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www.geographica.gs
Urbo Operational Dashboard
Location Intelligence
Urbo, key insights
➞ Operational
Dashboard
➞ Developed for
Telefónica
➞ Successfully
configured by
more than 13 cities
➞ Urbo means City
What does it do?
➞ It analyzes real-time data coming from sensors on different verticals, like:
parking, waste management, street lighting, environmental data, city alerts,
transport, etc.
➞ It shows nice visualizations for non-technical users
The aim of Urbo
➞ Improve the quality of life of
citizens, providing
information in real time
(traffic, air quality, waste
management, etc.)
➞ Optimizing the resources for
Public Administrations, as
well as promoting the
accuracy and agility in the
decision-making process
Quick Architecture overview
FIWARE
Urbo: Remarks
➞ Highly specialized PLPGSQL functions for advanced and spatial data operations:
best performance and reliability.
➞ Specific module for time demanding and processed-data driven tasks. Scheduling
+ queueing.
Currently: Machine Learning
➞ Prevent through prediction unwanted events, like traffic jams.
➞ Relocate or create new facilities and resources for optimizing their indicators, like
parkings.
➞ Optimize repetitive tasks, like waste collection. We are trying not to focus in the vehicle
routing problem, but instead on predicting which containers need to be collected on the
coming shift. We can optimize the workload by not doing unnecessary work.
Let’s take a look!
Thanks.
www.geographica.gs
Paula Juliá
Project Manager
paula@geographica.gs

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Urbo Operational Dashboard Provides Real-Time City Insights

  • 2. Urbo, key insights ➞ Operational Dashboard ➞ Developed for Telefónica ➞ Successfully configured by more than 13 cities ➞ Urbo means City
  • 3. What does it do? ➞ It analyzes real-time data coming from sensors on different verticals, like: parking, waste management, street lighting, environmental data, city alerts, transport, etc. ➞ It shows nice visualizations for non-technical users
  • 4. The aim of Urbo ➞ Improve the quality of life of citizens, providing information in real time (traffic, air quality, waste management, etc.) ➞ Optimizing the resources for Public Administrations, as well as promoting the accuracy and agility in the decision-making process
  • 6. Urbo: Remarks ➞ Highly specialized PLPGSQL functions for advanced and spatial data operations: best performance and reliability. ➞ Specific module for time demanding and processed-data driven tasks. Scheduling + queueing.
  • 7. Currently: Machine Learning ➞ Prevent through prediction unwanted events, like traffic jams. ➞ Relocate or create new facilities and resources for optimizing their indicators, like parkings. ➞ Optimize repetitive tasks, like waste collection. We are trying not to focus in the vehicle routing problem, but instead on predicting which containers need to be collected on the coming shift. We can optimize the workload by not doing unnecessary work.