Midterm #1 Review Flashcards
Cartography
the art, science, and technology of making, using, and studying maps
Reference maps
focused on accurate description of landscape, no emphasis on any one feature (topographic maps)
Thematic maps
result of some spatial analysis, communicating spatial distribution of phenomena (ex. graph shows emphasis on vegetation type)
What is classified as a map?
communicating information regarding spatial data
Definition of a map according to Muehrcke, 1978
“A map is any geographical image of the environment”
Definition of a map according to Burough, 1987
“A map is a set of points, lines, and areas that are defined by their non-spatial attributes”
Babylonian world map
600 BC clay tablet, flat earth, 7 islands, parallel vertical lines are euphrates river, 8 cities (circles) surround Babylon
Ptolemy’s world map
first example of a projection map (lat-long coordinate system), 150 AD, influenced Islamic cartography through 15th century and European cartography towards the end of this period
Medieval Christianity (Europe)
biblical explanations of geography, 1300s AD T-O Mappaemundi, world depicted as a letter T (land masses Asia, Europe, Africa) within an O (worlds water/ oceans), used for dominance and power reasons
10th century Arabia
greek, persian, Indian geographical lore. Islamic tenets, Al Wardi’s (1457 AD) world map
Yo ji tu (12 century Asia)
functional and scientific methods, time of the compass, etched in stone then rubbed onto paper
- more focused on trade, governance, navigation
Age of Exploration: (15th to 16th century)
- interest maps spurred by European subjugation of new lands and ppl
- re-discovery of Ptolemy’s geography introduced projections to renaissance mapmakers
- new instruments + printing processes
- professional class of mapmakers began to emerge
Maps for colonization: late 16th to late 18th century
- increased use of maps by officials, soldiers, clergy, scientists, middle class merchants
- maps as tools of commerce and government, towards colonization
- expanded role of science in cartography
- continued role of visual arts in cartography
Mercator’s projection and direction
mercator’s projection does not preserve size or distance, it does preserve direction
Surveys and Thematic cartography, for the nation: late 18th to 19th century
- western mathematical style spreads throughout world = standardized techniques, symbols, projections, scales
- traditional cartography does continue, some hybrids
- thematic cartography: distributions of phenomena
More maps for more people: 20th century
- maps readily available to general population
- new methods for data acquisition and printing (printing maps became cheaper, mass production)
- military fuels interest in maps and world geography (war)
Digital Mapping: 1970 - present
- daily imaging of earth’s surface
- global positioning system (GPS)
- PCs, internet, GIS (most maps created by non-cartographers), crowdsourcing
Global Positioning System (GPS)
current location, real-time updates, data may be and mined to create value-added apps
How can remote sensing instruments image the earth?
they can image the earth at various spectral, spatial, and temporal resolutions:
- spatial: size of pixel
- temporal: how often does spacecraft pass?
- Spectral: wavelength of energy measured
Predictive policing
spatial analysis of arrest data to target policing activity
- potential ethical problems: arrest data is not crime data, arrest data is racialized/biased, history does not necessarily repeat itself
Key changes in maps over time
- REFERENCE mapping for baseline info and navigation, THEMATIC to learn about phenomena
- New tech (compass, computer) and new social org. (bureaucracies, divisions of labor)
- maps wield power for govs., militaries, BUT increasingly made/read by gen. pub.
GeoAI
- AI fused with geospatial data, science, and tech. for real world understanding
- ex. “foresters and landowners use GeoAI to give them knowledge about volumes and species of trees without a time-consuming on-site inspection”
Development of GIS: Historic use of multiple theme maps
- Series of base thematic maps to portray layers of data
- Mapping cholera in central London (1854)
→ Dr. John Snow - identified a contaminated well by mapping incidents of infection
→ early example of spatial analysis
First Law of Geography
Everything is related to everything else, but near things are more related than distant things
- Waldo Tobler, 1970
Limitations to paper maps
- Difficult and expensive to store, retrieve and update
- Difficult to perform quantitative analysis
Canada Geographic Information System (CGIS)
- Produced stats to aid development of land management plans over large rural areas
- Major technical innovations: internet data structure, overlay and area measurements, scanner for map input
- Used in the 90s but could not compete with other vendors
What is a GIS?
1) A database: system in which most of the data are spatially indexed, and upon which a set of procedures can be operated in order to answer queries (Smith et al., 1987)
2) Tools: GIS is a powerful set of tools for collecting, storing, retrieving at will, transforming and displaying spatial data from the real world (Burrough, 1986)
3) People: An automated set of functions that provides professionals with advanced capabilities for the storage, retrieval, manipulation and display of geographically located data (Ozemoy et al., 1981)
Data models
set of rules and/or constructs used to describe and represent aspects of the real world in a computer
The vector data model
vector model can be outdated (tree falls), using lines for rivers does not explain how water flows there, do not know width/depth
Choropleth map
uses colour to show spatial distance of some phenomenon
Point numbers
are feature IDs that have coordinates
Polygons
will have closing coordinates (closed shape)
Topology
set of rules that model relationships between neighbouring points, lines, and polygons and determines how they share geometry
Dimensionality
- feature class: collections of geographic features
- graph structure: nodes (to and from points), faces (area), edges/arcs (borders)
Why does topology matter?
- data quality (topological errors)
- spatial analysis
→ how shapes relate to one another
→enables certain types of spatial analysis to occur
Table analysis - attribute queries
- filter attribute data based on queries
- queries are structured ways of asking questions about data
SQL
AND: only the alike traits (venn diagram middle part)
OR: inclusive, everything combined (entire venn diagram)
XOR: either A or B, not both (only A or B on diagram)
NOT: exclude specific conditions (everything but A and B covered)
Table joins
- how you import and build geospatial data
- join additional attribute tables to feature classes via a key field column
→ need at least one key field (key fields must have some overlap)
→ key fields do not need the same name, just matching type & values
Characteristics of vector data
- Real world entities rep. by Points, Lines, Polygons
- Only polygons cover an areal extent
- Only features are encoded (not space between)
Feature class
a table with a shape field containing point, line, or polygon geometries for geographic feature (each row is a feature)
Raster dataset
contains rasters which rep. continuous geographic phenomena
Rasters grid the world
images are a form of raster (zoomed in pixelation)
→ colour is stored in each pixel, coded as wavelength
How are rasters structured?
Each cell (basic unit of all rasters) has a value:
- Integers
→ represent a category
→ An integer representing e.g. land use
→ e.g. 51 might mean “cropland”
→ Integer-based rasters have
attributes (zones – areas with
equal values) - NoData (#)
→ raster data can show that nothing is between - Float/double (decimals)
→ Magnitude (precipitation)
→ Height (elevation)
→ both continuous
How are rasters structured?
square cell is typically equal in size on each side
Advantages of storing data as a raster
- A simple data structure—A matrix of cells with values
representing a coordinate and sometimes linked to an
attribute table. Native structure for satellite imagery (grid
of pixels). - Basemaps!
- The ability to perform fast overlays with complex datasets
- A powerful format for advanced spatial and statistical
analysis - The ability to represent continuous surfaces (elevation,
temperature, rainfall, etc.)
Continuous phenomena
- boundaries are not easily defined, often some reference or relation to a base
- ex. elevation (masl), soil types, edges of forests, boundaries of wetlands
Discrete phenomena
has known precise and definable boundaries
Elevation data
- elevation is a continuous phenomenon
- we get data from surveying, lidar, public agencies, etc.
- use it for basemapping, topography, slope, aspect, hillshade, water drainage, etc.
Applications: satellite and drone imagery
- Satellite imagery = imagery from satellites like Landsat
- “bands” corresponding to different wavelengths
- remote sensing
- use for viewing land cover, land use
- turning pixel values (which indicate light reflected back to space) into categories of land cover/use
Applications: land cover / land use
- LC = classification of physical, ecological, and human made features covering the earth (forest, wetland, water)
- LU = interpretation of what human activities occur (forestry, conservation, parking lot)
- satellite imagery → classification
- for conservation planning
What is the difference between cell values representations based on area in cell?
- Centre points: cell value represents a measured value at centre point of the cell. Example: raster of elevation.
- Entire area of cell: cell value represents a sampling of a phenomenon, and the value is presumed to represent the whole cell square.
Challenges with raster data
- Mixed pixels: a problem in converting data
- A question of scale and resolution, but is not fully
solved with greater resolution - Solution: emphasize feature of interest (water in this
case), make >50%, create another class (ex. edges)
Modelling real world entities: Raster vs. vector
- Factors: data availability, what you’re trying to represent
→ Raster: continuous surfaces (ex. Land cover)
→ Vectors: networks (ex. roads), discrete areas (ex.
Provincial boundaries), multiple attributes (ex. COVID
cases, pop in province) - Computing constraints (ex. File size)
Map generalization
refers to the process of resolving conflicts associated with too much detail, too many features, or too much information to map
Generalization methods
Simplification
1. Nth point
2. angle threshold
3. distance threshold
4. douglas-peucker method
The nth point algorithm
- Eliminate every nth vertex along the line.
- If N = 2, then every second vertex is deleted.
The angle threshold algorithm
eliminates a vertex if the interior angle at the vertex is greater than a specified tolerance
The distance threshold algorithm
eliminates any vertex that is closer to the preceding vertex than a specified tolerance distance, t
Douglas-Peucker algorithm
- Join the first and last vertices (FL)
- Draw perpendicular line (P) from FL to intermediate vertex (I) furthest from FL
- If P < tolerance (T), then
→ Delete all vertices between first and last vertices
→ New line segment connects first and last - If P > T, then
→ Keep vertex I
→ Split original line at I & repeat process for new line segments
Map scale
- refers to the factor of reduction of the world so it fits on a map
- ratio of map distance to earth distance
→ map scale = (map distance) / (earth distance)
Large scale
~ 1:30,000 or larger
- detailed data, small portion of earths surface
- small mapped area
- more detail
- zoomed in
Small scale
~ 1:300,000 or smaller
- generalized data, large portion of earths surface
- less detail
- zoomed out
Coordinate systems
- Coordinate systems are frameworks that are used to define unique positions
- For instance, in geometry we use x (horizontal) and y (vertical) coordinates to define points on a 2D plane
- The coordinate system that is most commonly used to define locations on the three-dimensional earth is called the geographic coordinate systems (GCS)
Components of Geographic Coordinate System
- angular measurements
- origins (centre of earth, Greenwich meridian)
- datum (shape of earth & anchor to it)
Science of earth measurement
- earth is not flat
- because gravity, earth is actually a big lumpy mass that we call a geoid
- as such the earth is not a sphere but in many cases we might treat it as a spheroid/ellipsoid
Earth as an ellipsoid
- Earth’s rotation and resulting centrifugal forces make it not a perfect sphere
- a does not equal b
a = semi major axis (equatorial radius)
b = semi minor axis parallel to the rotational axis of the earth (polar radius)
Earth as a geoid
- Geoid: shape of the earth if we got rid of land and extended the sea
- Not an ellipsoid!
- Sea level would not be the same everywhere
- Not noticeable, but affects precise survey work
Climate change = changes to earth’s shape
- As we warm the planet, we are melting glaciers and ice sheets
- The shift in mass has an impact on the configuration of the planet.
- “That mass on the surface of Earth pushes down on Earth and actually changes its shape,” said Dr. Davis of Columbia University
- In effect, through climate change, our species is altering gravity across the planet.
Earth as an ellipsoid vs. geoid
“Reference” ellipsoids are commonly used as surrogates for geoids so as to simplify the mathematics involved in relating a coordinate system grid with a model of the earth’s shape
Datum orient ellipsoids to geoid
- Datums model the size and shape of earth through reference ellipsoids
- They also anchor reference ellipsoids to earth’s geodetic surface via well-measured control points
- ex. NAD27, NAD83, WGS84
Implication
datums with different reference ellipsoids will have incompatible coordinates; different places on earth will have different coordinates depending on the ellipsoid
Datum shift
same coord’s but different datums = different places
What is a map projection?
Map projections are used to
transform the spherical 3D earth (coordinates) onto 2D
planar surfaces.
Types of projection
- Gnomonic: azimuthal projection that uses centre of earth as perspective point
- Stereographic: perspective projection of sphere onto the plane
- Orthographic: azimuthal perspective projection onto a tangent or secant plane
Rhumb lines
- lines of constant bearing
- your relation to north is always the same
- in mercator projection, all straight lines are rhumb lines
Great circle
- shortest distance between two points, typically changing relation to north
- in gnomonic projection, all straight lines are great circles = lines of constant direction
Developable surfaces
plane, cylinder, cone
3 families of map projections
- azimuthal (planar)
- cylindrical
- conic
Universal Transverse Mercator
- use for study areas with small east-west extent (<6º longitude)
- works by cylinder oriented with equator
Equal interval classification
- size of each class is equal
- works well when data range is equally populated
Quantile classification
- equal # of classification per category
- log data
Spatial dimensions
- zero: points
- one: lines
- two: areas/polygons
- three: elevation
Geographic attribute data
- observed or measured facts about phenomena
- can be used to draw conclusions
Nature of data: Accuracy
the extent to which attributes correspond to their real world counterparts
- how true the data is
Nature of data: precision
- the detail with which attributes are represented
- how detailed the data/measurements are
Attribute data levels of measurement
Categorical: nominal and ordinal
Numerical: interval and ratio
