GISCI 241 Chap.2 Spatial Data, Scale and Projections
Spatial Data, Scale and Projections
Spatial analysis depends on how the world is represented. Raster stores values in a continuous grid and supports surface operations and map algebra. Vector stores discrete points, lines and polygons with attributes and relationships. This chapter also separates accuracy from precision, shows how scale changes visible patterns, and explains why every flat map projection distorts at least one spatial property.
The correct model and coordinate reference come from the phenomenon and measurement, not from a default file type or basemap.
What this chapter covers
- 01
Raster cells, values, layers and map algebra
- 02
Vector geometry, topology and attributes
- 03
Accuracy, precision, error and uncertainty
- 04
Scale, aggregation and false spatial precision
- 05
Geographic and projected coordinate systems
- 06
Conformal, equal-area and UTM decisions
Worked example · free
Build a mixed flood workflow
- +1Use raster thresholding for low terrain because elevation is a continuous field sampled across every cell.
- +1Overlay a flood extent with property polygons to identify intersected parcels and state the partial-intersection rule.
- +1Use connected river-centreline geometry for along-channel distance rather than straight-line raster distance.
- +1Document coordinate reference, cell support, boundary uncertainty and any raster-vector conversion.
Key terms
- Raster
- A continuous grid of cells in which each location stores a value or band stack.
- Vector
- A spatial model using points, lines and polygons linked to attributes and relationships.
- Topology
- Spatial relationships such as connection, adjacency and containment among vector features.
- Accuracy
- Closeness of a measurement or derived position to an accepted reference value.
- Precision
- Repeatability or fineness of measurement, which can be high even when a result is biased.
- Projection
- A mathematical transformation from Earth's curved coordinate surface to a flat plane.
- UTM zone
- One of the longitudinal zones used by the Universal Transverse Mercator coordinate system.
Spatial Data, Scale and Projections FAQ
When should I use raster instead of vector?
Use raster when the phenomenon varies continuously across space or when cell-by-cell arithmetic is central, such as elevation, temperature or multispectral imagery. Use vector when discrete objects, exact geometry, attributes or network relationships dominate. Many workflows combine both, but every conversion changes support, boundaries or interpolation.
Can data be precise but inaccurate?
Yes. Repeated observations can cluster tightly while all are displaced from the accepted location. That pattern is high precision with low accuracy. Extra decimal places also create numerical precision without proving observational accuracy. Check reference control, datum, calibration and transformation before trusting a finely printed coordinate.
Why does projection choice matter for analysis?
Flattening Earth inevitably distorts distance, area, shape or direction. Choose the property required by the question and a projection suited to the study extent. Equal-area supports area comparisons, while conformal preserves local angles. On-screen alignment from automatic reprojection does not remove measurement distortion or datum uncertainty.
Why can two aligned layers still produce a wrong measurement?
Software may project layers on the fly so they look aligned even when datum, coordinate reference, support or source accuracy differ. A valid measurement requires compatible reference systems, suitable distortion properties and an uncertainty statement tied to the original observations. Visual coincidence on screen is therefore a diagnostic clue, not sufficient evidence of analytical compatibility.
Exam move
Build a two-column raster versus vector table from operations rather than definitions. Add continuous surface, discrete object, map algebra, topology, storage, boundary behaviour and conversion loss. Next draw four target diagrams for accurate and precise, accurate and imprecise, inaccurate and precise, and neither.
For projections, attach one decision question to each property: area comparison, local angle, distance route or direction. Practise identifying the coordinate reference and unit before calculating anything. When reviewing a map, ask whether the apparent precision comes from the observation, the transformation or merely the display.
Close every method answer with the support area and uncertainty inherited from the least reliable input. Work three conversion cases: turn a shoreline vector into a raster, contour a continuous elevation raster, and aggregate a fine grid to coarser cells. For each case, name the rule that decides the new values and one feature that may be lost.
Create a metadata checklist containing coordinate reference, datum, units, resolution, extent, acquisition date and transformation history. Apply it before opening any measurement tool. Then practise projection choices by writing the property to preserve before naming a projection family.
An area-comparison problem should lead to equal-area reasoning, while a local angular problem points toward conformal reasoning; neither decision comes from which map looks familiar. For accuracy and precision, describe the pattern first and the cause second. A tight displaced cluster is precise but inaccurate, and extra decimal places cannot remove the displacement.
Finish with a mixed-data workflow that combines an elevation raster, river line and parcel polygons. Explain why each model suits its operation, how overlay or conversion connects them, and which input limits the precision of the result. This integrated exercise mirrors the way later change and drone analyses combine data structures.