The Future of Open Data and Its Relationship with the Private Sector
This post discusses the role that the private sector can and should play in ensuring open data reaches its fullest potential in the public arena.
Posts By Amen Ra Mashariki
Analytics in Motion Part 4: Impacting the Resident
The most important part of the analytics process within an urban environment is impacting the resident.
Analytics in Motion Part 3: Developing the Analytics Solution
The next to last – and what many believe is the most important phase of a citywide analytics project – is the development of an analytics solution. Here’s how you can build your analytics solution in order to solve problems.
Open Data and Trust in Government
This is just one example of the power of an initiative that is still in its most nascent stage. With respects to open data and building trust in government, the best is yet to come.
Analytics in Motion Part 2: Executable Analytics Solution
If you can identify a useful analytics question and keep it well-scoped, your chances of an executable analytics solution go up dramatically.
What Happens When Data Fails?
The government relies on data to perform a number of critical services and simply to function. But what happens when data fails?
Analytics in Motion Part 1: Data Drills for a More Responsive City
Data drills are a mechanism for helping a city to baseline where they are with citywide data practices. They’re also a mechanism for helping a city improve their ability to identify, understand and use data to solve a city challenge when requested.
Community Outreach VS. Predictive Policing for Safer Communities
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.
How Cities Can Get Smarter About Machine Learning
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.
Building Transparency and Fairness into Public-Facing Algorithms
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.