Drew Mackie & David Wilcox

• Section 1 of the network mapping paper
• Download full paper - 12Mb pdf

Other sections:

• 2. Network analysis
• 3. Network mapping examples

This section

• Networkistas - four strands of network interest
  
• Why Analyse Networks?
• Mapping Assets in Networks
  
• Identifying Assets
  
• Asset mapping
• NetThreat
• Networks and Ageing
  
• We need to talk about Kevin

What are networks - and why are they increasingly important?

We are all familiar with networks. They are evident in the pattern of roads on a map; the power lines connecting the sockets in our homes to the national grid; the patterns of who knows who in any community. And we may realise that networks are becoming more important because the Internet allows almost anyone or anything to be connected. From all these examples it is clear that some places and people are better connected than others, and that networks are links and hotspots of various types.

If you are in a rich network it is fairly easy to connect with apparently distant people through a few links. You make use of relationships if you are well-connected - “who you know matters as much as what you know” because you can always find someone to ask or to help. You can build on the relationships, become more widely trusted, make things happen more easily.

But…if Facebook, Google and other Internet giants can encourage you to make more connections and share your content, they will know your interests and target you with adverts in exchange for free use of their networks. If security services can see where your connections are they may not need to know what you are saying - they can see whether your associations look threatening just by looking at your networks.

Online networks change the way business is done. They enable connections within organisations between workers that can bypass management, wherever they are in the world; they can enable people to set up businesses even though they never meet; and they enable businesses to cut out middle-level suppliers, as Amazon has done to a lot of retailers. In this set of notes we’ll provide some guidance on how to:

• Understand networks in more detail
• Describe these networks through mapping
• Use network mapping to show how to make more of our assets, particularly in local communities
• Recognise different types of network enthusiasts
• See where influence may lie in networks - and why some people are resistant to developing networks
• Use software for network mapping and analysis

The networks that you will find illustrated in this document have been prepared in the course of Drew Mackie Associates projects over the last 8 years. These projects cover:

• Regeneration
• Consultation and Engagement
• Public, private and community community organisations
• Linked ideas and projects
• Physical planning

The principle of network analysis is really very simple. If you can identify a number of things and how they connect together, then you have a network. The things themselves are represented by blobs known as nodes. The links that connect them are shown by lines. Computer analysis can show how central nodes are to the operation of the network.

In the first part of this document, we discuss general network ideas. The second part shows a number of examples taken from our work over the last 8 years and covers network of people, places and ideas.

The sheet below shows the sort of people who have an interest in networks. This was prepared for the informal “Networkista” group with members from all over the UK. Meetings have been held in Manchester and London to discuss the value of the network approach.

Drawing a network may help to understand the the various patterns of relationship but as the size of network increases it becomes more difficult to see these patterns and some more subtle dimensions come into play. For instance the node with the most connections may not be the most central to the network. It may be the centre of an intense sub-network which is on the periphery of the network as a whole. On the other hand a node with few connections may be the crucial bridge between parts of the network. Social Network Analysis (SNA) is a computer based method of measuring various forms of centrality (see Section 2 for a more detailed exploration of SNA)

People that might find this analysis useful are:

• Indicates who may be best to influence or gain support from
• Shows the range of contacts that you should ensure have been talked to to gain maximum exposure
• Traces the flows of influence through the network

• Shows patterns of interest and activity
• Indicates the distribution and type of assets
• Plots the flows between asset holders
• Explores shared stories
• Shows what clusters of organisations work best together and who should be brought together for the project or programme

• Plots changes in Social Capital (network density) over the life of a programme
• Records improvements in communication and asset sharing
• Indicates the role of programme organisers in facilitating change

• Shows the distribution of available skills and resources
• Gives an indication of delivery efficiency
• Indicates the willingness to share assets and information across organisations

Social Network Analysis is good tool for telling us where the key parts of a network are and how they might exert influence. In recent work, we have been comparing the position of an organisation or department with the assets it controls. This can often show up a mismatch where:

• The department or organisation is very central to the network but has few skills and resources. This means that, although it has the potential to influence the network and/or control flows of information, it does not have the resources to exercise such control effectively.
• The department or organisation is peripheral to the network but is well stocked with skills and resources. Thus it has the means to act effectively but doesn't command the position to do so.

We are constantly amazed at how often these anomalies occur. In one case, the body responsible for the implementation of a major regeneration project was recognised as being skilled, but had virtually no resources. In another, a well resourced department with highly skilled staff was carrying out mundane duties at the edge of the network.

So the point is that network analysis can identify these mismatches and suggest ways in which they can be resolved. And this could be really useful in gauging the present and potential effectiveness of a network. So why is it often difficult to persuade prospective clients to undertake this sort of analysis? The sequence often unfolds like this:

We are approached by a middle manager who feels that SNA could benefit their complex organisation - or we are already working on a job where SNA appears to be useful.
After discussion we are asked to put a proposition to senior management. This contains examples of networks whose performance is impeded by under-resourced but central departments.
Senior management don't like it at all. And who can blame them. The analysis will likely show that they don't have the influence they want (or think they have) or the resources they need. It's a threat.
Because the process is risky, even a committed client can be wary of the work and can be sceptical that the simple one page questionnaire that we use can really suffice. This has been especially problematic in work with health organisations whose culture is evidence based. Although there are sections of the NHS's “Evidence” website devoted to SNA, the method is not well known in_ _health circles. Advocates of SNA's structural rather than numerical approach can be vulnerable to criticism from colleagues and have to feel confident in justifying the method and it's routines. This can lead to over complication in data acquisition so that the method doesn't appear simplistic.

We were recently asked to map the network of attendees at a national conference. The organisers were enthusiastic. However, senior staff realised that we were about to explore and encourage network links between participants when their own website was incapable of facilitating these. The invitation was withdrawn.

Often a network analysis will indicate that the real structure of an organisation or group of organisations is nothing like the ideal structure perceived by senior management. Cross connections between departments and informal links between individuals create a complex pattern of relationships which are essential to the way the network works. These grow and reconfigure constantly as the activities of the network change and any analysis is a snapshot of the state of the network at any one time.

But senior managers want stability and clarity. Often they will see the mapping of the real structure as a threat because it may indicate:

• They are not in the strongest position to control the network
• Lines of communication bypass them
• Skills and resources are not effectively deployed

Now each of the above would be useful knowledge to a creative manager. A knowledge of SNA would be another tool in the managers box. Yet often they feel that it will invite criticism of their role or capabilities. When we are asked to map a network it is often because the members realise that there is something wrong with the present structure. When we explain what the analysis will do it often touches on the sensitivities of key individuals and often they are not prepared to face this. Almost all the network mapping we have done has been part of a larger task where we have chosen to use this method.

Another factor is that very few senior managers know about SNA. This is not a tool that they have faith in. Indeed this is not a tool in general use in the UK although widely used in the US and Australia.

As an organization, whether public, private or community, knowledge of what assets you control is vital to planning your future. These assets can be financial, physical, organisational or knowledge based. Knowing what you have let's you think better about what you want to do.

It is becoming clear that the predominantly needs based approach to neighbourhood development has a number of unfortunate consequences:

Areas are seen as “problems” to be solved and this perception percolates down to the people on the ground, both community and professional. Money is targeted at solving problems and supporting problem based organisations. The result is fragmented and partial support for the area and the sidelining of natural community strengths and resources.

The solution to local problems is increasingly seen as external to the community - and this leads to all sorts of complaints about public bodies: “when are they going to do something.” and “why should we do it when they're paid to?” The possibility of locally generated action is increasingly linked to external resources - staffing, funding etc - and the lack of these becomes a justification for inaction.

The proliferation of bodies that are tasked to address local needs becomes a problem in itself. Local people often don't know who is responsible for what. Worse, these bodies are often in competition for funds and resist the collaboration that would maximise the use of scarce resources.

Thus, community effort at local level is often fragmented and this is reinforced by the sporadic and partial availability of external resources.

Asset Based Community Development (ABCD) builds on existing assets. These can be tangible (buildings, equipment etc.) or non-tangible (passion, skill, willingness to share). Local networks are crucial to this approach and can be seen as indicators of social capital.

All communities and organisations have assets. Assets can be a number of things:

• Skills
• Funding (both revenue and capital)
• Premises
• Equipment
• Staffing
• Knowledge
• Experience
• Networks

Identifying these is a useful start. But a mere list is not enough. We need to know who controls them, where they’re located, how they relate to each other and how stable they are. Creating an Asset Map is a useful way of bringing this information together.

An asset map can be of two types:

• Geographic: the location of assets is depicted on Google Maps or a printed map of the area. This is good for showing capital assets - buildings and land - but less good at showing asset ownership, skills and the more intangible assets like community networks.
• Organisational: mapping the network of organisations and individuals allows us to see how the assets are distributed and controlled. Comparing the ownership of assets with their centrality in the network can show if they are deployed in the most effective way. Software can be used to plot the networks that connect community groups and organisations and that link them to local and national agencies. This can then assist planning the delivery of programmes and projects.

SNA is a good way of exploring the links between people, organisation's or ideas and identifying the most influential elements in the resulting network. Specialist software is used to plot the network map and to measure how central its various nodes are. This can be very useful in locating “hubs” (nodes which are central to the network as a whole) and “gateways” (nodes which act as the entry points to subnetworks). Clusters of nodes which hang together naturally can also be shown.

This is not just an academic approach. Business organisations use SNA to help structure their operations. The police and security services use it to track patterns of crime and potential terrorist threat. The military use it to create and improve flexible command structures in complex and fast moving battlefields. The best funded institution studying the uses of SNA is the Centre for Network Science at the US Military College in West Point. So network analysis is serious stuff.

While network analysis can be incredibly useful in plotting relationships and identifying patterns it is not inherently good at storing information about the nodes and links which form a network. This is partly because of the design of the software commonly used and the interests of researchers. Most SNA is carried out using UCINET, (an academic research tool that carries out the analysis) and NETDRAW which creates a visual representation of the network. Neither of these tools is capable of containing information about the nodes or links beyond their role in the network. The same is true of the foremost commercial applications such as Valdis Krebs' InFlow and Karen Stephenson's  NetForm.

A problem is that the first “Wow” reaction to the visualisation of a complex network is soon followed by a “So What”. In our experience, networks only come alive when we can compare centrality measures with the assets held by particular nodes or links. When a node organisation is very central but has few skills and resources, that creates a real problem for the network as a whole, posing questions around whether to strengthen the node's resources, limit it's responsibility or bypass it altogether.

Recent software development allows the attachment of attributes and information to nodes and links, thus creating a “network database”.

Networks are a way of looking at things. They are a useful perspective for understanding and influencing how organisations and individuals interact. The process of ageing can be viewed in terms of changing network patterns as we move through life. The growth or loss of connections depends on many factors - health, employment, mobility, education, recreation, procreation and so on. Connections are formed and broken as we age and the patterns at different life stages influence our opportunities.

The diagrams that follow indicate the process. Of course, a real individual will display different patterns based on family circumstance, local context, economic status and culture. Real life’s patterns will be more complex and varied. The patterns shown indicate the principle.

Figure 1 above shows the networks that might surround a child of pre-school age - parents, various relatives, friends at a nursery and various health professionals. In Figure 2, the child is going to primary school and is developing friends and interests there. The number of immediate connections and further network connections is expanding.

Figures 3 and 4 show the expansion of contacts and links as the the subject moves to secondary school and then into early adulthood. Some of the original contacts and links disappear through death (grandparents), become weaker as people move away (Aunts Uncles / Cousins) but generally the pattern changes through the relationship with a partner and the birth of children. Work becomes a focus of interest and friendship

In Figure 5 some connections disappear through death or the drifting away that happens in any family. However, grandchildren appear and new relationships are forged with the family of a partner. Interests will change as some of the early enthusiasms for active sport that requires fitness (running, football?) become difficult to maintain and more leisurely pursuits emerge (yoga, walking?).

In Figure 6, many of the earlier relationships have disappeared and those that remain become weakened (dotted lines). The collapse of one relationship - going to a club, say because of lack of mobility - leads to weakening relationships with friends which in turn can affect interactions with other interests. at this stage in life, forming new friendships and interests becomes more difficult anyway (deafness?) and the tendency may be to replace these with passive interests (TV?).

OK the story outlined above is simplistic. Real families will be much more complex and dramatic, coloured by argument, sudden opportunity and many crises that occur over a lifespan. As the traditional family unit becomes less common, the complexities of post-divorce and changing partnerships emerge (see the work of Eric Widmer on this. In the examples above I’ve not included the relationship with the health and care sectors that colour many older people’s lives or with social services and the financial support that many families and individuals will depend on. The illustrations are meant to demonstrate a principle - that networks determine a large part of our lives.

OK, so this is a way of looking at the ageing process. What practical use is it?

• it suggests that we look at the social context of a person to seek out strengths and weaknesses.
• as people reach later life we should be trying to prepare for that by the introduction of new activities and connections in middle age to forestall the later collapse of links
• we should concentrating on the network of links beyond those immediate to the older person. These have a role in supporting friendships and activities
• the use of personal tech - tablets, smartphones etc. - can keep the wider network in touch and facilitate more face to face activity

This note is relevant to some of the network maps you will se in the Examples section where we are modelling street patterns. In 1959 Kevin Lynch wrote “The Image of the City”. It was one of the most influential books in the newly emerging discipline of Urban Design. It described a study of the Back Bay area of Boston based on the perceptions of the people who lived there. From this study, Lynch developed a system of analysis of urban areas based on the five elements that constantly recurred in the descriptions people gave of the area. These are:

• Nodes
• Pathways
• Edges
• Districts
• Landmarks

Nodes are the connections in the street pattern. Pathways are both vehicular and pedestrian. Together these form the physical network that holds a place together.

Edges are the discontinuities in that network often caused by major transit routes or by topographical features - rivers, sudden changes in level, etc. Districts are the areas defined by these discontinuities or by clustering effects in the Node/Pathway network.

Landmarks are the significant features - buildings, monuments, landscape items - that people cite as way finders and may mark places to meet.

Lynch's work has fallen out of fashion. This is partly because it is not an architecturally oriented language and, at least in the UK, architects have claimed ownership of the Urban Design territory. However the emerging popularity of a network perspective in many fields makes his analysis seem very up to date. We have recently been applying network analysis techniques to examine the structure of urban areas and the results consistently confirm the Lynch model. Here's how:

• Using specialised network software, we can draw the map of pathways and nodes very easily. This doesn't require interpretation. It can be drawn from any ordinance survey map. The network can then be analysed using the principle of centrality which scores the value of nodes in terms of how often they are passed through as a proportion of all pathways in the network. OK that sounds abstract, but when you compare the centrality results with a townscape assessment of the same place the results are amazingly consistent.
• The software will identify clusters of nodes that hang together. This is not just a measure of how near they are to each other but of how they work as sub-networks. One measure of centrality will identify gateways into these clusters. Again, when these clusters are compared to the townscape assessment, they map onto areas that can be identified because of their character or use. These are Lynch's districts.
• Where there are significant discontinuities in the network - where some of the clusters are not well connected into adjacent parts of the network - this will show up in the network analysis. Often this occurs because of physical barrier - a river, a rail line or a sharp change in level. This models the edges in Lynch's analysis.
• The last of Lynch's categories concerns landmarks. This is the least structural of the categories and refers to significant buildings, topographical or landscape features. However, the software consistently identifies nodes that contain significant landmarks.

Now I would be the first to admit that this list doesn't include the activities that make a city. Rather it refers to the framework within which these activities take place and some of us believe that the location and intensity of these activities is influenced by the network of streets, junction nodes, edges and districts.

We have mapped a range of towns and city centres and observed that:

• Nodes which score high in centrality (particularly betweenness centrality) also tend to be visually significant points - as if their importance in the structure of pathways has led to the location of significant development. Landmarks such as significant buildings and monuments tend to be located on these nodes.
• A cluster analysis reveals areas which generally correspond to recognised districts.
• Discontinuities in the network map reflect edges that have a strong influence on the centrality of nodes and formation of clusters.

The illustration below is from the study of Boston used in “the Image of the City”. It shows the distribution of pathways, edges, nodes, districts and landmarks in the Back Bay area of the city as derived from the sketch maps of interviewees.