Temporal and Spatial Attractiveness Characteristics of Wuhan Urban Riverside from the Perspective of Traveling

Abstract

Improving the attractiveness of urban waterfronts has become an important objective to promote economic development and improve the environmental quality. However, few studies have focused on the inherent characteristics of urban waterfront attractiveness. In this study, mobile phone signaling data and the TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) were used to construct the attractiveness evaluation system of the riverside in Wuhan. The OLS (ordinary least squares) regression model was used to analyze the relationship between the POI (point of interest) and the attractiveness of river waterfronts. Furthermore, the high-or-low-value aggregation classification of research units was performed according to attractiveness and the POI indicators to reveal the influencing factors of the attractiveness of the Wuhan urban riverside. Results showed the following. (1) The high-value distribution of attractiveness of the river waterfronts in Wuhan presented regional aggregation characteristics, and the attractiveness of economically developed areas was high. (2) Consumer POIs (CPOIs) and outdoor recreation POIs (RPOIs) had a positive effect on the attractiveness of the riverside in Wuhan, while housing POIs (HPOIs), public service POIs (OPOIs), and the high degree of POI mixing had a negative impact on the attractiveness of the urban riverside. (3) The high–high agglomeration spaces were mainly concentrated in the economically developed areas of the city center, which are mainly open spaces where urban cultural activities are held, while the low–low agglomeration spaces were mostly gathered in the suburban areas. The spatial distribution of the high–low agglomeration spaces, which are mainly green open spaces, was relatively fragmented, while the low–high agglomeration spaces, which are mainly freight terminals, linear walks, and residential areas, were near the city center.

Full text

Land 2022, 11, 1434. https://doi.org/10.3390/land11091434 www.mdpi.com/journal/land
Article
Temporal and Spatial Attractiveness Characteristics of Wuhan
Urban Riverside from the Perspective of Traveling
Yuting Chen 1, Bingyao Jia 1, Jing Wu 1,2,*, Xuejun Liu 1,2 and Tianyue Luo 3
1 School of Urban Design, Wuhan University, Wuhan 430072, China
2 Hubei Habitat Environment Research Center of Engineering and Technology, Wuhan 430072, China
3 Wuhan Transportation Development Strategy Institute, Wuhan 430070, China
* Correspondence: jing.wu@whu.edu.cn
Abstract: Improving the attractiveness of urban waterfronts has become an important objective to
promote economic development and improve the environmental quality. However, few studies
have focused on the inherent characteristics of urban waterfront attractiveness. In this study, mobile
phone signaling data and the TOPSIS (Technique for Order Preference by Similarity to an Ideal
Solution) were used to construct the attractiveness evaluation system of the riverside in Wuhan. The
OLS (ordinary least squares) regression model was used to analyze the relationship between the
POI (point of interest) and the attractiveness of river waterfronts. Furthermore, the high-or-low-
value aggregation classification of research units was performed according to attractiveness and the
POI indicators to reveal the influencing factors of the attractiveness of the Wuhan urban riverside.
Results showed the following. (1) The high-value distribution of attractiveness of the river water-
fronts in Wuhan presented regional aggregation characteristics, and the attractiveness of economi-
cally developed areas was high. (2) Consumer POIs (CPOIs) and outdoor recreation POIs (RPOIs)
had a positive effect on the attractiveness of the riverside in Wuhan, while housing POIs (HPOIs),
public service POIs (OPOIs), and the high degree of POI mixing had a negative impact on the at-
tractiveness of the urban riverside. (3) The high–high agglomeration spaces were mainly concen-
trated in the economically developed areas of the city center, which are mainly open spaces where
urban cultural activities are held, while the low–low agglomeration spaces were mostly gathered in
the suburban areas. The spatial distribution of the high–low agglomeration spaces, which are
mainly green open spaces, was relatively fragmented, while the low–high agglomeration spaces,
which are mainly freight terminals, linear walks, and residential areas, were near the city center.
Keywords: mobile phone signaling data; attractiveness; OLS; waterfront
1. Introduction
Urban waterfronts are the parts of a city that are adjacent to water bodies (rivers,
lakes, oceans, bays, creeks, and so on) [1,2] that carry social, ecological, and economic
attributes such as urban ecology [3], culture [4], economy [5], and politics [6]. From the
early 18th century to the 19th century, urban waterfronts functioned as ports, warehouses,
and factories [1]. With the rapid development of the economy, the attractiveness of wa-
terfronts has gradually been recognized by planners and citizens and used for public pur-
poses [1]. Since the 1980s, urban development has brought many environmental and so-
cio-economic problems to urban waterfronts [7–10], so waterfront areas have gradually
become the focus of urban planning and construction interventions [11]. Since the 21st
century, the revitalization of waterfront areas has usually been accompanied by the opti-
mization of urban functions and an important way for cities to improve their competitive-
ness [12]. In recent years, the research on the renewal of urban waterfronts has become a
development trend in cities around the world [13]. Increasing studies are considering the
renewal of urban waterfronts as an important approach to boosting the vitality of cities
Citation:
Chen, Y.; Jia, B.; Wu, J.;
Liu, X.; Luo, T.
Temporal and Spatial
Attractiveness Characteristics of
Wuhan Urban Riverside from the
Perspective of Traveling.
Land 2022,
11
, 1434. https://doi.org/10.3390/
land11091434
Academic Editors: Heesup Han
Received: 24 July 2022
Accepted: 25 August 2022
Published:
30 August 2022
Publisher’s Note:
MDPI stays neu-
tral with regard to jurisdictional
claims in published maps and institu-
tional affiliations.
Copyright:
© 2022 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 (https://cre-
ativecommons.org/licenses/by/4.0/).

Land 2022, 11, 1434 2 of 22
[14–16]. The renewal planning of waterfronts is increasingly becoming an important part
of sustainable urban development strategies [17].
Although the issue of urban waterfront renewal has been given increasing attention,
problems remain such as insufficient facility functions, low environment quality, and in-
sufficient utilization of the waterfront, which in turn lead to low attractiveness [16,18].
Therefore, exploring the attractive characteristics of urban waterfronts and proposing ef-
fective methods to enhance them are crucial to the future construction of urban water-
fronts. Unlike the vitality measured by density [19], attractiveness is difficult to define and
quantify [20]. Biernacka, M. and Kronenberg, J. et al. proposed that a place is attractive
when people are willing to spend their time there and it meets their personal needs, ex-
pectations, and preferences [21,22]. With the progress of research, scholars gradually be-
gan to pay attention to urban spatial attractiveness. In the spatial scale, urban spatial at-
tractiveness can be divided into three types. At the macro-level, studies have concentrated
on the economic attractiveness between cities or regions [23–25], tourism attractiveness
[20,26], and the characteristics of population migration [27–30], exploring socioeconomic
strategies for urban development. At the meso-level, studies have tended to focus on fac-
tors influencing attractiveness (e.g., accessibility [21,22,31], facilities and vegetation distri-
bution [32–35], spatial comfort [36] and biodiversity [37,38]), crowd perception features
[35,39], and spatiotemporal distribution features [40,41]. At the micro-level, digital tech-
niques such as eye tracking [42] and 3D simulation [43] are used to explore the dynamic
characteristics of the visual attractions of urban streets from the pedestrian perspective
[44]. Although studies on attractiveness are extensive, they are mainly on the regional
scale and focus more on the attractiveness of urban green open spaces than urban water-
fronts.
In terms of research methods, early studies on the attractiveness of urban waterfronts
mostly explored people’s preferences and usage of public spaces through questionnaires,
interviews, and field research [31–33,45–48]. However, the rise in big data and the devel-
opment of related technologies in recent years have brought new exploration methods for
quantitative research on urban spatial attractiveness [49]. Banet, K. et al. used bicycle
travel trajectory data to explore the characteristics of user dwell time, revealing the spatial
pattern of urban attractiveness [40]. Cai, L. et al. proposed the concept and model of the
hotspot attractiveness indicator to explore the spatiotemporal distribution of hotspots and
determine the degree of attractiveness to residents [41]. Huang, H. L. et al. used taxi tra-
jectory data to establish a time-dependent attraction function to represent the attractive-
ness of a destination to passengers from the perspective of dynamic demand [50]. Among
all of the related big data, mobile phone signaling data have the advantages of large cov-
erage and sufficient samples and contain the traveling information of users in different
periods [51–54]. Therefore, mobile phone signaling data can accurately describe the trav-
eling patterns of users and help in the study of the spatiotemporal characteristics of urban
spatial attractiveness from the perspective of traveling [55,56].
In general, this study established indicators from the spatial and temporal dimen-
sions to measure the attractiveness of the riverside area in Wuhan, adopting the TOPSIS
(Technique for Order Preference by Similarity to an Ideal Solution) to calculate the total
attractiveness score, then used the OLS (ordinary least squares) model to study the rela-
tionship between the POI (point of interest) and attractiveness before finally proposing
suggestions for attractiveness improvement and the promotion of urban waterfronts.
However, it cannot be neglect that mobile phone signaling data only provide the time and
location of users but does not engage with any of the other variables that assist in deter-
mining an individual or community’s attractiveness to a location. Therefore, this work
only offers an additional method but not as a standalone approach for determining the
attractiveness of the urban waterfront. The remainder of this paper is organized as fol-
lows. Section 2 presents the materials including an introduction of the study area and data.
In Section 3, the methodology is discussed including an introduction of the research
framework and mathematical models. Section 4 discusses the results including the

Land 2022, 11, 1434 3 of 22
attractiveness distribution characteristics, the total attractiveness score, and the regression
model and classification results. Sections 5 and 6 present the discussion and the conclu-
sions, respectively.
2. Materials
Materials are the basis of research. In this part, we introduce the study areas, the data
sources, and the pre-processing progress.
2.1. Study Area
Wuhan (113°41′–115°05′ E, 29°58′–31°22′ N) is the capital city of Hubei Province,
China [57]. This city is traversed by the Changjiang River and its tributary, the Han River,
and is geographically divided into three towns: Wuchang, Hankou, and Hanyang [58].
The Third Ring Road is the dividing line between Wuhan and its suburbs. The areas
within the Third Ring Road are highly developed and economically prosperous, while
those outside the Third Ring Road are relatively underdeveloped [59]. This study takes
the core area within the Third Ring Road of the Changjiang River in Wuhan as the study
area. As shown in Figure 1, the study area is the core area of the Changjiang River in
Wuhan, China, with a length of 23.74 km. To study the attractiveness characteristics of the
riversides, we divided 46 research units according to the road network, with an average
area of 0.36 km2 and a total area of approximately 16.6 km2. Table 1 lists the names, cate-
gories, and districts of different research units in Figure 1. The table reveals that most of
the research units belong to the landscape park category, while the rest are cultural spots,
residential areas, public buildings, and transportation hubs.
Figure 1. The research area and research units.
Table 1. The research unit information.
ID
Name
Category
District
ID
Name
Category
District
1
River Landscape
Park Landscape park Hankou 24
Hongshan Beach
Park Landscape park Wuchang
2
Hankou River Beach
Park Phase IV
Landscape park Hankou 25
Yangsigang Yangtze
River Bridge Park
Landscape park Wuchang

Land 2022, 11, 1434 4 of 22
3
Hankou River Beach
Park Phase III
Landscape park Hankou 26
BAPU Street em-
bankment Beach
Landscape park Wuchang
4
Hankou River Beach
Park Phase III
Landscape park Hankou 27
Changjiang Zidu res-
idential community
Residential com-
munity
Wuchang
5
Fengfan Square,
Hankou River Beach
Park Phase II
Cultural spot,
landscape park Hankou 28 Under building / Wuchang
6
Zhiyin Dock,
Hankou River Beach
Park Phase II
Cultural spot,
landscape park Hankou 29
Meihuayuan residen-
tial community
Residential com-
munity Wuchang
7
Hankou River Beach
Park Phase I
Landscape park Hankou 30
Jiefanglu residential
community
Residential com-
munity
Wuchang
8
Culture Plaza of
Hankou River Beach
Park, Hankou River
Beach Park Phase I
Landscape park Hankou 31 Zhonghualu Dock Transportation
hub Wuchang
9
Wuhan Harbor, Wu-
han Science and
Technology Museum
Transportation
hub, public
building
Hankou 32 Dadikou Square,
Wuchang Beach Park
Landscape park Wuchang
10
Wuhanguan Dock
Cultural spot
Hankou
33
Wuchang Beach Park
Landscape park
Wuchang
11
Temple of the
Dragon King
Cultural spot,
landscape park
Hankou 34 Wuchang Beach Park
Landscape park Wuchang
12 Nananzui Park Landscape park Hanyang 35
Moon Bay Square of
Wuchang River
Beach Park
Landscape park Wuchang
13
Qingchuan Pavilion,
Dayu Square Park
Cultural spot,
landscape park
Hanyang 36 Moon Bay Dock
Transportation
hub
Wuchang
14
Chaozong Park
Landscape park
Hanyang
37
Wuchang Beach Park
Landscape park
Wuchang
15
Yingwuzhou Culture
Plaza
Cultural spot Hanyang 38 Wuchang Beach Park
Landscape park Wuchang
16
Hanyang beach Park
Landscape park
Hanyang
39
Wuchang Beach Park
Landscape park
Wuchang
17 Under building / Hanyang 40
Qingshan River
Beach Park Phase I Landscape park Wuchang
18 Under building / Hanyang 41
Qingshan River
Beach Park Phase I
Landscape park Wuchang
19 Under building / Hanyang 42
Qingshan River
Beach Park Phase I
Landscape park Wuchang
20
Wuhan International
EXPO Center
Public building Hanyang 43
Qingshan River
Beach Park Phase I
Landscape park Wuchang
21
Yangsigang bridge
Beach Park
Landscape park Hanyang 44
Qingshan River
Beach Park Phase II
Landscape park Wuchang
22
Hongshan Beach
Park
Landscape park Wuchang 45
Jieteng jianjiu Music
and Sports Park
Cultural spot,
landscape park
Wuchang
23
Hongshan Beach
Park
Landscape park Wuchang 46
Jieteng jianjiu Music
and Sports Park
Cultural spot,
landscape park
Wuchang

Land 2022, 11, 1434 5 of 22
2.2. Data Sources and Pre-Processing
As for the data, we obtained mobile phone signaling and POI data as the research
data sources and then pre-processed them for further research.
2.2.1. Mobile Phone Signaling Data
The mobile phone signaling data used in this study comes from the Smart Steps dig-
ital platform (accessed on 11 August 2022, http://www.smartsteps.com/) of China Uni-
com, which is one of the three major telecommunications operators in China. This study
selected the mobile phone signaling data of the riverside area within the Third Ring Road
of Wuhan in June 2021 including the average and cumulative values of data on weekdays
and weekends such as the user density, user travel distance, user OD data, the number of
users arriving in 24 h, and the average dwell time and dwell frequency of the users. As
shown in Table 2, the study area had 162,020 mobile phone base stations with different
coverage accuracies, and the closer they were to the central city, the higher their accuracy.
A total of 73,469 base stations have a coverage of 250 m.
Table 2. The mobile phone base station information of the Wuhan riverside area.
Length
Num
Length
Num
Length
Num
250 m
73,469
2000 m
4299
16,000 m
135
500 m
64,206
4000 m
2154
32,000 m
37
1000 m
17,177
8000 m
539
96,000 m
4
Total number:162,020
Additionally, we declare that the collection and processing of the data was under-
taken by China Unicom from whom we obtained our dataset for this study. In the process
of collection, it was not allowed to query user details including user ID and specific loca-
tion, etc. The final returned data only included the statistical cumulative number of users
in a certain period of time or in a certain region. Therefore, in this study, the use of mobile
signaling data fully complies with the ethical and legal standards related to user privacy.
2.2.2. POI Data
In this study, the POI data within the 200 m buffer area of the research unit along the
river was selected, and 37,101 data items (including location and type of each POI) were
obtained. According to the POI characteristics, the data were divided into four categories,
namely, consumer POI (CPOI), outdoor recreation POI (RPOI), public service POI (OPOI),
and housing POI (HPOI). Among them, CPOI includes catering and shopping services,
RPOI includes scenic spots, HPOI includes residential and living services, and OPOI in-
cludes public service facilities. Through data preprocessing, a distribution map of the POI
points in the study area was obtained (as shown in Figure 2). The figure shows that the
POI points are mainly distributed in the riverside area of Hankou, followed by the river-
side area of Wuchang.

Land 2022, 11, 1434 6 of 22
Figure 2. The POI distribution of the Wuhan riverside area.
3. Methodology
This part mainly discusses how to use certain methods for research. The research
framework, the calculation method of attractiveness indicators, TOPSIS, and the OLS re-
gression model are discussed.
3.1. Research Framework
This study aimed to explore the spatiotemporal characteristics of attractiveness on
the urban riverside area of Wuhan and then design a framework to study attractiveness
from the perspective of traveling combined with POI data. As shown in Figure 3, this
study used mobile phone signaling data to establish indicators from the time and space
dimensions, exploring the spatiotemporal characteristics of the attractiveness distribu-
tion. First, the research units were divided by the road network and the Changjiang River,
and the mobile phone signaling data were cleaned and processed to obtain information
such as the numbers of users on the weekends and working days in each research unit,
the origin and destination of users, the number of hourly visit of users, and the average
number of users dwelling in each research unit, their dwelling frequency, and stay dura-
tion. Then, we established the spatial dimension indicators (i.e., the spatial density, diver-
sity, and distance) and the time dimension indicators (i.e., temporal stability, dwell time,
and dwell frequency) to obtain the spatiotemporal distribution characteristics of water-
front attractiveness. After normalizing and standardizing the data, the TOPSIS was used
to comprehensively evaluate different indicators, and the total score of waterfront attrac-
tiveness was obtained. Finally, using the OLS regression model, the correlation between
the POI density, POI mixing degree, and waterfront attractiveness was calculated, and a
strategy for improving the attractiveness of the waterfront in Wuhan is proposed.

Land 2022, 11, 1434 7 of 22
Figure 3. The research framework.
3.2. Calculation of Attractiveness Indicators
On the basis of the above research framework, this study constructed an attractive-
ness indicator system based on the time and space dimensions. In existing studies, den-
sity, dwell time, dwell frequency, and travel distance are common indicators for measur-
ing attractiveness [33,40,41,48], and the mobile phone signaling data used in this study
also contained these variables. However, to study the spatiotemporal characteristics of
attractiveness more comprehensively, this study attempted to combine the characteristics
of mobile phone signaling data to provide abundant observation indicators of attractive-
ness. Referring to the research of Liu, Song et al. [60], this study added spatial diversity
and time diversity to measure attractiveness. Each indicator is discussed in detail below,
and the calculation method is explained in Table 3.
Table 3. The spatiotemporal attractiveness indicators.
Dimension
Indicator
Calculation Formula
Description
Spatial
Space Density  = 
 
is the number of users of
each research unit,  is the
area of each research unit.
Space Distance  = 
 
is the sum of user travel
distance of each research
unit,  is the number of us-
ers of each research unit.
Space Diversity  = 


is the number of origins of
users in research unit i.
Temporal Dwell Time  = 
 
is the sum of the user
Dwell Time per study unit,
 is the number of users of
each research unit.

Land 2022, 11, 1434 8 of 22
Dwell Frequency  = 
 
is the sum of the user
Dwell Time per study unit,
 is the number of users of
each research unit.
Time Diversity  = (.


 )

.
refers to the proportion
of the number of users arriv-
ing in research unit i to the
total number of users arriv-
ing in 24 h a day during pe-
riod j.
1. Space Density
The space density indicator measures the density of users in a research unit. The
larger the value, the larger the ability of a certain unit to gather a larger number of people.
2. Space Distance
The space distance indicator measures the travel distance of users in the research
unit. The larger the value, the stronger the ability of a certain unit to attract people from
farther places.
3. Space Diversity
The space diversity indicator measures the number of user origins in the research
unit. The larger the value, the more diverse the regions of origin of the people that can be
attracted by a certain unit.
4. Dwell Time
Dwell time measures the average dwell time of users in the research unit. The larger
the value, the greater the ability of a certain unit to attract people into staying for a long
time.
5. Dwell Frequency
Dwell frequency measures the average dwell frequency of users in the research unit.
The larger the value, the greater the ability of a certain unit in attracting people to visit
several times.
6. Dwell Diversity
The time diversity indicator measures the diversity of user arrival times in the re-
search unit, and the larger the value, the stronger the ability of a certain unit to attract
people at different times of the day.
3.3. TOPSIS
Given the diversity of the spatiotemporal attractiveness indicators, their quantitative
evaluation is a typical multi-attribute decision-making problem. Thus, this study adopted
the TOPSIS method to comprehensively evaluate attractiveness. On the basis of the simi-
larity of the studied alternatives to the ideal solution, the optimal solution in the TOPSIS
model should have the shortest distance from the positive ideal solution and the farthest
distance from the negative ideal solution. TOPSIS has been proven to be a reasonable and
feasible performance evaluation method [61]. When conducting TOPSIS analysis, deter-
mining whether the indicator is a gain (bigger is better) or cost type (smaller is better) is
critical [62]. The basic assumptions are outlined as follows. For each study unit, the higher
the values of its space density, space distance, space diversity, dwell time, dwell fre-
quency, and time diversity indicators, the more attractive it is. Therefore, all selected in-
dicators were classified as a gain type.
=ln()

 ln , =
(= 1, 2, … , )
(1)

Land 2022, 11, 1434 9 of 22
According to the indicators obtained above, a standardized evaluation matrix was
established, and the entropy weight method was used to determine the weights of various
evaluation indicators. In Formula (1),  is the information entropy; k is the number of
indicators; and  is the weight of each indicator.
=()

 ,=()

 ,
(2)
=



++
(3)
Afterward, as shown in Formulas (2) and (3), we calculated the sum of the distance
( and  ) of the attractiveness indicator of each research unit to the positive ideal so-
lution  and the negative ideal solution . Finally, we obtained the relative approach
degree  of each research unit; the closer the value is to 1, the better the evaluation object.
Thus, the standardized value was used in this study as the total score of attractiveness.
3.4. OLS Regression Model
After a comprehensive evaluation of various indicators of attractiveness, this study
used the OLS regression model to study the relationship between the total attractiveness
score and POI (including POI density and mixing degree of the different POI types). The
OLS regression model is a common and effective statistical model for exploring the rela-
tionship between variables.
=+

 +
(4)
In Formula (4),  represents the dependent variable;  represents the j-th POI indi-
cator;  represents the coefficient of each independent variable; and  represents the re-
sidual.
= (.


 ) (5)
In addition, for the POI mixing degree, as shown in Formula (5), this study adopted
the Simpson value, where . is the ratio of the number of j-type POIs to all of the POIs
in each research unit.
4. Results
This section presents the spatiotemporal distribution characteristics of attractiveness,
the TOPSIS evaluation result, the OLS regression result, and the type analysis.
4.1. Spatiotemporal Distribution Characteristics of Attractiveness
4.1.1. Space Density
Space density is a measure of user density and represents the ability of a research
unit to gather crowds. As shown in Figure 4, the high-density areas in the riverside area
of Wuhan were mainly concentrated in the central area of the city and were mostly cul-
tural spots and landscape parks such as the Temple of Dragon King, the Nananzui Park,
and the Culture Plaza of the Hankou River Beach Park. The units with low spatial density
were mainly concentrated in the urban fringes, with only a small scattered distribution.
Overall, the space density during weekends was much lower than that during weekdays,

Land 2022, 11, 1434 10 of 22
and the relative value of the space density of the landscape park research units on week-
ends was decreased.
Figure 4. The space density distribution of the riverside area of Wuhan.
In general, the space density of the riverside area of Wuhan presented a significant
centripetal distribution, and many people gathered in the cultural spot and landscape
park category research units, but the space density of the landscape park category re-
search units decreased on weekends.
4.1.2. Space Distance
The space distance indicator is a measure of the distance traveled by a user, indicat-
ing the ability of a research unit to attract people from distant places. As shown in Figure
5, the research units with a high space distance in the riverside area of Wuhan had regional
aggregation characteristics, especially the transportation hub such as the research units
where the Qingchuan and Zhonghualu Docks are located, which had the highest space
distance value, followed by the research unit where high-quality landscape parks and res-
idential areas such as Hankou River Beach Park Phase IV and Baishazhou Bridge River
Beach were located. The space distance distributions during weekends and weekdays
were similar, but the space distance of the landscape parks and cultural spots during
weekends was higher than that during the weekdays, indicating that people were willing
to access the city’s riverside area for activities on the weekends.

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Figure 5. The space distance distribution of the riverside area of Wuhan.
In general, the distribution of spatial distance in the waterfront area of Wuhan pre-
sented regional aggregation characteristics; waterway transportation hubs had the high-
est spatial distance value, and people were willing to come to the landscape parks and
cultural spots in the riverside area from farther places for activities on the weekends.
4.1.3. Space Diversity
The space diversity indicator presents the number of user origins, indicating the abil-
ity of a research unit to attract people from different regions. As shown in Figure 6, the
distribution pattern of the units with high spatial diversity in the Wuhan riverside showed
regional aggregation characteristics, indicating that the units were mainly distributed in
the research units where high-quality landscape parks and residential areas such as the
Er’qi Yangtze River Bridge, Fengfan Square, the Culture Plaza of Hankou River Beach
Park, and the Temple of the Dragon King are located. Overall, the space diversity during
weekdays was higher than that during the weekends, especially the space diversity of the
residential category research units, which decreased the most on weekends, indicating
that the source of people visiting riverside residential areas during weekdays was rela-
tively more diverse than that during the weekends.
Figure 6. The space diversity distribution of the riverside area of Wuhan.

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In general, the research units with high space diversity in the riverside of Wuhan
showed regional aggregation characteristics in different areas. High-quality landscape
parks and residential areas can attract people from different areas, and the source of peo-
ple visiting the riverside residential areas on the weekdays was relatively more diverse
than that during the weekends.
4.1.4. Dwell Time
The dwell time measures the average dwell time of users and represents the ability
of a research unit to attract people to stay for a long time. As shown in Figure 7, the units
with a high dwell time in the riverside area of Wuhan were mainly marginally distributed,
and most were landscape parks such as Hankou River Beach Park Phase IV, the Baisha-
zhou Bridge River Beach Park, and the Qingshan River Beach Park, and the research units
where residential areas or public buildings such as the Wuhan International EXPO Center,
are located. The distributions of dwell time during the weekdays and weekends were ba-
sically the same, and the dwell time during the weekends was slightly higher than during
the weekdays.
Figure 7. The dwell time distribution of the riverside area of Wuhan.
In general, the research units with a high dwell time in the Wuhan riverside were
mostly marginally distributed, and the research units where the landscape parks, residen-
tial areas, and public buildings were located had long dwell times.
4.1.5. Dwell Frequency
Dwelling frequency measures the average dwell frequency of users, indicating the
ability of a research unit to attract crowds to visit multiple times. As shown in Figure 8,
the units with a high dwell frequency in the riverside area of Wuhan were mainly scat-
tered in distribution, and most of them were cultural spots and well-built landscape parks
such as the research units where the Temple of Dragon King, the Nananzui Park, the Cul-
ture Plaza of Hankou River Beach Park, and the Wuchang River Beach Park are located.
In addition, the research unit where the residential area is located also had a high dwell
frequency. The areas with low dwell frequency were mainly distributed in the urban
fringe area, and a few were scattered. The dwell frequency during the weekdays was gen-
erally higher than that during the weekends, and the dwell frequency of some landscape
parks was relatively higher during the weekends than that during the weekdays.

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Figure 8. The dwell frequency distribution of the riverside area of Wuhan.
In general, the research units in Wuhan riverside with high dwell frequency pre-
sented a scattered distribution. Cultural spots and well-built landscape parks can attract
people to visit and stay many times.
4.1.6. Time Diversity
The time diversity indicator measures the diversity of the user arrival times and rep-
resents the ability of a research unit to attract crowds at different times of the day. As
shown in Figure 9, the distribution of units with high time diversity had regional aggre-
gation characteristics. Most of the units were mainly where landscape parks such as
Hankou River Beach Park Phase IV, the Baishazhou Bridge River Beach Park, Nananzui
Park, and the Temple of Dragon King, are located, public buildings such as the Wuhan
International EXPO Center, and residential areas. The spatial distributions of time diver-
sity during the weekdays and weekends were similar, but the time diversity of the re-
search unit where the transportation hub is located was lower during the weekends than
the weekdays, and the time diversity of a few research units where landscape parks are
located was relatively higher during the weekends than the weekdays.
Figure 9. The time diversity distribution of the riverside area of Wuhan.

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In general, the distribution pattern of time diversity in the Wuhan riverside showed
regional aggregation characteristics. Cultural spots and well-built landscape parks could
attract crowds in highly diverse times of the day, while the time diversity of the transpor-
tation hubs was relatively lower on the weekends than on the weekdays.
This part analyzes the spatiotemporal attractiveness characteristics of the riverside
area of Wuhan from the spatial and temporal dimensions. The specific findings are as
follows:
1. The difference between the spatial dimensions on the weekends and weekdays was
larger than that between the time dimensions.
2. The distribution of high values of various indicators was highly varied, presenting
four main types: centripetal aggregation, marginal distribution, regional aggrega-
tion, and scattered distribution. Among the indicators, space density showed a cen-
tripetal distribution, dwell time a marginal distribution, space distance, space diver-
sity, and time diversity a regional aggregation, and dwell frequency a scattered dis-
tribution.
3. A significant correlation existed between the type of research unit and the high value
of the indicator. The research units with a high space density were mostly cultural
spots and landscape parks; the research units with a high space distance were mostly
transportation hubs, cultural spots, and landscape parks; the research units with high
space diversity and dwell frequency were landscape parks and residential areas; and
the research units with high dwell time and time diversity were landscape parks,
public buildings, and residential area.
4.2. TOPSIS Evaluation Result
4.2.1. Weight Calculation Result
After standardizing the indicators, the entropy weight of the TOPSIS method was
used to calculate the spatiotemporal attractiveness indicators of the Wuhan riverside area
on the weekends and work days. Then, we obtained information such as the weight and
relative approach degree of each indicator. As shown in Table 4, overall, the weight of the
spatial indicators was slightly larger than that of the time indicators. Among the indica-
tors, space density had the largest weight (40.31% on weekdays, 37.23% on weekends),
followed by dwell frequency (35.61% on weekdays and 32.43% on weekends), and both
played a decisive role in the total attractiveness score. In addition, the weights of space
density and dwell frequency on working days were slightly higher than those on the
weekends.
Table 4. The weight distribution of the attractiveness indicators of the riverside area of Wuhan.
Dimension Indicator
Work
Day
Average
Work
Day
Standard
Deviation
Work
Day
Weight
Rest Day
Average
Rest Day
Standard
Deviation
Rest
Day
Weight
Spatial
Space Density
Space Diversity
0.069
0.201
0.148
0.156
40.31%
10.21%
0.07
0.159
0.149
0.145
37.23%
10.28%
Space Distance
0.619
0.298
7.02%
0.5
0.315
10.67%
Temporal
Dwell Time
Dwell Frequency
0.551
0.14
0.203
0.217
3.70%
35.61%
0.518
0.145
0.22
0.219
4.26%
32.43%
Time Diversity
0.756
0.237
3.15%
0.7
0.294
5.13%
In general, the indicator of the spatial dimension had a higher weight than that of the
time dimension, and the space density and dwell frequency were the most important in-
dicators for determining the total attractiveness score.

Land 2022, 11, 1434 15 of 22
4.2.2. Overall Attractiveness Evaluation Result
After calculating the relative approach degree of each research unit according to the
TOPSIS model, we normalized it to obtain the total attractiveness score of each research
unit as the total attractiveness evaluation result for working days and weekends in the
riverside area within the Third Ring Road of Wuhan. As shown in Figure 10, the highly
attractive areas of the waterfront showed regional aggregation characteristics. The re-
search units where high-quality landscape parks and cultural spots are located were the
most attractive, followed by the research units where urban public buildings and some
residential areas are located. The difference in the distribution of attractiveness between
weekends and weekdays was small, but the research units where landscape parks such as
the Wuchang and Baishazhou Bridge River Beach Parks are located were relatively more
attractive on the weekends than on the weekdays. Research units where transport hubs
such as the Zhonghualu and Qingchuan Docks are located were relatively less attractive
on the weekends than on the weekdays.
Figure 10. The spatiotemporal attractiveness distribution of the riverside area of Wuhan.
In general, the distribution of attractiveness in the riverside area of Wuhan presents
regional aggregation characteristics. Most of the areas with high attractiveness were re-
search units where high-quality landscape parks and cultural spots are located, and the
research units where few landscape parks are located were relatively more attractive on
the weekends, and those where the transport hubs are located were less attractive on the
weekends than on the weekdays.
4.3. OLS Regression Result
On the basis of the discussion in Sections 4.1 and 4.2, to continue studying the rela-
tionship between different types of POI and POI mixing degrees and attractiveness, the
OLS regression model was used to analyze the attractiveness during the weekends and
weekdays and the density of different types of POI and the Simpson value of POI (repre-
senting POI mixing degree) were studied. As shown in Table 5, the independent variables
passed the collinearity test (VIF < 7.5) and the significance test. The adjusted R2 indicates
that the independent variables had 68.9% and 63.2% explanation levels for attractiveness
on the weekdays and weekends.

Land 2022, 11, 1434 16 of 22
Table 5. The OLS regression results.
Variables
Beta (Week-
day)
t (Weekday)
Beta (Week-
end)
t (Weekend)
VIF
CPOI
1.361
7.466 ***
1.424
7.183 ***
4.811
RPOI
0.291
2.716 **
0.344
2.951 **
1.666
HPOI
−0.758
−5.099 ***
−0.721
−4.457 ***
3.198
OPOI
−0.379
−2.231 **
−0.598
−3.237 **
4.173
Simpson
−0.212
−2.268 **
−0.181
−1.788 *
1.259
Adjusted R
2
: 0.689
Adjusted R
2
: 0.632
* Significant at the 0.1 level, ** Significant at the 0.05 level, *** Significant at the 0.001 level.
The beta values in Table 5 show that the CPOI (1.361 on the weekdays and 1.424 on
the weekends) had the strongest positive correlation with waterfront attractiveness on the
weekdays and weekends, followed by the RPOI (0.291 on the weekdays, 0.344 on the
weekends). This proves that consumer POI and outdoor recreation POI have a positive
effect on the attractiveness of the Wuhan riverside. However, HPOI (−0.758 on the week-
days, −0.721 on the weekends) and OPOI (−0.379 on the weekdays, −0.598 on the week-
ends) were negatively correlated with waterfront attractiveness, suggesting that residen-
tial-related POI and public service POI have a negative effect on the attractiveness of ur-
ban waterfronts. Particularly, we noted a negative correlation between the Simpson value
(−0.212 on the weekdays, −0.181 on the weekends) and attractiveness, indicating that areas
with a high POI mixing degree in Wuhan riverside areas are more likely to have low at-
tractiveness.
Combining previous conclusions, we can speculate that POI is not the main factor
that attracts people to visit urban waterfronts. Certain types of POI may reduce attractive-
ness, and areas with a high degree of POI mixing may not necessarily have high attrac-
tiveness. However, in some waterfront areas, the pleasant open space landscape may be
highly appealing to the crowd.
4.4. Type Analysis
To clearly explore the characteristics of waterfront attractiveness from the perspec-
tive of POI, we classified 46 research units from three perspectives: attractiveness scores,
POI density, and POI mixing degree. Combined with the results of the OLS regression,
the POIs were divided into positive classes (CPOI and RPOI) and negative classes (HPOI
and OPOI), and the positive (positive POI density) and negative values (negative POI
density) of each study unit were calculated. Finally, the four indicators of each research
unit, namely, the attractiveness, positive, negative, and Simpson values, were normalized,
and their averages were calculated. Then, the ones above the average were assigned to the
high score H class, and those below the average were assigned to the low score L class.
For example, if a research unit’s attractiveness score was above average, but all the other
indicators were below average, the unit was classified as “HLLL.”
According to the number and type of research units in each category, we determined
four categories of research units: “HHHH”, “HLLL”, “LHHH”, and “LLLL”. As shown in
Figure 11, the types of distribution on the weekdays and weekends were basically the
same.

Land 2022, 11, 1434 17 of 22
Figure 11. The type distribution of the riverside area of Wuhan.
1. The “HHHH” category was mainly distributed in the central area of the city such as
the unit where the Zhiyinhao Dock is located, which is a famous urban cultural tour-
ism area in Wuhan. This category of area often had strong centrality and attractive-
ness and belonged to the relatively economically developed region, where the POI
variety and quantity were also very high.
2. The “LLLL” category was mainly distributed in the urban fringe area and closer to
the suburbs than the “LLLH” category. This category belonged to a relatively under-
developed area with low POI density, POI mix, and attractiveness.
3. The “HLLL” category was scattered and mainly located in well-constructed land-
scape parks in the riverside area. This category’s high attraction was mainly its well-
designed and high-quality open spaces, which provide opportunities for the sur-
rounding crowd to commune with nature. People were attracted by the scenery and
environment here rather than the variety of POIs.
4. In the “LHHH” category, research units were mainly distributed in the central area
of the city. This type of area was low in attractiveness, but the density and mix of the
POIs were high. The surrounding areas were mostly residential areas with complete
infrastructure, and the part along the river is dominated by cargo terminals and lin-
ear walks. Thus, the area is unfavorable for people to stay, and the landscape and
activities of the place are relatively monotonous, resulting in low attractiveness to the
crowd.

Land 2022, 11, 1434 18 of 22
Through type analysis, we can obtain an in-depth understanding of the close rela-
tionship between the attractiveness and POI factors of the riverside area of Wuhan. Over-
all, the attractiveness, the POI density, and the POI mixing degree were generally higher
for research units near the city center, while the opposite was true for those further away
from the city center. However, the spatial quality and public cultural diversity of the re-
search units where landscape parks and cultural spots are located were the decisive fac-
tors of their attractiveness, and the density or abundance of POI did not largely determine
their attractiveness. Therefore, we believe that for urban riverside areas with low attrac-
tiveness, the space quality should be improved first to increase the landscape value or
accommodate many public activities, subsequently increasing the consumer and outdoor
recreation POIs.
5. Discussion
This study established indicators from the spatial and temporal dimensions to meas-
ure the attractiveness of the riverside areas within the Wuhan Third Ring Road and used
the TOPSIS method to calculate the total attractiveness indicator. Finally, we used the OLS
model to study the relationship between the POI and attractiveness. Our research revealed
the following.
1. The high-value distribution of attractiveness of the river waterfronts in Wuhan pre-
sented regional aggregation characteristics, and the attractiveness of the economi-
cally developed areas was high.
2. CPOIs and outdoor RPOIs had a positive effect on the attractiveness of the riverside
in Wuhan, while HPOIs, OPOIs, and the high degree of POI mixing had a negative
impact on the attractiveness of urban riverside.
3. The high–high agglomeration spaces were mainly concentrated in the economically
developed areas of the city center and were mainly open spaces where urban cultural
activities are held, while the low–low agglomeration spaces were mostly gathered in
the suburban areas. The spatial distribution of the high–low agglomeration spaces,
which are mainly green open spaces, was relatively fragmented, while the low–high
clusters, which are mainly freight terminals, linear walks, and residential areas, were
near the city center.
Through the above results, we found that common places with greater economic in-
vestment in the waterfront were more attractive to the users, but sometimes, the results
showed the opposite in different land categories. For landscape parks and cultural spots
with low economic investment but high attractiveness, the spatial quality and public cul-
tural diversity were the decisive factors of their attractiveness; for residential areas with
high economic invest but low attractiveness, the monotony and low quality of riverside
space led to their low attractiveness. In general, we believe that the attractiveness of the
riverside area is affected by economic and natural factors, and there are different strategies
to enhance the attractiveness for different land types.
6. Conclusions
6.1. Research Innovation
On the basis of the concept of attractiveness and the existing research, this research
studied the attractiveness of waterfront areas through quantitative analysis methods and
presented the spatiotemporal distribution characteristics of the riverside areas within the
Wuhan Third Ring Road from the perspective of traveling. Using mobile phone signaling
data, the characteristics of people’s travel in the city’s riverside area were studied at the
urban scale. The TOPSIS model was used to construct the attractiveness indicator system,
and the OLS regression model was used to explore the relationship between the POI and
attractiveness. Finally, the research units were classified and analyzed for a deeper under-
standing of the Wuhan riverside’s attractiveness.

Land 2022, 11, 1434 19 of 22
Our research results have a certain reference value for urban planning and design
and to research personnel in designing, constructing, and transforming the riverside area
of Wuhan and further enhancing the attractiveness of waterfront open spaces.
6.2. Future Construction Suggestions
This study revealed that the attractiveness of the riverside area of Wuhan presented
regional aggregation characteristics, and the attractiveness of the economically developed
regions was high. The areas with low attractiveness were generally characterized by in-
sufficient facilities, poor service, and monotonous space design. Thus, this study presents
recommendations for enhancing the attractiveness of waterfronts.
First, in future urban master planning, the balanced development of various regions
should be given more attention, especially the enhancement of the attractiveness of eco-
nomically underdeveloped riverside areas.
Second, the construction quality of landscape parks must be improved and the open-
ness of waterfront spaces must be increased to accommodate many urban public cultural
activities.
Finally, consumer and outdoor recreation POIs should be increased to enhance the
attractiveness of the city’s riverside areas.
6.3. Deficiencies and Limitations
This study also had some limitations. First, the mobile singling data were provided
by China Unicom Company, while users of other mobile companies were not included in
the study. Second, the mobile phone signaling data only provided the time and location
and did not engage in any of the other variables that assisted in determining an individual
or community’s attractiveness to a location. It only shows the objective observation of hu-
man stay, but does not present a subjective preference of attractiveness. Therefore, this
work only offers an additional method for determining the ‘attractiveness’ of a public
space, but not as a stand lone approach. Third, although we found that economic invest-
ment and natural factors are important influencing factors on attractiveness, we have not
drawn any specific conclusions on how these two factors affect the attractiveness and the
differences between them.
Therefore, in the future, we hope to add multisource data (such as social media data,
wearable device data, Wi-Fi data, traffic data, etc.) that present the individuals’ perception
and behaviors to make up for the limitations of signaling data as well as integrate more
urban economic and natural indicators to establish a model to explore their relationship
with attractiveness. In addition, studying the urban attractiveness for certain groups of
people from different aspects (such as age, gender, occupation, etc.) will also be beneficial.
Author Contributions: Conceptualization, J.W.; Methodology, Y.C. and B.J.; Software, B.J.; Valida-
tion, J.W., Y.C., and B.J.; Formal analysis, Y.C.; Investigation, J.W.; Resources, J.W.; Data curation,
T.L. and X.L.; Writing—original draft preparation, Y.C.; Writing—review and editing, J.W. and Y.C.;
Visualization, B.J.; Supervision, J.W.; Project administration, J.W.; Funding acquisition, J.W. All au-
thors have read and agreed to the published version of the manuscript.
Funding: This research was funded by The Young Top-notch Talent Cultivation Program of Hubei
Province, grant number 2021 Frist Batch and The APC was funded by Organization Department of
the CPC Hubei Provincial Committee.
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. We confirm that this manuscript
has not been published elsewhere and is not under consideration by another journal. All authors
have approved the manuscript and agree with submission to Science of the Total Environment.

Land 2022, 11, 1434 20 of 22
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