Yongqing Fang Historic District Regeneration Analysis with Synergistic Conversion Rates

Guangzhou, China's Yongqing Fang historic district is analyzed from 2016 to 2024. The dataset likely contains the research findings and analysis supporting a Dual-Helix Synergy Model for urban regeneration, authored by Liaoji Zheng and last updated in June 2026. The 19.6 KB document reports a 100% rise in economic revenue, a 92% increase in cultural capital, and a 78% growth in social trust over the study period.

Use Cases

• Modeling the coupling efficiency between spatial reproduction and value creation based on the synergistic conversion rate (SCR) metric.
• Analyzing the evolutionary pathway of urban regeneration nodes (Dormant Node to Symbiotic Engine) described in the study.
• Testing the proposed Dual-Helix Synergy Model in other historic district contexts for comparative research.
• Investigating the relationship between specific space typologies (Community Lounge, Professional Workshop) and gains in social trust or cultural capital.

Strengths

• Specific quantitative outcomes are reported, including economic revenue rising 100%, cultural capital 92%, and social trust 78% from 2016 to 2024.
• The study employs multiple analytical methods, including analytic network process (ANP), social network analysis, and spatiotemporal analysis.
• The data is shared under a permissive CC-BY-4.0 license, allowing for reuse and redistribution.

Limitations

• The dataset is a 19.6 KB document, indicating a very limited scope focused on a single case study's findings rather than raw observational data.
• Row count and column-level documentation are absent; the actual data structure and variables must be inferred from the document.
• Generalizability is explicitly noted as limited, as findings are derived from a single case in a specific institutional context.

Provenance

Source

Liaoji Zheng, via figshare.

Collection Method

The description suggests data was gathered through a case study of Yongqing Fang using analytic network process (ANP), social network analysis, and spatiotemporal analysis.

Time Range

2016 to 2024

Freshness

Last updated 2026-06-02 14:43:56.

Geography

Yongqing Fang, Guangzhou, China