1. Measuring Perceptions of Place Damien McCloud B.Sc(hons) M.Sc FBCart.S FRGS CGeog(GIS)
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12. Mapping residents perception of place Interpolated map for Q2 kriging and the range of values used. Resident location based on postcode Socio-cultural resource being measured
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24. Thank you for Listening Any Questions? [email_address]
Editor's Notes
3 Divisions: Infrastructure Buildings Consultancy
Arup is most well know for its Engineering and Architecture skills, as demonstrated by the built and highly visible infrastructure – many of which have become cultural icons of place world wide.
In Arup we look at different ways to engage with Cultural Heritage, both as practitioners and for increasing public enjoyment. Through the concept of value we engage with the less tangible aspects of cultural heritage. Understanding the way we value things plays a central vole in the way we measure, design and implement change . Bearing all this in mind we were interested in seeing how GIS could be used to demonstrate the publics perception of value as it related to Cultural Heritage. Further we wanted to explore methods of identifying social influences which may account for the way people value heritage.
In Arup we look at different ways to engage with Cultural Heritage, both as practitioners and for increasing public enjoyment. Through the concept of value we engage with the less tangible aspects of cultural heritage. Understanding the way we value things plays a central vole in the way we measure, design and implement change . Bearing all this in mind we were interested in seeing how GIS could be used to demonstrate the publics perception of value as it related to Cultural Heritage. Further we wanted to explore methods of identifying social influences which may account for the way people value heritage.
With this in mind we wanted to look at ways to develop strategies to reach social groups who were not benefiting from the Cultural heritage resources in their area and to help to co-ordinate improvements. However the first step was to identify who those groups were and why. In developing this tool, we joined forces with the HLF. The HLF give grants to a range of project UK wide and undertake analysis to understand the effectiveness of these improvements to visitor and local experiences. Clark & Maeer in particular assess the impact at the level of community well-being, to see how use, strengthened aspects of the local community. They identified that over half the respondents felt that such Cultural heritage sites were good places to relax, that they provided a community focus and helped drive social cohesion and inclusion. The HLF were keen to see GIS added to the work they were doing. We were keen to see whether we could use the data to identify a method which would highlight how different social groups reacted to either positively or negatively to cultural heritage resources and whether this could be a step towards developing strategies for improvement.
The results of a HLF resident survey in Wardown Park, Luton were selected. Wardown Park is considered by English Heritage to make a significant contribution to the local scene and is a registered Park & garden. It contains a landscaped walkway, a period home recast as a museum, and is a venue for various sporting activities. Luton also has areas which register highly in the East of England Deprivation Index. The results of the survey were supplied in the stats program SPSS which was converted to a csv file for export into GIS. In order to establish the key baseline two of the questions used in the questionnaire were used to set the Perception of Place and cultural framework, and 5 were used to develop the social/causal dimension.
Q2 set the Perception of place for Luton. Responses were mapped to residents postcode. As the analysis sought firstly to identify the geographic spread of perception of places values these were interpolated to show high and low values across the study area.
Wardown Park is marked as the redline and the dots represent each residents postcode. The interpolation identified a high clustering of values to the north west of the park, but this was not consistent for all sides of the park. The next step was to explore, within a cultural framework, what the residents thought of the park itself.
Perception of place was compared against cultural values as expressed through the HLF’s questions. These were broken down into concepts of Aesthetics, accessibility, health, social, safety and symbolic value.
The scores were consistently high except for safety and frequency of visits
Social data about the respondents was also visualised to assess for patterns or trends. However this didn’t provide any real results in terms of establishing linkely links between value of place and influence of social indicators (age, etc) on how people responded.
We decided to apply a regression analysis to the data to see if this would demonstrate likely relationships and therefore influences to explain the spatial variation in the responses. However the drawback to this was that traditional regression analysis applies a single and global results across the area and wouldn’t really explain the spatial variability which was occurring. As I wanted to explore the model spatially I needed to find a way to spatially regress the dependant variable of perception, against the independent variables of social indicators at each postcode point and visualise this within ArcGIS.
I decided to use the GWR programme developed by B, C and F specifically to address the problem of spatially varying data. This program allows the modelling of processes that vary over space and it develops a set of local parameter estimates for each relationship which can be imported into GIS and mapped to produce a surface parameter for each postcode. Significantly, the contribution of each sample point (i.e. the social indicator variables) is weighted according to their proximity to ‘y’ (the the point being sampled i.e. perception value at each sample point). The co-efficient of each result are determined by examining the points within a defined neighbourhood or bandwidth.
The results are outputted in a csv file and converted within GIS to explore not just the patterns and relationships about the data, but also the geographical process which may have generated the data. Two key outputs for this analysis are the parameter estimates (Parm 1, 2…) which demonstrate the degree of influence each variable had on the perception of place at that observation/sample point, and the t-value . The t-value maps the significance of the estimate at each point. It is important to visualise this with the parameter estimate as while the parameters may demonstrate a high, or where relevant, low or negative relationships, the results may not be significant to the model. Some examples of the results are illustrated below.
We decided to test this approach on census data per ward for the area. The intercept parameter for this analysis identified several negative relationships to the south (in orange) while it identified a strong positive relationship between Perception of Place and social indicators top the immediate west of the park.
The data range for ethnicity was very small to the point that any significant use of the data is limited. 53 of the 84 respondents were white but a greater proportion of those also scored lower values across the site and this may account for the results.
The results for this employment grade shows spatial varying without geographical trends, but with positive clusters to the south and east indicating that perhaps there is a relationship between the lower values here and this employment grade.
The analysis of the social indicators against the perception of place variable is intended to test whether the social indicators have any influence or relationship on the way residents perceive of the value of place, particularly where value of place is linked to the proximity of a significant cultural or heritage resource. Additionally the results did demonstrate some spatial trends which link to social influences. The main outcome of establishing these relationships is to target areas for improvements, i.e. improving accessibility for certain age groups, or ethnic groups, and to encourage social cohesion and inclusion within the community. The results have the potential to be the first stage in developing strategies to reach social groups which are not benefiting from their local cultural heritage resources. This is significant at the level of local government and for groups who manage such cultural heritage.
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