maps as numbers
DESCRIPTION
Maps as Numbers. GIS requires that both data and maps be represented as numbers. The GIS places data into the computer’s memory in a physical data structure (i.e. files and directories). Files can be written in binary or as ASCII text. - PowerPoint PPT PresentationTRANSCRIPT
Maps as Numbers
• GIS requires that both data and maps be represented as numbers.
• The GIS places data into the computer’s memory in a physical data structure (i.e. files and directories).
• Files can be written in binary or as ASCII text.• Binary is faster to read and smaller, ASCII can
be read by humans and edited but uses more space.
Organizing Data and Information
• Information can be organized as lists, numbers, tables, text, pictures, maps, or indexes.
• Clusters of information called data can be stored together as a database.
• A database is stored in a computer as files.
The GIS Database
• In a database, we store attributes as column headers and records as rows.
• The contents of an attribute for one record is a value.
• A value can be numerical or text.
Flat File Database
Record Value Value Value
Attribute Attribute Attribute
Record Value Value Value
Record Value Value Value
The GIS Database (cont)
• Data in a GIS must contain a geographic reference to a map, such as latitude and longitude.
• The GIS cross-references the attribute data with the map data, allowing searches based on either or both.
• The cross-reference is a link.
Feature Attribute Table
Fields
Records
Representation and Data Structures
Real world - phenomena that exist
Data model - an abstraction, identifying those phenomena and properties we deem relevant for our applications Data and file structures - computer representation and storage scheme of the data model, often shown as diagrams and lists
Representation and data structures
Important to note the selection process as we move from real world to data model...this reflects our conceptualization, and affects much of what we can do
Terms
Entities - those "things" in the real world we wish to represent (Rivers, buildings, soil types, wetlands) Objects - our representation in a data model, which generally includes both geometric information (spatial data) and descriptive information (aspatial or attribute data).
Entity
Object
A data model represents reality
The Data Model
• A logical data model is how data are organized for use by the GIS.
• GISs have traditionally used either raster or vector for maps.
Data ModelsRepresentation and Data Structures
Data Model – An consistent way of defining and representing spatial objects in a database, and of representing the relationships among the objects.
A data model includes at least two parts –
Coordinate data - pairs or triplets of numbers that define location
Attribute data - text, numbers, images, or other “non-
spatial” data
Discrete and Continuous Space
Vector Data model – Discrete space
Raster Data model – Continuous or discrete space
A raster data model uses a grid
• One grid cell is one unit or holds one attribute.
• Every cell has a value, even if it is “missing.” • A cell can hold a number or an index value
standing for an attribute.• A cell has a resolution, given as the cell size in
ground units.
Generic structure for a grid
Rows
Columns
Gridcell
Grid extent
Resolution23 Cell Value
Points as Cells
Line as a Sequence of Cells
Polygon as a Zone of Cells
The mixed pixel problem
W GW
W W G
W W G
W GG
W W G
W G G
W GE
W E G
E E G
Water dominates Winner takes all Edges separate
Raster – The Mixed Pixel Problem
Landcover map –Two classes, land or water
Cell A is straightforward
What category to assignFor B, C, or D?
Raster – The Storage Space/Resolution Tradeoff
Decreasing the Cell Size by one-half causes aFour-fold increase in the storage space required
Rasters – Discrete or Continuous Features
discrete continuous
RASTER
• Grids are poor at representing points, lines and areas, but good at surfaces.
• Grids are a natural for scanned or remotely sensed data.
• Grids suffer from the mixed pixel problem.
Vector format
Point - a pair of x and y coordinates(x1,y1)
Line - a sequence of points
Polygon - a closed set of lines
Node
vertex
Vector data are defined spatially:
Vectors Define Discrete Features
Representation – Enforced Uniformity
VECTOR• Vector data evolved the arc/node model in the 1960s.• In the arc/node model, an area consist of lines and a line consists of
points.• Points, lines, and areas can each be stored in their own files, with links
between them.• The topological vector model uses the line (arc) as a basic unit. Areas
(polygons) are built up from arcs.• The endpoint of a line (arc) is called a node. Arc junctions are only at
nodes.• Stored with the arc is the topology (i.e. the connecting arcs and left
and right polygons).
Basic arc topology
n1
n2
1
23A
B
Arc From To PL PR n1x n1y n2x n2y
1 n1 n2 A B x y x y
Topological Arcs File
TOPOLOGY
• Topological data structures dominate GIS software.• Topology allows automated error detection and
elimination.• Rarely are maps topologically clean when digitized or
imported.• A GIS has to be able to build topology from
unconnected arcs.• Nodes that are close together are snapped.• Slivers due to double digitizing and overlay are
eliminated.
Topology Matters
• The tolerances controlling snapping, elimination, and merging must be considered carefully, because they can move features.
• Complete topology makes map overlay feasible.
• Topology allows many GIS operations to be done without accessing the point files.
Although Raster is FasterVector is Correcter
• Vector can represent point, line, and area features very accurately.
• Vectors are far more efficient than grids.• Vectors work well with pen and light-
plotting devices and tablet digitizers.• Vectors are not good at continuous
coverages or plotters that fill areas.
No Decision is Final – We Can Convert
Comparisons, raster v.s. vector
Characteristics Positional Precision
Attribute Precision
Analytical Capabilities Data Structures
Storage Requirements Coordinate conversion Network Analyses
Output Quality
Can be Precise
Defined by cell size
Poor for continuous data
Good for continuous data
Good for spatial query, adjacency, area, shape analyses. Poor for continuous data. Most analyses limited to intersections. Slower overlays.
Spatial query more difficult, good for local neighborhoods, continuous variable modeling. Rapid overlays.
Often complex
Often quite simple
Relatively small
Often quite large
Usually well-supported
Often difficult, slow
Easily handled
Often difficult
Very good, map like
Fair to poor - aliasing
Vector Raster
Raster-Vector Data Model
Raster
Vector
Real World