{"id":231,"date":"2021-06-16T18:48:57","date_gmt":"2021-06-16T22:48:57","guid":{"rendered":"https:\/\/sites.bu.edu\/riselab\/?page_id=231"},"modified":"2025-02-05T21:55:12","modified_gmt":"2025-02-06T02:55:12","slug":"shape-up","status":"publish","type":"page","link":"https:\/\/sites.bu.edu\/riselab\/shape-up\/","title":{"rendered":"Shape-Up"},"content":{"rendered":"<p><a href=\"\/riselab\/files\/2022\/04\/Shape-Up-1-pager-Spring-2022.pdf\">Shape-Up 1-pager Spring 2022<\/a><\/p>\n<p><img loading=\"lazy\" src=\"\/riselab\/files\/2022\/03\/Toledo-banner-636x88.png\" alt=\"\" width=\"737\" height=\"102\" class=\"alignnone wp-image-304\" srcset=\"https:\/\/sites.bu.edu\/riselab\/files\/2022\/03\/Toledo-banner-636x88.png 636w, https:\/\/sites.bu.edu\/riselab\/files\/2022\/03\/Toledo-banner-1024x141.png 1024w, https:\/\/sites.bu.edu\/riselab\/files\/2022\/03\/Toledo-banner-768x106.png 768w, https:\/\/sites.bu.edu\/riselab\/files\/2022\/03\/Toledo-banner.png 1131w\" sizes=\"(max-width: 737px) 100vw, 737px\" \/><\/p>\n<p><strong>Shape-Up<\/strong> is a platform to help communities prevent gun violence by improving physical spaces. The Shape-Up algorithm learns what a high-risk location looks like in a particular city, using big data and machine learning. The algorithm analyzes aerial imagery to capture <strong>urban form;<\/strong> data on locations (e.g., parks, vacant lots, liquor stores) that play important <strong>functions<\/strong>; and demographic data to measure <strong>social disadvantage<\/strong>. The algorithm generates <strong><em>Shape-Up Scores<\/em><\/strong> of 1-10 for every single block in a given city. These scores predict the overall risk of gun violence at a particular location, with predictive accuracy that <a href=\"https:\/\/www.dropbox.com\/s\/vjh0izp2s6519tw\/SL1_working_paper.pdf?dl=0\">outperforms<\/a> traditional crime prediction approaches. They also measure the potential for reducing risk by addressing form, function, or disadvantage.<\/p>\n<p>Shape-Up Scores are designed to inform community discussions about how best to improve neighborhood safety. The ultimate decisions must rest on the <u>knowledge and priorities of community residents<\/u>.<\/p>\n<p>We continue to refine our processes for <strong>community-engaged data science<\/strong> because we are committed to the self-determination of the individuals and communities impacted by gun violence and other forms of structural disadvantage. Using funds to improve neighborhood spaces is a partial, but necessary, step to reverse legacies of <a href=\"https:\/\/pubmed.ncbi.nlm.nih.gov\/28579093\/\">racialized disinvestment<\/a> that contribute to violence. Reinvesting in neighborhood safety is consistent with calls to #<a href=\"https:\/\/www.fundpeacenow.com\/\">FundPeace<\/a>.<\/p>\n<p><em>Current Shape-Up partners:<\/em><\/p>\n<p><strong><u>Portland, OR:<\/u><\/strong> We\u2019re working with Portland\u2019s Community Safety Division to identify priority locations and interventions for addressing a recent surge in community gun violence. One particular issue in Portland is <strong><em>traffic<\/em><\/strong>: some community members have identified a connection between high volume\/high speed driving and recent shootings. A pilot program <a href=\"https:\/\/www.kgw.com\/article\/news\/local\/traffic-barrels-gun-violence-prevention\/283-5baf4743-e82a-4005-8e79-1c7b489d65eb\">using barrels to calm traffic<\/a> showed promising results. Accordingly, our Portland work incorporates traffic-related data. <a href=\"https:\/\/www.wweek.com\/news\/courts\/2021\/08\/18\/portland-safety-officials-believe-an-algorithm-can-pinpoint-the-citys-most-dangerous-places-and-make-them-safer\/\">Learn more<\/a>.<\/p>\n<p><strong><u>Toledo, OH:<\/u><\/strong> We\u2019re working with the Mayor&#8217;s Initiative to Reduce Gun Violence, ProMedica Health System, and other local agencies to diagnose risk factors for spatial patterns of gun violence in Toledo. In Toledo, a particular focus is the <strong><em>physical condition<\/em><\/strong> of land parcels\u2014we are leveraging a large-scale property survey conducted in 2015 and 2021 by the Lucas County Land Bank.<\/p>\n<p><em>Related RISE Lab work:<\/em><\/p>\n<p><strong><u>Boston, MA:<\/u><\/strong> We\u2019re working with the Boston Public Health Commission\u2019s Youth Organizing Institute to learn from young people (ages 14-19) about the physical and social factors that make a neighborhood safe for them. These youth can see a great deal of risk and protective factors that the Shape-Up algorithm can\u2019t \u2013 we\u2019re exploring what <strong>community-engaged data science <\/strong>strategies will work best to align these sources of knowledge.<\/p>\n<p><em>\u00a0<\/em><em>Past Shape-Up partners:<\/em><\/p>\n<p><strong><u>Albany, NY:<\/u><\/strong> Demolishing abandoned buildings can <a href=\"https:\/\/www.dropbox.com\/s\/546rzgu9f9zwiht\/Jay2019jbm.pdf?dl=0\">reduce gun violence<\/a>, at least in <a href=\"https:\/\/www.dropbox.com\/s\/w2z1lida0wdcpm1\/Jay%202021InjP.pdf?dl=0\">certain contexts<\/a>. In 2020, we worked with the City of Albany to help prioritize abandoned buildings for demolition. The Office of Neighborhood Stabilization had identified a list of buildings ready for demolition, but only had the resources to demolish a subset of these. We ran the Shape-Up algorithm for Albany and built a mapping interface (see below). Neighborhood groups reviewed the Shape-Up Scores, along with other information, and decided which buildings to move ahead for demolition. Their final selections were highly correlated with Shape-Up Scores, but also reflected a range of priorities in addition to violence prevention.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Shape-Up 1-pager Spring 2022 Shape-Up is a platform to help communities prevent gun violence by improving physical spaces. The Shape-Up algorithm learns what a high-risk location looks like in a particular city, using big data and machine learning. The algorithm analyzes aerial imagery to capture urban form; data on locations (e.g., parks, vacant lots, liquor [&hellip;]<\/p>\n","protected":false},"author":19021,"featured_media":0,"parent":0,"menu_order":4,"comment_status":"closed","ping_status":"closed","template":"","meta":[],"_links":{"self":[{"href":"https:\/\/sites.bu.edu\/riselab\/wp-json\/wp\/v2\/pages\/231"}],"collection":[{"href":"https:\/\/sites.bu.edu\/riselab\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/sites.bu.edu\/riselab\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/sites.bu.edu\/riselab\/wp-json\/wp\/v2\/users\/19021"}],"replies":[{"embeddable":true,"href":"https:\/\/sites.bu.edu\/riselab\/wp-json\/wp\/v2\/comments?post=231"}],"version-history":[{"count":10,"href":"https:\/\/sites.bu.edu\/riselab\/wp-json\/wp\/v2\/pages\/231\/revisions"}],"predecessor-version":[{"id":315,"href":"https:\/\/sites.bu.edu\/riselab\/wp-json\/wp\/v2\/pages\/231\/revisions\/315"}],"wp:attachment":[{"href":"https:\/\/sites.bu.edu\/riselab\/wp-json\/wp\/v2\/media?parent=231"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}