利用内置数据改善精选数据集

Wolfram 知识库中丰富的内置社会经济学数据集合可以用于改善外部数据源.

从美国住房和城市发展部（HUD）导入 Head Start 机构的地址数据集，作为开始.

In[1]:=

hs = ResourceData["Head Start Locations"][ All, {"CenterName", "CenterAddress", "CenterStateEntity", "CenterCityEntity", "CenterZipCodeEntity", "Coordinates"}];

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Take[hs, 5]

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使用选择算符提取位置的一个子集.

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hsChicago = hs[Select[#CenterCityEntity === Entity["City", {"Chicago", "Illinois", "UnitedStates"}] &]];

将它们表示在芝加哥地图上.

In[4]:=

GeoListPlot[hsChicago[All, #Coordinates &]]

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将 Head Start 地址分组，并根据邮政编码绘制它们的分布图.

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chicagoCounts = Length /@ GroupBy[hsChicago, #CenterZipCodeEntity &];

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GeoRegionValuePlot[chicagoCounts]

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检索在芝加哥邮编代码内有关学龄人口的内置数据，并将这些值与从 HUD 外部数据推导的 Head Start 地址计数绘图.

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zipcodes = Normal@Keys[chicagoCounts]

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chicagoMedian = EntityValue[zipcodes, EntityProperty["ZIPCode", "Population", {"Age" -> "SchoolAge"}], "EntityAssociation"];

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locsVSkids = Merge[{Normal@chicagoCounts, chicagoMedian}, Identity]; ListPlot[locsVSkids, FrameLabel -> {"Head Start locations", "School age population"}, ImageSize -> 550, PlotTheme -> "Detailed"]

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