Applied GIS - 3022.pptx

Mizan-Tepi University
College of Social Science and Humanities
Department of Geography and Environmental
Studies
Applied GIS
Instructor: Obang O. (M.Sc in GIS & RS)

Coordinate and Projection
• A coordinate system is a method for identifying the location of a
point on the earth. Coordinate systems use two numbers, a
coordinate, to identify the location of a point.
• Each of these numbers indicates the distance between the point and
some fixed reference point, called the origin. The origin has a
coordinate of 0, 0.
• The first number (X value) indicates how far left or right the point is
from the origin. The second number (Y value) indicates how far
above or below the point is from the origin.

Cont.
• All of these coordinate systems place a grid of vertical and horizontal
lines over a flat map of a portion of the earth. However, there are
hundreds of other coordinate systems used in different places around
the world to identify locations on the earth.
• A complete definition of a coordinate system requires understanding
of the following parameters :
– The projection that is used to draw the earth on a flat map
– The location of the origin
– The units that are used to measure the distance from the origin

Cont.
• A projection is the means by which you display the coordinate
system and your data on a flat surface, such as a piece of paper or a
digital screen.
• Mathematical calculations are used to convert the coordinate system
used on the curved surface of earth to flat surface.
• Some projection preserve shape, while others preserve distance.
Some preserve area or direction. The extent, location, and property
you want to preserve must inform your choice of map projection.

Cont.
• After defining the coordinate system that matches your data, you
may still want to use data in a different coordinate system.
• Since there is no perfect way to transpose a curved surface to a flat
surface without some distortion, various map projections exist that
provide different properties.
• This is when transformations are useful. Transformations is the
process of converting data between different geographic coordinate
systems.

Cont.
• Unless your data lines up, you'll encounter difficulties and
inaccuracies in any analysis and mapping you perform on the
mismatched data.
• A geographic datum transformation is a calculation used to convert
between two geographic coordinate systems to ensure that data is
properly aligned.
• Geographic coordinate systems describe how locations on the earth
are placed on a hypothetical reference spheroid.

Cont.
• GCS use angular units, degrees, to assign locations to coordinates
on a reference spheroid.
• There is more than one geographic coordinate system because each
is meant to best fit certain portions of the earth.
• The best geographic coordinate system to use depends on where
and how much of the earth's geography you are mapping.
• The transformation tools in the Projections and Transformations
toolset can be used to rectify these issues.

Cont.
• Whether you treat the earth as a sphere or a spheroid, you must
transform its three-dimensional surface to create a flat map.
• This mathematical transformation is commonly referred to as a map
projection.
• Symbols graphically describe, categorize, or rank geographic
features, labels, and annotation in a map to locate and show
qualitative and quantitative relationships.
• Symbols are graphic elements that are used in map displays

Cont.
• It allows you to define how each element will be displayed on the
map (symbols, colors, frames, text).
• The easiest way to apply symbols to features and graphics is to
choose from thousands stored in ArcGIS.
• It must be decided whether or not it is required to display the
information according to the attribute table of each layer.
• You can search for appropriate symbols by name or a keyword or
simply browse through a visual palette to find what you need.

Cont.
• In order to represent the real world through a map, it is necessary to
use appropriate symbols that can properly represent the nature of the
information.
• The symbol that conveys what really needs to be represented must
be carefully chosen. When a layer is first loaded into Arc Map it is
assigned symbols of the same size, shape and color.
• Symbols can be created and applied directly to features and
graphics, and optionally stored, managed, and shared in styles.

Cont.
• Styles are a place to store, organize, and share symbols and other map
components.

Cont.
• It can promote standardization across related map products by
ensuring consistency.

Cont.
• There are four basic symbol types:
• Markers are used to display point locations or embellish other
symbol types.
• Line symbols are used to display linear features and boundaries.
• Fill symbols are used to fill in polygons or other areas such as map
backgrounds.
• Text symbols are the font, size, color, and other text properties of
labels and annotation.

Cont.
• Spatial query refers to the process of retrieving a data subset from
a map layer by working directly with the map features.
• In a spatial database, data are stored in attribute tables and
feature/spatial tables.
• Structured query language (SQL) is a database “query” language
designed for extracting information from relational databases.
• Spatial queries can only be performed on a single vector file as they
can store multiple attributes simultaneously.

Cont.
• A spatial query selects spatial features based on their spatial
relationships to other features and are used to answer spatial
questions.
• These spatial questions can be translated into respective spatial
queries.
• For instance, a researcher needs to identify crime sites in a study
area, and another person tries to find locations of all traffic accidents
along some pre-defined roads.

Cont.
• In these situations, you can define a query expression to select a
subset of features for the layer displayed.
• You can type your own expression, or you can use the Query Builder
dialog box to help you set up your query expression
• For example: You might want to display only cities with a population
above a certain threshold (>50,000)
• If you only want to display and work with a subset of features in a
layer, you can apply a definition query to a layer.

Cont.
• Classification is the process of combining data into predefined
classes using some unique symbols or by a unique color
• Different classification methods can have a major impact on the
interpretability of a given map as the visual pattern presented is
easily distorted by manipulating the specific interval breaks of the
classification.
• In addition, the number of classes chosen to represent the feature of
interest will also significantly affect the ability of the viewer to
interpret the mapped information.

Cont.
• Including too many classes can make a map look overly complex and
confusing.
• Too few classes can oversimplify the map and hide important data
trends. It is effective to utilize four to six distinct classes.
• The equal interval (or equal step) classification method divides the
range of attribute values into equally sized classes.
• The number of classes is determined by the user. It is best used for
continuous datasets- precipitation, elevation or temperature

Cont.
• The advantage of equal interval method is that it creates a legend
that is easy to interpret and present to a nontechnical audience.
• The primary disadvantage is that certain datasets will end up with
most of the data values falling into only one or two classes, while few
or no values will occupy the other classes.
• The quintile classification places equal numbers of observations into
each class. It is best for data that is evenly distributed across its
range.

Cont.
• The advantage of this method is that it often excels at emphasizing
the relative position of the data values (i.e., which counties contain
the top 20 percent of the US population).
• The primary disadvantage is that features placed within the same
class can have wildly differing values, particularly if the data are
unevenly distributed across its range.
• In addition, the opposite can also happen whereby values with small
range differences can be placed into different classes, suggesting a
wider difference in the dataset than actually exists.

Cont.
• The natural breaks (or Jenks) utilizes an algorithm to group values
in classes that are separated by distinct break points.
• It is best used with data that is unevenly distributed but not skewed
toward either end of the distribution.
• One potential disadvantage is that it can create classes that contain
widely varying number ranges.
• In cases like this, it is often useful to either “tweak” the classes
following the classification effort or to change the labels to some
ordinal scale such as “small, medium, or large.”

Cont.
• A second disadvantage is that it can be difficult to compare two or
more maps because the class ranges are very specific to each
dataset.
• In these cases, datasets that may not be overly disparate may
appear so in the output graphic.
• The standard deviation method forms each class by adding and
subtracting the standard deviation from the mean of the dataset.
• It is best suited to be used with data that conforms to a normal
distribution

Cont.
• Labeling refers specifically to the process of automatically
generating and placing descriptive text for features on a map
• Label is a piece of text on the map that is dynamically placed and
derived from one or more feature attributes.
• Labeling is a fast way to add text to your map because you don't add
text for each feature manually.

Cont.
• This is useful if your data is expected to change or you are creating
maps at different scales.
• You can turn labels on or off by checking the box next to each layer
and label class to label on the Label Manager.
• You can use dynamic labeling for all features in a layer, or you can
use label classes to specify different labeling properties for features
within the same layer

Cont.
• Editing spatial data and attribute
• ArcGIS allows you to create and edit several kinds of data. You can
edit feature stored in shape files, geo-databases & tabular formats.
• This includes points, lines, polygons, text (annotations and
dimensions), multi-patches, and multi-points.
• You can also edit shared edges and coincident geometry using
topologies and geometric networks. Before you create or edit
features in Arc Map, you need to have an existing feature class to
edit.

Cont.
• If you don't have one, you can create a new geo-database feature
class or a shape file in the Catalog window.
• The Editor toolbar and Create Features window contain the most
frequently used feature editing tools.
• Once you have added the data you want to edit to Arc Map, you'll
follow a basic workflow:
• Choose the workspace and data frame you want to edit.
• Start an edit session (start editing).

Cont.
• Choose a feature template and construction tool from the Create
Features window.
• Set up additional editing properties or options, such as snapping.
• Create the new feature (such as by digitizing it on the map).
• Add or edit attributes of the feature.
• Save edits and stop editing.
• Editing attributes
• When you want to add, delete, or update attribute values, you can
use either the Attributes window or table

Cont.
• The Attributes window allows you to view and edit attributes of
features you have selected.
• You can open it by clicking the Attributes button on the Editor toolbar.
• The bottom of the Attributes window contains two columns: the
attribute fields of the layer you are viewing and the values of those
attribute properties.
• The attribute values that appear depend on what you click in the tree
at the top.

Cont.
• To modify a value for a single feature, click the feature and make
changes in the attribute value column.
• To modify values for all selected features in a layer at the same time,
click the layer name and make changes in the attribute value column.
• To modify values for just a few of the selected features in a layer,
click each feature you want to update so it is highlighted in the
Attributes window, and make the edits.

Cont.
• Topology contain features that share geometry i.e. a forest border
might be at the edge of a stream, lake polygons might share borders
with land-cover polygons and shorelines.
• Parcel polygons might be covered by parcel lot lines.
• When you edit these layers, features that are coincident should be
updated simultaneously so they continue to share geometry.
• Topology allows you to perform edits in this manner.

Cont.
• To edit shared geometry, you need to use topology.
• There are two kinds of topology used in ArcGIS: map topology and
geo-database topology.
• Creating a map topology is quick and allows you to edit features
that connect. A geo-database topology requires more effort to set up
and modify.
• It provides rules that define complex relationships about how the
features in one or more feature classes share geometry.

Cont.
• To activate a topology during an edit session, click the Select
Topology button on the Topology toolbar.
• If you have a geo-database topology in your table of contents, you
can edit shared features using geo-database topology.
• Otherwise, use the Select Topology dialog box to create a map
topology by specifying the layers that should be edited together.
• If you click a topology editing tool without having an active topology,
you are prompted to create a map topology using this dialog box.

Cont.
• Annotation is a way to store & place text on your maps. Each piece
of text stores its own position, text string and display properties.
• Labels, which are based on one or more attributes of features, are
the other primary option for placing text on maps.
• If the exact position of each piece of text is important, you should
store your text as annotation in a geo-database.
• Annotation provides flexibility in the appearance and placement of
your text because you can select individual pieces of text and edit.

Cont.
• Annotation can be stored in a map document or in feature classes in
a geo-database.
• You can convert labels to create new annotation.
• The Convert Labels to Annotation dialog box allows you to specify
what kind of annotation to create from the labels, which features to
create annotation for, and where the annotation will be stored.

Cont.
• Schematics are simplified representations of networks, intended to
explain their structure and make the way they operate
understandable.
• Schematics can be used to represent any type of network within a
defined space without scaling constraints.
• For example, a defined space is a piece of paper where numerous
pieces of information are displayed by optimizing the placement of
the features

Cont.
• The ArcGIS Schematics extension allows you to do the following:
• Automatically generate schematics from complex networks
• Check network connectivity
• Perform quality control of network data
• Optimize network design and analysis
• Forecast and plan (i.e. conduct modeling, simulation, and
comparative analysis)

Cont.
• Dynamically interact with geographic information system (GIS)
software through a schematic view
• Perform commercial and market analyses
• Model social networks
• Generate flowcharts
• Manage interdependencies

Cont.
• Schematic representations are used by the following industries and
utility companies:
• Telecommunications, Energy , Water and wastewater
• Transportation, Electric and gas
• Petroleum and pipeline
• Hydrology, Local government
• Defense and intelligence

Cont.
• Geo-database is a collection of geographic datasets of various types
or it is the dataset. It is the native data structure for ArcGIS and is
the primary data format used for editing and data management.
• While ArcGIS works with geographic information in numerous
geographic information system (GIS) file formats, it is designed to
work with and leverage the capabilities of the geo-database.
• It is the primary mechanism used to organize and use geographic
information in ArcGIS.

Cont.
• The geo-database is a "container" used to hold a collection of
datasets. There are three types geo-database
• File geo-databases—Stored as folders in a file system. Each
dataset is held as a file that can scale up to 1 TB in size. It is
recommended over personal geo-databases.
• Personal geo-databases - All datasets are stored within a Microsoft
Access data file, which is limited in size to 2 GB.
• Enterprise geo-databases / multiuser is unlimited in size and large
numbers of users. Stored in a relational database using Oracle,
Microsoft SQL Server, IBM DB2, IBM Informix, or PostgreSQL.

Cont.
• Creating a collection of these dataset types is the first step in
designing and building a geo-database. Users typically start by
building a number of these fundamental dataset types.
• Then they add to or extend their geo-databases 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.

Cont.
• Feature classes are homogeneous collections of features, each
having the same spatial representation (points, lines, or polygons)
and a common set of attribute columns, for example, a line feature
class for representing road centerlines.
• The four most commonly used feature classes are points, lines,
polygons, and annotation (the geo-database name for map text).

Cont.
• In the illustration below, these are used to represent four datasets for
the same area: (1) manhole cover locations as points, (2) sewer
lines, (3) parcel polygons, and (4) street name annotation.

Cont.
• Types of feature classes
• Vector features are versatile and frequently used geographic data
types, well suited for representing features with discrete boundaries,
such as streets, states, and parcels.
• The first three are supported in databases and geo-databases. The
last four are only supported in geo-databases.
• Points: Features that are too small to represent as lines or polygons
as well as point locations (such as GPS observations).

Cont.
• Lines represent the shape and location of geographic objects street
centerlines and streams, features that have length but no area, such
as contour lines and boundaries.
• Polygons: A set of many-sided area features that represents the
shape and location of homogeneous feature types such as states,
counties, parcels, soil types, and land-use zones.
• Annotation is map text including properties for how the text is
rendered.

Cont.
• For example, in addition to the text string of each annotation, other
properties are included such as the shape points for placing the text,
its font and point size, and other display properties. Annotation can
also be feature linked and can contain subclasses.

Cont.
• Dimensions is a kind of annotation that shows specific lengths or
distances, for example, to indicate the length of a side of a building or
land parcel boundary or the distance between two features.
• Dimensions are heavily used in design, engineering, and facilities
applications for GIS.

Cont.
• Multi-points are composed of more than one point. Multi-points are
often used to manage arrays of very large point collections, such as
lidar point clusters, which can contain literally billions of points.

Cont.
• Multi-patches: A 3D geometry used to represent the outer surface,
or shell, of features that occupy a discrete area or volume in three-
dimensional space.
• Multi-patches comprise planar 3D rings and triangles that are used in
combination to model a three-dimensional shell.
• Multi-patches can be used to represent anything from simple objects
(spheres and cubes) to complex objects iso-surfaces and buildings.

Cont.
• Creating geo-database in Arc Map

Cont.
• Geo-referencing is a technique of spatial positioning by means of
which a digital image is assigned a reference system based on
known coordinates.
• Some raster information is not associated with a reference system,
such as maps, scanned, topographic maps, aerial images, etc.
• To link an image to a known coordinate system, you must perform
the geo-referencing process

Cont.
• Scanned map datasets don't normally contain spatial reference
information (either embedded in the file or as a separate file)
• With aerial photography and satellite imagery, sometimes the
location information delivered with the imagery is inadequate, and the
data does not align properly with other data you have.
• Thus, to use some raster datasets in conjunction with your other
spatial data, you may need to align or geo-reference them to a map
coordinate system

Cont.
• Geocoding is the process of transforming a description of a location
such as a pair of coordinates, an address, or a name of a place to a
location on the earth's surface.
• You can geocode by entering one location description at a time or by
providing many of them at once in a table.
• In the real world, you find locations based on some description. This
might be a number and street name. It might include the name of the
city, state, or country or natural features, such as a drainage basin or
ecological region.

Cont.
• For example, if you needed to locate the address 380 New York St.,
Redlands, CA 92373 with the right street map, it would not take you
long to find the exact location.
• You might first find California, then find the city of Redlands. You
might also use a postal code map and locate the region covered by
the corresponding ZIP Code value.
• You would then locate the street and interpret where and on which
side of the 300 block the address is located.

Cont.
• Addresses come in many forms, ranging from the common address
format of house number, street name and succeeding information to
other location descriptions such as postal zone or census tract.
• An address includes any type of information that distinguishes a
place. Many common tasks are related to the geocoding process,
such as the creation and maintenance of address locators,
geocoding addresses, and getting addresses for point locations.
• With geocoded addresses, you can spatially display the address
locations and begin to recognize patterns within the information

Cont.
• Once you have created an address locator, you can begin using it to
geocode addresses. However, understanding how an address locator
prepares the input address data, searches the address attributes,
and matches addresses can help you improve both the performance
and accuracy of geocoding. i.e. 127 West Point Drive, Olympia, WA
98501

Cont.
• The 3D Analyst toolbox provides a collection of geo-processing
tools that enable a wide variety of analytical, data management, and
data conversion operations on surface models and three-dimensional
vector data.
• 3D Analyst tools provide the ability to create and analyze surface
data represented in raster, terrain, triangulated irregular network
(TIN), and LAS dataset formats.
• 3D data can be converted from a rich variety of formats, including
COLLADA, lidar, Sketch Up, Open Flight, and many other data types.

Cont.
• Analysis of geometric relationships and feature properties,
interpolation of raster and various triangulated irregular network (TIN)
models, and analysis of surface properties are only some of the
numerous functions provided by the 3D Analyst tools.
• Interactive 3D Analyst in Arc Map

Cont.
• 3D surface model is a digital representation of features, either real or
hypothetical, in three-dimensional space.
• Some examples are a landscape, an urban corridor, gas deposits
under the earth, and a network of well depths to determine water
table depth.
• A 3D surface is derived using specially designed algorithms that
sample point, line, or polygon data and convert it into a digital 3D
surface. ArcGIS can create and store four types of surface models:
raster, TIN, terrain datasets, and LAS datasets.

Cont.
• These surface models can be created from a variety of data sources
such as interpolation and triangulation
• A functional surface is a continuous field of values that may vary over
an infinite number of points. For example, points in an area on the
earth's surface may vary in elevation, proximity to a feature, or
concentration of a particular chemical.
• Any of these values may be represented on the z-axis in a three-
dimensional x,y,z coordinate system, so they are often called z-
values.

Cont.
• Raster, TIN, terrain, and LAS datasets are all types of a functional
surface.
• Surface models allow you to store surface information in a GIS.
Because a surface contains an infinite number of points, it is
impossible to measure and record the z-value at every point.
• A surface model approximates a surface by taking a sample of the
values at different points on the surface and interpolating the values
between these points.

Cont.
• GIS data can generally be categorized into two major types: raster
and vector. Vector data is defined by points, lines, and polygons and
their associated relationships that comprise geospatial data.
• Raster data is a rectangular matrix of cells, represented in rows and
columns. Each cell represents a defined square area on the earth's
surface and holds a value that is static across the entire cell.
• A surface can be represented as raster data, where each cell in the
data represents some value of real-world information

Cont.
• Surfaces represented as raster data are a form of continuous data. A
continuous surface represents phenomena in which each location on
the surface is a measure of the concentration level
• The fixed point may be a spot height derived from photogrammetric
methods, but interpolation between heights help form the digital
elevation model
• Since raster surfaces are usually stored in grid format with uniformly
spaced cells, the smaller the cells, the greater the locational
precision of the grid

Cont.
• TINs are a form of vector-based digital geographic data and are
constructed by triangulating a set of vertices (points).
• The vertices are connected with a series of edges to form a network
of triangles. TINs are a digital means to represent surface
morphology.
• There are different methods of interpolation to form these triangles,
such as Delaunay triangulation or distance ordering. ArcGIS supports
the Delaunay triangulation method.

Cont.
• The edges of TINs form contiguous, non-overlapping triangular
facets and can be used to capture the position of linear features that
play an important role in a surface, such as ridgelines or stream
courses.
• TIN models are less widely available than raster surface models and
tend to be more time consuming to build and process. The cost of
obtaining good source data can be high, and processing TINs tends
to be less efficient than processing raster data because of the
complex data structure.

Cont.
• TINs are typically used for high-precision modeling of smaller areas,
such as in engineering applications, where they are useful because
they allow calculations of planimetric area, surface area, and volume.

Cont.
• Terrain datasets are an efficient way to manage large point-based
data in a geo-database and produce high-quality, accurate surfaces
on the fly.
• Lidar, sonar, and elevation measurements can number from several
hundred thousand to many billions of points. Organizing, cataloging,
and generating 3D products from these types of data is difficult and
prohibitive in many cases.
• It allow you to overcome these data management hurdles, edit your
source data, and produce highly accurate TINs at varying
resolutions.

Cont.
• Terrain datasets are unique in that they can either embed or
reference the source data. Through the indexing of each point
measurement, a set of TIN pyramids is generated, each set with
successively fewer participating nodes (source points).
• This allows ArcMap and ArcGlobe to generate a TIN on the fly at
whatever resolution is needed for the scale of the viewer.
• Small-scale displays of data require fewer points, and thus, a lower-
resolution TIN is rendered

Cont.
• The LAS dataset allows you to examine LAS files, in their native
format, quickly and easily, providing detailed statistics and area
coverage of the lidar data contained in the LAS files.
• A LAS dataset can also store reference to feature classes containing
surface constraints. Surface constraints are break lines, water
polygons, area boundaries, or any other type of surface feature that
is to be enforced in the LAS dataset.
• A LAS file contains lidar point cloud data. For more information on
LAS files, see: Storing lidar data.

Cont.
• Vector Based Spatial data Analysis

Cont.
• The Tracking Analyst toolbox contains tools used to analyze
temporal data and prepare temporal data for use with the ArcGIS
Tracking Analyst extension.
• The ArcGIS Tracking Analyst extension provides a rich user interface
with all the tools you need to analyze, visualize, and process your
historical or real-time temporal data.
• Tracking Analyst extension is designed for mapping objects that
move or change status over time. Tracking Analyst gives you the
power to do the following

Cont.
• Bring geographic data containing dates and times (temporal data) to
life by adding it to a map as a tracking layer.
• Track objects in real time. Tracking Analyst supports network
connections to GPS units and other tracking and monitoring devices
so you can map your data in real time.
• Symbolize temporal data using time windows and other specialized
options for viewing data that changes through time.
• Play back temporal data using Tracking Analyst Playback Manager.
Data can be played back at different speeds in forward and reverse.

Cont.
• Analyze patterns in temporal data by creating data clocks.
• Create and apply actions on temporal data.
• Create animations using Tracking Analyst Animation tool.
• View tracking data in 3D using Arc Globe.
• Temporal data is any geographic data that contains time information.
• Using Add Temporal Data Wizard, just about any temporal
geographic data can be added to your map as a tracking layer.

Cont.
• Once your data has been added to your map, it can be brought to life
with the symbology options and analysis tools provided in Tracking
Analyst.

Cont.
• The Proximity toolset contains tools that are used to determine the
proximity of features within one or more feature classes or between
two feature classes.
• These tools can identify features that are closest to one another or
calculate the distances between or around them.

Cont.
• One of the most basic questions asked of a GIS is "what's near
what?"
• How close is this well to a landfill?
• Do any roads pass within 1,000 meters of a stream?
• What is the distance between two locations?
• What is the nearest or farthest feature from something?
• What is the distance between each feature in a layer and the
features in another layer?
• What is the shortest street network route from some location to
another?

Cont.
• Proximity tools can be divided into two categories depending on the
type of input the tool accepts: features or raster.
• The feature-based tools vary in the types of output they produce. For
example, the Buffer tool outputs polygon features, which can then be
used as input to overlay or spatial selection tools such as Select
Layer By Location.
• The Near tool adds a distance measurement attribute to the input
features. The raster-based Euclidean distance tools measure
distances from the center of source cells to the center of destination
cells. The raster-based cost-distance tools accumulate the cost of

Cont.
• Feature-based proximity tools
• For feature data, the tools found in the Proximity toolset can be used
to discover proximity relationships. These tools output information
with buffer features or tables.
• Buffers are usually used to delineate protected zones around
features or to show areas of influence.
• For example, you might buffer a school by one mile and use the
buffer to select all the students that live more than one mile from the
school to plan for their transportation to and from school.

Cont.
• You could use the multiring buffer tool to classify the areas around a
feature into near, moderate distance, and long distance classes for
an analysis.
• Buffers are sometimes used to clip data to a given study area or to
exclude features within a critical distance of something from further
consideration in an analysis.

Cont.
• Buffer and Multiple Ring Buffer create area features at a specified
distance (or several specified distances) around the input features.

Cont.
• The Near tool calculates the distance from each point in one feature
class to the nearest point or line feature in another feature class.
• You might use Near to find the closest stream for a set of wildlife
observations or the closest bus stops to a set of tourist destinations.
• The Near tool will also add the Feature Identifier and, optionally,
coordinates of and the angle toward the nearest feature. Below is an
example showing points near river features.

Cont.
• The points are symbolized using graduated colors based on distance
to a river, and they're labeled with the distance.

Cont.
• Point Distance calculates the distance from each point in one feature
class to all the points within a given search radius in another feature
class. This table can be used for statistical analyses, or it can be
joined to one of the feature classes to show the distance to points in
the other feature class.
• You can use the Point Distance tool to look at proximity relationships
between two sets of things.

Cont.
• For example, you might compare the distances between one set of
points representing several types of businesses (such as theaters,
fast food restaurants, engineering firms, and hardware stores) and
another set of points representing the locations of community
problems (litter, broken windows, spray-paint graffiti), limiting the
search to one mile to look for local relationships.
• You could join the resulting table to the business and problem
attribute tables and calculate summary statistics for the distances
between types of business and problems.

Cont.
• You might find a stronger correlation for some pairs than for others
and use your results to target the placement of public trash cans or
police patrols.
• You might also use Point Distance to find the distance and direction
to all the water wells within a given distance of a test well where you
identified a contaminant. Below is an example of point distance
analysis. Each point in one feature class is given the ID, distance,
and direction to the nearest point in another feature class.

Cont.
• Both Near and Point Distance return the distance information as
numeric attributes in the input point feature attribute table for Near
and in a stand-alone table that contains the Feature IDs of the Input
and Near features for Point Distance.

Cont.
• Create Thiessen Polygons creates polygon features that divide the
available space and allocate it to the nearest point feature. The result
is similar to the Euclidean Allocation tool for raster.
• Theissien polygons are used instead of interpolation to generalize a
set of sample measurements to the areas closest to them.
• Theissien polygons are sometimes also known as Proximal
polygons. They can be thought of as modeling the catchment area
for the points, as the area inside any given polygon is closer to that
polygon's point than any other

Cont.
• You might use Theissien polygons to generalize measurements from
a set of climate instruments to the areas around them or to quickly
model the service areas for a set of stores

Cont.
• The Distance toolset contains tools that create raster showing the
distance of each cell from a set of features or that allocate each cell
to the closest feature.
• Distance tools calculate the shortest path across a surface or the
corridor between two locations that minimizes two sets of costs.
• Distance surfaces are often used as inputs for overlay analyses; for
example, in a model of habitat suitability, distance from streams
could be an important factor for water-loving species, or distance
from roads could be a factor for timid specie

Cont.
• Euclidean distance
• Euclidean distance is straight-line distance, or distance measured "as
the crow flies." For a given set of input features, the minimum
distance to a feature is calculated for every cell.
• Below is an example of the output of the Euclidean Distance tool,
where each cell of the output raster has the distance to the nearest
river feature:

Cont.
• You might use Euclidean Distance as part of a forest fire model,
where the probability of a given cell igniting is a function of distance
from a currently burning cell.
• Euclidean allocation divides an area up and allocates each cell to the
nearest input feature. This is analogous to creating Theissien
polygons with vector data.
• The Euclidean Allocation tool creates polygonal raster zones that
show the locations that are closest to a given point.

Cont.
• If you specify a maximum distance for the allocation, the results are
analogous to buffering the source features.
• Below is an example of a Euclidean allocation analysis where each
cell of the output raster is given the ID of the nearest point feature:
• You might use Euclidean allocation to model zones of influence or
resource catchments for a set of settlements.

Cont.
• Below is an example of a Euclidean allocation analysis where each
cell within a specified distance of a point is given the ID of the
nearest point feature.
• For each cell, the color indicates the value of the nearest point; in the
second graphic, a maximum distance limits the allocation to buffer-
like areas. You might use Euclidean allocation with a maximum
distance to create a set of buffer zones around streams

Cont.
• Euclidean direction gives each cell a value that indicates the
direction of the nearest input feature. Below is an example of the
output of the Euclidean Direction tool where each cell of the output
raster has the direction to the nearest point feature.
• You might use Euclidean direction to answer the question, For any
given cell, which way do I go to get to the nearest store?

Cont.
• Cost distance
• In contrast with the Euclidean distance tools, cost distance tools take
into account that distance can also be measured in cost (for
example, energy expenditure, difficulty, or hazard) and that travel
cost can vary with terrain, ground cover, or other factors.
• Given a set of points, you could divide the area between them with
the Euclidean allocation tools so that each zone of the output would
contain all the areas closest to a given point.

Cont.
• However, if the cost to travel between the points varied according
to some characteristic of the area between them, then a given
location might be closer, in terms of travel cost, to a different point.
• An example of using the Cost Allocation tool, where travel cost
increases with land-cover type.
• The dark areas could represent difficult-to-traverse swamps, and
the light areas could represent more easily traversed grassland.

Cont.
• Compare the Euclidean allocation results with the Cost allocation
results.
• This is in some respects a more complicated way of dealing with
distance than using straight lines, but it is very useful for modeling
movement across a surface that is not uniform

Cont.
• Path distance tools extend the cost distance tools, allowing you to
use a cost raster but also take into account the additional distance
traveled when moving over hills, the cost of moving up or down
various slopes, and an additional horizontal cost factor in the
analysis.
• For example, two locations in a long, narrow mountain valley might
be further apart than one is from a similar location in the next valley
over, but the total cost to traverse the terrain might be much lower
within the valley than across the mountains.

Cont.
• Various factors could contribute to this total cost, for example: It is
more difficult to move through brush on the mountainside than
through meadows in the valley.
• It is more difficult to move against the wind on the mountain side than
to move with the wind and easier still to move without wind in the
valley.
• The path over the mountain is longer than the linear distance
between the endpoints of the path, because of the additional up and
down travel.

Cont.
• A path that follows a contour or cuts obliquely across a steep slope
might be less difficult than a path directly up or down the slope.
• The path distance tools allow you to model such complex problems
by breaking travel costs into several components that can be
specified separately.
• These include a cost raster (such as you would use with the Cost
tools), an elevation raster that is used to calculate the surface-length
of travel, an optional horizontal factor raster (such as wind direction),
and an optional vertical factor raster (such as an elevation raster).

Cont.
• In addition, you can control how the costs of the horizontal and
vertical factors are affected by the direction of travel with respect to
the factor raster.

Cont.
• The Corridor tool finds the cells between locations that minimize
travel cost using two different cost distance surfaces.
• For example, you might use the tool to identify areas that an animal
might cross while moving from one part of a park to another.
• Below are examples of two sets of factors that might affect the cost
of traveling across a landscape. In this case, one is land-cover type,
and the other is slope.

Cont.
• The Corridor tool combines the results of the Cost Distance analysis
for the two factors. The results can be reclassified to find the areas
where the combined costs are kept below a certain level for the
animal to travel within.

Cont.
• Interpolation predicts values for cells in a raster from a limited
number of sample data points.
• It can be used to predict unknown values for any geographic point
data, such as elevation, rainfall, chemical concentrations, and noise
levels.
• The assumption that makes interpolation a viable option is that
spatially distributed objects are spatially correlated.
• In other words, things that are close together tend to have similar
characteristics.

Cont.
• Using the above analogy, it is easy to see that the values of points
close to sampled points are more likely to be similar than those that
are farther apart.
• This is the basis of interpolation. A typical use for point interpolation
is to create an elevation surface from a set of sample measurements.
• Geostatistical Analyst also provides and extensive collection of
interpolation methods.

Cont.
• Interpolation tools create a continuous (or prediction) surface from
sampled point values.
• Visiting every location in a study area to measure the height,
concentration, or magnitude of a phenomenon is usually difficult or
expensive.
• Instead, you can measure the phenomenon at strategically dispersed
sample locations, and predicted values can be assigned to all other
locations.
• Input points can be either randomly or regularly spaced or based on
a sampling scheme.

Cont.
• There are a variety of ways to derive a prediction for each location;
each method is referred to as a model.
• With each model, there are different assumptions made of the data,
and certain models are more applicable for specific data—for
example, one model may account for local variation better than
another.
• Each model produces predictions using different calculations. The
interpolation tools are generally divided into deterministic and geo-
statistical methods.

Cont.
• The deterministic interpolation methods assign values to locations
based on the surrounding measured values and on specified
mathematical formulas that determine the smoothness of the
resulting surface. It include IDW (inverse distance weighting), Natural
Neighbor, Trend, and Spline.
• The geostatistical methods are based on statistical models that
include autocorrelation (the statistical relationship among the
measured points).

Cont.
• Because of this, geo-statistical techniques not only have the
capability of producing a prediction surface but also provide some
measure of the certainty or accuracy of the predictions. i.e. Kriging
• The remaining interpolation tools, Topo to Raster and Topo to Raster
by File, use an interpolation method specifically designed for creating
continuous surfaces from contour lines, and the methods also
contain properties favorable for creating surfaces for hydrologic
analysis.

Cont.
• Raster Based Spatial data Analysis
• Surfaces represent phenomena that have values at every point
across their extent. The values at the infinite number of points across
the surface are derived from a limited set of sample values.
• These may be based on direct measurement, such as height values
for an elevation surface, or temperature values for a temperature
surface; between these measured locations, values are assigned to
the surface by interpolation.

Cont.
• Surfaces can be represented using contour lines, arrays of points,
TINs, and raster; however, most surface analysis in GIS is done on
raster or TIN data.
• Surface analysis involves several kinds of processing, including
extracting new surfaces from existing surfaces, reclassifying
surfaces, and combining surfaces.
• Certain tools extract or derive information from a surface, a
combination of surfaces, or surfaces and vector data. Some of these
tools are primarily designed for the analysis of raster terrain surfaces.

Cont.
• The Slope tool calculates the maximum rate of change from a cell to
its neighbors, which is typically used to indicate the steepness of
terrain.

Cont.
• The Aspect tool calculates the direction in which the plane fitted to
the slope faces for each cell.
• The aspect typically affects the amount of sunlight it receives (as
does the slope); in northern latitudes places with a southerly aspect
tend to be warmer and drier than places that have a northerly aspect.

Cont.
• Hillshade shows the intensity of lighting on a surface given a light
source at a particular location. it can model which parts of a surface
would be shadowed by other parts.

Cont.
• Curvature calculates the slope of the slope that is, whether a given
part of a surface is convex or concave.
• Convex parts of surfaces, like ridges, are generally exposed and
drain to other areas. Concave parts of surfaces, like channels, are
generally more sheltered and accept drainage from other areas.
• The Curvature tool has a couple of optional variants, Plan and Profile
Curvature. These are used primarily to interpret the effect of terrain
on water flow and erosion.

Cont.
• The profile curvature affects the acceleration and deceleration of
flow, which influence erosion and deposition. The planiform curvature
influences convergence and divergence of flow.

Cont.
• Visibility tools are used to analyze the visibility of parts of surfaces.
The Line Of Sight tool identifies whether or not one location is visible
from another, and whether or not the intervening locations along a
line between the two locations are visible.
• An observer at the southern end of the line can see the parts of the
terrain along the line that are colored green, and cannot see the parts
of the terrain along the line that are colored red.
• In this case, the observer cannot see the fire in the valley on the
other side of the mountain.

Cont.
• The Observer Points tool identifies which observers, specified as a
set of points, can see any given cell of a raster surface.
• The View shed tool calculates, for each cell of a raster surface and a
set of input points (or the vertices of input lines), how many
observers can see any given cell.
• The observer has an offset to model the view from a fire tower 50
meters taller than the ground surface. Cells outside the observer's
view shed are blacked out in the image on the right.

Cont.
• Both the Observer Points and View shed tools also allow you to
specify observer, target offsets, and parameters that let you limit the
directions and distance that each observer can view.

Cont.
• The Cut Fill tool is used to calculate the amount of difference in each
cell for a before and after raster of the same area.
• This tool could be used to calculate the volume of earth that must be
brought to or removed from a construction site to reshape a surface.
• This tool works on two raster, and the results are presented as a
raster of the difference between the two layers.

Cont.
• The Raster Calculator tool allows you to create and execute Map
Algebra expressions in a tool.
• The Raster Calculator tool is specifically designed to offer the
following benefits:
• Implement single-line algebraic expressions.
• Support the use of variables in Map Algebra when in Model Builder.
• Apply Spatial Analyst operators on three or more inputs in a single
expression.
• Use multiple Spatial Analyst tools in a single expression.

Cont.
• It is designed to execute a single-line algebraic expression using
multiple tools and operators using a simple, calculator-like tool
interface.
• When multiple tools or operators are used in one expression, the
performance of this equation will generally be faster than executing
each of the operators or tools individually.
• There are four main areas in the tool dialog box that are used to
create a Map Algebra expression:

Cont.
• Map Algebra is a simple and powerful algebra with which you can
execute all Spatial Analyst tools, operators, and functions to perform
geographic analysis
• It is a simple and powerful algebra with which you can execute all
Spatial Analyst tools, operators, and functions to perform geographic
analysis.
• The Map Algebra has syntax, or a set of rules, that must be followed
to create a valid expression.

Cont.
• If these rules are not adhered to, the expression may be invalid and
will not execute, or you may get results you did not expect.
• The Map Algebra syntax used in this tool is the same, with the
following exceptions:
• You do not need to put the output raster name or the equal sign (=) in
the expression, since the output name is specified in the Output
raster parameter.
• You do not need to cast input data as a Raster object when using
operators.

Cont.
• Image classification refers to the task of extracting information
classes from a multiband raster image. The resulting raster from
image classification can be used to create thematic maps.
• Depending on the interaction between the analyst and the computer
during classification, there are two types of classification: supervised
and unsupervised.
• With the ArcGIS Spatial Analyst extension, there is a full suite of tools
in the Multivariate toolset to perform supervised and unsupervised
classification.

Cont.
• The classification process is a multi-step workflow; therefore, the
Image Classification toolbar has been developed to provide an
integrated environment to perform classifications with the tools.
• Not only does the toolbar help with the workflow for performing
unsupervised and supervised classification, it also contains additional
functionality for analyzing input data, creating training samples and
signature files, and determining the quality of the training samples
and signature files.
• The recommended way to perform classification and multivariate
analysis is through the Image Classification toolbar.

Cont.
• Supervised classification is an image classification approach that
is based on the training samples collected by the analyst.
• The training samples "teach" the software how to classify the rest of
the pixels in the image.
• It uses the spectral signatures obtained from training samples to
classify an image.
• With the assistance of the Image Classification toolbar, you can
easily create training samples to represent the classes you want to
extract.

Cont.
• You can also easily create a signature file from the training samples,
which is then used by the multivariate classification tools to classify
the image.
• Unsupervised classification is an image classification approach
that sorts the pixels in the image into clusters without the analyst's
intervention.
• The process is based solely on the distribution of pixel values in a
multidimensional attribute space.

Cont.
• It finds spectral classes (or clusters) in a multiband image without the
analyst’s intervention.
• The Image Classification toolbar aids in unsupervised classification
by providing access to the tools to create the clusters, capability to
analyze the quality of the clusters, and access to classification tools.
• The detailed steps of the image classification workflow are illustrated
in the following chart.

Cont.
• Overlay analysis combine the characteristics of several datasets into
one. You can then find specific locations or areas that have a certain
set of attribute values—that is, match the criteria you specify.
• This approach is often used to find locations that are suitable for a
particular use or are susceptible to some risk.
• In general, there are two methods for performing overlay analysis—
feature overlay (overlaying points, lines, or polygons) and raster
overlay.

Cont.
• Overlay analysis is used to find locations meeting certain criteria.it is
often best done using raster overlay (you can do it with feature data).
• Of course, this also depends on whether your data is already stored
as features or raster.
• It may be worthwhile to convert the data from one format to the other
to perform the analysis.
• In general, there are two methods for performing overlay analysis—
feature overlay (overlaying points, lines, or polygons) and raster
overlay

Cont.
• In raster overlay, each cell of each layer references the same
geographic location.
• That makes it well suited to combining characteristics for numerous
layers into a single layer.
• Usually, numeric values are assigned to each characteristic, allowing
you to mathematically combine the layers and assign a new value to
each cell in the output layer.

Cont.
• This approach is often used to rank attribute values by suitability or
risk, then add them to produce an overall rank for each cell.
• The various layers can also be assigned a relative importance to
create a weighted ranking (the ranks in each layer are multiplied by
that layer's weight value before being summed with the other layers).
• Three raster layers (steep slopes, soils, and vegetation) are ranked
for development suitability on a scale of 1 to 7.

Cont.
• When the layers are added, each cell is ranked on a scale of 3 to 21.
• New polygons are created by the intersection of the input polygon
boundaries. The resulting polygons have all the attributes of the
original polygons.

Cont.
•The end of the course

Applied GIS - 3022.pptx

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