This paper addresses a difficulty in large-scale long term laser localisation - how to deal with scene change. We pose this as a distraction suppression problem. Urban driving environments are frequently subject to large dynamic outliers, such as buses, trucks etc. These objects can mask the static elements of the prior map that we rely on for localisation. At the same time some objects change shape in a way that is less dramatic but equally pernicious during localisation - for example trees over seasons and in wind, shop fronts and doorways. In this paper, we show how we can learn in high resolution, the areas of our map that are subject to such distractions (low value data) in a place-dependent approach. We demonstrate how to utilise this model to select individual laser measurements for localisation. Specifically, by leveraging repeated operation over weeks and months, for each point in our map pointcloud we build distributions of the errors associated with that point for multiple localisation passes. These distributions are then used to determine the legitimacy of laser measurements prior to their use in localisation. We demonstrate distraction suppression as a front-end process to large scale localiser by incrementally adding 50km of error data to our base map and show that robustness is improved over the base system with a further 10km of urban driving.