Angelo Moretti

Angelo Moretti
Utrecht University | UU · Department of Methodology and Statistics

PhD

About

22
Publications
2,428
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64
Citations
Introduction
My research interests are in the area of survey statistics and methodology with a particular focus on social statistics. I work in the following areas: small area estimation, resampling techniques, variance estimation, survey calibration, survey design and estimation, survey sampling, data integration, propensity modelling. I am very interested in applications related to social indicators.
Additional affiliations
December 2019 - present
Manchester Metropolitan University
Position
  • Lecturer
January 2019 - December 2019
The University of Manchester
Position
  • Research Associate
September 2017 - December 2018
The University of Sheffield
Position
  • Research Associate
Education
September 2014 - March 2018
The University of Manchester
Field of study
  • Social Statistics
September 2012 - May 2014
Università di Pisa
Field of study
  • Master of Science in Marketing and Market Research
September 2009 - July 2012
Università di Pisa
Field of study
  • Bachelor's Degree in Economics

Publications

Publications (22)
Data
Description Dr. Angelo Moretti, Manchester Metropolitan University, a.moretti@mmu.ac.uk Dr. Adam Whitworth, University of Sheffield, a.whitworth@sheffield.ac.uk Dr. Megan Blake, University of Sheffield, m.blake@sheffield.ac.uk Introduction In the UK many are not food secure. Food security is the ability to consistently afford, access and utilise t...
Article
Full-text available
Objectives: Police-recorded crimes are used by police forces to document community differences in crime and design spatially targeted strategies. Nevertheless, crimes known to police are affected by selection biases driven by underreporting. This paper presents a simulation study to analyze if crime statistics aggregated at small spatial scales are...
Article
Full-text available
Spatial microsimulation encompasses a range of alternative methodological approaches for the small area estimation (SAE) of target population parameters from sample survey data down to target small areas in contexts where such data are desired but not otherwise available. Although widely used, an enduring limitation of spatial microsimulation SAE a...
Article
Full-text available
This paper sheds light on the impact of public attitudes towards climate change and energy disruption on the pricing of emission (carbon-intensive) and clean (low-emission) stocks. We develop a regional indicator of worries about climate change and energy disruption using data from the European Social Survey Round 8. We classify European regions as...
Presentation
This analysis draws on the survey conducted by YouGov for the Food Foundation in September and provides insights from August 2020. The analysis shows that household food insecurity is two-pronged: access or/and affordability.
Article
Full-text available
There is growing need for reliable survey-based small area estimates of crime and confidence in police work to design and evaluate place-based policing strategies. Crime and confidence in policing are geographically aggregated and police resources can be targeted to areas with the most problems. High levels of spatial autocorrelation in these varia...
Preprint
Full-text available
In this article, we aim to stress that the fight of COVID-19 needs clear and timely data collection plans. Without data to support decisions, we can only hope for a fortunate guess. We need synergies between different research communities, policy-makers, Official Statistics, health institutions, and private companies which may provide non-conventio...
Chapter
Open and crowdsourced data are becoming prominent in social sciences research. Crowdsourcing projects harness information from large numbers of citizens who voluntarily participate in one collaborative project, and allow new insights into people's attitudes and perceptions. However, these data may be affected by a series of biases that limit their...
Preprint
Full-text available
Police-recorded crimes are used by police forces to map crime patterns and design spatially-targeted strategies. Nevertheless, maps of crimes known to police are affected by selection biases driven by unequal crime reporting rates across social groups. This paper presents a simulation study to analyse the impact of selection biases on crime maps pr...
Article
Full-text available
Public attitudes to welfare are key issues in social policy research and practice given their important roles in shaping demands for different types of welfare policies as well as the political parameters within which those welfare decisions are made by governments. Research into headline trends has shown important hardenings in public attitudes to...
Article
Full-text available
In this article, we aim to stress that the fight of COVID-19 needs clear and timely data collection plans. Without data to support decisions, we can only hope for a fortunate guess. We need synergies between different research communities, policy-makers, Official Statistics, health institutions, and private companies which may provide non-conventio...
Preprint
Full-text available
Open and crowdsourced data are becoming prominent in social sciences research. Crowdsourcing projects harness information from large crowds of citizens who voluntarily participate into one collaborative project, and allow new insights into people’s attitudes and perceptions. However, these are usually affected by a series of biases that limit their...
Article
Factor analysis models are used in data dimensionality reduction problems where the variability among observed variables can be described through a smaller number of unobserved latent variables. This approach is often used to estimate the multidimensionality of well‐being. We employ factor analysis models and use multivariate empirical best linear...
Article
A range of data is of geographic interest but is not available at a small area level from existing data sources. Small area estimation (SAE) offers techniques to estimate population parameters of target variables to detailed scales based on relationships between those target variables and relevant auxiliary variables. The resulting indirect small a...
Article
Worry about crime is known to be higher in some European regions than others. However, cross-national surveys, which are the main source of information to map worry about crime across Europe, are designed to be representative of large areas (countries), and regions often suffer from small and unrepresentative sample sizes. This research produces re...
Article
Small area estimation (SAE) plays a crucial role in the social sciences due to the growing need for reliable and accurate estimates for small domains. In the study of well-being, for example, policy makers need detailed information about the geographical distribution of a range of social indicators. We investigate data dimensionality reduction usin...
Article
This article deals with the use of sample size dependent composite estimators in spatial microsimulation approaches for small area estimation. This approach has been applied to regression-based small area estimation approaches but never to our knowledge to spatial microsimulation approaches. In this paper, we extend the iterative proportional fitti...
Article
This article deals with mean squared error (MSE) estimation of a multivariate empirical best linear unbiased predictor (MEBLUP) under the unit-level multivariate nested-errors regression model for small area estimation via parametric bootstrap. A simulation study is designed to evaluate the performance of our algorithm and compare it with the univa...
Preprint
The use of spatially correlated random area effects is increasingly in use in small area estimation field. The spatial Empirical Best Linear Unbiased Predictor (SEBLUP), which borrows strength from correlated random area effects between neighbouring areas, have shown to reduce the estimates' variance and bias, both under simulated and real populati...
Thesis
Using multivariate statistical models in small area estimation (SAE) may improve the efficiency of the small area estimates over the univariate SAE. In this thesis, we study the multivariate SAE problem of multidimensional well-being indicators. We first investigate the univariate EBLUP for a single latent variable estimated through confirmatory fa...

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Projects

Projects (6)
Project
Police-recorded crimes are the main source of information used by police forces to analyse crime patterns, investigate the spatial concentration of crime, and design spatially targeted strategies. Police statistics are also used to design and evaluate crime prevention policies and to develop theories of crime and deviance. Nevertheless, crimes known to police are affected by biases and unreliability driven by unequal crime reporting rates across social groups and geographical areas. The measures of error that affect the reliability of crime statistics is an issue that merits deeper scrutiny, since it affects police everyday practices, criminal policies and citizens’ everyday lives. Yet it is an understudied issue, and the implications of data biases for crime mapping are unknown. Moreover, police analyses are moving towards the study of smaller levels of geography than ever before, such as street segments with highly homogeneous communities. Maps produced from police records are used to foreground the micro places where rates of recorded crimes are larger. This project will investigate the impact of data biases on crime maps produced from police records at the different spatial scales. Simulation studies and applications will be used to assess whether micro-level maps are affected by a larger risk of bias than maps produced at larger scales.
Project
This project aims to provide small area estimates of attitudes and new small area estimation methods relevant to this field.
Project
This project relates to my current research in the InGRID2 project at the University of Manchester, Social Statistics department.