What are the best types of maps for GIS data analysis and visualization?

1 Choropleth maps

Choropleth maps are one of the most popular types of maps for GIS data analysis and visualization. They show how a variable, such as population, income, or crime, varies across geographic areas, such as countries, states, or counties. Choropleth maps use colors or shades to represent the values of the variable, creating a visual contrast between areas. Choropleth maps are useful for comparing and summarizing spatial patterns, trends, and distributions of data. However, they also have some limitations, such as the choice of color scheme, the classification method, the size and shape of the areas, and the influence of outliers.

2 Dot density maps

Dot density maps are another common type of map for GIS data analysis and visualization. They show the density or frequency of a phenomenon, such as population, events, or occurrences, by placing dots or symbols on the map. Each dot or symbol represents a fixed number or proportion of the phenomenon, and the more dots or symbols there are in an area, the higher the density or frequency. Dot density maps are useful for showing the spatial distribution and concentration of data, especially when there are large variations or gaps in the data. However, they also have some challenges, such as the choice of dot size, color, and placement, the resolution and accuracy of the data, and the interpretation and perception of the dots.

3 Heat maps

Heat maps are a type of map for GIS data analysis and visualization that use colors or gradients to show the intensity or magnitude of a variable, such as temperature, precipitation, or traffic. Heat maps are similar to choropleth maps, but they do not rely on predefined geographic areas or boundaries. Instead, they use interpolation or smoothing techniques to create a continuous surface of values, creating a smooth transition between colors. Heat maps are useful for showing the spatial variation and range of data, especially when there are continuous or dynamic data. However, they also have some drawbacks, such as the choice of color scheme, the interpolation or smoothing method, the scale and extent of the map, and the representation and communication of uncertainty.

4 Point maps

Point maps are a simple but effective type of map for GIS data analysis and visualization. They show the location and attributes of individual features or entities, such as cities, landmarks, or customers, by placing points or markers on the map. Each point or marker can have different shapes, sizes, colors, or labels to represent different characteristics or categories of the features or entities. Point maps are useful for showing the spatial distribution and diversity of data, especially when there are discrete or specific data. However, they also have some issues, such as the choice of point style, color, and label, the clarity and readability of the map, and the potential for overlap and clutter.

5 Flow maps

Flow maps are a special type of map for GIS data analysis and visualization. They show the movement or direction of a phenomenon, such as migration, trade, or transportation, by using lines or arrows on the map. Each line or arrow can have different widths, lengths, colors, or labels to represent different quantities or qualities of the phenomenon. Flow maps are useful for showing the spatial dynamics and relationships of data, especially when there are linear or network data. However, they also have some difficulties, such as the choice of line style, color, and label, the accuracy and validity of the data, and the complexity and simplicity of the map.

6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?