Advantages of Using Topology Based Data Sets in GIS Questions

1) If the following data were stored as rasters, which ones would be discrete and which would be continuous: rainfall, soil type, voting districts, temperature, slope, and vegetation type?

2)List the main advantages of using topology-based data sets in GIS.

References

Kang-Tsung Chang (2016) Introduction to Geographic Information Systems, 9th edition, NY: McGraw-Hill ISBN19: 978-1-259-92964-9

Additional Reading Materials:

Wilpen Gorr, Kristen Kurland (2017) GIS Tutorial 1 for ArcGIS PRO, ESRI Press,

Michael Law, Amy Collins (2018) Getting to Know ArcGIS Desktop, 5th edition for ArcGIS 10.6, ESRI Press

GEOSPATIAL
INTELLIGENCE
LECTURE 3:
GIS DATA STRUCTURES
Professor, Dr. Sergei Andronikov
GIS Data Structures
Lecture outline:
 The Map as an abstraction of space
 Basic computer file structures;
 Database Structures;
 Graphic Representation of entities and
attributes;
 GIS Data models for multiple coverages
 Raster Vs. Vector
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Maps as Numbers
 GIS requires that both data and maps be represented
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as numbers.
Converting MAPS into NUMBERS requires that we
choose a STANDARD way to encode locations on the
earth.
A coordinate system is a STANDARTIZED method for
assigning codes to locations so that locations can be
found using the codes alone.
Standardized coordinate systems use absolute locations.
In a coordinate system, the x-direction value is the
easting and the y-direction value is the northing. Most
systems make both values positive.
Coordinate Systems
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FIVE Coordinate systems in the US
Based on projections and historical land
subdivision methods.
1. Geographic Coordinates
2. UTM – Universal Transverse Mercator
The basic unit is the meter. Adopted for much
R.S. work, topographic map, natural resource
database development.
The Military Grid System.
3. UPS – Universal Polar Stereographic Grid
For Polar regions.
Geographic Coordinates as Data
Coordinate Systems
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UTM
UTM zones in the lower 48
Military Grid Coordinates
Coordinate Systems (con-d)
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4. SPCS – State Plane Coordinate System.
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Unique set of coordinates for each state. Uses
Mercator or Lambert’s conformal conic projection tied
to a national geodetic framework.
Originally – to provide a permanent record of land
survey monument locations. Measured in feet. (State
name, Zone name, easting and northing values).
At 4 times accurate than UTM. Lack of coordination
between state borders.
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 5. PLSS – Public Land Survey System.
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As a tool for recording ownership of land. 1785
Geocoding
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The PROCESS inputting spatial data
into a GIS database by assigning
Geographical coordinates for each point,
line, and area entity.
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Obtaining coregistration between the
input maps, and attribute information.
A move from the real
World…
Cartographic conception
Computer abstraction
Cartographic
abstraction
The Map as an Abstraction
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You MUST create an explicit, rule-based
language that allows the computer to use its
digital view of the world to:
– to identify the spatial extent of each object;
– to locate it in a system of coordinates;
– to separate adjacent objects from one another;
– to identify and sort objects by orientation, size,
location, etc.
Basic File Structures
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A Map is broken down into a sequence of
numbers like attributes;
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
ASCII text – American Standard Code for
Information Interchange.
Binary is faster to read and smaller, ASCII can
be read by humans and edited but uses more
space.
Basic File Structures
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Computer File Structures allow the machine
to store, order, and search of pieces of data.
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BASIC Structures:
– Simple list;
– Ordered sequential files;
– Indexed files.
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Basic File Structures
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A DATABASE – a collection of multiple files.
A Database Structure or A Database
Management System – DBMS
Composed of combination of file structures,
and allow more complex method of managing
data.
BASIC types:
– Hierarchical Data Structure;
– Network System;
– Relational Database Structure.
Relational DBMS
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Based on relational algebra. Relies on set theory.
It provides a set of rules for the design and function of
the system.
A table can’t have any row that COMPLETELY
duplicates any other row of data. Each of the row must
be unique.
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A single (or even multiple) columns can be used for a search.
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A Primary Key is a search criterion for searching the
other columns in the database.
You can match data from one table to corresponding
data in another table by the use of RELATIONAL JOIN.
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Relational join
Relational DBMS
GEOSPATIAL
INTELLIGENCE
LECTURE 4:
GRAPHIC DATA STRUCTURES
Professor, Dr. Sergei Andronikov
Graphic Data Structure
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TWO fundamental methods of indication
geographical space.
Conceptual Models
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“Is the Spatial World a jig-saw puzzle of polygons, or a club-sandwich of
data layers ?” (Coucleis, 1992).
1. The space is occupied by ENTITIES described by their attributes.
Position could be mapped using a geometric coordinate system.
A. To define the entity. B. To list attributes, to define boundaries & location.
2. Variation of the attributes varies over the space as some continuous
mathematical function or FIELD
The simplest conceptual model represents geographical space in terms of
continuous Cartesian coordinates in 2 or 3 (4) dimensions. The attributes
vary smoothly.
The choice of conceptual model determines how information
can be later derived.
Different disciplines: municipal vs. urban landscapes
Spatial Data Models
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Formalized equivalent of the conceptual models.
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Most anthropogenic phenomena can be handled using the
entity approach.
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1. The simplest data model is a basic spatial entity which is further
specified by attributes and geographic location. This can be further
subdivided according to geographical data primitives: “a point”, “a line”, “a
polygon”
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2. With continuous field data the variation is too complex.
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It is necessary to divide geographical space into discrete spatial units. The
resulting tessellation is taken as a reasonable approximation of reality.
Both models assume that the phenomena can be specified exactly in
terms of both their attributes & spatial position
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Spatial Data Models
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The Vector Data Model of entities represents space as a series of
discrete entity-defined point, line or polygon units, geographically
referenced by Cartesian coordinates
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Continuous surfaces can be discretized into sets of single basic units:
square, triangular or hexagonal cells which are tessellated to form Spatial
representations
Triangulation in land surveying
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Alternative – regular grid or tessellation. 2D surface is divided into square
cells – PIXELS. Size is determined by the resolution – to represent the
variation of an attribute for a given purpose.
VOXELS – 3D equivalent to pixels. Pixels and voxels may be used as
5
entities.
Spatial Data Structure
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In life spatial information are represented by lines or
dotted shading. In GIS it is formalized into the vector
and raster methods.
VECTOR GIS:
Areas = lines = points = coordinates
wwwwwwwwwwwwwwwwwwwwwwwwwwwwwww
The Vector Model
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A vector data model uses points stored by their real
(earth) coordinates.
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Lines and areas are built from sequences of points in
order.
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Lines have a direction to the ordering of the points.
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Polygons can be built from points or lines.
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Vectors can store information about topology.
The Vector Formats
.PDF (Adobe format)
 .DWG (AutoCAD format)
 .DXF (AutoCAD Format)
 .KML (Google format)
 .LYR (ESRI format)
 .SHP (ESRI format)
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VECTOR
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At first, GISs used vector data and cartographic spaghetti
structures (unstructured vector data).
Vector data evolved the arc/node model in the 1960s.
The endpoint of a line (arc) is called a node. Arc junctions are
only at nodes.
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.
Stored with the arc is the topology (i.e. the connecting arcs and
left and right polygons).
RASTER GIS
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RASTER METHOD:
It serves to quantize or divide space as a series of
units of any geometric shape.
Often a series of squares – discrete GRID CELLS.
Uniform in size.
Raster Data structure do not provide precise
locational information.
A raster data model uses a GRID
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One grid cell is one unit or holds one attribute.
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Every cell has a value, even if it is “missing.”
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A cell can hold a number or an index value
standing for an attribute.
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A cell has a resolution, given as the cell size
in ground units.
Raster Files Formats
.TIFF
 .JPEG
 .GIF
 .PNG
 .BMP
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Generic structure for a GRID
Grid extent
Rows
Grid
cell
Resolution
Columns
Generic structure for a grid.
Rasters are faster…
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Points and lines in raster format have to move
to a cell center.
Lines can become fat. Areas may need
separately coded edges.
Each cell can be owned by only one feature.
As data, all cells must be able to hold the
maximum cell value.
Rasters are easy to understand, easy to read
and write, and easy to draw on the screen.
The mixed pixel problem
Water dominates
Winner takes all
Edges separate
W W
G
W G
G
W E
G
W W
G
W W
G
W E
G
W W
G
W G
G
E
G
E
Grids and missing data
GIS data layer as a grid with a large section of “missing data,” in this
case, the zeros in the ocean off of New York and New Jersey.
RASTER
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Advantage. The data form their own map in the computer’s
memory. Comparing grid cells require looking at the values in
the next and preceding row and column of the grid cells.
Disadvantage. Grids are poor at representing points, lines and
areas, but good at surfaces.
Grids are good only at very localized topology, and weak
otherwise.
Grids are a natural for scanned or remotely sensed data.
Grids suffer from the mixed pixel problem.
Grids must often include redundant or missing data.
Grid compression techniques used in GIS are run-length
encoding and quad trees.
The quad-tree structure
210
0
1
2
3
0
2
0
2
1
3
1
3
quadrant
number
The quad-tree structure. Reference to code 210.
Vectors just seemed more
correcter.
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Vector can represent point, line, and area
features very accurately LOCATIONALLY.
Vectors are far more efficient than grids
LOCATION-WISE.
Vectors work well with pen and light-plotting
devices and tablet digitizers.
Vectors are not good at continuous coverages
or plotters that fill areas.
TIN – Triangulated Irregular Network – must be
used to represent volumes.
Rasters and Vectors can be flat
files … if they are simple
Vector-based line
Raster-based line
Flat File
4753456 623412
4753436 623424
4753462 623478
4753432 623482
4753405 623429
4753401 623508
4753462 623555
4753398 623634
Flat File
0000000000000000
0001100000100000
1010100001010000
1100100001010000
0000100010001000
0000100010000100
0001000100000010
0010000100000001
0111001000000001
0000111000000000
0000000000000000
Beethoven is vector … Mozart is raster!
Factors to consider
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Is the situation/phenomena simple or complex?
Are the kinds of entities detailed or
generalized?
Do the database entities represent discrete
physical things or continuous field?
Are the attributes obtained by complete
enumeration or by sampling?
Will the database be used for descriptive,
administrative or analytical purposes?
Is the process static or dynamic ?
What Graphic Model to use for…
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A road transport information system
The location of fast food restaurants
The dispersion of pollution in groundwater
An emergency unit (police, fire, ambulance)
A tourist information system
The monitoring of vegetation change in upland areas
The location of landfill sites and environmental impact
assessment study
The incidence of landslides in mountainous regions
The monitoring of movement of airborne pollution after
Chernobyl accident
V
V
R
V
V
R
V+R
R
R
TOPOLOGY
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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.
Basic arc topology
3
n2
2
A
1
n1
B
Topological Arcs File
Arc
1
From To PL PR n1x n1y n2x n2y
n1 n2 A B x y
x y
Figure 3.5 A topological structure for the arcs.
13
11
2
12
10
7
POLYGON “A” 5
9
4
2
1
6
3
8
1
1xy
2xy
3xy
4xy
5xy
6xy
7xy
8xy
9xy
10 x y
11 x y
12 x y
13 x y
Points File
Arc/node map data structure with files
File of Arcs by Polygon
A: 1,2, Area, Attributes
1 1,2,3,4,5,6,7
2 1,8,9,10,11,12,13,7
Arcs File
Figure 3.4 Arc/Node Map Data Structure with Files.
Topology Matters
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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.
Slivers
Sliver
Unsnapped node
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GDB: Geodatabase Format
GDB – a repository of your spatial data inside a Relational
DMBS.
Contains all of your raster, vector data, tables, objects
GDB supports an Object-Oriented Vector data.
Entities are represented as OBJECTS with PROPERTIES,
BEHAVIOR, and RELATIONSHIPS
Object Types include Simple Objects, Geographic features
(objects with locations), networks and topology (with spatial
relationship with other features), annotation features, other.
GDB Model lets you define relationship between objects,
together with rules for maintaining their referential integrity.
The simplest GDB contains a number of independent
feature layers (each contains points, lines, areas,
annotation)
GDB: Geodatabase Format – 2
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GDB stores a feature data itself. User has 2 copies
Advantage – setting up default values for attributes
GDB stores topology, geometric networks,
behavior and validation rules
Feature Datasets contain related feature classes
with the same spatial reference
Domains are maintained in ArcCatalog as a property
of GDB rather than of a single feature class
GDB Annotation is another type of feature class (like
point, line) except it stores labels
Vector Data in GDB – 1
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Vectors are well-suited for discrete features. Vector Contents:
OBJECT CLASS. A DB table with which you can associate
behavior. “Owners” of “Land Parcels”
FEATURE CLASS. A collection of features. Points, lines,
polygons, annotation. Streams, counties, census tracts.
FEATURE ATTRIBUTES.
SPATIAL REFERENCE
SUBTYPES. A Set of classes for the members of a feature class.
Pipe Feature Class – subtypes: PVC, Iron, Concrete
FEATURE DATASET A collection of feature classes with the
same spatial reference. Analogous to ArcInfo coverages. Vital for
facilities networks, roads, environmental layers, census geogr.
RELATIONSHIPS. Association between two objects.
Vector Data in GDB – 2
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GEOMETRIC NETWORKS. A user-defined collection of feature
classes that form part of a connected network of edges,
junctions, and turns. Water network: valves and meters =
junctions, mains and service lines= edges.
The basic methodology for creating a GN is to determine which
feature classes will participate and what role each will play.
OPTIONALLY – a series of Network weights can be specified.
TWO methods:
A
NEW , empty GN
 A GN from existing simple features
Vector Data in GDB – 3
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PLANAR TOPOLOGIES. A user-defined collection of
feature classes that share geometry. Feature classes as
soil types, vegetation, terrain, water can share boundaries.
Update all.
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DOMAINS. Define the valid values for attributes as a range
or a set of value. TYPES:
 Range domain (GPA: from 0 to 4.0)
 Coded domain – allows certain values taken from a list
(PipeDiam: 1, 3, 6, 12).
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VALIDATION RULES. One or more constraints upon the
attribute values, topology or placement of features to
enforce the behavioral integrity of features. How features
are interconnected in networks. 6-inch & 4-inch pipes.
Raster in GDB
Why GDB?
 Enterprise OR Personal GDB
 Large Data holdings: edited, utilized, built
 Choice of creating a Mosaic or a Catalog
 Fast raster dataset display at any scale
 Enhanced raster catalog functionality
 Raster Data compression
 Taking advantage of the Relational DBMS:
security, multi-user access, recoverability, etc.
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Thank you for your
attention!
Questions?