sustainability
Article
A Spatial Visual Quality Evaluation Method for an Urban
Commercial Pedestrian Street Based on Streetscape
Images—Taking Tianjin Binjiang Road as an Example
Xiaofei Li and Chunyu Pang *
School of Landscape Architecture, Northeast Forestry University, Harbin 150040, China; adoplh@nefu.edu.cn
* Correspondence: pangchunyu2018@nefu.edu.cn
Abstract: As core public spaces in cities, urban commercial pedestrian streets are important destinations for local residents and foreign tourists, and confusion regarding the visual space of a commercial
pedestrian street sends direct environmental warning signals to pedestrians, affecting their visiting
decisions. In this paper, through an investigation consisting of the artificial field simulation of the
visual perception of pedestrians, we collect the corresponding street images and extract visual elements using the full convolutional network. Semantic segmentation is performed to obtain the visual
parameters of the street. According to the quantitative model, the visual elements are matched with
geographic elements, and a geographic information database is established to evaluate the spatial
visual quality of commercial pedestrian streets. (1) There is obvious spatial heterogeneity in the
spatial visual quality of different streets in commercial pedestrian streets. (2) The building heights,
street widths, as well as the street vegetation, facilities, and landscape vignettes are spatial elements
that shape the spatial visual quality of commercial pedestrian streets. (3) The main distribution of
commercial facilities and the distribution of active businesses have an important impact on the degree
of crowd gathering in a street space and the visual spatial quality of a street. This paper provides
comparable data collection methods and research methods for the visual spatial quality of commercial
pedestrian streets. This paper can also provide valuable data for the design, planning, and sustainable
renewal management and regulation of the visual perception of commercial pedestrian streets.
Citation: Li, X.; Pang, C. A Spatial
Visual Quality Evaluation Method for
an Urban Commercial Pedestrian
Keywords: geographical information system (GIS); commercial pedestrian street; sustainability; full
convolutional networks; spatial quality; streetscape images
Street Based on Streetscape
Images—Taking Tianjin Binjiang Road
as an Example. Sustainability 2024, 16,
1139. https://doi.org/10.3390/
1. Introduction
su16031139
Commercial pedestrian streets, as a street form of frequent contact with city residents,
pedestrians, and tourists in the walking process, are the spaces with the highest degree of
crowd gathering. Good street spatial visual quality significantly influences visits by local
residents, pedestrians, and tourists. Street visual quality is considered in the evaluation
of street spatial quality, according to the Landscape Visual Environment Assessment,
which explores the suitability of each element of the street to the main users and the
development of the street from the perspective of human vision both qualitatively and
quantitively. Street businesses show a high degree of correlation with the degree of crowd
concentration. In reality, different commercial streets have different development purposes,
functional combinations, and degrees of completion of construction, leading to differences
in visual–spatial quality between different commercial streets and between different parts
of the same commercial street. In the face of the actual needs of the development of
commercial pedestrian streets, the quantitative evaluation of the visual quality of street
space can systematically assess the strengths and weaknesses of street space as a whole,
which can help to clarify the direction of the sustainable optimization of the visual quality
of street space and the efficiency of the optimization work [1], and also play an important
role in optimizing the distribution of the functions of the commercial pedestrian street and
Academic Editor: Stephan Weiler
Received: 19 December 2023
Revised: 16 January 2024
Accepted: 23 January 2024
Published: 29 January 2024
Copyright: © 2024 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Sustainability 2024, 16, 1139. https://doi.org/10.3390/su16031139
https://www.mdpi.com/journal/sustainability
Sustainability 2024, 16, 1139
2 of 20
enhancing the experience of tourists. As consumer needs and expectations increasingly
favor social and experiential shopping street functions, the need for a sustainable evaluation
of the visual–spatial quality of streets is becoming more and more important [2]. This study
proposes a method and index system applicable to the evaluation of the visual–spatial
quality of commercial pedestrian streets from an objective perspective and conducts an
empirical study on the example of the commercial pedestrian street on Binjiang Road
in Tianjin. The findings will inform the further exploration of the impact of commercial
pedestrian street visual–spatial quality on pedestrian behavioral decision making and
aggregation and will be used to guide the generation of optimized strategies for commercial
pedestrian street spatial design, management, and sustainable regeneration.
At present, domestic and international research on the spatial quality of urban commercial pedestrian streets mainly focuses on the assessment of characteristics and the
innovation of research perspectives and methods on this basis. As commercial pedestrian
streets are block types with generally long pedestrian dwell times, both macro and micro
perspectives have been used in the assessment of characteristics. On the one hand, the
overall environmental characteristics and walking mechanisms [3] of walking behavior in
commercial pedestrian streets are studied at the macro level, such as walking safety [4–6],
accessibility [7], density of the blocks [5], density of functions [8], and degree of connectivity of the pedestrian system [9]. On the other hand, attention is paid to the experience of
walking at the micro level, such as walking evaluation [10], related recreational walking, the
green vision level [11], the street environment [12], the business negativity index [13], and
color coordination [14]. The research perspectives are divided into subjective and objective
perspectives. The subjective research perspective is mainly through the interaction with
pedestrians to obtain the evaluation of the spatial perception of the commercial pedestrian
street, usually using the interview method [15,16], questionnaire surveys [17], empirical
evaluations [18], etc. The objective research perspective involves comprehensively evaluating the quality of the space itself by quantitatively extracting different features of the space
itself, which is usually carried out using data sources such as street-view images, photographic images [19], wearable sensors [20], and web-based open-source APIs [21,22], and
by using research techniques such as machine learning [23] and statistical modeling [24,25].
In the construction of a street space quality evaluation system, street space is a part of
the urban built environment; in recent years, many scholars, regarding streets, combined
urban design quality models with different quantitative index systems for quality measurement, from the density, diversity, and design as the core of the 3D concept, extended to
supplement the “Destination Accessibility and Distance to Transit” of the 5D concept [26].
Many studies have constructed indicator systems for evaluation and measurement based
on this concept. For example, Ye Yu et al. [27] constructed a streetscape evaluation index
from the index system framework by using six dimensions, including the “green vision
index, sky visibility index, building interface, motorization rate, walkability, and diversity”.
Chen, L. et al., using the 5D framework including density, diversity, design, destination
accessibility, and distance to transit, explored the associations with pedestrian volume [28].
More scholars have quantitatively evaluated the street environment through the remaining
characteristics of the street, such as visual entropy [29], pedestrian presence percentage [30],
walkable area size [31], and other index systems on this basis. The indicator evaluation
systems of related studies are summarized in Table 1.
Table 1. Indicators for evaluating the visual–spatial quality of streets covered by existing studies.
Related Research
Han, J.; Dong, L. [29]
Xin, G et al. [30]
Evaluation Indicators
Visual entropy, color richness index, street width, building
height along the street, skyline change index, sky
openness index
Motorization, car presence, total pedestrian interface, diversity,
openness, pedestrian presence, street frontage space, green
visibility, pedestrian walkways, enclosure, building interface
Involving Visual Elements
Sky, buildings, motorways, non-motorways,
street furniture
Greenery, sky, buildings, walls, paving,
vehicles, motorways, footpaths, street
furniture, pedestrians, businesses
Sustainability 2024, 16, 1139
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Table 1. Cont.
Related Research
Chen, Z. et al. [31]
Ye et al. [32]
Long, Y et al. [1]
Tang and Long [33]
Evaluation Indicators
Sense of enclosure, permeability, grey space, spatial boundaries,
street furniture
Green vision, sky view, building interface, pedestrian space,
degree of motorization, diversity
Long-term street occupancy, crossing facilities, footpath widths,
separation of motor vehicles and non-motorized vehicles, street
furniture, street greening, aspect ratios of buildings on both
sides of the street
Wall continuity, intersection cross-section aspect ratio, green
occupancy, sky openness, enclosure
Involving Visual Elements
Buildings, roads, facilities, greenery,
non-motorized vehicles
Greenery, sky, buildings, motorways,
non-motorways, street furniture
Greenery, sky, buildings, walls, vehicles,
motorways, footpaths, street furniture
Greenery, sky, buildings, motorways,
non-motorized lanes, street furniture,
transport, pedestrians
To sum up, the works of the scholars listed above were from the street objective
perspective. As for the research data, they have mostly chosen the street-view images
provided by Baidu Map, Google Map, and Tencent Map as data to support their studies of
street spatial visual quality. Usually, the street-view images are taken by vehicle-mounted
cameras, which better cover the overall urban street space and comprehensively reflect the
characteristics of the urban street space environment [34]. However, there are differences
between what the vehicle-mounted camera is shooting and what the pedestrians are seeing.
Furthermore, due to the collection of street view data in different cities to collect the season,
time, and frequency, there is a big difference, and due to the scenic spots and commercial
pedestrian streets for the motor vehicle restrictions on access, the vehicle-mounted camera
cannot carry out the corresponding street-view image data collection. So, this type of
data source has some limitations when it comes to spatial quality assessment studies for
specific streets. Therefore, in this type of research, the object can be used to simulate the
visual perception of pedestrians through the experimental simulation in a way closer to the
pedestrian’s perspective of the street scene image data acquisition [35]. On this basis, it is
necessary to quantify the street features in the captured street-view images using image
semantic segmentation techniques for subsequent research. Currently, commonly used
semantic segmentation techniques include FCN, U-Net, SegNet, Deeplab, and PSPNet.
Among them, the Deeplab V3+ model is able to process input images with different scale
sizes and performs well in terms of computational time and computational memory, which
provides technical support for large-volume, high-precision, and high-efficiency recognition
and extraction of street scene images.
Overall, there has been a considerable accumulation of research results from a subjective research perspective. Based on the extensive use of big data, the focus of current
research has gradually shifted to quantitative research on the visual–spatial quality of
streets from an objective perspective. Determining the perception of street users by combining visual indicators of multiple street components is gradually becoming the dominant
research paradigm [36,37]. Therefore, this study proposes a method and index system applicable to the evaluation of the visual–spatial quality of commercial pedestrian streets from
an objective perspective and conducts an empirical study with the commercial pedestrian
street on Binjiang Road in Tianjin. The conclusions will provide more information about
the impact of commercial pedestrian street spatial visual quality and business formats on
pedestrian behavioral decisions and aggregation and will be used to guide the generation
of optimization strategies for commercial pedestrian street spatial design, management,
and sustainable regeneration.
2. Research Design
2.1. Selection of Research Subjects
This paper selects the Binjiang Road Commercial Walking Street in Tianjin as the object
of study (Figure 1) As one of the most prosperous commercial walking streets in Tianjin,
the Binjiang Road Commercial Street concentrates various business forms such as shopping
malls, catering, and service industries in Tianjin. The study area starts from Zhang Zizhong
Sustainability 2024, 16, 1139
Tianjin, the Binjiang Road Commercial Street concentrates various business forms such as
shopping malls, catering, and service industries in Tianjin. The study area starts from
Zhang Zizhong Road by the Haihe River and extends southwest to Nanjing Road, and
extends northwest to Duo Lun Road from Chifeng Road in the east.
However, considering that some streets are too long for the street-view 4images
obof 20
tained from the intersection sampling points alone to fully reflect the internal view of the
street, so mid-street sampling points will be set up for streets longer than 100 meters to
accurately
display
internal
view of the
Theses
points
numbered
accordingly
Road by the
Haihe River
andthe
extends
southwest
tostreet.
Nanjing
Road,
andare
extends
northwest
(Figure
1).
to Duo Lun Road from Chifeng Road in the east.
(a)
(b)
Figure 1. representation
Schematic representation
of the
study
and sampling
points:
(a)ofscope
of commercial
Figure 1. Schematic
of the study
area
and area
sampling
points: (a)
scope
commercial
pedestrian
street onRoad,
Binjiang
Road,(b)
Tianjin;
(b) numbering
and distribution
of sampling
points for
pedestrian street
on Binjiang
Tianjin;
numbering
and distribution
of sampling
points for
streetscape images.
streetscape images.
2.2. Research
Framework
However,
considering
that some streets are too long for the street-view images obThe
research framework
the visual–spatial
quality
method
tained from the
intersection
sampling of
points
alone to fully
reflectevaluation
the internal
viewfor
of urban
pedestrian
streets
based
onbe
streetscape
is longer
shown in
Figure
the street, commercial
so mid-street
sampling
points
will
set up forimages
streets
than
1002.m(1)toFirstly,
POI data
and road
data
studyTheses
area are
crawled
through the
GaudeMap API
accurately the
display
the internal
view
ofin
thethe
street.
points
are numbered
accordingly
(Figure 1). interface, and the geographic coordinates are matched. (2) Secondly, the image acquisition
and geographic coordinates matching are carried out for the intersections. (3) Classification
screening and road matching are performed on the acquired poi data. The acquired
2.2. Research
Framework
street-view
images are
with serial numbers,
the framework
Deeplab
The research framework
ofedited
the visual–spatial
quality and
evaluation
methodoffor
urban V3+ is
used for semantic segmentation and precision analysis of street-view images. Image samcommercial pedestrian streets based on streetscape images is shown in Figure 2. (1) Firstly,
pling points and POI analysis data are aggregated to the street geographic elements to
the POI data and road data in the study area are crawled through the GaudeMap API
form the overall street analysis data, show the overall spatial visual quality evaluation of
interface, and the geographic coordinates are matched. (2) Secondly, the image acquisition
different functional streets in the commercial pedestrian street, and analyze and discuss
and geographic coordinates matching are carried out for the intersections. (3) Classification
its sustainability observation and optimization.
screening and road matching are performed on the acquired poi data. The acquired
street-view images are edited with serial numbers, and the framework of Deeplab V3+
is used for semantic segmentation and precision analysis of street-view images. Image
sampling points and POI analysis data are aggregated to the street geographic elements to
form the overall street analysis data, show the overall spatial visual quality evaluation of
different functional streets in the commercial pedestrian street, and analyze and discuss its
sustainability observation and optimization.
2.3. Data Acquisition
The data collection methods are shown in Figures 3 and 4. (1) Street intersections are
important nodes for tourists to make behavioral decisions in street space, which determines
the visual information that tourists can receive and the place to go next, thus turn affecting
their overall perception of street space. Referring to existing studies [38], street-view images
were collected from each street intersection of Tianjin Binjiang Road Commercial Pedestrian
Street in each street direction (Figure 3). (2) Human visual perception is obtained by moving
slowly through the street, and the range encompassed by the pedestrian’s field of vision is
different in a stationary state and during slow traveling, for example, in the longitudinal
direction, the maximum field of vision can reach 25◦ above the horizontal line and 35◦
below the horizontal line when stationary, and the maximum field of vision can reach 20◦
above the horizontal line and 40◦ below the horizontal line during traveling (Figure 4).
In order to ensure that the street-view image is as close as possible to the real field of
view of the pedestrians, the shooting angle of view should meet the above field of view
Sustainability 2024, 16, 1139
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Sustainability 2024, 16, x FOR PEER REVIEW
5 of 22
requirements. The scope of image acquisition is the commercial pedestrian street of Binjiang
Road, including nearly 20 roads, such as Binjiang Road, Heping Road, and Liaoning Road.
Sustainability 2024, 16, x FOR PEER REVIEW
6 of 22
Sustainability 2024, 16, x FOR PEER REVIEW
6 of 22
elling, we take photos by placing a mobile phone or camera in front of our eyes and keeping
thewe
linetake
of sight
parallel
to the aground,
that the
photos in
wefront
takeof
meet
observation
elling,
photos
by placing
mobileso
phone
or camera
ourthe
eyes
and keeprequirements
a maximum
of viewso
ofthat
up to
above
horizontal
and 35°
ing the line of of
sight
parallel tofield
the ground,
the25°
photos
wethe
take
meet the line
observation
below
the horizontal
line whenfield
stationary,
a maximum
fieldthe
of view
of up to
20°and
above
requirements
of a maximum
of viewand
of up
to 25° above
horizontal
line
35°
the
horizontal
line and
40°when
below
the horizontal
when travelling
to ensure
that
are
below
the horizontal
line
stationary,
and a line
maximum
field of view
of up to
20°we
above
close
to the pedestrians’
observation
The collection
streetscape
imagesthat
tookwe
place
the horizontal
line and 40°
below theangle.
horizontal
line whenoftravelling
to ensure
are
during
clear
daylight
hours
from
early
May
to
early
June
2023,
and
a
total
of
168
close to the pedestrians’ observation angle. The collection of streetscape images took place
streetscape
images
fromhours
76 sampling
locations
collected.
the of
acquiduring clear
daylight
from early
Maywere
to early
JuneAfter
2023,comparing
and a total
168
sition
viewpoints
153 collected.
images are
selected
as the the
rawacquidata
streetscape
imageswith
fromthe
76 shooting
sampling conditions,
locations were
After
comparing
for
theviewpoints
street scenewith
image
study,
and the
data coding,
image segmentation,
and
sition
the
shooting
conditions,
153 images
are selected as
theparameter
raw data
calculation
are
carried
out.
for the street scene image study, and the data coding, image segmentation, and parameter
calculation
are carried
out. for evaluating the spatial quality of streets.
Figure
2. Research
framework
Figure 2. Research framework for evaluating the spatial quality of streets.
2.3. Data Acquisition
The data collection methods are shown in Figures 3 and Figures 4. (1) Street intersections are important nodes for tourists to make behavioral decisions in street space, which
determines the visual information that tourists can receive and the place to go next, thus
turn affecting their overall perception of street space. Referring to existing studies [38],
street-view images were collected from each street intersection of Tianjin Binjiang Road
Commercial Pedestrian Street in each street direction (Figure 3). (2) Human visual perception is obtained by moving slowly through the street, and the range encompassed by the
pedestrian’s field of vision is different in a stationary state and during slow traveling, for
example, in the longitudinal direction, the maximum field of vision can reach 25° above
the horizontal line and 35° below the horizontal line when stationary, and the maximum
field of vision can reach 20° above the horizontal line and 40° below the horizontal line
during3. traveling
(Figureimage
4). Inacquisition.
order to ensure that the street-view image is as close as
Figure
Street intersection
possible
to
the
real
field
of
view
of the pedestrians, the shooting angle of view should
Figure 3.
3. Street
Street intersection
intersection image
image acquisition.
acquisition.
Figure
meet the above field of view requirements. The scope of image acquisition is the commercial pedestrian street of Binjiang Road, including nearly 20 roads, such as Binjiang Road,
Heping Road, and Liaoning Road.
This paper uses Python to obtain road network and POI data from the Gaode Map
API interface. The road network data is cleaned by creating a geographic information database in ArcGIS 10.7 [39]. According to the intersection of the road network, line elements
obtain the location of the intersection point on the road network and extract the geographic coordinates of the point. A total of 52 line elements of the road network, 52 intersection sampling points, and 24 mid-street sampling points were obtained within the Binjiang Road Commercial Walking Street.
The image acquisition of Binjiang Road Commercial Walking Street is carried out
through the field research method. The images are captured in four directions, front and
view
.
Figure
field
view.
back, 4.
leftPedestrian
and right,
forofthe
street
crossing sampling points, and in two directions, front
Figure
4. Pedestrian
field ofof
view
and back,
for the middle
the .road sampling points. Considering the fact that pedestrians
2.4.
Databrowse
Handling
mostly
the surrounding environment by looking at their surroundings when trav2.4. Data
Handling
2.4.1.
Data
Classification and Encoding
2.4.1.For
Data
Classification
anddata,
Encoding
street
scene image
the final parameter value of each sampling point is the
average
value
of
its
parameter
value
in each
direction,
andofthis
calculation
For street scene image data, the final
parameter
value
each
samplingmethod
point is has
the
been
widely
used
byparameter
many scholars.
the aggregating
visual elements
of has
the
average
value
of its
valueHowever,
in each direction,
and thisofcalculation
method
Sustainability 2024, 16, 1139
6 of 20
This paper uses Python to obtain road network and POI data from the Gaode Map API
interface. The road network data is cleaned by creating a geographic information database
in ArcGIS 10.7 [39]. According to the intersection of the road network, line elements obtain
the location of the intersection point on the road network and extract the geographic
coordinates of the point. A total of 52 line elements of the road network, 52 intersection
sampling points, and 24 mid-street sampling points were obtained within the Binjiang
Road Commercial Walking Street.
The image acquisition of Binjiang Road Commercial Walking Street is carried out
through the field research method. The images are captured in four directions, front
and back, left and right, for the street crossing sampling points, and in two directions,
front and back, for the middle of the road sampling points. Considering the fact that
pedestrians mostly browse the surrounding environment by looking at their surroundings
when travelling, we take photos by placing a mobile phone or camera in front of our eyes
and keeping the line of sight parallel to the ground, so that the photos we take meet the
observation requirements of a maximum field of view of up to 25◦ above the horizontal
line and 35◦ below the horizontal line when stationary, and a maximum field of view of
up to 20◦ above the horizontal line and 40◦ below the horizontal line when travelling to
ensure that we are close to the pedestrians’ observation angle. The collection of streetscape
images took place during clear daylight hours from early May to early June 2023, and a
total of 168 streetscape images from 76 sampling locations were collected. After comparing
the acquisition viewpoints with the shooting conditions, 153 images are selected as the
raw data for the street scene image study, and the data coding, image segmentation, and
parameter calculation are carried out.
2.4. Data Handling
2.4.1. Data Classification and Encoding
For street scene image data, the final parameter value of each sampling point is the
average value of its parameter value in each direction, and this calculation method has
been widely used by many scholars. However, the aggregating of visual elements of the
streetscape image to the sampling point can only reflect the comprehensive feelings of
pedestrians at this sampling point, but cannot show the feelings of pedestrians at different
streets facing the street intersection and the specifics of that street space. Based on the
above considerations, in order to accurately represent the pedestrians’ feelings on the street,
this paper will take the intersections at both ends of the street as the starting and ending
points, and the average of the parameter values of the same element at all the sampling
points on the street in the section will be the overall parameter value of this element on
the street. Renamed coding is performed, with numeric codes identical to the numbering
of each sampling point in the streetscape image, and the alphabetic codes based on the
orientation of the content of the field of view reflected in the streetscape image relative
to the sampling point. The sampling point shooting direction is classified according to
the four directions of east, south, west, and north. If the image reflecting the street scene
is located in the sampling point within the range of 45◦ north of west–45◦ north of east,
then it is categorized as the northern direction, with the picture of the coding letter as “N”.
Similarly, pictures taken in the eastern direction are coded with the letter “E”, pictures
taken in the southern direction are coded with the letter “S”, and pictures taken in the
western direction are coded with the letter “W”. The sampling point in the middle of the
road is prefixed with “M”, e.g., a picture taken with the sampling point in the middle of
the road facing north, coded with the letter “M–N”.
Based on the above numeric and alphabetic codes, the sampling points are combined
and coded in the format of “numeric code–alphabetic code (direction)”. After the coding is
completed, the opposite street images of the sampling points at both ends of the same line
features are paired and analyzed, and these two sampling points are used as the starting
and ending points of the street corresponding to the linear element. For different road
Sustainability 2024, 16, 1139
middle of the road is prefixed with “M”, e.g., a picture taken with the sampling point in
the middle of the road facing north, coded with the letter “M–N”.
Based on the above numeric and alphabetic codes, the sampling points are combined
and coded in the format of “numeric code–alphabetic code (direction)”. After the coding
is completed, the opposite street images of the sampling points at both ends of the7 of
same
20
line features are paired and analyzed, and these two sampling points are used as the starting and ending points of the street corresponding to the linear element. For different road
directionsatatthe
theintersections,
intersections,the
theimage
imagecoding
codingmethod
methodand
andthe
thenumerical
numericalcalculation
calculationofof
directions
thefeatures
featurescontained
containedinineach
eachroad
roadline
linefeatures
featuresare
areshown
shown
Figure
the
inin
Figure
5. 5.
Figure 5. Street view image coding and spatial visual parameter calculation methods.
Figure 5. Street view image coding and spatial visual parameter calculation methods.
2.4.2.Image
ImageSegmentation
Segmentationand
andInspection
Inspection
2.4.2.
TheDeeplab
DeeplabV3+
V3+model
modelframework
framework(Figure
(Figure6)6)trained
trainedby
byYao
Yaoetetal.al.[40]
[40]using
usingthe
the
The
ADE_20Kopen
openimage
imagedataset
dataset selected
selected for this study
concatADE_20K
studyhas
hasaamloU
mloU(intersection
(intersectionand
and
concatenation
ratio:
the
ratio
intersection
and
concatenation
of the
true
predicted
enation ratio:
the
ratio
of of
thethe
intersection
and
concatenation
of the
true
andand
predicted
valvalues)
80.85%on
onthe
theCityscapes
Cityscapesdata
datatraining
trainingset,
set,which
whichisisaa model
model framework
framework with
with a
ues) ofof80.85%
a high
high level
level of accuracy in the
the same
same kind
kind of
ofhomogeneous
homogeneoussemantic
semanticsegmentation
segmentationmodel.
model.
The
Themodel
modelframework
frameworkcan
canidentify
identifyup
uptoto150
150classifications
classificationsofofelements
elementsfrom
fromstreet-view
street-view
images.
the
uniform
format
andand
sizesize
of the
datadata
output,
the size
theofsampled
images.Considering
Considering
the
uniform
format
of the
output,
the of
size
the samimages
is 1024ispixels
x 700 xpixels.
The image
data data
are normalized
to exclude
the effect
of
pled images
1024 pixels
700 pixels.
The image
are normalized
to exclude
the effect
brightness,
hue,hue,
and and
contrast
in the
on the
recognition
results.
of brightness,
contrast
in image
the image
on image
the image
recognition
results.
The processed image is fed into the Deeplab V3+ model framework for image segmentation, and the segmented elements of the image are organized and summarized.
Elements for measurement, including sky, trees, walls, streets, pedestrians, vehicles, and
facilities, were obtained. The main elements appearing in the images, such as sky, greenery, buildings, and roads, were manually selected by visual discrimination using Adobe
Photoshop CC 2015, and the results of the manual discrimination were compared with
the recognition results of the Deeplab V3+ model framework to check the accuracy of the
model recognition. Five randomly selected images from the captured street-view images
were used as comparison samples to compare the recognition results of the Deeplab V3+
model framework with the selected results of visual discrimination, and the results of this
study are shown in Table 2. From the results, the Deeplab V3+ model framework has better
semantic segmentation results in the street scene image dataset of Commercial Pedestrian
Street and Italian Style District in Binjiang Road, and the overall accuracy is up to 94.99%,
which meets the requirements of the research accuracy, based on the calculation of the
average of the errors of the five sample elements.
Sustainability 2024, 16, x FOR PEER REVIEW
8 of 22
Sustainability 2024, 16, 1139
8 of 20
Figure
6. Schematic
Schematic
diagram
of the
theof
Deeplab
V3+ semantic
semantic
segmentation
network
architecture.
Figure
6. Schematic
Schematic
diagram
ofDeeplab
the Deeplab
Deeplab
V3+ semantic
semantic
segmentation
network
architecture.
Figure
6.
diagram
of
V3+
segmentation
network
architecture.
Figure
6.
diagram
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mentation,
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ements
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ery, buildings,
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Five
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model
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Five Five
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the
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are shown
in Table
2. From
the results,
the Deeplab
V3+ model
framework
has betstudy
are shown
in Table
2. From
the results,
the Deeplab
V3+ model
framework
has better semantic
semantic
segmentation
results
in the
the
street
scenescene
image
dataset
of Commercial
Commercial
Pedester semantic
semantic
segmentation
results
instreet
the street
street
scene
image
dataset
of Commercial
Commercial
Pedester
segmentation
results
in
scene
image
dataset
of
Pedester
segmentation
results
in
the
image
dataset
of
Pedestrian
Street
and
Italian
Style
District
in
Binjiang
Road,
and
the
overall
accuracy
is
up
toup
trian
Street
and
Italian
Style
District
in
Binjiang
Road,
and
the
overall
accuracy
is
up to
to
triantrian
Street
and Italian
StyleStyle
District
in Binjiang
Road,
and the
accuracy
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Street
and Italian
District
in Binjiang
Road,
andoverall
the overall
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94.99%,
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research
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research
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sample
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of
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the
five
sample
elements.
of the average of the errors of the five sample elements.
Figure 6. Schematic diagram of the Deeplab V3+ semantic segmentation network architecture.
TableTable
2. Verification
Verification
results
of recognition
recognition
accuracy
of visual
visual
elements.
Table
2. Verification
Verification
results
of recognition
recognition
accuracy
of visual
visual
elements.
Table
2.
results
of
of
elements.
2.
results
of
of
elements.
Figure
6. Schematic
diagram
of the
Deeplabaccuracy
V3+accuracy
semantic
segmentation
network architecture.
Table 2. Verification results of recognition accuracy of visual elements.
Procedural
Identification/Visual
Identification
(Differences
in in
Procedural
Identification/Visual
Identification
(Differences
in
Ample
PointPoint
Ample
Point
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Identification/Visual
Identification
(Differences
in
Procedural
Identification/Visual
Identification
(Differences
Ample
Point
Ample
Visual
IdentificaVisual
IdentificaThe processed
image
is fed Identification/Visual
into the Deeplab V3+
model
framework
for in
image
segVisual
IdentificaVisual
IdentificaResults)
Results)
Image
EnLive
Image
Image
EnLive
Image
Procedural
Identification
(Differences
Results)
Ample
Point
Results)
Image
EnImage
Image
En- LiveLive
Image
tionthe
tionsegmented elements of the imageResults)
Live Image
mentation,Visual
and
are organized
and summarized.
Eltion
tion
coding
coding
Constructions
Sky Sky
RoadRoad
Greenery
Constructions Sky
Sky
Road
Greenery
Image
Encoding
Identification
Constructions
Road
Greenery
coding
coding
Constructions
Sky
Greenery
Constructions
Greenery
ements for measurement, including sky, trees, walls, streets, Road
pedestrians, vehicles,
and
facilities, were obtained. The main elements appearing in the images, such as sky, green58.19/57.24
12.30/12.10
22.40/22.67
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12.30/12.10 22.40/22.67
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58.19/57.24
22.40/22.67
3.84/3.52
ery, buildings, and roads,
were
manually12.30/12.10
selected
by visual
discrimination
using
Adobe
(0.95)
(0.20)
(−0.27)
(0.32)
(0.95)
(0.20)
(−0.27)
(0.32)
(0.20)
(−(−0.27)
0.27)
(0.32)
(0.95)(0.95)
(0.20)
(0.32)
(0.20)
(−0.27)
(0.32)
Photoshop CC 2015, and the(0.95)
results
of the manual
discrimination
were
compared
with
the
recognition
results
of
the
Deeplab
V3+
model
framework
to
check
the
accuracy
of
Sustainability
2024, 16,
x FOR
REVIEW
9 ofthe
22
Sustainability
2024,
16, xPEER
FOR PEER
REVIEW
9 of 22
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2024, 16,
x FOR
REVIEW
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Sustainability
2024,
16, xPEER
FOR PEER
REVIEW
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x FOR
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2024,
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FOR PEER
REVIEW
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model
recognition. Five randomly selected images from the captured street-view images
64.62/64.58
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21.62/22.37
0/0
64.62/64.58
10.32/10.21
21.62/22.37
0/0
64.62/64.58
10.32/10.21
21.62/22.37
0/0
64.62/64.58
10.32/10.21
21.62/22.37
0/0 0/0
64.62/64.58
10.32/10.21
21.62/22.37
6–N
6–N
6–N
were used as comparison samples
to compare
the
recognition
results
of the Deeplab
V3+
6–N
6–N
(0.04)
(0.11)
(−(−0.75)
0.75)(−0.75)
(0)
(0.04)(0.04)
(0.11)(0.11)
(0) (0)
(0.04)
(0.11)
(0)
(0.04)
(0.11)
(−0.75)
(0)
(−0.75)
model framework with the selected results of visual discrimination, and the results
of this
study are shown in Table 2. From the results, the Deeplab V3+ model framework has bet54.76/54.53
7.62/7.57
31.92/33.14
0.22/0.25
54.76/54.53
7.62/7.57
31.92/33.14
0.22/0.25
54.76/54.53
7.62/7.57
31.92/33.14
0.22/0.25
54.76/54.53
7.62/7.57
31.92/33.14
0.22/0.25
ter semantic segmentation
results
in the street
scene
image31.92/33.14
dataset
of Commercial
Pedes19–S
19–S
54.76/54.53
7.62/7.57
31.92/33.14
0.22/0.25
54.76/54.53
7.62/7.57
31.92/33.14
0.22/0.25
54.76/54.53
7.62/7.57
0.22/0.25
19–S
19–S
(0.23)
(0.05)
(−1.22)
(−0.03)
(0.23)
(0.05)
(−1.22)
(−0.03)
19–S
19–S
19–S
(0.23)
(0.05)
(−1.22)
(−0.03)
(0.23)
(0.05)
(−1.22)
(−0.03)
trian Street and Italian Style
District
in
Binjiang
Road,
and
the
overall
accuracy
is
up to
(0.23)
(0.05)
(−(−1.22)
1.22)(−1.22)
(−
0.03)
(0.23)(0.23)
(0.05)(0.05)
(−0.03)
(−0.03)
94.99%, which meets the requirements of the research accuracy, based on the calculation
of the average of the errors of the five sample elements.
53.59/54.24
13.28/13.18
25.88/25.37
1.99/1.47
53.59/54.24
13.28/13.18
25.88/25.37
1.99/1.47
53.59/54.24
13.28/13.18
25.88/25.37
1.99/1.47
53.59/54.24
13.28/13.1825.88/25.37
25.88/25.37 1.99/1.47
1.99/1.47
53.59/54.24
13.28/13.18
24–E
24–E
53.59/54.24
13.28/13.18
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1.99/1.47
53.59/54.24
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25.88/25.37
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24–E
24–E
24–E
(−0.65)
(0.10)
(0.51)
(0.52)
(−0.65)
(0.10)
(0.51)
(0.52)
24–E24–E
(−recognition
0.65)
(0.10)
(0.51)
(0.52)
Table 2. Verification results of
accuracy
of visual
elements.
(−0.65)
(0.10)
(0.51)
(0.52)
(−0.65)
(0.10)
(0.51)
(0.52)
(−0.65)
(0.10)(0.10)
(0.51)(0.51)
(0.52)(0.52)
(−0.65)
5M–N
5M–N
5M–N
5M–N
5M–N
Ample Point
Image En26–S
26–S
26–S
26–S
26–S
coding
26–S
26–S
5M–N
6–N
Live Image
Procedural Identification/Visual Identification (Differences in
Visual IdentificaResults)
73.38/70.71
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19.31/19.73
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73.38/70.71
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tion
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2.78/2.58
(2.67)
(0.19)
(
−
0.42)
(0.20)
Constructions
Sky (0.19)
Road(−0.42)
Greenery
(2.67)(2.67)
(0.19)
(−0.42)
(0.20)
(0.20)
(2.67)
(0.19)
(−0.42)
(0.20)
(2.67)
(0.19)
(−0.42)
(0.20)
(2.67)(2.67)
(0.19)(0.19)
(−0.42)
(0.20)(0.20)
(−0.42)
58.19/57.24
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2.5.
Parametric
Calculation
2.5.
Parametric
Calculation
(0.20)
(−0.27)
(0.32)
2.5.
Calculation
2.5. Parametric
Parametric
Calculation
2.5. Parametric
Parametric
Calculation(0.95)
2.5.
Parametric
Calculation
2.5.
Calculation
The
visual
elements
identified
from
the
street-view
image
are
calculated
usingusing
the the
The
visual
elements
identified
from
the
street-view
image
are
calculated
The
elements
identified
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are
using
the
The visual
visual
elements
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The visual
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are calculated
calculated
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The
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are
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The
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are
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formula
shown
below.
formula
shown
below.
formula
shown
below.
formula
shown
below.
formula
shown
below.
formula
shown
below.
formula
shown
below.
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quality
based
onof
each
street
line
feature,
which
simplifies
thethe
process
of of
results
of street
quality
based
on
each
street
linecan
feature,
which
simplifies
the process
Sustainability 2024, 16, 1139
9 of 20
Note: Y is an individual indicator element to be calculated. Yn is an element identified
by the street image involved in the calculation, and on the calculated street line features is
an element of the street image on both ends of the line features towards that element and
the sampling point in the street. Through the processing of the above formula, the sampling
results of each sampling point can roughly reflect the quantitative results of street quality
based on each street line feature, which simplifies the process of revealing and modeling
the mechanism of street quality perception based on street line features by pedestrians and
improves the feasibility of the operation process.
2.6. The Indicator Evaluation System
Considering that the people inside the commercial pedestrian street are walking as
the main way of passing, it is necessary to select evaluation indicators and visual elements
around the walking behavior of pedestrians, which should be able to represent and more
realistically reflect the real environment faced by pedestrians in the commercial pedestrian
street. It has been pointed out that there are three types of elements between the street
and the building: the street space, the transition space between the street and the building,
and the external surface of the building. In order to find the features and indicators
that are closely related to these three types of elements in the street, it is necessary to
analyze and interpret these three types of elements in the first place. First of all, for the
cognition of the street itself, it can be clear that the street space is formed by both sides
of the spatial elements enclosed. From the basic definition, when pedestrians walk in the
commercial pedestrian street, they first feel the intuitive feeling of people living at the
spatial scale [41]. Therefore, the length–width ratio of street space, such as the width of the
road, the height of the building and the height of the wall, which constitute the basic scale
of street space—the main parameter reflecting the street space. Secondly, when pedestrians
walk in the commercial pedestrian street, they feel not only the street, buildings, fences,
etc., but also the transition space extending from the road to the building and the transition
space contains the signboards, canopies, street lamps, and other facilities that depend on
the building and the street. In tourist destinations, the installation of such street facilities
is associated with the development of commercial, civic, and recreational sectors, and the
installation of street facilities is important for the enhancement of their functions and the
remodeling of their image [19]. It reflects the convenience in the visual–spatial quality of
the street. Finally, the building facade has a building façade or wall composition, which is
the main observation element for pedestrians in the commercial pedestrian street and the
enclosing element of the street space. Therefore, the measurement of the building facade
needs to focus on the overall scale. At the same time, considering that the building itself
is also the location of the shops in the commercial pedestrian street and the destination
for pedestrians, it is also necessary to study the business and crowd gathering in the
commercial pedestrian street. Research has shown that food and beverage storefronts can
better support the human sensory experience of seeing, hearing, and smelling, which in
turn attracts activities such as stopping, talking, and consuming. Banks, hotels, and offices,
as well as airline ticketing offices have a negative function, and the crowd mostly passes by
them, which does not attract people. Too many negative businesses will affect the overall
vitality of the pedestrianized commercial street and the visual quality of the street.
In summary, in combination with Table 3, scholars extracted the existing street spatial
quality evaluation indices. This paper establishes an evaluation system with nine indicators,
namely, the street green vision index, the street enclosure index, the sky openness index, the
pedestrian safety index, the street walkability index, the facility richness index, the crowd
gathering index, the negative business index, and the positive business index, to guide the
sustainable development of commercial pedestrian streets. The elements included in each
index and the calculation formula are shown in Table 3.
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Table 3. Table of selected indicators.
Evaluation Indicators
Street walkability index
Pedestrian safety index
Street enclosure index
Facility richness index
Crowd gathering index
Street green vision index
Sky openness index
Positive business index
Negative business index
Evaluation Note
It represents the degree of freedom of
choice that pedestrians have during
their walking tours, with higher
values proving that the roadway is of
higher quality.
Representing the extent to which
pedestrians are affected by vehicles
during the walking tour; the lower the
value, the higher the quality of
the road.
This refers to the degree to which the
street is enclosed by buildings, fences,
trees, etc., on both sides of the street.
Measures the number of amenities in
the street; the higher the value, the
higher the quality of the road section.
This refers to the number of people in
the roadway. The higher the value, the
more popular the road is and the
higher the quality of the road.
This refers to the percentage of
greenery visible to pedestrians as they
walk. The higher the value, the higher
the ecological quality of the road and
the higher the quality of the street.
This refers to the percentage of the sky
that is visible to pedestrians as they
walk. The higher the value, the richer
the spatial experience for pedestrians
and the higher the quality of the street.
The higher the proportion of active
businesses, the greater the overall
interaction of the roadway with
people and the higher the pedestrian
quality of the roadway.
A higher proportion of negative
businesses indicates that the overall
ability of the roadway to interact with
people is weaker, and the pedestrian
quality of the roadway is lower.
Quality Factor
Computational Formula
Roads
PAi = n1 ∑ni=1 W I n
Motor vehicles
STi = n1 ∑ni=1 CAn (i ∈
/ N∗ )
Buildings, walls,
greenery, roads
Signboards, signage,
seating, etc.
1
ENi = n
∑ni=1 ARn + n1 ∑ni=1 WAn + n1 ∑ni=1 TRn
(i ∈
/ N∗ )
1 n
n ∑i=1 Rn
1
RIi = n
∑ni=1 SI n + n1 ∑ni=1 FAn
(i ∈
/ N∗ )
O
Pedestrians
HUi = n1 ∑ni=1 PEn (i ∈
/ N∗ )
Greenery
GRi = n1 ∑ni=1 TRn (i ∈
/ N∗ )
Heavens
OPi = n1 ∑ni=1 SKn (i ∈
/ N∗ )
1
∑ni=1 POn
n
n ∑i=1 FOn
Positive business shops
PFi = n1
Negative format shops
NFi = n1
(i ∈
/ N∗ )
1
∑ni=1 NEn
(i ∈
/ N∗ )
n
n ∑i=1 FOn
Note: PAi denotes the Walking Freedom Index of the streetscape image, W In is the road pixels identified in the
image, and the sum is expressed as the total number of sky pixels in each streetscape image; STi is the pedestrian
safety index for street-view images, CAn is the proportion of car, van, and truck identification elements in the
image, the sum of which represents the total number of vehicle pixels in each street image; ARn is the proportion
of building pixels identified in the image, WAn is the proportion of wall pixels identified in the image, TRn is
the sum of the proportion of greenery pixels such as trees, grass, and plants identified in the image, Rn is the
proportion of road elements in the image. The street enclosure index is the ratio of the sum of pixels of building,
wall and greenery elements identified by the image to the pixels of road elements; RIn is the facility richness
index of the street-view image, SIn , FAn is the percentage of pixels on the image for street signs, facilities, etc.,
O is all pixels in each image. The sum of SIn , FAn represents the total number of pixels of street furniture in
each street-view image. HUi is the crowd gathering index, PEn is the proportion of pedestrian pixels identified
in the street-view image, the sum of which represents the total number of pedestrian pixels in each street-view
image; GRi is the street green vision index of the streetscape image, the sum of which is expressed as the total
number of green pixels in each streetscape image; OPi is the sky openness index, SKn is the proportion of sky
elements identified in the street-view image, the sum of which is expressed as the total number of sky pixels in
each street-view image; PFi is the positive business index, POn is the number of active shops identified within the
road buffer zone, FOn is the total number of shops identified in the road buffer zone, PFn indicates the proportion
of active shops to total shops; NFi is the number of negative shops identified in the road buffer, NEn indicates the
proportion of negative shops to the total number of shops.
3. Results and Analyses
3.1. Analysis of Visual Elements of Street Space
Based on the above metrics, the visual element data are aggregated to each intersection
sampling point, and the results of visual element extraction and calculation are visualized.
As shown in Figure 7, the visual elements in the overall spatial distribution of the
commercial pedestrian street on Riverside Road present different characteristics and dif-
Sustainability 2024, 16, 1139
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ferences, especially in the east–west direction along the main street and the north–south
direction along the main street contrast is more significant. (1) Road elements: The overall
percentages of road elements along the east–west direction of Heping Road and the north–
south direction of Binjiang Road are both higher, but the percentage of road elements along
Heping Road is higher than that along Binjiang Road, and the main street shows the characteristic of “high east–west and low north–south” directions. The main street is characterized
as “high in the east–west direction and low in the north–south direction”. This reflects the
phenomenon that there is more walkable space along Heping Road than along Binjiang
Road, but less amenities and greenery along the road. (2) Facility elements: in terms of the
distribution of facilities, Binjiang Road, Heping Road, and Henan Road are street areas with
a higher percentage of facilities, which reflecting the fact that the Binjiang Road and Heping
Road main streets are more complete in terms of facility configurations. Although the
facility elements in the sampling points of the two main streets show a staggered pattern,
the Binjiang Road main street has higher facility elements and a more complete facility
configuration than the Heping Road main street. The main street shows the characteristic
of “low east–west and high north–south”. The difference between the two main streets
in terms of facility elements is the opposite of how the roadway element behaves, corroborating the previous conjecture and reflecting the lesser placement of facilities along
Peace Road. (3) Greening elements: The streets along Binjiang Road and the road sections
connected to it have a lower proportion of greenery; the southern part of Binjiang Road
and the southern road sections intersecting with it have a higher proportion of greenery,
showing the characteristic distribution of “low in the north and high in the south”. The
difference between the two main streets in terms of greenery elements is also opposite to
that of the road elements, and a comparison between the two also confirms that there are
fewer greenery elements along Heping Road compared to those along Riverside Drive.
(4) Building elements: The distribution of building elements along Heping Road and Binjiang Road is relatively concentrated, and the values of the building elements are relatively
close to each other, and the distribution of the building elements in the whole area shows
a smooth characteristic. (5) Pedestrian element: The most intensively distributed areas
are along Binjiang Road, Shandong Road and the eastern half of Heping Road, where the
distribution of pedestrians along Binjiang Road is the most concentrated and continuous,
and the pedestrian element shows a high and smooth distribution, while the distribution
of pedestrians in the western half of Heping Road sampling points is less. The main street
shows a “high north–south direction and low east–west direction”. This is roughly the
same as the distribution of the road element and the greenery element, from which it can
be inferred that the facilities element and the greenery element can have a positive effect on
the gathering of pedestrians. (6) Sky element: the overall sky element along Heping Road
is high; the sky element along Binjiang Road is low; although some sampling points have
high sky element values, the overall area is still low. The main street shows the distribution
characteristics of “high east–west and low north–south” directions. This is the same as the
road element and the opposite of the pedestrian element. The characteristics of the road
elements are the same as those of the pedestrian elements, facilities and greenery elements.
It shows that compared to the sky, the arrangement of greenery and the completion of
facilities, the shopping street is more attractive for people to stop and rest.
The above results show that the Binjiang Road Pedestrian Street shows a clear “north–
south-east–west” distinction between the road, facilities, greenery, pedestrians, sky, and
other elements on the main street. The main street in the north– direction, Binjiang Road,
has more green vegetation areas in the street space, relatively well-equipped facilities, high
pedestrian density and more homogeneous distribution, poor sky view, and narrower
roads. The east–west main street, Peace Road, has a street space character that is roughly
opposite to it. The two main streets, on the other hand, show an overall homogeneity in
terms of architectural elements, indicating that the building heights and street widths of the
neighborhoods are more uniformly constrained and that the distribution of architectural
elements in the two areas is roughly the same and does not show significant differentiation.
Sustainability 2024, 16, 1139
which it can be inferred that the facilities element and the greenery element can have
positive effect on the gathering of pedestrians. (6) Sky element: the overall sky eleme
along Heping Road is high; the sky element along Binjiang Road is low; although som
sampling points have high sky element values, the overall area is still low. The main stre
shows the distribution characteristics of “high east–west and low north–south”
12 of 20 direction
This is the same as the road element and the opposite of the pedestrian element. The cha
acteristics of the road elements are the same as those of the pedestrian elements, facilit
and greenery
elements.
It shows that compared
the sky,
theasarrangement
It shows that although
there are
more long-established
shoppingtomalls,
such
Persuasions,of greene
and
the
completion
of
facilities,
the
shopping
street
is
more
attractive
for people
to st
within the commercial street, there is a conscious effort to unify the planning and design
in
and
rest.
the later stages of construction, resulting in a unified control of the building heights.
Sustainability 2024, 16, x FOR PEER REVIEW
13 of
(a)
(b)
(c)
(d)
(e)
(f)
Figure
7. Analysis
of visual
elements
at sampling
street spacepoints:
sampling
(a) percentage
Figure 7. Analysis
of visual
elements
at street
space
(a) points:
percentage
of streets;of streets;
percentage
of
facilities;
(c)
percentage
of
pedestrians;
(d)
percentage
of
buildings;
(e) percentage
(b) percentage of facilities; (c) percentage of pedestrians; (d) percentage of buildings; (e) percentage
green plants; (f) percentage of the sky.
of green plants; (f) percentage of the sky.
The
aboveand
results
show
that the Binjiang Road Pedestrian Street shows a cle
3.2. Street Space Visual
Quality
Business
Analysis
“north–south-east–west”
distinction
between
the road,
facilities,
Considering that road intersections contain visual
elements
from
all roadgreenery,
directionpedestrian
sky, and other elements on the main street. The main street in the north– direction, B
views, the average value of the proportion of visual elements can only indicate the percepjiang Road, has more green vegetation areas in the street space, relatively well-equipp
tion of the street environment at the intersection and cannot represent the situation faced
facilities, high pedestrian density and more homogeneous distribution, poor sky vie
and felt by pedestrians walking in the street space in the corresponding direction. The
and narrower roads. The east–west main street, Peace Road, has a street space charac
visual element information collected by pedestrians at the sampling points of road interthat is roughly opposite to it. The two main streets, on the other hand, show an over
sections is a synthesis of all the visual elements on the street space connected to the road
homogeneity in terms of architectural elements, indicating that the building heights a
street widths of the neighborhoods are more uniformly constrained and that the distrib
tion of architectural elements in the two areas is roughly the same and does not sho
significant differentiation. It shows that although there are more long-established sho
ping malls, such as Persuasions, within the commercial street, there is a conscious effo
to unify the planning and design in the later stages of construction, resulting in a unifi
Sustainability 2024, 16, 1139
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intersections, which can provide pedestrians with the basis for their behavioral decisionmaking for the next direction of travel, and therefore the line features corresponding to
the roads in different directions need to be further taken as the basic unit for quantitative
evaluation analysis of the visual quality on the street space.
According to the geographic location of the two ends of the road line features, the
street scene images collected at each sampling point of the Binjiang Road Commercial Street
are matched correspondingly. The visual quality quantification results of each street are
shown in the figure (Figure 8). The quantitative results of the evaluation indicators show
significant spatial differentiation, specifically: (1) The street walkability index: the overall
walking index of the Binjiang Road Commercial Walking Street is relatively moderate, and
only Binjiang Road along Hebei Road has the highest walking index, while the two small
roads along Heping Road has the lowest walking index. It can be seen from these that
Binjiang Road and Heping Road main street road width are more appropriate, showing an
overall more homogeneous distribution. (2) The walking safety index: The walking safety
index along Heping Road from Siping Dong Road to Chifeng Road is the highest, followed
by the overall walking safety index along Binjiang Road, which is also high. However,
the pedestrian safety index is generally low along most of the side streets connected to
the two main streets, which is related to the reality that the main streets do not allow
motorized vehicles except for ferries on the streets themselves, while the side streets are
only pedestrian-vehicle segmented but still allow motorized traffic to cross the streets. It
shows that as the main street of the commercial pedestrian street is the main place for
people to gather and disperse, the implementation of pedestrian-vehicle segregation can
play a more positive role for the personal safety of pedestrians. (3) The street enclosure
index: the lowest street enclosure indices are found on Hebei Road and Anshan Road,
and the enclosure indices are also lower on the east part of Hami Road, the main street
of Heping Road; the overall enclosure indices of the Binjiang Road main street and the
remaining main street part of Heping Road are more balanced, but the enclosure indices
of the side streets along Heping Road are higher. Index are higher. This is consistent with
the high road widths of the Binjiang Road and Heping Road main streets and the low road
widths of their side streets. (4) The facility richness index: It can be seen that the main street
of Binjiang Road and the side streets along it have the highest facility richness index, while
the main street along Heping Road is on the low side, and the side streets connected to
Heping Road have the lowest facility richness index. This is due to Binjiang Road itself, as
Tianjin is known as a traditional commercial pedestrian street of the overall construction
and maintenance of a longer period of time due to Heping Road along the east–west side
of the main street is the construction of the later so worse. It also suggests that the overall
amenities on main street at Riverside Drive are better laid out and more visually appealing
to the crowd. (5) The street greenness index: the street greenness index is higher in the
southern half of Hebei Road and Binjiang Road, but decreases the further north you go,
and is lower along the east–west main street of Heping Road. This corresponds to the
distribution of the facility richness index. The overall reflection is a richer visual quality
of greenery and amenities along the main street of Binjaing Road. (6) The sky openness
index: The sky openness indices along Binjiang Road are relatively homogeneous, except
for the lower sky openness indices along Hebei Road, which is generally higher along
Heping Road and is highest in the eastern section of the road. This reflects the fact that
both main streets are wider in terms of street width and that there is a unified plan. (7) The
positive business index: positive businesses are mainly concentrated in the north–south
direction of Binjiang Road and along the street, the distribution of positive businesses
along the north side of the east–west direction of Heping Road is less, and the proportion
of positive businesses is higher in the western middle section, which is in line with the
situation of the crowd gathering, forming a mutual corroboration. It shows that shops with
an active business format have a positive effect on people visiting and stopping by, and
are more likely to attract crowds. (8) The negative business index: Negative businesses are
scattered along Binjiang Road, but the number is high, mainly on Shandong Road, Henan
Sustainability 2024, 16, 1139
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Road, and Shanxi Road, and more widely distributed along Heping Road, but the value is
lower, which is in line with the situation that the overall crowd along Heping Road is less
concentrated. (9) The crowd gathering index: Streets with a high crowd gathering index are
concentrated on the Binjiang Road main street and its connected feeder roads, while streets
with a low gathering index gradually decrease from east to west along the Heping Road
main street, which is in line with the distribution of the facility richness index, positive
Sustainability 2024,
16, x FORindex,
PEER REVIEW
business
green looking index, the pedestrian safety index. It shows that the level of
amenities, active commercial shops, and pedestrian and vehicle segregation measures have
a positive effect on the visual perception of the crowd and are more appealing to them.
Figure 8. Cont.
(a)
(b)
(c)
(d)
(e)
(f)
16
Sustainability 2024, 16, x FOR PEER REVIEW
Sustainability 2024, 16, 1139
17 o
15 of 20
(g)
(h)
(i)
Figure
Analysis
of the
visualspace:
quality
street
space:
(a) the street
index; (b) the pe
Figure 8. Analysis
of the8.visual
quality
of street
(a)ofthe
street
walkability
index;walkability
(b) the pedestrian
trian
safety
index;
(c)
the
street
enclosure
index;
(d)
the
facility
richness
index;
(e) the street gr
safety index; (c) the street enclosure index; (d) the facility richness index; (e) the street green vision index;
vision index; (f) the sky openness index; (g) the positive business index; (h) the negative busi
(f) the sky openness index; (g) the positive business index; (h) the negative business index; (i) the crowd
index; (i) the crowd gathering index.
gathering index.
4. Discussion
In summary, the Binjiang Road Commercial Pedestrian Street in the north–south
From
the
results
of this study,
it can of
be the
seen
that street
the Binjiang
RoadRoad
Commercial
direction of Binjiang
Road
and
the east–west
direction
main
of Heping
destrian
Street
shows
obvious
spatial
differentiation
in
different
streets,
and forms
have some consistent characteristics in the overall distribution, but there are obvious
same
and
obvious
“centralized-discrete”
pattern
with
the
positive
and
negative
b
differences in the indicators of differences. There is a high degree of consistency between
nesses
in
each
street.
(1)
The
greenness
index,
crowd
concentration,
facility
richness,
the two main streets in terms of the street walking index, the walking safety index, and sky
the index
active businesses
the
space
also differ
between
the “north–south
openness, suggesting
thatofuniform
standardsof
are
setstreet
for road
widths
and that
restrictions
on
east–west”
main
streets,
but
there
is
a
high
degree
of
consistency
between
motorized vehicle access increase the level of safety for walking on the streets and the levelthe two m
streets in terms
of the
pedestrian
index,
thehas
pedestrian
safety index,
theindex,
sky openness
of crowd concentration.
The main
street
of Binjiang
Road
a high facility
richness
dex,
and
the
index
of
street
enclosure.
(2)
There
is
an
obvious
spatial
clustering
effec
indicating that it is well equipped with amenities. The street is also popular, with more
the
street
greenness
index,
the
facility
richness
index
and
the
crowd
gathering
inde
active businesses, a significant concentration of people, and a high street green vision index.
the street
space,shows
and thethe
clustering
are mainly on
Binjiang
Road
main street
The Heping Road
main street
oppositepoints
characterization
of the
these
elements,
with
the
subsidiary
streets
in
the
north–south
direction,
where
the
layout
of
active
the side streets intersecting along the two main streets generally having low road widths,industrie
rich. (3) It
can below
judged
that the building
street width
and the
vegetation, facili
a low facility richness
index,
distribution
of activeheight,
businesses,
low crowd
gathering
and
landscape
vignettes
placed
in
the
street
are
the
basic
visual
elements
that shape
indices, and low pedestrian safety indices for all of them. The overall greening level of the
spatial
quality
of
the
commercial
pedestrian
street.
(4)
The
distribution
of
major
entire commercial pedestrian street is at a low level, which is related to the fact that street comm
cial facilities and the distribution of active businesses have a significant impact on the le
builders usually build commercial complexes in pursuit of economic benefits, resulting
of congregation of people in the street space as well as the spatial quality of the st
in the need for more crowd dispersal and a large amount of walking space. The Binjiang
space.
Road Commercial Street is also only provided with shrubs and some trees in the centre
The higher the spatial distribution of the street walking index, the walking sa
of the street to contribute to the low overall greening level of the street. In summary, the
index, the facility richness index, the positive business index, and the street greening
higher the spatial distribution of the pedestrian index, the pedestrian safety index, the
richness of facilities, the active industry index, and the street greenness index, the higher
the corresponding concentration of people, and the street enclosure index and the sky
Sustainability 2024, 16, 1139
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openness index reflect the coordination between the road width and the building height of
each section of the commercial pedestrian street. On balance, the north–south-orientated
main street of Binjiang Road has a higher spatial quality than the east–west-orientated main
street of Heping Road.
It can be seen that the width of the street, the density of the buildings, and the level of
amenity provision are the basic elements of the pedestrian perception of the spatial quality
of the street. In contrast, the distribution of commercial facilities and the arrangement
of green spaces along the street will have a significant impact on the spatial quality of
the street. The design, sustainable regeneration, and management and maintenance of
characteristic commercial streets and general pedestrianized commercial streets should
maintain a focus on these elements and improve them.
4. Discussion
From the results of this study, it can be seen that the Binjiang Road Commercial
Pedestrian Street shows obvious spatial differentiation in different streets, and forms the
same and obvious “centralized-discrete” pattern with the positive and negative businesses
in each street. (1) The greenness index, crowd concentration, facility richness, and the index
of active businesses of the street space also differ between the “north–south and east–west”
main streets, but there is a high degree of consistency between the two main streets in terms
of the pedestrian index, the pedestrian safety index, the sky openness index, and the index
of street enclosure. (2) There is an obvious spatial clustering effect in the street greenness
index, the facility richness index and the crowd gathering index of the street space, and the
clustering points are mainly on the Binjiang Road main street and the subsidiary streets
in the north–south direction, where the layout of active industries is rich. (3) It can be
judged that the building height, street width and the vegetation, facilities and landscape
vignettes placed in the street are the basic visual elements that shape the spatial quality of
the commercial pedestrian street. (4) The distribution of major commercial facilities and
the distribution of active businesses have a significant impact on the level of congregation
of people in the street space as well as the spatial quality of the street space.
The higher the spatial distribution of the street walking index, the walking safety
index, the facility richness index, the positive business index, and the street greening
index, the higher the corresponding concentration of people, which echoes the S-O-R
(stimuli–organism–response) theory, which focuses on people’s perception and behaviors
in environmental stimuli and argues that the environmental stimuli affect the state of the
individual’s organism and then stimulate behaviors and responses [42,43]. The theory has
been introduced to the study of pedestrian behavior in recent years, and the S-O-R theory
provides a suitable theoretical tool for exploring the effects of microscale environmental
stimuli on individual psychology and behavior and for exploring the human–environment
relationship in pedestrian behavior. However, the existing research mainly reveals the
crowd’s behavioral willingness from the perspective of pedestrians’ subjective perception through questionnaires and interviews, and fewer quantitative studies on the spatial
approach to feature extraction corresponding to crowd aggregation. This paper carries
out the evaluation of the visual–spatial quality of commercial pedestrian streets based on
streetscape images, which is an active exploration of the quantification of spatial structural
differences of commercial pedestrian streets from the perspective of street feature extraction
and at the same time, it provides a richer connotation explanation of the microcosmic
human–land relationship of commercial pedestrian streets from the comparative view
of street spatial visual quality and business distribution. On the one hand, the unique
visual elements of the commercial pedestrian street and the spatial distribution of commercial business form the “aggregation–disaggregation” difference pattern of the street
space around the commercial business. On the other hand, the spatial concentration of
visual elements is also subject to spatial constraints to a certain extent, limiting the overall development of commercial pedestrian streets. Streets with inconsistent positioning,
slightly poorer degree of construction, or those constructed at a later stage will gradually
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deteriorate in terms of quality and vitality if they fail to break through the constraints. On
the one hand, it also reveals the uneven distribution of the street space pattern of Binjiang
Road Commercial Pedestrian Street, and on the other hand, it points out the direction of
the sustainable optimization of the street space of Commercial Pedestrian Street.
Based on the findings of this paper and the above discussion, the core elements influencing the spatial visual quality of commercial pedestrian streets and the potential spatial
correlations between each element are further refined. (1) The high congestion and abundance of amenities in areas with a concentration of businesses that exhibit a concentration
of people reflects the high correlation between the degree of business concentration and
accessibility to amenities on pedestrian travel choices that has been widely demonstrated
in existing research [36,40]. (2) The selective browsing and sustainable interaction of pedestrians walking in the commercial pedestrian street are the key prerequisites for pedestrians
to have a deep experience with the local humanities. Therefore, the type and spatial distribution of businesses should be in line with the actual role played by the local commercial
street and the situation, so as to carry out the sustainable continuation and optimization of
the type of business, which is an issue that should be taken seriously by the managers and
planners of the commercial pedestrian street. (3) The characteristic commercial pedestrian
street should fully integrate commercial and cultural resources, strengthen the interaction
between local culture and pedestrians in the visual space, provide pedestrians with a richer
visual feeling of progression and sustainable evolution between scenes, and avoid the
overall sense of incoherence and emptiness. (4) Different streets within the commercial
pedestrian street have the characteristic of unbalanced gathering of people. The large
difference in the gathering of people and the distribution is easy to cause spatial stickiness
in the gathering area, not only will reduce the mobility of people in the crowded area,
but also further affect the mobility of people in the other surrounding areas, resulting in
the overall spatial hot and cold difference in the commercial pedestrian street. It is not
conducive to the overall stability of the street and the sustainable development of the street.
Therefore, commercial pedestrian streets should make reasonable use of visual elements
and commercial elements to optimize the visual spatial pattern, and articulate and optimize
the spatial structure through the distribution of commercial types, etc., so as to balance the
differences in visual quality between different streets or use these differences to reclassify
the correspondence of commercial types, and enhance the order, hierarchy and sustainable
ornamental properties of the streets. (5) There is a potential correlation between the visual
quality of a street space and the its function As a gathering place of commercial functions,
the commercial pedestrian street itself is also consisted of different functional business,
and different functional clusters of the street in space and time experience is also different.
These elements together constitute the commercial pedestrian street of the multiple spatial
experience. Therefore, while improving the visual quality of the commercial pedestrian
space, it also provides space for the continuous development of economy, for the inheritance
of local culture and for the continuous display of the regional landscape.
Limited by the object, purpose and methodology of this study, there are also some
shortcomings in this study: (1) This paper only evaluates the spatial quality of commercial
pedestrian streets by objectively analyzing and quantifying the streetscape images and POI
data, but does not continue to be explored from the subjective perspective of the street user
when they walks. Therefore, the subsequent studies can combine questionnaire surveys and
other ways of representing pedestrians’ subjective decisions and perceptions to supplement
the assessment of the visual–spatial quality of commercial pedestrian streets from the
subject’s perspective and further explore the effects of different commercial pedestrian
street scenes on pedestrian attractiveness and aggregation. (2) Considering the important
impact of behavioral decisions on pedestrians’ senses during walking, this paper selects
street intersections and intermediate or equidistant points inside the street for street-view
image acquisition. However, this covers a limited continuous field of view for shops on
both sides of the street as well as the perception of the street during pedestrian walking.
Subsequent studies could supplement the street view data collection perspective by taking
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video footage of walks from the street median to both sides of the street. (3) Since the image
acquisition from the pedestrian’s point of view does not adequately represent the spatial
distribution of facilities, greenery and other features in the street, it is recommended to
consider how to incorporate the spatial distribution of features into it for more accurate
quantification and visualization in future research. (4) In the collection and shooting of
street-view images, there may be a situation where the crowd gathering degree is high in
different streets due to the large number of people on holidays, etc., and the values do
not differ much, which is not conducive to reflecting the differences in the distribution
of crowds on the streets. Therefore, it is suggested to add the use of heat map and aerial
drone photography for more detailed calculation and quantification of crowd distribution
and pedestrian flow in the subsequent research. (5) As a stage-by-stage exploration of
commercial pedestrian street spatial quality evaluation research, this paper mainly extracts
the main types of visual elements based on pedestrians’ visual perception of the overall
street environment at each sampling point and considers the combination of multiple
elements in the quantitative assessment. However, in the actual assessment, pedestrians
may pay more attention to microdetail elements, such as the continuity and consistency of
visual elements on the street that form a smooth landscape line. Whether the art form of the
building is unique and attractive, and whether the colors on the street are harmonious and
consistent with the overall style and cultural characteristics of the environment. Whether
the guide signs, directional signs, and landmarks on the streets are clear and unambiguous,
and can provide pedestrians with a good sense of navigation and direction. Therefore, it is
suggested that subsequent studies could use the attraction of elements from the subjective
perspective of pedestrians to further refine and screen the microelements that influence
pedestrians’ perception of the spatial quality of commercial pedestrian streets and to
conduct further evaluation studies. Such experiments can more accurately capture the
subjective feelings and preferences of pedestrians towards different types of commercial
walkways and provide more specific guidance and recommendations for sustainable design
and improvement of commercial walkways.
5. Conclusions
This paper focuses on the spatial visual quality of commercial pedestrian streets
and proposes a workflow and research methodology for evaluating the quality of streets
from two perspectives: visual quality and commercial quality, using a combination of
streetscape images and commercial pedestrian street POI data. (1) A quantitative model
of visual quality in parallel with commercial quality is developed by examining existing
research with the characteristics of commercial pedestrian streets. (2) Street-view images
were captured from various street intersections and sampling points in equidistant streets
within the commercial pedestrian street using manual simulation of pedestrian walks.
Subsequently, we used Deeplab V3+ structure to semantically segment the captured streetview images, extract the parameters of the visual elements in them, and verify their accuracy.
(3) A geographic information database was created in a Geographic Information System
(GIS) for visualizing the visual element field of view occupancy at each sampling point
and analyzing the spatial characteristics of the visual elements. This provides a basis for
further evaluating the spatial qualities of commercial walkways. (4) Taking the streets
represented by linear elements in GIS as the smallest unit of road data integration, we
calculated and visualized the spatial quality indicators of each street according to the
selected index system, and evaluated the visual spatial quality of commercial pedestrian
streets in a quantitative and sustainable assessment way.
In this study, streetscape images are used to characterize the objective environment,
and a database of streetscape images of different types of commercial pedestrian street
intersections and equidistant mid-street sampling points and a system of evaluation indices
to quantify the spatial quality of commercial pedestrian streets are established. This study
removes the limitations of previous ways of obtaining streetscape from non-pedestrian
perspectives, such as relying on vehicles, surveillance cameras, and remote sensing images,
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and adopts artificial simulation of pedestrians walking and browsing to collect streetscape
images so that the collection of streetscape images is closer to the real perceptions of the
scene in the process of pedestrian walking. In terms of research methodology, Deeplab V3+
is used in this paper for semantic segmentation and visual element recognition of street
scene images, and the method can provide a reference for street scene images and other
types of image data analysis. In terms of planning, design, and management of commercial
pedestrianized streets, the findings of this paper have some analogous application value
and can provide some valuable reference for the sustainable planning and management of
other pedestrianized commercial and pedestrian–vehicle–mixed commercial streets, e.g.,
the layout of active and passive businesses, the agreement on the abundance of facilities,
and the control of the width of the street.
Author Contributions: Conceptualization, X.L. and C.P.; methodology, X.L. and C.P.; software, X.L.;
validation, X.L. and C.P.; formal analysis, C.P.; investigation, X.L.; resources, C.P.; data curation,
X.L.; writing—original draft preparation, X.L.; writing—review and editing, C.P.; visualization, X.L.;
supervision, C.P.; project administration, C.P. All authors have read and agreed to the published
version of the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement: Data are contained within this article.
Conflicts of Interest: The authors declare no conflict of interest.
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