  Newly available data on the spatial distribution of retail activities in
cities makes it possible to build models formalized at the level of the single
retailer. Current models tackle consumer location choices at an aggregate level
and the opportunity new data offers for modeling at the retail unit level lacks
a theoretical framework. The model we present here helps to address these
issues. It is a particular case of the Cross-Nested Logit model, based on
random utility theory built with the idea of quantifying the role of floor
space and agglomeration in retail location choice. We test this model on the
city of London: the results are consistent with a super linear scaling of a
retailer's attractiveness with its floor space, and with an agglomeration
effect approximated as the total retail floorspace within a $325m$ radius from
each shop.
