Exploration of Strategies for Enhancing the Quality of Urban Space Based on Multi-Source Data Fusion

Abstract

This article, via empirical studies, investigates the influences of facility accessibility, facility correlation, and resident satisfaction on urban spatial quality. It is discovered that these three elements are positively correlated with urban spatial quality. Excellent facility accessibility and rational layout can elevate urban spatial quality, and resident satisfaction reflects the outcome of environmental optimization. On this basis, this article puts forward strategies of intensifying infrastructure construction, using multi-source data to optimize the transportation system, implementing humanistic care and promoting community interaction, promoting digital and intelligent management of the city, and paying attention to the cultural aesthetics of the city, with the aim of offering theoretical support and practical guidance for enhancing urban spatial quality, facilitating sustainable urban development and the improvement of residents’ quality of life.

1. Introduction

The quality of urban space is an important criterion for gauging the development level of a city, directly impacting residents’ quality of life and the sustainable development of the city. With the acceleration of urbanization, problems such as traffic congestion, insufficient facilities, and environmental deterioration are becoming increasingly prominent, threatening the sustainable development of cities. The root causes of these problems lie in the imperfections of urban spatial planning and management. For example, there are issues such as the irrational layout of facilities, the insufficient carrying capacity of the transportation system, and the uneven distribution of public service resources. Therefore, improving the quality of urban space has emerged as a key task of urban planning and management. Facility accessibility, facility relevance, and resident satisfaction are the key factors influencing the quality of urban space. Facility accessibility refers to the convenience of residents to reach various facilities, relevance refers to the complementarity between facilities, and residents’ satisfaction reflects residents’ recognition of the quality of space. To investigate the influence of these factors on the quality of urban space, this paper employs GIS technology, traffic data, and questionnaire surveys to construct a facility accessibility index, a facility correlation network, and a resident satisfaction evaluation system, and proposes strategies to improve the quality of urban space through empirical analysis: 1. intensifying infrastructure construction; 2. using multi-source data to optimize the transportation system; 3. implementing humanistic care and promoting community interaction; and 4. promoting digital and intelligent management of the city, paying attention to the cultural aesthetics of the city. We conducted research through the integration of multi-source data and analyzed the quality of urban space from multiple perspectives, thus enhancing the precision and depth. This can provide references for other cities and possesses practical value.

	Previous Research	This Research	Analysis of Gaps and Contributions
Research Objective	Focuses on solving a specific problem or verifying a certain hypothesis	Places more emphasis on innovation and practical application value, and pursues higher-level research goals	This research is more forward-looking and innovative in goal-setting, and attaches greater importance to practical application value.
Research Method	Mainly adopts traditional research methods, such as experiments, surveys, etc.	Introduces advanced technologies or methods, such as big data analysis, machine learning, etc.	This research uses more advanced technologies and methods, improving the efficiency and accuracy of the research.
Research Content	Relatively limited, conducting in-depth research on a certain field or problem	Covers a wider range of fields, with richer content, involving multiple subfields or interdisciplinary subjects	This research has a broader scope and richer content, which helps to form a comprehensive research perspective.
Data Processing	Data processing is relatively basic, mainly using statistical software (Stata 17.0) for analysis	Introduces more advanced data processing methods, such as deep learning, etc.	This research uses more advanced data processing methods, which can mine deeper-level information.
Results Analysis	Relatively simple, mainly verifying the correctness of the hypothesis	More in-depth, exploring the internal mechanisms and influencing factors	This research has a more in-depth and comprehensive results analysis, which can reveal more internal mechanisms and influencing factors.
Research Conclusion	Relatively limited, proposing solutions to specific problems	More universal, capable of providing new ideas and methods for related fields	The conclusion of this research is more universal, which can provide guidance for research and practice in related fields.
Innovation Points	Has certain innovation, but is limited to minor improvements in technology and methods	Has significant innovations in technology, methods, and theories	This research has significant innovations in multiple aspects, promoting the development of related fields.
Impact on the Future	Has a limited impact on the future, mainly providing inspiration for specific problems	Has a broader impact on the future, promoting the development and progress of related fields	This research has a broader impact on the future, providing new impetus and direction for the development of related fields.

Summary: By comparing the present study with previous ones in all respects, it is evident that this study demonstrates remarkable progress and innovation in numerous areas. In terms of objective setting, methodology selection, research domain, experimental design, data processing, results analysis, and innovation and future impacts, this study demonstrates a higher standard and makes greater contributions. These disparities highlight the significant value of this study in promoting the development and advancement of relevant fields.

2. Research Background and Significance

2.1. Research Background

With the acceleration of urbanization, the issue of urban space quality has become increasingly prominent, directly affecting residents’ quality of life and the sustainable development of cities. However, problems such as traffic congestion, insufficient facilities, and environmental deterioration have restricted the healthy development of cities. Therefore, effective methods are urgently needed to enhance urban space quality. Solving these problems requires a deep understanding of urban dynamics and the adoption of an integrated data-driven urban planning approach. Improving urban space quality has become a key task for urban planners, policymakers, and researchers to ensure cities remain long-term livable, resilient, and sustainable. Facility accessibility, facility connectivity, and residents’ satisfaction are the key factors influencing urban space quality. Facility accessibility determines the convenience of residents’ lives, facility connectivity determines the ease with which residents can enjoy diverse services, and residents’ satisfaction reflects their recognition of urban space quality. In-depth research on the impact of these factors on urban space quality can effectively promote the improvement of urban space quality.

2.2. Research Significance

The theoretical significance of this research lies in providing a theoretical framework for scientifically and systematically evaluating and improving the quality of urban space. By establishing a scientific evaluation system, it assesses the multiple attributes of urban space and proposes a model for evaluating urban space quality that integrates multi-source data. This model encompasses various data such as facility distance, scale, and resident satisfaction, providing a scientific basis for urban planning and design and offering actionable decision-making suggestions. In practice, the research conducts empirical analysis on the impact of facility accessibility, correlation, and resident satisfaction on urban space quality. Based on the assessment results, it proposes specific strategies to address the challenges faced by cities, guide urban planning and management, enhance space quality, and provide experience and cases for other cities, promoting the implementation of relevant policies.

3. Literature Review

3.1. Research on the Quality of Urban Space

Research on the quality of urban space is of great significance for improving urban planning and construction management. With the acceleration of urbanization, it has become increasingly important to scientifically assess and quantify the quality of urban public spaces. In recent years, the use of big data and machine learning technology to assess the quality of urban space has gradually emerged, providing a more efficient alternative to traditional survey methods.

Research shows that currently, the combination of street view images and machine learning technology has become a common method for assessing the quality of urban public spaces. This method can quantify various indicators of street space through image segmentation and semantic analysis, such as green coverage, color richness, and the accessibility of service facilities [1,2,3]. At the same time, a multi-dimensional assessment framework is being advocated, and the multi-attribute value theory is used to evaluate the quality of open spaces, considering attributes such as accessibility, livability, vitality, and identity. This multi-method framework provides a solid foundation for policy and investment decisions [4].

In the current urban development process, the following factors mainly affect the quality of urban space. In high-density cities, the green coverage, color richness, and accessibility of service facilities in street spaces are important factors affecting the quality of life of residents. Studies have shown that tourism and commercial activities have a significant impact on these indicators, and there are land-use conflicts between green spaces and service facilities [3].

Scholars suggest that in future research, attention should be paid to the fact that existing studies mainly focus on the assessment of spatial quality itself, and further research is needed in the future on the optimization of assessment methods as well as the externalities, influencing factors, and mechanisms of public space quality [2]. The multi-functionality and adaptability of space are important indicators of its quality. Future designs should consider the adaptability of space under different social conditions to cope with possible conflicts and changes [5].

3.2. Research on Facility Accessibility

Facility accessibility refers to the ability of individuals to access and utilize specific facilities, typically involving spatial or physical accessibility. It plays a crucial role in the planning of urban transportation systems and public service facilities.

Firstly, the definition of facility accessibility is that it generally refers to the spatial separation between the population and facilities and their interrelationships, encompassing the distribution of facilities and people’s capacity to use them [6,7,8]. It not only includes physical distance but also pertains to service quality and the affordability of users [6,9].

The measurement approach of the facility accessibility index employed in this study is to leverage POI data and transportation network data to calculate the convenience for residents to reach various types of facilities, namely “accessibility”.

Research indicates that facility accessibility is influenced by multiple factors:

-

Transportation system factors: including the availability of road networks and public transportation [8].

-

Land resource utilization: the geographical distribution of facilities and land-use patterns [8].

-

Time description: traffic conditions and facility usage needs at different time periods.

-

Individual characteristics: such as age and physical condition, which affect an individual’s ease of access to facilities [8,9].

Facility accessibility is a multi-dimensional concept involving various factors such as space, time, and individual characteristics. By employing multiple measurement methods and considering different influencing factors, the accessibility of facilities can be evaluated more comprehensively, thereby providing support for policymaking and urban planning.

3.3. Research on Facility Correlation

Facility correlation research mainly focuses on the spatial distribution characteristics and formation mechanisms of urban functional components. These functional components include various facilities in the city, such as education, health, culture, and entertainment, which play a key role in the optimization of urban layout and determine whether residents can conveniently enjoy diverse services [10]. Urban facility management (FM) plays an important role in the urban environment, promoting the realization of sustainable development goals and building a bridge between citizens and public and private practices [11].

There are various methods to measure the functional correlation of facilities. This paper assesses the correlation between different facilities by analyzing the correlation of POI data and identifies the facility combinations with strong correlations.

The distribution and functions of urban facilities are influenced by multiple factors. Urban scale and ecosystem services are key factors affecting the distribution of urban functional components [10]. Additionally, the accessibility and scarcity of urban facilities significantly impact housing prices and urban quality of life. The functional and supporting facilities of urban parks have an important impact on leisure satisfaction, indicating that the relationship between individuals and spatial conditions is one of the influencing factors [12]. The structural elements and functional purposes of the urban planning system also play a crucial role in affecting urban attractiveness.

Research on the functional correlation of urban facilities involves a comprehensive analysis of the definition, measurement methods, and influencing factors of urban functional components. Through methods such as gradient analysis and progress measurement frameworks, a better understanding of the distribution and functions of urban facilities can be achieved. Influencing factors include urban scale, ecosystem services, accessibility and scarcity of facilities, etc., which jointly affect the layout of the city and the quality of life of residents.

3.4. Research on Resident Satisfaction

Resident satisfaction is one of the important criteria for measuring the quality of urban space, involving multiple factors such as physical environment, social environment, and residents’ subjective feelings.

The subjective perception of the city image and the objective attributes of the physical space jointly influence residents’ satisfaction. Studies have shown that urban image factors, such as the overall impression of the regional appearance, have a significant positive impact on residents’ satisfaction, while certain physical space indicators, such as the level of green space, also significantly affect residents’ perception of the city image [13]. Residents usually choose residential areas that match their preferences, which makes the satisfaction levels of residents in different types of urban environments similar. Research suggests focusing on residents’ preferences to better represent and improve the quality of urban life [14]. Residents’ satisfaction plays a mediating role in sustainable urban development and affects residents’ loyalty to the city. Studies indicate that the relationship between the overall performance of the city and residents’ satisfaction is regulated by residents’ perception of the city’s sustainability dimensions [15]. Community environment, property management, and surrounding facilities are important factors affecting residents’ satisfaction. Research has found that the quality of the community environment has a greater impact on residents’ satisfaction than the conditions of individual residential units [16]. Residents’ satisfaction with urban green spaces is influenced by physical perception, aesthetic cognition, and psychological cognition. The biodiversity and landscape diversity of green spaces are important factors in enhancing residents’ satisfaction. The residents’ satisfaction survey questionnaire of this study is based on the above literature review.

4. Research Hypotheses and Sources of Data

4.1. Research Hypotheses

Based on existing research and theories, this paper proposes the following three hypotheses:

Hypothesis 1. 

Facility accessibility is positively related to urban spatial quality.

Hypothesis 2. 

Facility correlation is positively related to urban spatial quality.

Hypothesis 3. 

Residents’ satisfaction is positively related to urban spatial quality.

4.2. Data Collection and Processing

This study took Xi’an as an example to verify the three hypotheses proposed above. Empirical tests were conducted by collecting remote sensing data, POI data, and questionnaire data on Xi’an.

Remote sensing data: Remote sensing technology plays a key role in capturing large-scale and real-time information of the urban environment. Remote sensing images can provide accurate information on the distribution of urban elements, such as roads, buildings, green spaces, and water bodies. These data help assess the proximity of different areas to basic facilities such as transportation networks, public spaces, and residential areas, thereby comprehensively understanding the structure and spatial organization.

Point-of-interest (POI) data: POI data were another key data source used in this study. These data provide information on the location and distribution of various facilities and activities within the city, including public service facilities, commercial centers, and recreational spaces by analyzing the density, accessibility, and coverage of these facilities.

Questionnaire data: To complement the objective data obtained through remote sensing and POI, questionnaire data were used to collect residents’ subjective information about their urban life. These data included a wide range of urban evaluation indicators such as air quality, traffic congestion, safety, service accessibility, and overall satisfaction with the urban environment.

4.3. Indicator Construction and Analysis

(1)

Facility Accessibility Index

We selected the following model, using POI data and traffic network data to calculate the convenience of residents reaching various types of facilities, that is, “accessibility”. The model can assess the differences in the convenience of public service facilities for residents in different regions and identify areas with low accessibility.

The specific formula is as follows:

$$A={\displaystyle \sum \left(\frac{S_i}{D_i^{\alpha }}\right)}$$

(1)

A is facility accessibility index, S_i is the scale of the i-th facility, D_i is the distance from the residential area to the i-th facility, and α is the distance attenuation coefficient (usually positive, the farther the distance, the lower the accessibility).

(2)

Facility Correlation Index

We selected the following facility correlation model. Through the correlation analysis of POI data, the correlation between different facilities was evaluated to identify the facility combinations with strong correlation, which can provide a reference for the future supporting construction of facilities.

The specific formula is as follows:

$$G={\displaystyle \sum \left(\frac{S_i\cdot S_j\cdot C_{ij}}{D_{ij}^{\beta }}\right)}$$

(2)

G represents the facility association index, S_i and S_j respectively represent the scale of the i-th and j-th facilities, C_ij is the functional complementarity coefficient between facility S_i and facility S_j (determined based on factors such as facility type and function), D_ij represents the distance between facility S_i and facility S_j, and β is the distance attenuation coefficient.

(3)

Resident Satisfaction Index

We adopted the following resident satisfaction index model and collected data on residents’ satisfaction with urban spatial quality through questionnaire surveys. The relationships between resident satisfaction and factors such as facility density, accessibility, and diversity were analyzed.

The specific formula is as follows:

$$S={\displaystyle \sum \left(W_{\mathrm{i}}\cdot X_{\mathrm{i}}\right)}$$

(3)

S represents the residents’ satisfaction index, W_i indicates the weight of the i-th evaluation indicator (determined based on its importance), and X_i is the score of the i-th evaluation indicator (obtained through questionnaires or other means). The evaluation indicators included X₁—Your gender; X₂—Your age; X₃—Your educational attainment; X₄—Your occupation; X₅—Your satisfaction with transportation in Xi’an; X₆—Do you think the public transportation in Xi’an (such as subways and buses) is convenient?; X₇—Are you satisfied with the road conditions in Xi’an (such as road smoothness and traffic management)?; X₈—Can the public health facilities in Xi’an (such as hospitals and clinics) meet your needs?; X₉—Are you satisfied with the air quality in Xi’an?; X₁₀—Are you satisfied with the green environment in Xi’an (such as parks and green spaces)?; X₁₁—Your opinion on garbage disposal and environmental hygiene in Xi’an; X₁₂—How satisfied are you with the social welfare and public services in Xi’an (such as care for an aging society and educational resources)?; X₁₃—Are you satisfied with the public security situation in Xi’an?; X₁₄—Are you satisfied with the government services in Xi’an (such as work efficiency and government transparency)?; X₁₅—Are you satisfied with the housing conditions in Xi’an?; and X₁₆—Are you satisfied with the consumption level in Xi’an? Each evaluation indicator had five scoring options ranging from 1 to 5.

5. Empirical Research Results

5.1. Facility Accessibility

(1)

Empirical analysis

To clarify the spillover effect of facility scale S_i and facility distance D_i on the accessibility index A, we conducted an empirical analysis based on the benchmark model. Firstly, under the distance attenuation coefficient α = 0.015, the regression results of model (1) were established. In this model, the explanatory variables only include facility scale S_i, not facility distance D_i. Through analysis, the benchmark regression results of model (1) are obtained, which are used to evaluate the impact of facility scale on the accessibility index.

Model (2) adds facility scale S_i and facility distance D_i to the benchmark model for regression analysis. By comparing the results of these two models, it can be found that the role of facility scale S_i is very important in both models. Specifically, the regression coefficient of S_i is 0.981, with a very high significance level (p < 0.01, denoted by ***). The corresponding t-values are 180.856 and 181.52, further confirming the robustness of the results and indicating a significant positive correlation between facility scale and the accessibility index. This means that as the facility scale increases, the contribution of the facility to the accessibility index will enhance, and this effect is statistically significant and reliable.

The high goodness of fit (R² = 0.997) of the two models further confirms the significant role of facility scale in explaining the facility accessibility index. This indicates that the regression model has a very strong ability to explain, and the remaining unexplained variance is extremely small.

However, when facility distance D_i is included in model (2), it is found that its impact on the accessibility index is not significant. The regression coefficient of D_i is −1.66 × 10⁴, and the t-value is −1.573, which does not pass the significance test (p > 0.1). This indicates that facility distance has no significant impact on the facility accessibility index. Therefore, the research results show that within the framework of this study, the facility accessibility index A is affected by facility scale S_i, while the role of facility distance is relatively minor. (

Table 1. Facility accessibility regression.

	(1)	(2)
	A	A
Si	0.981 ***	0.981 ***
	(180.865)	(181.523)
Di		−1.66e+04
		(−1.573)
_cons	−986.837 ***	16,333.213
	(−3.845)	(1.483)
N	132	132
R2	0.997	0.997
F	32,708.763	16,577.032

Notes: *** p < 0.01.

)

The influence of the facility distance studied in this paper is not significant. There are three reasons for this, as follows.

• Influence of case studies: Different cities have distinct characteristics. Xi’an has a vast territory, and the influence of urban density on facility accessibility is limited. Therefore, the facility distance may not be the main influencing factor. In other cities, such as those with well-developed transportation or relatively low population density, the influence of facility distance on the accessibility index is more crucial.
• Influence of comprehensive factors: There are many factors affecting accessibility, such as traffic conditions and population density. These factors lead to the statistically insignificant influence of the facility distance. For example, even though the facility is far away, if the public transportation is well developed, good accessibility can still be achieved.
• Limitations of data collection: There may be limitations in the data collection, which will have a certain impact on the research results and may lead to a decrease in accuracy.

(2)

Residual analysis

Since the goodness of fit in the above experiment was 0.997, which may lead to overfitting, a residual test was added.

We first conducted a regression analysis on the D_i variable to obtain the residuals of the model, then drew a residual plot for analysis. The following are the results obtained.

From the results of the residual analysis, it can be seen that the mean value of the residuals should ideally be close to 0, and this item meets the requirement. There should be no obvious heteroscedasticity in the residuals, that is, the variance should be a constant. The graph is a straight line. The residuals should exhibit a normal distribution or an approximately normal distribution. The histogram basically conforms to the normal distribution, indicating that there is no situation of overfitting. (

Table 2. Results of the regression analysis of the Di variable.

Source	SS	df	MS	Number of obs = 132 F(0, 131) = 0.00 Prob > F=. R-squared = 0.0000 Adj R-squared = 0.0000 Root MSE = 0.01436
Model Residual	0 0.02700178	0 131	. 0.00020612
Total	0.02700178	131	0.00020612
DI	Coefficient	Std.err.	t	P > |t|	[95% conf.interval]
_cons	1.043801	0.0012496	835.30	0.000	1.041329	1.046273

, Figure 1, Figure 2, Figure 3 and Figure 4)

Overall, the research results show that in areas with higher facility accessibility, residents can more easily reach various facilities (such as schools, hospitals, parks, etc.), significantly improving the convenience of life, thereby promoting the improvement of urban spatial quality. This verifies the correctness of Hypothesis 1.

5.2. Facility Correlation

To examine the spillover effects of facility scale and distance, we conducted an empirical analysis based on the benchmark model. Under the assumption that C_ij = 1 and = 0.15, by incorporating the interaction term S_i∙S_j into Model (1), the benchmark regression results without considering the facility distance D_ij were obtained. In contrast, Model (2) simultaneously includes the interaction term S_i∙S_j and the facility distance D_ij in the regression.

A comparative analysis of the regression results of the two models reveals that the interaction term S_i∙S_j is statistically significant in both cases, with coefficients of 0.584 and 0.581, respectively, at a significance level of p < 0.01 (indicated by ***). These results suggest that the interaction of facility scales has a robust and positive impact on the dependent variable G, and the coefficients of the two models are very similar. This reinforces the view that the product of facility scales consistently influences the facility connection index in a significant manner.

Furthermore, the R² values of both models are 0.998, indicating that the models have high explanatory power, explaining 99.8% of the variance. This highlights the models’ advantage in fitting the data and emphasizes the role of the facility scale interaction term in explaining the connection index.

In sharp contrast, the facility distance D_ij is only included in Model (2), with a regression coefficient of −1.66e04 and a t-value of −1.573, which did not pass the significance test (i.e., p > 0.10). This indicates that facility distance has no significant impact on the dependent variable G. Therefore, the research results suggest that the facility connection index is mainly influenced by the product of scales, while the influence of facility distance is relatively weak (

Table 3. Facility association regression.

	(1)	(2)
	G	G
Sij	0.584 ***	0.581 ***
	(98.699)	(86.041)
Dij		−7.64e+09
		(−0.876)
_cons	−4.56e+08	1.34e+10
	(−0.638)	(0.846)
N	30	30
R2	0.998	0.998
F	9741.544	4823.950

*** p < 0.01.

).

In areas with strong facility correlation, various facilities form a good complementary and synergistic relationship, providing residents with diverse services. This diverse range of services not only meets the different needs of residents but also enhances the overall quality of the urban space. This supports Hypothesis 2.

5.3. Resident Satisfaction

To verify whether the questionnaire results truly reflected the situation and whether the questionnaire design was reasonable, reliability and validity tests, factor analysis, correlation tests, and Student’s t-test were conducted for verification.

(1)

Reliability Test

Reliability refers to the consistency of results when the same method is used to measure the same variable multiple times. The higher the reliability, the more reasonable the questionnaire design and the higher the credibility. In this test, a total of 16 items were included in the reliability analysis. The average covariance between the items was 0.4947366. A high value indicates strong correlations among the items, which is the basis for high reliability. According to the general standard of Cronbach’s α coefficient: above 0.9: very high (excellent); 0.8–0.9: high; 0.7–0.8: acceptable; 0.6–0.7: low; below 0.6: unacceptable. In this case, the α coefficient was 0.8931, indicating that the scale has high consensus in measuring the same latent variable. (

Table 4. Reliability test.

Item	Magnitude
Test scale	mean (unstandardized items)
Reversed item	X4
Average interitem covariance	0.4947366
Number of items in the scale	16
Scale reliability coefficient	0.8931

)

(2)

Validity Test

Validity refers to the degree to which a measurement method can effectively and accurately measure the variable to be measured. The closer the measurement result is to the required variable, the higher the validity. Conversely, the further it is, the lower the validity. Factor analysis was conducted using Stata software. Its determination coefficient is Det = 0.000, which is of the correlation matrix. A value close to 0 indicates that the correlation matrix may have multicollinearity or a strong linear relationship between variables. In factor analysis, the closer the determination coefficient of the correlation matrix is to 0, the more it supports the use of factor analysis, as this indicates a certain correlation between variables. For Bartlett’s test of sphericity, chi-squared = 1824.225, degrees of freedom = 120, p-value = 0.0000. The null hypothesis (Ho) of this test is that “the correlation matrix of the variables is an identity matrix”, that is, there is no significant correlation between the variables. p = 0.000 indicates rejection of the null hypothesis, indicating that there is a significant correlation between the variables and is suitable for factor analysis. In its KMO measure of sampling adequacy, KMO = 0.958. The KMO index is used to measure whether the correlation between variables is suitable for factor analysis, with a value range of 0 to 1: KMO > 0.9: excellent (very suitable for factor analysis); 0.8–0.9: very good; 0.7–0.8: moderate; <0.5: not suitable for factor analysis. In this case, KMO = 0.958, indicating that the data were very suitable for analysis. (

Table 5. Validity test.

Test Item	Magnitude
Determinant of the correlation matrix	Det = 0.000
Bartlett’s test of sphericity
Chi-squared	1824.225
Degrees of freedom	120
p-value	0.000
H0: variables are not intercorrelated
Kaiser–Meyer–Olkin measure of sampling adequacy	KMO = 0.958

)

(3)

Factor Analysis

Factor analysis is a commonly used dimensionality reduction method that is employed to explore the latent structure between variables and explain the relationships between variables through fewer factors. This analysis was based on 204 samples, using the principal factor method to extract factors, and ultimately retaining eight factors. This paper reports the results of factor analysis in detail, including characteristic values, factor loadings, and the uniqueness of variables.

Data overview and method of this analysis: sample size: 204; extraction method: principal factors; factor rotation: none; number of retained factors: 8; free parameters: 100. The fit of the factor model was evaluated through the chi-squared test. The chi-squared statistic χ²(120) = 1833.49/chi^2(120) = 1833.49 χ²(120) = 1833.49, with a significant p-value (p = 0.0000), indicating that the factor model has a good explanatory ability for the data. From the table, it can be seen that the eigenvalue of Factor 1 is much higher than that of other factors (7.47037), and its variance contribution rate is as high as 95.75%, indicating that Factor 1 is the dominant factor in this analysis. (

Table 6. Factor test table.

	Eigenvalue	Difference	Proportion	Cumulative
Factor 1	7.47037	6.96476	0.9575	0.9575
Factor 2	0.50561	0.28238	0.0648	1.0223
Factor 3	0.22322	0.07044	0.0286	1.0509
Factor 4	0.15278	0.04920	0.0196	1.0705
Factor 5	0.10358	0.01868	0.0133	1.0838
Factor 6	0.08490	0.03027	0.0109	1.0947
Factor 7	0.05463	0.02263	0.0070	1.1017
Factor 8	0.03199	0.05292	0.0041	1.1058
Factor 9	−0.02092	0.00963	−0.0027	1.1031
Factor 10	−0.03055	0.03138	−0.0039	1.0992
Factor 11	−0.06193	0.03130	−0.0079	1.0912
Factor 12	−0.09323	0.01788	−0.0119	1.0793
Factor 13	−0.11111	0.01021	−0.0142	1.0651
Factor 14	−0.12131	0.01692	−0.0155	1.0495
Factor 15	−0.13823	0.10976	−0.0177	1.0318
Factor 16	−0.24800	.	−0.0318	1.0000

)

The factor loading matrix shows the loading values of each variable on each factor. The absolute value of a variable’s loading on a factor (usually >0.4) indicates the greater contribution of that variable to the factor. In the table, it can be seen that variables X₅, X₇, X₈ and X₁₀ have higher loading values on Factor 1, suggesting that they are mainly influenced by Factor 1. Some variables (such as X₁ and X₃) have a wider distribution across factors. The uniqueness of the variables is shown in the table below. From the results, it can be seen that the uniqueness of X₅, X₁₂ and X₁₃ is relatively low, indicating that the factor model can explain the variance of these variable well s. The higher uniqueness of X₁ and X₃ indicates that the variance of these variables is largely unexplained by the factors. (

Table 7. Factor loading matrix.

Variable	Factor 1	Factor 2	Factor 3	Factor 4	Factor 5	Factor 6	Factor 7	Factor 8	Uniqueness
X1	0.0647	0.0421	0.03127	0.0529	0.0369	−0.0014	−0.0630	−0.0059	0.8881
X2	0.0250	0.4918	−0.0577	0.0191	0.0202	0.0503	0.0093	−0.0318	0.7498
X3	0.0087	−0.0617	0.0349	0.1597	−0.0136	0.1855	0.0507	0.0458	0.9301
X4	−0.0659	0.4829	0.0603	−0.0221	0.0042	−0.0422	0.0333	0.0155	0.7552
X5	0.7990	−0.0449	−0.2462	−0.0470	0.0520	−0.0134	0.0499	−0.0109	0.2912
X6	0.7658	0.0470	−0.0036	0.1068	0.1145	0.0481	−0.0045	−0.0246	0.3838
X7	0.7866	−0.0901	0.1681	−0.0773	0.0491	−0.0462	0.0803	0.0153	0.3277
X8	0.8016	0.0456	0.0002	−0.1157	0.0546	0.0924	−0.0996	0.0677	0.3159
X9	0.7685	0.0126	0.1165	−0.0884	−0.1287	−0.0049	0.1041	−0.0275	0.3597
X10	0.7885	−0.0531	0.0362	−0.1078	0.0600	0.0570	−0.0372	−0.0742	0.3488
X11	0.7951	0.0317	−0.0556	0.1678	−0.0497	−0.0416	0.0303	0.0586	0.3327
X12	0.8365	0.0409	−0.0414	−0.0680	0.0924	−0.0109	0.0237	0.0424	0.2813
X13	0.7556	−0.0412	0.0609	0.1582	−0.0927	−0.1028	0.0092	−0.0086	0.3793
X14	0.8023	0.0110	0.0354	−0.0195	−0.1287	0.1021	0.0164	−0.0090	0.3272
X15	0.7941	0.0057	−0.0574	0.1127	−0.0849	−0.0123	−0.0698	−0.0891	0.3332
X16	0.7689	0.0549	−0.0210	−0.0182	−0.1299	−0.0819	−0.0953	0.0590	0.3689

)

(4)

Correlation Analysis

Correlation refers to the degree of association between two variables and has three forms: positive correlation, negative correlation, and no correlation. The correlation coefficient ranges from 0 to 1, with a higher value indicating a stronger correlation and a value closer to 0 indicating a weaker correlation. The Pearson correlation coefficients among multiple variables are shown in the correlation coefficient matrix. The values on the diagonal of the matrix are always 1.000, indicating that each variable is perfectly correlated with itself. In the matrix, the following pairs of variables have a strong positive correlation. For X₁₂ and X₈, the correlation coefficient is 0.709 (p < 0.01), indicating a strong positive correlation between the two. For X₁₀ and X₈, the correlation coefficient is 0.679 (p < 0.01), indicating a strong positive correlation. For X₁₅ and X₁₂, the correlation coefficient is 0.668 (p < 0.01), further confirming the close positive correlation between the two.

Some variables have a weak correlation, but still have statistical significance. For example, X₄ and X₂ is 0.322 (p < 0.01), indicating a weak positive correlation. Many pairs of variables have a low correlation and are not statistically significant. For example, the correlation coefficient between X₆ and X₂ is 0.078 (p = 0.278, not significant). The correlation coefficients between X₁ and other variables are relatively low, with most p-values greater than 0.1 and no statistical significance. (

Table 8. Correlation analysis table.

Variables	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)	(11)	(12)	(13)	(14)	(15)	(16)
(1) X1	1.000
(2) X2	0.017	1.000
	(0.805)
(3) X3	0.009	−0.011	1.000
	(0.901)	(0.880)
(4) X4	0.020	0.322 *	−0.055	1.000
	(0.776)	(0.000)	(0.434)
(5) X5	−0.080	0.042	−0.024	−0.120	1.000
	(0.256)	(0.551)	(0.736)	(0.087)
(6) X6	0.075	0.076	0.035	−0.049	0.629 *	1.000
	(0.289)	(0.278)	(0.620)	(0.484)	(0.000)
(7) X7	0.107	−0.061	0.000	−0.065	0.614 *	0.599 *	1.000
	(0.126)	(0.390)	(0.999)	(0.359)	(0.000)	(0.000)
(8) X8	0.062	0.046	0.017	−0.033	0.641 *	0.611 *	0.629 *	1.000
	(0.378)	(0.518)	(0.809)	(0.641)	(0.000)	(0.000)	(0.000)
(9) X9	0.072	0.011	0.001	−0.019	0.592 *	0.552 *	0.662 *	0.586 *	1.000
	(0.305)	(0.871)	(0.994)	(0.786)	(0.000)	(0.000)	(0.000)	(0.000)
(10) X10	0.058	−0.006	−0.008	−0.079	0.634 *	0.607 *	0.644 *	0.679 *	0.624 *	1.000
	(0.406)	(0.930)	(0.914)	(0.262)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)
(11) X11	0.032	0.033	0.059	−0.030	0.641 *	0.614 *	0.600 *	0.606 *	0.606 *	0.581 *	1.000
	(0.651)	(0.644)	(0.401)	(0.665)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)
(12) X12	0.046	0.044	−0.022	−0.034	0.701 *	0.643 *	0.668 *	0.709 *	0.635 *	0.655 *	0.668 *	1.000
	(0.513)	(0.527)	(0.758)	(0.625)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)
(13) X13	0.092	−0.037	0.015	−0.036	0.602 *	0.620 *	0.619 *	0.569 *	0.548 *	0.583 *	0.636 *	0.629 *	1.000
	(0.193)	(0.603)	(0.829)	(0.614)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)
(14) X14	0.056	0.027	0.046	−0.051	0.628 *	0.613 *	0.625 *	0.651 *	0.669 *	0.625 *	0.614 *	0.663 *	0.576 *	1.000
	(0.425)	(0.707)	(0.509)	(0.466)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)
(15) X15	0.044	0.061	0.001	−0.094	0.628 *	0.608 *	0.577 *	0.606 *	0.604 *	0.638 *	0.663 *	0.643 *	0.628 *	0.657 *	1.000
	(0.531)	(0.389)	(0.988)	(0.179)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)
(16) X16	0.049	0.018	−0.043	−0.010	0.610 *	0.571 *	0.584 *	0.651 *	0.597 *	0.573 *	0.644 *	0.619 *	0.564 *	0.639 *	0.635 *	1.000
	(0.484)	(0.800)	(0.540)	(0.887)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)	(0.000)

* p < 0.1.

)

(5)

T-test

The following are the results of multiple one-sample t-tests. For each variable (X₁, X₂, ..., X₁₆), the hypothesis test compares the sample mean of the variable with the hypothesized value, which is the average score of the variable. Null hypothesis (Ho): The sample mean is equal to the given value. Alternative hypothesis (Ha): The sample mean is not equal to the given value. t-value: A measure of the difference between the sample mean and the hypothesized value. The larger the t-value, the greater the difference between the sample mean and the hypothesized value.

p-value: A measure of the fit between the observed data and the null hypothesis. If the p-value is less than a certain significance level (usually 0.5), the null hypothesis is rejected. Only the results of X₁, X₃, and X₄ are significant, while the others (X₂, X₅, X₆, ..., X₁₆) do not significantly deviate from the hypothesized value. Based on the p-value results, only those with p-values close to zero (such as X₁, X₃, and X₄) can reject the hypothesis, indicating that their sample means are significantly different from the hypothesized value. The p-values of other variables are close to 1, indicating that their means are not significantly different from the hypothesized value. (

Table 9. X₁ one-sample t-test.

Variable	Obs.	Mean		Std.Err.	Std.Dev.		95% Conf.Int.
X1	204	1.4951		0.0351	0.5012		1.4259–1.5643
Test Statistic			t = 28.4969			Degrees of freedom = 203
Ha: mean < 0.4951			Pr(T < t) = 1.0000
Ha: mean ! = 0.4951			Pr(|T| > |t|) = 0.0000			T
Ha: mean > 0.4951			Pr(T > t) = 0.0000

,

Table 10. X₂ one-sample t-test.

Variable	Obs.	Mean		Std.Err.	Std.Dev.		95% Conf.Int.
X2	204	3.0147		0.0994	1.4194		2.8188–3.2106
Test Statistic			t = 0.7890			Degrees of freedom = 203
Ha: mean < 2.9363			Pr(T < t) = 0.7845
Ha: mean ! = 2.9363			Pr(|T| > |t|) = 0.4310			T
Ha: mean > 2.9363			Pr(T > t) = 0.2155

,

Table 11. X₃ one-sample t-test.

Variable	Obs.	Mean		Std.Err.	Std.Dev.		95% Conf.Int.
X3	204	3.6422		0.0894	1.2767		3.4659–3.8184
Test Statistic			t = 14.4783			Degrees of freedom = 203
Ha: mean < 2.348			Pr(T < t) = 1.0000
Ha: mean ! = 2.348			Pr(|T| > |t|) = 0.0000			T
Ha: mean > 2.348			Pr(T > t) = 0.0000

,

Table 12. X₄ one-sample t-test.

Variable	Obs.	Mean		Std.Err.	Std.Dev.		95% Conf.Int.
X4	204	2.5441		0.1043	1.4899		2.3384–2.7498
Test Statistic			t = −8.7409			Degrees of freedom = 203
Ha: mean < 3.4559			Pr(T < t) = 0.0000
Ha: mean ! = 3.4559			Pr(|T| > |t|) = 0.0000			T
Ha: mean > 3.4559			Pr(T > t) = 1.0000

,

Table 13. X₅ one-sample t-test.

Variable	Obs.	Mean		Std.Err.	Std.Dev.		95% Conf.Int.
X5	204	2.7255		0.0799	1.1417		2.5679–2.8831
Test Statistic			t = −0.0001			Degrees of freedom = 203
Ha: mean < 2.7255			Pr(T < t) = 0.5000
Ha: mean ! = 2.7255			Pr(|T| > |t|) = 0.9999			T
Ha: mean > 2.7255			Pr(T > t) = 0.5000

,

Table 14. X₆ one-sample t-test.

Variable	Obs.	Mean		Std.Err.	Std.Dev.		95% Conf.Int.
X6	204	2.7402		0.0853	1.2182		2.5720–2.9084
Test Statistic			t = −0.0000			Degrees of freedom = 203
Ha: mean < 2.7402			Pr(T < t) = 0.5000
Ha: mean ! = 2.7402			Pr(|T| > |t|) = 1.0000			T
Ha: mean > 2.7402			Pr(T > t) = 0.5000

,

Table 15. X₇ one-sample t-test.

Variable	Obs.	Mean		Std.Err.	Std.Dev.		95% Conf.Int.
X7	204	2.6716		0.0844	1.2055		2.5052–2.8380
Test Statistic			t = −0.5809			Degrees of freedom = 203
Ha: mean < 2.7206			Pr(T < t) = 0.2810
Ha: mean ! = 2.7206			Pr(|T| > |t|) = 0.5619			T
Ha: mean > 2.7206			Pr(T > t) = 0.7190

,

Table 16. X₈ one-sample t-test.

Variable	Obs.	Mean		Std.Err.	Std.Dev.		95% Conf.Int.
X8	204	2.6765		0.8521	1.2170		2.5085–2.8445
Test Statistic			t = −0.0003			Degrees of freedom = 203
Ha: mean < 2.6765			Pr(T < t) = 0.4999
Ha: mean ! = 2.6765			Pr(|T| > |t|) = 0.9997			T
Ha: mean > 2.6765			Pr(T > t) = 0.5001

,

Table 17. X₉ one-sample t-test.

Variable	Obs.	Mean		Std.Err.	Std.Dev.		95% Conf.Int.
X9	204	2.6324		0.0869	1.2426		2.4608–2.8039
Test Statistic			t = −0.0005			Degrees of freedom = 203
Ha: mean < 2.6324			Pr(T < t) = 0.4998
Ha: mean ! = 2.6324			Pr(|T| > |t|) = 0.9996			T
Ha: mean > 2.6324			Pr(T > t) = 0.5002

,

Table 18. X₁₀ one-sample t-test.

Variable	Obs.	Mean		Std.Err.	Std.Dev.		95% Conf.Int.
X10	204	2.7206		0.0849	1.2138		2.5530–2.8881
Test Statistic			t = −0.0001			Degrees of freedom = 203
Ha: mean < 2.7206			Pr(T < t) = 0.4999
Ha: mean ! = 2.7206			Pr(|T| > |t|) = 0.9999			T
Ha: mean > 2.7206			Pr(T > t) = 0.5001

,

Table 19. X₁₁ one-sample t-test.

Variable	Obs.	Mean		Std.Err.	Std.Dev.		95% Conf.Int.
X11	204	2.7353		0.0807	1.1526		2.5762–2.8944
Test Statistic			t = −0.0001			Degrees of freedom = 203
Ha: mean < 2.7353			Pr(T < t) = 0.5000
Ha: mean ! = 2.7353			Pr(|T| > |t|) = 0.9999			T
Ha: mean > 2.7353			Pr(T > t) = 0.5000

,

Table 20. X₁₂ one-sample t-test.

Variable	Obs.	Mean		Std.Err.	Std.Dev.		95% Conf.Int.
X12	204	2.7549		0.0859	1.2272		2.5855–2.9243
Test Statistic			t = 0.0000			Degrees of freedom = 203
Ha: mean < 2.7549			Pr(T < t) = 0.5000
Ha: mean ! = 2.7549			Pr(|T| > |t|) = 1.0000			T
Ha: mean > 2.7549			Pr(T > t) = 0.5000

,

Table 21. X₁₃ one-sample t-test.

Variable	Obs.	Mean		Std.Err.	Std.Dev.		95% Conf.Int.
X13	204	2.7059		0.0804	1.1498		2.5471–2.8646
Test Statistic			t = −0.0002			Degrees of freedom = 203
Ha: mean < 2.7059			Pr(T < t) = 0.4999
Ha: mean ! = 2.7059			Pr(|T| > |t|) = 0.9998			T
Ha: mean > 2.7059			Pr(T > t) = 0.5001

,

Table 22. X₁₄ one-sample t-test.

Variable	Obs.	Mean		Std.Err.	Std.Dev.		95% Conf.Int.
X14	204	2.6863		0.0819	1.1701		2.5248–2.8478
Test Statistic			t = −0.0003			Degrees of freedom = 203
Ha: mean < 2.6863			Pr(T < t) = 0.4999
Ha: mean ! = 2.6863			Pr(|T| > |t|) = 0.9998			T
Ha: mean > 2.6863			Pr(T > t) = 0.5001

,

Table 23. X₁₅ one-sample t-test.

Variable	Obs.	Mean		Std.Err.	Std.Dev.		95% Conf.Int.
X15	204	2.7990		0.0826	1.1802		2.6361–2.9619
Test Statistic			t = 0.0002			Degrees of freedom = 203
Ha: mean < 2.799			Pr(T < t) = 0.5001
Ha: mean ! = 2.799			Pr(|T| > |t|) = 0.9998			T
Ha: mean > 2.799			Pr(T > t) = 0.4999

and

Table 24. X₁₆ one-sample t-test.

Variable	Obs.	Mean		Std.Err.	Std.Dev.		95% Conf.Int.
X16	204	2.7157		0.0798	1.1393		2.5584–2.8729
Test Statistic			t = −0.0002			Degrees of freedom = 203
Ha: mean < 2.7157			Pr(T < t) = 0.4999
Ha: mean ! = 2.7157			Pr(|T| > |t|) = 0.9999			T
Ha: mean > 2.7157			Pr(T > t) = 0.5001

)

Areas with higher resident satisfaction often imply that urban residents have higher recognition and satisfaction with the spatial quality of their area. The spatial quality of these areas is usually relatively high, reflecting a positive correlation between resident satisfaction and urban spatial quality. This verifies the validity of Hypothesis 3.

6. Enhancement Strategies

Based on the above empirical research findings, this paper puts forward the following strategies for improving the quality of urban space.

6.1. Intensify Infrastructure Construction

It can be observed from the urban space quality evaluation model and regression results that the popularization and stable supply of urban service facilities have a significant influence on the quality of urban space. Increasing investment in urban infrastructure can prevent urban hollowing and enhance the quality of life of citizens. Simultaneously, attention should be paid to the interrelationships among facilities, and multi-functional community centers, cultural facilities, etc., should be designed. To enhance the resilience and sustainability of the city, we need to keep a close eye on green ecological design and digital services.

6.2. Using Multi-Source Data to Optimize the Transportation System

We will optimize the urban transportation layout, establish a multi-level transportation system, and achieve complementarity among public transportation, private vehicles, and shared transportation means. By adopting an intelligent traffic system (ITS), we can optimize traffic signals, data analysis, artificial intelligence, traffic flow guidance, and traffic monitoring to improve fluidity and safety.

6.3. Implement Humanistic Care and Promote Community Interaction

To design diverse and open public spaces, regularly held cultural festivals, art exhibitions, and street performances can enhance community members’ sense of belonging and identity, stimulate citizens’ enthusiasm for participation, and create a harmonious community atmosphere. Furthermore, we can establish support and assistance networks through digital platforms or offline social organizations and set up community service centers to provide convenient social support.

6.4. Promote Digital and Intelligent Management of the City

Regarding urban infrastructure, intelligent street lamps, intelligent trash cans, and intelligent parking systems can be constructed to enhance the efficiency of urban management. Sensors and big data technology can monitor the operational status of the city in real time, optimize resource allocation, and reduce waste. Data-driven decision-making, big data, and cloud computing technology are used to analyze urban development and residents’ needs. Urban planning and resource allocation should be reasonably adjusted to achieve precise management. Intelligent security management employs multi-source data to prevent crime and medical and health management can monitor public health big data.

6.5. Paying Attention to the Cultural Aesthetics of the City

We will protect and restore historical buildings, promote cultural innovation, create public art projects with unique cultural symbols, and build industrial parks to enhance the cultural attractiveness and influence of the city.

7. Conclusions

Through the integration of multi-source data and the adoption of an empirical approach, this paper explores the impacts of facility accessibility, facility relevance, and residents’ satisfaction on the quality of urban space. Through this study, it was found that there is huge potential in improving the quality of urban space. Integrating multi-source data can help urban managers have a more comprehensive understanding of the operation status of the city so as to formulate precise strategies for improving the quality of urban space.

This study empirically examined the relationships among facility accessibility, facility relevance, residents’ satisfaction, and the quality of urban space. The results show that the above three variables all have a positive correlation with the quality of urban space. On this basis, this paper proposes strategies for improving urban space. The research findings of this paper illustrate the following.

(1)

The integration of multi-source data can more accurately display the distribution and utilization of urban resources, such as transportation, the environment, public facilities, etc. By deeply exploring these data, problems existing in urban operation can be discovered in a timely manner, providing a basis for optimizing urban planning and design.

(2)

The optimization of urban space through the integration of multi-source data should focus on human factors to meet people’s living needs. Driven by multi-source data, people pay more attention to the livability, convenience, and comfort of the city. Only by improving the quality of urban space can people’s satisfaction with urban living be enhanced.

(3)

The formulation of improvement strategies needs to be combined with the characteristics and development stages of the city. Different cities have different cultural and social backgrounds. Therefore, formulating strategies according to the specific situation of each city can ensure the effectiveness and feasibility of the strategies.

These research findings can provide certain reference value for the urban planning and spatial quality optimization of other cities. In the future, relevant research can further explore other factors affecting the quality of urban space and verify the effectiveness of the enhancement strategies proposed in this paper.