{"id":1323,"date":"2026-07-27T02:00:51","date_gmt":"2026-07-27T02:00:51","guid":{"rendered":"https:\/\/www.vpascode.com\/id\/docs\/uncategorized\/echarts-playbook\/scatter-chart-bivariate-distribution-and-point-clustering\/"},"modified":"2026-07-29T08:49:31","modified_gmt":"2026-07-29T08:49:31","slug":"scatter-chart-bivariate-distribution-and-point-clustering-guide","status":"publish","type":"ld_docs","link":"https:\/\/www.vpascode.com\/id\/docs\/vpascode-docs\/echarts-playbook\/scatter-chart-bivariate-distribution-and-point-clustering-guide\/","title":{"rendered":"Scatter Chart: Bivariate Distribution and Point Clustering"},"content":{"rendered":"

When exploring relationships or correlations between two continuous variables, the Scatter Chart<\/strong> (or Scatter Plot) is the primary analytical tool. By plotting data points across a two-dimensional grid based on numeric X and Y coordinates, scatter charts help reveal clusters, outliers, trends, and correlations<\/em> across complex datasets, such as marketing spend versus lead generation or employee experience versus salary.<\/p>\n

The Mechanics of Scatter Charts<\/h2>\n

Unlike category-driven column or line charts, scatter charts use numeric value axes for both dimensions. In Apache ECharts, setting type: 'scatter'<\/code> expects data formatted as two-element numerical coordinate arrays [x, y]<\/code> or data objects mapped across numerical xAxis<\/code> and yAxis<\/code> configurations.<\/p>\n

1. Essential Setup<\/h3>\n

To build a basic scatter chart, define type: 'value'<\/code> on both axes and provide numeric coordinate pairs within your series data array:<\/p>\n

\r\n
\r\n ECharts<\/span>\r\n \r\n <\/path><\/path><\/svg>\r\n Edit ECharts in VPasCode<\/span>\r\n <\/a>\r\n <\/div>\r\n
\r\n 
option = {\r\n    tooltip: {\r\n        trigger: 'item',\r\n        formatter: 'X: {c0}\r\nY: {c1}'\r\n    },\r\n    xAxis: {\r\n        type: 'value',\r\n        name: 'Ad Spend ($k)'\r\n    },\r\n    yAxis: {\r\n        type: 'value',\r\n        name: 'Conversions'\r\n    },\r\n    series: [\r\n        {\r\n            type: 'scatter',\r\n            symbolSize: 12,\r\n            data: [\r\n                [10.0, 8.04],\r\n                [8.07, 6.95],\r\n                [13.0, 7.58],\r\n                [9.05, 8.81],\r\n                [11.0, 8.33],\r\n                [14.0, 9.96],\r\n                [6.03, 7.24],\r\n                [12.0, 4.26],\r\n                [7.08, 4.82],\r\n                [5.02, 5.68]\r\n            ]\r\n        }\r\n    ]\r\n};<\/code><\/pre>                <\/div>\r\n                
\r\n                    \r\n                        <\/path><\/path><\/svg>\r\n                        Edit ECharts in VPasCode<\/span>\r\n                    <\/a>\r\n                <\/div>\r\n            <\/div>\r\n            \n
\"\"<\/p>\n
 <\/p>\n
Advanced Structural Techniques<\/h2>\n
Scatter charts can incorporate dynamic sizing to represent a third variable (bubble chart) or display multi-category cluster overlays.<\/p>\n
1. Variable Point Sizing (Bubble Effect)<\/h3>\n
Pass a custom function or data value mapping to symbolSize<\/code> to encode a third quantitative metric into point size:<\/p>\n            
\r\n                                
\r\n                    ECharts<\/span>\r\n                    \r\n                        <\/path><\/path><\/svg>\r\n                        Edit ECharts in VPasCode<\/span>\r\n                    <\/a>\r\n                <\/div>\r\n                                
\r\n                    
option = {\r\n    tooltip: {\r\n        trigger: 'item',\r\n        formatter: function (param) {\r\n            return 'Spend: $' + param.data[0] + 'k\r\n' +\r\n                   'Revenue: $' + param.data[1] + 'k\r\n' +\r\n                   'ROAS: ' + param.data[2] + 'x';\r\n        }\r\n    },\r\n    xAxis: {\r\n        type: 'value',\r\n        name: 'Spend'\r\n    },\r\n    yAxis: {\r\n        type: 'value',\r\n        name: 'Revenue'\r\n    },\r\n    series: [\r\n        {\r\n            name: 'Campaigns',\r\n            type: 'scatter',\r\n            symbolSize: function (data) {\r\n                return data[2] * 5; \/\/ Scale bubble size by third variable\r\n            },\r\n            data: [\r\n                [15, 120, 8.0],\r\n                [25, 180, 7.2],\r\n                [10, 50, 5.0],\r\n                [40, 380, 9.5],\r\n                [30, 210, 7.0]\r\n            ],\r\n            itemStyle: {\r\n                color: '#5470c6',\r\n                opacity: 0.7\r\n            }\r\n        }\r\n    ]\r\n};<\/code><\/pre>                <\/div>\r\n                
\r\n                    \r\n                        <\/path><\/path><\/svg>\r\n                        Edit ECharts in VPasCode<\/span>\r\n                    <\/a>\r\n                <\/div>\r\n            <\/div>\r\n            \n
\"\"<\/p>\n
 <\/p>\n
Fine-Tuning Layout and Styling<\/h2>\n
Controlling marker opacity and adding target trendlines prevents dense clusters from turning into solid visual blobs.<\/p>\n
1. Translucent Markers and Cluster Styling<\/h3>\n
Applying border strokes and semi-transparent item fills keeps overlapping data points visible within high-density clusters:<\/p>\n            
\r\n                                
\r\n                    ECharts<\/span>\r\n                    \r\n                        <\/path><\/path><\/svg>\r\n                        Edit ECharts in VPasCode<\/span>\r\n                    <\/a>\r\n                <\/div>\r\n                                
\r\n                    
option = {\r\n    grid: {\r\n        left: '10%',\r\n        right: '10%',\r\n        containLabel: true\r\n    },\r\n    xAxis: {\r\n        type: 'value',\r\n        scale: true \/\/ Auto-scale axis min\/max to fit data tightly\r\n    },\r\n    yAxis: {\r\n        type: 'value',\r\n        scale: true\r\n    },\r\n    series: [\r\n        {\r\n            type: 'scatter',\r\n            symbolSize: 16,\r\n            itemStyle: {\r\n                color: 'rgba(84, 112, 198, 0.6)',\r\n                borderColor: '#5470c6',\r\n                borderWidth: 2\r\n            },\r\n            data: [\r\n                [161.2, 51.6], [167.5, 59.0], [159.5, 49.2], [157.0, 63.0],\r\n                [155.8, 53.6], [170.0, 59.0], [159.1, 47.6], [166.0, 69.8],\r\n                [176.2, 66.8], [160.2, 75.2], [172.5, 55.2], [170.9, 54.2]\r\n            ]\r\n        }\r\n    ]\r\n};<\/code><\/pre>                <\/div>\r\n                
\r\n                    \r\n                        <\/path><\/path><\/svg>\r\n                        Edit ECharts in VPasCode<\/span>\r\n                    <\/a>\r\n                <\/div>\r\n            <\/div>\r\n            \n
\"\"<\/p>\n
 <\/p>\n
Strategic Best Practices<\/h2>\n
    \n
  • Enable scale: true<\/code> on Numeric Axes:<\/strong> Set scale: true<\/code> on both numeric axes so the plot auto-zooms to the relevant data range rather than forcing the origin at (0,0)<\/code>.<\/li>\n
  • Use Fill Transparency for Overlaps:<\/strong> When plotting hundreds of points, reduce fill opacity to around 0.4\u20130.6 so dense overlap zones appear naturally darker.<\/li>\n
  • Provide Contextual Axis Labels:<\/strong> Always label X and Y axes explicitly with measurement units (e.g., “$ in Thousands” or “Latency in ms”) so values are immediately clear without checking tooltips.<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"
    When exploring relationships or correlations between two continuous variables, the Scatter Chart (or Scatter Plot) is the primary analytical tool. By plotting data points across a two-dimensional grid based on numeric X and Y coordinates, scatter charts help reveal clusters,…<\/p>\n","protected":false},"author":3,"featured_media":0,"parent":1254,"menu_order":11,"template":"","meta":{"inline_featured_image":false,"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"default","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"set","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center 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Scatter Chart Guide | VPasCode Text to Diagram Tool<\/title>\n<meta name=\"description\" content=\"Analyze correlation trends between data variables using ECharts scatter plots in VPasCode, a diagram-as-code tool.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.vpascode.com\/id\/docs\/vpascode-docs\/echarts-playbook\/scatter-chart-bivariate-distribution-and-point-clustering-guide\/\" \/>\n<meta property=\"og:locale\" content=\"id_ID\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"ECharts Scatter Chart Guide | VPasCode Text to Diagram Tool\" \/>\n<meta property=\"og:description\" content=\"Analyze correlation trends between data variables using ECharts scatter plots in VPasCode, a diagram-as-code tool.\" \/>\n<meta property=\"og:url\" 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