Using an external data file – inflation indices

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There may be instances in which the analyst wishes to explore the likely impact of adding a new store at a given location. Rather than having to add this to the file (which may be france phone number list in production use and unable to be edited), the expression above can take any number of file or individual parameters – which means that we could have added the Glasgow postcode as an extra parameter directly in the expression to see the effect on say the mean minimum distance for our highest value customers.

Although the example outlined above uses UK Postcode information, the same technique can be utilised if we had latitude/longitude information. The GeoDist functions work on a worldwide basis using latitude/longitude information to provide the same location-based functionality such as nearest location, minimum distances etc. These functions have also been extended to allow for the use of lists of locations from columns of files.

The UK Government publishes data on sold house prices in the UK (2). This data contains information on 26 million houses that have been sold between 1995 and the present day. The dataset is limited to location-based information (a number of different fields such as postcode, address, district, county etc), a little bit about it (type, freehold/leasehold, old/new) and sale information (date of sale, sale price).



This dataset is useful in this context to illustrate a nice use-case of using a file in an expression as we have such a large date range. An obvious starting point for analysis is to look at the change in house prices over time.