| 摘要: |
| 从定性描述转向定量测度是当前城
市公共空间研究的趋势之一，开发定量模型
不仅能够辅助验证有关公共性失落的争议，
还能够使公共空间的对比研究更加客观直
接。本研究基于公共性概念在评估模型中的
作用将公共空间的定量研究划分为三大类：
第一类仅关注公共性的部分核心维度；第二
类将公共性视为公共空间整体品质的一部
分；第三类则是对公共性的完整评估。基于
模型的特性，本研究进一步将公共性的完整
评估模型划分为综合模型、简化模型、动态
模型三类，并总结了现有模型的优缺点。研
究发现，公共性定量评估模型主要围绕管理、
包容性、可达性构建指标体系，且不同文化
语境下采用的指标适配于各自的本地特质，
指标评价标准更加关注管理的权利和责任分
配、关注公共空间使用者权益。本研究以公
共性概念为切入点，系统综述围绕公共空间
的量化模型，以期推动公共空间的定量研究。 |
| 关键词: 公共空间 公共性 定量研究 测
度模型 多维度指标 |
| DOI：10.13791/j.cnki.hsfwest.20240821001 |
| 分类号: |
| 基金项目:国家自然科学基金重点项目（52130804）；广东省基础与应用基础研究基金（2023A1515010875）；
深圳市科技计划资助项目（RCBS20221008093306009）；国家自然科学基金青年科学基金项目（C类）（42401228） |
|
| Quantitative research of urban public space: Literature review on evaluation model of thepublicness |
|
LI Juan,DANG Anrong
|
| Abstract: |
| The term “public space”, as a distinct concept, first emerged in the sociological and
political philosophical discourses of the 1950s. It was later introduced into the fields of architecture,
urban design, and urban planning by scholars such as Lewis Mumford and Jane Jacobs. This new
terminology arose from the inadequacy of existing concepts to address evolving social trends and
ideologies. It marked a shift in urban studies from a utilitarian, function-driven perspective to one that
prioritizes the humanistic and social value of space. The focus also transitioned from the concrete
notion of “space” to the more abstract concept of the “public”, specifically the “publicness” of space.
Public spaces are traditionally regarded as being provided and maintained by the public sector.
However, in recent years, the growing involvement of the private sector has become increasingly
common, resulting in a rising number of public spaces being dominated by private entities. The
management of such spaces often tends to be exclusionary, with limited consideration for public
interests. Consequently, some scholars have proclaimed the "end of public space" or the "end of
public culture". The pervasive trends of privatization and commodification, combined with heightened
security concerns, have led to stricter control over public spaces. Despite these challenges, some
optimistic perspectives argue that effective management strategies can yield positive outcomes, and
private sector participation in the provision and management of public spaces may offer a more
suitable approach to organizing contemporary cities. The theoretical reflections on publicness have
led to a quantitative turn in public space research, with three main implications. Firstly, it involves a
shift from traditional qualitative descriptions to the quantitative measurement of publicness, which
develops measurement models to verify arguments about the fall of the publicness. This quantitative
approach allows researchers to assess whether urban development and practices have indeed
neglected publicness, whether the public sector performs better in ensuring publicness, and whether
privatization necessarily leads to its decline. Secondly, quantitative research enables a more explicit
and objective comparative analyses of public spaces, which helps to identify specific and nuanced
differences between them. Thirdly, through the development of quantitative models and indicators,
theoretical discussions of publicness are integrated with the physical characteristics of real urban
spaces. This bridges the gap between theory and practice, and makes the theories of publicness
practicable in guiding spatial planning and design. Therefore, this paper reviewed existing
quantitative models in public space studies and highlight their strengths and weaknesses to inspire
future studies. Based on the role that the concept of publicness plays in these measurement models,
this paper classifies the quantitative research on public space into three categories. The first category
includes models that explore partial dimensions of publicness aimed at probing the prominent changes
occurring in and around public spaces, such as increasing control, theming/fantasy, and
Disneyfication. These changes fundamentally address core issues central to the concept of publicness.
The second category consists of models designed to evaluate the quality of public space and generateuseful suggestions on how to improve it. In these models, characteristics of publicness are often implicitly regarded as components of “good” public space
qualities. Consequently, the indicators used to assess the quality of public space share significant similarities with those used in models that directly evaluate
publicness. The third category involves complete evaluations of publicness itself. This category is further concluded into three types: comprehensive models,
simplified models, and dynamic models. Through a systematic review, the study finds that the indicators used to evaluate the publicness of public spaces are
primarily developed around the dimensions of management, inclusiveness, and accessibility, while also aligning with specific local contexts. The grading
criteria for these indicators place greater emphasis on the obligations and responsibilities of management as well as the rights of public space users. Finally,
with advancements in AI technologies and the prevalence of smartphones and location-based services (LBS), it proposes two research directions for improving
the quantitative models of public space. Firstly, future research could leverage multi-source big data, such as social media data and mobile phone signaling data,
to better capture the utilization of public spaces. This approach would enable more precise measurements of public space usage, including user diversity, the
spatiotemporal distribution of space utilization, and usage intensity. Such methods could enhance the objectivity of diversity measurement in existing models.
Secondly, image semantic analysis could be utilized to identify spatial features related to publicness. Data from street view images, active sensing images,
wearable devices, and other sources could be analyzed using AI and deep learning techniques. These technologies can automatically identify and classify public
space features and elements, fostering more scientifically robust and data-driven research on public spaces. |
| Key words: public space publicness quantitative research measurement model multi-dimensional indicators |
