Choropleth Map

A choropleth map displays geographic regions colored according to a numerical variable. Each region (country, state, county, etc.) is shaded with a color intensity that represents the magnitude of a value, making geographic patterns and distributions immediately visible. Choropleth maps are powerful for showing how a measurement varies across geographic areas and identifying regional trends, hotspots, and disparities.

Best used for:

• Visualizing geographic distribution of metrics
• Comparing values across regions or countries
• Identifying geographic patterns and trends
• Showing population, economic, or demographic data by area
• Heat mapping regional performance or characteristics
• Communicating location-based insights

Common Use Cases

Business & Sales

• Sales performance by region or territory
• Market penetration and coverage analysis
• Customer density and distribution
• Store or branch performance by location
• Revenue or profit by geographic area
• Competitive presence mapping

Demographics & Population

• Population density by region
• Age distribution across areas
• Income levels by geography
• Education attainment by location
• Health metrics by region
• Migration and movement patterns

Government & Policy

• Election results by district
• Public service distribution
• Economic indicators by state
• Policy impact by region
• Resource allocation mapping
• Infrastructure coverage

Research & Analysis

• Disease prevalence by area
• Environmental data (pollution, temperature)
• Crime rates by location
• Access to services (healthcare, education)
• Agricultural production by region
• Risk assessment mapping

Options

Location/Region

Required - Column containing location identifiers.

Each value should correspond to a recognizable geographic region (e.g., country names, state codes, county names). Values must match standard geographic identifiers for proper mapping.

Examples: "California", "CA", "United States", "US", "06" (FIPS code)

Value

Required - Metric to visualize on the map.

Column

Select the numerical column containing the values to map. This determines the color intensity of each region.

Aggregation Function

Choose how to aggregate values if multiple rows per location:

Options:

• Sum - Total values for location
• Mean - Average value for location
• Count - Number of records per location
• Median - Middle value for location
• Min - Minimum value for location
• Max - Maximum value for location

Settings

Hide Empty Values

Optional - Exclude regions with no data.

When enabled, regions with no data are shown in a neutral color or left uncolored, rather than being treated as zero.

Map Projection

Optional - How the 3D Earth is projected onto 2D.

Different projections emphasize different geographic properties. Choose based on the area you're visualizing and what properties (shape, area, distance) are most important.

Options:

• Natural Earth - Balanced projection, good for world maps (default)
• Equirectangular - Simple projection, preserves latitude/longitude grid
• Mercator - Preserves angles, useful for navigation, distorts polar areas
• Orthographic - Globe view, shows one hemisphere

Understanding Choropleth Maps

Color Intensity

• Light colors: Low values
• Dark colors: High values
• Neutral/Gray: No data (if enabled)
• Color gradient: Smooth transition shows value ranges

Geographic Accuracy

• Region boundaries: Must match data identifiers
• Resolution: Depends on geographic level (country, state, county)
• Data matching: Location names must be standardized
• Missing regions: Indicate no data or matching failure

Data Representation

• Raw values: Absolute numbers (population, revenue)
• Rates/Ratios: Per capita, percentages, densities
• Normalized: Adjusted for population or area
• Categorical: Can also use discrete categories

Tips for Effective Choropleth Maps

1. Location Data Matching:

   • Use standard geographic identifiers (ISO codes, FIPS codes, full names)
   • Be consistent with naming (e.g., "USA" vs "United States" vs "US")
   • Clean data to match expected formats
   • Test with small dataset to verify matching
   • Use two-letter state codes for US states (e.g., "CA", "NY")

2. Data Normalization:

   • Raw counts: Misleading if regions vary in size or population
   • Per capita: Normalize by population for fair comparison
   • Density: Normalize by area (per sq km/mile)
   • Rates/Percentages: Better for comparing regions
   • Example: Use "cases per 100k population" not "total cases"

3. Color Scale Selection:

   • Sequential (Blues, Reds): For continuous data (low to high)
   • Diverging (Red-White-Blue): For data with meaningful midpoint
   • Categorical: For discrete categories
   • Ensure accessibility (colorblind-friendly)
   • Match colors to context (green = good, red = bad)

4. Map Projection:

   • World maps: Natural Earth or Equirectangular
   • Continental: Mercator or Natural Earth
   • Polar regions: Orthographic or specialized polar projection
   • Navigation: Mercator preserves angles
   • Consider audience familiarity with projection

5. Data Quality:

   • Handle missing data explicitly
   • Remove outliers if they compress the scale
   • Ensure complete coverage of relevant regions
   • Verify data accuracy before visualizing

6. Visual Clarity:

   • Include legend showing color scale
   • Add tooltips with exact values
   • Show region boundaries clearly
   • Consider zooming to region of interest
   • Provide context (title, units, timeframe)

Common Pitfalls and Solutions

Pitfall 1: Population Bias

Problem: Large population regions dominate in raw counts. Solution: Normalize by population (per capita) or area (density).

Pitfall 2: Missing or Mismatched Locations

Problem: Regions appear gray or missing. Solution: Standardize location names, use official codes (ISO, FIPS), check spelling.

Pitfall 3: Outliers Compress Scale

Problem: One extreme value makes other regions indistinguishable. Solution: Remove outliers, use logarithmic scale, or use quantile/binned color scales.

Pitfall 4: Inappropriate Color Scale

Problem: Colors don't intuitively match values. Solution: Use sequential for continuous, diverging for positive/negative, ensure low=light and high=dark.

Pitfall 5: Projection Distortion

Problem: Areas appear wrong size (e.g., Greenland looks huge). Solution: Choose projection appropriate for your region, use equal-area projections when size matters.

Example Scenarios

Sales Performance by State

US states colored by total sales volume (normalized by state GDP).

Population Density by Country

World map showing population per square kilometer.

Election Results by District

Voting results with diverging color scale (red vs blue).

COVID-19 Cases by Region

Cases per 100,000 population across regions over time.

GDP per Capita by Country

Economic indicator showing wealth distribution globally.

Interpreting Choropleth Maps

Reading the Map

1. Check the legend: Understand value range and color mapping
2. Look for patterns: Clusters, gradients, isolated regions
3. Compare regions: Side-by-side visual comparison
4. Note missing data: Gray or neutral regions
5. Consider normalization: Check if values are raw or per capita

Key Questions Answered

• Which regions have highest/lowest values?
• Are there geographic clusters or patterns?
• How does my region compare to others?
• Where are the hotspots or problem areas?
• Is there geographic disparity or uniformity?

Common Patterns

• Clusters: Adjacent regions with similar values
• Gradients: Smooth transitions across space
• Hotspots: Isolated high-value regions
• Core-periphery: High values in center, low on edges
• Regional disparities: Uneven distribution

Troubleshooting

Issue: Regions are not colored (showing gray)

• Solution: Check location names match standard identifiers, verify spelling, use ISO or FIPS codes, ensure location column is correctly assigned.

Issue: All regions are same or similar color

• Solution: Check value range (may be too narrow), remove outliers compressing scale, verify values are numeric, adjust color scale range.

Issue: Map shows wrong geographic area

• Solution: Verify location identifiers are correct level (country vs state vs county), check for mixed identifiers, use consistent naming.

Issue: Colors don't make intuitive sense

• Solution: Change color scale to match data type (sequential, diverging, categorical), reverse color scale if needed, use conventional colors (green=good, red=bad).

Issue: Can't see small regions

• Solution: Zoom to region of interest, use larger map size, consider alternative visualization for very small regions, add tooltips for details.

Issue: Projection distorts shapes

• Solution: Change projection to better fit your geographic area, use equal-area projection if size comparison is important, use orthographic for globe view.

Issue: Missing values treated as zero

• Solution: Enable "Hide Empty Values" to show missing data distinctly, clean data to explicitly mark nulls, use separate color for no-data regions.

Issue: Border boundaries are unclear

• Solution: Adjust border width and color in advanced settings, use contrasting colors, simplify map resolution if too detailed.