Drew Mackie & David Wilcox
The Internet provides the potential for extending and enhancing our personal and professional networks - offering scope for reducing social isolation, building community relationships, and supporting cooperation between and within organisations.
However, to realise the potential of networks we need to understand more about their nature, how to analyse, map and build them - whether online or not.
Drew Mackie - a networks specialist - has drafted this guide with David Wilcox.
This paper is licensed Creative Commons CC BY-NC-SA so you may remix, tweak, and build upon this work non-commercially, as long as you credit us and license any new creations under the identical terms.
Sections also available separately:
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:
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:
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:
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:
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:
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:
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:
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?
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 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:
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:
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.
It has become fashionable to talk of networks of organisations, people, computers, transport and so on. In organisations there is talk of being more “networky” and getting away from the older more hierarchical ways of doing things. Conferences are organised around “networking” both formal and informal.
Yet, the more that you listen to this network talk the more you realise that people mean very different things by the term “network”. The purpose of this paper is to explore what network thinking means and how networks can be mapped and analysed.
Why is this important and useful? The structure of a network will affect how influence and information is distributed. Certain members will be potentially more influential because of their position in the network. Mapping the network can give guidance on the easiest ways to distribute information, the links that should be there to improve the network, how to avoid bottlenecking and so on. Such network maps are used by commercial and government organisations to plot situations as divers as:
The first thing to be said is that a network is not a list. The term implies a set of connections between its members. These connections may consist of flows of information, power, money and so on, but the implication is that an influence of some sort is passing from one to the other.
Networks can be dense or sparse - meaning that the number of connections is great or small. The total number of possible connections in any a group of members of size n is given by the formula:
n x (n - 1) / 2
Thus, a network of 10 members has a total of 45 possible connections. The density of a network can be measured by comparing the number of actual links with number of possible links and expressing this as a percentage. However this measure should be used with care. The number of possible links increases dramatically with the number of nodes. In any real world network there will be a natural limit on the number of connections that any node may be able handle. Thus large networks will show much lower densities than small so of different size can’t be compared used such a density measure. A better gauge of density is the average number of connections per node as this can be applied across all scales.
For all the members to be connected into a network structure the number of links must be at least n - 1. Research shows that the best connected organisations are arranged so that any member can connect to another within three steps.
A key concept in the analysis of networks is centrality which holds that nodes in a network will have influence because of their position. There are several types of centrality. The following examples show the “kite” diagram developed by Professor David Krackhardt of Carnegie Mellon University to illustrate some of the basic properties of networks. Ten people make up the network and they are related in ways shown by the linking lines. Darker shading indicates how members score under various network measures.
Figure 1 - Degree Centrality is a measure of how many connections members have. In the diagram below, Diane has six connections. Fernando and Garth have 4. Carol, Andre, Beverly, Ed and Heather have 3. Ike has 2 and Jane has 1. Diane has the top degree centrality score.
Figure 2 - Closeness Centrality refers to the way that influence is spread through the network. In the diagram below, Fernando and Garth share the top score. Both Heather and Diane are next most influential followed by Andre and Beverly, then Carol and Ed with Ike and then Jane who is the least influential. It indicates their potential for influence because of their position in the network.
Figure 3 - Betweenness Centrality refers to the way that some nodes will control the access to parts of the network. Thus, in the diagram below, Heather is the only access to Ike and Jane. Such nodes are “gatekeepers” and can either restrict or facilitate the way that influence spreads to a cluster of nodes.
Figure 4 - Clusters of nodes can be identified in a network. A cluster is identified where the nodes connect more to each other than they do to the rest of the network. Thus, Ike, Jane and Heather form a cluster, to which Heather is the gatekeeper.
So, understanding the way that various nodes control the spread of information and influence in a network can be very useful in deciding where to exert pressure for change, how best to introduce ideas or information and how the network structure might be improved.
Many of the examples of social network analysis show vast maps with several thousand nodes and huge numbers of connections. Often, these will have been prepared using data mining techniques - the automatic recording of connections through email records or online social networks. These are compiled into matrices and the data set fed into the SNA software. The results are often hard to interpret.
When this approach is applied to the study of communities who use a mix of different connections with others - online, offline conversations, meetings, publications, letters and so on - the collection of data can be quite difficult. Commonly it will be based on interviews, surveys, workshops and other methods that will take time and staff commitment to organise and facilitate. So if you are studying a community with 3,000 members you will need to access these individually and in managed groups. This can be colossal task and recent UK examples have gained the method a reputation for being difficult to apply in real communities.
There is another approach that uses the characteristics of local networks to assist in the initial gathering of information. We must ask ourselves the question: “what do we want to use the analysis for?”. If it's to thoroughly understand the intricacies of local social networks, then we probably have to follow the route described above. If, on the other hand, we want to identify which individuals and organisations are potentially most central to the workings of community networks, there is a simpler method.
The SNA practitioner will usually be asked by a particular agency or group to carry out an analysis. This client will have their own set of contacts in the community as a starting point. Through interviews, questionnaires etc, this group will be asked to cite those they have most contact with in terms of the focus of the study - working relationships, information spread, political influence and so on.
The map and centrality analysis produced by this will certainly have been skewed in favour of the client's own network of contacts. Unsurprisingly, the client will turn out to be the most central node in the network. However the map will also show the contacts cited by those interviewed and connections between interviewees. A number of these will be linked with nodes other than the client list and score highly in terms of centrality. These become the targets for a second round of interviews or questionnaires concentrating on the nodes that were cited by contacts but not interviewed in the first wave. A large community may need several rounds of this process. Eventually the high centrality scores will emerge clearly without having to engage the whole community in the survey. This approach is sometimes known as “snowballing” in SNA literature.
The diagram illustrated above shows a network revealed in a two stage survey:
This process removes the skewing of the survey by the initial client contact selection and quickly focusses on the most central nodes without having to survey the whole community. Effectively it is using a networking approach to carry out the network survey. Complex and extensive networks may need several iterations of this method.
The study of networks has increased in popularity over the last few years. Until recently most of the examples of network analysis came from the US and Australia, but this is changing. A recent study of community networks in New Cross by the Royal Society of Arts gained a lot of interest as did a paper for the same organisation by Paul Ormerod stressing the understanding of network effects in economics. In the field of Public Health, the publication of “Connected” by Christakis and Fowler has raised interest in the network effects in alcoholism, smoking, sexually transmitted diseases and obesity.
The software used to study such phenomena and measure the centrality of network nodes has generally been derived from academic models in the US. UCINET, PAJEK, GEPHI and AGNA are popular in the academic community. Commercially, Valdis Krebs’ INFLOW and Karen Stephenson’s NETFORM are protected and can only be used under licence. Generally these programs are PC based and read their data from spreadsheet input. However, we find that the act of drawing makes them come alive in a way that spreadsheet input does not.
Most of the examples that you will find at the end of this document are created in yEd, a Java based programme that is used to create diagrams by drawing. For the last year, we have been using a web based application called Kumu which allows the storage of information directly in drawn nodes and links and lets you use that information to:
Of course the drawing input of data is appropriate to a certain size of network. Up to around 20 nodes, you can pretty well sketch the diagram on a bit of paper and gauge the centrality of nodes by hand - although in a really connected network this can become more difficult. The number of possible links in a network is n(n-1)/2, so 20 nodes have a possible 190 links.
The number of possible links rises steeply as the number of nodes increases as shown below:
Up to around 200 nodes, drawn input is OK. beyond that it becomes more difficult.
However this 200 node upper limit suits most organisational situations. It may not work for the mapping of sizeable communities (the recent mapping of the New Cross community in London contained around 3,000 nodes). Most of the work that we have done with local organisations yields less than 200 nodes.
The sheet above shows a simple questionnaire for eliciting information from an individual about:
This allows us to build a network map, carry out a Social Network Analysis to identify the most central individuals / organisations and to compare assets held with their position in the network.
Recently, we have been using online services to collect network information and to link that with audits of assets held by the various individuals and organisations. Using the right web based system can allow material to be collected in the field by tablet or smart phone. The information is returned online to a central database that we can use to construct a network map.
The following pages show snapshots of network mapping we have carried out over the last 8 years.
1 Berwick-upon-Tweed regeneration
2 Mapping Delivery in Consultation and Engagement
3 Belfast Community Tourism
4 Big Society Network
5 TSRC report appendix
6 The Children's EcoCity in Dunfermline
7 Our Society Ideas
9 Central Edinburgh street network
10 Borders settlement network
11 Stranraer networks
12 Irish Craft Networks
13 Asset Mapping in Croydon
Drew Mackie worked as part of a Kevin Murray Associates team to look at the way that a regeneration partnership was working in the historic town of Berwick-upon-Tweed. The results were used to suggest ways of strengthening the partnership and ensuring the most effective use of the assets it could control and / or influence.
The network map below shows the links between organisations and groups as cited in questionnaire. The highlighted nodes are those with greatest potential to influence the network because of their position.
Having established how central various nodes were, we asked those involved to indicate what skills and resources they and the people / organisations they cited had. This allowed us to compare network position with assets held. The results showed that some organisations with few skills and resources held very central positions thus adversely affecting the network.
This analysis was prepared for Belfast City Council some years ago to assess the network of departments and others involved in consultation and engagement. The analysis compared position in the network with perceived performance (assessed by themselves and by the other departments or grouped with which they worked) in terms of 5 types of Skills and Resources:
The network map shown below shows the relationships and sizes nodes according to betweenness centrality.
This map was prepared as part of a study of community tourism in Belfast conducted by TTC International. A series of workshops throughout the city collected comments from local people, politicians and professionals on what organisations might be able to deliver a community tourism strategy.
A conference was held by the Big Society Network in 2010. This consisted of a very ad hoc group of people gathered at the last minute by Twitter to catch the momentum of the incoming Coalition Government's Big Society agenda.
Around two thirds of participants (some refused to participate on principle and others just didn't return the survey sheets) completed a one question questionnaire asking - “Who do you work with most?”
The results were used to create the map below which showed that the organisations that might be expected to benefit from the government's Big Society policies were:
A more complete survey might have given more detail but these broad conclusions have been borne out by subsequent events.
The network map below was derived from the Third Sector Research Centre's “Beyond the Radar” conference in London in 2011. In response to the question “who do you work with most ?”, respondents listed organisations and gave their estimate of the skills and resources held by these bodies. From this we constructed the network map below.
The map below shows the clusters of organisations that work together.
This project is the latest in line of EcoCities stretching back 15 years. An EcoCity is an extended design exercise conducted with 40 primary school children aged around 11 in which they create a large model of their ideal ecologically friendly city. In Dunfermline, six schools participated and the EcoCity was part of a wider consultation on the future of the town. The pictures in the PDF below show the model.
Following the building of the model, a workshop was held with a broad range of stakeholders in the town - retailers, tourist operators, local community groups, local officials etc. We asked each attendee to list the organisations and groups they worked most with and used the result to generate the map below. This is now being used to lobby the most influential nodes to take forward the ideas from the EcoCity and from other engagement exercises.
The network map shown below is derived taken from a blog piece by David Wilcox. This was the culmination of an exercise in crowd sourcing ideas, voting on the most popular and then creating a network of connections.
We developed a graphical display of how the ideas can be clustered (slightly arbitrary, because we drew the lines connecting the ideas, and the software followed through). In the map, a cluster analysis shows 4 clear sets of ideas, each with a strong central node.
The clusters and most central nodes do not reflect the votes cast - but that's another issue.
Media4Me is a six nation EU project exploring how both online and offline media can benefit communities. The UK location for this is Fishermead in Milton Keynes and the project is managed by the National Association for Neighbourhood Management. We have been using a specially tailored version of our social media game to facilitate local workshops attended by community groups and agencies. The game results in a series of stories about how local individuals might benefit from particular social media strategies.
As part of this work, we used a number of community events to create a map of the community and agency networks in the area. In an open, day-long event, residents and agency representatives were asked to draw their network connections in response to the question: “ who do you connect with most”. As the results came in, the map was expanded in real time on a large screen so that participants could see it develop. Although such maps can look complex to outsiders, they are generally readily understood by the people that drew them.
Following this we associated useful information (contact details, websites, blogs etc) with the nodes on the network. This allowed the map to be used as a source of community information while keeping the network patterns up front.
Local community groups are now able to consider using this map and learning how to continuously update it as a guide to how local agencies work, how community groups relate to each other and who the key players are.
Quote from client:
“Drew's mapping of social connections in Fishermead added a great deal of value, principally by revealing, graphically, to the most civically active members of the community, the extent of social capital which already exists, but which too often was under-estimated. This in turn changed attitudes, and created new energy.“
Ben Lee, Director, National Association for Neighbourhood Management and senior staff at Shared Intelligence.
The maps here show a first attempt to apply Social Network Analysis to physical urban networks. They show the street network of central Edinburgh. The idea was to see if the most central nodes also had urban design significance. As a resident of the city, I believe that it does. The most central nodes in terms of network analysis are also places of urban design importance - places which have their own name (The Bottom of the Mound, Top o' the Walk, Frasers Corner etc.)
The map below uses the “natural clusters ” setting on YeD to group nodes. Again the clusters reflect recognisable areas of the city.
The map below was prepared to examine the centrality of road connections (nodes) on both sides of the Scots/English border. No survey work required here - just translated the road map into a YeD network.
The results showed that:
Street Network Analysis
This is a map showing the network of streets in Stranraer. It was prepared as part of a study of the regeneration of the waterfront area in the wake of the move of the Stena SeaLink Northern Ireland Ferry to a new site further up the coast. The map was prepared to show the key junctions in the movement system and to compare these with significant townscape elements. The maps were drawn with network software which also analysed the centrality of nodes.
A version of the Stranraer network map showing the “natural clusters” defined by the software is shown below. This was compared to a townscape analysis. The correlation with character areas was strong.
The assessment below was produced as part of the consultations associated with the Masterplanning of Stranraer Waterfront in 2009. At a Stakeholders Workshop, participants were asked to complete a short questionnaire indicating:
The first question allows us to draw a “map” of the relevant organisations. This is shown below. The nodes represent organisations and the links represent the working relationships specified in the questionnaire. The map shows the potential centrality of various departments and groups to the network of organisations.
Why does this matter? The position of an organisation within the network will help to determine the degree of influence that it has and how well it is placed to distribute information to the other organisations. This property (known as “centrality”) can be measured. In the map below, those organisations with the greatest centrality are shown in a darker colour. Thus “D&G Council” is the highest scorer not just because it has many links, but because of its position.
This project was commissioned by the Crafts Council of Ireland together with the 5 LEADER partnerships for Ballyhoura, Kilkenny, South Tipperary, West Cork and Wexford. We were asked to identify the crafts culture of these areas through:
Through an online questionnaire and a series of workshops held in the study areas, we identified the working links between the various participants in answer to the question: “who do you work most with?” This resulted in a network of 250 nodes and around 800 connections. The basic network is shown in Figure 1 below.
While the clustering of nodes is determined entirely by the network of connections, the node colour indicates the geographic location of an enterprise or organisation. There is a striking degree of correlation between the network clusters and geographic location. The cluster at the bottom of the diagram, for instance, is composed almost entirely of craft enterprises, suppliers, retailers and agencies located in Cork City and West Cork. This need not have been the case. The working clusters could have gathered around craft specialisms - ceramics, jewellery, woodworking etc. The network diagram shows that craftspeople tend to work with other craftspeople in their immediate geographic area.
The cluster immediately above that and to the right is composed mainly of nodes located in Kilkenny and Wexford and one of the main findings of the study was that these two areas cannot really be differentiated in network terms.
The cluster on the far left is composed of nodes in Limerick and Ballyhoura, while the cluster in the top middle has a mixture of nodes from different locations.
We also mapped the main road connections in the study area as shown in Figure 2. Nodes are a mixture of settlements and rural road junctions. Note that:
The road network
clusters of settlements / junctions in the road network
Figure 3 shows the results of a cluster analysis of the road network. Nodes are coloured according to which cluster they belong to, showing:
These comments echo much of the organisational analysis and show links between geographic distribution and operational and transport networks.
Although this work is experimental, it brings together a body of previous work that examines either organisational or geographic structures. The degree of congruence that we are finding is very encouraging for future comparisons.
In work for Croydon Voluntary Action (CVA), we have mapped the organisations and key individuals involved in Asset Based Community Development (ABCD) pilots in New Addington and Thornton Heath. This has involved:
The network map has been developed in Kumu. This online software allows us to progressively hand over the map to CVA and to monitor its use remotely as staff we have trained become more familiar with it and start to expand it.below shows the network of connections between key individuals and organisations involved in two pilot ABCD programmes in Croydon (New Addington and Thornton Heath).
The software allows us to attach information directly to nodes in a a sidebar and to carry out complex searches on various attributes assigned to nodes and connections.
The intention is that this should become an everyday method of recording the progress of the project. The metrics that SNA produces will serve as evaluators of: