Answer

Urban Structure and Space Syntax

In the book of The New Science of Cities, Michael Batty outlines that in order to understand towns and cities, people must view them as systems of flows and networks but not simply as places in a given space. He argues that, to understand space, an individual must understand flows, and to comprehend this flow, we must understand networks (Batty 2). Networks in this case refer to the relationship between objects that together make up a given city. Batty has drawn from a variety of fields such as physics, complexity sciences, transportation theory, urban geography, urban economics, regional science and his own work to introduce new theories and structures that deeply describe the functional process of the cities.

Batty acts as a pioneer in presenting the foundation of a new city science that defines flows and the connected networks. He also provides tools that can be applied in understanding and explaining the components of city structure. He introduced new simulation methods ranging from stochastic models to more evolutionary models in order to help aggregate the land-use transportation models (Batty 3). Batty uses these tools to make decision making models that can be used to predict the flows and interactions in the future cities. This sends a unique message to the future researchers that the design of cities should be done in a collective and coordinated way.

The most important component of city structures is the three laws of scales. The first component is frequency of city sizes. Through this component, a developed model can determine the size of a city based on the population of its occupants (Batty 5). The second component is the attribute of the changes in cities relative to one another and their sizes. This helps reflect diseconomies, economies of scales and negative allometry (Batty 6). The last law pertains to modelling the existing interactions between and within cities. Through these laws, it is established that spatial processes that determine the forms of cities are consistent in their application.

One important model that has been developed due to the continuous and coordinated researches is space syntax. Space syntax is a system that provides computational techniques and empirical theories for the analysis of spatial structures of the urban spaces (Jguirim et al. 4). Over the years space syntax has been widely used in the development of applied and empirical studies enabling people to understand the existing spatial structures in all urban networks.

Space syntax involve modelling of space as a network where the street intersections are nodes and the segments between these nodes are the edges. Graph-based operators are then applied to produce a group of computational measures that meet the global and local structural properties, for instance, connectivity, centrality and clustering (Koohsari et al. 91). The other critical quality of space syntax and urban structure is purely based on the relationship that arise from the underlying graph and not the actual location of the nodes and edges of the urban network.

Currently, a town can be interpreted with respect to the different points of view and at different scales. These differences create modelling challenge in providing a modelling framework that includes all these dimensions in the computational process. Space syntax acts as the best option to the solution of these dimensional problems. The program encompasses a connectivity measure which determines the number of nodes that are directly connected to the individual node. The centrality element gives the number of traversing routes that connects each node to one another (Koohsari et al. 121).

Importance of Computational Measures

Computational measures in a social network include connectivity, centrality and clustering. Connectivity gives the information of the robustness of a social network and the nodes that connects the various components of the network (Jiang 649). It also gives the density of roads, the average proportion of nodes connecting edges of the road. Centrality measure gives specific information about actors within the network. Roads with high degree of centrality have connections with many other roads within the city. Clustering helps create a super-network that enables the simplification of a very large network (Koohsari et al. 121). Clustering coefficient is normally used when giving the topological structure of any social network.

Urban Structure of Norfolk

Using space syntax to analyze the interconnection of roads in Norfolk reveals interesting results. The analysis indicate that the topology of Norfolk streets is far from being just random but form small worlds consisting of scale-free property. The scale free property of roads in Norfolk can be described in a number of ways. The first is that eighty percent of streets in the city with a street network consist of degrees that are less than the mean value of the network. Secondly, twenty percent of the streets have lengths that are greater than average value of the network. Out of the twenty percent roads, there are only one percent of streets which can accurately form the backbone of the street network (Bing et al. 76).

These results are derived from the fact that the degree and path length are significantly correlated. Therefore, lengthy streets have high degree of intersections (Jiang 3).

Degree is given by:

     n

m(G) = 1/n ∑_j-1R_ij  

Path length is given by:

 n     n

l(G) = 1/n∑∑d(i, j),                                                                                                                                (3)

From computational point of view, only streets within the same envelop, a rectangular area covering the street, are compared. Comparison of the degrees and length of the roads in all major cities creates a universal pattern that is represented in the graph below.

The graph indicates the topological pattern: 80% of streets having a degree less than the average m, while 20% having a degree greater than the average, out of the 20%, less than 1% of streets form a backbone of the street network (Jiang 6).

Clustering coefficient is normally applied to determine the clustering degree of roads in a given city. Application of the clustering coefficient reveals that the roads in Norfolk are divided into two unbalanced groups. First, on the surface, street networks have a simple structure. Secondly, at the cognitive level or underneath, there exist a consistent recurrences of the identified unbalanced pattern (Lamb 449). This scaling pattern explains why the image of Norfolk city can be formed in an individual’s mind with reference to street networks. The analysis reveals that twenty percent of well-connected streets account for over eighty percent of the traffic flow in Norfolk. Also, less than one percent of extremely organized and well-connected streets make up a cognitive map of the Norfolk city street network. From the findings, it is sound to conclude that in general, the government should not invest equally in all the streets, but mostly in the twenty percent of the streets that give the best reward using less effort.

Conclusion

To understand space, an individual must understand flows, and to comprehend this flow, we must understand networks. Critical computational measures used in a social network include connectivity, centrality and clustering. Just like most towns, the topological structure of Norfolk indicates 80% of streets having a degree less than the average m, while 20% having a degree greater than the average, out of the 20%, less than 1% of streets form a backbone of the street network.