PACT, Big Data tool to predict the risk of chronic social exclusion

Publications Bank of innovations

PACT, Big Data tool to predict the risk of chronic social exclusion

Universidad Politécnica de Madrid

Foto: Mark Bende, Unsplash

Algoritme desenvolupat a partir de tècniques de Big data i Machine learning, que preveu el risc de les persones beneficiàries d’ajudes públiques de patir una situació d’exclusió social crònica.

L’algoritme s’ha creat a partir de l’anàlisi anonimitzada de més de 16.000 casos i de 60 factors predictius. Mitjançant l’aprenentatge automàtic (machine learning) s’han determinat els deu factors principals de risc, i s’ha desenvolupat una aplicació en línia, accessible per als professionals de Serveis Socials des de qualsevol dispositiu, ordinador, tauleta o telèfon mòbil, que permet que aquests professionals coneguin amb precisió el risc d’exclusió social d’una persona, per facilitar-los així les decisions o actuacions professionals a prendre.

A més, en el marc del projecte PACT també s’ha creat i implementat un programari per a la gestió activa de casos, basat en l’anàlisi de riscos personals. Aquesta anàlisi permet identificar millor les necessitats de formació de cada persona atesa per facilitar la seva inserció socio-laboral.

Pact Project

Artificial Inteligence for identification of social vulnerability

Publications Bank of innovations

Artificial Inteligence for identification of social vulnerability

City of Espoo

Foto: City of Espoo

Big Data Analysis through Artificial Intelligence (IA) of multiple demographic databases to identify which people in a municipality need social help. This is a successful experiment carried out in 2017 by the City of Espoo (Finland), which has analyzed the social, health, and educational data of the entire population of the municipality, around 520,000 people, between the years 2002 and 2016.

Population data has been analyzed grouped according to various family environments, rather than by traditional methods of individual analysis. The results have allowed to identify, for example, 280 predictive factors in the field of childhood vulnerability and to show that, although none of these individual factors is a risk, the simultaneous occurrence of several factors can be a risk.

This pioneering experience of the Espoo City Council has shown that the analysis of a large volume of data from different origins through Artificial Intelligence techniques can play a very important role in the future of Social Services.

Espoo

The Opportunity Atlas, Big Data analysis to optimize the social support system

Publications Bank of innovations

The Opportunity Atlas, Big Data analysis to optimize the social support system

Seattle Housing Authority

Foto: Seattle Housing Authority

A tool for the analysis of anonymized census data in order to optimize the system of public subsidies to the vulnerable population based on the detailed knowledge of the characteristics and social differences between disadvantaged neighborhoods.

The tool has allowed detecting in several US big cities that nearby neighborhoods with very low incomes offer many different opportunities to leave poverty to the children who live there. Seattle Housing Authority has pioneered the analysis of the “Opportunity Atlas” data and concluded that, unlike other American cities, the differences between neighborhoods that offer high social mobility and those that do not, are invisible to the naked eye and only by means of this data analysis it is possible to identify them.

Among other results, the city of Seattle has discovered that most of the subsidies were allocated to neighborhoods with low social mobility and this has led to a rethinking of the social assistance system to promote the residence of vulnerable families in the neighborhoods with a higher social mobility rate.

Seattle Housing Authority

Les meves ajudes, an on-line tool to facilitate access to social assistance

Publications Bank of innovations

Les meves ajudes, an on-line tool to facilitate access to social assistance

Barcelona’s City Council

Foto: Ajuntament de Barcelona

A simulator called “Les meves ajudes”, allows on line consultation of the social aids a person is entitled to regardless of the Administration that manages them.

The simulator works anonymously and, based on the input, indicates which social aids may be requested, estimates the amount, and links to the procedures to be followed to request them.

Les meves ajudes

B·MINCOME, looking for the best way out of poverty

Publications Bank of innovations

B·MINCOME, looking for the best way out of poverty

Barcelona City Council

Foto: Barcelona City Council

B·MINCOME is a pilot project for the establishment of a municipal inclusion salary in the city of Barcelona, which is testing with one thousand vulnerable families in the ten most deprived Barcelona neighborhoods, the efficacy and efficiency of combining a stable financial help with active social-employment policies.

B·MINCOME is a project pilot to fight against poverty and inequality based in the development of an integral policy which combines a public passive policy (a Municipal Inclusive Support, a financial help that supplements the income of the selected people and families) with four active social-employment policies: a combined education and employment plan; actions for the encouragement of the socio-collaborative economy; grants for the rehabilitation of homes which may allow room rentals; and a program for participation in community networks.

B-Mincome

AFTS, An algorithm for risk of children neglect or abuse

Publications Bank of innovations

AFTS, An algorithm for risk of children neglect or abuse

Department of Human Services (DHS) Allegheny County, Pennsylvania

Photo: DHS Allegheny County

A Big Data System that provides social services professionals with an objective assessment of the situations of risk of child helplessness and helps them in the detection of cases and in the decision process of activation of the protocols for social intervention.

The Allegheny Family Screening Tool (AFST) is a tool based on specially designed algorithms that analyzes a large number of data from different sources of information. When social services receive information about a possible situation of helplessness or mistreatment, the algorithm calculates the risk index of the case by analyzing more than a hundred parameters such as criminal history, drug use, mental illness or History of child abuse of parents, guardians or people who live in the child’s home

The algorithm provides great accuracy in case detection and as a recent article in the New York Times concluded, “the Allegheny experience suggests that its screening tool is less bad at weighing biases than human screeners have been”.

Allegheny County

Self Sufficiency Matrix, matrix for evaluation independent-living capability

Publications Bank of innovations

Self Sufficiency Matrix, matrix for evaluation independent-living capability

Amsterdam Town Hall

Computer application that allows measuring a person’s ability to be self-sufficient, that is, to carry out daily activities independently

Until now, the assessment of an individual’s self-sufficiency was subjective, as it depended on the evaluator’s opinion. The Self Sufficiency Matrix (SSM) is based on eleven parameters that affect the effectiveness, productivity, and quality of life of the person. Income, housing, social network, and mental health are some examples. The tool allows the evaluator to obtain a relatively simple and comprehensive view of a complex concept.

The SSM captures “a snapshot” of a person’s specific moment. For this reason, generally, information older than 30 days is discarded. Additionally, the person’s background or future predictions about their state are not included. The program is divided into five levels of self-sufficiency, from less to more.

MyGov Social, personalised recommendations for social services users

Publications Bank of innovations

MyGov Social, personalised recommendations for social services users

AOC and County Council of Vallès Oriental

Administració Oberta de Catalunya (AOC) and the County Council of Vallès Oriental, with the support of Bismart, has developed the MyGov Social programme on social services to offer personalised and proactive recommendations to citizens with social needs based on the Big Data analysis of personal data and the behaviour of anonymous users with the same sociodemographic profile.

HIGEA Community, smart management of social and health organisations

Publications Bank of innovations

HIGEA Community, smart management of social and health organisations

AREP, Fundació Joia, FETB, Open TIC

Foto: Unsplash

Comprehensive cloud solution designed for the intelligent management of organizations with health, social and labor insertion intervention, which integrates the management of these three areas and exponentially improves the quality of care for the user through the SITT system (Sustainable, Integral and Transversal Technology).

While the current offer of software aimed at health and social management focuses on serving large corporations, this application is adapted to the management model of social, health and labor insertion organizations in the Third Sector through an econòmic investment sustainable.

The program has from the beginning the level of interoperability required by the Catalan Health Service (HC3) and the maximum security of the data. Sustainability comes from cloud data and secure access from anywhere (services, housing, etc.).

Community Higea

Smart Social Home Care, data for the elderly and vulnerable population

Publications Bank of innovations

Smart Social Home Care, data for the elderly and vulnerable population

Bismart

Large volumes of data for healthcare and social services planning

Smart Social Home Care collects data on social services, health, population, economic activity, use of basic supplies, waste management, and other aspects. The goal is to use this information to identify and predict groups and areas that need assistance.

The dashboards generated with this data allow professionals to visualize what is happening in their field and, thus, make better decisions to help the vulnerable population.