Anomalous distributions of birthdates across days of the month: An analysis using Spanish statistical records.

This study investigates birthdate patterns in a context of well-established civil registers and intensive migration inflows. Leveraging extensive Spanish microdata on residential variation flows and the Spanish Municipality Register, this research reveals new facets of the distributions of immigrants' birthdates across days of the month that differ significantly from those of non-migrants. The registered days of the month for birthdates are categorized into six distinct types based on the assumption that the anomalous distributions of birthdates will display rounding or simplifying patterns (digit preferences).

A detailed database of sub-annual Spanish demographic statistics: 2005-2021.

The big data revolution has made it possible to collect, transmit and exploit huge amounts of data. The potential this offer for data analysis, however, clashes with the limitations imposed by laws on protection of personal data. This paper details a new database (DEMOSPA0521) made after processing and summarising more than 868 million demographic records from Spain, corresponding to a period of seventeen years (2005-2021).

Dataset of an actual life-risk insurance portfolio.

The foundation of the insurance business is built on data, the latter being one of the most valuable assets of any insurer. In fact, the risk structure to which an insurance company is exposed can actually be deduced by reviewing its customer database. It is not surprising, therefore, that access to real insurance datasets is very limited.

Dataset of the COVID-19 post-lockdown survey conducted by GIPEyOP in Spain.

2020 was a year marked by COVID-19, an infectious disease caused by the SARS-CoV-2 virus. Since the official beginning of the pandemic (March 2020), the authorities in Spain have been imposing significant restrictions (mainly on mobility) to stop the spread of the disease. In October 2020, the research group GIPEyOP (Elections and Public Opinion Research Group from the University of Valencia) conducted a survey to analyse whether the Spanish population has maintained or modified their habits and customs once the strict measures imposed in Spain during the onset of the pandemic were relaxed.

Dataset of the COVID-19 lockdown survey conducted by GIPEyOP in Spain.

This article introduces a dataset that captures relevant information about the living conditions, feelings, and habits of residents in Spain during ninety nine days of home confinement. This and other measures, imposed by the Government of Spain to mitigate the impact of the pandemic on the population, have brought with them important economic, labor, and social changes, which have been accompanied by various modifications (some only temporary) in Spaniards habits and behaviours. Data collection was carried out through the implementation of a questionnaire with 33 questions, which was sent by email to the collaborators of GIPEyOP (Elections and Public Opinion Research Group from the University of Valencia).

Spanish electoral archive. SEA database.

This paper introduces the SEA database (acronym for Spanish Electoral Archive). SEA brings together the most complete public repository available to date on Spanish election outcomes. SEA holds all the results recorded from the electoral processes of General (1979-2019), Regional (1989-2021), Local (1979-2019) and European Parliamentary (1987-2019) elections held in Spain since the restoration of democracy in the late 70 s, in addition to other data sets with electoral content.

Lent impact on the seasonality of conceptions during the twentieth century in Spain.

In European past populations, religious canons shaped the seasonal distribution of marriages and births by means of banning weddings and sexual intercourse during important holidays within the religious calendar. In contemporary secularized societies, this seasonal modeling has disappeared. A few pieces of evidence have been gathered to explain how they have disappeared.

Elasticity as a measure for online determination of remission points in ongoing epidemics.

The correct identification of change-points during ongoing outbreak investigations of infectious diseases is a matter of paramount importance in epidemiology, with major implications for the management of health care resources, public health and, as the COVID-19 pandemic has shown, social live. Onsets, peaks, and inflexion points are some of them. An onset is the moment when the epidemic starts.

Assessing uncertainty of voter transitions estimated from aggregated data. Application to the 2017 French presidential election.

Inferring electoral individual behaviour from aggregated data is a very active research area, with ramifications in sociology and political science. A new approach based on linear programming is proposed to estimate voter transitions among parties (or candidates) between two elections. Compared to other linear and quadratic programming models previously published, our approach presents two important innovations.

Data from the GIPEyOP online election poll for the 2015 Spanish General election.

The general elections of 2015 in Spain took place in the middle of the Great Recession after several years of austerity economic policies. This election caused a political earthquake that shook the Spanish party system. During the campaign of that election, GIPEyOP (Elections and Public Opinion Research Group from University of Valencia) conducted a survey to collect relevant data about the electorate beliefs, intentions and motivations.

Combining Benford's Law and machine learning to detect money laundering. An actual Spanish court case.

Objectives: This paper is based on the analysis of the database of operations from a macro-case on money laundering orchestrated between a core company and a group of its suppliers, 26 of which had already been identified by the police as fraudulent companies. In the face of a well-founded suspicion that more companies have perpetrated criminal acts and in order to make better use of what are very limited police resources, we aim to construct a tool to detect money laundering criminals.

Methods: We combine Benford's Law and machine learning algorithms (logistic regression, decision trees, neural networks, and random forests) to find patterns of money laundering criminals in the context of a real Spanish court case.