Urban parks are one of the most common spaces for social interactions in modern cities. The design of park spaces, especially space configuration, has significant influences on people’s social behaviors in parks. In this study, the associations between space configurational attributes and social interactions were investigated using space syntax theory. An observation analysis of social behaviors was carried out in two urban parks in Beijing, China. Nine space configurational attributes, including depth to the gate, depth to the main road, connectivity, normalized angular integration (NAIN), and normalized angular choice (NACH) with three radii, were calculated using a segment model. The variance analysis and regression analysis reveal the strong joint effect of space type, space scale factors, and space configurational attributes on social interaction behaviors in parks. The personal interaction group contained 23% of the total observed people involved in social interactions. Pathway length, zone area, and NACH-10K (NACH with a radius of 10,000 m) are positively associated with the number of people involved in personal interactions. For the social interaction group (77% of the total observed people), the space scale and depth to main city road were found to have a positive and negative influence on social interaction intensity.
In the past decades, Space Syntax offers a series of theories and techniques to study the relationship between urban space and social-economic activities, and has been proved effective in analysis and design practices thanks to the open sources in the big data era. Taking the Chaoyang Square Renewal project in Jilin City, Jilin Province as an example, this article introduces the analyses of traffic volumes and visual integration of the square and the connected streets with modeling tools such as Segment Map and the intelligent multi-agent systems in visibility Graph Analysis. All these analyses provided a basis for the full design process, from conceptual design to proposal evaluation, in order to activate this site through introducing pedestrian vitality. Prospects on new technologies in Artificial Intelligence, such as machine learning, are also explored to promote the research of Space Syntax and related application in urban design.
Changes in the road environment of Tianjin were intended to speed up the traffic system and shift bicyclists to the metro, bus and car. The metro system was greatly expanded, along with a modernized bus fleet. App-based taxi services were also introduced. In 2007, travellers to the central area were intercepted to determine the starting point of their trip and their travel mode. The most time-efficient trips in 2007 were by bicycle (61%). These trips were re-enacted in 2017 using taxi and metro as it was no longer physically possible to replicate most of the original bicycle trips. Trips greater than 5 km in distance were somewhat faster by taxi than they were by bicycle, but overall, travel time by taxi was greater than by the bicycle for those same trips in 2007. A network analysis of road changes provides explanation why longer trips became more efficient while short trips became less efficient. Travel by metro alone was much longer than the other two methods, but the combination of app-based bicycles and metro would render this travel method the most efficient.
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