Virtual street audits are increasingly used by social service providers, community leaders and social workers to support place-based decisions in urban settings. Analytical tools can potentially augment decision making by making relevant information accessible in context to place-based interventions aimed at improving community wellbeing. I investigated if it was possible to discover a quantitative stochastic model that can be a reasonable fit with empirical data about the relationship between physical urban locations and the volumes of public services supplied to those locations. I analysed the dataset of San Jose’s Police Department call centre data spanning 10 years. I found that the Borel-Tanner distribution is a reasonable fit in 7 out of 10 trials conducted in this study. Relatively few urban sites were attributed to a large proportion of service volumes.  There was  high spatial concentration within ‘high needs’ sites. A significant proportion of these sites persist over the 10 year period.  I plotted these sites on a map to demonstrate their applicability to virtual audit applications.