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Environmental Analysis: Protected Zone Impact

Find industrial sites within protected water buffer zones to assess environmental compliance and risks.

Scenario Overview​

Goal: Identify industrial facilities that fall within 1km buffer zones around protected water bodies to evaluate environmental impact.

Real-World Application:

  • Environmental agencies monitoring compliance
  • NGOs assessing industrial pollution risks
  • Policy makers creating buffer zone regulations
  • Urban planners managing industrial zoning

Estimated Time: 15 minutes

Difficulty: ⭐⭐⭐ Advanced

Prerequisites​

Required Data​

  1. Industrial Sites Layer (points or polygons)

    • Industrial facility locations
    • Must include facility type/classification
    • Minimum 50+ sites for meaningful analysis

  2. Water Bodies Layer (polygons)

    • Rivers, lakes, wetlands, reservoirs
    • Protected status attribute (optional but useful)
    • Covers your study area

  3. Protected Zones (optional)

    • Existing environmental protection zones
    • Regulatory buffer boundaries

Sample Data Sources​

Option 1: OpenStreetMap

# Use QGIS QuickOSM plugin
# For water bodies:
Key: "natural", Value: "water"
Key: "waterway", Value: "river"

# For industrial sites:
Key: "landuse", Value: "industrial"
Key: "industrial", Value: "*"

Option 2: Government Data

  • Environmental Protection Agency (EPA) databases
  • National water quality databases
  • Industrial facility registries
  • Protected area boundaries (WDPA)

Backend Recommendation​

Spatialite - Best choice for this workflow:

  • Good performance for regional datasets (typically <100k features)
  • Robust buffer operations
  • Good geometry repair capabilities
  • No server setup required

Step-by-Step Instructions​

Step 1: Load and Inspect Data​

  1. Load both layers into QGIS:

    • water_bodies.gpkg or rivers_lakes.shp
    • industrial_sites.gpkg or factories.shp

  2. Check CRS compatibility:

    Right-click layer → Properties → Information
    Verify both use same projected CRS (e.g., UTM, State Plane)

  3. Verify geometry validity:

    Vector → Geometry Tools → Check Validity
    Run on both layers
CRS Requirements

Buffer operations require a projected coordinate system (meters/feet), not geographic (lat/lon). If your data is in EPSG:4326, reproject first:

Vector → Data Management Tools → Reproject Layer
Target CRS: Choose appropriate UTM zone or local projection

Step 2: Create 1km Buffer Around Water Bodies​

Option A: Using FilterMate (Recommended)

  1. Open FilterMate panel
  2. Select water_bodies layer
  3. Enter filter expression: 
    -- Keep all water bodies, prepare for buffer
    1 = 1
  4. Enable Geometry Modification → Buffer
  5. Set Buffer Distance: 1000 (meters)
  6. Buffer Type: Positive (expand)
  7. Click Apply Filter
  8. Export Result as water_buffers_1km.gpkg

Option B: Using QGIS Native Tools

Vector → Geoprocessing Tools → Buffer
Distance: 1000 meters
Segments: 16 (smooth curves)
Save as: water_buffers_1km.gpkg

Step 3: Filter Industrial Sites Within Buffer Zones​

Now the main FilterMate operation:

  1. Select industrial_sites layer in FilterMate
  2. Choose Backend: Spatialite (or PostgreSQL if available)
  3. Enter spatial filter expression:
-- Industrial sites intersecting 1km water buffers
intersects(
  $geometry,
  geometry(get_feature('water_buffers_1km', 'fid', fid))
)

Alternative using layer reference:

-- More efficient if buffer layer is already loaded
intersects(
  $geometry,
  aggregate(
    layer:='water_buffers_1km',
    aggregate:='collect',
    expression:=$geometry
  )
)
  1. Click Apply Filter
  2. Review results in map canvas (features should be highlighted)

Step 4: Add Distance Calculations (Optional)​

To see how far each industrial site is from protected zones:

  1. Open Field Calculator (F6)
  2. Create new field: 
    Field name: distance_to_water
    Field type: Decimal (double)

    Expression:
    distance(
      $geometry,
      aggregate(
        'water_buffers_1km',
        'collect',
        $geometry
      )
    )
  3. Features inside buffer will show 0 or small values

Step 5: Categorize by Risk Level​

Create visual categories based on proximity:

  1. Right-click filtered layer → Properties → Symbology
  2. Choose Categorized
  3. Use expression: 
    CASE
      WHEN "distance_to_water" = 0 THEN 'High Risk (Inside Buffer)'
      WHEN "distance_to_water" <= 500 THEN 'Medium Risk (0-500m)'
      WHEN "distance_to_water" <= 1000 THEN 'Low Risk (500-1000m)'
      ELSE 'No Risk (Outside Buffer)'
    END
  4. Apply color scheme (red → yellow → green)

Step 6: Export Results​

  1. In FilterMate, Export Filtered Features:

    Format: GeoPackage
    Filename: industrial_sites_environmental_risk.gpkg
    Include attributes: ✓ All fields
    CRS: Keep original or choose standard (e.g., WGS84 for sharing)

  2. Generate report (optional):

    # In Python Console (optional advanced step)
    layer = iface.activeLayer()
    total = layer.featureCount()
    high_risk = sum(1 for f in layer.getFeatures() if f['distance_to_water'] == 0)

    print(f"Total industrial sites in buffer: {total}")
    print(f"High risk (directly in water buffer): {high_risk}")
    print(f"Percentage at risk: {(high_risk/total)*100:.1f}%")

Understanding the Results​

What the Filter Shows​

✅ Selected features: Industrial sites within 1km of protected water bodies

❌ Excluded features: Industrial sites farther than 1km from any water body

Interpreting the Analysis​

High Risk Sites (distance = 0):

  • Directly within regulated buffer zones
  • May violate environmental regulations
  • Require immediate compliance review
  • Potential for water contamination

Medium Risk Sites (0-500m):

  • Close to buffer boundaries
  • Should be monitored
  • May need additional safeguards
  • Future buffer expansions could affect them

Low Risk Sites (500-1000m):

  • Within analytical buffer but outside typical regulation
  • Useful for proactive planning
  • Lower immediate concern

Quality Checks​

  1. Visual inspection: Zoom to several results and verify they're actually near water
  2. Attribute check: Ensure facility types match expectations
  3. Distance validation: Measure distance in QGIS to confirm buffer accuracy
  4. Geometry issues: Look for sites on buffer boundary (may indicate geometry problems)

Best Practices​

Performance Optimization​

For Large Datasets (>10,000 industrial sites):

  1. Simplify water body geometry first:

    Vector → Geometry Tools → Simplify
    Tolerance: 10 meters (maintains accuracy)

  2. Use spatial index (automatic in PostgreSQL, manual in Spatialite):

    Layer → Properties → Create Spatial Index

  3. Pre-filter water bodies to protected areas only:

    "protected_status" = 'yes' OR "designation" IS NOT NULL

Backend Selection:

Features    | Recommended Backend
--------    | -------------------
< 1,000     | OGR (simplest)
1k - 50k    | Spatialite (good balance)
> 50k       | PostgreSQL (fastest)

Accuracy Considerations​

  1. Buffer distance units: Always verify units match your CRS:

    Meters: UTM, State Plane, Web Mercator
    Feet: Some State Plane zones
    Degrees: NEVER use for buffers (reproject first!)

  2. Geometry repair: Water bodies often have invalid geometries:

    Vector → Geometry Tools → Fix Geometries
    Run before buffer operation

  3. Topology: Overlapping water bodies may create unexpected buffer shapes:

    Vector → Geoprocessing → Dissolve (union all water bodies)
    Then create single unified buffer

Regulatory Compliance​

  • Document methodology: Save FilterMate expression history
  • Version control: Keep original data + filtered results + metadata
  • Validation: Cross-reference with official regulatory databases
  • Updates: Re-run analysis when industrial registry is updated

Common Issues​

Issue 1: "No features selected"​

Cause: CRS mismatch or buffer distance too small

Solution:

1. Check both layers are in same projected CRS
2. Verify buffer distance: 1000 in meters, not degrees
3. Try larger buffer (e.g., 2000m) for testing
4. Check water bodies actually exist in your study area

Issue 2: "Geometry errors" during buffer​

Cause: Invalid water body geometries

Solution:

Vector → Geometry Tools → Fix Geometries
Then re-create buffers

Issue 3: Performance very slow (>2 minutes)​

Cause: Large datasets without optimization

Solutions:

1. Create spatial indexes on both layers
2. Simplify water body geometry (10m tolerance)
3. Switch to PostgreSQL backend
4. Pre-filter to smaller area of interest

Issue 4: Buffer creates strange shapes​

Cause: Geographic CRS (lat/lon) instead of projected

Solution:

Reproject BOTH layers to appropriate UTM zone:
Vector → Data Management → Reproject Layer
Find correct zone: https://epsg.io/

Next Steps​

Advanced Techniques​

1. Multi-Ring Buffers (graduated risk zones):

Create 3 separate buffers: 500m, 1000m, 1500m
Categorize facilities by which buffer they fall into

2. Proximity to Nearest Water (not just any water):

-- Find distance to closest water body only
array_min(
  array_foreach(
    overlay_nearest('water_bodies', $geometry),
    distance(@element, $geometry)
  )
)

3. Temporal Analysis (if you have facility age data):

-- Old facilities in sensitive areas (highest risk)
"year_built" < 1990 
AND distance_to_water < 500

4. Cumulative Impact (multiple facilities near same water body):

-- Count facilities affecting each water body
WITH risk_counts AS (
  SELECT water_id, COUNT(*) as num_facilities
  FROM filtered_sites
  GROUP BY water_id
)
-- Show water bodies with >5 nearby facilities

Further Learning​

Summary​

✅ You've learned:

  • Creating buffer zones around water bodies
  • Spatial intersection filtering with industrial sites
  • Distance calculation and risk categorization
  • Geometry validation and repair
  • Backend-specific optimization techniques

✅ Key takeaways:

  • Always use projected CRS for buffer operations
  • Fix geometry errors before spatial analysis
  • Choose backend based on dataset size
  • Document methodology for regulatory compliance
  • Visual validation is essential

🎯 Real-world impact: This workflow helps environmental agencies identify compliance risks, supports evidence-based policy making, and protects water quality by highlighting facilities requiring monitoring or remediation.