Predictive policing is being used by many municipal and federal law enforcement agencies both domestically and internationally to decrease crime. But community outreach with data-driven strategies can be more effective.
You want to hear a secret? City governments are still at the beginning stages of understanding how best to optimize the use of machine learning (ML) algorithms to make city services more efficient. Here’s why.
How can you tell which data is useful and which is not?
Algorithms are made up of someone’s opinion and experience. Therefore, we should ensure that the opinion and experience of people who will be impacted by the algorithm is captured in the design of the algorithm such that we can remove as much bias as humanly possible.
Detecting and responding to any type of infectious disease requires geographic accuracy along with the ability to track and gather data. A GIS platform allows you to visualize and analyze information layered onto a map in order to understand relationships, patterns and trends.
Rarely is spatial analysis seen as a competency that should be shared horizontally through all organizations. But hiring data scientists to lead this charge is a key piece to the puzzle of solving urban challenges.
Use this four-step process to evidence-based decision making.
We need more than just data scientists doing city work; we need a strong ecosystem that will support a smart team of urban data scientists as they assist city leadership in driving successful outcomes for citizens.
The tasks of using and sharing big data for government seems huge. However, the benefits and potential cost savings would outweigh the costs.
The goal of urban analytics is to help make cities safer, smarter, cleaner, healthier, equitable, sustainable and economically vibrant for all of us who live, work, play, learn and strive in them.