Towards an Improved Rural Data Infrastructure for rural Scotland

Objectives & Methods

This page describe the four project modules which have been devised. In loose terms, Modules A, B and D deal with policy-related research, whereas Module C provides complementary applied empirical research

Module	Purpose	Leader(s)
A	To determine needs and priorities of data users	Neil Bayfield
B	To assess the provision of infrastructure data	Alistair Geddes
C	To apply and develop spatial data integration methods	David Elston, Matthew Hodgson, Alistair Geddes
D	To assess future rural change scenarios	Mark Shucksmith

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Module A: To determine needs & priorities of data users

Rationale

The aim of this module is to identify key data sets, the main types of data users, and the priorities that users ascribe to different types of data and to other potential data sets.

Initial work aims to make a preliminary identification of 'core' data and the main users of them. The major effort will be to investigate the priorities that different users attach to different types of data in a variety of rural land use change contexts, and in relation to the rural change scenarios which are being developed in Module D. Examples of contexts which could be covered might include: the location of new roads, housing developments, quarries and open cast coal fields, woodland and conservation grant schemes; or enabling developments for old landscapes, interpretative and access schemes; or establishing integrated transport links.

A method based on multiple attribute utility theory will be used to explore the relative importance of (a) different types of social, economic and environmental data, and (b) different types of data within individual disciplines.

Initially, two consultative workshops are envisaged, each lasting two days. The first will be organised for 'expert' colleagues, including those from the research centres in which the members of the project team work. The second 'stakeholder' workshop will involve representatives of data users, holders and transmitters (e.g. planners, developers, regulators, and NGOs).

Priority rankings for different types of data will be produced from this workshop, and these will reveal the differences between the experts' and stakeholders' assessments. They will help to draw conclusions about the levels of precision that are needed for different types of tasks, by different user groups. Moreover, they will help to probe under-use or lack of awareness of data sets, and also whether there are recombinant or presently missing data sets that could be developed.

Key Objectives

A1 To identify core social, economic and environmental data sets for investigation.

A2 To identify current users of core data sets.

A3 To identify what data are used by different categories of users, and for what purposes they are used.

A4 To identify users' awareness of data sets, whether there are important under-used or missing data, and whether recombinant data could be developed.

A5 To assess priorities for social, economic and environmental data in relation to different user groups, data types, and sizes of organisation.

Module A: Activity plan [READS FROM LEFT TO RIGHT]

Objective (and Links to Other Objectives)	Task(s)	Responsibility	Timing
A1 Identify core social economic and environmental data sets (Links to B1, C1, C2)	i. Agree restricted set of key data sets for investigation	Project Team	Oct 1999
A2 Identify current users of core data sets	i.Conduct questionnaire survey of data users, holders and transmitters	Neil Bayfield	Mar 2000
	ii. Conduct interviews with data users, holders and transmitters		Mar 2000
A3 Identify what data are used by different categories of users, and for what purposes they are used (Link to D3)	i. Conduct questionnaire survey of data users, holders and transmitters	Neil Bayfield	Mar 2000
	ii. Conduct interviews of data users, holders and transmitters		Mar 2000
A4 Identify users' awareness of data sets, whether there are important under-used or missing data, and whether recombinant data could be developed (Links to B2, C2, D3)	i. Run 'pilot' workshops with staff from partner organisations to test consultation method and use of requisite decision modelling software	Neil Bayfield	Nov 1999
	ii. Run 'expert' workshop with specialists		Dec 1999
	iii. Run 'stakeholder' workshop with representatives of data users (e.g. planners, developers, regulators, NGOs)		Mar 2000
A5 Assess priorities for social, economic and environmental data in relation to different user groups, data types, and sizes of organisation (Link to D3)	i. Prepare draft findings from Objectives A1-A4	Neil Bayfield	Mar 2000
	ii. Write journal paper		May 2001

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Module B: To assess the provision of infrastructure data

Rationale

The concept of a data 'infrastructure' focuses on the connections between data providers, particularly those in the public sector, and the availability and suitability of data from them for use by existing or potential data-using organisations. The infrastructure of spatial data is viewed with special regard. Two main reasons for this are, firstly, because such data are generally expensive to collect and maintain, and thus the cost-efficiency of such data is a concern, and secondly, because the data are often made available for different geographical units, making it difficult to use them together. Typically, attention is paid to three key constituents of a spatial data infrastructure: (a) the identification of commonly used core data; (b) the broadcast of descriptive 'metadata'; and (c) participation in multilateral co-ordinated ventures. This third component addresses different kinds of partnerships, including strategic service level agreements, ad hoc project-specific arrangements and voluntary arrangements based on social networks (Craglia, 1999). In the UK, the National Geospatial Data Framework (NGDF) promulgated by the Ordnance Survey is probably the most well-publicised infrastructure initiative.

This module sets out to describe and assess the current conditions which frame the spatial data infrastructure for applied research on rural change. It takes on the ideas presented in the earlier appraisal made by Haines-Young and Watkins (1996). However, whereas their review was mainly restricted to national land use / land cover data sets, this research aims for broader cross-scale and cross-sector coverage. This is necessary, if it is anticipated that rural policies will pose integrated challenges for economic development and social welfare alongside sound environmental management - at sub-national as well as national levels. In this context, attention to the availability and adequacy of biophysical data sets alone is simply not sufficient. Moreover, although the infrastructure is significantly characterised by the data provision responsibilities of central government departments and research organisations, other important creators and users of spatial data include local authorities (and perhaps other 'small' bodies), and they too may have rural concerns. Overall therefore, this module aims to investigate what data are provided, by whom and why, considering such factors as the value attached to data, policies on enabling information access and preserving privacy, and awareness of current and future users.

References:
Craglia, M. (1999). The development of local GI infrastructure in the UK. Presentation at GIS 99, London.
Haines-Young R and Watkins C (1996). The rural data infrastructure. International Journal of Geographical Information Systems 10(1): 21-36.

Key Objectives

B1 To describe and assess the characteristics of the current rural data infrastructure.

B2 To assess the impacts of infrastructure initiatives.

Module B: Activity plan [READS FROM LEFT TO RIGHT]

Objective (and Links to other Objectives)	Task(s)	Responsibility	Timing
B1 Describe and assess the characteristics of the current rural data infrastructure (Link to A1, C2, D3) and B2 Assess the impacts of infrastructure initiatives (Link to A4, D3)	i.Review existing research on data infrastructure and collate information about current infrastructure initiatives	Alistair Geddes	Oct 1999 - Dec 1999
	ii. Conduct initial interviews with representatives of key data set providers and staff from data infrastructure initiatives. Preliminary analysis and write-up		Oct 1999 - Jan 2000
	iii. Conduct more detailed interviews with same interviewees Task i, where necessary, regarding: mission and structure; nature of data; data provision policies; and participation in infrastructure initiatives. Analysis of fresh information and 2nd draft write-up		Feb 2000- Mar 2000
	iv. Conduct workshop with interviewees to present and discuss findings. Post-workshop revision of draft findings		Mar 2000
	v.Write journal paper		May 2001

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Module C: To apply & develop spatial data integration methods

Rationale

Spatial data sets that can be handled by current geographical information systems (GIS) technology come in many forms, resulting from differences in the ways in which the data are collected, computerised and processed. These differences impose constraints on the joint use of environmental and socio-economic variables, either for informal comparison or for formal integration. Generally, data on environmental phenomena are recorded with respect to different spatial units than socio-economic characteristics. Additionally, data sets about rural phenomena often have certain other characteristics which must be taken into account. Commonly, 'population' data sets contain only small numbers of observations at relatively coarse resolutions (such as surveys of workers or residents in the countryside recorded for district or postcode areas, or even counts of wildlife numbers in kilometre squares). In contrast, data on some environmental characteristics, such as land cover, vegetation and certain land uses is becoming more detailed as a result of the developments in high resolution remote-sensing technology.

In this module, the meaning attached to 'spatial data integration' centres on analysis methods for reconciling these differences. Broadly three close sets of methods are considered, for visualising and exploring data, and for dis-aggregating zone-based data sets. 'Visualising' concerns the use of maps and other views, in interactive and possibly linked modes, for garnering initial information about the variation and relationships shown by different data sets. This will draw on the functions of up-to-date GIS packages and, if relevant, other statistical and data-viewing software. 'Exploring' refers to methods for simplifying the complexities of large, multivariate data sets, also for attaining better understanding of their distribution and correspondence. Appropriate statistical or computational techniques will be used here. The interest in 'dis-aggregating' addresses the issue that data are sometimes needed for geographic area frameworks (e.g. parishes, postcode areas or grid squares) other than those for which they are supplied. Thus, techniques for transferring data between different frameworks are needed. The intention is to develop generalisable statistical techniques for dis-aggregating zone-based data into smaller (non-hierarchical) area partitions, which could subsequently be re-aggregated to different zoning frameworks and/or combined with other data.

Key Objectives

C1 To investigate and apply modern statistical or computational methods for the structured exploration of multi-dimensional data sets, particularly data reduction methods.

C2 To develop and apply methods for matching data based on different zonal systems.

Module C: Activity plan [READS FROM LEFT TO RIGHT]

Objective (and Links to other Objectives)	Task(s)	Responsibility	Timing
C1 Investigate and apply methods for the structured exploration of multidimensional rural data sets, particularly data reduction methods (Links to A1, C2)	i.Review existing research on exploratory spatial data analysis	Alistair Geddes	Oct 1999 - Jan 2000
	ii. Identify potential techniques for rural data analysis and reporting		Oct 1999 - Mar 2000
	iii. Investigate the strengths and weaknesses of different methods for establishing relationships amongst spatial data		Apr 2000 - Dec 2000
	iv. Report on findings under i-iii		Oct 2000 - Dec 2000
C2 Develop and apply methods for matching data based on different zonal systems (Links to A1, A4, B1, C1)	i. Review existing methods for spatial dis-aggregation and re-aggregation	Matthew Hodgson and David Elston	Oct 1999 - Dec 1999
	ii. Modify or create methods for comparison of variables recorded with respect to different spatial units		Oct 1999 - Jun 2000
	iii. Test the effectiveness and efficiency of the respective methods		Apr 2000 - Sep 2000
	iv. Report on findings under i-iii		Oct 2000 - Dec 2000
	v. Write research paper		May 2001

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Module D: To assess future rural change scenarios

Rationale

A key issue for the project is to link knowledge of the current situation with possible future requirements, geared towards a data framework which would provide better support for analyses and reporting on the environmental and socio-economic dimensions of rural land use change. Whilst some relevant information will be obtained from Module A, which surveys current users and will make an assessment of their future needs, this will possibly miss key user groups who may not currently require such data or indeed may not yet 'exist'. One way to deal with this problem is to adopt a visioning approach, where a range of future rural change scenarios are examined specifically in terms of their implications for a rural data framework. For example, a "business as usual" scenario would suggest a progressive need for linking agricultural production and environmental data (i.e. linked to cross-compliance) but the data user community might be largely unaltered. On the other hand, an enhanced European rural development policy based on local community involvement and action might imply the need of integrating a much wider range of data, at a much finer spatial grain and for a host of new users (e.g. including local communities).

In this module, three objectives have been established to develop and analyse a range of credible rural change scenarios, and to link these back to the analysis of the current rural data infrastructure. These scenarios will be based around the three agricultural/rural development policy scenarios for the UK set out in the Land Use Research Review (Birnie et al. 1995). Another page on this website provides a fuller summary of the scenarios. Overall, the purpose of these objectives is to provide recommendations for a future rural data infrastructure which takes account of possible changing demands for data.

A fourth objective is staged to take on the production of the Final Report from the findings of these objectives with the results from Modules A,B and C.

References:
Birnie RV, Morgan RJ, Bateman D, McGregor MJ, Potter C, Shucksmith DM, Thompson TRE and Webster JPG (1995). Review of Land Use Research in the UK. MLURI, Aberdeen.

Key Objectives

D1 To develop rural change scenarios with explicit assumptions.

D2 To describe the change scenarios and assess their implications in terms of data infrastructure.

D3 To compare the scenario analysis with the assessment of the current rural data infrastructure (from Modules A & B).

D4 To produce a final report which includes specific recommendations for development of a rural data infrastructure.

Module D: Activity plan [READS FROM LEFT TO RIGHT]

Objective (and Links to other Objectives)	Task(s)	Responsibility	Timing
D1 Develop rural change scenarios with explicit assumptions	i. Agree the range of feasible scenarios	Mark Shucksmith	Oct 1999
	ii. Identify key assumptions underpinning scenarios		Nov 1999
	iii. Agree standard description format for change scenarios		Nov 1999
D2 Describe change scenarios	i. Produce description for each scenario	Mark Shucksmith	Dec 1999
	ii. Define the impacts of each scenario in terms of implications for data infrastructure		Dec 1999 - Mar 2000
	iii. Identify the common elements		Dec 1999 - Mar 2000
D3 Comparison with existing analysis (Links to A3, A4, A5, B1, B2)	i. Compare core elements of 'future' data infrastructure with present situation	Mark Shucksmith	Mar 2000 - Oct 2000
	ii. Reporting, to identify: (a) core data sets, (b) data infrastructure requirements and (c) critical development issues		Mar 2000 - Oct 2000
D4 Produce Final Report	i. Collate findings from Modules A,B C and D ii. Review current situation of the rural data infrastructure iii. Assessment of future needs iv. Recommendations for development of infrastructure	Project Team	Oct 2000 - Mar 2001

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