October 2026 — Week 5 of 9

Applied Spatial Analysis Using QGIS - Spatial Proximity & Multi-Criteria Analysis

Applied Spatial Analysis Using QGIS — GIS for Surveyors (2026 Cohort) · 11 Oct 2026 · 45:08

Applied Spatial Analysis Using QGIS - Week 5 Spatial Proximity & Multi-Criteria Analysis

Course: Applied Spatial Analysis Using QGIS - 2026 Cohort (GIS for Surveyors)

Session: Week 5 of 9 - 11 Oct 2026 (45:08)

Video: Week 5 - Spatial Proximity & Multi-Criteria Analysis to YouTube playlist Applied Spatial Analysis Using QGIS - Live Sessions and Labs

Data: roads, rivers/water bodies, buildings, admin layers from training_utm36n (UTM 36N)

Deliverable: buildings in proximity zone identified via buffer + spatial join (e.g. buildings_in_road_buffer with affected_m2 and affected_ha as per lab guide)

Digital archive note - I write this after every live session so students who missed power or signal can catch up offline, and so our community has a searchable record for the future.

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Why Week 5 Exists

Week 4 taught us to ask questions of attribute data. Week 5 is where we start asking questions about location and distance - spatial relationships. How many buildings fall within a 50 m no-build setback from roads? Which buildings would be impacted if a water body flooded to 100 m? These are classic spatial analysis questions.

The plan, live on screen:

1. Recap the importance of data readiness before analysis

2. Create and use a Study Area boundary

3. Clip all relevant layers to the study area (normalize and improve performance)

4. Understand Buffer, Clip, Overlay (Intersection/Union), Spatial Join

5. Apply buffers to roads (filtered by expression) and water bodies

6. Use Spatial Join to identify impacted vs unimpacted buildings

7. Wrap up with deliverable and next steps

1) Data Preparation First

Before running spatial analysis, datasets must be prepared. We emphasized: ensure you have all needed datasets before framing questions; normalize through selection/filtering/reprojection; create a Study Area; then clip every layer to that boundary. Clipping makes datasets manageable and improves QGIS performance.

2) Study Area & Clipping

We created a Study Area polygon, styled it clearly, then used Vector → Geoprocessing → Clip (also accessible via Processing Toolbox and Locator) to cut roads, rivers/water bodies, and buildings to the study area boundary. After clipping, original full-extent layers were removed from the layer stack and replaced with subset layers.

3) Buffers and Expressions

Buffers create proximity zones. We filtered roads using an SQL expression to extract main roads (primary/secondary/tertiary), exported as main_roads, styled and labelled. We then ran a 30 m buffer on main roads. We also applied a 100 m buffer around water bodies to model a setback/protection zone.

4) Spatial Join to Find Impacts

The key question posed: "How many buildings were impacted?" Using Spatial Join (Join attributes by location), we joined buildings with the 100 m water buffer to identify which buildings fell within or touched the buffer zone. This produced building_impacted_buff100. We also inverted/selected the remainder to export buildings_unimpacted_buff100m.

5) Key Takeaways

  • CRS is critical - work in UTM (metres) for distance-based analysis
  • Study Area + Clip normalizes data and improves performance
  • Buffer defines proximity; Spatial Join/Intersection quantify what falls inside
  • Filtering with expressions (e.g. road class) keeps analysis focused
  • Keep your layer stack organized (subsets only after clipping)

    • 6) Wrap-up

    We reviewed results, discussed the week's deliverable context (buildings_in_road_buffer with affected_m2, affected_ha), reminded about the weekly quiz, mentioned raster analysis intro for next week, and closed the session.

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    Archive created from live session notes and what_we_covered_today.txt