Tabular information is the basis of geographic features, allowing you to visualize, query, and analyze your data. In the simplest terms, tables are made up of rows and columns, and all rows have the same columns. In ArcGIS, rows are known as records and columns are fields. Each field can store a specific type of data, such as a number, date, or piece of text.
Feature classes are really just tables with special fields that contain information about the geometry of the features. These include the Shape field for point, line, and polygon feature classes and the BLOB field for annotation feature classes. Some fields, such as the unique identifier number (ObjectID) and Shape, are automatically added, populated, and maintained by ArcGIS.
ArcGIS allows you to associate records in one table with records in another table through a common field, known as a key. You can make these associations in several ways, including by joining or relating tables temporarily in your map or by creating relationship classes in your geodatabase that maintain more permanent associations. For example, you could associate a table of parcel ownership information with the parcels layer, since they share a parcel ID field.
Sources of tabular information
There are lots of sources of tabular data, and ArcGIS can take advantage of many formats. Tabular information could be stored as tables in folders or databases, text files, queries on databases, and so on. In addition, if you have spatial data, you probably already have tabular attributes that describe those geographic features.
File-based tables are stored in folders on disk. Some examples of file-based sources of tabular information include the following:
- dBASE tables, the format used with shapefiles
- INFO, the format used with coverages
- Text files, such as those created in a text editor and delimited by commas or tabs
- Many other sorts of tables, including those generated in other programs, such as Microsoft Excel, either accessed directly in ArcGIS or through the OLE DB functionality
Tables in a database or geodatabase can contain some types of information that file-based tables do not support. For example, database or geodatabase tables can store BLOB or raster field types. In addition, databases and geodatabases provide capabilities to extend the functionality of tables, such as maintaining data integrity and managing transactions.
You can perform queries on these database or geodatabase tables to create new tables. The Make Query Table tool, for example, allows you to apply a SQL expression to one or more tables. The query can be used to join the tables or return a subset from the original data.
To learn more about what you can do with these different kinds of tables, see About tabular data sources.
Tasks you can perform with tables and attribute information
There are many mapping, analysis, and data management tasks you can perform using tabular data.
Tables allow you to map and visualize your data. For example, you can classify or categorize attributes to symbolize a layer. You can use population values to symbolize major cities with a larger symbol than would be used for smaller towns and villages. You can also specify that a different color be used to represent each type of land use in a parcel layer. In addition, you can use the attribute values to generate text to label each parcel feature. In the graphic below, the parcels are symbolized by the type of land use, then labeled with their parcel ID values.
Attribute data helps you perform spatial queries and analyses. For example, you can examine the distribution of features with certain attributes by using ArcMap to select the features that contain attributes you want to examine. In the graphic below, the features with a LAND_USE value of UNK (unknown) are selected using an attribute query.
When information in your geodatabase or database changes, you can update your attributes. For example, you'll need to update your database when land use or property ownership changes or the unknown values are classified. If you have a feature class representing some pipes with a field for the diameter, you can easily change the attributes when the crew removes an 8-inch pipe and replaces it with a 6-inch pipe. You can edit tabular values within either the Table window or the Attributes window, which shows attributes of only individual, selected features.
The geodatabase includes functionality that allows you to enhance, maintain, and enforce the integrity of your tabular data. For example, by establishing attribute domains, you can set up rules that specify the valid values for the records in your table. So when updating the pipe diameter attributes, you can use attribute domains to ensure that the diameter is appropriate for that section of pipe. Range domains, for example, ensure that the values you enter are within a valid range. The graphic below shows the use of coded value domains when editing, which allows you to choose a value from a predefined list and avoid making typographic errors.
ArcGIS also allows you to convert data in a table into spatial data. For example, a commonly converted data source is a list of coordinates obtained from using a GPS unit in the field. You can easily add such x,y data to ArcMap to display it.
The graphics below show a text file containing the x,y locations of hydrants (top graphic) and the points displayed in ArcMap on the map and in the Table window (bottom graphic).
Learn more about adding x,y data as a layer
If you have a list of addresses, you can use geocoding to match them to known street locations to create point features. In addition, through linear referencing, you can indicate events along line features with just an identifier and a location.
Learn more about geocoding
Learn more about linear referencing
Tables are also at the root of data models, which are templates that you can use to set up your geodatabase to better model real-world phenomena.
Learn more about data models
Geographic Information System/Attributes
Attributes are non-spatial characteristics that describe spatial entities. Attrubutes are commonly arranged in tables where a row is equivalent to one entity and a column is equivalent to one attribute, or descriptor of that entity.
(picture of data table)
Typically, each row relates to a single object in a spatial data model. It is also typical for each object to have multiple attributes that describe the object. All attributes are often displayed in a table format. Attributes can be stored on a computer using a flat file format or in a database management system.
For example, consider that we have a spatial data model that stores the location of fire hydrants. In order for each object to represent a fire hydrant, we would need to store their positions. In addition to positional information, we would also store attributes that would describe those fire hydrants.
(Image of fire hydrant)
In this example, we are storing color, service date, and flow as three attributes that describe this particular fire hydrant at this particular position on Earth. The position, color, service date, and flow will be stored as one row in an attribute table that will contain four columns because there are four descriptors for this fire hydrant.
Computer representations[edit | edit source]
Computers fundamentally "think" differently than humans. While humans see numbers, letters, pictures, and sounds, a computer only sees zeros and ones, which is known as binary data. Therefore, we need a way to translate the numbers, sounds, and videos, as humans know it, to a form in which a computer can understand and store the information.
Computer scientists have created data structures that can be used to translate our information into a format which a computer can store in its memory. This data structure is known as a data type.
There are four typical data types that we use in GIS:
In order to use the computer's memory most efficiently, it is important that we specify which data type we are going to use to store information in the computer's memory. It is important to let the computer know which operations are allowed for each data point stored in that memory location using a specific data type.
Key Facts[edit | edit source]
Attribute Data Types
- Attributes are stored in computer memory
- The data type of the attribute needs to be specified for efficient use of memory and determination of operation applicability.
There are four typical data types:
Attribute tables in ArcMap[edit | edit source]
Attribute tables in ArcMap are made up of rows and columns. Rows are known as records, and columns are known as fields. Each column (field) stores specific information about the layer. To view a layer's attribute table, right lick the desired layer in the "Table of Contents", and select "Open Attribute Table".
An attribute table's menu bar consist of a "Table Options" drop down button, "Related Tables" drop down button, "Select by Attributes" button, "Switch Selection" button, "Clear Selection" button, "Zoom" button, and lastly a "Delete Field" button.
Current fields cannot be altered. However, by adding a new field, modified data can be added. Different filed data types can include short number, long numbers, text, and dates. To add a new field, click on the "Table Options" and select "Add Field". Select a proper name for the field, keeping in mind that field names cannot contain spaces or special characters. Also, select an appropriate type.
The short and long integer type is used for storing exact integers (no fractions). The float and double type is used for storing fractional numeric data. (The main difference between the numeric storage is the storable range and the byte size. It's always best to used the minimum storage required.) The text type can store letters and numbers, such as an address. Calculations cannot be performed on numbers stored in a text field. The date type stores dates and/or times. The BLOB type stores items such as images or binary numbers for coding. Refer here for more details pertaining to field types.
Now that the new field is created, it can be populated with appropriate data. Additionally, further analysis can be performed on the field. For example, by right clicking on the field one can perform calculations on numerical data, or sort and search for text data.
These are some basic steps to become more skilled with attribute tables in ArcMap. However, more in-depth information can be found here or a video lesson here.
A set of data elements arranged in rows and columns. Each row represents a single record. Each column represents a field of the record. Rows and columns intersect to form cells, which contain a specific value for one field in a record.
Nonspatial information about a geographic feature in a GIS, usually stored in a table and linked to the feature by a unique identifier. For example, attributes of a river might include its name, length, and sediment load at a gauging station.
A column in a table that stores the values for a single attribute.
An alternative name specified for fields, tables, files, or datasets, which is more descriptive and user-friendly than the actual name.
A row in a table.
Appending the fields of one table to those of another through an attribute or field common to both tables. A join is usually used to attach more attributes to the attribute table of a geographic layer.
An operation that establishes a temporary connection between records in two tables, using a key common to both.
An item in the geodatabase that stores information about a relationship. A relationship class establishes a permanent connection between records in two tables, using a key common to both.
In a geodatabase, a mechanism for enforcing data integrity. Attribute domains define what values are allowed in a field in a feature class or nonspatial attribute table. If the features or nonspatial objects have been grouped into subtypes, different attribute domains can be assigned to each of the subtypes.
In geodatabases, a subset of features in a feature class or objects in a table that share the same attributes. For example, the streets in a streets feature class could be categorized into three subtypes: local, collector, and arterial. Creating subtypes can be more efficient than creating many feature classes or tables in a geodatabase.
A table containing results from a query. You can create a query table by using the Make Query Table geoprocessing tool.
In ArcMap, a request that examines feature or tabular attributes based on user-selected criteria and displays only those features or records that satisfy the criteria.
In ArcGIS, a system-managed value that uniquely identifies a record or feature.
There are two components to GIS data: spatial information (coordinate and projection information for spatial features) and attribute data. Attribute data is information appended in tabular format to spatial features. The spatial data is the where and attribute data can contain information about the what, where, and why. Attribute data provides characteristics about spatial data.
Types of Attribute Data
Attribute data can be store as one of five different field types in a table or database: character, integer, floating, date, and BLOB.
The character property (or string) is for text based values such as the name of a street or descriptive values such as the condition of a street. Character attribute data is stored as a series of alphanumeric symbols.
Aside from descriptors, character fields can contain other attribute values such as categories and ranks. For example, a character field may contain the categories for a street: avenue, boulevard, lane, or highway. A character field could also contain the rank, which is a relative ordering of features. For example, a ranking of the traffic load of the street with “1” being the street with the highest traffic.
Character data can be sorted in ascending (A to Z) and descending (Z to A) order. Since numbers are considered text in this field, those numbers will be sorted alphabetically which means that a number sequence of 1, 2, 9, 11, 13, 22 would be sorted in ascending order as 1, 11, 13, 2, 22, 9.
Because character data is not numeric, calculations (sum, average, median, etc.) can’t be performed on this type of field, even if the value stored in the field are numbers (to do that, the field type would need to be converted to a numeric field). Character fields can be summarized to produced counts (e.g. the number of features that have been categorized as “avenue”).
Integer and floating are numerical values (see: the difference between floating and integer values). Within the integer type, the is a further division between short and long integer values. As would be expected, short integers store numeric values without fractional values for a shorter range than long integers. Floating point attribute values store numeric values with fractional values. Therefore, floating point values are for numeric values with decimal points (i.e numbers to the right of the decimal point as opposed to whole values).
Numeric values will be sorted in sequentially either in ascending (1 to 10) or descending (10 to 1) order.
Numerical value fields can have operations performed such as calculating the sum or average value. Numerical field values can be a count (e.g. the total number of students at a school) or be a ratio (e.g. the percentage of students that are girls at a school).
Date fields contains date and time values.
BLOB stands for binary large object and this attribute type is used for storing information such images, multimedia, or bits of code in a field. This field stores object linking and embedding (OLE) which are objects created in other applications such as images and multimedia and linked from the BLOB field
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4.1 Feature Versus Attributes
As discussed in Chapter One, geographic data represent spatial locations (i.e., a feature) and non-spatial attributes measured at certain times. For instance, a city (a feature with a spatial location) can contain an endless number of attributes. Geographic data for a specific city may include attributes such as its population, the types of public transportation, and various land use patterns. Over recent years, software developers have created variations on standard query languages (SQL) that incorporate spatial queries. The dynamic nature of geographic phenomena complicates the issue further, however. The need to pose spatio-temporal queries challenges geographic information scientists (GIScientists) to develop ever more sophisticated ways to represent geographic phenomena, thereby enabling analysts to interrogate their data in more sophisticated ways.
4.1.1 Tables: Location versus Attribute
To explore the differences between a location and its attributes, view the table below of a geographic database originating from the U.S. Census Bureau and imported into ESRI’s ArcMap program. Each row in the attribute table refers to a feature’s location on the map, with numerous attributes associated with it. In this example, each object refers to a state that includes attribute data including information such as its FID (unique identifier), shape (polygon), state abbreviation, full state name, a FIPS code (unique code assigned to each state), and the longitude and latitude coordinates. As you can see, the fifth row highlighted in light blue is selected and the mapping program automatically links to the spatial representation of the state of California, also outlined in light blue. This functionality allows users to manipulate, query, and select features and their attributes in the table, while viewing changes dynamically on the map.
Figure 4.2. Screenshot of an attribute table and a linked map in ESRI’s ArcMap™ program.
Credit: Jennifer M. Smith, Department of Geography, The Pennsylvania State University; state boundaries simplified in map shaper and data from the U.S. Census Bureau.
Vector Attribute Data¶
|Objectives:||In this topic we describe how attribute data are associated with vector features and can be used to symbolise data.|
|Keywords:||Attribute, database, fields, data, vector, symbology|
If every line on a map was the same colour, width, thickness, and had the same label, it would be very hard to make out what was going on. The map would also give us very little information. Take a look at figure_map_attributes for example.
Figure Attributes on map:
Maps come to life when colour and different symbols are used to help you to tell one type of feature from the next. Can you tell the difference between rivers, roads and contours using the map on the left? Using the map on the right it is much easier to see the different features.
In this topic we will look at how attribute data can help us to make interesting and informative maps. In the previous topic on vector data, we briefly explained that attribute data are used to describe vector features. Take a look at the house pictures in figure_house.
Figure House 1:
Every feature has characteristics that we can describe. These can be visible things, or things we know about the feature (e.g. year built).
The geometry of these house features is a polygon (based on the floor plan of the house), the attributes we have recorded are roof colour, whether there is a balcony, and the year the house was built. Note that attributes don’t have to be visible things –– they can describe things we know about the feature such as the year it was built. In a GIS Application, we can represent this feature type in a houses polygon layer, and the attributes in an attribute table (see figure_house_gis).
Figure House 2:
A houses layer. House features have attributes that describe the houses’ roof colour and other properties. The attribute table (lower image) lists the attributes for the house areas shown on the map. When a feature is highlighted in the table, it will appear as a yellow polygon on the map.
The fact that features have attributes as well geometry in a GIS Application opens up many possibilities. For example we can use the attribute values to tell the GIS what colours and style to use when drawing features (see figure_style_by_attribute). The process of setting colours and drawing styles is often referred to as setting feature symbology.
Figure Feature Style 1:
In a GIS Application, we can draw features differently depending on their attributes. On the left we have drawn house polygons with the same colour as the roof attribute. On the right we colour coded houses according to whether they have a balcony or not.
Attribute data can also be useful when creating map labels. Most GIS Applications will have a facility to select an attribute that should be used to label each feature.
If you have ever searched a map for a place name or a specific feature, you will know how time consuming it can be. Having attribute data can make searching for a specific feature quick and easy. In figure_search_by_attribute you can see an example of an attribute search in a GIS.
Figure Feature Search 1:
In a GIS Application, we can also search for features based on their attributes. Here we see a search for houses with black roofs. Results are shown in yellow in the map, turquoise on the table.
Finally, attribute data can be very useful in carrying out spatial analysis. Spatial analysis combines the spatial information stored in the geometry of features with their attribute information. This allows us to study features and how they relate to each other. There are many types of spatial analysis that can be carried out, for example, you could use GIS to find out how many red roofed houses occur in a particular area. If you have tree features, you could use GIS to try to find out which species might be affected if a piece of land is developed. We can use the attributes stored for water samples along a river course to understand where pollution is entering into the stream. The possibilities are endless! In a later topic we will be exploring spatial analysis in more detail.
Before we move on to attribute data in more detail, let’s take a quick recap.
Features are real world things such as roads, property boundaries, electrical substation sites and so on. A feature has a geometry (which determines if it is a point, polyline or polygon) and attributes (which describe the feature). This is shown in figure_features_at_glance.
Figure Feature Summary 1:
Vector features at a glance.
Attributes in detail¶
Attributes for a vector feature are stored in a table. A table is like a spreadsheet. Each column in the table is called a field. Each row in the table is a record. Table table_house_attributes shows a simple example of how an attribute table looks in a GIS. The records in the attribute table in a GIS each correspond to one feature. Usually the information in the attribute table is stored in some kind of database. The GIS application links the attribute records with the feature geometry so that you can find records in the table by selecting features on the map, and find features on the map by selecting features in the table.
|Attribute Table||Field 1 : YearBuilt||Field 2: RoofColour||Field 3: Balcony|
Table House Attributes 1: An attribute table has fields (columns) and records (in rows).
Each field in the attribute table contains contains a specific type of data –– text, numeric or date. Deciding what attributes to use for a feature requires some thought and planning. In our house example earlier on in this topic, we chose roof colour, presence of a balcony and month of construction as attributes of interest. We could just as easily have chosen other aspects of a house such as:
- number of levels
- number of rooms
- number of occupants
- type of dwelling (RDP House, block of flats, shack, brick house, etc)
- year the house was built
- area of floor space in the house
- and so on....
With so many options, how do we make a good choice as to what attributes are needed for a feature? It usually boils down to what you plan to do with the data. If you want to produce a colour coded map showing houses by age, it will make sense to have a ‘Year Built’ attribute for your feature. If you know for sure you will never use this type of map, it is better to not store the information. Collecting and storing unneeded information is a bad idea because of the cost and time required to research and capture the information. Very often we obtain vector data from companies, friends or the government. In these cases it is usually not possible to request specific attributes and we have to make do with what we get.
If a feature is symbolised without using any attribute table data, it can only be drawn in a simple way. For example with point features you can set the colour and marker (circle, square, star etc.) but that is all. You cannot tell the GIS to draw the features based on one of its properties in the attribute table. In order to do that, you need to use either a graduated, continuous or unique value symbol. These are described in detail in the sections that follow.
A GIS application will normally allow you to set the symbology of a layer using a dialog box such as the one shown in in figure_single_symbol_1. In this dialog box you can choose colours and symbol styles. Depending on the geometry type of a layer, different options may be shown. For example with point layers you can choose a marker style. With line and polygon layers there is no marker style option, but instead you can select a line style and colour such as dashed orange for gravel roads, solid orange for minor roads, and so on (as shown in figure_single_symbol_2). With polygon layers you also have the option of setting a fill style and color.
Figure Single Symbol 1:
When using simple symbols, the feature is drawn without using an attribute to control how it looks. This is the dialog for point features.
Figure Single Symbol 2:
There are different options when defining simple symbols for polyline and polygon features.
Sometimes vector features represent things with a changing numerical value. Contour lines are a good example of this. Each contour usually has an attribute value called ‘height’ that contains information about what height that contour represents. In earlier in this topic we showed contours all drawn with the same colour. Adding colour to the contours can help us to interpret the meanings of contours. For example we can draw low lying areas with one colour, mid-altitude areas with another and high-altitude areas with a third.
Figure Graduated Symbol 1:
The height attribute of contours can be used to separate the contours into 3 classes. Contours between 980 m and 1120 m will be drawn in brown, those between 1120 m and 1240 m in green and those between 1240 m and 1500 m in purple.
Figure Graduated Symbol 2:
Our map after setting graduated colours for our contours.
Setting colours based on discrete groups of attribute values is called Graduated Symbology in QGIS. The process is shown in Illustrations figure_graduated_symbol_1 and figure_graduated_symbol_2. Graduated symbols are most useful when you want to show clear differences between features with attribute values in different value ranges. The GIS Application will analyse the attribute data (e.g. height) and, based on the number of classes you request, create groupings for you. This process is illustrated in table_graduated_1.
|Attribute Value||Class and Colour|
Table Graduaded 1: Graduated colour breaks up the attribute value ranges into the number of classes you select. Each class is represented by a different colour.
Continuous Colour Symbols¶
In the previous section on Graduated Colour symbols we saw that we can draw features in discrete groups or classes. Sometimes it is useful to draw features in a colour range from one colour to another. The GIS Application will use a numerical attribute value from a feature (e.g. contour heights or pollution levels in a stream) to decide which colour to use. Table table_continuous_1 shows how the attribute value is used to define a continuous range of colours.
|Attribute Value||Colour (no classes or grouping)|
Table Continuous 1: Continuous colour symbology uses a start colour (e.g. light orange shown here) and an end colour (e.g. dark brown shown here) and creates a series of shades between those colours.
Using the same contours example we used in the previous section, let’s see how a map with continuous colour symbology is defined and looks. The process starts by setting the layers properties to continuous colour using a dialog like the one shown in figure_continuos_symbol_1.
Figure Continuous Symbol 1:
Setting up continuous colour symbology. The contour height attribute is used to determine colour values. Colours are defined for the minimum and maximum values. The GIS Application will then create a gradient of colours for drawing the features based on their heights.
After defining the minimum and maximum colours in the colour range, the colour features are drawn in will depend on where the attribute lies in the range between minimum and maximum. For example if you have contour features with values starting at 1000 m and ending at 1400 m, the value range is 1000 to 1400. If the colour set for the minimum value is set to orange and the colour for the maximum value is black, contours with a value of close to 1400 m will be drawn close to black. On the other hand contours with a value near to 1000 m will be drawn close to orange (see figure_continuous_symbol_2).
Figure Graduated Symbol 2:
A contour map drawn using continuous colour symbology
Unique Value Symbols¶
Sometimes the attributes of features are not numeric, but instead strings are used. ‘String’ is a computer term meaning a group of letters, numbers and other writing symbols. Strings attributes are often used to classify things by name. We can tell the GIS Application to give each unique string or number its own colour and symbol. Road features may have different classes (e.g. ‘street’, ‘secondary road’, ‘main road’ etc.), each drawn in the map view of the GIS with different colours or symbols. This is illustrated in table_unique_1.
|Attribute Value||Colour class and symbol|
Table Unique 1: Unique attribute values for a feature type (e.g. roads) can each have their own symbol.
Within the GIS Application we can open/choose to use Unique Value symbology for a layer. The GIS will scan through all the different string values in the attribute field and build a list of unique strings or numbers. Each unique value can then be assigned a colour and style. This is shown in figure_unique_symbol_1.
Figure Unique Symbol 1:
Defining unique value symbology for roads based on the road type.
When the GIS draws the layer, it will look at the attributes of each feature before drawing it to the screen. Based on the value in the chosen field in the attribute table, the road line will be drawn with suitable colour and line style (and fill style if its a polygon feature). This is shown in figure_unique_symbol_2.
Figure Unique Symbol 2:
A roads vector layer symbolised using a unique value per road type.
Things to be aware of¶
Deciding which attributes and symbology to use requires some planning. Before you start collecting any GeoSpatial data, you should ensure you know what attributes are needed and how it will be symbolised. It is very difficult to go back and re-collect data if you plan poorly the first time around. Remember also that the goal of collecting attribute data is to allow you to analyse and interpret spatial information. How you do this depends on the questions you are trying to answer. Symbology is a visual language that allows people to see and understand your attribute data based on the colours and symbols you use. Because of this you should put a lot of thought into how you symbolise your maps in order to make them easy to understand.
What have we learned?¶
Let’s wrap up what we covered in this worksheet:
- Vector features have attributes
- Attributes describe the properties of the feature
- The attributes are stored in a table
- Rows in the table are called records
- There is one record per feature in the vector layer
- Columns in the table are called fields
- Fields represent properties of the feature e.g. height, roof colour etc.
- Fields can contain numerical, string (any text) and date information
- The attribute data for a feature can be used to determine how it is symbolised
- Graduated colour symbology groups the data into discrete classes
- Continuous colour symbology assigns colours from a colour range to the features based on their attributes
- Unique value symbology associates each different value in the chosen attribute column with a different symbol (colour and style)
- If the attribute of a vector layer is not used to determine its symbology, it is drawn using a single symbol only
Now you try!¶
Here are some ideas for you to try with your learners:
- Using the table that you created in the last topic, add a new column for the symbology type you would use for each feature type and have the learners identify which symbology type they would use (see table_example_symbols_1 for an example).
- Try to identify which symbology types you would use for the following types of vector features:
- points showing pH level of soil samples taken around your school
- lines showing a road network in a city
- polygons for houses with an attribute that shows whether it is made of brick, wood or ‘other’ material.
|Real world feature||Geometry Type||Symbology Type|
|The school flagpole||Point||Single Symbol|
|The soccer field||Polygon||Single Symbol|
|The footpaths in and around the school||Polyline||Have your learners count the number of learners using each footpath in the hour before school and then use graduated symbols to show the popularity of each footpath|
|Places where taps are located||Point||Single symbol|
|Classrooms||Polygon||Unique value based on the grade of the learners in the classroom|
|Fence||Polyline||Have your learners rate the condition of the fence around your school by separating it into sections and grading each section on a scale of 1-9 based on its condition. Use graduated symbols to classify the condition attribute.|
|Classrooms||Polygon||Count the number of learners in each classroom and use a continuous colour symbol to define a range of colours from red to blue.|
Table Example Symbols 1: An example of a table that defines the feature types and the kind of symbology you would use for each.
Something to think about¶
If you don’t have a computer available, you can use transparency sheets and a 1:50 000 map sheet to experiment with different symbology types. For example place a transparency sheet over the map and using different coloured koki pens, draw in red all contour lines below 900 m (or similar) and in green all lines above or equal to 900 m. Can you think of how to reproduce other symbology types using the same technique?
In the section that follows we will take a closer look at data capture. We will put the things we have learned about vector data and attributes into practice by creating new data.
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