Small Area Estimation in the Era of Big Data, Crowdsourced Data and Non-Probability Web Surveys. Topics in Poverty, Social Exclusion and Crime |
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| Coordinator 1 | Dr Angelo Moretti (University of Manchester) |
| Coordinator 2 | Mr David Buil-Gil (University of Manchester) |
Small area estimation methods are gaining relevance in academic research in the social sciences due to the growing need for reliable estimates at small geographical levels and for small domains. In the study of poverty, social exclusion and crime, policy-makers require detailed information about the geographical distribution of a large variety of social indicators (e.g. fear of crime, crime rates, poverty measures, unemployment). Unfortunately, the most diffused social sample surveys are not designed to be representative at small area level. Indeed, we are in the presence of the so-called “unplanned domains” phenomenon, where domain membership is not incorporated in the sampling design. Thus, the sample size in each domain is random (and may be large or small) and in many cases zero. In this latter case, design-based estimation methods may produce a large variability in the estimates. Here, indirect model-based estimation methods, in particular small area estimation approaches, can be used to predict target parameters for the small areas.
The Internet and the new information and communication technologies offer new opportunities to collect data on social problems. These data refer to non-probabilistic samples, big data, open data and crowdsourced data. Such data offers many advantages over traditional approaches to data collection. However, open and crowdsourced data are criticised due to biases arising from participants self-selection and due to non-probability sampling designs. These new forms of data can be used in different ways in the context of small area estimation techniques:
1. As covariates in small area models.
2. To validate small area estimates (external validation).
3. As target variables in small area models.
In this session we particular welcome substantive and methodological papers that address these issues in poverty, social exclusion and crime. Moreover, we are interested in how to provide measures of uncertainty of the small area estimates obtained using these data (mean squared error or confidence intervals). Applications based on relevant data for users are important.