A quantile panel examination of the moderation effects of guardianship on residential burglary.

Do informal and formal mechanisms of guardianship work together to reduce residential burglary? In this article we argue that informal guardianship moderates the relationship between formal mechanisms of guardianship and residential burglary. Formal guardianship requires some level of social cohesion and trust to be effective against residential burglary. We test this argument with the use of robust panel quantile methods controlling for time effects, spatial effects, and alternative explanations. Using Mexico City neighborhood crime and census data, we show evidence of a moderating weakening effect of informal guardianship on the previous relationship, particularly in deprived neighborhoods and only in the upper quantiles of the residential burglary distribution. In addition, the moderation effects seem to have weakened over time. In sum, the combination of guardianship mechanisms seems to have been more effective in high burglary risk deprived neighborhoods, although their combination seems to have become less relevant.

Clinical Characteristics of Coronavirus Disease (COVID-19) in Mexican Children and Adolescents.

BACKGROUND: We analyzed the demographic, clinical, and diagnostic data of children and adolescents in Mexico, from the first case of coronavirus disease (COVID-19) to 28 February 2022. METHODS: Using the open databases of the Ministry of Health and a tertiary pediatric hospital, we obtained demographic and clinical data from the beginning of the COVID-19 pandemic until 28 February 2022. In addition, quantitative reverse-transcription polymerase chain reaction outputs were used to determine the viral load, and structural protein-based serology was performed to evaluate IgG antibody levels. RESULTS: Of the total 437,832 children and adolescents with COVID-19, 1187 died. Of these patients, 1349 were admitted to the Hospital Infantil de Mexico Federico Gómez, and 11 died. Obesity, asthma, and immunosuppression were the main comorbidities, and fever, cough, and headache were the main symptoms. In this population, many patients have a low viral load and IgG antibody levels. CONCLUSION: During the first 2 years of the COVID-19 pandemic in Mexico, children and adolescents had low incidence and mortality. They are a heterogeneous population, but many patients had comorbidities such as obesity, asthma, and immunosuppression; symptoms such as fever, cough, and headache; and low viral load and IgG antibodies.

Detection of hierarchical crowd activity structures in geographic point data.

The pervasive adoption of GPS-enabled sensors has lead to an explosion on the amount of geolocated data that captures a wide range of social interactions. Part of this data can be conceptualized as event data, characterized by a single point signal at a given location and time. Event data has been used for several purposes such as anomaly detection and land use extraction, among others. To unlock the potential offered by the granularity of this new sources of data it is necessary to develop new analytical tools stemming from the intersection of computational science and geographical analysis. Our approach is to link the geographical concept of hierarchical scale structures with density based clustering in databases with noise to establish a common framework for the detection of crowd activity hierarchical structures in geographic point data. Our contribution is threefold: first, we develop a tool to generate synthetic data according to a distribution commonly found on geographic event data sets; second, we propose an improvement of the available methods for automatic parameter selection in density-based spatial clustering of applications with noise (DBSCAN) algorithm that allows its iterative application to uncover hierarchical scale structures on event databases and, lastly, we propose a framework for the evaluation of different algorithms to extract hierarchical scale structures. Our results show that our approach is successful both as a general framework for the comparison of crowd activity detection algorithms and, in the case of our automatic DBSCAN parameter selection algorithm, as a novel approach to uncover hierarchical structures in geographic point data sets.