spatial analysis and modeling

• Spatial is relating to the position, area, shape and size of things.

• Spatial describes how objects fit together in space, on earth.

• Data are facts and statistics collected together for reference or analysis.

• Spatial data are data that are connected to a place in the Earth.

• Spatial data are data/information about the location and shape of, and relationships among, geographic features, usually stored as coordinates and topology

1Spatial Analysis and Modelling by Tadele Feyssa, Wollega

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• Principally, there are three spatial data components that need to be stored for GIS data:

geometric data,

thematic data, and

a link identification (ID) for the geometric and the thematic component.


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Spatial data link between the geometric component

(which deals with the location of the data by means, for

example, of a reference coordinate system) and the

thematic component (it provides the attribute values of

the data, e.g. names, and other identifiers (IDs) of the

data).

Object or feature needs to be geometrically and

thematically described 3Spatial Analysis and Modelling by Tadele Feyssa, Wollega

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• Components of Spatial Data

Spatial data


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• All GIS software has been designed to handle spatial data.

• Spatial data are characterized by information about position connections with other features and details of non-spatial characteristics


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SPATIAL DATA

SPATIAL NON-SPATIAL

Wolega University

ADDRESS NAME

Block 41

MAP DATABASE

Block 32


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SPATIAL DATA CRITERIA:

• X-Y Coordinate System

• Shape

• Area/Size

• Perimeter

• Distance

• Neighborhood


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WHAT ARE ELEMENTS OF SPATIAL DATA?

BuildingTopography

Land use

UtilitySoil Type

Roads

District

Land Parcels

Nature of Geography Objects


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ATTRIBUTES:

• Explains about spatial data

• Relevant non-spatial data

• Words or Numbers

• Qualitative methods

• Quantitative methods


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NATURE OF SPATIAL DATA

(GEOGRAPHIC OBJECTS)

• spatial component

– relative position between objects

– coordinate system

• attribute component

– explains spatial objects characteristics

• spatial relationship

– relationship between objects

• time component

– temporal element


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Analysis

Analysis is the process of inferring meaning from data.

Analysis is carried visually in a GIS

Analysis in a GIS can also be carried out by

measurements, statistical computations, fitting models to

data values other operation

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Spatial analysis concept

Spatial analysis is the process by which we turn

raw data into useful information

Spatial analysis is the crux of GIS because it

includes all of the transformations, manipulations,

and methods that can be applied to geographic

data to add value to them, to support decisions,

and to reveal patterns and anomalies that are not

immediately obviousSpatial Analysis and Modelling by Tadele Feyssa, Wollega

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In a narrow sense, spatial analysis has been described as

a method for analyzing spatial data, while in a broad sense

it includes revealing and clarifying processes, structures,

etc., of spatial phenomena that occur on the Earth’s

surface.

Ultimately, it is designed to support spatial decision-

making, and to serve as a tool for assisting with regional

planning and the formulation of government policies,

among other things.13Spatial Analysis and Modelling by Tadele Feyssa, Wollega

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Spatial Analysis


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The data analysis domain of a GIS, includes a variety

of data processing functions that aim at deriving

spatial relationships, patterns and trends, that are

implicit in the source data.

The results of data analysis may be used immediately

for spatial problem solving and decision making or as

input for further spatial analysis and modeling.

Spatial data analysis is computing from existing,

stored spatial data new information that provides new

insight15Spatial Analysis and Modelling by Tadele Feyssa, Wollega

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Spatial analysis could be either for prescriptive or

predictive applications.

Prescriptive model:

Used for planning & site selection.

This involve the use of criteria & parameters to

quantify environmental, economic & social factors.

The model enumerates a number of conditions to be

met.16Spatial Analysis and Modelling by Tadele Feyssa, Wollega

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Predictive model:

A forecast is made of the likelihood of future events.

Various spatial data layers used (raster or vector).

Analytical questions, such as why or what if.

It is intended to construct models and perform

predictions.

E.g. Pollution, erosion, landslides.


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Application

Road construction

Construction in mountainous areas is complex

engineering task, Cost factors, such as the number of

tunnels & bridges to be constructed,

Volume of rock & soil to be removed.

GIS can help to compute such costs on the basis of an

up-to-date DEM and soil map.


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Modeling

Models are used in many different ways, from simulations

of how the world works, to evaluations of planning

scenarios, to the creation of indicators of suitability or

vulnerability

Model is a simplification of reality in be viewed as a

model.


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Modeling

In the field of GIS, modelling provide understanding of the

way the world works with sufficient precision and

accuracy to allow prediction and confident decision-

making.

Modeling concern the way in which analyses are carried

out using standard functionality


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2 GIS Analysis Functions

This chapter is organized to take you from data to

information and ultimately to decision-making.

It covers some of the options in GIS for data analysis.

Data analysis is the most interesting part of a GIS project.

B/c it is where one can start to find answers to some of

his/her questions, and use GIS to help develop new

questions for research.


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GIS Analysis Functions …Analysis that is undertake with GIS may lead to new

information that will inform decision making.


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Once the data input process is complete and your GIS layers

are preprocessed, you can begin the analysis stage.

GIS analysis functions use the spatial and non-spatial

attribute data to answer questions about real-world

Analyzing geographic data requires critical thinking and

reasoning.

When use GIS to address real-world problems, you will come

up against the question that which analysis function you want

to use and to solve the problems 23Spatial Analysis and Modelling by Tadele Feyssa, Wollega

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You look for patterns, associations, connections,

interactions, and evidence of change through time and

over space.

GIS helps you analyze the data sets and test for spatial

relationships, but it does not replace the necessity for you

to think spatially.


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GIS Analysis Functions…

What makes GIS unique is the ability to:

link data to spatial locations and

query and summarize these data based on specific analysis

requirements.

Functionally, GIS provides a sophisticated tool for

reporting the results of a database.

These reports may be for an entire dataset (or table) or

for a portion of the dataset (e.g., based on the results of a

query or data summary)25Spatial Analysis and Modelling by Tadele Feyssa, Wollega

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Analysis FUNCTIONS of GIS Includes:

Measurements

Query

Extraction

Proximity

Classification

Topology

Network analysis …


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MEASUREMENT

Measurements are simple numerical values that describe

aspects of geographic data.

Measurement functions in GIS includes

Distance,

Perimeter and

Area


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Many types of interrogation ask for measurements

we might want to know the total area of a parcel of land, or the

distance between two points, or the length of a stretch of road

and in principle all of these measurements are obtainable by

simple calculations inside GIS.

Comparable measurements by hand from maps can be very tedious

and error-prone.

28

Tadele F Spatial Analysis and Modelling


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Measuring Distance

There are many ways to measure distance.

Most GIS programs have a ruler button that allows you to

measure distances across a map.

After clicking the button, you point on the map where you

want to begin your distance measurement and then click

at the ending point (or intervening points that define the

path you want to measure).


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Many vector-based systems measure distances along

existing vector line networks, like streets, sewers, and

railroads

Example: shortest distance from Wollega University to

Nekemte bas station

This type of distance measurement relies on topological

network relationships


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Measuring Area/Perimeter

Many vector systems automatically generate area and

perimeter measurements for polygon features and store

these values in prescribed attribute fields.

The systems that do not have this automatic function do

provide a way for you to generate area and perimeter and

store the results in user-defined fields.

Once calculated and stored, you can select multiple

polygon features and sum their area and perimeter


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MEASUREMENT

DISTANCE

Perimeter

AREA/SIZE

A

B

C

D

X Y5 KM

A- B = 20 = 40%

B- C = 20 = 40%

C - D= 10 = 20%

2

10 km32Spatial Analysis and Modelling by Tadele Feyssa, Wollega

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QUERY

Queries are the most basic of analysis operations, in

which the GIS is used to answer simple questions posed

by the user.

No changes occur in the database, and no new data are

produced with these type of selection


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Cont…

The operations vary from simple and well-defined queries

like ‘how many houses are found within 1 km of this point’,

to vaguer questions like ‘which is the closest city to Los

Angeles going north’, where the response may depend on

the system’s ability to understand what the user means by

‘going north’


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Attribute Query (Boolean Selection)

It involves picking features based on query

expressions, which use Boolean algebra (and, or, not),

set algebra (>, <, =, >=, <=),

arithmetic operators (=, -, *, /), and

user-defined values.

Simply put, the GIS compares the values in an attribute field with

a query expression that you define


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Cont…

For example, if you want to select every restaurant

whose price is considered inexpensive, you would

use a query expression like “PRICE = $”

where “PRICE” is the attribute field under

investigation,

“=” is the set algebra operator, and “$” is the value


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Spatial Selection (Spatial Searches/query)

While attribute queries select features by sorting through

records in a data file, spatial selection chooses features from

the map interface.

In most cases, it selects features from one layer that fall within

or touch an edge of polygon features in a second layer (or an

interactively drawn graphic polygon).


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Cont….

There are many types of spatial selection like point

in polygon, it is a spatial operation in which points

from one feature dataset are overlaid on the

polygons of another to determine which points are

contained within the polygons.

38



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Cont….

There are many types of spatial selection like point

in polygon, it is a spatial operation in which points

from one feature dataset are overlaid on the

polygons of another to determine which points are

contained within the polygons.

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Extracting portions of data helps to isolate specific areas for

further processing or data analysis.

Similar to queries and selection sets, extraction functions can

reduce the size of datasets and/or facilitate more complex

interpretation.

Queries and selection sets also allow to isolate portions of a

dataset

Extraction techniques differ in that these portions of data are

isolated in a permanent way - through the creation of new

data layers 40Spatial Analysis and Modelling by Tadele Feyssa, Wollega

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Cont…

GIS software packages provide a suite of tools to extract

data, the most useful being, clip,select, and split

Extracts input features that overlay the clip features.

Working much like a cookie-cutter.

This is particularly useful for creating a new feature class—

also referred to as study area or area of interest (AOI)—that

contains a geographic subset of the features in another,

larger feature class41Spatial Analysis and Modelling by Tadele Feyssa, Wollega

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Cont…Use this tool to cut out a piece of one feature class using

one or more of the features in another feature class as a

cookie cutter

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Clip is useful for developing a subset of features from a series of existing data layers to match a common boundary. Eg Addis Ababa city planner might wish to look at a street network layer, but only those streets falling within a Addis Ababa city boundary. Assume that the street network includes Finfine special zone. Clipping would be useful in order to permanently extract the street features matching the extent of the city boundary.


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Split is used to divide an input layer into two or more

independent layers:

based on geographically corresponding features in a split layer

Input output layer

The Split Features dataset must be polygons. 43Spatial Analysis and Modelling by Tadele Feyssa, Wollega

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Extracts features from an input feature class or input

feature layer, typically using a select or Structured Query

Language (SQL) expression and stores them in an output

feature class.

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Eg. Urban planner might wish to look

at only double-line streets in the

particular municipality of interest.

In this case, he or she would execute

a selection query to extract only those

desired features to a new layer


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CLASSIFICATION

Classification is the procedure of identifying a set of

features as belonging to a group and defining patterns.

Some form of classification function is provided in every

GIS.

Classification is important because it defines patterns.

One of the important functions of a GIS is to assist in

recognizing new patterns.

Classification is done using single data layers, as well as

with multiple data layers as part of an overlay operation. 45Spatial Analysis and Modelling by Tadele Feyssa, Wollega

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When you perform a classification, you group similar

features into classes by assigning the same symbol to each

member of the class.

Aggregating features into classes allows you to spot

patterns in the data more easily.

The definition of a class range determines which features

fall into that class and affect the appearance of the map.


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By altering the class breaks (the boundary between

classes), you can create very different-looking maps.

Classes can be created manually, or you can use a

standard classification scheme.


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TOPOLOGY

In geodatabases, topology is the arrangement

that defines how point, line, and polygon features share

coincident geometry.

For example, street centerlines and census blocks share

common geometry, and adjacent soil polygons share their

common boundaries.

Topology is the science and mathematics of relationships used

to validate the geometry of vector entities, and for operations

such as network tracing and tests of polygon adjacency48Spatial Analysis and Modelling by Tadele Feyssa, Wollega

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Topological or topology-based data are useful for

detecting and correcting digitizing errors (e.g. two lines in

a roads vector layer that do not meet perfectly at an

intersection).

Topological errors violate the topological relationships

that are either required by a GIS package or defined by

the user

Topology is necessary for carrying out some types of

spatial analysis, such as network analysis.49Spatial Analysis and Modelling by Tadele Feyssa, Wollega

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Topological Errors

There are different types of topological errors and they can

be grouped according to whether the vector feature types

are polygons or polylines.

Topological errors with polygon features can include

unclosed polygons, gaps between polygon borders or

overlapping polygon borders.


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Topological Errors

A common topological error with polyline features is that

they do not meet perfectly at a point (node).

This type of error is called an undershoot if a gap exists

between the lines, and an overshoot if a line extend

beyond the line

The result of overshoot and undershoot errors are so-called

'dangling nodes' at the end of the lines.


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Dangling nodes are acceptable in special cases, for

example if they are attached to dead-end streets

Arc-node data model

Arc: a series of points that start and end at a node

Node: an intersection point where two or more arcs meet

Nodes that are close together are snapped.

Slivers due to double digitizing and overlay are

eliminated.52Spatial Analysis and Modelling by Tadele Feyssa, Wollega

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Topological errors

53

Undershoots (1) occur when digitized vector lines that should connect to each

other don't quite touch. Overshoots (2) happen if a line ends beyond the line it

should connect to. Slivers (3) occur when the vertices of two polygons do not

match up on their borders.Spatial Analysis and Modelling by Tadele Feyssa, Wollega

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Slivers

Sliver

A common error in overlaying

polygon layers is Silvers

Silvers are very small polygons along

correlated or shared boundary lines


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Unsnapped node


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OVERLAY ANALYSIS

Overlay is one of the most common and powerful GIS

functions.

It investigates the spatial association of features by “vertically

stacking” feature layers to investigate geographic patterns

and determine locations that meet specific criteria.

An overlay operation combines the geometries and attributes

of two feature layers to create the output

Feature layers to be overlaid must be spatially registered and

based on the same coordinate systems56Spatial Analysis and Modelling by Tadele Feyssa, Wollega

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Feature type and overlay

There are two group of overlay operations

The first group uses two polygon layers as input

The second group uses one polygon layer and other layer

which may contain points or lines


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Overlay operation can be classified as:

Point-in-polygon overlay

Line-in-polygon overlay

Polygon-on-polygon overlay


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Poin

t-in

-po

lygo

n o

verl

ay

Lin

e-i

n-p

oly

go

n o

verl

ay


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Polygon-on-polygon overlay


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Overlay Methods

Overlay methods are based on the Boolean connectors

AND, OR and XOR

Intersect uses the AND connector

Union uses the OR connector.

Differences uses XOR connector

Union preserves all features from the inputs

The area extent of the output combines the area extents

of both input layers62Spatial Analysis and Modelling by Tadele Feyssa, Wollega

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AND


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Union requires that both input layers be polygon layers

Intersect preserves only those features that fall within the

area extent common to the inputs

The input layers may contain different feature types

although in most cases one of them is a point, line or

polygon


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OR


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XOR

Symmetrical difference preserves features that fall within

the area extent that is common only to one of the inputs.

In other words symmetrical difference is opposite to

intersect in terms of the outputs area extent

Symmetrical difference requires polygons for both inputs


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Identity preserves only features that fall within the area

extent of the layer defined as the input layer the other

layer is call the identity of layer

The input layer may contain points, lines or polygon and

the identity layer is a polygon layer


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Application of Overlay

An overlay operation combines features and attributes

from the input layers

The overlay output is useful for query and modelling

purposes.

For example a company who is looking a parcel that is

zoned a commercial area, not subject for flooding and not

more than a mile from heavy duty road may use overlay

method to identify the area


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NEIGHBORHOOD FUNCTIONS

Neighborhood operations consider the characteristics of

neighboring areas around a specific location.

The principle here is to find out the characteristics of the

vicinity, here called neighborhood, of a location.

After all, many suitability questions, for instance, depend

not only on what is at the location, but also on what is near

the location.

Thus, the GIS must allow us ‘to look around locally’.69Spatial Analysis and Modelling by Tadele Feyssa, Wollega

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To perform neighbourhood analysis, we must

1. state which target locations are of interest to us, and

what is their spatial extent,

2. define how to determine the neighbourhood for each

target,

3. define which characteristic(s) must be computed for

each neighbourhood.

These functions either modify existing features or create

new feature layers, which are influenced, to some degree,

by the distance from existing features


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For example, our target is Nekemte Hospital. Its

neighborhood can be defined as

• an area within 2 km distance, as the crow flies, or

• an area within 2 km travel distance, or

• all roads within 500 m travel distance, or

• all other clinics within 15 minutes travel time, or

• all residential areas, for which the clinic is the closest

clinic71Spatial Analysis and Modelling by Tadele Feyssa, Wollega

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Proximity computation

In proximity computations, we use geometric distance to

define the neighborhood of one or more target locations.

All GIS programs provide some neighborhood analyses,

which include buffering, interpolation, Theissen polygons,

and various topographic functions.

The most common and useful technique is buffer zone

generation.

Another technique based on geometric distance that is

Thiessen polygon generation.72Spatial Analysis and Modelling by Tadele Feyssa, Wollega

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Buffering

Buffering works based on proximity concept

Feature for buffering may be points, lines or polygons

Buffering around point create a circle

Around lines a series of elongated buffer zones around

each line segment

A buffer around a polygon creates an extended area from

the polygon boundaries


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Buffering around point

Buffering Around lines

Buffering around a polygon

Buffering uses distance measurements from selected features

We must know the measurement unit of features we are dealing

with74Spatial Analysis and Modelling by Tadele Feyssa, Wollega

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Application of Buffering

Buffering creates a buffer zone data set

A buffer zone often treated as a protection zone and is

used for planning and regulatory purposes

A city may require a buffer zone of 500m for alcohol

trading from school

A 30m buffer zone along river bank may needed to protect

a river


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A buffer zone may be treated as a neutral zone and as a

tool for conflict resolution

Buffering zone also used for identifying suitable sites for

different purposes

Buffering also can be applied for sampling methods.

EG A stream network can be buffered at regular distances

to analyse vegetation variations as one moves away from

the stream


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Thiessen Polygons (Analysis)

It Creates Thiessen polygons from point input features.

Each Thiessen polygon contains only a single point input

feature. Any location within a Thiessen polygon is closer

to its associated point than to any other point input

feature.

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This tool is used to divide the area covered by the point input features into

Thiessen or proximal zones. These zones represent full areas where any

location within the zone is closer to its associated input point than to any other

input pointSpatial Analysis and Modelling by Tadele Feyssa, Wollega

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NETWORK ANALYSIS Network is any system of interconnected linear features

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


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What is network analysis?Solving problems involving networks

Its goal is efficiency – Saving time and money.

Tools like

• Network data (connectivity is needed)

• Network analysis software – A GIS is also required to

network analysis 79Spatial Analysis and Modelling by Tadele Feyssa, Wollega

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Network Analysis

Network analyses involve analyzing the flow of networks—

a connected set of lines and point nodes.

These linear networks most often represent features such

as rivers, transportation corridors (roads, railroads, and

even flight paths), and utilities (electric, telephone,

television, sewer, water, gas).

Point nodes usually represent pickup or destination sites,

clients, transformers, valves, and intersections. People,

water, consumer packages, kilowatts, and many other

resources flow to and from nodes along linear features.

Each linear feature affects the resource flow.80Spatial Analysis and Modelling by Tadele Feyssa, Wollega

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For example, a street segment might only provide flow in

one direction (a one-way street) and at a certain speed.

Network analysis tools help you analyze the “cost” of

moving through the network.

“cost” can be money, time, distance, or effort.

The three major types of network analyses include route

selection (optimal path or shortest path), resource

allocation, and network modeling.


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• Route Selection attempts to identify the least “cost” route.

• You might want to find the shortest path between your

home and a weekend destination

• In any route selection routine, two or more nodes,

including an origin and a destination point, must be

identified and be able to be visited on the network.

• Sometimes there are a large number of possible routes.

• It is the job of the network analysis algorithm to determine

the least cost route. 82Spatial Analysis and Modelling by Tadele Feyssa, Wollega

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•

• Resource Allocation, the second major type of network

analysis, involves the distribution of a network to nodes.

• To do this, you define one or more allocation nodes on

the network.

• Territories of linear features, like streets, are defined

around each of these allocation nodes.

• The linear features are usually assigned to the nearest

node, where distance is measured in time, length, money,

or effort.


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What do the tools do?

GIS allows you to solve common network problems, such as finding the

best route across a city, finding the closest emergency vehicle or

facility, identifying a service area around a location, servicing a set of

orders with a fleet of vehicles, or choosing the best facilities to open or

close

What do the tools do?

Direct path analysis – eg finding the shortest path between your office

and home

Optimum routing - helping a pizza deliveryman visit numerous houses

in the most time – efficient manner, that include

length of the lines, their capacity, maximum travel

rate and time

Closest facility analysis – eg finding the closest hospital to an

automobile accident

Drive time analysis- Helping a store to determine how many customers are within 5 driving miles

Driving directions- the systems of computation also allow deriving directions

ArcGIS Network Analyst ExtensionSolving transportation problems

Route Closest Facility Service Area

Vehicle Routing

ProblemLocation-Allocation

Origin-Destination

Cost Matrix

Route

Network Analyst can find the best way to get from one location to

another or to visit several locations

What's the best route?

Whether finding a simple route between two locations or one that

visits several locations, people usually try to take the best route. But

"best route" can mean different things in different situations.

The best route can be the quickest, shortest, or most scenic route,

depending on the impedance chosen. If the impedance is time, then

the best route is the quickest route. Hence, the best route can be

defined as the route that has the lowest impedance, where the

impedance is chosen by the user. 87

Spatial Analysis and Modelling by Tadele Feyssa, Wollega University

eg Routing from Wollega University to Nekemte Bus Station

88

Shortest route analysis by

considering different

origins and destinations


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Closest facility

The closest facility solver finds the cost of travelling

between incidents (i.e. specified points/ locations) and

facilities and determines which are nearest to the other

Finding the closest hospital to an accident, the closest

police cars to a crime scene, and the closest store to a

customer's address are all examples of closest facility

problems


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The closest facility problem to search for 5 schools within a

10 minute drive from Nekemte First Square and Chalalaki.

Any schools that take longer than 10 minutes to reach are not

included in the results. This can be visualized in the following

figure

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School closest facility mapping


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Service Area Analysis

The service area solver generates polygons or lines that cover all edges within a given distance, travel time or other impedance unit from the predefined facility/facilities

With the ArcGIS Network Analyst extension, you can find service areas around any location on a network.

A network service area is a region that encompasses all accessible streets

For instance, the 5-minute service area for a point on a network includes all the streets that can be reached within five minutes from that point.

Service areas created by Network Analyst also help evaluate accessibility. Concentric service areas show how accessibility varies with impedance. Once service areas are created, you can use them to identify how much land, how many people, or how much of anything else is within the neighborhood or


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One of many uses of GIS analysis tools is to build models.

What is a model?

A model is a simplified representation of a phenomena or a

system.

A map is a model.

So are the vector and raster data models for representing

spatial features.


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A model helps us better understand a phenomenon or a

system by retaining the significant features and

relationships of reality.

Often used to identify locations that meet specific criteria

Can be used to infer an unknown quality or quantity using

relationships with known or measurable quantities or

qualities

Can be used to generate new data


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Classification of GIS Models

Descriptive or Prescriptive

Deterministic or Stochastic

Static or Dynamic

Deductive or Inductive


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A model may be descriptive or prescriptive.

Descriptive model describes the existing conditions of

spatial data

Prescriptive model offers a prediction of what the

conditions could be or should be.

Eg If we use maps as analogies, a vegetation map would

represent a descriptive model and a potential natural

vegetation map, a prescriptive model.


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The vegetation map shows existing vegetation, whereas the

potential natural vegetation map predicts the vegetation that

could occupy a site without disturbance or climate change


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Both deterministic and stochastic models are mathematical

models represented by equations with parameters and

variables.

A stochastic model considers the presence of some

randomness in one or more of its parameters or variables,

but a deterministic model does not


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A dynamic model emphasizes:

the changes of spatial data and the interactions between variables,

Whereas a static model deals with the state of spatial data

at a given time

Many environmental models such as groundwater pollution

and soil water distribution are best studied as dynamic

models


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A deductive model represents the conclusion derived from a

set of premises.

These premises are often based on scientific theories or

physical laws

An inductive model represents the conclusion derived from

empirical data and observations.

Eg To assess the potential for a landslide one can use a

deductive model based on laws in physics or use an

inductive model based on recorded data from past

landslides101Spatial Analysis and Modelling by Tadele Feyssa, Wollega

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The development of a model follows a series of steps.

1st step is to define the goals of the model

This is similar to defining a research problem

What is the phenomenon to be modeled?

Why is the model necessary?

What spatial and time scales are appropriate for the model?

One can use a conceptual or schematic model to show the essential

structure of the model


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2nd step is to break down the model into elements and to

define the properties of each element and the interactions

between the elements

A flowchart may used as a useful tool for linking the

elements

Also at this step, one will gather mathematical equations

of the model and use tools in a GIS to carry out the

computation


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3rd step is the implementation and calibration of the model

Data are needed for running and calibrating the model

4th Validate the model


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GIS can assist the modeling process in several ways.

First, a GIS can be used a tool that can process, display,

and integrate different data sources including maps, digital

elevation models (DEMs), GPS (global positioning system)

data, images, and tables

These data are needed for the implementation, calibration,

and validation of a model.


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A GIS can function as a database management tool and,

at the same time, is useful for modeling-related tasks such

as exploratory data analysis and data visualization.

Second, models built with a GIS can be vector-based or

raster-based

The choice depends on the nature of the model, data

sources, and the computing algorithm


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Third, the distinction between raster-based and vector-

based models does not preclude GIS users from integrating

both types of data in the modeling process.

Fourth, the process of modeling may take place in a GIS or

require the linking of a GIS to other computer programs.


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There are three scenarios for linking a GIS to other computer

programs

A loose coupling involves transfer of data files between the

GIS and other programs through import and export

A tight coupling gives the GIS and other programs a

common user interface. eg, the GIS can have a menu

selection to run a simulation program on soil erosion.

An embedded system packages the GIS and other

programs with shared memory and a common interface108Spatial Analysis and Modelling by Tadele Feyssa, Wollega

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This Chapter includes


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Binary model uses logical expressions to select spatial

features from composite feature layers from a composite

map or multiple grids

The output of binary model is in binary format:

1 ( ) for spatial features that meet the selection criteria and

0 ( ) for features that do not

Binary model can be the extension of data query


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Site selection is the most common application of the

binary model

A siting analysis determines if a unit area meets a set of

selection criteria for locating a certain activities

Two approaches may be used to run a siting analysis

One is to evaluate the preselected sites

The second is to evaluate all potential areas


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For example a municipality wants to select potential industrial sites that meets the following criteria

At least 5 acres in size

Commercial zone

Not subject to flooding

Not more than 1 mile from a heavy-duty road

Less than 10 percent slope

Operationally the task involves the following


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Operationally, the task involves the following from steps

Gather all layers (land use, flood potential, road and

slope) relevant to the selection criteria

Select heavy duty roads from the road layer and create a

1 mile buffer zone around them

Intersect the road buffer zone layer and other layers

Select sites which are equal to or larger than 5 acres


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Application of Binary models

Change detection is a simple application of the binary mode

By overlaying two maps representing land covers at two

different points in time, one can query the attribute data

of the composite map to find, for example, where forested

land has been converted to housing development


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Application of Binary models

Siting analysis is probably the most common application of

the binary model

A siting analysis determines if a unit area (a polygon or a

cell) meets a set of selection criteria for locating for example

a landfill

There are at least two approaches to conducting a siting

analysis.


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On evaluates a set of nominated or preselected sites

And the other evaluates all potential sites

Although the two approaches may use different sets of

selection criteria, they follow the same approach for

evaluation


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12

3

12

3

4

ID Suit

1

2

3

3

1

2

ID Type

1

2

3

21

18

6

12

34

5 67

Suit = 2 AND Type = 18

ID

1

2

3

5

6

7

Suit Type

3

3

1

2

2

1

21

18

18

21

6

6

4 2 18

++

An illustration of a vector-based binary model. The two maps at the top are

overlaid so that their spatial features and their attributes of Suit and Type are

combined. A logical expression, Suit = 2 AND Type = 18, results in the selection

of polygon 4 in the output.

117Spatial Analysis and Modelling by Tadele Feyssa,

Wollega University

1 1 1 4

3 2 4 4

3 3 3 4

4 4 4 4

1 1 1 3

3 2 2 3

3 3 4 4

3 3 4 4

Grid 1 Grid 2

([Grid1] = 3)

AND

([Grid2] = 3)=

An illustration of a raster-based binary model. A query statement, ([Grid1] = 3)

AND ([Grid2] = 3), results in the selection of 3 cells in the output.


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An index model calculates the index value for each unit

area and produces a ranked map based on the index

values.

An index model is similar to a binary model in that both

involve multi-criteria evaluation and both depend on overlay

operations for data processing

But an index model produces for each unit area an index

value rather than a simple yes or no


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The primary consideration in developing an index model,

either vector-based or raster, is the method for computing

the index value

The weighted linear combination method is probably the

most common method for computing the index value


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To build an index model with the selection criteria of slope, aspect, and elevation, the weighted linear combination method involves evaluation at three levels.

The first level of evaluation determines the criterion weights (e.g., Ws for slope)

The second level of evaluation determines standardized values

for each criterion (e g, sl, s2, and s3 for slope)

The third level of evaluation determines the index (aggregate) value for each unit area


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Weighted linear combination is a common method for

computing the index value

For example combination of slope aspect and elevation

Weighted linear combination involves evaluation at three

levels


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To build an index model with the selection criteria of slope, aspect, and elevation, the

weighted linear combination method involves evaluation at three levels. The first level of

evaluation determines the criterion weights (e.g., Ws for slope). The second level of

evaluation determines standardized values for each criterion (e.g., s1, s2, and s3 for slope).

The third level of evaluation determines the index (aggregate) value for each unit area. 123Spatial Analysis and Modelling by Tadele Feyssa,

Wollega University

12

3

1

2

3

0.640.46

0.14

0.26

0.440.68

0.56

12

3

4

56

7

(S_V * 0.4) + (T_V * 0.6)

+

+

ID Suit

1

2

3

3

1

2

S_V

1.0

0.2

0.52

ID Type

1

2

3

21

18

6

T_V

0.4

0.1

0.83

ID

1

2

3

4

5

6

7

S_V

0.2

1.0

1.0

0.2

0.5

0.5

0.5

T_V

0.4

0.1

0.1

0.1

0.4

0.8

0.8

An illustration of a vector-based index

model. First, the Suit and Type values of

the two input maps are standardized

from 0.0 to 1.0. Second, the two maps

are overlaid. Third, a weight of 0.4 is

assigned to the map with Suit and a

weight of 0.6 to the map with Type.

Finally, the index values are calculated

for each polygon in the output by

summing the weighted values. For

example, Polygon 4 has an index value

of 0.26 (0.5 *0.4+ 0.1*0.6).


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7 21

32 49

1 3

5 2

45 57

63 31

0.2 0.6

0.8 1.0

0.2 0.6

1.0 0.4

0.6 0.8

1.0 0.4

0.12 0.36

0.48 0.60

0.04 0.12

0.20 0.08

0.12 0.16

0.20 0.08

0.28 0.64

0.88 0.76

x 0.6 x 0.2 x 0.2

Input

grids

Standardize cell values

into 0.0-1.0 scale

Multiply by

criterion weights

Calculate index values by summing

weighted criterion values

An illustration of a raster-based index model. First, the cell values of each input grid

are converted into the standardized scale of 0.0 to 1.0. Second, the index values in

the output grid are calculated by summing the products of each grid multiplied by its

assigned weight. For example, the index value of 0.28 is calculated from: 0.2*0.6 +

0.2*0.2 + 0.6*0.2, or 0.12 + 0.04 + 0.12. 125Spatial Analysis and Modelling by Tadele Feyssa,

Wollega University

First the relative importance of each criterion or factor is evaluated against the other criteria

In most cases we use expert-derived paired comparison for evaluating criteria

This method involves performing ratio estimates for each pair of criteria

For instance if criterion A is considered to be three times more important than criterion B, then 3 is recorded for A/B and 1/3 for B/A


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Application of the Index model

Index model are commonly used for suitability analysis

and vulnerability analysis

A suitability analysis ranks areas for their appropriateness

for a particular use

A vulnerability analysis assess areas for their

susceptibility to a hazard or a disaster

Both analysis requires careful consideration of criteria and

criterion weights


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3. Regression Model

Regression model relates a dependent variable to a

number of independent (explanatory) variables in an

equation which can then be used for prediction or

estimation

Regression model can use an overlay operation in GIS to

combine variables needed for the analysis

There are two types of regression model; linear and

logistic regression


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Regression Model…

Some GIS packages are capable of performing linear or

logistic regression analysis

Both Arclnfo Workstation and IDRISI have commands to

build raster-based linear or logistic models

In fact it is not powerful like statistical analysis packages


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Linear Regression Model

A multiple linear regression model is defined by

Y= a+b1Xi +b2x2+……bnxnWhere y is the dependent variable, Xi is the

independent variable I and b1,….. bn are the regression

coefficients a is the intercept

The primary purpose of linear regression is to predict

values of y from values of Xi


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regression

Dependent Independent variables

Predicted Predictor variables

Response variable Explanatory variables

Outcome variable Covariables

xβ ... xβ xβα y ii2211

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Logistic Regression Model

logistic regression is used when the dependent variable is

categorical (e g., presence or absence) and

the independent variables are categorical, numeric,or both.

Although having the same form as linear regression,

logistic regression uses the logit of y as the dependent

variable

133Spatial Analysis and Modelling by Tadele Feyssa, Wollega University

logit(p)=ln(p/(1-p))=a+b1*x1+b2*x2+b3*x3

Regression analysis applications

Regression analysis can be used for a large variety of

applications:

Modeling traffic accidents as a function of speed, road

conditions, weather, and so forth, to inform policy aimed

at decreasing accidents.

Modeling property loss from fire as a function of variables

such as degree of fire department involvement, response

time, or property values.


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There are three primary reasons you might want

to use regression analysis:

To model some phenomenon to better understand it.

The basic objective is to measure the extent that changes

in one or more variables jointly affect changes in another.

Example: Understand the key characteristics of the

habitat for some particular endangered species of bird

(perhaps precipitation, food sources, vegetation,

predators) to assist in designing legislation aimed at

protecting that species.


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It is mainly applied for bird habitat identification, rainfall-

triggered land slide model, predicting grass land bird

habitat attitude towards national park designation


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4. Process ModelsA process model integrates existing knowledge about the

environment process in the real world into:

a set of relationships and equations for quantifying the processes

A process model offers both a predictive capability and an

explanation that is inherent in the proposed processes

Therefore process models are by definition predictive and

dynamic models


Environmental models are very complex and data

intensive

Environmental models are typically process models

because they must deal with the interaction of many

variables including physical variables such as climate,

topography, vegetation, and soils as well as cultural

variables such as land management


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Revised Universal Soil Loss Equation (RUSLE)

RUSLE is a model that is widely used to estimate average annual nonchannelized soil loss.

Soil erosion is an environmental Process that involves climate, soil properties, topography, soil surface conditions and human activities

A well known model of soil erosion is the Revisited Universal Soil Loss Equation (RUSLE)

RUSLE predicts the average soil loss carried by runoff from specific field slopes in specified cropping and management systems from range land


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RUSLE is a multiplicative model with six factors

A= R*K*L*S*C*P

Where A is average soil loss

R- is the rainfall fun off erosivity factor

K is the soil erodibility factor

L is the slope length factor

S is the slope steepness factor

C is crop management factor(land cover) and

P = Support practice factor (conservation)


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L and S can be combined into single topographic factor LS

Slope length is defined as the horizontal distance from the

point of origin of overland flow to the point where either the

lope gradient decreases enough that deposition begins or

the flow is concentrated in a defined channel

RUSLE permits the estimation of long-term soil loss in a wide

range of environmental settings.

RUSLE is the primary means for estimating soil loss on farm fields

and rangelands141

Spatial Analysis and Modelling by Tadele Feyssa, Wollega University

The RUSLE module not only allows the user to estimate average

annual soil loss for existing conditions, it permits one to simulate

how landuse change (C factor), climate change (R factor), and/or

changes in conservation/management practices (P factor), will

affect soil loss.


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142

Critical Rainfall Model

Land slide is defined as a down slope movement of a

mass of soil and a rock material

A landslide hazard model measures the potential of

landslide occurrence within the given area

GIS has been employed in the past decades for

development of landslide models


There are two types of landslide models; Physically based

and statistical based models

Regression model is an example of statistical based land

slide model

Critical Rainfall model is an example of physically based

landslide model

The infinite slope model defines slope stability as the ratio of

the available shear strength (Stabilizing forces) including soil

and root cohesion to the shear of stress (destabilizing forces). 144Spatial Analysis and Modelling by Tadele Feyssa, Wollega

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The Critical Rainfall model combines the infinite slope model with a steady hydrologic model to predict the critical rainfall Qcr that can cause landslide Qcr can be calculated

Qcr = T sinѲ (a/b) (ps/pw)(1- sinѲ-C/cosѲtanØ)

T is saturated soil transmissivity

Ѳ is local slope angel

a is the upslope contributing drainage area

b is the unit contour length (the raster resolution)

Ps is wet soli density

Ø is the internal friction angle of the soil

Pw is the density of water

C is combined cohesion


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Critical rainfall model is regularly used for predicting

shallow landslides triggered by rainfall events


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Digital Elevation Model (DEM)

The land surface has been the object of mapping,

modelling and analysis for several years

Map makers devised many techniques for terrain

mapping; such as contouring, hill shading, 3D-view

perspectives, etc.

Geomorphologists have developed measures of the land

surface which include slope, aspect and surface curvature


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Terrain mapping is no more a subject of specialists.

GIS has made it relatively easy to incorporate them in

many application areas

Slope and aspect play a regular role in hydrological

modelling, snow cover evaluation, soil mapping land slide

area delineation and soil erosion modelling


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Most GIS packages treat elevation data (Z values) as

attribute data at point or cell location

However, in 3-D model an additional coordinate to x and y

In raster data modelling the z value corresponds to the

cell value

In vector data modelling the z value will be stored as

attribute data


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Digital elevation model and TIN

Digital elevation model is a data input for terrain mapping

The two most common data input for terrain mapping and analysis are DEM and TIN

Digital elevation model (DEM) is based on raster data analysis

Triangulated irregular network (TIN) is based on vector based data analysis

We can’t use TIN and DEM together but we can change TIN to DEM or DEM to TIN


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DEM

A DEM represents a regular array of elevation points

A point based DEM must be converted before using this

data for terrain mapping

This conversion simply places each point in the DEM at

the centre of a cell in elevation raster

Therefore DEM and elevation raster can be used

interchangeably


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2301m


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TIN

A TIN approximates the land surface with a series of non-

overlapping triangles

Elevation values (Z values) along x, y, coordinates are

stored at nodes that make up the triangles

Unlike the DEM the TIN is based on an irregular

distribution of elevation points


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Terrain Mapping

There are different kinds of terrain mapping

Contouring is the most common for the terrain mapping it

has contour interval and base contour

The arrangement and pattern of contour lines reflect the

topography

Contour lines don’t intersect one an other and will not stop

in the middle of the map


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Vertical Profiling

A vertical profile shows changes in elevation along a line

such as a road or a stream


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Hill Shading

Hill shading simulates how the terrain looks with

interaction between sunlight and surface features

A mountain slope directly facing incoming light will be very

bright

A slope opposite to it is will be dark.

Four factors control the visual effects of hill shading


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The sun’s azimuth is the direction of the incoming light

ranging from 0 (due north to 360 in a clockwise direction

Typically the default for the sun’s azimuth is 315 with

The sun’s altitude is the angle of the incoming light

measured above the horizon between 0 and 90 degree


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The other two factors are slope and aspect

Slope ranges between 0 and 90 degrees

Aspect ranges between 0 and 360 degrees

Slope measures the rate of change of elevation at a

surface location

It can be expressed in percent or degree

Aspect is the directional measures slope


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Aspect starts with 0 degree at North pole and measures

clockwise and ends with 360 degree


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Viewshed Analysis

Viewshed refers to the portion of the land surface that is visible from one or more viewpoints

The process for deriving viesheds is called viewshed or visibility analysis

A veiwshed analysis requires two input datasets

The first is usually point layer containing one or more view points such as a layer containing communication tower

The second input is DEM (an elevation raster or a TIN which represents the land surface


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The line-of-sight operation is the basis for veiwshed

analysis

The line of sight also called sight-line, connects the

viewpoint and the target

GIS can display a sightline with symbols for the visible

and invisible portions a long the sightline


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Parameters of Viewshed Analysis

A number of parameters can influence the result of a viewshed analysis

The first parameter is the viewpoint

After the view point is determined its elevation should be increased by the height of a physical structure

Fore example a forest lookout station is usually 15 to 20 meters high.

The height of the observation station can be added as an offset value to the elevation at the station


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The second parameter is the viewing azimuth which sets

horizontal angle limits of the view

Azimuth system for measuring direction is based on the

360 degrees found in a full circle.

Viewing radius is the third parameter which sets the

search distance for deriving visible areas.

Other parameters include vertical viewing angle limits, the

earth’s curvature, tree height and building height


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http://www.physicalgeography.net/physgeoglos/a.html

Application areas of viewshed

•Urban Planning

•Cell phone tower placement

•Location for wind turbines

•Conservation projects

Military purpose

•Many more!


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Watershed Analysis

A watershed refers to an area defined by topographic

divides that drains surface water to a common outlet

A watershed is often used as a unit area for the

management and planning of water and other resources

Watershed analysis refers to the process of using DEMs

and following water flow to delineate stream networks and

watersheds


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Traditionally watershed boundary can be drawn manually onto a topographic map

The person who draws the boundaries uses topographic features on the map to determine where a divide is located

Today computer based watershed analysis can do this job in a fraction of time

Delineation of watershed can tale place at different spatial scales


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Delineation of watershed can be area based on point based

An area based method divides a study area into a series of

watershed one for each stream section

A point based method on the other hand derives a watershed

for each select point

The select point may be an outlet a gauge station or a dam

Both methods follow a series of steps, starting with a filled

DEM


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A filled DEM is void of depressions or sinks

A depression is a cell or cells surrounded by higher elevation values thus represent an area of internal drainage

Although some depressions are real most of them are the outcome of DEM imperfection

Therefore the Depression must be removed from a elevation raster

A common method fro removing a depression is to increase the cell value to the lowest overflow point out of the sink


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http://www.britannica.com/bps/media-view/144763/1/0/0




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Treating depressions

routing through false depressions:

filling

fill-in


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Flow Direction

A flow direction raster shows the direction water will flow

out of each cell of a filled elevation raster

Flow directions are commonly determined using single

using single or multiple flow direction methods

D8 is a popular single flow direction method

The D8 assigns a cell’s flow direction to the one of its 8

surrounding cells that has the steepest distance-weighted

gradient


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D8’ (8 direction) method , sets the flow direction towardthe lowest of the eight neighboring cells, in relation to thecentre cell.

Therefore, flow is allowed in one of eight possible directions, assuming that water will travel along the steepest down slope path

Based on the 3 x 3 cell neighborhood , flow would be directed from the centre cell (elevation of 8) to the southwest cell (elevation of 4).


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Multiple flow direction methods allow flow divergence or flow bifurcation

The flow direction of the centre cell can be determined by first calculating the distance between weighted gradient to each to each of its 8 neighbors.

For the immediate neigbours the gradient is calcualted by dividing the elveation difference between the center and the neighbour cell by 1

For the four corner neighbours, the gradient is calculated by dividing the elevation difference by 1.414

The result shows the steepest gradient and therefore the flow direction is from the centre to the steepest direction


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Flow Direction for cell

1014 1011 1004

1019 1015 1007

1025 1021 1012

+1 +4 +11

-4 +8

-10 -6 +3

+0.71 +4 +7.78

-4 +8

-7.07 -6 +2.12

1

11.414

Cell Elevation Elevation difference Distance weight

Distance weight Gradient Flow direction

32 64 128

16 1

8 4 2

Flow direction

Number186Spatial Analysis and Modelling by Tadele Feyssa, Wollega

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Flow accumulation

A flow accumulation raster tabulates for each cell number

of cells that will flow to it.

The tabulation is based on the flow direction raster

With the appearance of spanning tree a flow accumulation

raster records how many upstream cells will contribute

drainage to each cell

A flow accumulation raster can be interpreted in two ways.


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First, Cells having high accumulation values generally

correspond to stream channels

Whereas cell having an accumulation value of zero

generally correspond to ridge lines

Second if multiplied by the cell size, the accumulation

value equals the drainage area


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Stream network

A stream network can be derived from a flow accumulation raster

The derivation is based on the channel initiation threshold which represents the amount of discharge needed to maintain a channel head with contributing cells serving as a surrogate for discharge

A threshold value of 500 for example means that each cell of the drainage network has a minimum of 500 contributing cells


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Stream links

After a stream network is derived from a flow

accumulation raster, each section of the stream raster line

is assigned a unique value and is associated with a flow

direction

A stream link raster therefore resembles a topology based

stream layer

The intersections or junctions are like nodes and the

stream sections between junctions are like arcs reaches


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Area wide watershed

The final step is to delineate a watershed for each stream

section

This operation uses the flow direction raster and the

stream link raster as the inputs

A denser stream network (based on a smaller threshold

value) will have more but smaller watersheds


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Point Based watershed

Sometimes we use point based for watershed analyis.

The points are called pour points or outlets

Delineation of water sheds based on individual pour

points follow the same procedure for delineating area

wide water sheds delineation

The only difference to substitute a point raster for a

stream link raster


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In a point raster a cell representing a pour point must be

located over a cell that is part of the stream link

If a pour point is not located directly over a stream link, it

will result in a small incomplete watershed for the outlet

The relative location of the pour point to a stream network

determines the size of a point based watershed


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We use the snap pour point command to snap pour point

to the cell the highest flow accumulation within a used

defined search distance

The snap pour point operation should be considered part

of the data processing for delineating point based

watersheds


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Application of water shed analysis

Water shed analysis is mainly used for natural resource

management

• Floodplain Management

• Land Use Planning

• Invasive Species

• River Function

• Water Supply


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