ECOMETRICS IN THE AGE OF BIG DATA: MEASURING AND ASSESSING "BROKEN WINDOWS" USING LARGE-SCALE ADMINISTRATIVE RECORDS

被引:92
|
作者
O'Brien, Daniel Tumminelli [1 ,2 ,3 ]
Sampson, Robert J. [4 ]
Winship, Christopher [5 ]
机构
[1] Northeastern Univ, Sch Publ Policy & Urban Affairs, Boston, MA 02120 USA
[2] Northeastern Univ, Sch Criminol & Criminal Justice, Boston, MA 02120 USA
[3] Harvard Univ, Radcliffe Inst Adv Study, Boston Area Res Initiat, Cambridge, MA 02138 USA
[4] Harvard Univ, Social Sci, Cambridge, MA 02138 USA
[5] Harvard Univ, John F Kennedy Sch Govt, Cambridge, MA 02138 USA
来源
基金
美国国家科学基金会;
关键词
ecometrics; urban sociology; big data; computational social science; physical disorder; broken windows; 311; hotlines; NEIGHBORHOOD ENVIRONMENTS; DISORDER; SCIENCE; RISK;
D O I
10.1177/0081175015576601
中图分类号
O1 [数学]; C [社会科学总论];
学科分类号
03 ; 0303 ; 0701 ; 070101 ;
摘要
The collection of large-scale administrative records in electronic form by many cities provides a new opportunity for the measurement and longitudinal tracking of neighborhood characteristics, but one that will require novel methodologies that convert such data into research-relevant measures. The authors illustrate these challenges by developing measures of "broken windows" from Boston's constituent relationship management (CRM) system (aka 311 hotline). A 16-month archive of the CRM database contains more than 300,000 address-based requests for city services, many of which reference physical incivilities (e.g., graffiti removal). The authors carry out three ecometric analyses, each building on the previous one. Analysis 1 examines the content of the measure, identifying 28 items that constitute two independent constructs, private neglect and public denigration. Analysis 2 assesses the validity of the measure by using investigator-initiated neighborhood audits to examine the "civic response rate" across neighborhoods. Indicators of civic response were then extracted from the CRM database so that measurement adjustments could be automated. These adjustments were calibrated against measures of litter from the objective audits. Analysis 3 examines the reliability of the composite measure of physical disorder at different spatio-temporal windows, finding that census tracts can be measured at two-month intervals and census block groups at six-month intervals. The final measures are highly detailed, can be tracked longitudinally, and are virtually costless. This framework thus provides an example of how new forms of large-scale administrative data can yield ecometric measurement for urban science while illustrating the methodological challenges that must be addressed.
引用
收藏
页码:101 / 147
页数:47
相关论文
共 50 条
  • [1] Using a Probabilistic Model to Assist Merging of Large-Scale Administrative Records
    Enamorado, Ted
    Fifield, Benjamin
    Imai, Kosuke
    AMERICAN POLITICAL SCIENCE REVIEW, 2019, 113 (02) : 353 - 371
  • [2] Stratifying risk using large-scale electronic health records data
    Perlis, R. Y.
    McCoy, T.
    Wiste, A.
    Ostacher, M.
    Castro, V.
    BIPOLAR DISORDERS, 2015, 17 : 12 - 12
  • [3] Readmission characteristics of elective pediatric circumcisions using large-scale administrative data
    Roth, Joshua D.
    Keenan, Alison C.
    Carroll, Aaron E.
    Rink, Richard C.
    Cain, Mark P.
    Whittam, Benjamin M.
    Bennett, William E., Jr.
    JOURNAL OF PEDIATRIC UROLOGY, 2016, 12 (01) : 27.e1 - 27.e6
  • [4] Understanding inequality in US farm subsidies using large-scale administrative data
    Yu, Jisang
    Lim, Sunghun
    AMERICAN JOURNAL OF AGRICULTURAL ECONOMICS, 2024,
  • [5] Measuring Brand Favorability Using Large-Scale Social Media Data
    Zhang, Kunpeng
    Moe, Wendy
    INFORMATION SYSTEMS RESEARCH, 2021, 32 (04) : 1128 - 1139
  • [6] The Problems with Neighbors: An Examination of the Influence of Neighborhood Context Using Large-Scale Administrative Data
    Liu, Yan
    Wang, Siqin
    Cheshire, Lynda
    URBAN AFFAIRS REVIEW, 2023, 59 (01) : 238 - 274
  • [7] Large-Scale Data-Driven Financial Risk Modeling using Big Data Technology
    Stockinger, Kurt
    Heitz, Jonas
    Bundi, Nils
    Breymann, Wolfgang
    2018 IEEE/ACM 5TH INTERNATIONAL CONFERENCE ON BIG DATA COMPUTING APPLICATIONS AND TECHNOLOGIES (BDCAT), 2018, : 206 - 207
  • [8] Performance Evaluation of Big Data Frameworks for Large-Scale Data Analytics
    Veiga, Jorge
    Exposito, Roberto R.
    Pardo, Xoan C.
    Taboada, Guillermo L.
    Tourino, Juan
    2016 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA), 2016, : 424 - 431
  • [9] Survey of Large-Scale Data Management Systems for Big Data Applications
    Wu, Lengdong
    Yuan, Liyan
    You, Jiahuai
    JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY, 2015, 30 (01) : 163 - 183
  • [10] Building a Big Data Platform for Large-scale Security Data Analysis
    Lee, Jong-Hoon
    Kim, Young Soo
    Kim, Jong Hyun
    Kim, Ik Kyun
    Han, Ki-Jun
    2017 INTERNATIONAL CONFERENCE ON INFORMATION AND COMMUNICATION TECHNOLOGY CONVERGENCE (ICTC), 2017, : 976 - 980