Wolfram Data Repository

Immediate Computable Access to Curated Contributed Data

Sample Data: Toronto Murders

Source Notebook

Locations of murders in Toronto annotated with marks including victim age, victim sex, type, murder method, and year

Details

Locations of murders in Toronto in the observation region that is the polygon shape of Toronto, annotated with marks including victim age, victim sex, type, murder method, and year.

Examples

Basic Examples (1) 

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

Summary of the spatial point data:

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

Visualizations (3) 

Plot the spatial point data:

In[3]:=
ListPlot[ResourceData[\!\(\* TagBox["\"\<Sample Data: Toronto Murders\>\"", #& , BoxID -> "ResourceTag-Sample Data: Toronto Murders-Input", AutoDelete->True]\), "Data"]]
Out[3]=

Visualize the smooth point density:

In[4]:=
density = SmoothPointDensity[ResourceData[\!\(\* TagBox["\"\<Sample Data: Toronto Murders\>\"", #& , BoxID -> "ResourceTag-Sample Data: Toronto Murders-Input", AutoDelete->True]\), "Data"]]
Out[4]=
In[5]:=
Show[density["DensityVisualization"], ListPlot[ResourceData[\!\(\* TagBox["\"\<Sample Data: Toronto Murders\>\"", #& , BoxID -> "ResourceTag-Sample Data: Toronto Murders-Input", AutoDelete->True]\), "Data"], PlotStyle -> Black], Frame -> True]
Out[5]=

Visualize the data with annotations:

In[6]:=
Show[Graphics[{Opacity[.2], ResourceData[\!\(\* TagBox["\"\<Sample Data: Toronto Murders\>\"", #& , BoxID -> "ResourceTag-Sample Data: Toronto Murders-Input", AutoDelete->True]\), "ObservationRegion"]}], PointValuePlot[ResourceData[\!\(\* TagBox["\"\<Sample Data: Toronto Murders\>\"", #& , BoxID -> "ResourceTag-Sample Data: Toronto Murders-Input", AutoDelete->True]\), "Data"], {1 -> None, 2 -> Automatic, 3 -> None, 4 -> None, 5 -> None}, PlotLegends -> Automatic], PlotLabel -> "Victim sex"]
Out[6]=

Analysis (5) 

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

In[7]:=
nnG = NearestNeighborG[ResourceData[\!\(\* TagBox["\"\<Sample Data: Toronto Murders\>\"", #& , BoxID -> "ResourceTag-Sample Data: Toronto Murders-Input", AutoDelete->True]\), "Data"]]
Out[7]=
In[8]:=
maxR = nnG["MaxRadius"]
Out[8]=
In[9]:=
DiscretePlot[nnG[r], {r, maxR/100, maxR, maxR/100}, AxesLabel -> {"radius", "probability"}]
Out[9]=

NearestNeighborG as the CDF of nearest neighbor distribution can be used to compute the mean distance between a typical point and its nearest neighbor - the mean of a positive support distribution can be approximated via a Riemann sum of 1- CDF. To use Riemann approximation create the partition of the support interval from 0 to maxR into 100 parts and compute the value of the NearestNeighborG at the middle of each subinterval:

In[10]:=
step = maxR/100; middles = Subdivide[step/2, maxR - step/2, 99]; values = nnG[middles];

Now compute the Riemann sum to find the mean distance between a typical point and its nearest neighbor:

In[11]:=
Total[(1 - values)*step]
Out[11]=

Test for complete spatial randomness:

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

Fit a Poisson point process to data:

In[13]:=
Clear[\[Mu]]; EstimatedPointProcess[ResourceData[\!\(\* TagBox["\"\<Sample Data: Toronto Murders\>\"", #& , BoxID -> "ResourceTag-Sample Data: Toronto Murders-Input", AutoDelete->True]\), "Data"], PoissonPointProcess[\[Mu], 2]]
Out[14]=

Gosia Konwerska, "Sample Data: Toronto Murders" from the Wolfram Data Repository (2022)  

Data Resource History

  • Date Created:

Source Metadata

  • Citation:
    • Brown PE (2015). Model-based geostatistics the easy way. Journal of Statistical Software, 63(12), pp. 1-24.

Data Downloads

Publisher Information

  • Prepared for the Wolfram Data Repository By: Gosia Konwerska
    (Wolfram Research, Inc.)
  • Publisher of Record: Gosia Konwerska