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Citation: Liu, Y.; Chen, L.; Qi, M.;
Kong, D. Construction of a Spatial
Equalization Assessment System for
Medical Facilities. Buildings 2024,14,
1265. https://doi.org/10.3390/
buildings14051265
Academic Editor: Haifeng Liao
Received: 27 March 2024
Revised: 18 April 2024
Accepted: 26 April 2024
Published: 30 April 2024
Copyright: © 2024 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
buildings
Article
Construction of a Spatial Equalization Assessment System for
Medical Facilities
Yi Liu 1, Lulu Chen 2,3 ,* , Mu Qi 1, * and Dezheng Kong 4
1School of Art and Archaeology, Hangzhou City University, Hangzhou 310015, China; liuyi@hzcu.edu.cn
2School of Architecture, Harbin Institute of Technology, Harbin 150006, China
3Key Laboratory of National Territory Spatial Planning and Ecological Restoration in Cold Regions, Ministry
of Natural Resources, Harbin 150006, China
4Wuzhou Engineering Consulting Group Co., Ltd., Hangzhou 310052, China; kdz347104464@163.com
*Correspondence: chenlulu@hit.edu.cn (L.C.); muqi@hzcu.edu.cn (M.Q.)
Abstract:
The spatial equalization of medical facilities can alleviate the wastage of medical resources
and improve the efficiency of medical services. Therefore, it is necessary to carry out spatially
balanced planning and assessment of medical facilities in cities. Existing studies on the balanced
planning, design, and evaluation of medical facilities have been conducted from the perspective of
hospital buildings in terms of spatial utilization efficiency, service satisfaction, and their physical
environment on one hand, and from the perspective of regional planning of medical facilities in
terms of spatial accessibility to medical facilities and the suitability of medical facilities to the social
environment on the other hand. This study hopes to break down the boundaries of each perspective
and effectively integrate the architecture, planning, and social well-being of medical facilities, taking
spatial equilibrium as the core, in order to establish a spatial equilibrium system for medical facilities
and achieve a spatial equilibrium-based assessment of the current state of medical facilities. First, the
factors influencing the spatial equilibrium of hospital buildings with the support of the system and
environment of hospital buildings are determined. Second, the indicators of the spatial equilibrium
of hospital buildings are extracted through the consideration of influencing factors, and the indicator
weights are determined by discussing the degree to which they contribute to the influence of the
operation of hospital building spatial equilibrium systems, thus forming a system of equilibrium
indicators for hospital buildings. Finally, a spatial equilibrium evaluation model for hospital buildings
is established to assess the effects of equilibrium. The results obtained in this study provide insights
into the regional planning of medical facilities and the design of hospital buildings.
Keywords: medical facilities; equilibrium systems; assessment models; hospital design
1. Introduction
Medical facilities are a form of integration between public health and the planning of
urban public service facilities [
1
]. They play an important role in cities, in terms of public
safety, residents’ health, and social services [
2
,
3
]. The service efficiency and fairness of
public health spaces can be improved by enhancing the role of medical facilities’ spatial
equilibrium [
4
]. To establish the spatial equilibrium of medical facilities, it is important to
systematically assess the current spatial service condition of hospital buildings, establish
reasonable assumptions, and find key elements [
5
]. Overall, the existing literature on the
assessment of spatial balance in healthcare facilities involves three disciplines: architecture,
urban planning, and public health [6–8].
In the field of architecture, the balanced evaluation of hospital buildings revolves
around the rational use of space [
9
,
10
]. For example, Rose, SJ et al. (2022) have used the
Post-occupancy Evaluation method for evaluation of the use of nurses in an acute care
nursing unit before and after relocation to a new hospital. The survey questions included
work efficiency and productivity, the design of the patient room and support spaces,
Buildings 2024,14, 1265. https://doi.org/10.3390/buildings14051265 https://www.mdpi.com/journal/buildings
Buildings 2024,14, 1265 2 of 20
information systems, and environmental conditions [
8
]. Nimlyat, PS et al. (2018) have
studied indoor environmental quality (IEQ), methods of comparison through subjective or
objective assessment, and the internal environment of buildings in a way that was related
to health, comfort, and well-being [
11
]. Related studies also include the quality of hospital
premises and user satisfaction, the physical environment of hospitals, the efficiency of the
use of healthcare space, and assessment of the comfort of healthcare environments [
12
–
15
].
In the field of urban planning, studies on the spatial equilibrium of healthcare facilities
are more focused on exploring the accessibility and siting of facilities [
16
–
18
]. Hu, W et al.
(2018), taking the city of Shenzhen as an example, constructed a comprehensive assessment
method that included three dimensions: supply and demand, travel convenience, and
equilibrium [
19
]. Nakamura et al. (2017) used both distance measures and the enhanced
two-step floating catchment area (E2SFCA) method to compare the number of hospitals in
the neighborhood and the E2SFCA score with regard to the amount and equity of access to
hospitals [
20
]. Xing, LJ et al. (2024) attempted to explicitly define and classify urban health
resources considering active and passive health demands through building a conceptual
framework. An integrated approach framework, including the global collaborative location
quotient (GCLQ), the Gaussian 2SFCA method, and Gini coefficients, was constructed to
evaluate the proximity, complementarity, accessibility, and equity of multi-tiered health
resources in Guangzhou [21].
In public health, assessment of the equilibrium of healthcare facilities is focused on
multidimensional indicators and categories of elements, usually including one or more of
the social, economic, health, and environmental dimensions [
22
–
24
]. Social and economic
assessment studies include social health benefits, financial expenditures for the construc-
tion of healthcare facilities, service volume, and hospital class, while health and social
environment assessments include the number of facilities in the region, the surrounding
environment, land-uses, the health needs of the population, and the experience of accessing
healthcare [
25
–
27
]. Conley et al. (2023) have shown that hospitals can improve the quality
of healthcare and population health through addressing equity and the social and structural
determinants of health. They performed a cross-sectional analysis of non-profit hospital
community health needs assessments (CHNAs) and implementation strategies (ISs) from a
national sample of 474 non-profit hospitals in the U.S. [
28
]. Singh, SR et al. (2023) employed
a multivariate logistic regression model to examine the association between hospitals’ use
of equity as a guiding theme in the assessment of community health needs and binary
indicators of alignment for six common community health needs: access to care, chronic
illness, obesity, mental health, substance use, and social determinants of health. This study
explores the relationship between non-profit hospitals’ use of equity as a guiding theme in
the development of their community health needs assessments and the level of alignment
between the health needs identified in the community health needs assessment and those
addressed in a hospital’s implementation strategies [
29
]. JW et al. (2018) developed and
applied a measure to categorize and estimate the potential impact of a hospital’s community
health activities on population health and equity [30].
The studies have their own focus, and the general issues related to evaluation can
be summarized into the site evaluation of medical facilities in the field of urban planning,
the service efficiency of hospital buildings, and the evaluation of the healing capacity
of the environment, as well as the post-use evaluation of hospitals in the field of public
health, satisfaction with medical services, coverage, and assessment of the types of medical
functions [
5
,
9
,
31
,
32
]. However, although specialized assessment is considered an effective
way to update and develop the design of hospital buildings, it lacks relevance and therefore
is not effective in practical application. In addition, rational hospital building planning is
not a problem that can be solved independently at the level of urban planning, monolith
construction, or public health; it is a complex system from planning to monoliths to
internal structures.
Therefore, the main purpose of this study is to effectively integrate the architecture,
urban planning, and social well-being of healthcare facilities taking the spatial equilibrium
Buildings 2024,14, 1265 3 of 20
as a core concept, in order to establish an assessment system for the spatial equilibrium
of healthcare facilities and vertically sort out the planning and architectural dimensions
of healthcare facilities. This is expected to facilitate practical assessment of the spatial
equilibrium of healthcare facilities, including the regional basic conditions, the location of
hospitals, the environment of the hospital, the organization of the moving line, and the
functional setup. As a result, this study mainly discusses three questions: (1) What are the
elements that affect the spatial balance of hospital buildings? (2) How does one extract
effective evaluation indices of spatial balance for hospital buildings? And (3) How does
one systematically and flexibly assess the balanced effect of hospital buildings?
2. Methods and Data
2.1. Research Methods
This study regards the spatial equilibrium of hospital buildings as a system. Ad-
ditionally, under the guidance of system synergy theory, in order to extract the spatial
equilibrium indicators of hospital buildings, we first need to determine the factors af-
fecting the spatial equilibrium of hospital buildings and analyze and extract them with
the support of the system and the environment from the macro–medium–micro point of
view [
33
,
34
]. Second, considering the self-organization relationship of each subsystem
within the system—and with the help of the factors influencing the equilibrium within the
hospital building
space—we
further extract indicators affecting the spatial equilibrium of
the hospital building. Moreover, through discussing their synergistic and correlative roles
within the system, as well as the degree of their contribution to the impact of the system’s
operation, we determine the weights of the indicators reflecting the spatial equilibrium of
the hospital building. Finally, with reference to the subordination relationships between
the various sub-systems of the hospital building, we establish the index dimensions with
reference to these relationships and form the spatial equilibrium index system of hospital
buildings. On this basis, the spatial balance assessment model of hospital buildings is
formed by applying the method of multiple linear regression. This method effectively
combines the qualitative and quantitative spatial balance of hospital buildings, uses mathe-
matical models to address the fuzzy and subjective problems related to the spatial balance of
hospital buildings, and transforms the subjective evaluation of multiple factors in hospital
buildings into a scientific and effective evaluation model in order to enhance its operability
and applicability [35,36].
We summarize the basic process as follows (see Figure 1). First, we extract the factors
influencing the spatial equilibrium of hospital buildings. Then, we determine the indicators
of spatial equilibrium (focus group discussions and expert interviews were used to find
indicators, and the data processing method was SPSS- Statistical Product and Service
Solutions). Next, we establish the basic dimensions of the indicators, then form the index
system for the spatial balance of hospital buildings. Finally, using the method of multiple
linear regression, we form the spatial balance measurement model for hospital buildings.
Buildings 2024, 14, x FOR PEER REVIEW 4 of 20
Figure 1. Research framework.
• Focus group method
This is a qualitative research method which is commonly used in social science re-
search. It is conducted by a research-trained investigator, who talks to a group of respond-
ents using a semi-structured approach (i.e., one in which some of the interview questions
are pre-determined). In this study, first, the information that had been summarized in
advance and ready for discussion was made available to the group members through the
establishment of a focus group. The moderator stated that the main purpose of the discus-
sion was for the initial extraction of influencing factors from the two levels of spatial equi-
librium research in hospital buildings, and the members were asked to list the relevant
factors as exhaustively as possible.
• Exploratory factor analysis (EFA)
This is a commonly used method for data reduction and exploratory data analysis to
identify potential variable structures and measurement dimensions. In this study, the cor-
responding functional relationships in SPSS were used to perform exploratory factor anal-
ysis for the selection of indicators through the process of balanced indicator extraction.
The focus was on four indicators of the factor entries: eigenvalues of the factors, included
entries of the factors, maximum loadings and differences, and factor loadings of the di-
mension in which they are located.
• Multiple linear regression
Multiple linear regression modeling is a statistical method used to study the relation-
ship between a dependent variable and multiple independent variables. In this study, the
spatial balance measurement model of hospital buildings was constructed using multiple
linear regression. The spatial equilibrium index of hospital buildings was considered as
the independent variable, and linear regression was used to fit the spatial equilibrium of
hospital buildings. Equation (1) shows the form of the general expression of the multiple
linear regression model:
Y = β0 + β1X1 + β2X2 + …… + βi Xi + µ (1)
Y—predicted value of dependent variable;
Xi—independent variable input values;
i—number of explanatory variables, i = 1,2,…,n;
βi—regression coefficient;
β0—constant term;
µ—random error constant.
Figure 1. Research framework.
Buildings 2024,14, 1265 4 of 20
•Focus group method
This is a qualitative research method which is commonly used in social science research.
It is conducted by a research-trained investigator, who talks to a group of respondents using
a semi-structured approach (i.e., one in which some of the interview questions are pre-
determined). In this study, first, the information that had been summarized in advance and
ready for discussion was made available to the group members through the establishment
of a focus group. The moderator stated that the main purpose of the discussion was for the
initial extraction of influencing factors from the two levels of spatial equilibrium research in
hospital buildings, and the members were asked to list the relevant factors as exhaustively
as possible.
•Exploratory factor analysis (EFA)
This is a commonly used method for data reduction and exploratory data analysis
to identify potential variable structures and measurement dimensions. In this study,
the corresponding functional relationships in SPSS were used to perform exploratory
factor analysis for the selection of indicators through the process of balanced indicator
extraction. The focus was on four indicators of the factor entries: eigenvalues of the factors,
included entries of the factors, maximum loadings and differences, and factor loadings of
the dimension in which they are located.
•Multiple linear regression
Multiple linear regression modeling is a statistical method used to study the relation-
ship between a dependent variable and multiple independent variables. In this study, the
spatial balance measurement model of hospital buildings was constructed using multiple
linear regression. The spatial equilibrium index of hospital buildings was considered as
the independent variable, and linear regression was used to fit the spatial equilibrium of
hospital buildings. Equation (1) shows the form of the general expression of the multiple
linear regression model:
Y = β0 + β1X1 + β2X2+......+βiXi + µ(1)
Y—predicted value of dependent variable;
Xi—independent variable input values;
i—number of explanatory variables, i = 1, 2, . . ., n;
βi—regression coefficient;
β0—constant term;
µ—random error constant.
2.2. Screening of Factors Influencing Spatial Equilibrium in Hospital Buildings
2.2.1. Data Extractions
In the form of functional clusters, hospital monoliths, and regional planning suites,
variables impacting the spatial equilibrium of hospital buildings were screened.
•Functional cluster dimension
Users of hospital buildings are directly impacted by functional groups. Using a general
hospital as an example, the functional circle of a hospital building can include an outpatient
functional circle, an emergency functional circle, and an inpatient functional circle, based
on the demand for medical care by the public. The formation of functional clusters is based
on the delineation of the functional circle and consideration of the connection efficiency
between the functions and the frequency of use issues. The balanced factors influencing
functional groupings were extracted from the functional groupings of general hospitals
and primary healthcare institutions, which are drawn and arranged in Figure 2. (Qing
Han, Beijing Architecture University, drew a schematic diagram of the functional circle
and functional groups of the general hospital in a master’s thesis (MSc) and elaborated the
drawing method. Our research takes this as the basis for collating and drawing).
Buildings 2024,14, 1265 5 of 20
Buildings 2024, 14, x FOR PEER REVIEW 5 of 20
2.2. Screening of Factors Influencing Spatial Equilibrium in Hospital Buildings
2.2.1. Data Extractions
In the form of functional clusters, hospital monoliths, and regional planning suites,
variables impacting the spatial equilibrium of hospital buildings were screened.
• Functional cluster dimension
Users of hospital buildings are directly impacted by functional groups. Using a gen-
eral hospital as an example, the functional circle of a hospital building can include an
outpatient functional circle, an emergency functional circle, and an inpatient functional
circle, based on the demand for medical care by the public. The formation of functional
clusters is based on the delineation of the functional circle and consideration of the con-
nection efficiency between the functions and the frequency of use issues. The balanced
factors influencing functional groupings were extracted from the functional groupings of
general hospitals and primary healthcare institutions, which are drawn and arranged in
Figure 2. (Qing Han, Beijing Architecture University, drew a schematic diagram of the
functional circle and functional groups of the general hospital in a master’s thesis (MSc)
and elaborated the drawing method. Our research takes this as the basis for collating and
drawing.)
Figure 2. Functional group structure of hospital buildings at different levels.
Figure 2. Functional group structure of hospital buildings at different levels.
•Dimensionality of a single entity
The monolithic dimension—which is based on the functional grouping
dimension—first
treats the hospital district as a whole. It then determines the relationship between the
building monoliths at the sites of the inpatient department, outpatient building, infectious
disease building, and so on and extracts the factors that influence the spatial equilib-
rium of these monoliths through regulatory analysis of the floor area, functional location,
and site flow line, among others. Second, various hospital types with varying medical
levels—such
as general hospitals, specialized hospitals, and primary medical and health-
care institutions—are extracted as influencing factors, based on the medical level.
•Regional planning dimension
The hospital building’s accessibility, density, and other features are the primary focal
points of the characteristic refinement of the balanced influencing factors at the level of hos-
pital building planning. In the regional planning layout, the hospital building is regarded
as a rational and balanced form of spatial point coordinates. This is particularly evident in
the balanced relationship between the number of hospital buildings and the population.
Buildings 2024,14, 1265 6 of 20
2.2.2. Specific Operations
Focus group discussions and expert interviews were carried out for the extraction
of impact factors. First, the initial extraction of the influencing factors was carried out
based on the focus group methodology, through conducting a discussion on the aggregated
information from the three areas mentioned above. The focus group members included
5 hospital architects, 2 staff members from the urban public space planning department,
1 academic in the field of public architecture research, 4 students in urban and environ-
mental construction-related disciplines, 1 doctor, and 3 nurses, a total of 16 people, who
were randomly divided into 2 groups for the discussion. The meeting lasted 2 h and was
recorded by a note taker.
After the impact factors were determined twice and the impact factor data tables were
finally organized, the impact factors that were discussed in the focus groups were organized
as Excel tables and sent to the 5 experts via email and paper copies. The five experts first
needed to check the initial extraction of the influencing factors and discern the reliability of
the influencing factors. Secondly, the experts were to categorize the extracted influencing
factors and provide advice and guidance on the subsequent hierarchy of influencing factors.
In summary, the features of the factors influencing the spatial equilibrium of hospi-
tal buildings at each level were defined and refined in accordance with the hierarchical
division of influencing factors. The factors influencing the spatial equalization of hospital
buildings shown in Table 1were generated using the index screening of multidimensional
equalization influencing factors, based on the characteristics of the influencing factors.
Table 1. Factors affecting the spatial balance of hospital buildings.
Tier 1 Composition Tier 2 Composition Factors Influencing Spatial
Equilibrium
Regional planning
Quota
Number of hospitals in the region
Number of people in the region
Number of hospital beds
Site selection
Distance to medical care
Relevance among hospitals
Scope of Coverage
Hospital building levels
Environment
Parking spaces
Barrier-free design
Environmental healing capabilities
Kinetic organization Efficiency of rescue and treatment
Differentiation of streamlines
Function setting
Hospital level
Spatial resilience
Type of function
Spatial scale
2.3. Scale Creation for the Extraction of Indicators of Spatial Balance
To derive the specific indicators of spatial equilibrium, an indicator extraction scale
must be prepared. The indicators of hospital buildings’ spatial equilibrium can then be
obtained by scoring the indicators in the indicator scale and statistically analyzing the data.
2.3.1. Research Objective
The audience and the hospital environment are the primary foci of research on the
scale of the balance of the hospital building space. First, the waiting area, the diagnosis and
treatment area, the ward area, and other transportation spaces are the spatial objects of the
scale development and questionnaire research [
37
–
39
]. Second, as this research uses two
types of questionnaires—the expert questionnaire and the social questionnaire—the target
respondents include professionals involved in hospital planning and management services;
hospital users such as medical staff, patients, and their families; and administrators, as well
as those connected to the expert questionnaire.
Buildings 2024,14, 1265 7 of 20
2.3.2. Statistical Methods
Using the SPSS 24.0 software, entry analysis, exploratory factor analysis, correlation
analysis, reliability and validity analysis, and validation factor analysis were carried out on
the gathered data. The Amos 24.0 software was used for validation factor analysis. The
first test sample (n = 128) was used for entry analysis, exploratory factor analysis, factor
and total score correlation analysis, validation factor analysis, the validity scale validity
test, and the reliability test. The formal administration sample (n = 238) was used for
these analyses.
2.3.3. Calibration Tools
Cronbach’s alpha coefficient was utilized in conjunction with the discrimination and
correlation coefficients as calibration tools for item analysis in order to assess the reliability
of the items. The KMO measure and Bartlett’s spherical test were used in the exploratory
factor analysis of the items to identify the factors. A KMO value between 0.9 and 0.5 can
be used for the factor analysis: values greater than 0.9 are very suitable for the factor
analysis, while values below 0.5 are discarded. The higher the value, the higher the
factor’s contribution rate. These findings align with the reference value range used in
related studies.
2.3.4. Scale Creation
The single unit itself, medical function grouping, and hospital building regional
planning were all included in the extraction of spatial balance indicators. Numerous levels
and types of influencing factors are involved in each aspect. The indicators were compiled
from earlier research, and those that appeared four or more times on average were initially
kept. The role of spatial balance is also considered in the emergency medical service, along
with the actual research conducted in its early stages and the opinions gathered from focus
groups. Two indicators of reserved emergency land and emergency conversion space were
added and, in accordance with the state of the integration of network medical service at the
moment, an indicator of the network consulting service in the function setting was also
added. Two indicators—the hospital’s functional diversion situation and the length of time
it takes for an admission to a consultation in the flow organization—should be added in
accordance with the research characteristics of spatial equilibrium. Using the information
above as a guide, this paper first numbered the indicators with question items to represent
the spatial balance indicators of medical facilities, which are displayed in Figure 3.
2.4. Extraction of Equilibrium Indicators
2.4.1. Design of Questionnaires
The importance of the indicators was ranked one by one in the form of a list of
questions in an importance questionnaire. The questionnaire was divided into seven levels,
ranging from unimportant to very important, with values from 1 to 7. Simultaneously, a link
to enhance and expand the indicators by adding or changing them as desired was included.
2.4.2. Sample Features
In the first sample, 150 questionnaires were distributed. Of these, 132 were returned,
and 128 valid questionnaires were obtained through screening, yielding an 85.3% validity
rate. The screening criteria were as follows:
1
the entire questionnaire missed
≥
two entries;
2
the tendency to answer was constant; and
3
the answers showed regularity. There were
48 men and 80 women, with ages between 28 and 67 and an average age of 42
±
10 years
old, among the valid questionnaires.
The formal administration sample received 300 questionnaires in total. Of these,
254 were returned, and 238 valid questionnaires (or 79.3% of the total) were obtained
after screening. A total of 129 men and 109 women, aged 24–65 with an average age of
37 ±17 years, completed the valid questionnaires.
Buildings 2024,14, 1265 8 of 20
Buildings 2024, 14, x FOR PEER REVIEW 8 of 20
added in accordance with the research characteristics of spatial equilibrium. Using the
information above as a guide, this paper first numbered the indicators with question items to
represent the spatial balance indicators of medical facilities, which are displayed in Figure 3.
Figure 3. Initial selection of indicators.
2.4. Extraction of Equilibrium Indicators
2.4.1. Design of Questionnaires
The importance of the indicators was ranked one by one in the form of a list of ques-
tions in an importance questionnaire. The questionnaire was divided into seven levels,
ranging from unimportant to very important, with values from 1 to 7. Simultaneously, a
link to enhance and expand the indicators by adding or changing them as desired was
included.
2.4.2. Sample Features
In the first sample, 150 questionnaires were distributed. Of these, 132 were returned,
and 128 valid questionnaires were obtained through screening, yielding an 85.3% validity
rate. The screening criteria were as follows: ① the entire questionnaire missed ≥ two en-
tries; ② the tendency to answer was constant; and ③ the answers showed regularity.
Figure 3. Initial selection of indicators.
2.4.3. Analysis of Entries
The questionnaire scale questions are described in such a way that the following
conclusions were reached: in the topic-to-total-score correlation; the correlation coefficients
of T17, T18, T25, T26, T27, T28, T29, T30, T35, and T36 and the entry value were less than
three in the discriminant method analysis; and no statistical significance was found for
deletion in the high and low subgroups. For all T17s, T25s, T27s, T28s, T29s, T30s, T35s, and
T36s, the Cronbach Alpha after entry deletion was greater than or equal to 0.890, and the
correlation coefficient of the total score was less than 0.400. Therefore, it was recommended
that T17, T25, T27, T28, T29, T30, T35, and T36 be excluded, and the scale was adjusted to
28 entries (Table 2).
Buildings 2024,14, 1265 9 of 20
Table 2. Analysis of spatial equilibrium indicator entries for hospital buildings.
Serial
Number
Deterministic
Value pCorrelation
Coefficient
Cronbach’s
ARemark
T1 5.984 0.000 0.454 ** 0.887 reservation
T2 7.000 0.000 0.553 ** 0.885 reservation
T3 6.034 0.000 0.502 ** 0.886 reservation
T4 6.153 0.000 0.480 ** 0.886 reservation
T5 5.179 0.000 0.443 ** 0.887 reservation
T6 6.827 0.000 0.510 ** 0.886 reservation
T7 6.309 0.000 0.559 ** 0.885 reservation
T8 5.596 0.000 0.580 ** 0.884 reservation
T9 7.839 0.000 0.605 ** 0.884 reservation
T10 7.319 0.000 0.620 ** 0.884 reservation
T11 7.425 0.000 0.572 ** 0.885 reservation
T12 7.632 0.000 0.606 ** 0.884 reservation
T13 6.809 0.000 0.575 ** 0.884 reservation
T14 6.030 0.000 0.586 ** 0.884 reservation
T15 6.712 0.000 0.590 ** 0.884 reservation
T16 6.512 0.000 0.597 ** 0.884 reservation
T17 2.311 0.024 00.150 0.893 exclude
T18 3.767 0.000 0.343 ** 0.889 reservation
T19 5.025 0.000 0.486 ** 0.886 reservation
T20 5.727 0.000 0.539 ** 0.885 reservation
T21 5.319 0.000 0.499 ** 0.886 reservation
T22 3.641 0.001 0.408 ** 0.888 reservation
T23 5.276 0.000 0.514 ** 0.886 reservation
T24 5.717 0.000 0.542 ** 0.885 reservation
T25 2.663 0.010 0.334 ** 0.890 exclude
T26 3.618 0.001 0.386 ** 0.888 reservation
T27 3.434 0.001 0.252 ** 0.889 reservation
T28 2.057 0.044 0.195 * 0.891 exclude
T29 2.747 0.008 0.246 ** 0.890 exclude
T30 2.830 0.006 0.233 ** 0.891 exclude
T31 4.920 0.000 0.437 ** 0.887 reservation
T32 5.165 0.000 0.494 ** 0.886 reservation
T33 6.021 0.000 0.512 ** 0.886 reservation
T34 6.302 0.000 0.504 ** 0.886 reservation
T35 2.407 0.019 0.194 * 0.891 exclude
T36 2.854 0.006 0.261 ** 0.890 exclude
*p< 0.05, ** p< 0.01.
2.4.4. Exploratory Factor Analysis (EFA)
The factor entries in this study were determined using the following four criteria.
•The factor’s eigenvalue must be greater than 1.
•Each factor has at least three entries.
•
The item’s maximum loading value on both dimensions is greater than 0.4 and the
difference is less than 0.1.
•The factor loadings of the dimension where the item is located are lower than 0.4.
These factors suggest that the item does not have a high degree of differentiation.
The factor loadings of T18 and T26 were less than 0.4 in the exploratory factor analysis;
however, the KMO values prior to the exploratory factor analysis were all above 0.5, and
Bartlett’s spherical test values all reached the significance level.(Table 3) As a result, we
continued to analyze the 26 entries while excluding the question items T18 and T26.
Buildings 2024,14, 1265 10 of 20
Table 3. KMO values and Bartlett’s test for sphericity.
KMO Quantity of Sampling Suitability 0.890
Bartlett’s sphericity test
rough chi-square 2689.744
degrees of freedom 325
significance 0.000
Ultimately, the variables exhibited strong correlation, Bartlett’s spherical test rejected
the initial hypothesis, and the scale’s KMO statistic of 0.890 was appropriate for factor
analysis. The first four male factors that were extracted after a second ANOVA on the
scale’s question items revealed four male factors with eigenvalues larger than 1. With a
good degree of explanation, the four male factors accounted for 74.631% of the variance of
all the variables, according to their cumulative variance contribution rate of 74.631%. The
26 question items underwent factor rotation to obtain the matching factor loading tables.
In order to create a table of rotated factor loadings, factor rotation was applied to the
four extracted common factors. The results showed that T10, T15, T12, T8, T13, T16, T11,
T14, and T9 had high loadings on the first factor; T1, T3, T2, T5, T4, T6, and T7 had high
loadings on the second factor; T24, T19, T21, T20, T22, and T23 had high loadings on the
third factor; and T34, T31, T33, and T32 had high loadings on the fourth factor. These
results were consistent with the expected division of dimensions, indicating good validity.
This allowed for the determination of the hospital building’s spatial equilibrium index and
dimensions (Table 4).
Table 4. The rotated component matrix.
Serial Number
Ingredients
1 2 3 4
T10 0.873
T13 0.855
T12 0.854
T15 0.851
T8 0.847
T16 0.844
T11 0.826
T9 0.824
T14 0.804
T1 0.872
T3 0.868
T2 0.846
T5 0.833
T4 0.826
T6 0.820
T7 0.802
T19 0.865
T21 0.863
T24 0.860
T20 0.853
T22 0.851
T23 0.817
T34 0.894
T31 0.889
T33 0.870
T32 0.846
The indicators are crucial for the spatial balance of hospital buildings, according to
the results of the Cronbach’s a test for the overall scale and sub-dimension of the spatial
balance scale (Cronbach’s a = 0.885).
Buildings 2024,14, 1265 11 of 20
2.4.5. Validation of Indicators
•Structural validity
The structural validity of the spatial balance indices of hospital buildings was good,
and the validation factor analysis diagram is displayed in Figure 4. As indicated in Table 5,
the spatial balance index X
2
/DF of hospital buildings was lower than 3; the RMR and
RMSEA were lower than 0.08; and the CFI, TLI, IFI, and GFI were all greater than 0.9.
Buildings 2024, 14, x FOR PEER REVIEW 11 of 20
T16 0.844
T11 0.826
T9 0.824
T14 0.804
T1 0.872
T3 0.868
T2 0.846
T5 0.833
T4 0.826
T6 0.820
T7 0.802
T19 0.865
T21 0.863
T24 0.860
T20 0.853
T22 0.851
T23 0.817
T34 0.894
T31 0.889
T33 0.870
T32 0.846
The indicators are crucial for the spatial balance of hospital buildings, according to
the results of the Cronbach’s a test for the overall scale and sub-dimension of the spatial
balance scale (Cronbach’s a = 0.885).
2.4.5. Validation of Indicators
• Structural validity
The structural validity of the spatial balance indices of hospital buildings was good,
and the validation factor analysis diagram is displayed in Figure 4. As indicated in Table
5, the spatial balance index X²/DF of hospital buildings was lower than 3; the RMR and
RMSEA were lower than 0.08; and the CFI, TLI, IFI, and GFI were all greater than 0.9.
Figure 4. Confirmatory factor analysis diagram.
Figure 4. Confirmatory factor analysis diagram.
Table 5. Index validity analysis.
Fitness Index X2/df RMR RMSEA CFI TLI IFI GFI
Fitting values 1.161 0.048 0.024 0.990 0.989 0.990 0.918
•Convergent validity
Table 6reveals that the factor loadings corresponding to the topics of site selection,
functional settings, flow organization, and site environment were all greater than 0.5,
suggesting that the topics corresponding to each latent variable are highly representative.
The convergent validity analysis was performed on the spatial balance indices of hospital
buildings. Furthermore, the convergent validity was optimal, as each latent variable’s mean
variance (AVE) was greater than 0.5 and the combined reliability (CR) was greater than 0.7.
Table 6. Indices convergence analysis.
Dimension AVE CR
Site selection 0.656 0.93
Function setting 0.637 0.94
Kinetic organization 0.634 0.912
Environment 0.634 0.874
•Distinguishing validity
Table 7displays the results of the distinguishing validity analysis for the spatial
balance index of hospital buildings. It can be seen that the correlation coefficients for site
selection, functional settings, flow organization, and site environment were all less than the
square root of the corresponding AVE. This suggests some degree of differentiation and
Buildings 2024,14, 1265 12 of 20
correlation between the latent variables, meaning that the distinguishing validity of the
scale data is reasonable.
Table 7. Discriminant validity of indicators.
Site Selection Function
Setting
Kinetic
Organization Environment
Site selection 0.810
Function setting 0.147 0.798
Kinetic
organization 0.170 0.198 0.796
Environment 0.125 0.103 0.185 0.796
•Establishment of indicators
Consequently, the hospital building’s spatial balance indicator system was created,
and the weights of each indicator were determined based on the validation analysis of
the aforementioned indicators of spatial balance, in conjunction with the analysis of the
indicators of structural validity, convergence validity, and zoning validity. The specific
calculation formulas are as follows: first, the standardized path coefficients of each observed
variable measured in accordance with the model were weighted to obtain the indicator
weights of each observed variable.
There are a total of 26 index factors and 4 categories of influencing factors in the hospi-
tal buildings spatial balance evaluation index system. The four categories of influencing
factors were classified in accordance with the characterization of the spatial equilibrium
analysis of hospital buildings in the preceding section, with reference to the construction
specifications of the hospital buildings. Each indicator factor was then interpreted to
establish the evaluation standard. Table 8displays the index weights and interpretations.
Table 8. Weight of indicators.
Dimension Subject Weights Interpretation of Indicators
Site selection
Distance to hospital 0.139
The greater the distance, the lower the grade; ideally, general hospitals
should be reachable by car in thirty minutes, and primary healthcare
facilities should be reachable on foot in fifteen minutes.
Convenience of referrals 0.145
The ease of referral routes to upper (lower) level hospitals that facilitate
prompt referral is preferred; otherwise, the larger the gap, the lower the
rank. The availability of ambulance vehicles in the hospital area is
also preferred.
Coverage 0.143
In other words, the larger the gap, the lower the grade. General hospitals
are able to efficiently connect medical services with the primary healthcare
institutions under their control, and the primary healthcare institutions are
able to meet all community health services with superior focus, covering
more than three kilometers.
Surrounding
transportation
convenience
0.142
The wider the gap, the lower the grade; alternatively, convenient public
transportation in the area and a direct rail connection to the compound
are preferred.
Building site reserved
for future development 0.147
This considers whether any land in the hospital’s vicinity has been set
aside for development and construction, and how the grade is determined
based on that area while also considering the hospital’s present state.
Reserve land for
emergencies 0.147
If a site is successfully integrated with the infectious disease area and the
hospital area is designated for emergency medical care, it receives a higher
grade; if not, it receives a lower grade.
Site area 0.137
The larger the gap, the lower the grade; therefore, it is better to have a
hospital that serves medical needs without placing undue strain on the
surrounding land and environment.
Buildings 2024,14, 1265 13 of 20
Table 8. Cont.
Dimension Subject Weights Interpretation of Indicators
Function
setting
Hospital level 0.113
Level 3A hospitals are preferred (and so on, in descending order),
based on the hospital’s rating.
Emergency transition space 0.111
The hospital has areas such as wards, labs, operating rooms, and
so on that can be temporarily converted in an emergency. The
more areas that can be used for this purpose, the higher the grade.
Daily clinic volume 0.110 Preferably, general hospitals should have more than
1000 outpatient visits per day.
Function type 0.113
All three of the aforementioned aspects are deemed to be
excellent, failing which they will be eliminated one-by-one.
Functional rooms are fully furnished and have room set aside for
emergencies, and the functional settings satisfy hospital-level
functional requirements.
Functional partition 0.112
Functional zoning is clearly established, along with the hospital
area’s superior efficiency of medical services, as opposed to the
principle that the wider the gap, the lower the grade.
Orientation of wards and
consultation rooms 0.110
It is ideal if 80% of the wards and consultation rooms face south;
if not, the impact on medical operations and the amount of light
in the wards and consultation rooms are determining factors.
Web-based
consultation services 0.113
The higher the rank, the more online medical service items (e.g.,
online booking, online billing, online consultation, hospital ward
registration, and so on) that are offered.
Corridor dimensions 0.108
To meet the superior standard, waiting areas should
accommodate patient needs for consultation and treatment, as
well as wheelchair mobility. The larger the gap, the lower
the rating.
Functional room scales 0.109 Based on meeting the regulated area, the higher the grade, the
more effectively it is used.
Kinetic
organization
Functional streaming 0.160
The hospital area features distinct areas for outpatient care,
emergency care, medical examinations, maternity and child
healthcare, wards, infectious disease control, and other functions
of the diversion line or entrance. The more gaps there are in the
function of the exhaustive flow line, the worse the grade will be.
Time from admission
to consultation 0.160
The sooner a patient is seen, the better; general hospitals want to
see them within 30 min of admission and primary care
organizations within 10. The larger the gap, the lower the grade.
Pedestrian–vehicle separation 0.171
It is preferable to have distinct pedestrian and vehicle flow lines
with distinct lanes for each type of traffic; otherwise, the grade
will be lower when the gap is larger.
Patient–doctor triage 0.172
There is never a perfect point where patient flow and healthcare
workers meet; there are some places where staff-only access is
advantageous, and vice versa.
Clean-sewage diversion 0.168
Using smart rail logistics vehicles may vary, depending on the
situation. No crossing of personnel and dirt flow lines is excellent;
some crossing of personnel and dirt flow lines is good, and
vice versa.
Ease of access to the
medical process 0.170
According to the medical treatment flow, everything goes
smoothly, every area is understood, and less folding is
preferred—the larger the gap, the worse the grade.
Buildings 2024,14, 1265 14 of 20
Table 8. Cont.
Dimension Subject Weights Interpretation of Indicators
environment
Aboveground parking setup 0.248
Temporary parking spaces, parking spaces for emergency
vehicles, staff parking spaces, patient parking spaces, parking
spaces for logistics vehicles, and temporary parking spaces (or set
up in the underground), along with the hospital area of vehicles
parked in an orderly manner, are required for the organization of
the best outcome, or else decreasing one by one.
Accessible design 0.241
Facilities free of barriers, such as elevators, accessible restrooms,
barrier-free ramps, and barrier-free handrails, are arranged well
or excellently; the more gaps, the worse the grade.
Healing space setting 0.254 According to real senses, each room should have a comfortable
and balanced temperature; if not, the rating will be lower.
Number of entrances and exits
0.257
To meet the preferred standard, general hospitals should have
three entrances and exits; otherwise, the larger the gap, the lower
the grade.
3. Results and Discussion
3.1. Spatial Equilibrium Measurement Model Construction for Hospital Buildings
Using the multiple linear regression method, the spatial balance assessment model
for hospital buildings at the single-unit level was formed based on the established spatial
balance index system for hospital buildings.
3.1.1. Variable Selection
First, through the establishment of the spatial equilibrium index system for hospital
buildings, the independent variables of the spatial equilibrium model of hospital buildings
were set as follows: site selection; function setting, flow organization, and site environment.
Second, the Pearson correlation coefficient test revealed a significant positive corre-
lation (p< 0.05) between site selection, functional settings, flow organization, site envi-
ronment, and the hospital building’s spatial balance, further supporting the relationship
between the independent variables and spatial balance. It is evident that the indicator
system can be established and that the relationships between the study variables have been
validated (Table 9).
Table 9. Test of correlation.
Site Selection Function Setting Kinetic
Organization Environment Spatial Equilibrium of
Hospital Buildings
Site selection 1
Function setting 0.140 * 1
Kinetic organization 0.160 ** 0.186 ** 1
Environment 0.115 0.094 0.165 ** 1
Spatial equilibrium of
hospital buildings 0.473 ** 0.366 ** 0.393 ** 0.400 ** 1
*p< 0.05, ** p< 0.01.
3.1.2. Model Construction
First, the spatial equilibrium of hospital buildings was set as the dependent variable (Y),
and the important relevant variables—site selection, functional settings, flow organization,
and site environment—were taken as independent variables (X). Equation (2) illustrates
Buildings 2024,14, 1265 15 of 20
how the relevant variables were substituted into Equation (1) to obtain the assumed
regression model:
Spatial equilibrium of hospital buildings (Y) = β0 + β1×site selection (X1) +
β2×Function Setting (X2) + β3×kinetic organization (X3) + β4×
environment (X4) + µ
(2)
Second, using the least squares method with the assumption that the sum of squares
of the errors is minimized, the regression coefficients of the multivariate regression model
were estimated. The model fit of the regression coefficients was solved using the linear
regression function of the statistical program SPSS 24.0 in order to construct the model
(the confidence interval for parameter setting was set to 95%, or p< 0.05). The linear
relationship between the explanatory variables and the established explanatory variables
was significant, as indicated by the regression equation statistic F value of 63.520 and the
p-value of 0.000 (see Table 10). On this basis, we concluded that a linear regression model
could be established for the spatial equilibrium model of hospital buildings.
Table 10. One-way ANOVA.
Square Sum Degrees of
Freedom Equalize the Square F Significance
Regression 143.831 4 35.958
63.520
0.000
Residuals 159.071 281 0.566
Total 302.901 285
Third, the coefficients of all of the dependent variables in this linear regression model
were determined and tested for significance. Table 11 displays the findings. The constant
term
β
0 and the regression coefficients
β
i in Equation (2) must be solved to obtain the
unstandardized coefficient B. Consequently, Equation (3) illustrates the regression equation
of the spatial equilibrium of hospital buildings.
Table 11. Coefficient test of regression equation.
Unstandardized Coefficients Standardized
Coefficient tSignificance
Covariance Statistics
B Standard Errors Beta Tolerances VIF
(Constant) −1.450 0.372 −3.900 0.000
site selection 0.385 0.046 0.367 8.300 0.000 0.955 1.047
Function
setting 0.265 0.049 0.242 5.451 0.000 0.950 1.053
Kinetic
organization 0.255 0.048 0.241 5.363 0.000 0.929 1.076
Environment 0.286 0.043 0.295 6.698 0.000 0.962 1.040
The multiple linear regression analysis indicated that the spatial equilibrium of hospi-
tal buildings is significantly influenced positively by site selection, functional settings, flow
organization, and site environment. As a result, the following model was derived:
y = −1.450 + 0.385×Site selection + 0.265 ×Function Setting + 0.255 ×Kinetic
organization + 0.286 ×Environment (3)
Fourth, the residuals of the regression model were analyzed. The residual of the
model is the difference between the predicted value obtained after solving and the actual
value, which represents the error of model fitting. As shown in Figure 5a, the model’s
residuals basically conform to the normal distribution. The relationship between the
measured cumulative probability and the predicted cumulative probability is depicted
Buildings 2024,14, 1265 16 of 20
in the residual normal P-P plot in Figure 5b. As the residual distribution is essentially
distributed along the graph’s diagonal, the residual distribution’s normality is confirmed.
The residuals were dispersed, rather than clustered, in the scatter plot of the residuals
displayed in Figure 5c. Each of these outcomes demonstrates the model’s validity and the
reasonableness of the residuals.
Buildings 2024, 14, x FOR PEER REVIEW 16 of 20
Table 11. Coefficient test of regression equation.
Unstandardized Coefficients Standardized Coefficient t Significance Covariance Statistics
B Standard Errors Beta Tolerances VIF
(Constant) −1.450 0.372 −3.900 0.000
site selection 0.385 0.046 0.367 8.300 0.000 0.955 1.047
Function seing 0.265 0.049 0.242 5.451 0.000 0.950 1.053
Kinetic organi-
zation 0.255 0.048 0.241 5.363 0.000 0.929 1.076
Environment 0.286 0.043 0.295 6.698 0.000 0.962 1.040
The multiple linear regression analysis indicated that the spatial equilibrium of hos-
pital buildings is significantly influenced positively by site selection, functional seings,
flow organization, and site environment. As a result, the following model was derived:
y = −1.450 + 0.385× Site selection + 0.265 × Function Seing + 0.255 × Kinetic
organization + 0.286 × Environment (3)
Fourth, the residuals of the regression model were analyzed. The residual of the
model is the difference between the predicted value obtained after solving and the actual
value, which represents the error of model fiing. As shown in Figure 5a, the model’s
residuals basically conform to the normal distribution. The relationship between the
measured cumulative probability and the predicted cumulative probability is depicted in
the residual normal P-P plot in Figure 5b. As the residual distribution is essentially dis-
tributed along the graph’s diagonal, the residual distribution’s normality is confirmed.
The residuals were dispersed, rather than clustered, in the scaer plot of the residuals
displayed in Figure 5c. Each of these outcomes demonstrates the model’s validity and the
reasonableness of the residuals.
(a) Residual histogram (b) Residual normal P-P plot (c) Residual scaer plot
Figure 5. Confirmatory factor analysis diagrams.
Fifth, the goodness-of-fit test results are displayed in Table 12. According to the table,
the explanatory variables in the model have an explanatory strength for the explanatory
variables, to a certain extent, with explanatory strength reaching 47.5%. The R-square
value was 0.475, and the adjusted R-square was 0.467.
Table 12. Multiple linear regression fit test.
R R-Square Adjusted R-Square Errors in Standardized Estimates
0.689 0.475 0.467 0.752
Figure 5. Confirmatory factor analysis diagrams.
Fifth, the goodness-of-fit test results are displayed in Table 12. According to the table,
the explanatory variables in the model have an explanatory strength for the explanatory
variables, to a certain extent, with explanatory strength reaching 47.5%. The R-square value
was 0.475, and the adjusted R-square was 0.467.
Table 12. Multiple linear regression fit test.
R R-Square Adjusted R-Square Errors in Standardized Estimates
0.689 0.475 0.467 0.752
3.2. Spatial Balance Evaluation Rating for Hospital Buildings
In this paper, an assessment system for hospital building spatial equilibrium was es-
tablished, the factors that influence a hospital building’s spatial equilibrium were extracted,
and an index system was created. A better fit was also established for the purpose of
predicting hospital building spatial equilibrium. A multivariate linear regression model
was then constructed, and its validity was confirmed. On this basis, the hospital building
spatial equilibrium prediction score was divided into five levels. Table 13 displays the
degree to which each level (i.e., A, B, C, D, and E) is represented.
Table 13. Evaluation grade of spatial equilibrium of hospital building and its explanation.
Level Level Description Point Value
A
Excellent: the hospital building units that have been evaluated effectively
fulfill the requirement for spatial balance with respect to site environment,
flow organization, functional settings, and so on.
117–90
B
Better: the assessed hospital building units, taking into account site selection,
functional settings, flow organization, site environment, and so on, satisfy the
requirement for spatial balance.
89–80
C
Medium: in terms of site selection, functional settings, flow organization, site
environment, and so on, the evaluated hospital building units essentially
satisfy the requirement for spatial balance.
79–70
Buildings 2024,14, 1265 17 of 20
Table 13. Cont.
Level Level Description Point Value
D
General: in terms of siting, functional settings, flow organization, site
environment, and so on, the spatial balance of the assessed hospital building
units is average.
69–60
E
Mediocre: in terms of site selection, functional settings, flow organization, site
environment, and so on, the spatial balance of the assessed hospital building
units is unsatisfactory and urgently needs to be improved.
59–0
3.3. Comparison of Research Results
The spatial equilibrium assessment model of medical facilities shows that the spatial
equilibrium of medical facilities is influenced by the location of the medical facilities,
the environment of the hospital, the functional settings, and the organization of the flow
lines, with the degree of influence decreasing in order. The reason for this is that the
functional settings and flow organization are basically the same for hospitals of the same
level under the hierarchical system of Chinese hospitals, so the differentiation is not
obvious. On the contrary, there is relative flexibility for the environment and location of
hospitals. Therefore, to improve the balance of medical facilities, it is necessary to improve
the rationality of the functional configuration and flow organization of hospitals at the
national macro-control level, and at the meso-level of medical facilities, it is more important
to focus on the site selection in the pre-construction stage, as well as the design of the
hospital’s environment.
At the same time, the spatial balance influence factor of medical facilities is more
targeted than the factors obtained from related studies in various disciplines in previous
studies, which can be regarded as a systematic organization and secondary refinement of
the factors from related studies in individual fields, with more emphasis on the correlation
between the factors [
40
–
42
]. For example, in previous studies, the factors extracted from
the architectural point of view would include the scale of functional rooms, emergency
space conversion, etc., but would not be related to the distance to medical care and the
accessibility of the surrounding traffic; in fact, they are related from the point of view of
the accessibility and fairness of medical care. Therefore, this study achieves a systematic
approach to spatial equilibrium and produces an assessment system and evaluation model
with more practical value than previous independent studies in separate subject areas.
However, due to the consideration of the size of the assessment system, the number of
factors influencing the assessment of spatial equilibrium is small compared to the number
of factors obtained from the previous studies in each subject area. This is also a problem
of the depth and breadth of the study, which needs to be further explored and refined in
subsequent studies.
4. Conclusions
The spatial equilibrium of medical facilities reflects the fairness of social public services.
In this paper, the spatial equilibrium of medical facilities was first extracted, and the factors
influencing the spatial equilibrium of medical facilities were obtained. After that, an index
scale for the spatial equilibrium of medical facilities was formulated according to the
influencing factors, which was tested and examined to extract the spatial equilibrium index
of medical facilities. The indices were further verified and weighted to establish a spatial
equilibrium index system for medical facilities. The spatial equilibrium assessment model
for medical facilities was established with multiple linear regression, based on the spatial
equilibrium index system for medical facilities.
The study of spatial equilibrium of medical facilities is an interdisciplinary, multilevel,
complex, and lengthy research work. In subsequent research, the first step is to evaluate
exemplary hospitals using the evaluation model established in this study, conduct an
empirical study of the evaluation model, and make adjustments and additions to the model
Buildings 2024,14, 1265 18 of 20
accordingly. This empirical study has already been carried out and will be described in our
next paper. Second, as the sampling data used in this study mostly originated from the
same region, data from other regions can be sampled in the next step of the study in order
to consider geographical characteristics and conditions, test the universality, and further
improve the accuracy of the model. It is hoped that further research by more scholars will
improve the study of the spatial balance of medical facilities in the future, thus enhancing
the fairness and balance of China’s public health services from the perspective of the health
environment and public space.
Author Contributions:
Conceptualization, Y.L. and L.C.; Methodology, Y.L. and L.C.; Software,
Y.L.; Validation, L.C. and M.Q.; Formal analysis, Y.L. and M.Q.; Investigation, Y.L., L.C., M.Q. and
D.K.; Resources, L.C. and D.K.; Data curation, Y.L. and D.K.; Writing—original draft, L.C. and
D.K.; Writing—review and editing, M.Q.; Visualization, D.K.; Supervision, L.C. and M.Q.; Project
administration, Y.L.; Funding acquisition, Y.L. All authors have read and agreed to the published
version of the manuscript.
Funding:
This research was funded by [Scientific Research Project of Zhejiang Provincial Department
of Education] grant number [Y202351407].
Data Availability Statement:
The original contributions presented in the study are included in the
article, further inquiries can be directed to the corresponding author.
Conflicts of Interest:
Author Dezheng Kong was employed by the company Wuzhou Engineering
Consulting Group Co., Ltd. The remaining authors declare that the research was conducted in the
absence of any commercial or financial relationships that could be construed as a potential conflict
of interest.
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