Choropleth Mapping with the Quartile Method

Choropleth maps are powerful tools for visually representing geographic data variations. Among the different classification methods, the quartile method stands out for its ability to reveal patterns and outliers in a nuanced way. Let's embark on a cartographic journey to understand this method!

Imagine a vast landscape of data points:

• Each point represents a geographic area (like a country, state, or county) with a corresponding data value (e.g., income, literacy rate, crime rate).
• Our goal is to create a map that divides these data points into meaningful groups based on their values.

Enter the quartiles:

• The quartile method slices the data distribution into four equal parts:
  • Q1 (First Quartile): Represents the 25% of data points with the lowest values.
  • Q2 (Second Quartile): Also known as the median, it marks the middle 50% of data points.
  • Q3 (Third Quartile): Encompasses the 25% of data points with the highest values.
• Each quartile threshold becomes a boundary for classifying the data points.

Painting the map with colors:

• Each geographic area is assigned a color based on its quartile classification.
• For instance, areas falling in Q1 might be colored blue, Q2 green, Q3 yellow, and the top 25% (outliers exceeding Q3) red.
• This color scheme creates a visual hierarchy, highlighting areas with relatively low, medium, high, and exceptionally high data values.

Benefits of the quartile method:

• Reveals natural breaks in the data: Unlike equal interval methods, which can be sensitive to outliers, the quartile method adapts to the data's inherent distribution.
• Highlights patterns and outliers: Areas with similar values are grouped together, making it easier to spot spatial trends. Outliers stand out due to their distinct color, prompting further investigation.
• Useful for skewed data: If the data distribution is skewed (e.g., income often follows a right-skewed distribution), the quartile method ensures a balanced representation with equal portions in each quartile.

A visual example:

• Imagine a choropleth map of income levels across countries. Using the quartile method, low-income countries might be shown in blue, middle-income in green, and high-income in yellow. A few exceptionally wealthy nations might stand out in red.
• This map instantly reveals which countries fall into each income bracket, allowing for easy comparison and identification of potential economic clusters or outliers.

Remember:

• The quartile method is not a one-size-fits-all solution. Consider the nature of your data and the message you want to convey when choosing a classification method.
• Tools like GIS software and online mapping platforms often offer built-in functionalities for creating choropleth maps with different classification methods, including the quartile method.

Steps involved in creating a choropleth map using the quartile method:

1. Gather and prepare your data:

• Ensure your data points have accurate geographic locations (e.g., latitude and longitude coordinates or administrative boundaries) and corresponding data values for the chosen variable.
• Clean and organize your data, ensuring consistency and removing any errors or outliers that might skew the analysis.

2. Calculate the quartiles:

• Sort your data values in ascending order.
• Find the positions of the quartiles:
  • Q1: Divide the total number of data points by 4 and round down to the nearest integer.
  • Q2 (Median): Divide the total number of data points by 2 and round down to the nearest integer.
  • Q3: Add the number of data points in Q1 and Q2 to 1 and round down to the nearest integer.

3. Classify data points:

• Assign each data point to a quartile based on its value:
  • Data points with values less than or equal to the value at Q1 position belong to Q1.
  • Data points with values between Q1 and Q2 (excluding value at Q2) belong to Q2.
  • Data points with values between Q2 and Q3 (excluding value at Q3) belong to Q3.
  • Data points with values greater than the value at Q3 position belong to the outlier category.

4. Choose your color scheme:

• Select a color palette that effectively distinguishes the quartiles and outliers. Consider using a sequential color scheme for the quartiles (e.g., blue to green to yellow) and a distinct color for outliers (e.g., red).
• Ensure the color scheme is accessible and appropriate for your audience.

5. Create the map:

• Use your chosen mapping software or platform to create a base map with your geographic areas (e.g., countries, states, counties).
• Join the data points with their corresponding geographic areas.
• Apply the chosen color scheme to each area based on its quartile classification.
• Include a legend explaining the color scheme and data classification.

6. Analyze and interpret the map:

• Look for spatial patterns and trends in the distribution of data across the map.
• Identify areas with high, low, or outlier values and investigate potential underlying factors.
• Use the map to communicate your findings and insights to your audience.

Additional tips:

• Consider the purpose of your map and choose the number of quartiles accordingly. For more detailed analysis, you can use more quartiles (e.g., quintiles or octiles).
• Pay attention to scale and map projections to ensure accurate representation of spatial relationships.
• Add title, labels, and other annotations for clarity and context.

By following these steps, you can create informative and visually compelling choropleth maps using the quartile method, revealing valuable insights from your geographic data.

Reference:

References for Choropleth Mapping with the Quartile Method:

Explanation:

• "Choropleth Maps - A Guide to Data Classification" by GIS Geography: This article provides a clear and concise explanation of different classification methods for choropleth maps, including the quartile method. It highlights the advantages and applications of this method. https://storymaps.arcgis.com/stories/871fe556c40b4d40b7a465c6f135ac88
• "Creating and using a choropleth map—ArcGIS Insights" by Esri Documentation: This guide offers a step-by-step explanation for creating choropleth maps with various classification methods, including using the quartile method within ArcGIS Insights. https://visme.co/blog/how-to-make-a-choropleth-map/
• "Directions" article by Michael P. Peterson: This article discusses the use of quantile classification in choropleth maps within the context of exploratory data analysis and emphasizes its effectiveness in revealing patterns and outliers. https://www.directionsmag.com/article/3363