Exploring a new method to identify hidden danger space in the post-pandemic period: Taking Beian city in China as a case

Abstract

COVID-19 has become a major public health emergency in the world, which seriously affects the normal operation of cities. Epidemic prevention and control is not only needed in big cities, but also in small and medium-sized cities. In view of this, the paper takes Beian city, China as the research area. This study establishes a street network model through spatial syntax, and predicts the crossing potential and arrival potential of its street network. This will play a reference role for traffic flow control in Beian city. The article uses emerging data. Through GIS spatial analysis method, we identify the hidden danger space of city. Therefore, this summarizes the places where people are easy to gather and some problems of the current situation of the city. The results show that: (1) Beian bridge and Wuyuer street have a good traffic potential. The intersection of Longjiang Road and Beidahuang street and the intersection of Tianyuan North Road and Baocheng road have good accessibility. (2) The intersection of Ping’an Street and Shanghai road is a potential hidden danger space of the city, and the focus of epidemic prevention and control. (3) The coverage rate of urban community medical services to residential land is 58.61%, and the existing medical infrastructure is insufficient. Under public health emergencies, the paper will argue a new development ideas for health and safety small town planning by visualizing the hidden danger space of the city.

Keywords

Post-pandemic hidden danger space emerging data GIS analysis space syntax

1. Introduction

COVID-19 is another major public health emergency after China’s SARS virus in twenty-first Century. The emergence of the epidemic has greatly affected the normal operation of the city, as well as people’s living order and physical and mental health.

The spread of the virus in China has been brought under control for the time being. However, the overall global epidemic situation is still not optimistic [1, 2, 3]. At present, all parts of China are in the stage of normalization of the epidemic. Local sporadic cases will turn the region or city into a closed control state again.

China has taken many preventive and control strategies against COVID-19, and closed management strategy has played a significant role in dealing with the situation.

For example, community units are closed, public gathering places are closed, companies adopt flexible working system, workers work at home, teachers teach online, etc. [4].

Many disciplines pay attention to infectious diseases, but the attention of urban planning to infectious diseases is not very high. Although there are some requirements for disaster prevention and mitigation in the compilation system, it is not enough to face the severe situation of COVID-19. Those contents are relatively shallow. They are limited to disaster prevention channels and medical and health site selection, and lack of in-depth and practical epidemic prevention and control research [5]. In recent years, it has become increasingly clear in the field of urban planning that health is very important for human settlements. Some scholars put forward opinions on epidemic control from the aspects of public policy, layout of medical facilities and urban governance [6, 7].

Figure 1.

Article research framework diagram.

Li et al. [8] discussed the scale change of COVID-19 confirmed case clusters in Huangzhou District of Huanggang City. The study identified the infectious cases geographically by GIS method, and studied the hypothetical relationship between the building environment attribute and COVID-19 case aggregation area by structural equation model. The results showed that commercial activity and transportation infrastructure had a statistically significant direct and indirect impact on the number of confirmed cases of infectious diseases. Bai et al. [9] took Luoshan County of Henan Province as an example and analyzed the accessibility of urban spatial transportation in Luoshan County by using space syntax. From the perspective of urban vitality and physical space isolation, the author points out effective traffic management suggestions to control the spread of the epidemic. Wang et al. [10] evaluated the service scope and construction quality of public service facilities in Nanjing Chengxian Street community living circle through POI data and GIS analysis. The author points out the construction scheme of life circle and emergency community public health and epidemic prevention circle in the post epidemic era. Xu et al. [11] analyzed the spatio-temporal evolution characteristics of the epidemic through text analysis, mathematical statistics and spatial analysis. The authors evaluated the accessibility of confirmed cases and put forward corresponding prevention and control suggestions. Liu et al. [12] used principal component analysis to evaluate the impact of the built environment on the incidence of infectious diseases in King County, USA, and explored research methods for infectious diseases in urban areas. The results indicate that the built environment has a multifaceted impact on COVID-19 cases. The density of built environment was positively correlated with morbidity.

Infectious diseases occur more frequently and affect a larger area in large cities. However, with convenient transportation facilities and large population mobility, many diseases spread from point to surface in small towns and then spread on a large scale. So control and epidemic prevention in small towns is crucial [13]. As a port city, Heihe city has repeatedly been the source of the spread of the epidemic in China. Beian city is a subordinate city of Heihe City and a typical port city. For China, strictly controlling imported cases from abroad has become a major challenge for epidemic prevention and control [14].

The most effective way to prevent transmission is to implement self physical isolation at home. Such measures may effectively reduce population mobility and personnel aggregation, and prevent crowd cross infection [15, 16]. Through the analysis of the integration and selection of the street network in Beian city, the study predicts the arrival potential and crossing potential of each street in the city. We predict the hidden danger space in Beian city by using ArcGIS spatial analysis method. The analysis results can be used as a reference for the daily prevention of infectious diseases and the mitigation of the impact of new sudden infectious diseases in Beian city. Consequently, the study of street network aggregation and urban spatial form is of positive significance to the control of public health emergencies such as COVID-19 in port cities [17].

2. Overview

2.1 Research objects

Beian city is located in the north of Heilongjiang Province, China, on the edge of Songnen Plain. Its administrative region belongs to Heihe city. The city is located in the center of the “Golden Triangle” composed of Harbin, Qiqihar and Heihe. Beian city is 330 km away from the provincial capital Harbin in the South and 246 km away from Heihe, one of the first open cities along the border in China. It is 240 kilometers away from Qiqihar, the second largest city in the province, and only 62 kilometers away from Wudalianchi World Geopark, a world-famous tourist attraction [18]. Beian has a total area of 7194 square kilometers and a population of 480000.

Figure 2.

Location analysis map of Beian city. Beian city is the hub of highway transportation in the northern part of Heilongjiang Province, China. The main surrounding cities are Harbin, Qiqihar, Heihe, Yichun and Suihua.

2.2 Research scope

The paper defines the research scope as the main urban area of Beian city, which is surrounded by national highway 202, Heqinghua middle road and various urban land.

Figure 3.

Definition of the study area in Beian city. There are three exits in Beian city, which is also the starting point of the main road in the urban area.

2.3 Research data source

Unlike previous articles that only use traditional data, the study combines traditional data with emerging data. We conduct on-site research and capture data on the Internet. By downloading the satellite image map of Beian city on Baidu map, the researcher defined the road network and street layout within the research scope. The research data is the POI data obtained by Baidu map. Each data contains attribute information such as name, longitude, latitude, category and administrative division location. According to the industry classification of POI by Baidu map, the author extracts the facilities related elements to be studied in this paper, and classifies and analyzes them [19, 20, 21].

Figure 4.

Data sources in this document. Traditional data sources are on the left and emerging data sources are on the right. The paper combines traditional data with emerging data.

3. Methodology

3.1 GIS spatial analysis method

The average nearest neighbor analysis method refers to the nearest distance between points. This analysis method judges the spatial pattern of the city by calculating the average distance between the nearest neighbor pairs and the average distance between the nearest neighbor pairs in the random distribution. The average nearest neighbor tool will get five values: average observation distance, expected average distance, nearest neighbor index, Z-score and $p$ -value. The “nearest neighbor index” is expressed as the ratio of “average observation distance” to “expected average distance”. The expected average distance is the average distance between neighborhoods in a hypothetical random distribution. If its index is less than 1, the pattern is clustering. If its index is greater than 1, the pattern tends to be discrete or competitive.

Kernel density analysis. The average nearest neighbor can only get whether the spatial pattern in the city is in a discrete state, but can not get the location of aggregation or dispersion. The kernel density analysis can calculate the density distribution of the whole region according to the value and distribution of the input point elements. It will generate a continuous grid graph to estimate the probability density distribution function of observation samples. When analyzing the spatial distribution of points with geographical location characteristics by kernel density, the probability of geographical events is different in different locations. Points is high, and the probability of occurrence in areas with sparse points is low [22].

Figure 5.

Schematic diagram of kernel density analysis.

Facility service area analysis. The paper applies the service area tool in ArcGIS network analysis to study the current service scope of POI points of various public service facilities. The traditional facility service range is usually directly centered by the facility and used as a buffer according to a certain radius. In this way, the actual situation of urban roads is not considered, so the accuracy of calculating urban service range is insufficient. The service area tool can simulate the flow of individuals in the point line network data set. It can accurately calculate the reachable range of people on foot or by car and various facilities according to the situation of urban road network [23].

Figure 6.

Diagram of kernel density analysis.

3.2 Space syntax

Segment model analysis in space syntax is a method applied to the analysis of spatial topology and geometric configuration. It takes the street as the basic part of the street network, considers the connection of topology, metric distance and angular distance, and can effectively predict social activities. The angular distance analysis of segment model under the restriction of metric radius is helpful to detect the arrival path and crossing traffic path in urban network structure [24].

The integration mainly reflects the closeness of a small-scale space in the spatial fabric system with other spaces. It can usually reflect the aggregation degree or dispersion degree of the flow of people and vehicles in the space. If the space has a high integration, it will show that the elements of the whole space are closely related and the stronger the aggregation [25]. When the spatial system has a low integration, the spatial elements are relatively independent, the correlation between the elements is weak, and the aggregation ability of people and vehicles in this space is poor. The integration of angle segment analysis can well predict the potential of each segment to become a popular destination. In other words, the integration measures the minimum angle path from all starting points to all ending points in the system, and can predict the arrival traffic potential of each line segment [26].

Hillier’s integration calculation formula $=$ (NC * NC)/TD Integration formula of angle segment analysis $=$ NC/MD $=$ NC/(TD/NC) $=$ NC * NC/TD

Choice refers to the number of times a unit acts as the transition unit of the minimum topological steps of any two units in a spatial system. What it shows is the size of the pedestrian flow of the space unit in the overall space. The choice indicates the potential of each line segment element to be selected, including the choice of pedestrians with small radius or the choice of drivers with large radius. Therefore, the choice represents the crossing traffic potential of line segments in the spatial system. The calculation method of selectivity is defined according to Turner’s research: “there is the shortest path between all starting and ending elements. When the shortest path passes through a node, the selectivity of that node will be accumulated.”

Space syntax theory has proved its unique role in the study of social and spatial relevance. In the paper, we use Depthmap software guided by space syntax theory to analyze the topological relationship of streets in Beian city, and transform the spatial analysis at the drawing level into quantitative data [27]. The combination of integration and choice helps to find the line segments in the system that have the potential to become destinations and crossing paths. This can narrow the scope and focus on the street elements that are less numerous and more meaningful [28, 29].

4. Analysis and discussion

4.1 Street space network form in Beian city before and after the epidemic

We build the space syntax segment model of Beian city in CAD, and import the completed axis map into Depthmap software for calculation. By analyzing the changes of urban integration and choice, the paper explores the accessibility and urban form before and after the epidemic [30].

According to the actual situation before and after the epidemic, we have drawn two kinds of axis maps respectively: Before the epidemic, the city operated normally and people traveled normally and freely. There is no epidemic prevention station in the residential area, and its internal roads are connected with the main and secondary roads of the city. After the outbreak of the epidemic, residents were isolated at home. Epidemic prevention stations are set at the main entrances and exits of each residential area, and other entrances and exits are prohibited. The main traffic flow of the city is the circulation of medical personnel and prevention and control management personnel, as well as material delivery vehicles and emergency rescue vehicles.

4.1.1 Integration analysis before and after the epidemic in Beian city

Figure 7.

Integration analysis of Beian city before the epidemic.

Figure 8.

Integration analysis of Beian city after the epidemic.

Figure 7 shows that the traffic flow of the city is mainly concentrated in Beidahuang street, Wuyuer street and Longjiang Road. After the outbreak of the epidemic, its internal roads were closed. Figure 8 shows that Beidahuang street, Wuyuer street, Youyi street and Qinghua West Road have high arrival potential, which is slightly different from that of the road section before the epidemic. The comparison between Figs 7 and 8 shows that the vitality of urban streets before the outbreak of the epidemic is high.

4.1.2 Choice analysis before and after the epidemic in Beian city

Figure 9.

Choice analysis of Beian city before the epidemic.

Figure 10.

Choice analysis of Beian city after the epidemic.

Figure 9 shows that Beidahuang street, Beian bridge, Wuyuer street, Qinghua road and Qinghua West Road have high traffic crossing potential. Figure 10 shows that Beidahuang street, Wuyuer street and Qinghua West Road have high choice and high crossing potential.

4.2 Spatial distribution characteristics of POI

According to the daily life needs before and after the epidemic, this study extracts the elements related to commercial service facilities and public service facilities, and focuses on analysis. Their categories include six types of facilities: medical services, social welfare, business services, life services, cultural and sports activities and education [31].

4.2.1 Overall distribution of service facilities

In the text, the average nearest neighbor method is used to analyze the overall distribution characteristics of Beian city. According to the calculation results of ArcGIS, the nearest neighbor ratio is 0.23, the Z score is $-$ 68.97, the $P$ value is 0.00, the average observation distance is 15.22 m, and the expected average distance is 66.75 m. Figure 11 shows that the service facilities show obvious aggregation characteristics in the city, and there are obvious regional differences in aggregation and distribution. The probability of random distribution of this state is less than 1%.

Figure 11.

Average nearest neighbor summary.

4.2.2 Analysis and evaluation of urban facility service area based on Residents’ needs

During the epidemic, the most important daily requirements of urban residents are drugs and food, followed by commercial services, social welfare, life services, education and other service facilities.

Figure 12.

Kernel density analysis of points of interest (POI) in Beian city.

Figure 12 shows that the facilities in Beian are basically concentrated in the southeast. Living service facilities and commercial service facilities are mainly concentrated in the Nanjing section of the pedestrian street in the southeast of Beian city. The places where people are easy to gather are mainly pedestrian street Nanjing Road, followed by Longjiang Road and Shanghai road. However, their distribution is uneven, and there are few service facilities in the north and west of the city. Educational facilities, sports facilities and social welfare facilities are evenly distributed in cities. But overall, these facilities are less. The overall distribution of medical service facilities is uneven, and they are mainly concentrated in the Nanjing section of the southeast region. There is a lack of medical facilities in the north and southwest of the city.

Generally speaking, the gathering places of facilities and the places where people are easy to gather are the intersection areas of Nanjing Road, Longjiang Road, Shanghai Road, Ping’an Street and Heping Street. These areas have high vitality, and the population may be prone to aggregation.

4.3 Accessibility analysis of public service facilities

Network analysis in ArcGIS can accurately construct urban traffic network, including road linearity, road patency, vehicle speed, one-way line, viaduct and so on. It takes the facility as the center of the circle and the service radius as the radius of the circle. The network analysis method accurately simulates the area that the facility can cover within the service radius according to the traffic distance, and then calculates the shortest vehicle path and the service area of the facility [32].

Beian city has no viaduct, subway entrance and tunnel traffic, and its traffic network is relatively single. Therefore, we do not consider one-way traffic, intersection turning and road patency when building a traffic network data set. Then, the article uses the network analyst toolbar in GIS to create a new service area analysis and load public service facilities.

The paper sets the scope of primary school service facilities as 10 minutes’ walk, middle school service facilities as 15 minutes’ walk, community medical facilities as 10 minutes’ walk, general hospital as 15 minutes’ drive, social welfare facilities as 15 minutes’ walk, and cultural facilities as 15 minutes’ walk.

Figure 13.

Network analysis diagram of Beian city.

Table 1

Relevant data of various service facilities

Name of service facilities	Service area/ha	Service area after integration/ha	Covered residential land area/ha	Residential service rate	Urban service rate
Primary school	1392.56	786.18	372.74	58.28%	32%
Middle school	3247.97	1255.55	517.71	85.76%	51.14%
Community hospital	1372.5	804.46	353.95	58.61%	32.74%
General hospital	6098.38	1153.66	428.99	71.03%	46.99%
Culture and sports	5025.73	1343.83	491.79	81.46%	54.72%
Social welfare	6221.8	1386.97	502.17	83.11%	56.47%

Figure 13 shows that the coverage rate of primary schools in urban residential land is 58.28%. They are underserved in the north and east of the city. There is a lack of primary education resources in the north of the city; The service area of secondary school facilities is relatively comprehensive, basically covering urban residential land, and only a small part in the south of the city is not covered; The coverage of secondary school services in urban residential land is 85.76%. Its service scope is relatively comprehensive, basically covering urban residential land, only a small part of the southern part of the city is not covered; Therefore, the northern part of the city lacks community medical facilities; The coverage rate of community medical service in urban residential land is 58.61%. Its service coverage is comprehensive in the south of the city, while a large area of residential land in the north of the city is not covered; The coverage rate of the service scope of the general hospital in the urban residential land is 71.03%. The service scope in the central part of the city is comprehensive, and a small part of the northern and southern parts of the city are not covered; The coverage rate of cultural and sports services in the urban residential land area is 81.46%. Its service scope is comprehensive, and only a small part of the residential land in the south of the city is not covered; The coverage rate of social welfare services in urban residential land area is 83.11%. Its overall service scope is good. Only a few residential areas in the south of the city are not covered.

5. Conclusion

The study uses space syntax theory, kernel density and network analysis in ArcGIS, and discusses the location of hidden danger space in the city during the epidemic. In the face of long-term and repeated normalization of the epidemic, cities should control external hidden dangers, identify internal hidden dangers and control them for a long time. We examined some problems arising from public health emergencies. We have proposed quantitative analysis and evaluation of city space accessibility for COVID-19’s prevention and control. The following conclusions are drawn from the intuitive analysis results and visual expression:

(1)

Beian bridge and Wuyuer street have good accessibility. From the street network model of Beian city, the sections such as Beian bridge and Wuyuer street have high traffic potential. Wuyuer street and Beian bridge, as the main urban roads, are the main roads for the traffic flow of other cities. The traffic flow control of Wuyuer street and Beian bridge should be more rigorous. Statistics and epidemic prevention measures shall be taken for the information of vehicles and personnel entering other cities. This may provide a reliable basis for the control of the epidemic.

(2)

The intersection of Ping’an Street and Shanghai road is the hidden danger space during the urban epidemic. It can be seen from the characteristics of spatial structure that the intersection of Longjiang Road and Beidahuang street and the intersection of Tianyuan North Road and Baocheng road may produce the phenomenon about crowd aggregation. The intersection of Ping’an Street and Shanghai road is not only the gathering place of commercial service and living service facilities, but also the gathering place of people. These two regions have high vitality space potential and may be the focus of prevention and control during the epidemic.

(3)

The scope of urban infrastructure services is insufficient. The network analysis chart of the city clearly shows that the medical infrastructure of the city (community medical service facilities and general hospital service facilities) is insufficient. In particular, the residential land in the north of the city lacks medical facilities. As an important guarantee to combat the epidemic, medical infrastructure is an important facility to improve the comprehensive emergency response capacity of cities. This shows that the emergency supporting facilities in Beian city are still not perfect. The city should add community medical facilities in its north and serve the residents in the north of the city. Not only to deal with the epidemic prevention and control, it will also have an important impact on the future development of urban infrastructure. COVID-19 pneumonia is mainly transmitted by human to human beings, and reducing the flow of personnel is the main measure to cope with the epidemic. A reasonable service radius of facilities will greatly reduce the flow of people across districts. Therefore, a reasonable layout of facilities is necessary in planning. This will reduce the necessary activities of people to a small scale as much as possible, which may play a positive role in epidemic prevention and control and production and living order.

This research creatively deals with complex space from the perspective of configuration. We combines the space syntax of spatial parsing as a combination of relationships with GIS network analysis, which has a powerful spatial analysis function. In the post-pandemic period, this will help to find the hidden danger space of the city, and also obtain some problems faced by urban facilities, and take measures on the basis. In case of sudden infectious events, the author suggests that epidemic prevention measures should be strengthened in areas with strong population concentration to prevent large-scale infectious accidents in the future. Temporary medical centers should be built in areas lacking medical facilities in cities. This will not only play a positive role in controlling order, but also improve the quality of life of urban residents.

Footnotes

Funding

This work was supported by Teaching and research project of Northeast Forestry University in 2022 (DGY2022-32).

References

Yang

. Planning thinking triggered by public health emergencies – reply to the 2020 New Coronavirus pneumonia emergency conference. Urban planning; 2021.

Zhang

. Epidemic prevention and control of infectious diseases should be integrated into urban comprehensive disaster prevention and mitigation planning as soon as possible: A writing conference on COVID-19 emergencies in 2020. Urban Planning; 2021.

Xiong

. Thoughts on prevention and control planning of urban public space under public health emergencies. Space governance for high-quality development – Proceedings of 2020 China Urban Planning Annual Conference (01 Urban Safety and Disaster Prevention Planning); 2021. pp. 546-557.

Liang

Gong

Yang

. Thoughts and suggestions on issues related to the construction of resilient cities in the context of epidemic prevention and control. Constr Technol. 2020; (6): 31-34.

Tang

. Community governance challenges in COVID-19 epidemic prevention and control: from the perspective of urban rural planning and public health. Nanjing Soc Sci. 2020; (3): 8-14+27.

Luan

Zhang

Qin

. Regional dissemination characteristics and planning strategies of major epidemics – Reflection on 2019 New Coronavirus pneumonia epidemic situation. Urban Plann. 2021; 45(3): 57-70+80.

Allam

Jones

. Pandemic stricken cities on lockdown. Where are our planning and design professionals now, then and into the future? Land Use Policy. 2020; 97: 104805.

Peng

. Built environment and early infection of COVID-19 in urban districts: A case study of Huangzhou. Sustainable Cities Soc. 2021; 66: 10.

Bai

Chen

Zhang

. Evaluation of urban form and traffic accessibility in response to public health emergencies – A case study of Luoshan County, Henan Province. Archit Culture. 2021; (5): 144-145.

10.

Wang

Cai

Sun

. Research on community life circle planning in the context of post epidemic era – Taking Chengxian Street Community in Nanjing as an example. Spatial governance for high-quality development – Proceedings of 2021 China Annual Urban Planning Conference (19 Housing and Community Planning); 2021. pp. 221-234.

11.

Wang

. Study on temporal and spatial evolution of COVID-19 epidemic and accessibility of medical services in Wenzhou. J Northwest Univ (Nat Sci Ed). 2021; 51(2): 231-242.

12.

Liu

Guan

. The impacts of the built environment on the incidence rate of COVID-19: A case study of King County, Washington. Sustainble Cities Soc. 2021; 74: 103144.

13.

Tang

. Research on disaster prevention and reduction planning of small and medium-sized cities and towns under the guidance of the concept of resilient City: Taking sudden public health events as an example. J Hunan City Univ (Nat Sci Ed). 2021; 30(2): 27-31.

14.

Dong

. Research on the coordinated governance of politics and society under the modern epidemic crisis – Centered on the cholera epidemic in the port city of the Yangtze River Delta in 1932. China’s Third Sector Res. 2021; 21(1): 51-75+257.

15.

Wang

. Vision of China’s future urban construction reform: In the perspective of comprehensive prevention and control for multi disasters. Sustainable Cities Soc. 2021; 64: 102511.

16.

Xiao

Liang

. Research on healthy city model for public health emergencies. Spatial governance for high-quality development – Proceedings of 2020 China Annual Urban Planning Conference (01 Urban Safety and Disaster Prevention Planning); 2021. pp. 60-69.

17.

Zheng

. Assessment of lively street network based on geographic information system and space syntax. Multimedia Tools Appl. 2017; 76(17): 17801-17819.

18.

Xue

Pan

. Study on the urban features of small towns in Northern Xinjiang – Taking the overall urban design of Beian city as an example. 2018 Urban Development and Planning Papers; 2018. 1721-1726.

19.

Dong

Peng

Guo

. Quantitative assessment method on urban vitality of metro-led underground space based on multi-source data: A case study of Shanghai Inner Ring area. Tunnelling Underground Space Technol. 2021; 116: 104108.

20.

Liu

Yin

. Visualizing and exploring POI configurations of urban regions on POI-type semantic space. Cities. 2020; 99: 102610.

21.

Wei

Wang

. Urban planning response in the era of big data. Planner. 2014; 30(8): 5-11.

22.

Zhu

Yang

. Evaluation of village development potential and village classification based on multi-source data. J Agric Res Environ. 2021; 38(6): 1142-1151.

23.

. Analysis and evaluation of public service facilities service area based on GIS – Taking the central urban area of Wei County as an example. Housing Technol. 2020; (2020): 10+20-56.

24.

Yamu

Van Nes

Garau

. Bill Hillier’s legacy: Space syntax-a synopsis of basic concepts, measures, and empirical application. Sustainability. 2021; 13(6): 3394.

25.

Kim

Penn

. Linking the spatial syntax of cognitive maps to the spatial syntax of the environment. Environ Behav. 2004; 36(4): 483-504.

26.

Yang

. Research on multi-scale urban spatial network morphology based on spatial syntax. Tsinghua University; 2017.

27.

Alalouch

Al-Hajri

Naser

Al Hinai

. The impact of space syntax spatial attributes on urban land use in Muscat: Implications for urban sustainability. Sustainable Cities Soc. 2019; 46: 101417.

28.

Lee

Jung

. Analysis on the urban street network of Korea: Connections between topology and meta-information. Phys Soc. 2018; 497: 15-25.

29.

Wei

Sun

Luan

. Evaluation and optimization of traffic organization efficiency of commercial blocks based on spatial syntax – A case study of Taidong pedestrian street in Qingdao. Archit Culture. 2022; (2): 165-167.

30.

Jung

Choi

. Analysis of traffic volume using space syntax model supplemented by accessibility factor in downtown Daegu. J Korea Plann Assoc. 2010; 45(5): 129-140.

31.

Wang

Cheng

. Study on the coupling between public service facilities and residential space based on POI data – Taking Guandu District of Kunming as an example. Urban Archit. 2021; 18(34): 102-106.

32.

Liu

. Evaluation and planning countermeasures of public service facilities based on network analysis. Shanxi Archit. 2021; 47(21): 31-34.