This paper presents the development and analysis of a system aimed at
evaluating socioeconomic data from Brazilian cities, with the aim of helping to
understand regional disparities and formulate more efficient public policies.
Data were collected from reliable sources, such as IBGE, DATASUS and INEP,
covering education, health and income indicators, which were processed,
organized and analyzed using tools such as Dask, Pandas and Cython, which
optimized the processing of large volumes of information.
The system was structured in a layered architecture, with a backend
developed in Flask and compiled in C to improve performance, and an
interactive and responsive frontend, implemented with Tailwind CSS,
Semantic UI and visualization libraries such as Plotly. The results were
presented in dynamic graphs that allow the exploration of data such as the
evolution of GDP per capita, average wage and health indicators, highlighting
regional disparities and development patterns.
The analyses carried out highlighted the reduction in the number of
school enrollments after the pandemic, the growing differences in average
salaries between regions, and the prevalence of specific health problems in
cities such as Manaus. The system proved to be a practical and effective tool
for transforming complex data into accessible insights, contributing to urban
planning and the competitiveness of Brazilian cities. It is concluded that the
integration of socioeconomic data with modern technologies can promote more
informed, equitable, and sustainable public management.
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