Spatio-temporal variations in the urban rhythm: the travelling waves of crime

This is the final version. Available from EDP Sciences via the DOI in this record.In the last decades, the notion that cities are in a state of equilibrium with a centralised organisation has given place to the viewpoint of cities in disequilibrium and organised from bottom to up. In this perspective, cities are evolving systems that exhibit emergent phenomena built from local decisions. While urban evolution promotes the emergence of positive social phenomena such as the formation of innovation hubs and the increase in cultural diversity, it also yields negative phenomena such as increases in criminal activity. Yet, we are still far from understanding the driving mechanisms of these phenomena. In particular, approaches to analyse urban phenomena are limited in scope by neglecting both temporal non-stationarity and spatial heterogeneity. In the case of criminal activity, we know for more than one century that crime peaks during specific times of the year, but the literature still fails to characterise the mobility of crime. Here we develop an approach to describe the spatial, temporal, and periodic variations in urban quantities. With crime data from 12 cities, we characterise how the periodicity of crime varies spatially across the city over time. We confirm one-year criminal cycles and show that this periodicity occurs unevenly across the city. These ‘waves of crime’ keep travelling across the city: while cities have a stable number of regions with a circannual period, the regions exhibit non-stationary series. Our findings support the concept of cities in a constant change, influencing urban phenomena—in agreement with the notion of cities not in equilibrium.Leibniz AssociationArmy Research OfficeScience Without Borders program (CAPES, Brazil

(Looking) Back to the Future: using space-time patterns to better predict the location of street crime

Crime analysts attempt to identify regularities in police recorded crime data with a central view of disrupting the patterns found. One common method for doing so is hotspot mapping, focusing attention on spatial clustering as a route to crime reduction (Chainey & Ratcliffe, 2005; Clarke & Eck, 2003). Despite the widespread use of this analytical technique, evaluation tools to assess its ability to accurately predict spatial patterns have only recently become available to practitioners (Chainey, Tompson, & Uhlig, 2008). Crucially, none has examined this issue from a spatio-temporal standpoint. Given that the organisational nature of policing agencies is shift based, it is common-sensical to understand crime problems at this temporal sensitivity, so there is an opportunity for resources to be deployed swiftly in a manner that optimises prevention and detection. This paper tests whether hotspot forecasts can be enhanced when time-of-day information is incorporated into the analysis. Using street crime data, and employing an evaluative tool called the Predictive Accuracy Index (PAI), we found that the predictive accuracy can be enhanced for particular temporal shifts, and this is primarily influenced by the degree of spatial clustering present. Interestingly, when hotspots shrank (in comparison with the all-day hotspots), they became more concentrated, and subsequently more predictable. This is meaningful in practice; for if crime is more predictable during specific timeframes, then response resources can be used intelligently to reduce victimisation

Micro-Scale, Meso-Scale, Macro-Scale, and Temporal Scale: Comparing the Relative Importance for Robbery Risk in New York City

We compare the relative importance of four dimensions for explaining the micro location of robberies: 1) the micro spatial scale of street segments; 2) the meso spatial scale surrounding the street segment; 3) the temporal pattern, and 4) the macro-scale of the surrounding 2.5 miles. This study uses crime, business, and land use data from New York City and aggregates it to street segments and hours of the day. Although the measures capturing the micro-scale of the street segment explained the largest amount of unique variance, the measures capturing temporal scale across hours of the day (and weekdays) explained the next largest amount of unique variance. The measures of the characteristics in the 2.5 miles macro scale explained the next largest amount of unique variance, and combined with the measures at the meso-scale explained nearly as much of the variance as the street segment measures

Crime and space in intermediate cities: fine-tuning a model of territorial analysis in Gerona, Tarragona and Lérida

This article investigates the location logic of petty larcenies and mugging with violence and intimidation, for the period: 2008-2011, in three intermediate cities of Catalonia. The aim is to validate and fine-tune previously-formulated hypotheses on crimes which follow patterns of a structural nature, that is, which have a tendency towards territorial stability linked to urban features and functions. These crimes have been compared with crimes against public order and assaults. This study has enabled a definition of the type of areas that attract and stabilise such phenomena; this makes possible consideration of the characteristics of public space that can favour the citizen's sense of securi

Fast and slow change in neighbourhoods: characterization and consequences in Southern California

Due to data limitations, most studies of neighbourhood change within regions assume that change over the years of a decade is relatively constant from year-to-year. We use data on home loan information to construct annual measures of key socio-demographic measures in neighbourhoods (census tracts) in the Southern California region from 2000 to 2010 to test this assumption. We use latent trajectory modelling to describe the extent to which neighbourhood change exhibits temporal nonlinearity, rather than a constant rate of change from year to year. There were four key findings: (1) we detected nonlinear temporal change across all socio-demographic dimensions, as a quadratic function better fit the data than a linear one in the latent trajectories; (2) neighbourhoods experiencing more nonlinear temporality also experienced larger overall changes in percent Asian, percent black, and residential stability during the decade; neighbourhoods experiencing an increase in Latinos or a decrease in whites experienced more temporal nonlinearity in this change; (3) the strongest predictor of racial/ethnic temporal nonlinearity was a larger presence of the group at the beginning of the decade; however, the racial and SES composition of the surrounding area, as well as how this was changing in the prior decade, also affected the degree of temporal nonlinearity for these measures in the current decade; (4) this temporal nonlinearity has consequences for neighbourhoods: greater temporal nonlinear change in percent black or Latino was associated with larger increases in violent and property crime during the decade, and the temporal pattern of residential turnover or changing average income impacted changes in crime. The usual assumption of constant year-to-year change when interpolating neighbourhood measures over intervening years may not be appropriate

SOPHIA

The Iraqi Insurgency (2003–2011) has commonly been characterized as demonstrating the tendency for violence to cluster and diffuse at the local level. Recent research has demonstrated that insurgent attacks in Iraq cluster in time and space in a manner similar to that observed for the spread of a disease. The current study employs a variety of approaches common to the scientific study of criminal activities to advance our understanding of the correlates of observed patterns of the incidence and contagion of insurgent attacks. We hypothesize that the precise patterns will vary from one place to another, but that more attacks will occur in areas that are heavily populated, where coalition forces are active, and along road networks. To test these hypotheses, we use a fishnet to build a geographical model of Baghdad that disaggregates the city into more than 3000 grid cell locations. A number of logistic regression models with spatial and temporal lags are employed to explore patterns of local escalation and diffusion. These models demonstrate the validity of arguments under each of three models but suggest, overall, that risk heterogeneity arguments provide the most compelling and consistent account of the location of insurgency. In particular, the results demonstrate that violence is most likely at locations with greater population levels, higher density of roads, and military garrisons

The scaling of crime concentration in cities

All crime data are official open data sets that are available as described in the Supporting Information file available at https://doi.org/10.1371/journal.pone.0183110.s001Crime is a major threat to society’s well-being but lacks a statistical characterization that could lead to uncovering some of its underlying mechanisms. Evidence of nonlinear scaling of urban indicators in cities, such as wages and serious crime, has motivated the understanding of cities as complex systems - a perspective that offers insights into resources limits and sustainability, but that usually neglects details of the indicators themselves. Notably, since the nineteenth century, criminal activities have been known to occur unevenly within a city; crime concentrates in such way that most of the offenses take place in few regions of the city. Though confirmed by different studies, this concentration lacks broad analyses on its characteristics, which hinders not only the comprehension of crime dynamics but also the proposal of sounding counter-measures. Here, we developed a framework to characterize crime concentration which divides cities into regions with the same population size. We used disaggregated criminal data from 25 locations in the U.S. and the U.K., spanning from 2 to 15 years of longitudinal data. Our results confirmed that crime concentrates regardless of city and revealed that the level of concentration does not scale with city size. We found that the distribution of crime in a city can be approximated by a power-law distribution with exponent α that depends on the type of crime. In particular, our results showed that thefts tend to concentrate more than robberies, and robberies more than burglaries. Though criminal activities present regularities of concentration, we found that criminal ranks have the tendency to change continuously over time - features that support the perspective of crime as a complex system and demand analyses and evolving urban policies covering the city as a whole.CAPES FoundationArmy Research Offic