@article {tucker_who_2021,
	title = {Who {\textquoteleft}Tweets{\textquoteright} Where and When, and How Does it Help Understand Crime Rates at Places? Measuring the Presence of Tourists and Commuters in Ambient Populations},
	journal = {Journal of Quantitative Criminology},
	volume = {37},
	number = {2},
	year = {2021},
	pages = {333{\textendash}359},
	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.},
	issn = {0748-4518, 1573-7799},
	doi = {10.1007/s10940-020-09487-1},
	url = {https://link.springer.com/10.1007/s10940-020-09487-1},
	author = {Tucker, Riley and O{\textquoteright}Brien, Daniel T. and Ciomek, Alexandra and Castro, Edgar and Wang, Qi and Nolan Edward Phillips}
}
@article {obrien_how_2021,
	title = {How and Why is Crime More Concentrated in Some Neighborhoods than Others?: A New Dimension to Community Crime},
	journal = {Journal of Quantitative Criminology},
	year = {2021},
	abstract = {Objectives  Much recent work has focused on how crime concentrates on particular streets within communities. This is the first study to examine how such concentrations vary across the neighborhoods of a city. The analysis evaluates the extent to which neighborhoods have characteristic levels of crime concentration and then tests two hypotheses for explaining these variations: the compositional hypothesis, which posits that neighborhoods whose streets vary in land usage or demographics have corresponding disparities in levels of crime; and the social control hypothesis, which posits that neighborhoods with higher levels of collective efficacy limit crime to fewer streets. Methods We used 911 dispatches from Boston, MA, to map violent crimes across the streets of the city. For each census tract we calculated the concentration of crime across the streets therein using the generalized Gini coefficient and cross-time stability in the locations of hotspots. Results Neighborhoods did have characteristic levels of concentration that were best explained by the compositional hypothesis in the form of demographic and land use diversity. In addition, ethnic heterogeneity predicted higher concentrations of crime over and above what would be expected given the characteristics of the individual streets, suggesting it exacerbated disparities in crime. Conclusions  The extent to which crime concentrates represents an underexamined aspect of how crime manifests in each community. It is driven in part by the diversity of places in the neighborhood, but also can be influenced by neighborhood-level processes. Future work should continue to probe the sources and consequences of these variations.},
	issn = {0748-4518, 1573-7799},
	doi = {10.1007/s10940-021-09495-9},
	url = {http://link.springer.com/10.1007/s10940-021-09495-9},
	author = {O{\textquoteright}Brien, Daniel T. and Ciomek, Alexandra and Tucker, Riley}
}
@article {ciomek_influence_2020,
	title = {The influence of firearms trafficking on gunshot injuries in a co-offending network},
	journal = {Social Science \& Medicine},
	volume = {259},
	year = {2020},
	pages = {113114},
	abstract = {Individuals at the greatest risk of gunshot victimization are often prohibited from legally acquiring guns in the U.S. due to prior felony convictions or other disqualifications. Prohibited persons often rely on others {\textendash} such as friends, family members, fellow gang members, and gun brokers {\textendash} to acquire firearms. This study examines whether the sources of guns recovered from high-risk individuals differ relative to the sources of guns recovered more generally in a major U.S. city, and whether illegally-diverted guns are associated with increased gunshot victimization risk. Using official data, we recreate the co-offending network of individuals in Boston who were arrested or contacted by the police with at least one other person between 2007 and 2014. Firearms trace data are then used to develop measures of the shortest distance between individuals and firearms in their immediate network. Results suggest guns with markers of illegal diversion are more likely to be recovered in the highest risk sector of the network and that the probability of gunshot victimization increases with decreased distance to an individual linked to firearms with markers of illegal trafficking.},
	issn = {02779536},
	doi = {10.1016/j.socscimed.2020.113114},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0277953620303336},
	author = {Alexandra M. Ciomek and Anthony A. Braga and Papachristos, Andrew V.}
}
@database {640510,
	title = {Boston 911 Calls},
	number = {106 total downloads},
	year = {2019},
	author = {Alexandra M. Ciomek and Daniel T. O{\textquoteright}Brien}
}
@database {640509,
	title = {Massachusetts Census Indicators Dataverse},
	number = {960 total downloads},
	year = {2016},
	author = {Daniel T. O{\textquoteright}Brien and Alexandra M. Ciomek}
}
