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Self‐exciting point processes allow modeling the temporal location of an event of interest, considering the history provided by previously observed events. This family of point processes is commonly used in several areas such as criminology, economics, or seismology, among others. The standard formulation of the self‐exciting process implies assuming that the underlying stochastic process is dependent on its previous history over the entire period under analysis. In this paper, we consider the possibility of modeling a point pattern through a point process whose structure is not necessarily of self‐exciting type at every instant or temporal interval. Specifically, we propose a mixture point process model that allows the point process to be either self‐exciting or homogeneous Poisson, depending on the instant within the study period. The performance of this model is evaluated both through a simulation study and a case study. The results indicate that the model is able to detect the presence of instants in time, referred to as change points, where the nature of the process varies.

The behavior and spatial distribution of crime events can be explained through the characterization of an area in terms of its demography, socioeconomy, and built environment. In particular, recent studies on the incidence of crime in a city have focused on the identification of features of the built environment (specific places or facilities) that may increase crime risk within a certain radius. However, it is hard to identify environmental characteristics that consistently explain crime occurrence across cities and crime types. This article focuses on the assessment of the effect that certain types of places have on the incidence of property crime, robbery, and vandalism in three cities of the Valencian region (Spain): Alicante, Castellon, and Valencia. A nonlinear effects model is used to identify such places and to construct a risk map over the three cities considering the three crime types under research. The results obtained suggest that there are remarkable differences across cities and crime types in terms of the types of places associated with crime outcomes. The identification of high‐risk areas allows verifying that crime is highly concentrated, and also that there is a high level of spatial overlap between the high‐risk areas corresponding to different crime types.

The aim of our work is to determine the influence that socio-economic and demographic factors have had on crimes that have taken place during the period 2010–2018 in the communes of the Metropolitan Region of Chile, as well as the existence of possible spatial or temporal effects. We address 12 kinds of crime that we have grouped into two main types: against people and against property. Our interest focuses on crimes against people, using crimes against property as an additional covariate in order to investigate the existence of the broken-windows phenomenon in this context. The model chosen for our analysis is a spatial panel model with fixed effects. The results highlight that covariates such as infant mortality, birth rate, poverty and green areas have a significant influence on crimes against people. Regarding the spatio-temporal covariates, one effect observed is that there is a displacement of crime towards neighbouring communes, leaving open a new line of study to discover the causes of this displacement.

Importance Limited information on the transmission and dynamics of SARS-CoV-2 at the city scale is available. Objective To describe the local spread of SARS-CoV-2 in Valencia, Spain. Design, Setting, and Participants This single-center epidemiological cohort study of patients with SARS-CoV-2 was performed at University General Hospital in Valencia (population in the hospital catchment area, 364 000), a tertiary hospital. The study included all consecutive patients with COVID-19 isolated at home from the start of the COVID-19 pandemic on February 19 until August 31, 2020. Exposures Cases of SARS-CoV-2 infection confirmed by the presence of IgM antibodies or a positive polymerase chain reaction test result on a nasopharyngeal swab were included. Cases in which patients with negative laboratory results met diagnostic and clinical criteria were also included. Main Outcomes and Measures The primary outcome was the characterization of dissemination patterns and connections among the 20 neighborhoods of Valencia during the outbreak. To recreate the transmission network, the inbound and outbound connections were studied for each region, and the relative risk of infection was estimated. Results In total, 2646 patients were included in the analysis. The mean (SD) age was 45.3 (22.5) years; 1203 (46%) were male and 1442 (54%) were female (data were missing for 1); and the overall mortality was 3.7%. The incidence of SARS-CoV-2 cases was higher in neighborhoods with higher household income (β2 [for mean income per household] = 0.197; 95% CI, 0.057-0.351) and greater population density (β1 [inhabitants per km2] = 0.228; 95% CI, 0.085-0.387). Correlations with meteorological variables were not statistically significant. Neighborhood 3, where the hospital and testing facility were located, had the most outbound connections (14). A large residential complex close to the city (neighborhood 20) had the fewest connections (0 outbound and 2 inbound). Five geographically unconnected neighborhoods were of strategic importance in disrupting the transmission network. Conclusions and Relevance This study of local dissemination of SARS-COV-2 revealed nonevident transmission patterns between geographically unconnected areas. The results suggest that tailor-made containment measures could reduce transmission and that hospitals, including testing facilities, play a crucial role in disease transmission. Consequently, the local dynamics of SARS-CoV-2 spread might inform the strategic lockdown of specific neighborhoods to stop the contagion and avoid a citywide lockdown. ; This study was supported by the Innovation, Universities, Science and Digital Society Council through the Valencia Innovation Agency (AVI); grant 851255 from the European Research Council under the European Union's Horizon 2020 research and innovation program (Dr Zanin); grant MDM-2017-0711 from the Spanish State Research Agency through the Severo Ochoa and María de Maeztu Program for Centers and Units of Excellence in Research and Development (Dr Zanin); and from the Universitat de Valencia (Drs Iftimi and Lozano). ; Peer reviewed