Visualisation Techniques for Facilitating Decision Making in Urban Planning
Andrew J Rixon, Stewart Burn
CSIRO, Australia
<andrew@anecdote.com.au> <stewart.burn@csiro.au>
Abstract
Public participatory geographical information systems (PPGIS) are becoming widely recognised as powerful tools for informed participatory decision-making processes within urban planning projects. This paper presents a case study discussing how a PPGIS software tool was designed and used to both facilitate dialogue and discussion amongst key stakeholders engaged in an urban development project and provide representation to the communities located around the development site. In particular, the paper demonstrates a new methodology for visualisation of qualitative social data which enables stakeholders to explore community perceptions and attitudes. The paper concludes by considering how this new methodology for visualizing qualitative social data may improve collaborative decision making by stakeholders as well as providing a means towards community representation and empowerment.
Introduction
There has been a significant increase in stakeholder and community involvement in planning and management strategies since the publication of Arnstein’s (1969) seminal text on citizen participation. Participation is now recognised as an important component of community development and neighbourhood planning approaches. Australia, as a signatory to United Nations Local Agenda 21, has legislatively recognised local participation as fundamental to ecologically sustainable development. In particular, strong community involvement in planning processes ensures that the values and knowledge of the community is considered, which enables communities to take ownership and charter their futures in a more sustainable manner (Howard 1998).
To enhance the community participation in planning there is a need for geographical information systems (GIS) technologies to be more amenable to public participatory processes. In this respect, Public Participatory GIS (PPGIS) applications are now emerging as ways to address such issues of stakeholder and community involvement, empowerment and governance (McCall 2003).
Common to PPGIS approaches is the use of visualisation and mapping techniques with a group of participants to enable knowledge elicitation and interaction. Visualisation is a powerful technique for breaking down the barriers of traditional communication and tapping into group creativity. In particular, as discussed by (Morgan 1993), visualisation and metaphor are powerful tools for facilitating organisational change. More specifically, utilising visualisation techniques within group facilitation may provide key benefits such as:
Helping to support the development of ideas.
Helping to ensure that participants from different cultural and educational backgrounds can intuitively access the information presented.
Helping to include selected ideas into the group’s collective graphic memory (Lugt 2000).
It is unclear in general, however, whether graphic visualisation techniques (such as capturing ideas with pictures rather than words) employed in group brainstorming processes may interfere with the divergent thinking phase required in brainstorming activities. As suggested by (Lugt 2000): ‘The problem appears to be that no matter how quick the sketch, making a graphic representation on paper takes more time and contemplation than simply verbalizing the idea. This evokes some kind of a first idea screening, introducing judgement into the divergent thinking phase’.
Interestingly, unlike the limitations provided by traditional visualisation techniques used for brainstorming within general group facilitation processes, PPGIS approaches appear to provide a key strength in this area. PPGIS enables residents to articulate their preferences spatially via GIS, and strengthens and deepens communication about neighbourhood issues (Talen 2000). Indeed, local communities and residents can use PPGIS very effectively to characterise their local environment and incorporate their local knowledge into the GIS (Talen 2000).
Like all group processes, PPGIS benefits from a facilitator who is skilled in guiding participants through the process, enabling dialogue and discussion to emerge. As with conventional facilitation, successful outcomes for PPGIS approaches are more likely with a facilitator skilled in building trust and relationships with the group participants (Ball 2002). Apart from managing the group’s interaction processes, the platform and technologies utilised by PPGIS has seen the facilitator taking on extra responsibilities to enable participants to explore and get access to:
the local area coverages available in GIS.
the types of neighbourhood features involved (e.g. streets, intersections).
the means for basic navigation of the GIS tool functionality (e.g. turn layers on and off, change scale, zoom to areas of interest).
the ways in which local knowledge can be explored and evaluated within a GIS context (Talen 2000).
As PPGIS approaches find new ways to explore the experiences and perceptions of community in everyday life, such as ‘sense of place’ (Pretty et al. 2003), the need for suitable, complementary visualisation approaches becomes critical. Such new visualisation approaches for PPGIS must recognise that notions such as the sense of place associated with particular localities and particular groups of people is often qualitative, fuzzy, metaphorical or mystical (McCall 2003).
A key strength of PPGIS in mapping local knowledge is its potential for GIS thematic layers to reflect the social or environmental images obtained from a diverse group of participants. Current GIS interfaces, however, require specialist knowledge and are weak in providing general public access and usability. Such interfaces provide little capacity to handle qualitative interpretations of space or capability for expression of personal ideas of place and locality. As such, enhanced capabilities for PPGIS software applications are sought to support both the public participants and the PPGIS facilitator in the development of better interfaces, data models, and qualitative interpretations and expressions of data (Carver 2001; Ball 2002).
Inspired by (McCall 2003) and seeking to extend the work and principles drawn from the “mapmaker’s dream of mapping the intangible” (Bosworth et al. 1998), this paper addresses a new approach for including fuzzy, qualitative social data into PPGIS visualisations. In particular, the paper demonstrates a technique by which qualitative social data can be visualised at the street level for an urban locale. The development of the means for the visualisation of qualitative social data within the PPGIS application was created to give stakeholders and urban planning professionals the ability to consider local community perceptions and attitudes within the case study area. To facilitate scenario discussions for changes to street structure, space syntax theory was used to provide just-in-time visualisation of the impact of street changes on traffic flow throughout the urban street network.
Finally, this paper seeks to contribute to the field of community informatics (Gurstein 2000) addressing how the use of information and communications, specifically PPGIS software, may facilitate collaborative decision making regarding urban planning as well as providing an opportunity for representation and empowerment of communities within regional and urban planning environments.
Visualising traffic flows
Introduction to Space Syntax Theory
Space syntax models the spatial configuration of urban spaces by using graph theory representations. Such a configuration of space identifies patterns that can be used to study urban structures and human behaviour (Hillier et al. 1984). Street structures are generalised into space syntax models by abstracting the streets into nodes and edges, with the points of intersection between streets as edges (see Figure 1).
Figure 1: A fictive small town and its graph representation.
Once the urban street structure has been ‘decomposed’ into its representative ‘graph’, the techniques of space syntax analysis can be applied. The common measures used within space syntax analysis are connectivity and integration. Connectivity measures the number of nodes that interconnect a given node; in graph theory this is known as the degree. Integration is a measure of a street’s mean depth within its urban environment, and is dependent on the radius (or depth) chosen. See (Jiang et al. 2002) for a detailed description of these measures and how to calculate them.
The integration of a node within a pedestrian network has been found to relate empirically to the amount of utilisation it receives (Hillier 1996). Higher connectivity nodes offer greater accessibility to a larger number of other nodes, resulting in proportionally greater utilisation. This holds true in urban street contexts as well. Sreets that offer greater access to a large number of other streets act as conduits for more origin/destination pairs than streets with less connectivity. Such streets consequently experience more throughput than less connected and accessible streets.
Integration is a quantitative measure of this connectivity applied to pedestrian movement, also known as ‘movement potential’. Using this method, it is possible to analyse a whole city and produce a coloured map in which, based on the analysis, the relative importance of a particular route within the system, can be shown. As noted by (Steadman 2004), space syntax theory does not in itself aim to model the absolute rates of movement in an area, but rather demonstrates a statistical distribution based on the spatial configuration.
Implementing Space Syntax
To facilitate discussion of scenarios based on changes to street structure and integration within a case study area, PPGIS as a software tool utilises space syntax techniques. In particular, the space syntax approach provides the capability to perform rapid, just-in-time computations to allow the visualisation of movement potentials based on the current or proposed street structure and connectivity.
The key steps in implementing the space syntax model within the PPGIS software were:
Iterate through all lines (i.e. the streets) held in the GIS datafile (ESRI shapefile) to obtain a collection of nodes. For each node, a street intersection which shares the node defines the new edge for that particular node.
Calculate specific space syntax indicators such as ‘Connectivity’ and ‘Integration’.
Visualise
the GIS datafile by using the simple graded colour scale (red,
orange, yellow, green, blue, violet) ()
which first finds the maximum value and proportionality and
assigns each colour based on the lines’ space syntax score
relative to this maximum value.
Current components for working with GIS data (such as ESRI’s MapObjectsLite control) have reached a high degree of sophistication and usability, easily enabling development and prototyping of such algorithms. Figure 2 shows an example of a visualisation from a space syntax model for movement potentials based on a certain street configuration in the case study area.
Visualising social elements
Social data was collected for the case study area by using a telephone survey of participants in the area. The telephone survey was designed to allow for the investigation and determination of social indicators such as ‘sense of place’ and ‘community satisfaction’, as well as general demographics and usability statistics specific to the case study area.
The telephone survey included questions allowing for:
Boolean (yes/no) responses: e.g. ‘Do you walk in this neighbourhood?’.
Scaled responses (1–5: 1 = strongly disagree to 5 = strongly agree): e.g. ‘I feel safe living in this neighbourhood’.
Qualitative responses (captured narrative): e.g. ‘What are the best things about living in this area?’1
Figure 2: Colour grading of movement potentials calculated by space syntax theory.
The social survey data responses were stored within a Microsoft Access database, keyed on street name/street ID. This provided a simple link to the spatial/visual data held within the GIS database. Thjs was also keyed on street name/street ID.
As discussed, the social survey allowed for a mixture of Boolean, scaled and qualitative responses all of which are scored quantitatively. For each street and any given indicator, the PPGIS software tool collates the scores for the set of questions which comprise the indicator, and creates an aggregate score for that indicator as applied to a particular street. The algorithm operates over the network of streets within the current GIS database and then visualises all the streets and the indicator using the graded colour scale discussed previously.
When an indicator is chosen which contains all Boolean, Scaled and Qualitative responses, the algorithm first calculates the Boolean score, Scaled score and Qualitative score for each street and then performs a simple addition operation between all the indicator component scores to form the street’s total score. Future changes to the algorithm would see that weightings were used to help overcome any scale bias which may result from the simple summation operation used.
Table 1 demonstrates a sample set of questions which comprised a sense-of-place indicator. For each question, the response is quantified and then scored for each street in the case study area. The scored value is aggregated to form the sense-of-place indicator and then visualised based on a relative scale across all the streets contained within the entire case study area.
Table 1: Sample questions comprising the sense-of-place indicator
Question |
Type |
What would you say are the best things about living in this neighbourhood? Great place to live |
Qualitative |
People are always moving in and out of this neighbourhood |
Scaled |
It is very important to me to live in this neighbourhood |
Scaled |
I would be unhappy if I had to move from this neighbourhood |
Scaled |
How long have you lived in this neighbourhood? |
Scaled |
By choosing specific questions, it is possible for unique indicators to be created for and by participants and stakeholders. These indicators form the basis for performing visualisations within the PPGIS software tool. Examples of such visualisations created and used by stakeholders during a facilitated session were:
Traffic Flow Options – Preferences of Speed Limits.
Traffic Flow Options – Preferences of Pedestrian Overpass.
Sense of Place – Overall.
Community Satisfaction – Overall.
Community Satisfaction – Based on Services.
Figure 3 illustrates an example of a visualisation of the sense-of-place indicator within the case study region. The simple colour-coding technique employed within the space syntax visualisation was used to present gradations for the indicator.
Figure 3: Visualisation of total sense of place in survey area.
Conclusions
Design principles are emerging to aid in building more usable systems from the perspective of both the end users, as well as providing tools for more seamless group facilitation. A design philosophy proposed for building ‘tools which inform dialogue debate and deliberation’ emphasises a key technique of progressive revelation of information to the user (Pereira et al. 2005).
The PPGIS software tool described in this paper followed this design principle by:
Integrating social data with GIS data.
Utilising simple colour scaling to visualise features and ‘indicators’ on the GIS map.
Providing the capacity for the PPGIS facilitator to design flexible visualisation queries that allowed for ‘real-time’ exploration of the social data/perceptions by stakeholders.
The design technique of progressively revealing levels of detail within the application demonstrated simplicity and usability amongst a varied group of participants. In particular, the simple colour grading used to visualise both movement patterns and the social perceptions and attitudes from the case study area, provided a useful way to engage stakeholders in discussions on the effects of changing street structure within their local areas of interest, as well as providing access to the virtual community represented within the PPGIS software.
This paper has considered a case study demonstrating how PPGIS software provided a visualisation tool aiding group facilitation, contributing to stakeholder decision making and discussion in urban planning scenarios. Moreover this approach has provided a means for community representation amongst the key stakeholders and decision makers.
The ability to capture and visualize a community’s social data provides a powerful means for representation of that community in board level discussions. We found this representation to be invaluable in helping stakeholders to develop a real understanding and appreciation of their communities. Finally, the ability to visualize a community’s social data provides a powerful means for engagement of the community itself in dialogue and understanding around core issues and aspects of urban planning and development. Such engagement is the first step to community empowerment, a strong basis by which communities and urban planning professionals can charter their way towards a sustainable future.
Appendix A
Question Type |
Question |
Scaled 1-5 (Strongly Disagree-Strongly Agree) |
It is very import to me to live in this neighbourhood |
Scaled 1-5 (Strongly Disagree-Strongly Agree) |
People are always moving in and out of this neighbourhood |
Scaled 1-5 (Strongly Disagree-Strongly Agree) |
I don't have much confidence about the future of this area |
Scaled 1-5 (Strongly Disagree-Strongly Agree) |
This is a good place for children to grow up |
Scaled 1-5 (Strongly Disagree-Strongly Agree) |
I feel safe living in this neighbourhood |
Scaled 1-5 (Strongly Disagree-Strongly Agree) |
It is easy for me to get to where I need to go in my neighbourhood |
Scaled 1-5 (Strongly Disagree-Strongly Agree) |
There is not much to do in this area |
Scaled 1-5 (Strongly Disagree-Strongly Agree) |
I would be unhappy if I had to move from this neighbourhood |
Scaled 1-5 (Strongly Disagree-Strongly Agree) |
This area is well maintained |
Scaled 1-5 (Strongly Disagree-Strongly Agree) |
People here would get together to protect the interests of the neighbourhood |
Boolean |
Do you rollerblade in this neighbourhood for enjoyment? |
Boolean |
Do you jog in this neighbourhood for enjoyment? |
Boolean |
Do you cycle in this neighbourhood for enjoyment? |
Boolean |
Do you walk in this neighbourhood for enjoyment? |
Boolean |
Visit for: Aboriginal cultural centre |
Boolean |
Visit for: Sporting events |
Boolean |
Visit to see what's happening |
Boolean |
Visit for: Fun and recreation |
Boolean |
Visit for: Peace and quiet |
Qualitative |
What are the things that you least like about living in this neighbourhood? |
Qualitative |
Why did you move here? |
Qualitative |
What would you say are the best things about living in this neighbourhood? |
Qualitative |
What activities do you do in this neighbourhood for enjoyment? |
Scaled 1-5 (Highly Unsatisfactory-Highly Satisf) |
The control of traffic in your area |
Scaled 1-5 (Highly Unsatisfactory-Highly Satisf) |
The opportunities for young people's recreation in your neighbourhood-for example, skateboard parks |
Scaled 1-5 (Highly Unsatisfactory-Highly Satisf) |
The condition of parks in your neighbourhood |
Scaled 1-5 (Highly Unsatisfactory-Highly Satisf) |
The accessibility of these services to you |
Scaled 1-5 (Highly Unsatisfactory-Highly Satisf) |
The range of different shops and services in your neighbourhood |
Scaled 1-5 (Highly Unsatisfactory-Highly Satisf) |
The variety of different green areas in your neighbourhood |
Scaled 1-5 (Highly Unsatisfactory-Highly Satisf) |
The level of traffic noise in your neighbourhood |
Scaled 1-5 (Highly Unsatisfactory-Highly Satisf) |
The walking distance to shops from where you live |
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1 For a listing of the questions asked in the case study see Appendix A. Preserve the anonymity of the case study area used, some questions have been removed.