Lab 1: Reading, Exploring, and Projecting Spatial Data

Duration: 2–3 hours  ·  Format: Python (Jupyter notebook)  ·  Prerequisite: Lab 0  ·  Assessment: sign-off quiz submitted to LEARN

1Learning Objectives

• Import and use core Python geospatial libraries (geopandas, matplotlib, pandas)
• Read spatial data from multiple formats (GeoPackage, Shapefile, GeoJSON, CSV)
• Inspect GeoDataFrame properties: geometry, attributes, CRS, and extent
• Understand coordinate reference systems and reproject data using .to_crs()
• Create basic choropleth maps using matplotlib
• Export spatial data to GeoPackage format

2Before You Start

You need the encn205 environment from Lab 0. If you have not done Lab 0 yet, do it first — it installs everything this lab depends on.

Basic Python knowledge is assumed: variables, lists, loops, and functions.

To start work, open your Miniforge/Anaconda Prompt (Windows) or Terminal (macOS/Linux) and run the usual two commands:

conda activate encn205
jupyter lab

Then use the JupyterLab file browser to open Lab1_SpatialData_CRS.ipynb and run the cells in order with Shift + Enter.

3Step-by-Step Instructions

Section 1: Environment Setup

Import geopandas, pandas, and matplotlib. Verify all libraries load without errors. If you see an ImportError, ask your tutor for help installing the missing package.

Section 2: Understanding Geometry Types

Create Point, LineString, and Polygon objects using Shapely. Understand which geometry type represents which real-world feature. Visualise all three types side by side.

Section 3: Reading Spatial Data from Files

Load the Natural Earth world countries dataset using gpd.read_file(). This dataset ships with geopandas so no download is needed. Plot the world map to verify the data loaded correctly.

Section 4: Inspecting GeoDataFrame Properties

Use .head(), .info(), .crs, .total_bounds, and .geom_type to explore the dataset. Filter to New Zealand and inspect its properties. Complete the YOUR TURN exercise.

Section 5: Creating GeoDataFrames from CSV

Create a sample borehole dataset as a pandas DataFrame with Easting/Northing columns. Convert to a GeoDataFrame using gpd.points_from_xy(). Set the CRS to EPSG:2193 (NZTM2000).

Section 6: CRS and Reprojection

Check the CRS of your borehole data. Reproject from NZTM2000 to WGS84 using .to_crs(). Compare coordinates before and after reprojection. Calculate distances in both CRS to understand why a projected CRS is essential for engineering measurements.

Section 7: Choropleth Mapping

Create choropleth maps using .plot(column=..., cmap=...). Experiment with different colour maps and classification schemes. Complete the water table depth mapping exercise.

Section 8: Writing Data to GeoPackage

Save your borehole GeoDataFrame to a GeoPackage file using .to_file(). Add a second layer (WGS84 version) to the same file. Verify by reading the data back.

4Assessment: Lab Sign-Off

Step 1 — Self-check

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

• Successfully loaded and inspected spatial data (show .head(), .crs, .info())
• Reprojected data between NZTM2000 and WGS84, and can explain the difference
• Created at least two choropleth maps with appropriate colour schemes
• Saved data to GeoPackage and verified the output by reading it back
• Can answer: why must all layers share the same CRS for spatial analysis?

Step 2 — Take the sign-off quiz

1. Download Lab1_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_Lab1_<yourID>.png.

Step 3 — Submit

Upload ENCN205_Lab1_<yourID>.png to the Lab 1 Sign-Off submission on LEARN.

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