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Who ‘Tweets’ Where and When, and How Does it Help Understand Crime Rates at Places? Measuring the Presence of Tourists and Commuters in Ambient Populations

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Abstract

Objectives

Test the reliability of geotagged Twitter data for estimating block-level population metrics across place types. Evaluate whether the proportion of Twitter users on a block at a given time who are local residents, inter-metro commuters, or tourists is correlated with incidences of public violence and private conflict for four different time periods: weekday days, weekday nights, weekend days, and weekend nights.

Methods

DBSCAN* machine learning technique is used to estimate the home clusters of 54,249 Twitter users who sent at least one geotagged tweet in Boston. Public violence and private conflict are measured using geocoded 911 dispatches. ANOVA models are used to evaluate how the presence of our three groups of interests varies across three types of block-level land usage. Hierarchical linear regression models are used to evaluate whether the proportion of commuters and tourists at census tract- and block-levels are predictive of crime events across the four time periods of interest.

Results

We find evidence that Twitter data has limited reliability across residential blocks due to data sparseness. For non-residential blocks, we find that commuter and tourist presence at the block-level are positively associated with both public violence and private conflict, but that these effects are not stable across time periods. Commuters and tourists only effect violence during weekday days, and the effects of commuters and tourists on private conflict are only statistically significant during weekday days and weekend days.

Conclusions

Consistent with routine activities and crime pattern theories, the influx of outsiders in a given location impacts the likelihood of crime occurring there. While we find that data from Twitter users can be valuable for measuring block-level ambient populations, it appears this is not true for residential blocks. Future research may further consider how the characteristics of Twitter users may inform spatial patterns in crime.

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Notes

  1. Spatial information is generated only for users who opt-in.

  2. Due to hardware issues, data was not collected for 37 days.

  3. The code used to estimate home locations has been made available at: https://github.com/BARIBoston/home-location-estimate. To facilitate interpretation of this process, we have also included pseudo-code detailing the steps taken. See “Appendix A”.

  4. Among users with multiple tweet clusters, 46% had their home location identified on the basis of length of time between first and last tweet in the cluster and 54% had their home location identified on the basis of geographic compactness.

  5. An initial DBSCAN utilizing user data from mid-2017 to early-2019 identified home clusters for 20,790 users (38.63% of all users). Upon introducing user data from 2016, home clusters were identified for another 2297 users.

  6. Because our strategy for differentiating locals, commuters, and tourists requires the use of home location, individuals without home clusters have been removed from the analytic sample.

  7. We treat the following holidays and the night periods preceding them as weekends: New Years, President’s Day, Memorial Day, 4th of July, Labor Day, Columbus Day, Veteran’s Day, Thanksgiving, and Christmas.

  8. Of all public violence and private conflict events in 2018, respectively, over 99% were geocoded to a census block.

  9. Percentages only account for blocks with one or more parcels.

  10. Because we observe minimal between-tract variation in one of our outcomes of interest, private conflict, during some day-time periods, we have also replicated the block-level only models for weekday nights, weekend days, and weekend nights as OLS models. See “Appendix B”.

  11. This model utilized a sample that included all four time periods of interest.

  12. In interpreting these models, we assessed whether our data meet modeling assumptions. Linearity was evaluated by extracting model residuals and visually plotting them against the values of the original outcome variable, with results indicating that our models satisfy this assumption. To evaluate homoscedasticity, model residuals were extracted, the absolute values were squared, and Levene’s Test was used to evaluate whether there are statistically significant differences between blocks (see: Glaser 2004), with results suggesting we satisfy this assumption. Normality of residuals was assessed visually using Q-Q plots, with results suggesting our models generally fail to satisfy this assumption. See limitations section for discussion of the ramifications.

  13. Notably, the coefficient for tourist presence lost statistical significance when the model was reevaluated using an OLS strategy to account for the low degree of between-tract variation in private conflict.

References

  • Albanese JS (1985) The effect of casino gambling on crime. Fed Probat 49:39–44

    Google Scholar 

  • Andresen MA (2006) Crime measures and the spatial analysis of criminal activity. Br J Criminol 46(2):258–285

    Article  Google Scholar 

  • Andresen MA (2010) Diurnal movements and the ambient population: an application tomunicipal-level crime rate calculations. Canad J Criminol Crim Just 52(1):97–109

    Article  Google Scholar 

  • Andresen MA (2011) The ambient population and crime analysis. Prof Geograph 63(2):193–212

    Article  Google Scholar 

  • Andresen MA, Jenion GW (2010) Ambient populations and the calculation of crime rates and risk. Secur J 23(2):114–133

    Article  Google Scholar 

  • Balduini M, Della Valle E, Dell'Aglio D, Tsytsarau M, Palpanas T, Confalonieri C (2013) Social listening of city scale events using the streaming linked data framework. In International Semantic Web Conference: 1-16. Springer, Berlin, Heidelberg

  • Bendler J, Brandt T, Wagner S, Neumann D (2014) Investigating crime-to-twitter relationships in urban environments—facilitating a virtual neighborhood watch. In Twenty second european conference on information systems. Tel Aviv, Israel

  • Bernasco W, Ruiter S, Block R (2017) Do street robbery location choices vary over time of day or day of week? A test in Chicago. J Res Crime Delinq 54(2):244–275

    Article  Google Scholar 

  • Biagi B, Detotto C (2014) Crime as tourism externality. Reg Stud 48(4):693–709

    Article  Google Scholar 

  • Biagi B, Brandano MG, Detotto C (2012) The effect of tourism on crime in Italy: a dynamic panel approach. Econ Open-Access Open-Assessment E-J 6:1–24

    Article  Google Scholar 

  • Boggs SL (1965) Urban crime patterns. Am Sociol Rev 6:899–908

    Article  Google Scholar 

  • Boivin R (2018) Routine activity, population (s) and crime: spatial heterogeneity and conflicting Propositions about the neighborhood crime-population link. Appl Geogr 95:79–87

    Article  Google Scholar 

  • Boivin R, Felson M (2018) Crimes by visitors versus crimes by residents: the influence of visitor inflows. J Quant Criminol 34(2):465–480

    Article  Google Scholar 

  • Boy John D, Uitermark Justus (2016) How to study the city on Instagram. PLoS ONE 11(6):e0158161

    Article  Google Scholar 

  • Boyd D, Crawford K (2012) Critical questions for big data: provocations for a cultural, technological, and scholarly phenomenon. Inf Commun Soc 15(5):662–679

    Article  Google Scholar 

  • Braga AA, Apel R, Welsh BC (2013) The spillover effects of focused deterrence on gang violence. Evaluat Rev 37(3–4):314–342

    Article  Google Scholar 

  • Brantingham PL, Brantingham PJ (1993) Environment, routine and situation: toward a pattern theory of crime. Adv Criminol Theory 5(2):259–294

    Google Scholar 

  • Brantingham P, Brantingham P (2013) Crime pattern theory. In Environmental criminologyand crime analysis. Willan, pp 78–93

  • Clarke RV, Weisburd D (1994) Diffusion of crime control benefits: observations on the reverse of displacement. Crime Prevent Stud 2:165–184

    Google Scholar 

  • Cohen LE, Felson M (1979) Social change and crime rate trends: a routine activity approach. Am Sociol Rev 44(4):588–608

    Article  Google Scholar 

  • Eck John E (2018) Regulation for high-crime places: theory, evidence, and principles. Ann Am Acad Polit Soc Sci 679:106–120

    Article  Google Scholar 

  • Farley JE (1987) Suburbanization and central-city crime rates: new evidence and a reinterpretation. Am J Sociol 93(3):688–700

    Article  Google Scholar 

  • Felson M (2013) Routine activity approach. In: Environmental criminology and crime analysis. Willan, pp 70–77

  • Felson M, Boba RL (eds) (2010) Crime and everyday life. Sage

  • Felson M, Boivin R (2015) Daily crime flows within a city. Crime Sci 4(31)

  • Felson M, Cohen LE (1980) Human ecology and crime: a routine activity approach. Human Ecol 8(4):389–406

    Article  Google Scholar 

  • Gao S, Yang JA, Yan B, Hu Y, Janowicz K, McKenzie G (2014) Detecting origin-destination mobility flows from geotagged tweets in greater Los Angeles area. In Eighth international conference on geographic information science (GIScience’14)

  • Gerber Matthew S (2014) Predicting crime using Twitter and kernel density estimation. Decis Support Syst 61:115–125

    Article  Google Scholar 

  • Giacopassi DJ, Stitt BG, Nichols M (2000) Including tourists in crime rate calculations for new casino jurisdictions: what difference does it make? Am J Crim Just 24(2):203–215

    Article  Google Scholar 

  • Glaser RE (2004) Levene’s robust test of homogeneity of variances. Encycloped Statist Sci

  • Grinols EL, Mustard DB, Staha M (2011) How do visitors affect crime? J Quant Criminol 27(3):363–378

    Article  Google Scholar 

  • Haberman CP, Ratcliffe JH (2015) Testing for temporally differentiated relationships among potentially criminogenic places and census block street robbery counts. Criminology. 53(3):457–483

    Article  Google Scholar 

  • Hipp JR, Kim YA (2019) Explaining the temporal and spatial dimensions of robbery: differences across measures of the physical and social environment. J Crim Just 60:1–12

    Article  Google Scholar 

  • Hipp JR, Bates C, Lichman M, Smyth P (2018) Using social media to measure temporal ambient population: does it help explain local crime rates? Justice Quarterly, pp 1–31

  • Jacobs J (1961) The Death and Life of Great American Cities. Random House, New York

  • Lazer D, Pentland A, Adamic L, Aral S, Barabási AL, Brewer D, Jebara T (2009) Computational social science. Science 323(5915):721–723

    Article  Google Scholar 

  • Lenormand M, Picornell M, Cantú-Ros OG, Tugores A, Louail T, Herranz R, Ramasco JJ (2014) Cross-checking different sources of mobility information. PLoS ONE 9(8):e105184

    Article  Google Scholar 

  • Malleson N, Andresen MA (2015a) The impact of using social media data in crime rate calculations: shifting hot spots and changing spatial patterns. Cartograph Geograph Inf Sci 42(2):112–121

    Article  Google Scholar 

  • Malleson N, Andresen MA (2015b) Spatio-temporal crime hotspots and the ambient population. Crime Sci 4(1):10

    Article  Google Scholar 

  • Malleson N, Andresen MA (2016) Exploring the impact of ambient population measures on London crime hotspots. J Crim Just 46:52–63

    Article  Google Scholar 

  • Mburu LW, Helbich M (2016) Crime risk estimation with a commuter-harmonized ambient population. Ann Am Assoc Geograph 106(4):804–818

    Google Scholar 

  • McNeill G, Bright J, Hale SA (2017) Estimating local commuting patterns from geolocated Twitter data. EPJ Data Science. 6(1):24

    Article  Google Scholar 

  • Mislove A, Lehmann S, Ahn Y-Y, Onnela J-P, Rosenquist JN (2011) Understanding the demographics of twitter users. In: ICWSM

  • Montolio D, Planells-Struse S (2016) Does tourism boost criminal activity? Evidence from a top touristic country. Crime Delinq 62(12):1597–1623

    Article  Google Scholar 

  • Novak J, Ahas R, Aasa A, Silm S (2013) Application of mobile phone location data in mapping of commuting patterns and functional regionalization: a pilot study of Estonia. J Maps. 9(1):10–15

    Article  Google Scholar 

  • O’Brien DT, Phillips N, De Benedictis-Kessner J, Shields M, Sheini S (2018) 2018 Geographical Infrastructure for the City of Boston. edited by Boston Area Research Initiative

  • O'Brien DT and Sampson RJ (2015) Public and private spheres of neighborhood disorder: Assessing pathways to violence using large-scale digital records. J. Res. Crime Delinq. 52(4):486–510

  • O’Brien DT, Sampson RJ, Winship C (2015) Ecometrics in the age of big data: measuring and assessing “broken windows” using large-scale administrative records. Sociol Methodol 45(1):101–147

    Article  Google Scholar 

  • Ochrym RG (1990) Street crime, tourism and casinos: an empirical comparison. J Gambl Stud 6(2):127–138

    Article  Google Scholar 

  • Palanca-Tan R, Garces LPDM, Purisimia ANC, Zaratan AGL (2015) Tourism and crime: evidence from the Philippines. Southeast Asian Stud 4(3):565–580

    Google Scholar 

  • Phillips NE, Levy BL, Sampson RJ, Small ML, Wang RQ (2019) The social integration of American cities: network measures of connectedness based on everyday mobility across neighborhoods. Sociol Methods Res 0049124119852386

  • Ristea A, Andresen MA, Leitner M (2018a) Using tweets to understand changes in the spatial crime distribution for hockey events in Vancouver. Canad Geographer 62(3):338–351

    Article  Google Scholar 

  • Ristea A, Kurland J, Resch B, Leitner M, Langford C (2018b) Estimating the spatial distribution of crime events around a football stadium from georeferenced tweets. ISPRS Int j Geo-Inf 7(2):43–68

    Article  Google Scholar 

  • Roman CG, Reid SE (2012) Assessing the relationship between alcohol outlets and domestic violence: routine activities and the neighborhood environment. Violence Vict 27(5):811–828

    Article  Google Scholar 

  • Sampson RJ (2006) Collective efficacy theory: lessons learned and directions for future inquiry. Taking Stock Status Criminol Theory 15:149–167

    Google Scholar 

  • Sampson RJ, Raudenbush SW, Earls F (1997) Neighborhoods and violent crime: a multilevel study of collective efficacy. Science 277(5328):918–924

    Article  Google Scholar 

  • Stitt BG, Nichols M, Giacopassi D (2003) Does the presence of casinos increase crime? An examination of casino and control communities. Crime Delinq 49(2):253–284

    Article  Google Scholar 

  • Stults BJ, Hasbrouck M (2015) The effect of commuting on city-level crime rates. J Quant Criminol 31(2):331–350

    Article  Google Scholar 

  • Taylor RB (1988) Human territorial functioning: an empirical, evolutionary perspective on individual and small group territorial cognitions, behaviors, and consequences. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • Wang Q, Taylor JE (2015) Process map for urban-human mobility and civil infrastructure data collection using geosocial networking platforms. J Comput Civ Eng 30(2)

  • Wang X, Brown DE, Gerber MS (2012) Spatio-temporal modeling of criminal incidents using geographic, demographic, and Twitter-derived information. In: Intelligence and securits informatics, Lecture Notes in Computer Science IEEE

  • Wang Q, Phillips NE, Small ML, Sampson RJ (2018) Urban mobility and Neighborhood isolation in America’s 50 largest cities. Proc Natl Acad Sci 115(30):7735–7740

    Article  Google Scholar 

  • Williams Matthew L, Burnap Pete, Sloan Luke (2017) Crime sensing with big data: the affordances and limitations of using open-source communications to estimate crime patterns. Br J Criminol 57:320–340

    Google Scholar 

  • Zhang H, Song W (2014) Addressing issues of spatial spillover effects and non-stationarity in analysis of residential burglary crime. GeoJournal 79(1):89–102

    Article  Google Scholar 

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Authors and Affiliations

  1. Northeastern University, 431 Churchill Hall, 360 Huntington Avenue, Boston, MA, 02115, USA

    Riley Tucker, Daniel T. O’Brien, Edgar Castro & Qi Wang

  2. Boston Area Research Initiative, Northeastern & Harvard Universities, Boston, MA, 02115, USA

    Riley Tucker, Daniel T. O’Brien, Alexandra Ciomek, Edgar Castro, Qi Wang & Nolan Edward Phillips

  3. Harvard University, Boston, MA, USA

    Alexandra Ciomek & Nolan Edward Phillips

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  1. Riley Tucker
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  2. Daniel T. O’Brien
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Correspondence to Riley Tucker.

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Appendices

Appendix A

Pseudocode demonstrating home location strategy using DBSCAN*

figure a

Appendix B

See Table 6.

Table 6 OLS regression models examining the block-level covariates of private conflict
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Tucker, R., O’Brien, D.T., Ciomek, A. et al. Who ‘Tweets’ Where and When, and How Does it Help Understand Crime Rates at Places? Measuring the Presence of Tourists and Commuters in Ambient Populations. J Quant Criminol 37, 333–359 (2021). https://doi.org/10.1007/s10940-020-09487-1

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