Wolfram 语言™

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"}];

In[2]:=

Take[hs, 5]

Out[2]=

使用选择算符提取位置的一个子集.

In[3]:=

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 &]]

Out[4]=

将 Head Start 地址分组，并根据邮政编码绘制它们的分布图.

In[5]:=

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

In[6]:=

GeoRegionValuePlot[chicagoCounts]

Out[6]=

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.

In[7]:=

zipcodes = Normal@Keys[chicagoCounts]

Out[7]=

In[8]:=

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

In[9]:=

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

Out[9]=