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Introduction to Data Models used in Geographic Information Systems

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... Examples Zonal Function Global function Examples Spatial Modeling Introduction to Data ... = = Focal Mean (moving average ... Concept of Spatial Objects ... – PowerPoint PPT presentation

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Title: Introduction to Data Models used in Geographic Information Systems


1
Introduction to Data Models used in Geographic
Information Systems
  • Miles Logsdon
  • mlog_at_u.washington.edu

Garry Trudeau - Doonesbury
2
GIS - consists of
  • Components
  • People, organizational setting
  • Procedures, rules, quality control
  • Tools, hardware software
  • Data, information
  • Functions
  • Data gathering
  • Data distribution

3
Geographic Data ( i.e. not spatial information)
  • Spatial Data
  • location
  • shape
  • relationship among features
  • Descriptive Data
  • attributes, or
  • characteristics of the features

Spatial Data the spatial attribute is
explicitly stated and linked to the thematic
attribute for each data item.
4
Map Projections cont.
  • Map Projection Properties
  • Conformality, Shape is preserved
  • Equaldistant
  • Azimuths (directions)
  • Scale
  • Area

5
Coordinate Systems . A system that allow you to
use numeric values to identify any point in space
latitude/longitude, Universal Transverse
Mercator, State Plane Coordinates
Cartesian Coordinates two axis crossing at
right angles identifying position on a flat
surface Spherical Coordinates the angle
between an axis (or axes) and a base line that
runs through the origin point.
Much Thanks Denis Dean CSU
6
  • Winkel tripel projection (1921) National
    Geographic standard
  • Average the X and Y from the Aitoff and
    Equirectangular projections
  • a modified planner, secant, normal aspect
    projections
  • Robinson projection (1963) Rand McNally.
  • the orthophanic projection (right appearing,
    or Pseudocylindrical Projection with Pole Line
  • a secant Tangency at 38N-38S, normal aspect
    projections

Much Thanks Denis Dean CSU
7
  • Mercator Projection
  • Cylindrical, Tangent, and normal
  • Compression (distortion of the poles)
  • Universal Transverse Mercator (UTM) Projection
    and Coordinate system
  • Cylindrical, secant (1950), and transverse
  • Identical to Guass-Kruger projection
  • USA uses Clarke 1866 spheroid
  • 60 zones, North sets of coordinates all positive
  • Coordinates in meters

Much Thanks Denis Dean CSU
8
  • Washington State Plane Coordinate System
  • Lambert Conformal Conic projections
  • North American Datum 1983
  • Zones North and South
  • units - often feet for NAD 27, meters NAD83

Much Thanks Denis Dean CSU
9
Spatial Information
  • Three Attributes of Geographic Data that
    constitutes Information
  • Thematic (Value Variable)
  • Nominal, name, label
  • Ordinal, rank ordered
  • Interval / Ratio, measurement on a scale
  • Spatial (location)
  • Temporal

After Sinton, 1978 Components of spatial
information time, space, theme
(attribute) Sounds obvious. One must be fixed,
one controlled, one measured.
10
Spatial - thematic value types
200
Sta. 94, DOC 4.9
Stream,3
Former Land Fill
100
FOREST
URBAN
Duvall, pop 1170
Brush Creek, 2
FOREST
AGRICULTURE
100
200
Snoqualmie River, 1
WELL
11
Geographies
Layers, Coverages, Themes
Land use
Soils
Streets
Hydrology
Parcels
12
Concept of Spatial Objects
  • POINTS
  • LINES
  • AREA

13
Spatial Encoding - RASTER
POINT
0
0
0
0
0
1
0
0
0
5
5
3
AREA
3
3
1
1
1
2
LINE
1
0
0
0
1
0
1
0
14
Spatial Encoding - VECTOR
a single node with NO area
POINT
- x, y
- x1, y1 - x2, y2 . . - xN, yN
LINE
a connection of nodes (vertices)
beginning with a to and ending with a
from
(Arcs)
Area (Polygons)
a series of arc(s) that close around a
label point
- x1, y1 - x2, y2 . . - xN, yN (closure Point)
15
Vector - Topology
Descriptive
Spatial
Object
VAR1 VAR2
1 2 3
x1,y1 x2,y2 x3,y3
1 2 3
VAR1 VAR2
Fnode Tnode x1y1, x2y2
1
1 2
1 2
1 2 xxyy, xxyy 2 3
xxyy,xxyy
3
2
2
1
VAR1 VAR2
2
3
1 2
1 2
10, 11, 12, 15 10, .
15
10
1
4
12
5
11
16
Raster Data Model
17
ArcGIS Geodatabase
At its most basic level, the geodatabase is a
container for storing spatial and attribute data
and the relationships that exist among them.
Geodatabases are created, edited, and managed
using the standard menus and tools in ArcCatalog
and ArcMap.
A database or file structure used primarily to
store, query, and manipulate spatial data.
Geodatabases store geometry, a spatial reference
system, attributes, and behavioral rules for
data. Various types of geographic datasets can be
collected within a geodatabase, including feature
classes, attribute tables, raster datasets,
network datasets, topologies, and many others.
Geodatabases can be stored in IBM DB2, IBM
Informix, Oracle, Microsoft Access, Microsoft SQL
Server, and PostgreSQL relational database
management systems, or in a system of files, such
as a file geodatabase.
18
Personal geodatabase for Microsoft Access A
personal geodatabase for Microsoft Access can be
read by multiple people at the same time, but
edited by only one person at a time. A personal
geodatabase for Access has the .mdb file
extension (a format used by Microsoft Access) and
has a maximum size of 2 gigabytes (GB). Vector
data is stored in the database, while raster data
is referenced. Personal geodatabases for Access
are appropriate for smaller workgroups and for
managing small to moderately sized datasets.
File geodatabase The file geodatabase is a new
geodatabase type released at ArcGIS 9.2. A file
geodatabase, which has the .gdb file extension,
stores datasets in a folder of files on your
computer. File geodatabases work across operating
systems and can store individual datasets up to 1
terabyte (TB) in size. While only one person can
edit an individual item in a file geodatabase at
a time, multiple people can view and query the
data stored in a file geodatabase at the same
time. File geodatabases are the recommended
native data format for ArcGIS. Multiuser
geodatabase A multiuser geodatabase is typically
found in larger organizations where multiple
users need to view and edit the GIS database at
the same time. Multiuser geodatabases support
versions and replication and require ArcSDE
technology and a database management system
(DBMS) such as Informix, Microsoft SQL Server, or
Oracle.
19
Display
Source
20
Feature Dataset is a collection of feature
classes with the same spatial reference.
Feature Class is a collection of features that
share the same geometry type (point, line, or
polygon).
Nonspatial Table contains attribute data
associated with feature classes
1 Feature Dataset
Stand alone Feature Classes
5 Feature Classes
Feature datasets primarily store feature classes
that have topological relationships
(connectivity, adjacency, containment).
21
Types of Geodatabases
Coverages CAD dBase INFO tables Shapefiles
ERDAS Imagine ESRI Grid JPEG MrSID TIFF
22
  • The file geodatabase uses an efficient data
    structure that is optimized for performance and
    storage. File geodatabases use about one-third of
    the feature geometry storage required by
    shapefiles and personal geodatabases for Access.
    File geodatabases also allow you to compress
    vector data to a read-only format to reduce
    storage requirements even further.
  • File geodatabases have no storage size limit.
    Individual datasets within a file geodatabase,
    such as a feature class or table, do have a size
    limit of 1 terabyte (TB).
  • The file geodatabase offers improved performance.
    For example, it can easily support individual
    datasets containing over 300 million features and
    datasets that can scale beyond 500 GB per file,
    while maintaining very fast performance.
  • The file geodatabase offers less restrictive
    editing locks. Locking can be done per table
    instead of on the entire database.
  • Lastly, the file geodatabase is supported by many
    platforms, including Windows and UNIX (Solaris
    and Linux).

23
Set Selections
1 2 3 4 5 6 7 8 9 10
Reduce Select - RESEL GT 5 6 7 8 9 10 Add
Select - ASEL EQ 5 5 6 7 8 9
10 Unselect - UNSEL GE 9 5 6 7 8
Null Select - NSEL 1 2 3 4 9 10
24
AND, OR, XOR
25
Spatial Overlay - UNION
1
1
2
1
3
6
2
4
5
2
3
7
8
11
3
12
9
10
4
5
13
14
16
17
15
A B C D
102 103
102 A
A 102 B
102
26
Spatial Overlay - INTERSECT
1
1
1
2
2
2
3
3
4
5
3
6
7
4
5
8
9
A B C D
102 103
A 102 B
102 A 103 B
103
27
Spatial Overlay - IDENTITY
1
1
1
2
5
2
3
4
2
3
6
7
3
8
9
4
5
10
11
12
13
A B C D
102 103
A A
102 B 103 B

28
Spatial Poximity - BUFFER
Constant Width
Variable Width
29
Spatial Poximity - NEAR
Assign a point to the nearest arc
30
Spatial Proximity - Pointdistance
DISTANCE
2,045 1,899 1,743
1 2 3
1 2 3
31
Spatial Proximity - Thiessen Polygons
32
(No Transcript)
33
Map Algebra
  • In a raster GIS, cartographic modeling is also
    named Map Algebra.
  • Mathematical combinations of raster layers
  • several types of functions
  • Local functions
  • Focal functions
  • Zonal functions
  • Global functions
  • Functions can be applied to one or multiple layers

34
Local Function
  • Sometimes called layer functions -
  • Work on every single cell in a raster layer
  • Cells are processed without reference to
    surrounding cells
  • Operations can be arithmetic, trigonometric,
    exponential, logical or logarithmic functions

35
Local Functions Example
  • Multiply by constant value

2 0 1 1 2 3 0 4
1 1 2 3 2
6 0 3 3 6 9 0 12
3 3 6 9 6
X 3
  • Multiply by a grid

2 0 2 2 3 3 3 3
2 2 2 1 1
4 0 2 2 6 9 0 12
2 2 4 3 2
2 0 1 1 2 3 0 4
1 1 2 3 2

X
36
Focal Function
  • Focal functions process cell data depending on
    the values of neighbouring cells
  • We define a kernel to use as the neighbourhood
  • for example, 2x2, 3x3, 4x4 cells
  • Types of focal functions might be
  • focal sum,
  • focal mean,
  • focal max,
  • focal min,
  • focal range

37
Focal Function Examples
  • Focal Sum (sum all values in a neighborhood)

2 0 1 1 2 3 0 4 2
1 1 2 2 3 3 2
(3x3)
12 13

17 19
  • Focal Mean (moving average all values in a
    neighborhood)

2 0 1 1 2 3 0 4 4
2 2 3 1 1 3 2
1.8 1.3 1.5 1.5 2.2 2.0 1.8 1.8 2.2 2.0
2.2 2.3 2.0 2.2 2.3 2.5
(3x3)

38
Zonal Function
  • Process and analyze cells on the basis of zones
  • Zones define cells that share a common
    characteristic
  • Cells in the same zone dont have to be
    contiguous
  • A typical zonal function requites two grids
  • a zone grid which defines the size, shape and
    location of each zone
  • a value grid which is processed
  • Typical zonal functions
  • zonal mean,
  • zonal max,
  • zonal sum,
  • zonal variety

39
Zonal Function An Example
  • Zonal maximum Identify the maximum in each zone

2 2 1 1 2 3 3 1
3 2 1 1 2 2
1 2 3 4 5 6 7 8 1
2 3 4 5 6 7 8
5 5 8 8 5 7 7 8
7 8 8 8 8 8

Useful when we have different regions
classified and wish to treat all grid cells of
each type as a single zone (ie. Forests, urban,
water, etc.)
40
Global function
  • In global functions -
  • The output value of each cell is a function of
    the entire grid
  • Typical global functions are distance measures,
    flow directions, or weighting measures.
  • Useful when we want to work out how cells
    relate to each other

41
Golbal Function An Example
  • Distance Measures Euclidean distance based upon
    cell size

1 1 1
2
2 1 0 0 1.4 1 1 0 1
0 1 1 1.4 1 1.4 2

Or some function which must consider all cells
before determining the value of any cell
(cost associated with a path across the surface)
42
Examples
outgrid zonalsum(zonegrid, valuegrid) outgrid
focalsum(ingrid1, rectangle, 3, 3) outgrid
(ingrid1 div ingrid2) ingrid3
43
Spatial Modeling
  • Spatial modeling is analytical procedures applied
    with a GIS. Spatial modeling uses geographic data
    to attempt to describe, simulate or predict a
    real-world problem or system.
  • There are three categories of spatial modeling
    functions that can be applied to geographic
    features within a GIS
  • geometric models, such as calculating the
    Euclidean distance between features,
  • coincidence models, such as topological overlay
  • adjacency models (pathfinding, redistricting, and
    allocation)
  • All three model categories support operations on
    spatial data such as points, lines, polygons,
    tins, and grids. Functions are organized in a
    sequence of steps to derive the desired
    information for analysis.
  • The following references are excellent
    introductions to modeling in GIS
  • Goodchild, Parks, and Stegaert. Environmental
    Modeling with GIS. Oxford University Press, 1993.
  • Tomlin, Dana C. Geographic Information Systems
    and Catograhic Modeling. Prentice Hall, 1990.
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