地図 on Visualizing.JP

Gridded Cartogram

Tue, 31 Mar 2026 00:00:00 +0900

A gridded cartogram represents each region as an equally sized grid cell, such as a square or hexagon, while roughly preserving geographic arrangement. It is also known as a mosaic cartogram, tile grid map, or grid map. Because every region receives the same visual area, small regions are not visually suppressed by large geographic areas.

Historical Background

The gridded cartogram is not tied to a single inventor or paper, but it became especially visible in data journalism and web visualization during the 2010s.

In the United States, tile grid maps representing the 50 states as equal squares became common in election coverage by outlets such as FiveThirtyEight, NPR, and Bloomberg. They helped address a familiar problem: large states such as Alaska, Montana, and Texas dominate ordinary maps, while smaller but often politically important states on the East Coast can nearly disappear.

Similar designs have been used for EU member states, and in Japan for the 47 prefectures. Recent research has also explored algorithms that automatically optimize grid layouts while preserving geographic relationships as much as possible.

Data Structure

The required data is simple compared with many other cartogram types.

Data	Description	Example
Region identifier	Code or name for each region	State code, prefecture code
Grid coordinate	Row and column position	`(row, col)`
Statistical value	Value encoded by color	Population density, turnout, infection rate
Category	Optional value for color grouping	Party, region group
Label	Text shown inside the cell	CA, NY, Tokyo, Osaka

Boundary polygons are not required. The core input is a layout table that assigns each region to a grid position. This layout is often designed manually, although automatic layout methods also exist.

Purpose

The main purpose is to remove the visual bias caused by geographic area. On an ordinary map or choropleth, large territories dominate perception and small territories are easy to miss. A gridded cartogram solves this by giving each region one equal cell. Data values are then represented primarily through color, much like a choropleth map.

Use Cases

State-level election results in the United States
Prefecture-level statistics in Japan
Policy comparisons across EU member states
Country-level indicators such as vaccination rates or education levels
Small multiples showing regional change over time
Sports league comparisons arranged by home location

Characteristics

Every region has equal visual weight.
Geographic arrangement is approximate, not exact.
Data cannot be encoded by area because all cells have the same size.
The technique is easy to implement on the web with tables, CSS Grid, or SVG.
Designing a readable layout can be time-consuming when there are many regions.

How to Read It

Each cell represents one region. Since the cells are equal in size, do not interpret area as magnitude. Read values through color and use the legend to understand the scale or category.

The layout usually preserves a rough north-south and east-west relationship, but adjacency in the grid does not necessarily mean actual geographic adjacency. Labels are therefore important for identifying each region.

Design Notes

Design the layout so readers can infer approximate location.
Add clear labels because shape no longer identifies the region.
Use accessible color schemes, such as ColorBrewer palettes.
Choose squares or hexagons according to the number of regions and layout constraints.
Avoid overloading cells with too many small marks.
Explain the layout logic when the arrangement may not be obvious.

Alternatives

Continuous cartogram: Distorts area in proportion to data while maintaining topology.
Non-contiguous cartogram: Scales each region independently while preserving shape.
Pseudo-continuous cartogram: Replaces regions with circles, squares, or other geometric marks sized by data.
Choropleth map: Uses true geographic shapes and encodes values by color, but area bias remains.

Summary

Gridded cartograms are effective when the goal is to compare regions fairly without letting geographic size dominate perception. They are especially useful in journalism and interactive visualization because they are simple, compact, and easy to scan, provided that the layout and labels are carefully designed.

References

`1`	`- [Cartogram - Wikipedia](https://en.wikipedia.org/wiki/Cartogram)`

Pseudo-Continuous Cartogram

Mon, 30 Mar 2026 00:00:00 +0900

A pseudo-continuous cartogram replaces geographic regions with geometric shapes such as circles, squares, or hexagons, and sizes those shapes in proportion to a data variable. Dorling cartograms and Demers cartograms are representative examples. By discarding the exact shape of each region, the method makes quantitative comparison easier while still roughly preserving geographic position.

Historical Background

The best-known pseudo-continuous cartogram is the Dorling cartogram, proposed by British geographer Daniel Dorling in 1996. Dorling addressed the limitations of continuous cartograms, which distort shapes, and non-contiguous cartograms, which break adjacency, by taking a different route: replace each region with a simple circle.

In a Dorling cartogram, each region is represented by a circle whose area is proportional to the value. A force-directed layout adjusts positions so circles do not overlap while remaining as close as possible to their original geographic locations.

The Demers cartogram later introduced a square-based variant. Squares can produce a more orderly appearance and make labeling easier. Hexagonal variations have also appeared, expanding the range of possible geometric marks.

Data Structure

Data	Description	Example
Region identifier	Code or name for each region	ISO code, prefecture code
Centroid	Representative position used for initial placement	Latitude and longitude
Statistical value	Numeric value that determines area	Population, GDP, medal count
Category	Optional variable for color	Party, region, group

Boundary polygons are not strictly required. Centroids and values are enough to generate a basic version, although boundary information can improve layout quality.

Purpose

The purpose is to preserve geographic context while making quantitative comparison clearer. Complex regional shapes often make area comparison difficult. By replacing all regions with the same type of mark, such as a circle, the reader can compare size more directly.

This is also useful when boundaries are visually complicated or when the exact outline is less important than relative magnitude.

Use Cases

Olympic medal counts by country
Population or GDP comparisons by country
Election results by state or district
Infectious disease case counts by region
Industrial output by prefecture
Branch counts or sales by market area

Characteristics

Area can encode a quantitative value.
Geographic position is approximate but recognizable.
Original shapes and exact adjacencies are lost.
Overlap avoidance and layout algorithms strongly affect readability.
Circles support smooth comparison; squares can support tighter packing and labeling.

How to Read It

Read the size of each circle, square, or hexagon as the data value. Because humans often misread area, the legend should include reference sizes. Color may encode categories or another quantitative measure.

The position of each mark indicates approximate location, not exact geography. If two marks are near one another, that suggests a geographic relationship, but the final layout may be shifted to avoid overlap.

Design Notes

Scale mark area, not diameter, to the data value.
Include a size legend with meaningful reference values.
Keep labels readable and avoid excessive overlap.
Use restrained color so size remains legible.
Explain that the geography is schematic.

Alternatives

Continuous cartogram: Preserves topology but distorts the entire map.
Non-contiguous cartogram: Preserves region shape while scaling each region independently.
Gridded cartogram: Gives every region equal size and encodes values by color.
Proportional symbol map: Places scaled symbols on a normal map rather than replacing regions.

Summary

Pseudo-continuous cartograms are useful when relative magnitude matters more than exact geographic form. By replacing regions with comparable geometric marks, they make values easier to compare while retaining enough spatial arrangement for readers to recognize the geography.

Non-Continuous Cartogram

Sun, 29 Mar 2026 00:00:00 +0900

A non-continuous cartogram scales each region independently in proportion to a data variable while preserving the original shape of that region. Because each region is usually scaled around its centroid, gaps appear between regions and adjacency is not preserved. The advantage is that individual regions remain recognizable, unlike in many continuous cartograms where shapes are heavily distorted.

Historical Background

Non-continuous cartograms were proposed by Judy Olson in 1976 as an alternative to continuous cartograms. Olson focused on the difficulty readers face when regions are distorted so much that they are no longer recognizable. By scaling each region independently, the method makes calculation easier and the result more intuitive.

This approach made the trade-off explicit: lose topological continuity in exchange for shape fidelity and simpler interpretation.

Data Structure

Data	Role
Region geometry	Original polygon shape to be scaled
Region identifier	Key for joining geometry and data
Statistical value	Determines the scaled area
Centroid or anchor point	Center around which the region is scaled
Optional category	Used for color or grouping

Purpose

The purpose is to compare values through area while keeping the recognizability of each region. It is useful when the exact shape of a country, state, or prefecture matters, but geographic adjacency is less important.

Use Cases

Population or GDP by country
Election results by state or prefecture
Regional production or resource comparisons
Public health burden by administrative area

Characteristics

Region shapes are preserved.
Areas encode values.
Adjacency and shared borders are lost.
Gaps between regions are part of the design.
The method is easier to generate than many continuous cartograms.

Design Notes

Make clear which variable controls area.
Use labels because regions move apart visually.
Keep a reference outline or original map nearby if recognition is difficult.
Avoid using the map for distance or adjacency interpretation.

Summary

Non-continuous cartograms are effective when shape recognition and area comparison matter more than adjacency. They provide a simpler and often more readable alternative to continuous cartograms, but the reader must understand that the geography has been separated into independently scaled pieces.

Continuous Cartogram

Sat, 28 Mar 2026 00:00:00 +0900

A continuous cartogram distorts geographic areas in proportion to a data variable while preserving adjacency, or topology. Regions expand or shrink according to values such as population or GDP, producing a map that looks as if a rubber sheet has been stretched. Neighboring regions remain connected, and borders do not break apart.

Historical Background

Cartograms have a long history in thematic cartography, but continuous cartograms became more practical as computational methods improved. The core problem is difficult: change the area of each region to match a value while keeping the map connected and recognizable.

Different algorithms have been proposed to solve this trade-off. Some simulate physical diffusion or rubber-sheet deformation; others optimize geometry to balance area accuracy and shape preservation. The result is always a compromise between statistical accuracy and geographic recognizability.

Data Structure

Data	Description	Example
Boundary geometry	Polygon shapes for each region	Country or prefecture boundaries
Region identifier	Key linking geometry and data	ISO code, prefecture code
Statistical value	Variable used to determine area	Population, GDP, emissions
Optional attributes	Values used for color or labeling	Region group, rate, category

Unlike gridded or Dorling cartograms, a continuous cartogram requires polygon geometry because the shape of the map itself is transformed.

Purpose

The main purpose is to make a map’s visual area correspond to a meaningful data value rather than land area. This is useful when land area is misleading. A population cartogram, for example, enlarges densely populated regions and shrinks sparsely populated ones, making the visual map better match where people actually live.

Use Cases

Population cartograms
GDP or economic output by country
Election maps weighted by population or electorate
Disease burden by region
Carbon emissions and energy consumption
Trade or migration comparisons

Characteristics

Area can represent quantitative values directly.
Topological relationships are preserved.
Shapes can become distorted and unfamiliar.
Small regions may still be hard to label.
Algorithm choice affects both accuracy and readability.

How to Read It

Read the area of each region as the encoded value. A large-looking region is not necessarily geographically large; it has a large value in the chosen data variable.

Because the map remains connected, neighboring relationships are still meaningful. However, distances, angles, and shapes are no longer geographically accurate. Use the legend and title to confirm what variable controls the distortion.

Design Notes

State clearly which variable controls area.
Add labels or reference outlines when distortion makes regions hard to identify.
Avoid mixing area distortion with a confusing color scale.
Use color for a different but related variable only when the relationship is important.
Consider whether the audience can still recognize the geography after distortion.

Alternatives

Pseudo-continuous cartogram: Uses circles or squares sized by value.
Non-contiguous cartogram: Scales regions independently while preserving shape.
Gridded cartogram: Gives regions equal size and uses color for data.
Choropleth map: Preserves geographic shape but is affected by area bias.

Summary

Continuous cartograms are powerful when the map should show the geography of a phenomenon rather than the geography of land. They preserve adjacency while reshaping area to match data, but their effectiveness depends on whether readers can still understand the distorted geography.

Proportional Flow Map

Thu, 26 Mar 2026 00:00:00 +0900

A proportional flow map represents movement between locations by varying line width continuously in proportion to a data value. Migration, trade volume, traffic, and other quantitative flows can be read through the width of the connecting bands. Charles Joseph Minard’s 1869 map of Napoleon’s Russian campaign is one of the most famous examples.

Historical Background

The history of proportional flow maps is closely tied to Minard, a French civil engineer. From around 1845, Minard created maps showing freight movement, population movement, trade, and military campaigns with bands whose widths represented quantity.

His map of Napoleon’s 1812-1813 campaign shows the shrinking size of the army as a proportional band, while also integrating direction, geography, and temperature. Edward Tufte later praised it as one of the finest statistical graphics ever made.

Data Structure

Data	Role
Origin	Starting location
Destination	Ending location
Flow value	Determines line width
Route geometry	Straight or curved path
Category	Optional color or grouping

Purpose

The purpose is to communicate not only where flows go, but also how large each flow is. Continuous width scaling supports more accurate comparison than a purely categorical line symbol.

Use Cases

Migration between regions
Trade flows between countries
Freight and logistics routes
Commuting patterns
River or energy flows
Historical movement narratives

Design Notes

Scale width carefully so large flows do not overwhelm the map.
Use transparency or bundling when many flows overlap.
Clarify whether width represents absolute value, rate, or volume.
Avoid too many crossing lines.
Use arrows only when direction is not otherwise clear.

Summary

Proportional flow maps are powerful when the magnitude of movement is central to the story. They require careful width scaling and route design, but they can reveal both spatial connection and quantitative weight in a single map.

Graduated Flow Map

Wed, 25 Mar 2026 00:00:00 +0900

A graduated flow map represents flows between locations by assigning line widths to discrete classes rather than scaling them continuously. For example, flows may be shown as thin, medium, and thick lines. This reduces visual complexity while still communicating the approximate magnitude of movement, especially when values span a very wide range.

Historical Background

Flow maps date back to the nineteenth century, especially the work of Charles Joseph Minard. The graduated flow map applies the cartographic idea of classification to flow width, just as choropleth maps classify values into color ranges.

As GIS made it easier to draw hundreds or thousands of flows, graduated widths became a practical way to keep maps readable.

Data Structure

Data	Role
Origin and destination	Define the flow
Flow value	Assigned to a class
Classification rule	Equal interval, quantile, natural breaks, or manual
Line style	Width used for each class
Category	Optional color or line type

Purpose

The purpose is to show relative flow magnitude without making every small numeric difference visible. It is useful when the reader needs to understand broad levels rather than exact values.

Use Cases

Regional migration categories
Transportation volume classes
Trade intensity groups
Commuting flows with broad bands
Network flow maps for reports or print

Design Notes

Explain the class breaks.
Keep the number of width classes small.
Use a legend with representative values.
Consider graduated flow when proportional width would create extreme line differences.
Avoid class breaks that hide important thresholds.

Summary

Graduated flow maps trade numeric precision for readability. They are especially useful for dense or wide-ranging flow data where continuous proportional widths would make the map difficult to read.

What Is MapLibre Tile (MLT)?

Wed, 28 Jan 2026 00:00:00 +0900

MapLibre Tile (MLT) is a new vector tile format for the MapLibre ecosystem. It builds on the context established by Mapbox Vector Tile (MVT), but is redesigned with modern large-scale geospatial data and next-generation GPU rendering, including WebGPU, in mind. Official MapLibre materials describe support for MLT sources in MapLibre GL JS and MapLibre Native through encoding: "mlt" in a style JSON source.

This article first reviews what vector tiles are, then summarizes the differences between MLT and MVT and how MLT is used in MapLibre.

What Are Vector Tiles?

Vector tiles divide a map into zoom levels and tile coordinates (z, x, y), but instead of serving each tile as an image, they serve geometric features: points, lines, polygons, and their attributes.

Raster tiles contain images such as PNG or JPEG. They are fast to draw, but their style is hard to change after delivery.
Vector tiles contain roads, buildings, points of interest, boundaries, and attributes. The client applies style rules, so color, line width, labels, and visibility can change dynamically.

MVT has been the representative vector tile specification used by many renderers, including MapLibre.

What to Look For

Lines and labels remain crisp while zooming
Vector tiles can change what features and labels are shown at different zoom levels while preserving crisp rendering.
The same data can have different visual styles
Because data and style are separated, a map can be redesigned by changing style rules rather than regenerating all data.
Rendering cost moves to the client
Download size, decoding, filtering, and polygon tessellation can become bottlenecks. MLT is designed to reduce this preprocessing cost.
Complex attributes are increasingly important
Modern geospatial data often has nested attributes, lists, 3D coordinates, and measured values. MLT is designed with these future requirements in view.

MVT Basics

MVT stores tiled vector data efficiently with Protocol Buffers. Its strength is its ecosystem: tools, renderers, debuggers, and production experience. But the format reflects assumptions from roughly a decade ago. With larger datasets and GPU-centered rendering, more work has to be done through schema design and optimization.

What MLT Tries to Improve

MLT is designed around several ideas:

Column-oriented layout: improves compression and filtering by organizing data by columns rather than only by features.
Separation of storage and in-memory formats: supports efficient transfer while allowing faster runtime processing.
GPU-friendly design: supports moving expensive work such as polygon tessellation earlier in the pipeline.
Richer types: anticipates complex attributes, 3D coordinates, and measured values.

MVT and MLT Compared

Aspect	MVT	MLT
Basic idea	Established vector tile format	Redesigned for next-generation rendering
Storage model	Feature-oriented	More column-oriented
Decoding and processing	Mature ecosystem, but heavy data can be costly	Designed for faster compression, decoding, and filtering
Polygon preparation	Runtime tessellation can be a bottleneck	Supports pre-tessellated storage concepts
Attribute types	Mostly simple types	Designed with complex and future types in mind

MapLibre Support

MapLibre documents MLT as a supported vector source encoding. A style can specify encoding: "mlt" in a vector source.

{
  "version": 8,
  "sources": {
    "basemap": {
      "type": "vector",
      "url": "https://example.com/tiles/style-or-tileset.json",
      "encoding": "mlt"
    }
  },
  "layers": [
    {
      "id": "roads",
      "type": "line",
      "source": "basemap",
      "source-layer": "transportation"
    }
  ]
}

MapLibre GL JS release notes and examples also describe MLT support, making it a practical part of the MapLibre stack rather than only a specification proposal.

Why MLT Now?

The assumptions behind vector tiles are changing. Data volume is growing, browsers can use the GPU more effectively, and rendering stacks increasingly benefit from data structures that are easier to transfer and process in parallel. MLT responds to this shift by treating the format itself as part of the performance solution.

Summary

Vector tiles serve map features instead of images, allowing dynamic styling. MVT made this approach mainstream. MLT is MapLibre’s attempt to modernize the format for larger data, faster decoding, better filtering, and GPU-friendly rendering. Its use through encoding: "mlt" positions it as a concrete evolution of the MapLibre ecosystem.

References

Isoline Map

Wed, 10 Dec 2025 00:00:00 +0900

An isoline map visualizes continuous spatial change by connecting points with the same value. Contour lines, isotherms, and isobars are familiar examples. Unlike choropleth maps, which color administrative areas, isoline maps emphasize the continuity of the phenomenon itself.

Historical Background

The idea of isolines is old. Edmond Halley produced an early scientific isoline map in 1701 showing magnetic declination in the Atlantic. In the eighteenth and nineteenth centuries, contour lines and isotherms became important tools in topography, meteorology, oceanography, and geology.

With GIS, isolines can now be generated from observation points using interpolation methods such as kriging, inverse distance weighting, and splines.

Data Structure

Data	Role
Observation points	Locations with measured values
Values	Temperature, elevation, pressure, travel time, etc.
Interpolation method	Generates a continuous surface
Contour interval	Determines which values are drawn as lines

Purpose

The purpose is to reveal gradients and spatial structure. Isolines show how values change across space, where peaks and valleys occur, and how smoothly a phenomenon varies.

Use Cases

Elevation contours
Weather maps
Sea-level or ocean temperature maps
Air pressure and wind analysis
Travel-time accessibility maps

Design Notes

Choose contour intervals carefully.
Label lines when exact values matter.
Avoid drawing too many lines.
Combine with color only when it improves clarity.
Note the interpolation method when values are estimated.

Summary

Isoline maps are effective for continuous phenomena. They help readers see gradients and patterns, but the result depends strongly on interpolation and contour interval choices.

Isopleth Map

Wed, 10 Dec 2025 00:00:00 +0900

An isopleth map is a thematic map that represents the distribution of a continuous quantity by coloring areas bounded by lines of equal value. Observation points are interpolated into a continuous surface, and zones between equal-value boundaries are filled with color or shading. It is useful for seeing phenomena such as temperature, humidity, air pollution, and population density as spatial fields.

Historical Background

The term “isopleth” is associated with a classification proposed by John K. Wright in 1944. Wright distinguished lines connecting directly measurable point values from lines representing values such as ratios or densities that are derived for areas. This distinction helped formalize contour-based thematic mapping.

From the 1960s onward, computer-based interpolation made it easier to extract isolines and fill the resulting areas. With the spread of GIS in the 1990s, isopleth maps became standard in meteorology, environmental monitoring, and spatial analysis.

Data Structure

Data element	Description	Example
Observation coordinates	Locations of measurement points	Weather stations, air-quality monitors
Observed value	Numeric value at each point	Temperature, PM2.5, population density
Interpolation method	Algorithm for estimating the surface	Kriging, IDW, spline
Classification method	How value ranges are divided	Equal interval, quantile, Jenks
Color scheme	Colors assigned to classes	Sequential gradient, stepped palette

Purpose

The purpose is to communicate the spatial spread and intensity of a continuous variable through color-filled areas. Compared with an isoline map, which uses only lines, an isopleth map gives a stronger visual impression of high and low regions.

Use Cases

Weather maps for temperature, humidity, and wind speed
Air-pollution maps for PM2.5, ozone, or NO2
Urban heat-island analysis
Ocean maps for sea surface temperature and salinity
Noise or radiation exposure mapping

Characteristics

Aspect	Description
Target	Spatially continuous quantities
Input	Discrete observation points plus interpolation
Representation	Colored areas bounded by equal-value lines
Strength	Makes broad distribution patterns easy to see
Weakness	Depends heavily on interpolation and classification
Tools	ArcGIS, QGIS, Python, R

How to Read It

First check the legend to understand the color order and units. Then note how the classes were created, because equal intervals, quantiles, and natural breaks can produce different impressions from the same data.

Remember that the map may show estimated values, not direct observations, especially in areas far from measurement points. If possible, inspect the observation locations as well.

Design Notes

Use a sequential palette for one-directional values and a diverging palette when there is a meaningful midpoint.
State the classification method and number of classes.
Use color-vision-friendly palettes.
Include units, interpolation method, data source, and uncertainty notes.
Keep area boundaries subtle so color remains the main signal.

Alternatives

Method	Feature	Suitable when
Isoline map	Shows equal-value lines only	You need to read gradients or structure precisely
Heat map	Colors pixels continuously	A smoother surface is desired
Choropleth map	Colors administrative areas	The purpose is administrative comparison
Raster map	Uses grid cells	Resolution should be explicit

Summary

Isopleth maps are useful for showing continuous spatial phenomena as colored surfaces. They are common in weather, environmental, and urban analysis, but their credibility depends on appropriate interpolation and classification.

References

1
2

- [Contour line - Wikipedia](https://en.wikipedia.org/wiki/Contour_line)
- [Isarithmic map - Wikipedia](https://en.wikipedia.org/wiki/Isarithmic_map)

Why Web Map Data Sometimes Mentions EPSG:4326 and Sometimes EPSG:3857

Sun, 16 Nov 2025 00:00:00 +0900

An EPSG code is an identifier assigned to coordinate reference systems, datums, projections, and related geospatial definitions. It tells software which coordinate system a geographic dataset uses.

Web mapping has a common source of confusion:

Should data for a web map be EPSG:4326, or EPSG:3857?

Documentation for Foursquare, Google Maps, Mapbox, MapLibre, Kepler.gl, Cesium, QGIS, and ArcGIS can appear to say different things. The reason is that there are two different uses of coordinates: input data and map rendering or tiles.

Conclusion

User-uploaded data should generally be in EPSG:4326.
Map tiles and rendering often use EPSG:3857 internally.

Input data layer -> EPSG:4326
Map tile and rendering layer -> EPSG:3857

Many people get confused because documentation may describe different layers of the system.

GIS tools such as QGIS and ArcGIS can handle many projections, so they are outside the main scope of this simplified web-map explanation.

GeoJSON Is Fixed to WGS84 Longitude/Latitude

The GeoJSON specification, RFC 7946, states that coordinates must be in WGS84 longitude and latitude. In practice, this means EPSG:4326.

Therefore, web mapping tools that accept GeoJSON generally assume input data in EPSG:4326. This is true across MapLibre, Mapbox, Kepler.gl, Cesium, Google Maps, and OpenLayers.

Map Tiles Use EPSG:3857

Google Maps popularized Web Mercator, EPSG:3857, and the web tile system based on z/x/y coordinates. OpenStreetMap, Mapbox Vector Tile, and many other web map systems follow this convention.

In other words, map tiles are usually in 3857 even though the data you upload is 4326.

Note on Cesium: Cesium accepts EPSG:4326-style longitude and latitude input, but rendering uses Earth-Centered, Earth-Fixed coordinates, EPSG:4978. The structure is still the same: input and rendering coordinates are different.

Internal Processing

A typical web map pipeline looks like this:

User data in EPSG:4326 longitude and latitude.
The map engine projects the data internally.
The display is aligned to the tile/rendering coordinate system, usually EPSG:3857.

So documentation that says “4326” may be correct for input, while documentation that says “3857” may be correct for tiles or rendering.

Does Any Tool Require EPSG:3857 as Input?

Almost none, if by “input” we mean ordinary user data such as GeoJSON. The main exception is vector tile generation. When data is being prepared as map tiles, then EPSG:3857 becomes relevant.

Do not confuse ordinary web map upload data with GIS servers or OGC services such as WMS and WMTS. Those systems can support many projections and have different design goals.

Method	Role	Projection	Feature	Typical use
Map tiles / XYZ tiles	De facto web map tile delivery	Usually EPSG:3857	Fast, cacheable, compatible with Google Maps and OSM	Web maps, apps, MapLibre, Mapbox, Kepler
GIS server	GIS-oriented map delivery	Flexible	Supports WMS, WMTS, tiles, analysis workflows	Government GIS, surveying, business systems
WMS / WMTS	OGC standard services	Flexible	More projection flexibility than web tiles	Public GIS and precision-oriented systems

Summary

Misunderstanding	Cause
“I do not know which EPSG to upload.”	Input coordinates and rendering coordinates are mixed up.
“Google Maps mentions both 4326 and 3857.”	One refers to input data; the other to tiles.
“MapLibre renders in 3857, so should input be 3857?”	GeoJSON input is still based on 4326.

For ordinary web map data, prepare longitude and latitude in EPSG:4326. Let the map engine project it for display.

References

3D Choropleth Map

Sat, 11 Oct 2025 00:00:00 +0900

A 3D choropleth map extends a choropleth map by using both color and height to represent values for geographic areas. Each region may be extruded into a prism whose height corresponds to a statistic such as population density or income.

Historical Background

Choropleth maps date back to early nineteenth-century statistical cartography. Three-dimensional extrusion became common with modern GIS, WebGL, and tools such as ArcGIS, Mapbox, deck.gl, and Kepler.gl.

Design Notes

Use with caution because perspective can mislead.
Avoid hiding small or low regions behind tall ones.
Provide interaction and 2D alternatives.
Clarify whether height, color, or both encode the value.

Summary

3D choropleth maps can be visually impactful, but they introduce occlusion and viewpoint dependency. They are best used when spatial extrusion supports exploration rather than replacing careful comparison.

Bivariate Choropleth Map

Sat, 11 Oct 2025 00:00:00 +0900

A bivariate choropleth map represents two variables on one map. Instead of using one color scale for one variable, it combines two color axes so each region’s color represents a paired value.

Purpose

The purpose is to show the spatial relationship between two variables, such as income and education, or vulnerability and exposure.

Design Notes

Use a simple 2x2 or 3x3 legend.
Choose variables that have a meaningful relationship.
Avoid too many color classes.
Make sure readers understand the mixed-color legend.

Summary

Bivariate choropleth maps are powerful for showing two-variable spatial patterns, but they require careful legends and restrained color design.

Dot Density Map

Sat, 11 Oct 2025 00:00:00 +0900

A dot density map represents geographic distribution through the density of dots. Each dot stands for a fixed quantity, such as 100 people or one household, and dots are placed within areas to show regional patterns.

Purpose

The purpose is to show spatial concentration and spread while avoiding the area bias of choropleth maps. Dense dot clusters indicate high values or high concentration.

Design Notes

State what one dot represents.
Avoid implying exact locations when dots are randomly placed within areas.
Use small dots and appropriate transparency.
Consider dasymetric placement when internal distribution is known.

Summary

Dot density maps are useful for showing population and other distributed quantities. They communicate density intuitively, but readers must understand that dots may represent quantities, not exact locations.

Dot Map

Sat, 11 Oct 2025 00:00:00 +0900

A dot map plots individual observations as dots at their geographic locations. One dot represents one event, object, or observation. It is also called a dot distribution map, and is useful for showing the geographic distribution of precise locations such as earthquakes, stores, or incidents.

Historical Background

Dot maps have roots in nineteenth-century cartography. John Snow’s 1854 cholera map in London is a famous early example of plotting individual cases geographically.

Design Notes

Use dot maps when locations themselves matter.
Avoid overplotting by using transparency or clustering.
Do not confuse dot maps with dot density maps, where one dot may represent many units.
Provide enough basemap context for interpretation.

Summary

Dot maps are direct and powerful when the data consists of individual locations. They show where observations occur without aggregating them into regions.

Geography-Based Connection

Sat, 11 Oct 2025 00:00:00 +0900

A geography-based connection map visualizes network relationships on top of geographic space. Nodes such as cities or locations are placed on a map, and edges connect them to show movement, communication, logistics, or other relationships.

Historical Background

The form is related to nineteenth-century flow maps and later airline route maps, communication network maps, and logistics maps. Modern GIS and network tools make it easy to overlay network structure on digital maps.

Use Cases

Air routes
Internet and communication cables
Logistics and supply chains
Migration or commuting connections
Urban and regional networks

Design Notes

Avoid too many overlapping edges.
Use bundling or filtering for dense networks.
Clarify whether line width represents volume.
Preserve geographic context without overwhelming the network.

Summary

Geography-based connection maps are useful when the location of relationships matters. They combine network structure with geographic context.

Space-Time Cube

Sat, 11 Oct 2025 00:00:00 +0900

A space-time cube integrates time and geographic space in a three-dimensional visual form. The X and Y axes represent location, while the Z-axis represents time. This makes it possible to see spatial and temporal change together.

Historical Background

The concept is rooted in time geography, developed by Swedish geographer Torsten Hagerstrand in the 1960s. His ideas about space-time prisms showed how human activity is constrained by both space and time. Later GIS and 3D visualization tools made space-time cubes practical.

Data Structure

Data	Role
X/Y location	Geographic position
Time	Vertical axis
Event or path	Point, line, or trajectory
Attribute	Color, size, or annotation

Design Notes

Use interaction or multiple views for readability.
Clarify the time direction.
Avoid clutter from too many trajectories.
Provide 2D map and timeline references when needed.

Summary

Space-time cubes are useful when spatial and temporal patterns must be understood together. They are powerful but often require interaction to read well.

Vector Field Map

Sat, 11 Oct 2025 00:00:00 +0900

A vector field map shows direction and magnitude at spatial locations using arrows or glyphs. It is used for data such as wind, ocean currents, electric fields, and magnetic fields. Arrow angle represents direction, while length or color represents strength.

Historical Background

Vector field visualization developed with physics and fluid dynamics. Michael Faraday’s field-line diagrams and James Clerk Maxwell’s mathematical work on fields helped establish visual and mathematical thinking about directional forces. Modern weather and ocean maps now routinely use vector field visualization.

Data Structure

Data	Role
Location	Point where vector is measured
Direction	Arrow angle
Magnitude	Arrow length or color
Optional time	Animation or temporal comparison

Design Notes

Avoid drawing too many arrows.
Use consistent scaling for magnitude.
Consider streamlines for dense flow.
Use animation carefully for time-varying fields.
Include a legend for arrow length or color.

Summary

Vector field maps are effective for directional spatial data. They help readers see flow, force, or movement patterns, but density and scaling must be managed carefully.