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www.sei.se/relu
Characterizing Rural England using GIS
Steve Cinderby, Meg Huby, Anne Owen
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Scoping study in the Rural Economy & Land Use Programme
Aim:
To integrate natural and social science data into a spatial
dataset that can be used for analysis to inform rural policy-
making and provide a knowledge base for furthering policy
integration
Characterizing Rural England Using GIS
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The Super Output Area• This study uses the new Census Super Output Areas
(SOAs) as the base unit for aggregation.
• SOAs are a new geography designed to improve the reporting of small area census statistics. It is intended that they will eventually become the standard across UK National Statistics.
• Lower level SOAs have a minimum population of 1000 people with a mean of 1500 people. For rural SOAs, areas range from 0.16km2 to 684km2 with a mean size of 18.2km2.
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The Rural Definition
• Classification based on underlying hectare square grid
• Each square classified into one of 9 “morphological” categories – e.g. small town, village, hamlet
• Each square assigned a score based on the sparsity of the surrounding area
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The Rural Definition for OAs
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The Rural Definition for SOAs
• Super Output Areas are either rural or urban
• SOA is either sparse or less sparse
• Rural SOAs are either town or village/hamlet
• 2 urban and 4 rural types
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Spatial Integration• The 2001 Census and 2004 Indices of Deprivation use
the Super Output Area as their areal unit.
• Other variables, particularly environmental datasets, use a different geography, which need to be integrated at SOA level.
• The problems of geographic integration to a common base unit are well known.
• This project aims to characterise, minimise and represent errors and uncertainty when data is portrayed at SOA level.
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Distribution of data
• Uniform
• Patchy
• Continuously varying
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Geography of data
• Point
• Line
• Area
• Surface
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Resolution of data
Low High
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Distribution of non SOA level data
• Data that have not been collected at SOA level must be assigned to SOAs
• The nature of the assignation is determined according to the underlying distribution of the data
• Additional data are required to determine the geography of the distribution
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Case Study I: Bird species richness
• Captured at 10km grid square level
• Resolution is low
• Assume uniform distribution throughout grid square
• Apply area weighted averaging technique to construct data at SOA level
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23 29
35 41
60 m2 80 m2
30 m220 m2
(23 x 60)/190 = 7.26
(29 x 80)/190 = 12.21
(35 x 20)/190 = 3.68
(41 x 30)/190 = 6.47
190 m2
(7.26 + 3.68 + 12.21 + 6.47) = 30 (2 s.f.)
Area Weighted Technique
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Case Study II: Voter participation
• Captured at parliamentary ward level
• Resolution is low
• Assume patchy distribution of population settlements
• Apply population weighted averaging technique to construct data at SOA level
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0.72 x 900 = 648
0.53 x 600 =
3181500 people
(648 + 318) / 1500 = 64.4%
Population Weighted Technique
72% 53%
900
600
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Case Study III: Air Pollution
• 1km grid square level
• Resolution is high
• Distribution is continuously varying
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When ‘average’ is not appropriate
• A weighted average technique masks variation in the data and information on very high, or very low values is lost
• When considering pollution data, it may be more appropriate to consider maximum pollution found in an SOA rather than the mean
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Pollution: averaging problem
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Case Study IV: Impact of Tourism
• Calculate an indicator showing the effect of tourism on Rural SOAs
• Use point data of visitor numbers to tourist sites with line data of road network
• Aim to show tourist ‘intensity’ along area adjacent to roads
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Tourist Influence
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Legend
Tourist influence
High
Low
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Tourist Influence along roads
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Conclusion• Problems of combining data together spatially do not
arise because the data is either environmental or socio-economic
• They depend on the nature of the data
• Each type must therefore be considered on a case by case basis, using supplementary data on the underlying distribution for mapping to SOA level