Methodology
To create ‘box plots’ and composite bar charts to clarify the patterns seen between different time periods. Box plots (sometimes called box-and-whisker plots) illustrate the median value in a range with the ‘box’ identifying the upper quartile (75%) and lower quartile (25%) of all the values. The ‘whisker’ lines illustrate the range of values between 1% and 25%, and 75% and 100%, thereby offering an indication of skewness and dispersal of data values.
Method
Deprivation can be considered a “latent variable” or “latent construct” as it is not directly observable. It is an abstract psychological concept that can only be inferred from other measurable variables, and in this sense is a derived variable. This informs how deprivation indices have been constructed in the past and present, which are context specific (in terms of their input variables) for their time. Fundamentally though, deprivation can be considered where people are lacking access to a resource that can be considered important to attain a basic standard of living, with the prevailing view that restriction to these resources affects people to differing extents. Nonetheless deprivation metrics help local and national governments plan better allocations of resources for those most in need. In the 19th century deprivation mapping was carried out by Charles Booth, Joseph Rowntree and subsequently his son Seebohm. A range of spatially-linked measures have been produced during the 20th century to guide policy makers, including the Carstairs Score from the 1981 census (Carstairs and Morris 1989:11), the Townsend Index compiled in 1988 (Townsend et al.
1986), the Jarman Underprivileged Area Score during the 1980s and the Index of Local Conditions from the 1991 census (Department for the Environment, Transport and Regions).
The index employed in the impact indicator for the case study hubs is the Index of Multiple Deprivation (IMD), released in 2000 at ward level, then in 2004 and 2007 at Lower Super Output Area (LSOA). The latter have the advantage of containing a more homogenous socio-economic population (c1500 households) than the on-average larger ward populations (Morgan and Baker 2006:29). These indices are calculated on a shorter cycle then the
In the IMD 2004 Geographical Barriers domain, the variables measure the population-weighted average road distance (in km) to
• a GP surgery (Source: National Health Service Information Authority, 2003)
• a general store or supermarket (Source: copyright © Pitney Bowes MapInfo Ltd, 2002)
• a primary school (Source: DfES, 2001–02)
• a Post Office or sub post office (Source: Post Office Ltd, 2003)
In the IMD 2007 Geographical Barriers domain, the variables measure the population-weighted average road distance (in km) to
• a GP surgery (Source: National Health Service Information Authority, 2005)
• a general store or supermarket (Source: copyright © Pitney Bowes MapInfo Ltd, 2005)
• a primary school (Source: DfES, 2004–05)
• a Post Office or sub post office (Source: Post Office Ltd, 2005)
Fig. 7.17 (left): Domains and sub-domains in the Index of Multiple Deprivation (D.G.L.C. 2007a) rural areas. It has been included as a basic but
‘blanket’ accessibility measure to essential facilities over the whole 10km analysis zone attributed at several scales from the individual to a neighbourhood or institution, or a sub-class therefore that very little can be understood from these measures of individual experience of multiple deprivation dimensions, but nonetheless, restriction to a range of resources will give a picture of the different facets of deprivation faced in an area relative to other areas (Noble et al. 2008:10).
There are many pitfalls to circumnavigate when choosing indices particularly in association with combined or composite measures. While a composite index can be useful as it encapsulates several aspects of deprivation, there can be logistical and theoretical issues beneath the surface that should be understood before adoption of a measure. For example, it is common practice to standardise units of measures which removes the element of hidden weighting, or having to construct a weighting scheme, but it is debatable whether this is desirable (Senior 2002:129) There are several reasons why one could choose to transform deprivation variables; to reduce the effect of an over-dominating indicator (Thunhurst 1985:95), and/or to convert the distribution to a normal dispersal of values (although others have countered that this is only necessary for some statistical analyses such as PCA or Factor Analysis). Not doing so could introduce a heightened risk of double counting where there are highly correlated variables (Carr-Hill and Sheldon 1991:702). Finally transformations can help re-address any problems of indices cancelling each other out, and the IMD 2004 and 2007 are founded upon a weighted cumulative model which leads to limited cancellation effects (Noble et al. 2008:11). When dealing with composite multiple domain indices, there is the question of weighting variables. Three main methods are utilised as there is often very little opportunity to weight domains based on empirically-backed values/reasons due to the cost of researching the real impact, hence they are more often either equally weighted or arbitrarily differentially weighted. Ranking issues known in the first IMD2000 include their symmetrical nature and equidistant indicator values that can erroneously cancel each other out. This was calibrated by the transformation of the rank
Therefore the following sub-indicators (the composite measure and the Geographical Barriers measure) are ‘worked examples’ intended to demonstrate how such a method could help planners monitor and plan for deprivation reduction.
This indicator could highlight the location of local areas that can be considered to be at risk of spatially entrenched deprivation, and that could benefit from MUTP-related intervention, such as improved accessibility, increasing the mix of housing tenure, improved 'walkability' or permeability.
Lessons Learnt for planners & decision-makers
Changes in relative deprivation can be considered as a ‘knowable’ process, whereby the causes that change deprivation may become clearer over (possibly a long) time, although
the relationship is complicated. One can however generate ‘good practice’ guidelines from experience to aid decision makers to ‘Sense’ (with deprivation indices for example),
‘Analyse’ (the creation of GIS-based indicator), and ‘Respond’ (what MUTP-related interventions are deemed appropriate and anticipated to be the most beneficial). Comparison between the hubs provides the chance to consider the patterns of change to be context specific, of have an underlying generic relationship.
Cross-influence with meta themes
A sub-domain of the IMD, the Income domain (which accounts for 22.5% of the IMD), is also an input variable in the Social Exclusion indicator (in chapter 8.2) to provide a general picture of the dispersal of income-related poverty. By not including the entire IMD when exploring social exclusion, this avoids the double counting with supplementary employment datasets.
The base dataset for this measure is the Index of Multiple Deprivation 2004 (IMD04) and 2007 (IMD07) published by the Department for Communities And Local Government at Lower Super Output Area level, clipped to the LSOAs surrounding the CTRL stations at Ebbsfleet and Ashford within the 10km ‘wider context’ analysis zone. The SE England Government Office Region (GOR) is adopted as a regional context and is located within the appendix 10.5. It is anticipated that the maps demonstrate the changing levels of deprivation around the hubs. Also this indicator illustrates a potential methodology for understanding where and how much deprivation (relative and absolute) exists in the case study areas before and after MUTP delivery.
The two variables are the combined Index of Multiple Deprivation (IMD), and the Geographical Barriers sub-domain (distances from a range of key local services). The entire dataset of LSOAs in England (n = 32,482) were ranked, 1 being the most deprived, from which the sub-datasets where extracted for Ashford, Ebbsfleet and SE England GOR. It is evident that when one employs a ranking metric what the visual outputs demonstrate is the relative deprivation of an LSOA over others under analysis, without explicit reference to precisely the deprived nature of that LSOA. In order to provide sufficient information that can be used to respond to the relevant research questions, box-and-whisker plots and bar charts further clarify the spatial patterning in the maps. This section initially assesses the IMD 2004 and 2007 in Ashford, then Ebbsfleet and closes with a combined descriptive bar chart that compares the absolute deprivation ranking deciles, and changes in rankings of relative deprivation in quintiles for Ashford and Ebbsfleet compared to SE England.
The GIS map outputs are the 10km analysis zones of the two hubs, with the ‘decile classification’ bands fixed at 1/10 of the ranking range (for the whole country). Some of the analysis areas do not have the most deprived class, hence the map only illustrates the subsequent nine classes. All the colour ramps and value bands in this indicator are identical to facilitate ease of comparison between areas and time periods. A miniature vertical bar chart accompanies each mapset with the percentage of LSOAs per decile class in order to quickly assimilate the dispersion. The red vertical dashed line on these bar charts indicates where the class becomes progressively more deprived (to the left) or less deprived (to the right). These bar charts are the basis for the comparison chart at the close of the section and will be examined in greater detail and clarity.