SlideShare a Scribd company logo
1 of 24
The data behind the

    graphic
Right click the
         theme to get this
           dialog menu.




Click “Open Attribute Table” to
see the data behind the graphic
“Options” button
  provides another
  dialog menu that
    lets you work
   with table data


                  Field Names or Columns


Rows or Records


  Add a new field,
  that can then be
   “calculated” or
  manually entered

        A table can be added to a layout
               or exported as DBF
Name of New
  Field (10
 Characters)




Field Type
 Options
Symbology properties

    are different depending
    on if you are working
    with points, lines, or
    polygons.
    The easiest is to work

    with is Single Symbol
    features where
    everything in the layer
    is displayed using the
    same symbol
    Can use nominal,

    ordinal, ratio or interval
    data
Some symbol methods may not be useful (pie

    charts for example)
    Markers

Some symbol methods may not be useful (pie

                          charts for example)
                                Lines or Arcs
Nominal (categories), Ordinal, Interval and Ratio
  
      (Quantities) can be used with different methods
      Fills and outlines
  

                                               Nominal data
                                                 example




Ratio Data
 Example
Category data

    symbology
    comes next
    It displays data

    by unique values
    of a field, or
    multiple fields
    Nominal, ordinal,

     ratio or interval
    data
Next, comes the
 
            quantities
           symbology
              method
  It uses a number
    field in the table
  to display data by
    classified values
 Ratio and interval
                  data
Six different ways to classify data, with an

    added manual method for infinite freedom
Equal Interval

    Defined Interval

    Quantile

    Natural Breaks

    Geometrical Interval

    Standard Deviation

Categorical (Qualitative)

        Grouping based on some quality
    ◦
        Labels or categories
    ◦
        E.g.; Sex = Male or Female
    ◦
        Nominal or Ordinal
    ◦
         Nominal the order is not important
           E.g.: Sex = male or female
         Ordinal the order is important
           E.g.; Rank = Officer, Sergeant, Lieutenant, etc
    ◦ Can be binary or non-binary
         Binary = only two values (male or female)
         Non-Binary = More than two (red, blonde, brunette, etc)
Measurement (Quantitative)

    ◦ Grouping based on some quantity or value
    ◦ Always numbers
    ◦ Discrete or continuous
      Discrete = only certain values are possible and data
       could have gaps (1, 2, 3, or 4)
      Continuous = Any value along some interval (any value
       between 1 and 4 (ie: 3.24211)
    ◦ Interval or ratio
      In interval data the interval between values is important
       (ie; temperature of 30 compared to 110 means
       something)
      Ratio data is the best, and the “0” value can be
       informative (ie; a grid can have 0 crimes, or any value
       up to infinity)
http://www.socialresearchmethods.net/kb

    /index.php
Number of
    Equal Interval (ratio, Interval)
                                                 classes desired
    ◦ The range between the classifications is   thedetermines
                                                      interval
      same




                                                      Take the
                                                  high value-low
                                                   value and for
                                                   each of the 5
                                                 classes, the value
                                                     is 199.61
Defined Interval (ratio, interval)

    ◦ Similar to the equal interval, but here, we
      define what the interval will be and thus
      establish the classes




                                               In this case the
                                               interval was set
                                                to 150, and so
                                                the number of
                                                   classes is
                                                determined by
                                                  the interval
Quantile (ratio, interval)

    ◦ A percentage of the values in the class
      falling with the range. Each class contains
      an equal number of features.




                                             Each of the 10
                                             classes has the
                                            same number of
                                             features within
                                              each class, or
                                            makes up 10% of
                                            the total records
Natural Breaks (ratio, interval)

    ◦ Breaks the data where there are natural
      holes between values




                                     Use test exam score example
Geometrical Interval (ratio, interval)
    
        ◦ This is a classification scheme where the
          class breaks are based on class intervals
          that have a geometrical series. This
          ensures that each class range has
          approximately the same number of values
          with each class and that the change
          between intervals is fairly consistent.
 The interval is
determined by a
   geometric
equation (large
   and small
    changes
 depending on
 breaks in data)
Standard Deviation (ratio, interval)

    ◦ Classes are determined by mean and
      standard deviation of values. Can display
      by 1, ½, ¼ standard deviations as needed
Right click the theme and choose properties,
   
       then choose the Labels tab


                                                You can
                                             combine fields
                                              for labeling
  Can label all
  features the
 same, or use a
 query to label
    features
   differently

                                               Infinite text
                                                 choices
Use a field in the
   data for the
  labels that is
      good




 Control over placement options, scale
  at which labels draw and styles are
               available
Use to select data by a SQL query


                    Click this

More Related Content

Similar to Fundamentalsof Crime Mapping Arc Gis Tables

Introduction to spss: define variables
Introduction to spss: define variablesIntroduction to spss: define variables
Introduction to spss: define variablesAzmi Mohd Tamil
 
Lecture 13 classification_methods
Lecture 13 classification_methodsLecture 13 classification_methods
Lecture 13 classification_methodsAnkit Singh
 
Classification Systems
Classification SystemsClassification Systems
Classification SystemsJohn Reiser
 
Evaluation of multilabel multi class classification
Evaluation of multilabel multi class classificationEvaluation of multilabel multi class classification
Evaluation of multilabel multi class classificationSridhar Nomula
 
Presentation of data
Presentation of dataPresentation of data
Presentation of datamaryamijaz49
 
Introduction to statistical terms
Introduction to statistical termsIntroduction to statistical terms
Introduction to statistical termsDr Bryan Mills
 
Frequency Distributions and Graphs
Frequency Distributions and GraphsFrequency Distributions and Graphs
Frequency Distributions and Graphsmonritche
 
BasicStatistics.pdf
BasicStatistics.pdfBasicStatistics.pdf
BasicStatistics.pdfsweetAI1
 
Kendall's ,partial correlation and scatter plot
Kendall's ,partial correlation and scatter plotKendall's ,partial correlation and scatter plot
Kendall's ,partial correlation and scatter plotBharath kumar Karanam
 
Data Representations
Data RepresentationsData Representations
Data Representationsbujols
 
Spss measurement scales
Spss measurement scalesSpss measurement scales
Spss measurement scalesNaveed Saeed
 
ScoreWeek 1.Measurement and Description - chapters 1 and 2.docx
ScoreWeek 1.Measurement and Description - chapters 1 and 2.docxScoreWeek 1.Measurement and Description - chapters 1 and 2.docx
ScoreWeek 1.Measurement and Description - chapters 1 and 2.docxpotmanandrea
 
3.2 measures of variation
3.2 measures of variation3.2 measures of variation
3.2 measures of variationleblance
 
Discriminant analysis using spss
Discriminant analysis using spssDiscriminant analysis using spss
Discriminant analysis using spssDr Nisha Arora
 
1.8 discretization
1.8 discretization1.8 discretization
1.8 discretizationKrish_ver2
 
Spss basic Dr Marwa Zalat
Spss basic Dr Marwa ZalatSpss basic Dr Marwa Zalat
Spss basic Dr Marwa ZalatMarwa Zalat
 

Similar to Fundamentalsof Crime Mapping Arc Gis Tables (20)

Introduction to spss: define variables
Introduction to spss: define variablesIntroduction to spss: define variables
Introduction to spss: define variables
 
Lecture 13 classification_methods
Lecture 13 classification_methodsLecture 13 classification_methods
Lecture 13 classification_methods
 
Classification Systems
Classification SystemsClassification Systems
Classification Systems
 
Evaluation of multilabel multi class classification
Evaluation of multilabel multi class classificationEvaluation of multilabel multi class classification
Evaluation of multilabel multi class classification
 
Presentation of data
Presentation of dataPresentation of data
Presentation of data
 
Datamining
DataminingDatamining
Datamining
 
Histograms
HistogramsHistograms
Histograms
 
Data Rollout
Data RolloutData Rollout
Data Rollout
 
Introduction to statistical terms
Introduction to statistical termsIntroduction to statistical terms
Introduction to statistical terms
 
Frequency Distributions and Graphs
Frequency Distributions and GraphsFrequency Distributions and Graphs
Frequency Distributions and Graphs
 
BasicStatistics.pdf
BasicStatistics.pdfBasicStatistics.pdf
BasicStatistics.pdf
 
Kendall's ,partial correlation and scatter plot
Kendall's ,partial correlation and scatter plotKendall's ,partial correlation and scatter plot
Kendall's ,partial correlation and scatter plot
 
Data Representations
Data RepresentationsData Representations
Data Representations
 
Spss measurement scales
Spss measurement scalesSpss measurement scales
Spss measurement scales
 
ScoreWeek 1.Measurement and Description - chapters 1 and 2.docx
ScoreWeek 1.Measurement and Description - chapters 1 and 2.docxScoreWeek 1.Measurement and Description - chapters 1 and 2.docx
ScoreWeek 1.Measurement and Description - chapters 1 and 2.docx
 
3.2 measures of variation
3.2 measures of variation3.2 measures of variation
3.2 measures of variation
 
Discriminant analysis using spss
Discriminant analysis using spssDiscriminant analysis using spss
Discriminant analysis using spss
 
1.8 discretization
1.8 discretization1.8 discretization
1.8 discretization
 
Spss basic Dr Marwa Zalat
Spss basic Dr Marwa ZalatSpss basic Dr Marwa Zalat
Spss basic Dr Marwa Zalat
 
Spss software
Spss softwareSpss software
Spss software
 

More from Osokop

Fundamentalsof Crime Mapping Geocoding
Fundamentalsof Crime Mapping GeocodingFundamentalsof Crime Mapping Geocoding
Fundamentalsof Crime Mapping GeocodingOsokop
 
Fundamentalsof Crime Mapping 8
Fundamentalsof Crime Mapping 8Fundamentalsof Crime Mapping 8
Fundamentalsof Crime Mapping 8Osokop
 
Fundamentalsof Crime Mapping 7
Fundamentalsof Crime Mapping 7Fundamentalsof Crime Mapping 7
Fundamentalsof Crime Mapping 7Osokop
 
Fundamentalsof Crime Mapping 6
Fundamentalsof Crime Mapping 6Fundamentalsof Crime Mapping 6
Fundamentalsof Crime Mapping 6Osokop
 
Fundamentalsof Crime Mapping 5
Fundamentalsof Crime Mapping 5Fundamentalsof Crime Mapping 5
Fundamentalsof Crime Mapping 5Osokop
 
Fundamentalsof Crime Mapping 4
Fundamentalsof Crime Mapping 4Fundamentalsof Crime Mapping 4
Fundamentalsof Crime Mapping 4Osokop
 
Fundamentalsof Crime Mapping 3
Fundamentalsof Crime Mapping 3Fundamentalsof Crime Mapping 3
Fundamentalsof Crime Mapping 3Osokop
 
Fundamentalsof Crime Mapping 2
Fundamentalsof Crime Mapping 2Fundamentalsof Crime Mapping 2
Fundamentalsof Crime Mapping 2Osokop
 
Fundamentalsof Crime Mapping 1
Fundamentalsof Crime Mapping 1Fundamentalsof Crime Mapping 1
Fundamentalsof Crime Mapping 1Osokop
 
Fundamentalsof Crime Mapping Tactical Analysis Concepts
Fundamentalsof Crime Mapping Tactical Analysis ConceptsFundamentalsof Crime Mapping Tactical Analysis Concepts
Fundamentalsof Crime Mapping Tactical Analysis ConceptsOsokop
 

More from Osokop (10)

Fundamentalsof Crime Mapping Geocoding
Fundamentalsof Crime Mapping GeocodingFundamentalsof Crime Mapping Geocoding
Fundamentalsof Crime Mapping Geocoding
 
Fundamentalsof Crime Mapping 8
Fundamentalsof Crime Mapping 8Fundamentalsof Crime Mapping 8
Fundamentalsof Crime Mapping 8
 
Fundamentalsof Crime Mapping 7
Fundamentalsof Crime Mapping 7Fundamentalsof Crime Mapping 7
Fundamentalsof Crime Mapping 7
 
Fundamentalsof Crime Mapping 6
Fundamentalsof Crime Mapping 6Fundamentalsof Crime Mapping 6
Fundamentalsof Crime Mapping 6
 
Fundamentalsof Crime Mapping 5
Fundamentalsof Crime Mapping 5Fundamentalsof Crime Mapping 5
Fundamentalsof Crime Mapping 5
 
Fundamentalsof Crime Mapping 4
Fundamentalsof Crime Mapping 4Fundamentalsof Crime Mapping 4
Fundamentalsof Crime Mapping 4
 
Fundamentalsof Crime Mapping 3
Fundamentalsof Crime Mapping 3Fundamentalsof Crime Mapping 3
Fundamentalsof Crime Mapping 3
 
Fundamentalsof Crime Mapping 2
Fundamentalsof Crime Mapping 2Fundamentalsof Crime Mapping 2
Fundamentalsof Crime Mapping 2
 
Fundamentalsof Crime Mapping 1
Fundamentalsof Crime Mapping 1Fundamentalsof Crime Mapping 1
Fundamentalsof Crime Mapping 1
 
Fundamentalsof Crime Mapping Tactical Analysis Concepts
Fundamentalsof Crime Mapping Tactical Analysis ConceptsFundamentalsof Crime Mapping Tactical Analysis Concepts
Fundamentalsof Crime Mapping Tactical Analysis Concepts
 

Recently uploaded

Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...apidays
 
A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)Gabriella Davis
 
Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin WoodPolkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin WoodJuan lago vázquez
 
🐬 The future of MySQL is Postgres 🐘
🐬  The future of MySQL is Postgres   🐘🐬  The future of MySQL is Postgres   🐘
🐬 The future of MySQL is Postgres 🐘RTylerCroy
 
Boost PC performance: How more available memory can improve productivity
Boost PC performance: How more available memory can improve productivityBoost PC performance: How more available memory can improve productivity
Boost PC performance: How more available memory can improve productivityPrincipled Technologies
 
Boost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdfBoost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdfsudhanshuwaghmare1
 
presentation ICT roal in 21st century education
presentation ICT roal in 21st century educationpresentation ICT roal in 21st century education
presentation ICT roal in 21st century educationjfdjdjcjdnsjd
 
Real Time Object Detection Using Open CV
Real Time Object Detection Using Open CVReal Time Object Detection Using Open CV
Real Time Object Detection Using Open CVKhem
 
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers:  A Deep Dive into Serverless Spatial Data and FMECloud Frontiers:  A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FMESafe Software
 
Exploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone ProcessorsExploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone Processorsdebabhi2
 
Top 10 Most Downloaded Games on Play Store in 2024
Top 10 Most Downloaded Games on Play Store in 2024Top 10 Most Downloaded Games on Play Store in 2024
Top 10 Most Downloaded Games on Play Store in 2024SynarionITSolutions
 
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...Miguel Araújo
 
Tata AIG General Insurance Company - Insurer Innovation Award 2024
Tata AIG General Insurance Company - Insurer Innovation Award 2024Tata AIG General Insurance Company - Insurer Innovation Award 2024
Tata AIG General Insurance Company - Insurer Innovation Award 2024The Digital Insurer
 
The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024Rafal Los
 
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024The Digital Insurer
 
Strategies for Landing an Oracle DBA Job as a Fresher
Strategies for Landing an Oracle DBA Job as a FresherStrategies for Landing an Oracle DBA Job as a Fresher
Strategies for Landing an Oracle DBA Job as a FresherRemote DBA Services
 
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024The Digital Insurer
 
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...Drew Madelung
 
Deploy with confidence: VMware Cloud Foundation 5.1 on next gen Dell PowerEdg...
Deploy with confidence: VMware Cloud Foundation 5.1 on next gen Dell PowerEdg...Deploy with confidence: VMware Cloud Foundation 5.1 on next gen Dell PowerEdg...
Deploy with confidence: VMware Cloud Foundation 5.1 on next gen Dell PowerEdg...Principled Technologies
 
Partners Life - Insurer Innovation Award 2024
Partners Life - Insurer Innovation Award 2024Partners Life - Insurer Innovation Award 2024
Partners Life - Insurer Innovation Award 2024The Digital Insurer
 

Recently uploaded (20)

Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
Apidays New York 2024 - The Good, the Bad and the Governed by David O'Neill, ...
 
A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)A Domino Admins Adventures (Engage 2024)
A Domino Admins Adventures (Engage 2024)
 
Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin WoodPolkadot JAM Slides - Token2049 - By Dr. Gavin Wood
Polkadot JAM Slides - Token2049 - By Dr. Gavin Wood
 
🐬 The future of MySQL is Postgres 🐘
🐬  The future of MySQL is Postgres   🐘🐬  The future of MySQL is Postgres   🐘
🐬 The future of MySQL is Postgres 🐘
 
Boost PC performance: How more available memory can improve productivity
Boost PC performance: How more available memory can improve productivityBoost PC performance: How more available memory can improve productivity
Boost PC performance: How more available memory can improve productivity
 
Boost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdfBoost Fertility New Invention Ups Success Rates.pdf
Boost Fertility New Invention Ups Success Rates.pdf
 
presentation ICT roal in 21st century education
presentation ICT roal in 21st century educationpresentation ICT roal in 21st century education
presentation ICT roal in 21st century education
 
Real Time Object Detection Using Open CV
Real Time Object Detection Using Open CVReal Time Object Detection Using Open CV
Real Time Object Detection Using Open CV
 
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers:  A Deep Dive into Serverless Spatial Data and FMECloud Frontiers:  A Deep Dive into Serverless Spatial Data and FME
Cloud Frontiers: A Deep Dive into Serverless Spatial Data and FME
 
Exploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone ProcessorsExploring the Future Potential of AI-Enabled Smartphone Processors
Exploring the Future Potential of AI-Enabled Smartphone Processors
 
Top 10 Most Downloaded Games on Play Store in 2024
Top 10 Most Downloaded Games on Play Store in 2024Top 10 Most Downloaded Games on Play Store in 2024
Top 10 Most Downloaded Games on Play Store in 2024
 
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
Mastering MySQL Database Architecture: Deep Dive into MySQL Shell and MySQL R...
 
Tata AIG General Insurance Company - Insurer Innovation Award 2024
Tata AIG General Insurance Company - Insurer Innovation Award 2024Tata AIG General Insurance Company - Insurer Innovation Award 2024
Tata AIG General Insurance Company - Insurer Innovation Award 2024
 
The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024The 7 Things I Know About Cyber Security After 25 Years | April 2024
The 7 Things I Know About Cyber Security After 25 Years | April 2024
 
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
 
Strategies for Landing an Oracle DBA Job as a Fresher
Strategies for Landing an Oracle DBA Job as a FresherStrategies for Landing an Oracle DBA Job as a Fresher
Strategies for Landing an Oracle DBA Job as a Fresher
 
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
Bajaj Allianz Life Insurance Company - Insurer Innovation Award 2024
 
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
Strategies for Unlocking Knowledge Management in Microsoft 365 in the Copilot...
 
Deploy with confidence: VMware Cloud Foundation 5.1 on next gen Dell PowerEdg...
Deploy with confidence: VMware Cloud Foundation 5.1 on next gen Dell PowerEdg...Deploy with confidence: VMware Cloud Foundation 5.1 on next gen Dell PowerEdg...
Deploy with confidence: VMware Cloud Foundation 5.1 on next gen Dell PowerEdg...
 
Partners Life - Insurer Innovation Award 2024
Partners Life - Insurer Innovation Award 2024Partners Life - Insurer Innovation Award 2024
Partners Life - Insurer Innovation Award 2024
 

Fundamentalsof Crime Mapping Arc Gis Tables

  • 1.
  • 2. The data behind the  graphic
  • 3. Right click the theme to get this dialog menu. Click “Open Attribute Table” to see the data behind the graphic
  • 4. “Options” button provides another dialog menu that lets you work with table data Field Names or Columns Rows or Records Add a new field, that can then be “calculated” or manually entered A table can be added to a layout or exported as DBF
  • 5. Name of New Field (10 Characters) Field Type Options
  • 6. Symbology properties  are different depending on if you are working with points, lines, or polygons. The easiest is to work  with is Single Symbol features where everything in the layer is displayed using the same symbol Can use nominal,  ordinal, ratio or interval data
  • 7. Some symbol methods may not be useful (pie  charts for example) Markers 
  • 8. Some symbol methods may not be useful (pie  charts for example)  Lines or Arcs
  • 9. Nominal (categories), Ordinal, Interval and Ratio  (Quantities) can be used with different methods Fills and outlines  Nominal data example Ratio Data Example
  • 10. Category data  symbology comes next It displays data  by unique values of a field, or multiple fields Nominal, ordinal,  ratio or interval data
  • 11. Next, comes the  quantities symbology method  It uses a number field in the table to display data by classified values  Ratio and interval data
  • 12. Six different ways to classify data, with an  added manual method for infinite freedom
  • 13. Equal Interval  Defined Interval  Quantile  Natural Breaks  Geometrical Interval  Standard Deviation 
  • 14. Categorical (Qualitative)  Grouping based on some quality ◦ Labels or categories ◦ E.g.; Sex = Male or Female ◦ Nominal or Ordinal ◦  Nominal the order is not important  E.g.: Sex = male or female  Ordinal the order is important  E.g.; Rank = Officer, Sergeant, Lieutenant, etc ◦ Can be binary or non-binary  Binary = only two values (male or female)  Non-Binary = More than two (red, blonde, brunette, etc)
  • 15. Measurement (Quantitative)  ◦ Grouping based on some quantity or value ◦ Always numbers ◦ Discrete or continuous  Discrete = only certain values are possible and data could have gaps (1, 2, 3, or 4)  Continuous = Any value along some interval (any value between 1 and 4 (ie: 3.24211) ◦ Interval or ratio  In interval data the interval between values is important (ie; temperature of 30 compared to 110 means something)  Ratio data is the best, and the “0” value can be informative (ie; a grid can have 0 crimes, or any value up to infinity)
  • 17. Number of Equal Interval (ratio, Interval)  classes desired ◦ The range between the classifications is thedetermines interval same Take the high value-low value and for each of the 5 classes, the value is 199.61
  • 18. Defined Interval (ratio, interval)  ◦ Similar to the equal interval, but here, we define what the interval will be and thus establish the classes In this case the interval was set to 150, and so the number of classes is determined by the interval
  • 19. Quantile (ratio, interval)  ◦ A percentage of the values in the class falling with the range. Each class contains an equal number of features. Each of the 10 classes has the same number of features within each class, or makes up 10% of the total records
  • 20. Natural Breaks (ratio, interval)  ◦ Breaks the data where there are natural holes between values Use test exam score example
  • 21. Geometrical Interval (ratio, interval)  ◦ This is a classification scheme where the class breaks are based on class intervals that have a geometrical series. This ensures that each class range has approximately the same number of values with each class and that the change between intervals is fairly consistent. The interval is determined by a geometric equation (large and small changes depending on breaks in data)
  • 22. Standard Deviation (ratio, interval)  ◦ Classes are determined by mean and standard deviation of values. Can display by 1, ½, ¼ standard deviations as needed
  • 23. Right click the theme and choose properties,  then choose the Labels tab You can combine fields for labeling Can label all features the same, or use a query to label features differently Infinite text choices Use a field in the data for the labels that is good Control over placement options, scale at which labels draw and styles are available
  • 24. Use to select data by a SQL query  Click this