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1.		The location of each UPRN is mapped with the x,y coordinates from the Property Database. The ward boundaries have been overlaid. Dot mapping gives an indication of housing density and street patterns, but not much else. Instead, the dot map is usually transformed into other types of maps. Copyright: LB Camden

2.		Each UPRN can be labelled with the ward code it falls within. Then the ward code is used as a grouping factor to create summary statistics for each ward for some measure, and the ward polygons shaded according to value gradients. This creates a choropleth map. Copyright: LB Camden

3.		UPRNs can similarly be grouped into fixed size cells which are shaded according to value gradients. This captures small neighbourhoods and gives an impression of changes in value at a higher resolution. Copyright: LB Tower Hamlets

4.		The data can be transformed into density maps showing the number of people or addresses that meet a certain set of criteria e.g. persons aged 65+ per square km. Once shaded by value, the highest density hotspots can be converted into polygons representing areas of highest concentration in order to create meaningful zones from the 'bottom-up' for, say, targeting initiatives. Copyright: LB Tower Hamlets

5.		UPRNs, and therefore population characteristics and counts linked to UPRN, can be extracted for any shape or size of areas: in this map there are PCT locality boundaries, Sure Start boundaries, and bespoke hotspots. Copyright: LB Brent

6.		This is a GP catchment area map showing the number of persons per GP based on people's nearest GP surgery. It is a good way of looking at the equity of access to health care because the size and population characteristics of each area can be computed exactly. Copyright: LB Brent

7.		This map illustrates the number of people per grid cell that meets a certain criteria. It gives a basic simple impression of clusters. Copyright: LB Brent

8.		This map is another example of using a shaded density map. This example illustrates varying levels of risk from coronary heart disease based on an analysis of risk factors. The numbers at risk with a given risk profile can then be accurately computed within any size or shape of area. Copyright: LB Islington

9.		The red areas on this map are derived from density hotspots (described in map 4) to illustrate that pockets of deprivation do not always match ward boundaries (in blue). Copyright: LB Brent/Gillian Harper

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