Author ORCID Identifier
https://orcid.org/0000-0002-2765-4819;
https://orcid.org/0000-0001-6024-3790;
https://orcid.org/0000-0001-9379-0694;
https://orcid.org/0000-0002-6970-444X;
Document Type
Article
Disciplines
Computer Sciences, Geosciences, (multidisciplinary)
Abstract
The dataset offers a comprehensive information to analyse cities and neighbourhood that are potentially unsafe for women, this information has been collected for four cities: Toluca (Mexico), Valencia (Spain), Dublin (Ireland) and San Francisco (USA). The collection includes quantitative and qualitative variables obtained and processed from open data, georeferenced publications from a social media platform, and points located through participatory mapping sessions.
The data is structured in raw format, organized by country and city, and categorized according to the data source used while processing, which allows unrestricted access with most data analysis software and it does not depend on specific licenses. This format includes both geometric information and associated attributes allowing reusability and analysis in different environments.
Additionally, the release of this data allows developing models tailored to specific local contexts and represents a significant advance in open data access as stated in the Sustainable Development Goal 5 (SDG 5), especially in relation to indicator 5.2.2. In general, this indicator faces a lack of sufficient data for accurate measurement, which limits the ability to accurately assess and address gender-based violence. By providing an open and flexible resource, the dataset not only facilitates comparative research and informed policymaking, it also supports the international commitment for transparency and contributes to filling existing gaps in information on violence and insecurity.
DOI
https://doi.org/10.1016/j.dib.2024.111251
Recommended Citation
Hernandez Zetina, Sandra Lucia; Anquela Julian, Ana Belen; Martin Furones, Angel Esteban; Martinez Montes, Carlos; and Fernandez Noguerol, Santos, "Integration of Data Sets for Modelling Gender Violence and Perception of Insecurity" (2025). Articles. 35.
https://arrow.tudublin.ie/diraaart/35
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License.
Publication Details
https://www.sciencedirect.com/science/article/pii/S2352340924012137
doi:10.1016/j.dib.2024.111251