12 min read

To Classify or Not to Classify

If you’ve been following this blog, you’ll recognize that I’ve been pretty obsessed with crime data recently. Louisville’s open crime data has been a great open source, continuously updating data set that has allowed me to explore new plotting techniques, interesting time series analysis and more. Today, I’m exploring the feasibility of classifying crimes by their crime type using this data set. Why you might ask? Well, as per usual, mainly because it’s fun, but this sort of model could easily see use in the real world. A successful model will give me insight into what features have predictive power for certain crimes. This information can be used to allocate manpower, for departmental budgeting and a whole host of other things. In developing this model, I’ve bumped–yet again– into a fundamental truth in data science – your data is the most valuable asset you have. Sometimes, your model just needs better data(or better feature engineering) and without it, even the most sophisticated model in the world won’t give you satisfactory results. With that said, let’s dive into the analysis.

Preparing the data

First things first, I need to massage my data a bit. The original data is a little over a million rows and has 25 variables. Many of those are redundant(month and month_reported) or uninformative(yday) for my purposes, so I just eliminate those right away. Next, I have to decide how fine grained my predictions should be. I have 16 different crime types. That’s not an unworkable number of classes to predict with something like multinomial logistic regression, but just going by gut initial feeling, I don’t think I’ll get very good accuracy. From my past analysis, I know that this data set is very unbalanced.

Var1 Freq
arson 0.000
assault 0.134
burglary 0.092
disturbing the peace 0.013
drugs/alcohol violations 0.172
dui 0.001
fraud 0.035
homicide 0.001
motor vehicle theft 0.040
other 0.128
robbery 0.022
sex crimes 0.008
theft/larceny 0.178
vandalism 0.086
vehicle break-in/theft 0.078
weapons 0.011

As you can see above, crimes such as theft and drug/alcohol violations make up 17% of the total number of crimes each, while arson is barely represented in the data. This means predicting something like arson will be extremely difficult without some very informative variables. Additionally, do I really care about all the crime distinctions. While vehicle break-in/theft and motor vehicle theft are indeed different, ultimately they are both thefts. Thankfully, the UCR(Uniform Crime Reports) provides some guidance for this sort of aggregation. There are two parts to the UCR, part 1 and part 2. Part 1 consists violent crimes and property crimes and part 2 is mainly “lesser” offenses such as vandalism, but also includes offenses such as sex crimes and fraud. The UCR can be used to (somewhat) rigorously divide the various crime types, but, ultimately, what sort of granularity to use is a modeling decision. It seems reasonable that violent crimes may share characteristics that could lead to performant models. This also seems true for something like property crimes. Part 2 of the UCR is a different bag of worms. It contains such a diverse range of crimes. Fraud doesn’t appear to have much in common with DUI’s or prostitution. The entirety of part 2 is really just a mixed bag of offenses. After a good bit of test modeling, it didn’t seem possible to classify part 2 crimes reliably in some sort of ‘other’ category. The characteristics of the crimes were just too varied. In the end, I decided to simply predict whether a crime was a property crime or a violent crime. It may not be the ideal choice, but all modeling requires tough choices and finesse.

With that decision out of the way, I can label my data and cut out any remaining uninformative variables. The head of the resulting data set can be seen below.

Table continues below
premise_type zip_code year month hour day_of_week
highway / road / alley 40210 2005 May 16 Sunday
parking lot / garage 40205 2009 Feb 12 Saturday
bar / night club 40202 2005 Jan 23 Saturday
residence / home 40229 2005 Jan 10 Sunday
grocery / supermarket 40229 2005 Jan 1 Saturday
residence / home 40215 2005 Jan 9 Sunday
weekday season lmpd_beat lat lng crime
Weekend Spring 223 38.24 -85.79 property
Weekend Winter 513 38.23 -85.7 property
Weekend Winter 134 38.26 -85.75 property
Weekend Winter 724 38.12 -85.65 violent
Weekend Winter 724 38.09 -85.67 property
Weekend Winter 436 38.18 -85.78 property

Looking at some quick, exploratory plots, it’s pretty easy to see that there are small, but noticeable differences in many of variables. We tend to see more violent crime on the weekend than on weekdays. With property crime, this is reversed. Similarly, there are more violent crimes in spring/summer than in fall/winter, but for property crimes we only really notice a slight bump in summer and then a sharp drop during winter. These are the sort of things our logistic regression model should pick up on to fit the model.

Crime by Day of Week

Figure 1: Crime by Day of Week

Crime by Season

Figure 2: Crime by Season

Making the Model

With the data prepared, now I can flex the full power of R. First, I create a training and a testing split with the data. I’m just using a 75/25 split, though this can easily be adjusted depending on the computing power you have at your disposal. With the data split, now I need to see how well represented each class is in my data set.

Var1 Freq
property 0.7
violent 0.3

As you can see above, the data set is quite unbalanced. Louisville sees 2.3 times as many property crimes as violent crimes. In my test modeling, leaving the data this imbalanced significantly reduced the accuracy of the model. To address unbalanced data, you typically either upsample or downsample the data. With upsampling you are effectively making random copies of the smaller class until the classes are even. With downsampling you are removing data from the larger class until they are even. While, in general, it’s not a good idea to throw away data, in this case the performance was very similar. Since upsampling had similar performance but much longer training times, I ended up using downsampling.

Finally, all that’s left to do is build the model. In this post, I’m building a fairly standard logistic regression model. I wanted to try out the nnet package, so I use their multinom function which fits a logistic regression model using a single layer neural network. I could just as easily have used the base glm package from R, but it was a decent bit slower in the model training–plus it is always fun to play around with a new package.

One note on cross-validation: initially, I built all of my models using the caret package. With caret, you have a world of training, testing, evaluation and validation techniques at your disposal. These inital models used repeated cross-validation from caret to fit the logistic regression model. I planned to use these more robust models in the final report, but somewhere along the pipeline the model size blew up and I was left with 100+MB model sizes. Since this is going up on github and I didn’t want to fiddle around with those model sizes I ditched the cross-validation and fit the model with the original nnet package.

# Cross validation setup. I found that when I used caret to build this model, 
# the saved model size blew up way too quickly(100+MB models). Since this is just a
# blog post and not a production model, I went back and built the model using
# the multinom function directly from the nnet package.
# Test set performance was similar either way.

# fitControl <- trainControl(method = 'repeatedcv', number = 2)
# set.seed(123)
# fit1 <- train(Class ~., data = downsampled_train, method = 'multinom',
#               trControl = fitControl, verbose = TRUE)

# Model takes a couple minutes to train, so I prebuilt and saved the model. 
# Just loading it in here

# fit1 <- multinom(Class ~., data = downsampled_train, maxit = 100)
# saveRDS(fit1, "nnet_logistic_class_model.rds", compress = 'gzip')

fit1 <- readRDS(here::here("content", "post",
                           "Data", "nnet_logistic_class_model.rds"))

Model Evaluation

Now that the logistic regression model is built I can plug the test set data into the model and see how I did. The simple model was able to classify about 80% of violent crimes correctly, but only 55% of the property crimes, yielding about 63% accuracy overall. That’s a pretty disappointing number. Given the unbalanced nature of the data set, if I just predicted property on every entry, I’d get about 70% accuracy–sure I’d miss every violent crime, but sacrifices must be made!

crime acc
property 0.554
violent 0.799
## Confusion Matrix and Statistics
##           Reference
## Prediction property violent
##   property    54299    8423
##   violent     43705   33483
##                Accuracy : 0.6274        
##                  95% CI : (0.6249, 0.63)
##     No Information Rate : 0.7005        
##     P-Value [Acc > NIR] : 1             
##                   Kappa : 0.2845        
##  Mcnemar's Test P-Value : <2e-16        
##             Sensitivity : 0.5540        
##             Specificity : 0.7990        
##          Pos Pred Value : 0.8657        
##          Neg Pred Value : 0.4338        
##              Prevalence : 0.7005        
##          Detection Rate : 0.3881        
##    Detection Prevalence : 0.4483        
##       Balanced Accuracy : 0.6765        
##        'Positive' Class : property      

Even with this poor performance, I don’t think the model is a complete failure. What if we have skewed costs associated with identifying violent versus property crimes. Let’s just say, hypothetically, that every property crime costs the taxpayer $50 and every violent crime costs $150(where did these come from? I pulled them from thin air.) And let’s say that, for some reason, if we are able to identify the crime type correctly, we can mitigate the cost of that crime by 50%. If this were the case, then it would be beneficial to value the identification of violent crimes more highly–even at the expense of missing more property crimes. For example, using the test set and my made up dollar figures we have a total crime cost of $8,736,000 when naively predicting all crimes as property crimes, but only $7,317,400 when using our logistic regression model.

Of course the above numbers are made up, but I think they illustrate a point nicely – just because your model is a poor fit doesn’t meant it’s not valuable and just because your model is a great fit doesn’t mean it’s valuable. In this case, the overall model needs a ton of improvement, but there are several nice features already present. Model specificity(the ability to identify violent crimes) is quite high and model precision(when I predict a property crime, how often is it actually a property crime?) is also really high. The main problem seems to be that the model is vastly over predicting violent crimes at the cost of missing a huge amount of property crimes–my recall/sensitivity is just 55%. In other words, of the 98,004 property crimes in the test set, I only identified 55% of them. Those that I did identify as property crimes were most often labeled correctly, but I wasn’t able to identify a large portion of the property crimes at all. This makes me think that my features may not have very distinct delineations between violent and property crimes. If you look at Fig.1 and Fig.2 again, you can see that while there are differences between property and violent crimes, they are relatively minor. This could be a case where I need better feature engineering or just more features/data to really hone in on the difference between the categories. Just off the top of my head, I would imagine that adding features such as ‘time since last violent crime in zip code’ would help to differentiate the two categories. It would also be interesting to see what sort of correlation the categories have with things like severe weather, political data, or housing data. There’s a whole host of information you could add to the data set to improve the model, but that’s something for a different post (and probably a more serious model).

Confusion Matrix

Figure 3: Confusion Matrix

As usual, this exploration has been extremely educational. It appears that I need a little more work (and probably some better features) to really develop a sufficiently performant model. Then again, depending on the purpose of the model, this could already be useful. I’d be interested in exploring how far I could push the model. I think better feature engineering is the most obvious way to improve, but perhaps this would be a use case for a more “advanced” model like a random forest or a neural net. I could even see this being an interesting application of a ARIMA model + regression model that I believe I mentioned back in my forecasting post. But I think for the time being I’ll leave it here. As usual, the full code can be found in this blogs github. Until next time!


I know above I said I decided to work with just two crime categories, but if you know me at all, you’d know I couldn’t resist playing around with the full slate of crimes. I follow essentially the exact same data preparation and modeling procedures, except this time instead of just violent and property crimes, I now have robbery, vandalism, homicide, etc. This means I’m now fitting a multinomial logistic regression model with 16 different classes, many of which are significantly underrepresented in the data set.

In addition to calculating the accuracy as I did for the two class model, I also generated predictions through random sampling for the sake of comparison. For those, I randomly sampled one of the 16 crime types for each entry and used that as the prediction. Since there are 16 crime types, I’d expect to see about 0.062 accuracy for each crime type (I have a 1/16 chance of just randomly picking the correct crime).

crime_type acc sim_acc
assault 0.219 0.068
burglary 0.311 0.065
disturbing the peace 0.283 0.070
drugs/alcohol violations 0.195 0.068
dui 0.714 0.073
fraud 0.407 0.067
homicide 0.124 0.071
motor vehicle theft 0.097 0.066
other 0.113 0.065
robbery 0.075 0.072
sex crimes 0.357 0.059
theft/larceny 0.319 0.067
vandalism 0.018 0.067
vehicle break-in/theft 0.423 0.068
weapons 0.290 0.070

The random sampling accuracy is exactly as I expected. As you can see, the model outperforms random sampling by a good amount, but it is nothing to write home about. Overall accuracy sits at a pretty abysmal 23.5%. We see similar metrics as we did on the two class model. Specificity is high, sensitivity is low, but in this case, precision is pretty low as well. This is actually pretty informative. Since I am seeing the same sort of metric on both sets of classification models, it gives me more confidence that the model needs better features. It couldn’t reliably distinguish between violent and property crimes given the features, and it gets even worse when we try to more specifically classify the crimes using the same set of features. Anyway, no real additional analysis here. Just a tasty little bonus morsel.