Using GIS to map market research data

How do maps make survey data leap off the page? How can hotspots of opinions help organisations make decisions?

Below are a few examples based on our earlier work (the images are highly compressed – the original maps are very easy to read).

The visual styles, the colours, the question types can all change – but you can start to see how much value a map can add to interpreting survey results.

Here (above) an overview of data for the London boroughs gives us broad insight into the scales of opinion expressed and where we can home in on centres of dissatisfaction (the darkest areas). National-scale maps can be compelling too, given the right volume and distribution of data. Online interactive maps allow data visualisations to be dynamically adapted as we zoom in and out, so that we can perceive relevant patterns at each scale.

Anecdotally, we know that satisfaction with where respondents live is likely to be tied to underlying social and economic factors; by identifying where negative views are concentrated, we can start to identify these drivers. The same approach works for any survey question where opinions are likely to vary across different locations, such as retail choices by proximity, transport preferences, voting behaviour, home and personal media takeup, etc.

Above we’ve zoomed into the same data at a local scale; the semi-transparent data surface allows us to see how hotpots relate to the underlying geography down to the scale of individual streets.  The statistically smoothed data surface is based on individual respondents, not aggregated zones, helping us perceive hotspots which span geographic boundaries, and to have confidence in data despite localised sample sizes.

Mapping uses location to integrate different datasets and allow easy comparisons. The local-scale perception data (above) can be directly compared with neighbourhood types defined by Output Area Classification (OAC), the free-to-use geodemographic system based on government Census data (below). This is the simplest high-level OAC classification; each group breaks down into multiple sub-groups if this level of detail is needed. Of course, geo-demographics help us understand how views differ by socio-economic types and to predict propensities in equivalent neighbourhoods. Other neighbourhood classification systems can also be mapped.