GIS 5103 – Fundamentals of GISLecture 83D GIS.docx

GIS 5103 – Fundamentals of GIS
Lecture 8
3D GIS
Dimensionality
A dimension is the minimum amount of coordinates needed to
define the mathematical space of an object.
Lecture 8 – 3D GIS
2DA figure that has only length & height as its dimensions. 2D
shapes lie on a flat surface; known as plane figures or plane
shapes. While they have areas, 2D shapes have no volume.2D
figures are plotted on two axes, the x- and y-axes.
3DLength + Height + WidthX + Y + Z Volume + Surface
AreaExamples include: 3D multi-patch building, roof, interior
floors, and foundation would all contain different z-values for
the same 2D coordinate. Aircraft's 3D position or a walking

trail up a mountain, would only have a single z-value for each
X,Y location.
2.5DHighly used in GIS to represent Z data that is not
continuous = 3DZ data does not need to be elevation. Pollution,
# of cases, etc.
3D file types .3dd.sxdWhen you import an ArcGlobe or
ArcScene document:First the .3dd file opens by default in
global mode Secondly the .sxd file opens in local mode. Any
new blank scene view defaults to global mode.
Data TypesVector FeaturesPoint / Line / Polygon Surface
typesTIN (Triangular Irregular Networks)DEM’s (Digital
Elevation Models)Raster SurfaceLAS Dataset
3D StylesPointsGeometric
ShapesModelsMarkersImportedLinesTextured
SymbolsGeometric ShapesPolygonsTexture FillBasic Colors
Perspective ViewPerspective drawing is the most common

drawing mode in 3D, where features in the foreground are
shown larger than those off in the distance. This matches the
way we see the world in our day-to-day lives, and the result is a
realistic representation of 3D content. All scenes open in
perspective viewing mode. You can switch between Perspective
Perspective View and Parallel Isometric View viewing modes
using the Drawing Mode drop-down menu in the Scene group on
the View tab.
Parallel / Isometric ViewParallel drawing renders the 3D view
using a parallel projection, where features of the same physical
size are rendered on-screen identically, regardless of their
distance from the viewing camera. Parallel drawing is useful for
architectural drawings, as well as for representing statistical
data in a 3D view, such as extruded shapes symbolizing numeric
values.
3D Analyst ToolsData Conversion/PreparationTxt / Binary /
Feature class / Raster / TINSurface CreationInterpolation /
LASD CreationSurface AnalysisAspect / Slope / Contour /
Feature Interpolation3D Operator & VisibilitySkyline / Inter-
visibility / Sun Shadow Analysis
There are five exploratory analysis tools:The Line of Sight tool
creates sight lines to determine if one or more targets are
visible from a given observer location.The View Dome tool
determines the parts of a sphere that are visible from an
observer located at the center.The Viewshed tool determines the
visible surface area from a given observer location through a

defined viewing angle.The Slice tool visually cuts through the
view's display to reveal hidden content.The Cut and Fill tool
provides volume calculations while visually demonstrating the
surface area where the ground will be filled or removed.
Exploratory Analysis ToolsThe first three analysis tools
determine the visible sight lines, field of view, or spherical
visibility coverage. The Slice tool temporarily removes
displayed content using clipping planes as volumes.The Cut and
Fill tool calculates volumetric displacement using a planar
surface. In all cases, the results are temporary and must be
saved as feature classes if you want to reload the interactive
objects again.
illumination propertiesA unique element to authoring scenes is
that you can also define illumination properties. This includes
properties such as the time of day, whether or not the sun casts
a shadow, and how much ambient light is used. In global mode,
you can also simulate atmospheric diffusion.
By the end of this week you should be able to:
Understand dimensionality List the differences between 2D vs
2.5D vs 3D Know the data types, Views, & Scene Types for 3D
GIS Understand Surfaces & How to create/find themFamiliarize
yourself with the Exploratory & Analyst toolsList the unique
properties of 3D GIS vs Traditional 2D mapsComprehend the
issues of 3D GIS

GIS 5103 – Fundamentals of GIS
Lecture 7
Intro to R-Studio & Python for GIS
Week 7
Winter 2019
What is R-Studio & why should I use it?Free open-source
integrated coding language.Powerful computing/programing &
simple UIAlso very popular
Intro to R & Python - Lecture 7
Applications &
CapabilitiesStatisticsGraphicsProgramingCalculatorGISNeural
NetworksEtc.
Installinghttps://courses.edx.org/courses/UTAustinX/UT.7.01x/
3T2014/56c5437b88fa43cf828bff5371c6a924/
Set working DirectoryDefining the working area / confining
thinking space / Files of interest located here

Import DataFiles of interest located in working directory.
Manipulate & Organize Data
Various functions for parameters of interest
Able to work with data and create data.
Convert, Transpose, data with single commands.
Plots / DiagramsplotMF(object.cls)plotMF(object)
Most Popular Uses of RData ManagementConversion, sub-
setting, merging, transformationsStatistical & Mathematical
AnalysisANOVA’s, Modeling, RegressionsProgramming /
Operations / System InterfaceAutomation, Special
ConditionsGraph / Data Display / ConfigurationData
Visualization, High CustomizationSimulationsHigh level
scenario modeling

Variables
Can define characters/numbers as nearly anything
x = 5
First_Name = Mike
f = (sin(y) / tan(sum(z)) * variance(y/z)
Name – Type – Size – Value
Created Title– character/matrix/list – Bytes/KB/MB – Output
Functions & Syntax
Mean(x, trim = 0, na.rm = FALSE, …)
Function(input, argument 1, argument 2, …)
*Commas used to separate arguments/inputs*
Help Dialogue

What is Python & why should I use it?Object-orientated, high
level programming language with dynamic semanticBuilt in data
structures, dynamic typing/bindingUsed as glue to connect
programs / components together.Supports modules / packages /
code re-use
Python Libraries = R-Studio PackagesGIS
LibrariesGDALOGRPyprojShapelyMapnikMySQLPostGISSpati
aLite
How to download Pythonhttps://realpython.com/installing-
python/
How to Pip install
librarieshttps://packaging.python.org/tutorials/installing-
packages/
How we will use python & Libraries
Example of ArcGIS & python
https://developers.arcgis.com/python/sample-

notebooks/chennai-floods-analysis/
GIS AutomationPythonR-StudioModel Builder
Common Automated processesAddress systemsData
UpdatingMap TemplatingSpatial JoinsDigitizing
CalculationsModelsWork Flows
After reviewing this lecture you should be able to:
List applications/uses of a coding languagesKnow many of the
popular input file typesUnderstand/Replicate functions, syntax,
styleKnow the names of essential packages/librariesList
commonly automated tasksBe able to download & Install the
softwares
2
Foundations of Geographic Information Science
GIS 5103
Fall 2019 - Week Six
Vector Analysis

Objectives:
By the end of week seven you will be able to:
Describe Measurements in GIS;
Perform data queries in ArcGIS Pro
Explain techniques used in reclassification of data
Describe Buffering Functions and map overlays
Earlier in this course we covered some of the questions that can
be answered by GIS. Over the past few weeks we have been
discussing different types of data, data formats, data models,
data creation and editing, and finding/correcting errors in data.
Once we have our software, computer hardware, personnel, and
data in place we can start to answer questions with our system.
This week we will take a look at some of the questions a GIS
can answer and also
some of the tools GIS uses to answer the questions like the ones
listed below:
Where are particular features found?
What geographical patterns exist?
Where have changes occurred over a given period?
Where do certain conditions apply?
What will the spatial implications be if an organization takes a
certain action?
Common questions answered by GIS

Questions for GIS Analysis
Of course “where” is the predominant question but there are
some additional questions
beyond just where. Below are some examples of the questions
beyond where.
Location: what is at or where is it? E.g., where is the nearest
Mexican restaurant to my house?
Patterns: what spatial patterns exist? E.g., where do most of the
students live in Boston?
Trends: what has changed since? Has the number of deer
incidents on roads
reduced since commuters have used the deer whistles?
Conditions: where can I find something close to, within or by?
Modeling/Implications: what if? An example may include: if a
new baseball park were built adjacent to Gillette stadium, how
would traffic flow be affected?
Our data analysis procedures assist us in answering these
questions. Let us look at them more closely now.
Making Measurements in GIS
Calculating lengths, perimeters and areas are common
applications in GIS. For example, measuring the length of a
road from a digital map is pretty straightforward. However, a
GIS can make measurements on other types of features as well.
There are a number of tools at your disposal and depending on
the type of data you are using (raster or vector). The next few
slides will introduce you to some of those measuring tools and
techniques.

a. Length (distance); b. Perimeter; c. Area
Vector GIS measurements: (a) distance;
(b) area; and (c)perimeter can be measured as the sum of
straight line lengths.
The figure to the right shows how these
quantities are calculated by the GIS, but as users it is much
easier for us. All that is needed is to click on the measure tool
in the toolbar of the ArcMap GIS software. The GIS does the
rest.
In Vector GIS, length, perimeter and area data can be stored as
attributes in a database. By storing the lengths and areas as
attributes we can select features meeting a certain criteria and
sum the areas or lengths. For example how many acres of
wetlands do we have in a city?
Measurements in GIS
The figure above from Esri shows 3 and 5 minute drive time
polygons from a downtown area. Drivetime polygons can be
measured using either a straight line distance or by using road
networks and traffic conditions.
Example of a Measurement
Queries in GIS
Queries are the main data retrieval operations that are useful at

all stages of GIS analysis for everything from data quality
checking to a final analysis.
Features may be selected by two primary methods:
Select by Location - select features based on their location
relative to features in another layer. For instance, if you want
to know how many homes were affected by a recent flood, you
could select all the homes that fall within the flood boundary.
You can use a variety of selection methods to select the point,
line, or polygon features in one layer that are near or overlap
the features in the same or another layer. (Esri, 2017)
Select by Attribute - Select By Attributes allows you to provide
a SQL query expression that is used to
select features that match the selection criteria. (Esri, 2017).
An example procedure for selecting features by attribute might
include:
A prospective buyer wants to know where all the 3 bedroom
houses are for under $175,000 (apparently it’s possible, just
not in Boston). The ArcGIS user can call the Selection menu in
the software and then by pressing the “select by attributes”
option. This will provide a new window with
fields, unique values and numerical operators within it. This
window allows
the attribute table to be queried. A command line query question
may then look like:
status=Y + Bedroom=3+ saleprice=$175000.
Where: status refers to house availability; 3 refers to the desired
number of bedrooms; and sale price refers to… well, the sale
price,and a dream sale price at that for any buyer in Boston.
Select by Attribute

Selecting by attributes involves the use of Boolean Operators
(Pg. 380) in which use the conditions OR, AND, or NOT. In
Esri’s ArcMap software we can use the “Select by Attributes”
dialog box to build an expression. The dialog box to the right
lists the layer “zebra_mussel” we want to select features from,
the attribute fields “STATE” and “YEAR”. The buttons and
dialog box
can be used to select all of the points from th. e feature layer
“zebra_mussel” that are in the state of New York and are from
2005.
We can use this dialog box to build very complex expressions
and even save the expressions for future use. The next slide
will show an example of how more complex expressions can be
built.
Select by Attributes
Queries Continued
This image illustrates how an attributes or non-spatial query is
executed in ArcGIS. In previous chapters we had
discussed how SQL Queries can be used to select features from
an attribute table. Notice the query “SELECT * FROM
Parcel_Poly_BT_2008_LOCID WHERE: BEDROOMS = 3
AND FULLBATHS= 2 AND TOTALVALUE<175,000.
Essentially, the query is telling the software to find all of the
homes in Waltham that have 2 full baths, 3 Bedrooms, that cost
less than $175,000.By using operators such as “AND”,
“OR”,“NOT”,etc. we can build queries based on whatever
criteria we choose. I can execute this query by clicking on the
“Apply” button. The parcels that are selected can then be

exported to create a new layer or table. We can also modify our
query to refine our selection. You will have an opportunity to
gain some hands-on experience working with these queries in
the co-requisite with this course; GIS5102.
An example procedure of selecting features by location might
include:
An analyst wants to know where all the osprey nests are within
a distance of
0.5 miles to a major highway. The ArcGIS user can call the
Selection menu in the software and then by pressing the
“selecting by location” tool.
This will allow the user to use the operators: “are within a
distance
of” (0.5miles to freeway) and “contain” (osprey nests) in order
to carry out the query.
In the following slide, an example is included of selecting
particular soil types by query as part of a step to produce a soil
erosion model. It is important to realize that these techniques
can be used sequentially and do not have to be standalone.
Queries Continued
Example of a typical spatial query
A spatial query is used to find features that are adjacent, within
a distance of, are contained by, crossed by, etc. The graphic to
the right looks similar to the “Select by Attributes” dialog box
on the previous slides but the main difference is that instead of
using SQL queries or Boolean operators we are able to build
expressions to find features that are near, within, are within a
distance of, touching, or intersecting another feature.

The following set of slides illustrates how a “Spatial Query”is
executed. In this following example we will be selecting all of
the parcels within 300 feet of our selected parcel (highlighted
in red).
We can select features adjacent to or within a specified
distance of a selected feature using “Select by Location”.
Notice the “Select by Location” toolbox that now appears on
the screen. The first dropdownbox allows me to specify which
operation I would like to use. In the second box, the
“Parcel_Poly” layer is checked off. By checking off the
“Parcel_Poly” layer I am specifying which layer I will be
querying. I could also use the select by locations dialog to
query features that are in separate layers, for instance I could
also create a query that would find all of the hydrants within
300 feet of the selected property. The drop-down menu at the
bottom of the dialog box allows me specify the type of location
query I would like to use. In this example I will use the “are
within a distance of” option.
At the bottom of the dialog box I can specify a distance that I

would like to use. In this case I will use 300 feet. Once I have
built the select by location query I will click on the apply
button and all of the parcels within 300 feet of the subject
property are now highlighted in red.
Earlier in the chapter we looked at ways of finding features that
are within a certain distance of a selected feature or features.
This distance is commonly referred to as a buffer. Buffers are
drawn based on a user specified distance and vary in shape
based upon whether the feature being buffered is a point (a), a
line (b), or a polygon (c). The diagram below illustrates how
these shapes are drawn. If multiple features are being buffered
and the buffer polygons overlap you can dissolve the buffer
boundaries if you choose to create a continuous buffer polygon.
Buffering and neighborhood functions
a b
c
We discussed one way to use a buffer is to designate an area for
special protection. In this example, 1000 foot buffer zones
(purple) around the rail network (black) are established to
define an oil spill impact area. This is what buffers around line
features look like.
An Example of a Buffer

The graphic above illustrates a buffer polygon that was created
to show an area 300 feet around a parcel polygon. The buffer is
shown in blue. When buffering a polygon, the buffer that is
created emulates the shape of the object being buffered. In this
case a polygon.
An Example of a Buffer
Map Overlay: Data Integration
One of the most basic questions asked of a GIS is "What's on
top of what?" For example:
What land use is on top of what soil type?
What parcels are within the 100-year floodplain? ("Within" is
just another way of saying "on top of.")
What roads are within what counties?
What wells are within abandoned military bases?
To answer such questions before the days of GIS, cartographers
would create maps on clear plastic sheets and overlay these
sheets on a light table (remember Week 1) to create a new map
of the overlaid data.
Because overlay yields such valuable information, it was
paramount to the development of GIS. There are two types of
overlays in GIS Vector and Raster

An overlay operation is much more than a simple merging of
line work; all the attributes of the features taking part in the
overlay are carried through, as shown in the
example to the right, where parcels (polygons) and flood zones
(polygons) are overlaid
(using the Union tool) to create a new polygon dataset. The
parcels are split where they are crossed by the flood zone
boundary, and new polygons created. The FID_flood value
indicates whether polygons are outside (-1) or inside the flood
zone, and all polygons retain their original land-use category
values. Esri 2017.
The total area of each land-use type in the flood zone can be
calculated by selecting all polygons within the flood zone
(using the Select Layer By Attribute tool, for example) and
summarizing the area by land- use type (using the Frequency
tool). Following is a chart illustrating the result of this
calculation. (Esri 2018).
There are a number of vector overlay tools that are common to
most commercially available GIS softwares. The graphic to the
right lists some of these tools. The tools you use vary based on
the output you desire but they generally involve creating or
updating new datasets.
You will have a chance to use some of these tools in your
analysis class and also in GIS5201 – Advanced
Spatial Analysis.

We have now looked at some of the common tools and
techniques used to conduct a
GIS analysis. Now that we have described some of the
techniques and tools how and when do we apply them? Also,
how and where do we begin? Below are five points to consider
when conducting a GIS analysis.
Frame the question – Begin by defining the objectives of your
analysis. What are the primary questions you are trying to
answer?
Explore and prepare data – Do you have the data you need to
conduct the analysis? Do you have to create new data or
acquire it from another source? These are critical questions.
You may not be able to acquire or develop the data you need
from the sources you have access to within the time frame or
cost associated with the analysis.
Choose analysis methods and tools – What tools or methods do
you need to perform your analysis?
Perform the analysis – Your analysis should be conducted a
number of times to make sure your
results make sense. You should have a good working knowledge
of the subject matter.
Examine and refine results – Examine your results and refine
your output products. Are your results repeatable? Your maps
and tables should be designed to be easily understood by your
intended audience.
Common questions answered by GIS

GIS5103 – Fall 2019
Week 4 Database Management
Outline
Differentiate between relational database management systems
(RDBMS), databases, and data files.
Explain why we select the database approach for GIS
Define what a relational database is
Describe how databases are created
Describe GIS database applications
Database Management System (DBMS)
What is a Database Management System (DBMS)?
A DBMS is a system [or software] designed to manage a
database and allow user-
selected operations to be performed on the data.
1.
2.
3.
4.
It is a logical construct for the storage and retrieval of
information. It puts the database into a geographic coordinate
system.
Attribute data models are needed for the DBMS.
It contains data definition language; a data-entry module; a data
update module; a report generator; and query language.

What is the difference between and database and a database
management system?
A database is a simply a collection of related data. This can
include non-computerized data such as those found in a ‘phone
or address book. It can also include information contained in
spreadsheets or also in relational databases or databases
comprised of multiple tables that are connected or related to
each other using common fields or
“keys” found in each of the tables.
Data in a computer database, however, are managed and
accessed through a database management system (DBMS). The
database management system is used to manage, access the
database
The Database is distinct from the DBMS
In order for there to be a DBMS, there must be a database. In
order for there to be a database there must be data files
Bolstad’s Figure 8-4. It shows the database approach to data
handling data.
First comes the Data File, then the database and then the DBMS
So, what is the difference between a data file and a database?
Heywood discusses this on p.110. For simplicity, below is a
comparison in tabular form.
The image above from Screencast illustrated the differences
between a flat file (like a spreadsheet) and the relational
database model structure (RDBMS).

Creating the Database, 337-339
To design a database, the user must:
Identify the features (i.e. entities) to be represented (e.g.,
forests, trails, and recreational features. These are all nouns);
Identify the appropriate tables for a relational database. What
is the ID# that could allow a relational join for e.g. (Forest-
ID)?
Identify the feature’s attributes
(e.g., Trail Difficulty)
Identify relationships between the features (e.g., what
activities, trails, can be explored at each of the forests? What
trails are in each forest and how difficult area they?);
Figure 8-6: Forest data in a relational database structure.
Creating the Database
Keys or Key ID’s are extremely important in a relational
database. They allow us to combine and display data from other
tables and also allow us to store, query, and edit data more
efficiently. There are two types of keys, primary and foreign.
A primary key, also called a primary keyword, is a key in a
relational database that is unique for each record. It is a
unique identifier, such as a driver license number, telephone
number (including area code), or vehicle identification number
(VIN). In the example to the left, the Forest-ID would be the
primary key. A relational database must always have one and
only one primary key.
A foreign key is a set of one or more columns in a table that
refers to the primary key in another table. There isn’t any
special code, configurations, or table definitions you need to

place to officially “designate” a foreign key. For example,
trail name.
Cardinality
In the context of databases, cardinality refers to the uniqueness
of data values contained in a column.
High cardinality columns are those with very unique or
uncommon data values. For example, in a
database table that stores bank account numbers, the “Account
Number” column should have very high
cardinality – by definition, every item of data in this column
should be totally unique.
Normal cardinality columns are those with a somewhat unique
percentage of data values. For instance, if a table holds
customer information, the “Last Name” column would have
normal cardinality. Not every last name will be unique (for
example, there will likely be several occurrences of “Smith”)
but on the whole, the data is fairly non-repetitive.
Low cardinality columns are those with very few unique values.
In a customer table, a low cardinality column would be the
“Gender” column. This column will likely only have “M” and
“F” as the range of values to choose from, and all the
thousands or millions of records in the table can only pick one
of these two values for this column.
Cardinality relationships between tables can take the form of
one-to-one, one-to-many (whose reversal is many-to-one) or
many-to-many. These terms simply refer to the relationships of
data between the tables. For example, the relationship between
the “Customers” table and the “Bank Accounts” table is one-to-

many, that is, one customer can have several accounts, but one
account cannot belong to more than one customer. That is, of
course, assuming this bank has never heard of joint accounts!
Cardinality
Cardinality is commonly expressed in the following ways:
Databases
In many tabular databases (e.g., Dbase, SQL Server, ORACLE,
Excel etc.), it is possible to list data, create queries, and sort
the results.
Linking a tabular database to a GIS, however, provides
additional “spatial”
capabilities that enhance the value of the data to provide useful
information.
The “geographic search” is the secret to GIS data retrieval, in
addition to the
ability to get back on demand data that were previously stored.
Let’s take a look some examples of GIS software and the
“spatial” database
facilities they provide.
Multitier Architecture
.
Figure 8-5: Data are stored and accessed from the databases on
the bottom tier and requests to view the data (queries) are made
from the clients on the top tier consisting of desktop and web
based products. Application servers act as the middle-men,
processing transactions from the clients to the databases on the

bottom tier. In well designed systems there are rules or policies
setup that control how the data is accessed and by whom. We
will look at this in more detail layer in the lecture.
Spatial Database facilities in ArcGIS
ESRI’s ArcGIS software is packaged with tutorials for
ArcCatalog, ArcMap, ArcToolbox, and any extensions that you
purchase. ESRI maintains on-line resources for teaching GIS in
higher education, which includes links to lab exercises. The
software you are using in the analysis course is, of course, ESRI
software.
GIS and image processing software developed at Clark Labs -
an educational and research institution at Clark University in
Worcester, Massachusetts - is called Idrisi. It is easy to use
while providing professional-level GIS, image processing, and
spatial statistics analysis on Windows computers. It originally
focused on handling raster data.
Database facilities in Idrisi http://www.clarklabs.org/
The historical approach to data handling is that a different
version of the visitor’s details may be stored in separate
databases. Additionally, data may be duplicated, errors may
occur during transcription, and data storage mechanisms are
often inflexible etc.
Why Choose a Database Approach?

Card index record from ski school manual database
It is also very difficult to update files using the traditional
approach, as
illustrated in this example for a hardcopy index card held for an
individual.
We are striving for the database approach for data handling
Key Points for databases:
They are collections of related material shared by different
users;
Data is organized for easy reference with no duplication (e.g.,
think of a phone book);
Computer databases are superior to hardcopy ones because
they are easier to update and data can be adapted to different
uses (e.g., unlike the phonebook).
Relational Databases
Example of a relational attribute table data for hotels in Happy
Valley, including the
terminology
/ ID number
Earlier this term you were introduced to flat file and
hierarchical attribute tables. Relational attribute files are

different to flat files and hierarchical files (see next slide for
comparison). As we can see here, by organizing the data into 2D
tables that contain records for each entity / feature, it is
possible to “query” these data using a GIS, to find out which
hotels are the cheapest or which is 5 star etc.
Examples will be given in later slides.
New attribute tables can be created using the relational ID
number.
This is 1 example of the flexibility of computer databases over
hardcopy forms, in that they can be adapted to different
purposes to illustrate different relationships.
You will be doing these types of exercises during your
analysis course.
Creating New Attribute Tables from Existing Ones
Retrieval Operations
Retrieval operations may be applied to either spatial or attribute
data. For example, if searches are made to attribute data then
the user would find or browse an entity.
As we saw in the last slide, and as you have experienced in your
analysis course, this can be used for data reorganization by
selecting, renumbering, and sorting the data.
Similarly, new attributes can be calculated by computation of
selected values.

Spatial Retrieval Operations
In a spatial (map) database, records are features or themes.
The spatial equivalent of an “attribute find” is locate. The GIS
then
highlights the result.
Spatial equivalents of the DBMS queries result in locating sets
of features or building new GIS layers.
Queries
Functions, query language and applying algorithms could be
difficult requiring a
command line approach. For example:
find in states where state_name = ‘California’
<1 record in result>
use states
calculate in states population_density = population / area
<50 records in result>
restrict in states where population_density > 1000
<20 records selected in result>
So you are coming into this field at a good time because it
recent years it has become A LOT more user friendly.
Querying via SQL
At the highest level, a query in SQL is comprised of three key
components or operations: SELECT, FROM, and WHERE.

Intuitively, these operations used together tell your DBMS/GIS
to SELECT some subset or fields of data FROM some data set
WHERE some such condition is met.
Retrieval User Interface
Now the Retrieval User Interface simplifies the process. Most
GIS packages use the GUI (Graphical User Interface) of the
computer’s operating system to support both a menu-type query
interface and a macro or programming language.
SQL (Standard Query Language) is a standard interface that
works with relational databases and is supported by many GIS
software packages.
So, let us examine a few things we can do operations with the
GUI and SQL in the next slides. You will also look at this in
more detail in week 8 (chapter 9) and you will be applying
these techniques extensively in the analysis course.
I will start with spatial retrieval operations. Then go on to
illustrate an application from my own work that uses complex
retrieval operations. Finally, I will finish this week’s lecture
component by discussing GIS applications using the web
interface.
Examples of Attribute Searches and Queries
Identify– The image below shows a commonly used tool in GIS.
The user clicks on the “identify” tool (circled in red) and then
clicks on a feature in the map which will then display all of the
attributes associated with that feature. This tool provides a
quick
“snapshot” of attributes associated with a feature and does not
allow the user to save or
work with the popup.

Examples of Attribute Searches and Queries
Select by Attributes – The “select by attributes” tool allows the
user to build a SQL query and then view the results of the
query on the map display. The user can also see the results in
the layer’s attribute table, further refine the query, or export
the data into a new table. The example below
shows features from the 2013 EPA_Temperature_2013 layer
greater than January 2nd 2013 and less than or equal to April
1st 2013. Another advantage of using this tool is that more
complex queries can be saved an re-used at a later date.
Examples of Spatial Searches and Queries
– Select by Location
In GIS there will be instances where you will be required to
find features that are near or that intersect other features.
While the select by attributes tool we saw on the previous slide
is a powerful tool it is limited in that it can only use SQL
language to select features. The Select by Location is an
example of a tool that can be used to select features from one
layer that intersect, touch, are within a distance of, etc. a
feature or features in another layer. The example to the right
retrieves all of the 2009 seismic events that are within a mile
of the selected countries. Once the data is selected in can be
exported to another feature layer or viewed as an attribute table
which can be exported.

GIS5103 – Fall 2019
Week 5
Data Input, Throughput, & Quality
By the end of this week you should be able to:
Describe methods of data input
Describe Digitizing sources ‐ the cartographic base
Contrast Digitizing methods within a GIS (Tablet vs. “Heads
Up”)
Describe Data editing (correcting digitizing errors,
re‐projections
and generalizations)
Define accuracy and precision
Describe the importance of data standards and metadata
List sources of errors in a GIS project
Differentiate spatial and attribute errors
Describe techniques for coping with errors in Data
The Data Stream

The focus of this week’s lecture component is on creating GIS
or spatial data from a variety of
sources. We will also be looking at how GIS data is updated.
Digitizing GIS Data
As we have discussed over the past several weeks one of the
primary features of a GIS is its ability to integrate data from a
variety of sources. Depending on how the data is to be used the
need to convert the data or extract features from it may arise.
Fortunately there are a number of techniques and tools available
to assist in acquiring data for
a new GIS is straightforward these days. Some of the sources
for this data can include some of the following:
GPS (Global Positioning Systems) has become a major source
of new GIS data. Data acquired through GPS can be highly
spatially accurate (1cm horizontal) depending upon the type of
receiver used.
Digital map images such as scanned maps, LiDAR , satellite
data, and aerial photographs (Ortho photos) are now often used
as a cartographic base for digitizing; this is another way to
create new GIS data layers.
Remotely sensed satellite data are becoming an important
source of GIS data as the cost of data falls, free data becomes
available and new types of data emerge.
The following examples show the different spatial scales
commonly found
with remote sensing data. Examples are also provided that show
how they can be used to digitize temporal changes or changes
to features over time (urbanization) or after an event (Natural
Disaster).

Satellite data are raster data. The amount of spatial detail
represented by one pixel of a raster
image determines its spatial resolution. So, an image with one
meter spatial resolution means
that each pixel in the image represents one square meter on the
ground. With improvements in
cameras and digital image acquisition the cost of acquiring
aerial images has decreased. It is now possible to obtain
imagery with a 6” pixel resolution for a few thousand dollars.
30 meters
10 meters
5 meters
The Different Spatial Resolutions
Image Resolutions Continued
2 meters
1 foot
In cartographic applications, higher spatial resolutions are often
favored. However, for applications such as weather forecasting
or to monitor seasonal plant growth on a
yearly basis for North America, coarser spatial resolutions are
favored. While this type of data
can be extremely useful there are data storage considerations
and large file sizes to consider which may impact the
accessibility of these images especially in web based
applications.
1 meter
Where to obtain remote sensing data

There are a number of useful websites from which remote
sensing data (satellite images) can be downloaded. Here are
some examples:
Free satellite image data @ University of Maryland:
http://glcf.umd.edu/data/
It is also possible to BUY data! e.g.
http://gs.mdacorporation.com/products/index.asp
Free Digital Terrain data:
http://www.webgis.com/terraindata.html
Digitizing within a GIS
Sometimes digitizing is necessary if:
We need new to input new features.
Map features are incorrectly mapped.
Updates are needed for existing features.
And so, just what is digitizing?
Digitizing is the process of capturing map data in a GIS layer
by tracing points, lines, or polygons from a map or image by
using a mouse or a “puck” on on a digitizing tablet.
It can be done by either using a digitizing tablet, or by
digitizing directly from a satellite image or a scanned map on
screen.
On‐screen or “heads up” digitizing creates a spatial dataset by
tracing over features displayed on a computer monitor with a
mouse. The newly created dataset picks up the spatial reference
of the source document and results in a string of points with (x,

y) values. You will learn how to digitize in tutorial 6 of your
analysis class but we will examine how we can do this now as
well.
Digitizing table and PC workstation
A few years ago, the digitizing table was widely used in GIS.
This method allows features to be input into a GIS
database as points, lines or polygons but often requires
extensive data clean-up and editing. With the updates made to
GIS software this method of data entry is essentially obsolete
and has been replaced by on-screen digitizing.
Digitizing directly on screen
Digitizing directly on screen or “heads up” digitizing, is the
approach I am most familiar with and it is what you will be
doing in the analysis class.
In this case, you use your computer mouse to digitize paper
maps, aerial photos, or other images displayed in the GIS. We
can refer to this layer as a stable base map (that is geocoded) ‐
recall from slide 15 that the newly created dataset picks up
the spatial reference of the source document? Below is an
example, where the outline of fluvial features (blue) and
contour lines (brown) have been digitized. Only the fluvial
features can have been digitized directly from the image on the
left, however.
Selecting points to digitize
A vertex
Digitizing is not difficult to do. You can choose to digitize

either a point, a line (snap to feature) or a polygon (snap to
end). Snapping is a routine embedded within the GIS that
makes sure the separate vertices in a line or an area are
connected. That is, it ensures there are no gaps. This is
important if calculations or correlations are going to be
performed on the new layers. In these cases,
gaps would cause errors. The next slide shows how snapping
can be achieved –it uses topology
Geocoding Address Data
What is it?
Geocoding address data is the process of relating an address to a
geographic location (such as latitude/longitude coordinates) or
geographic area (such as census tract, block group, block, or
ZIP code). The address itself then is used to determine the
geographical coordinates.
Geocoding can be affected by the quality of data, e.g., incorrect
spelling and the use of different abbreviations (e.g., for Street
and Avenue). Therefore, the use of standards is relevant to
geocoding address data.
Address Matching
Address matching is the process of geocoding street addresses
to a street network.
(Modified) ESRI’s definition of address matching:
A process that compares an address or a table of addresses to
the address attributes of a reference dataset (e.g., locations may
be determined based on address ranges stored for each street
segment). This determines whether a particular address falls

within an address range associated with a feature in the
reference dataset.
If it does, it is considered a match and a location can be
returned. The next slide provides an example of how an address
can be converted to a point feature and drawn along a line
segment based upon an address range.
Address Matching
In most instances a geocoding service or engine is built upon a
street network in which line segments are assigned an address
number range on the left and right side of the streets, a city or
twin name, and a zip code. When a user enters an address the
geocoding engine finds a match along a line segment and places
the location of the address (a point) based upon where it falls
along the street line segment.
GIS Services: Address Matching Resources
Creating a geocoding engine can be a time consuming process.
However, there are some commercially available geocoding
services that allow us to enter a table of addresses for
geocoding
and then download the results and add them to our GIS
database. Some geocoding services are free and some charge a
fee.
http://www.batchgeo.com
https://geomap.ffiec.gov/FFIECGeocMap/GeocodeMap1.aspx
https://www.census.gov/geo/maps-data/data/geocoder.html
Error, Accuracy, and Precision

Until quite recently, people involved in developing and using
GIS paid little attention to the problems caused by error,
inaccuracy, and imprecision in spatial datasets. Surely, a GIS is
too powerful for error?
Not true! It is now recognized that error, inaccuracy, and
imprecision, if left unchecked, can make the results of a GIS
analysis almost worthless, it is like putting garbage in means
getting garbage out.
But where do these errors come from?
Since a GIS can collate and cross‐reference many types of data
by location and can integrate many discrete datasets (which is
the heart of its power), it can also inherit error from the
imported datasets.
17
Error, Accuracy, and Precision
We can discuss error in terms of data quality.
Data quality refers to the relative accuracy and precision of a
GIS database. These facts are often documented in data quality
reports.
Errors may exist both in map data (which can be reduced by
maintaining topological integrity) and also attribute data.
Next we will discuss the differences between accuracy and
precision in GIS data.
18
Data Accuracy
Accuracy is the degree to which information on a map, or in a

database, matches true values. The level of accuracy required
for particular applications and GIS analyses varies greatly.
Highly accurate data can be very difficult and costly to produce
and maintain often requiring highly accurate GPS and/or survey
data or the acquisition of LiDAR or remotely sends data
(satellite imagery, aerial photographs, etc.).
In discussing a GIS database, it is possible to consider
horizontal and vertical (spatial) accuracy with respect to
geographic position (i.e. how close is the mapped feature to its
actual location on the earths surface) as well as attribute
accuracy (i.e. do the values in the Attribute table match the real
world values).
19
Data Accuracy
1:1,200 ± 1 m
1:2,400 ± 2 m
1:4,800 ± 4 m
1:10,000 ± 8.5 m
1:12,000 ± 10 m
1:24,000 ± 12 m
1:63,360 ± 32 m
1:100,000 ± 50 m
This means that when we see a point on a map we have its
"probable" location within a certain area. For example, a sewer
manhole shown on a 1:1,200 scale map should be within 1
meter (3 ft) of it’s true location on the ground to be considered
within the 1:1,200 accuracy standard. It’s important to
understand the limitations of your data as well. While a layer
of sewer manholes within the 1:1200 data standard may be
suitable for broad planning or maintenance activities, using this

data at scales it was not intended (engineering purposes for
example) for can have negative and potentially costly
consequences.
20
Defining Precision
Precision refers to the level of measurement and exactness of
description in a GIS database.
The level of precision required for particular applications varies
greatly. Engineering projects such as road and utility
construction require very precise information measured
to the millimeter. Demographic analyses of marketing or
electoral trends can often make do
with less. For example to the closest zip code or precinct
boundary. Highly precise data can also be very difficult and
costly to collect.
Precise data ‐ no matter how carefully measured ‐ may be
inaccurate and vice-versa. Let’s try to understand this better by
using Bolstad’s diagram on page 625, which I have included in
the next slide.
21
Accuracy and Precision
Points (yellow circles) are digitized to represent the center of
the cloverleaf intersection. Average accuracy is high when the
average of the points falls near the true location, as in the
panels on the left side of the figure. Precision is high when the
points are all clustered near each other (top panels). A group of

points may be accurate, but not precise (lower left), or precise,
but not accurate (upper right). We typically strive for a
process that provides both accuracy and precision (upper left),
and avoid low accuracy and low precision (lower right).
22
Sources of Inaccuracy and Precision
Let us take a minute to discuss attribute accuracy and precision,
that is the non‐spatial data linked to lo
cation.
Inaccuracies may result from mistakes of many sorts, including
basic data entry. With respect to precision, precise attribute
information describes phenomena in great detail. For example,
a precise description of a person living at a particular address
might include gender, age, income, occupation, level of
education, and many other characteristics. A less precise
description might include just income or gender. It is the
application that will determine whether precise or less precise
data are needed.
There are many sources of error that may affect the quality of a
GIS dataset. Some are quite obvious, but others can be difficult
to discern. Few of these will be automatically identified by the
GIS itself.
It is the user's responsibility to prevent them.
Sources of error can be divided into 3 main categories:
Conceptual errors;
Errors arising through data processing; and
Errors arising from source data
23

Errors Arising from Source Data
Age of Data
Data sources may be too old to be useful or relevant to current
GIS projects. Past collection standards may be unknown,
non‐existent, or currently acceptable. For instance, John
Wesley Powell's nineteenth century survey data of the Grand
Canyon lacks the precision of data that can be developed and
used today. Additionally, erosion, deposition, and other
geomorphic processes will have modified the landscape.
Therefore, reliance on old data could skew, bias, or negate
results.
Density of Observations
The number of observations within an area is a guide to data
reliability and they should be known by the map user. An
insufficient number of observations may not provide the level
of resolution required to adequately perform spatial analysis
and determine the patterns GIS projects seek to resolve or
define.
24
There are four ways we describe errors in spatial data
Positional accuracy – describes how close the locations of
objects represented in a digital dataset correspond to the true
locations of the real-world entities.

Attribute accuracy – summarizes how different the attributes are
from their real world values.
Logical Consistency – reflects the presence, absence, or
frequency of inconsistent data. Tests for logical consistency
often require comparisons among themes (i.e. all buildings
must be on dry land).
Completeness – Describes how well a layer reflects all of the
real world features it is supposed to represent.
The next slide shows a figure from the text (Figure 14-3, Page
624) that depicts examples of these types of errors.
25
26
Data Standards
Regular checks and tests should be employed during a project to
make sure that standards are being followed. This allows a
designer to pinpoint difficulties at an early stage and correct
them. Establishing data standards helps with data exchange –
unfortunately, the history of GIS data exchange has been
chaotic and has been wasteful in the past.
Examples of good data standards include:
USGS, National Mapping Program Standards,
http://nationalmap.gov/standards//

Spatial Data Transfer Standard
http://mcmcweb.er.usgs.gov/sdts/
27
2
GIS 5103
Fall 2019 - Week 3
Spatial Data Modeling
By the end of Week Three you should be able to:
Provide a definition of a ‘spatial data model’
Explain how spatial entities are used to create a data model
Distinguish between rasters and vectors
Describe a spatial data structure
Explain what topology is
List the advantages and disadvantages of different types of
spatial models

File Types
Files in a GIS may be binary data files. They can be read by
computers (a program or a hardware understands in advance
exactly how it is formatted) but not people.
All executable programs and most numeric data files are stored
in binary files.
Or they can be ASCII text. ASCII files can be read by humans.
Binary is faster to read and smaller.
Raster and Vector Data
A file must have a mental model of how the physical data
represent a geographic feature.
GIS traditionally use either raster or vector models to store
data.
Rasters represent phenomenon distributed continuously over a
large area.
Vectors are object based (point, line, polygon).

Raster Data Example
Satellite data has a RASTER data structure (GRID
CELL), composed of matrix of pixels;
Each pixel records the “radiation intensity” measured by the
remote sensing instrument called its radiometric resolution.
The intensity of radiation is represented by a data number
(DN), that is, the “brightness value”.
One grid cell (pixel) has one attribute that represents its data
number/ brightness value (even if 0); a cell also has a spatial
resolution, given as the cell size in ground units.
Grid Structure of Raster Data, p.81-82
Vector Data Example
Spatial feature files that are object (vector) based:
Have identifiable boundaries or spatial extent;
Relevant to an intended application;
They have “ATTRIBUTES” – descriptive characteristics of a
feature.
For example, a tax parcel can have a rectangular shape and be
represented by a polygon, that is the feature’s geometry. The
owner name, parcel id, property value, etc. would be
considered the

polygon’s attributes.
Attribute data are stored in tables comprised of a matrix of
numbers and values
and are stored in rows and columns, like a spreadsheet, e.g.
attribute tables.
OBJECTS may be:
Exact, e.g., man-made objects; or
Inexact (fuzzy), e.g., soil types and forest stands etc.
Recall: spatial objects represented by vector files may be
represented either as
points, lines or polygons (areas) [slides 9-11, class 2].
Vectors vs Rasters:
Vectors can store information about topology (relational space,
p. 51-52).
GIS Data File Formats
Most GIS systems can import different data formats, or use
utility programs to convert them;
Data formats can be industry standard or commonly accepted
standard;
Example vector formats: DLG (Digital Line Graph) and TIGER
(Topologically Integrated Geographic Encoding and
Referencing system), shp. (ESRI)
Personal, File, and Enterprise GeoDatabase (ESRI);
Most raster formats are digital image formats, e.g. Tiff (Tagged
Image File
Format) or DEMs (Digital Elevation Models).

Data Models
Once you have determined which features you would like to
map or create data
layers for you next have to decide how you wish to portray
those features
and also consider the following:
How the geographic features are to be represented for each
theme (for example, as points, lines, polygons, or rasters) along
with their tabular attributes
How the data will be organized into datasets, such as feature
classes, attributes, raster datasets, and so forth
What additional spatial and database elements will be needed
for integrity rules, for implementing rich GIS behavior (such as
topologies, networks, and raster catalogs), and defining spatial
and attribute relationships between datasets.
Representation
Each GIS database design begins with a decision as to what the
geographic representations will be for each dataset. Individual
geographic entities can be represented as:
Feature classes (sets of points, lines, and polygons)
Imagery and rasters (aerial photography, grid datasets)
Continuous surfaces that can be represented using features
(such as contours), rasters (digital elevation models [DEM]), or
triangulated irregular networks (TINs) using terrain datasets
Attribute tables for descriptive data – these tables can be stand-
alone tables that contain information related to a feature in a

feature class – such as an inspection, an activity (catch basin
cleaning, tree maintenance, etc.)
Data Themes
Geographic representations are organized in a series of data
themes (sometimes referred to as thematic layers). A key
concept in a GIS is one of data layers, or themes. A data theme
is a collection of common geographic elements such as a road
network, a collection of parcel boundaries, soil types, an
elevation surface, satellite imagery for a certain date, well
locations, and so on.
The concept of a thematic layer was one of the early notions in
GIS. Practitioners thought about how the geographic
information in maps could be partitioned into logical
information layers—as more than a random collection of
individual objects (such as a road, a bridge, a hill, a house, a
peninsula). These early GIS users organized information in
thematic layers that described the distribution of a phenomenon
and how it should be portrayed across a geographic extent.
These layers also provided a protocol (capture rules) for
collecting the representations (as feature sets, raster layers,
attribute tables, and so on).
In GIS, thematic layers are one of the main organizing
principles for GIS database design.
Each GIS will contain multiple themes for a common
geographic area. The collection of themes acts as layers in a
stack. Each theme can be managed as an information set

independent of other themes. Each has its own representations
(points, lines, polygons, surfaces, rasters, and so on). Because
the various independent themes are spatially referenced, they
overlay one another and can be combined in a common map
display. Plus, GIS analysis operations, such as overlay, can fuse
information between themes.
GIS datasets are collections of representations for a data
theme
Geographic data collections can be represented as feature
classes and raster-based datasets in a GIS database.
Many themes are represented by a single collection of
homogeneous features such as a feature class of soil type
polygons and a point feature class of well locations. Other
themes, such as a transportation framework, are represented by
multiple datasets (such as a set of spatially related feature
classes for streets, intersections, bridges, highway ramps, and
so on).
Raster datasets are used to represent continuous surfaces, such
as elevation, slope, and aspect, as well as to hold satellite
imagery, aerial photography, and other gridded datasets (such
as land cover and vegetation types).
GIS datasets are collections of representations for a data
theme – Cont.
Both the intended use and existing data sources influence
spatial representations in a GIS. When designing a GIS

database, users have a set of applications in mind. They
understand what questions will be asked of the GIS. Defining
these uses helps to determine the content specification for each
theme and how each is to be represented geographically. For
example, there are numerous alternatives for representing
surface elevation: as contour lines and spot height locations
(such as hilltops, peaks), as a continuous terrain surface (a
TIN), or as shaded relief. Any or all of these may be relevant
for each particular GIS database design. The intended uses of
the data will help to determine which of these representations
will be required.
Frequently, the geographic representations will be
predetermined to some degree by the available data sources for
the theme. If a preexisting data source was collected at a
particular scale and representation, it will often be necessary to
adapt your design to use it.
Individual GIS datasets often are collected in concert with other
data
layers
While each GIS dataset can be used independently of other GIS
data, it is often quite important to collect datasets in concert
with other information layers so that the fundamental spatial
behavior and spatial relationships are maintained and consistent
between the related GIS data layers. Here are a few examples
that help to illustrate this concept:
The spatial relationships between elevation, landform, soil type,
slope, vegetation, surficial geology, and other terrain
properties are typically compiled in unison to characterize
environmental resource units. Understanding the science behind
these spatial relationships helps to build a consistent, logical
database where features from each data layer are consistent

with each other.
Topographic basemap information is compiled in an integrated
manner. Hydrography, transportation, structures, administrative
boundaries, and other topographic map layers are compiled in
unison. These cartographic representations in the map display
are built in an integrated manner to communicate clearly and
accurately and draw attention to key map locations.
Individual GIS datasets often are collected in concert with other
data
layers – Cont.
In each of these cases, a data model defines a collection of
related data themes that fit into an overall information
framework. Each framework is essentially a collection of
related data themes that are best captured in unison with each
other. The data capture guidelines follow sound scientific
principles about their spatial behavior and relationships. Each
theme plays an important part in the holistic characterization of
a particular landscape. For example:
Terrain landscape. Topographic maps, elevation, drainage
network, transportation network, map features, cross-country
movement, and so forth
Urban landscape. Buildings, critical infrastructure, and so forth
Imagery landscape. Satellite and aerial, local, regional, and
national assets, and so forth
Human landscape. Demographics (population characteristics),
cultural centers, citizens, administrative districts and zones and
so forth

Workforce landscape. Mobile workforce tracking, service
centers, traffic
conditions, warehouses, and so forth
Sensor landscape. Camera locations, devices, and so forth
Operations and plans landscape. Zones of control, planned
movements,
response, and so forth
This concept of collecting integrated data themes in unison is
one of the key design principles used in each of the ArcGIS
Data Models.
Topology
Topology is a collection of rules that, coupled with a set of
editing tools and techniques, enables the geodatabase to more
accurately model geometric relationships. ArcGIS implements
topology through a set of rules that define how features may
share a geographic space and a set of editing tools that work
with features that share geometry in an integrated fashion. A
topology is stored in a geodatabase as one or more
relationships that define how the features in one or more
feature classes share geometry. The features participating in a
topology are still simple feature classes—rather than modifying
the definition of the feature class, a topology serves as a
description of how the features can be spatially related.
Why Topology?
Topology has long been a key GIS requirement for data

management and integrity. In general, a topological data model
manages spatial relationships by representing spatial objects
(point, line, and area features) as an underlying graph of
topological primitives—nodes, faces, and edges. These
primitives, together with their relationships to one another and
to the features whose boundaries they represent, are defined by
representing the feature geometries in a planar graph of
topological elements.
Why topology?
Topology is a collection of rules that, coupled with a set of
editing tools and techniques, enables the geodatabase to more
accurately model geometric relationships. ArcGIS implements
topology through a set of rules that define how features may
share a geographic space and a set of editing tools that work
with features that share geometry in an integrated fashion. A
topology is stored in a geodatabase as one or more relationships
that define how the features in one or more feature classes share
geometry. The features participating in a topology are still
simple feature classes—rather than modifying the definition of
the feature class, a topology serves as a description of how the
features can be spatially related.
Why topology?
Topology is fundamentally used to ensure data quality of the
spatial relationships and to aid in data compilation. Topology
is also used for analyzing spatial relationships in many

situations, such as dissolving the boundaries between adjacent
polygons with the same attribute values or traversing a network
of the elements in a topology graph.
Topology can also be used to model how the geometry from a
number of feature classes can be integrated. Some refer to this
as vertical integration of feature classes.
Ways that features share geometry within a topology
Features can share geometry within a topology. Here are some
examples among adjacent
features:
Area features can share boundaries (polygon topology).
Line features can share endpoints (edge-node topology).
In addition, shared geometry can be managed between feature
classes using a geodatabase topology. For example:
Line features can share segments with other line features.
Area features can be coincident with other area features. For
example, parcels can
nest within blocks.
Line features can share endpoint vertices with other point
features (node topology).
Point features can be coincident with line features (point
events).
The next slide shows some commonly used topology rules found
in ArcGIS.
28

What are the advantages of topology?
The results are that topological primitives (nodes, edges, and
faces) and their relationships to one another and their features
can be efficiently discovered and assembled. This has several
advantages:
Simple feature geometry storage is used for features. This
storage model is open, efficient, and scales to large sizes and
numbers of users.
This simple features data model is transactional and is
multiuser. By contrast, the older topological storage models
will not scale and have difficulties supporting multiple editor
transactions and numerous other GIS data management
workflows.
Geodatabase topologies fully support all the long transaction
and versioning capabilities of the geodatabase. Geodatabase
topologies need not be tiled, and many users can
simultaneously edit the topological database—even their
individual versions of the same features if necessary.
Feature classes can grow to any size (hundreds of millions of
features) with very strong performance.
Topology can be added this to an existing schema of spatially
related feature classes. The alternative is that you must
redefine and convert all your existing feature classes to new
data schemas holding topological primitives.
There need only be one data model for geometry editing and

data use, not two or more.
It is interoperable because all feature geometry storage adheres
to simple features specifications from the Open Geospatial
Consortium and ISO.
Data modeling is more natural because it is based on user
features (such as parcels, streets, soil types, and watersheds)
instead of topological primitives (such as nodes, edges, and
faces). Users will begin to think about the integrity rules and
behavior of their actual features instead of the integrity rules of
the topological primitives. For example, how do parcels
behave? This will enable stronger modeling for all kinds of
geographic features. It will improve our thinking about streets,
soils types, census units, watersheds, rail systems, geology,
forest stands, land forms, physical features, and on and on.
Geodatabase topologies provide the same information content as
maintained topological implementations—either you store a
topological line graph and discover the feature geometry (like
coverages) or you store the feature geometry and discover the
topological elements and relationships (like geodatabases).
In cases where users want to store the topological primitives, it
is easy to create and post topologies and their relationships to
tables for various analytic and interoperability purposes (such
as users who want to post their features into an Oracle Spatial
warehouse that stores tables of topological primitives).
At a pragmatic level, the ArcGIS topology implementation
works. It scales to extremely large geodatabases and multiuser
systems without loss of performance. It includes validation and
editing tools for building and maintaining topologies in
geodatabases. It includes rich and flexible data modeling tools
that enable users to assemble practical, working systems on file

systems, in any relational database, and on any number of
schemas.

GIS 5103 – Fundamentals of GISLecture 83D GIS.docx

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