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Lecture 3
GIS Data Models
Data Formats
EEOS 381 - Spatial Databases and
GIS Applications
EEOS 381 - Spring 2015: Lecture 3 2
OverviewOverview
GIS Data Models
Common GIS Data Formats
EEOS 381 - Spring 2015: Lecture 3 3
OverviewOverview
Key points:– It is important to understand what
model to use, based on the application
– The model determines what specific format you use
– The format may determine what types of analysis you perform
EEOS 381 - Spring 2015: Lecture 3 4
Data ModelData Model
General definition:
–Abstraction or representation of objects and processes in the real world, incorporating properties relevant to the application at hand
EEOS 381 - Spring 2015: Lecture 3 5
GIS Data ModelGIS Data Model
Definition:
–Digital representation of
geographic objects (spatial data)
in GIS software
•includes relationships between and
attributes of objects
•doesn’t include all of reality
•in context of a digital environment
EEOS 381 - Spring 2015: Lecture 3 6
GIS Data ModelsGIS Data Models
The role of a data model in GIS
Levels of GIS data model abstraction
EEOS 381 - Spring 2015: Lecture 3 7
GIS Data ModelsGIS Data Models
Levels of abstraction:
Reality Real-world phenomena - e.g. wells, streets, lakes
Conceptual ModelDecide which objects are applicable, what relationships exist among them, what processes they participate in
Logical ModelList objects, with names, descriptions, behavior, interaction, location, what GIS will do
Physical ModelSpecific file and table names, attributes, object relationships, processes (commands)
EEOS 381 - Spring 2015: Lecture 3 8
GIS Data ModelsGIS Data Models
Example implementation:
Reality Wells, dry cleaners, streets
Conceptual ModelAsk - How does pollution from dry cleaners and major roads affect public water supplies (wells and reservoirs)?
Logical ModelUse ArcGIS to compare wells (points), reservoirs (polygons), dry cleaners (points) and streets (lines), with buffer and proximity operations; focus on wells with 100+ gallons per minute yield and major roads, in eastern Mass.
Physical ModelBUFFER shapefile WELLS_PT, join to GPM table on YIELD field; determine how many dry cleaners are within 1 mile of large wells and proximity to reservoirs and wells to major roads; store in Oracle-based ArcSDE geodatabase
EEOS 381 - Spring 2015: Lecture 3 9
GIS Data Models – 2 Conceptual ViewsGIS Data Models – 2 Conceptual Views
Discrete objects
– World is empty except where occupied by objects with well-defined locations and/or boundaries
• e.g. wells, streets, lakes
Fields
– Measurements may be made at any location over a continuous surface
• e.g. elevation, temperature, population density
EEOS 381 - Spring 2015: Lecture 3 10
GIS Data ModelsGIS Data Models
EEOS 381 - Spring 2015: Lecture 3 11
GIS Data ModelsGIS Data Models
Raster is a data model– space is divided into array (rows and
columns) of cells
– each cell (pixel, or picture element) in a layer is the same size and has a homogeneous value
• cell size refers to resolution (10m, 1 foot, etc.)
– usually associated with field view
– includes images, elevation models, surfaces
EEOS 381 - Spring 2015: Lecture 3 12
GIS Data ModelsGIS Data Models
Raster - examples
Aerial (ortho) photograph Land use types
EEOS 381 - Spring 2015: Lecture 3 13
GIS Data ModelsGIS Data Models
Raster– Cells may belong to
zones (groups of cells with same values, usually representing the same feature)
– Can include ‘NODATA’ -null values (out of range of dataset or no information available for that cell)
– Some image formats can include attributes (value attribute table)
EEOS 381 - Spring 2015: Lecture 3 14
GIS Data ModelsGIS Data Models
Raster– Advantages:
• A simple data structure—a matrix of cells with values, representing a coordinate, sometimes linked to an attribute table.
• A powerful format for intense statistical and spatial analysis; perform overlays with complex data faster than with vector data.
– “Spatial Analyst” extension in ArcGIS
• The ability to represent continuous surfaces and perform surface analysis.
• The ability to uniformly store points, lines, polygons, and surfaces.
• Compression
EEOS 381 - Spring 2015: Lecture 3 15
GIS Data ModelsGIS Data Models
Raster– Disadvantages:
• Inherent spatial inaccuracies due to the cell-based feature representation, especially if low resolution.
• Datasets can be very large.
EEOS 381 - Spring 2015: Lecture 3 16
GIS Data ModelsGIS Data Models
Vector is a data model– points - single coordinate values
– lines (arcs) - strings of connected points
– polygons (areas) - enclosed lines
– usually associated with discrete object view
– stores geography and attributes
EEOS 381 - Spring 2015: Lecture 3 17
GIS Data ModelsGIS Data Models
Vector – the basics
POINT - location with a set of coordinates (0-D)
LINE – connected string of points (1-D)
POLYGON – area defined by a line (2-D)
2 line segments (a direct line between two points) shown here
EEOS 381 - Spring 2015: Lecture 3 18
GIS Data ModelsGIS Data Models
(topological junction, or
endpoint of line)
(direct connection between two nodes)
(sequence of line segments)
(directed sequence of nonintersecting line
segments with nodes at each end)
(an area defined by an outer ring
without inner rings)
(sequence of any line segments with
closure)
(curve string)
(an area defined by an outer ring with
inner rings)
(a link between two nodes, with one
direction designated)
Vector (other objects/definitions)
EEOS 381 - Spring 2015: Lecture 3 19
GIS Data ModelsGIS Data Models
Vector–Advantages:
• Precise values
• Efficient storage
• Topological relationships
• High-quality cartographic output
• Useful for a variety of spatial analysis operations
EEOS 381 - Spring 2015: Lecture 3 20
GIS Data ModelsGIS Data Models
Vector–Disadvantages:
• Poor for storing continuous surfaces(e.g. elevation models)
• Overlay operations can be time-consuming and computer intensive (need lots of RAM)
EEOS 381 - Spring 2015: Lecture 3 21
GIS Data ModelsGIS Data Models
Vector–Simple vs. Topologic features:
• Simple - a.k.a. “spaghetti model” - no inherent connectivity relationships
• Topologic - simple features with defined spatial relationships
Spaghetti – 4 linear features
Topologic- 14 linear features- 13 nodes
NodeLine
EEOS 381 - Spring 2015: Lecture 3 22
GIS Data ModelsGIS Data Models
Spaghetti Data Model
– No details of logical relationships between
objects
• The line shared by two adjacent polygons is stored
separately (twice) in the computer
• Spatial relationships are only implied
– Efficient for cartographic display but not data
storage
– At first, GIS used vector data and
cartographic spaghetti structures
EEOS 381 - Spring 2015: Lecture 3 23
GIS Data ModelsGIS Data Models
Topology– Connectivity: chains are connected at which nodes?
– Direction: defined by a “from node” and a “to-node”of a chain
Example analysis:Modeling flow through the
connecting lines in a network
EEOS 381 - Spring 2015: Lecture 3 24
GIS Data ModelsGIS Data Models
Topology
– Adjacency: which polygons are on the left and which are on the right side of a chain?
Example analysis:Identifying adjacent
features;Combining adjacent polygons with similar
characteristics
EEOS 381 - Spring 2015: Lecture 3 25
GIS Data ModelsGIS Data Models
Topology
– Inclusion: simple spatial objects (node, chain, smaller polygon) are within a polygon
Example analysis:Overlaying geographic
features
EEOS 381 - Spring 2015: Lecture 3 26
GIS Data ModelsGIS Data Models
Network– Type of topologic vector data model (see pgs 218-219 in
book)
– Models flow of goods and services (e.g. routes of roads, rivers, utility lines)
• Radial - flow in one direction (e.g. upstream, downstream)
• Looped - intersections allowed, choices for flow allowed
“Network Analyst”extension in
ArcGIS contains tools for this type
of analysis
EEOS 381 - Spring 2015: Lecture 3 27
GIS Data ModelsGIS Data Models
Regions–Type of
topologic vector data model
–Groups of polygons in coverages
– “Multi-part”polygons
EEOS 381 - Spring 2015: Lecture 3 28
GIS Data ModelsGIS Data Models
Routes– Composite line features
• Created from sections (whole or partial arc)
• contain “M” values (measures along route)
• Ex.: All the arc segments in ALL_ROADS that make up Interstate 90, treated as one feature in MAJOR_ROUTES
EEOS 381 - Spring 2015: Lecture 3 29
GIS Data ModelsGIS Data Models
Linear Referencing System (LRS)– Uses a relative position along an already
existing linear feature, without explicit x,y coordinates. Location is given as a position, or measure, along it (distance, or percent along).
• Have “base layer” of lines, plus a series of related “event tables”
– Address, Speed Limit, Route Number tables, etc…
• Highways/city streets (MassDOT), railroads, rivers, and pipelines, water and sewer networks
• Dynamic segmentation / “flat file”
– See pages 219-221 in textbook
EEOS 381 - Spring 2015: Lecture 3 30
GIS Data ModelsGIS Data Models
Linear Referencing System (LRS)
1 “Base” arc
Speed limit
# of lanes
3 “Flat file”arcs
ID = 1
55 mph 45 mph
30 mi.
0 100
3 lanes 2 lanes
ID = 1 2 3
3551
2453
3452
ID SPEEDLIMIT NUMLANES
2
3
NUMLANES
6001
100601
ID F_MEAS T_MEAS
45
55
SPEEDLIMIT
100
30
T_MEAS
30
0
F_MEAS
1
1
ID
2
1
IDBase arcs feature class
attribute table
Flat file arcs feature class
attribute table
SPEEDLIMIT Table NUMLANES TableLRS Tables
60 mi.
EEOS 381 - Spring 2015: Lecture 3 31
GIS Data ModelsGIS Data Models
TIN (Triangular Irregular Network)
– Topologic data model for surfaces (e.g. elevation) made up of connected triangles (faces)
– Triangle nodes have X,Y,Z values
– Triangles may be sized differently, based on original data density
– See pages 219-221 in textbook
EEOS 381 - Spring 2015: Lecture 3 32
GIS Data ModelsGIS Data Models
TIN
As viewed in ArcScene
EEOS 381 - Spring 2015: Lecture 3 33
GIS Data ModelsGIS Data Models
Terrain Dataset– a multiresolution, TIN-based surface built from
measurements stored as features in a geodatabase.
They're typically made from LiDAR, sonar, and
photogrammetric sources. Terrains reside in the
geodatabase, inside feature datasets with the features
used to construct them.
EEOS 381 - Spring 2015: Lecture 3 34
GIS Data ModelsGIS Data Models
Annotation
– text labels (vector features)
– fixed position, size, orientation
• anno does NOTreposition as you pan and zoom
–N/A for shapefiles (only in GDB and coverages)
EEOS 381 - Spring 2015: Lecture 3 35
GIS Data ModelsGIS Data Models
Object-Relational– Everything stored in database tables
• attributes, geometry in RDBMS
– Defined relationships between objects
– Can store topology
– Can design with CASE (Computer-Aided Software
Engineering) tools (like MS Visio) to produce UML
(Unified Modeling Language) diagrams (see pages
221-226 in textbook)
– Download models from esri.com for various
industries
– Geodatabases (ArcSDE, Personal and File)
EEOS 381 - Spring 2015: Lecture 3 36
GIS Data ModelsGIS Data Models
Object-Relational UML Diagram
An example of a CASE tool (Microsoft Visio) The UML model
is for a utility water system
EEOS 381 - Spring 2015: Lecture 3 37
GIS Data ModelsGIS Data Models
Object-Relational DiagramA water-facility object model
EEOS 381 - Spring 2015: Lecture 3 38
DefinitionDefinition
Format - The pattern into which data
(coordinates, attributes, indexes, spatial
reference, etc.) is systematically arranged for
use on a computer. A file format is the specific
design of how information is organized in the
file. (All GIS data is a file on disk at the most
basic level).
– For example, ArcInfo has specific, proprietary
formats used to store coverages. DLG, DEM, and
TIGER are geographic datasets with different file
formats. ESRI has also developed Shapefiles and
Geodatabases.
EEOS 381 - Spring 2015: Lecture 3 39
GIS Data FormatsGIS Data Formats
Common raster formats:– GeoTIFF, TIFF, BIL, BIP
– MrSID (.SID), JPG, JPEG 2000
– GRID, DEM
– ERDAS IMAGINE (.IMG)
– Intergraph - CIT, COT
– ER Mapper
– ADRC
– NTIF - National Image Transfer Format
– Geodatabase “raster datasets”
EEOS 381 - Spring 2015: Lecture 3 40
Raster - file components:– Image file (.tif, .sid, ... )
– Header (“world”) file (.tfw, sdw, …):
– Auxiliary file (.aux) - stores spatial reference
– Reduced raster resolution (.rrd or .ovr) – stores pyramid levels
GIS Data FormatsGIS Data Formats
1.0000000000000000.0000000000000000.000000000000000
-1.000000000000000237000.500000000000000897999.500000000000000
Cell size (x-scale)
Coordinates of center of upper left pixel
Rotation terms
Cell size (y-scale)
EEOS 381 - Spring 2015: Lecture 3 41
GIS Data FormatsGIS Data Formats
Common vector formats:– Shapefile, Coverage, Geodatabase “feature classes”
– DXF, DWG - CAD-based
– MapInfo - MIF
– DLG
– TIGER, VPF
– ASCII, DBF
– SDTS - Spatial Data Transfer Standard
– SDC - Smart Data Compression
– XML, GML
EEOS 381 - Spring 2015: Lecture 3 42
DefinitionsDefinitions
A feature is a point, line, or polygon in a
dataset that represents a real-world object
A feature class is a collection of features,
categorized by the type of geometry used to
define the feature (e.g., how the coordinates
are stored, as a point, line, or polygon)
– “polygon feature class”, “arc feature class”, “point
feature class”, etc.
– Should represent
similar objects
EEOS 381 - Spring 2015: Lecture 3 43
Common ArcGIS FormatsCommon ArcGIS Formats
Coverage
Shapefile
Geodatabase(“geographic database”)
– Personal, File
– Spatial Database Engine (SDE)
File-baseddata modelFile-baseddata model
DBMS-baseddata model(aka Object data model)
DBMS-baseddata model(aka Object data model)
Vector
Vector & Raster
EEOS 381 - Spring 2015: Lecture 3 44
GIS Data Formats - ShapefileGIS Data Formats - Shapefile
Developed by ESRI (ArcView 2)
Stored on disk in folders
Consists of a set of files
– .shp – spatial geometry
– .shx – spatial geometry index
– .dbf – dBASE file (feature attributes)
–optional others (.prj, .sbn, .sbx, .ain, .aih, .aig, …)
alwayspresent
EEOS 381 - Spring 2015: Lecture 3 45
GIS Data Formats - ShapefileGIS Data Formats - Shapefile
Simpler than coverages - useful for mapmaking and some kinds of analysis.
Fast display (especially when local)
Single feature class (geometry) per shapefile
– Point (points and multipoints) or
– Line (simple lines and multipart polylines) or
– Polygon (simple and multipart)
No topology or annotation
10-character max. field names (dbf limitation)
May be edited in ArcGIS and ArcView GIS 2x+
Open format (specs available); may be produced from other applications
EEOS 381 - Spring 2015: Lecture 3 46
GIS Data Formats - CoverageGIS Data Formats - Coverage
Developed by ESRI, c.1981
Traditional (Arc/Info) format for complex geoprocessing, high-quality geographic data, and sophisticated spatial analysis.
Stores features and attributes for thematically associated data
Can explicitly store topology (features stored only once) - use BUILD or CLEAN commands (vs. “spaghetti data model”
EEOS 381 - Spring 2015: Lecture 3 47
GIS Data Formats - CoverageGIS Data Formats - Coverage
Stored on disk as a directory (folder) of files, with more files in associated ‘info’ directory
Attributes in INFO format (tables)
Coverage folder stored in a “workspace” - a special name for a folder with a coverage (or
Grid or TIN)
Workspace
Coverages
View in Windows Explorer View in ArcCatalog
EEOS 381 - Spring 2015: Lecture 3 48
GIS Data Formats - CoverageGIS Data Formats - Coverage
Multiple feature classes can be grouped and
stored in one coverage
– Primary (label point, arc, polygon, node)
– Secondary (tics, links, annotation)
– Compound (routes/sections, regions; built from
primary features) – like “multi-part features”
Edit in ArcInfo Workstation only
Polygons can’t have “holes” (because of
“universal polygon” (i.e. the background)
You cannot have points and polygons in the
same coverage
EEOS 381 - Spring 2015: Lecture 3 49
GIS Data Formats - CoverageGIS Data Formats - Coverage
(point attribute table)
(arc attribute table)
(route attribute table)
(polygon attribute table)
(node attribute table)
<cover>.RAT<route>
EEOS 381 - Spring 2015: Lecture 3 50
GIS Data Formats - CoverageGIS Data Formats - Coverage
Explicit topology
–Connectivity (arc-node topology) -arcs connect to each other at nodes
EEOS 381 - Spring 2015: Lecture 3 51
GIS Data Formats - CoverageGIS Data Formats - Coverage
Explicit topology
–Area Definition (polygon-arc topology) -Arcs that connect to surround an area define a polygon
EEOS 381 - Spring 2015: Lecture 3 52
GIS Data Formats - CoverageGIS Data Formats - Coverage
Explicit topology
–Contiguity (adjacency) - Arcs have direction and left and right sides
EEOS 381 - Spring 2015: Lecture 3 53
GIS Data Formats - CoverageGIS Data Formats - Coverage
Coverage attribute tables have “Sacred Items”
– Point/Polygon: AREA, PERIMETER, <COVER>#, <COVER>-ID
– Arc: <COVER>#, <COVER>-ID, FNODE#, TNODE#, LPOLY#, RPOLY#, LENGTH
Topology between feature classes managed
with sacred items
– Ex.: <cover># in .PAT (polygon attribute table) relates to
LPOLY# and RPOLY# in .AAT (arc attribute table)
– <cover># = 1 in polygon coverages’ “universal
polygon” (hidden in ArcGIS Desktop)
EEOS 381 - Spring 2015: Lecture 3 54
Data Format ConversionData Format Conversion
Workflow may dictate that data
need to be in another format
In ArcMap, Right-
click layer in Table
of Contents and
choose Data >
Export Data > and
select format
EEOS 381 - Spring 2015: Lecture 3 55
Data Format ConversionData Format Conversion
Right-click layer(s) in ArcCatalog
EEOS 381 - Spring 2015: Lecture 3 56
Use ArcToolbox Conversion Tools
ArcInfo license and installation of ArcInfo Workstation required for Coverage conversion tools
Data Format ConversionData Format Conversion
EEOS 381 - Spring 2015: Lecture 3 57
DistributionDistribution
Process of moving data from one location
to another
Copy/paste in ArcCatalog if source and
destination are both accessible, otherwise:
– Coverage – export to “Arc/Info Export File” (a.k.a
“interchange file”) in ArcToolbox
• ASCII file with .e00 extension
• User then “Imports” file with ArcToolbox (ArcInfo)
– Shapefile – send all components or use WinZip,
PKZIP, StuffIt, etc., to send all in one file
– Geodatabase – Export to XML, plus other options