The impact of historical nodes and links of the city on adopting knowledge networks strategy

الملخص EN

The city was formed historically as a node for human activities.

Its formation depends on the resources and relations in areas of production and distribution; therefore, it was divided into special zones with different economic activities that depend on them (industrial, recreational, commercial, residential areas and other uses of land).

The physical and functional features of the city reflected imposed social and digital systems.

The Technological advances have expanded the role of knowledge in everyday life that established the concept of knowledge networks.

This study deals with the concept of knowledge networks as an updated multidimensional strategy which imposes its ways to deal with the historic urban system as a part of the larger one, where the developed technology imposes its needs of the organizational vision of the city as part of the global system.

This study hypothesis that the knowledge networks is a concept not separated from the historic space networks in cities.

The study also hypothesis that these historic networks, which were formed from nodes and links impose the strategic vision to adopt and promote the knowledge networks into urban level.

This study aims to clarify the concept of knowledge networks as an updated strategy for the urban growth, shows the role of nodes, and links which had grown historically in cities to adopt and promote strategies for the knowledge networks.

This research adopted a method to achieve its aims, through studying the cognitive network mechanism and the role of urban characteristics as guidelines to strengthen these networks in a number of global examples, and then use the global experience to build a vision for network strategy in the Iraqi city to produce a new urban pattern.

The main conclusion is that the historic urban ties act as a mutual force along with the global mechanism of knowledge network so that each city would show its special response to the network pattern.