Transcript Chapter 3 Digital Representation of Geographic Data Digital geographic data • are numerical representations that describe real-world features and phenomena • must be in.

Chapter 3
Digital Representation of Geographic Data
Digital geographic data
• are numerical representations that describe
real-world features and phenomena
• must be in digitial form and organized as a
geographic database for use in a GIS
• are dynamic, in contrast to the static data
displayed on a conventional map (i.e., paper)
Conceptual model for organizing
geographic data for analysis ?
Geographic matrix*
• geographic data described according to
location (columns) and attributes (rows)
• it facilitates areal differentiation, the study of
differences among various locations
see figure 3.1
*(Berry, 1964)
Real world data exist as:
Objects - buildings, highways, cities
Phenomena - terrain, temperature, ethnicity
Data models for GIS
• Object-based (vector)
• Field-based (raster)
Object-based model (vector)
geographic space is populated by discrete
and identifiable objects
An object:
• Has identifiable boundaries or spatial extent
• Is relevant to some intended application
• Is describable by one of more attributes
(characteristics)
• Exact objects - are generally man-made
features with precise
boundaries
• Inexact objects - are generally natural
features with transitional,
or “fuzzy” boundaries
objects are represented as:
• Points
• Lines
• Polygons
Field-based model (raster)
geographic space is populated by one or
more spatial phenomena
Spatial phenomena
are real-world features that vary continuously
over space with no obvious or specific extent
and are represented as surfaces
the surfaces in a field-based model can be
conceptualized as being composed of:
• Grid cells or pixels
– regular tessellations
• Polygons (i.e., triangles)
– irregular tessellations
Representation of spatial
relationships ?
• Geometric - when adjacent features share
common boundary
• Proximal - when one feature is “close” to
another one
see Figures 3.5 and 3.6
Representation of temporal
relationships ?
Temporal scaling
1 : 7200
To be usable, digital data must:
• Be properly encoded
• Be properly organized
Logical organization
focuses upon data classification and
geocoding
Physical organization
focused upon the way in which the data are
stored in the computer’s memory
Levels of data measurement
•
•
•
•
Nominal
Ordinal
Interval
Ratio
zero)
grouped by category
rank-order
numerical values
numerical values with a
true origin (absolute
Data classification schemes
Descriptive names
– identifying classes and subclasses
– may be based upon form or function
(“high-rise” vs commercial”)
Definitions
– descriptions of classes and subclasses
Data classification schemes
• example
see Figure 3.8
• Criteria
see page 70
(Rhind and Hudson, 1980)
Geographic data precision
• Computer numbers are discrete, whereas
real world values are continuous
• When the original data contain more precise
measurements than those supported by the
computer, rounding occurs and precision is
reduced
• GIS coordinates are normally stored as
floating-point numbers (real numbers) in
double-precision mode to minimize the
impact of rounding during data processing.
Database organization
attribute (stored field) = one data item
record (tuple) = group of related items
data file = collection related records
ASCII files (alphanumeric)
Binary files (0 and 1)
Digitial data files are commonly
referred to as:
• Layers
• Themes
• Coverage
Raster geographic data
representation
• Is best employed to represent geographic
phenomena that are continuous over a large
area
• use tessellations to model a surface
tessellations
are geometric arrangements (triangular,
square, or hexagonal) of figures that
completely cover a flat surface
note the need for map projection!
reasons for the popularity of
raster data format:
• compatibility with different types of hardware
devices for data capture and output
• compatibility with bit-mapped images
• compatibility with grid-oriented coordinate
systems (i.e., plane rectangular )
Nature and characterisitics of
Raster data
• Geographic data is subdivided into grid cells
• Linear dimension of each pixel defines the
spatial resolution
• Grid size should be one-half the minimum
mapping unit (smallest object to be
represented)
• One value (character, integer, or floatingpoint number) assigned to each grid cell
• These values can be used for computations
(like interpolation of contours) or as codes
linked to a look-up table or color palette
Map layers
• In a raster database, each individual
attribute (characteristic) is stored in a
separate file
• thus data processing requires the use of
multiple map layers
downside
• Identities of individual spatial objects are lost
in a raster data model
upside
• Since the data is stored in a linear array and
the dimensions of database (rows and
columns) is know, there is no need to store the
coordinates of the cells in the data file
WARNING!!!
You must know the raster data format and
data compression algroithm used to construct
the files that you are using for a particular
project.
Principles of raster data
compression
• Raster data files tend to be quite large,
requiring large amounts of storage space
and making data transmission problematic
file size is a function of:
• Resolution
number of pixels
• Bit depth
8 bit (28) 0-255
Run length encoding
adjacent cells in one row are treated as group
See figure 3.18
Quadtree data model
is a hierarchical tessellation model that used
grid cells of variable sizes