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Available online at www.sciencedirect.com
ScienceDirect
journal homepage: www.keaipublishing.com/foar
RESEARCH ARTICLE
Vitality evaluation of the waterfront space in
the ancient city of Suzhou
Yingxiang Niu a, Xiaoyan Mi a,*, Zhao Wang b
a
School of Architecture, Tianjin University, Tianjin, 300072, China
School of Information Technology and Electrical Engineering, The University of Queensland, Brisbane,
Qld, 4072, Australia
b
Received 8 November 2020; received in revised form 18 June 2021; accepted 4 July 2021
KEYWORDS
Waterfront space;
Influence factor;
Vitality evaluation
model;
Crowd activity;
Hash algorithm
Abstract With a history spanning thousands of years, the water system of Suzhou is an indispensable carrier of urban space and civil culture. Quantificational model analysis of the waterfront in the ancient city of Suzhou has significant implications for the future design of
waterfront space and the establishment of an evaluation method to determine the vitality
of such space. In this study, a vitality evaluation model was first constructed using river attributes, the spatial type of waterfront areas, vision accessibility, transportation accessibility,
and combined new data extracted using spatial factor analysis. Second, a vitality evaluation
matrix was established using the analytic hierarchy process to simulate the vitality of waterfront spaces. Third, a hash algorithm was employed to determine the fitting degree between
the vitality model of Suzhou’s waterfront space and crowd activity. The different areas between them were found and then the factor evaluation process was adjusted on this basis
of analyzing the causes. Thus, this study identified the factors influencing the vitality of Suzhou’s waterfront. Furthermore, this research constructed a model for evaluating the vitality of
waterfront spaces. Finally, some guidelines were presented regarding the design and implementation of waterfront spaces in urban design.
ª 2021 Higher Education Press Limited Company. Publishing services by Elsevier B.V. on behalf
of KeAi Communications Co. Ltd. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
* Corresponding author.
E-mail address: xiaoyan_mi@tju.edu.cn (X. Mi).
Peer review under responsibility of Southeast University.
https://doi.org/10.1016/j.foar.2021.07.001
2095-2635/ª 2021 Higher Education Press Limited Company. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Please cite this article as: Y. Niu, X. Mi and Z. Wang, Vitality evaluation of the waterfront space in the ancient city of Suzhou, Frontiers of
Architectural Research, https://doi.org/10.1016/j.foar.2021.07.001
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1. Introduction
through fitting degree analysis of residents’ use efficiency.
Moreover, the factors influencing waterfront space vitality in
the ancient city of Suzhou and the degrees of influence are
clarified. A quantitative model for the waterfront space vitality of the historical water system is constructed.
Furthermore, this study proposes a reasonable argumentation and verification method and quantitative data support
for the empirical research on waterfront space vitality.
With rich water networks and a history of thousands of
years, Suzhou is known as the “Oriental Venice.” Waterfront spaces such as lakesides and riversides play a critical
role in urban spaces. They not only constitute a natural
ecological transition between water and land to improve
urban environments and enrich urban landscapes but also
foster a close connection between nature and urban crowds
to improve urban spatial quality and characteristics. A
quantitative evaluation of waterfront space vitality is of
great significance in the creation of a healthy social life
among urban residents, to the efficient design and renewal
of urban spaces, and to the effective allocation and
improvement of service facilities for the urban public.
In recent years, following the emergence of various new
types of data and on the basis of traditional research
methods (Yin, 2019), numerous studies have measured the
vitality of urban spaces using urban planning and research
methods pertaining to sociology and geography (Yang and
Shao, 2018). These studies have focused on a certain type
of urban space, including residential areas, streets, parks,
and scenic spots, to examine the impact of one or several
types of elements pertaining to space vitality. Many of the
studies have examined waterfront space vitality through
methods such as employing the plot ratio as the evaluation
standard of development intensity to examine the influence
mode and the degree of development intensity with regard
to waterfront space vitality (Zhang and Guo, 2019).
Furthermore, some scholars have used the characteristics
of certain elements to measure the vitality of waterfront
space through processes such as measuring the level of
shoreline accessibility by taking into account the number of
adjacent shoreline roads and road intersections (Zhang,
2018). The density of road intersections within each
waterfront space unit was used to measure block texture
(Yang et al., 2018), and the distribution of public service
facilities within a waterfront space was measured using the
density of these facilities (Wang and Ma, 2020). Location
centrality was measured by the shortest linear distance
between the waterfront space and city center (Chu, 2002).
Furthermore, researchers have examined the influence of
factors such as shoreline accessibility, block texture, and
location centrality on waterfront spaces. The majority of
studies have highlighted distinct factors, directly or indirectly analyzing their influence on waterfront space vitality. Therefore, researchers should develop a comprehensive
research model to determine the types and levels of the
factors that influence waterfront space vitality.
This research is based on the relationship between the
historical water system and modern social life in the ancient
city of Suzhou. Influencing factors are selected from the
perspectives of physical space and crowd perception.
Drawing from previous research, the current study contributes to the analysis of physical space elements such as river
attributes and street types and explores the influencing
factors of three-dimensional spaces, such as visual accessibility. Furthermore, it establishes an evaluation model for
analyzing the vitality of waterfront spaces based on urban
planning and sociological research methods and uses a hash
algorithm to demonstrate the rationality of the model
2. Analysis of the spatial characteristics of the
waterfront in the ancient city of Suzhou
2.1. The study’s definition of waterfront space
Waterfronts constitute a broad concept, covering multiple
categories of spaces, including riversides, lakesides, coastal
areas, and wetland. The waterfront spaces examined in this
research were urban waterfront spaces, which is “a general
term for a certain area connected by land and water in a city”
that is generally formed by water areas, water boundaries,
and land areas (Yang et al., 2018). In line with the scope of
this researchdthe actual situation of the waterfront space in
Suzhoudthe current study defines waterfront space as the
land space within 60 m of a waterfront.
2.2. Characteristics of the waterfront space in
Suzhou
The water system in Suzhou originated from the Grand
Canal of the Suzhou Section. A complete water system
network has been created following the flow of water into
the urban area of Suzhou. At present, the chessboard
pattern observed in the Pingjiang Tu region of the Song
Dynasty has been maintained. Overall, Suzhou has a system
of water flowing around buildings and is surrounded by the
water system. Suzhou’s buildings are mostly located along
the rivers, resulting in a waterfront space that is mostly
parallel to the rivers. A characteristic waterfront pattern
constituting adjacent rivers, land, and streets has thus
been formed (Fig. 1; Li et al., 2014). This unique waterfront
space serves as the main line of the urban space that
connects streets, markets, and other major open spaces.
The rivers in Suzhou stretch for 35 km in total, and more
than 20 rivers and 180 bridges can be found in the city (Lu,
2016). The ancient city’s water network is divided into
three levels: the moat, the trunk river system, and tributaries (Zhang, 2016). The moat forms a ring, and the trunk
river system comprises three vertical and three horizontal
forms. The tributaries connect the trunk river system to the
network, in turn connecting thousands of households.
3. Establishment of a vitality evaluation model
for Suzhou’s waterfront
3.1. Selection of vitality-influencing elements and
hierarchical structure model establishment
Highly concentrated crowd activity in an urban space is a
critical manifestation of city vitality (Yin, 2019), which is
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Fig. 1 Schematic map of waterfront space pattern. (a) Source: Self-painted by author based on Pingjiang Map (Song Dynasty). (b)
Source: Self-painted by author based on Gusu City Map (Qing Dynasty). (c) Source: Self-painted by author.
analysis results. However, this paper mainly studies the
influencing factors of waterfront space vitality from the
perspective of the surrounding environment without paying
attention to the influence of factors such as river attributes
and spatial type.
On the other hand, it was found that the control and
guiding elements of waterfront space included building
retreat, waterfront footpath layout, revetment form, dock
settings and rail form by consulting the Urban Design
Guidelines of Suzhou City (2019).
Therefore, on the basis of a systematic review of previous
literature and relevant design guidelines, this study selected
the influencing factors of waterfront space vitality in Suzhou
and summarized different dimensions combined with relevant expert consultation to establish a systematic evaluation system of waterfront space vitality. The influencing
factors of waterfront space vitality were finally summarized
into multiple dimensions such as traffic, location, building,
land use, facilities, waterbody and riverbank. Combined
with field research of the spatial characteristics, crowd
activities and actual use of the ancient city of Suzhou, the
transportation accessibility was used to measure the traffic
and location factors of the waterfront space. In view of the
building density and development intensity in the ancient
city is relatively average, the land use functions and facilities distribution were summarized as functional mixing
degree. The water body was summarized as river attributes.
The relationship among the waterfront space, adjacent
buildings and roads was summarized into three spatial types
to reflect the combination type of waterfront space and
hydrophilicity of riverbank. Considering the influence of
three-dimensional space elements, visual accessibility was
added as an influencing factor.
Finally, the first-level indicators of waterfront space
vitality in Suzhou were summarized into functional type,
river attributes, land spatial types, vision accessibility and
traffic accessibility. On this basis, the first-level indicators
were refined. Through the analysis of the architectural type
and land use function of the waterfront space in Suzhou,
the functional type was subdivided into education (referring to schools, research institutions and training institutions, etc.), health care (referring to hospitals,
pharmacies, clinics, healthcare institutions, etc.), daily life
(referring to daily life service places, such as beauty and
hair salons, express delivery sites, intermediary agencies,
telecom offices, laundry, maintenance sites, printing
among the important criteria for measuring the quality of
an urban space. This research analyzed the characteristics
of the vitality of waterfront spaces by examining the material space representation of crowd concentration and
proposed a quantitative evaluation model of this vitality.
In this study, it is found that there are few literatures
related to the analysis of waterfront space vitality by
referring to related keywords in the CNKI and SCI database
in the past ten years. The previous literature mainly
focused on the influence of single elements such as
greenway (Qian et al., 2017), public space (Yang and Shao,
2018) and terrain (Dong et al., 2020) on waterfront space
vitality or explored the indirect vitality factors such as
publicity (Wang et al., 2020) and connectivity (Da, 2013) of
waterfront space. Overall, there are few studies on the
systematic construction of the waterfront space vitality
influencing system. In addition, the data sources of these
studies are mostly questionnaires or field observations,
which have no reference significance in the study of largescale urban waterfront space.
Among them, Fang Qian decomposed the components of
urban waterfront space from the perspective of health
orientation (Qian, 2010). Moreover, the study screened out
the influencing factors of waterfront space such as traffic,
water bodies, greening and activity facilities from its
theoretical analysis results. However, this paper mainly
focused on the components of waterfront space without
paid attention to the land use types and building functions
adjacent to the waterfront space. Ting Da indirectly
explored the influencing factors of the urban waterfront
space vitality from the perspective of influencing the
waterfront space connectivity and established the evaluation system of waterfront connectivity with the connectivity calculation method (Da, 2013). The study screened
out the influencing factors of the waterfront space vitality
including transportation, open space, housing, shopping,
sports facilities and other types of POI. However, there is a
lack of research on river attributes and vision accessibility
in three-dimensional space. Weiqiang Wang et al. introduced the quantitative method to explore he influence of
hinterland area development on the vitality of waterfront
public space with the help of multi-source data such as
microblog, aerial image, population distribution and facility
distribution (Wang and Ma, 2020). Moreover, the influence
factors such as surrounding facilities mixing degree and
surrounding housing were screened out based on the
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related to vitality. River attributes were subdivided into
water width, water depth and water quality. The spatial
types were subdivided into no street type, street type
(pedestrian) and street type (vehicle) combined with field
research on the street types of waterfront space in Suzhou.
Traffic accessibility was divided into non-motorized transportation accessibility, mass transit accessibility and private car accessibility through the analysis of crowd travel
modes.
Finally, according to the selected influencing factors and
the logically structured relationships among them, a
waterfront space vitality evaluation model comprising 3
levels and 19 factors was established (Fig. 2).
3.2. Weight calculation of the influencing factors
through the analytic hierarchy process
This study aimed at establishing a vitality evaluation matrix
of the waterfront space in Suzhou. The influencing factors
of the waterfront space vitality had obvious hierarchies and
the relationship between the hierarchies was top-down. In
addition, the factors of both the same and the adjacent
levels were independent without interdependence and
mutual dominance relationship. Therefore, analytic hierarchy process was chosen for weight assignment of influencing factors rather than other methods.
In the analytic hierarchy process, the research object is
considered as a system and decisions are made by means of
decomposition, comparative judgment, and comprehensive
thinking (Wang et al., 2018). This process constitutes an
organic combination of qualitative and quantitative
methods. Furthermore, decision-making problems are
transformed using multiple factors that are difficult to
quantify into a multilevel and single-objective problem.
Fig. 2 Hierarchical structure model of the waterfront space
vitality in Suzhou. Source: Self-painted by author.
shops), housing (referring to ordinary residence, villa,
business residence, etc.), shopping (referring to shopping
places, such as shopping malls, stores, convenience stores,
vegetable markets), catering (referring to various restaurants, beverage shops, pastry shops, fast food restaurants,
etc. that exist independently and open to the outside),
sport (referring to sports venues; fitness centers, etc.),
landscape (referring to scenic spots), open space (referring
to square and green spaces, etc.) and hotel (referring to
places that include accommodation functions) highly
Table 1
Weights of the influencing factors.
Target layer
Criterion layer
Weight
Factor layer
Weight
Spatial vitality evaluation
of the waterfront in the
ancient city of Suzhou
Functional Mixing Degree
0.2029
River Attributes
0.3018
Land Spatial Type
0.1477
Vision Accessibility
Transportation Accessibility
0.0892
0.2583
Education
Health care
Daily life
Housing
Shopping
Catering
Sport
Landscape
Hotel
Open space
Water Width
Water Depth
Water Quality
No Street
Street (Pedestrian)
Street (Vehicle)
Vision Accessibility
Non-motorized Transportation
Accessibility
Mass Transit Accessibility
Private Car Accessibility
0.0086
0.0111
0.0252
0.009
0.0243
0.0261
0.0089
0.0365
0.0171
0.0362
0.0892
0.0756
0.0337
0.0182
0.0991
0.0304
0.0892
0.1707
4
0.0648
0.0228
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hierarchy process. The relative importance of each
element was determined by comparing all element pairs
and using the method of expert group judgment. Expert
group judgment was employed to overcome the limitation
of subjective factors. When constructing a judgment matrix, the judgment matrix of the criterion layer was first
constructed, following which the judgment matrices for
the factor layers under each criterion layer were constructed. In the judgment matrix, a comparison scale from
1 to 9 was used to determine the relative importance of
each factor and combine the experts’ overall ranking of the
relative importance of the various factors influencing
waterfront space vitality in Suzhou. Thereafter, the overall
order of the relative importance of the influencing factors
was determined using the group judgment method, and the
weight of each influencing factor was determined based on
this determination. The study sent out questionnaires to 30
experts, including 20 local urban planning engineers who
had participated in compiling the urban design guidelines
of Suzhou and 10 environmental behavioral experts. A total
of 30 questionnaires were distributed and received. After
analysis and calculation, the consistency ratio of 28 questionnaires was less than 0.1, which met the one-time test
and was considered as valid questionnaires. Based on the
statistical analysis of 28 valid questionnaire data, the
weight of each factor in criterion layer and factor layer was
obtained. The specific values are shown in Table 1.
The vitality evaluation model for Suzhou’s waterfront
space was constructed as follows formulas:
Fig. 3 Data process flow diagram. Source: Self-painted by
author.
The impact of each element on the result is quantified by
comparing all element pairs.
The study’s evaluation model was based on the hierarchical structure model constructed using the analytic
Fig. 4
Distribution map of the functional mixing degree. Source: Self-painted by author.
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Table 2
Statistics of the river attributes.
River name
Water quality
(type)
Water
width (m)
River name
Water quality
(type)
Water
width (m)
The first straight River
The second straight River
The third straight River
The first horizontal River
The second horizontal River
The third horizontal River
Lvmenneicheng River
Cangqiaobang River
Pingmen River
Zhongshi River
Beiyuan River
Xiqilin River
V
IV
IV
IV
V
V
IV
IV
IV
IV
IV
IV
5
3~10
5~6
5~6
5
5~6
6~8
4
4~4.5
4~5
4
4
Huxiangshi River
Liuzhi River
Xuanqiao River
Xinqiao River
Loumenneicheng River
Dongyuananqu River
Panmenneicheng River
Zhuhui River
Xuejia River
Miaojiabang River
Moat
IV
IV
IV
IV
IV
IV
V
V
V
V
V
6
5~6.6
1.8
5
4
4
10~12
6~8
6
5
30~100
Transportation Accessibility Z 0.1707 Non-motorized
Transportation Accessibility þ 0.0648 Mass Transit
Accessibilityþ0.0228 Private Car Accessibility
(5)
Waterfront Space Vitality Z 0.2029 Functional Mixing
Degree þ 0.3018 River Attributes þ 0.477 Land Spatial
Type þ 0.0892 Vision Accessibility þ 0.2583 Transportation Accessibility.
(1)
3.3. Classified calculation of the factors influencing
waterfront space vitality in Suzhou
Among the above factors, the following relationships
were observed.
To clarify the waterfront space vitality in Suzhou, the
influencing factors were classified and calculated. The
waterfront space function utilized Baidu map open data,
that is, POI data. The water width, depth, and quality data
of the waterfront space were collected from the website of
the Suzhou Municipal Government and through field
research. Street type data were collected through field
research and visual and transportation accessibility was
calculated and analyzed using Suzhou urban road network
data.
In this study, all types of data were imported into the
ArcGIS platform for data preprocessing, and the preliminary
Functional Mixing Degree Z 0.0086 Education þ 0.0111 Health Care þ 0.0252 Daily Life þ 0.0090 Housing þ
0.0243 Shopping þ 0.0261 Catering þ 0.0089 Sport þ
0.0365 Landscape þ 0.0171 Hotel þ 0.0362 Open
Space
(2)
River Attributes Z 0.0756 Water Width þ 0.0337 Water
depth þ 0.1925 Water Quality
(3)
Land Spatial Type Z 0.0182 No Street Type þ 0.0991 Street Type (Pedestrian) þ 0.0304 Street Type (Vehicle)(4)
Fig. 5
Comprehensive evaluation of the river attributes. Source: Self-painted by author.
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data results were obtained for subsequent research. Fig. 3
illustrates the specific processing flow.
3.3.1. Function type analysis of Suzhou’s waterfront
space
Using the ArcGIS platform, the kernel density of 10 types of
point of interest (abbreviated as POI in the following) data
that were highly correlated with waterfront space vitality
in Suzhou and its surrounding area was analyzed (Fig. 4(a)).
Each type of POI data was superimposed according to
formula (2) through the analytic hierarchy process to create
a comprehensive evaluation map of the waterfront spatial
function mixing degree in Suzhou (Fig. 4(b)).
The comprehensive evaluation map revealed that the
place with the highest degree of functional mixing in Suzhou was the middle part of the Xinkai River, followed by the
historical and cultural blocks of Pingjiang Road, the west
section of the Daoqian River, and the areas around the
Zhongshi River. These areas are rich in types and numbers
of POI. Furthermore, the various service facilities in these
areas are relatively concentrated and fully functional.
3.3.2. River attributes analysis
The river attributes were analyzed to obtain statistics
pertaining to the water depth, width, and quality data and
to calculate basic data as per the quantitative standard
assignment calculation. The river attributes were analyzed
and calculated according to formula (3), and depth, and the
calculation results were visualized through the ArcGIS
platform.
The average water depth of the moat was 2.8 m, and the
water depth was approximately 2.5 m during the dry season
(Lu, 2016). The average water depth of the moat was 2.8 m,
and the water depth was approximately 2.5 m during the
dry season. Considerable siltation was present in the tributaries, the average water depth of which was 1e2 m. As
per the quantitative standard, each river was graded based
on its depth; that is, 0e2 m was assigned 1 point, and >2 m
was attributed 2 points.
The water width was calculated from the relevant data
and literature (Lu, 2016). Table 2 presents the calculated
width of each river in Suzhou. Each river was graded according to its width as per the quantitative standard; that
is, 0e10 m was assigned 1 point; 10e30 m was assigned 2
points; and >30 m was assigned 3 points.
Water quality information for each river in Suzhou was
obtained by considering the average water quality at each
observation point in a week (Table 2) from the website of
the Suzhou Environmental Protection Bureau and relevant
documents (Li et al., 2014). Each river was graded according to water quality as follows: fifth grade water
quality was assigned 1 point and fourth grade water quality
was assigned 2 points.
The weights of water quality, water width and water
depth were obtained using the analytic hierarchy process.
Fig. 5 presents the comprehensive evaluation results.
Finally, the ArcGIS platform was employed to visualize the
comprehensive evaluation results of river attributes (Fig. 6).
Fig. 6 Distribution map of the river attributes. Source: Selfpainted by author.
type, pedestrian street type, and vehicle street type. The
spatial sequence of the pedestrian street type was riveresidewalkebuilding, and that of the vehicle street type
(vehicle) was riverecarriagewayebuilding. The spatial
sequence of no street type was riverebuilding.
3.3.3. Land spatial type analysis
Field research and relevant literature review (Zhang, 2016)
highlighted three river space types in Suzhou: no street
Fig. 7 Distribution map of the space types. Source: Selfpainted by author.
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waterfront space and size of the surrounding open space.
The areas with better sight were found to be concentrated
along the moat and on both sides of Ganjiang road; the
other river channels had poor sight because the river in
these areas is narrower and the surrounding buildings are
dense.
The comprehensive spatial type analysis map demonstrated that the majority of the water system in Nanyuan
was of the no street type. The Pingjiang River system primarily comprised the river and street types, whereas the
second Henghe River and Suzhou University water system
were mostly of the street type.
On the basis of the influence of different land spatial types
on waterfront space and accessibility, the influence weights
of the three street types were determined through expert
marking. Considering this weights as the quantification
standard, this study analyzed the waterfront space spatial
types in Suzhou according to formula (4) and visualized the
calculation results using the ArcGIS platform (Fig. 7).
3.3.5. Classification analysis of waterfront space traffic
accessibility
The main modes of transportation in Suzhou are nonmotorized transportation (including walking, bicycles,
rickshaws and two-wheeler, etc.), private cars, and mass
transit (buses and subways). Therefore, the transportation
accessibility of the city’s waterfront space was comprehensively analyzed using the ArcGIS platform from the
following three aspects: non-motorized transportation
accessibility, private car accessibility, and mass transit
accessibility (Fig. 9(a)). Non-motorized transportation
accessibility was calculated by constructing Suzhou’s road
network and employing the origin-destination (abbreviated
as OD in the following) cost matrix to calculate the accessibility from each point on the road network to the riverside
3.3.4. Analysis of the vision accessibility of the
waterfront space
By constructing the raster data topography of Suzhou and
the viewing routes along the river, observation points were
selected along the routes to analyze the vision accessibility
of the waterfront space. As illustrated in Fig. 8, the
comprehensive analysis map of vision accessibility indicated that the sight was mainly affected by the width of the
Fig. 8
Distribution map of the vision accessibility. Source: Self-painted by author.
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Fig. 9 Distribution map of the transportation accessibility. (a) Classification analysis of transportation accessibility. (b)
Comprehensive analysis of transportation accessibility. Source: Self-painted by author.
visualized through the ArcGIS platform (Fig. 10). The
overall vitality of the waterfront space in Suzhou was found
to be relatively high; however, the vitality values of the
waterfront spaces in different regions were discovered to
be relatively different, as reflected by Guanqian Street in
the middle of the Xinkai River, the Pingjiang River, and the
western section of the Daoqian River. The Lumen Inner City
River and north section of the Xueshi River were observed
to be highly active. However, the waterfront space surrounding the moat was found to have relatively low vitality.
roads. The accessibility of private cars was calculated to
set the capacity values for different roads. Mass transit
accessibility was calculated by adding bus stations, subway
stations, and subway lines on the road network and by using
the OD cost matrix to calculate the accessibility of each bus
station, subway station in the ancient city to the subway
stations, and the bus stations along the riverside road.
The comprehensive evaluation map of the waterfront
space’s traffic accessibility was created by superimposing
the different weights according to formula (5). The map
(Fig. 9(b)) demonstrated that the intersection of the old
city center with the north area had relatively high traffic
accessibility, whereas that of the southern residential area
with the northeast area was relatively low.
4. Analysis of the fitting degree of waterfront
space vitality in Suzhou
3.4. Comprehensive analysis of waterfront space
vitality in Suzhou
By establishing the vitality model of Suzhou’s waterfront
space, quantitative analysis of the use of this space was
conducted from the perspective of urban analysis.
Furthermore, to verify the rationality of the vitality evaluation matrix, the actual data pertaining to crowd activities in the waterfront space were selected for
demonstration. Finally, a hash algorithm was used to
determine the degree of fit. Then the different areas between them was found and the factor evaluation process
was adjusted on this basis of analyzing the causes.
On the basis of the classification of influencing factors and
their respective weights in the predetermined matrix for
waterfront space vitality evaluation, the current study
superimposed and calculated the value of waterfront space
vitality in Suzhou using the grid calculator in ArcGIS according to formula (1). Fig. 10 displays the vitality of each
waterfront space, and the calculation results have been
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4.1. Analysis of crowd distribution in Suzhou’s
waterfront space
This study selected vitality map data from WeChat to
examine the crowd distribution in Suzhou’s waterfront
space. To ensure high scientific validity of the results, data
were collected every 2 h from 6 to 24 points on three days
representative of work days, holidays, and weekends,
respectively (Guo et al., 2020). Subsequently, the crowd
distribution was analyzed for nuclear density; finally, the
vitality map data pertaining to 10 periods were superimposed
in equal proportion to obtain the waterfront spatial crowd
distribution map of the day (Fig. 11(a). (to facilitate comparison, the color and legend digital ranges of the crowd
distribution maps in each period were adjusted uniformly).
The crowd distribution maps of working days, weekends,
and holidays (Fig. 11(a)) were superimposed and analyzed
according to the weights determined using the analytic
hierarchy process to obtain the actual crowd distribution
map in Suzhou (Fig. 11(b)). The crowd distribution map of
the waterfront space indicated that the fewest people are
present on working days in all three periods, whereas the
number of people was highest on the weekends; however,
the differences in overall number of people and the size of
the trend in the three periods were relatively small. The
crowd distribution centers in the three periods were
Guanqian Street in the middle section of the Xinkai River,
Fig. 10 Comprehensive simulation of the waterfront space
vitality. Source: Self-painted by author.
Fig. 11 Crowd map of the waterfront space. (a) Crowd distribution analysis of work days, weekends and holidays. (b) Crowd
distribution analysis of waterfront space. Source: Self-painted by author.
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Fig. 12
Comparison of the waterfront space vitality evaluation with the crowd distribution. Source: Self-painted by author.
process was employed to construct the relevant evaluation
system and study the relationship between the influencing
factors. Third, the influence mechanism of the various
influencing factors on actual commuters of the waterfront
space was explored. Fourth, a crowd distribution map of the
waterfront space was constructed, and a hash algorithm and
the vitality evaluation model were employed to analyze the
degree of fit between the evaluation results and actual
crowd distribution. A high degree of fit was discovered,
verifying the rationality of the evaluation model.
1. This study established a quantitative model for evaluating waterfront space vitality based on historical water
systems; this model was, in turn, employed to determine
the types and levels of factors that influenced waterfront
space vitality.
2. This research led to certain quantitative conclusions
on the vitality characteristics of the waterfront space in
Suzhou.
the Suzhou University area, and the Suzhou Library area.
Furthermore, the population was more evenly distributed
on working days, and the density at crowd gathering points
was higher on weekends and holidays.
4.2. Hash algorithm fitting degree analysis of
waterfront space vitality evaluation and crowd
distribution
A hash algorithm was employed to calculate the fitting
degree between the waterfront space vitality evaluation
and crowd distribution to verify the rationality of the vitality evaluation model (Fig. 12). A grid with size approximately 1 m 1 m was established with the power of 2, and
image similarity was analyzed through Python programming
with this accuracy. The fitting degree between the waterfront space vitality evaluation and crowd distribution was
concluded to be 95.31%, which is relatively high.
In general, the current study’s simulation results of the
model for waterfront space vitality evaluation in Suzhou
were highly consistent with the actual crowd activity. The
weight of each influencing factors in the model can reflect
the degree of importance attached to the crowd attraction to
waterfront space and can be used as a practical foundation
for design and renewal of the city’s waterfront space.
First, three types of factors were discovered to influence
waterfront space vitality. The functional type of the
waterfront space also constitutes the functional mixing
degree, and the influencing factors include education,
health care, daily life, housing, shopping, catering,
sports, landscapes, open spaces, and hotels. Waterfront
spatial form elements were river attributes (water
width, depth, and quality), spatial types (namely street
types: no street type, pedestrian street type, and
vehicle street type), and the vision accessibility of the
city’s waterfront space. The transportation accessibility
of the waterfront space had three factors: nonmotorized transportation accessibility, mass transit
accessibility, and private car accessibility.
Second, river attributes were discovered to exert the
most critical impact on waterfront space vitality in
Suzhou, followed by transportation accessibility, functional mixing degree, and land spatial type. Visual
accessibility was the least influential factor.
5. Conclusion
On the basis of the relationship between historical water
systems and modern social life and by using new data, the
current study conducted a comprehensive analysis of the
function elements, form elements, and transportation elements of Suzhou’s waterfront space to establish a vitality
evaluation model of the space from the perspective of material space and crowd perception. First, the factors influencing waterfront space vitality were defined through
quantitative analysis. Second, the analytic hierarchy
11
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MODEL
Y. Niu, X. Mi and Z. Wang
Third, the overall waterfront space vitality in Suzhou
was found to be relatively high, and the vitality of the
waterfront space in the center was clearly higher than
that in the surrounding areas, such as Pingjiang Road,
Taohuawu, the second Henghe waterfront space around
the Guanqian Street commercial area, and other historical and cultural blocks. The waterfront space vitality
around the moat was relatively low, indicating that
waterfront space vitality in Suzhou is closely integrated
with commercial behavior and historical culture; however, its integration with daily life is weak.
Fourth, the waterfront space in areas with higher functional density had higher vitality, such as the waterfront
space with sports, catering, and landscape areas.
Fifth, transportation accessibility had a strong impact on
waterfront space vitality, and a positive correlation
existed between them. The street type rivers offer more
amenities, leisure facilities, and public spaces; therefore, they have higher vitality compared with no street
type rivers.
Sixth, visual accessibility was found to have a weak
impact on waterfront space vitality, indicating that the
waterfront space in Suzhou is not sufficiently hydrophilic, which is a common problem among the waterfront spaces in historical water systems.
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3. The practical application of the evaluation model for
waterfront space vitality is promising.
First, the model can clarify the factors influencing urban
waterfront space, and quantitative analysis can be
performed for waterfront space design.
Second, quantitative suggestions can be presented for
the optimization of service facilities and restoration of
urban waterfront space based on the model.
Third, the model can be used to make effective suggestions pertaining to the layout and functional setting
of the urban waterfront space.
Declaration of competing interest
The authors declare that they have no known competing
financial interests or personal relationships that could have
appeared to influence the work reported in this paper.
Acknowledgements
This research was supported by the National Natural Science Foundation of China (No.51708398), Beiyang Scholar
of Tianjin University (2019XRG-0025) and the National Key
Research and Development Program of China (No.
2018YFC0704700).
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