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Use and Understanding of the Scottish Government Urban Rural Classification

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7 USING OTHER GEOGRAPHIC CLASSIFICATIONS

7.1 There are various geographical classification systems in use and this chapter looks at which ones are known to respondents, both online and telephone. Quantitative data from the online responses shows the level of awareness and usage of 5 main tools in addition to the SG Urban Rural Classification. During the telephone interviews, reasons for use or non-use were explored in more detail. The online questionnaire prompted respondents with a list of classifications or databases, but also gave them the opportunity to add any others. Further information on these, and other classifications, is given in Appendix 3. The prompted list consisted of:

  • The Scottish Index of Multiple Deprivation ( SIMD);
    • designed to identify area concentrations of multiple deprivation across all of Scotland presented at data zone level.
  • PAF (Postcode Address File);
    • the Royal Mail's database of UK addresses.
  • ACORN or Mosaic;
    • socio-demographic segmentation systems.
  • DEFRA's Classification of Local Authority Districts and Unitary Authorities in England;
    • a 6-fold urban rural classification.
  • The Randall Definition;
    • o a means of classifying local authorities based on population density; using the Randall Classification 14 Local Authorities are classed as rural.

Awareness of classifications

7.2 All respondents were asked to say which classifications they had heard of and were prompted with the list of 5 classifications. Respondents were provided with an opportunity to add any others of which they were aware. Results are shown in chart 7.1 alongside awareness of the SG Urban Rural Classification.

7.3 As shown in chart 7.1, the highest level of awareness was for the Scottish Index of Multiple Deprivation ( SIMD) at 88% of all respondents, followed by the Scottish Government Urban Rural Classification (81%). Only 28 respondents said they were aware of other classifications;

  • ONS output area classification (8 mentions);
  • Carstairs Deprivation Scores (3);
  • Scottish Enterprise - Economic typology of places across Scotland (2);
  • SuperProfiles (2);
  • Fragile Areas / HIE Fragile Areas (2);
  • CAMEO (2).

Chart 7.1 Awareness of Classifications

Chart 7.1 Awareness of Classifications

Source: QPA4 & PA20; All respondents, n = 412

7.4 Sub group analysis shows:

  • Awareness of SIMD was highest among those in Scottish Government, Local Authorities and NHS Boards;
  • Awareness of Acorn or MOSAIC was highest among Universities / Colleges;
  • Awareness of DEFRA's Classification of Local Authority Districts and Unitary Authorities in England was highest among those in Universities / Colleges and Private Consultancies;
  • Awareness of PAF was highest among those in Scottish Government;
  • With exception of SIMD, awareness of each classification was higher among analysts than those involved in policy;
  • Awareness of SIMD was 86%+ for all roles.

Usage of Classifications

7.5 As with the question on awareness, respondents were prompted with a list of 5 classifications and given the opportunity to add any others they used. Results are shown in chart 7.2 alongside use of the SG Urban Rural Classification. As shown in chart 7.2, highest levels of usage for these classifications were for SIMD (89%) and the SG Urban Rural Classification (72%). Other classifications were used by around one in three or less respondents.

Chart 7.2 Usage of Classifications

Chart 7.2 Usage of Classifications

Source: QPA5; All respondents aware of SG Urban Rural Classification, n = 296; QPA20: All respondents aware of each classification, n = 262

7.6 Sub-group analysis shows:

  • Highest levels of usage for SIMD were seen within NHS Boards (93%), Local Authorities (89%) and Voluntary Organisations (86%).

7.7 After identifying other classifications ever used in the course of their work, respondents were asked which they used most often. This question did not include the SG Urban Rural Classification.

Table 7.1 Usage of other classifications

Total
(254)

SG & others*
(100)

Local Authority
(53)

Other
(101)

Policy
(73)

Analyst
(181)

%

%

%

%

%

%

SIMD

76

74

89

71

84

73

PAF

11

17

6

8

5

13

DEFRA's Classification of Local Authority Districts and Unitary Authorities in England

6

3

2

10

4

6

Randall

3

5

2

1

4

2

Acorn or MOSAIC

2

-

-

5

-

3

Source: PA20b
*This category comprises Scottish Government, Scottish Government Agencies and the Scottish Parliament.

Other Urban Rural Classifications

7.8 Other urban rural classifications identified as being used most often included:

  • DEFRA's Classification of Local Authority Districts and Unitary Authorities in England (14 respondents);
  • Randall (7 respondents).

7.9 These classifications were reported as used most often by a mixture of policy respondents and analysts.

DEFRA's Classification of Local Authority Districts and Unitary Authorities in England

7.10 DEFRA's Classification of Local Authority Districts and Unitary Authorities in England, was used by 14 respondents (3 policy and 11 analysts); 6 respondents reported that they used this classification for 'academic analysis'.

7.11 This classification was used most commonly in the topic area of Communities / Population Change and Transport.

7.12 Of the 13 respondents who commented on the benefits of using DEFRA's Classification of Local Authority Districts and Unitary Authorities in England, 8 said that it is easy to work with.

7.13 Comments on limitations or complications involved in using this classification were identified by 3 respondents, one mention each for difficulty of use, lack of transparency / clarity, limited number of geographies.

7.14 Frequency of use for DEFRA's Classification of Local Authority Districts and Unitary Authorities in England was reported as an average of 3.77 times per year.

Randall Definition

7.15 The Randall Definition, a Local Authority urban rural definition, was used by 7 respondents (3 policy and 4 analysts); 3 respondents mentioned that they use Randall 'for statistical publications'.

7.16 The main topic areas where Randall was used were Communities / Population Change (2 analysts) and Housing (2 policy).

7.17 Main benefits identified were:

  • 'Allows comparisons to be made across urban / rural areas throughout Scotland' (5 respondents)
  • Easy to work with (2 respondents);
  • Provides uniformity when allocating funding" (2 SG policy respondents).

7.18 Comments on limitations or complications involved in using Randall were identified by 4 SG respondents:

  • Information may be out of date (1 policy);
  • Lack of precision / too general (2 policy) and, on a similar note, Some LAs are too big to be categorised as either purely urban or rural (2 analysts);
  • Can produce illogical / anomalous classifications (2 policy).

7.19 Frequency of use for Randall was reported as 3 times per year.

Other Geographic Classifications / Databases

7.20 Other non Urban Rural Classifications identified as being used most often included:

  • SIMD (193 respondents);
  • PAF (28 respondents);
  • Acorn or Mosaic (5 respondents).

7.21 While both policy respondents and analysts reported that they use SIMD and PAF, Acorn / Mosaic was only selected by analysts. All 5 using Acorn / Mosaic were analysts working outwith local or Scottish Government.

Scottish Index of Multiple Deprivation ( SIMD)

7.22 The Scottish Index of Multiple Deprivation ( SIMD) was mentioned by 193 respondents (47% of the 412 total). The SIMD was designed with the purpose of identifying area concentrations of multiple deprivation across Scotland and is presented at data zone level; this means that small pockets of deprivation can be identified. It provides a 'scale' of deprivation.

Who uses SIMD and why?

7.23 The different purposes that SIMD and the SG Urban Rural Classifications have been designed for is reflected in the number of respondents who report using both the SG Urban Rural Classification and SIMD, either in conjunction or separately, depending on the work being undertaken.

7.24 Highest usage of SIMD was among policy respondents. In relation to sector, usage was highest among respondents working within the NHS and Local Authorities.

7.25 Looking at Scottish Government Directorates; all respondents within Health reported that they used SIMD while usage was lowest for those within Environment.

7.26 138 respondents who reported that they use SIMD most often also used the SG Urban Rural Classification.

7.27 The pattern of usage for SIMD is similar to that of the SG Urban Rural Classification, with highest proportions of analysts within Local Authorities claiming to use this to 'define eligibility for funding' or to 'help with monitoring of single outcome agreements'. Highest proportions of analysts using SIMD 'as a standard variable in analysis' were from the Scottish Government.

7.28 A difference between reasons given for using SIMD, as opposed to those given for using the SG Urban Rural Classification, appears in relation to single outcome agreements. Although similar numbers use both classifications (188 and 210 respectively), SIMD is more commonly used in relation to single outcome agreements, especially within Local Authorities.

7.29 SIMD (24%) is also used slightly more than the SG Urban Rural Classification (17%) for defining eligibility for funding, and this use is more common among users in local government than in central government.

Topic areas for which SIMD is used

7.30 Respondents reported that the main topic areas where they use SIMD were Health and Wellbeing (57%), Communities / Population Change (52%) and Economy/ Economic Development (41%).

Benefits of using SIMD

7.31 Both policy and analyst respondents were asked to say what they felt were the benefits of using SIMD. The main response from both types of respondent was that it is an officially recognised classification that fits with other Scottish Government datasets; this was the main response given by analysts in all sectors.

7.32 Policy respondents working within the Scottish Government and SG agencies, however, were more likely to identify 'fits with policy requirement' as the main benefit.

Limitations / complications in using SIMD

7.33 Both analyst and policy respondents were asked to identify any limitations or complications in using SIMD. As with the question on the SG Urban Rural Classification, answers were not pre-coded and respondents were free to provide free-text responses. Again there was a smaller number of responses (n=66) and corresponding small numbers giving any one answer. The following perceptions came from both policy and analyst respondents unless stated:

  • Limited use in rural areas (24 respondents); this answer was given by 9 of those working in Local Authorities;
  • Difficult to use / statistical literacy needed (6);
  • Lack of continuity / frequent updates make it difficult to quantify change over time (6 analysts);
  • Not suitable for areas with low population density (6);
  • Data zones don't match LA boundaries / post code areas (5).

7.34 In recognition of the fact that SIMD and the SG Urban Rural Classification were designed for different purposes, respondents chose whichever one best fitted the analysis they were undertaking at the time.

Frequency of use

7.35 Respondents were asked how regularly, on average, they used SIMD and 29% of those using SIMD said they use it every month or more; this is a higher figure than the users of the SG Urban Rural Classification where only 12% reported that they used the classification every month or more frequently.

PAF

7.36 PAF, (Postcode Address File), was used most often by 28 respondents, only 4 of whom were policy respondents. 12 of the 24 analyst respondents said that they use PAF 'as a standard variable in analysis.

7.37 The main topic areas where PAF was used were Communities / Population Change and Education and Lifelong Learning; both mentioned by 8 respondents.

7.38 The main benefit, given by 20 out of 24 analyst respondents in relation to PAF, was that it is available at postcode level.

7.39 Only 5 respondents identified limitations or complications in using PAF. Cost, lack of precision and the need to keep it up to date were each identified by 2 respondents.

7.40 Frequency of use for PAF was an average use of 4.13 times per year.

ACORN or Mosaic

7.41 ACORN or Mosaic, socio-geodemographic profiling tools, were used by 5 analysts; 3 mentioned using them 'for statistical publications' and 'as a standard variable in analysis'.

7.42 The main topic areas where Acorn or Mosaic were used were given as Housing and Economy / Economic Development (2 mentions each).

7.43 The main benefit identified was that these are available at a postcode level (4 mentions).

7.44 Only 2 analysts mentioned a limitation / complication involved in using Acorn or Mosaic and this was cost.

7.45 Frequency of use was an average of 5.4 times per year.

Overall usage of the Scottish Government Urban Rural Classification and/or other classifications

7.46 This section looks at different classifications in use and examines whether this usage relates to the respondents' job function or work sector, topic areas, or other datasets and sources that are also in use. As table 7.2 shows, the highest proportion of respondents used a combination of the SG Urban Rural Classification and other(s), while only a small minority used the SG Urban Rural Classification only.

Table 7.2 Usage of classifications

Total
(412)

SG & others*
(171)

Local Authority
(92)

Other
(149)

Policy
(133)

Analyst
(279)

%

%

%

%

%

%

Do not use any classification

30

34

37

21

32

29

Use SGUR only

7

7

2

9

11

5

Use SGUR and other(s)

45

44

42

49

38

49

Use other(s) only

18

15

18

21

20

17

Source: PA5, PA20x
* This category comprises Scottish Government, Scottish Government Agencies and the Scottish Parliament.

7.47 123 (30% of the total) do not use any form of classification in their work:

  • This figure includes analysts and policy respondents from across all sectors; 70 of these respondents reported that they do make a distinction between urban and rural areas in their work and 47 were aware of other types of classifications. Given that 70 respondents do make a distinction but do not use any form of classification, this issue was examined further in the telephone interviews to ascertain why this might be the case.

7.48 There were two key reasons provided by respondents participating in the telephone interviews for not using any classification in their work. First, the nature of their work meant that it was not perceived as being necessary to use any of the available classifications. Second, a small number of respondents (primarily those involved in policy) noted that their use is indirect. These respondents were provided with information by colleagues who have used one of the available classifications, for example, in briefing papers or publications. As one policy respondent noted,

"I have never used the Scottish Government Urban Rural Classification, although that doesn't mean to say I haven't used data produced by colleagues who have used it. There are probably a number of people within this organisation who will be using it."

7.49 27 (7%) use only the SG Urban Rural Classification:

  • Again this figure included analysts and policy respondents from across most sectors, although more than half were policy respondents; the main topic area for which the SG Urban Rural Classification was being used was Economy / Economic Development (12 respondents).

7.50 187 (45%) use the SG Urban Rural Classification and also use one or more other classifications:

  • The other classification most frequently mentioned was the Scottish Index of Multiple Deprivation ( SIMD); 167 respondents reported they use the SG Urban Rural Classification and also use SIMD. The SG Urban Rural Classification was used mainly in the area of Communities / Population Change (91 respondents) while Health and Wellbeing was the main topic area for those who also used SIMD (70 respondents).

7.51 75 (18%) use one or more other classifications but do not use the SG Urban Rural Classification:

  • This included 51 who only used one classification. Again SIMD was the classification most frequently mentioned (by 55 respondents); 35 respondents used SIMD in the area of Health and Wellbeing and 31 in the area of Communities / Population Change.

Using the Scottish Government Urban Rural Classification and other classifications

7.52 Analysts who use the SG Urban Rural Classification were asked whether they used different classifications in conjunction with the SG Urban Rural Classification or for different purposes (base = 144):

  • 28 used the SG Urban Rural Classification in conjunction with other classifications;
    • this was highest amongst University / College analysts (7 out of 20).
  • 17 used other classifications for different purposes;
    • highest amongst Local Authorities (5 out of 22).
  • 99 answered no, they do not use different classifications in conjunction with the SG Urban Rural Classification or for different purposes;
    • All of the 7 Voluntary Organisation analysts answered no to this question.
  • Classifications identified as being used in conjunction with each other or for different purposes included (base = 32):
    • SG Urban Rural Classification and SIMD / SG Urban Rural Classification and SIMD in conjunction (8 respondents);
    • Using SIMD only but using it for a variety of purposes (8);
    • SIMD and Acorn for a variety of purposes (2);
    • Derived bespoke classifications for different purposes (2).
  • Reasons for using classifications in conjunction with each other included (base = 26):
    • They measure different things / provide more than one dimension of analysis (4 respondents);
    • They are the best available / best suited to purpose (2);
    • Offers a sufficiently local base to be meaningful in analysing communities (2).

7.53 In order to understand why different classifications were being used, the respondents participating in the telephone interviews were asked to explain how they make a decision on which classification(s) to use. Respondents reported that the classification(s) were selected primarily according to each particular project being undertaken and its objectives. In some instances, classifications were used in conjunction with each other or for different purposes because they had previously been used and there was a requirement for consistency in data reporting over time. As had already been mentioned in responses to the online survey, respondents again indicated that each classification was distinct in its own right and used for specific purposes.

UK wide analysis involving urban and rural classifications

7.54 One of the questions asked of analysts, but not policy respondents, in the online survey was whether respondents conduct cross-border or UK wide analysis that involves using different urban rural classifications:

  • Less than a fifth of analysts (24 out of 147) who responded said that they do, with highest proportions from within Universities / Colleges (12).

7.55 Analysts were then asked to identify any issues in using more than one urban rural classification for cross-border or UK-wide analysis. Nineteen respondents gave an answer. The main issues identified were:

  • Different classifications / different definitions (7);
  • Lack of comparability (with rest of UK) (4 out of the 5 who said this were from Universities / Colleges).

In summary,

  • Amongst other classifications, the Scottish Index of Multiple Deprivation ( SIMD) was identified as being used most often by most respondents.
  • The pattern of usage for SIMD was similar to that noted for the SG Urban Rural Classification, although SIMD is used more than the SG Urban Rural Classification in relation to single outcome agreements and slightly more for defining eligibility for funding.
  • A key benefit identified by analysts for SIMD is that is it an officially recognised classification that fits with other SG datasets; policy respondents note that SIMD fits with policy requirements. SIMD is of limited use in rural areas as populations in rural areas are more sparse and mixed so concentrations of multiple deprivation are less likely to occur.
  • The classification with highest levels of awareness is SIMD, followed closely by the SG Urban Rural Classification. Awareness of other classifications varied; while almost half were aware of PAF, only 1 in 10 knew of the Randall Definition.
  • SIMD has the highest level of usage among respondents; closely followed by the SG Urban Rural Classification. Less than one in three respondents used any other classification.
  • Almost 90% of the users of the SG Urban Rural Classification also used at least one other geographic classification systems, most commonly the SIMD.
  • Some online respondents conducting cross-border or UK-wide analysis face difficulties because of the use of different urban rural classifications which are not comparable.