Ensuring a high quality of life for citizens is a fundamental objective for every city, with public safety representing one of the most critical challenges to social sustainability and equitable urban development. This study analyzes the spatiotemporal dynamics of violent crime in Mexico City across pre-pandemic, pandemic, and post-pandemic periods (2019–2023) to evaluate how COVID-19 mobility restrictions were associated with changes in crime patterns. Using publicly available crime reports, we applied Seasonal-Trend decomposition using Loess (STL) and Moran’s I to examine four violent crimes: homicide, robbery, kidnapping, and rape. The results reveal crime-specific pandemic-related patterns. While robbery showed a sustained decline, its spatial clustering intensified significantly, with Moran’s I increasing from 0.22 to 0.59, indicating highly localized risk zones. Conversely, rape exhibited a steady increase that appeared unaffected by lockdown measures, while maintaining significant spatial autocorrelation. Likewise, Cuauhtémoc borough persisted as the main urban hotspot across all phases. Overall, crime did not decline uniformly during the pandemic; instead, mobility restrictions reshaped the geographic distribution and concentration of specific offenses. This research contributes to the understanding of crime dynamics during and after a public health emergency.
Yanil Contreras-Jiménez, Carolina Palma-Preciado, M. Torres-Ruiz et al.· Geographies· 0 citations
This paper presents a supervised multi-label framework for detecting multidimensional perceived risk in HIV-related Reddit discourse. A longitudinal corpus of 329,707 texts collected from r/hivaids and r/HIV between 2015 and 2025 was analyzed to identify three risk dimensions: transmission risk, health deterioration risk, and social stigma risk. A stratified sample of 2,000 texts was annotated by domain experts, achieving substantial inter-annotator agreement (Cohen's κ = 0.74-0.81). A RoBERTa-base model was fine-tuned using class-weighted binary cross-entropy loss and per-class threshold optimization. The proposed model achieved a macro-F1 score of 0.87 and a macro-AUC-ROC of 0.97, outperforming 12 baseline models, including traditional machine learning, neural network, and alternative transformer-based approaches. Ablation experiments confirmed the importance of transformer fine-tuning and class weighting, while also showing that handcrafted features provided only marginal gains. Applied to the full corpus, the model revealed significant upward trends in transmission risk and health deterioration risk, strong co-occurrence between transmission and stigma-related discourse, and distinct information-seeking patterns across risk categories. The findings demonstrate that transformer-based multi-label learning can support scalable, reproducible analysis of HIV-related health perceptions in online communities, with potential applications in public health surveillance, communication strategy design, and digital intervention planning.
A. Abadian, Abdullah, Zulaikha Fatima et al.· Scientific Reports· 0 citations
