Assessing the Impact of Ferry Transit on Urban Crime.
In 2017, over a dozen ferry stations were introduced across the NYC region on multiple dates, serving roughly 10,000 customers per day. We measure a negative association between these stations and crime reduction, a significant decline of 11 crimes per week (11%) at a one-mile radius around the stat...
| Publicado en: | Urban Affairs Review Vol. 59; no. 6; pp. 1950 - 1973 |
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| Autores principales: | , |
| Formato: | Artículo |
| Publicado: |
Sage Publications Inc.
Nov2023
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=172779660&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 172779660 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 10780874 UAR jtl: Urban Affairs Review issn: 10780874 maglogo: Y pubinfo: dt: Nov2023 vid: 59 iid: 6 pid: 344 pub: Sage Publications Inc. artinfo: ui: 172779660 10.1177/10780874221143047 ppf: 1950 ppct: 23 formats: tig: atl: Assessing the Impact of Ferry Transit on Urban Crime. aug: au: Weber, Bryan Cappellari, Paolo affil: Department of Economics, City University of New York, 14771College of Staten Island, Staten Island, NY, USA Information Systems & Informatics, City University of New York, College of Staten Island, Staten Island, NY, USA su: New York (N.Y.) Crime Ferries Random forest algorithms sug: subj: Crime New York (N.Y.) Scenic and Sightseeing Transportation, Water Deep sea, coastal and Great Lakes water transportation by ferries Inland water transportation by ferries Ferries Random forest algorithms keyword: causal random forest machine learning port transportation urban causal random forest machine learning port transportation urban ab: In 2017, over a dozen ferry stations were introduced across the NYC region on multiple dates, serving roughly 10,000 customers per day. We measure a negative association between these stations and crime reduction, a significant decline of 11 crimes per week (11%) at a one-mile radius around the stations, and about 1 crime per week (32%) over the extremely narrow base of crime at the station itself. We also find no evidence of crime displacement. This study first utilized a traditional difference-in-differences methodology, but we also used a new tool, the causal random forest. Both methodologies are compared and contrasted with an eye toward user understanding. The results of our analysis are consistent and coherent across all the different methodologies, with the causal random forest finding more pronounced effects by taking into account two major factors: the propensity of the regions for treatment, and the interaction between elements of interest. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
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