The catchphrase ‘location, location, location’ might seem lacklustre, but break it down and it has a hidden insight: the need for multiplicity. Simply relying on a single piece of location data can be inadequate or even misleading. This is why location analytics, which can cope with multiple data sources, is so valuable.
Take the example that an expensive restaurant needs to situated in an area with wealthy clientele, but a simple local household incomes map may not be a sufficient research tool. For instance, a prospective site at a railway station might be in a low-income neighbourhood where residents work unusual hours such as night shifts. However, at peak morning and early evening times the station may be filled with people from high-paying jobs catching a connection to or from a city’s financial district.
In this situation a high-quality restaurant cooking meals to order with a relaxed dining atmosphere would do badly: commuters won’t have time to use it, and locals may be unable to afford it, or need to eat outside of traditional opening hours. Instead, a takeaway cafe that also uses high-quality ingredients and charges premium prices could do very well by capturing the trade of people passing through, particularly those who grab breakfast on the run.
Likewise, a gym could be located in an area dominated by families with young children or pensioners, but still be a viable business because of its proximity to the workplaces of rushed but cash-rich 20-somethings who like to keep fit.
This is all part of the new pattern of people living, working, shopping and socialising in a much wider range of locations these days. Indeed, the 2011 census showed the average commute to work was more than a mile longer than a decade earlier, while a 2011 survey found out-of-town retail parks continue to get bigger as the rate of empty shops in the town centre increases.
This is a challenge when choosing a location for a retail outlet or other service location, but location analytics can be the answer. Location analytics is the science (though some would call it an art) of turning raw numerical data into graphical form, such as on maps. The beauty of this technique is that you can factor in multiple sources of data and quickly identify sweet spots where all the factors are favourable. You can also change the combination of data sources used to see the changes almost immediately, rather than have to start the analysis from scratch.