): 0.23",
                "Urban score (building density): 0.85",
                "Industry score (emissions): 0.18",
                "Environment score (green spaces): 0.62",
                "ML Cluster: 17"
            ],
            "grid_cell": "POLYGON ((39.2276462 -6.7774635, 39.2276462 -6.765873249999999, 39.2408298 -6.765873249999999, 39.2408298 -6.7774635, 39.2276462 -6.7774635))",
            "latitude": -6.769338746644935,
            "longitude": 39.23542821539876,
            "primary_reason": "Optimized for air quality data and spatial coverage",
            "proximity_warnings": [],
            "site_category": "Urban Background",
            "suitability_score": 0.4218
        }

Use Cases

This tool can power:

Government air quality monitoring networks,
NGO and academic research,
Smart city infrastructure planning,
Environmental justice analysis.

It’s especially powerful for applications where data coverage, public health, and urban planning intersect.

Why It Matters

Air pollution affects billions globally. Poorly placed sensors = incomplete data. This tool helps ensure every microenvironment is captured, from busy highways to quiet rural zones.

Better data → better policies → healthier lives.

Ready to Try?

If you're a developer, urban planner, or researcher, this script is a valuable addition to your toolkit. Load your polygon, tune the config, and let the optimizer do the hard work of placement, spacing, and scoring.

Stay tuned for our upcoming integration with interactive mapping tools and APIs for real-time deployment!

The code recides in spatial micro service

Smart Sensor Deployment: Optimizing Air Quality Monitoring with Python

Overview

Details

Smart Sensor Deployment: Optimizing Air Quality Monitoring with Python

Overview

Details