Using Google Trends Data to forecast homicide mortality
The case of Mexico
Bibliographic Data
| ID | 21184530 |
|---|---|
| Authors | Eduardo Vazquez (0009-0001-2240-1847, Universidad Anáhuac México Sur), Eliud Silva (0000-0003-0499-0446, Universidad Anáhuac México Sur) |
| Year | 2025 |
| Publication date | 2025-09-24 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Población y Salud en Mesoamérica (JOURNAL) |
| Journal identifiers | ISSN: 1659-0201 • E-ISSN: 1659-0201 |
| Publisher | Universidad de Costa Rica (PUBLISHER • CR) |
| DOI | 10.15517/c57dzg81 |
| OpenAlex | W4415397573 |
| Language | EN |
| References cited | 17 |
Introduction: In Mexico a major public safety concern is how to predict and reduce homicides to implement effective mitigation policies. Methodology: This study aims to compare traditional forecasting models —ARIMA and Vector Autoregressive (VAR)—with and without Google Trends data, the research explores ways to enhance prediction accuracy. Using homicide records from the National Institute of Statistics and Geography (INEGI, for its Spanish acronym) and Google Trends data from 2006–2020, the study highlights the integration of real-time online data to complement official statistics. Results: Considering a forecast horizon of 15 months up to March 2020, results show that VAR models with Google Trends provide the best performance for both female and male homicides. Conclusions: The findings underscore the potential of integrating digital data sources into traditional models to provide more accurate and timely tools for public safety planning and intervention
Autoregressive integrated moving average · Data integration · Homicide · Official statistics · Public use · Autopsy Techniques and Outcomes · COVID-19 epidemiological studies · Data-Driven Disease Surveillance
| Citation velocity | historical |
|---|---|
| Highly cited | No |