Characterization of public space forms in traditional Chinese villages based on spatial syntax: Zhangli village as an example

Figures & data

Figure 1. Cai’s Ancient Residence Complex.

Table 1. Map of the five different phases.

Time	State	Event
Around 1867 A.D.	Initial planning to build residential homes.	Cai Qichang built Qichang House and Youzhu House.
1883–1889 A.D.	First peak of construction	Formation of the first closed cluster, initialization of the distribution of spatial patterns and ideas.
1893–1899 A.D.	Low volume of construction and careful construction.	Constructed the most representative and highest-ranking Caiqian house in the Cluster.
1904–1907 A.D.	Second peak of construction.	Construction is essentially complete, with overall order and separate clusters.
1911 A.D.	The pattern of the settlement is basically formed.	Construction of defenses (e.g., gun emplacements, posts, double walls, etc.).

Table 2. Description of spatial syntactic feature parameters.

Characteristic Parameters	Description	Equation Expression
Integration Value	The standardized index of the reciprocal of the depthvalue is divided into global integration and localintegration. It refers to the degree of agglomeration ordispersion of a node in the system and other nodes in theglobal or within a certain range. The larger the value, themore convenient the position and it also reflects thecentrality of the node relative to the system.	$I_k=\frac{n\left[lo{g}_2\left(\frac{\mathit{n}+2}{3}-1\right)\right]+1}{\left(\mathit{n}-1\right)\left(D_{\mathit{Mk}}-1\right)}$ In the formula, $D_{\mathit{Mk}}$ is the average depth value ofnode, $\mathit{k}$ is the average depth value of the node, $\mathit{n}$ isthe total number of nodes
Depth Value	The average of the shortest distance (that is, the minimum number of steps) from one node to all other nodes in the space system is the average depth value of the node. It reflects the reachability of the node in the sense of topology, and the lower the value, the more convenient it is	$\mathrm{Mdi}=\sum _{\mathrm{j}=1}^{\mathrm{n}}\mathrm{dij}/\mathrm{n}$
Intelligibility Value	The correlation coefficient between connectivity andglobal integration is utilized to measure the difficulty ofperceiving the entire spatial system from the local level (0< R2 < 0.5 denotes poor system intelligibility, 0.5 < R2<0.7 signifies good system intelligibility and 0.7 < R2 < 1indicates strong system intelligibility)	${\mathrm{R}}^2=\frac{{\left[\mathrm{\Sigma }\left({\mathrm{L}}_{\mathrm{k}}-\mathrm{L}\right)\sum \left({\mathrm{I}}_{\mathrm{k}}-\mathrm{I}\right)\right]}^2}{\sum {\left({\mathrm{L}}_{\mathrm{k}}-\mathrm{L}\right)}^2\sum {\left({\mathrm{I}}_{\mathrm{k}}-\mathrm{I}\right)}^2}$ In the formula, ${\mathrm{L}}_{\mathrm{k}}$ is the connectivity of node $\mathrm{k}$; $\mathrm{L}$is the mean value of all node connectivity; ${\mathrm{I}}_{\mathrm{k}}$ is theintegration of node $\mathrm{k}$; $\mathrm{I}$is the mean of integration of all nodes
Visual Graph Analysis	Visual graph analysis is an intuitive method of spatial analysis because itpresents the perspective of able-sighted occupants (Turner 2003). It canreflect the visible area in the analysis space from a certain point. The expression of the numerical value in this graph can be decremented fromwarm to cool. In theory, it should be conducted in a three-dimensional space.but for simplicity, space is usually reduced to a two-dimensional continuousplan in visual graph analysis.	$\mathrm{Integration}\left({\mathrm{a}}_{\mathrm{n}}\right)=\frac{\left[\mathrm{n}\left(\mathrm{lo}{\mathrm{g}}_2\frac{\mathrm{n}}{3}-1\right)+1\right]\left(\frac{\mathrm{n}}{2}-1\right)}{\frac{\left(\mathrm{n}-1\right)\left(\mathrm{n}-2\right)}{3}\left(\frac{\mathrm{TotalDepth}\left({\mathrm{a}}_{\mathrm{n}}\right)}{\mathrm{n}-1}-1\right)}$

Figure 2. Research framework.

Figure 3. Public space (a).

Figure 4. Public space (b).

Figure 5. Public space (c).

Figure 6. Google Earth high-definition remote sensing image map.

Figure 7. Conservation Plan for Zhangli Village.

Figure 8. Axial view of Zhangli village.

Figure 9. Axial Integration Degree of Cai’s Ancient Residence.

Figure 10. Comprehensibility analysis of the axes of Cai’s Ancient Residence.

Figure 11. Analysis of the depth values of the four entrance/exit axes.

Figure 12. Analysis of the depth value of the point-like spatial axis of the ancestral hall and the academy.

Figure 13. Analysis of the depth values of the axes of the linear space “Alleyways and gate courts”.

Figure 14. Analysis of the depth value of the spatial axis of the surface of “Theater Square” and “Village Square”.

Figure 15. Visual integration analysis map of Cai’s Ancient Residence.

Figure 16. Depth value analysis of the four entrances and exits of the Cai’s Ancient Residence.

Figure 17. Actual view of the ancestral temple and the academy.

Figure 18. The Ancestral Hall and the academy and Schematic Distribution of Areas.

Figure 19. Actual view of the alleyways and gate courts.

Figure 20. The alleyways and Gate courts and Schematic Distribution of Areas.

Figure 21. Actual view of Theater Square and Village Square.

Figure 22. Theater Square, Village Square and Schematic Distribution of Areas.

Figure 23. Schematic diagram of the surrounding environment of the Cai’s Ancient Residence.

Turner, A. 2003. “Analysing the Visual Dynamics of Spatial Morphology.” Environment and Planning B: Planning and Design 30 (5): 657–676. https://doi.org/10.1068/b12962.

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