Machine learning used millions of data points to discover top locations 

From Dublin to Donegal, Ireland’s most desirable places have been discovered using data analytics. SAS, the leader in analytics, used its capabilities in artificial intelligence to create the list from large volumes of publicly available data, including city studies, social media sites, review sites like TripAdvisor, geodata and reports from statistical services and international agencies.

Dublin dominated the rankings, with its suburbs of Artane, Sallynoggin, Drumcondra, Blackrock, Donnycarney, Rathgar and Irishtown all securing a place on the list. The towns of Killarney in County Kerry and Bundoran in Donegal also put in strong showings to be placed among Ireland’s best.

The locations achieved high marks across eight different categories generated by machine learning, comprising living expenses, safety & infrastructure, healthcare, restaurants & shopping, the environment, culture, attractiveness to families, and education & employment.

The findings emerged from the Paradise Found project which analysed nearly 150,000 locations worldwide in 193 countries, comprising more than five million data points. It identified the best seven places globally, with West Perth in Australia topping the list:

1. West Perth, Australia
2. Feijenoord, Rotterdam, Netherlands
3. New York, NY, United States
4. Sandy Bay, Australia
5. Hebden Bridge, United Kingdom
6. Zurich, Switzerland
7. Woodinville, WA, United States

The machine learning process used all the publicly available data that analysts could obtain and an algorithm then determined its importance. The algorithm learns from the data instead of using any model assumptions, so is the sole arbiter of the factors that describe a location – and then uses this information to determine which is the best.

The key criteria were combined with indicators for quality of life, for example the price of common groceries such as a kilogram of bananas, the distance covered by pavements, the number of trees, the width of footpaths and the number of hours a person spends in traffic jams each year.

“The data doesn’t lie,” explained John Spooner, Head of Data Science at SAS UK & Ireland. “When putting together a conventional survey, it’s all too easy for unconscious bias to creep in when selecting the criteria to use when determining which data should be collected and analysed. For Paradise Found, however, we processed all the available data and allowed machine learning algorithms to decide which criteria are truly important. This way, no aspect can be ignored simply because no one was looking for it.

“This allowed us to demonstrate what analytics and machine learning are capable of — namely, finding patterns in data from a completely impartial perspective. In this particular case it showed how analytics can come up with a list of places that are different to what people might first think of based on their own opinions and preferences.”

SAS’ free, online Paradise Configurator tool can be used to find your own perfect place in just a few clicks. Rather than giving equal weight to each of the eight criteria above, the tool allows someone to select how important each of these criteria are to them, so they can generate their own unique ‘analytical paradise’.