Smart Cities

Analytics Engines is designing a new software tool to help tackle illegal dumping

Illegal Dumping featured image

Analytics Engines is designing a new software tool to help tackle illegal dumping in the Dublin region, a problem which costs over €1.5million each year.  There are a number of reasons for illegal dumping and fly tipping these are a mixture of lack of information and lack of finances. Some of the reasons include people not being able to pay for their waste, people putting out waste on the wrong day, people choosing not to pay for their waste, and people not realising they have to pay for their waste.  With the current population for the area in excess of 1.3million and predicted to continue rising, this is a problem that needs an innovative solution to help tackle it.

Using our data platform, Analytics Engines XDP, we will bring together data and systems from across all the councils in the Dublin region, creating a single view of all the information available.  Then using inbuilt advanced analytics capabilities, we are applying machine learning and predictive models to aid in tackling this problem.

This tool will automatically provide the Dublin Councils with real-time monitoring information from across the region.  It will act as a central hub for all the councils involved highlighting in real time when illegal dumping is taking place by using all of the different existing data points.

We are building predictive models based on historical data to predict where and when illegal dumping may take place, allowing for proactive data driven decision to enable targeted allocation of resources.

The tool will be based on proven machine learning techniques using large volumes of data to identify patterns and categorise events from disparate data sets, becoming more accurate and efficient as the models are trained and the data available increases. Each time a data set is added the tool becomes more effective, learning from previous successes. The question then becomes not, ‘what data can we analyse’ but ‘what data can be made available’ to build the most comprehensive real-time picture and also predict future events.

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