Home - Advanced and Predictive Analytics Network in Local Government

Welcome to APAN

Responding to the increasing use of predictive analytics within councils, the LGA has created a new online network for practitioners, bringing together practical help, support and an area for discussion on the use of advanced and predictive analytics into one place.

The Advanced & Predictive Analytics Network in Local Government (APAN) provides a network for local government data analysts and other invited experts to discuss, share and consider the opportunities, benefits and risks of using advanced analysis and predictive analytics in local government settings. 

This is an interactive space where councils can share learning and support one another in a rapidly evolving area of innovation.

In time, we hope for the network to feature a range of discussions on the potential for data use to aid policy decisions and provide evidence for interventions. The network priorities will be determined by our users of this service. It will give councils an opportunity to showcase their use of projects, offer the evidence for their decisions, as well as share the systems and algorithms deployed for wider transparency, sharing and scrutiny.  

We set out more detail about the partner organisations we are working with and the features in this network and how to use them in the "about pages" link.

Announcements

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10 Apr 2024 - 17:41

We are excited to announce we have three openings for Digital Advisers to join our team in delivering our 2024/25 programme for Cyber, Digital and Technology | Local Government Association. These are great opportunities to work in a policy role (one vacancy) or a project delivery role (two vacancies) - each focused on supporting and promoting the work councils do to deliver modern digital public services. It’s a really exciting time to work with us as we enter our seventh year. Loads more details in the job advert. https://lnkd.in/edHJG_Rs

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26 Mar 2024 - 08:29

This live online course explores the principles, practices and ethics of using AI when analysing qualitative data. 

By the end of the couse you will be:

  • Have a critical awareness of the range of AI-assisted qualitative analysis tools
  • Be equipped to consider their ethical incorporation into analysis practice
  • Have practical, hands-on experience with using CoLoop and MAXQDA’s AI-assisted tool

The course will cover:

  • Historical context of AI-assisted qualitative analysis
  • Planning for and setting up AI-assisted qualitative analysis projects
  • The ethics of incorporating AI-assisted tools into qualitative analysis
  • Using AI-assisted tools to transcribe, explore and summarise qualitative data
  • Using AI-assisted tools to generate ideas for qualitative coding
  • Using AI-assisted tools to identify patterns and relationships
  • Reporting on the use of AI-assisted tools in qualitative analysis
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(Members only)

How to join

Anyone may view the advanced and predictive analytics resources section .

The network's real work takes place with the interactions and discussions between its members in the discussion section (which requires users to be logged in). If you wish to participate in discussions, other activities or with information you would like to share by uploading into the resources section, then please join the network.  Membership is free and open to any local government officials.  You require to log in using the button at the top right of this screen.

Not signed up yet?  This hub is hosted by Local Government's Knowledge Hub.   You have to be registered and can sign up via Knowledge Hub here.

Requests to join are automatically accepted from those with a local government email address. Requests to join from other parties will be reviewed on a case-by-case basis as wider membership is by invitation only and is based upon expertise and responsibilities to Local Government.

Advanced and Predictive Analytics Network in Local Government Logo
Advanced and Predictive Analytics Network in Local Government
Restricted group | Started - November 2020 | Last activity - Yesterday