Henrique Alves

Henrique Alves

Searcher

Independent Researcher, Brazil

Biography

I am a Statistician working in Data Science and Data Analysis, focused on transforming raw data into strategic information to support decision-making.

I have experience in data extraction, processing, and analysis, development of management reports, and support for Business Intelligence (BI), using statistical methods and analytical tools to generate clear and actionable insights.

I work with a strong results-oriented mindset, clear communication with business areas, and a focus on solving real-world problems through data.

Areas of interest:

  • Data Science
  • Data Analysis
  • Business Intelligence
  • Applied Statistics

Download my resumé.

Número de acessos: web counter
Interests
  • spatio-temporal geoestatistical modeling
  • hypothesis testing for the p-dimensional population mean vector
  • bootstrap methods, intensive computer simulation
Education
  • PhD in Agricultural Statistics and Experimentation, 2020

    Federal University of Lavras

  • Master's degree in Agricultural Statistics and Experimentation, 2015

    Federal University of Lavras

  • Degree in Statistics, 2013

    Federal University of Ouro Preto

Skills

Rlogo
R

90%

estatistica
Statistics

100%

matematica
Mathematic

90%

Experience

 
 
 
 
 
Estatístico Pleno
Mar 2025 – Jun 2025 Brasília - Brasil

Responsibilities include:

  • Analysing
  • Modelling
  • Deploying
 
 
 
 
 
Estatístico Pleno
Jan 2024 – Mar 2025 Brasília - Brasil

Responsibilities include:

  • Analysing
  • Modelling
  • Deploying
 
 
 
 
 
Instituto de Pesquisa Econômica Aplicada - Ipea - Diest
Researcher
May 2020 – Present Rio de Janeiro - Brasil

Responsibilities include:

  • Analysing
  • Modelling
  • Deploying

Recent Posts

Income Inequality and Sexual Violence in Brazil (2015–2025)

Abstract

This study examines the temporal and spatial dynamics of rape victimization rates in Brazil between 2015 and 2025, disaggregated by sex and Federative Units. Using standardized rates per $100,000$ inhabitants, the analysis integrates descriptive statistics, three-year weighted moving averages, consistency indicators, and Local Indicators of Spatial Association (LISA) to identify structural patterns, temporal persistence, and localized spatial dependence. The results reveal pronounced regional disparities, with the North and Central-West regions consistently exhibiting the highest levels of female victimization. The LISA analysis further demonstrates that these elevated rates are not randomly distributed but form statistically significant and persistent High-High spatial clusters, indicating strong geographic concentration. Conversely, Low-Low clusters observed in parts of the Northeast and Southeast suggest spatially structured areas of lower vulnerability. Although male victimization rates are substantially lower, partially similar spatial patterns emerge, albeit with weaker and more fragmented spatial autocorrelation. The findings also indicate that, despite short-term fluctuations — particularly during the COVID-19 period — several states exhibit persistent upward trends, reinforcing the interpretation of structural rather than episodic dynamics. The temporary disruption and subsequent reconfiguration of spatial clusters further highlight the resilience of underlying spatial processes. Overall, the combined use of temporal and spatial analytical tools provides a more nuanced understanding of sexual violence in Brazil, emphasizing the coexistence of regional inequality, spatial dependence, and trend persistence. These findings underscore the need for geographically targeted public policies and improvements in data quality and reporting systems.

Projetos

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