3: Building Blocks of GIS Flashcards
WHAT IS GEOGRAPHIC INFORMATION
Information about stuff on the earth’s surface
WHERE, WHAT and perhaps WHEN
Examples: landform, soil, geology, attributes, property
also… data, info, knowledge
two types: geometric vs attribute
LIST 5 BASIC GEOMETRIC PROPERTIES OF FEATURES
Points, lines, polygons, network, volume
GIVE EXAMPLES OF GIS SPATIAL MODEL TYPES
Raster and Vector
Data model is an abstraction of the real world, which one you use depends on what you are modelling
raster for continuous data
vector for discrete data
GIVE EXAMPLES OF GIS DATABASE STRUCTURES (3)
Data Structure: how the data is stored, what does the file structure look like
Relational database model - attribute files and geographic data are stored in different files
ex: SQL structure query language used to query the data in a relational database
Hierarchical database model - like a family tree, 1 to 1 or 1 to many
Network database model - many to many but time consuming
LIST USES FOR A DIGITAL ELEVATION MODEL
modelling features visibility assessment slope aspect hillshade identify route of landslide or flooding
What is fuzzy logic?
Some geographic features change so are difficult to accurately represent… ex: water level of a lake
What is the importance of scale when representing features
Everything we represent in GIS is based on the scale of the data, a city could be a point at one scale or an area at another, display scale is different than the scale of data
Two types of data to represent geographic info/features
Discrete - a point or separate individual objects
Continuous - a field of data with no breaks
How is spatial data stored in Vector models?
Points, lines, polygons
Each point has a unique identifier, an x coordinate and a y coordinate. Attributes are linked by the unique identifier
How is spatial data stored in Raster models?
Grid of cells/pixels with values, sometimes just a binary system is used to show the presence of the phenomena where 0=no, 1=yes
one attribute in each cell per layer
different layers for different attributes
Points
no spatial dimensions (width or length)
has a location (x,y)
can be real or virtual
Lines
has length but not width
has two locations, start and end which have coordinates
can have intermediate points if complex
can be real or virtual
Polygons
just like a line but ends at the first point
has two dimensions, length and width
has perimeter and area
can be real or virtual
What are vector models good at representing?
Discrete data
Socio-economic data
What are raster models good at representing?
Surfaces and continuous data
elevation, temperature, air pressure, salinity, pH
Advantages/disadvantages of raster
Better suited for spatially continuous data
Better for visualisations and modelling
Fast and efficient processing
Large files
What is a DEM
Digital elevation model (raster)
Continuous surface of cells with elevation (height) displayed as shaded by height
Disadvantages: File size is large! Cell is averaged to might miss low or high points
Visibility analysis
use DEM to see calculate the viewshed seen from a specific point (observer)
example: military use, cell towers, urban planning/landscape
Slope analysis maps
calculate slope angle (degree/steepness)
or aspect (direction of slope face)
example: solar gain for solar panel or building houses oriented to the sun
TIN Surface
Triangulated irregular network
an efficient way of storing 3D model (not raster) no redundant data, just collect points at critical features
creates a network of irregular triangles in which lines between points have a constant slope
Networks
the connected line features with a hierarchy and sometimes also a direction
example: streams or road network
often used for routing (google maps)
Advantages/disadvantages of Vector
Can represent features more accurately
Processing can be heavy/slow
VECTOR data model
Best for discrete features
Location referenced in x,y coordinates
Coordinates linked into points, lines, polygons
Attributes referenced by unique ID to tables
Data comes from GPS, digitisation, LINZ
RASTER data model
location is referenced by a grid cell in a matrix (rectangular array)
attribute is represented by a single value for that cell
data come from images (remote sensing), scanned maps, elevation data
best for continuous features like elevation, temperature, soil type, land use
