While a significant amount of data is collected by business units within the City and by stakeholders and partner agencies, there is tremendous opportunity to connect and share the information generated by these disparate data sources for analysis and more informed decision-making.
Through Richmond’s Smart Cities Challenge initiative, information from internal and external sources will be collected, analyzed and shared to make more informed and time-sensitive, operational decisions. Check out our Smart Cities Challenge map to find out about how we plan to collect this data.
Data will be analyzed using machine learning based technology which will identify factors and patterns that create real time predictive models deployed in a neural network.
This knowledge will be used to support the development and implementation of integrated response plans which identify the resources and data required both internally and externally to address the identified risks. Key information will also ensure rapid assessment post-disaster to decrease loss of life and return to business as usual for the community.
Key activities to be undertaken over the next five years to contribute to this outcome include:
Installation of City-wide sensors to monitor water pressure, water quality, sanitary sewer water levels, drainage system water levels, air quality, and rain sensors. Sensors will provide day to day information on system operation and capacity as well as post disaster assessment of municipal utilities and water levels.
Installation of seismic sensors on municipal facilities, integrated with existing sensors on provincially-owned infrastructure such as bridges, tunnels and schools. This data will then be combined with initial structural assessment that will facilitate rapid post seismic assessment of these facilities.
Drone based LiDAR technology for regular dike assessment and inspection as well as post disaster dike and initial building assessment.
Installation of infrastructure to support smart street lights and smart traffic cameras at high risk intersections.