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Dan, I've just updated that old post, thanks!

Here are some pieces of code to visualize the larger trend suggested by @Jonathan Wallace:

• Crime in the United States by Volume and Rate per 100,000 Inhabitants, 1992-2011:

  crime=Import["https://www.fbi.gov/about-us/cjis/ucr/crime-in-the-u.s/2011/crime-in-the-u.s.-2011/tables/table-1/output.xls","Data"][[1,4;;-36,1;;-5]];
  i=1992;interpreters[{year_,population_,violentCrime_,violentCrimeRate_,murder_,murderRate_,rape_,rapeRate_,robbery_,robberyRate_,aggravatedAssault_,aggravatedAssaultRate_,propertyCrime_,propertyCrimeRate_,burglary_,burglaryRate_,larceny_,larcenyRate_,motorVehicle_,motorVehicleRate_}]:=<|
  "year"->DateObject[{i++}],
  "population"->Interpreter["Integer"][population],"crime"->Interpreter["Integer"][violentCrime],
  "murder"->Interpreter["Integer"][murder],
  "rape"->Interpreter["Integer"][rape],
  "robbery"->Interpreter["Integer"][robbery],
  "assault"->Interpreter["Integer"][aggravatedAssault],
  "property"->Interpreter["Integer"][propertyCrime],
  "burglary"->Interpreter["Integer"][burglary],
  "larcency"->Interpreter["Integer"][larceny],
  "motor"->Interpreter["Integer"][motorVehicle],"crime rate"->Interpreter["Number"][violentCrimeRate],"murder rate"->Interpreter["Number"][murderRate],"rape rate"->Interpreter["Number"][rapeRate],"robbery rate"->Interpreter["Number"][robberyRate],"assault rate"->Interpreter["Number"][aggravatedAssaultRate],"property rate"->Interpreter["Number"][propertyCrimeRate],"burglary rate"->Interpreter["Number"][burglaryRate],"larcency rate"->Interpreter["Number"][larcenyRate],"motor rate"->Interpreter["Number"][motorVehicleRate]|>;
  crimeUS=Dataset[interpreters/@Drop[crime,1]]
  

crimeUS[DateListPlot, {"year", "murder"}]

DateListPlot of Crime by Volume:

DateListPlot[Transpose@Normal@Values@crimeUS[All, 3 ;; 11], {1992}, 
 PlotTheme -> "Detailed", 
 PlotLegends -> Normal@Keys[crimeUS[1, 3 ;; 11]]]

DateListLogPlot of Crime by Rate per 100,000 Inhabitants:

DateListLogPlot[
 Transpose@Normal@Values@crimeUS[All, 12 ;; 20], {1992}, 
 PlotTheme -> "Detailed", 
 PlotLegends -> Normal@Keys[crimeUS[1, 12 ;; 20]]]

• Homicides by Firearm compared to other countries (2007 only):

  homicides = Import["homicides.m"]
  

Percentage of homicides by firearm:

    GeoRegionValuePlot[homicides[All, #country -> #percentage &]]

Number of homicides by firearm:

GeoRegionValuePlot[homicides[All, #country -> #number &]]

Homicide by firearm rate per 100,000 population:

GeoRegionValuePlot[homicides[All, #country -> #rate &]]

• Murder Victims by Weapon, 2008-2012

  murderVictims=Dataset[<|#[[1]]-><|"2008"->IntegerPart@#[[2]],
  "2009"->IntegerPart@#[[3]],
  "2010"->IntegerPart@#[[4]],
  "2011"->IntegerPart@#[[5]],
  "2012"->IntegerPart@#[[6]]|>&/@Import["https://www.fbi.gov/about-us/cjis/ucr/crime-in-the-u.s/2012/crime-in-the-u.s.-2012/offenses-known-to-law-enforcement/expanded-homicide/expanded_homicide_data_table_8_murder_victims_by_weapon_2008-2012.xls/output.xls"][[1,5;;-5,1;;-4]]|>]
  

PieCharts:

Column[PieChart[murderVictims[Join[Range[3,10],{-1}],#],
ChartLegends->Normal@Keys[murderVictims[Join[Range[3,10],{-1}],#]],
ChartElementFunction->ChartElementDataFunction["GlassSector","GradientDirection"->"Angular"],
LabelingFunction->"RadialOutside",
ChartStyle->10,
PlotLabel->#]&/@{"2008","2009","2010","2011","2012"}]

Attachments:

@Marina, I've updated my CDF file in the original post.

It seems the line:

shootingsAssociHype = 
  Flatten[{#[[1 ;; 2]], #[[5]], #[[3]], 
      StringReplace[statesAssoci[#[[4]]], 
       " " -> ""], "UnitedStates"}] & /@ shootingsSplitHype;

Should be replaced by:

shootingsAssociHype = 
  Flatten[{#[[1 ;; 2]], #[[5]], #[[3]], 
      StringReplace[statesAssoci[StringTrim[ToUpperCase[#[[4]]]]], 
       " " -> ""], "UnitedStates"}] & /@ shootingsSplitHype;

Because in the shootings file, the state postal code is not always in uppercase, and sometimes it contains space that can only be deleted by StringTrim.

The new CDF file will also show how many entries are deleted because it cannot find a coordinate for a certain location.

@Vitaliy, Thank you for sharing with me the Postal code already available in WL. I think this can further simply my code to be:

divisions = Entity["Country", "UnitedStates"][EntityProperty["Country", "AdministrativeDivisions", {}]];
adms=AdministrativeDivisionData[divisions, {"StateAbbreviation", "Entity"}];
statesAssoci=AssociationThread[adms[[All,1]],adms[[All,2]]];
shootingsSplitHype =Flatten[{#[[2]], #[[4]] + #[[5]], {StringSplit[StringReplace[#[[6]], " " -> ""], ","]}, #[[7]] },1] & /@ shootings;
shootingsAssociHype =Flatten[{
#[[1 ;; 2]], 
#[[4]], 
{If[
Length[#[[3]]]==2,
CityData[Entity[
"City" ,  {#[[3,1]], statesAssoci[ToUpperCase[StringTrim[#[[3,2]]]]][[2, 1]],
statesAssoci[ToUpperCase[StringTrim[#[[3,2]]]]][[2, 2]]}], "Coordinates"],
Missing["NotAvailable"]
]
}},1] &/@ shootingsSplitHype;

For some reason, Entity[] only works for me in Wolfram Cloud, so I have not tested out the complete code yet.

Very sad discussion...

We tend to magnify primacy, but what does a larger data set reveal?

• Here's crime in the US on a decade timetime: FBI Table 1
• Murder Victims by Firearms
• Homicides by Firearm over a decade, compared to other countries
• Bureau of Justice Statistics: a more positive data set

I would be very curious to see the different ways the Wolfram Language can visualize the larger trend.