GEOG 380 - Short Answers

What is a horizontal datum and what is a vertical datum as they relate to
mapping and GISc? Describe the different components of the datums. What
are the different types of datums? Provide examples. Diagrams may help
you answer this question

Horizontal datum

A horizontal datum defines positions on Earth’s surface in the horizontal direction: latitude, longitude, eastings, and northings. It answers the question: “Where is this feature on Earth?”

A horizontal datum usually includes several components:

1. Reference ellipsoid
   A smooth mathematical model of Earth’s shape, slightly flattened at the poles.

2. Origin and orientation
   Defines where the coordinate system begins and how it is aligned to Earth.

3. Geodetic control network
   A set of accurately surveyed points used to anchor the datum to the real Earth.

4. Coordinate system
   The system used to express locations, such as latitude/longitude or UTM eastings and northings.

Examples of horizontal datums include NAD27, NAD83, and WGS84. WGS84 is commonly used by GPS.

Vertical datum

A vertical datum defines elevation or depth. It answers the question: “How high or low is this feature?”

A vertical datum usually includes:

1. Reference surface
   Often mean sea level, the geoid, or a tidal reference surface.

2. Benchmark network
   Physical points with known elevations.

3. Height system
   Defines what type of height is being measured, such as orthometric height or ellipsoidal height.

Examples include CGVD28, CGVD2013, and NAVD88.

A key distinction is that GPS gives height relative to the ellipsoid, while many maps and DEMs use height relative to the geoid, which approximates mean sea level.

Types of datums

There are several major types of datums:

1. Local datums

A local datum is designed to fit one region very well. It may not align perfectly with the rest of the world.

Example: NAD27, based on the Clarke 1866 ellipsoid and used historically in North America.

2. Geocentric/global datums

A geocentric datum is centred on Earth’s centre of mass and is useful for global positioning systems.

Example: WGS84, used by GPS.

3. Horizontal datums

These define horizontal position.

Examples: NAD83, WGS84, NAD27.

4. Vertical datums

These define elevation or depth.

Examples: CGVD2013, NAVD88, mean sea level, chart datum.

Describe the difference between raster and vector data models as they are
used in GISc. What are the ‘building’ blocks of each data model? How are
attributes used in each model? What are the strengths and weaknesses of
each data model? Diagrams may help you answer this question.

Raster vs. vector data models in GISc

In GIS, raster and vector are two main ways of representing geographic information.

A raster data model represents space as a grid of cells or pixels. Each cell has a value that represents some attribute of the area covered by that cell. Raster data is commonly used for continuous surfaces such as elevation, temperature, precipitation, slope, land cover, or satellite imagery.

Raster model

+---+---+---+---+

| 5 | 5 | 6 | 7 |

+---+---+---+---+

| 4 | 5 | 6 | 7 |

+---+---+---+---+

| 3 | 4 | 5 | 6 |

+---+---+---+---+

A vector data model represents geographic features using discrete geometric objects: points, lines, and polygons. Vector data is commonly used for features with clear boundaries, such as roads, buildings, rivers, property parcels, cities, or administrative boundaries.

Building blocks

The building blocks of a raster are cells/pixels. Each cell has a location based on its row and column in the grid, and each cell stores a value.

The building blocks of a vector are:

Points: single x-y coordinate locations, such as wells, cities, or sample sites.
Lines: connected points, such as roads, rivers, or faults.
Polygons: closed lines enclosing an area, such as lakes, land parcels, or soil units.

Attributes

In a raster, attributes are stored as cell values. For example, in a DEM, each cell value may represent elevation in metres. In a land-cover raster, each value may represent a class, such as forest, water, urban, or agriculture.

In a vector model, attributes are stored in an attribute table. Each feature has a row in the table, and the columns describe properties of that feature. For example, a road line may have attributes such as road name, speed limit, surface type, and road class.

Strengths and weaknesses

Raster strengths: Raster data is very good for continuous data and surface analysis. It works well for imagery, elevation models, slope, aspect, suitability modelling, and map algebra. Raster overlay is often simple because cells line up in a grid.

Raster weaknesses: Raster data can require large file sizes, especially at high resolution. Boundaries may look blocky or pixelated, and accuracy depends heavily on cell size. Raster is less ideal for representing precise features such as property lines or road centre lines.

Vector strengths: Vector data is very good for discrete features with clear boundaries. It can represent shapes precisely and usually has smaller file sizes for simple features. It is well suited for networks, cadastral mapping, roads, boundaries, and detailed attribute tables.

Vector weaknesses: Vector data is less ideal for continuous surfaces. Overlay and spatial analysis can be more complex because points, lines, and polygons must be geometrically processed. It may also have topology errors, such as gaps, overlaps, or disconnected lines.

Summary

Raster data represents the world as cells in a grid, while vector data represents the world as points, lines, and polygons. Raster is strongest for continuous surfaces and imagery, while vector is strongest for discrete objects with clear boundaries. In GISc, the best choice depends on the type of phenomenon being mapped and the type of analysis being performed.

Scale and resolution are two important interrelated concepts in GIScience. Describe the different ways spatial scale is used in GIScience. What are the different types of resolution and how do they relate to spatial scale?

Scale and resolution in GIScience

Scale describes the relationship between the real world and its representation in GIS or on a map. Resolution describes the level of detail that can be detected or represented. They are related because larger-scale or higher-resolution data usually shows more detail, while smaller-scale or lower-resolution data shows less detail.

Different uses of spatial scale

Map scale is the ratio between distance on the map and distance on the ground. For example, a map scale of 1:10,000 is a large-scale map because it shows a small area in high detail. A map scale of 1:1,000,000 is a small-scale map because it shows a large area in less detail.

Observation or measurement scale refers to the scale at which data were collected. For example, a GPS survey, an aerial photo, and a satellite image may all measure the same area but at different levels of detail.

Analysis scale refers to the scale used for GIS operations. For example, analyzing vegetation patterns at the scale of individual trees gives different results than analyzing vegetation by entire forest stands or regions.

Operational scale refers to the real-world size of the process being studied. Some processes occur at local scales, such as soil erosion on a slope, while others occur at regional or global scales, such as climate patterns.

Cartographic scale refers to how features are generalized for display. At smaller map scales, features are simplified, merged, or removed so the map remains readable.

Types of resolution

Spatial resolution is the ground area represented by one pixel or cell in a raster. For example, a 1 m raster can show much finer detail than a 30 m raster. Spatial resolution is closely related to scale because finer resolution supports larger-scale, more detailed mapping.

Spectral resolution is the number and width of wavelength bands recorded by a sensor. For example, a multispectral image may have a few broad bands, while a hyperspectral image has many narrow bands. Higher spectral resolution allows better separation of materials such as vegetation, water, and soil.

Temporal resolution is how often data are collected for the same place. For example, a satellite that images an area every day has higher temporal resolution than one that images it every 16 days. This matters for monitoring change through time.

Radiometric resolution is the sensor’s ability to detect small differences in energy or brightness. It is often described by bit depth. For example, 8-bit data have 256 possible values, while 16-bit data have many more possible values.

Thematic resolution refers to the level of detail in attribute categories. For example, a land-cover map with classes like “urban,” “forest,” and “water” has lower thematic resolution than one with categories like “deciduous forest,” “coniferous forest,” “low-density urban,” and “high-density urban.”

Relationship between scale and resolution

Scale and resolution are closely connected, but they are not exactly the same. A large-scale map should use high-resolution data because it shows more detail. A small-scale map can use lower-resolution data because fine details may not be visible or useful.

For example, a 1 m DEM is appropriate for detailed local analysis, such as slope around a road or building site. A 30 m DEM may be better for regional terrain analysis because it covers a larger area with less detail.

Large scale / high resolution:

1:10,000 map → small area → high detail

Small scale / low resolution:

1:1,000,000 map → large area → low detail

In GIScience, scale can refer to map scale, measurement scale, analysis scale, process scale, or cartographic scale. Resolution describes the detail of spatial, spectral, temporal, radiometric, or thematic information. In general, larger-scale studies require higher-resolution data, while smaller-scale studies use more generalized, lower-resolution data.

Name and describe the 8 different elements of a map. Indicate how they would rank (1 = most important and 8 = least important) in a visual hierarchy.

Rank	Map element	Description
1	Map area / content area	The main part of the map where the geographic information is shown. This is the most important element because it communicates the main spatial message.
2	Title	Tells the reader what the map is about, including the topic, location, and sometimes time period.
3	Legend	Explains the symbols, colours, line types, and categories used in the map area.
4	Scale	Shows the relationship between distance on the map and distance in the real world, often as a scale bar or representative fraction.
5	Orientation	Shows direction, usually with a north arrow. It is especially important if north is not at the top.
6	Inset map	Provides extra location context or shows a smaller area in more detail.
7	Data source	Identifies where the data came from, and may include author, date, projection, and datum information.
8	Frame line / neat line	A border around the map or layout that organizes the design and separates the map from the page background.

A strong visual hierarchy means the map area/content area should be the most visually dominant, because it contains the main information. The title and legend should also be easy to find because they explain what the map shows and how to interpret it. Elements such as scale, orientation, inset map, data source, and neat line are important, but they should be less visually dominant so they support the map without distracting from it.