ENV103 Digital Earth
The Complete Study & Assessment Guide · S2 2026ENV103 Overview
- University of Auckland
- Level 1 course
- School of Environment
- Four labs in Excel and QGIS
- Group poster and reflection
ENV103 Digital Earth is the University of Auckland’s first-year introduction to geospatial science, taught by the School of Environment and drawing on geography, Earth science and environmental science.
- Assessed by Laboratories 40%, the in-lab test 30%, lecture quizzes 10%, the poster 15% and the reflection 5%.
- Hardest step Moving from following lab steps to explaining what a classification, join or buffer actually did to the data.
- Software Microsoft Excel for data wrangling and QGIS for mapping, overlay and raster analysis.
What you get in the full ENV103 Study & Assessment Guide
- 12 chapters, 52 pages — every assessed topic, week by week
- 36 original practice questions, each with a model answer
- 12 worked examples, solved the way ENV103 marks
- 17 computed figures, redrawn from the course’s own numbers
- Before-you-submit checklist for the Laboratories (40%) — what markers reward, what costs marks
Free on this page: assessment table, chapter map, one worked example, key terms, FAQ. The rest is in the full study & assessment guide.
How ENV103 is assessed
| Component | Weight | Format |
|---|---|---|
| Laboratories | 40% | Four lab assignments at 10% each, submitted as a PDF proforma; part of each lab mark comes from an exercise handed in at the end of the session |
| Test | 30% | In-lab and in person in Week 8, 1 hour, closed book on a computer and invigilated; covered the first two labs; controlled (lane 1), no GenAI |
| Lecture Quizzes | 10% | Ten quizzes done in person in lecture; two may be missed and are replaced by your average |
| Poster | 15% | Group A2 poster applying geospatial data to a real-world issue; submitted as one PDF per group |
| Reflection | 5% | Individual reflection on the poster’s data, using the course proforma |
All five weights come from the course’s Assessment Overview page and total 100%. To pass, your aggregate across the assessment groups must be at least 50%. Late work loses 10% of the maximum mark per day or part day, applied automatically in Canvas.
What other students are asking about ENV103
- Where can I find lecture notes or study guides for ENV103 at auckland?
- The ENV103 study guide in AskSia Library covers Digital Earth in 12 chapters, with the assessment table, a chapter-by-chapter topic map, key terms and a worked example, free to read on this page.
- Is there a final exam in ENV103?
- No final exam is listed in the offering this AskSia Library guide is built from; ENV103 is assessed by Laboratories 40%, Test 30%, Lecture Quizzes 10%, Poster 15%, Reflection 5%.
- What does ENV103 cover on map projections, datums and coordinate systems?
- In AskSia Library's ENV103 guide, the Map Projections, Datums and Coordinate Systems chapter explains distortion trade-offs, conformal and equal-area projections, datums, NZTM2000 and EPSG 2193 coordinates.
- What does ENV103 cover on maps as messengers: cartographic design?
- In AskSia Library's ENV103 guide, the Maps as Messengers: Cartographic Design chapter explains map definitions, reference and thematic maps, the five cartographic steps, visual weight and the optical centre.
- What does ENV103 cover on classifying data for thematic maps?
- In AskSia Library's ENV103 guide, the Classifying Data for Thematic Maps chapter explains equal interval, quantile and natural breaks, number of classes, ColorBrewer schemes and histograms.
What ENV103 covers
Follow the course from space, place and scale through projections, map design and classification, into data models and quality, the four labs in Excel and QGIS, spatial analysis, remote sensing and the politics of representation.
Spatial Thinking: Space, Place and Scale
Digital Earth, space measured by coordinates and distance, place and meaning, scale from individual to globalThis chapter sets up the vocabulary the whole course depends on.
- Digital Earth and the four course questions
- Space measured by coordinates and distance
- Euclidean distance versus network distance
- Place, meaning and geographical imagination
- Scale from individual to global
- Naming and mapping as acts of power
Map Projections, Datums and Coordinate Systems
Distortion trade-offs, conformal and equal-area projections, datums, NZTM2000 and EPSG 2193 coordinatesEvery flat map distorts the curved Earth, so this chapter explains what each family of projections keeps and gives up, how geodesy, datums and projections fit together, and how New Zealand fixes its coordinates with NZTM2000, EPSG 2193. It ends with reading real easting and northing values, the skill behind every distance and area you calculate in the labs.
- Why all flat maps distort
- Conformal and equal-area projections
- Planar, conical and cylindrical surfaces
- Projections matched to country shape
- Geodesy, datums and projections
- NZTM2000 parameters and EPSG 2193
Maps as Messengers: Cartographic Design
Map definitions, reference and thematic maps, the five cartographic steps, visual weight and the optical centreMaps are constructions of a view of the world, not neutral copies of it.
- Maps as constructions, not reconstructions
- Reduced, selective and symbolised
- Where, what, how much, which and when
- Tangible, virtual and mental maps
- Reference maps and thematic forms
- Five cartographic steps
Classifying Data for Thematic Maps
Equal interval, quantile and natural breaks, number of classes, ColorBrewer schemes and histogramsA thematic map rarely shows raw values; it groups them into classes. This chapter explains why classification helps the eye, why about seven classes is a practical ceiling, and how equal interval, quantile and natural breaks place their boundaries differently.
- Why classify a data series
- The magical number seven
- Equal interval classification
- Quantile classification
- Natural breaks classification
- Fixed interval and standard deviation
Vector and Raster Data Models
Points, lines and polygons, raster extent and resolution, shapefiles, GeoPackages and newer formatsSpatial data link a location, often a time, and an attribute. This chapter separates the two ways GIS stores them: vector points, lines and polygons with attribute tables, and raster grids defined by extent and resolution.
- Data versus information
- Location, attribute and time
- Points, lines and polygons
- Raster extent and resolution
- Shapefile problems and the GeoPackage
- GeoJSON, Feather and Parquet
Data Collection, Accuracy and the MAUP
Primary and secondary data, LiDAR, InSAR, sonar and GNSS, precision and accuracy, scale and zoning effectsData are gathered by surveys, observation, monitoring and sensors, and every route leaves its mark on quality.
- Primary and secondary data
- Volunteered geographic information
- LiDAR, SAR, InSAR and sonar
- GNSS and positioning from four satellites
- Precision versus accuracy
- Top-down and bottom-up data
Wrangling Geospatial Data in Excel
Lab 1: filtering treatment-plant data, cleaning census codes, charts and distance from eastings and northingsLab 1 trains the step that comes before any map: getting data into shape.
- Text, CSV and Excel file formats
- Filtering to two treatment plants
- AVERAGE formulas and the fill handle
- Cleaning census codes such as −999
- Line, stacked and box-and-whisker charts
- Euclidean distance from eastings and northings
Building a First Map in QGIS
Lab 2: project CRS, imports and joins, cleaning a GeoPackage, convex hull, buffer and print layoutLab 2 is the first map.
- The QGIS window and Project CRS
- Importing vector, CSV and raster data
- Table joins on a shared ID field
- Exporting and cleaning a GeoPackage
- Minimum bounding geometry and convex hulls
- Buffers and temporary layers
Buffers, Overlays and Population Density
Lab 3: buffer, intersect and aggregate, spatial joins, population density choropleth and inset mapsLab 3 strings the vector tools together.
- Buffer, intersect and aggregate
- Clip, intersect and difference compared
- Table joins versus spatial joins
- Filtering suburbs with an expression
- Area with $area and population density
- Graduated symbols and labelled filters
Spatial Analysis, Autocorrelation and Models
Tobler's first law, dispersed, random and clustered patterns, correlation and causation, species distribution modelsSpatial analysis turns layered data into explanation.
- Definitions of spatial analysis
- Layering as the first step
- Tobler's first law and distance decay
- Dispersed, random and clustered patterns
- Autocorrelation is not an error
- Correlation versus causation
Remote Sensing and Raster Analysis
Platforms, electromagnetic radiation, the four resolutions, image enhancement, sea level and Lab 4 slope analysisRemote sensing measures electromagnetic radiation from a surface without touching it.
- Ground, sky and space platforms
- Electromagnetic radiation and reflectance
- Active and passive sensors
- Spatial, spectral, temporal and radiometric resolution
- Histogram stretching and enhancement
- Satellite altimetry and sea-level rise
Representation, Ownership and Data Ethics
Why maps are political, data origins, corporate control, bias and consent, and the reflection questionsThe last part of the course asks why representations of a Digital Earth are political and why owning it matters.
- Why representations are political
- Borders, names and omission
- Maps as constructions revisited
- Three levels of data origin
- Corporate control and data bias
- Surveillance, consent and de-anonymisation
The course asks four questions: what our digital earth is, why spatial data are important, how data are measured and represented, and what contextual factors influence those data. It answers them by teaching GIS, mapping, remote sensing, modelling and big-data analytics with GeoAI, alongside the limits of the data and the way quantitative and qualitative evidence support decisions. The lecture series runs in three parts.
Maps and data cover space, place and scale, map projections and New Zealand’s NZTM2000 coordinates, cartographic design, data classification, vector and raster models, data collection and quality, including the Modifiable Areal Unit Problem. Essential methods cover spatial analysis and autocorrelation, remote sensing from ground to space, navigation and GNSS, the art of modelling and AI applications to remote sensing.
The final part places Digital Earth in political and economic context, asking why representations are political and why owning the data matters.
Alongside the lectures, five lab sessions build practical skill: wrangling wastewater, census and housing data in Excel; a first QGIS map of Auckland’s volcanic field; buffers, overlays and a population-density choropleth; and raster analysis of elevation and slope on Waiheke Island, with the last session a helpdesk for the group poster.
Assessment is laboratories, an in-lab test, weekly lecture quizzes, a group poster on a real-world spatial problem and an individual reflection on the ethics of the poster’s data. This guide follows that structure chapter by chapter, works an example with real arithmetic in every chapter, and ends with checklists for the poster and the reflection.
Worked example · free
How far apart are the Māngere and Oneroa treatment plants?
- +1Difference in easting: 1,778,490.68 − 1,757,733.35 = 20,757.33 m.
- +1Difference in northing: 5,927,764.55 − 5,907,257.31 = 20,507.24 m.
- +1Distance = √(20,757.33² + 20,507.24²) = √(430,866,749 + 420,546,892) = √851,413,641.
- +1√851,413,641 ≈ 29,179 m, about 29.2 km, valid because NZTM2000 coordinates are projected metres.
Key terms
- Geographic Information System
- A Geographic Information System is software and method for storing, analysing and mapping spatial data.
- NZTM2000
- NZTM2000 is New Zealand Transverse Mercator 2000, the national projected coordinate system, EPSG 2193.
- Projection
- A projection is a method of representing Earth's curved surface on a flat map, always with some distortion.
- Choropleth map
- A choropleth map shades defined areas by a rate or category for each area.
- Natural breaks
- Natural breaks is a classification that places class boundaries at large jumps in the data.
- Raster
- A raster is a grid of cells, each storing one value for the area it covers.
- Vector layer
- A vector layer stores discrete features as points, lines or polygons with attributes.
- Buffer
- A buffer is the zone within a set distance of a feature.
- Spatial join
- A spatial join transfers attributes between layers according to their locations.
- Spatial autocorrelation
- Spatial autocorrelation is the similarity, or dissimilarity, of values according to how near they are in space.
- MAUP
- The MAUP, or Modifiable Areal Unit Problem, is the bias introduced when results depend on the chosen aggregation units.
- Remote sensing
- Remote sensing is measuring radiation reflected or emitted by a surface from a distance.
- GNSS
- GNSS, or Global Navigation Satellite System, is any satellite network, such as GPS, used to fix positions.
ENV103 FAQ
How is the course assessed?
Through coursework and one in-lab test. Laboratories carry 40%, the in-lab test 30%, lecture quizzes 10%, the group poster 15% and the individual reflection 5%, as listed on the Assessment Overview page. You need an aggregate of at least 50% across the groups to pass, so steady lab work matters more than any single task.
What was the in-lab test like?
It ran in Week 8 in your normal lab stream slot: one hour, in person, closed book on a computer and invigilated, with no internet, translator apps or GenAI. It tested the first two labs. The ungraded practice test released in Week 7 used an Excel bar chart, a QGIS map, short multiple-choice items and a definition question.
How are the lab assignments marked?
Each of the four labs is worth 10%. Part of each mark comes from an exercise handed in at the end of the session, and the rest from the proforma submitted as a PDF, where short interpretation questions sit alongside finished maps or charts. In each of Labs 1 to 3 the finished graphs or maps are the single largest item.
What does the group poster involve?
Groups choose a topic from the teaching team's menu, urban greenness and equity, hiking suitability, flood risk and housing, or Auckland transport, and produce an A2 poster with research questions, three key papers, at least two maps using different techniques, three supporting figures, conclusions, a contributions section and references. Adding datasets beyond those supplied is rewarded.
Is this course hard for first-year students?
The course materials point to three pressures: each lab builds on the software skills of the one before, the in-lab test worth 30% was timed at one hour without internet access, and the expected workload is about ten hours a week. The Start Here page also warns that putting off lectures to watch later tends to lead to problems.
Do I need prior GIS or coding experience?
The labs start from first steps, opening Excel to sort and filter data and then setting up a first QGIS project, and they are written for beginners with video walkthroughs. A little arithmetic is needed for distances, areas and class widths, but the methods are taught in the labs and in this guide.
Which software does the course use?
Microsoft Excel for organising data and building charts in Lab 1, and QGIS, the open-source GIS, for mapping, overlay and raster analysis from Lab 2 onwards. The lab computers run the QGIS long-term release, and the NextGIS QuickMapServices plugin supplies basemaps.
Can I use AI tools in this course?
The course follows the Faculty of Science two-lane approach. The in-lab test was controlled, lane 1, with no GenAI allowed. The lecture quizzes, lab assignments, poster and reflection are uncontrolled, lane 2, so AI may assist, but you are responsible for everything you submit. Sia can explain classifications, joins or projections step by step while you learn.
Questions students ask in each ENV103 chapter
- What is the difference between space and place?
- Space concerns position and measurement: coordinates, distances and directions that anyone can reproduce. Place adds what a location means to people, including its history, its names and its social character. A park has one set of coordinates but can be a refuge, a sports ground or a contested site, depending on who you ask. Chapter 01 · Spatial Thinking: Space, Place and Scale
- Why does Greenland look as big as Africa on many maps?
- Those maps use Mercator or Web Mercator, which preserve direction but enlarge areas increasingly with distance from the equator. Greenland lies far north, so it is drawn far larger than its true size relative to Africa, which straddles the equator. Equal-area projections such as Gall-Peters remove that inflation at the cost of shape. Chapter 02 · Map Projections, Datums and Coordinate Systems
- What does it mean that maps are constructions?
- A map is made by someone for a purpose, so every map selects some features, leaves others out and symbolises what remains. It presents a view of the world rather than reproducing it. Recognising that helps you read maps critically and explain your own design choices honestly. Chapter 03 · Maps as Messengers: Cartographic Design
- How many classes should a choropleth have?
- Around five to seven usually works. The lecture draws on Miller's magical number seven: people struggle to separate more than about seven shades at a glance. The Week 3 apps show three classes hiding real variation in Auckland's density and ten classes being too many to tell apart. Chapter 04 · Classifying Data for Thematic Maps
- When should I use raster rather than vector data?
- Use raster for phenomena that vary continuously across space, such as elevation, temperature or slope. Use vector for discrete features with clear edges, such as bus stops, roads or suburb boundaries. Satellite images are rasters by nature, while most administrative data arrive as vectors. Chapter 05 · Vector and Raster Data Models
- What is the Modifiable Areal Unit Problem?
- It is a bias that appears when individual data are aggregated into arbitrary areas. The same data and method can give different results if the units change size, the scale effect, or if their boundaries are redrawn, the zoning effect. Gerrymandering is the zoning effect used deliberately. Chapter 06 · Data Collection, Accuracy and the MAUP
- How do I calculate distance from easting and northing in Excel?
- Take the difference in eastings and the difference in northings between the house and the plant, square both, add them and take the square root. Because NZTM2000 coordinates are in metres, the result is a straight-line distance in metres. Fix the plant cells with dollar signs before filling the formula down. Chapter 07 · Wrangling Geospatial Data in Excel
- Why did my buffer disappear after I reopened the project?
- Processing outputs start as temporary layers. Saving the project does not save them, so they vanish when the project closes. Right-click the layer and choose Make Permanent, or export it, straight after running the tool. Chapter 08 · Building a First Map in QGIS
- What is the difference between clip and intersect?
- Both keep only the overlapping area, but clip returns the input layer's features and attributes alone, while intersect combines features and attributes from both layers. That is why the volcano analysis uses intersect: each piece needs the volcano's name and the census population together. Chapter 09 · Buffers, Overlays and Population Density
- What is Tobler's first law of geography?
- Everything is related to everything else, but near things are more related than distant things. It underpins spatial autocorrelation and explains why neighbouring areas often share characteristics, which the lecture illustrates with a map of the Index of Multiple Deprivation. Chapter 10 · Spatial Analysis, Autocorrelation and Models
- Which four resolutions describe remote-sensing data?
- Spatial resolution is the ground size of a pixel, spectral resolution the number and width of wavelength bands, temporal resolution how often a place is imaged, and radiometric resolution how finely brightness is recorded. They interact, so improving one often means accepting less of another. Chapter 11 · Remote Sensing and Raster Analysis
- Why are maps described as political?
- Maps decide what is shown, named and left out, and those choices carry power. A border can appear differently depending on who views a web map and from where, names can record a colonial view, and omission can make communities invisible. Reading a map critically means asking whose view it constructs. Chapter 12 · Representation, Ownership and Data Ethics
How to prepare for the assessments
Treat the course as two linked tracks. The concept track, Chapters 1 to 6 and 10 to 12, is best learned through short written explanations: after each lecture, write three or four sentences that define the week’s key idea and give an Auckland example, because the quiz and lab interpretation questions reward exactly that. The practical track, Chapters 7 to 9 and the Lab 4 material in Chapter 11, is learned only by doing.
Rebuild each lab from a blank project with the instructions closed, save after every major step, and make temporary layers permanent straight away. For every map, state the coordinate system, the classification method and the units, and check the mandatory elements before export.
With the poster and reflection still ahead, plan backwards from the deadlines: settle the research questions and data first, divide the map work by strength, and leave time to read the rubric against the finished layout. For the reflection, list every dataset you used and its origin before drafting any dot point.