Tile Grid Map
A map where every region is represented by an equally sized tile arranged in a grid — ensuring that small states get the same visual weight as large ones.
// 01 — The chart
What it looks like
A tile grid map of U.S. states showing voter turnout. Each state gets an equal-sized tile, preventing Alaska and Texas from dominating the visual.
// 02 — Definition
What is a tile grid map?
A tile grid map replaces the complex geographic shapes of a traditional map with equally sized tiles — usually squares or hexagons — arranged in a grid that roughly approximates real-world geography. Each tile represents one region (a state, province, or country) and gets the same visual weight regardless of its actual land area.
This solves a fundamental problem with standard maps: large-area regions (like Alaska or Western Australia) dominate the visual even if they have small populations, while small-area regions (like Rhode Island or Singapore) are nearly invisible. In a tile grid map, every region is equally readable.
Tile grid maps are especially popular in data journalism and political coverage. They’re ideal for showing per-state or per-country metrics where each unit of geography is equally important to the story, such as election results, policy comparisons, or demographic snapshots.
Origin: Tile grid maps gained mainstream popularity around 2015 when NPR and The Washington Post used them for U.S. election coverage. The technique draws from earlier ideas in cartography about equal-area representations and was formalised by data visualisation practitioners seeking alternatives to choropleth maps.
// 03 — Anatomy
Parts of a tile grid map
// 04 — Usage
When to use it — and when not to
- Every region should have equal visual weight (elections, policy comparisons)
- Small regions would be invisible on a standard map (Rhode Island, Luxembourg)
- You want to show a categorical or per-capita variable, not an absolute count
- Your audience knows the approximate geography and can locate regions by position
- You need to embed small charts or sparklines inside each tile (small multiples)
- Clean, modern editorial or dashboard aesthetics are important
- Geographic area itself is the variable of interest (use a cartogram instead)
- Precise spatial relationships or borders matter to the analysis
- Your audience is unfamiliar with the region and cannot orient from a grid
- You have too many regions (>60) — the grid becomes hard to arrange meaningfully
- Continuous spatial patterns (like weather or terrain) need to be shown
- You need to overlay point data, routes, or other spatial features
// 05 — Reading guide
How to read a tile grid map
Follow these steps to interpret any tile grid map you encounter.
Read the title and legend
Understand what variable is being shown and how it maps to colour (or icons, or embedded charts). Is it a rate, a category, an election result, or something else?
Orient yourself on the grid
Find a few anchor regions you know well. In a U.S. tile grid, start with Texas in the south-centre, California on the left, and Florida in the south-east. This anchors your spatial reading.
Scan for colour patterns
Look for clusters of similar colours (regional trends), isolated standouts (outliers), or gradients from one side of the map to another (geographic correlations).
Check individual tiles
Use the label inside each tile to identify specific regions. Compare a tile’s colour to the legend to read its value. Look for the darkest and lightest tiles.
Remember equal weight
Unlike a standard map, no tile is bigger than another. Every region gets the same visual importance, so your eye naturally gives equal attention to small and large geographic areas.
// 06 — Common mistakes
Mistakes to watch out for
Poor grid arrangement
If the tile layout doesn’t approximate real geography well, readers cannot orient themselves. A tile grid map only works because people use spatial memory to locate regions. Test your layout with real users to confirm they can find key regions quickly.
Showing absolute counts instead of rates
Because every tile is the same size, showing absolute numbers (like total population) is misleading — California’s tile is the same size as Wyoming’s. Use per-capita rates, percentages, or categories to match the equal-area visual treatment.
Too many colours or categories
Tile grid maps work best with a small number of colour classes (2–5 for sequential data, 2–3 for categorical). More classes make individual tiles hard to classify by eye.
Missing labels
Without abbreviation labels inside tiles, readers must rely entirely on position to identify regions. Always include short text labels, especially for less well-known geographies.
Inconsistent tile spacing
Uneven gaps between tiles create the false impression of groupings or clusters. Keep gutters uniform so the grid reads as a neutral layout rather than implying spatial relationships that don’t exist.
// 07 — Real-world examples
Where you’ll see tile grid maps used
Journalism: U.S. election results by state
News outlets like NPR and Bloomberg use tile grid maps to show which party won each state. The equal-size tiles prevent Alaska from visually overwhelming Rhode Island, giving each state’s result equal prominence.
Data JournalismPublic policy: Healthcare access by state
A policy institute maps whether each state has expanded Medicaid, with a simple two-colour scheme. The tile grid ensures that the count of states in each category is visually obvious without area distortion.
Public PolicyBusiness: Regional sales performance dashboard
A retail company uses a tile grid of sales regions, each containing a sparkline showing monthly revenue trends. The equal tile sizes let the viewer compare sparkline shapes across all regions simultaneously.
Business Analytics// 08 — At a glance
Quick reference
// 09 — Variations
Types of tile grid maps
The tile grid concept has several useful variations depending on tile shape and content.
Square tile grid
The most common variant. Squares pack neatly and are easy to label. Used widely for U.S. state maps.
Hexagonal tile grid
Hexagons have six neighbours instead of four, creating a more organic-looking layout with better adjacency.
Bivariate tile grid
Uses a two-dimensional colour scale to encode two variables simultaneously within each tile.
Tile grid with sparklines
Each tile contains a small inline chart (sparkline, bar, or icon) to show trends or multi-value data per region.
// 10 — FAQs
Frequently asked questions
What is a tile grid map?+
A tile grid map replaces the complex geographic shapes of a traditional map with equally sized tiles — usually squares or hexagons — arranged in a grid that roughly approximates real-world geography. Each tile represents one region (a state, province, or country) and gets the same visual weight regardless of its actual land area.
When should you use a tile grid map?+
Use a tile grid map when every region should have equal visual weight (elections, policy comparisons). It also works well when small regions would be invisible on a standard map (Rhode Island, Luxembourg), and when you want to show a categorical or per-capita variable, not an absolute count.
When should you avoid a tile grid map?+
Avoid a tile grid map when geographic area itself is the variable of interest (use a cartogram instead). It is also a poor fit when precise spatial relationships or borders matter to the analysis, or when your audience is unfamiliar with the region and cannot orient from a grid.
Is a tile grid map suitable for dashboards?+
Yes — a tile grid map can work well in dashboards as long as the panel is large enough for readers to perceive the encoded values, has a clear title, and includes the legend or axis labels needed to interpret it.
What category of chart is a tile grid map?+
Tile Grid Map belongs to the Geospatial family of charts. Charts in that family are designed to answer the same kind of question, so they often work as alternatives when one doesn't quite fit your data.
How do you read a tile grid map?+
Start with the axis labels and legend, then look at the overall shape before zooming into individual marks. Compare prominent features against the rest of the data, and verify any conclusion against the underlying numbers when precision matters.