# A Cross-Level Exploratory Analysis of “Neighborhood Effects” on Urban Behavior: An Evolutionary Perspective

## Abstract

**:**

## 1. Introduction

## 2. Conceptualizing and Operationalizing “Neighborhood”

#### Multiple Neighborhood Levels and Cross-Level Interactions

## 3. Conceptual Model

## 4. Hypotheses, Data, and Methods

#### 4.1. Hypotheses

- (1)
- is higher for individuals who violated the property code in time t-1;
- (2)
- increases as a direct function of the individual’s level-1 neighborhood violation rate; but such that;
- (3)
- this “neighborhood effect” is amplified in cooperative level-2 neighborhoods (and, by extension, is muted in non-cooperative level-2 neighborhoods).

#### 4.2. Data

#### 4.3. Methods

Variable | Mean | Standard Deviation |
---|---|---|

Individual Violation_{t-1} * | 0.14 | n/a |

Level-1 Neighborhood Violators_{t-1} | 0.12 | 0.11 |

Level-2 Neighborhood Violators_{t-1} | 0.12 | 0.07 |

Ownership (imputed) * | 0.47 | n/a |

Number of parcels in Level-1 Neighborhood | 36.9 | 24.4 |

Number of parcels in Level-2 Neighborhood | 106.3 | 48.4 |

n | 12,260 |

## 5. Results

Variable | Without Product Term | Full Model |
---|---|---|

Coefficient (Standard Error) | Coefficient (Standard Error) | |

Individual Violation_{t-1} | 0.41 *** (0.05) | 0.41 *** (0.05) |

Level-1 Neighborhood Violators_{t-1} | 0.49 * (0.23) | 2.91 *** (0.39) |

Level-2 Neighborhood Violators_{t-1} | 2.46 *** (0.35) | 4.41 *** (0.43) |

(Level-1 Neighborhood Violators_{t-1} x Level-2 Neighborhood Violators_{t-1}) | - | −14.57 *** (1.85) |

Ownership | −0.57 *** (0.04) | −0.56 *** (0.04) |

Intercept | −2.74 *** (0.04) | −2.98 *** (0.05) |

_{t-1}predictor is set to 0, the Ownership predictor is set to 1, the Level-1 Neighborhood Violators

_{t-1}predictor is allowed to range from 0 to 1 in increments of 0.01, and the moderating Level-2 Neighborhood Violators

_{t-1}predictor is evaluated for three different scenarios: (1) the mean level of 0.12; (2) a “low” value of 0.00, or a 100% reduction in the number of level-2 violators relative to the mean; and (3) a “high” value of 0.24, or a 100% increase in the number of level-2 violators relative to the mean. For each of these three level-2 neighborhood contexts, 1000 simulation draws are taken from the estimated relogit model for every possible value in the above-specified range of the level-1 neighborhood variable. Doing this allows for calculation of the expected probability that a cooperative homeowner violates the property code in period t, given (1) the t-1 behavior of actors in his or her level-1 neighborhood and (2) his or her presence in a high-, mean-, or low-violation level-2 neighborhood as defined above. Because the expected value of the dependent variable is a simulation of the predicted probability of observing a violation (given a draw of the estimated coefficients from their sampling distributions), this process further allows for the construction of confidence intervals around all of the expected probabilities [70,71].

**Figure 3.**Simulating violation behavior for cooperative owner occupants in three level-2 neighborhood contexts.

## 6. Discussion and Conclusions

## Appendix

**Table A1.**Regression estimation results, dependent variable = fraction of parcels committing new code violations in 2008–2009.

Variable | OLS | Spatial Lag |
---|---|---|

Coefficient (Standard Error) | Coefficient (Standard Error) | |

Median Owner Occupied Home Value (in $000s) | −0.0001 ** (3.9 × 10^{−5}) | −7.0× 10^{−5}* (3.4× 10^{−5}) |

Poverty Rate | 0.0384 ** (0.015) | 0.0314 * (0.013) |

Fraction of Population (25 and Older) without a High School Diploma | 0.0497 * (0.020) | 0.0300 (0.017) |

Racial Diversity | 0.0249 ** (0.007) | 0.0159 ** (0.006) |

Tenure Diversity | 0.0157 (0.008) | 0.0091 (0.007) |

Fraction of Parcels with Violations (Spatial Lag) | - | 0.5290 *** (0.063) |

Intercept | 0.0341 ** (0.010) | 0.009 (0.009) |

R-squared | 0.229 | 0.401 ^{a} |

Robust LM (Lag) | 7.004 ** | - |

Robust LM (Error) | 0.538 | - |

^{a}Indicates pseudo-R

^{2}value; LM = Lagrange Multiplier; Estimation carried out with in GeoDa [64]; n = 284 census block groups (3 of 287 total census block groups were dropped due to missing data).

## Conflicts of Interest

## References and Notes

- Gerald Suttles. The Social Construction of Communities. Chicago: The University of Chicago Press, 1972. [Google Scholar]
- Albert Hunter. “The urban neighborhood: Its analytical and social contexts.” Urban Affairs Quarterly 14 (1979): 267–88. [Google Scholar] [CrossRef]
- Robert J. Chaskin. “Perspectives on neighborhood and community: A review of the literature.” Social Service Review 71 (1997): 521–47. [Google Scholar] [CrossRef]
- Rick Grannis. “The importance of trivial streets: Pedestrian street networks and geographic patterns of residential segregation.” American Journal of Sociology 103 (1998): 1530–64. [Google Scholar] [CrossRef]
- Rick Grannis. “T-communities: Pedestrian street networks and residential segregation in Chicago, Los Angeles, and New York.” City & Community 4 (2005): 295–321. [Google Scholar] [CrossRef]
- Rick Grannis. From the Ground up: Translating Geography into Community through Neighbor Networks. Princeton: Princeton University Press, 2008. [Google Scholar]
- Deborah G. Martin. “Enacting neighborhood.” Urban Geography 24 (2003): 361–85. [Google Scholar] [CrossRef]
- George C. Galster. “What is neighborhood? An externality-space approach.” International Journal of Urban and Regional Research 10 (1986): 243–61. [Google Scholar] [CrossRef]
- Deborah G. Martin. “‘Place-framing’ as place-making: Constituting a neighborhood for organizing and activism.” Annals of the Association of American Geographers 93 (2003): 730–50. [Google Scholar] [CrossRef]
- Robert E. Park. “The urban community as a spatial pattern and a moral order.” In The Urban Community. Edited by Ernest W. Burgess. Chicago: University of Chicago Press, 1926, pp. 21–31. [Google Scholar]
- William Grigsby, Morton Baratz, and Duncan Maclennan. The Dynamics of Neighborhood Change and Decline. Oxford: Pergamon, 1987. [Google Scholar]
- Jerome Rothenberg, George C. Galster, Richard V. Butler, and John R. Pitkin. The Maze of Urban Housing Markets: Theory, Evidence, and Policy. Chicago: The University of Chicago Press, 1991. [Google Scholar]
- Kenneth Temkin, and William M. Rohe. “Social capital and neighborhood stability: An empirical investigation.” Housing Policy Debate 9 (1998): 61–88. [Google Scholar] [CrossRef]
- George C. Galster, Jackie M. Cutsinger, and Ron Malega. The Social Costs of Concentrated Poverty: Externalities to Neighboring Households and Property Owners and the Dynamics of Decline. Washington: National Poverty Center, 2006. [Google Scholar]
- Peter Kitchen, and Allison M. Williams. “Measuring neighborhood social change in Saskatoon, Canada: A geographic analysis.” Urban Geography 30 (2009): 261–88. [Google Scholar] [CrossRef]
- Hendrik Wagenaar. “Governance, complexity, and democratic participation: How citizens and public officials harness the complexities of neighborhood decline.” The American Review of Public Administration 37 (2007): 17–50. [Google Scholar] [CrossRef]
- Peter Somerville, Ellen van Beckhoven, and Ronald van Kempen. “The decline and rise of neighborhoods: The importance of neighborhood governance.” European Journal of Housing Policy 9 (2009): 25–44. [Google Scholar] [CrossRef] [Green Version]
- Christopher Jencks, and Susan E. Mayer. “The social consequences of growing up in a poor neighborhood.” In Inner-City Poverty in the United States. Edited by Laurence E. Lynn and Michael G.H. McGeary. Washington: National Academies Press, 1990, pp. 111–86. [Google Scholar]
- Ingrid Gould Ellen, and Margery Austin Turner. “Does neighborhood matter? Assessing recent evidence.” Housing Policy Debate 8 (1997): 833–66. [Google Scholar] [CrossRef]
- Robert D. Dietz. “The estimation of neighborhood effects in the social sciences: An interdisciplinary approach.” Social Science Research 31 (2002): 539–75. [Google Scholar] [CrossRef]
- Robert J. Sampson, Jeffrey D. Morenoff, and Thomas Gannon-Rowley. “Assessing ‘neighborhood effects’: Social processes and new directions in research.” Annual Review of Sociology 28 (2002): 443–78. [Google Scholar] [CrossRef]
- Ruth Lupton. 'Neighborhood Effects': Can We Measure them and Does It Matter? London: Centre for Analysis and Social Exclusion, London School of Economics, 2003. [Google Scholar]
- George C. Galster. “The mechanism(s) of neighborhood effects: Theory, evidence, and policy implications.” In Neighborhood Effects Research: New Perspectives. Edited by Maarten van Ham, David Manley, Nick Bailey, Ludi Simpson and Duncan Maclennan. Dordrecht: Springer, 2012, pp. 23–56. [Google Scholar]
- Charles F. Manski. “Identification of endogenous social effects: The reflection problem.” The Review of Economic Studies 60 (1993): 531–42. [Google Scholar] [CrossRef]
- Charles F. Manski. “Economic analysis of social interactions.” The Journal of Economic Perspectives 14 (2000): 115–36. [Google Scholar] [CrossRef]
- George C. Galster. “On the nature of neighborhood.” Urban Studies 38 (2001): 2111–24. [Google Scholar] [CrossRef]
- Maarten van Ham, David Manley, Nick Bailey, Ludi Simpson, and Duncan Maclennan. Neighbourhood Effects Research: New Perspectives. Houten: Springer Netherlands, 2012. [Google Scholar]
- Mei-Po Kwan. “The uncertain geographic context problem.” The Annals of the Association of American Geographers 102 (2012): 958–68. [Google Scholar] [CrossRef]
- Mei-Po Kwan. “How GIS can help address the uncertain geographic context problem in social science research? ” Annals of GIS 18 (2012): 245–55. [Google Scholar] [CrossRef]
- John M. Clapp, and Yazhen Wang. “Defining neighborhood boundaries: Are census tracts obsolete? ” Journal of Urban Economics 59 (2006): 259–84. [Google Scholar] [CrossRef]
- Ana V. Diez Roux, and Christina Mair. “Neighborhood and health.” Annals of the New York Academy of Sciences 1168 (2010): 125–45. [Google Scholar] [CrossRef] [PubMed]
- Ron Johnston, Carol Propper, Rebecca Sarker, Kelvyn Jones, Anne Bolster, and Simon Burgess. “Neighbourhood social capital and neighbourhood effects.” Environment and Planning A 37 (2005): 1443–59. [Google Scholar] [CrossRef]
- Ron Johnston, Kelvyn Jones, Carol Propper, and Simon Burgess. “Region, local context, and voting at the 1997 General Election in England.” American Journal of Political Science 51 (2007): 640–54. [Google Scholar] [CrossRef]
- Anthony C. Gatrell. “Concepts of space and geographical data.” In Geographic Information Systems: Principles and Applications. Edited by David J. Maguire, Michael F. Goodchild and David W. Rhind. London: Taylor & Francis, 1991, pp. 119–34. [Google Scholar]
- Sheila R. Foster. “Collective action and the urban commons.” Notre Dame Law Review 87 (2011): 57–134. [Google Scholar]
- Russell Weaver. “Evolutionary theory and neighborhood quality: A multilevel selection-inspired approach to studying urban property conditions.” Applied Research in Quality of Life, 2014. [Google Scholar] [CrossRef]
- Robert J. Sampson, Stephen W. Raudenbush, and Felton Earls. “Neighborhoods and violent crime: A multilevel study of collective efficacy.” Science 277 (1997): 918–24. [Google Scholar] [CrossRef] [PubMed]
- Andrew Gelman. “Multilevel (hierarchical) modeling: What it can and cannot do.” Technometrics 48 (2006): 432–35. [Google Scholar] [CrossRef]
- Seth E. Spielman, and John R. Logan. “Using high-resolution population data to identify neighborhoods and establish their boundaries.” Annals of the Association of the American Geographers 103 (2013): 67–84. [Google Scholar] [CrossRef] [PubMed]
- Christopher S. Fowler. “Segregation as a multiscalar phenomenon and its implications for neighborhood-scale research: The case of south Seattle 1990–2010.” Urban Geography, 2015. [Google Scholar] [CrossRef]
- Brady Baybeck. “Sorting out the competing effects of racial context.” The Journal of Politics 68 (2006): 386–96. [Google Scholar] [CrossRef]
- David W. Cash, W. Neil Adger, Fikret Berkes, Po Garden, Louis Lebel, Per Olsson, Lowell Pritchard, and Oran Young. “Scale and cross-scale dynamics: Governance and information in a multilevel world.” Ecology and Society 11 Article 8. (2006). Available online: http://www.ecologyandsociety.org/vol11/iss2/art8/ (accessed on 2 November 2015). [Google Scholar]
- Kevin Lynch. The Image of the City. Cambridge: The MIT Press, 1960. [Google Scholar]
- Brian Skyrms. The Stag Hunt and Social Structure. Cambridge: Cambridge University Press, 2004. [Google Scholar]
- Brian Skyrms. The Evolution of Social Contract, 2nd ed. Cambridge: Cambridge University Press, 2014. [Google Scholar]
- Martin A. Nowak. Evolutionary Dynamics: Exploring the Equations of Life. Cambridge: Harvard University Press, 2006. [Google Scholar]
- Richard McElreath, and Robert Boyd. Mathematical Models of Social Evolution: A Guide for the Perplexed. Chicago: University of Chicago Press, 2007. [Google Scholar]
- Philipp Langer, Martin A. Nowak, and Christoph Hauert. “Spatial invasion of cooperation.” Journal of Theoretical Biology 250 (2008): 634–41. [Google Scholar] [CrossRef] [PubMed]
- Martin A. Nowak, and Sarah Coakley, eds. Evolution, Games, and God: The Principle of Cooperation. Cambridge: Harvard University Press, 2012.
- David Sloan Wilson. Evolution for Everyone: How Darwin’s Theory Can Change the Way We Think about Our Lives. New York: Delta, 2007. [Google Scholar]
- David Sloan Wilson. The Neighborhood Project: Using Evolution to Improve My City, One Block at a Time. New York: Little, Brown and Company, 2011. [Google Scholar]
- Samuel Bowles, and Herbert Gintis. A Cooperative Species: Human Reciprocity and Its Evolution. Princeton: Princeton University Press, 2013. [Google Scholar]
- Herbert Gintis. The Bounds of Reason: Game Theory and the Unification of the Behavioral Sciences, rev. ed. Princeton: Princeton University Press, 2014. [Google Scholar]
- David Sloan Wilson, and Edward O. Wilson. “Rethinking the theoretical foundation of sociobiology.” The Quarterly Review of Biology 82 (2007): 327–48. [Google Scholar] [CrossRef] [PubMed]
- Samir Okasha. Evolution and the Levels of Selection. Oxford: Clarendon, 2006. [Google Scholar]
- Brian Skyrms. Social Dynamics. Oxford: Oxford University Press, 2014. [Google Scholar]
- Russell Weaver, and Sharmistha Bagchi-Sen. “Evolutionary analysis of neighborhood decline using multilevel selection theory.” Annals of the Association of American Geographers 104 (2014): 765–86. [Google Scholar] [CrossRef]
- Russell Weaver. “Re-framing the urban blight problem with trans-disciplinary insights from ecological economics.” Ecological Economics 90 (2013): 168–76. [Google Scholar] [CrossRef]
- Robert D. Putnam. “E pluribus unum: Diversity and community in the twenty-first century.” Scandinavian Political Studies 30 (2007): 137–74. [Google Scholar] [CrossRef]
- Violations for the final two quarters of 2009 were not available in the database at the time of the data request.
- U.S. Census Bureau. “TIGER Geodatabases.” 2015. Available online: https://www.census.gov/geo/maps-data/data/tiger-geodatabases.html (accessed on 2 November 2015). [Google Scholar]
- Charles Kadushin. Understanding Social Networks. Oxford: Oxford University Press, 2012. [Google Scholar]
- James Jaccard, and Robert Turrisi. Interaction Effects in Multiple Regression, 2nd ed. Thousand Oaks: Sage, 2003. [Google Scholar]
- Luc Anselin. Exploring Spatial Data with GeoDa: A Workbook. Urbana: Center for Spatially Integrated Social Science, 2005. [Google Scholar]
- Dan O’Brien. “Managing the urban commons: The relative influence of individual and social incentives on the treatment of public space.” Human Nature 23 (2012): 467–89. [Google Scholar] [CrossRef] [PubMed]
- Dan 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 24 (2003): 304–27. [Google Scholar] [CrossRef]
- Gary King, Robert O. Keohane, and Sidney Verba. Designing Social Inquiry: Scientific Inference in Qualitative Research. Princeton: Princeton University Press, 1994. [Google Scholar]
- Gary King, and Langche Zeng. “Logistic regression in rare events data.” Political Analysis 9 (2001): 137–63. [Google Scholar] [CrossRef]
- Kosuke Imai, Gary King, and Olivia Lau. “Relogit: Rare events logistic regression for dichotomous dependent variables.” 2010. Available online: http://gking.harvard.edu/zelig (accessed on 23 May 2013).
- Gary King, Michael Tomz, and Jason Wittenberg. “Making the most of statistical analyses: Improving interpretation and presentation.” American Journal of Political Science 44 (2000): 347–63. [Google Scholar] [CrossRef]
- Kosuke Imai, Gary King, and Olivia Lau. “Zelig: Everyone’s Statistical Software.” 2010. Available online: http://projects.iq.harvard.edu/zelig/documentation (accessed on 23 May 2013).
- Steven A. Julious. “Using confidence intervals around individual means to assess statistical significance between two means.” Pharmaceutical Statistics 3 (2004): 217–22. [Google Scholar] [CrossRef]
- Mirjam J. Knol, Wiebe Pestman, and Diederick Grobbee. “The (mis)use of overlap of confidence intervals to assess effect modification.” European Journal of Epidemieology 26 (2011): 253–54. [Google Scholar] [CrossRef] [PubMed]
- Ian MacGregor-Fors, and Mark E. Payton. “Contrasting diversity values: Statistical inferences based on overlapping confidence intervals.” PLoS ONE 8 (2013): 1–4. [Google Scholar]
- Ernst Fehr, and Urs Fischbacher. “The economics of strong reciprocity.” In Moral Sentiments and Material Interests: The Foundations of Cooperation in Economic Life. Edited by Herbert Gintis, Samuel Bowles, Robert Boyd and Ernst Fehr. Cambridge: MIT Press, 2005, pp. 151–92. [Google Scholar]

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**MDPI and ACS Style**

Weaver, R.
A Cross-Level Exploratory Analysis of “Neighborhood Effects” on Urban Behavior: An Evolutionary Perspective. *Soc. Sci.* **2015**, *4*, 1046-1066.
https://doi.org/10.3390/socsci4041046

**AMA Style**

Weaver R.
A Cross-Level Exploratory Analysis of “Neighborhood Effects” on Urban Behavior: An Evolutionary Perspective. *Social Sciences*. 2015; 4(4):1046-1066.
https://doi.org/10.3390/socsci4041046

**Chicago/Turabian Style**

Weaver, Russell.
2015. "A Cross-Level Exploratory Analysis of “Neighborhood Effects” on Urban Behavior: An Evolutionary Perspective" *Social Sciences* 4, no. 4: 1046-1066.
https://doi.org/10.3390/socsci4041046