The events in Ferguson, Missouri between Officer Darren Wilson and Michael Brown are tragic, chaotic, and unfortunately part of a larger pattern of police-citizen conflict that occasionally escalates into deadly force. These events are also not simply about what happened on August 9, 2014, but about the growing distrust between citizens and the police who patrol that community. Ferguson has resonated across the country, not because of the merits of this one particular interaction -- where the facts are still uncertain -- but because of other similar, but less deadly policing tactics in certain urban communities.
Calls for better police training, body-worn cameras, new racial profiling legislation, and/or federal investigations are welcome steps, but they all fail to capture the simmering day-to-day tension that is created by certain types of stop and frisk patrols. At their core, the Ferguson protests are not just about a death, but about the day-to-day life of people of color who share a growing resentment for perceived police harassment.
How can police and communities track this tension before it boils over into riots and civil unrest? How can we measure the cost/benefit of certain types of stop and frisk practices, with metrics including community sentiment as well as community safety?
Interestingly, new crime mapping and predictive technologies ordinarily used to prevent crime can be adapted to identify patterns of problematic police-citizen interaction. While the technologies are only just being made available to better-funded police departments, this "stop and track" solution does offer an additional remedy for those searching for an answer to prevent the next Ferguson situation.
In jurisdictions like New York City, Chicago, and Los Angeles, police are employing sophisticated crime mapping technologies to map crime. Police track each reported crime, call for service, and arrest in terms of date, time, and event. In cities required to keep track of each police-citizen encounter, the data is even more granular. In New York City, for example, we know exactly where each stop took place because a UF-250 police form is filled out, uploaded into a computer and then mapped.
This crime mapping technology has proved quite effective in reducing crime rates in certain neighborhoods, shifting scarce police resources to areas of higher crime, and organizing the rather chaotic reality of urban street policing. Yet, because it is police technology, it necessarily focuses on crime (it is "crime mapping" after all), and not the conduct of police officers in relation to the crime. So, we know a burglary happened on a particular block, but not necessarily where the police were patrolling when they failed to prevent that burglary. Obviously, to stop the next burglary, understanding why that "missed opportunity" occurred would be important. Or, we know that a police officer encountered a suspect and did not arrest him, but not the qualitative nature of the encounter. Essentially, the crime mapping software used in areas like New York City only tells us half the equation -- we know where the crime is and where police interact with citizens, but we do not know where the police were located when crime occurred or the nature of their interactions with citizens. Simply put, we are missing half of the picture that might provide a warning sign of increasing tension between the community and the police or even about individual police officers.
Community distrust of police grows from two contradictory, but interlocking sources. First, certain communities are subject to more crime due to inadequate police intervention. Second, in an attempt to aggressively respond to crime, innocent people in those communities may be stopped and questioned without any accountability for the police error. As can be seen in the outpouring of stories after Ferguson, certain communities, thus, feel that the police are not on their side, and only out to harass them.
If crime mapping technologies were redirected to also capture information about the conduct of the police important insights might emerge. First, understanding where police officers are in relation to the location of crime should improve police efficiency. Most police cars are (or soon will be) fitted with GPS technology. Some departments have begun adding RFID chips to police badges to map police officer movement throughout the day. These innovations, while obviously invasive to the police officer's personal freedom, would allow police administrators to map police patterns throughout the day. Overlaying the crime reports, police administrators would be able to more effectively utilize their officers to respond in real time to crime patterns.
More importantly, once this granular level of police officer mapping was established, it would be possible to track individual officers on patrol and observe patterns of citizen involvement. A police officer who made fifty civilian stops a day with no recovered contraband, might be flagged as an officer who was stopping people without the requisite reasonable suspicion or probable cause. Studying individual police districts might allow for systemic comparisons about the appropriate level of police citizen encounters compared to other districts. Arrest efficiency metrics could be studied. Officers who had fewer stops, but more recovered contraband could be studied to see what made them more effective. Officers who simply appeared to be stopping everyone on the street could be re-trained. While having a better understanding of officers' conduct would not end all arbitrary stops on the street, it would provide an additional measure of accountability not currently in practice.
Finally, in addition to the quantitative study of stops per successful recovery, crime mapping technologies and the reporting requirements could also include a qualitative measure of community sentiment. If police officers were required to report the citizen's reaction to the police stop, you would also have a measure of community sentiment. The underlying assumption of the Supreme Court's logic allowing police to approach and question all citizens for any reason without violating the Constitution is that police are generally acting in the best interests of the community. The underlying assumption of allowing Fourth Amendment stops based on a lesser standard of reasonable suspicion, is that police should be encouraged to investigate possible crimes, even if they do not have the higher level of probable cause. Both assume that the police are acting in good faith and that the community supports this pro-active intervention. But, tracking, measuring, and evaluating those assumptions are not regularly done. If police were required to inquire about the citizen's reaction to being stopped (erroneously) or questioned (innocently), police administrators would be able to gain a better sense of the tensions in the community. Hotspots of community anger could be mapped. In those jurisdictions that already require a report for every contact, all that would be required is an additional comment about the interaction. To keep police officers honest about the interaction, random audits of people stopped could be done to compare the reported sentiment to the actual sentiment.
While devastating for the people involved, an incident like the shooting in Ferguson might not have created the same local outrage, if it had been an isolated event. Knowing about the pattern of community tension, could allow police administrators to address community sentiment with more targeted and sensitive interventions.
If at the end of the day, a crime map could be created that mapped crime, police patrols, police-citizen encounters, and citizen sentiment, police administrators and civic leaders would have a much better accounting of possible trouble areas in the city. While it would not prevent individual tragedies from occurring, and it cannot solve the very difficult challenges faced by police officers involved in dangerous situations every day, it would provide an ability to study the problem in a new and innovative way.