Expert Opinions on Improving Femicide Data Collection across Europe: A Concept Mapping Study

Femicide, defined as the killings of females by males because they are females, is becoming recognized worldwide as an important ongoing manifestation of gender inequality. Despite its high prevalence or widespread prevalence, only a few countries have specific registries about this issue. This study aims to assemble expert opinion regarding the strategies which might feasibly be employed to promote, develop and implement an integrated and differentiated femicide data collection system in Europe at both the national and international levels. Concept mapping methodology was followed, involving 28 experts from 16 countries in generating strategies, sorting and rating them with respect to relevance and feasibility. The experts involved were all members of the EU-Cost-Action on femicide, which is a scientific network of experts on femicide and violence against women across Europe. As a result, a conceptual map emerged, consisting of 69 strategies organized in 10 clusters, which fit into two domains: “Political action” and “Technical steps”. There was consensus among participants regarding the high relevance of strategies to institutionalize national databases and raise public awareness through different stakeholders, while strategies to promote media involvement were identified as the most feasible. Differences in perceived priorities according to the level of human development index of the experts’ countries were also observed.

Constrained self-organizing feature map to preserve feature extraction topology

In many classification problems, it is necessary to consider the specific location of an n-dimensional space from which features have been calculated. For example, considering the location of features extracted from specific areas of a two-dimensional space, as an image, could improve the understanding of a scene for a video surveillance system. In the same way, the same features extracted from different locations could mean different actions for a 3D HCI system. In this paper, we present a self-organizing feature map able to preserve the topology of locations of an n-dimensional space in which the vector of features have been extracted. The main contribution is to implicitly preserving the topology of the original space because considering the locations of the extracted features and their topology could ease the solution to certain problems. Specifically, the paper proposes the n-dimensional constrained self-organizing map preserving the input topology (nD-SOM-PINT). Features in adjacent areas of the n-dimensional space, used to extract the feature vectors, are explicitly in adjacent areas of the nD-SOM-PINT constraining the neural network structure and learning. As a study case, the neural network has been instantiate to represent and classify features as trajectories extracted from a sequence of images into a high level of semantic understanding. Experiments have been thoroughly carried out using the CAVIAR datasets (Corridor, Frontal and Inria) taken into account the global behaviour of an individual in order to validate the ability to preserve the topology of the two-dimensional space to obtain high-performance classification for trajectory classification in contrast of non-considering the location of features. Moreover, a brief example has been included to focus on validate the nD-SOM-PINT proposal in other domain than the individual trajectory. Results confirm the high accuracy of the nD-SOM-PINT outperforming previous methods aimed to classify the same datasets.