Applied GIS

COURSE MATERIAL
COURSE TITLE - APPLIED GIS
COURSE CODE - GeES2083
Module Name and Code: SPATIAL DATA ACQUISITION AND ANALYSIS, GeESM2081
ADIGRAT UNIVERSITY
COLLEGE OF SOCIAL SCIENCE AND HUMANITIES
DEPARTMENT OF GEOGRAPHY AND ENVIRONMENTAL STUDIES
1
Compiled by - Dr. Zubairul Islam, Associate Professor, Department of geography and Environmental studies.
zubairul@gmail.com
Compiled by : Dr. Zubairul Islam

COURSE CONTENT AND SCHEDULE OF ACTIVITIES
Week / day CONTENTS SLIDES
1st Week CHAPTER ONE: GIS Data Processing 6-17
Day 1&2 1.1 - GIS Data Processing using Arc Toolbox
Day 3 1.2 - GIS Data Processing using Model builder
CHAPTER TWO: Spatial Analysis 18-40
Day 4 2.1. Interpolation
Day 5 2.2. Hydrology
2Compiled by : Dr. Zubairul Islam

2nd Week CHAPTER THREE: GEO - DATA MANAGEMENT 41-52
Day 1 3.1. Introduction
Day 1&2 3.2. Creating Geodatabase
Day 3 3.3. Building Topology
Day 3 3.4. Working with Geodatabase & Validating
Work with topology
CHAPTER FOUR: ROUTE NETWORKING 53-64
Day 4 4.1 - Route Networking with ArcGIS
Day 5 4.2 - Route Networking with Google Earth

3rd Week
CHAPTER FIVE: POINT PATTERN ANALYSIS AND SPATIAL STATISTICS
65-73
Day 1 5.1 – Average Nearest Neighbor Analysis
Day 2 5.2 – Measuring Geographical Distribution
CHAPTER SIX: 3D ANALYSIS 74-86
Day 3 6.1. Digital Terrain Model (DTM)
Day 3 6.2. How to collect Aster DEM Data
Day 4 6.3 - Surface Analysis from Aster Dem Data
Day
4&5
Lab 6.3.1 - Contours,
Lab 6.3.2 - Angle of slope,
Lab 6.3.3 - Steepest down slope direction (aspect),
Lab 6.3.4 - Shaded relief (hill shade), and
Lab 6.3.5 - View shed (line of sight).

RECOMMENDED MODE OF ASSESSMENT
Continuous assessment………………… (60%)
•Lab 1& 2…10 + 10………1st week Friday After noon & Saturday
Morning Shift – Ch 1 & 2
•Lab 1& 2… 10 + 10………2nd week Friday After noon &
Saturday Morning Shift– Ch 3 & 4
•Group project work …..20…..3rd week Friday for submission – Ch
5 & 6
Final exam…………………………….40% ………… All Chapters

1.1 - GIS Data Processing using ArcToolbox
1.2 - GIS Data Processing using Model builder
CHAPTER ONE

1 - GIS Data Processing using ArcToolbox and Model builder
1.1 - GIS Data Processing or Geoprocessing
The fundamental purpose of geoprocessing is to allow
you to automate your GIS tasks. Almost all uses of GIS
involve the repetition of work, and this creates the need
for methods to automate, document, and share multiple-
step procedures known as workflows. Geoprocessing
supports the automation of workflows by providing a
rich set of tools and a mechanism to combine a series of
tools in a sequence of operations using models and
scripts.

The kinds of tasks to be automated can be as following:
1. Data from one format to another.
2. The tasks can be quite creative, using a sequence of
operations to model and analyze complex spatial
relationships—for example, calculating optimum paths
through a transportation network, predicting the path of
wildfire, analyzing and finding patterns in crime locations,
predicting which areas are prone to landslides, or predicting
flooding effects of a storm event.
A typical geoprocessing tool performs an operation on an ArcGIS
dataset (such as a feature class, raster, or table) and produces a
new dataset as the result of the tool. Each geoprocessing tool
performs a small yet essential operation on geographic data, such
as projecting a dataset from one map projection to another,
adding a field to a table, or creating a buffer zone around features.
ArcGIS includes hundreds of such geoprocessing tools.

1.2 - GIS Data Processing using ArcToolbox
Geoprocessing is carried out using tools stored in ArcToolbox.

10
• Toolbox: Container
for toolsets and tools
– Note: cannot have a
toolbox within a toolbox
• Toolset: Logical
container of tools
and other toolsets
(i.e. folder)
• Tool: Single
geoprocessing
operation (includes
dialogs, models,
and scripts)
Inside of ArcToolbox
Toolbox
Toolset
Tools
Compiled by : Dr. Zubairul Islam

Compiled by : Dr. Zubairul Islam 11
Environment settings
• “Customizable defaults”
• Common parameters that are applied to all
tools within a geoprocessing session
• Settings work (“persist”) in all environments
(dialogs, command line, models, scripts,)
Coverage environment
Comparison between prj files
Precision for new coverages
General settings
Current workspace
Output coordinate system
Raster settings
Cell size
Mask
Geodatabase raster
Statistics
Compression
Geodatabase settings
XY Domain
M and Z Domains

Lab 1.1.1 – Clipping Features
1. Add data
2. Analysis Tool
3. extract
4. clip
5. Input feature
6. Clip features
7. Output feature class
8. Ok
RESULT

The Elements of Model Builder
• Model diagram window
– Input variable
– Tool
– Derived data variable
• Toolbar
– Add Data or Tools
– Layout
– Zoom and Pan
– Add connection
– Run
1.2 - GIS Data Processing using Model builder

Building a model: constructing
• Create a new model
• Drag tool into model
• Drag data into model
or onto a tool
• Link data and tool

Building a model: running and documenting
• Save and rename the
model
• Run model
• Document the model

Element States
• Not ready to run
(parameters not set)
• Ready to run
• Has been run
(note the grey
shadow)

LAB 1.2.1 – CLIP FEATURES WITH MODEL
1. Open model builder
2. Drag files to be clipped
3. Drag files used to clip
4. Drag clip tool
5. Double clip tool & set data
6. Right click & add to display
7. run
RESULT

2 - Spatial Analysis
2.1 - Interpolation,
2.2 - Buffering,
2.3 - Hydrology

Spatial Analysis
Spatial Analysis is the process of examining the
locations, attributes, and relationships of features in
spatial data through analytical techniques in order to
address a question or gain useful knowledge. Spatial
analysis extracts or creates new information from
spatial data.

2.1 - Interpolation
Interpolation create a
continuous surface from
discrete samples with
measured values, such as
elevation or chemical
concentration. There are
several interpolation tools,
and each has a variety of
parameters that influence
the resulting surface.

The simplest interpolation tool is Inverse Distance Weighted.
Inverse Distance Weighted (IDW)
To predict a value for any unmeasured location, IDW uses the
measured values surrounding the prediction location. The
measured values closest to the prediction location have more
influence on the predicted value than those farther away. IDW
assumes that each measured point has a local influence that
diminishes with distance. It gives greater weights to points closest
to the prediction location, and the weights diminish as a function
of distance, hence the name inverse distance weighted.

LAB 2.1.1 - INTERPOLATION
1. Add data
2. Open spatial analysis
3. Click option
4. General & set wo dir + An. Mask
5. Set extent
6. Ok
First set analysis mask and extent

1. Interpolation
2. Inverse distance weighted
3. Input points
4. Put field
5. ok
Interpolation
Result
One Raster interpolated image will be added to TOC.

Buffering
Creates buffer polygons to a specified distance around the Input
Features. An optional dissolve can be performed to remove
overlapping buffers.
FULL—A buffer will be generated on both sides of the line. If
the input is a polygon the result will include the area inside the
polygon. This is the default.
LEFT—the buffer will be generated on the LEFT side of the
line.
RIGHT—the buffer will be generated on the RIGHT side of the
line.
OUTSIDE_ONLY—the area inside of the input polygon
features will excluded from the resulting buffer.
Buffering options
SIDE

The distance used to create buffer zones around Input Features.
Either a value or a numeric field can be used to provide buffer
distances.
If a negative buffer distance is specified, the buffer offsets will
be generated inside, instead of outside, of the input features. This
is only valid for polygon feature classes.
DISTANCE
Specifies whether a dissolve will be performed to remove
buffer feature overlap.
NONE—Individual buffer for each feature is maintained,
regardless of overlap. This is the default.
ALL—Dissolves all the buffers together into a single feature
and removes any overlap.
LIST—Dissolves by a given list of fields.
DISSOLVE

EXERCISE - BUFFERING
1. Add Data to Buffer
2. Go to Analysis tool & Click on Buffer
3. Input Data to buffer
4. Give distance with unit
5. OK
RESULT
One new shape file will be
Added in TOC as Buffer.
NOTE – Buffering may be with line, point or polygon features.
Follow following steps for Buffering

2.3 - Hydrology
An understanding of the shape of the earth's surface is useful for
many fields, such as regional planning, agriculture, and forestry.
These fields require an understanding of how water flows across
an area and how changes in that area may affect that flow.
When modeling the flow of water, you may want to know where
the water came from and where it is going.
STEPS TO HYDROLOGICALANALYSIS
1. WATER FLOW DIRECTION
This function takes a surface as input and outputs a raster
showing the direction of flow out of each cell. If the output drop
raster option is chosen, an output raster is created showing a
ratio of the maximum change in elevation from each cell along
the direction of flow to the path length between centers of cells
and is expressed in percentages. 27Compiled by : Dr. Zubairul Islam

HOW FLOW DIRECTION WORKS
The direction of flow is determined by finding the direction of
steepest descent, or maximum drop, from each cell. This is
calculated as:
Maximum drop = change in z-value / distance
The distance is determined between cell centers. Therefore if the
cell size is one, the distance between two orthogonal cells is one and
the distance between two diagonal cells is 1.414216, the square root
of two. If the maximum descent to several cells is the same, the
neighborhood is enlarged until the steepest descent is found.
When a direction of steepest descent is found, the output cell is
coded with the value representing that direction.

There are eight valid output directions relating to the eight adjacent
cells into which flow could travel. This approach is commonly
referred to as an eight direction (D8) flow model and follows an
approach presented in Jensen and Domingue (1988).
If all neighbors are higher than the processing cell, the processing cell is a sink and has
an undefined flow direction. Cells with undefined flow direction can be flagged as sinks
using the Sink function. To obtain an accurate representation of flow direction across a
surface, the sinks should be filled.

Creates a raster identifying all sinks or areas of internal drainage.
Sinks
A sink is a cell or set of spatially connected cells whose flow
direction cannot be assigned one of the eight valid values in a
flow direction raster. This can occur when all neighboring cells
are higher than the processing cell or when two cells flow into
each other, creating a two-cell loop.
Sinks are considered to have undefined flow directions and are
assigned a value that is the sum of their possible directions. For
example, if the steepest drop and, therefore, flow direction, are
the same to both the right (1) and left (16), the value 17 would be
assigned as the flow direction for that cell.

FILL
Fills sinks in a surface raster to remove small imperfections in the
data.
Sinks (and peaks) are often errors due to the resolution of the data
or rounding of elevations to the nearest integer value.
Sinks should be filled to ensure proper delineation of basins and
streams. If the sinks are not filled, a derived drainage network
may be discontinuous.
FOR EXAMPLE - 39°26'34.593"E 14°16'6.761"N
SO
RATER DEM - FILL – FLOW DIRECTION

Flow accumulation
The Flow Accumulation function calculates accumulated flow as
the accumulated weight of all cells flowing into each downslope
cell in the output raster. If no weight raster is provided, a weight
of one is applied to each cell, and the value of cells in the output
raster will be the number of cells that flow into each cell.
In the graphic below, the top left image shows the direction of
travel from each cell and the top right the number of cells that
flow into each cell.
Cells with a high flow
accumulation are areas of
concentrated flow and may be
used to identify stream
channels.

Stream networks
Stream networks can be delineated from a digital elevation
model (DEM) using the output from the Flow Accumulation
function. Flow accumulation in its simplest form is the number
of upslope cells that flow into each cell. By applying a threshold
value to the results of the Flow Accumulation function using
Map Algebra (or the Con tool in geoprocessing), a stream
network can be delineated. For example, the expression to
create a raster where the value one represents a stream
network on a background of No Data could be:
streamnet = con (flowacc > 100, 1)

LAB 2.3.1 - HYDROLOGY
The hydrology tools can be applied individually or used in sequence
to create a stream network or delineate watersheds.
The sequence is as following:
First - Creating a Depression less DEM
Second - Creating Flow direction from Depression less DEM
Third - Creating Flow accumulation from Flow direction
Fourth - Classify FlowAcc_Flow1 values
Fifth - Creating drainage network of high-flow cells
Sixth - Creating features from drainage network

The first step in any of the hydrologic modeling tools in ArcGIS is to fill the
elevation raster. You must start with a surface that has no sinks. Sinks are areas
of internal drainage, that is, areas that do not drain out anywhere.
The reason that sinks need to be filled in is because a drainage network is built
that finds the flow path of every cell.
First - Creating a Depressionless DEM
1. Add Data
2. Click on fill tool
3. Input raw DEM data
RESULT - Fill_adigrat
will be added in TOC

Second - Creating Flow direction from Depressionless DEM
Flow direction is important in hydrologic modeling because in
order to determine where a landscape drains, it is necessary
to determine the direction of flow for each cell in the
landscape
1. Click on flow direction tool
2. Input filled DEM data
RESULT - FlowDir_Fill1

Third - Creating Flow accumulation from Flow direction
Flow accumulation is the next step in hydrologic modeling. By
selecting cells with the greatest accumulated flow, we are able to
create a network of high-flow cells. These high-flow cells should
lie on stream channels and at valley bottoms.
1. Click on Flow accumulation tool
2. Input FlowDir_Fill1 data
RESULT - FlowAcc_Flow1

Fourth - Classify FlowAcc_Flow1 values
By selecting cells with the greatest accumulated flow, we are able to create a network
of high-flow cells. These high-flow cells should lie on stream channels and at valley
bottoms.
Classify FlowAcc_Flow1 values to know levels of water accumulation
1. Right click on First Classify FlowAcc_Flow1
2. Properties
3. Symbology
4. Classify
5. Increase no. of classes
6. Apply
7. OK

Fifth - Creating drainage network of high-flow cells
Once flow accumulation is calculated, it is customary to identify
those cells with high flow. This can be done with Raster
Calculator.
1. Open Spatial analysis
2. Choose Raster calculator
3. Give Condition
4. Click evaluate
RESULT - drainage network
Note- You may increase or decrease no of streams by changing
the values at field calculator 39Compiled by : Dr. Zubairul Islam

Sixth - Creating features from drainage network
Converts a raster representing a linear network to features
representing the linear network.
1. Open Stream to feature tool
2. Input calculation as stream raster
3. Input FlowDir_Fill1
4. OK
RESULT
Note- You may merge and name the streams.

CHAPTER 3 – GEO - DATA MANAGEMENT
3.1 - Introduction
3.2 - Creating Geodatabase
3.3 - Building Topology
3.4 - Working with Geodatabase

What is geodata?
Geodata is information about geographic locations that is stored in a format that
can be used with a geographic information system (GIS).
Geodata can be stored in a database, geodatabase, shapefile, coverage, raster
image, or even a dbf table or Microsoft Excel spreadsheet. The following is a list
of geodata that can be used with Esri GIS software along with links to topics
describing them
What is geodatabase?
The geodatabase is a collection of geographic datasets of various types.
Fundamental datasets in the geodatabase
The geodatabase contains three primary dataset types:
1. Feature classes
2. Raster datasets
3. Tables
3.1 - Introduction

Creating a collection of these dataset types is the first step in designing and building a
geodatabase. Users typically start by building a number of these fundamental dataset
types. Then they add to or extend their geodatabases with more advanced capabilities
(such as by adding topologies, networks, or subtypes) to model GIS behavior, maintain
data integrity, and work with an important set of spatial relationships.
The geodatabase is a "container" used to hold a collection of datasets. There are three
types:
1. File geodatabases—Stored as folders in a file system. Each dataset is held as a file
that can scale up to 1 TB in size. The file geodatabase is recommended over
personal geodatabases.
2. Personal geodatabases—All datasets are stored within a Microsoft Access data file,
which is limited in size to 2 GB.
3. ArcSDE geodatabases—Also known as multiuser geodatabases. Stored in a
relational database using Oracle, Microsoft SQL Server, IBM DB2, IBM Informix, or
PostgreSQL. These geodatabases require the use of ArcSDE and can be unlimited in
size and numbers of users.
Types of Geodatabase

LAB 3.2 - CREATING GEODATABASE
Example - CREATING GEODATABASE OF ADIGRAT UNIVERSITY
A. FIRST PLAN PARTS OF GEODATABASE
This exercise may be divided into two parts
A. FIRST PLAN PARTS OF GEODATABASE

OPEN ARC CATALOG & GO TO FOLDER TO SAVE GEODATABASE
1. RIGHT CLICK - NEW
2. PERSONAL GEODATABASE
3. NAME & RIGHT CLICK
4. NEW
5. FEATURE DATASAT
6. NAME-NEXT-IMPORT –ADU IMAGE
NEXT-NEXT-FINISH
7. RIGHT CLICK ON FEATURE DATA – NEW
8. FEATURE CLASS
9. NAME – ADU
10. TYPE – POLYGONLINEPOINT
B. SECOND DEVELOP GEODATABASE WITH ARC CATALOG AS PER PLAN
NOTE- REPEAT POINT 7–10 TO MAKE ALL FEATURES AS PER YOUR PLAN

11. RIGHT CLICK ON FEATURE NAMED AS “BUILDING” CLICK PROPERTIES WRITE FIELD NAMES & SET THEIR PROPERTIES
DO SAME FOR OTHER FEATURE CLASSES

CREATE LAYER FROM FEATURE CLASSES
MAKE NEW FOLDER UNDER GEODATABASE NAMED AS LAYERS
MAKE NEW FOLDER UNDER GEODATABASE NAMED AS LAYERS
1. RIGHT CLICK ON GEODATABASE
2. NEW
3. FOLDER
4. RENAME AS LAYER
1. RIGHT CLICK ON FEATURE
2. CREATE LAYER
3. CHOOSE CREATED FLODER LAYER
4. SAVE

Topology is a mathematical approach that allows us to structure
data based on the principles of feature adjacency and feature
connectivity.
It is in fact the mathematical method used to define spatial
relationships. Without a topologic data structure in a vector based
GIS most data manipulation and analysis functions would not be
practical or feasible.
A topology is a set of rules behaviors that models how point line
and polygon shares the geometry. For example : adjacent features
such as two counties ,will share a common edges.
3.3 - Building Topology & Editing Geodatabase

• Manage share geometry (i.e. constrain how feature shares
geometry)
• Define and enforce data integrity rules (e.g. no gap should
exist between parcel features.)
• Support topological relationship queries and navigation (e.g.
have the ability to identify adjacent and connected features, find
the shared edges and navigate along a series of connected
edges.)
• Construct feature from unstructured geometry (e.g. the
ability to construct polygon from lines)
WHY TOPOLOGY

STEPS
1. RIGHT CLICK ON FEATURE DATASAT
2. NEW
3. TOPOLOGY
4. NEXT
5. TOLERANCE – 1 M
6. SELECT FEATURES
7. NEXT
8. ADD RULES
9. NEXT
10. FINISH
LAB 3.3.1 - Building Topology

LAB 3.4- WORKING WITH GEODATABASE & VALIDATING WORK WITH TOPOLOGY
1. OPEN ARCMAP & ADD ADU GEOREFERENCE IMAGE
2. ADD ALL LAYERS ONE BY ONE
4. START EDITING
5. CREATE NEW FEATURE
6. SET TARGET
7. TAKE PENCIL
8. START WORK
 NOTE – DO NOT FORGET TO SAVE YOUR WORK
WORKING WITH GEODATABASE

VALIDATE WORK WITH TOPOLOGY
STEPS
1. ADD TOPOLOGY FROM GEODATABASE
2. OPEN TOPOLOGY TOOL
3. CLICK ON VALIDATE ENTIRE TOPOLOGY
4. OPEN ERROR INSPECTOR
5. SEARCH NOW
IT WILL SHOW IF ANY ERROR IS THERE
PLS NOTE - ERRORS MAY BE CORRECTED MANUALLY OR AUTOMATIC

CHAPTER 4 -
NETWORKS , NETWORK MODELLING AND ANALYSIS
4.1 – Networks
4.2 - Network Modeling
4.3 - Route network & Analysis with Google Earth

4 - Networks , Network Modelling and analysis
ArcGIS Network Analyst 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, or servicing a set of orders with
a fleet of vehicles.

If the Network Analyst Extension is not enabled, on the Tools menu, click
Extensions, and in the Extensions dialog box, click Network Analyst and close
the Extensions dialog box.
FIRST – ENABLE EXTENTION
1. TOOLS
2. EXTENTION
3. CHECK NETWORK ANALYST
LAB - 4.1 - Networks
Cont…

1. CHOOSE FOLDER
2. RIGHT CLICK ON ROADS
3. NEW NETWORK DATASAT
4. NEXT
5. NEXT
6. NEXT
7. NEXT
8. NEXT
9. YES
10. YES
11. NEXT
12. Display Length Unit
13. Apply
14. OK - Next
15. Finish
16. Yes
TAKE YOUR ROUTE AND POINT DATA IN A FOLDER

17. Import network dataset data into Arcmap
18. Select required data
19. Add ------------Yes
20. Take Network Analyst toolbar & click on NA
21. NEW ROUTE
22. SHOW NA WINDOW
23. CREATE NETWORK LOCATION
24. MARK 2 POINTS AS MEKELLE & GONDAR
25. CLICK ON SOLVE… NOTICE ROUTE
26. CLICK ON DIRECTION FOR MORE DETAILS

RESULT

LAB - 4.2 - NETWORK MODELING
1. ADD DATA – ROADS, NETWORK DATASAT, POINTS TO BE CONNECTED
2. CLICK TOOLBOX
3. EXPAND NAT & ANALYSIS
4. START MODEL BUILDER
5. DRAG MAKE ROUTE LAYER
6. DOUBLE CLICK ON MAKE ROUTE LAYER

7. INPUT ANALYSIS NETWORK
8. CHECK REORDER STOPS
9. PRESERVE_BOTH – APPLY – OK
10. DRAG ADD LOCATIONS
11. DRAG CITIES
12. TAKE CONNECTOR
13. CITIES TO ADD LOCATIONS
14. ROUTE TO ADD LOCATIONS

15. DRAG SOLVE
16. CONNECTOR
17. NETWORK ANALYST TO SOLVE
18. RIGHT CLICK NETWORK ANALYST
19. ADD TO DISPLAY
20. MODEL
21. RUN ENTIRE MODEL
Minimize model window &
See the result
Result & explanations

4.3 - Route network & Analysis with Google Maps
STEPS
1. https://maps.google.com/
2. Directions
3. Give locations
4. Search
Google maps provide directions for driving, public transit, biking,
walking, and flying
LAB 4.3
Route networking with Google Map
RESULT

5.1 - Average Nearest Neighbor Analysis
5.2 – Measuring Geographical Distribution
CHAPTER 5
Point Pattern Analysis and Spatial Statistics

Point pattern analysis (PPA) is the study of the spatial
arrangements of points in (usually 2-dimensional) space.
Point pattern analysis
has become an extremely
important application in
recent years, particularly
in crime analysis, in
epidemiology, and in
facility location planning
and management.
Four patterns of 256 points
5.1 - Average Nearest Neighbor Analysis

Point Pattern Analysis with ArcGis
Average Nearest Neighbor Analysis
The Average Nearest Neighbor Distance tool measures the
distance between each feature centroid and its nearest neighbor's
centroid location. It then averages all these nearest neighbor
distances.
If the average distance is less than the average for a hypothetical
random distribution, the distribution of the features being
analyzed are considered clustered.
If the average distance is greater than a hypothetical random
distribution, the features are considered dispersed.
The index is expressed as the ratio of the observed distance
divided by the expected distance (expected distance is based on a
hypothetical random distribution with the same number of
features covering the same total area).

Calculations
di …… is the distance between feature i and its nearest neighbor feature.
n …… correspond to the total no of features
A …… is the total study area 68Compiled by : Dr. Zubairul Islam

Interpretation
If the index (Average Nearest Neighbor ratio) is less than 1, the
pattern exhibits clustering. If the index is greater than 1, the trend
is toward dispersion.
Results for this statistic are sensitive to changes in the study area.
For these reasons, comparing results from this statistic are most
appropriate when the study area is fixed: comparing average
nearest neighbor distances for different types of retail stores within
a particular county or comparing a single type of retail for a fixed
study area over time, for example.

LAB 5.1.1: Average Nearest Neighbor Analysis
1. Add point data with area
2. Expand spatial stat. tool
3. Nearest neighbor analysis
4. Input point data
5. Display output graphically
6. Optionally give study area
7. Ok
Result & Interpretation

5.2 – Measuring Geographical Distribution
5.2.1 – Measuring Central Feature
Identifies the most centrally located feature in a point feature class.
The Central Feature tool identifies the most centrally located
feature in a point, line, or polygon feature class. Distances from
each feature centroid to every other feature centroid in the dataset
are calculated and summed. Then the feature associated with the
shortest accumulative distance to all other features (weighted if a
weight is specified) is selected and copied to a newly created
output feature class.
Potential applications
For example, if you wanted to build a performing arts center, you could calculate the
central feature. The Central Feature tool is useful for finding the center when there is
travel between the features and the center.

LAB 5.2.1: Measuring Central Feature without weight
1. Add point data
2. Expand spatial stat.
3. Measuring Geographical Distribution
4. Central Feature
5. Input data
6. OK

LAB 5.2.2 : Measuring Central Feature with weight
1. Add point data
2. Expand spatial stat.
3. Measuring Geographical Distribution
4. Central Feature
5. Input data
6. Give weight field
7. OK

CHAPTER - 6
3D analysis and visualization of geospatial data
(TIN Creation, TIN surface, Digital Terrain
Modelling and analysis etc.)

The general term digital terrain model (DTM) may be used to refer
to any of the following surface representations when in digital
form.
6.1 - DIGITAL TERRAIN MODEL (DTM)
1. DEM (digital elevation model):
The term DEM is applied to those datasets which originate as
continuous surfaces. Example - Aster DEM Data
2. TIN (Triangulated Irregular Network)
A TIN data model is composed of nodes, edges, triangles, hull
polygons, and topology.
lines of equal elevation, drawn at a given interval (e.g. every
10 or 50 m.)
3. Contour lines:

6.2 - HOW TO COLLECT ASTER DEM DATA
Link - http://gdem.ersdac.jspacesystems.or.jp/search.jsp

ASTER GDEM Characteristics
1. Tile Size - 3601 x 3601 (1°-by-1°)
2. Geographic coordinates - Geographic latitude and longitude
3. DEM output format - GeoTIFF, signed 16 bits, and 1m/DN
referenced to the WGS84/EGM96 geoid

6.3 - Surface Analysis from Aster Dem Data
Surface analysis involves identifying a specific pattern within a
dataset. Patterns that were not readily apparent in the original
raster dataset surface can be derived.
Surface analysis can be done using tools in Spatial Analyst and
3D Analyst.
3D Surface analysis may be practiced as follows:
Lab 6.3.4 - Shaded relief (hill shade), and

1. 3D Analyst
2. Surface Analysis
3. Contour
4. Input Surface
5. Set Contour Interval (Opt.)
6. Set Output features
7. OK
RESULT
Note – You may label contour by right click on contour in TOC -
property – label – label f in this layer – label f (contour)

1. Data management tool
2. Projections & Transformations
3. Raster
4. Project Raster
5. Input Raster
6. Set Output raster dataset
7. Click on output coordinate sys.
8. Select (Projected Coordinate System –
UTM – WGS 1984 - WGS 1984 UTM Zone 37N.prj)
9. OK
10. OK
First (If data is Geographical coordinate system then change
it to Projected as following:
Cont….

1. Set Layer
2. 3D Analyst
3. Surface Analyst
4. Slope
5. Input Surface
6. Opt degree or %
7. Set Output raster
8. OK
Slope
RESULT AND INTERPRETATION
Note – You may try Slope in Percentage

1. Set Layer
2. 3D Analyst
3. Surface Analyst
4. Aspect
5. Input Surface
6. Output Layer
7. OK

1. Add Aster Dem Data
2. Right click
3. Property
4. Symbology
5. Stretched
6. Use hill shade effect
7. Standard deviation
8. Apply
9. OK
Please Note - Actual elevation values will appear
Lab 6.3.4 - Shaded relief (hill shade)

1. Zoom in area of interest
2. Click on line of sight tool
3. Draw line
Line with Red and Green color will be generated.
Red shows non visible part and
Green shows visible part.
Optionally you may change observer and target elevation, It will change the results Try

THE END

Applied GIS

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