One number per place, compared across the map
Unemployment rate by country, median income by state, vote share by county. A choropleth is built exactly for this: one value, one shade, one glance to find the extremes.
Map Chart Templates
Turn location-tagged data into a shaded map people can actually read correctly. Start from a template built around countries, states or regions and skip the part where you fight a mapping library.
Modelos nesta categoria
Uma visão rápida do que tem aqui. Clique para abrir qualquer um.
Sobre Maps
Map chart templates take data that's tagged to a place (a country, a US state, a county, a region) and encode a value as color across that geography, so the reader sees the pattern before they read a single number. The category's core member is the choropleth map: every area on the map gets a shade drawn from your data, darker or more saturated for higher values, so a reader spots the hot spots and cold spots at a glance without scanning a table.
What distinguishes a good map chart from a misleading one is almost never the visual design. It's what value you chose to shade by. Shade by a raw count and a choropleth quietly turns into a population map: California and Texas light up on nearly any metric just because they have more people and more of everything. Use a rate, a per-capita figure or a percentage instead, and the map actually answers the question you're asking. This is the one thing worth getting right before you touch a template.
This hub is deliberately narrow for now. It covers the static, single-snapshot choropleth. If your geography needs to change or move over time, that's a job for an animated format instead, and PlotSet's dedicated /maps hub lists the full set of supported geographies (countries, US states, and more) if you need to check coverage before you start.
Tipos de gráfico nesta categoria
Cada um abre seu próprio criador grátis com dados de amostra já carregados.
Modelos de vídeo animado nesta categoria
A mesma ideia, renderizada como MP4.
Trabalhando com dados geográficos?
O hub de mapas cobre cada tipo de mapa que o PlotSet oferece, incluindo os fora desta categoria.
Melhor usado para
Spotting regional clusters a table would hide
Rows in a spreadsheet don't show you that a value is high across an entire coastline or a whole region. Color on a map does. Clustering jumps out instantly in a way a sorted list can't replicate.
A rate or percentage, not a raw total
CO2 emissions per capita, smartphone ownership rate, percentage of households with broadband. These are exactly the metrics choropleth maps were designed to carry, because they're already normalized for area and population.
A rate that needs to change over time
Emissions per capita by year, unemployment rate by quarter, election results across several cycles. A static choropleth handles one snapshot well; for a series that needs to build region by region, pair it with the map tour video template instead.
Como construir um
De uma planilha a um gráfico publicado, passo a passo.
Confirm your geography is supported
Check that your data matches a supported boundary set (countries, US states, or another region PlotSet recognizes) before you upload, so every row has somewhere to land on the map.
Open the choropleth map template
It opens with sample data already bound in, so you can see the two-column shape it expects: a place name or code, and the value to shade by.
Upload your spreadsheet and pick the right value
Import a CSV or Excel file, or paste from a spreadsheet. Make sure the column you're shading by is a rate or a normalized figure, not a raw count, unless population really is what you're mapping.
Publish, embed or export
Publish for an interactive embed with hover tooltips per region, or export a static PNG, SVG or PDF for a report or slide deck.
Abra o editor e comece com qualquer modelo nesta categoria. Já vem com dados de amostra.
Abrir o editorMaps: perguntas frequentes
A map where each region (country, state, county) is shaded according to a data value, typically on a color scale from light to dark. It's the standard way to show how a metric varies across geography at a glance, without forcing the reader to read individual numbers.
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