Big data for our land

MarghERita, the Emilia-Romagna Region Data Platform, exploits integration and analysis tools and techniques in order to collect data from different sources: inside or outside bodies, both public and crowdsourcing.

Thanks to the Big Data Analysis, it’s possible to expand the informative relationships map with:

• predictive analysis to support decision-making processes, based on business analytics tools integrated with Artificial Intelligence (AI), Machine Learning (ML) and Geographic Information System (GIS)
• Big Data Mining and Visualization services, which are able to satisfy the governance needs for the territory generating maps, infographics and interactive information.

Some of the guiding principles trasversal to the different use cases:

• Use of Big Data Mining
• Development of models of Artificial Intelligence (AI) able to predict phenomena
• Visualizations with applications for analysis and simulation, based on interactive maps
• Development of infographics able to facilitate the storytelling of what was discovered by the models so crafted
• An elastic approach with the possibility to access to external sources

First areas of investigation

1. Support for the prevention and management of environmental risk: it uses predictive modeling to simulate the impact that hypothetical natural phenomena can have on the territory (rock slides, floods, etc.) and shows the practical impacts that they can have on the affected territories and possible countermeasures (i.e: identify river flood points, predict the flow routes of flood waters, etc.).
   Objectives
   a. Identify the critical issues present in the regional territory in terms of environmental risk
   b. Update emergency plans based on indications revealed by the data.
   
   
2. Support to the re-organization of the healthcare Emergency-Urgency web: it uses location analytics techniques to support the re-organization of the Emilia-Romagna region emergency-urgency sites (Emergency Room).
   Objectives
   a. To support the territorial emergency-urgency planning with a data-driven approach in order to predict the impacts and benefits of such re-organization
   b. To correctly manage the different levels of criticality of every patient
   c. To reduce the waiting time
   d. To plan the healthcare personnel aid as best as possible.
   
   
3. Support for the control of traffic flows and the prediction and mitigation of road accidents: it develops models that identify dangerous zones to prevent car accidents and to identify the road sections on which it is most advantageous to encourage the use of public transport.
   Objectives
   a. To Identify the most critical points in the regional road network
   b. To adopt clustering techniques to suggest the most advantageous actions in order to mitigate the accident ratio on the street system (i.e: intervene on traffic limits and on the physical characteristics of the road arch, etc.).
   
   
4. Support for the planning and reduction of pollution: it’s a simulation environment based on cartography, it identifies the spreading dynamics of the main atmospheric pollution indicators and collects data on the causes of such phenomena (traffic, industrial and private energy consumptions, etc.) and on their effects (Air quality indicators).
   Objectives
   a. To support the correlation analysis on the air quality of  the territory of the Emilia-Romagna Region
   b. To simulate the real consequences that political actions can have on the environment, such as traffic limitation initiatives or energy saving, helping to plan them.