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运行 2015 芝加哥马拉松的数据

2015年 10 月 11日芝加哥马拉松吸引了 45,000 名选手来到芝加哥. 37,000 以上的选手完成了比赛,并且每位选手的表现都被准确记录下来. 使用含有该数据的实体库研究并可视化选手的特征和其表现.

ResourceObject 加载该马拉松的一个实体库.

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marathonEntityStore = ResourceData[ ResourceObject[ Association[ "Name" -> "2015 Chicago Marathon Data", "UUID" -> "7dc77972-cfc3-48dc-8d08-0292c6d2a929", "ResourceType" -> "DataResource", "Version" -> "1.0.0", "Description" -> "2015 Chicago Marathon participant data", "ContentSize" -> Quantity[1990.2215919999999`, "Megabytes"], "ContentElements" -> {"Content"}]]]
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注册该部分数据库.

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PrependTo[$EntityStores, marathonEntityStore];

提取选手的总人数,并采用隐式定义的实体类,提取男性和女性的参加人数.

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EntityValue["ChicagoMarathon2015", "EntityCount"]
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Table[EntityValue[ EntityClass["ChicagoMarathon2015", "Gender" -> gender], "EntityCount"], {gender, {Entity["Gender", "Male"], Entity["Gender", "Female"]}}]
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随机选定五名选手.

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RandomEntity["ChicagoMarathon2015", 5]
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查看特定选手所存储的属性.

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Entity["ChicagoMarathon2015", "Runner145"]["PropertyAssociation"]
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提取选手的国籍记录,并创建包含常见国籍的表格.

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nationalityTallies = Reverse[SortBy[ Tally[EntityValue["ChicagoMarathon2015", EntityProperty["ChicagoMarathon2015", "Country"]]], Last]];
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TextGrid[Join @@@ Transpose[ Partition[Take[Reverse[Sort[Reverse /@ nationalityTallies]], 80], 20]], Alignment -> {{{Decimal, Left}}, Automatic}, Dividers -> {{{Thick, True}}, {{True}}}, Frame -> Thick, Background -> {Automatic, {{LightBlue, None}}}] // TraditionalForm
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可视化从所有国家到芝加哥的测地路径.

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With[{chicago = Entity["City", {"Chicago", "Illinois", "UnitedStates"}]}, GeoGraphics[{Darker[Green], GeoPath[{chicago, #} & /@ nationalityTallies[[All, 1]], "Geodesic"]}, GeoRange -> "World", GeoProjection -> "Robinson", GeoCenter -> chicago]]
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构建显示美国参赛者住址的美国地图热图.

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anyUS = EntityClass["ChicagoMarathon2015", "Country" -> Entity["Country", "UnitedStates"]] // EntityList;
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allUSCities = DeleteMissing[ EntityValue[anyUS, EntityProperty["ChicagoMarathon2015", "CityState"]]]; talliedUSCities = Tally[allUSCities]; coordsUS = EntityValue[talliedUSCities[[All, 1]], "Position"]; cityPositions = Transpose[{coordsUS, talliedUSCities[[All, -1]]^0.5}]; projectionUS = {"LambertAzimuthal", "Centering" -> GeoPosition[{37.1558, -95.883}]}; data = {GeoGridPosition[#1, projectionUS][[1]], #2} & @@@ cityPositions; weightedData = WeightedData @@ Transpose[data]; cityDensityP = SmoothKernelDistribution[weightedData, "Silverman"]; cityDensity[{lat_Real, lon_Real}] := With[{xy = First[GeoGridPosition[GeoPosition[{lat, lon}], projectionUS]]}, Flatten[{xy, PDF[cityDensityP, xy]}]]; area = GeoVariant[Entity["Country", "UnitedStates"], "DefaultMapArea"]; {{latminUS, latmaxUS}, {lonminUS, lonmaxUS}} = GeoBounds[area];
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cityPlot = ContourPlot[ Sqrt[Last[cityDensity[{lat, lon}]]], {lon, lonminUS, lonmaxUS}, {lat, latminUS, latmaxUS}, Frame -> False, PlotRange -> All, Contours -> 100, MaxRecursion -> 2, ColorFunction -> ColorData["DarkRainbow"], PlotRangePadding -> 0, ContourStyle -> None];
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GeoGraphics[{GeoStyling[{"GeoImage", cityPlot}], Polygon[area], Gray, Opacity[1], PointSize[0.001], Point[coordsUS]}, GeoRange -> area]
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从分割均值找出每个变化的选手数目.

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allkm = Table[ Normal[allTimeSplits[[i]][2 ;;, "Time"]], {i, Length[allTimeSplits]}];
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allsplitbins = DeleteMissing[Transpose[allkm], 2];
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meanall = Table[N[Mean[allsplitbins[[i]]]], {i, Length[allsplitbins]}]
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marathondistances = (allTimeSplits[[1]])[All, "Split"] // Normal
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differencesall = Table[{marathondistances[[i + 1]], allsplitbins[[i]] - meanall[[i]]}, {i, Length[allsplitbins]}];
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allHistograms = Histogram[#2, {60}, PlotLabel -> NumberForm[#1, {3, 1}]] & @@@ differencesall;

生成每个分割的直方图.

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Grid[Partition[allHistograms, UpTo[3]]]
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绘制选手分割和均值间的不同的平滑内核分布图.

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skd = Table[ SmoothKernelDistribution[ QuantityMagnitude[differencesall[[i, 2]]]], {i, Length[differencesall]}];
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Plot[ Evaluate[Table[PDF[skd[[i]], t], {i, Length[skd]}]], {t, -8000, 10000}, PlotRange -> All, PlotLegends -> Table[If[IntegerQ[QuantityMagnitude[diff = differencesall[[i, 1]]]], diff, Round[diff, .1]], {i, Length[differencesall]}] ]
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