“风景-遗存”对公众时空行为的影响机制研究——以首钢工业遗址公园为例
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邱思嘉/女/北京林业大学园林学院在读硕士研究生/研究方向为风景园林规划与设计 |
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宝力格/男/蒙古族/北京林业大学园林学院在读硕士研究生/研究方向为风景园林规划与设计 |
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徐静/女/苗族/北京林业大学园林学院在读硕士研究生/研究方向为风景园林规划与设计 |
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魏方/女/博士/北京林业大学园林学院副教授/研究方向为当代风景园林设计理论、景观感知、可持续更新 |
收稿日期: 2024-12-10
修回日期: 2025-01-19
网络出版日期: 2025-12-12
基金资助
北京市社会科学基金青年项目“基于公共价值提升的北京工业遗存分级保护与景观重构利用研究”(19YTC040)
版权
Research on the Influence Mechanism of “Scenery-Relics” Landscape on Public Spatio-Temporal Behavior and Perception Preference in Industrial Wasteland: A Case Study of Shougang Industrial Heritage Park
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QIU Sijia is a master student in the School of Landscape Architecture, Beijing Forestry University. Her research focuses on landscape planning and design |
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BAO Lige (Mongol) is a master student in the School of Landscape Architecture, Beijing Forestry University. His research focuses on landscape planning and design |
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XU Jing (Miao) is a master student in the School of Landscape Architecture, Beijing Forestry University. Her research focuses on landscape planning and design |
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WEI Fang, Ph.D., is an associate professor in the School of Landscape Architecture, Beijing Forestry University. Her research focuses on contemporary landscape design theory, landscape perception, and sustainable renewal |
Received date: 2024-12-10
Revised date: 2025-01-19
Online published: 2025-12-12
Copyright
后工业时期大量的城市工业废弃地减缓了城市化进程与经济发展,导致环境污染、生物多样性下降和城市空间碎片化,对居民生活质量和幸福感产生影响,城市工业废弃地逐渐变成被遗弃的空间。“风景-遗存”作为一种新兴的城市绿地类型展现出巨大的改造潜力,研究其对公众时空行为的影响机制,可为工业废弃地有机更新提供新的方向。
结合时空行为轨迹技术,通过计算空间指标度量样本空间的自然度,通过游客时空行为轨迹分布、轨迹点密度分布、轨迹点速度分布探究后工业景观中游客的行为模式。
对于客观指标,建筑面积占比与停留行为呈显著负相关,水体面积占比与通行行为呈极显著负相关;景观形状指数与漫步行为呈显著正相关,香农-威纳指数、辛普森指数与穿越行为呈现极显著正相关;自然要素与停留行为呈显著正相关。对于主观情感指标,除人工干预和管理程度与停留行为呈现显著正相关外,其余指标与时空行为无显著相关性。
从时空行为轨迹视角对后工业景观中游客行为模式进行研究,有助于了解后工业景观与游客行为模式之间的时空关系,可为后工业景观设计改造、城市绿地结构优化与工业废弃地有机更新提供理论参考。
邱思嘉 , 宝力格 , 徐静 , 魏方 . “风景-遗存”对公众时空行为的影响机制研究——以首钢工业遗址公园为例[J]. 风景园林, 2025 , 32(3) : 41 -48 . DOI: 10.3724/j.fjyl.LA20240107
During the de-industrialization period, large areas of urban industrial wastelands have slowed the process of urbanization and economic development. These sites, associated with environmental pollution, loss of biodiversity, and urban spatial fragmentation, have negatively influenced residents’ quality of life and well-being, eventually turning into abandoned spaces. The reclamation of industrial wastelands can effectively promote the development of these accounting components. Moreover, the “Scenery-Relics” concept, as a novel form of green space, has demonstrated significant potential for transformation, offering new directions for enhancing ecosystem cultural services.
The spatio-temporal behavioral trajectory technique provides an accurate and efficient method for the research on public behavioral preferences in post-industrial landscapes. Based on the aforesaid technique, to quantify the naturalness of sample spaces, spatial metrics are employed to measure their natural degree. By examining the trajectory patterns of tourists in post-industrial landscapes with varying degrees of naturalness, the number and density of trajectory points are used to investigate the behavioral patterns of tourists.
The results indicate a significant association mechanism between the spatio-temporal behavior trajectories of tourists and the “Scenery-Relics” of industrial land renewal. Various spatial elements and subjective emotional indicators within the naturalness of industrial landscapes exert certain influence on tourists’ spatio-temporal behavior. Overall, the proportion of buildings is inversely related to tourists’ stay time; the higher the proportion of buildings among all landscape elements, the shorter tourists’ stay. In terms of spatial indicators regarding the naturalness of industrial landscape, there is no significant correlation between behavior distribution and the proportions of paved areas, pedestrian pathways, vehicular roadways, or green spaces. However, the proportion of buildings shows a significant negative correlation with stay behavior distribution, while the proportion of water bodies exhibits a significant negative correlation with traffic behavior. The number of patches is not significantly related to behavior distribution, but the landscape shape index shows a significant positive correlation with strolling behavior. Shannon's Diversity Index and Simpson's Diversity Index demonstrate significant positive correlations with rapid traffic behavior. While naturalness and artificial elements are not significantly correlated with behavior distribution, natural elements show a significant positive correlation with stay behavior, and the number of plant layers is positively correlated with stay behavior. However, the data on subjective emotional indicator do not follow a normal distribution (p<0.05). Satisfaction, plant species, naturalness, and a sense of security show no significant relationship with behavior distribution. In contrast, artificial interventions and management are significantly positively correlated with stay behavior, indicating that tourists’ subjective perception of Shougang Industrial Heritage Park reveals a significant correlation between artificial interventions, management, and tourists’ behavior trajectories. Additionally, relevant research on the San Gaolu Xiuchi and Qunming Lake areas further confirms that within the natural elements of landscape spaces, the area of water bodies and the richness of plant spatial diversity are positively correlated with tourists’ stay time. Larger water body areas and more diverse plant landscapes can effectively attract tourists to stay.
Examining the behavior patterns of tourists in post-industrial landscapes from the perspective of spatio-temporal trajectory can help elucidate the spatio-temporal relationships between post-industrial landscapes and tourist behaviors. This approach provides a theoretical reference for the design and transformation of post-industrial landscapes, the optimization of urban green space structure, and the organic renewal of industrial wastelands.
表1 景观格局指数计算方法及意义Table 1 Calculation method and significance of landscape pattern index |
| 指标 | 计算式 | 式注 | 意义 |
| 景观形状指数 | ${\rm{ESI}} = {P^2}/A$ | ESI表示景观形状指数,P表示斑块的边缘长度,A表示斑块的面积 | 描述植被斑块的几何形状 |
| 斑块数量 | 描述景观破碎化程度 | ||
| 香农-威纳指数 | H ’$ = - \displaystyle\sum\nolimits_{i = 1}^S {{P_i} \times {\rm{ln}}{P_i}} $ | H’表示香农-威纳指数,S表示群落中物种的总数,P i 表示第i个物种的个体数占群落中总个体数的比例,ln表示自然对数。当群落中只有1个物种时,香农-威纳指数为0,即群落多样性为0。群落内物种数增加或物种相对多度越均衡,香农-威纳指数则随之上升;若不同物种相对多度差异增大,则香农-威纳指数降低 | 描述生态系统的物种多样性,评估生态系统的健康状况和稳定性 |
| 辛普森指数 | $D = 1 - \displaystyle\sum {P_i^2} $ | D为辛普森指数;P i 为第i个物种的个体数占群落中总个体数的比例 | 描述种群优势状况 |
表2 人工介入程度表征Table 2 Quantitative characterization of degree of manual intervention |
| 赋值 | 人工要素 | 自然要素 |
| 0 | 全面覆盖,有大型建筑或装饰性结构 | 完全没有自然要素 |
| 1 | 结构,无论是建造的雕像、喷泉,还是服务设施(酒吧、加油站)的面积占据场地总面积的90%~100%;存在大型建筑或装饰性结构 | 自然要素小于等于场地总面积的10% |
| 2 | 75%~90%或更多的面积被人工要素占据;存在大型建筑或装饰性结构 | 自然要素小于等于场地总面积的25% |
| 3 | 50%~75%的场地被人工要素占据 | 几种自然要素(草本植物、树木)的面积占据场地总面积的25%~50% |
| 4 | 人工要素存在且明显,但没有压倒自然要素;人工要素占据场地总面积的40%~50%,大型建筑或装饰性结构不存在或只占据场地的一小部分 | 多种不同的自然要素(草本、灌木、树木)的面积占场地总面积的50%~60% |
| 5 | 人工要素数量少,占据场地总面积不到40%~25%,没有突出的自然要素 | 自然要素面积占场地总面积的60%~75% |
| 6 | 人工要素少,其中一些人工要素突出;人工要素覆盖不到场地总面积的15%~25% | 原始自然生态系统,经过高度改造/改变,但保留了大量自然要素,自然要素面积占场地总面积的75%~85% |
| 7 | 人工要素少且不突出,覆盖不到场地总面积的10%~15% | 原始自然生态系统占场地总面积的85%~90% |
| 8 | 可能有0~10%的人工要素(不干扰自然要素的存在)用以促进娱乐和教育用途 | 原始自然生态系统的自然要素和多样化自然要素占场地总面积的90%~100% |
| 9 | 用于访问场地和教育所必需的人工要素占场地总面积的0~5%;只有一些可能以某种方式固定的人工要素 | 原始自然生态系统的自然要素占场地总面积95%~100% |
| 10 | 完全没有人工要素 | 所有原始场地的自然要素占场地总面积的100% |
表3 游客行为模式分类Table 3 Classification of tourist behavioral patterns |
| 行为模式 | 行走情况 | 速度区间 |
| 停留 | 站立 | 0~1.5 km/h |
| 漫步 | 低速行走 | >1.5~2.5 km/h |
| 通行 | 中速行走 | >2.5~4.0 km/h |
| 穿越 | 高速行走 | >4.0~6.0 km/h |
| 跑步/ | 高速通行 | >6.0 km/h |
表4 空间要素面积占比与行为模式的相关性Table 4 Correlation between area proportion of spatial elements and behavioral patterns |
| 行为模式 | 相关系数 | |||||
| 硬质铺装面积占比 | 车行道面积占比 | 人行道面积占比 | 建筑面积占比 | 水体面积占比 | 绿地面积占比 | |
| 注:*表示在0.05级别(双尾)相关性显著;**表示在0.01级别(双尾)相关性显著。 | ||||||
| 停留 | −0.144 | 0.135 | −0.159 | −0.433* | −0.500 | 0.307 |
| 漫步 | 0.205 | 0.279 | 0.274 | −0.017 | −0.500 | 0.211 |
| 通行 | 0.305 | 0.198 | 0.210 | 0.079 | −1.000** | 0.191 |
| 穿越 | 0.238 | 0.068 | −0.064 | −0.021 | | 0.080 |
表5 景观格局指数与行为模式的相关性Table 5 Correlation between landscape pattern index and behavioral patterns |
| 行为模式 | 景观格局指数 | |||
| 景观形状指数 | 斑块数量 | 香农-威纳指数 | 辛普森指数 | |
| 注:*表示在0.05级别(双尾)相关性显著;**表示在0.01级别(双尾)相关性显著。 | ||||
| 停留 | 0.217 | 0.081 | 0.355 | 0.403 |
| 漫步 | 0.436* | 0.050 | 0.200 | 0.264 |
| 通行 | 0.056 | −0.149 | 0.287 | 0.404 |
| 穿越 | −0.125 | −0.155 | 0.521** | 0.532** |
表6 人工介入程度与行为模式的相关性Table 6 Correlation between degree of manual intervention and behavioral patterns |
| 行为模式 | 相关系数 | ||
| 自然度 | 人工要素 | 自然要素 | |
| 注:*表示在0.05级别(双尾)相关性显著。 | |||
| 停留 | 0.319 | 0.241 | 0.407* |
| 漫步 | −0.024 | −0.216 | 0.231 |
| 通行 | −0.003 | −0.132 | 0.199 |
| 穿越 | 0.042 | −0.051 | 0.079 |
表7 主观情感指标与行为模式的相关性Table 7 Correlation between subjective emotion indicators and behavioral patterns |
| 行为模式 | 相关系数 | |||||||||
| 满意度 | 宁静程度 | 空间历史文化特征明显程度 | 人工干预和管理程度 | 植物种类丰富度 | 自然演替程度 | 安全感 | 平坦开敞程度 | 私密性 | 开展社交活动适宜性 | |
| 注:*表示在0.05级别(双尾)相关性显著。 | ||||||||||
| 停留 | −0.314 | −0.464 | 0.086 | 0.829* | −0.543 | −0.771 | −0.657 | 0.029 | −0.406 | 0.145 |
| 漫步 | 0.257 | −0.029 | −0.200 | −0.314 | −0.257 | −0.086 | −0.086 | −0.543 | −0.406 | −0.290 |
| 通行 | −0.371 | 0.319 | −0.257 | −0.486 | 0.543 | 0.371 | 0.371 | 0.086 | 0.580 | −0.667 |
| 穿越 | 0.257 | −0.464 | 0.086 | 0.600 | −0.429 | −0.200 | −0.200 | 0.029 | −0.638 | 0.725 |
文中图表均由作者绘制,其中
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