Class 08: Chicago Crime Data



Chicago Crimes

Our dataset for today contains observations of reported crimes from the city of Chicago. Our prediction task is to figure out, given details of when and where the crime occured, what the mostly likely type of crime was commited. Loading in the dataset see that the crime type is given as a number from 1 to 3:

crimes <- read_csv("")

The corresponding crime names are:

crime_names <- c("criminal_damage", "narcotics", "theft")

Notice that this dataset has been balanced. That is, there are an approximately equal number of each crime type in both the training and validation sets (also in the test set, but you cannot see that here):

table(crimes$crime_type, crimes$train_id)
##     test train valid
##   1    0  6013  2039
##   2    0  6029  1976
##   3    0  5958  1985

This has been done by down-sampling. These crimes do not occur with equal probabilities in the raw data; I have taken a subset of the data in such a way that the probabilities are equal. This makes building models easier and is a common trick that I will generally do for you on the lab data.

Multivariate prediction

Now that we have three categories, if we were to fit a linear regression on the response this would no longer make sense. The model would be assuming that the second category is somehow in-between the other two categories. We could modify the procedure in two ways:

  • one-vs-one: take each pair of crimes and fit a LM or GLM seperating these two groups. Final predictions use every model to determine, head to head, which class each testing point should belong to.
  • one-vs-all: build a seperate model for each class, trying to seperate each class from the rest.

With only a small number of classes, both of these can work well. When the number of categories is large, the first takes a lot of computational resource to compare all pairs of models. The second becomes hard because it has a tendency to make every point look like the “all” (since it dominates any individual group).

We could implement either of these strategies ourselves. Some models, such as GAMs, don’t directly implement any other way of doing multi-class prediction and that would be the only approach if we wanted to use them. The e1071 package will do the one-vs-one when given multiple classes (so be carefuly giving it too many classes). Today we will see a package that does a tweak on the one-vs-many for logistic regression and an entirely different way of approaching the problem that avoids the multiclass issue in its entirety.

Multinomial regression

The nnet package provides a function multinom that generalizes the logistic regression in the glm function. It requires almost no special settings; just supply a formula as usual but with a categorical response. The function will print out verbose information letting you know how quickly it converges.

model <- multinom(crime_type ~ poly(longitude, latitude, degree = 3),
                  data = crimes)
## # weights:  33 (20 variable)
## initial  value 26366.694928 
## iter  10 value 25491.235282
## iter  20 value 25472.801010
## iter  30 value 25472.552858
## iter  40 value 25398.449574
## iter  50 value 25191.828655
## iter  60 value 25191.277334
## iter  70 value 25191.171933
## iter  80 value 25180.456887
## iter  90 value 25122.465513
## iter 100 value 25120.012891
## final  value 25120.012891 
## stopped after 100 iterations

The predicted values from the predict function give, by default, the class predictions:

crimes$y_pred <- predict(model, newdata = crimes)

We could, if needed, get the predicted probabilities for each class by setting the type option to “probs”. The output is a matrix with one column per class. We will see uses for these in the future:

head(predict(model, newdata = crimes, type = "probs"))
##           1         2         3
## 1 0.2962009 0.4837351 0.2200640
## 2 0.2923040 0.4543063 0.2533897
## 3 0.2831502 0.2675520 0.4492978
## 4 0.3047145 0.3285162 0.3667693
## 5 0.2969050 0.4745688 0.2285262
## 6 0.3013706 0.4368505 0.2617789

Confusion Matrix

The classification rate that we saw last time still work as a good measurment of how well our predictions run. Remember though that with more classes, even “good” classification rates will generally be lower. Random guessing in the two class model yields a 50% rate; here it gives a 33% rate.

tapply(crimes$y_pred == crimes$crime_type, crimes$train_id, mean)
##      test     train     valid 
##        NA 0.4601111 0.4643333

With more than two classes, there is more than one kind of error. Which crimes, for example, are we having trouble distinguishing? The confusion matrix shows

table(y = crimes$crime_type, y_pred = crimes$y_pred)
##    y_pred
## y      1    2    3
##   1 2867 2881 2304
##   2 1995 4500 1510
##   3 2198 2044 3701

So, criminal damage and narcotics seem harder to distinguish based on location along. These types of metric will be very useful going forward.

Neural networks?

You might wonder why we are using a neural network package for fitting multinomial models. This is a very old package; I can’t find the exact original publication date but I believe it was pre version 2.0 of R (2004-10-04). The neural networks here do not have the functions that you might be familiar with from another class or research project. However, there is a close relationship between neural networks and regression theory. We will ook at this in just a couple of weeks.

Nearest Neighbors

Now, we can look at an entirely different approach to prediction. As I mentioned in the course introduction, I think of models as coming in two categories: local and global. Everything we have seen is inherently global, though we have tried to create local effects through non-linearities and basis expansion techniques.

The nearest neighbor classification algorithm does something very simple: categorize each point with whatever category is more prominent within the nearest k training points. The package we will use for this is FNN, for fast nearest neighbors.


In order to use nearest neighbors, we need to create a model matrix. FNN does not accept a formula input.

X <- as.matrix(select(crimes, longitude, latitude))
y <- crimes$crime_type
X_train <- X[crimes$train_id == "train",]
y_train <- y[crimes$train_id == "train"]

Once we have the data, the knn function is used to run the nearest neighbors algorithm. We have only to set the parameter k; here set to 100.

crimes$y_pred <- knn(train = X_train, test = X,
                     cl = y_train, k = 100)

The classification can be investigated and we see that it is better in this case than the linear model.

tapply(crimes$y_pred == crimes$crime_type, crimes$train_id, mean)
##      test     train     valid 
##        NA 0.5277222 0.4993333

Plotting the data, we can see just how local the model actually is:

qmplot(longitude, latitude, data = crimes,
       color = y_pred) +
  viridis::scale_color_viridis(discrete = TRUE)

plot of chunk unnamed-chunk-14

The nearest neighbors algorithm is of course very sensitive to the choice of k. You can tune it using the validation set. Also, the function knn.reg can be used to do nearest neighbors for prediction of a continuous response (notice the use of “regression” to contrast with “classification”).


A main problem with the nearest neighbors algorithm is defining what “near” means. This will be an ongoing issue, but for now notice that if we build a model matrix with very different variables (such as, time, latitude, and income) the algorithm will basically ignore any variables that have a very different scale. By default knn just uses ordinary Euclidean distances.

One simple fix is to use the scale function on the data matrix X:

X <- scale(X)
X_train <- X[crimes$train_id == "train",]

It does not change much here because latitude and longitude have different scales. In other applications it drastically changes the fit.

Spatial-temporal plots

I wanted to show you an interesting plot of the predictions for the Chicago crime data. In order to make the plot most readable, lets cut time into six buckets:

crimes$h6 <- as.numeric(cut(crimes$hour, 6, label = FALSE))

Now, I’ll fit a multinomial model interacting longitude, latitude, and time.

model <- multinom(crime_type ~ poly(longitude, latitude, h6, degree = 3),
                  data = crimes)
## # weights:  63 (40 variable)
## initial  value 26366.694928 
## iter  10 value 24958.814394
## iter  20 value 24940.603824
## iter  30 value 24940.391270
## iter  40 value 24898.447973
## iter  50 value 24636.616310
## iter  60 value 24616.746591
## iter  70 value 24616.034670
## iter  80 value 24506.612973
## iter  90 value 24478.634633
## iter 100 value 24478.583671
## final  value 24478.583671 
## stopped after 100 iterations
crimes$y_pred <- predict(model, newdata = crimes)

The resulting plot shows how the propensity for each crime type changes throughout the day and over the city.

qmplot(longitude, latitude, data = crimes, color = factor(y_pred)) +
  viridis::scale_color_viridis(discrete = TRUE) +

plot of chunk unnamed-chunk-18

This is a good example of how predictive modelling can be used for meaninful data analysis.