Our initial research has brought our group to investigate which factors are the greatest determinants of the level of force used by police officers during incidents. Once arriving at a set of variables we would like to understand the narrative behind why changes in certain factors (for example: demographics/area/time_of_day/number_of_officers/type_of_resistance) influence the use of force. In addition, a case study of several officers will be used in order to analyze whether the same officer is meting out the same level of force regardless of changing environment or whether the same factors applicable to groups of police officers are influencing individual officer decisions.

Research Questions

1. Relational Analytics

a. What is the breakdown into categories of force used as a percentage of the total incidents?

b. Demographics shares of incidents involving the use of force in comparison to the demographics of the beat/area/neighborhood where incident occurred.

c. What are the possible values that can be used to quantify force within the dataset

i. Which action categories/force types are we considering at what “level”

d. What geographic group are most affected by a given type of action?

e. What are the most common type of resistance levels among incidents of use-of-force?

2. Visualization

a. A highlight table can be used to illustrate the high impact one factor can have on the level of force used regardless of changes in other variables such as geographic location

b. A scatterplot can be used to show our analysis if our findings reveal two continuous variables of interest being correlated i.e. number of commendations/medals is directly correlated with a high proportion of an officer’s misconduct allegations being related to use of excessive force.

c. A circle view chart can also be used for comparative analysis of the different variables of interest and how they underlie the number of use of force incidents.

d. One interactive visual we can create is similar to the NCAA bracket predictor tagged below with the difference being that instead of each node showing chance of winning the tournament broken down by team, it would instead show chance of variable being present or co-present with others in a use-of-force incident

i. https://thepowerrank.com/visual/NCAA_Tournament_Predictions

3. Data Cleaning and Integration

a. Does the number of allegations filed previously against an officer (or group of officers) affect the monetary compensation of a settlement

b. Do previous uses of force by an officer (or group) affect the monetary compensation of a settlement

c. What percentage of law enforcement officers who are named in settlements have been named in previous settlements

d. What percentage of settlements are early in an officer’s career?

4. Graph Analytics

a. Are officers who are accused of using force alone more likely to reoffend than officers accused with other officers?

b. If groups are more likely, is there a group size that leads to the largest proportion of officers reoffending?

5. Machine Learning and Text Analytics

a. Can we predict how aggressive an officer (what level of force) will act following an allegation that was sustained?

i. Specifically, to answer this question, we will look at the force_type used by officers following an allegation that was sustained. We will take into consideration to make sure the type of charge and resistance_subjects/resistance_levels are similar before and after a sustained allegation to draw a predictive analysis.

b. Can we predict if a use-of-force incident is likely to happen?

i. For this, we would like to see if we can come up with a decision-tree like system comparing all the attributes in the trr_data to create a predictive analysis for use-of-force incidents.

c. Are certain complaint report tags correlated to relationships between officer and complainant demographics?

d. We would like to cluster levels-of-force tags based on how common they are used with police officers with a complaints in the 99th percentile.