Methodology
Developing Understanding Scottish Places involved the following steps:
- Geography: defining the geography of towns to be included in USP
- Socio-demographic typology: setting the context of each town
- Size classification: grouping towns by resident settlement size
- Inter-relationship model: this explores how towns inter-relate and to what extent they are independent, interdependent or dependent
Geography
Understanding Scottish Places uses nationally recognised boundary definitions at the locality level. This permits the use of a wide range of national government data, as smaller geographies (such as output areas) can readily be assembled into localities. Localities were preferred to the geography settlement as the criterion for defining a settlement was more demanding and would have excluded places on the edge of conurbations which have their own town identities. The definition of locality is taken from the 2011 census as is the list of localities used:
'Localities correspond to the more recognisable towns and cities of Scotland which can be found within settlements. They also have a minimum rounded population of 500 people or more' (1).
Localities are taken to be the nearest administrative definition of towns available. On the USP web interface the term 'towns' is used rather than 'locality'.
In Scotland there are 509 localities with a resident population of 1000 or more, representing an increase from 479 in 2014. There are many reasons for this including population increase, growth and repositioning of towns and new towns being developed on sites proximate to major cities.
These 509 towns form the geographical base of the Understanding Scottish Places platform. Only data sets which are available for all 509 localities are used in USP. This reflects the fact that USP is about towns, and not about local authorities or other geographies, and that consistency and comparability across Scotland are key principles of the platform.
Socio-Demographic Typology
The 2022 Scottish Census provides data on a range of demographic, social and economic indicators. Data for a series of variables were downloaded for each of the 509 localities and are used to derive a typology of Scottish towns. The variables were selected with the statistical clustering procedure K-Means in mind. The approach follows that used by Shepherd for England in 2009 (2). Some pre-analysis of variables was carried out to understand the dimensions of the data and to ensure that the variables would offer sufficient breadth and variety to be meaningful.
| Population change |
|
|---|---|
| Household change |
|
| Population |
|
|---|---|
| Car ownership |
|
| Tenure |
|
| Household composition |
|
| Age (% total population) |
|
| Employment (% working age 16-74) |
|
|---|---|
| Occupation (% working age 16-74) |
|
| Deprivation |
|
| Social Grade (% households aged 16-64) |
|
| Sector Diversity |
|
| Distance travelled to work |
|
| Education (% all people aged 16 and over) |
|
|---|---|
| Public services |
|
| Commercial |
|
|---|
| Distance travelled to study |
|
|---|---|
| Charity |
|
| Environment assets |
|
|---|
USP Clustering Typology
A typology was developed using a clustering technique K-Means Clustering is a data driven clustering procedure which seeks to maximise differences between clusters, with cases being as similar as possible to others in any cluster and as dis-similar to cases in other clusters as possible. Unlike other procedures it does not start from a theoretical basis and established groupings to which cases are added, nor does it merely divide the range of values for cases into classes. K-means clustering is based upon numerical distance between cases when represented by scores on the variables. The number of clusters dictates the number of centroids and the distances are measured from these. The cases are grouped by minimising the distances between cases. The process is iterative and stops when no cases can be moved between groups.
The clustering procedure involved repeating the process for different numbers of potential clusters. A judgement was made between too much detail (too many groups of 1 locality) and too much generality.
F values are calculated which are indicative of the contribution of each variable to the final analysis. Final centre clusters are used to define the clusters. Not all cases in a cluster share all characteristics of the cluster to the same degree. They are generally more like each other, however, than the cases in other clusters.
USP used a six-fold clustering model drew on a large set of 2022 census variables covering life-stage, housing, employment and welfare. Those clusters are:
- Aspiring and Active towns
- Comfortable Mature towns
- Prosperous University
- Affluent towns
- Least Privileged towns
- Diverse Social Circumstances
Locality size
It is logical to expect that locality size, in terms of resident population, will impact on a locality's function and service provision. The clusters therefore have to be disaggregated in terms of the size of the towns. The table shows the size categories used, which provide compatibility with the Scottish Government's Urban-rural classification size bands. (3)
| Size definitions | Number of Localities |
|---|---|
| 0-2999 | 248 |
| 3000-9999 | 170 |
| 10000-49999 | 80 |
| 50000 and over | 11 |
Combining the typology with size categories 19 different groupings (the typology) were established (i.e. there were 5 types in the typology which contained no town entries).
| Size definitions | Cluster | Number of Localities |
|---|---|---|
| 0-2999 | 1 | 41 |
| 2 | 66 | |
| 3 | 60 | |
| 4 | 0 | |
| 5 | 29 | |
| 6 | 52 | |
| 3000-9999 | 1 | 34 |
| 2 | 41 | |
| 3 | 27 | |
| 4 | 0 | |
| 5 | 21 | |
| 6 | 47 | |
| 10000-49999 | 1 | 23 |
| 2 | 17 | |
| 3 | 12 | |
| 4 | 1 | |
| 5 | 5 | |
| 6 | 22 | |
| 50000 and over | 1 | 1 |
| 2 | 5 | |
| 3 | 0 | |
| 4 | 0 | |
| 5 | 0 | |
| 6 | 5 |
Inter-Relationship Model
The next level of Understanding Scottish Places was to apply an inter-relationship model. The inter-relationship model is framed by the Centre for Local Economic Strategies (CLES) research over the last ten years about the resilience of place. (5) The CLES resilience model explores the assets and relationships which places have between the public, commercial, and social sectors and how that shapes the functioning of their economies. The inter-relationship model explores this further by identifying the extent to which places are reliant or otherwise upon neighbouring localities for these assets and relationships.
In the Understanding Scottish Places (USP) project, we have developed a way of exploring this through developing a set of indicators. These indicators enable us to define the relative independence, interdependence, and dependence of the 509 towns in Scotland with a population of over 1000 people. We have called this our inter-relationship model.
An independent town will have a high number of public; commercial; and social economy assets in relation to its population. This will include GP surgeries, shops and charities. It will have a diverse sector base in terms of jobs. Residents will travel short distances to work and study and the town will attract people from neighbouring towns to access its assets.
A dependent town will have a low number of public; commercial; and social economy assets in relation to its population. It will be reliant on singular sectors in terms of jobs. Residents will travel longer distances to work and study and the town will be reliant on neighbouring towns for assets and jobs.
An interdependent town will sit somewhere between independent and dependent towns. For some public, commercial and social economy assets it may have a high number in relation to its population and for others a low number. A balance of people will work and study in the town with others reliant on neighbouring towns.
Approach
Twelve indicators are used which explore the inter-relationships within and between towns in Scotland. These indicators were chosen on the basis of data being nationally available to populate them. The indicators portray three things:
- There are indicators which detail the number of certain assets in the town when compared to its resident or working age population;
- There are indicators which detail the diversity of the business and employment base in the town;
- There are indicators which detail the distance people resident in the town travel to work and to study.
To populate each indicator, data has been gathered from a range of sources for each of the 509 towns in Scotland with a population of over 1000 people. The 12 indicators and associated data sources are as follows:
- Number of registered charities - this is the number of charities based in the town in relation to the resident population. Data has been gathered from the Scottish Charity Regulator (OSCR);
- Number of GP’s and Dentists - this is the number of GP’s and Dentists in the town in relation to the resident population. Data has been gathered from Registers of GPs and Surgeries at the Information Services Division at NHS Scotland;
- Number of hospitals - this is the number of hospitals in the town in relation to the resident population. Data has been gathered from the Information Services Division at NHS Scotland;
- Number of children in primary schools - this is the number of children in primary schools based in the town in relation to its resident population. Data has been gathered from Education Scotland and the Scottish Council of Independent Schools;
- Number of children in secondary schools - this is the number of children in secondary schools based in the town in relation to its resident population. Data has been gathered from Education Scotland and the Scottish Council of Independent Schools;
- Number of jobs - this is the number of jobs in the town in relation to its working age population (16-64). Data has been gathered from the Business Register and Employment Survey;
- Diversity of jobs - this is the number of jobs in particular sectors in the town and the extent to which it is diverse or reliant in sector terms. Data has been gathered from the Business Register and Employment Survey;
- Public sector jobs - this is the number of jobs in the town in the public sector in relation to all jobs. Both low and high numbers of jobs are a sign of reliance on either the public sector or other sectors. Data has been gathered from the Business Register and Employment Survey;
- Number of shops - this is the number of shops in the town in relation to its resident population. Data has been gathered from the Scottish Assessors Association;
- Distance travelled to work - this is the distance travelled by the working age residents of the town to reach their job. Data has been gathered from the 2011 Census;
- Distance travelled to study - this is the distance travelled by students resident in the town to reach the place of their studies. Data has been gathered from the 2011 Census;
- Diversity of retail offer - this is the percentage of retail types in the town in relation to 37 different retail types. The higher the percentage the greater diversity of retail types. Data has been gathered from the Business Register and Employment Survey.
The data has then been analysed utilising the appropriate one of three portrayals outlined earlier (number of assets; diversity of business and employment base; and distance travelled). For each indicator, towns have then been split into sevenths depending on their position across the 509 towns and given an appropriate score. Towns in the top seventh on each indicator (a high number of shops per resident, for example) have scored 6 and towns in the bottom seventh (a low number of shops per resident, for example) have scored -6 with increments of 4, 2, 0, -2, and -4 in between.
Once each indicator has been analysed for each town, the individual scores have been added to derive a total for the town. The highest aggregated total score is 62 with the lowest -62. The difference (124) between highest and lowest is taken and splits the towns into seven equal increments. For example, towns scoring between 42 and 62 are in the increment representing the most independence. They have been subsequently provided with the following assessments.
- First increment - Independent
- Second increment - Independent to Interdependent
- Third increment - Interdependent to Independent
- Fourth increment - Interdependent
- Fifth increment - Interdependent to Dependent
- Sixth increment - Dependent to Interdependent
- Seventh increment - Dependent
This gives each town their overarching score and assessment using our inter-relationship model.
Independent towns have a high number of assets in relation to their population; a strong diversity of jobs; and residents travel shorter distances to travel to work and study. These towns will attract people from neighbouring towns to access their assets and jobs.
Independent to Interdependent towns have a good number of assets in relation to their population. These towns have a good diversity of jobs; and residents on the whole travel shorter distances to travel to work and study. These towns attract people from neighbouring towns to access some of their assets and jobs.
Interdependent to Independent towns have a good number of assets in relation to their population. They have some diversity of jobs; and residents largely travel shorter distances to work and study, although some travel longer distances. These towns attract people from neighbouring towns to access some of their assets and jobs.
Interdependent towns have a medium number of assets in relation to their population; average diversity of jobs; and residents travel a mix of short and long distances to travel to work and study. These towns are attractors of people from neighbouring towns who come to access some assets and jobs but they are also reliant on neighbouring towns for other assets and jobs.
Interdependent to Dependent towns have a low number of assets in relation to their population. They have some diversity of jobs; and residents travel largely longer distances to work and study, although some travel shorter distances. They are reliant on neighbouring towns for some assets and jobs.
Dependent to Interdependent towns have a low number of assets in relation to their population. They have a poor diversity of jobs; and residents on the whole travel longer distances to work and study. They are reliant on neighbouring towns for some assets and jobs.
Dependent towns have a low number of assets in relation to their population; a reliance on one sector for jobs; and residents travel longer distances to work and study. They are reliant on neighbouring towns for assets and jobs.
Footnotes
- National Records of Scotland (2014) Mid-2012 Population Estimates for Settlements and Localities in Scotland
- Shepherd, J. (2009) A typology of the smaller rural towns of England , Birkbeck College, university of London.
- Lupton, R., Fenton, A., Tunstall, R. and Harris, R. (2011) Place typologies and their policy applications , DCLG Case Report 65.
- Scottish Government (2012) Urban-rural classification.
- CLES (2011) Productive Local Economies: Creating resilient places and CLES (2014) Developing resilient town centres.