Wolfram Research

Sample Data: Leukaemia NW England

Source Notebook

Locations of leukaemia in N.W. England from 1982 to 1998 (inclusive), annotated with age, gender, deprivation index (higher values indicating less affluence), and subject type (case or control) marks

Details

Locations of leukaemia in N.W. England from 1982 to 1998 (inclusive) in the observation region that is the polygon of N.W. England, annotated with age, gender, deprivation index (higher values indicating less affluence), and subject type (case or control) marks.

Examples

Basic Examples (2) 

Retrieve the data:

In[1]:=
ResourceData[\!\(\*
TagBox["\"\<Sample Data: Leukaemia NW England\>\"",
#& ,
BoxID -> "ResourceTag-Sample Data: Leukaemia NW England-Input",
AutoDelete->True]\), "Data"]
Out[1]=

Summary of the spatial point data:

In[2]:=
ResourceData[\!\(\*
TagBox["\"\<Sample Data: Leukaemia NW England\>\"",
#& ,
BoxID -> "ResourceTag-Sample Data: Leukaemia NW England-Input",
AutoDelete->True]\), "Data"]["Summary"]
Out[2]=

Visualizations (3) 

Plot the spatial point data:

In[3]:=
Show[Graphics[{Opacity[.1], ResourceData[\!\(\*
TagBox["\"\<Sample Data: Leukaemia NW England\>\"",
#& ,
BoxID -> "ResourceTag-Sample Data: Leukaemia NW England-Input",
AutoDelete->True]\), "ObservationRegion"]}], ListPlot[ResourceData[\!\(\*
TagBox["\"\<Sample Data: Leukaemia NW England\>\"",
#& ,
BoxID -> "ResourceTag-Sample Data: Leukaemia NW England-Input",
AutoDelete->True]\), "Data"], AspectRatio -> Full], Axes -> True]
Out[3]=

Plot the data annotated with gender (3rd annotation) marks:

In[4]:=
PointValuePlot[ResourceData[\!\(\*
TagBox["\"\<Sample Data: Leukaemia NW England\>\"",
#& ,
BoxID -> "ResourceTag-Sample Data: Leukaemia NW England-Input",
AutoDelete->True]\), "Data"], {1 -> None, 2 -> None, 3 -> Automatic, 4 -> None}, PlotLegends -> Automatic]
Out[4]=

Plot the data annotated with type (4th annotation) marks:

In[5]:=
PointValuePlot[ResourceData[\!\(\*
TagBox["\"\<Sample Data: Leukaemia NW England\>\"",
#& ,
BoxID -> "ResourceTag-Sample Data: Leukaemia NW England-Input",
AutoDelete->True]\), "Data"], {1 -> None, 2 -> None, 3 -> None, 4 -> Automatic}, PlotStyle -> {Magenta, Gray}, PlotLegends -> Automatic]
Out[5]=

Analysis (3) 

Compute probability of finding a point within given radius of an existing point - NearestNeighborG is the CDF of the nearest neighbor distribution:

In[6]:=
nnG = NearestNeighborG[ResourceData[\!\(\*
TagBox["\"\<Sample Data: Leukaemia NW England\>\"",
#& ,
BoxID -> "ResourceTag-Sample Data: Leukaemia NW England-Input",
AutoDelete->True]\), "Data"]]
Out[1]=
In[7]:=
maxR = nnG["MaxRadius"]
Out[7]=
In[8]:=
DiscretePlot[nnG[r], {r, maxR/100, maxR, maxR/100}, AxesLabel -> {"radius", "probability"}]
Out[8]=

Mean distance between a typical point and its nearest neighbor (for positive support distribution can be approximated via a Riemann sum of 1-CDF):

In[9]:=
step = maxR/100;
partition = Table[{k, k + step}, {k, 0, maxR, step}];
values = nnG[Mean /@ partition];
In[10]:=
Total[(1 - values)*step]
Out[10]=

Test for complete spacial randomness:

In[11]:=
SpatialRandomnessTest[ResourceData[\!\(\*
TagBox["\"\<Sample Data: Leukaemia NW England\>\"",
#& ,
BoxID -> "ResourceTag-Sample Data: Leukaemia NW England-Input",
AutoDelete->True]\), "Data"], {"PValue", "TestConclusion"}]
Out[12]=

Gosia Konwerska, "Sample Data: Leukaemia NW England" from the Wolfram Data Repository (2021) 

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