1
. See the video at https://
2 . All quotations that follow are taken directly from the source, including any grammatical or typographical errors.
3 . S. Goldsmith and S. Crawford, The Responsive City: Engaging Communities through Data-Smart Governance (San Francisco: Jossey-Bass, 2014); T. Newcombe, “Is the Cost of 311 Systems Worth the Price of Knowing?,” Governing, March 2014.
4 . E. Ostrom, “Crossing the Great Divide: Coproduction, Synergy, and Development,” World Development 24 (1996):1073–1087; G. P. Whitaker, “Coproduction: Citizen Participation in Service Delivery,” Public Administration Review 40 (1980): 240–246.
5 . Pew Research Center, The Web at 25 in the U. S., February 27, 2014.
6 . United Nations, 2014 Revision of the World Urbanization Prospects, July 10, 2014.
7 . L. Bettencourt and G. B. West, “A Unified Theory of Urban Living,” Nature 467, no. 7318 (2010): 912–913.
8 . Goldsmith and Crawford, The Responsive City.
9 . A. M. Townsend, Smart Cities (New York: W. W. Norton, 2013).
10 . A. M. Townsend, Making Sense of the New Urban Science (New York: NYU Wagner Rudin Center for Transportation Policy and Management, 2015).
11 . P. Szanton, Not Well Advised (New York: Russell Sage Foundation and the Ford Foundation, 1981).
12 . M. Batty, “The Pulse of the City,” Environment and Planning B: Planning and Design 37 (2010): 575–577.
13 . D. Boyd and K. Crawford, “Critical Questions for Big Data: Provocations for a Cultural, Technological, and Scholarly Phenomenon,” Information, Communication and Society 15, no. 5 (2012): 662–679; D. Lazer R. Kennedy, G. King, and A. Vespignani, “The Parable of Google Flu: Traps in Big Data Analysis,” Science 343, no. 6176 (2014): 1203–1205; R. Wagner-Pacifici, J. W. Mohr, and R. L. Breiger, “Ontologies, Methodologies, and New Uses of Big Data in the Social and Cultural Sciences,” Big Data and Society , July–December 2015, 1–11.
14 . Newcombe, “Is the Cost of 311 Systems Worth the Price of Knowing?”
15 . J. Legewie and M. Schaeffer, “Contested Boundaries: Explaining Where Ethnoracial Diversity Provokes Neighborhood Conflict,” American Journal of Sociology 122, no 1 (2016): 125–161.
16 . A. E. Lerman and V. Weaver, “Staying Out of Sight? Concentrated Policing and Local Political Action,” Annals of the American Academy of Political and Social Science 651 (2014): 202–219.
17 . J. R. Levine and C. Gershenson, “From Political to Material Inequality: Race, Immigration, and Request for Public Goods,” Sociological Forum 29 (2014): 607–627.
18 . D. Offenhuber, “Infrastructure Legibility—A Comparative Analysis of Open311-Based Citizen Feedback Systems,” Cambridge Journal of Regions, Economy and Society 8, no. 1 (2015): 93–112.
19 . S. L. Minkoff, “NYC 311: A Tract-Level Analysis of Citizen-Government Contacting in New York City,” Urban Affairs Review 52, no. 2 (2016): 211–246.
20
. City of Boston, CityScore 2016,
April 20, 2016, http://
21 . T. Trenkner, “Public Officials of the Year: Nigel Jacob and Chris Osgood, Co-chairs, Mayor’s Office of New Urban Mechanics, City of Boston,” Governing, October 27, 2011.
22 . M. Reis, “5 U.S. Cities Using Technology to Become Smart and Connected,” Forbes, August 15, 2014; B. Cohen, “The 10 Smartest Cities in North America,” Co.Design, December 3, 2012.
23 . G. Hardin, “The Tragedy of the Commons,” Science 162, no. 3859 (1968): 1243–1248.
24 . C. Booth, Life and Labour of the People in London (London: Macmillan, 1903); J. Jacobs, The Death and Life of Great American Cities (New York: Random House, 1961).
25 . J. Q. Wilson and G. L. Kelling, “Broken Windows: The Police and Neighborhood Safety,” Atlantic Monthly 127 (March 1982): 29–38.
26 . R. J. Sampson, Great American City: Chicago and the Enduring Neighborhood Effect (Chicago: University of Chicago Press, 2012).
27 . E. Ostrom, J. Burger, C. B. Field, R. B. Norgaard, and D. Policansky, “Revisiting the Commons: Local Lessons, Global Challenges,” Science 284, no. 5412 (1999): 278–282; E. Ostrom, Understanding Institutional Diversity (Princeton, NJ: Princeton University Press, 2005); E. Ostrom, Governing the Commons: The Evolution of Institutions for Collective Action (Cambridge, MA: Harvard University Press, 1990).
28 . R. J. Sampson and W. B. Groves, “Community Structure and Crime: Testing Social Disorganization Theory,” American Journal of Sociology 94, no. 4 (1989): 774–802; R. Bursik and H. G. Grasmick, Neighborhoods and Crime: The Dimensions of Effective Community Control (New York: Lexington Books, 1993); E. Silver and L. L. Miller, “Sources of Informal Social Control in Chicago Neighborhoods,” Criminology 42, no. 3 (2004): 551–583.
29 . R. B. Taylor, Human Territorial Functioning: An Empirical, Evolutionary Perspective on Individual and Small Group Territorial Cognitions, Behaviors and Consequences (Cambridge: Cambridge University Press, 1988); B. B. Brown and I. Altman, “Territoriality and Residential Crime,” in Environmental Criminology, ed. P. J. Brantingham and P. L. Brantingham (Beverly Hills, CA: Sage, 1981), 56–76; B. B. Brown, “Territoriality,” in Handbook of Environmental Psychology, ed. D. Stokols and I. Altman (New York: Wiley, 1987), 505–531.
30 . R. Ardrey, The Territorial Imperative (New York: Atheneum, 1966).
31 . Taylor, Human Territorial Functioning; Brown, “Territoriality”; J. J. Edney, “Human Territoriality,” Psychological Bulletin 81, no. 12 (1974): 959–975; I. Altman, “Territorial Behavior in Humans: An Analysis of the Concept,” in Spatial Behavior of Older People, ed. L. A. Pastalan and D. A. Carson (Ann Arbor: University of Michigan Press, 1970), 1–24.
32 . B. B. Brown and C. M. Werner, “Social Cohesiveness, Territoriality and Holiday Decorations: The Influence of Cul-de-Sacs,” Environment and Behavior 17 (1985): 539–565; C. M. Werner, S. Peterson-Lewis, and B. B. Brown, “Inferences about Homeowners’ Sociability: Impact of Christmas Decorations and Other Cues,” Journal of Environmental Psychology 9, no. 4 (1989): 279–296; M. O. Caughy, P. J. O’Campo, and J. Patterson, “A Brief Observational Measure for Urban Neighborhoods,” Health and Place 7 (2001): 225–236; P. B. Harris and B. B. Brown, “The Home and Identity Display: Interpreting Resident Territoriality from Home Exteriors,” Journal of Environmental Psychology 16 (1996): 187–203.
33 . R. B. Parks, P. C. Baker, L. Kiser, R. Oakerson, E. Ostrom, V. Ostrom, S. L. Percy, M. B. Vandivort, G. P. Whitaker, and R. Wilson, “Consumers as Coproducers of Public Services: Some Economic and Institutional Considerations,” Policy Studies Journal 9 (1982): 1001–1011; E. Ostrom, “Metropolitan Reform: Propositions Derived from Two Traditions,” Social Science Quarterly 53 (1972): 474–493.
34 . Ostrom, “Crossing the Great Divide.”
35 . A. L. Schneider, “Coproduction of Public and Private Safety: An Analysis of Bystander Intervention, ‘Protective Neighboring,’ and Personal Protection,” Western Political Quarterly 40 (1987): 611–630; D. H. Folz, “Recycling Solid Waste: Citizen Participation in the Design of a Coproduced Program,” State and Local Government Review 23 (1991): 92–102; M. J. Marschall, “Parent Involvement and Educational Outcomes for Latino Students,” Review of Policy Research 23 (2006): 1053–1076; K. J. Powers and F. Thompson, “Managing Coprovision: Using Expectancy Theory to Overcome the Free-Rider Problem,” Journal of Public Administration Research and Theory 4, no. 2 (1994): 179–196.
36 . C. H. Levine, “Citizenship and Service Delivery: The Promise of Coproduction,” Public Administration Review 44 (1984): 178–187; T. Bovaird, “Beyond Engagement and Participation: User and Community Coproduction of Public Services,” Public Administration Review 67, no. 5 (2007): 846–860.
37 . J. A. Fodor, Modularity of Mind: An Essay on Faculty Psychology (Cambridge, MA: MIT Press, 1983); J. Tooby and L. Cosmides, “The Psychological Foundations of Culture,” in The Adapted Mind: Evolutionary Psychology and the Generation of Culture, ed. J. H. Barkow, L. Cosmides, and J. Tooby (New York: Oxford University Press, 1992), 19–136.
38 . S. P. Osborne and K. Strokosch, “It Takes Two to Tango? Understanding the Co-production of Public Services by Integrating the Services Management and Public Administration Perspectives,” British Journal of Management 24 (2013): S31–S47; S. P. Osborne, “Delivering Public Services: Time for a New Theory?,” Public Management Review 12 (2010): 1–10.
39 . Lerman and Weaver, “Staying Out of Sight?”; Levine and Gershenson, “From Political to Material Inequality”; Minkoff, “NYC 311”; B. Y. Clark, J. L. Brudney, and S. G. Jang, “Coproduction of Government Services and the New Information Technology: Investigating the Distributional Biases,” Public Administration Review 73 (2013): 687–701; J. Fountain, “Connecting Technologies to Citizenship,” in Technology and the Resilience of Metropolitan Regions, ed. M. A. Pagano (Champaign: University of Illinois Press, 2014), 25–51.
1 . Then mayor Martin O’Malley (later governor of the state of Maryland) claims he borrowed the idea from Chicago, but Baltimore was the first to fully adopt the 311 system as we know it now, and the one that has been in continuous operation longest.
2 . Based on a research project conducted by a graduate student in Northeastern University’s master of urban informatics program. Data available upon request.
3 . S. Johnson, “What a Hundred Million Calls to 311 Reveal about New York,” Wired, November 1, 2010.
4 . T. Newcombe, “Is the Cost of 311 Systems Worth the Price of Knowing?,” Governing, March 2014.
5 . Because the list is not exhaustive, to those who would like such a treatment, I recommend Anthony Townsend’s report on “the new urban science” in A. M. Townsend, Making Sense of the New Urban Science (New York: NYU Wagner Rudin Center for Transportation Policy and Management, 2015). That said, given the rapid growth of the field, even this work might now be considered outdated.
6 . R. J. Sampson, S. W. Raudenbush, and F. Earls, “Neighborhoods and Violent Crime: A Multilevel Study of Collective Efficacy,” Science 277 (1997): 918–924.
7 . A. A. Braga, A. V. Papachristos, and D. M. Hureau, “The Concentration and Stability of Gun Violence at Micro Places in Boston, 1980–2008,” Journal of Quantitative Criminology 26 (2010): 33–53.
8 . J. Patinkin, “These Digital Maps Could Revolutionize Nairobi’s Minibus Taxi System,” Next City, February 11, 2014.
9 . M. Batty, “The Pulse of the City,” Environment and Planning B: Planning and Design 37 (2010): 575–577.
10 . S. Grauwin, S. Sobolevsky, S. Moritz, I. Gó dor, and C. Ratti, “Towards a Comparative Science of Cities: Using Mobile Traffic Records in New York, London and Hong Kong.” arXiv, 2014. 1406.440 [physics.soc-ph].
11 . K. Batygin and M. E. Brown, “Evidence for a Distant Giant Planet in the Solar System.” Astronomical Journal 151, no. 2 (2016).
12 . D. Lazer et al., “Computational Social Science,” Nature 323 (2009): 721–723.
13 . A. P. Masucci, K. Stanilov, and M. Batty, “Limited Urban Growth: London’s Street Network Dynamics since the 18th Century,” Public Library of Science 8, no. 8 (2013): e69469.
14 . A. Mavrogianni, A., et al., “The Comfort, Energy and Health Implications of London’s Urban Heat Island,” Building Services Engineering Research and Technology 32, no. 1 (2011): 35–52.
15 . D. Offenhuber, D. Lee, M. I. Wolf, S. Phithakkitnukoon, A. Biderman, and C. Ratti, “Putting Matter in Place,” Journal of the American Planning Association 78, no. 2 (2012): 173–196; D. Offenhuber, Waste Is Information (Cambridge, MA: MIT Press, 2017).
16 . P. Santi, G. Resta, M. Szell, S. Sobolevsky, S. H. Strogatz, and C. Ratti, “Quantifying the Benefits of Vehicle Pooling with Shareability Networks,” Proceedings of the National Academy of Sciences 111 (2014): 13290–13294.
17
. https://
18 . A. A. Braga, D. M. Hureau, and A. V. Papachristos, “The Relevance of Micro Places to Citywide Robbery Trends: A Longitudinal Analysis of Robbery Incidents at Street Corners and Block Faces in Boston,” Journal of Research in Crime and Delinquency 48, no. 1 (2011): 7–32.
19 . F. J. Odling-Smee, K. N. Laland, and M. W. Feldman, Niche Construction: The Neglected Process in Evolution (Princeton, NJ: Princeton University Press, 2003).
20
. https://
21 . S. Goldsmith and S. Crawford, The Responsive City: Engaging Communities through Data-Smart Governance (San Francisco: Jossey-Bass, 2014).
23
. https://
24 . T. Small, “South Bend Saves Millions with Smart Sewers,” ABC57 News, 2016.
25 . Z. Friend, “Predictive Policing: Using Technology to Reduce Crime,” FBI Law Enforcement Bulletin, April 9, 2013.
26 . City of Boston, “Ordinance to Eliminate Public Nuisance Precipitated by Problem Properties in the City,” in Ordinances , ed. Boston City Council (Boston: City of Boston, 2011), Chapter 16–55.
27
. City of Minneapolis, MN, Problem Properties Unit,
2015, http://
28 . Though the benefits from these changes are mixed. See New York City Department of Transportation, Making Safer Streets (New York: New York City Department of Transportation, 2013).
29 . For example, the effects of homelessness on school success. See J. Fantuzzo and S. Perlman, “The Unique Impact of Out-of-Home Placement and the Mediating Effects of Child Maltreatment and Homelessness on Early School Success,” Children and Youth Services Review 29 (2007): 941–960.
30 . J. Lin, “Center in Phila. Helps Battle Veteran Homelessness,” Philadelphia Inquirer, January 2, 2014.
31 . E. M. Kitzmiller, “The Circle of Love: South Carolina’s Integrated Data System,” IDS Case Study (Philadelphia: Actionable Intelligence for Social Policy, University of Pennsylvania, 2014).
32 . K. Giammarise, “Allegheny County DHS Using Algorithm to Assist in Child Welfare Screening,” Pittsburgh Post-Gazette, April 9, 2017.
33 . A. M. Townsend, Smart Cities (New York: Norton, 2013).
34 . E. Gordon and J. Baldwin-Philippi, “Playful Civic Learning: Enabling Lateral Trust and Reflection in Game-Based Public Participation,” International Journal of Communication 8 (2014): 759–786.
35 . Y. Cabannes, “Participatory Budgeting: A Significant Contribution to Participatory Democracy,” Environment and Urbanization 16 (2004): 27–46.
36 . T. Kuhn, The Structure of Scientific Revolutions (Chicago: University of Chicago Press, 1962, reprinted 1996).
37 . R. E. Park and E. W. Burgess, The City (Chicago: University of Chicago Press, 1925).
38 . L. Bettencourt, “The Kind of Problem a City Is: New Perspectives on the Nature of Cities from Complex Systems Theory,” in Decoding the City, ed. D. Offenhuber and C. Ratti (Basel: Birkhauser, 2014), 168–179; L. Bettencourt and G. B. West, “A Unified Theory of Urban Living,” Nature 467, no. 7318 (2010): 912–913.
39 . Park and Burgess, The City.
40 . Sampson, Raudenbush, and Earls, “Neighborhoods and Violent Crime”; T. Leventhal and J. Brooks-Gunn, “The Neighborhoods They Live In: The Effect of Neighborhood Residence on Child and Adolescent Outcomes,” Psychological Bulletin 126, no. 2 (2000): 309–337; R. J. Sampson, Great American City: Chicago and the Enduring Neighborhood Effect (Chicago: University of Chicago Press, 2012); A. V. Diez Roux and C. Mair, “Neighborhoods and Health,” Annals of the New York Academy of Sciences 1186 (2010): 125–145.
41 . M. Schlapfer, L. M. A. Bettencourt, S. Grauwin, M. Raschke, R. Claxton, Z. Smoreda, G. B. West, and C. Ratti, “The Scaling of Human Interactions with City Size,” Journal of the Royal Society Interface 11 (2014); L. Bettencourt, J. Lobo, D. Helbing, C. Kü hnert, and G. B. West, “Growth, Innovation, Scaling, and the Pace of Life in Cities,” Proceedings of the National Academy of Sciences 104 (2007): 7301–7306.
42 . Lazer et al., “Computational Social Science.”
43 . A. E. Lerman and V. Weaver, “Staying Out of Sight? Concentrated Policing and Local Political Action,” Annals of the American Academy of Political and Social Science 651 (2014): 202–219.
1 . This has been presented with greater technical detail in D. T. O’Brien, R. J. Sampson, and C. Winship, “Ecometrics in the Age of Big Data: Measuring and Assessing ‘Broken Windows’ Using Administrative Records,” Sociological Methodology 45 (2015): 101–147.
2 . D. Boyd and K. Crawford, “Critical Questions for Big Data: Provocations for a Cultural, Technological, and Scholarly Phenomenon,” Information, Communication and Society 15, no. 5 (2012): 662–679; N. M. Richards and J. H. King, “Big Data Ethics,” Wake Forest Law Review 49 (2014): 393–432; R. Kitchin, The Data Revolution (Woburn, MA: SAGE Publications, 2014); O. Tene and J. Polonetsky, “Big Data for All: Privacy and User Control in the Age of Analytics,” Northwestern Journal of Technology and Intellectual Property 11, no. 5 (2013): 240–273.
3 . R. Kitchin and G. McArdle, “What Makes Big Data, Big Data? Exploring the Ontological Characteristics of 26 Datasets,” Big Data and Society 3, no. 1 (January–June 2016):1–10.
4 . C. Anderson, “The End of Theory: The Data Deluge Makes the Scientific Method Obsolete,” Wired, June 23, 2008.
5 . M. Pigliucci, “The End of Theory in Science?” EMBO Reports 10, no. 6 (2009): 534.
6 . D. Lazer, R. Kennedy, G. King, and A. Vespignani, “The Parable of Google Flu: Traps in Big Data Analysis,” Science 343, no. 6176 (2014): 1203–1205.
7 . C. Booth, Life and Labour of the People in London (London: Macmillan, 1903); H. Mayhew, London Labor and the London Poor (London: Griffin, Bohn, 1862); R. E. Park and E. W. Burgess, The City (Chicago: University of Chicago Press, 1925).
8 . But see R. B. Taylor, Breaking Away from Broken Windows: Baltimore Neighborhoods and the Nationwide Fight against Crime, Grime, Fear, and Decline (Boulder, CO: Westview Press, 2001).
9 . S. W. Raudenbush and R. J. Sampson, “Ecometrics: Toward a Science of Assessing Ecological Settings, with Application to the Systematic Social Observation of Neighborhoods,” Sociological Methodology 29, no. 1 (1999): 1–41; R. J. Sampson and S. W. Raudenbush, “Systematic Social Observation of Public Spaces: A New Look at Disorder in Urban Neighborhoods,” American Journal of Sociology 105, no. 3 (1999): 603–651; R. J. Sampson, S. W. Raudenbush, and F. Earls, “Neighborhoods and Violent Crime: A Multilevel Study of Collective Efficacy,” Science 277, no. 5328 (1997): 918–924.
10 . D. Cohen, S. Spear, R. Scribner, P. Kissinger, K. Mason, and J. Wildgren, “ ‘Broken windows’ and the Risk of Gonorrhea,” American Journal of Public Health 90, no. 2 (2000): 230–236; T. Leventhal and J. Brooks-Gunn, “The Neighborhoods They Live In: The Effect of Neighborhood Residence on Child and Adolescent Outcomes,” Psychological Bulletin 126, no. 2 (2000): 309–337; C. R. Browning, B. Soller, and A. L. Jackson, “Neighborhoods and Adolescent Health-Risk Behavior: An Ecological Network Approach,” Social Science and Medicine 125 (2015): 163–172; I. Kawachi and L. F. Berkman, eds., Neighborhoods and Health (Oxford: Oxford University Press, 2003); C. L. Gibson, C. J. Sullivan, S. Jones, and A. R. Piquero, “ ‘Does It Take a Village?’ Assessing Neighborhood Influences on Children’s Self-Control,” Journal of Research in Crime and Delinquency 47, no. 1 (2010): 31–62.
11 . The full analysis is presented in greater detail in O’Brien, Sampson, and Winship, “Ecometrics in the Age of Big Data.”
12
. https://
13 . Boyd and Crawford, “Critical Questions for Big Data”; W. G. Skogan, Disorder and Decline (Berkeley: University of California Press, 1992).
14 . J. Q. Wilson and G. L. Kelling, “Broken Windows: The Police and Neighborhood Safety,” Atlantic Monthly 127 (March 1982): 29–38.
15 . M. Wen, L. C. Hawkley, and J. T. Cacioppo, “Objective and Perceived Neighborhood Environment, Individual SES and Psychosocial Factors, and Self-Rated Health: An Analysis of Older Adults in Cook County, Illinois,” Social Science and Medicine 63 (2006): 2575–2590; T. J. Haney, “ ‘Broken Windows’ and Self-Esteem: Subjective Understandings of Neighborhood Poverty and Disorder,” Social Science Research 36, no. 3 (2007): 968–994; K. P. Theall, Z. H. Brett, E. A. Shirtcliff, E. C. Dunn, and S. S. Drury, “Neighborhood Disorder and Telomeres: Connecting Children’s Exposure to Community Level Stress and Cellular Response,” Social Science and Medicine 85 (2013): 50–58; M. S. Mujahid, M. Shen, D. Gowda, S. A. Jackson, B. Sanchez, A. V. Diez Roux, S. Shea, and D. R. Jacobs, “Relation between Neighborhood Environments and Obesity in the Multi-ethnic Study of Atherosclerosis,” American Journal of Epidemiology 167, no. 11 (2008): 1349–1357; A. Dulin-Keita, K. Casazza, J. R. Fernandez, M. I. Goran, and B. Gower, “Do Neighbourhoods Matter? Neighbourhood Disorder and Long-term Trends in Serum Cortisol Levels,” Journal of Epidemiology and Community Health 66, no. 1 (2012): 24–29.
16 . Cohen et al., “ ‘Broken Windows’ and the Risk of Gonorrhea.”
17 . H. Meltzer, L. Doos, P. Vostanis, T. J. Ford, and R. Goodman, “The Mental Health of Children Who Witness Domestic Violence,” Child and Family Social Work 14 (2009): 491–501.
18 . Sampson and Raudenbush, “Systematic Social Observation of Public Spaces.”
19 . D. S. Massey, “Segregation and Stratification: A Biosocial Perspective,” Du Bois Review 1 (2004): 7–25.
20 . Raudenbush and Sampson, “Ecometrics.”
21 . Taylor, Breaking Away from Broken Windows, 5.
22 . Taylor, Breaking Away from Broken Windows ; Cohen et al., “ ‘Broken Windows’ and the Risk of Gonorrhea”; Skogan, Disorder and Decline; D. T. O’Brien and D. S. Wilson, “Community Perception: The Ability to Assess the Safety of Unfamiliar Neighborhoods and Respond Adaptively,” Journal of Personality and Social Psychology 100, no. 4 (2011): 606–620; C. E. Ross and J. Mirowsky, “Disorder and Decay: The Concept and Measurement of Perceived Neighborhood Disorder,” Urban Affairs Review 34 (1999): 412–432; M. O. Caughy, P. J. O’Campo, and J. Patterson, “A Brief Observational Measure for Urban Neighborhoods,” Health and Place 7 (2001): 225–236; C. D. M. Furr-Holden, M. J. Smart, J. L. Pokorni, N. S. Ialongo, P. J. Leaf, H. D. Holder, and J. C. Anthony, “The NIfETy Method for Environmental Assessment of Neighborhood-Level Indicators of Violence, Alcohol, and Other Drug Exposure,” Prevention Science 9, no. 4 (2008): 245–255; A. G. Rundle, M. D. M. Bader, C. A. Richards, K. M. Neckerman, and J. Teitler, “Using Google Street View to Audit Neighborhood Environments,” American Journal of Preventive Medicine 40, no. 1 (2011): 94–100.
23 . All cases that had geographic reference and were received between March 1, 2010, when a standardized data entry form was implemented, and June 29, 2012. An additional 30,855 cases had no geographic reference.
24
. https://
25 . Ross and Mirowsky, “Disorder and Decay.”
26 . All items loaded on the given factor at .4 or greater, except for four exceptions that were maintained based on conceptual similarity: abandoned buildings loaded at .36 on the factor of uncivil use; requests to empty a litter basket loaded >.3 on both trash and graffiti, and was maintained on the former factor based on its substantive content; two items were added to the housing factor, as they were conceptually identical to the definition of the factor and likely did not load in the factor analysis because of their low frequency.
27 . We summed counts for all case types within each of the five factors for each neighborhood. These measures had substantial outliers and were all log-transformed before analysis. We then conducted a pair of confirmatory factor analyses that compared a one-factor structure in which all five measures loaded together to the two-factor structure. The two-factor model was superior by all measures. It had better fit (CFI = .82 vs. 61; SRMR = .07 vs. 10; Δχ2 df=1 = 89.27, p < .001), and accounted for 42 percent of the variation across factors, as opposed to 26 percent. One will note that although the two-factor model was stronger, it still had a poor fit. Because the hypothesis in question was the efficacy of a one- or two-factor model, there were no assumptions that the components of each were completely independent. Thus, we took the exploratory step of examining modification indices, leading to the addition of a covariance between uncivil use and trash, greatly improving fit (CFI = .95, SRMR = .05, χ2 df=5 = 24.26, p < .001). The parameter estimates presented in Figure 2.1 are from this final model.
28 . J. McCorriston, D. Jurgens, and D. Ruths, “Organizations Are Users Too: Characterizing and Detecting the Presence of Organizations on Twitter,” in Proceedings of the 9th International AAAI Conference on Web and Social Media, ed. D. Quercia and B. Hogan (Palo Alto, CA: Association for the Advancement of Artificial Intelligence, 2015), 650–653.
29 . S. Messick, “Validity of Psychological Assessment: Validation of Inferences from Persons’ Responses and Performances as Scientific Inquiry into Score Meaning,” American Psychologist 50, no. 9 (1995): 741–749.
30 . Defined as intersection to intersection or intersection to dead end.
31 . Our audits covered all streets in a neighborhood, leading us to observe streetlight outages and garbage in many different contexts, from residential streets, to commercial streets, to isolated industrial areas, that might be more or less likely to elicit 311 reports, independent of the local community’s generalized tendency to do so. For the sidewalk audits, it was also necessary to control for the quality of each sidewalk: three reports of a sidewalk with a score of 50 is a different reflection of a community’s responsiveness to public issues than three reports of a sidewalk with a score of 10. To address these issues, we utilized multilevel models that nested streets within neighborhoods (tracts for sidewalks and census block groups for streetlight outages) and could accommodate predictors at both levels. See S. W. Raudenbush, A. S. Bryk, Y. F. Cheong, and R. T. Congdon Jr., HLM 6: Hierarchical Linear and Nonlinear Modeling (Lincolnwood, IL: Scientific Software International, 2004). The second-level residuals (i.e., the expected score for each neighborhood after controlling for street-level characteristics) were then used as the neighborhood-level estimates of civic response rate.
32 . A test of the variance component for two-month intervals of the second level was significant (χ2 df=53 = 80.80, p < .01) and was greater than the two other intervals that also featured significant or nearly significant variation (one month: χ2 df=54 = 78.39, p < .05; two weeks: χ2 df=54 = 68.61, p < .10).
33 . Because most sidewalks elicited zero reports, we compared two models, each controlling for sidewalk quality, one predicting the likelihood of generating any requests (a logit model) and the other predicting the number of requests across those with one or more (a log-link model). There was significant variation between tracts in a sidewalk generating any reports, controlling for the sidewalk quality index (χ2 df=155 = 425.16, p < .001), but not in the number of reports per sidewalk (χ2 df=152 = 179.58, p < .10), making the former the preferable measure. In addition, sidewalk quality did not significantly predict more reports for a sidewalk, suggesting it is a less effective measure for the purposes here (β = .001, p = ns, odds ratio = 1.001).
34 . S. Verba, K. L. Schlozman, and H. E. Brady, Voice and Equality: Civic Volunteerism in American Politics (Cambridge, MA: Harvard University Press, 1995); R. Putnam, Making Democracy Work: Civic Traditions in Modern Italy (Princeton, NJ: Princeton University Press, 1993).
35 . For example, total road length, dead ends. This idea was developed with my colleague Jeremy Levine. See also J. R. Levine and C. Gershenson, “From Political to Material Inequality: Race, Immigration, and Request for Public Goods,” Sociological Forum 29 (2014): 607–627.
36 . This list was expanded to 77 case types in later studies of custodianship that covered a longer time period of the data. See Chapter 3 for a full list.
37 . We regressed the number of snowplow requests (log-transformed to adjust for a skewed distribution) on the total population, road length, and the length of dead end roads and extracted the residuals as an adjusted measure of snowplow requests.
38 . To be concurrent with the audits, the 311 indicators are calculated using only reports from 2011, comprising 161,703 cases with geographic reference across 154 case types. In this time, there were 29,439 constituent users, accounting for 38 percent of all requests for service. Users who made one or more reports as a department member at any time (including 2010 or 2012) were removed because city employees differ from other constituents in their motivation for making reports. This excluded five individuals, a number that is low because for many employee-specific case types user IDs were stripped before data sharing. Two CBGs were excluded from analysis because there were concerns that calls from there might not reflect usage of the CRM system by actual residents: (1) the CBG that contains City Hall, because many reports without an address are attributed to that location, and (2) the CBG that contains a large park, zoo, and golf course but includes the houses that ring the park.
39 . The model analyzed those 195 CBGs with a measure for propensity to report streetlight outages and had good fit (CFI = .95, SRMR = .06, χ2 df=9 = 25.44, p < .01).
40 . We again used multilevel models to nest observations in neighborhoods and account for street characteristics (see note 31 for more details). Because sampling for the garbage audit occurred at the tract level, CBGs varied in the number of street segments that were rated. In order to be certain that neighborhood-level measures were reliable, the ensuing analysis was limited to the 196 CBGs with ten or more street segment measures. See also Sampson and Raudenbush, “Systematic Social Observation of Public Spaces.”
41 . These measures were log-transformed to better approximate normality.
42 . n = 135 residential CBGs.
43 . The response rate was standardized and centered before the interaction was calculated. The physical disorder measures were left uncentered, with a minimum of zero, so that the response rate would adjust up or down in proportion to the total number of actual reports.
44 . Because the time points of these data sources vary, we analyzed their relationship to the CRM-based measure for the most concurrent year: 2010 for the American Community Survey and Boston Neighborhood Survey, and 2011 for 911. Models were run for N = 121 residential census tracts.
45 . Raudenbush and Sampson, “Ecometrics.”
46 . Taylor, Breaking Away from Broken Windows; R. J. Sampson and S. W. Raudenbush, “Seeing Disorder: Neighborhood Stigma and the Social Construction of ‘Broken Windows,’ ” Social Psychology Quarterly 67, no. 4 (2004): 317–342; L. Franzini, M. O. Caughy, S. M. Nettles, and P. O’Campo, “Perceptions of Disorder: Contributions of Neighborhood Characteristics to Subjective Perceptions of Disorder,” Journal of Environmental Psychology 28, no. 1 (2008): 83–93; B. B. Brown, D. D. Perkins, and G. Brown, “Incivilities, Place Attachment and Crime: Block and Individual Effects,” Journal of Environmental Psychology 24 (2004): 359–371.
47 . O’Brien, Sampson, and Winship, “Ecometrics in the Age of Big Data.”
48 . For each interval setting, the original database was split into intervals of the given size, starting with March 1, 2010, and ending with the last complete interval. Exemplars were always measured for a given time interval as the number of public reports in a region obtaining exemplar status over the course of the 365 days preceding the end of the interval.
49 . One will note that these require the incorporation of the number of exemplars, measured for the full year preceding the last day of the given time window. Consequently, the first time window analyzed must be that which ends at or after the end of the twelfth month of the available database, diminishing the number of measurements per tract. For this reason, this analysis does not examine change over time.
50 . Lazer et al., “The Parable of Google Flu.”
51 . D. T. O’Brien and R. J. Sampson, “Public and Private Spheres of Neighborhood Disorder: Assessing Pathways to Violence Using Large-Scale Digital Records,” Journal of Research in Crime and Delinquency 52, no. 4 (2015): 486–510.
52 . D. T. O’Brien and B. W. Montgomery, “The Other Side of the Broken Window: A Methodology That Translates Building Permits into an Ecometric of Investment by Community Members,” American Journal of Community Psychology 55 (2015): 25–36.
53 . J. Legewie and M. Schaeffer, “Contested Boundaries: Explaining Where Ethnoracial Diversity Provokes Neighborhood Conflict,” American Journal of Sociology 122, no. 1 (2016): 125–161.
54 . D. Quercia, R. Schifanella, L. M. Aiello, and K. McLean, “Smelly Maps: The Digital Life of Urban Smellscapes,” in Proceedings of the 9th International AAAI Conference on Web and Social Media, ed. D. Quercia and B. Hogan (Palo Alto, CA: Association for the Advancement of Artificial Intelligence, 2015), 327–336; D. Quercia, N. K. O’Hare, and H. Cramer, “Aesthetic Capital: What Makes London Look Beautiful, Quiet, and Happy?,” in Proceedings of the 17th ACM Conference on Computer Supported Cooperative Work (New York: ACM, 2014), 945–955; L. M. Aiello, R. Schifanella, D. Quercia, and F. Aletta, “Chatty Maps: Constructing Sound Maps of Urban Areas from Social Media Data,” Royal Society Open Science Journal 3 (2016).
55 . J. D. Boy and J. Uitermark, “How to Study the City on Instagram,” PLoS One 11, no. 6 (2016): e0158161.
56 . D. Lazer et al., “Computational Social Science,” Science 323, no. 5915 (2009): 721–723.
1 . M. Rawson, Eden on the Charles (Cambridge, MA: Harvard University Press, 2010).
2 . G. Hardin, “The Tragedy of the Commons,” Science 162, no. 3859 (1968): 1243–1248.
3 . F. Alcock, “Bargaining, Uncertainty, and Property Rights in Fisheries,” World Politics 54, no. 4 (2002): 437–461–; X. Basurto, “How Locally Designed Access and Use Controls Can Prevent the Tragedy of the Commons in a Mexican Small-Scale Fishing Community,” Society and Natural Resources 18, no. 7 (2005): 643–659; A. Begossi, “Fishing Spots and Sea Tenure—Incipient Forms of Local Management in Atlantic Forest Coastal Communities,” Human Ecology 23, no. 3 (1995): 387–406; F. Berkes, “Local-Level Management and the Commons Problem: A Comparative Study of Turkish Coastal Fisheries,” Marine Policy 10, no. 3 (1986): 215–229.
4 . E. Ostrom, Governing the Commons: The Evolution of Institutions for Collective Action (Cambridge, MA: Harvard University Press, 1990).
5 . S. F. Pires and W. D. Moreto, “Preventing Wildlife Crimes: Solutions That Can Overcome the ‘Tragedy of the Commons,’ ” European Journal on Criminal Policy and Research 17, no. 2 (2011): 101–123.
6 . P. Kollock and M. Smith, “Managing the Virtual Commons,” in Computer-Mediated Communication: Linguistic, Social, and Cross-Cultural Perspectives, ed. S. H. Herring (Amsterdam: Benjamins, 1996), 109–128.
7 . H. Demsetz, “Toward a Theory of Property Rights,” American Economic Review 62 (1967): 347–359; O. E. G. Johnson, “Economic Analysis, the Legal Framework and Land Tenure Systems,” Journal of Law and Economics 15 (1972): 259–276; R. J. Smith, “Resolving the Tragedy of the Commons by Creating Private Property Rights in Wildlife,” CATO Journal 1 (1981): 439–468.
8 . See, for example, W. Ophuls, “Leviathan or Oblivion,” in Toward a Steady State Economy, ed. H. E. Daly,(San Francisco: Freeman, 1973), 215–230; R. L. Heilbroner, An Inquiry into the Human Prospect (New York: Norton, 1974); D. W. Ehrenfeld, Conserving Life on Earth (Oxford: Oxford University Press, 1972).
9 . Ostrom, Governing the Commons ; E. Ostrom, J. Burger, C. B. Field, R. B. Norgaard, and D. Policansky, “Revisiting the Commons: Local Lessons, Global Challenges,” Science 284, no. 5412 (1999): 278–282; E. Ostrom, Understanding Institutional Diversity (Princeton, NJ: Princeton University Press, 2005).
10 . Ostrom, Governing the Commons.
11 . R. J. Sampson, Great American City: Chicago and the Enduring Neighborhood Effect (Chicago: University of Chicago Press, 2012); R. J. Sampson and S. W. Raudenbush, “Systematic Social Observation of Public Spaces: A New Look at Disorder in Urban Neighborhoods,” American Journal of Sociology 105, no. 3 (1999): 603–651; R. J. Sampson, S. W. Raudenbush, and F. Earls, “Neighborhoods and Violent Crime: A Multilevel Study of Collective Efficacy,” Science 277, no. 5328 (1997): 918–924; J. D. Morenoff, R. J. Sampson, and S. W. Raudenbush, “Neighborhood Inequality, Collective Efficacy, and the Spatial Dynamics of Urban Violence,” Criminology 39, no. 3 (2001): 517–560.
12 . R. J. Sampson and W. B. Groves, “Community Structure and Crime: Testing Social Disorganization Theory,” American Journal of Sociology 94, no. 4 (1989): 774–802; P. E. Bellair, “Informal Surveillance and Street Crime: A Complex Relationship,” Criminology 38 (2000): 137–170; B. D. Warner and P. W. Rountree, “Local Social Ties in a Community and Crime Model: Questioning the Systemic Nature of Informal Social Control,” Social Problems 44 (1997): 520–536; W. Steenbeek and J. R. Hipp, “A Longitudinal Test of Social Disorganization Theory: Feedback Effects among Cohesion, Social Control, and Disorder,” Criminology 49 (2011): 833–871.
13 . A. Bandura, Social Foundations of Thought and Action (Englewood Cliffs, NJ: Prentice Hall, 1986). See also R. Wickes, J. R. Hipp, E. Sargeant, and R. Homel, “Collective Efficacy as a Task Specific Process: Examining the Relationship between Social Ties, Neighborhood Cohesion and the Capacity to Respond to Violence, Delinquency and Civic Problems,” American Journal of Community Psychology 52 (2013): 115–127.
14 . Compare J. Coleman, The Foundations of Social Theory (Cambridge, MA: Harvard University Press, 1990).
15 . See, for example, R. Boyd, H. Gintis, S. Bowles, and P. J. Richerson, “The Evolution of Altruistic Punishment,” in Moral Sentiments and Material Interests, ed. H. Gintis, S. Bowles, and E. Fehr (Cambridge, MA: MIT Press, 2005), 3531–3535; H. Gintis S. Bowles, R. Boyd, and E. Fehr, “Explaining Altruistic Behavior in Humans,” Evolution and Human Behavior 24, no. 3 (2003): 153–172; O. Gurerk, B. Irlenbusch, and B. Rockenbach, “The Competitive Advantage of Sanctioning Institutions,” Science 312, no. 5770 (2006): 108–111; M. Milinski, D. Semmann, and H. J. Krambeck, “Reputation Helps Solve the ‘Tragedy of the Commons,’ ” Nature 415, no. 6870 (2002): 424–426; E. Fehr and S. Gachter, “Altruistic Punishment in Humans,” Nature 415, no. 6868 (2002): 137–140.
16 . J. Elster, Explaining Social Behavior: More Nuts and Bolts for the Social Sciences (Cambridge: Cambridge University Press, 2007).
17 . See, for example, R. Ardrey, The Territorial Imperative (New York: Atheneum, 1966).
18 . G. Brown, “Claiming a Corner at Work: Measuring Employee Territoriality in Their Workspaces,” Journal of Environmental Psychology 29 (2009): 44–52.
19 . For a scientific analysis of this phenomenon, see M. Costa, “Territorial Behavior in Public Settings,” Environment and Behavior 44 (2012): 713–721.
20 . J. J. Edney, “Human Territoriality,” Psychological Bulletin 81, no. 12 (1974): 959–975; I. Altman, “Territorial Behavior in Humans: An Analysis of the Concept,” in Spatial Behavior of Older People, ed. L. A. Pastalan and D. A. Carson (Ann Arbor: University of Michigan Press, 1970), 1–24; B. B. Brown, “Territoriality,” in Handbook of Environmental Psychology, ed. D. Stokols and I. Altman (New York: Wiley, 1987), 505–531; R. B. Taylor, Human Territorial Functioning: An Empirical, Evolutionary Perspective on Individual and Small Group Territorial Cognitions, Behaviors and Consequences (Cambridge: Cambridge University Press, 1988).
21 . J. L. Pierce, T. Kostova, and K. T. Dirks, “Toward a Theory of Psychological Ownership in Organizations,” Academy of Management Review 26, no. 2 (2001): 298–310 at 299. See also G. Brown, T. B. Lawrence, and S. L. Robinson, “Territoriality in Organizations,” Academy of Management Review 30, no. 3 (2005): 577–594.
22 . C. M. Werner, S. Peterson-Lewis, and B. B. Brown, “Inferences about Homeowners’ Sociability: Impact of Christmas Decorations and other Cues,” Journal of Environmental Psychology 9, no. 4 (1989): 279–296; B. B. Brown and C.M. Werner, “Social Cohesiveness, Territoriality and Holiday Decorations: The Influence of Cul-de-Sacs,” Environment and Behavior 17 (1985): 539–565.
23 . M. O. Caughy, P. J. O’Campo, and J. Patterson, “A Brief Observational Measure for Urban Neighborhoods,” Health and Place 7 (2001): 225–236.
24 . Brown, “Claiming a Corner at Work”; Brown, Lawrence, and Robinson, “Territoriality in Organizations.”
25 . J. L. Pierce and I. Jussila, “Collective Psychological Ownership within the Work and Organizational Context: Construct Introduction and Elaboration,” Journal of Organizational Behavior 31 (2010): 810–834.
26 . B. B. Brown and I. Altman, “Territoriality and Residential Crime,” in Environmental Criminology, ed. P. J. Brantingham and P. L. Brantingham (Beverly Hills, CA: Sage, 1981), 56–76.
27 . Brown and Werner, “Social Cohesiveness, Territoriality and Holiday Decorations”; P. B. Harris and B. B. Brown, “The Home and Identity Display: Interpreting Resident Territoriality from Home Exteriors,” Journal of Environmental Psychology 16 (1996): 187–203; B. B. Brown, D. D. Perkins, and G. Brown, “Incivilities, Place Attachment and Crime: Block and Individual Effects,” Journal of Environmental Psychology 24 (2004): 359–371; R. O. Pitner, M. Yu, and E. Brown, “Making Neighborhoods Safer: Examining Predictors of Residents’ Concerns about Neighborhood Safety,” Journal of Environmental Psychology 32 (2012): 43–49.
28
. https://
29 . The methodology that follows was originally presented on a smaller corpus of data in D. T. O’Brien, “Custodians and Custodianship in Urban Neighborhoods: A Methodology Using Reports of Public Issues Received by a City’s 311 Hotline,” Environment and Behavior 47 (2015): 304–327.
30 . City employees were identified as anyone who either had at least one report whose case type included “Employee” or “Internal” in the name, had at least one report generated by the smartphone app that allows city employees to manage their own work queue and report new cases, or had “.gov” or “.ma.us” in their e-mail address. This amounted to 3,320 employees. There were also 26,781 accounts with zero reports that we exclude from analysis here. This could happen because either the person made a report that had no geographical reference, which means we excluded it before analysis, or because he or she created an account when downloading the city’s smartphone app BOS:311 but never used it to make a report.
31 . C. J. Coulton, J. Korbin, T. Chan, and M. Su, “Mapping Residents’ Perceptions of Neighborhood Boundaries: A Methodological Note,” American Journal of Community Psychology 29, no. 2 (2001): 371–383; A. M. Guest and B. A. Lee, “How Urbanites Define Their Neighborhoods,” Population and Environment 7, no. 1 (1984): 32–56; N. Sastry, A. R. Pebley, and M. Zonta, “Neighborhood Definitions and the Spatial Dimensions of Daily Life in Los Angeles” (paper presented at the Annual Meeting of the Population Association of America, Minneapolis, MN, 2002).
32 . L. D. Frank, T. L. Schmid, J. F. Sallis, J. Chapman, and B. E. Saelens, “Linking Objectively Measured Physical Activity with Objectively Measured Urban Form,” American Journal of Preventive Medicine 28, no. 2, Suppl. 2 (2005): 117–125; N. Colabianchi, M. Dowda, K. Pfeiffer, D. Porter, M. J. Almeida, and R. R. Pate, “Towards an Understanding of Salient Neighborhood Boundaries: Adolescent Reports of an Easy Walking Distance and Convenient Driving Distance,” International Journal of Behavioral Nutrition and Physical Activity 4, no. 1 (2007): 66.
33 . Sastry, Pebley, and Zonta, “Neighborhood Definitions and the Spatial Dimensions of Daily Life in Los Angeles.”
34 . Density reachability and connectivity clustering, or DBSCAN. See C. Hennig, fpc: Flexible Procedures for Clustering (Comprehensive R Archive Network, 2014).
35 . Density reachability and connectivity clustering used a minimum distance of 300 m, approximating the diameter around the home for the narrowest definition of neighborhood, or a radius of 150 m. This produced clusters of reports for every custodian with at least two public reports within 300 m of each other. Any reports that did not fit these criteria were classified as one-case clusters. The nearest report to home in each distinguished “home clusters” from those located in other places around the city. This measure had a Poisson distribution, with an “elbow” at 300 m, suggesting this as the most appropriate cut point for home clusters. The same threshold was then used to classify one-case clusters. See Hennig, fpc .
36 . A somewhat different analysis of these data for this question was originally presented in D. T. O’Brien, E. Gordon, and J. Baldwin-Philippi, “Caring about the Community, Counteracting Disorder: 311 Reports of Public Issues as Expressions of Territoriality,” Journal of Environmental Psychology 40 (2014): 320–330.
37 . There were 765 respondents (response rate = 21 percent), 743 of whom could be merged with a particular user account. Of these respondents, 674 completed all items used in the analyses.
38 . W. A. Fischel, The Homevoter Hypothesis: How Home Values Influence Local Government Taxation, School Finance, and Land-Use Policies (Cambridge, MA: Harvard University Press, 2005).
39 . W. Seo and B. von Rabenau, “Spatial Impacts of Microneighborhood Physical Disorder on Property Resale Values in Columbus, Ohio,” Journal of Urban Planning and Development 137, no. 3 (2011): 337–345.
40 . A repeated-measures analysis of variance (ANOVA) indicated that these differences were significant (F = 360, p < .001), and post-hoc tests confirmed this for all pairwise comparisons (Tukey’s adjustment, p < .001 for all tests).
41 . These analyses were limited to survey respondents having a home address associated with their account and living in a census tract from which at least three residents participated in the survey (final n = 427 individuals in 81 tracts). Models predicting reporting in different geographical contexts were limited to custodians only, meaning they could only include those custodians from census tracts with at least three other custodians represented in the survey sample (n = 211 individuals in 48 census tracts).
42 . Derived from models with a logit link.
43 . A version of the analysis that follows based on a smaller corpus of the data was presented originally in D. T. O’Brien, “Using Small Data to Interpret Big Data: 311 Reports as Individual Contributions to Informal Social Control in Urban Neighborhoods,” Social Science Research 59 (2016): 83–96.
44 . This is a hurdle model, with the two models specified independently. Again, demographic factors are controlled for and a logit link is used.
45 . Before the analysis, outliers with more than 100 custodial reports (91 individuals; 0.2 percent of custodians) were removed, and both sets of counts were log-transformed to account for the Poisson distribution.
46 . This approach is limited to the 2,549 custodians who made both types of reports and had home addresses on file.
47 . Based on the binomial function, (kn)*an−k*bk=5*.84*.21=.41.
48 . The total number of possible combinations was actually 8,933, but I limited the study to those in which the expected count was greater than .5 (i.e., could be rounded to 1 or greater).
49 . % diff=obs.−exp.exp.
50 . The significance of this result was confirmed by a curvilinear regression, which found that the proportion of incivility reports was a positive, quadratic predictor of the percentage difference between observed and expected (β = .74, p < .001).
1 . C. Hilbe, A. Traulsen, T. Rö hl, and M. Milinski, “Democratic Decisions Establish Stable Authorities That Overcome the Paradox of Second-Order Punishment,” Proceedings of the National Academy of Sciences 111, no. 2 (2014): 752–756; J. Henrich et al., “Costly Punishment across Human Societies,” Science 312, no. 5781 (2006): 1767–1770.
2 . M. A. McKean, “The Japanese Experience with Scarcity: Management of Traditional Common Lands,” Environmental Review 6 (1982): 63–88; M. A. McKean, “Management of Traditional Common Lands (iriaichi) in Japan,” in Conference on Common Property Resource Management (Washington, DC: National Academy Press, 1986), 533–589.
3 . E. Ostrom, Governing the Commons: The Evolution of Institutions for Collective Action (Cambridge, MA: Harvard University Press, 1990).
4 . McKean, “Management of Traditional Common Lands (iriaichi) in Japan,” 559.
5 . Ostrom, Governing the Commons.
6 . This argument and the analysis that follows builds on ideas originally presented in D. T. O’Brien, “Lamp Lighters and Sidewalk Smoothers: How Individual Residents Contribute to the Maintenance of the Urban Commons,” American Journal of Community Psychology 58 (2016): 391–409.
7 . S. B. Sarason, The Creation of Settings and the Future Societies (San Francisco: Jossey-Bass, 1972).
8 . E. Seidman, “Back to the Future, Community Psychology: Unfolding a Theory of Social Intervention,” American Journal of Community Psychology 16 (1988): 3–24; E. Seidman, “Pursuing the Meaning and Utility of Social Regularities for Community Psychology,” in Researching Community Psychology: Issues of Theories and Methods, ed. P. H. Tolan, C. Keys, F. Chertok, and L. Jason (Washington, DC: American Psychological Association, 1990), 91–100; E. Seidman, “An Emerging Action Science of Social Settings,” American Journal of Community Psychology 50 (2012): 1–16.
9 . The geographic range of reporting was estimated for those who had a home address on file as the farthest distance of a reported issue from the custodian’s home (calculated using the Pythagorean equation, (xr−xh)2+(yr−yh)2, where the subscripts r and h indicate the location of the report and the home, respectively). Using three reports as a cut point generated a greater distinction between groups in geographic range (t = 11.2, p < .001) than using two reports (t = 4.6, p < .001) or four reports (t = 10.6, p < .001).
10 . W. J. Wilson, The Truly Disadvantaged (Chicago: University of Chicago Press, 1987).
11 . D. M. Kennedy, A. M. Piehl, and A. A. Braga, “Youth Violence in Boston: Gun Markets, Serious Youth Offenders, and a Use-Reduction Strategy,” Law and Contemporary Problems 59, no. 1 (1996): 147–196.
12 . T. D. Clear, D. R. Rose, E. Waring, and K. Scully, “Coercive Mobility and Crime: A Preliminary Examination of Concentrated Incarceration and Social Disorganization,” Justice Quarterly 20, no. 1 (2003): 33–64.
13 . Compare M. S. Granovetter, “The Strength of Weak Ties,” American Journal of Sociology 78, no. 6 (1973): 1360–1380.
14 . M. Small, Villa Victoria: The Transformation of Social Capital in a Boston Barrio (Chicago: University of Chicago Press, 2004); P. G. Foster-Fishman, C. Collins, and S. J. Pierce, “An Investigation of the Dynamic Processes Promoting Citizen Participation,” American Journal of Community Psychology 51 (2013): 492–509.
15 . For those 311 accounts without mappable home addresses, census geography of residence was estimated as the geography from which the individual made the most reports. This technique was accurate for those with known home addresses 93 percent of the time for census tracks and 91 percent of the time for block groups.
16 . Because all measures of behavioral composition featured a positive skew, they were log-transformed before regression analysis.
17 . To eliminate any shared variance between the interaction effect and the main effects, it was first regressed on the two component variables, a process known as residual centering, which is more effective than the traditional mean centering. See C. E. Lance, “Residual Centering, Exploratory and Confirmatory Moderator Analysis, and Decomposition of Effects in Path Models Containing Interactions,” Applied Psychological Measurement 12, no. 2 (1988): 163–175; T. D. Little, J. A. Bovaird, and K. F. Widaman, “On the Merits of Orthogonalizing Powered and Product Terms: Implications for Modeling Interactions among Latent Variables,” Structural Equation Modeling 13 (2006): 497–519. The residual (i.e., the unique variance of the interaction factor) was then entered into the equation.
18 . For comparison, I ran two simpler models that did not differentiate between types of users, once with the total number of custodians as a lone predictor and again with custodians per square mile as the lone predictor. Though both were significant (total number: β = .35, p < .001; number per sq. mile: β = .48, p < .001), these models explained less variation than the full model with typical custodians, exemplars, and their interaction (ANOVA comparisons p < .001). This further justifies the division of custodians into two groups. See Appendix D for complete models.
19 . The initial introduction of the interaction effect improved the model, explaining an additional 2 percent of the variance, but because of the small sample size, this was a nonsignificant change. It also made all predictors nonsignificant. Upon trimming the model, the interaction effect was the last remaining predictor. Note that the lower level of significance in this model results from the smaller sample size, N = 54.
20 . For sidewalks, the parameters for the behavioral composition were nearly identical (typical custodians: β = .38, p < .001; exemplars: β = .30, p < .001; interaction: β = .14, p < .05). Of the new predictors, only Hispanic population marginally predicted greater reporting than accounted for by the three measures of the behavioral composition (β = .16, p < .10). For streetlight outages, the small sample size called for a stepwise regression. The four demographic variables were entered into the model first, none of which were significant predictors. The interaction was entered next as the strongest remaining predictor (β = .31, p < .05). The further introduction of either typical or exemplar custodians was nonsignificant, though they maintained their correlations with reporting when controlling for demographics (typical custodians: β = .27; exemplars: β = .26; both p -values <= .10).
21
. https://
22 . This maintains the previous ratio of ∼10 percent of custodians being exemplars.
23 . B. B. Brown and I. Altman, “Territoriality and Residential Crime,” in Environmental Criminology, ed. P. J. Brantingham and P. L. Brantingham (Beverly Hills, CA: Sage, 1981), 56–76.
24 . In addition to the predictors related to the main hypotheses, these models controlled for day of the week and season of the year, whether a report was for snow removal (often made by one-time callers), and street length.
25 . The discerning and statistically savvy reader will probably note that, with this multivariate distinction between typical and exemplar custodians, it is possible to use cluster analysis to create a more sophisticated algorithm for differentiating between the groups than the arbitrary dividing line of three reports of public issues. I have conducted such an analysis using total reports, estimated geographical range of custodianship, incivilities reported, reports made on main streets, and reports made on nonresidential streets as the defining variables for clusters. Surprisingly, the resulting clusters only muddy the waters. The ability to predict whether a person will engage in the types of tasks associated with exemplars was far greater with the dichotomy based on a simplistic reporting threshold than with the division based on a clustering algorithm. This seemed to result from the fact that the variance in estimated geographical range of reporting dominated the k -means clustering algorithm, obscuring to some extent deviations in the other forms of behavior.
26 . For the case of exemplars, this is calculated as length*issuesm*P(exemplar), where issuesm is the expected count of issues per meter, specific to a road’s zoning type (calculated from the entire database of all issues, excluding any duplicate reports), and P (exemplar ) is based on the road- and tract-level characteristics (e.g., zoning, main vs. side streets) and the parameters from the multilevel models in the previous analysis.
27 . As before, we measure the latter in terms of the density of typical custodians and the raw number of exemplars.
28 . It is important to note that for factors that are presumed to operate at the individual level, such as affluence, these conclusions are vulnerable to the ecological fallacy—that factors measured at the group level can be used as evidence of individual-level effects. See G. King, A Solution to the Ecological Inference Problem (Princeton, NJ: Princeton University Press, 1997). For example, while it might seem that individuals with greater access to resources are more likely to participate in 311, it is also possible that what really matters is living in a neighborhood with greater overall access to resources, regardless of one’s own socioeconomic standing.
29 . J. M. Darley and B. Latané , “Bystander Intervention in Emergencies: Diffusion of Responsibility,” Journal of Personality and Social Psychology 8, no. 4 (1968): 377–383.
30 . J. Jacobs, The Death and Life of Great American Cities (New York: Random House, 1961).
31 . M. Pattillo-McCoy, Black Picket Fences: Privilege and Peril among the Black Middle Class (Chicago: University of Chicago Press, 2000).
32 . Ibid.
33 . Ostrom, Governing the Commons.
1 . Reported in D. T. O’Brien, D. Offenhuber, J. Baldwin-Philippi, and E. Gordon, “Uncharted Territoriality in Coproduction: The Motivations for 311 Reporting,” Journal of Public Adminstration Research and Theory 27 (2017): 320–335.
2 . W. Barnes and B. Mann, Making Local Democracy Work: Municipal Officials’ Views about Public Engagement (Washington, DC: National League of Cities Center for Research and Innovation, 2009); K. A. McComas, “Trivial Pursuits: Participant Views of Public Meetings,” Journal of Public Relations Research 15, no. 2 (2003): 91–115.
3 . T. O’Reilly, “Government as a Platform,” in Open Government, ed. D. Lathrop and L. Ruma (Sebastopol, CA: O’Reilly, 2010), 11–40; E. Ferro and F. Molinari, “Making Sense of Gov 2.0 Strategies: ‘No Citizens, No Party,’ ” Journal of eDemocracy 2, no. 1 (2010): 56–68; B. S. Noveck, Wiki Government: How Technology Can Make Government Better, Democracy Stronger, and Citizens More Powerful (Washington, DC: Brookings Institution Press, 2009).
4 . S. Goldsmith and S. Crawford, The Responsive City: Engaging Communities through Data-Smart Governance (San Francisco: Jossey-Bass, 2014).
5 . J. S. Evans-Cowley, “Planning in the Age of Facebook: The Role of Social Networking in Planning Processes,” GeoJournal 75, no. 5 (2010): 407–420; D. G. Freelon, T. Kriplean, J. Morgan, W. L. Bennett, and A. Borning, “Facilitating Diverse Political Engagement with the Living Voters Guide,” Journal of Information Technology and Politics 9, no. 3 (2012): 279–297.
6 . B. Barber, Strong Democracy: Participatory Politics for a New Age (Oakland: University of California Press, 1984).
7 . R. B. Parks, P. C. Baker, L. Kiser, R. Oakerson, E. Ostrom, V. Ostrom, S. L. Percy, M. B. Vandivort, G. P. Whitaker, and R. Wilson, “Consumers as Coproducers of Public Services: Some Economic and Institutional Considerations,” Policy Studies Journal 9 (1982): 1001–1011; E. Ostrom, “Metropolitan Reform: Propositions Derived from Two Traditions,” Social Science Quarterly 53 (1972): 474–493; G. P. Whitaker, “Coproduction: Citizen Participation in Service Delivery,” Public Administration Review 40 (1980): 240–246; C. H. Levine, “Citizenship and Service Delivery: The Promise of Coproduction,” Public Administration Review 44 (1984): 178–187; S. Percy, “Citizen Participation in the Co-production of Urban Services,” Urban Affairs Quarterly 19, no. 4 (1984): 431–436.
8 . Y. Cabannes, “Participatory Budgeting: A Significant Contribution to Participatory Democracy,” Environment and Urbanization 16 (2004): 27–46.
9 . E. Gordon and J. Baldwin-Philippi, “Playful Civic Learning: Enabling Lateral Trust and Reflection in Game-Based Public Participation,” International Journal of Communication 8 (2014): 759–786.
10 . See, for example, J. Alford, “Why Do Public-Sector Clients Coproduce? Toward a Contingency Theory,” Administration and Society 34 (2002): 32–56.
11 . Parks et al., “Consumers as Coproducers of Public Services”; Ostrom, “Metropolitan Reform.”
12 . See, for example, Whitaker, “Coproduction”; Levine, “Citizenship and Service Delivery”; Percy, “Citizen Participation in the Co-production of Urban Services.”
13 . E. Ostrom, “Crossing the Great Divide: Coproduction, Synergy, and Development,” World Development 24 (1996): 1073–1087 at 1079.
14 . D. Osborne, “Reinventing Government,” Public Productivity and Management Review 16, no. 4 (1993): 349–356; C. Hood, “A Public Management for All Seasons?,” Public Administration 69, no. 1 (1991): 3–19.
15 . S. P. Osborne and K. Strokosch, “It Takes Two to Tango? Understanding the Co-production of Public Services by Integrating the Services Management and Public Administration Perspectives,” British Journal of Management 24 (2013): S31–S47; S. P. Osborne, “Delivering Public Services: Time for a New Theory?,” Public Management Review 12 (2010): 1–10.
16 . Ostrom, “Crossing the Great Divide.”
17 . Osborne and Strokosch, “It Takes Two to Tango?”; Osborne, “Delivering Public Services”; S. P. Osborne, Z. Radnor, and G. Nasi, “A New Theory for Public Service Management? Toward a (Public) Service-Dominant Approach,” American Review of Public Administration 43, no. 2 (2012): 135–158.
18 . E. Mayo and H. Moore, eds., Building the Mutual State: Findings from the Virtual Thinktank (London: New Economics Foundation / Mutuo, 2002).
19 . T. Bovaird, “Beyond Engagement and Participation: User and Community Coproduction of Public Services,” Public Administration Review 67, no. 5 (2007): 846–860.
20 . A. Joshi and M. Moore, “Institutionalized Co-production: Unorthodox Public Service Delivery in Challenging Environments,” Journal of Development Studies 40, no. 4 (2004): 31–49; Bovaird, “Beyond Engagement and Participation”; T. Brandsen and V. Pestoff, “Co-production, the Third Sector and the Delivery of Public Services: An Introduction,” Public Management Review 8, no. 4 (2006): 493–501; D. Cepiku and F. Giordano, “Co-production in Developing Countries,” Public Management Review 16, no. 3 (2014): 317–340.
21 . D. H. Folz, “Recycling Solid Waste: Citizen Participation in the Design of a Coproduced Program,” State and Local Government Review 23 (1991): 92–102; M. J. Marschall, “Parent Involvement and Educational Outcomes for Latino Students,” Review of Policy Research 23 (2006): 1053–1076; K. J. Powers and F. Thompson, “Managing Coprovision: Using Expectancy Theory to Overcome the Free-Rider Problem,” Journal of Public Administration Research and Theory 4, no. 2 (1994): 179–196.
22 . This stands in contrast to more extensive efforts to uncover the motivations that characterize public-sector employees See B. E. Wright, “Public-Sector Work Motivation: A Review of the Current Literature and a Revised Conceptual Model,” Journal of Public Administration Research and Theory 11, no. 4 (2001): 559–586; J. L. Perry, “Antecedents of Public Service Motivation,” Journal of Public Administration Research and Theory 7, no. 2 (1997): 181–197. It also stands in contrast to such efforts to uncover the motivations behind volunteerism. See E. G. Clary and M. Snyder, “The Motivations to Volunteer: Theoretical and Practical Considerations,” Current Directions in Psychological Science 8, no. 5 (1999): 156–159.
23 . J. C. Thomas, “Citizen, Customer, Partner: Rethinking the Place of the Public in Public Management,” Public Administration Review 73, no. 6 (2013): 786–796.
24 . Osborne, “Reinventing Government”; Hood, “A Public Management for All Seasons?”
25 . Levine, “Citizenship and Service Delivery”; Bovaird, “Beyond Engagement and Participation.”
26 . J. Fledderus, T. Brandsen, and M. Honingh, “Restoring Trust through the Co-production of Public Services: A Theoretical Elaboration,” Public Management Review 16, no. 3 (2014): 424–443.
27 . See, for example, M. J. Marschall, “Citizen Participation and the Neighborhood Context: A New Look at the Coproduction of Local Public Goods,” Political Research Quarterly 57 (2004): 231–244.
28 . Joshi and Moore, “Institutionalized Co-production.”
29 . J. A. Fodor, Modularity of Mind: An Essay on Faculty Psychology (Cambridge, MA: MIT Press, 1983); J. Tooby and L. Cosmides, “The Psychological Foundations of Culture,” in The Adapted mind: Evolutionary Psychology and the Generation of Culture , ed. J. H. Barkow, L. Cosmides, and J. Tooby (New York: Oxford University Press, 1992), 19–136.
30 . Alford, “Why Do Public-Sector Clients Coproduce?”; J. Alford, Engaging Public Sector Clients: From Service-Delivery to Co-production (Basingstoke: Palgrave Macmillan, 2009).
31 . B. Y. Clark, J. L. Brudney, and S.-G. Jang, “Coproduction of Government Services and the New Information Technology: Investigating the Distri butional Biases,” Public Administration Review 73, no. 5 (2013): 687–701; J. Fountain, “Connecting Technologies to Citizenship,” in Technology and the Resilience of Metropolitan Regions, ed. M. A. Pagano (Champaign: University of Illinois Press, 2014), 25–51; J. R. Levine and C. Gershenson, “From Political to Material Inequality: Race, Immigration, and Request for Public Goods,” Sociological Forum 29 (2014): 607–627; A. E. Lerman and V. Weaver, “Staying Out of Sight? Concentrated Policing and Local Political Action,” Annals of the American Academy of Political and Social Science 651 (2014): 202–219; S. L. Minkoff, “NYC 311: A Tract-Level Analysis of Citizen-Government Contacting in New York City,” Urban Affairs Review 52, no. 2 (2016): 211–246.
32 . Clark, Brudney, and Jang, “Coproduction of Government Services and the New Information Technology”; Fountain, “Connecting Technologies to Citizenship”; Levine and Gershenson, “From Political to Material Inequality”; Lerman and Weaver, “Staying Out of Sight?”
33 . The nine activities were sufficiently correlated to justify combining them as a single index of civic activities (Cronbach’s α = .76).
34 . We linked survey respondents to the Boston voter file via exact and approximate (fuzzy) string matching in R. Exact matches were identified as unique combinations of name and address or name and phone number in both files. Next, a fuzzy string-matching algorithm was applied to unmatched sets of name and address fields, such that similar names (e.g., “Stephen” and “Steve”) could constitute a match. We then manually checked all approximate matches for false positives. The analyses reported here include all fuzzy matches but were rerun restricting the analysis to exact matches, producing results that were qualitatively identical.
35 . Z. L. Hajnal and P. G. Lewis, “Municipal Institutions and Voter Turnout in Local Elections,” Urban Affairs Review 5 (2003): 645–668.
36 . A. Lijphart, “Unequal Participation: Democracy’s Unresolved Dilemma,” American Political Science Review 91, no. 1 (1997): 1–14.
37 . Final N = 439 individuals in 82 tracts in multilevel models.
38 . There was no significant variation in custodians across neighborhoods, suggesting that people living in any neighborhood were about equally likely to have the opportunity to report at least one public issue (ICC = .02; χ2 df=81 = 90.95, p = ns ).
39 . Both of these had Poisson distributions, requiring the use of a log-link model.
40 . Limited to those custodians from census tracts with at least three other custodians represented in the survey sample (N = 220 individuals in 50 census tracts).
41 . In contrast to the model predicting custodianship, both reports within the home neighborhood and size of home neighborhood had significant variation at the neighborhood level (reports: ICC = .08, p < .001; size: ICC = .10, p < .001), indicating the importance of controlling for similarity in reporting between neighbors owing to shared environment.
42 . There was no significant neighborhood-level variation regarding whether someone reported in any of these three contexts.
43 . Clark, Brudney, and Jang, “Coproduction of Government Services and the New Information Technology”; Fountain, “Connecting Technologies to Citizenship”; Levine and Gershenson, “From Political to Material Inequality”; Lerman and Weaver, “Staying Out of Sight?”
44 . B. B. Brown, “Territoriality,” in Handbook of Environmental Psychology, ed. D. Stokols and I. Altman (New York: Wiley, 1987), 505–531; D. D. Perkins, A. Wandersman, R. C. Rich, and R. B. Taylor, “The Physical Environment of Street Crime: Defensible Space, Territoriality and Incivilities,” Journal of Environmental Psychology 13 (1993): 29–49; R. B. Taylor, Human Territorial Functioning: An Empirical, Evolutionary Perspective on Individual and Small Group Territorial Cognitions, Behaviors and Consequences (Cambridge: Cambridge University Press, 1988).
45 . As described in Bovaird, “Beyond Engagement and Participation.”
46 . E. Melhuish and J. Barnes, Towards Understanding Sure Start Local Programmes: Summary of Findings from the National Evaluation (London: Department for Education and Skills, 2004).
47 . Cepiku and Giordano, “Co-production in Developing Countries.”
48 . Gordon and Baldwin-Philippi, “Playful Civic Learning.”
49 . S. Haselmayer, “What Boston’s Snow Crisis Can Teach Us about Solving Problems in New Ways,” The Atlantic CityLab, February 19, 2015.
50 . Ostrom, “Crossing the Great Divide.”
51 . Marschall, “Citizen Participation and the Neighborhood Context.”
52 . Alford, “Why Do Public-Sector Clients Coproduce?”
53 . Perkins et al., “The Physical Environment of Street Crime”; B. B. Brown and C. M. Werner, “Social Cohesiveness, Territoriality and Holiday Decorations: The Influence of Cul-de-Sacs,” Environment and Behavior 17 (1985): 539–565; P. B. Harris and B. B. Brown, “The Home and Identity Display: Interpreting Resident Territoriality from Home Exteriors,” Journal of Environmental Psychology 16 (1996): 187–203. Note that I do not report it in this book, but this correlation also holds within our survey data. See D. T. O’Brien, E. Gordon, and J. Baldwin, “Caring about the Community, Counteracting Disorder: 311 Reports of Public Issues as Expressions of Territoriality,” Journal of Environmental Psychology 40 (2014): 320–330.
54 . Marschall, “Citizen Participation and the Neighborhood Context.”
55 . Osborne and Strokosch, “It Takes Two to Tango?”; Osborne, “Delivering Public Services.”
56 . See, for example, S. Shahrabani, U. Benzion, and G. Y. Din, “Factors Affecting Nurses’ Decision to Get the Flu Vaccine,” European Journal of Health Economics 10 (2009): 227–231.
57 . Bovaird, “Beyond Engagement and Participation.”
58 . Levine, “Citizenship and Service Delivery.”
59 . M. Jakobsen and S. C. Andersen, “Coproduction and Equity in Public Service Delivery,” Public Administration Review 73, no. 5 (2013): 704–713; R. Warren, M. S. Rosentraub, and K. S. Harlow, “Coproduction, Equity, and the Distribution of Safety,” Urban Affairs Review 19, no. 4 (1984): 447–464.
1 . J. Q. Wilson and G. L. Kelling, “Broken Windows: The Police and Neighborhood Safety,” Atlantic Monthly 127 (March 1982): 29–38.
2 . D. Weisburd, J. Hinkle, A. A. Braga, and A. Wooditch, “Understanding the Mechanisms Underlying Broken Windows Policing: The Need for Evaluation Evidence,” Journal of Research in Crime and Delinquency 52, no. 4 (2015): 589–608; R. B. Taylor, Breaking Away from Broken Windows: Baltimore Neighborhoods and the Nationwide Fight against Crime, Grime, Fear, and Decline (Boulder, CO: Westview Press, 2001); W. G. Skogan, Disorder and Decline (Berkeley: University of California Press, 1992); B. E. Harcourt and J. Ludwig, “Broken Windows: New Evidence from New York City and a Five-City Social Experiment,” University of Chicago Law Review 73, no. 1 (2006): 271–320; B. E. Harcourt, Illusion of Order: The False Promise of Broken Windows Policing (Cambridge, MA: Harvard University Press, 2001).
3 . R. J. Sampson and S. W. Raudenbush, “Systematic Social Observation of Public Spaces: A New Look at Disorder in Urban Neighborhoods,” American Journal of Sociology 105, no. 3 (1999): 603–-651.
4 . T. Meares, “Law of Community Policing and Public Order Policing,” in Encyclopedia of Criminology and Criminal Justice, ed. G. Bruinsma and D. Weisburd (New York: Springer, 2014), 2823–2827.
5 . D. Weisburd and J. E. Eck, “What Can Police Do to Reduce Crime, Disorder, and Fear?” Annals of the American Academy of Political and Social Science 593 (2004): 42–65.
6 . M. Jakobsen, “Can Government Initiatives Increase Citizen Coproduction? Results of a Randomized Field Experiment,” Journal of Public Administration Research and Theory 23, no. 1 (2013): 27–54.
7 . This experiment was also described as part of a discussion of the relevance of humans’ evolved territoriality to the management of 311 systems in D. T. O’Brien, “311 Hotlines, Territoriality, and the Collaborative Maintenance of the Urban Commons: Examining the Intersection of a Coproduction Policy and Evolved Human Behavior,” Evolutionary Behavioral Sciences 10, no. 2 (2016): 123–141.
8 . Matched triplets were constructed via the following process. First, to control for regional characteristics, all three tracts had to be in the same planning district, large regions of the city with historical and social identity (e.g., Fenway, South Boston). Second, standardized measures of difference between the tracts composing all eligible triplets were calculated based on four demographic measures related to custodianship—prevalence of home ownership, level of median income, and proportion black and proportion Hispanic—and previous measures of custodianship at the neighborhood level. This produced an ordered list of the overall consistency in custodianship and relevant sociodemographics for all triplets of census tracts contained in the same planning district. A final selection of five triplets was then made, with two rules: (1) no planning district could produce more than one triplet, and (2) a triplet was included from each of the city’s three most disadvantaged districts, which also have the lowest overall engagement with 311, in order to maximize the potential effect of the intervention.
9 . We placed flyers within or against the front doorway of each building, with additional flyers at buildings with multiple units, distributed in such a way as to have one given for each unit if possible. We agreed with our public sector collaborators that we would not place the flyers in mailboxes, thereby keeping on the right side of federal law that allows only mail delivered by the U.S. Postal Service to be placed in mailboxes.
10 . (kn)*an−k*(1−a)k=5*1/34*2/31=.04, where n is the number of times an event happens in k attempts, and a is the probability of the event.
11 . A. Ryan, “Missing Link: App Will Connect Citizens,” Boston Globe, October 12, 2010.
12 . T. Trenkner, “Public Officials of the Year: Nigel Jacob and Chris Osgood,” Governing, October 27, 2011.
13 . C. McDowell and M. Chinchilla, “Partnering with Communities and Institutions,” in Civic Media: Technology, Design, Practice, ed. E. Gordon and P. Mihalidis (Cambridge, MA: MIT Press, 2016), 461–480.
14 . T. O’Reilly, “Government as a Platform,” in Open Government, ed. D. Lathrop and L. Ruma (Sebastopol, CA: O’Reilly, 2010), 11–40; E. Ferro and F. Molinari, “Making Sense of Gov 2.0 Strategies: ‘No Citizens, No Party,’ ” Journal of eDemocracy 2, no. 1 (2010): 56–68; B. S. Noveck, Wiki Government: How Technology Can Make Government Better, Democracy Stronger, and Citizens More Powerful (Washington, DC: Brookings Institution Press, 2009); S. Johnson, Future Perfect: The Case for Progress in a Networked Age (New York: Penguin Books, 2012).
15 . E. Morozov, “From Slacktivism to Activism,” Foreign Policy, September 5, 2009.
16 . E. Zuckerman, “New Media, New Civics?” Policy and Internet 6 (2014): 151–168.
17 . For robustness, a reanalysis treating all General Requests as public issues produced practically identical results (e.g., the more inclusive approach found that 65 percent of custodians made only one report, rather than 68 percent).
18 . D. T. Campbell and J. Stanley, Experimental and Quasi-experimental Designs for Research (New York: Rand McNally, 1963).
19 . S. L. Morgan and C. Winship, Counterfactuals and Causal Inference (New York: Cambridge University Press, 2007).
20 . This is mathematically equivalent to creating weighted match scores on a single categorical variable.
21 . Certain supplemental analyses will, however, leverage those individuals who did have home addresses on file for more robust tests, albeit with a smaller sample.
22 . The accuracy of this estimate was evaluated for the 38,830 individuals with home addresses. The estimates were found to be nearly identical statistically to the true maximum distance (r = .999).
23 . The linear estimate was based on the regression equation true range = est. range * 1.009 − 6.453.
24 . Log-transformed outcome variable in a backwards regression that also controlled for total population and traditional users.
25 . Because both outcome measures of interest had a Poisson distribution, a logit link was used.
26 . The regression predicted log (diameter +1) in order to account for skew while also attending to diameters less than 1. The intercept for calls was 0.72 and that for geographic range was 3.05.
27 . An alternative approach would be to analyze the 301 BOS:311 users with home addresses on file, thus creating a within-subjects design. For these individuals, the median true distance from home was 310 m. Again, this reflects an expansion in range but not an overwhelming one. Of these 301 individuals, 292 had actually made public reports using both the hotline and BOS:311, making for a within-subjects experimental design that compares the geographical range of their reports made via BOS:311 versus those made via traditional channels. The median maximum distance from home for hotline reports for these individuals was 129 m; the same value for BOS:311 reports was 270 m.
28 . New samples n = 588 custodians in 107 census tracts with three or more users and n = 315 custodians in 69 census tracts with three or more custodians. Using this expanded sample had no impact on the qualitative interpretations of models run in previous chapters and in fact generally strengthened all findings.
29 . 4.6 vs. 3.6 public issues and 319 m vs. 500 m in range when comparing a white male of average education and age with average levels of territorial motivation. These estimates are notably higher than those of the full data set, but this is because of the greater levels of custodianship exhibited by those in the survey sample.
30 . M. Dimock, C. Doherty, and A. Kohut, Majority Says the Federal Government Threatens Their Personal Rights (Washington, DC: Pew Research Center, 2013); American National Election Studies, American National Election Studies Guide to Public Opinion and Electoral Behavior (Ann Arbor: Center for Political Studies, Institute for Social Research, University of Michigan, 2012); R. Putnam, Making Democracy Work: Civic Traditions in Modern Italy (Princeton, NJ: Princeton University Press, 1993).
31 . S. Mettler, The Submerged State: How Invisible Government Policies Undermine American Democracy (Chicago: University of Chicago Press, 2011).
32 . R. W. Buell and M. I. Norton, “The Labor Illusion: How Operational Transparency Increases Perceived Value,” Management Science 57, no. 9 (2011): 1564–1579; R. W. Buell, T. Kim, and C.-J. Tsay, “Creating Reciprocal Value through Operational Transparency,” Management Science 63, no. 6 (2016): 1673–1695.
33 . R. W. Buell, E. Porter, and M. I. Norton, Surfacing the Submerged State: Operational Transparency Increases Trust in and Engagement with Government (Cambridge, MA: Harvard Business School, 2016).
34 . At the time, it was Citizens Connect, though for consistency we refer to it as BOS:311.
35 . The sample is considerably larger than the number of registered 311 users who had made reports through BOS:311 presented in the previous section. This is because the app-based data can differentiate between individuals based on their device without the individual having created a registered account.
36 . This is an average of 17 months per user. This is greater than the 14 months over which the messages were sent so that the panel models could capitalize on baseline behavior occurring in advance of the program.
37 . True home address when available, or estimated home as in the previous section.
38 . A. Fung, H. R. Gilman, and J. Shkabatur, “Six Models for the Internet + Politics,” International Studies Review 15 (2013): 30–47.
1 . N. Selwyn, “Reconsidering Political and Popular Understandings of the Digital Divide,” New Media and Society 6, no. 3 (2004): 341–362.
2 . U.S. Department of Commerce, Falling through the Net III: Defining the Digital Divide (Washington, DC: National Telecommunications and Information Administration, U.S. Department of Commerce, 1999).
3 . J. B. Horrigan and M. Duggan, Home Broadband 2015 (Washington, DC: Pew Research Center, 2015).
4 . Controlling for income. See J. van Dijk and K. Hacker, “The Digital Divide as a Complex and Dynamic Phenomenon,” The Information Society 19, no. 4 (2003): 315–326.
5 . P. J. Tichenor, G. A. Donohue, and C. N. Olien, “Mass Media Flow and Differential Growth in Knowledge,” Public Opinion Quarterly 34, no. 2 (1970): 159–170.
6 . A. van Deursen and J. van Dijk, “The Digital Divide Shifts to Differences in Usage,” New Media and Society 16, no. 3 (2004): 507–526.
7 . J. van Dijk, The Deepening Divide: Inequality in the Information Society (London: SAGE, 2005); P. Dimaggio and E. Hargittai, “From the ‘Digital Divide’ to ‘Digital Inequality’: Studying Internet Use as Penetration Increases” (working paper, Center for Arts, Culture and Policy Studies, Princeton University, 2001); K. Mossberger, C. J. Tolbert, and M. Stansbury, Virtual Inequality: Beyond the Digital Divide (Washington, DC: Georgetown University Press, 2003); M. Warschauer, Technology and Social Inclusion (Cambridge, MA: MIT Press, 2003).
8 . E. Hargittai, “Second-Level Digital Divide: Differences in People’s Online Skills,” First Monday 7, no. 4 (2002).
9 . van Deursen and van Dijk, “The Digital Divide Shifts to Differences in Usage.”
10 . E. Kontos, K. D. Blake, W.-Y. S. Chou, and A. Prestin, “Predictors of eHealth Usage: Insights on the Digital Divide from the Health Information National Trends Survey 2012,” Journal of Medical Internet Research 16, no. 7 (2014): e172.
11 . M. Anderson and J. B. Horrigan, Smartphones Help Those without Broadband Get Online, but Don’t Necessarily Bridge the Digital Divide (Washington, DC: Pew Research Center, 2016).
12 . T. Newcombe, “Is the Cost of 311 Systems Worth the Price of Knowing?,” Governing , March 2014.
13 . D. Shemek, S. Winograd, and K. R. Vining,Case Studies for Consolidated Public Safety Dispatch Center Feasibility Study: The Next Steps (Cleveland: Center for Public Management, Maxine Goodman Levin College of Urban Affairs, Cleveland State University, 2011).
14
. https://
15
. https://
16 . Note that the system did not start at a true “0,” as some municipalities already had contracts with SeeClickFix, and in some areas constituents had used the SeeClickFix app even though their municipality did not have a contract.
17 . 3 / 2010–2 / 2011, the first whole year with clean records. The system was introduced in 2008.
18 . E. Ostrom, “Crossing the Great Divide: Coproduction, Synergy, and Development,” World Development 24 (1996): 1073–1087.
19 . Ibid.
20 . K. Layne and J. Lee, “Developing Fully Functional e-Government: A Four Stage Model,” Government Information Quarterly 18, no. 2 (2001): 122–136; M. J. Moon, “The Evolution of e-Government among Municipalities: Rhetoric or Reality?,” Public Administration Review 62, no. 4 (2002): 424–433; D. M. West, “E-Government and the Transformation of Service Delivery and Citizen Attitudes,” Public Administration Review 64, no. 1 (2004): 15–27.
21 . B. S. Jimenez, K. Mossberger, and Y. Wu, “Municipal Government and the Interactive Web: Trends and Issues for Civic Engagement,” in E-Governance and Civic Engagement: Factors and Determinants of e-Democracy, ed. A. Manoharan and M. Holzer (Hershey, PA: Information Science Reference, 2011), 251–271.
22 . Moon, “The Evolution of e-Government among Municipalities.”
23 . C. J. Tolbert, K. Mossberger, and R. McNeal, “Institutions, Policy Innovation, and e-Government in the American States,” Public Administration Review 68, no. 3 (2008): 549–563.
24 . R. Eyestone, “Confusion, Diffusion, and Innovation,” American Political Science Review 71, no. 2 (1977): 441–447; V. Gray, “Innovation in the States: A Diffusion Study,” American Political Science Review 67, no. 4 (1973): 1174–1185; C. Knill, “Introduction: Cross-National Policy Convergence: Concepts, Approaches and Explanatory Factors,” Journal of European Public Policy 12, no. 5 (2005): 764–774.
25 . F. S. Berry and W. D. Berry, “Innovation and Diffusion Models in Policy Research,” in Theories of the Policy Process, ed. P. A. Sabatier, 2nd ed. (Boulder, CO: Westview Press, 2007), 223–260.
26 . H. J. Yun and C. Opheim, “Building on Success: The Diffusion of e-Government in the American States,” Electronic Journal of e-government 8, no. 1 (2010): 71–82.
27 . C. Z. Mooney, “Modeling Regional Effects on State Policy Diffusion,” Political Research Quarterly 54, no. 1 (2001): 103–124.
28 . M. Z. Sobaci and K. Y. Eryigit, “Determinants of e-Democracy Adoption in Turkish Municipalities: An Analysis for Spatial Diffusion Effect,” Local Government Studies 41, no. 3 (2015): 445–469.
29
. Massachusetts Department of Revenue, Municipal Databank,
http://
30 . That is, free cash at year end. See Jimenez, Mossberger, and Wu, “Municipal Government and the Interactive Web.”
31 . L. Anselin, R. J. Florax, and S. J. Rey, eds., Advances in Spatial Econometrics: Methodology, Tools and Applications (Berlin: Springer, 2004).
32 . Importantly, these are not formal spatial lag models based on maximum likelihood estimates. We were most interested in the question of how many neighborhood municipalities were also in the program, whereas a spatial lag model would explicitly predict outcomes based on the proportion of neighboring municipalities in the program. Theoretically, the former would better capture the magnitude of social influence. We also tested a model of “any neighboring municipality.” In the end, the total count measure was the only one with predictive power for any of the outcome measures.
33 . Ostrom, “Crossing the Great Divide.”
34 . Based on population density, with 3,000 people / sq. mile as the dividing point between rural and suburban and 10,000 people / sq. mile as the dividing point between suburban and urban.
35 . This classification was only true for 5 of the 64 municipalities (1 suburban and 4 rural), none of which participated in the survey.
36 . This stands somewhat in contradiction to SeeClickFix’s records, which state that only 10 of these municipalities had robust buy-in and promotion. This could be a function of differing definitions of what constitutes a thorough launch effort or could reflect incomplete communication between municipalities and SeeClickFix.
37 . S. Goldsmith and S. Crawford, The Responsive City: Engaging Communities through Data-Smart Governance (San Francisco: Jossey-Bass, 2014).
1 . U.S. Department of Education, Application for Grants Under the Promise Neighborhoods Program (Washington, DC: U.S. Department of Education, 2010).
2 . P. Tough, Whatever It Takes: Geoffrey Canada’s Quest to Change Harlem and America (Wilmington, MA: Mariner Books, 2009).
3 . U.S. Department of Education, Application for Grants Under the Promise Neighborhoods Program.
4 . J. A. Johnson, “From Open Data to Information Justice,” Ethics and Information Technology 16, no. 4 (2014): 263–274.
5 . C. Martin, “Barriers to the Open Government Data Agenda: Taking a Multi-level Perspective,” Policy and Internet 6, no. 3 (2014): 217–240.
6 . M. Boychuk, M. Cousins, A. Lloyd, and C. MacKeigan, “Do We Need Data Literacy? Public Perceptions Regarding Canada’s Open Data Initiative,” Dalhousie Journal of Interdisciplinary Management 12 (2016): 1–26.
7 . P. Vahey, L. Yarnall, C. Patton, D. Zalles, and K. Swan, “Mathematizing Middle School: Results from a Cross-Disciplinary Study of Data Literacy” (paper presented at American Educational Research Association Annual Conference, San Francisco, 2006). See also B. L. Madison and L. Steen, eds., Quantitative Literacy: Why Numeracy Matters for Schools and Colleges (Princeton, NJ: National Council on Education and the Disciplines, 2003).
8 . Vahey et al., “Mathematizing Middle School.”
9 . Policy Map, policymap.com.
10 . Data USA, datausa.io.
11
. Racial Dot Map,
http://
12
. Detroit, MI Tax Parcel Map,
https://
13 . Crime Mapping, crimemapping.com.
14
. City of Boston 311 Map,
https://
15
. City of Chicago Open Grid,
https://
16 . K. A. Lynch, Image of the City (Cambridge, MA: MIT Press, 1960).
17 . See, for example, C. J. Coulton, J. Korbin, T. Chan, and M. Su, “Mapping Residents’ Perceptions of Neighborhood Boundaries: A Methodological Note,” American Journal of Community Psychology 29, no. 2 (2001): 371–383; C. E. Dunn, “Participatory GIS—A People’s GIS?,” Progress in Human Geography 31 (2007): 616–637; G. Brown, M. F. Schebella, and D. Weber, “Using Participator GIS to Measure Physical Activity and Urban Park Benefits,” Landscape and Urban Planning 121 (2014): 34–44.
18 . R. Bhargava, Data Therapy, 2016, datatherapy.org.
19 . K. O’Connell, Y. Lee, F. Peer, S. M. Staudaher, A. Godwin, M. Madden, and E. Zegura, “Making Public Safety Data Accessible in the Westside Atlanta Data Dashboard” (paper presented at Bloomberg Data for Good Exchange Conference, New York, 2016).
20 . M. P. Johnson, “Data, Analytics and Community-Based Organizations: Transforming Data to Decisions for Community Development,” I / S 11, no. 1 (2015): 49–96.
21 . Ibid.
22 . R. Stoecker, “The Research Practices and Needs of Non-profit Organizations in an Urban Center,” Journal of Sociology and Social Welfare 34, no. 4 (2007): 97–120.
23 . D. Hackler and G. D. Saxton, “The Strategic Use of Information Technology by Nonprofit Organizations: Increasing Capacity and Untapped Potential,” Public Administration Review 67, no. 3 (2007): 474–487.
24 . Johnson, “Data, Analytics and Community-Based Organizations.”
25 . M. Gurstein, “Open Data: Empowering the Empowered or Effective Data Use for Everyone?,” First Monday 16, no. 2 (2011).
26 . A. S. Taylor, S. Lindley, T. Regan, D. Sweeney, V. Vlachokyriakos, L. Grainger, and J. Lingel, “Data-in-Place: Thinking Through the Relations between Data and Community,” in Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (New York: ACM, 2015), 2863–2872.
27
. M. P. Johnson and S. Jani, “Measuring Success: Community Analytics for Local Economic Development, 2017, https://
1 . New York City Department of Transportation, Making Safer Streets (New York: New York City Department of Transportation, 2013).
2 . E. Fishman, “Bikeshare: A Review of Recent Literature,” Transport Reviews 36 (2016): 92–113.
3 . T. O’Reilly, “Government as a Platform,” in Open Government, ed. D. Lathrop and L. Ruma (Sebastopol, CA: O’Reilly, 2010), 11–40.
4 . J. B. Carr and J. L. Doleac, The Geography, Incidence, and Underreporting of Gun Violence: New Evidence Using ShotSpotter Data , (Washington, DC: Brookings Institution, 2016); Y. Irvin-Erickson, N. G. La Vigne, N. Levine, and S. Bieler, “What Does Gunshot Detection Technology Tell Us about Gun Violence?,” Applied Geography 86 (2017): 262–273.
5 . O’Reilly, “Government as a Platform.”
6 . E. Ostrom, Governing the Commons: The Evolution of Institutions for Collective Action (Cambridge, MA: Harvard University Press, 1990).
1 . A distinction created by the Boston Redevelopment Authority for administrative purposes but based on historically salient regions, many of which are once-independent municipalities that were annexed. Using an analysis of variance (ANOVA), the planning districts account for about 50 percent of the variation in ethnic composition and median income across census tracts.
2 . S. W. Raudenbush, A. S. Bryk, Y. F. Cheong, and R. T. Congdon Jr., HLM 6: Hierarchical Linear and Nonlinear Modeling (Lincolnwood, IL: Scientific Software International, 2004).
3 . This number diminishes with some measures that allow greater time between identification of an outage and reporting, being that those reported by city employees in that time span were removed.