{"id":19041,"date":"2019-09-16T07:00:45","date_gmt":"2019-09-16T12:00:45","guid":{"rendered":"https:\/\/gisgeography.com\/?p=19041"},"modified":"2025-04-04T09:12:31","modified_gmt":"2025-04-04T14:12:31","slug":"map-types","status":"publish","type":"post","link":"https:\/\/gisgeography.com\/map-types\/","title":{"rendered":"25 Map Types for Building Unbeatable Maps"},"content":{"rendered":"<style>.kb-image19041_1f46b7-71 .kb-image-has-overlay:after{opacity:0.3;}<\/style>\n<div class=\"wp-block-kadence-image kb-image19041_1f46b7-71\"><figure class=\"aligncenter\"><img loading=\"lazy\" decoding=\"async\" width=\"850\" height=\"380\" src=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/09\/Map-Types-Feature.jpg\" alt=\"Map Types Feature\" class=\"kb-img wp-image-61415\" srcset=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/09\/Map-Types-Feature.jpg 850w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/09\/Map-Types-Feature-300x134.jpg 300w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/09\/Map-Types-Feature-678x303.jpg 678w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/09\/Map-Types-Feature-768x343.jpg 768w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/09\/Map-Types-Feature-50x22.jpg 50w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/09\/Map-Types-Feature-80x35.jpg 80w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/09\/Map-Types-Feature-200x89.jpg 200w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/09\/Map-Types-Feature-425x190.jpg 425w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/09\/Map-Types-Feature-550x246.jpg 550w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/09\/Map-Types-Feature-115x51.jpg 115w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/09\/Map-Types-Feature-360x161.jpg 360w\" sizes=\"auto, (max-width: 850px) 100vw, 850px\" \/><\/figure><\/div>\n\n\n\n<div class=\"wp-block-group\" style=\"padding-top:var(--wp--preset--spacing--30);padding-bottom:var(--wp--preset--spacing--30)\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<h3 class=\"wp-block-heading\">25 Examples of Map Types<\/h3>\n\n\n\n<p>Looking for some cartographic inspiration? Well, I\u2019m a big fan of using different <strong>map types<\/strong>.<\/p>\n\n\n\n<p>Unfortunately, the best ones are often overlooked. <\/p>\n\n\n\n<p>But not today.<\/p>\n\n\n\n<p>&#8230;Because today we\u2019ll review <strong>25 types of maps<\/strong> that make a huge difference to the map reader. <\/p>\n\n\n\n<p>Each one you can easily add to your mapping repertoire to communicate data more effectively. <\/p>\n\n\n\n<p>Ready?<\/p>\n<\/div><\/div>\n\n\n\n<div class=\"wp-block-group\" style=\"padding-top:var(--wp--preset--spacing--40);padding-bottom:var(--wp--preset--spacing--40)\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<h3 class=\"wp-block-heading\">1. Firefly maps<\/h3>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"alignright\"><img loading=\"lazy\" decoding=\"async\" width=\"300\" height=\"183\" src=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Firefly-Map-300x183.jpg\" alt=\"Firefly Map\" class=\"wp-image-19044\" srcset=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Firefly-Map-300x183.jpg 300w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Firefly-Map-50x30.jpg 50w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Firefly-Map-200x122.jpg 200w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Firefly-Map-425x259.jpg 425w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Firefly-Map-550x335.jpg 550w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Firefly-Map-115x70.jpg 115w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Firefly-Map.jpg 628w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/figure>\n<\/div>\n\n\n<p>Firefly maps use <strong>lightsaber-glowing symbology<\/strong> with a dark desaturated backdrop. Its greatest strengths are its cinematic effects and glowing appeal. <\/p>\n\n\n\n<p>For example, their radiance stands out for earthquake events, transportation networks, and population <a href=\"https:\/\/gisgeography.com\/point-clustering-arcgis-pro\/\">cluster maps<\/a>.<\/p>\n<\/div><\/div>\n\n\n\n<div class=\"wp-block-group\" style=\"padding-top:var(--wp--preset--spacing--40);padding-bottom:var(--wp--preset--spacing--40)\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<h3 class=\"wp-block-heading\">2. Dot maps<\/h3>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"alignright\"><img loading=\"lazy\" decoding=\"async\" width=\"300\" height=\"183\" src=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Dot-Map-0-300x183.png\" alt=\"Dot Map\" class=\"wp-image-19110\" srcset=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Dot-Map-0-300x183.png 300w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Dot-Map-0-50x30.png 50w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Dot-Map-0-200x122.png 200w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Dot-Map-0-425x259.png 425w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Dot-Map-0-550x335.png 550w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Dot-Map-0-115x70.png 115w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Dot-Map-0.png 628w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/figure>\n<\/div>\n\n\n<p><a href=\"https:\/\/gisgeography.com\/dot-maps\/\">Dot maps<\/a> scatter dots in an area based on value. More dots means a higher value. And vice versa. <\/p>\n\n\n\n<p>Dot maps don&#8217;t necessarily show the exact location. They <strong>randomly spread out points<\/strong> in a given area best for showing density differences across regions. <\/p>\n\n\n\n<p><strong>READ MORE:<\/strong> <a href=\"https:\/\/gisgeography.com\/dot-distribution-graduated-symbols-proportional-symbol-maps\/\">Dot Distribution vs Graduated Symbols<\/a><\/p>\n<\/div><\/div>\n\n\n\n<div class=\"wp-block-group\" style=\"padding-top:var(--wp--preset--spacing--40);padding-bottom:var(--wp--preset--spacing--40)\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<h3 class=\"wp-block-heading\">3. Graduated symbols<\/h3>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"alignright size-medium\"><img loading=\"lazy\" decoding=\"async\" width=\"300\" height=\"183\" src=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Graduated-Symbols-Map-0-300x183.png\" alt=\"Graduated Symbols Map\" class=\"wp-image-19055\" srcset=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Graduated-Symbols-Map-0-300x183.png 300w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Graduated-Symbols-Map-0-50x30.png 50w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Graduated-Symbols-Map-0-200x122.png 200w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Graduated-Symbols-Map-0-425x259.png 425w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Graduated-Symbols-Map-0-550x335.png 550w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Graduated-Symbols-Map-0-115x70.png 115w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Graduated-Symbols-Map-0.png 628w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/figure>\n<\/div>\n\n\n<p>Graduated symbol maps <strong>resize symbols based on value<\/strong>. For example, a small coffee cup pictogram represents low consumption and vice versa. <\/p>\n\n\n\n<p>Symbols include circles, pictograms, and volumetric shapes. For depicting quantity differences, graduated symbol maps are a useful choice.<\/p>\n<\/div><\/div>\n\n\n\n<div class=\"wp-block-group\" style=\"padding-top:var(--wp--preset--spacing--40);padding-bottom:var(--wp--preset--spacing--40)\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<h3 class=\"wp-block-heading\">4. Vector direction maps<\/h3>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"alignright\"><img loading=\"lazy\" decoding=\"async\" width=\"300\" height=\"183\" src=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Wind-Map-300x183.png\" alt=\"Wind Map\" class=\"wp-image-19052\" srcset=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Wind-Map-300x183.png 300w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Wind-Map-50x30.png 50w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Wind-Map-200x122.png 200w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Wind-Map-425x259.png 425w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Wind-Map-550x335.png 550w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Wind-Map-115x70.png 115w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Wind-Map.png 628w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/figure>\n<\/div>\n\n\n<p>Vector direction maps rotate symbols based on the angle. For example, wind maps use <strong>vector arrows<\/strong> that spin how the wind blows. <\/p>\n\n\n\n<p>If you want to depict wind speed, you adjust the size of the arrows. For example, large arrows depict big gusts of wind.<\/p>\n<\/div><\/div>\n\n\n\n<div class=\"wp-block-group\" style=\"padding-top:var(--wp--preset--spacing--40);padding-bottom:var(--wp--preset--spacing--40)\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<h3 class=\"wp-block-heading\">5. Distributive flow<\/h3>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"alignright\"><img loading=\"lazy\" decoding=\"async\" width=\"300\" height=\"183\" src=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Distributive-Flow-300x183.png\" alt=\"Distributive Flow\" class=\"wp-image-19058\" srcset=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Distributive-Flow-300x183.png 300w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Distributive-Flow-50x30.png 50w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Distributive-Flow-200x122.png 200w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Distributive-Flow-425x259.png 425w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Distributive-Flow-550x335.png 550w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Distributive-Flow-115x70.png 115w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Distributive-Flow.png 628w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/figure>\n<\/div>\n\n\n<p>Distributive flow maps <strong>curl like fingers<\/strong> branching off to their destination depicting direction and movement. <\/p>\n\n\n\n<p>Although it\u2019s not necessarily the accurate route being traveled, it&#8217;s an abstract way of how commodities or information travel from an origin to multiple destinations. <\/p>\n<\/div><\/div>\n\n\n\n<div class=\"wp-block-group\" style=\"padding-top:var(--wp--preset--spacing--40);padding-bottom:var(--wp--preset--spacing--40)\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<h3 class=\"wp-block-heading\">6. Density-equalizing cartograms<\/h3>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"alignright\"><img loading=\"lazy\" decoding=\"async\" width=\"300\" height=\"183\" src=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Density-equalizing-cartogram-300x183.png\" alt=\"Density-equalizing cartogram\" class=\"wp-image-19059\" srcset=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Density-equalizing-cartogram-300x183.png 300w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Density-equalizing-cartogram-50x30.png 50w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Density-equalizing-cartogram-200x122.png 200w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Density-equalizing-cartogram-425x259.png 425w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Density-equalizing-cartogram-550x335.png 550w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Density-equalizing-cartogram-115x70.png 115w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Density-equalizing-cartogram.png 628w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/figure>\n<\/div>\n\n\n<p>Density-equalizing (contiguous) cartograms are your traditional cartograms. <\/p>\n\n\n\n<p>Like all cartograms, they <strong>bulge the size of geographic areas<\/strong> from representative values.  Although it distorts each feature, each feature remains connected.<\/p>\n\n\n\n<p><strong>READ MORE:<\/strong> <a href=\"https:\/\/gisgeography.com\/flow-maps\/\">Flow Maps<\/a><\/p>\n<\/div><\/div>\n\n\n\n<div class=\"wp-block-group\" style=\"padding-top:var(--wp--preset--spacing--40);padding-bottom:var(--wp--preset--spacing--40)\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<h3 class=\"wp-block-heading\">7. Voronoi diagram<\/h3>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"alignright\"><img loading=\"lazy\" decoding=\"async\" width=\"300\" height=\"183\" src=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Voronoi-Diagram-0-300x183.png\" alt=\"Voronoi Diagram\" class=\"wp-image-19063\" srcset=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Voronoi-Diagram-0-300x183.png 300w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Voronoi-Diagram-0-50x30.png 50w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Voronoi-Diagram-0-200x122.png 200w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Voronoi-Diagram-0-425x259.png 425w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Voronoi-Diagram-0-550x335.png 550w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Voronoi-Diagram-0-115x70.png 115w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Voronoi-Diagram-0.png 628w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/figure>\n<\/div>\n\n\n<p>Voronoi diagrams best understand the proximity and distance of features. It starts with seed points that <strong>divide regions<\/strong> for each point. <\/p>\n\n\n\n<p>Each region shows the closest region for a point. So If you place a point in any region, then it\u2019s the closest to that seed point.<\/p>\n<\/div><\/div>\n\n\n\n<div class=\"wp-block-group\" style=\"padding-top:var(--wp--preset--spacing--40);padding-bottom:var(--wp--preset--spacing--40)\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<h3 class=\"wp-block-heading\">8. Choropleth<\/h3>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"alignright\"><img loading=\"lazy\" decoding=\"async\" width=\"300\" height=\"183\" src=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Choropleth-Map-300x183.png\" alt=\"Choropleth Map Types\" class=\"wp-image-19064\" srcset=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Choropleth-Map-300x183.png 300w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Choropleth-Map-50x30.png 50w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Choropleth-Map-200x122.png 200w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Choropleth-Map-425x259.png 425w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Choropleth-Map-550x335.png 550w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Choropleth-Map-115x70.png 115w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Choropleth-Map.png 628w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/figure>\n<\/div>\n\n\n<p>Choropleth maps <strong>vary the shading<\/strong> of each area based on its value. It&#8217;s one of the most common map types because they&#8217;re easy to understand and add a bit of color.  <\/p>\n\n\n\n<p>They&#8217;re different from heat maps because they require a geographic boundary. We assume the entire boundary is homogeneous with its assigned shading.<\/p>\n\n\n\n<p><strong>READ MORE:<\/strong> <a href=\"https:\/\/gisgeography.com\/voronoi-diagram-thiessen-polygons\/\">Voronoi Diagrams and Thiessen Polygons<\/a><\/p>\n<\/div><\/div>\n\n\n\n<div class=\"wp-block-group\" style=\"padding-top:var(--wp--preset--spacing--40);padding-bottom:var(--wp--preset--spacing--40)\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<h3 class=\"wp-block-heading\">9. Heat Maps (Point-density)<\/h3>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"alignright\"><img loading=\"lazy\" decoding=\"async\" width=\"300\" height=\"183\" src=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Heat-Map-300x183.png\" alt=\"Heat Map Point-density\" class=\"wp-image-19066\" srcset=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Heat-Map-300x183.png 300w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Heat-Map-50x30.png 50w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Heat-Map-200x122.png 200w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Heat-Map-425x259.png 425w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Heat-Map-550x335.png 550w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Heat-Map-115x70.png 115w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Heat-Map.png 628w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/figure>\n<\/div>\n\n\n<p>Heat maps color-code using the <strong>density of points<\/strong>.  For example, if there is a high density of crimes in an area, it shades the area as red.  <\/p>\n\n\n\n<p>But fewer crimes are assigned a blue shading. By using this type of map, you can pinpoint the safest and most dangerous places in a city. And they&#8217;re not tied to a boundary like choropleth maps.<\/p>\n<\/div><\/div>\n\n\n\n<div class=\"wp-block-group\" style=\"padding-top:var(--wp--preset--spacing--40);padding-bottom:var(--wp--preset--spacing--40)\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<h3 class=\"wp-block-heading\">10. Isochrone<\/h3>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"alignright\"><img loading=\"lazy\" decoding=\"async\" width=\"300\" height=\"183\" src=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Isochrone-Map-0-300x183.jpg\" alt=\"Isochrone Map\" class=\"wp-image-19070\" srcset=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Isochrone-Map-0-300x183.jpg 300w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Isochrone-Map-0-50x30.jpg 50w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Isochrone-Map-0-200x122.jpg 200w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Isochrone-Map-0-425x259.jpg 425w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Isochrone-Map-0-550x335.jpg 550w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Isochrone-Map-0-115x70.jpg 115w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Isochrone-Map-0.jpg 628w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/figure>\n<\/div>\n\n\n<p>Isochrone maps reveal the geographic extent to which one can travel. If you start at a given point, it shades how far you can travel <strong>within an amount of time<\/strong>. <\/p>\n\n\n\n<p>For example, fire stations measure their drive-time distances using a city&#8217;s road network. If they know which areas they have difficulty providing service for, it gives insight into where to build new fire stations.<\/p>\n\n\n\n<p><strong>READ MORE:<\/strong> <a href=\"https:\/\/gisgeography.com\/finding-service-areas-using-arcgis-network-analyst\/\">Isochrone Maps<\/a><\/p>\n<\/div><\/div>\n\n\n\n<div class=\"wp-block-group\" style=\"padding-top:var(--wp--preset--spacing--40);padding-bottom:var(--wp--preset--spacing--40)\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<h3 class=\"wp-block-heading\">11. Dasymetric<\/h3>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"alignright\"><img loading=\"lazy\" decoding=\"async\" width=\"300\" height=\"183\" src=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Dasymetric-300x183.png\" alt=\"Dasymetric maps\" class=\"wp-image-19073\" srcset=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Dasymetric-300x183.png 300w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Dasymetric-50x30.png 50w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Dasymetric-200x122.png 200w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Dasymetric-425x259.png 425w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Dasymetric-550x335.png 550w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Dasymetric-115x70.png 115w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Dasymetric.png 628w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/figure>\n<\/div>\n\n\n<p>Dasymetric and choropleth maps are both thematic mapping techniques. But dasymetric maps classify <strong>quantitative aerial data<\/strong>.  <\/p>\n\n\n\n<p>For example, the population can be aggregated into areal census tracts. In a dasymetric map, each census tract is assumed to be homogenous.<\/p>\n<\/div><\/div>\n\n\n\n<div class=\"wp-block-group\" style=\"padding-top:var(--wp--preset--spacing--40);padding-bottom:var(--wp--preset--spacing--40)\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<h3 class=\"wp-block-heading\">12. Space-time cubes<\/h3>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"alignright\"><img loading=\"lazy\" decoding=\"async\" width=\"300\" height=\"183\" src=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Space-time-cubes-300x183.jpg\" alt=\"Space-time cubes\" class=\"wp-image-19075\" srcset=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Space-time-cubes-300x183.jpg 300w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Space-time-cubes-50x30.jpg 50w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Space-time-cubes-200x122.jpg 200w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Space-time-cubes-425x259.jpg 425w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Space-time-cubes-550x335.jpg 550w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Space-time-cubes-115x70.jpg 115w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Space-time-cubes.jpg 628w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/figure>\n<\/div>\n\n\n<p><a href=\"https:\/\/gisgeography.com\/space-time-cubes\/\">Space-time cubes<\/a> are slices of time stacked up in a three-dimensional space. Older time cubes are on the bottom. New slices of time are on top. <\/p>\n\n\n\n<p>So they&#8217;re ideal to show how values change in geographic space. For usability, it takes time to get accustomed to it. But in terms of 4D mapping techniques, it\u2019s one of a kind.<\/p>\n<\/div><\/div>\n\n\n\n<div class=\"wp-block-group\" style=\"padding-top:var(--wp--preset--spacing--40);padding-bottom:var(--wp--preset--spacing--40)\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<h3 class=\"wp-block-heading\">13. Topographic<\/h3>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"alignright\"><img loading=\"lazy\" decoding=\"async\" width=\"300\" height=\"183\" src=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Topographic-Map-1-300x183.jpg\" alt=\"Topographic Map\" class=\"wp-image-19112\" srcset=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Topographic-Map-1-300x183.jpg 300w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Topographic-Map-1-50x30.jpg 50w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Topographic-Map-1-200x122.jpg 200w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Topographic-Map-1-425x259.jpg 425w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Topographic-Map-1-550x335.jpg 550w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Topographic-Map-1-115x70.jpg 115w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Topographic-Map-1.jpg 628w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/figure>\n<\/div>\n\n\n<p>Topographic maps are all-purpose maps. Generally, they include terrain, natural and man-made features. From topographic surveys, we construct these map types. <\/p>\n\n\n\n<p>Fast-forward to today, they still hold tremendous value in infrastructure, military, and resource planning.<\/p>\n<\/div><\/div>\n\n\n\n<div class=\"wp-block-group\" style=\"padding-top:var(--wp--preset--spacing--40);padding-bottom:var(--wp--preset--spacing--40)\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<h3 class=\"wp-block-heading\">14. Contour (Isolines)<\/h3>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"alignright\"><img loading=\"lazy\" decoding=\"async\" width=\"300\" height=\"183\" src=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Contour-Map-300x183.jpg\" alt=\"Contour Maps\" class=\"wp-image-19078\" srcset=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Contour-Map-300x183.jpg 300w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Contour-Map-50x30.jpg 50w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Contour-Map-200x122.jpg 200w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Contour-Map-425x259.jpg 425w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Contour-Map-550x335.jpg 550w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Contour-Map-115x70.jpg 115w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Contour-Map.jpg 628w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/figure>\n<\/div>\n\n\n<p>Contour maps have lines with constant values joining points of equal elevation. <\/p>\n\n\n\n<p>If contour lines are closely spaced together, the terrain is steep. But if they are widely spaced apart, it\u2019s a gradual incline.<\/p>\n\n\n\n<p><strong>READ MORE:<\/strong> <a href=\"https:\/\/gisgeography.com\/contour-lines-topographic-map\/\">Contour Lines on Topographic Maps<\/a><\/p>\n<\/div><\/div>\n\n\n\n<div class=\"wp-block-group\" style=\"padding-top:var(--wp--preset--spacing--40);padding-bottom:var(--wp--preset--spacing--40)\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<h3 class=\"wp-block-heading\">15. Statistical<\/h3>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"alignright\"><img loading=\"lazy\" decoding=\"async\" width=\"300\" height=\"183\" src=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Data-Viz-Maps-0-300x183.png\" alt=\"Data Viz Maps\" class=\"wp-image-19084\" srcset=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Data-Viz-Maps-0-300x183.png 300w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Data-Viz-Maps-0-50x30.png 50w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Data-Viz-Maps-0-200x122.png 200w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Data-Viz-Maps-0-425x259.png 425w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Data-Viz-Maps-0-550x335.png 550w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Data-Viz-Maps-0-115x70.png 115w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Data-Viz-Maps-0.png 628w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/figure>\n<\/div>\n\n\n<p>Data viz meet maps. When you put bar graphs, pie charts, and line plots on maps, suddenly statistics have a location. <\/p>\n\n\n\n<p>And the saying goes: without geography, you&#8217;re nowhere. So with <strong>Tableau-style visualizations<\/strong>, you put <a href=\"https:\/\/gisgeography.com\/data-visualization\/\">data visualizations<\/a> on the map.<\/p>\n<\/div><\/div>\n\n\n\n<div class=\"wp-block-group\" style=\"padding-top:var(--wp--preset--spacing--40);padding-bottom:var(--wp--preset--spacing--40)\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<h3 class=\"wp-block-heading\">16. Non-contiguous cartogram<\/h3>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"alignright\"><img loading=\"lazy\" decoding=\"async\" width=\"300\" height=\"183\" src=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Non-contiguous-cartogram-300x183.png\" alt=\"Non-contiguous cartogram\" class=\"wp-image-19085\" srcset=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Non-contiguous-cartogram-300x183.png 300w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Non-contiguous-cartogram-50x30.png 50w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Non-contiguous-cartogram-200x122.png 200w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Non-contiguous-cartogram-425x259.png 425w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Non-contiguous-cartogram-550x335.png 550w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Non-contiguous-cartogram-115x70.png 115w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Non-contiguous-cartogram.png 628w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/figure>\n<\/div>\n\n\n<p>Non-contiguous cartograms resize features based on values and keep their shape intact. But features don\u2019t have to stay connected. <\/p>\n\n\n\n<p>They can freely move to scatter them like <strong>pieces of a jigsaw puzzle<\/strong>. So features scale up or down according to value. But the tradeoff is that you lose their precise placement.<\/p>\n<\/div><\/div>\n\n\n\n<div class=\"wp-block-group\" style=\"padding-top:var(--wp--preset--spacing--40);padding-bottom:var(--wp--preset--spacing--40)\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<h3 class=\"wp-block-heading\">17. Dorling cartogram<\/h3>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"alignright\"><img loading=\"lazy\" decoding=\"async\" width=\"300\" height=\"183\" src=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Dorling-Cartogram-300x183.png\" alt=\"Dorling Cartogram\" class=\"wp-image-19088\" srcset=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Dorling-Cartogram-300x183.png 300w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Dorling-Cartogram-50x30.png 50w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Dorling-Cartogram-200x122.png 200w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Dorling-Cartogram-425x259.png 425w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Dorling-Cartogram-550x335.png 550w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Dorling-Cartogram-115x70.png 115w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Dorling-Cartogram.png 628w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/figure>\n<\/div>\n\n\n<p>The Dorling cartogram uses shapes like circles and rectangles to <strong>depict the area<\/strong>. By using shapes, it&#8217;s easier to recognize patterns. <\/p>\n\n\n\n<p>But its main weakness is that you lose its geographic reference. It can get to such a point that you don&#8217;t know what you&#8217;re looking at.<\/p>\n<\/div><\/div>\n\n\n\n<div class=\"wp-block-group\" style=\"padding-top:var(--wp--preset--spacing--40);padding-bottom:var(--wp--preset--spacing--40)\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<h3 class=\"wp-block-heading\">18. Surface maps<\/h3>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"alignright\"><img loading=\"lazy\" decoding=\"async\" width=\"300\" height=\"183\" src=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Surface-Maps-300x183.png\" alt=\"Surface Maps\" class=\"wp-image-19089\" srcset=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Surface-Maps-300x183.png 300w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Surface-Maps-50x30.png 50w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Surface-Maps-200x122.png 200w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Surface-Maps-425x259.png 425w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Surface-Maps-550x335.png 550w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Surface-Maps-115x70.png 115w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Surface-Maps.png 628w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/figure>\n<\/div>\n\n\n<p>Surface maps take a set of known values. Then, they create a surface <strong>predicting the unknown ones<\/strong> in between. <\/p>\n\n\n\n<p>This 3D surface (isopleth) is common for rainfall, temperature, and elevation. They differ from choropleth maps because they&#8217;re not tied to a geographic boundary or region.<\/p>\n\n\n\n<p><strong>READ MORE:<\/strong> <a href=\"https:\/\/gisgeography.com\/cartogram-maps\/\">Cartogram Maps<\/a><\/p>\n<\/div><\/div>\n\n\n\n<div class=\"wp-block-group\" style=\"padding-top:var(--wp--preset--spacing--40);padding-bottom:var(--wp--preset--spacing--40)\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<h3 class=\"wp-block-heading\">19. Schematics<\/h3>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"alignright\"><img loading=\"lazy\" decoding=\"async\" width=\"300\" height=\"183\" src=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Schematics-300x183.png\" alt=\"Schematics\" class=\"wp-image-19092\" srcset=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Schematics-300x183.png 300w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Schematics-50x30.png 50w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Schematics-200x122.png 200w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Schematics-425x259.png 425w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Schematics-550x335.png 550w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Schematics-115x70.png 115w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Schematics.png 628w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/figure>\n<\/div>\n\n\n<p>Schematics significantly <strong>simplifies maps<\/strong>. For example, subway maps, traffic lights, and electric networks use schematics as a simplified view. <\/p>\n\n\n\n<p>At a cost, schematics lose orientation and a true base map. But readers can gain greater clarity.<\/p>\n<\/div><\/div>\n\n\n\n<div class=\"wp-block-group\" style=\"padding-top:var(--wp--preset--spacing--40);padding-bottom:var(--wp--preset--spacing--40)\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<h3 class=\"wp-block-heading\">20. Network flow<\/h3>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"alignright\"><img loading=\"lazy\" decoding=\"async\" width=\"300\" height=\"183\" src=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Network-Flow-300x183.png\" alt=\"Network Flow\" class=\"wp-image-19094\" srcset=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Network-Flow-300x183.png 300w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Network-Flow-50x30.png 50w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Network-Flow-200x122.png 200w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Network-Flow-425x259.png 425w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Network-Flow-550x335.png 550w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Network-Flow-115x70.png 115w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Network-Flow.png 628w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/figure>\n<\/div>\n\n\n<p>Network flow maps <strong>show movement<\/strong> along with an established network. For example, flight paths, transportation networks, and communication systems use network flow maps. <\/p>\n\n\n\n<p>The purpose is to display the traffic on the map. For instance, thick lines have a denser flow. But thin lines have less traffic.<\/p>\n<\/div><\/div>\n\n\n\n<div class=\"wp-block-group\" style=\"padding-top:var(--wp--preset--spacing--40);padding-bottom:var(--wp--preset--spacing--40)\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<h3 class=\"wp-block-heading\">21. Coxcomb chart (Polar area chart)<\/h3>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"alignright\"><img loading=\"lazy\" decoding=\"async\" width=\"300\" height=\"183\" src=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Coxcomb-Chart-Maps-300x183.png\" alt=\"Coxcomb Chart Maps\" class=\"wp-image-19097\" srcset=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Coxcomb-Chart-Maps-300x183.png 300w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Coxcomb-Chart-Maps-50x30.png 50w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Coxcomb-Chart-Maps-200x122.png 200w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Coxcomb-Chart-Maps-425x259.png 425w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Coxcomb-Chart-Maps-550x335.png 550w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Coxcomb-Chart-Maps-115x70.png 115w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Coxcomb-Chart-Maps.png 628w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/figure>\n<\/div>\n\n\n<p>The Coxcomb chart is a twist on the pie chart. Like petals on a flower, each petal is packed with single or <strong>multiple slices of information<\/strong>. <\/p>\n\n\n\n<p>For example, petals in a coxcomb chart can radially display months of a year. If the petals are large, it has a higher quantity like this wildlife bird strike map.<\/p>\n<\/div><\/div>\n\n\n\n<div class=\"wp-block-group\" style=\"padding-top:var(--wp--preset--spacing--40);padding-bottom:var(--wp--preset--spacing--40)\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<h3 class=\"wp-block-heading\">22. Timeslice maps<\/h3>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"alignright\"><img loading=\"lazy\" decoding=\"async\" width=\"300\" height=\"183\" src=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Timeslice-Maps-300x183.png\" alt=\"Timeslice Maps\" class=\"wp-image-19099\" srcset=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Timeslice-Maps-300x183.png 300w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Timeslice-Maps-50x30.png 50w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Timeslice-Maps-200x122.png 200w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Timeslice-Maps-425x259.png 425w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Timeslice-Maps-550x335.png 550w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Timeslice-Maps-115x70.png 115w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Timeslice-Maps.png 628w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/figure>\n<\/div>\n\n\n<p>Timeslice maps portray a journey by dividing the map canvas into <strong>chunks of time<\/strong>. For example, the path of a tsunami divides the map canvas in minutes. <\/p>\n\n\n\n<p>Napoleon\u2019s March is a timeslice map in three dimensions over days. These types of maps are hard to implement but extremely effective at communicating time.<\/p>\n\n\n\n<p><strong>READ MORE:<\/strong> <a href=\"https:\/\/gisgeography.com\/best-maps-webmaps\/\">The Maps Hall of Fame<\/a><\/p>\n<\/div><\/div>\n\n\n\n<div class=\"wp-block-group\" style=\"padding-top:var(--wp--preset--spacing--40);padding-bottom:var(--wp--preset--spacing--40)\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<h3 class=\"wp-block-heading\">23. Hexagonal Binning<\/h3>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"alignright\"><img loading=\"lazy\" decoding=\"async\" width=\"300\" height=\"183\" src=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Hexbin-Maps-300x183.png\" alt=\"Hexbin Maps\" class=\"wp-image-19102\" srcset=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Hexbin-Maps-300x183.png 300w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Hexbin-Maps-50x30.png 50w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Hexbin-Maps-200x122.png 200w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Hexbin-Maps-425x259.png 425w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Hexbin-Maps-550x335.png 550w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Hexbin-Maps-115x70.png 115w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Hexbin-Maps.png 628w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/figure>\n<\/div>\n\n\n<p>Rather than square grids, the future of mapping is <strong>hexagons<\/strong>. Actually, circles make the best grids because their perimeter has equal distances from a center point. <\/p>\n\n\n\n<p>Overall, <a href=\"https:\/\/gisgeography.com\/hexagon-binning\/\">hexagon binning reduces sampling bias<\/a>. But you can&#8217;t connect neighboring circles into a continuous grid. So this is why we use hexbins for sampling which resemble circles.<\/p>\n<\/div><\/div>\n\n\n\n<div class=\"wp-block-group\" style=\"padding-top:var(--wp--preset--spacing--40);padding-bottom:var(--wp--preset--spacing--40)\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<h3 class=\"wp-block-heading\">24. Word (typography) maps<\/h3>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"alignright size-medium\"><img loading=\"lazy\" decoding=\"async\" width=\"300\" height=\"183\" src=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/09\/Word-Maps-300x183.jpg\" alt=\"Word Maps\" class=\"wp-image-97261\" srcset=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/09\/Word-Maps-300x183.jpg 300w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/09\/Word-Maps.jpg 628w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/figure>\n<\/div>\n\n\n<p>Word maps assign <strong>dominant keywords<\/strong> to features filling in the area of the map. <\/p>\n\n\n\n<p>Word placement is key for this type of map in terms of rotation, scaling, and skewing. Large words suggest an influential topic. Whereas small words put less emphasis on a keyword.<\/p>\n<\/div><\/div>\n\n\n\n<div class=\"wp-block-group\" style=\"padding-top:var(--wp--preset--spacing--40);padding-bottom:var(--wp--preset--spacing--40)\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<h3 class=\"wp-block-heading\">25. Radial flow<\/h3>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"alignright\"><img loading=\"lazy\" decoding=\"async\" width=\"300\" height=\"183\" src=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Radial-flow-300x183.png\" alt=\"Radial flow\" class=\"wp-image-19104\" srcset=\"https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Radial-flow-300x183.png 300w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Radial-flow-50x30.png 50w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Radial-flow-200x122.png 200w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Radial-flow-425x259.png 425w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Radial-flow-550x335.png 550w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Radial-flow-115x70.png 115w, https:\/\/gisgeography.com\/wp-content\/uploads\/2019\/04\/Radial-flow.png 628w\" sizes=\"auto, (max-width: 300px) 100vw, 300px\" \/><\/figure>\n<\/div>\n\n\n<p>In radial flow maps, lines <strong>radiate from an origin<\/strong>. Then, they radiate outwards to single or multiple destination nodes. <\/p>\n\n\n\n<p>Each hub links the movement of a phenomenon. It has a directional flow line which is often in a radial pattern.<\/p>\n<\/div><\/div>\n\n\n\n<div class=\"wp-block-group\" style=\"padding-top:var(--wp--preset--spacing--40);padding-bottom:var(--wp--preset--spacing--40)\"><div class=\"wp-block-group__inner-container is-layout-constrained wp-block-group-is-layout-constrained\">\n<h3 class=\"wp-block-heading\">All hail the cartographer<\/h3>\n\n\n\n<p>Cartographers are heroes in any organization.<\/p>\n\n\n\n<p>And every hero has a secret.<\/p>\n\n\n\n<p>Don&#8217;t go through life keeping yours hidden :)<\/p>\n\n\n\n<p>Let these map types help you <strong>discover your full potential<\/strong> to become the hero you&#8217;ve always wanted to be.<\/p>\n\n\n\n<p>So which map types are you eyeing in your next masterpiece?<\/p>\n\n\n\n<p>Let us know with a comment below.<\/p>\n<\/div><\/div>\n","protected":false},"excerpt":{"rendered":"<p>From cartograms to contour maps, cartographers are like heroes. And every hero has a secret. If you want to build unbeatable maps, here are 25 map types.<\/p>\n","protected":false},"author":2,"featured_media":19117,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_kad_blocks_custom_css":"","_kad_blocks_head_custom_js":"","_kad_blocks_body_custom_js":"","_kad_blocks_footer_custom_js":"","_kad_post_transparent":"default","_kad_post_title":"default","_kad_post_layout":"default","_kad_post_sidebar_id":"","_kad_post_content_style":"default","_kad_post_vertical_padding":"default","_kad_post_feature":"","_kad_post_feature_position":"","_kad_post_header":false,"_kad_post_footer":false,"_kad_post_classname":"","footnotes":""},"categories":[108],"tags":[437],"class_list":["post-19041","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-maps-cartography","tag-map-types"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.6 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>25 Map Types for Building Unbeatable Maps - GIS Geography<\/title>\n<meta name=\"description\" content=\"From cartograms to contour maps, cartographers are like heroes. 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