In my perpetual quest to walk the line between readability and informativeness, I have divided the colors into groups, roughly modeled after their electromagnetic frequencies, and then created four subdivisions within each color group. I think the added level of complication is worth it. There are the shades of purple on the north, which should come as no surprise. The blues on the northwest and southwest sides almost form a ring around the West Side red zone in Garfield Park. The greens interestingly seem to signal transitionary areas. That is, they are bordered on either side by decent and decidedly dangerous areas. A significant question is whether the crime rate varies uniformly over these areas or changes rapidly somewhere in the middle, with the side bordering on safe areas being itself also safe and the area bordering on the dangerous areas also itself being dangerous. I suspect it is, more often than not, the latter, judging by my various ill-advised bike rides throughout this fair city. I have posted the various VC Indexes below the map. The average for the city is at about 528, which is between the darkest and second darkest green on the map.
And the data:
| Comm Area |
VC |
| Edison Park |
44.28686 |
| Norwood Park |
49.00007 |
| Forest Glen |
57.48499 |
| OHare |
69.6598 |
| Mount Greenwood |
82.63223 |
| Jefferson Park |
105.153 |
| Dunning |
105.6732 |
| North Park |
116.6262 |
| North Center |
138.1757 |
| Beverly |
144.0694 |
| Montclare |
148.0778 |
| Lincoln Square |
149.3367 |
| Portage Park |
157.8527 |
| Edgewater |
161.9378 |
| Albany Park |
168.9074 |
| Clearing |
174.0806 |
| Lincoln Park |
174.5109 |
| Lake View |
179.3639 |
| Bridgeport |
180.1214 |
| Irving Park |
181.0791 |
| West Ridge |
185.4949 |
| Hyde Park |
217.5492 |
| Hegewisch |
223.2749 |
| East Side |
232.0843 |
| Garfield Ridge |
237.0339 |
| Uptown |
247.0979 |
| West Elsdon |
279.7194 |
| Lower West Side |
288.6189 |
| West Lawn |
290.3425 |
| Avondale |
295.498 |
| McKinley Park |
298.2283 |
| Brighton Park |
309.3131 |
| Logan Square |
310.0839 |
| Near North Side |
313.2287 |
| South Lawndale |
322.2777 |
| Kenwood |
325.3636 |
| Belmont Cragin |
328.1414 |
| West Town |
339.2626 |
| Hermosa |
339.594 |
| Rogers Park |
341.6309 |
| Armour Square |
373.6478 |
| Douglas |
383.4679 |
| Ashburn |
386.6232 |
| Archer Heights |
391.0434 |
| Gage Park |
396.4393 |
| Near South Side |
419.1248 |
| Oakland |
464.0129 |
| Morgan Park |
467.6648 |
| Pullman |
541.2687 |
| Calumet Heights |
569.624 |
| Near West Side |
587.5236 |
| South Deering |
629.5593 |
| New City |
680.6183 |
| Washington Heights |
691.9897 |
| Loop |
754.428 |
| Grand Boulevard |
772.8288 |
| Chicago Lawn |
805.917 |
| Humboldt Park |
849.7532 |
| Riverdale |
856.7843 |
| Burnside |
872.367 |
| Austin |
890.2048 |
| South Shore |
892.1938 |
| South Chicago |
905.1898 |
| West Pullman |
907.0116 |
| Avalon Park |
925.9841 |
| Auburn Gresham |
988.0668 |
| Roseland |
1025.911 |
| Chatham |
1093.954 |
| North Lawndale |
1179.835 |
| Woodlawn |
1181.694 |
| Washington Park |
1346.766 |
| Greater Grand Crossing |
1362.73 |
| West Englewood |
1404.563 |
| Englewood |
1449.434 |
| West Garfield Park |
1477.607 |
| East Garfield Park |
1606.766 |
| Fuller Park |
1861.264 |
| AVERAGE |
528.6458 |
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And the data:
I think you may be right about variability within neighborhoods. Their way of dividing communities seems to group some farily diverse areas within one community designation. For example, Uptown contains areas usually thought of as Andersonville and Ravenswood, and Near North contains the old Cabrini neighborhood, Streeterville, and part of Old Town.
One question — did you filter out larceny (in its myriad of forms) from the index crimes list, or are you considering property crimes to be violent? I imagine this might have a big effect on some neighborhoods like the near north or Lincoln Park in which you might expect to have an abnormally high rate of retail theft and theft from cars.
Ah yes, I should put it how it’s calculated. It’s a weighted average, 5 times the number of murders plus 4 times the manslaughter plus 3 times the criminal sexual assaults plus the aggravated batteries plus the aggravated assaults plus the robberies. It does not include burglaries or larcenies. I.e. If your car gets stolen, it doesn’t get counted, unless it gets stolen with you in it. I have the property crime map in previous entries. It’s as up to date as this one, but I don’t spend as much time on it because it’s not as interesting.
Hmm, I am begnining to see the faint glimmer of a cluster analysis on the horizon.
*** Potentially boring stats to follow ***
Since the community boundaries are kind of arbitrary, one could assemble an alternative by using the police beat level data and 3D cluster analysis. If you assigned the VC crime index to one of the axis and latitude and longitude to the other two you could generate more of a true picture of the “crime neighborhoods” in Chicago. This would group police beats together in a cluster if they were geographically close to one another and had similar crime rates.
Of course, unless you specified a greater weight to the VC index in the model, you would be assuring that geographic similarity would be more important than crime data similarity in assembling the clusters. This could be seen as a weakness, or I think it also could be justified on the basis of common sense. Although it is possible for geographically discontiguous areas to have the same crime rates for the same reasons, I would want the model to make this unlikely.
***Potentially boring stats warning has been lifted. This message has been brought to you by the emergency stats notification network***
This might be more trouble than it is worth, but it also might demonstrate a quality of crime distribution that I suspect contributes to its effect. I am going to guess that in the blighted areas of Chicago you’d find large clustes of mid-high level crime punctuated by small clusters of exceptionally high levels of crime. In the nicer neighborhoods I am going to guess you’d find large clusters of low crime punctuated by small clusters of mid-high or high crime.
Setting aside differences in the rate of crime overall, this distribution pattern could make crime more difficult to tolerate in the blighted neighborhoods than the nicer neighborhoods. It would be easier to avoid the high crime neighborhoods as a developer and renter if they are limited in size and surrounded by better neighborhoods. This would leave the residents of even the small violent neighborhoods on the North Side better off than the residents of the mid-high crime level neighborhoods on the south side (who would be surrounded by large tracts of dysfunctional neighborhoods).
For a hypothetical example, compare a resident of the Cabrini neighborhood (still relatively dangerous by North side standards) to a resident of South Shore (relatively safe by SS standards). The latter would have to travel through at least one other mid-high crime neighborhood to work or recreate in a lower crime area. The former would have to walk 4 blocks to do the same.
Hmm, beat breakdown, eh? My raw data does indeed have beat info. I would have to go back down and set up a completely new set of spreadhseets to process it (as my current ones are broken down by Community Area), but I could do it. The problem is that I population-normalize all of my data, and I do that using census data that is broken down by Community Area, not by beat. So I could theoretically find the raw number of crimes in each beat, but comparison of the crime RATES across beats without knowing the populations would be imposssible.
Hmm, that would introduce a source of error. You could always divide the population of the community areas evenly across all beats within the community (provided that all beats are in 1 and only 1 community area). Otherwise this seems impossible to overcome.
Cluster analysis of community level data might also be interesting. This is particularly true if you were not all that bothered by a failure to generate a pretty 3D graph of the results and were willing to expand into 5 or 6 D space. I could imagine communities clustered by drug crime, violent crime, property crime, location from center, rent value, and average income. There might be discernable patterns in the arrangement of these factors.
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