End-to-end GIS case study demonstrating how epidemiological, demographic, and spatial data can be integrated to estimate dengue incidence and support public health decision-making.
This project analyzes reported dengue cases in Niterói (Rio de Janeiro, Brazil) during 2024 and estimates neighborhood-level incidence rates (cases per 100,000 inhabitants).
Rather than relying on raw case counts, the analysis adjusts for population size, providing a more meaningful indicator for identifying priority areas for surveillance and vector-control actions.
- Clean and standardize epidemiological records
- Integrate dengue notifications with census data
- Calculate neighborhood-level incidence rates
- Produce an interactive choropleth map
- Support data-driven public health decisions
- Data cleaning and standardization
- Case aggregation by neighborhood
- Integration with population data
- Incidence rate calculation
- Spatial join with neighborhood boundaries
- Interactive mapping and visualization
- R
- tidyverse
- sf
- leaflet
- ggplot2
- plotly
- R Markdown
.
├── README.md
├── GeoDengue_Report.html
├── GeoDengue_Report.Rmd
├── data/
│ ├── Banco_dengue_teste.xlsx
│ ├── censo2217762015942.csv
│ └── Limite_de_Bairros.geojson
└── LICENSE
The analysis covers 51 neighborhoods and demonstrates a complete GIS workflow, from raw epidemiological records to interactive spatial visualization.
Main outputs include:
- Neighborhood-level dengue incidence rates
- Interactive web map
- Top incidence ranking
- Population-adjusted epidemiological indicators
- GIS Analysis
- Spatial Data Processing
- Data Cleaning & Standardization
- Spatial Joins
- Epidemiological Analysis
- Interactive Web Mapping
- Data Visualization
- Reproducible Analytics with R Markdown
The complete analysis is available in:
📄 GeoDengue_Report.html
- Dengue notification database (2024)
- Population estimates by neighborhood
- Neighborhood administrative boundaries (GeoJSON)
This project is licensed under the MIT License.