The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Block Crime and Fear: Defensible Space, Local Social Ties, and Territorial Functioning
Abstract
Why do some blocks have more crime, or their residents have higher fear levels, than other blocks? In an effort to answer this question we proposed a model that incorporated physical defensible space features, local social ties, and territorial functioning. The model was tested using data from a multistage, stratified sample of 687 Baltimore households on 63 blocks. At each household, surveys were completed and on-site physical features were photographed and subsequently rated. Records of police activity on each block were also obtained. Our model explained significant portions of crimes of violence to persons (18%) and block fear (37%). It was also able to predict a significant amount (13%) of the variation in individual-level fear. At the block level: defensible space features dampened crime and fear but not as strongly as expected; and local social ties dampened crime and fear directly, and indirectly via an enhancement of territorial functioning. A model predicting individual fear levels, controlling for block context, was also supported. Our successful modeling of block dynamics suggests that these entities may profitably be treated as small-scale social units or groups. The pattern of findings has also confirmed suggestions made by others that physical factors alone cannot be relied on to preserve local order and feelings of security. Finally, the block-level linkages between local social ties and territorial attitudes clarify how territorial attitudes reflect, and may contribute to, the development of group-based norms regarding appropriate behaviors in on-block settings.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Defensible Space Undefended: Social Factors in Crime Control Through Environmental Design
Merry, S. E. (1981) · Urban Affairs Quarterly · doi:10.1177/107808748101600401 · Cited by 178
Cited by
Neighborhoods and Violent Crime: A Multilevel Study of Collective Efficacy
Sampson, R. J., Raudenbush, S. W., & Earls, F. (1997) · Science · doi:10.1126/science.277.5328.918 · Cited by 11,999
Leventhal, T., & Brooks-Gunn, J. (2000) · Psychological Bulletin · doi:10.1037/0033-2909.126.2.309 · Cited by 3,550
Systematic Social Observation of Public Spaces: A New Look at Disorder in Urban Neighborhoods
Sampson, R. J., & Raudenbush, S. W. (1999) · American Journal of Sociology · doi:10.1086/210356 · Cited by 2,485
NEIGHBORHOOD INEQUALITY, COLLECTIVE EFFICACY, AND THE SPATIAL DYNAMICS OF URBAN VIOLENCE*
MORENOFF, J. D., SAMPSON, R. J., & RAUDENBUSH, S. W. (2001) · Criminology · doi:10.1111/j.1745-9125.2001.tb00932.x · Cited by 1,874
Fear of crime: A review of the literature
Hale, C. (1996) · International Review of Victimology · doi:10.1177/026975809600400201 · Cited by 1,579
THE LAW OF CRIME CONCENTRATION AND THE CRIMINOLOGY OF PLACE*
WEISBURD, D. (2015) · Criminology · doi:10.1111/1745-9125.12070 · Cited by 1,064
The measurement of fear of crime
Ferraro, K. F., & Grange, R. L. (1987) · Sociological Inquiry · doi:10.1111/j.1475-682x.1987.tb01181.x · Cited by 908
Measuring human perceptions of a large-scale urban region using machine learning
Zhang, F., Zhou, B., Liu, L., Liu, Y., Fung, H. H., Lin, H., & Ratti, C. (2018) · Landscape and Urban Planning · doi:10.1016/j.landurbplan.2018.08.020 · Cited by 810
A Difference-in-Differences Analysis of Health, Safety, and Greening Vacant Urban Space
Branas, C. C., Cheney, R. A., MacDonald, J. M., Tam, V. W., Jackson, T. D., & Ten Have, T. R. (2011) · American Journal of Epidemiology · doi:10.1093/aje/kwr273 · Cited by 599
Covington, J., & Taylor, R. B. (1991) · The Sociological Quarterly · doi:10.1111/j.1533-8525.1991.tb00355.x · Cited by 527
Self-citation"The Concentration and Stability of Gun Violence at Micro Places in Boston, 1980-2008
Braga, A. A., Papachristos, A. V., & Hureau, D. M. (2010) · Journal of Quantitative Criminology · doi:10.1007/s10940-009-9082-x · Cited by 470
Hot Spots of Bus Stop Crime: The Importance of Environmental Attributes
Loukaitou-sideris, A. (1999) · Journal of the American Planning Association · doi:10.1080/01944369908976070 · Cited by 299
Social Cohesion, Criminal Victimization and Perceived Risk of Crime in Brazilian Neighborhoods
Villarreal, A., & Silva, B. F. A. (2006) · Social Forces · doi:10.1353/sof.2006.0073 · Cited by 202
Neighborhood Interventions to Reduce Violence
Kondo, M. C., Andreyeva, E., South, E. C., MacDonald, J. M., & Branas, C. C. (2018) · Annual Review of Public Health · doi:10.1146/annurev-publhealth-040617-014600 · Cited by 201
Environmental Design, Crime, and Prevention: An Examination of Community Dynamics
Taylor, R. B., & Gottfredson, S. (1986) · Crime and Justice · doi:10.1086/449128 · Cited by 174
Self-citationAn Ex Post Facto Evaluation Framework for Place-Based Police Interventions
Braga, A. A., Hureau, D. M., & Papachristos, A. V. (2011) · Evaluation Review · doi:10.1177/0193841x11433827 · Cited by 106
BEYOND COMMUNITY: REACTIONS TO CRIME AND DISORDER AMONG INNER‐CITY RESIDENTS
CARVALHO, I., & LEWIS, D. A. (2003) · Criminology · doi:10.1111/j.1745-9125.2003.tb01004.x · Cited by 105
Paydar, M., Kamani-Fard, A., & Etminani-Ghasrodashti, R. (2017) · Cities · doi:10.1016/j.cities.2016.10.002 · Cited by 98
Risk‐Focused Policing at Places: An Experimental Evaluation
Weisburd, D., Morris, N. A., & Ready, J. (2008) · Justice Quarterly · doi:10.1080/07418820801954647 · Cited by 47
Building collective action at crime hot spots: Findings from a randomized field experiment
Weisburd, D., Gill, C., Wooditch, A., Barritt, W., & Murphy, J. (2021) · Journal of Experimental Criminology · doi:10.1007/s11292-019-09401-1 · Cited by 46
The validity of incivility measures in public housing
Piquero, A. (1999) · Justice Quarterly · doi:10.1080/07418829900094371 · Cited by 29
Taylor, R. B., & Ratcliffe, J. H. (2020) · Criminology & Public Policy · doi:10.1111/1745-9133.12514 · Cited by 16
Self-citationUsing Feminist Geography to Understand Feelings of Safety and Neighborhood Image
Zavattaro, S. (2019) · Journal of Health and Human Services Administration · doi:10.1177/107937391904200202 · Cited by 11
Metadata
- Authors
- TAYLOR, R. B., GOTTFREDSON, S. D., & BROWER, S.
- Venue
- Journal of Research in Crime and Delinquency
- Publisher
- SAGE Publications
- Published
- 1984-11
- Volume
- 21
- Issue
- 4
- Pages
- 303-331
- ISSN
- 0022-4278 (ISSN-L)
- 1552-731X
- OpenAlex
- W2168702601
- Cited by
- 468
- References
- 1
- FWCI
- 25.53
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
