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Int. J. Environ. Res. Public Health 2021, 18, 9444. https://doi.org/10.3390/ijerph18189444 www.mdpi.com/journal/ijerph
Article
Bibliometric Analysis and Research Trend Forecast of Healthy
Urban Planning for 40 Years (1981–2020)
Bingyao Jia, Yuting Chen and Jing Wu *
School of Urban Design, Wuhan University, Wuhan 430072, China; 2017301530047@whu.edu.cn (B.J.);
2018302091004@whu.edu.cn (Y.C.)
* Correspondence: jing.wu@whu.edu.cn
Abstract: The history of healthy city planning can be traced back to the beginning of the 19th century.
Since the industrialization period, the harsh living conditions of cities and the outbreak of infectious
diseases have promoted the coordinated development of urban planning and public health, and
people have gradually realized the importance of urban design and planning to the health of resi-
dents. After searching keywords related to health city and urban planning, and excluding repeated,
non-English, and unrelated papers, this work retrieved 2582 documents as the basic data (timespan
is 1 January 1981–31 December 2020, retrieval time is 28 January 2021). Additionally, CiteSpace was
used to analyze document co-citation, cooperation network, and topic co-occurrence. Subsequently,
random forest algorithm was used to predict the probability of citation. Overall, this work found
that the hot spots of healthy urban planning are physical activity, green space, urban green space,
and mental health. It also shows the diversification of themes and the development trend of cross-
fields in the field of healthy urban planning. In addition, the article found that two factors, namely,
the average number of citations of the first author and whether the article belongs to the field of
environmental research, have a great impact on the number of citations of the article. This work is
of practical significance to relevant practitioners and researchers, because it provides guidance for
hot topics and future research directions in the field of healthy urban planning.
Keywords: healthy urban planning; spatial planning; bibliometric analysis; CiteSpace;
random forest
1. Introduction
The squalid living conditions of industrialized cities and communicable disease out-
breaks in the 19th century gave rise to both the urban planning and public health profes-
sions [1–3], which emphasizes that urban planners, decision makers, and health officials
have the responsibility to solve the increasingly serious urban health problems [4–12].
Additionally, at the beginning of the 19th century, Canada’s public health committee
stated that good urban planning is essential to the preservation of the environment and
people’s health [13].
In the 1980s, the World Health Organization (WHO) officially proposed the healthy
city project in Europe in 1986 and took five years as a phase to promote the development
of healthy cities [14,15]. This project mainly paid attention to health education [16], health
equality [17,18], community health [19], health assessment [20], health-related policies
[21,22], and project diplomacy [23,24]. Additionally, in terms of urban planning, the initial
goals of the project were the introduction of new systems and methods for the construc-
tion of healthy cities, and the promotion of various departments fully participating in
healthy urban planning.
In the 1990s, with the broad spread of health city projects, more countries formed a
cooperative network and integrated healthy city planning into their national policies.
Citation: Jia, B.; Chen, Y.; Wu, J.
Bibliometric Analysis and Research
Trend Forecast of Healthy Urban
Planning for 40 Years (1981–2020).
Int. J. Environ. Res. Public Health 2021,
18, 9444. https://doi.org/10.3390/
ijerph18189444
Academic Editor: Francesco Aletta
Received: 8 August 2021
Accepted: 1 September 2021
Published: 7 September 2021
Publisher’s Note: MDPI stays neu-
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Copyright: © 2021 by the authors. Li-
censee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and con-
ditions of the Creative Commons At-
tribution (CC BY) license (http://crea-
tivecommons.org/licenses/by/4.0/).
Int. J. Environ. Res. Public Health 2021, 18, 9444 2 of 27
Germany further strengthened their health city network at the annual general meeting
(AGM) in Greifswald, and emphasized the importance of health impact assessment and
urban planning [25]. Korea undertook the enactment of the National Health Promotion
Act in 1995, and emphasized in 1998 that at the city level, healthy cities are handled either
by the planning department and health department or the planning department and pub-
lic health center [26].
At the beginning of the 21st century, concepts such as City Health Development Plan-
ning (CHDP) [27] and Healthy Urban Planning (HUP) [28], have been proposed succes-
sively by the WHO, which emphasized that compared with health service planning, it is
more meaningful to deal with urban spatial planning based on the new dynamics of urban
management and the principle of the integration of health and urban planning. The con-
cept of healthy urban planning (HUP) was introduced in 2003 by the WHO in The Fourth
Phase of the WHO European Healthy Cities Network (2003–2007): Goals and Require-
ments, which refers to the encouragement and support of city planners to take health into
planning strategies and initiative consideration and emphasize equity, well-being, sus-
tainable development, and community safety [15,29]. Additionally, more countries placed
urban planning in an important position when formulating their national health policy
and laws. Brazil integrated urban planning strategies into The National Health Promotion
Policy (PNPS) for healthy city development from 2006 to 2016 [30]. France launched
French Act No. 2009-879, French Act No. 2010-788, and French Order No. 2011-210, which
promoted its health agency ARS to position itself as a key participant in healthy urban
planning [31]. In the past ten years, Sydney, Australia explored systematically how the
concept of health as an urban planning issue infiltrated institutional norms of urban stra-
tegic planning policy, and realized A Plan for Growing Sydney (APGS) in 2014 [32]. In
2016, China released the Healthy China 2030 plan and emphasized that cities should inte-
grate health into urban planning and design as a first step towards the integration of
health into all policies. [33,34]
In terms of research, the themes of healthy city planning are widely distributed,
which relates to impact of urban morphology [35–37], ecological planning (e.g., urban heat
island) [38–47], and landscape re-source equity [48–54] on urban residents’ physical and
mental health, public health [55,56], and the cross application of urban planning, the per-
ception and evaluation of the urban environment [57–62] as well as the space design
[63,64] that is based on human behavior. To have a more comprehensive understanding
of the research status and development trends in the field of healthy urban planning,
grasping the research hotspots and future trends in the numerous literatures is crucial.
Hence, the bibliometric and holistic analyses of the macroscopic and the progress of
healthy urban planning in the past 40 years are needed. This work will use CiteSpace and
the random forest algorithm to show the overall status and future trends of healthy urban
planning research from different perspectives, providing researchers with theoretical fo-
cus, research frontiers, and a valuable reference in the field of healthy urban planning.
The rest of this paper is organized as follows: In Section 2, we introduce the research
design and approach, data collection, and then data analysis. Section 3 illustrates the re-
sults in detail, including the CiteSpace part and the random forest part. Section 4 summa-
rizes the whole paper and significant results are discussed in this section. Section 5 con-
cludes this work and plans for future research.
2. Data and Methodology
Bibliometric analysis is used for the quantitative analysis of books, articles, or other
publications [65,66] and has been applied in various professional fields in recent years to
visualize the status, characteristics, evolution, and development trend of knowledge
[67,68]. With information development and technology improvement, many visualization
tools have emerged in recent years, such as VOSviewer, BibExcel, Sci2, Gephi, and
CiteSpace [69–71]. These tools can integrate information in the field. However, in the anal-
ysis software CiteSpace [72], users can directly use the data downloaded from the Web of
Int. J. Environ. Res. Public Health 2021, 18, 9444 3 of 27
Science (WOS) database to set time slices to extract information. Additionally, in compar-
ison with VOSviewer and SCi2, CiteSpace can provide further analyze and complete il-
lustrations, including network betweenness centrality [69]. The co-occurrence network
can represent time, frequency, and centrality simultaneously, and the cluster view can use
the cluster set extracted from the title, keywords, or abstract [73].
This work will use CiteSpace to conduct a bibliometric analysis on the field of healthy
urban planning. To have a deeper understanding of future research trends in this field,
this work will also use the random forest algorithm to explore the influencing factors of
literature citations using the characteristics of the article (e.g., journal impact factor, author
related factors, whether it is a single author, keywords’ density, key field coverage, first
author’s country), and key research fields are used to predict hotspots and trends in the
field of healthy urban planning.
In summary, this work combines two different methods to jointly analyze the current
and future research focus of healthy urban planning. As for the research design, we pre-
sented an outline of the research process in Figure 1, which shows the following four steps
of our workflow: data acquisition, data processing, results, and further discussion.
Figure 1. The outline of research design.
Int. J. Environ. Res. Public Health 2021, 18, 9444 4 of 27
2.1. Data Acquisition and Processing
In step one for data acquisition, this work uses the core set of the WOS as the data
source. Considering that our research focuses on healthy urban planning, which is the
combination of health city and urban planning, we searched for papers of which the re-
search topics, titles, or keywords contain “healthy city” (including synonyms) and the
WOS categories that are highly related to urban planning (urban studies, regional urban
planning, architecture, and area studies). The specific search formula is TS = (health city
OR health cities OR health urban) OR TI = (health city OR health cities OR health urban)
OR AK = (health city OR health cities OR health urban) AND WC = (“urban studies” OR
“regional urban planning” OR “architecture” OR “area studies”). The time span is 1 Jan-
uary 1981–31 December 2020, and the retrieval time is 28 January 2021.
In step two for data processing, we firstly sorted out the papers of which the docu-
ment type was articles (including reviews) and excluded non-English papers. Secondly,
we removed the repeated articles. Finally, we manually excluded papers that were unre-
lated to the theme of healthy urban planning; finally, 2582 articles were obtained for
CiteSpace analysis. The workflow above followed the criteria of Database Search [74],
which is the most used study identification method. Many other bibliometric analyses also
follow this strategy [75–77]. As for the review articles searched out that related to healthy
urban planning, we found that many of them are periodical summaries of the WHO
healthy city project [15,28,29,78], some studied city environmental factors and urban
health, including factors such as air pollution [79,80], transportation system [81], climate
change[82,83], urban green space[84], and indicator system[85,86], and some summarized
the research and practice of healthy city planning in different regions or countries such as
China[87], America[88], and Brazil[17]. These reviews also help us know more about our
research topic.
Then, the random forest algorithm analysis of this work uses the literature data of
2582 articles obtained in the WOS as the basis. The initial data from the WOS for each
article included information of the Web of Science Core Collection field tags, such as AU
(author), TI (title), etc. We selected terms that could be used in random forest part, reor-
ganized them into 22 forecast influence factors, and divided them into the following three
types: publication, author, and document. After calculation and statistics, the influencing
factors such as the categories, names, sources, and statistical methods of all the influencing
factors, were listed in Table 1. Then, the data were cleaned further. Articles that had anon-
ymous authors (18), or the ones for which the number of citations was 0 (406), along with
the ones for which the diversity of research directions was 0 (186), and those whose jour-
nal impact factor was 0 (152) were excluded. Finally, 1820 articles were selected for the
follow-up predictions.
Table 1. Table of influencing factors.
Category
Name
Field Name in
Random
Forest
Analysis
Name in
Metadata
from WOS
S
ources of Influencing Factors and
Statistical Methods
Publicatio
n-
related
factors
Total cited
impact factor Impact_Factor
SO
Data come from the core index of
JCR
®
, and the experimental data are
the latest publication index data. If
the publication is not included in
JCR, then its value is set to 0
because it lacks influence.
Author-
related
factors
The number of
times the first Avg_Ci AF, Z9
Statistics appear in the data set, the
first author of all papers published,
and the c
itation frequency of these
Int. J. Environ. Res. Public Health 2021, 18, 9444 5 of 27
author’s articles
have been cited
papers are added, then it is divided
by the total number of papers to
determine the number of times the
first author’s articles have been
cited.
Nationality of
the first author
(Select the 5
countries with
the largest
number of
documents)
Name of
Countries C1 Metadata from papers in Web of
Science
Single author Single_Author
AF Metadata from papers in Web of
Science
Article-
related
influencin
g factors
WOS
classification of
papers (Select
the 10
categories with
the largest
number of
documents)
Names of
Categories WC Metadata from papers in Web of
Science
The number of
pages PG PG Metadata from papers in Web of
Science
Number of
paper
references
NR NR Metadata from papers in Web of
Science
The number of
times the paper
is cited
Z9 Z9 Metadata from papers in Web of
Science
Paper keyword
density KW_dense DE
Statistics in the data set, the total
number of top ten keywords, which
are defined as key keywords, the
number of key keywords contained
in each article is defined as the
keyword density of the paper
Diversity of
paper research
direction
WC_differ WC
Statistics of the top 10 WOS
categories in the data set are
defined as key research directions.
The number of key research
directions in each
article is defined
as the diversity of paper research
directions.
2.2. Research Methods
2.2.1. Bibliometric Method Based on CiteSpace
In this work, we used CiteSpace to conduct bibliometric analysis in the field of
healthy urban planning from the aspects of cooperation network at the national, institu-
tional, and individual scales; co-citation analysis of highly cited authors, journals and ref-
erences; and theme analysis such as keywords co-occurrence.
Int. J. Environ. Res. Public Health 2021, 18, 9444 6 of 27
(1) Analysis process
This research used CiteSpace 5.7.R3 (64-bit)-(c)2003-2021 Chaomei Chen to analyze
the co-citation status, cooperation status, and sudden changes (burst detection of words
or items through time) of keywords to determine the internal structure of the field,
knowledge database of different periods of time, research hotspots, and research fron-
tiers[89–93]. After obtaining the basic information of the literature (e.g., author, title, ab-
stract, keywords, citations, publication journal, organization source, publication year, and
publication number) from the WOS database, the data are imported into CiteSpace for
deduplication processing and then proceed to the analysis process.
(2) Analysis type
a. Cooperation Network Analysis
Research on the country and institutional cooperation network is conducive to ex-
ploring the spatial and geographic distribution of published articles. Additionally, the au-
thor of the study has a key role in reflecting the research ability and evaluating the devel-
opment of the academic field.
b. Co-citation Analysis
We must pay attention to the citation frequency of articles to identify the core authors
and journals in a certain field. Moreover, from the topic distribution of the reference liter-
ature, we can see the knowledge base of the healthy urban planning field and provide
reference values for subsequent research.
c. Thematic Co-occurrence Analysis
The co-occurrence of keywords can reflect the research hotspots of the subject
field and provide auxiliary support for scientific research effectively.
Additionally, the
keyword explosive detection map is to detect keywords with a rapid increase in frequency
in the short term, which reflects the occurrence of hot spots more directly.
2.2.2. Random Forest Algorithm
Referring to previous studies [94], we used the random forest algorithm in machine
learning to predict articles that may be cited more times in the field of healthy urban plan-
ning in the future. This analysis enables this work to determine the factors that have a
greater impact on the number of citations in the literature. The appearance of these factors
with literature and field characteristics is likely to make the literature more cited in the
future. This work will provide healthy urban planning practitioners and scholars with a
reference for popular directions in the future.
(1) Modeling process
Random forest is an ensemble learning algorithm that is based on decision trees,
which was proposed in 2001 by Leo Breiman [95] along with Bagging ensemble learning
theory [96] with random subspace method [97]. At present, the algorithm is widely used
in many fields, such as in biological information [98,99], economics and finance [94,100],
computer vision recognition [101], and speech recognition [102]. However, it is still rarely
used in the research of urban planning. This work used the R language to implement the
random forest prediction model and to comprehensively measure the impact of different
influencing factors on the number of citations of the article by calling the random forest
algorithm package using the characteristic factors of journals, documents, and authors,
such as journal impact factor, author’s writing influence, whether it is a single author, key
keyword density, coverage of key areas, the country of the first author, and key research
areas. As a supplement to the analysis of CiteSpace, it can predict the research hotspots
and trend areas of healthy city planning from a more complete perspective. The specific
process is shown in Figure 2.
Int. J. Environ. Res. Public Health 2021, 18, 9444 7 of 27
Figure 2. R language modeling process.
(2) Algorithm steps
a. Random sampling was performed on the sample data set with replacement to
obtain a data set with the same size sample.
b. Specify the mtry value, that is, randomly generate mtry variables for the binary
tree on the node, and the choice of the binary tree variables still meets the prin-
ciple of minimum node impurity.
c. Establish a fully grown decision tree to train all the extracted data sets.
d. The final result is obtained by counting the average of the possible results of all
decision trees.
According to the above steps, we organize the data set and build a random forest
model. The model structure is shown in Figure 3. To prevent model overfitting, that is, to
reduce the prediction performance of the model in other data sets, this article first ran-
domly divides the literature sample into the following two disjoint sets: the training set
(80% of the data) is used to build the model, and the test set (20% of the data) is used to
evaluate the performance of the model.
Figure 3. Random forest citation likelihood analysis model.
(3) Parameter Selection
a. Root mean square error (RMSE).
The root mean square error (RMSE) corresponds to the square root of the ratio of the
square of the deviation between the predicted value and the true value to the number of
observations n. It is used to measure the deviation between the predicted value and the
true value. The smaller the value is, the smaller the deviation between the predicted value
and the true value is, and the higher the prediction accuracy of the model is. Thus, the
prediction model needs to seek to minimize the RMSE of the model training set.
Int. J. Environ. Res. Public Health 2021, 18, 9444 8 of 27
b. Decision tree (ntree).
This work uses the test set to evaluate the performance of the model for predicting
the number of citations in the literature. During the parameter selection process, the num-
ber of decision trees (ntree) is fixed to 500 for the following two reasons: first, the more
trees exist, the more wasteful the computer’s performance will be. Second, after a certain
number of models, the performance of the model remains basically stable, and the im-
provement of model performance with the increase in the number of trees is very small.
Subsequently, this work optimizes the value of the number of predictors (mtry) randomly
selected by each tree by searching in discrete intervals of 1…60.
c. K-fold cross-validation.
To establish a more accurate prediction model under the condition of a smaller
amount of data, this paper used K-fold cross-validation to perform k = 10 repeated k-fold
cross-validation, which means that the training set is divided into k sub-samples, a single
sub-sample is retained as the verification data, the other k-1 samples are used for training,
the cross-validation is repeated k times, each sub-sample is verified once, the results are
averaged k times, and an optimal result is finally obtained.
Figure 4 shows the RMSE under different mtry parameter settings. Finally, mtry = 14,
which is the parameter selection to minimize RMSE, was selected. On the test set, the op-
timized model that was run achieved an RMSE of 17.506, which means that the citations
predicted by the algorithm may have an error of ±17.506 times, which is only 1.04% rela-
tive to the highest citations, thereby proving that the model has an accurate forecast of the
number of citations, and the error is within the acceptable range.
Figure 4. Model selection during training phase, Model selection: in-sample RMSE across different
parameters for mtry (number of randomly selected predictors for each tree).
d. Ranking of Influencing Factors
In the random forest prediction model, increased mean squared error (%IncMSE) re-
fers to the expected value of the square of the difference between the estimated value of
the parameter and the true value of the parameter caused by the change in the predictive
index. The larger the value is, the greater the impact of the factor on the prediction model
is. This article uses %IncMSE value (the positive and negative values are only used to
represent the degree of influence, and the degree of influence of positive values is greater
than the negative value) to determine the factors that have the greatest impact on the
number of citations in the literature.
Int. J. Environ. Res. Public Health 2021, 18, 9444 9 of 27
3. Result
After data processing, a series of networks were generated to determine the state for
healthy urban planning research. The 2582 articles were listed according to their year of
publication. Then, networks of country, institution, author, journal, keywords, were de-
rived using CiteSpace. Then, the outcome of random forest algorithm showed the most
important influence factor of an article in this field as well as the future trend of the
healthy urban planning area.
3.1. Publishing Analysis
The analysis of publishing was divided into the following two parts: publication vol-
ume and publication year, then the WOS category analysis of the literature. The analysis
of the publication volume and the publication year can clearly show the development
process of the healthy urban planning area. Additionally, the WOS category analysis of
the literature can clearly show the distribution of disciplines in the research field of
healthy urban planning.
3.1.1. Publication Volume and Publication Year
Figure 5 shows the relationship between the annual publication volume and time of
documents related to healthy urban planning for 40 years, that is, from 1981 to 2020, show-
ing an exponential growth as a whole (Trend line function y = 443.56
.
). From the
figure, the development process of the healthy urban planning can be clearly seen.
Figure 5. Number of published papers on healthy urban planning (1981–2020).
According to the growth curve of healthy urban planning research, it is divided into
the following three stages: preparation period, budding period, and development period.
Notably, its stage node has a corresponding relationship with the WHO healthy city pro-
ject.
(1) Preparation period 1981–1992 (I). Before 1993, less than 10 articles on healthy ur-
ban planning were issued each year, and the growth was extremely slow. This phenome-
non may be related to the initial proposal of the concept of a healthy city, and the research
was in the exploratory stage.
(2) Budding period 1993–2007 (II, III, IV). A series of research results on healthy urban
planning appeared in this period [19,21,103], and some basic theories and empirical re-
search on healthy city planning were presented. At this stage, the number of documents
steadily increased, from a minimum of 13 documents per year to a maximum of 34 docu-
ments per year in 15 years. A total of 330 articles were published at this stage.
(3) Development period 2008–2020 (V, VI). Since 2008, issues related to healthy cities
have gradually become one of the most concerned issues for scientists, government
Int. J. Environ. Res. Public Health 2021, 18, 9444 10 of 27
decision makers, various countries, and international organizations in the related fields.
Moreover, the number of documents has grown rapidly since 2008, and its growth rate is
much greater than the preparation and budding periods. In 2020, 407 related documents
were produced, and 2236 documents were produced in 12 years.
In general, the amount of literature related to healthy city planning has increased
exponentially in 40 years, and the growth cycle of its literature is basically the same as the
development cycle of healthy city projects. The promotion and publicity of healthy city
projects have had a certain impact on the academic research of healthy urban planning.
3.1.2. Category Analysis
The exported literature covers 44 WOS categories. Table 2 shows the top 15 subject
categories. The distribution of subject categories shows that the field of healthy urban
planning places great emphasis on urban, environmental, regional, and geographic issues.
In general, healthy city planning research has a strong interdisciplinary nature. In addi-
tion to the fields related to urban research, it also has a certain degree of intersection with
economics, environmental science, and ecology.
Table 2. Top 15 WOS subject categories based on publications.
Web of Science Category
Rank
Counts
Urban Studies 1 1961
Environmental Studise 2 1289
Regional Urban Planning 3 1061
Geography 4 590
Ecology 5 364
Forestry 6 333
Plant Sciences 7 333
Geography Physical 8 293
Development Studies
9
278
Architecture 10 185
Area Studies 11 164
Economics 12 74
Environmental Sciences
13
72
Biodiversity Conservation 14 70
Green Sustainable Science Technology 15 59
3.2. Bibliometric Analysis of CiteSpace Documents on Healthy Urban Planning
There are three analysis types of this work using CiteSpace, including Cooperation
network analysis, Co-citation Analysis, and Thematic Co-occurrence Analysis. From these
networks, the development context of the healthy urban planning area can be completely
presented.
3.2.1. Cooperation Network Analysis
Cooperation networks exist among countries, institutions, as well as authors. We
used CiteSpace to build the following three individual networks: a country, an institu-
tional, and an author cooperation network to explore the cooperation relationship within
the field of healthy urban planning.
(1) Country Cooperation Network Analysis
The country cooperation network of healthy urban planning research is shown in
Figure 6. The size of the node indicates that the number of articles published in different
countries or regions varies. The larger the node is, the more times it appears in the coop-
erative network. The figure shows that the nodes in the United States, the United King-
dom, China, Australia, and Canada are relatively large, indicating that these countries
Int. J. Environ. Res. Public Health 2021, 18, 9444 11 of 27
have made greater contributions to the scientific research cooperation network. In addi-
tion to Denmark, Scotland, Finland, Switzerland, and Sweden, China, Brazil, New Zea-
land, and France are the four countries that have formed a closed and connected graphic,
that is, they have formed a certain scale of cooperation. Most other countries have not
formed a certain scale of cooperative groups.
Figure 6. Visualization map of countries participating in healthy urban planning research.
Table 3 lists the top 10 countries with the most occurrences in the cooperation net-
work, the time of their first appearance in the cooperation network, as well as their cen-
trality. Notably, the higher the centrality is, the higher the importance of the node is. In
terms of centrality: China < Canada, Germany, Spain, and India < Italy < Netherlands <
Australia < United States. The centrality of the United Kingdom reaches the maximum of
0.64, which shows that the United Kingdom and the United States, Australia, the Nether-
lands, and many other countries are in an important position in the cooperation network
of healthy city planning.
Table 3. Top 10 countries based on frequency.
Country Frequency Centrality Year
USA 921 0.24 1992
China 310 0 2003
England 310 0.64 1995
Australia
229
0.61
1995
Canada 147 0.05 1998
Germany 72 0.05 1999
Italy 66 0.11 2003
Netherlands
61
0.14
1995
Spain 58 0.05 2009
India 51 0.05 1999
Int. J. Environ. Res. Public Health 2021, 18, 9444 12 of 27
(2) Institutional Cooperation Network Analysis
As shown in Figure 7, some research institutions are relatively concentrated, thereby
resulting in some major institution clusters. Few institutions have formed cooperative
clusters of a certain scale, but many institutions have strong centrality. The University of
Hong Kong (0.3) and University of Illinois (0.24), have high centrality. They are fruitful
contributors in this field, with more publications and more in-depth cooperation.
Figure 7. Visualization map of institutions participating in healthy urban planning research.
Table 4 lists the top 10 institutions with the most occurrences in the cooperation net-
work. Among the top 10 institutions, seven originated in the United States, and the rest
are from China and Australia. This finding reflects the outstanding research results of the
United States in the field of healthy urban planning.
Table 4. Institutions based on publications.
Rank
Institution Publications Centrality Country
1 The University of Hong Kong 41 0.3 China
2 Arizona State University 38 0.17 USA
3 The University of Melbourne 35 0.06 USA
4
The University of Michigan
27
0.08
USA
5 University of Illinois 26 0.24 USA
6 US Forest Service 21 0.05 USA
7 Tongji University 19 0.1 China
8
Columbia University
18
0.06
USA
9 University of Florida 17 0 USA
10 The University of Queensland 16 0.17 Australia
Int. J. Environ. Res. Public Health 2021, 18, 9444 13 of 27
(3) Author Cooperation Network Analysis
Based on 2582 articles by 6550 different authors, Figure 8 vividly depicts the collabo-
rative network of authors in healthy urban planning. Many authors tend to work with a
small group of authors, which leads to several major author groups. For example, the au-
thor clusters centered on Ulrika K Stigsdotter, Jasper Schipperijn, Billie Gilescorti, Mo-
hammad Javad Koohsari, etc., all have a closed loop with cross-connections in the figure,
which represents the formation of a certain scale of scientific research cooperation. In ad-
dition, some authors, such as William C Sullivan and Dongying Li, Yi Liu and Ye Liu,
Justin Morgenroth, and Ade Kearns, also have some small-scale scientific research coop-
eration. Overall, the authors of the healthy urban planning research have formed part of
a small-scale collaborative network, but no author has high centrality.
Figure 8. Visualization map of authors participating in healthy urban planning research.
Table 5 lists the top 10 authors and their countries and affiliates with the most ap-
pearances in the cooperative network from 1981 to 2020. Among them, Reid Ewing (10) of
the University of Utah in the United Kingdom, who first entered the cooperation network
in 2008, appeared most frequently in the cooperation network. William C Sullivan (8), C
Y Jim (8), Ye Liu (8), Dagmar Haase (7), David J Nowak (7), Ulrika K Stigsdotter (7), Jasper
Schipperijn (7), and Billie Gilescorti (7) followed. Most of the authors who are included in
the table are in a relatively large-scale cooperation network, and the authors from the
United States, China, and Denmark account for the majority from the perspective of coun-
try distribution.
Table 5. Top 10 most productive authors in healthy urban planning research: 1981–2020.
Freq
Author Year Country
Institution
10 Reid Ewing 2008 UK University of Utah
9 Mohammad Javad Koohsarik 2009 Iran University of Tehran
8 William C Sullivan 2009 USA University of Illinoi
8
C Y Jim
2011
China
University of Hong Kong
7 Ye Liu 2017 China The Chinese University of
Hong Kong
7 Dagmar Haase 2013 Germany
Humboldt Universität zu
Berlin
7 David J Nowak 2012 USA USDA Forest Service
7 Ulrika K Stigsdotter 2010 Denmark
University of Copenhagen
Int. J. Environ. Res. Public Health 2021, 18, 9444 14 of 27
7 Jasper Schipperijn 2010 Denmark
University of Copenhagen
7 Billie Gilescortig 2013 Australia
University of Melbourne
3.2.2. Co-Citation Analysis
In this work, we conducted the co-citation analysis from the perspectives of the au-
thor, journals, and references, in order to identify the core authors and journals of the
healthy urban planning area and the knowledge bases of this area.
(1) Author Co-cited Network Analysis
Figure 9 shows the author co-citation network, where one node represents an author.
In the chart generated by CiteSpace, the color corresponds to the year of publication (Fig-
ure 9). The lines between authors represent co-citation relationships.
Figure 9. Visualization map of co-cited authors participating in healthy urban planning research.
Table 6 lists the top five authors who have been cited. The most cited author is WHO
(frequency 237, centrality 0), because WHO, as the proponent of healthy cities and advo-
cates of healthy urban planning, has laid the foundation for the development of the entire
discipline. The second place is Harting T (frequency 149, centrality 0.57), the third place is
Kaplan R (frequency 131, centrality 0.45), and the fourth place is Ulrich RS (frequency 130,
centrality 0.06). They all appeared in “The benefits of nature experience: Improved affect
and cognition” [59] as the author of the reference.
Int. J. Environ. Res. Public Health 2021, 18, 9444 15 of 27
Table 6. Top 5 cited authors and their highly cited articles: 1981–2020.
Freq
Centrality
Author
Year Most Cited Articles Citations
237 0 WHO 2012 Green justice or just green? Provision of
urban green spaces in Berlin, Germany 215
149 0.57 Hartig T
2015 The benefits of nature experience:
Improved affect and cognition 218
131 0.45
Kaplan
R 2015 The benefits of nature experience:
Improved affect and cognition 218
130 0.06 Ulrich
RS 2015 The benefits of nature experience:
Improved affect and cognition 218
119 0.54 Maas J 2015
Spatial planning for multifunctional green
infrastructure: Growing resilience in
Detroit
189
(2) Network Analysis of Co-cited Journals
According to the analysis of the co-cited journals, the distribution of the journals cited
has a vivid reflection in the field of healthy urban planning.
What can be seen from Figure 10 and Table 7 is that Landscape and Urban Planning is
the journal with the most co-citations (812 co-citations, centrality 0.61), and its citations
and centrality are much higher than other journals. This result indicates that Landscape and
Urban Planning has a high reference value for research in the field of healthy urban plan-
ning. The second journal is Health & Place (529 total citations, centrality 0.32), and the third
is Urban Forestry & Urban Greening (484 total citations, centrality 0.25). These three journals
serve as the core nodes that have established connections with other nodes.
Figure 10. Visualization map of co-citation journal in healthy urban planning research.
Int. J. Environ. Res. Public Health 2021, 18, 9444 16 of 27
Table 7. Top 10 productive journals in healthy urban planning research: 1981–2020.
Journal Frequency Centrality
Year
Impact
Factor
Landscape and Urban Planning 812 0.61 2013
5.441
Health & Place 529 0.32 2014
3.29
Urban Forestry & Urban Greening
484
0.25
2015
4.021
Social Science & Medicine 466 0.06 2014
3.616
Urban Studies 428 0.09 2014
2.828
American Journal of Public Health 362 0.01 2015
6.464
Cities
330
0.17
2016
4.802
Environment and Planning A:
Economy and
Space 325 0.06 2015
2.855
Journal of Environmental Psychology
322
0.02
2015
2.64
American Journal of Preventive Medicine
306 0.07 2015
4.42
(3) Co-citation Analysis of References
Figure 11 shows a cluster view of references for healthy urban planning and presents
the network of co-cited documents. A total of 12 clusters of different sizes are provided in
the figure, some of which offer a powerful reference for the study of healthy urban plan-
ning. The biggest cluster is #0 mental wellbeing, which focuses on the impact of urban
green space landscapes on residents’ psychology [104–108]. The second is #1 urban green
spaces, which focuses on the behavior, emotion, health, and spatial quality evaluation of
people related to urban green spaces[109–113].The third is #4 safe communities, which
focuses on the study of the accessibility characteristics of urban space and its impact on
the health of residents[114–117].
Figure 11. Cluster view of references in healthy urban planning research.
3.2.3. Thematic Co-Occurrence Analysis
Keywords are important in our research of the healthy urban planning area. In this
work, we used the co-occurrence of keywords, the timeline of keywords, as well as the
burst detection of keywords to identify the hot spots of this research area.
(1) Co-occurrence of Key Words
Figure 12 shows the co-occurrence analysis view of keywords, in which the size of
the node represents the frequency of the keyword appearing, and the connection in the
node represents the co-occurrence of the keyword in the same document. The more co-
occurrences, the thicker the connection, which shows the relevance between keywords.
Int. J. Environ. Res. Public Health 2021, 18, 9444 17 of 27
Figure 12 shows the relationship between physical activity and keywords, such as green
space, public health, neighborhood, and urban green space, indicating that the research
on urban green space is related to human activities, public health, and other topics.
Figure 12. Co-occurrence view of key words in healthy urban planning research.
The words health, city, urban, and environment are too broad in meaning to be ana-
lyzed. What can be seen from Figure 12 and Table 8 are that the keywords with larger
nodes include physical activity (count: 239, centrality: 0.12), neighborhood (count: 147,
centrality: 0.08), green space (count: 145, centrality: 0.07), public health (count: 141, cen-
trality: 0.02), and space (count: 120, centrality: 0.09). Centrality means that a node con-
structs bridges to two unrelated nodes that measures the importance of the nodes in the
network. The keywords with greater centrality are more important in the field, but they
are not necessarily related to co-occurrence frequency. The keywords with a lower co-
occurrence frequency may also have higher centrality. For example, ecosystem service
(count: 106, centrality: 0.14) and park (count: 92, centrality: 0.12) also have high centrality.
In general, the keyword co-occurrence view provides an objective perspective that shows
that the hot spots in the field of healthy urban planning are physical activity, green space,
urban green space, and mental health.
Table 8. Top 20 keywords based on count.
Count
Centrality
Year
Keyword
Count
Centrality
Year
Keyword
239
0.12
2009
physical activity
105
0.05
2011
perception
147 0.08 2009
neighborhood 98 0 2015
mental health
145 0.07 2012
green space 96 0.01 2011
urbanization
141 0.02 2011
public health 92 0.12 2012
park
120
0.09
2013
space
90
0.05
2011
land use
118 0 2011
community 88 0.09 2013
urban green space
114 0.02 2011
built environment
87 0.04 2013
china
112 0.04 2010
quality 82 0.03 2014
landscape
110
0.16
2010
walking
78
0.02
2015
climate change
106 0.14 2014
ecosystem service 77 0.05 2015
access
Int. J. Environ. Res. Public Health 2021, 18, 9444 18 of 27
(2) Timeline of Key Words
Figure 13 depicts a timeline view of keywords that shows the dynamics of keywords
in different clusters over time. Overall, the most sustainable cluster is #3 urban green
space. From 2002 to 2020, new keywords, including health, city, impact, quality, environ-
ment perception, park, preference, stress, landscape, inequality, forest, tree, natural envi-
ronment, and other key words, have appeared continuously. The research on urban green
space also mainly revolves around evaluation, perception, fairness, and other aspects. In
addition, #0 physical activity is the largest cluster, and its research content focuses on the
community design, accessibility, and residents’ health. The remaining clusters also show
other focus points of healthy urban planning, such as accessibility (#2 urban park access),
climate issues (#8 extreme heat), group research (#4 adolescent achievement, #7 subjective
wellbeing), and new technology (#9 using remote sensing data). In general, the research
on healthy urban planning not only focuses on the planning and design of urban space
and green space resources but also considers the health factors of urban residents. More-
over, the clustering of keywords has a certain inclusion relationship with the clusters co-
cited in the literature, which reflects that the field of healthy urban planning is gradually
expanding and becoming diversified.
Figure 13. Timeline view of keywords.
(3) Burst Detection of Key Words
Figure 14 illustrates the keyword explosive detection map, which shows the explo-
sive dynamic changes in keywords in the field of healthy urban planning in the past 40
years. In this figure, red color represents the occurrence of burst of keywords, and green
color represents no occurrence of burst. In general, the burst of keywords is mainly con-
centrated in the 20 recent years, which echoes the conclusions in the publication analysis.
After removing keywords with broad meanings, such as health and city, the keyword
with the longest outbreak time is urbanization (2011–2016). The other keywords have a
shorter outbreak time, generally within 3 years, which reflects from the side that the key
words in the field of healthy urban planning are changing rapidly and are not sustainable.
Int. J. Environ. Res. Public Health 2021, 18, 9444 19 of 27
Figure 14. Burst detection of keywords.
3.3. Prediction of Citation Possibility of Healthy City Planning Literature
The result of the random forest algorithm of this work can be presented from the
following three perspectives that can show the most significant characteristics of this anal-
ysis: overall result, the WOS field, and country or region.
3.3.1. Overall Result
Table 9 lists the top 20 influencing factors that have the greatest impact on the num-
ber of citations of papers in the field of healthy urban planning. Taken together, the char-
acteristic factors of the document itself have a dominant influence on the number of cita-
tions of the document. For example, the %IncMSE value of the number of times the first
author’s articles have been cited ranks first, and the total cited impact factor, the number
of pages, and whether it is a single author all ranked in the top five. The influence of au-
thor and field diversity are also important. The two influencing factors, namely, whether
it is a single author and the diversity of paper research direction, also have a higher
%IncMSE value, which shows that multi-author cooperation and multi-disciplinary liter-
ature have the potential to be highly cited to a certain extent. In addition, the impact of
specific areas and national indicators are described below.
Table 9. All the features listed by IncMSE, Feature selection: all the predictors for predicting the
number of citations of papers in the healthy urban planning area.
Rank Field Name %IncMSE
1 Avg_Ci 42.5297737
2
PG
3.0033313
3
Impact_Factor
3.0017694
4 Environmental_Studies 2.1920207
5 Single_Author 1.9137673
6 WC_differ 1.6044975
7 Peroples_R_China 0.8693223
Int. J. Environ. Res. Public Health 2021, 18, 9444 20 of 27
8 Geography 0.5209746
9 Regional_Urban_Planning 0.3672396
10 Ecology 0.1049450
11
Pbulic_Administration
0.0000000
12
Physical_Geography
0.0000000
13 Development_Studies -0.1207923
14 NR -0.3937910
15 Urban_Studies -0.7585883
16 Plant_Sciences -1.0216501
17 Forestry -1.2453836
18 England -2.3478475
19
USA
-2.3765915
20
Australia
-2.7250626
21 KW_dense -3.0610388
22 Canada -3.9821623
3.3.2. WOS Field
Table 10 shows the ranking of influencing factors in the WOS classification. The
%IncMSE value of Environmental Studies reached 2.1920207. The existence of this field
will greatly increase the frequency of the literature citations. The research on healthy ur-
ban planning and the cross-correlation of the environmental field may have a higher cita-
tion in the future. The analysis of this article shows that compared with papers related to
public management, those related to the environment are the most praised by researchers
in terms of the number of citations. In addition, Geography (%IncMSE:0.5209746), Re-
gional and Urban Planning (%IncMSE:0.3672396), and Ecology (%IncMSE:0.1049450) have
higher %IncMSE values.
Table 10. Impactors’ rank (WOS categories).
Rank WOS Categories %IncMSE
1 Environmental Studies 2.1920207
2 Geography 0.5209746
3 Regional and Urban Planning 0.3672396
4 Ecology 0.1049450
5 Public Administration 0.0000000
6
Physical Geography
0.0000000
7 Development Studies −0.1207923
8 Urban Studies −0.7585883
9 Plant Sciences −1.0216501
10
Forestry
−1.2453836
3.3.3. Country or Region
Table 11 shows the ranking of the influencing factors of different countries or regions.
What we can derive from the table is that China has the highest %IncMSE value, and other
countries have a relatively lower %IncMSE, which may indicate that Chinese researchers
have made relatively significant progress in the field of healthy urban planning. However,
this does not mean to deny the contribution of any other country in this area. Every coun-
try has made great contribution to heathy urban planning research. It is only the objective
result produced from the selected 2582 articles.
Int. J. Environ. Res. Public Health 2021, 18, 9444 21 of 27
Table 11. Impactors’ rank (country).
Rank Country %IncMSE
1 China 0.8693223
2 England −2.3478475
3
USA
−2.3765915
4 Australia −2.7250626
5 Canada −3.9821623
4. Discussion
In terms of the volume of publications, the number of documents related to healthy
urban planning has generally shown an exponential increase in 40 years, and the growth
cycle of its documents is basically the same as the development cycle of healthy urban
projects. The promotion and publicity of healthy city projects has an impact on academic
research in healthy urban planning. Then, we found out that the field of healthy urban
planning is highly interdisciplinary. In addition to the fields related to urban research, it
also has a certain degree of intersection with economics, environmental science, and ecol-
ogy.
In terms of cooperation networks, at the level of national cooperation, except for Den-
mark, Scotland, Finland, Switzerland, and Sweden, four other countries, China, Brazil,
New Zealand, and France, have each formed a certain scale of cooperation, and most of
the other countries have not formed a certain scale of cooperation groups. The United
Kingdom, the United States, Australia, the Netherlands, and many other countries play
an important role in the cooperation network of healthy urban planning. At the level of
institutional cooperation, the University of Hong Kong and University of Illinois are fruit-
ful contributors in this field with a higher number of and more in-depth publications. At
the micro level of author cooperation, some small-scale cooperation networks have been
formed but without very central authors.
In terms of co-citation analysis, among the authors, WHO, Hartig T, and Kaplan are
among the top three in terms of citations. In terms of the journals co-cited, Landscape and
Urban Planning, Health & Place, and Urban Forestry & Urban Greening were the core journals
in the field of healthy urban planning. In terms of co-cited references, the impact of urban
green space landscape on residents’ psychology; the behavior, emotion, health and spatial
quality evaluation of people related to urban green space; and the walkability of the space
and its impact on the health of residents, partial clustering provides a powerful reference
for the study of healthy urban planning.
In terms of thematic co-occurrence, the hot spots in the field of healthy urban plan-
ning are physical activity, green space, and mental health. According to the analysis of the
keyword timeline, the research of healthy urban planning not only focuses on the plan-
ning and design of urban space and green space resources but also considers the health
factors of urban residents. Moreover, the clustering of keywords has a certain inclusion
relationship with the clusters co-cited in the literature, which reflects that the field of
healthy urban planning is gradually expanding and becoming diversified. In addition,
through explosive testing, the hot words of healthy urban planning changed rapidly and
were not sustainable.
After having a more comprehensive understanding of the field of healthy urban plan-
ning, this work used the random forest algorithm to establish a model to predict and rank
the factors that influence the citation of literature in the field of healthy urban planning.
Taken together, top three influence factors which have dominant influence on the number
of citations of the document are: the number of times the first author’s articles have been
cited, the number of pages, the journal`s impact factor. The two influencing factors,
namely, whether it is a single author and the diversity of the research direction, also have
high %incMSE values, showing that multi-author cooperation and multi-disciplinary lit-
erature have the potential of being highly cited to a certain extent. Among the influencing
Int. J. Environ. Res. Public Health 2021, 18, 9444 22 of 27
factors related to the WOS fields, the %IncMSE values of Environmental Studies, Geogra-
phy, Regional and Urban Planning, and Ecology are all high, suggesting that the fre-
quency of the citations of documents which overlap with these fields in healthy urban
planning research may increase to some extent. Among the five countries with the largest
number of publications in the field of healthy urban planning, China’s %incMSE value
ranks first, which may indicate that Chinese researchers have made relatively significant
progress in the field of healthy urban planning. Therefore, according to this result, it may
be a good choice to cooperate with Chinese researchers. However, this does not mean to
deny the contribution of any other country in the research of this field. It is only the ob-
jective result produced from the selected 2582 articles. What we really want to do is en-
courage international cross-country cooperation and enhance the international coopera-
tion network in this field
5. Conclusions
Healthy urban planning is a field under rapid development. Over the past few dec-
ades, countries have made unremitting efforts to make our cities healthier. In this work,
CiteSpace data visualization analysis and a random forest algorithm are used to analyze
the articles on the healthy urban planning area in the WOS core database from 1981 to
2020 (as of 31 December 2020). The conclusions are as follows:
(1) The field of healthy city planning has developed rapidly in the past 40 years, the
number of annual articles of healthy urban planning research has increased from 2 to 407
exponentially. In the meantime, this field is growing more and more interdisciplinary.
(2) A certain scale of cooperation network has been formed, where the United King-
dom, the United States, Australia, etc., and the University of Hong Kong, University of
Illinois, etc., are in an important position. This may be relative to the promotion of the
WHO healthy city project. Additionally, Landscape and Urban Planning, Health & Place, and
Urban Forestry & Urban Greening are the core journals in the field of healthy urban plan-
ning, having the highest co-cited frequency. Moreover, the research hotspots are wide and
changing rapidly, mainly focusing on physical activity, urban green space, mental health,
etc., which also reflects that the field of healthy city planning is gradually expanding and
diversifying.
(3) Based on the analysis result of the random forest algorithm, for related research-
ers,
it is advantageous to consider international cross-country cooperation, interdiscipli-
nary themes, and multi-author cooperation when studying in the field of healthy urban
planning in the future, especially considering fields such as Environmental Studies, Ge-
ography, Regional and Urban Planning, and Ecology.
The emerging trends and patterns identified using CiteSpace and the prediction re-
sults of the random forest algorithm provide novel, interesting, and comprehensive views
on how to conduct healthy urban planning research. Based on the conclusion, this article
has provided some suggestions for future research: First, the research direction of future
healthy urban planning should not only focus on the planning and design of urban space
and green space resources but also consider the physical and mental health of urban resi-
dents. Specifically, the research on green space needs to consider factors such as human
activities and mental health. Second, this study found that the field of healthy urban plan-
ning is highly interdisciplinary. At the same time, multi-author cooperation and multi-
disciplinary literature have the potential to be highly cited, thereby showing that the field
of healthy urban planning is looking forward to the new cross-cutting of knowledge to
bring about differences to the field. Healthy urban planning itself involves multi-discipli-
nary knowledge, such as urban research, geography, environmental science, landscape
architecture, public health, and medicine. Strengthening the cross-cooperation among
various disciplines and conducting joint research can help analyze the fundamentals of
healthy urban development and understand the essential relationship between the city
and people’s healthy life and also provide us with multiple perspectives to explore the
characteristics of the rules behind the harmonious relationship of people and city.
Int. J. Environ. Res. Public Health 2021, 18, 9444 23 of 27
Despite the contribution of this work, the research still has some shortcomings and
limitations. First, the research data are only selected to analyze the literature data in the
largest global database of scientific publications (i.e., the WOS), but other international
databases [118] are not used. Databases, such as Pubmed and Google Scholar, are not in-
cluded, which may reduce the comprehensiveness and completeness of the research. Sec-
ond, when using the random forest model to predict the number of citations, the selection
of indicators, such as the number of influencing factors, the number of cross-validation of
the training set, and other parameters, can be further optimized through a larger number
of experiments to obtain a more accurate model.
Author Contributions: Conceptualization, J.W.; Data curation, Y.C.; Formal analysis, B.J. and Y.C.;
Funding acquisition, J.W.; Investigation, B.J. and Y.C.; Methodology, B.J. and J.W.; Software, B.J. and
Y.C.; Supervision, J.W.; Validation, B.J. and J.W.; Visualization, Y.C.; Writing—original draft, B.J.
and Y.C.; Writing—review & editing, J.W. All authors have read and agreed to the published ver-
sion of the manuscript.
Funding: This research was funded by the National Natural Science Foundation of China (NSFC)
Youth Program, grant number 51808409.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Not applicable.
Conflicts of Interest: The authors declare no conflict of interest.
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