Project description
Big data policing for crime prevention
The ERC-funded BIGDATPOL project emerges as a beacon for the future of crime prevention in Europe. Big data policing models incorporate variables such as crime data, socio-economic factors, and opportunity characteristics, and big data sources such as images or mobile phone data, thereby highlighting the need for a holistic understanding of these models. Confronting the current disjointed landscape of big data policing, the project seeks to merge expertise, foster interdisciplinary cohesion, and uphold rigorous scientific standards. Consequently, it aims to harmonise statistical, criminological, economical, legal, and ethical facets into an evidence-based model, contributing to a safer and fairer future for our communities. This approach leverages historical data to predict and pre-empt potential crime hotspots, optimising police resources and ultimately curbing crime rates.
Objective
Big data policing is an innovative strategy that uses historical data to forecast when and where there is a high risk of new crime events in order to use police resources more efficiently and proactively, and ultimately reduce crime rates. Big data policing models can consist of variables based on crime data available in police databases (e.g. previous crime events), socio-economic data (e.g. poverty index, residential mobility), opportunity characteristics (e.g. the presence of shops, distance to nearest highway), data from new technologies (e.g. intelligent cameras) and other known predictors of crime (e.g. police patrol intensity).
However, there are several research gaps that need to be addressed. First, knowledge about and expertise in big data policing in Europe is currently fragmented. Second, there is a lack of interdisciplinarity with regard to big data policing studies, and yet the involvement of several disciplines is required when studying the issue. Third, there is a lack of scientific evaluations of big data policing models.
The overarching objective of this ERC project is to unite and integrate the statistical-methodological, criminological, legal and ethical dimensions of big data policing in an evidence-based model that will be tested by different randomized controlled trials and built on the principles of an international (i.e. European) and interdisciplinary approach. The latter aim should be enabled by incorporating and conducting different PhD tracks focusing on these specific dimensions, which should allow better knowledge, insights and understanding of big data policing to be developed. This approach is innovative and radically different from the existing commercial and economic initiatives, which lack transparency on their predictive reliability and validity, effectiveness and legal and ethical safeguards.
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
This project's classification has been validated by the project's team.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
This project's classification has been validated by the project's team.
Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.1 - European Research Council (ERC)
MAIN PROGRAMME
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Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
HORIZON-ERC - HORIZON ERC Grants
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) ERC-2022-COG
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
9000 GENT
Belgium
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.