Intermediate Gis

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GIS

Back 1

Geographical Information Systems is any computer‐based system for capturing,
storing, analyzing & managing data, data which are spatially referenced

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Geographic Information Systems vs. Science

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ƒ Geographical Information Systems(GIS) is any computer‐based
systemfor capturing,storing, analyzing&managing data, data
which are spatially referenced
ƒ Geographic Information Science (GISc orGIScience) isthe
academic theory behind the development, use, and application of
geographic information systems(GIS).GISc also addresses
f d tlunamental issuesrai d se by the use ofGIS and reltd ae
information technologies
ƒ Cartography
ƒ Remote sensing & Image processing Remote sensing & Image processing
ƒ Geodesy
ƒ Surveying
ƒ Photogrammetry

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map

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a system of layers

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vector data

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a method of storing, representing or displaying spatial data in
digital form. It consists of using coordinate pairs (x,y) to represent
locations on the earth. Features can take the form of single points, lines,
arcs or closed lines
polygon (set of connected line segments, with the same start/end point), line (simple: set of coordinate pairs, nodes; detailed: multiple pairs, nodes and vertices), point (single coordinate pair)
-features (house, lake, etc)
--attributes
---size, type, length, etc

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raster data

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A method of storing,
representing or displaying spatial data in
digital form.
It consists of using cell data (not
necessarily square) arranged in a regular
grid pattern in which each unit (pixel or
cell) within the grid is assigned an
identifying value based on its
characteristics
-pixels
-a location and value
-satellite images and aerial photos are already in this format

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what can GIS do for you?

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1. Identify a Location, and tell you where you are…
2. Identify Distributions, Relationships & Trends…
3. Integrate Data from Diverse Sources…
4. Combine & Overlay Data to solve Spatial Problems..
5. Decision Support and Resource Management
6 Gis can aid modelling future events

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model

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A Model is an idealized and simplified
representation of reality

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Spatial Modeling

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process of manipulating
and analyzing geographical data to generate
useful information for solving complex problems

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Geographic Representation

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ƒ Representation –How we depict or convey
ideas orinformation aboutthe real world
ƒ Itinvolves choices choices about
ƒ Scale – level of detail, whatto omit
(g ) eneralization)
ƒ Format ‐ Vector or Raster
ƒ Temporal Temporal decisions decisions – whattime period
ƒ Attributes – data type, data classification

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spaghetti vector model

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The spaghetti model is a simple model that stores the data in an unstructured way. This model just stores the name of every object, followed by the coordinates the object is composed ofSince the objects are not related to each other, no topological information is included and the consistency can not be verified. The spaghetti data model has a simple structure, every object is described independently of the others. The same coordinates may appear several times, therefore it needs large amounts of storage space.

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topological vector model

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Another model is the topological model. As new elements, this model introduces nodes and edges. A node is a distinguished point that connects one or several arcs. An edge is a line composed by a start and an end node. Every object is composed by a less complex object. For example, a polygon is the composition of several arcs which are defined earlier. The advantage of this model is the topological information it is containing: every object includes information about the elements it is related to. Since every geometrical is only stored once, there is no redundancy.

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topology

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The Mathematics and Science of geometrical
relationships used to validate the geometry of vector entities

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Topological Relationships

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The properties of geographic
objects that do not change when the forms are bent,
stretched, or undergo similar transformations

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Typical Topological relationships

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Typical Topologicalrelationships
ƒ Connectivity (Lines)
ƒ Directionality (Lines)
ƒ Adj y ( yg ) acency (Polygon)
ƒ Exhaustive (Polygon)
ƒ Planar Topology (No overlaps)
ƒ Non-Planar (Overlaps allowed) Planar(Overlaps allowed)
ƒ Dangles

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Intra‐layer Relationships

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overlap & connectivity

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Shapefile

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ƒ A vector data storage format for storing the location,
shape, and attributes of geographic features.
ƒ A shapefile is stored in a set of related files and contains
one feature class

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Types of Geographic Attributes

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1. Nominal
2. Ordinal
3. Interval
4. Ratio
5. Cyclic

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Nominal

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distinguishes one entity from another (names, SSN)

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Ordinal

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values have a natural order (***movie ratings)

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interval

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difference between values are meaningful and it's along a scale (temperature)

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ratio

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proportions between values are meaningful...value of zero (weight)

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Cyclic data

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used in special cases for directional data, months of the year, compass directions

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Discrete Objects

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vector representation
Representing the geographic world as objects with well
defined boundaries, in otherwise empty space
(a mountain)

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Continuous Fields

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raster representation
Represents the real world as a finite number of variables,
each one defined at every possible location
(elevation)

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Whenwould you use a Inset map

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1. To show a primarymap area in relation to a largermore
recogni blzae area
2. To enlarge imported or congested areas
3. To show alternate thematic topicsthat are related to the
mapstheme, or different different dates ofthe same theme

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Representative Fraction

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The Ratio ofmap distance to earth
distance, and i dincatesthe extentto whi hc a geographic
region has been reduced fromit’s actualsize

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balance

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ƒ Balance refersto the organization ofmap
elements elements and the empty space,resulting resulting in
visual harmony and equilibrium

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contrast

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Contrastrefersto the visual differences betweenmap
featuresthat allow usto distinguish one feature from
another. This can be implemented through:
ƒ Spacing
ƒ Size
ƒ Perspective Height
ƒ Orientation (Shading)
ƒ Shape
ƒ Arrangement
ƒ ALL aspects of COLOR
ƒ FigureGround Relations

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visual hierarchy

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Visual Hierarchy refersto the order ofthe
g p rahicalrepresentation of yourmap
information

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colorblindness

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ƒ If you wantto delineate features using color, choose
blends of yellow or blue, very few people have
problems with identifying these colors.
ƒ Convert your colorsto gray scales, ifthere is a tonal
difference color blind users can still differentiate by
tone.
ƒ Think of using differenttexturestomark areas.
ƒ Unfortunately Unfortunately thisisn t' easy to do inGoogle Earth.

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Data Classification

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1. Equal Intervals
2. Quantiles
3. Natural Breaks
4. Mean‐Standard Deviation
5. Optimal
6. Manual
7. Geometrical Interval

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Equal Intervals

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Dividesthe range of
attribute valuesinto equal‐
sized sub ranges
ƒ Advantages
ƒ Easy Calculation
ƒ Easy Interpretation
ƒ NoGaps
ƒ Disadvantages
ƒ Does not consider data
distribution along the number
line

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Quantiles

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Equal numbers of
observations are placed in
each class
ƒ Advantages
ƒ Easy (manual) Calculation
ƒ Allows use ofthe complete
colorspectrum
ƒ Disadvantages
ƒ Identical Data values MAY be
placed in different classes
ƒ Again,failsto consider how
data is distribution along the
numberline

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Natural Breaks

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Classes are based on natural
groupingsih t nerenin the
data through examination of
the histogram
ƒ Advantages
ƒ Minimizesthe differences
between data valuesin the same
class,&maximize the differences
between classes
ƒ Disadvantages
ƒ Data ranges are usually uneven

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Mean – StandardDeviation

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This classification scheme shows
you how much a feature feature s'
attribute value variesfrom the
mean.
ƒ Advantages
ƒ Considers how data is distributed
along the numberline
ƒ If data are normally di ib d stributed,
then the Mean is a natural
dividing point
ƒ Di d tsavanages
ƒ ONLY works well for data that
are normally distributed

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Geometrical Interval

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This is a classification scheme
wh hl bk here the class breaks are
based on class intervals that
have a geometrical series.
– An algorithm creates these
geometrical intervals by minimizing
the square sum of element per class
– Ensures that each class range has
approximately the same number of
values with each class and that the
change between intervals is fairly change between intervals is fairly
consistent.
– Produces a result that is visually
appealing and cartographically appealing and cartographically
comprehensive

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Generalization

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the process ofreducing the
information information content content ofmaps, due to scale,
map purpose, intended audience, and/or
technical constraints
-moving from a large scale to a small scale map, you will have issues with congestion/conflict...communication is hampered, features collide due to reduction in scale, looks busy, messy
-why generalize: reduce complexity, improve aesthetic quality

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coordinate system

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A coordinate system is a reference system used to represent the locations
of geographic features, imagery, and observations such asGPS locations within a common geographic framework.
Each coordinate system is defined by:
Its measurement framework, which is either geographic (3 D) or projected (planimetric) (2 D)
 Unit of measurement 
The definition of the map projetion for projected coordinate systems
Other measurement system properties such as a spheroid of reference; a datum; and projection parameters like one or more standard parallels, a central meridian, and possible shifts in the x and y directions

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Geographic coordinate systems

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global or spherical coordinate 
system such as latitude longitude, typically expressed as Degree 
Minute Seconds (DD:MM:SS) or Decimal Degrees (DD)

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Projected coordinate systems

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 coordinate system that provides 
various methods to project the earth's spherical surface onto a 
two dimensional Cartesian coordinate plane.

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geographical coordinate system or GRATICULE

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latitude, longitude

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latitude

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Latitude is an angular distance, North or South of the Equator measured
from the center of the earth

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longitude

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Longitude is an angular distance, East or West of a point on the Earth’s
surface, measured from the center of the earth

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datum

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 Every Geographic Coordinate System includes a unit of 
measurement, a prime meridian, and a datum 
 A datum defines the position of the spheroid relative to the 
center of the earth

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Map Projection Technique

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1. reduce earth's size to that of an imaginary globe
2. project the graticule from reference globe onto the developable surface

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reference globe

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A model of the earth at a reduced scale, that is used to project the 
landmasses and graticule onto a flat map

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developable surface

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A mathematically definable 
surface onto which the land 
masses and graticule are 
projected from the reference 
globe

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class

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This refers to the overall appearance of the graticule, once the projection process
is complete. There are three common classes: Cylindrical, Conic or Planar

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cylindrical class

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Cylindrical Characteristics
 Lines of longitude are straight, 
equally spaced
 Lines of latitude are straight, 
parallel and intersect lines of long 
at right angels
Distinguishing Features
 The spacing of the parallels 
distinguishes one type of 
cylindrical projection from 
another

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conic class

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Conic Characteristics
 Lines of longitude are straight 
lines of equal length, radiating 
from a central point (poles)
 Lines of latitude are concentric 
circular arcs centered around one 
of the poles
Distinguishing Features
 “Pie Wedge” Shape
 The angular extent of the wedge, 
and the spacing of the parallels 
distinguish one conic projection 
from another

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Planar Class

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Planar Characteristics
 Lines of longitude are straight, 
equally spaced, parallel lines that 
radiate from the center
 Lines of latitude appear as 
equally spaced concentric circles, 
centered about a point
Distinguishing Features
 Again, The spacing of the 
parallels distinguishes one type of 
planer projection from another

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case

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The Case of a projection relates to how the 
developable surface is positioned with respect to 
the reference globe
Case can be described as 
 Tangent (@ a single parallel) or 
 Secant (@ two parallels)

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how case effects distortion

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-projection distortion inside secant lines makes features slightly smaller
-secant lines are the only part of the projection plane without distortion
-projection distortion outside secant lines makes features slightly larger

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mercator v. lambert conformal conic

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The Mercator projection is a tangent
cylindricaltype, shown here in its 
familiar equatorial aspect (cylinder 
wrapped around the equator). 
The Lambert Conformal Conic
projection is a secant conic type. In 
this instance, the cone onto which 
the surface was projected intersected 
the Earth along two lines of latitude: 
20 North and 60 North.

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Aspect

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The Aspect of a Projection deals with the placement of then projections center with
respect to the earth’s surface. A projection can have one of three aspects:
Equatorial, Polar or Oblique

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 FOUR Spatial Relationships

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There are FOUR Spatial Relationships that can be 
preserved or distorted by a particular map 
projection:
-area (equivalent, or equal area)
-shape (conformal)
-distance (equidistant)
-direction (azimuthal projections)

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mollweide

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EQUAL area

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mercator

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conformal

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Geographical Coordinate System

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Spherical Coordinates (lat/long or decimal degrees

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Projected Coordinate Systems

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Rectangular/Planimetric Coordinate System
(derived from Cartesian Coordinates)

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Geocoding

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 the process of finding associated 
geographic coordinates (often expressed as latitude 
and longitude) from other geographic data, such as 
street addresses, or zip codes (postal codes).

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Georeferencing

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 Aligning geographic data to a known 
coordinate system so it can be 
viewed, queried, and analyzed with 
other geographic data. 
Georeferencing may involve shifting, 
rotating, scaling, skewing, and in 
some cases warping, rubber 
sheeting, or orthorectifying the data
 Use ground control pointsto 
“anchor” the unreferenced image to 
a georeferenced, map, vector data 
or other georeferenced image

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(Rubbersheeting)

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When you Georeference your raster dataset, you 
define its location using map coordinates and assign 
the coordinate system of the data frame. 
The general steps for Georeferencing a raster dataset are 
 Add the raster dataset that you want to align with your projected 
data in ArcMap. 
 Add control points that link known raster dataset positions to known 
positions in map coordinates. 
 Save the georeferencing information when you're satisfied with the 
alignment (also referred to as registration). 
 Optionally, permanently transform the raster dataset.

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distributed GIS

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There are four distinct components of significancet o
distributed GIS:
ƒ The location of the user and the user interface,U
ƒ The location of thedata,D
ƒ The location wherethe data are processed,P
ƒ The area thati sthe focus of the project, the subject
location,S

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Object‐level metadata & Collection‐level metadata

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ƒ Automatesearchanddiscovery
ƒ Assessfitnessforuse
ƒ Provideinformationforeffectivehandling
ƒ Provideusefulinformationondataset‘scontents

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Geolibraries

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Digital Libraries for user defined geographic location

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VirtualrealityandAugmentedreality

Back 67

ƒ VR:databaseencapsulatedresearchenvironments
ƒ AR:combininginformationfromadatabasewith
informationderiveddirectlythroughthesenses

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location based services

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information service provided by a spatially aware device, which is capable of modifying the information depending on location

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gis service

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program executed at a remote site that performs some specific GIS task

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service oriented architecture

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non commercial applications of GIService (emergency management, public awareness)

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geovisualization

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refers to a set of tools and techniques supporting geospatial data analysis through the use of interactive visualization
-emphasizes knowledge construction over knowledge storage or info transmission
-communicates geospatial information in ways that, when combined with human understanding, allow for data exploration and decision making processes

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volunteered geographic info

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harnessing of tools to create, assemble, and disseminate geographic data provided voluntarily by individuals

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crowd sourcing data

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-outsourcing a task to the community at large, via an open call
(data capture, technology design)

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open street map

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project aimed squarely at creating and providing free geographic data such as street maps to anyone who wants them

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5 steps to add data to open street map

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collect data
upload data
create/edit osm data
label data, and add details
render and use maps

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where/how do we collect data?

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-GPS - systematic ground surveys using a handheld GPS unit and anotebook, digital camera
-local knowledge - augmenting already present vector data
-imagery: trace features (no importing of names due to copyright) and sources (purchased or donated imagery, yahoo & landsat), personal maps or photography

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ushaidi

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built for info collection, visualization, and interactive mapping
-website that initially developed to map reports of post election violence in kenya

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quantum (open source desktop GIS)

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-official project of OSGeo
-support vector and raster
Quantum GIS provides a continously growing number of capabilities provided by core functions and plugins. You can visualize, manage, edit, analyse data, and compose printable maps

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open source software

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-open source software can be defined as computer software for which the human readable source code is made available under a copyright license (or arrangement such as the public domain) that meets the Open source definition
-this permits users to use, change, and improve the software, and to redistribute it in a modified or modified form. it is very often developed in a public, collaborative manner

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primary data collection

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direct measurement

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secondary data collection

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reused from earlier studies or obtained from other systems
-data capture (direct entry)
-data transfer (importing existing digital data)

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primary raster & vector collection class examples

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raster: digital satellite remote sensing images and digital aerial photographs
-vector: gps and survey (x,y,z locations), geocoding (addresses to x,y)

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secondary raster & vector collection class examples

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raster: scanned maps or photographs, digital elevation models from map contours, or spot heights, or satellite imagery
-vector: existing vector data, manual digitizing from scanned maps

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remote sensing

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 Remote Sensing isthemeasurement of physical,
chemical, and biological properties of objects
without direct contact
 Satellite Imagery
 Aerial Photography

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Surveying

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Surveying isthe art and science ofmeasuring the
surface ofthe earth and itsfeatures.

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Geodetic surveys

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take into account the true shape of the earth

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GPS

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A constellation ofGlobal
Positioning System(GPS)
satellites orbiting the earth is
used to determine the
position(s) of ground receivers

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plane surveys

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Plane surveystreatthe earth as a flatsurface
 Horizontalsurveys determine the position offeatures on the ground
 Verticalsurveys determine the elevation or heights offeatures

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human errors in digitizing

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undershoots and overshoots (where things meet, for example)
-invalid polygons (shapes that don't fit...dangling segment)
-sliver polygons (dooesn't fit)

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topology

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math & science of geometrical relationships used to validate the geometry of vector entities

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typical topological relationships

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connectivity & direcitonality (lines)
adjacency & exhaustive (polygon)
planar topology (no overlaps)
dangles

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intra layer relationships

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overlap and connectivity

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uncertainty

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complexity in the way real world is conceived, represented, and analyzed
-it is inevitable
-never take data as truth, esp 2nd hand
-uncertainty in analyzed data can be greater than you expect
-work w/ multiple sources of data whenever possible
-avoid spurious precision

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u1 conception

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vagueness (construction of artifical units of analysis)
-what is out of confidence in the placement of boundaries? what are the implications?
ambiguity
-linguistic ambiguity
-direct versus indirect indicators

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u2 representation

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-vector - discrete objects
--discrete objects aggregated for simplicity, or privacy reasons
-raster - continuous field
--it is classification of each cell that builds into the representation
--resolution can lead to uncertainty
--mixel (mixed pixel)

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accuracy

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degree to which information on a map or in database matches true or accepted value

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precision

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refers to the level of measurement and exactness of description in a GIS database

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bias

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consistent error throughout a dataset

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u3 analysis

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3 ways in which gis analyst can deal with error:
1. pinpoint the ways error can propogate (and degree of distortion that will arise)
2. model within zone spatial distribution
3. external validation through external diverse data sources

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spatial autocorrelation

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data from locations near one another in space are more likely to be similar than data from locations remote from one another

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Effects of Aggregation & Scale

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 Data collection does not alwaystake the
analysis processinto account
--You have to careful how you cross attribute info
 Ecological fallacy
 MAUP
 Scale

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MAUP

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modifiable areal unit problem
-problem associated with aggregate data sources
-use of arbitrary spatial units
-problem of scale
-ecological fallacy (errors due to performing analyses on aggregate data when trying to reach conclusions on the individual units)

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Concatenation

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the integration oftwo ormore different data sources
 Using information fromone to “FIX” the other

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Conflation

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replace two ormore versions ofthe same information
with a single version
 Sort ofmerged re‐versioning

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data quality

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-fitness for use of data for an intended purpose
-with GIS, you can integrate diverse data sets...but sometimes the data is used in ways different from its original intent
-GIS programs hide underlying data quality issues
-important because without it we do not know what confidence to put in the results of our analysis

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when is error introduced?

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data collection phase
data input and editing phase
methodological phase

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broad types of error

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spatial errors, attribute errors, conceptual errors, procedural/analytic error (logical error)

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specific types of error

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 Positional accuracy
 Accuracy of content
 Sources of variation in data
 Errors Arising Through Processing
 Numerical Errors
 Errorsin Topological Analysis
 Classification andGeneralization Problems.
 Digitizing andGeocoding Errors

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data model

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simplified or abstracted representation of reality

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Spatial Data Model

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-simplified or abstracted representation of geographic reality
a set of constructs for representing Geographical objects, data, processes, and relationships in the digital environment, for the purposes of analysis and complex problem solving

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data modeling cycle

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often move from real world elements that are the subject of data capture, to a conceptual model, usually a diagram composed of the relevant features and objects
-there is often a cyclical iterative process before one gets to create a physical geodatabase, leading to recreation of the database

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conceptual model

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The conceptual model is a human-oriented, often partially structured, model of selected objects and processes that are thought relevant to a particular problem domain.
The conceptual modeling phase begins with definition of the main types of objects to be represented in the GIS and concludes with a conceptual description of the main types of objects and relationships between them – in a very specific (attribute table specific way)

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LOGICAL MODEL

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LOGICAL MODEL
The logical model is an implementation-oriented representation of reality that is often expressed in the form of diagrams and lists.
The logical modeling phase leads to the creation of diagrams and lists describing the names of objects, their behavior, and the type of interaction between objects

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physical model

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The physical model portrays the actual implementation in a GIS, and often comprises tables stored as files or databases
The physical modeling phase involves describing the exact files or database tables used to store the data, the relationships between objects types, and the precise operations that can be performed. Basically making the geodatabase that will house your elements (very specific)

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Simplified Modeling Process for Problem Solving

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Identify the problem
Breakdown (simplify) the problem
Organize the data required to solve the problem
Develop a clear & logical flowchart using well defined operations
Run the model and modify it if necessary

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GIS data models

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many different types, different scales, levels of detail
-raster
-vector
-TIN
-object data model

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raster data model

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-uses an array of cells or pixels to represent real world objects
-cells in each line of the image are mirrored by an equivalent row of numbers/letters/classes in the file structure
-especially suited to mapping continuous spatial features or phenomenon, such as elevation, temp, rainfall - anything w/ significant changes in value over large areas
The advantages of storing your data as a raster are as follows:
A simple data structure—A matrix of cells with values representing a coordinate and sometimes linked to an attribute table
A powerful format for advanced spatial and statistical analysis
The ability to represent continuous surfaces and perform surface analysis
The ability to uniformly store points, lines, polygons, and surfaces
The ability to perform fast overlays with complex datasets
-negative: spatial inaccuracies due to limits imposed by raster dataset cell dimensions

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cell dimension

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length and width in surface units

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level of detail

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trade off between spatial detail and file size

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spatial precision

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Positional accuracy is assumed to be no better than one half of the cell size – WHY? – A cell coordinate is usually defined as an area in the center of the cell – but this coordinate applies to the entire area covered by the cell – therefore positional accuracy is…… You need to take the cell size into account when creating raster data as it affects the desired accuracy of your data

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Data Assignment

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Point Physical Value – Taking a point reading an assigning the value throughout the cell, eg information read from stations, like elevation - temperature
Statistical value – some averaged values across the cell
Classification Data – discrete values assigned to cells based on classification process
Point/Line/Polygon reassignment – point/line – usually attribute the cell with a value if the feature crosses it at all, or some accepted proportion.

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Within cell variation

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Each represents a given area on the ground and is assigned a value that is considered to apply to the entire cell – if the variable is uniform across the cell, this is OK, but what about instances where there is this intracellular variation. – it may be a winner take all approach. The feature that takes up the largest amount of the cell gets the assignment – or you may set one feature type as preferential and it gets the assignment

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vector data model

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consists of the idea of using coordinate pairs to represent locations on the earth
-precise data representation, small file storage, and quality of cartographic output
-Vector Data is more commonly adopted for what we call discrete data view (specific objects, rather than continuous surfaces). Cities, airports, schools, roads, admin boundaries, rather than temperature/rainfall etc

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object data modeling

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key behind this is to look at a collection of geographic objects, and the relationship between those objects
-encapsulate complex systems that may be a combination of many diff features and feature classes (or layers)

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inheritance

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Object classes are often defined hierarchically, taking advantage of one of the most important concepts in object-oriented systems: inheritance. It is possible to define more general classes, containing the structure of a generic type of object, and then specialize this class by creating subclasses. The subclasses will inherit all properties of the parent class and add some more of its own. For instance, when creating an object class to represent water network nodes, one might define subclasses to be pump and reservoir, which would inherit generic water node characteristics such as code and location, along with their relationship with water pipes, and aggregate specific characteristics such as pump throughput and reservoir capacity

For a city, a building may be commercial, may be food, restaurant, versus grocery store.

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encapsulation/behavior

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-each object can carry a description about its state and behavior
--state=properties or attributes
--behavior = methods or operations that can/can't be performed on that object
-ability to hide from the user the internal structure of an object...it is only possible to manipulate the object's data using a set of predefined functions, thus ensuring data independence. the internal definition of the data structure can then change, without influencing what the user perceives

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principles of object data modeling

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identity, inheritance, encapsulation

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identity

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things in the world are instances of classes, which form hierarchies
-Each subclass shares the properties of the class

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rules for object data models

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attribute, relationship, connectivity, geographic

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Static Modeling

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the series of steps required to achieve some final result
Identifying available land for development
Siting of a new school/store

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Dynamic modeling

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performed in a similar fashion, but has additional parameter requiring several iterations of the model
Disease outbreak
Scenario repeated over several counties/state

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model builder

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models are workflows that string together sequences of geoprocessing tools, feeding the output of one tool into another tool as input

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triangulated irregular network

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TINs are a form of vector-based digital geographic data and are constructed by triangulating a set of vertices (points). The vertices are connected with a series of edges to form a network of triangles. 

Height observations are joined together with straight lines to create a mosaic of irregular triangles
The vertices of the triangles (the point that the data is collected from) ideally represent peaks, depression, while the edges represent ridges and valleys (linear features)

The surface of the individual triangles provides us with area (polygon), gradient (slope) and orientation (aspect)

Advantages
Much more efficient storage of data (Only the points need to be stored to create the TIN surface)
Advantages of TINs are
o the density of sampled points can be adjusted to reflect relief
o they incorporate the original sample points
o easy to calculate elevation, slope, aspect, and line-of-sight

Limitations of TINs are
o They are

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geometric networks

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can only travel in one direction at a time

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transportation networks

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allow travel in both directions

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network

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A network is a system of interconnected elements, such as edges (lines) and connecting junctions (points), that represent possible routes from one location to another.