Method
We'll want to take advantage of the characteristics of the emergent structure of our network to inform how we might distribute our public urban programmes.
We'll focus on the connectivity of the nodes and paths. For the paths, we'll use the space syntax algorithm developed by Bill Hillier and colleagues at UCL as it measures connectivity but weighting it with certain other parameters such as centrality, and has been shown to be a more accurate representation of how people use a network.
Connectivity can help us to determine likely concentrations of flows within a network. Highly connected nodes and paths will be more likely to be used in the course of a typical user's travels through a network.
Therefore, placing infrastructures at these nodes and paths, we will be able to serve larger percentage of the population than lesser connected network components.
Likewise, these highly used network components will be attractors for private investment, or social mixing programmes, such as retail, food, drink and entertainment uses, as these uses benefit from exposure to traffic.
These social mixing programmes will in turn again amplify the attractivity of the highly connected nodes and paths, resulting in the potential for large concentrations of people and a vibrant street life.
We'll want to plan for this eventuality by developing a methodology for providing adequate space for these activities in the appropriate locations throughout our urban fabric.
In this section, we'll begin with an analysis of the distribution network generated from our sample tissue. We will examine the nodes and paths of the network, ranking them by their level of connectivity and integration, respectively.
