Urban Commercial Space Vitality Evaluation Method Based on Social Media Data: The Case of Shanghai

Abstract

Social media has rapidly intervened in the interaction between urban consumers and commercial space, further reshaping the structure of urban commercial space. This study employed the social, spatial, and subjective dimensions of geographies of consumption as the theoretical framework. Based on the data from five social media platforms, including Douyin, REDnote, Weibo, Dianping, and Baidu Index, we constructed a multi-level evaluation method of “attention level–activity degree–experience quality” and applied it to measure the dynamics of the shopping malls in Shanghai to investigate their mechanism of generating urban commercial space vitality. The findings indicate that the “core + core–periphery + multi-center + circle structure, agglomeration, and balance” is the primary pattern of urban commercial space in Shanghai. The differences in business formats, consumer positioning, and consumption culture revealed by the social media data are conducive to clarifying the scale of the regional consumption space and the logic of urban commercial evolution. The main contribution of this study is the demonstration that this evaluation method rooted in social media has the potential to generalize the measurement of urban commercial space in major cities in China. We also propose corresponding countermeasures and suggestions for developing urban commercial space in Shanghai.

1. Introduction

In the mobile Internet era, social media is transforming how people perceive and experience places, thus reshaping the interaction between digital and urban environments. The real-time nature, interactivity, and personalization of social media information dissemination and diffusion further accelerate the trends of business district transformation, stock upgrading, popular Internet check-ins, and the debut economy in urban commercial space. Social media is altering people’s consumption habits and preferences [1,2], while initiating the process of the individual becoming a subject of online communication [3]. Consumption activities are evolving under the influence of social media, as the process of attracting attention to a space is experienced in the field; consumer attention has become a new economic factor to seize [4], and the demand for consumption experiences is increasing [5]. These evolutions of consumption patterns redistribute consumers into different commercial spaces, which in turn directly accelerates the functional iteration of commercial spaces, thus affecting their overall spatial distribution characteristics. This study explores the development of urban commercial space using social media data and establishes a systematic evaluation method to measure the vitality of this space. Therefore, we aim to reveal its internal operational logic and provide strong support for the planning and development of urban commercial space.

As a preferred tool for data generation, social media, rooted in the interconnections between users and local stakeholders, has become a significant innovation driver [6]. Social media-supported research in urban geography is primarily focused on three directions. First, identification and classification studies of urban spatial structures are based on user geo-location [7,8,9], user check-ins [10,11], user comments [12,13], UGC [14,15], and other data for identifying the internal structure of the city and its functional patterns. They reduce the cost of data and increase the coverage of people and places simultaneously [12]. Second is research on social media’s mechanism of influence on urban space, where existing research is concentrated on exploring the expansion and remodeling of urban space by social media [16,17]. Further, such research targets the role of social media in shifting the sociospatial structure through geospatial [18,19,20] information, which provides a new viable option for urban space upgrading and renewal. Third, some scholars have employed social media in urban governance and development research. They apply social media information to disaster assessment and response [21,22,23] to narrow the distance between fragmented discourses of social media users on issues such as land policy [24,25], infrastructure construction [26,27,28], and urban image construction [29], providing a significant tool for the successful implementation of urban policies. Nowadays, urban commercial spaces mainly capture consumers’ attention through social media, and consumers’ reproduction through social media further amplifies commercial vitality; thus, social media Internet traffic has become a significant criterion to measure urban commercial vitality. Compared to traditional surveys or commercial registration methods, social media data have greater accessibility and wider consumer coverage, and the local consistency of social media platforms therefore ensures the replicability of the evaluation method of this study.

However, with the lack of integration of multiple social media platforms, most existing studies based on social media have centered around a single social media indicator or platform and have commonly ignored the integration of virtual and real space, which has posed a hidden problem for the promotion and practical implementation of this research method. This study belongs to the third direction, aiming to establish a method for evaluating the vitality of urban commercial space which is founded on the multi-platform and multi-indicator fusion of social media data. It can reveal the mechanism of urban commercial vitality in the era of social media, explore the problems in urban commercial space development, and subsequently propose strategies and recommendations. These real-time communication results supported by social media hedge the lag of traditional commercial policy management to a certain extent, while their underlying logic, based on consumer feedback, as being characterized by obvious humanism, is a significant guide for the planning and construction of People’s Cities.

Currently, research on urban commercial space primarily delves into the structure and boundary of commercial space, the location and mode of commercial activities, the behavior of the production and consumption sides, etc. Research on the characteristics of urban commercial space layouts utilizing POI data [30,31], mobile phone check-in data [32], and social media data [33] has also appeared widespread under the application of big data technology. As a new “urban core” [34] in contemporary urban space, shopping malls face new strategic transformations in the new retail era through their offline experience and service advantages [35]. However, scholars are predominantly rooted in studies targeted at shopping venues in business district measurement models, such as Reilly’s Retail Gravity Model, Converse Breakpoint Model, the Huff model, the Rushton Behavioral Space Model, and Wilson’s Shopping Model, where there are less in-depth consumer emotional and cognitive levels to explore. This study takes the urban shopping mall as the object of its study, trying to build an evaluation method of urban commercial space vitality around the complexity of individual consumption activities. Therefore, we can externalize the evaluation results of shopping malls to the overall urban commercial space, which is more operable with the adjustment of commercial management and the regulation of urban planning, and to clarify the focus of the structural upgrading of commercial areas.

Geographies of consumption have relatively evolved from macro-consumption places to micro-consumption practices, explaining the complex meanings embedded in places through how consumption is linked to various spaces. The analytical dimensions of geographies of consumption proposed by Mansvelt include socialities, spatialities, and spatialities of consumption, which explain consumption behaviors under the influence of social structures, cultures, and social relations; the spatial distributions and interpretations of consumption activities; and the consumption behaviors determined by consumers’ sentiments and values, respectively [36,37]. This theory of the geography of consumption is a crucial inspiration to this study in constructing an evaluation method based on social media from the consumer’s end. The results also provide innovative ideas for policy diversions in commercial districts, further amplifying the dominant position of consumers in the structural adjustment and overall development of urban commercial spaces.

Existing studies have investigated the intersection between social media and consumption spaces, but two main deficiencies remain in previous research. On the one hand, there is a neglect of the interaction between virtual and real space from the social media perspective, and researchers have yet to fully demonstrate the advantages of social media data in observation and prediction. On the other hand, research has lacked consumer behavior logic, and therefore, the spatial analysis process has been detached from the redirection of consumption marketing trends. This study constructs an evaluation method of urban commercial space based on the theoretical framework of socialities, spatialities, and the subjectivity of geographies of consumption. We examine Shanghai as an application case, analyzing the vitality pattern of its urban commercial space, further identifying the problems in Shanghai’s commercial space development, and proposing corresponding countermeasures contrapuntally.

2. Analytical Framework

2.1. Conceptual Framework Based on the Geography of Consumption

Geography has extensively participated in consumption studies against the backdrop of the conversion from a production-orientated to a consumption-orientated society [38]. Discussions of geographies of consumption have broadened to encompass the linkages between the production of goods beyond their purchase and their use, appropriation, and disposal [39], where consumption instead connects people, commodities, and places [40]. Some of these studies have explored the spatial relations of consumption by tracing commodities’ social and spatial journeys. In contrast, others explore human–place relations regarding consumers’ identities, subjectivities, and affective functions.

Mansvelt’s analytical framework of geographies of consumption integrates spatial, subjective, and social elements, focusing on social relations in consumption space. The socialities of consumption characterize interpersonal social interactions in terms of consumption practices, highlighting how people’s roles influence and intervene in social practices and consumption relations. Social values guide the formation and change in consumer behavior in social interactions. The spatialities of consumption emphasize meaningful places and spaces produced or connected by consumption, rather than production and consumption itself, elucidating how consumption practices shape spatial relations. The subjectivities of consumption accentuate how individual and collective behaviors, discourses, relations, and imaginaries form and construct subjects of consumption [41]. The emergence of the subjects is a manifestation of the power relations established by the regular functioning of consumption discourses [42].

Social interactions mature under the influential trend of social media communication, where consumers and their behaviors are constantly creating new consumption spaces and relations. Consumer discourses derived from their own experiences intermittently reshape media power and spatial structures in spatial interactions (Figure 1).

Correspondingly, the consumption mode for capturing users’ attention to experiences in the field is increasingly evident in the current commercial activities. The SICAS model proposed by DCCI mirrors the consumer behavior logic of Sense–Interest and Interactive–Connect and Communicate–Action–Share [43]. Fueled by social media, the attention economy [44] and the experience economy [4] have become new trends in commercial space development. Therefore, this study constructs a three-dimensional evaluation system of attention level–activity degree–experience quality under the theoretical framework of socialities, spatialities, and spatialities of consumption, and collects the social media data of Shanghai’s major shopping malls through the Internet to measure the social attention, spatial dynamics, and subjective experiences of the leading carriers of Shanghai’s urban commercial spaces to reveal the city’s commercial vitality distribution and internal patterns.

2.2. Analytical Framework for the Evaluation Method

Attention level is the starting point of the consumption process from the social media perspective. In the digital age, attention penetrates the deeper layers of economic activities by giving users the dual identity of consumers and producers [44], rapidly participating in social practices and consumption relationships. Nowadays, social media and users possess a panoramic perception anytime and anywhere through the dynamic perception network, constituting a controlled relationship of mutual observation. Information pushing and retrieval, content creation and sharing, and real-time capturing by convergence media are the primary frameworks of contemporary competition for capturing consumer attention and for the evolution of consumption relations. Therefore, this study formulates indicators of informativeness, topicality, and inclusiveness to duplicate the process of consumer choices being shaped by commercial activities and draws the results of shopping malls’ attention levels with multi-media information.

Activity degree is a commercial space practice that combines consumers’ attention levels, which returns the evaluative perspective to geographic space [45]. We attempt to discuss how consumer attention is captured, shaped, and consumed in geospatial contexts, and to discern the spatial relationship between consumers and social media. The activity degree under the spatialities of consumption precisely emphasizes the urban commercial space’s richness, solidity, and resilience, operationalizing the analysis to the spatial characteristics of physical commercial activities.

Experience quality probes into how consumption functions in place-based social identities, highlighting the significance of the separation of production and consumption roles [46]. Consumption is no longer merely an elementary physical behavior but can convey the personality and preferences of the consumer [47]. Similarly, consumers’ comments and feedback on urban commercial spaces have emerged as a pivotal reference to business vitality, backed by the endorsement of consumers’ actual experiences. With the current consumption trend of attracting consumer attention to the field of fulfilling experiences, social media has evolved into a daily life diary and a portfolio for consumers with the status of an experience seeker [4]. Therefore, this study establishes a connection between the spatialities of consumption and the consumer experience evaluation, investigating the quantitative values of urban shopping malls and their respective comparisons.

3. Materials and Methods

3.1. Study Area

This method was implemented in Shanghai, a city located in eastern China, with 16 districts: Huangpu, Xuhui, Changning, Jing’an, Putuo, Hongkou, Yangpu, Minhang, Baoshan, Jiading, Pudong, Jinshan, Songjiang, Qingpu, Fengxian, and Chongming. The city covers an area of 6340.5 square kilometers with a population of 24.87 million, serving as China’s third most densely populated city. Shanghai is the most urbanized city in China, with an urbanization rate of 89.5%, implying a prominent urban consumption potential.

As the city with China’s most substantial consumption power and a globally renowned international metropolis, Shanghai’s business sector plays a crucial role in the urban rise and development [48]. In 2024, total retail sales of consumption goods in Shanghai reached RMB 1.79 trillion, maintaining the top position among Chinese cities for five consecutive years. The commercial development of Shanghai is notably representative and influential, reflecting the overall trend and characteristics of commercial development in China.

Shanghai’s shopping malls vary in scale and format, resulting in the formation of central hubs for urban consumers and consumption activities. Further, they have progressed into the core network nodes of Shanghai’s urban commercial space. Therefore, this study designated Shanghai’s shopping malls as specific research objects to measure the pattern of urban commercial space vitality and its distribution law.

3.2. Research Method

Based on the analytical framework of the previous evaluation method, this study instituted a three-dimensional evaluation method of urban shopping malls on the socialities, spatialities, and subjectivities of consumption, including attention level, activity degree, and experience quality. This method can reflect the evolution of the consumption space and the relationship triggered by the transformation of attention in social interactions from the perspective of social media. In addition, it described the subjective experience and further reshaped the social consumption connection. Simultaneously, to eliminate the interference of different scales caused by varied data sources and to maintain the original data distribution characteristics, this study utilized linear normalization to normalize the original data of each indicator. The range of values was [0, 1].

3.2.1. Attention Level

Informativeness is the accessibility of information to be obtained and retrieved by social media users through the Internet platform, reflecting the degree of information exposure of shopping malls. This study selected the brand index, news index, and search index of shopping malls with keywords included on Baidu Index from January 2022 to August 2024. Subsequently, we weighted and merged the data, employing the entropy weighting method, and calculated how much the corresponding shopping mall had captured consumer attention through direct information over the past few years, leading to social interaction. The entropy weighting method is as follows:

$$y_{ij}={x^{\prime }}_{ij}\sum _{i=1}^m{x^{\prime }}_{ij}$$

(1)

$$e_j=-{\displaystyle \frac{1}{\ln \left(n\right){\sum }_{i=1}^m{y}_{ij}{lny}_{ij}}}$$

(2)

$$d_j=1-e_j$$

(3)

where y_ij is the share of the shopping mall i value under the index j, e_j is the information entropy value of the index j, and d_j is its information utility value. The greater the entropy value of the indicator, the lower its information utility, thereby diminishing its role in informativeness and reducing its weight.

Topicality is the enthusiasm of users towards topics about consumption content in posts on various social media platforms, which is fundamentally based on UGC and a modest portion of PGC and OGC. The exchange and flow of attention occur continuously, along with the connections between spontaneous creation on the consumption side and creation on the production side, ultimately forming intimate social interaction-type attention [49]. This study selected video data from Douyin, post data from Weibo, note data from REDnote, and Dianping data from January 2022 to August 2024 to calculate the topicality of shopping malls. The entropy weighting method was also introduced to calculate the objective weights of likes, comments, favorites, and shares in topicality in order to merge the indexes, taking Douyin as an example. Further, we employed the number of monthly active users of each social media platform to weigh its topicality, respectively. The equations are as follows:

$$r_j={\displaystyle \frac{w_j}{{\sum }_{j=1}^4{w}_j}}$$

(4)

$$z_i=\sum _{i=1}^4{r}_i{z}_{ij}$$

(5)

where w_j is the number of monthly active users of the social media platform j, r_j is the share of the social media platform j, z_ij is the topicality of the shopping mall i on the platform j, and z_i is the shopping mall i’s overall topicality.

Inclusiveness is the degree of exposure of consumer content in the form of live broadcasting on convergence media, which integrates a variety of media forms of video, audio, text, and pictures. Convergence media, with efficient resource integration and complementary capabilities, can capture consumer attention and is the latest expansion of digital technology to foster the socialities of consumption. This study selected the monthly associated live broadcasts of Douyin platform live shops in the relevant shopping malls in July 2024 from the Feigua database. Subsequently, we carried out the summation of statistics in terms of shopping malls, and the equation used is as follows:

$$L_i=\sum _{j=1}^n{m}_{ij}$$

(6)

where m_ij is the number of monthly associated live broadcasts of the live shop j in the shopping mall i, n is the number of live broadcast stores, and L_i is the shopping mall i’s overall inclusiveness.

3.2.2. Activity Degree

Variegation is a term initially used in biology. However, in this study, we leverage variegation to characterize the abundance of the commodities and activities in urban commercial spaces, quantified by the distribution of commercial formats. Spatial relations materialize in diverse consumption activities, and their internal structure illustrates the construction, representation, and reproduction of the consumption space. This study selected the format data from the homepage of the shopping mall from the app Dianping in August 2024, including the formats of the food, shopping, beauty salon, parent–child outing, leisure and fun, learning and training, sports and fitness, and life services. Subsequently, we applied Shannon’s Diversity Index combined with the completeness of the distribution of commercial formats and the commercial volume. Ultimately, we used the entropy weighting method to combine these three elements to assess the overall variegation. The equations are as follows:

$$p_{ij}={\displaystyle \frac{k_{ij}}{{\sum }_{j=1}^8{k}_{ij}}}$$

(7)

$$H_i=-\sum _{j=1}^8{p}_{ij}ln{p}_i$$

(8)

$$C_i={\displaystyle \frac{c_i}{8}}$$

(9)

$$k_i=\sum _{j=1}^8{k}_{ij}$$

(10)

where k_ij is the number of shops of the format j in the shopping mall i, p_ij is the proportion of the format j in the shopping mall i, H_i is Shannon’s Diversity Index of the shopping mall i, c_i is the number of shop formats in the shopping mall i, C_i is the completeness of the distribution of the formats in the shopping mall i, and k_i is the commercial volume in the shopping mall i.

Stability is the range of fluctuation of the topicality of the urban commercial space from a temporal perspective, indicating the shopping malls’ competences in maintaining their vitality levels. It examines the stabilization properties of the commercial space itself and its consumption links. This study selected the topicality of Douyin videos and Weibo posts since October 2018 and January 2016, respectively. Further, we merged and measured them by the number of monthly active users. The equations are as follows:

$${STdev}_{x_i}=\sqrt{{\displaystyle \frac{{\sum }_{i=1}^n{\left(x_i-\overline{x}\right)}^2}{n-1}}}$$

(11)

$$v_i={\displaystyle \frac{1}{{STdev}_{x_i}+0.0001}}$$

(12)

where x_ij is the topicality of the shopping mall i on the social media platform j, STdev_xij is the standard deviation of the topicality of the shopping mall i on the platform j over time, and v_i is the stability of the shopping mall i on the platform j.

Vulnerability is the capability of urban commercial spaces to counteract economic shocks before and during sudden social events [50]. The pain of the COVID-19 pandemic on the urban economy is continuously spreading, and the consumption stagnation is still severe. Therefore, the significance of the vulnerability quantification in urban commercial spaces has become imminent. Considering the timeline of Shanghai’s lockdown and control of the epidemic, this study investigated video data from Douyin and post data from Weibo, respectively, in 8 months before and after April 2022, and obtained the vulnerability by weighting their number of monthly active users. The equations are as follows:

$$V_r={\displaystyle \frac{Y_{ptr}-Y_{btr}}{Y_{btr}}}$$

(13)

$$V_i={\displaystyle \frac{\left(Y_{pti}-Y_{bti}\right)/Y_{bti}-\left(Y_{ptr}-Y_{btr}\right)/Y_{btr}}{\left|\left(Y_{ptr}{-Y}_{btr}\right)/Y_{btr}\right|}}$$

(14)

where V_r is the vulnerability of all shopping malls in the district, and V_i is the shopping mall i’s vulnerability in the district. Y_btr and Y_bti are the average monthly topicality of all shopping malls in the district and the shopping mall i from September 2021 to April 2022, respectively. Y_ptr and Y_pti are the average monthly topicality of all shopping malls in the district and the shopping mall i from May to December 2022, respectively.

3.2.3. Experience Quality

Horizontal evaluation is consumers’ subjective perceptions based on the overall condition of the environment, facilities, and services of the shopping mall at a stationary time node, which is an essential reference for external consumers to seek a satisfactory consumption experience in the age of social media. This study selected the shopping mall rating on Dianping as the value of horizontal evaluation, which is a concentrated manifestation of the subjectivity of consumption.

Vertical comparison is the ranking of each shopping mall based on the quantitative ratings and experience feedback of a massive number of consumers, corresponding to the complexity and latency of consumption [51]. It is the permutation of urban commercial space by aggregating individual behaviors and discourses. This study collected data from the shopping mall list on Dianping and assigned equidistant scores based on the rankings. The equation is as follows:

$$S_i=M-\left(r_i-1\right)\times {\displaystyle \frac{M-m}{N-1}}$$

(15)

where S_i is the value assigned to the shopping mall i, M is the highest value and is set to 1, m is the lowest value and is set to 0, N is the number of shopping malls, and r_i is the ranking of the shopping mall i.

3.3. The Comprehensive Strength Index Model

3.3.1. Proportional Weighting Method

A certain degree of incomplete and unstable coverage of social media data inevitably exists [52]. Given the varied data quality of each social media platform and the current situation of shopping mall operations, we employed the proportion weighting method to assign weights to the secondary indicators to optimize the data accuracy. The equations are as follows:

$$m_j={\displaystyle \frac{s_{jv}}{s_{jt}}}$$

(16)

$$r_j={\displaystyle \frac{m_j}{{\sum }_{j=1}^n{m}_j}}$$

(17)

where s_jv is the amount of valid data for the indicator j, s_jt is the total amount of data, and r_j is the weighted proportion of the indicator j.

3.3.2. TOPSIS Model

The TOPSIS Model serves as a ranking evaluation technique by approximating an ideal value and is widely employed in multi-objective decision analysis. This method encompasses the quantitative study and analysis of multi-indicators across various objects. Firstly, it selects the ideal value for each indicator and subsequently calculates the closeness of each indicator to the ideal target. Ultimately, the method assesses the relative merits of existing objects and ranks their performance accordingly [53]. The model can objectively assess each object based on the indicator values, revealing the distance between each object and the ideal target. This study introduced the TOPSIS Model to determine the dimensions and comprehensive strength index, and the calculation steps are as follows:

• Construct the Weighted Normal Matrix;

In this study, we organized shopping mall data with their evaluation indicators into a weighted normal matrix, where each row represented a shopping mall, and each column represented an evaluation indicator.

$$R={\left\{r_j{P}_{ij}\right\}}_{m\times n}$$

(18)

where r_j is the weight of the indicator j, P_ij is the value corresponding to the indicator j of the shopping mall i, m is the number of shopping malls, and n is the number of indicators.

2.

Determine the Ideal Solution and Negative Ideal Solution;

The ideal solution, generally the maximum number in the efficiency indicator, is optimal for all indicators, and the negative ideal solution is the inverse. According to the characteristics of the indicators in the matrix, we determined the ideal solution and the negative ideal solution.

$$X^{+}=\left\{x_1^{+},x_2^{+},{\dots ,x}_j^{+}\right\}$$

(19)

$$X^{-}=\left\{x_1^{-},x_2^{-},{\dots ,x}_j^{-}\right\}$$

(20)

where $x_j^{+}$ is the ideal solution for the indicator j and $x_j^{-}$ is the negative ideal solution for the indicator j.

3.

Calculate the Distance from the Ideal Solution and the Negative Ideal Solution;

This study employed the Euclidean distance to calculate the distance between each shopping mall and the ideal or negative ideal solutions. The closer the distance, the stronger the attributes; the farther the distance, the weaker the attributes.

$$D_i^{+}=\sqrt{\sum _{j=1}^n{\left(x_j^{+}-x_{ij}\right)}^2}$$

(21)

$$D_i^{-}=\sqrt{\sum _{j=1}^n{\left(x_j^{-}-x_{ij}\right)}^2}$$

(22)

where $x_j^{+}$ and $x_j^{-}$ are the ideal solution and negative ideal solution for the indicator j, respectively. D_i⁺ and D_i⁻ are the proximity of the shopping mall i to the ideal solution and the negative ideal solution, respectively, and x_ij is the value of the shopping mall i under the indicator j.

4.

Construct the Weighted Normal Matrix;

Calculate the secondary indicators and the comprehensive strength index for each shopping mall based on the distance of the shopping malls from the ideal solution and the negative ideal solution.

$$C_i={\displaystyle \frac{D_i^{-}}{D_i^{+}+D_i^{-}}}$$

(23)

where C_i is the relative proximity of the shopping mall i. The higher the relative proximity, the stronger the attribute of the relevant indicator.

3.4. Data Resources

The data for the evaluation method (

Table 1. Evaluation method description and data sources.

Dimension	Indicator	Description	Data Resource	Duration
Attention Level	Informativeness	Exposure of shopping malls on social media platforms and users’ initiative to search for information about shopping malls.	Baidu Index website	January 2022–August 2024
	Topicality	User enthusiasm for topics about shopping malls in posts via various social media platforms.	Douyin, REDnote, Weibo, and Dianping websites
	Inclusiveness	Shopping mall exposure in the form of convergence media live broadcasting.	Feigua	July 2024
Activity Degree	Variegation	Distribution and diversity of commercial formats of shopping malls on social media platforms.	Dianping app	August 2024
	Stability	Fluctuations of the topicality about shopping malls on social media platforms over time.	Douyin and Weibo websites	October 2018–August 2024 (Douyin) January 2016–August 2024 (Weibo)
	Vulnerability	The capability of shopping malls to rebound commercial activity in response to sudden social events.	Douyin and Weibo websites	September 2021–April 2022, May 2022–December 2022
Experience Quality	Horizontal Evaluation	Evaluation of overall quantitative user perceptions of shopping malls on social media platforms.	Dianping website	August 2024
	Vertical Comparison	Ranking of shopping malls based on user perception ratings on social media platforms.	Dianping website

) used in this study were all found on social media. Specifically, the data utilized to measure informativeness originated from the Baidu Index; the data on topicality originated from Douyin, REDnote, Weibo, and Dianping; and the data on inclusiveness stemmed from Feigua. We collected data from the app Dianping for variegation, and concurrently, stability and vulnerability were primarily derived from topicality. The data exploring horizontal evaluation and vertical comparison were from Dianping. In addition, we gathered the list of shopping malls from the project database of WinShang and the number of monthly active users of each platform from QuestMobile. Under the premise of complying with the terms of use and service agreement of the target API, we legally obtained data through the API crawler, and ensured data quality by keyword screening and manual screening to exclude irrelevant data.

3.4.1. WinShang

As an integrated marketing platform for commercial real estate and retail businesses, WinShang compiles a multitude of professional information on commercial real estate, including urban complexes, shopping malls, community businesses, commercial streets, etc. The website possesses China’s most extensive branded merchant expansion site and commercial real estate project database, which is highly authoritative and representative. The data collected from WinShang’s project database included the addresses, opening years, and business statuses of Shanghai’s shopping malls, totaling 470 operating malls.

3.4.2. Baidu Index

Baidu Index is a data analysis tool underpinned by Baidu search data, which can supply the data of keywords on Baidu’s webpage in three dimensions: search index, news index, and brand index. It captures how consumers access straightforward information on the Internet, allowing us to gauge users’ level of attention and consumption willingness. This study obtained data on the searches, information, and brand indexes of shopping malls through web crawler technology. In addition, we excluded shopping malls with vague regional pointing and ultimately retained 54 shopping malls with keywords.

3.4.3. Douyin

Douyin is the leading short video social media in China. Its fundamental operational logic is to provide users with a short video generating platform and to accurately recommend short videos through advanced computer algorithms. Covering people of all ages and consumption levels, the substantial user base of Douyin indicates data with wide consumer coverage. Therefore, through the web crawler, this study gathered video data from the Douyin website with shopping malls as the search term, including the number of video likes, comments, favorites, and shares. Specifically, we collected 95,462 valid entries from 470 shopping malls after data cleansing.

3.4.4. REDnote

REDnote propagates consumer experiences and lifestyles by allowing users to share graphical notes, possessing the differentiated advantage of users sharing honest opinions, enhancing the conversion rate of offline word-of-mouth compared to Douyin. Thanks to the application of big data and artificial intelligence technology, the platform has also ascended to be a significant driving force for social media growth lately. This study crawled the note and video data, including the number of likes, with shopping malls used as the search term on the REDnote website. After data cleansing, we collected 30,859 valid entries from 470 malls.

3.4.5. Weibo

Over its 16 years of development, Weibo has forged a profound accumulation of users and intimate inter-user associations. The representative Weibo Hot Search and Weibo Super Topic modules facilitate the precise sharing of public information. This study crawled Weibo post data Weibo with shopping mall as the search term, including the number of likes, comments, and forwardings. Ultimately, we collected 58,224 valid entries from 470 shopping malls after data cleansing.

3.4.6. Dianping

Dianping is an interactive platform that provides information about merchants in catering, shopping, leisure and entertainment, and life services. It publishes consumption reviews, which combine the geographic location with users’ personalized consumption demands to provide consumers with messages about consumption services in a physical space. This study crawled the shopping malls’ homepage data from Dianping, including the number of comments, favorites, and the overall rating, and simultaneously obtained the shopping mall ranking data. After data cleansing, we procured 425 valid entries.

3.4.7. QuestMobile

QuestMobile is a professional intelligence platform that serves the mobile Internet industry, with a service product line for data research covering the whole life cycle of social media apps. The website publishes professional analysis reports on the users and services of social media platforms monthly or quarterly. In this study, we employed data from May 2024 from QuestMobile, including the number of monthly active users of Douyin, REDnote, Weibo, and Dianping (

Table 2. Monthly active users of various social media platforms.

Social Media Platform	Number of Monthly Active Users (Million)
Douyin	743
REDnote	199
Weibo	485
Dianping	121

).

3.4.8. Feigua

Feigua, as an expert data analysis platform specializing in short video and live e-commerce in China, supplies big data assessments of Douyin’s e-commerce and live broadcasting in its Douyin module, and its local life location database provides users with live broadcasting e-commerce data in combination with geographic location. This study aggregated data on the number of associated live broadcasts in shopping malls in the past 30 days, sourced from Feigua. Ultimately, we identified 169 shopping malls with live broadcasting shops operating in convergence media.

4. Results

4.1. Evaluation Results of Secondary Indicators

The method for evaluating the commercial vitality of shopping malls consisted of three dimensions: attention level, activity degree, and experience quality.

Table 3. Weights of shopping mall evaluation indicators in Shanghai.

Dimension	Indicator	Weight
Attention Level	Informativeness	0.0793
	Topicality	0.6772
	Inclusiveness	0.2435
Activity Degree	Variegation	0.2852
	Stability	0.3574
	Vulnerability	0.3574
Experience Quality	Horizontal evaluation	0.6296
	Vertical comparison	0.3704

illustrates the weights of each indicator, and Figure 2 presents the data distribution across these three dimensions. Under the socialities of consumption, topicality was prominent in the evaluation indicators due to its higher data coverage and representativeness. Meanwhile, inclusiveness also played a considerable role in the prevailing trend of convergence media. The data distribution of attention levels elaborated a clear divergence between low and high values, with 77.2% of shopping malls concentrated in the bottom 10% of the value range, indicating significant differences in shopping malls’ ability to capture consumer attention. Only a few shopping malls garnered substantial attention on social media.

The activity degree corresponded to the spatialities of consumption, where the three indicators—variegation, stability, and vulnerability—carried approximately equal weights and collectively determined the richness and robustness of consumer activity spaces. Under this criterion, the overall value of the activity degree was the most undesirable compared to the other two dimensions. The data distribution, characterized by a low density of high values and a high density of low values, resembled that of attention level, with only a few commercial spaces exhibiting diverse, stable, and resilient characteristics thoroughly.

In the dimension of experience quality, horizontal evaluation was more influential due to its more comprehensive inclusion of subjects. Combined with vertical comparison, it interpreted the critical role of consumer subjectivity in consumption spaces. The final score distribution contrasted sharply with the previous two dimensions, with shopping malls showing varying degrees of clustering in the high and medium score ranges, reflecting the generally effective performance of shopping malls in Shanghai regarding experience quality, with only a few shopping malls deviating from consumer experience goals.

Therefore, how to capture consumer attention, influence consumers’ social choices, and indirectly enhance the quality of spatial relationships in consumption has solidified into a new growth opportunity for urban commercial spaces in Shanghai.

From the perspective of the interrelationships among attention level, activity degree, and experience quality, this study initially categorized shopping malls into four types—high attention–high experience, high attention–low experience, low attention–high experience, and low attention–low experience—based on the average values of socialities and subjectivities (Figure 3). In addition, we have attached the list of the top 50 shopping malls in the Appendix A (

Table A1. List of top 50 shopping malls by evaluation results.

Ranking	Shopping Mall	C	Ranking	Shopping Mall	C
1	Global Harbor	0.819	26	MAX City	0.598
2	Metro City	0.775	27	TaiKoo Li QianTan	0.598
3	IFC Mall	0.751	28	Grand Gateway 66	0.598
4	New World Daimaru	0.747	29	Wujiaochang Wanda Plaza	0.594
5	Jing’an Joy City	0.743	30	Link Plaza Qibao	0.593
6	Xintiandi North Block	0.739	31	Raffles City Changning	0.59
7	Takashimaya	0.703	32	L + Mall	0.59
8	Jiangqiao Wanda Plaza	0.692	33	Raffles City North Bund	0.589
9	IAPM mall	0.679	34	Fengxian Paradise Walk	0.587
10	New World Comprehensive Consumption Circle	0.679	35	THE HUB	0.585
11	K11	0.654	36	Century Link Tower	0.583
12	D Mall Shopping Center	0.646	37	The Bund Finance Center	0.582
13	Raffles City Huangpu	0.644	38	Panlong Tiandi	0.58
14	Sinar Mas Plaza	0.637	39	Mitsui Shopping Park LaLaport	0.579
15	Bailian ZX Creative Fun Zone	0.63	40	Super Brand Mall	0.576
16	The Mixc	0.624	41	Songjiang Wanda Plaza	0.574
17	Jing’an Kerry Center	0.619	42	Plaza 66	0.574
18	HKRI Taikoo Hui	0.619	43	Jing’an Dairongcheng	0.573
19	Cloud 9 Changning	0.611	44	Suhewan MixC Cityland	0.572
20	World Financial Center	0.61	45	Shimao Festival City	0.571
21	HOPSON ONE	0.604	46	Zhang Garden	0.571
22	SML Center Huangpu	0.604	47	Nanxiang Incity MEGA	0.57
23	Shanghai Tower	0.603	48	Baoshan Wanda Plaza	0.57
24	Sogo Shanghai	0.601	49	Grand Ocean Crystal Tian’an Qianshu	0.567
25	Shanghai Aegean Sea	0.6	50	Hongqiao Center Art Park Dadaocheng	0.56

C stands for the relative proximity or distance of each shopping mall from the positive and negative ideal solutions.

). Subsequently, we could conduct a detailed analysis of spatialities at the activity level.

High attention–high experience shopping malls, accounting for 20.6% in the first quadrant, simultaneously met external social needs and internal personal preferences. The majority of such malls were distributed in the urban core, with representative examples including Shanghai New World Daimaru, IFC Mall, Takashimaya, Metro City, and Global Harbor. However, several commercial spaces exhibited weak spatial relationships, necessitating the exploration of practical pathways to boost spatial connections beyond the commercial loop of attracting attention and achieving consumer experiences.

High attention–low experience shopping malls represented 5.1% of the total. The low distribution of these malls inversely illustrated the principle that shopping malls capable of satisfying consumers’ social attributes can generally provide essential consumption experiences for subjectivity. We found that most of these malls suffered from long operating histories, outdated infrastructure, or social media hype, urgently requiring the renewal and transformation of stock.

Low attention–high experience shopping malls, accounting for 47.0%, commonly bypassed attracting consumer attention during commercial consumption. Typically, they were regional large-scale shopping malls with a radiation range limited to the surrounding residential areas, such as Pinault Printemps-Redoute, Brilliance, and Paradise Walk, which ordinarily provided satisfactory consumption experiences for residents.

Low attention–low experience shopping malls comprised 27.2% of the total, exhibiting weak activity degrees simultaneously. They had low operational stability and limited influence within the overall commercial space of Shanghai.

4.1.1. Results of Attention Level

The attention level comprised three secondary indicators: informativeness, topicality, and inclusiveness. Shopping malls that had successfully established strong consumer relationships in terms of information exposure, topic creation, and convergence media dissemination were mainly situated in the urban core area, such as Shanghai New World Daimaru, IFC Mall, Shanghai Center Building, Takashimaya, Metro City, Jing’an Joy City, Global Harbor, and Xintiandi North Block. The grid map of the attention level (Figure 4) presents a ring-shaped distribution overall. Values decreased at an approximate rate from the city center outward, indicating that consumer attention radiated from the city center with a relatively even distribution of social consumption relationships in the suburbs. Further, the district-level cartogram (Figure 4) illustrates the distribution trends and anomalies of the attention level intuitively, in which Qingpu, Jinshan, Fengxian, Songjiang, Jiading, Baoshan, and Chongming all highlighted a significant area compression regarding attention level, while Huangpu, Jing’an, Xuhui, and Changning exhibited varying levels of expansion. The cartogram visually demonstrates the internal logic behind the evolution of urban social consumption relationships by depicting the differences in attention levels between the urban core and the periphery of Shanghai’s commercial space.

4.1.2. Results of Activity Degree

The activity degree, including the variegation, stability, and vulnerability, appeared insignificant in the agglomeration and differentiation compared with the attention level, from which the differences between shopping malls in both spatial vertical operation and parallel time are gradually narrowing. The grid map of the activity degree (Figure 5) suggests the initial formation of a “one core with several centers” distribution pattern. In addition to the commercial activity core in the city center, agglomeration centers are beginning to emerge in the suburbs, and in contrast, other suburban areas still exhibit a uniform consumption space attribute. Similarly, the district-level cartogram (Figure 5) presents a corresponding reduction in the expansion of the urban center’s area and a compression of the suburbs’ area, indicating weak commercial space activity levels in Jinshan and Chongming. The spatial system continues to shape the urban commercial vitality pattern.

4.1.3. Results of Experience Quality

Experience quality comprises the two secondary indicators of horizontal evaluation and vertical comparison, reflecting consumer subjectivity both individually and collectively. The agglomeration and dispersion patterns of experience quality are relatively pronounced. Further, there is a notable distribution pattern of “one core with several centers” regarding activity degree, and the characteristics of the multi-level urban commercial space distribution, which emphasizes clustering and balancing, are evident (Figure 6). Simultaneously, the district-level cartogram (Figure 6) articulates how the expansion and compression of consumer experiences have gradually diminished the natural geographical differences in spatial scale among districts. Only a few suburban districts, such as Qingpu, Jinshan, and Chongming, still exhibit inconsequential experience-related economic utilities, which require enhancing their vitality regarding attention level, activity degree, and experience quality.

4.2. Comprehensive Evaluation Results

The urban commercial space of Shanghai exhibits a spatial structure described as “Core + Core–Periphery + Several-center, Circle structure, clustering and balancing together.” The core precisely consists of Huangpu, Jing’an, Xuhui, Changning, and Hongkou, which encompass six of the top ten shopping malls—Global Harbor, Metro City, Shanghai New World Daimaru, Jing’an Joy City, Xintiandi North Block, and Takashimaya. Additionally, 64% of the top fifty shopping malls lie in the core region, forming a good spatial relationship that attracts consumers and fulfills their experiential demands. The core–periphery serves as the transitional area from the municipal core to the regional multi-centers, mainly located within the scope of Minhang and Yangpu. Although the commercial vitality in these regions gradually declines, they still maintain a coherent and active commercial environment. Moreover, the core–periphery business formats and consumer groups are gradually evolving from high-end consumption to livelihood protection. The multi-centers, including Baoshan, Jiading, Qingpu, Songjiang, Jinshan, and Pudong, feature several regional or community-level commercial centers. Typically, a large-scale shopping mall serves as the primary regional anchor in these centers, with notable examples being the IFC Mall in Pudong, Jiangqiao Wanda Plaza and Nanxiang Incity MEGA in Jiading, Wanda Plaza in Songjiang, and Paradise Walk in Fengxian. The multi-centers suggest distinct spatial relationships and consumer cultures compared to the core and core–periphery. Overall, the city exhibits a radiating trend from the core to the multi-centers, culminating in a balanced geographical distribution that meets consumers’ social and subjective needs across various regions.

The district-level cartogram of urban commercial space vitality (Figure 6) demonstrates that consumer activities reshape the natural geographical distribution of the city. The five core districts, which initially account for less than 3% of Shanghai’s total area, occupy the main scale of the urban center in commercial activities and play a decisive role in commercial functions. Moreover, the core–periphery and multi-centers imply varying degrees of distortion, forming a hierarchical and balanced association between consumption and space. In contrast, the commercial spaces in Qingpu, Jinshan, Fengxian, and Chongming exhibit significant compression. Despite the overall balance, commercial vitality gaps in the marginal areas still exist, urgently necessitating the optimization of commercial space relationships and the emphasis on commercial space layout planning.

To further clarify the layout structure of Shanghai’s commercial space, this study statistically analyzes the gradient distribution of the urban commercial space vitality with Park Hotel as the center and each kilometer as a unit of measurement (Figure 7). Within a 20 km radius of the city center, the area—consisting of the urban commercial space core, core–periphery, and parts of the multi-centers—appears highly dense in commercial space vitality, with three-quarters of Shanghai’s shopping malls concentrated in the area. The region, which extends from 20 to 40 km in radius, is moderately dense, with commercial space vitality declining to less than half of that in the highly dense region. Some peak areas within this zone host the commercial centers of various districts. Beyond 40 km lies the sparse region, with only a few active areas distributed towards the urban fringe.

In addition, the correlation analyses of the evaluation results with the number of retail shops and the business areas of the shopping malls demonstrate significant correlations, respectively. They all manifest a clear circle structure, with consistent demarcation points on each rung of the ladder (Figure 8). The evaluation results are highly consistent with the physical commercial spaces, indicating that this method is highly applicable and feasible in the evaluation of urban commercial space.

In summary, Shanghai’s commercial vitality circle structure transitions at a gentle pace, with commercial centers appearing intermittently as the radius expands. The characteristics of the agglomeration and equilibrium of urban commercial space are pronounced.

5. Discussion

5.1. Research Progress

In urban geography, existing studies on urban commercial space have focused on the layout of the general urban commercial space [54] macroscopically, the analysis of the social relationship-oriented trends of specific commercial spaces mesoscopically [55], and the assessment of the shopping mall site selection [35], the renewal and optimization of commercial streets [56], and the expansion of underground commercial space [57] microscopically. Most studies that utilize social media data tend to rely on metrics from a single platform, determining the distribution patterns of urban spatial activities through geo-location or measuring spatial attributes via sentiment analysis. Further, these analysis results founded on geospatial data primarily remain superficial, focusing solely on objective phenomena derived from quantitative models. While some studies on social media emphasize the interpretation of the socialities of consumption, few have successfully integrated both the social aspects and the spatial characteristics of consumption to highlight the socialities, spatialities, and subjectivities of consumption. In addition, due to a lack of systematic thinking, social media data exploitation appears simplistic in general, not yet achieving the in-depth integration of social media information and physical commercial space.

Theoretically, this study established a method for evaluating three dimensions anchored in Mansvelt’s analytical dimensions of geographies of consumption, supplying an in-depth analysis of the internal logic of consumption in urban commercial space. In terms of data, this study integrated data from five social media platforms through exhaustive investigations of user positioning and dissemination patterns across various platforms, covering the data on searches, UGC, live broadcasts, etc. Regarding the evaluation system, the indicator of sociality, adherent to the laws of media dissemination, synthesized various types of social media; the indicator of spatiality measured the vitality attributes of urban commercial space from multiple angles; the indicator of subjectivity considered both user internal evaluations and external rankings, leveraging the advantages of media data thoroughly and achieving a profound transformation from single indicators to a diversified integration. Methodologically, in combination with the entropy weighting method and proportional weighting method, this study employed the TOPSIS model to calculate the evaluation results scientifically. We investigated the gradient distribution of commercial vitality in Shanghai and visually presented the vitality of each shopping mall through cartograms, effectively illustrating the overall layout of Shanghai’s urban commercial spaces. In addition, this study integrated digital metrics and spatial analysis. The spatial visualization of the results of each dimension and the comprehensive evaluation results can accurately capture consumers’ attention and consumption experiences in each commercial zone, and then determine the commercial patterns. In turn, the spatial visualization and gradient analysis can make a basic judgment on the levels of the commercial zones.

5.2. Research Findings

In turn, the method of this study demonstrates high applicability in the evaluation of urban commercial space in Shanghai, where social media data can be used to measure the vitality of urban commercial space effectively. The latest developments in the consumption market are first released on social media, and consumers are used to posting their personal consumption experiences on social media. Social media is therefore highly sensitive to consumer preferences and consumption market dynamics. The quantitative results provide real-time and stable evaluations of the socialities, spatialities, and subjectivities of consumption, which provide crucial references for urban planners and business policy makers. Our findings are split into three aspects.

Firstly, the spatial patterns of the attention level, activity degree, and experience quality of Shanghai’s urban commercial space generally exhibit minor differences in detail. Each dimension presents a clear pattern of agglomeration in the urban core, radiating outward. The core and multi-centers are arranged in concentric circles, highlighting a distribution that emphasizes clustering and balance. The socialities, spatialities, and subjectivities of consumption are significantly coordinated, where consumer experiences are primarily satisfied on the premise of capturing attention. The consumer subject injects discursive power into the evolution of the consumption space structure, and the production of consumer attention is constructed upon the dual relationship between subjective experience and spatial practice.

Concurrently, based on the comprehensive evaluation of urban commercial space, Shanghai’s urban commercial space is characterized primarily by a pattern of “Core + Core Periphery + Several-centers, Circle structure, clustering and balancing together”. The core area serves as the heart of commercial activity, transitioning outward through the core–periphery to several commercial centers distributed evenly across the city. Overall, the urban commercial space exhibits a balanced circular hierarchical structure. However, urban fringe areas still have commercial vitality gaps, reflecting the complex patterns and fundamental laws of Shanghai’s consumption society and space. Shanghai Sectoral Planning of Commercial Space (2022–2035) [48] also describes the spatial planning system in detail, dividing multi-level commercial centers and several characteristic commercial functional areas, and formulating strategies for upgrading the main city, new towns, and other areas, respectively. The conclusions of this study are basically consistent with the spatial structure of the upper plan. The hierarchical structure and the balanced layout of the multi-centers of ‘Core + Core periphery + Several-centers, Circle structure, clustering and balancing together’ are in line with the status quo and the basic planning framework.

Finally, from the perspective of individual shopping malls, the evaluation process follows the consumption logic of capturing consumer attention and then satisfying the subjective experience in physical commercial space. Social media data reveal the different attributes of various shopping malls in terms of business formats, marketing models, consumer orientations, architectural environments and scales, and even consumption culture. This clarifies the hierarchy and scale of consumption spaces within designated areas and elucidates the mechanisms for the development of commercial consumption space both regionally and throughout the entire city. The division of commercial centers at each level in the planning and development guidance also stems from shopping malls [48], and their commercial capacity, layout structure, and overall image are all closely related to the shopping mall entity. Therefore, the measurements of this study resting on the shopping mall have a significant reference value for the planning of each commercial center.

5.3. Development Proposal

The results from the shopping malls cover whole areas of Shanghai, and the social media-based judgments of consumer attention, consumption experience, and spatial performance can assist commercial policy makers in adjusting their commercial business strategies promptly. Based on the aforementioned conclusions, we propose development suggestions for the planning paths of Shanghai’s urban commercial space for urban planners.

At the entity level of the shopping mall, the logic of the attention economy and experience economy prove to be inspiring. Given the current attention level and experience quality status, it is necessary to transform stocks and to develop long-term strategies for creating incremental spaces reasonably. The stock transformation in the urban core is not supposed to deviate from the area’s commercial positioning and consumption level. It is important to promptly upgrade external architectural configurations, internal business formats, and specific stores. Adopting emerging consumption models—such as the debut economy, internet celebrity economy, goods economy, and bazaar economy—can enhance the interactive relationship between consumers and commercial consumption spaces. For incremental spaces in the suburbs, apart from large-scale commercial projects in the central area, the deployment of small- and medium-sized commercial assets should be reasonably adjusted with a livelihood orientation, positioned at the regional and community levels, and should target the fixed customer flow within the area. Unlike the large-scale shopping malls in the urban core, which aim to be attention capturing, these suburban spaces should prioritize meeting the daily needs of local residents. Through precise positioning and adaptive adjustments, each shopping mall can effectively operate and form a commercial space network where consumption hotspots and consumption plains coexist harmoniously.

At the regional level, Shanghai’s urban commercial space structure, characterized by “Core + Core Periphery + Several-center, Circle structure, clustering and balancing together” is firmly coordinated and rooted in the city’s population, industry, transportation structure, and consumption culture. There should be a shift from the paradigm of mechanically defining hierarchical levels or scopes through artificial means or undertaking large-scale spatial transformations to disrupting the current agglomeration and dispersion patterns. The relationships within the consumption space in the core area are relatively robust, while the multi-centers exhibit considerable fluidity. The transition from the core–periphery to the multi-centers should align with the principles of the commercial consumption market, accompanied by appropriate government regulation.

The government’s role in commercial space planning should center on the strategic level, setting phased development goals for each region based on the overarching aim of becoming an international consumption center city and clarifying each area’s commercial space development positioning. The core area’s structural updates and adjustments and the evolution of multi-center scales should adhere to consumption space relationships oriented around consumer attention and experiences. For the edge areas with commercial vitality gaps, it is necessary to learn from the development experience of multi-center commercial spaces and implement strong controls to enhance the overall coherence and coordination within the urban commercial landscape.

5.4. Research Limitations

Notably, while social media provides a novel perspective, it also has inherent limitations, particularly through the commercialized exaggerated operations and the inflation of online public opinion, which ought to be further mitigated. Simultaneously, limited by the platform rules and anti-crawlers, we utilize the proportional weighting method to address the problem of uneven data coverage. The homogenization of consumer attention conversion overlooks the significant differences in the salience of the attention economy across various types of commercial consumption. Additionally, to some extent, the simplistic calculation of commercial formats in spatial practice reduces the penetration rate of the evaluation method in different commercial spaces. Moreover, the measurement of consumer experience is derived solely from the ratings and rankings from Dianping, a single platform, while in terms of physical consumption behaviors, consumers’ subjective experiences involve more complex demands and diverse feedback.

Future research should focus more on consumer identity and relationships between humans and space. We expect to optimize acquisition channels further and thoroughly analyze consumer discourse power. While maintaining the breadth of the evaluation method, we intend to enhance our precise grasp of commercial space, providing a fundamental reference for the stable operation and quality upgrade of urban commercial space.

6. Conclusions

The urban commercial space evaluation method integrates data from multiple social media platforms to synthesize the data characteristics and user attributes. Underpinned by the geography of consumption theoretically, the evaluation indicators merge the plural forms of social media communication, the multiple nature of the consumption space, and the multi-dimensional experiences of consumers. When applied to the shopping malls in Shanghai, the distribution pattern of the commercial space vitality appears as “core + core periphery + multi-center, circling structure, agglomeration and balance” and development suggestions are put forward. Thus, this study’s method of evaluating urban commercial space vitality using social media data offers significant potential for further popularization and application. Social media provides a pivotal reference for exploring the social relationship between urban consumers and commercial spaces. We can also apply this method to assess the urban commercial spaces of other cities in China. However, due to the inherent limitations of social media data, it is necessary to approach data utilization with a rigorous mindset.

This study’s evaluation method is highly adaptable and contributes to the planning and development of urban commercial space, with social media data, as a yardstick for urban commercial space, possessing significant internal and external advantages. Therefore, the evaluation method and findings suggested in this study furnish an innovative perspective on urban commercial space planning and management to monitor the dynamics of vitality in real-time, effectively coordinating the urban virtual and real space and the proposed systematic approach to promote the healthy development of urban commercial environments.