Unlocking the Power of Spatial Data: Building Maps with Multiple Layers
Choropleth maps are a valuable tool for understanding geographic regions and the patterns of data that exist within them. This is particularly relevant in the field of international development, where researchers and policymakers often need to understand the distribution of resources, poverty, or development indicators across different countries or regions. Choropleth maps allow us to visualize this data and identify areas that may require more attention or resources. For example, a choropleth map of literacy rates across different regions in a developing country can help policymakers identify areas with low literacy rates and allocate resources to improve access to education. Similarly, a choropleth map of access to healthcare facilities can help identify areas where healthcare infrastructure needs to be strengthened. By using choropleth maps, we can better understand the unique challenges faced by different regions and develop more targeted and effective strategies to address them.
So how do we use these kinds of maps to learn and make decisions, and what do we do if we have multiple layers of information that we want to understand?
Univariate Maps: Understanding a Single Variable
A univariate map displays data related to a single variable or feature. This means that the map shows the distribution, density, or magnitude of one variable across a geographic area. Univariate maps are useful for identifying spatial patterns and variations in a single variable, such as population density, income, or temperature. They are often displayed using a range of colors or shading to represent the values of the variable, with darker colors indicating higher values and lighter colors indicating lower values.
Bivariate Maps: Understanding the intersection between two variables
In contrast, a bivariate map displays data related to two variables or features on the same map. This means that the map shows how the distribution, density, or magnitude of one variable relates to the other variable across a geographic area. Bivariate maps are useful for exploring the relationship between two variables, such as crime rates and poverty rates, or temperature and precipitation. They are often displayed using two different color schemes or shading patterns to represent the values of both variables, with different colors indicating different levels or ranges of values for each variable.
But what if we want to add more layers to a map? How do we see the difference between multiple factors in an interpretable way? That’s when we may turn to the development of a weighted index.
Weighted Indices: Analyzing Multiple Layers
Creating a weighted index that can be turned into a choropleth map involves a few key steps. First, we need to select the variables that are relevant to the topic or research question we are exploring. For example, if we are interested in creating an index to measure economic well-being, we may choose variables such as median income, unemployment rate, poverty rate, and educational attainment.
Next, we need to determine the weight or importance of each variable in the index. This involves assigning a score to each variable that reflects its relative importance in measuring the overall concept we are interested in. For example, we may decide that median income should be weighted more heavily than educational attainment because it has a greater impact on economic well-being.
Once we have determined the weights for each variable, we can calculate the weighted index by multiplying each variable score by its weight and summing the results. This will give us a single index score for each geographic area, such as a county or state
Finally, we can turn the weighted index into a choropleth map by assigning a color or shading to each area based on its index score. This will allow us to visually represent the distribution of the index across the geographic area of interest. By following these steps, we can create a powerful tool for understanding and communicating complex data in an easy way to interpret and understand.
