Geospatial Data Structures
GEOG3222: Geographical
Information Science II
Dr. Jed Long
Department of Geography
Western University
Lecture Overview
Lecture Overview
• Review definition of geospatial data
• Geospatial data representation
• Data models: objects and field
• Vector
• Raster
What are Geospatial Data?
What are Spatial Data?
• Spatial data are a collection
of measurements taken at
specific locations
• Data that are mappable
http://www.ci.fort-collins.co.us/gis/what-is.php
Components of Geospatial Data
Components of Spatial Data
1) Location – describes where a “thing” is
2) Attribute – provides information about
http://www.ci.fort-collins.co.us/gis/what-is.php
the “thing”
Components of Geospatial Data
Components of Spatial Data
Examples
Tree
Location: A single tree
Attributes:
• Tree height
• Tree species
• Tree health
Components of Geospatial Data
Components of Spatial Data
Examples
Healthcare
• Location: hospital location
• Attribute:
• Hours
• Specialties
• Patients
http://gis.broward.org/gisdata/hospitals.htm
Representing Geospatial Data
Representing Spatial Data
• Need some way storing real world phenomena in a
digital format.
• First consideration ….
How do we represent spatial phenomena?
Representing Geospatial Data
Representing Spatial Data
Data representations impact how we:
• Conceptualize
• Analyze
• Interpret
Representation & Conceptualization
Representation & Conceptualization
What are the fundamental properties of
geospatial data?
Two fundamental properties
Properties of “things” in the real world
The spatial relationships between “things”
Objects and Fields
Data Models
• Objects are things that are mapped using
discrete boundaries
• Fields are continuously varying across
geographical space
OINT
Field
OBJECT
Representing Spatial Data
ENTITY
ENTITY
FIELD FIELD
Data Models
(conceptual)
Data Representations
(practical)
POINT
LINE
LINE
VECTOR
AREA
AREA
RASTER
RASTER
TIN
TIN
Data Models
Objects and Fields
Philosophical argument that dates back to
ancient Greece about the nature of reality:
empty container full of distinct objects or
continuously varying field of phenomena
Object Models
Object models
• Object model = entity model
• Space is conceptualized as a collection of selfcontained objects and relationships between
objects
• Phenomena under study are self-contained
objects
• Objects described by attributes
Field Models
Field models
• Considers that phenomena have spatially
continuous attributes
• A value is possible at an infinite number of point
locations on a surface
• Note – pencil method to
determine field or object
OINT
Field
OBJECT
Representing Spatial Data
ENTITY
ENTITY
FIELD FIELD
Data Models
(conceptual)
Data Representations
(practical)
POINT
LINE
LINE
VECTOR
AREA
AREA
RASTER
RASTER
TIN
TIN
Object Representations
ENTITY
 Points
 Lines
 Polygons
POINT
LINE
AREA
Spatial Points
• Pair of coordinates
• 2-D
• Polar coordinates
• Latitude, Longitude
• Cartesian coordinates
• { X, Y }
• Maybe some attributes
• { X, Y, Attributes }
Soil Samples: Zinc Concentration
Spatial Lines
Spatial Lines represent linear
features, often called polylines.
• Examples:
• Roads
• Rivers
• Power-lines
Spatial Lines
• Polylines = poly (many) + lines
• Collections of connected coordinate pairs
• First point not same as last
• { (x1,y1), (x2,y2), … , (xn, yn) }
• Typically, non self-intersecting…
Spatial Polygons
Spatial Polygons represent
areal features.
• Examples:
•
•
•
•
•
Continents
Lakes
Parks
States/Counties etc.
Cities/Urban area
Spatial Polygons
• Polygons are collections of points
• First point = end point
• “close the loop”
• Can have holes, etc.
• Discrete = “floating”
• E.g., Lakes
• Lattice = “connected”
• E.g., States/counties
Field Representations
ENTITY
FIELD
• Rasters
• Lattice
• Tins (will
talk about
later)
LINE
AREA
RASTER
TIN
Raster
Space is divided into smaller units
Space is tessellated
A tesselation is defined as the process to
cover a surface through the repeated use
of a single shape.
Raster
A raster can use any
reasonable geometric
shape, as long as it can be
connected in such a way
as to create a continuous
surface.
Grids
• Square pixel rasters are the most
common
• Easy to deal with mathematically
• Efficient to store
Raster Data
Raster data represent continuous
spatial variables with a regularly
patterned spatial unit (e.g., pixels).
• Examples:
• Elevation (DEM)
• Satellite Imagery
• Land cover
Vector to Raster
Choosing Representation
Should the data be represented with
object of field?
Often the “thing” being studied could be
conceptualized as either
E.g., Forest polygons
• Continuous forest coverage
• Groups of similar tree - stand
Representation Weakness: #1
• Multi-temporal spatial data
• Change through time
Representation Weakness: #2
• Uncertainty
• Many natural
processes are fuzzy
• A forest patch is both
naturally easy to
think of as an object,
a collection of
objects (trees) and a
continuously varying
field