Is it possible that good policing may be an influence here?

This a map of London showing the the levels of deprivation in each Ward based on the average score of the Lower Super Output Areas scores calculated for the English Indices of Deprivation 2007. As I have discussed in previous post criminology theories suggest that the higher the deprivation the higher the crime. So as I have carried out my violence analysis at Ward level I can investigate if there is correlation between my deprivation values for each Ward and my various incident occurrence values. First I am analysing robbery incidents.

The correlation between the two arrays of scores is 0.58 but goes up to 0.66 if the West End outliers are removed. More interesting is to plot the two set of values against each other to determine a linear regression line. This line has an equation that describes its slop and where it bisects the y axis. It also has an  R squared value or coefficient of determination. This shows the degree of influence that deprivation has on the occurrence of robbery incidents (to put it simply). See here for a more detail. In this case it is 0.34 or just over third.

Now each Ward has a new value that can be measured above or below the line by using the equation to determine the score if it were on the line and subtracting that score from it actual score. This new score can be mapped to show those Wards that have a higher and lower score than predicted by the line.

We know from previous post that entertainment venues has a high influence on the occurrence of violence, including robbery, which explains some of the high scores. The Wards that do not have that "excuse" and have high scores are therefore interesting. So are the Wards with lower than predicted scores, especially those shown in green. Is it possible that good policing may be an influence here?

Comparing Geodemographics with IMD

Table taken from "Perceptions of crime and anti-social behaviour:Findings from the 2008/09 British Crime Survey"

In the last post but one I discussed the English Indices of Multiple Deprivation (IMD). In this post I introduce the Geodemographic Classification created from the 2001 Census by various academics for the Office of National Statistics. It originally was classified at Output Area but I am using the larger unit classification of Lower Super Output Area that was published in 2008. Guidance can be found here and the data here.

National Statistics 2001 Area Classification of Lower Super Output Areas showing Supergroups, Groups and Subgroups in the London Borough of Camden

Comparing Area Classification with IMD ranks (the lower the rank the higher the deprivation)

The reason I have used the LSOA scale is that it allows comparison with the IMD. I have shown this above. I have devised a hybrid scatter graph to display the combination of ordinal data - IMD and nominal data - the geodemographic to aid in understanding the similarities and differences between the two datasets.

The table above gives an example of IMD and Area classifications being used for analysis purposes. This comes from a relevant and interesting recent publication that I will return to in later posts.

Deprived

"Indices of Deprivation are an important tool for identifying the most disadvantaged areas in England so that resources could be appropriately targeted" (Noble et al 2008). These indices are subtly different from geodemographic classifications that I have discussed previously on this blog. Geodemographics is the “analysis of people by where they live” (Sleight 1997 quoted in Harris, Sleight and Webber 2005 page 2), whereas Indices of Multiple Deprivation (IMD) can be said to be the collation of data pertaining to place with reference to people who live and work there. The emphasis is on the most deprived areas but the whole of England is covered at the detail of Lower Super Output Area. This means that there is a grading and ranking of deprivation throughout England from 1 - the most deprived to 32,482 the least deprived.

The IMD brings together 38 different indicators which cover seven officially recognised domains of deprivation: Income, Employment; Health and Disability; Education, Skills and Training; Housing and Services; Living Environment; and Crime. A numerical value is produced for each LSOA for each domain.

Okay that's the preamble. You can find out more if you wish by following the reference links above.

So why am I interested in the IMD? Well if you remember from my discussion of incivility theories previously there is a suggestion in broad terms that areas that are deprived (or in colloquial terms, rundown, uncaredfor, etc.) are characterised by high crime and disorder and that high crime and disorder (in a chicken and egg type way) contributed to the area becoming deprived.

So if we assume that these theories are true and stretch that truth to the limit we can make a simple relationship for analysis purposes:

"The higher the deprivation the higher the crime; the lower the deprivation the lower the crime."

To allow this to work you need good measures of deprivation and good measures of crime and disorder.

Comparing the Multiple Deprivation Score with the Crime and Disorder Domain Scores in Camden showing no correlation between the two

Comparing the Multiple Deprivation Score with the other Domain Scores in Camden showing high correlation between the two

Comparing the Multiple Deprivation Score with the other Domain Scores in Camden showing no correlation between the two

Comparing Camden with London

Comparing Residential Burglaries in Camden in 2009 with Multiple Deprivation Scores and Crime and Disorder Scores showing no correlation


Above I have presented various statistics to show that in Camden, in common with the rest of London the selected recorded crime figures are not a good indication of multiple deprivation. Income, employment, health and education appear to be better single domain indicators. This is partly to do with higher weightings these domains are given in the multiple indices.

There are two broad possibilities - the incivility theories are wrong or the measures are inaccurate or incomplete. Having studied the way the figures are compiled I am leaning towards the crime and disorder figures being incomplete in their scope.