Spatial Analysis: Techniques, Tools & Applications

Core Concepts of Spatial Analysis

Spatial analysis examines geographic data to reveal patterns, relationships, and trends. It transforms raw spatial data into actionable insights for decision-making. Key techniques include:

1. Buffering

Definition: Creates a zone (buffer) around spatial features (points, lines, polygons) at a specified distance. Used to analyze proximity relationships.

How it works:

• Input: A feature (e.g., a school, hospital, or road).
• Output: A polygon representing all locations within a set distance (e.g., 500m) of the feature.
• Example: Identifying areas within 1km of a river to assess flood risk.

Real-world use:

• Pathao: Uses buffering to determine rider pickup zones near restaurants or offices, optimizing delivery routes.
• NTC (Nepal Telecom): Analyzes buffer zones around cell towers to plan network expansion in underserved areas.

Worked Example: Problem: A hospital in Kathmandu wants to identify all schools within a 1km radius for emergency evacuation planning. Steps:

1. Load hospital location (point feature).
2. Apply a 1km buffer.
3. Overlay with a school layer to extract intersecting schools. Output: 12 schools fall within the buffer zone.

Advantages/Disadvantages:

---

2. Overlay Analysis

Definition: Combines multiple spatial layers to derive new information. Common types:

• Union: Merges all input layers.
• Intersection: Identifies common areas across layers.
• Identity: Retains attributes from one layer while overlaying another.

How it works:

• Input: Two or more layers (e.g., land use + elevation).
• Output: A new layer showing combined attributes (e.g., "forested areas above 2000m").

Real-world use:

• eSewa: Uses overlay analysis to map areas with high electricity demand (from consumer data) with power grid capacity to plan upgrades.
• Nepal Police: Overlays crime hotspots with population density to deploy patrols efficiently.

Worked Example: Problem: A municipality wants to identify agricultural land suitable for solar farms (flat terrain + low population density). Steps:

1. Load elevation layer (identify flat areas <5% slope).
2. Load land-use layer (extract agricultural zones).
3. Overlay with population density to exclude high-density areas. Output: 450 hectares of suitable land.

---

3. Network Analysis

Definition: Analyzes movement along linear features (roads, rivers, pipelines) to solve routing, connectivity, or service-area problems.

Key Techniques:

• Shortest Path: Finds the optimal route between two points (e.g., A* algorithm).
• Service Area: Determines reachable locations within a time/distance (e.g., "all areas within 30 minutes of a fire station").
• Location-Allocation: Assigns demand points (e.g., schools) to supply points (e.g., hospitals) efficiently.

Real-world use:

• Daraz: Uses network analysis to optimize last-mile delivery routes, reducing costs.
• NTC: Plans fiber-optic cable routes by analyzing terrain and existing infrastructure.

Worked Example: Problem: Find the fastest route from Pokhara to Chitwan via Prithvi Highway, avoiding toll roads. Steps:

1. Load road network layer (with speed limits).
2. Set start (Pokhara) and end (Chitwan) points.
3. Apply constraints (avoid toll roads).
4. Run shortest-path analysis. Output: Route via Pokhara–Butwal–Chitwan (280km, 4.5 hours).

Comparison Table: Network Analysis Tools

---

4. Terrain Analysis

Definition: Examines elevation data (DEMs) to derive slope, aspect, viewsheds, and watersheds.

Key Techniques:

• Slope Analysis: Measures steepness (critical for landslide risk).
• Aspect: Determines direction of slope (e.g., south-facing slopes get more sunlight).
• Viewshed: Identifies visible areas from a viewpoint (e.g., for tower placement).
• Watershed Delineation: Defines drainage basins.

Real-world use:

• Nepal Electricity Authority (NEA): Uses slope analysis to site hydropower dams in stable, high-elevation areas.
• ICIMOD: Maps glacier melt zones using aspect and elevation data to predict water availability.

Worked Example: Problem: Assess landslide risk in Dhading District using slope and rainfall data. Steps:

1. Load DEM (Digital Elevation Model) of Dhading.
2. Calculate slope (steepness >30° = high risk).
3. Overlay with rainfall data (high rainfall + steep slope = critical zones). Output: 12 villages fall in high-risk areas.

---

5. Geostatistics

Definition: Applies statistical methods to spatial data to identify patterns (e.g., hotspots, clusters, trends).

Key Techniques:

• Hotspot Analysis: Identifies clusters of high/low values (e.g., crime, pollution).
• Interpolation: Estimates values between sample points (e.g., predicting rainfall in unsampled areas).
  • Methods: Inverse Distance Weighting (IDW), Kriging.
• Spatial Autocorrelation: Measures how nearby features are similar (e.g., Moran’s I).

Real-world use:

• Khalti: Uses geostatistics to detect fraudulent transaction clusters by analyzing location + time patterns.
• WHO Nepal: Maps malaria hotspots using interpolation of case reports.

Worked Example: Problem: Predict air pollution levels in Kathmandu’s Thapathali using 10 monitoring stations. Steps:

1. Collect PM2.5 data from stations.
2. Apply Kriging interpolation to estimate values across the city. Output: A pollution map showing Thapathali as a hotspot (PM2.5 > 50 µg/m³).

---

In the Real World

1. Pathao’s Dynamic Routing:
   • Uses network analysis to recalculate rider routes in real-time, avoiding traffic jams detected via GPS. For example, during Dashain, Pathao reroutes drivers away from congested areas like Thamel to reduce wait times by 20%.

2. NTC’s 4G Tower Planning:

   • Employs overlay analysis to combine population density maps with terrain data (using DEMs) to place towers in areas with the highest demand and minimal obstruction. This reduced dead zones in rural areas like Doti by 35%.

3. eSewa’s Load Shedding Prediction:

   • Applies geostatistics to historical electricity consumption data (layered by neighborhood) to predict load shedding patterns. During summer, eSewa flags high-risk zones (e.g., Lalitpur-7) for preemptive alerts.

---

Exam Tip

This unit is heavily tested on:

1. Definitions: Know the difference between buffering (distance-based) and overlay (attribute-based). Examiners often ask: "When would you use intersection vs. union?"
2. Worked Examples: Be ready to solve 2–3 step problems (e.g., "Find hospitals within 500m of schools"). Always show:
   • Input layers.
   • Analysis method (e.g., "1km buffer + overlay").
   • Output interpretation.
3. Tool Applications: Link techniques to software:
   • QGIS: Free for overlay/buffering.
   • ArcGIS: Advanced network analysis.
   • Python (GDAL): For automation.
4. Real-world Scenarios: Expect questions like:
   • "How would Daraz use spatial analysis to reduce delivery costs?" (Answer: Network analysis for route optimization.)
   • "Explain how NTC could use terrain analysis to expand 5G coverage." (Answer: DEM + slope to avoid mountainous obstacles.)
5. Diagrams: Always sketch a simple flowchart or layer overlay in exams. For example:

   [Layer 1: Roads] → [Buffer 2km] → [Overlay with Schools] → [Result: 15 schools within reach]
   

6. Common Pitfalls:
   • Ignoring scale (e.g., buffering a point at 1km vs. 10km gives different results).
   • Misapplying interpolation (IDW assumes distance = similarity; Kriging accounts for spatial trends).
   • Forgetting constraints (e.g., rivers block buffers in real-world scenarios).