Additionally, the study found that the diffusion of benefit and crime displacement have similar likelihoods of occurring. The probability of positive and negative effects are the same. At the same time the measure of the effects are lacking. Guerette mentions that in many cases displacement may be undetectable. Offenders may move to other jurisdictions, other offenses that are difficult to detect or offenses that have a low reporting rate. Crime displacement and diffusion of benefit have measuring problems. Lack of data means there still exists a gap in research that needs to be filled. Crime displacement in relation to predictive policing needs to be researched to conclusively determine how predictive policing relates to crime displacement.
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It is true, the poor-quality data may have data censoring, systematic bias, and relevance. However, it may also have missing data. Crimes like rape suffer from under reporting. This lack of data means that the algorithm will not detect or accurately predict crimes that suffer under reporting. Under reported crimes cause inaccuracies in the analysis of the algorithms because the algorithms don’t have the complete accurate data they need. This would lead to police officers becoming worse at detecting those crimes because the under reported crimes would be given less emphasis to crack down on. Having a lot of information on a certain crime means attention will be given to those crimes that can be solved easily. It also means that crime with less information will be given less attention because it is not incorporated as accurately in the data. This missing data problem also relates to the feedback loop …show more content…
Someone from the SPD said, “We continue to utilize best practices through the use of crime analysts and technology, but currently have no formal application for predictive policing.” A possible answer to the conflict within predictive policing may be normal crime analysis and technology. This combination maintains a closer connection between the data and police officers. In this situation it is still within the realm of police officers to understand the reasoning that was used to arrive at a conclusion. Given the data a human should be able to detect hot spots and crime trends, police officers have been doing that for years before PredPol came in play, and it seems they’ll continue
It is true, the poor-quality data may have data censoring, systematic bias, and relevance. However, it may also have missing data. Crimes like rape suffer from under reporting. This lack of data means that the algorithm will not detect or accurately predict crimes that suffer under reporting. Under reported crimes cause inaccuracies in the analysis of the algorithms because the algorithms don’t have the complete accurate data they need. This would lead to police officers becoming worse at detecting those crimes because the under reported crimes would be given less emphasis to crack down on. Having a lot of information on a certain crime means attention will be given to those crimes that can be solved easily. It also means that crime with less information will be given less attention because it is not incorporated as accurately in the data. This missing data problem also relates to the feedback loop …show more content…
Someone from the SPD said, “We continue to utilize best practices through the use of crime analysts and technology, but currently have no formal application for predictive policing.” A possible answer to the conflict within predictive policing may be normal crime analysis and technology. This combination maintains a closer connection between the data and police officers. In this situation it is still within the realm of police officers to understand the reasoning that was used to arrive at a conclusion. Given the data a human should be able to detect hot spots and crime trends, police officers have been doing that for years before PredPol came in play, and it seems they’ll continue