Proportional symbols

What is Layer Styling in GIS?

In Geographic Information Systems (GIS), layer styling is a technique that visually represents geographic data on maps. In web cartography, layer styling is essential for displaying data in a way that is informative and visually engaging. There are various types of GIS layer styling:

1. Single Symbol Styling

2. Styling by One Attribute

3. Styling by Multiple Attributes

Attributes in this context refer to the fields within the layer's attribute table. The chosen style determines how data appears on the map and what specific information it communicates.

Why Use Styling by Multiple Attributes?

Applying multiple attributes in GIS layer styling can reveal insights and help users engage with complex datasets more effectively. Here are some main reasons for using multi-attribute styling:

• Categorize Data: Grouping points by multiple attributes (e.g., store type, sales level) highlights key information quickly, enabling users to identify patterns and trends.

• Emphasize Values: Changing colors or sizes based on attribute values can draw attention to important data points on the map, such as high-risk areas in environmental studies.

• Data Comparison: Using distinct symbols or colors across points allows users to easily compare data, making web cartography an efficient tool for analysis.

Application of Multi-Attribute Styling in GIS

Styling layers by multiple attributes is valuable across many fields that require a detailed representation of data. Here’s how it applies in various sectors:

1. Socio-Economic Geography: Socio-economic maps often require the layering of multiple statistics, such as population, economy type  and GDP. Multi-attribute styling makes it possible to assign each metric to a visual variable—size, color, or symbol—thereby creating an intuitive map for complex data analysis.

2. Urban Planning: Urban planners use multi-attribute styling in GIS to create city development plans. For example, they might use symbols to represent facility type (schools, sports facilities, cultural centers), size for facility importance (local, regional, federal), and color for facility status (existing, planned, under renovation).

3. Mining Industry: Mapping mineral deposits requires a detailed display of deposit information. Here, a multi-attribute styling might use symbols for mineral types, color to indicate reserve status (active/inactive), and size to show reserve levels. This type of styling makes it easier for analysts to interpret complex geological data.

Configuring Multi-Attribute Layer Styling in GISCARTA

Here’s a step-by-step guide to setting up multi-attribute layer styling:

1. Access Layer Properties: Start by navigating to the layer's settings. Click on the three dots to the right of the layer name and select the "Properties" tab.

   

   
   

2. Choose Properties: After clicking on the three dots, a pop-up window will appear in which you need to select the Properties tab.

   

   
   

3. Navigate to Styling: Here you can configure the layer to display a:

• Single Symbol

• Symbol by One Attribute

• Symbol by Multiple Attributes

• Charts

• Heat Map

• Bivariate Map

Choose “Symbol by Multiple Attributes”

4. Attribute Configuration: Now you need to customize styling by several attributes. In GISCARTA styling is available by three attributes.

In GISCARTA, styling by multiple attributes enables rich and dynamic map visualizations using three key attributes: symbol, color, and size.

1. Styling by Symbol

   

   
   Choose an Attribute: Select an attribute (a field in the attribute table) to control the symbol displayed on the map.

   

   
   Select a Highlighting Method:

• Unique Values: Assign each unique value its own symbol, ideal for attributes with limited values.

• Set Intervals: Manually set specific intervals, useful for predefined classifications (e.g., city size based on population ranges).

• Equal Intervals: Divide the full value range into equal segments, suited for attributes with uniform distribution.

• Equal countt (Quantile): Create groups of equal size to account for uneven data distributions.

• Natural Brakes (Jenks): Automatically define classes with larger gaps between values.

After selecting an attribute and method, choose the symbols from the Symbol section by clicking on the default Circle icon. From here, you can select from the built-in library or upload custom SVG icons.

2. Styling by Color

The next attribute manages the color of the icons on the map.

1. Choose an Attribute: Select the field that will dictate icon color in the Attribute section.

2. Define Value Settings: In the Assignment Method section, select how values will be displayed.

3. Configure Color Display: Access the color toolbar by clicking on the color icon in the Symbol column. Available settings include:

• Fill Color: Adjust the primary color fill.

• Outline Color and Style: Define the border color and appearance.

• Outline Thickness: Set the border width.

You can disable fill or outline if needed for specific map designs.

3. Styling by Size

The final attribute manages the symbol's size.

1. Choose an Attribute: Select a size-controlling attribute in the same manner as the previous settings.

2. Adjust Symbol Size: Click on the symbol (Circle icon) in the Symbol column to reveal a slider, allowing you to set precise symbol size.

Incorporating multiple attributes into point layer styling enhances the usability of web cartography tools like GISCARTA. It allows organizations and analysts to deliver complex information more efficiently, making it easier to interpret multiple data types simultaneously. This level of customization proves especially valuable in scenarios where displaying comprehensive information at once can significantly help decision-making.

In summary, multi-attribute styling is an advanced GIS capability that enables users to leverage powerful, customized map visualizations. For sectors such as socio-economic planning, urban development, and mining, these visual tools provide a new dimension of clarity and insight essential for effective data analysis and presentation.