Introduction to Geographic Data Mapping

Data maps merge geographic data, like maps, with extra layers of information. This helps visualize patterns otherwise buried in spreadsheets. For instance, maps showing religious or economic trends highlight historical influences and socio-economic factors. They are valuable in public health, business analytics, and climate science, guiding data-driven decisions where geography plays a crucial role.

A choropleth map visualizes data by shading geographic areas, like countries or states, using a color scale. It effectively shows relative rates or percentages, and is an intuitive way to compare data across regions. However, it may misrepresent data if granularity is large or if extreme values skew color scales. Darker shades usually represent ‘more’. It’s important to use sequential/diverging color schemes for accurately representing scaled data and avoid categorical color scales, which have no inherent order.

Dot density maps use dots to represent values across regions, illustrating both distribution and concentration. While each dot might represent a single data point or a fixed quantity, their placement is generally random within a geographic area. These maps work best at suitable scales and require equal area projections for accuracy.

Cartograms are illustrative maps that distort area or spatial relationships to represent data like population or travel time. Unlike traditional maps aiming for geospatial accuracy, cartograms use distortion to visualize data patterns. There are two types:

• Map distortion cartograms show distorted geographic maps where the size of an area (often a country or state) corresponds to a data variable.
• Diagrammatic cartograms use schematic methods to organize geography by data relationships, such as travel times.

A spike map visualizes data using vertical spikes at specific locations. The height of each spike corresponds to the data’s magnitude at that point. This hybrid of a map and 3D bar chart is effective for highlighting extreme values that standard maps might overlook—especially when comparing urban centers to rural areas. Spike maps do a good job at resolving geographic overlap in congested areas, but can also obscure smaller values and require specifically formatted data.

Story maps transform data storytelling by integrating maps, text, images, and visualizations, all within a single medium. They excel at making complex geographical data accessible, guiding the viewer through a structured narrative with immersive geographic arguments. Typically used in public outreach and education, story maps create engaging experiences that enhance understanding of spatial data. Ideal for illustrating trends, changes, or proposals with geographic implications, they tell multimedia stories with intentional pacing.

Geographic data on maps can be broken down into three main forms: points, lines, and polygons. This classification allows complex geographies to be digitally represented.

• Points: Zero-dimensional, defined by latitude and longitude (e.g., ATM locations)
• Lines: One-dimensional features, connecting multiple points (e.g., roads)
• Polygons: Two-dimensional enclosed areas (e.g., administrative boundaries)

Non-geospatial data, like income level or religion, can be incorporated into maps by connecting it to geographic features. This can only happen when there’s a field in the data that matches the geography on the map, like postal codes or county names. Identifiers must match the geographic units on the map to successfully join data.