Integration of data sets for modelling gender violence and perception of insecurity

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.

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Publication Details

Journal
Data in Brief
Published
2024-12-24
DOI
https://doi.org/10.1016/j.dib.2024.111251
Citations
1
Primary Topic
Data Analysis with R
Type
article
Field-Weighted Citation Impact
0.35

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article

Integration of data sets for modelling gender violence and perception of insecurity

Ana Belén Anquela Julián, Ángel Martín, Sandra Lucía Hernández Zetina, Carlos Martinez Montes et al.
1 citations
Data in Brief
Data Analysis with R
0.35
article

Integration of data sets for modelling gender violence and perception of insecurity

Ana Belén Anquela Julián, Ángel Martín, Sandra Lucía Hernández Zetina, Carlos Martinez Montes, Santos Fernández Noguerol
article en
1 citations

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.

Data in BriefVol. 58
Universidad Autónoma de la Ciudad de México (MX), Universidad Autónoma del Estado de México (MX), Technological University Dublin (IE), Universitat Politècnica de València (ES), Universidad Nacional Autónoma de México (MX)
Generalitat Valenciana, Universidad Autónoma del Estado de México, Technological University Dublin, Universitat Politècnica de València, Conselleria de Innovación, Universidades, Ciencia y Sociedad Digital, Generalitat Valenciana
Gender equality
Openalex Percentile: Top 30%
Data Analysis with R
0.35
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