We're sorry but this page doesn't work properly without JavaScript enabled. Please enable it to continue.

Mapping COVID-19 epidemic data using FOSS

Formal Metadata

Mapping COVID-19 epidemic data using FOSS
Title of Series
Number of Parts
CC Attribution 3.0 Germany:
You are free to use, adapt and copy, distribute and transmit the work or content in adapted or unchanged form for any legal purpose as long as the work is attributed to the author in the manner specified by the author or licensor.
Release Date2023

Content Metadata

Subject Area
This talk focuses on comprehending spatial and temporal patterns of the COVID-19 pandemic in the Trentino region, Italy. To achieve this, a comprehensive database has been developed and continually updated. The region's significance lies in its role as a transportation corridor and a popular tourist destination, both of which have influenced the virus's spread. The dataset captures COVID-19 cases, recoveries, deaths, and age groups on a daily basis at the municipal level from March 2020 to 2022. The project emphasizes privacy, aggregating data weekly and applying a threshold to protect small numbers. The use of official data from the local Health Authority ensures data validity and patient confidentiality. The data management system is powered by a free and open-source relational database system (MySQL), ensuring geographic data processing and storage. A user-friendly WebGIS interface has been developed, prioritizing clear data presentation on both large screens and mobile devices. This interface enhances user exploration while distributing processing load between server and client sides. Geospatial data from the OpenStreetMap project underpins the cartography, and a virtual machine integrates software and data on the server side. Leaflet Javascript libraries enable data rendering, ensuring flexibility across various devices. Data exchange between server and client is facilitated through geojson tables, dynamically generated based on user requests. Overall, the project aims to support decision-making through data-driven insights into COVID-19 spread and its temporal evolution.