The information revolution is creating and publishing vast data sets, such as records of business transactions, environmental statistics and census demographics. In many application domains, this data is collected and indexed by geo-spatial location. The discovery of interesting patterns in such databases through visual analytics is a key to turn this data into valuable information. Challenges arise because newly available geo-spatial data sets often have millions of records, or even far more, they are from multiple and heterogeneous data sources, and the output devices have significantly changed, e.g. high-resolution pixilated displays are increasingly available in both wall-sized and desktop units . New techniques are needed to cope with this scale. In this paper we focus on ways to increase the scalability of pixel-based visual interfaces by adding task on hands scenarios that tightly integrate the data analyst into the exploration of geo-spatial data sets.