The privatisation of the water industry in 1989 highlighted the need for greater precision in planning water resources. The absence of any large scale research on the pattern of domestic water use (DWU) accentuated the ignorance of the utilisation of more than 50% of supplies.
With the gradual introduction of domestic metering during the mid 1980s, there has been a considerable growth in information in this area. Since privatisation, the number of metered properties has risen and now amounts to 6% of the population. The data accumulated on the subject of domestic water use have increased the ability to understand better some of the processes involved, although there remained two major stumbling blocks on the way to analysing fully these data.
Firstly, the nature of the meters and the frequency with which they are read makes the understanding of the components which make up domestic water use almost impossible; and secondly, the profile of metered households is, by its nature, biased towards new properties which often have meters installed in them automatically, and small households, who opt to have a meter installed as they perceive financial gain resulting from it.
Under such circumstances the data containing the results of two surveys conducted by Yorkshire Water in 1992 are used in a new approach to geographical modelling. In the first stage the components with the highest DWU coefficients are determined by statistical means. A microsimulation technique, which lies beyond the scope of this work, is used in the second stage to model the spatial distribution of domestic water use in Leeds by using household components derived from the data by statistical means. The uniqueness of this thesis is in its association of these two techniques.
The overall conceptual analysis of all the issues involved in DWU, together with results of the two analyses, allow a better understanding of domestic water use of all properties, whether they possess a meter or not, from the smallest geographical unit - the household - to any spatial aggregation required.
The implications of this model for policy formulation and management strategy are numerous. The ability to forecast demand whilst incorporating environmental and economic scenarios, combined with the ability to concentrate on any geographical scale, renders this approach extremely useful in future developments which the water industry is about to enter
