Do more with data

Putting data on a map makes it easier to see and understand. More than this, it allows spatial statistical methods to be used, to achieve deeper understanding. It avoids some pitfalls of other analysis methods, and can create decision-making tools of fine-scale sensitivity.

Changes in London's commercial floorspace analysed alongside multiple market drivers by Geofutures

Spatial data analysis: more than just visualisation

 

Long and weighty books are written about spatial statistical methods, and using them is not for the faint-hearted. In (very) brief, with the right skills at your disposal, the key things they offer your organization are:

 

The ability to combine unrelated data and analyse relationships between factors

As an example, integrating Census-based demographic data, constituency-based voting behaviour, topographic flood risk data and address-based property value data might reveal information of value. By linking data values at address level, the differences in data geography between the component datasets no longer make the datasets impossible to compare.

 

The ability to understand local variations

Data for geographic zones, such as local authority areas, electoral wards or TV regions, may offer aggregated ‘average’ values for the whole zone. These have some use, but plainly every business, household or individual within that zone does not represent that average.

Spatially analysing data at finer scales such as Census output areas, postcodes or individual addresses reveals hotspots and common characteristics at a level useful to understand local influences within zones, and how they spill over these theoretical boundaries.

 

The ability to test the strength of relationships

Robust statistical methods often call for a test of significance – finding out mathematically whether an observed phenomenon is likely to be a coincidence, or if a causal relationship can safely be attributed. Adding a spatial element to a significance test allows us to consider how close in space an observed data point is to the factor which might be influencing, and assign significance accordingly.

It’s a ‘real-world’ addition to the statistical methods we have available, and it’s available to you when you work with Geofutures to analyse your data.