Unlike choropleth maps, cartograms encode data using area rather than color, resulting in distorted geographic boundaries. In this example, states are rescaled around their centroid, preserving local shape but not topology. Inspired by Zachary Johnson. Non-continguous cartogram design invented by Judy Olsen. U.S. state and county boundaries from the U.S. Census Bureau, simplified using GDAL and MapShaper.
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| "class": "Technology", | |
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| "class": "Never-Ending Language Learning", | |
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| "class": "Dependency grammar", | |
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| "class": "Physical body", | |
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| "class": "Abstract and concrete", | |
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| ], | |
| "Community": [ | |
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| "class": "Creativity", | |
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| "class": "Artificial intelligence", | |
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| "class": "Education", | |
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| "class": "Speech recognition", | |
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| "class": "Linguistics", | |
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| "class": "Entropy (information theory)", | |
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| "class": "Technology", | |
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| "class": "Machine translation", | |
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| { | |
| "class": "Storage (memory)", | |
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| { | |
| "class": "Light", | |
| "similarity": 0.051400043000192824 | |
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| { | |
| "class": "Perception", | |
| "similarity": 0.050634665682152365 | |
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| { | |
| "class": "Programming language", | |
| "similarity": 0.048724704627334144 | |
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| { | |
| "class": "Mass", | |
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| { | |
| "class": "Biology", | |
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| "class": "Skill", | |
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| "class": "Engineering", | |
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| "class": "Universe", | |
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| "class": "Recursion", | |
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| "class": "Idea", | |
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| { | |
| "class": "Physical body", | |
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| { | |
| "class": "The Matrix", | |
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| "class": "Relational database", | |
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| { | |
| "class": "Information", | |
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| { | |
| "class": "Deep learning", | |
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| "class": "Thought", | |
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| { | |
| "class": "Dependency grammar", | |
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| { | |
| "class": "Knowledge representation and reasoning", | |
| "similarity": 0.039611273911789896 | |
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| { | |
| "class": "Space", | |
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| "class": "Belief", | |
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| "class": "Natural science", | |
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| "class": "Data compression", | |
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| "class": "Inductive reasoning", | |
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| { | |
| "class": "Science", | |
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| { | |
| "class": "Physics", | |
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| "class": "Meaning (linguistics)", | |
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| { | |
| "class": "Supply and demand", | |
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| { | |
| "class": "Neuroscience", | |
| "similarity": 0.03178093614535593 | |
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| { | |
| "class": "Sensory system", | |
| "similarity": 0.031031766924514988 | |
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| { | |
| "class": "Expert system", | |
| "similarity": 0.030839923744141837 | |
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| { | |
| "class": "Sentence (linguistics)", | |
| "similarity": 0.02924480127646601 | |
| }, | |
| { | |
| "class": "The Matrix (franchise)", | |
| "similarity": 0.02842276997395581 | |
| }, | |
| { | |
| "class": "Graph database", | |
| "similarity": 0.02773607964996245 | |
| }, | |
| { | |
| "class": "Automatic Language Translator", | |
| "similarity": 0.027717993912665603 | |
| }, | |
| { | |
| "class": "Multilayer perceptron", | |
| "similarity": 0.02742330921520509 | |
| }, | |
| { | |
| "class": "The Matrix Reloaded", | |
| "similarity": 0.02703802672049581 | |
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| { | |
| "class": "Visual system", | |
| "similarity": 0.026514728964831306 | |
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| { | |
| "class": "Natural language", | |
| "similarity": 0.0262358928693357 | |
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| { | |
| "class": "Brainstem", | |
| "similarity": 0.02547265654838682 | |
| }, | |
| { | |
| "class": "Phrase", | |
| "similarity": 0.025213879496392883 | |
| }, | |
| { | |
| "class": "Conveyed concept", | |
| "similarity": 0.025162484175576514 | |
| }, | |
| { | |
| "class": "Demand", | |
| "similarity": 0.02495601563131364 | |
| }, | |
| { | |
| "class": "Central nervous system", | |
| "similarity": 0.024758188132792746 | |
| }, | |
| { | |
| "class": "Spacetime", | |
| "similarity": 0.02475143759026334 | |
| }, | |
| { | |
| "class": "Conceptual framework", | |
| "similarity": 0.024546976651943445 | |
| }, | |
| { | |
| "class": "Structure", | |
| "similarity": 0.020012860195176858 | |
| }, | |
| { | |
| "class": "Code", | |
| "similarity": 0.019942803090650496 | |
| }, | |
| { | |
| "class": "Hierarchical control system", | |
| "similarity": 0.01971525730756035 | |
| }, | |
| { | |
| "class": "Hierarchical database model", | |
| "similarity": 0.01967333910953013 | |
| }, | |
| { | |
| "class": "Clause", | |
| "similarity": 0.01861186357973814 | |
| }, | |
| { | |
| "class": "Cognitive robotics", | |
| "similarity": 0.01813601052207878 | |
| }, | |
| { | |
| "class": "The Matrix Revolutions", | |
| "similarity": 0.017366537050369245 | |
| }, | |
| { | |
| "class": "Inference engine", | |
| "similarity": 0.01562903919069506 | |
| }, | |
| { | |
| "class": "Supply (economics)", | |
| "similarity": 0.014353395835352976 | |
| }, | |
| { | |
| "class": "Autoencoder", | |
| "similarity": 0.013152426262299125 | |
| }, | |
| { | |
| "class": "Abstract and concrete", | |
| "similarity": 0.012979925426081264 | |
| }, | |
| { | |
| "class": "Neural pathway", | |
| "similarity": 0.011489703890022074 | |
| }, | |
| { | |
| "class": "Never-Ending Language Learning", | |
| "similarity": 0.010987716305764237 | |
| }, | |
| { | |
| "class": "Neo4j", | |
| "similarity": 0.00627527037561404 | |
| }, | |
| { | |
| "class": "Knowledge acquisition", | |
| "similarity": 0.005835225446711936 | |
| } | |
| ] | |
| } |
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| <!DOCTYPE html> | |
| <meta charset="utf-8"> | |
| <title>Non-Contiguous Cartogram</title> | |
| <style> | |
| .land { | |
| fill: #fff; | |
| stroke: #ccc; | |
| } | |
| .state { | |
| fill: #ccc; | |
| stroke: #666; | |
| } | |
| </style> | |
| <body> | |
| <script src="http://d3js.org/d3.v3.min.js"></script> | |
| <script src="http://d3js.org/topojson.v1.min.js"></script> | |
| <script> | |
| // Ratio of Obese (BMI >= 30) in U.S. Adults, CDC 2008 | |
| var valueById = [ | |
| NaN, .187, .198, NaN, .133, .175, .151, NaN, .100, .125, | |
| .171, NaN, .172, .133, NaN, .108, .142, .167, .201, .175, | |
| .159, .169, .177, .141, .163, .117, .182, .153, .195, .189, | |
| .134, .163, .133, .151, .145, .130, .139, .169, .164, .175, | |
| .135, .152, .169, NaN, .132, .167, .139, .184, .159, .140, | |
| .146, .157, NaN, .139, .183, .160, .143 | |
| ]; | |
| var path = d3.geo.path(); | |
| var svg = d3.select("body").append("svg") | |
| .attr("width", 960) | |
| .attr("height", 500); | |
| d3.json("/d/4090846/us.json", function(error, us) { | |
| svg.append("path") | |
| .datum(topojson.feature(us, us.objects.land)) | |
| .attr("class", "land") | |
| .attr("d", path); | |
| svg.selectAll(".state") | |
| .data(topojson.feature(us, us.objects.states).features) | |
| .enter().append("path") | |
| .attr("class", "state") | |
| .attr("d", path) | |
| .attr("transform", function(d) { | |
| var centroid = path.centroid(d), | |
| x = centroid[0], | |
| y = centroid[1]; | |
| return "translate(" + x + "," + y + ")" | |
| + "scale(" + Math.sqrt(valueById[d.id] * 5 || 0) + ")" | |
| + "translate(" + -x + "," + -y + ")"; | |
| }) | |
| .style("stroke-width", function(d) { | |
| return 1 / Math.sqrt(valueById[d.id] * 5 || 1); | |
| }); | |
| }); | |
| </script> |
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