{"id":36506,"date":"2018-11-13T20:05:31","date_gmt":"2018-11-14T01:05:31","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/improving-public-safety-with-machine-learning\/"},"modified":"2018-11-13T20:05:31","modified_gmt":"2018-11-14T01:05:31","slug":"improving-public-safety-with-machine-learning","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/improving-public-safety-with-machine-learning\/","title":{"rendered":"Improving Public Safety with Machine Learning"},"content":{"rendered":"<p>The biggest mission of NYPD if to increase public safety by effectively allocating public, limited, resources. In the criminal landscape, firearms incidents play a significant role in public safety and is one of the main focuses of the police department. In New York City, firearms are used in 68% of murders and 41% of robberies<sup>1<\/sup>. With a limited visibility of the all incidents that occurs in NYC, the government relies in the proactivity of the community to respond to gunshot incidents that may lead to a low efficiency in human resource allocation and effectiveness of further investigations<\/p>\n<p><strong>Increasing efficiency by increasing response to incidents<\/strong><\/p>\n<p>Since the end of 2016, the NYPD began the roll out of a system called ShotSpotter (SST), a technology that process sounds from acoustic sensors placed in the streets to identify and locate gunfire incidents in real time<sup>1<\/sup>. Moreover, SST matched the series of sounds to its existing database of gunfire and classify the sound as a gunfire incident or not, after a positive identification, the SST traces the original location and send the information to the police department.<\/p>\n<p>This technology aimed to <strong>increase the response of the department to gunfire by solving the main restrictions the government operated. <\/strong>Until ShotSpotter, the department relies on \u201c911 calls\u201d to act and therefore has limited visibility of the occurrences of gun related incidents in the city.<\/p>\n<ul>\n<li>NYPD reports state that approximately 80% of the gunshots are not reported<sup>1<\/sup>: The community often are discouraged reporting the incident due to fear of the investigation or the belief that they could handle the situation themselves.<\/li>\n<li>In a dense city as NY, Shotgun sounds to identify and determinate its origin: An interview with the CEO of ShotSpotter shows the challenge of correctly identifying the specific sound pattern of a gunshot<sup>2<\/sup> (see link below). In addition, imprecise 911 calls lead to a larger response time to the incident.<\/li>\n<\/ul>\n<p><strong>Initial Results:<\/strong>\u00a0 By 2017, only 66% of the incidents reported by ShotSpotter weren\u2019t reported through \u201c911 calls\u201d and the response time for ShotSpotter identification was only 8 minutes lower than any other response time in the department\u00a0<sup>3,4<\/sup>.<\/p>\n<p><strong>New way of investigating incidents<\/strong><\/p>\n<p>The implementation of SST, changed the way NYPD structured their operation and introduced a new integrated way of investigating gunshot incidents. The integration of the SST sound database and the NYPD internal crime database, Domain Awareness System (DAS), enabled the department to <strong>connect incidents with ongoing investigations (see picture below) even when the incident doesn`t have a physical evidence <\/strong>(e.g. bullet, victim, gun, etc)<\/p>\n<p><a href=\"https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/1-7.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter size-large wp-image-36210\" src=\"https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/1-7-1024x991.png\" alt=\"\" width=\"640\" height=\"619\" srcset=\"https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/1-7-1024x991.png 1024w, https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/1-7-300x290.png 300w, https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/1-7-768x744.png 768w, https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/1-7-600x581.png 600w, https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/1-7.png 1069w\" sizes=\"auto, (max-width: 640px) 100vw, 640px\" \/><\/a><\/p>\n<p>Image:\u00a0 \u201cThe way moving forward\u201d NYPD Publication (2016)<\/p>\n<p><strong>Mastering predictive analysis to prevent crime<\/strong><\/p>\n<p>The amount of data storage that SST and DAS, opens a wide range of possibilities to improve services via machine learning. Recently, police departments across US are working on developing predictive models that goes beyond the reactive way of combating crime. The innovating efforts have a new focus of using the <strong>ability of foreseeing incidents as a way of preventing crime.<\/strong><\/p>\n<p>In the next years, predictive analysis (mass) implementation is around <strong>identifying and mapping criminality profile of different regions.<\/strong> In the past year, the NYPD has been more actively introducing tools such as Hunchlab, or ShotSpotter Mission, uses historical data to maps each shift regions based on its risk assessment (types of events such as robbery, homicide, etc) and probability of having an incident in that period. The mapping based of risk profile allows the department to allocate resources effectively, not only in terms of number of patrols but also based on expertise.<\/p>\n<p><a href=\"https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/2-7.png\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-36255\" src=\"https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/2-7.png\" alt=\"\" width=\"646\" height=\"375\" srcset=\"https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/2-7.png 791w, https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/2-7-300x174.png 300w, https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/2-7-768x446.png 768w, https:\/\/d3.harvard.edu\/platform-rctom\/wp-content\/uploads\/sites\/4\/2018\/11\/2-7-600x348.png 600w\" sizes=\"auto, (max-width: 646px) 100vw, 646px\" \/><\/a><\/p>\n<p>Image: Chantele Dubois, \u201c The future of AI and predictive Policing\u201d (2017)<\/p>\n<p><strong>Recommendation in a social perspective<\/strong><\/p>\n<p>The amount of data and the development of predictive analysis opens a door to optimize decision making that goes beyond crime prevention based on law enforcement perspective.<\/p>\n<p>In the short term, mapping can further help allocation resources: decision about police stations, awareness campaigns and human capital allocation. In the mid-long term, mapping can be the initial step in targeted public actions to reduce criminality that goes beyond law enforcement. They can help other departments to create education and social programs to help reduce criminality by changing the region social reality.<\/p>\n<p><strong>Challenges moving forward<\/strong><\/p>\n<p>The advances in predictive analysis related to public security have opened a series of discussions about bias and discrimination based on income, race and other factors related to inequality issues in the country. The machine learning process utilizes historical data to make decisions and predictions, therefore it presents limitations in foreseeing structural changes in societies.<\/p>\n<p><strong>Questions remaining<\/strong><\/p>\n<p>Given the new trend of predictive, what are the social consequences that law enforcement predictive models can generate? how the limitations of the predictive model can be minimized in cities that are going through a series of structural changes (migration, increase population density, high economic growth)?<\/p>\n<p>(Word count: 781)<\/p>\n<p>&nbsp;<\/p>\n<p>[1] Tami Lin, Malgorzata Rejniak, \u201cSmarter New York City: How City Agencies Innovate\u201d Columbia University Press (2018)<\/p>\n<p>[2] Cold Call Podcast, \u201cShotSpotter: A gunfire detection business looks for a new market\u201d (2017) https:\/\/hbswk.hbs.edu\/item\/shotspotter-a-gunfire-detection-business-looks-for-a-new-market, accessed in November 2018<\/p>\n<p>[3] Rocco Parascandola, \u201cEXCLUSIVE: NYPD ShotSpotter gunfire sensors improve rates of 911 calls, arrests\u201d (2017) http:\/\/www.nydailynews.com\/new-york\/nypd-shotspotter-gunshot-detection-rate-improves-article-1.3010990, accessed in November 2018<\/p>\n<p>[4] New York Police Department, \u201cReport of the Finance Division on the Fiscal 2019 Preliminary Budget and the Fiscal 2018 Preliminary Mayor\u2019s Management Report for the New York Police Department\u201d Finance Division (2018)<\/p>\n<p>[5] New York Police Department, \u201cThe way moving forward\u201d NYPD Publication (2016)<\/p>\n<p>[6] Chantele Dubois, \u201c The future of AI and predictive Policing\u201d (2017) https:\/\/www.allaboutcircuits.com\/news\/the-future-of-ai-and-predictive-policing\/, accessed in November 2018<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Machine learning usage to increase public safety by increasing the response of the NYPD to gunfire by solving the main restrictions the government operated<\/p>\n","protected":false},"author":11874,"featured_media":36507,"comment_status":"open","ping_status":"closed","template":"","categories":[],"class_list":["post-36506","hck-submission","type-hck-submission","status-publish","has-post-thumbnail","hentry","hck-taxonomy-organization-new-york-police-department","hck-taxonomy-industry-public-administration","hck-taxonomy-country-united-states"],"connected_submission_link":"https:\/\/d3.harvard.edu\/platform-rctom\/assignment\/rc-tom-challenge-2018\/","yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.3 - 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