Maps are powerful tools for visualizing data, and within ArcGIS Online Map Viewer, smart mapping styles offer dynamic ways to explore and interpret your information. When it comes to numeric data, one of the most intuitive and impactful methods is using symbol size to represent quantities or comparisons. This technique, often referred to as “Map Compare Size,” allows map creators to visually communicate magnitudes and differences directly on the map itself, making complex datasets accessible and understandable at a glance.
This article delves into the world of “map compare size” within Map Viewer, focusing on how you can leverage different styling options to effectively visualize and compare numeric data using symbol sizes. We will explore various techniques, primarily focusing on the ‘Counts and Amounts (size)’ style, but also touching upon other related styles where size plays a crucial role in data comparison, such as ‘Color and Size’, ‘Charts and Size’, and ‘Relationship and Size’. By understanding these styles, you can create compelling maps that not only display data but also tell a story of comparison and magnitude, enhancing the viewer’s understanding and insights.
Utilizing Counts and Amounts (Size) for Direct Data Comparison
The ‘Counts and Amounts (size)’ style in Map Viewer is specifically designed to visualize numeric data through an ordered sequence of symbol sizes. This method is particularly effective for showcasing raw counts or ranked categories, where larger symbols directly correlate to larger numerical values. It provides an immediate visual hierarchy, allowing viewers to quickly grasp the relative magnitude of data points across the map. This style is applicable to points, lines, and areas, with polygons being represented by proportional points overlaid on the polygon shapes.
Imagine wanting to display the annual average daily traffic across a city. Using ‘Counts and Amounts (size)’, you can represent each traffic point with a circle whose size is proportional to the traffic volume. Instantly, areas with higher traffic will be represented by larger circles, making it easy to identify traffic hotspots and compare traffic flow across different locations.
To implement ‘Counts and Amounts (size)’ styling, follow these steps within Map Viewer:
- Begin by applying a style to your map layer, as detailed in the general “Apply a style” documentation within Map Viewer. This typically involves selecting the layer you wish to style.
- Within the Styles pane, locate and select the ‘Counts and Amounts (size)’ style. You may need to click on it to ensure it’s active, and then click ‘Style options’ to customize further.
- Theme Selection: Map Viewer offers various themes for symbol styling, each designed to convey data stories in slightly different ways. Explore the available themes to find one that best suits your data and the message you want to communicate. These themes adjust how size is applied to the data ranges, offering different visual emphasis.
- Data Normalization: If your raw data is not already normalized or standardized, consider using the ‘Divided by’ dropdown menu to transform raw counts into rates or percentages. Normalization is crucial when comparing data across areas of different sizes. For instance, population density (population per square kilometer) is a normalized measure, while raw population count is not. Standardizing your data ensures fair comparisons on the map.
- Symbol Customization: Click on the symbol displayed under ‘Symbol style’ to delve into detailed symbol customization. Here, you can adjust the color, stroke, and opacity of your proportional symbols. Align these symbol properties with your map’s overall design and the data narrative.
- Inverting Size Ramp: The ‘Invert size ramp’ option allows you to reverse the default size order. Typically, larger values are represented by larger symbols. Inverting this can sometimes be useful for specific data stories where smaller symbols for larger values might be more intuitive.
- Histogram and Data Distribution: The histogram is a vital tool for understanding your data’s distribution. Adjust the bounding handles along the histogram to fine-tune how symbol sizes are applied to your data ranges. By manipulating these handles, you can emphasize certain data ranges and refine the map’s message. Experiment with handle positions, using the histogram and calculated average to guide your adjustments.
- Size Range Adjustment: Define the ‘Size range’ to control the minimum and maximum symbol sizes in pixels. You can use the default range or specify a custom range to better fit your map’s scale and visual clarity. Keeping ‘Adjust size automatically’ checked is recommended for responsive symbol scaling across different zoom levels.
- Polygon Styling (if applicable): If you are mapping polygon data, you can further customize the polygon’s fill and outline properties within the ‘Symbol style’ options. The ‘Show background symbol’ toggle controls whether the underlying polygon shapes are visible beneath the proportional symbols. You can customize the background polygon symbol style (e.g., transparency, outline color) or hide it entirely if it distracts from the proportional symbols.
- Handling Missing Data: Use the ‘Show features with no value’ toggle to decide whether to display locations with missing data. If you choose to display them, you can define a specific symbol style and label to represent these missing values, ensuring data completeness on your map.
- Legend Control: The ‘Include in legend’ toggle allows you to control whether the size ramp is included in the map legend. You might choose to hide it for thematic maps where the size comparison is visually obvious and a legend might be redundant.
- Data Classification: The ‘Classify data’ option allows you to categorize your data into classes, simplifying the visual representation. Choose a classification method and the number of classes to generalize the map. This can be helpful for making broader comparisons and reducing visual complexity.
- Transparency by Attribute: Enhance your map by applying transparency based on an attribute using ‘Transparency by attribute’. This allows you to visually layer another dimension of data onto your size-based symbols.
- Symbol Rotation (Point Symbols): For point symbols, ‘Rotation by attribute’ lets you rotate symbols based on a second numeric attribute. This adds another layer of information to your map. For example, symbol size could represent population, while rotation could represent population growth rate.
After customizing the style options, click ‘Done’ to apply the styling to your map or ‘Cancel’ to discard changes.
Exploring Other Map Compare Size Styles
While ‘Counts and Amounts (size)’ is the primary style for direct size comparison, Map Viewer offers other styles that incorporate size as a visual variable to enhance data interpretation and comparison.
Color and Size: A Dual-Variable Approach
The ‘Color and Size’ style allows you to represent two numeric attributes simultaneously, using color to encode one attribute and symbol size to encode the other. This style is powerful for showing relationships between two different, but potentially related, numeric datasets. For instance, you could map population density (color) and total population (size). This combination allows viewers to compare areas based on both concentration and overall magnitude.
When styling a single attribute with ‘Color and Size’, you can also use it to highlight data points above or below a certain threshold using different colors and proportional symbol pairs, making comparisons against a benchmark visually striking.
Charts and Size: Comparing Proportions and Totals
For categorical data, the ‘Charts’ and ‘Charts and Size’ styles are invaluable. ‘Charts’ uses pie or donut charts to represent the proportions of different categories within each feature. ‘Charts and Size’ extends this by additionally sizing the chart symbol proportionally to the sum of all categories for each feature. This allows for comparison not only of the category proportions within each location but also the overall magnitude across locations. For example, in visualizing sales data for different product categories across regions, ‘Charts and Size’ would allow you to compare the proportion of each product’s sales within each region (using chart slices) and compare the total sales volume across regions (using chart size).
Relationship and Size: Unveiling Multivariate Relationships
The ‘Relationship and Size’ style is designed for advanced multivariate data exploration. It uses color to display the relationship between two numeric attributes (similar to the ‘Relationship’ style) and then uses symbol size to represent a third numeric attribute. This style is highly effective for uncovering complex relationships within your data. For example, you could visualize the relationship between income and education level (using color gradients) and simultaneously represent population size (using symbol size), allowing for a rich, multi-layered data comparison on a single map.
Best Practices for Effective Map Compare Size Visualizations
To maximize the effectiveness of “map compare size” visualizations, consider these best practices:
- Clarity and Simplicity: Ensure your symbol sizes are easily distinguishable and avoid overcrowding the map with too many symbols, which can obscure the underlying geographic information and make comparisons difficult.
- Appropriate Data Normalization: Always consider normalizing your data when comparing counts across different sized geographic units. Using rates or percentages instead of raw counts often leads to more meaningful and accurate comparisons.
- Thoughtful Symbol Design: Choose symbol colors, shapes, and sizes that are visually harmonious and support your data story. Ensure sufficient contrast between symbol sizes and map background for readability.
- Effective Legend Use: A clear and informative legend is crucial. Ensure your legend accurately represents the size ramp and data ranges. However, for very intuitive size-based maps, consider whether a legend is truly necessary, prioritizing visual clarity.
- Contextual Information: Provide contextual information through map titles, descriptions, and labels to guide viewers’ interpretation of the size comparisons being presented.
- User Interaction: Leverage Map Viewer’s interactive capabilities. Allow users to zoom, pan, and query features to explore the size-based data comparisons in detail. Pop-ups can provide exact values and further context when users click on symbols.
Conclusion
“Map compare size” is a fundamental and powerful technique for data visualization within Map Viewer. By effectively utilizing styles like ‘Counts and Amounts (size)’, ‘Color and Size’, ‘Charts and Size’, and ‘Relationship and Size’, you can create maps that not only display data but also facilitate immediate visual comparisons and deeper data insights. Mastering these styles allows you to transform raw numeric data into compelling visual narratives, enhancing understanding and communication through the intuitive language of map symbology. Whether you are analyzing traffic patterns, population distributions, sales figures, or complex multivariate relationships, leveraging symbol size in Map Viewer is a key strategy for effective data-driven storytelling.
