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常见图表","f7c7b341",{"type":1088,"value":1089,"toc":3463},"minimark",[1090,1094,1098,1102,1106,1157,1161,1166,1206,1209,1212,1239,1242,1245,1277,1280,1283,1310,1313,1316,1329,1332,1335,1362,1365,1368,1395,1398,1401,1428,1432,1435,1438,1441,1550,1552,1555,1587,1589,1592,1624,1626,1629,1661,1664,1667,1694,1696,1699,1731,1733,1736,1768,1771,1775,1778,1894,1900,1902,1905,1937,1939,1942,1974,1978,1981,2008,2012,2015,2047,2051,2054,2086,2091,2174,2185,2188,2193,2207,2212,2215,2217,2221,2303,2412,2414,2417,2449,2453,2456,2488,2492,2495,2527,2531,2534,2566,2568,2571,2603,2605,2608,2640,2642,2646,2713,2726,2728,2731,2763,2765,2768,2800,2802,2805,2837,2839,2843,2941,2946,2948,2951,2983,2985,2988,3020,3022,3025,3057,3059,3062,3094,3096,3100,3196,3209,3211,3214,3246,3248,3251,3283,3285,3288,3320,3322,3325,3357,3359,3363,3366,3369,3372,3375,3378,3381,3384,3387,3390,3393],[1091,1092,1093],"h1",{"id":1093},"数据可视化的常见图表",[1095,1096,1097],"p",{},"本文主要介绍数据可视化的常见图表，主要包括其基础信息、构成与视觉通道、适用场景、优缺点和常见变体",[1091,1099,1101],{"id":1100},"一比较与排序图表","一、比较与排序图表",[1103,1104,1105],"h2",{"id":1105},"图表特性对比",[1107,1108,1109,1122],"table",{},[1110,1111,1112],"thead",{},[1113,1114,1115,1119],"tr",{},[1116,1117,1118],"th",{},"维度",[1116,1120,1121],{},"表现",[1123,1124,1125,1137,1147],"tbody",{},[1113,1126,1127,1134],{},[1128,1129,1130],"td",{},[1131,1132,1133],"strong",{},"数据量大",[1128,1135,1136],{},"平行坐标图、词云图、环柱状图能承载大量数据；柱状图、哑铃图、雷达图数据多时可读性下降",[1113,1138,1139,1144],{},[1128,1140,1141],{},[1131,1142,1143],{},"信息精确性",[1128,1145,1146],{},"高：子弹图、柱状图、哑铃图（依托坐标轴）；低：词云图、平行坐标图与雷达图、环柱状图",[1113,1148,1149,1154],{},[1128,1150,1151],{},[1131,1152,1153],{},"传递效率",[1128,1155,1156],{},"高效表现数值：哑铃图、柱状图、环柱状图；词云图靠面积\u002F颜色直观看关键词；雷达图、子弹图、平行坐标图维度多，比较时耗时更长",[1158,1159,1160],"h3",{"id":1160},"柱状图",[1162,1163],"img",{"src":1164,"width":1165},"\u002F数据可视化\u002Fbar-chart.png","50%",[1167,1168,1169,1176,1182,1188,1194,1200],"ul",{},[1170,1171,1172,1175],"li",{},[1131,1173,1174],{},"基础信息","：以矩形高度为测度的统计图，比较两个及以上类别的定量指标；由横纵轴、矩形柱、图例组成，可纵向或横向。",[1170,1177,1178,1181],{},[1131,1179,1180],{},"视觉通道","：类别→颜色+位置；数值→长度。横轴放定类\u002F定序\u002F定距数据，纵轴放定量数据。",[1170,1183,1184,1187],{},[1131,1185,1186],{},"适用场景","：分类数据的对比与排序，堆叠可同时看数值变化与占比；类别过多时不适用。",[1170,1189,1190,1193],{},[1131,1191,1192],{},"注意","：纵轴须从 0 起；柱间距约为柱宽一半；同组同色系、强调用对比色；多系列按逻辑排序",[1170,1195,1196,1199],{},[1131,1197,1198],{},"优缺点","：优点是分类对比直观；缺点是类别多则标签拥挤、不擅长表达关系\u002F层级\u002F分布、依赖坐标轴设置易误导。",[1170,1201,1202,1205],{},[1131,1203,1204],{},"变体","：横向条形图（类别多时适用）、径向条形图（极坐标、空间利用率高）、双向柱状图（正反对比）、象形柱状图（具象语义）、3D 柱状图",[1158,1207,1208],{"id":1208},"环柱状图",[1162,1210],{"src":1211,"width":1165},"\u002F数据可视化\u002Fcircular-bar-chart.png",[1167,1213,1214,1219,1224,1229,1234],{},[1170,1215,1216,1218],{},[1131,1217,1174],{},"：柱状图沿圆周弯曲，柱子变成绕圆心的弧形条，紧凑美观。",[1170,1220,1221,1223],{},[1131,1222,1180],{},"：类别→颜色+位置；数值→面积（弧长+半径）。精度低于长度，不宜精确对比（玫瑰图用长度，性质不同）。",[1170,1225,1226,1228],{},[1131,1227,1186],{},"：类别多、想省横向空间且重设计感，如排行榜、活动榜单。",[1170,1230,1231,1233],{},[1131,1232,1192],{},"：内圆半径别太短；不宜表现差距过大或过于接近的数据；纵轴尽量统一；半径平方关系会夸大差异；数据太少改用玫瑰图\u002F柱状图；复杂比较不宜用极坐标。",[1170,1235,1236,1238],{},[1131,1237,1204],{},"：环形柱状堆叠图、玫瑰图（南丁格尔图）、半圆柱状图。",[1158,1240,1241],{"id":1241},"子弹图",[1162,1243],{"src":1244,"width":1165},"\u002F数据可视化\u002Fbullet-chart.png",[1167,1246,1247,1252,1257,1262,1267,1272],{},[1170,1248,1249,1251],{},[1131,1250,1174],{},"：狭小空间内同时展示实际值、目标值与评价区间，取代仪表盘式表达。",[1170,1253,1254,1256],{},[1131,1255,1180],{},"：类别→颜色视觉通道（背景色条）；数值→坐标轴位置（数据条、目标刻度）。",[1170,1258,1259,1261],{},[1131,1260,1186],{},"：仪表盘、KPI 监控，业绩完成进度，判断指标是否达标；适用不超过 5 条定量数据、2–5 个定性区间。",[1170,1263,1264,1266],{},[1131,1265,1192],{},"：标签按从大到小、从左到右排列；主标签用纯黑、刻度用浅灰；定性区间 3–5 个为宜；主标记一般垂直居中",[1170,1268,1269,1271],{},[1131,1270,1198],{},"：优点是空间小信息密度高，缺点是有学习成本。",[1170,1273,1274,1276],{},[1131,1275,1204],{},"：反向子弹图（负面值）、层叠子弹图（阶段数据）",[1158,1278,1279],{"id":1279},"哑铃图",[1162,1281],{"src":1282,"width":1165},"\u002F数据可视化\u002Fdumbbell-chart.png",[1167,1284,1285,1290,1295,1300,1305],{},[1170,1286,1287,1289],{},[1131,1288,1174],{},"：一条线连两个圆点，表示同一对象在两种状态下的数值，形似哑铃。",[1170,1291,1292,1294],{},[1131,1293,1180],{},"：区分高低值→颜色+位置；比较数值大小→位置；比较极差→长度。水平轴定量、垂直轴定性。",[1170,1296,1297,1299],{},[1131,1298,1186],{},"：前后变化或两组对比，如改革前后、男女差异、去年与今年、外界刺激后的变化。",[1170,1301,1302,1304],{},[1131,1303,1192],{},"：数据边界差异大而端点差异小时杠杆过短不易观察，需颜色辅助；只适合比较起止两点；数据过多过密则失效，变化幅度难精确区分时改用双折线图或瀑布图。",[1170,1306,1307,1309],{},[1131,1308,1204],{},"：火柴图、纵向哑铃图、多变量哑铃图、甘特图。",[1158,1311,1312],{"id":1312},"马赛克图",[1162,1314],{"src":1315,"width":1165},"\u002F数据可视化\u002Fmosaic-chart.png",[1167,1317,1318,1324],{},[1170,1319,1320,1323],{},[1131,1321,1322],{},"构成与视觉通道","：纵横轴 + 色块 + 图例，表现二维数据的比较关系。",[1170,1325,1326,1328],{},[1131,1327,1186],{},"：二维分类变量比较、边际分析。",[1158,1330,1331],{"id":1331},"雷达图",[1162,1333],{"src":1334,"width":1165},"\u002F数据可视化\u002Fradar-chart.png",[1167,1336,1337,1342,1347,1352,1357],{},[1170,1338,1339,1341],{},[1131,1340,1174],{},"：多变量数据沿中心发散的多轴排布，各指标连成闭合多边形。",[1170,1343,1344,1346],{},[1131,1345,1180],{},"：类别→位置；性能比较→面积（形状）。",[1170,1348,1349,1351],{},[1131,1350,1186],{},"：少数对象的多维综合评价与对比，如能力模型、产品多属性；适用 5 组以内、不超过 30 维。",[1170,1353,1354,1356],{},[1131,1355,1192],{},"：组数和变量都不宜过多（多边形\u002F坐标轴过密、上层遮挡下层）；数值需先标准化以便同级比较；不擅长精确比较；形状面积随值的平方增长，会夸大微小差异。",[1170,1358,1359,1361],{},[1131,1360,1204],{},"：填充雷达图、极坐标图（含南丁格尔玫瑰图）。",[1158,1363,1364],{"id":1364},"平行坐标图",[1162,1366],{"src":1367,"width":1165},"\u002F数据可视化\u002Fparallel-coordinates.png",[1167,1369,1370,1375,1380,1385,1390],{},[1170,1371,1372,1374],{},[1131,1373,1174],{},"：一组平行纵轴表示不同变量，每条记录在各轴取值连成折线，用于高维\u002F多元数据。",[1170,1376,1377,1379],{},[1131,1378,1180],{},"：类别→颜色；变量在某属性下的表现→位置。Y 轴定序、X 轴属性类别、颜色区分数据种类。",[1170,1381,1382,1384],{},[1131,1383,1186],{},"：探索多变量关系、发现聚类与异常。",[1170,1386,1387,1389],{},[1131,1388,1192],{},"：颜色不宜多以免线条混乱；适当增大轴间距提升可分辨性、必要时缩放；保证线条归属清晰；各轴量纲可能不同，需标准化。",[1170,1391,1392,1394],{},[1131,1393,1204],{},"：三维平行坐标图、星形平行坐标图。",[1158,1396,1397],{"id":1397},"词云图",[1162,1399],{"src":1400,"width":1165},"\u002F数据可视化\u002Fword-cloud.png",[1167,1402,1403,1408,1413,1418,1423],{},[1170,1404,1405,1407],{},[1131,1406,1174],{},"：词语按频率以不同字号、颜色排布，字号越大权重越高。",[1170,1409,1410,1412],{},[1131,1411,1180],{},"：颜色→颜色视觉通道；大小→面积；图形\u002F区域图片→形状；位置→位置。文本内容定类、大小定比、颜色定量。",[1170,1414,1415,1417],{},[1131,1416,1186],{},"：快速展示高频关键词、舆情与主题热点的概览传播。",[1170,1419,1420,1422],{},[1131,1421,1192],{},"：文字不可重叠；选与主题相关的轮廓图；个别权值过大时取 log 压缩差距；文本太少或权重区分不明显则效果差；面积难感知精确数值、长词易造成视觉误差；不绘无意义高频词。",[1170,1424,1425,1427],{},[1131,1426,1204],{},"：形状词云、分类着色词云。",[1091,1429,1431],{"id":1430},"二局部与整体可视化图像","二、局部与整体可视化图像",[1095,1433,1434],{},"与比较排序可视化图形不同，整体与局部可视化图形重点关注局部占整体的比重，有时也被称为占比类可视化图形，更多使用面积来表示整体与局部的关系",[1095,1436,1437],{},"很多可视化图形可同时展示比较于排序以及整体与局部的关系，比如矩形树图",[1095,1439,1440],{},"整体与局部可视化图形可以看做对定量树形层级关系的一种可视化，可以分为两层与多层，单路径和多路径",[1107,1442,1443,1457],{},[1110,1444,1445],{},[1113,1446,1447,1451,1454],{},[1116,1448,1450],{"align":1449},"left","图表",[1116,1452,1453],{"align":1449},"优势",[1116,1455,1456],{"align":1449},"局限性",[1123,1458,1459,1472,1485,1498,1511,1524,1537],{},[1113,1460,1461,1466,1469],{},[1128,1462,1463],{"align":1449},[1131,1464,1465],{},"维恩图",[1128,1467,1468],{"align":1449},"展示相交关系",[1128,1470,1471],{"align":1449},"仅适合少量集合",[1113,1473,1474,1479,1482],{},[1128,1475,1476],{"align":1449},[1131,1477,1478],{},"饼图",[1128,1480,1481],{"align":1449},"简单易懂的占比显示",[1128,1483,1484],{"align":1449},"分类过多效果差，信息少",[1113,1486,1487,1492,1495],{},[1128,1488,1489],{"align":1449},[1131,1490,1491],{},"环形图",[1128,1493,1494],{"align":1449},"显示文本信息，支持多环环比",[1128,1496,1497],{"align":1449},"需避免过度嵌套",[1113,1499,1500,1505,1508],{},[1128,1501,1502],{"align":1449},[1131,1503,1504],{},"旭日图",[1128,1506,1507],{"align":1449},"多层数据比例显示",[1128,1509,1510],{"align":1449},"复杂度随层级增加",[1113,1512,1513,1518,1521],{},[1128,1514,1515],{"align":1449},[1131,1516,1517],{},"圆锥树图",[1128,1519,1520],{"align":1449},"分类型数据一目了然",[1128,1522,1523],{"align":1449},"不宜过于细节，存在空间留白",[1113,1525,1526,1531,1534],{},[1128,1527,1528],{"align":1449},[1131,1529,1530],{},"矩形树图",[1128,1532,1533],{"align":1449},"空间利用率高",[1128,1535,1536],{"align":1449},"级别过多不可准确读",[1113,1538,1539,1544,1547],{},[1128,1540,1541],{"align":1449},[1131,1542,1543],{},"漏斗图",[1128,1545,1546],{"align":1449},"比较各业务流程",[1128,1548,1549],{"align":1449},"更适用于流程数据",[1158,1551,1478],{"id":1478},[1162,1553],{"src":1554,"width":1165},"\u002F数据可视化\u002Fpie-chart.png",[1167,1556,1557,1562,1567,1572,1577,1582],{},[1170,1558,1559,1561],{},[1131,1560,1174],{},"：把一个圆按各部分占比切成扇形，表现部分占整体的比例。",[1170,1563,1564,1566],{},[1131,1565,1180],{},"：类别→颜色视觉通道；占比→面积，形状，角度",[1170,1568,1569,1571],{},[1131,1570,1186],{},"：2-9个类别占比情况，如市场份额、预算分配。",[1170,1573,1574,1576],{},[1131,1575,1192],{},"：分类不宜超过五六块（多的较小的标为其他）、份额最大的放12点方向，不要用3d，分割类别表示强调，排序并标注百分比",[1170,1578,1579,1581],{},[1131,1580,1198],{},"：优点是占比直观，缺点是切片多难读、不适合精确比较",[1170,1583,1584,1586],{},[1131,1585,1204],{},"：分割饼图（强调），华夫饼图（识别小比例差异）、斯贝图（饼图加玫瑰图）、玫瑰图、双层饼图（重属关系）、复合饼图（复杂数据）",[1158,1588,1491],{"id":1491},[1162,1590],{"src":1591,"width":1165},"\u002F数据可视化\u002Fdonut-chart.png",[1167,1593,1594,1599,1604,1609,1614,1619],{},[1170,1595,1596,1598],{},[1131,1597,1174],{},"：中间挖空的饼图，中心可放总数或标题。",[1170,1600,1601,1603],{},[1131,1602,1180],{},"：类别→颜色视觉通道；占比→形状，长度。",[1170,1605,1606,1608],{},[1131,1607,1186],{},"：用途与饼图相同，更现代美观，常用于仪表盘和看板。",[1170,1610,1611,1613],{},[1131,1612,1192],{},"：份额最大的放12点方向，分割类别表示强调，中间挖空面积要大",[1170,1615,1616,1618],{},[1131,1617,1198],{},"：优点是美观、中心可复用，缺点同饼图",[1170,1620,1621,1623],{},[1131,1622,1204],{},"：多层环形图、进度环形图、变形环形图",[1158,1625,1504],{"id":1504},[1162,1627],{"src":1628,"width":1165},"\u002F数据可视化\u002Fsunburst-chart.png",[1167,1630,1631,1636,1641,1646,1651,1656],{},[1170,1632,1633,1635],{},[1131,1634,1174],{},"：环形的层级结构图，从内到外逐圈表示数据层级关系（层级和归属）（原点越近层级越高）",[1170,1637,1638,1640],{},[1131,1639,1180],{},"：类别→颜色视觉通道；层级→同心环位置；占比→弧长。",[1170,1642,1643,1645],{},[1131,1644,1186],{},"：带上下级归属的构成数据，如多级分类销售、目录占用。",[1170,1647,1648,1650],{},[1131,1649,1192],{},"：颜色，饱和度区分分类，层级深或分类多时外圈细碎，需交互下钻",[1170,1652,1653,1655],{},[1131,1654,1198],{},"：优点是同时表达占比与层级，缺点是层级多难读。",[1170,1657,1658,1660],{},[1131,1659,1204],{},"：可下钻旭日图、带空值旭日图",[1158,1662,1663],{"id":1663},"圆堆积图",[1162,1665],{"src":1666,"width":1165},"\u002F数据可视化\u002Fcircle-packing.png",[1167,1668,1669,1674,1679,1684,1689],{},[1170,1670,1671,1673],{},[1131,1672,1174],{},"：用大圆套小圆表示包含关系，圆面积代表数值。",[1170,1675,1676,1678],{},[1131,1677,1180],{},"：类别→颜色；占比大小→面积；嵌套→位置。",[1170,1680,1681,1683],{},[1131,1682,1186],{},"：表现层级结构和相对规模，如组织、分类、预算的嵌套构成。",[1170,1685,1686,1688],{},[1131,1687,1192],{},"：层级不要太细，2到3个，表达精确性不建议用，分类占比太小难排布，不要选相差过大\u002F过小的数据值，不难精确比较",[1170,1690,1691,1693],{},[1131,1692,1204],{},"：气泡层级图、打包圆图。",[1158,1695,1530],{"id":1530},[1162,1697],{"src":1698,"width":1165},"\u002F数据可视化\u002Ftreemap.png",[1167,1700,1701,1706,1711,1716,1721,1726],{},[1170,1702,1703,1705],{},[1131,1704,1174],{},"：把整体区域递归切成矩形，面积表示数值，嵌套或相邻表示层级。",[1170,1707,1708,1710],{},[1131,1709,1180],{},"：类别→颜色视觉通道；占比→面积；层级→包含。",[1170,1712,1713,1715],{},[1131,1714,1186],{},"：有限空间里展示大量带层级的占比数据，如磁盘占用、品类构成。",[1170,1717,1718,1720],{},[1131,1719,1192],{},"：分类占比太小时难区分，颜色边框要设计好。不适合没有权重的数据，层级不要太多，按层级最高级别优先级排序",[1170,1722,1723,1725],{},[1131,1724,1198],{},"：优点是空间利用率高、容量大，缺点是面积精度有限。",[1170,1727,1728,1730],{},[1131,1729,1204],{},"：可下钻树图、多类别树图。",[1158,1732,1543],{"id":1543},[1162,1734],{"src":1735,"width":1165},"\u002F数据可视化\u002Ffunnel-chart.png",[1167,1737,1738,1743,1748,1753,1758,1763],{},[1170,1739,1740,1742],{},[1131,1741,1174],{},"：用自上而下逐级收窄的梯形条表示流程各阶段的数量递减。",[1170,1744,1745,1747],{},[1131,1746,1180],{},"：本流程不同环节-> 颜色；各环节留存数量->长度；各阶段转化趋势-> 角度（斜率）",[1170,1749,1750,1752],{},[1131,1751,1186],{},"：分析转化流程流量分析，如从浏览到下单的各环节流失。商业分析，医疗分析",[1170,1754,1755,1757],{},[1131,1756,1192],{},"：各阶段须有先后和递减逻辑",[1170,1759,1760,1762],{},[1131,1761,1198],{},"：优点是流失一目了然、便于找瓶颈，缺点是只适合单向递减流程。",[1170,1764,1765,1767],{},[1131,1766,1204],{},"：金字塔图、对称漏斗图。",[1769,1770],"hr",{},[1091,1772,1774],{"id":1773},"三分布类可视化图","三、分布类可视化图",[1095,1776,1777],{},"在可视化中用于展示定量数据在其取值范围中的分布特征",[1107,1779,1780,1798],{},[1110,1781,1782],{},[1113,1783,1784,1786,1789,1792,1795],{},[1116,1785,1450],{},[1116,1787,1788],{},"适用样本量",[1116,1790,1791],{},"能看的分布信息",[1116,1793,1794],{},"多组对比",[1116,1796,1797],{},"主要短板",[1123,1799,1800,1819,1838,1857,1875],{},[1113,1801,1802,1807,1810,1813,1816],{},[1128,1803,1804],{},[1131,1805,1806],{},"直方图",[1128,1808,1809],{},"50 条以上单组",[1128,1811,1812],{},"形态、集中、离散、偏态",[1128,1814,1815],{},"弱，叠加易乱",[1128,1817,1818],{},"对组距敏感、只适合单组",[1113,1820,1821,1826,1829,1832,1835],{},[1128,1822,1823],{},[1131,1824,1825],{},"密度图",[1128,1827,1828],{},"中到大、最多 5 组",[1128,1830,1831],{},"平滑形态、概率分布",[1128,1833,1834],{},"中，半透明可叠",[1128,1836,1837],{},"带宽影响大、纵轴不直观",[1113,1839,1840,1845,1848,1851,1854],{},[1128,1841,1842],{},[1131,1843,1844],{},"箱线图",[1128,1846,1847],{},"不超过 12 组",[1128,1849,1850],{},"中位数、四分位、离群",[1128,1852,1853],{},"强",[1128,1855,1856],{},"看不出双峰、隐藏形态",[1113,1858,1859,1864,1867,1870,1872],{},[1128,1860,1861],{},[1131,1862,1863],{},"小提琴图",[1128,1865,1866],{},"大样本、多组",[1128,1868,1869],{},"形态 + 四分位双重信息",[1128,1871,1853],{},[1128,1873,1874],{},"小样本不可靠、复杂",[1113,1876,1877,1882,1885,1888,1891],{},[1128,1878,1879],{},[1131,1880,1881],{},"脊线图",[1128,1883,1884],{},"多组有序类别",[1128,1886,1887],{},"分布随时间\u002F类别演变",[1128,1889,1890],{},"极强",[1128,1892,1893],{},"有遮挡、读数不准",[1895,1896,1897],"blockquote",{},[1095,1898,1899],{},"数据组数承载力：直方图 \u003C 密度图 \u003C 小提琴图 \u003C 脊线图；样本贴合度反过来：小提琴图 \u003C 密度图 \u003C 直方图。",[1158,1901,1806],{"id":1806},[1162,1903],{"src":1904,"width":1165},"\u002F数据可视化\u002Fhistogram.png",[1167,1906,1907,1912,1917,1922,1927,1932],{},[1170,1908,1909,1911],{},[1131,1910,1174],{},"：把连续变量切成相连区间，用矩形高度表示落入各区间的频数，表现分布。",[1170,1913,1914,1916],{},[1131,1915,1180],{},"：数值的组距，频数密度，频率→位置；频数→面积；",[1170,1918,1919,1921],{},[1131,1920,1186],{},"：50条以上的定量数据，观察集中趋势、离散程度和形态，如成绩、年龄分布。",[1170,1923,1924,1926],{},[1131,1925,1192],{},"：合适的组距（不能过大过小），区间宽度明显影响样貌，过宽掩盖细节、过窄杂乱。样本量>= 50 个 单个类别单个变量和柱状图比，宽度有意义",[1170,1928,1929,1931],{},[1131,1930,1198],{},"：优点是分布形态直观，缺点是对分组敏感。",[1170,1933,1934,1936],{},[1131,1935,1204],{},"：含拟合曲线直方图、镜像直方图、坐标变换直方图，分组直方图",[1158,1938,1825],{"id":1825},[1162,1940],{"src":1941,"width":1165},"\u002F数据可视化\u002Fdensity-plot.png",[1167,1943,1944,1949,1954,1959,1964,1969],{},[1170,1945,1946,1948],{},[1131,1947,1174],{},"：直方图的平滑版本，用连续曲线估计概率分布，曲线下面积代表概率。",[1170,1950,1951,1953],{},[1131,1952,1180],{},"：类别-> 颜色； 取值范围-> 位置；种类，特征-> 形状 ；到x轴距离->长度",[1170,1955,1956,1958],{},[1131,1957,1186],{},"：展示平滑分布形态、对比多组分布，如不同人群收入分布。",[1170,1960,1961,1963],{},[1131,1962,1192],{},"：不超过4个，各曲线和横轴围起来面积为1，要连续变量，不适合起伏波动过大的，极值差异不能过大，重叠要用半透明。带宽越大曲线越平滑，样本大数据密小带宽，样本小数据不密大带宽",[1170,1965,1966,1968],{},[1131,1967,1198],{},"：优点是平滑、便于叠加，缺点是纵轴是密度不直观。",[1170,1970,1971,1973],{},[1131,1972,1204],{},"：核密度图、二维密度图、堆叠密度图。",[1158,1975,1977],{"id":1976},"箱线图-多组数据分布特征比较","箱线图 （多组数据分布特征比较）",[1162,1979],{"src":1980,"width":1165},"\u002F数据可视化\u002Fbox-plot.png",[1167,1982,1983,1988,1993,1998,2003],{},[1170,1984,1985,1987],{},[1131,1986,1174],{},"：用\"箱子加须\"概括最小值、下四分位、中位数、上四分位、最大值，箱外点为离群值。",[1170,1989,1990,1992],{},[1131,1991,1180],{},"：类别→颜色，位置；数据分布→长度+位置",[1170,1994,1995,1997],{},[1131,1996,1186],{},"：比较多组数据的水平和离散度并发现异常，如各班成绩对比。看骗态分布，异常值集中较大值一侧右偏，较小值一侧左偏",[1170,1999,2000,2002],{},[1131,2001,1192],{},"：不超过12组，不能提供骗态尾重精确度量，数据不宜过少。无序分组按中位数排列，上限和下限的计算，样本数据的选择",[1170,2004,2005,2007],{},[1131,2006,1204],{},"：带数据点箱线图、分组箱线图、横向箱线图。",[1158,2009,2011],{"id":2010},"小提琴图不同组连续数据异同分析分布特征比较","小提琴图（不同组连续数据异同分析，分布特征比较）",[1162,2013],{"src":2014,"width":1165},"\u002F数据可视化\u002Fviolin-plot.png",[1167,2016,2017,2022,2027,2032,2037,2042],{},[1170,2018,2019,2021],{},[1131,2020,1174],{},"：箱线图与密度图结合，箱体两侧对称画出密度曲线，外形像小提琴。",[1170,2023,2024,2026],{},[1131,2025,1180],{},"：区间数值→位置；密度→长度；分组-> 颜色；数据量-> 面积",[1170,2028,2029,2031],{},[1131,2030,1186],{},"：对比多组数据时同时关注分布形状，如不同处理组分布。每个月温度性差异分布",[1170,2033,2034,2036],{},[1131,2035,1192],{},"：要用大数据量，只能比较分布情况没有准确概率密度信息，不能展示异常值",[1170,2038,2039,2041],{},[1131,2040,1198],{},"：优点是信息丰富、可显多峰，缺点是较复杂、要求高。",[1170,2043,2044,2046],{},[1131,2045,1204],{},"：分边小提琴图、叠加散点的小提琴图。",[1158,2048,2050],{"id":2049},"脊线图同一维度几个数据分布情况","脊线图（同一维度几个数据分布情况）",[1162,2052],{"src":2053,"width":1165},"\u002F数据可视化\u002Fridgeline-plot.png",[1167,2055,2056,2061,2066,2071,2076,2081],{},[1170,2057,2058,2060],{},[1131,2059,1174],{},"：把多组密度曲线沿纵向错落叠放，像连绵山脊，又称山脊图。",[1170,2062,2063,2065],{},[1131,2064,1180],{},"：位置颜色和形状",[1170,2067,2068,2070],{},[1131,2069,1186],{},"：展示分布随时间或类别的演变，如各月温度分布、各地区收入分布。",[1170,2072,2073,2075],{},[1131,2074,1192],{},"：连续变量，不适合较小起伏波动，权值差异不能过大，面积大小适当，要连贯的定序数据",[1170,2077,2078,2080],{},[1131,2079,1198],{},"：优点是节省空间，缺点是有遮挡、不便精确读数。",[1170,2082,2083,2085],{},[1131,2084,1204],{},"：渐变着色脊线图。",[1095,2087,2088],{},[1131,2089,2090],{},"直方图与密度图的区别",[1107,2092,2093,2107],{},[1110,2094,2095],{},[1113,2096,2097,2099,2103],{},[1116,2098],{"align":1449},[1116,2100,2101],{"align":1449},[1131,2102,1806],{},[1116,2104,2105],{"align":1449},[1131,2106,1825],{},[1123,2108,2109,2122,2135,2148,2161],{},[1113,2110,2111,2116,2119],{},[1128,2112,2113],{"align":1449},[1131,2114,2115],{},"纵坐标",[1128,2117,2118],{"align":1449},"数据出现频数",[1128,2120,2121],{"align":1449},"数据出现概率",[1113,2123,2124,2129,2132],{},[1128,2125,2126],{"align":1449},[1131,2127,2128],{},"横坐标",[1128,2130,2131],{"align":1449},"数据区间",[1128,2133,2134],{"align":1449},"数据取值",[1113,2136,2137,2142,2145],{},[1128,2138,2139],{"align":1449},[1131,2140,2141],{},"适用数据",[1128,2143,2144],{"align":1449},"离散和连续均可",[1128,2146,2147],{"align":1449},"连续数据",[1113,2149,2150,2155,2158],{},[1128,2151,2152],{"align":1449},[1131,2153,2154],{},"表现形式",[1128,2156,2157],{"align":1449},"离散式",[1128,2159,2160],{"align":1449},"连续式",[1113,2162,2163,2168,2171],{},[1128,2164,2165],{"align":1449},[1131,2166,2167],{},"功能",[1128,2169,2170],{"align":1449},"展示数据在不同区间内的分布情况",[1128,2172,2173],{"align":1449},"展示数据出现的概率分布",[1167,2175,2176,2179,2182],{},[1170,2177,2178],{},"直方图适合单组数据，密度图可绘制多组（不超过5组）；",[1170,2180,2181],{},"直方图样本量足够大；密度图具有足够数量；",[1170,2183,2184],{},"直方图对样本数据直接利用，图形更贴合样本；密度图观察概率分布更准确美观",[1095,2186,2187],{},"共同的局限性是？如果样本量不足，它们就无法准确反映数据的真实分布形态，容易产生误导性的结论",[1095,2189,2190],{},[1131,2191,2192],{},"小提琴图与密度图的区别",[1167,2194,2195,2198,2201,2204],{},[1170,2196,2197],{},"小提琴图是箱线图和密度图的合体；",[1170,2199,2200],{},"小提琴的纵轴代表数值，横轴代表不同属性；宽度代表出现的概率分布=密度图纵轴\n密度图的纵轴代表数据出现的频率，横轴代表具体数值",[1170,2202,2203],{},"小提琴图可表示四分位数范围和置信区间；密度图可表示峰值、拐点；",[1170,2205,2206],{},"小提琴图可表示多个属性，密度图表示多属性易引起混淆",[1895,2208,2209],{},[1095,2210,2211],{},"可展示的数据组数：直方图 \u003C 密度图 \u003C 小提琴图\n样本数据的贴合程度：小提琴图 \u003C 密度图 \u003C 直方图",[1095,2213,2214],{},"既想要展示分布，又想更多组比较",[1769,2216],{},[1091,2218,2220],{"id":2219},"四时间趋势图","四、时间趋势图",[1107,2222,2223,2236],{},[1110,2224,2225],{},[1113,2226,2227,2230,2233],{},[1116,2228,2229],{},"想表达的重点",[1116,2231,2232],{},"首选",[1116,2234,2235],{},"备选",[1123,2237,2238,2249,2260,2271,2281,2292],{},[1113,2239,2240,2243,2246],{},[1128,2241,2242],{},"单纯看变化趋势",[1128,2244,2245],{},"折线图",[1128,2247,2248],{},"平滑折线、阶梯图",[1113,2250,2251,2254,2257],{},[1128,2252,2253],{},"强调累积总量",[1128,2255,2256],{},"面积图",[1128,2258,2259],{},"堆叠面积图",[1113,2261,2262,2265,2268],{},[1128,2263,2264],{},"多条序列横向对比、省空间",[1128,2266,2267],{},"地平线图",[1128,2269,2270],{},"小型多图（small multiples）",[1113,2272,2273,2276,2279],{},[1128,2274,2275],{},"多类别此消彼长",[1128,2277,2278],{},"河流图",[1128,2280,2259],{},[1113,2282,2283,2286,2289],{},[1128,2284,2285],{},"拆解增减归因",[1128,2287,2288],{},"瀑布图",[1128,2290,2291],{},"堆叠柱状图",[1113,2293,2294,2297,2300],{},[1128,2295,2296],{},"金融行情 OHLC",[1128,2298,2299],{},"烛形图",[1128,2301,2302],{},"OHLC 条形图",[1107,2304,2305,2319],{},[1110,2306,2307],{},[1113,2308,2309,2311,2314,2316],{},[1116,2310],{"align":1449},[1116,2312,2313],{"align":1449},"适用情况",[1116,2315,1453],{"align":1449},[1116,2317,2318],{"align":1449},"劣势",[1123,2320,2321,2336,2351,2366,2381,2396],{},[1113,2322,2323,2327,2330,2333],{},[1128,2324,2325],{"align":1449},[1131,2326,2245],{},[1128,2328,2329],{"align":1449},"呈现一个或多个数值变量随时间或有序类别变化的趋势",[1128,2331,2332],{"align":1449},"a. 趋势展示最为直观b. 简洁，类别过多时不会重叠",[1128,2334,2335],{"align":1449},"a. 相对于瀑布图，不能精确展示数据的变化量b. 不能展示部分与整体的关系",[1113,2337,2338,2342,2345,2348],{},[1128,2339,2340],{"align":1449},[1131,2341,2256],{},[1128,2343,2344],{"align":1449},"强调数值变量随时间或有序类别变化的趋势，不易显示数值",[1128,2346,2347],{"align":1449},"a. 对数据变动很有清晰的展示能力b. 堆叠面积图对多组数据对比比较明显c. 更具有视觉突出性",[1128,2349,2350],{"align":1449},"a. 不适合展示具体的数值b. 不适合类别更多的数据",[1113,2352,2353,2357,2360,2363],{},[1128,2354,2355],{"align":1449},[1131,2356,2267],{},[1128,2358,2359],{"align":1449},"适合比较不同分类之间的时间模式差异",[1128,2361,2362],{"align":1449},"可以极大地节省空间",[1128,2364,2365],{"align":1449},"读图的难度比较大",[1113,2367,2368,2372,2375,2378],{},[1128,2369,2370],{"align":1449},[1131,2371,2278],{},[1128,2373,2374],{"align":1449},"呈现总体以及每个个体随时间变化的趋势，强调的是部分与整体的关系",[1128,2376,2377],{"align":1449},"a. 方便考察部分与整体的关系b. 在数据量大且波动幅度大时，具有突出的视觉结构",[1128,2379,2380],{"align":1449},"a. 视觉效果受类别数量的限制，不能过多或过少b. 比较复杂，最好采用交互界面c. 相对于折线图，不适合数据差异值很大的情况",[1113,2382,2383,2387,2390,2393],{},[1128,2384,2385],{"align":1449},[1131,2386,2288],{},[1128,2388,2389],{"align":1449},"呈现变量随时间或有序类别变化的趋势，以及正值和负值的累积效应",[1128,2391,2392],{"align":1449},"a. 更方便考察某一阶段某属性增加或减少的数值或不同分类的数值对总体数值的影响b. 能更好地展示演变过程",[1128,2394,2395],{"align":1449},"a. 相对于折线图，不易判断趋势b. 不适合展示趋势过于平稳的数值c. 相对于折线图，也不适合数据差异过大的情况",[1113,2397,2398,2403,2406,2409],{},[1128,2399,2400],{"align":1449},[1131,2401,2402],{},"烛台图",[1128,2404,2405],{"align":1449},"呈现股票最高价格、最低价格、开盘价与收盘价以及之间的价差",[1128,2407,2408],{"align":1449},"a. 与折线图相比，展示的信息更为丰富b. 在金融工具的价格分析上，实用性和适用性十分广泛c. 对数据变动有更清晰的展示能力",[1128,2410,2411],{"align":1449},"a. 比较适合展示一只股票，多只股票在同一张图可能会有重叠b. 表现趋势时没有折线图清晰",[1158,2413,2245],{"id":2245},[1162,2415],{"src":2416,"width":1165},"\u002F数据可视化\u002Fline-chart.png",[1167,2418,2419,2424,2429,2434,2439,2444],{},[1170,2420,2421,2423],{},[1131,2422,1174],{},"：把按时间排列的数据点用线段连接，表现数值随时间变化。",[1170,2425,2426,2428],{},[1131,2427,1180],{},"：类别→颜色；数值→坐标轴位置；",[1170,2430,2431,2433],{},[1131,2432,1186],{},"：连续时间序列，如股价、流量、温度变化。",[1170,2435,2436,2438],{},[1131,2437,1192],{},"：使用不同颜色的实线，不要虚线，所有折线最大最小值差异不要过大，横轴密度适当",[1170,2440,2441,2443],{},[1131,2442,1198],{},"：优点是趋势清晰、可多线对比，缺点是不适合类别数据。",[1170,2445,2446,2448],{},[1131,2447,1204],{},"：多系列折线图、阶梯图、光滑折线图、带置信区间折线图。",[1158,2450,2452],{"id":2451},"面积图趋势大小","面积图（趋势大小）",[1162,2454],{"src":2455,"width":1165},"\u002F数据可视化\u002Farea-chart.png",[1167,2457,2458,2463,2468,2473,2478,2483],{},[1170,2459,2460,2462],{},[1131,2461,1174],{},"：在折线图下方填充颜色，用面积强调累积总量及变化。",[1170,2464,2465,2467],{},[1131,2466,1180],{},"：数值→平面\u002F坐标轴视觉通道；类别→颜色视觉通道。",[1170,2469,2470,2472],{},[1131,2471,1186],{},"：强调随时间累积的规模感，或堆叠表现构成随时间变化。",[1170,2474,2475,2477],{},[1131,2476,1192],{},"：堆叠时上层基线起伏难精确读数。可加性，不得有负值。2到3组，比较同一分类数据",[1170,2479,2480,2482],{},[1131,2481,1198],{},"：优点是总量感强，缺点是多系列上层不易读、易遮挡。",[1170,2484,2485,2487],{},[1131,2486,1204],{},"：堆叠面积图、百分比堆叠面积图、河流图。",[1158,2489,2491],{"id":2490},"地平线图实践序列可视化节约纵向空间-数据基于某个基准值变化趋势","地平线图（实践序列可视化节约纵向空间 数据基于某个基准值变化趋势）",[1162,2493],{"src":2494,"width":1165},"\u002F数据可视化\u002Fhorizon-chart.png",[1167,2496,2497,2502,2507,2512,2517,2522],{},[1170,2498,2499,2501],{},[1131,2500,1174],{},"：为节省纵向空间设计的时间序列图，将面积按数值带分层折叠、用颜色深浅表示高低。",[1170,2503,2504,2506],{},[1131,2505,1180],{},"：变量→位置；区分→颜色 ；幅度->位置",[1170,2508,2509,2511],{},[1131,2510,1186],{},"：一屏内对比很多条时间序列，如众多传感器或股票走势。",[1170,2513,2514,2516],{},[1131,2515,1192],{},"：有阅读门槛、需理解折叠与配色",[1170,2518,2519,2521],{},[1131,2520,1198],{},"：优点是极省空间、适合密集多序列，缺点是不直观、不便精确读数。",[1170,2523,2524,2526],{},[1131,2525,1204],{},"：双向地平线图（区分正负）。",[1158,2528,2530],{"id":2529},"河流图-不同时间段多分类值叠加","河流图 (不同时间段多分类值叠加)",[1162,2532],{"src":2533,"width":1165},"\u002F数据可视化\u002Fstreamgraph.png",[1167,2535,2536,2541,2546,2551,2556,2561],{},[1170,2537,2538,2540],{},[1131,2539,1174],{},"：居中堆叠的面积图，各系列围绕中心轴上下展开，像流动的河，又称主题河流图。",[1170,2542,2543,2545],{},[1131,2544,1180],{},"：时间→水平位置；类别→颜色视觉通道；数值→条带厚度+垂直位置。",[1170,2547,2548,2550],{},[1131,2549,1186],{},"：展示多类别此消彼长和总体演变，如各话题热度随时间变化。",[1170,2552,2553,2555],{},[1131,2554,1192],{},"：基线浮动难精确读数、类别多显杂乱",[1170,2557,2558,2560],{},[1131,2559,1198],{},"：优点是有动感、表现趋势与构成演变，缺点是读数不准。",[1170,2562,2563,2565],{},[1131,2564,1204],{},"：堆叠面积图、流图（streamgraph）。",[1158,2567,2288],{"id":2288},[1162,2569],{"src":2570,"width":1165},"\u002F数据可视化\u002Fwaterfall-chart.png",[1167,2572,2573,2578,2583,2588,2593,2598],{},[1170,2574,2575,2577],{},[1131,2576,1174],{},"：用一系列悬浮柱子表现初始值经多次增减到达终值的过程，每柱接上一柱终点。",[1170,2579,2580,2582],{},[1131,2581,1180],{},"：顺序→位置；占值→长度；增减→颜色（正值\u002F负值）。",[1170,2584,2585,2587],{},[1131,2586,1186],{},"：财务分析，如从营收到净利润的逐项增减、预算构成变化，累计效应。",[1170,2589,2590,2592],{},[1131,2591,1192],{},"：要区分增、减和小计，正负配色一致",[1170,2594,2595,2597],{},[1131,2596,1198],{},"：优点是把累积过程拆清、便于归因，缺点是只适合递推累积。",[1170,2599,2600,2602],{},[1131,2601,1204],{},"：堆叠瀑布图、带连接线瀑布图。",[1158,2604,2299],{"id":2299},[1162,2606],{"src":2607,"width":1165},"\u002F数据可视化\u002Fcandlestick-chart.png",[1167,2609,2610,2615,2620,2625,2630,2635],{},[1170,2611,2612,2614],{},[1131,2613,1174],{},"：金融行情专用，每根蜡烛表示一周期的开、收、最高、最低价，又称 K 线图。",[1170,2616,2617,2619],{},[1131,2618,1180],{},"：时间→水平位置；价格区间→垂直位置+长度；涨跌→颜色视觉通道（红涨绿跌\u002F空心实心）。",[1170,2621,2622,2624],{},[1131,2623,1186],{},"：股票、期货等价格分析的标准图表。",[1170,2626,2627,2629],{},[1131,2628,1192],{},"：构成要开盘，收盘，最高，最低价格，多组数据不得差异过大、需专业背景、配色含义要约定",[1170,2631,2632,2634],{},[1131,2633,1198],{},"：优点是信息量大、便于读盘，缺点是普通读者门槛高。",[1170,2636,2637,2639],{},[1131,2638,1204],{},"：OHLC 条形图、带均线和成交量的组合图。",[1769,2641],{},[1091,2643,2645],{"id":2644},"五地理特征图","五、地理特征图",[1107,2647,2648,2663],{},[1110,2649,2650],{},[1113,2651,2652,2654,2657,2660],{},[1116,2653,1450],{},[1116,2655,2656],{},"编码维度",[1116,2658,2659],{},"适合的数据类型",[1116,2661,2662],{},"注意点",[1123,2664,2665,2681,2697],{},[1113,2666,2667,2672,2675,2678],{},[1128,2668,2669],{},[1131,2670,2671],{},"分级地图",[1128,2673,2674],{},"颜色 + 行政边界",[1128,2676,2677],{},"区域聚合的密度\u002F比率",[1128,2679,2680],{},"大区域视觉权重过高，宜用比率非绝对量",[1113,2682,2683,2688,2691,2694],{},[1128,2684,2685],{},[1131,2686,2687],{},"蜂窝热力图",[1128,2689,2690],{},"颜色 + 等大网格",[1128,2692,2693],{},"离散点集（签到、打车）",[1128,2695,2696],{},"网格大小决定细节，丢失行政边界",[1113,2698,2699,2704,2707,2710],{},[1128,2700,2701],{},[1131,2702,2703],{},"变形地图",[1128,2705,2706],{},"面积 + 区域位置",[1128,2708,2709],{},"数值大小本身（人口、GDP）",[1128,2711,2712],{},"形状失真，读者需要适应",[1895,2714,2715],{},[1095,2716,2717,2718,2721,2722,2725],{},"共性：都解决「空间分布」问题，区别在于",[1131,2719,2720],{},"保留地理形态","（分级 > 蜂窝 > 变形）和",[1131,2723,2724],{},"规避面积偏差","（变形 > 蜂窝 > 分级）正好相反，按取舍二选一",[1158,2727,2671],{"id":2671},[1162,2729],{"src":2730,"width":1165},"\u002F数据可视化\u002Fchoropleth-map.png",[1167,2732,2733,2738,2743,2748,2753,2758],{},[1170,2734,2735,2737],{},[1131,2736,1174],{},"：按行政区划给区域填色，用颜色深浅表示各区域数值，又称等值区域图。",[1170,2739,2740,2742],{},[1131,2741,1180],{},"：区域→地理位置；数值→颜色深浅（亮度\u002F饱和度）。",[1170,2744,2745,2747],{},[1131,2746,1186],{},"：指标在空间上的分布，如各省人口密度、各地区销售额。",[1170,2749,2750,2752],{},[1131,2751,1192],{},"：受区域面积影响大、宜用密度或比率",[1170,2754,2755,2757],{},[1131,2756,1198],{},"：优点是地理分布一目了然，缺点是大区域视觉权重过高、绝对量易误导。",[1170,2759,2760,2762],{},[1131,2761,1204],{},"：双变量分级地图、分级符号地图。",[1158,2764,2687],{"id":2687},[1162,2766],{"src":2767,"width":1165},"\u002F数据可视化\u002Fhexbin-map.png",[1167,2769,2770,2775,2780,2785,2790,2795],{},[1170,2771,2772,2774],{},[1131,2773,1174],{},"：把地理空间划成等大六边形网格，用颜色表示落入其中的密度或数值。",[1170,2776,2777,2779],{},[1131,2778,1180],{},"：空间位置→网格位置；密度\u002F数值→颜色深浅（亮度\u002F饱和度）。",[1170,2781,2782,2784],{},[1131,2783,1186],{},"：表现点数据的空间聚集，如打车热点、签到分布。",[1170,2786,2787,2789],{},[1131,2788,1192],{},"：六边形大小要选当，过大丢细节、过小稀疏",[1170,2791,2792,2794],{},[1131,2793,1198],{},"：优点是规避面积偏差、呈现密度聚集，缺点是失去精确边界。",[1170,2796,2797,2799],{},[1131,2798,1204],{},"：方格网热力图、核密度热力图。",[1158,2801,2703],{"id":2703},[1162,2803],{"src":2804,"width":1165},"\u002F数据可视化\u002Fcartogram.png",[1167,2806,2807,2812,2817,2822,2827,2832],{},[1170,2808,2809,2811],{},[1131,2810,1174],{},"：按某数值有意改变各区域的大小或形状，让面积直接代表数值，又称统计示意地图。",[1170,2813,2814,2816],{},[1131,2815,1180],{},"：区域→地理位置；数值→面积（变形程度）。",[1170,2818,2819,2821],{},[1131,2820,1186],{},"：强调数值本身而非真实面积，纠正\"大区域抢眼\"问题。",[1170,2823,2824,2826],{},[1131,2825,1192],{},"：区域扭曲偏离真实形态、需适应",[1170,2828,2829,2831],{},[1131,2830,1198],{},"：优点是数值直接可比、纠正面积偏差，缺点是地理形状失真。",[1170,2833,2834,2836],{},[1131,2835,1204],{},"：连续变形地图、马赛克网格地图。",[1769,2838],{},[1091,2840,2842],{"id":2841},"六相关性图","六、相关性图",[1107,2844,2845,2863],{},[1110,2846,2847],{},[1113,2848,2849,2851,2854,2857,2860],{},[1116,2850,1450],{},[1116,2852,2853],{},"变量数",[1116,2855,2856],{},"视觉编码",[1116,2858,2859],{},"典型用途",[1116,2861,2862],{},"局限",[1123,2864,2865,2884,2903,2922],{},[1113,2866,2867,2872,2875,2878,2881],{},[1128,2868,2869],{},[1131,2870,2871],{},"散点图",[1128,2873,2874],{},"2",[1128,2876,2877],{},"位置",[1128,2879,2880],{},"看两变量相关、聚类、离群",[1128,2882,2883],{},"点多易重叠",[1113,2885,2886,2891,2894,2897,2900],{},[1128,2887,2888],{},[1131,2889,2890],{},"气泡图",[1128,2892,2893],{},"3–4",[1128,2895,2896],{},"位置 + 面积（+ 颜色）",[1128,2898,2899],{},"关系叠加规模",[1128,2901,2902],{},"面积读数不准",[1113,2904,2905,2910,2913,2916,2919],{},[1128,2906,2907],{},[1131,2908,2909],{},"相关图",[1128,2911,2912],{},"多变量两两",[1128,2914,2915],{},"颜色 + 大小 + 矩阵位置",[1128,2917,2918],{},"建模前特征筛查",[1128,2920,2921],{},"只衡量线性、不说因果",[1113,2923,2924,2929,2932,2935,2938],{},[1128,2925,2926],{},[1131,2927,2928],{},"热力图",[1128,2930,2931],{},"两个类别维度",[1128,2933,2934],{},"颜色 + 行列位置",[1128,2936,2937],{},"时段\u002F类别交叉强度",[1128,2939,2940],{},"颜色精度低、不友好色盲",[1895,2942,2943],{},[1095,2944,2945],{},"两点关系用散点，加规模用气泡，多变量整体扫描用相关图，两类别交叉强度用热力图。",[1158,2947,2871],{"id":2871},[1162,2949],{"src":2950,"width":1165},"\u002F数据可视化\u002Fscatter-plot.png",[1167,2952,2953,2958,2963,2968,2973,2978],{},[1170,2954,2955,2957],{},[1131,2956,1174],{},"：每条数据用一个点画在两变量构成的平面上，通过分布观察两变量关系。",[1170,2959,2960,2962],{},[1131,2961,1180],{},"：变量一→水平位置；变量二→垂直位置；可附加类别→颜色视觉通道。",[1170,2964,2965,2967],{},[1131,2966,1186],{},"：研究相关性，看正相关、负相关、聚类和离群点，如身高与体重。",[1170,2969,2970,2972],{},[1131,2971,1192],{},"：点多会重叠，可用透明度、抖动或采样",[1170,2974,2975,2977],{},[1131,2976,1198],{},"：优点是揭示关系直观、能发现异常，缺点是只表现两变量。",[1170,2979,2980,2982],{},[1131,2981,1204],{},"：气泡图、带拟合线散点图、散点矩阵、分面散点图。",[1158,2984,2890],{"id":2890},[1162,2986],{"src":2987,"width":1165},"\u002F数据可视化\u002Fbubble-chart.png",[1167,2989,2990,2995,3000,3005,3010,3015],{},[1170,2991,2992,2994],{},[1131,2993,1174],{},"：在散点图基础上用点的大小表示第三个变量，可再用颜色加第四维。",[1170,2996,2997,2999],{},[1131,2998,1180],{},"：变量一→水平位置；变量二→垂直位置；第三变量→面积；可附加类别→颜色视觉通道。",[1170,3001,3002,3004],{},[1131,3003,1186],{},"：关系之外还想表达规模，如各国人均收入、预期寿命与人口规模。",[1170,3006,3007,3009],{},[1131,3008,1192],{},"：面积比较不精确、应按面积映射、大气泡会遮挡",[1170,3011,3012,3014],{},[1131,3013,1198],{},"：优点是维度丰富，缺点是面积读数不准。",[1170,3016,3017,3019],{},[1131,3018,1204],{},"：动态气泡图、分面气泡图。",[1158,3021,2909],{"id":2909},[1162,3023],{"src":3024,"width":1165},"\u002F数据可视化\u002Fcorrelogram.png",[1167,3026,3027,3032,3037,3042,3047,3052],{},[1170,3028,3029,3031],{},[1131,3030,1174],{},"：用矩阵展示多变量两两之间的相关系数，又称相关系数矩阵图。",[1170,3033,3034,3036],{},[1131,3035,1180],{},"：变量对→矩阵位置；相关强度→颜色深浅+图形大小；正负→颜色视觉通道（双色渐变）。",[1170,3038,3039,3041],{},[1131,3040,1186],{},"：多变量分析中快速筛查相关变量，建模前的特征分析。",[1170,3043,3044,3046],{},[1131,3045,1192],{},"：反映统计相关非因果、只衡量线性",[1170,3048,3049,3051],{},[1131,3050,1198],{},"：优点是一次概览大量关系，缺点是不能说明因果、变量多时格子小。",[1170,3053,3054,3056],{},[1131,3055,1204],{},"：圆点相关图、聚类相关热图。",[1158,3058,2928],{"id":2928},[1162,3060],{"src":3061,"width":1165},"\u002F数据可视化\u002Fheatmap.png",[1167,3063,3064,3069,3074,3079,3084,3089],{},[1170,3065,3066,3068],{},[1131,3067,1174],{},"：用颜色矩阵表示二维表格各单元格数值，颜色越深越大。",[1170,3070,3071,3073],{},[1131,3072,1180],{},"：行列类别→位置；数值→颜色深浅（亮度\u002F饱和度）。",[1170,3075,3076,3078],{},[1131,3077,1186],{},"：两类别维度交叉下的数值分布，如各时段各星期活跃度、基因表达矩阵。",[1170,3080,3081,3083],{},[1131,3082,1192],{},"：色阶选择和归一化关键、可配数值标注",[1170,3085,3086,3088],{},[1131,3087,1198],{},"：优点是密集矩阵里快速发现热点，缺点是颜色精度有限、对色盲不友好。",[1170,3090,3091,3093],{},[1131,3092,1204],{},"：聚类热力图、日历热力图、注释热力图。",[1769,3095],{},[1091,3097,3099],{"id":3098},"七网络关系","七、网络关系",[1107,3101,3102,3120],{},[1110,3103,3104],{},[1113,3105,3106,3108,3111,3114,3117],{},[1116,3107,1450],{},[1116,3109,3110],{},"节点排布",[1116,3112,3113],{},"关系编码",[1116,3115,3116],{},"适合规模",[1116,3118,3119],{},"强项",[1123,3121,3122,3141,3160,3178],{},[1113,3123,3124,3129,3132,3135,3138],{},[1128,3125,3126],{},[1131,3127,3128],{},"网络图",[1128,3130,3131],{},"自由布局（力导向等）",[1128,3133,3134],{},"连线",[1128,3136,3137],{},"中到大",[1128,3139,3140],{},"表达任意复杂关系、发现关键节点",[1113,3142,3143,3148,3151,3154,3157],{},[1128,3144,3145],{},[1131,3146,3147],{},"弧形图",[1128,3149,3150],{},"节点排在一条直线",[1128,3152,3153],{},"上\u002F下方弧线",[1128,3155,3156],{},"中等",[1128,3158,3159],{},"节点有自然顺序时整洁清晰",[1113,3161,3162,3167,3170,3173,3175],{},[1128,3163,3164],{},[1131,3165,3166],{},"和弦图",[1128,3168,3169],{},"节点排在圆环",[1128,3171,3172],{},"圆内带状弧 + 带宽",[1128,3174,3156],{},[1128,3176,3177],{},"表现双向流动和关系强度",[1113,3179,3180,3185,3188,3191,3193],{},[1128,3181,3182],{},[1131,3183,3184],{},"桑基图",[1128,3186,3187],{},"多列分阶段排布",[1128,3189,3190],{},"带状流 + 带宽",[1128,3192,3137],{},[1128,3194,3195],{},"流量守恒，看多级分流汇聚",[1895,3197,3198],{},[1095,3199,3200,3201,3204,3205,3208],{},"关系无方向无流量用网络图\u002F弧形图，",[1131,3202,3203],{},"有强度","用和弦图，",[1131,3206,3207],{},"有流量且分阶段","用桑基图。",[1158,3210,3128],{"id":3128},[1162,3212],{"src":3213,"width":1165},"\u002F数据可视化\u002Fnetwork-graph.png",[1167,3215,3216,3221,3226,3231,3236,3241],{},[1170,3217,3218,3220],{},[1131,3219,1174],{},"：用节点表示对象、连线表示关系，展现整个关系网络结构。",[1170,3222,3223,3225],{},[1131,3224,1180],{},"：对象→节点位置+大小+颜色视觉通道；关系→连线；关系强度→连线粗细\u002F颜色。",[1170,3227,3228,3230],{},[1131,3229,1186],{},"：社交关系、知识图谱、依赖关系等无固定层级的关联。",[1170,3232,3233,3235],{},[1131,3234,1192],{},"：节点多易成\"毛线团\"、需布局与交互",[1170,3237,3238,3240],{},[1131,3239,1198],{},"：优点是直接表达复杂关系、发现关键节点，缺点是规模大时杂乱。",[1170,3242,3243,3245],{},[1131,3244,1204],{},"：有向网络图、力导向图、分群网络图。",[1158,3247,3147],{"id":3147},[1162,3249],{"src":3250,"width":1165},"\u002F数据可视化\u002Farc-diagram.png",[1167,3252,3253,3258,3263,3268,3273,3278],{},[1170,3254,3255,3257],{},[1131,3256,1174],{},"：把节点排在一条直线上，用上方或下方弧线连接有关系的节点，是网络图的线性简化。",[1170,3259,3260,3262],{},[1131,3261,1180],{},"：对象→轴上位置+颜色视觉通道；关系→弧线；关系强度→弧线粗细。",[1170,3264,3265,3267],{},[1131,3266,1186],{},"：节点有自然顺序时展示连接，如人物关系、序列间关联。",[1170,3269,3270,3272],{},[1131,3271,1192],{},"：节点排序影响疏密和美观、连线多会交叠",[1170,3274,3275,3277],{},[1131,3276,1198],{},"：优点是结构清晰整洁，缺点是只适合中等规模。",[1170,3279,3280,3282],{},[1131,3281,1204],{},"：双向弧形图、带权重弧形图。",[1158,3284,3166],{"id":3166},[1162,3286],{"src":3287,"width":1165},"\u002F数据可视化\u002Fchord-diagram.png",[1167,3289,3290,3295,3300,3305,3310,3315],{},[1170,3291,3292,3294],{},[1131,3293,1174],{},"：把各类别排在圆环上，用圆内带状弧连接，带宽表示关系强弱。",[1170,3296,3297,3299],{},[1131,3298,1180],{},"：类别→环上位置+颜色视觉通道；关系强度→带宽（弧带宽度）。",[1170,3301,3302,3304],{},[1131,3303,1186],{},"：多对象间双向流动或关联，如国家间贸易、人群迁徙、跳转关系。",[1170,3306,3307,3309],{},[1131,3308,1192],{},"：类别多时弧带密集、配色排序要精心",[1170,3311,3312,3314],{},[1131,3313,1198],{},"：优点是美观、表现双向关系和强度，缺点是读数不精确。",[1170,3316,3317,3319],{},[1131,3318,1204],{},"：有向和弦图、分组和弦图。",[1158,3321,3184],{"id":3184},[1162,3323],{"src":3324,"width":1165},"\u002F数据可视化\u002Fsankey-diagram.png",[1167,3326,3327,3332,3337,3342,3347,3352],{},[1170,3328,3329,3331],{},[1131,3330,1174],{},"：用宽度不等的带状流表现数量从一组节点流向另一组，带越宽流量越大、流量守恒。",[1170,3333,3334,3336],{},[1131,3335,1180],{},"：类别→节点位置+颜色视觉通道；流量→带宽；流向→位置方向。",[1170,3338,3339,3341],{},[1131,3340,1186],{},"：能量、资金、用户在多阶段间的流转分配，如能源流向、转化路径、预算分配。",[1170,3343,3344,3346],{},[1131,3345,1192],{},"：节点多时交叉混乱、需合理排序",[1170,3348,3349,3351],{},[1131,3350,1198],{},"：优点是流向流量一目了然、表现多级分流汇聚，缺点是细小流量不易比。",[1170,3353,3354,3356],{},[1131,3355,1204],{},"：能量流图、漏斗式桑基图、时间桑基图。",[1769,3358],{},[1091,3360,3362],{"id":3361},"八一些图像区别","八、一些图像区别",[1158,3364,3365],{"id":3365},"柱形图与直方图",[1095,3367,3368],{},"外形最像但本质不同。柱形图比较离散类别，横轴是互不相连的分类，柱间有间隙、可任意排序，高度表示某类别的数值；直方图描述连续变量的分布，横轴是连续数值区间，柱子紧挨无间隙、顺序不可乱，高度表示某区间内的频数。一个用来\"比大小\"，一个用来\"看分布\"。",[1158,3370,3371],{"id":3371},"饼图与玫瑰图",[1095,3373,3374],{},"饼图用扇形角度表示占比，所有扇形半径相同，只靠角度区分大小；玫瑰图（南丁格尔玫瑰图）把半径也用来表示数值，角度往往相等而半径不同，靠半径甚至面积比较。饼图强调\"占整体的比例\"，玫瑰图更像绕成一圈的柱状图、强调数值对比，且面积编码会放大差异、更易误导。",[1158,3376,3377],{"id":3377},"堆叠面积图与堆叠柱状图",[1095,3379,3380],{},"两者都用堆叠表现构成随某维度的变化，区别在横轴和侧重点。堆叠面积图横轴是连续时间，用填充面积强调总量和构成的连续演变，适合看趋势；堆叠柱状图横轴通常是离散类别或离散时间点，用一根根柱子表示每个点的构成，适合在几个节点上做比较。想看\"随时间平滑变化\"用前者，想看\"几个节点上的构成对比\"用后者。",[1158,3382,3383],{"id":3383},"其他常见区分",[1095,3385,3386],{},"散点图与气泡图的区别在于气泡图多用点的大小表达第三个变量；",[1095,3388,3389],{},"折线图与面积图的区别在于面积图填充了线下区域、更强调总量；环形图与饼图的区别只是中心是否挖空；",[1095,3391,3392],{},"分组柱状图与堆叠柱状图的区别在于前者并排便于逐项比较、后者上下堆叠便于看总量和构成。",[1107,3394,3395,3405],{},[1110,3396,3397],{},[1113,3398,3399,3402],{},[1116,3400,3401],{},"易混对",[1116,3403,3404],{},"区分",[1123,3406,3407,3415,3423,3431,3439,3447,3455],{},[1113,3408,3409,3412],{},[1128,3410,3411],{},"柱形图 vs 直方图",[1128,3413,3414],{},"比大小（离散类别） vs 看分布（连续区间）",[1113,3416,3417,3420],{},[1128,3418,3419],{},"饼图 vs 玫瑰图",[1128,3421,3422],{},"角度表占比 vs 半径\u002F面积表数值",[1113,3424,3425,3428],{},[1128,3426,3427],{},"堆叠面积图 vs 堆叠柱状图",[1128,3429,3430],{},"连续时间趋势 vs 离散节点构成",[1113,3432,3433,3436],{},[1128,3434,3435],{},"散点图 vs 气泡图",[1128,3437,3438],{},"两维 vs 三维（加点大小）",[1113,3440,3441,3444],{},[1128,3442,3443],{},"折线图 vs 面积图",[1128,3445,3446],{},"趋势 vs 总量感",[1113,3448,3449,3452],{},[1128,3450,3451],{},"环形图 vs 饼图",[1128,3453,3454],{},"中心挖空 vs 实心，编码本质相同",[1113,3456,3457,3460],{},[1128,3458,3459],{},"分组柱状图 vs 堆叠柱状图",[1128,3461,3462],{},"并排比子项 vs 上下看总量与构成",{"title":3464,"searchDepth":3465,"depth":3465,"links":3466},"",4,[3467],{"id":1105,"depth":3468,"text":1105,"children":3469},2,[3470,3472,3473,3474,3475,3476,3477,3478,3479,3480,3481,3482,3483,3484,3485,3486,3487,3488,3489,3490,3491,3492,3493,3494,3495,3496,3497,3498,3499,3500,3501,3502,3503,3504,3505,3506,3507,3508,3509,3510],{"id":1160,"depth":3471,"text":1160},3,{"id":1208,"depth":3471,"text":1208},{"id":1241,"depth":3471,"text":1241},{"id":1279,"depth":3471,"text":1279},{"id":1312,"depth":3471,"text":1312},{"id":1331,"depth":3471,"text":1331},{"id":1364,"depth":3471,"text":1364},{"id":1397,"depth":3471,"text":1397},{"id":1478,"depth":3471,"text":1478},{"id":1491,"depth":3471,"text":1491},{"id":1504,"depth":3471,"text":1504},{"id":1663,"depth":3471,"text":1663},{"id":1530,"depth":3471,"text":1530},{"id":1543,"depth":3471,"text":1543},{"id":1806,"depth":3471,"text":1806},{"id":1825,"depth":3471,"text":1825},{"id":1976,"depth":3471,"text":1977},{"id":2010,"depth":3471,"text":2011},{"id":2049,"depth":3471,"text":2050},{"id":2245,"depth":3471,"text":2245},{"id":2451,"depth":3471,"text":2452},{"id":2490,"depth":3471,"text":2491},{"id":2529,"depth":3471,"text":2530},{"id":2288,"depth":3471,"text":2288},{"id":2299,"depth":3471,"text":2299},{"id":2671,"depth":3471,"text":2671},{"id":2687,"depth":3471,"text":2687},{"id":2703,"depth":3471,"text":2703},{"id":2871,"depth":3471,"text":2871},{"id":2890,"depth":3471,"text":2890},{"id":2909,"depth":3471,"text":2909},{"id":2928,"depth":3471,"text":2928},{"id":3128,"depth":3471,"text":3128},{"id":3147,"depth":3471,"text":3147},{"id":3166,"depth":3471,"text":3166},{"id":3184,"depth":3471,"text":3184},{"id":3365,"depth":3471,"text":3365},{"id":3371,"depth":3471,"text":3371},{"id":3377,"depth":3471,"text":3377},{"id":3383,"depth":3471,"text":3383},"https:\u002F\u002Fpicx.zhimg.com\u002Fv2-9d5838b8f53bdda7c1ef071f2e767833_r.jpg?source=d16d100b","md",true,{"uuid":3515,"slots":3516},"b8d70910-c4f4-11f0-83bd-25018b4642b8",{},33,{"title":1085,"description":1097},"posts\u002F数据可视化\u002F2026-06-14-数据可视化-基本图像",[24],"ah6pcokiAoB1JNCGde2xOrIghm3XW-kHU8y0tlT2lQI",1790443284161]