Lab 2: Vector Analysis — Infrastructure Exposure Assessment

Duration: 2–3 hours  ·  Format: Python (Jupyter notebook)  ·  Prerequisite: Lab 1  ·  Assessment: sign-off quiz submitted to LEARN

1Learning Objectives

• Join attribute tables to spatial data using .merge()
• Filter GeoDataFrames using attribute queries
• Perform spatial joins using gpd.sjoin()
• Create buffer zones around infrastructure features
• Clip spatial data to a study area boundary
• Compute spatial aggregations using .dissolve() and .groupby()
• Build interactive web maps with .explore()

2Engineering Scenario

You are a graduate engineer at a consulting firm assessing coastal infrastructure exposure for the Christchurch City Council. Your task is to determine which buildings, roads, and communities are at risk from sea-level rise in the 0–5 m elevation zone. You will combine census, infrastructure, and hazard data to produce an exposure report.

3Step-by-Step Instructions

Section 1: Setup and Data Creation

Load libraries and the Christchurch coastal zone datasets — buildings, pipes, statistical areas, and a coastal hazard zone.

Section 2: Attribute Queries

Filter GeoDataFrames by attribute values. Identify residential buildings in the low-elevation zone. Practice combining multiple filter conditions with & and | operators.

Section 3: Table Joins (.merge())

Merge a valuation table (plain DataFrame) to buildings (GeoDataFrame) using a shared key. Verify the result retains geometry. Map buildings by capital value.

Section 4: Spatial Joins (gpd.sjoin())

Assign each building to its statistical area using a spatial join. Summarise building counts and values per area. Practice spatial join with pipe data.

Section 5: Buffer Analysis

Create 50 m buffer zones around pipes. Use a spatial join to find buildings within buffer zones. Understand how buffer distance depends on CRS units.

Section 6: Clipping

Extract buildings within a focus area boundary using gpd.clip(). Compare the full dataset to the clipped result.

Section 7: Dissolve and Aggregation

Merge statistical areas by deprivation category. Aggregate population and other attributes. Compare individual vs dissolved maps.

Section 8: Interactive Maps

Create interactive web maps with .explore(). Add multiple layers, tooltips, and popups. Experiment with different basemap tiles.

Section 9: Exposure Assessment

Combine all techniques: filter at-risk buildings, spatial join to areas, summarise by deprivation level. Produce a final exposure map and summary table.

4Assessment: Lab Sign-Off

Step 1 — Self-check

Before you start the quiz, make sure you can do each of these:

• Loaded a multi-layer GeoPackage and described its contents
• Performed attribute queries to filter features by multiple criteria
• Completed a spatial join and explained how it differs from a table join
• Created buffer zones and identified affected buildings
• Produced the exposure summary table by deprivation level
• Created at least one interactive map using .explore()

Step 2 — Take the sign-off quiz

1. Download Lab2_SignOff.html from LEARN and open it in your web browser.
2. Enter your full name and University ID, then answer the 10 questions.
3. You need 8 out of 10 to pass. You can retake it as often as you like — each attempt draws a new set of questions, and every answer comes with an explanation.
4. When you pass, click Download Sign-Off PNG. This saves a file called ENCN205_Lab2_<yourID>.png.

Step 3 — Submit

Upload ENCN205_Lab2_<yourID>.png to the Lab 2 Sign-Off submission on LEARN.

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