A study on dynamic trend ofurban hotspots using SNS data
Main contributions：• A new SNS-based method for identifying urban hotspots• The dynamic changing trend of urban hotspots• The driving factors of dynamic trend of urban hotspots

Yujie Ren, Shichen Zhao, Kyushu University
Department of Urban Design, Planning and Disaster Management
Graduate School of Human-Environment Studies
Contact: ren.yujie.994@s.kyushu-u.ac.jp, 080-7982-6567

概要：
In order to explore the emergence and formation mechanism of urban hotspots, this study
collected Sina Weibo check-in data to design an urban hotspot identification method, andanalyzed the dynamic trend of the location, attribute, and function of urban hotspots in thecentral area of Nanjing.
The results showed that: (1) There are 309 urban hotspots in the downtown area of Nanjing,which respectively bear the functions of daily living space, public service areas, tourist attractions
and entertainment centers. (2) From the perspective of location, the urban hotspots will explodeto the city's commercial center and tourist attractions along the main road in the afternoon ofnon-working days. (3) In terms of basic attributes, the number, area and popularity of urban
hotspots obey the characteristics of reaching maximum value on weekends and holidays (4) Fromthe perspective of functional changes, the urban hotspots centered on well-known universities
and parks will merge into large urban hotspots of entertainment centers in the afternoon of nonworking days; the edge of urban large-scale entertainment center urban hotspots will differentiate
and derive some small-scale urban hotspots of daily life spaces; the urban scenic spots around theresidential area will be converted into daily life spaces in the weekday morning to undertake theleisure activities of the surrounding residents.
In addition, through qualitative and quantitative analysis of the driving factors of the dynamicchange of urban hotspots, we found that large-scale population migration and the commuting
mode of residents may be the main reason of urban hotspot displacement, and the population
density and accessibility of public transportation facilities are the main driving forces on thenumber, area and popularity of urban hotspots.
This work takes urban researchers one more step toward detecting urban hotspots real-timely.

Introduction: Motivation of understanding of urban hotspot dynamically• Dynamic urban hotspots had shown a significant impact on urban system.
•The aggregation of public facilities is the main reason for the change of housing price(Leung et al., 2006);•

High density tourist flow and scenic spots will affect the operation of urban transportation
system (Nilsson et al., 2000);•The passenger flow of urban central business district is also dynamically related to urban
PM2.5 emission (Traversi et al. 2009).

• The research on urban hotspots remained room for improvement.
•The research on urban hotspots based on theoretical methods and econometric models can
not accurately identify the number and scope of hotspots (Gulia et al. 2017).

•The clustering research based on POIs from digital maps neglects the dynamics of urban
residents and the real-time situation of using these hotspots (SM et al., 2019).

Methodology: The detailed technical scheme of this study

Figure 1. Technical scheme

Methodology: Sina Weibo check-in data
Table 1. Basic information of check-in dataDateTime

LeisureOthersPublic service

Residence

Tourism Transportation

WorkSum

2014.04.27

06:00-12:00

63479

18029

51519

19233

11336

26367

11595

2015582014.04.27

12:00-18:00

12802433883

109489

35052

19279

51333

21126

3981862014.04.27

18:00-24:00

84169

23173

70310

24467

17880

33772

14558

2683292014.04.29

06:00-12:00

55714

20372

59175

26653

10920

26620

12299

2117532014.04.29

12:00-18:00

58616

27255

66721

33039

11654

30383

17720

2453882014.04.29

18:00-24:00

40370

16459

42125

23496

8429

23524

9934

1643372014.05.01

06:00-12:00

65841

16040

45755

19519

11873

32697

10736

202461

2014.05.01

12:00-18:00

84430

24759

63591

28885

17846

42292

16025

2778282014.05.01

18:00-24:00

127069

37535

91992

42091

26154

77797

22483

425121The original check-in points are divided by the Weibo platform into 200 types, this study
merged the original types into 7 activity categories (see details from Table 1) according to land
use regulations, check-in activities and Yan et al.‘s work (Yan et al., 2019).
A total of 2,394,961 SNS check-in data was collected in the core area of Nanjing from Sina
Application Programming Interface (API) using a crawler tool written in Python.

Methodology: How to cluster the check-in points into groups?
In order to find the hotspots accurately, a new method of self-adaptive determination ofDBSCAN algorithm parameters (KANN-DBSCAN) is selected. In this algorithm, candidate Eps
parameters are generated by K-means nearest neighbor method, and corresponding MinPts
parameters are obtained by mathematical expectation (Bharti, 2019).

Figure 2. KANN-DBSCAN clustering with MinPts = 4
In this work, the KANN-DBSCAN algorithm was run in RStudio 3.6 with R language.

Methodology: How to detect the of scope of urban hotspots?
After clustering all check-in points into clusters and removing noise points, thiswork had to forward another step to detect the scope of each urban clusters. Drawing theboundary for a cloud of points had always been the main research field for the computer
vision (CV). Among all the related CV algorithms, convex hull and concave hull are thetwo most popular ones.

Figure 4. Convex hull (left) and concave hull (right)
The concave hull has shown a great performance in dealing with real world problems,
like detecting the urban boundaries (Craig, 2017). As a result, in this work, the concave
hull of check-in clusters was defined as the urban hotspots, and the number of check-inactivities and area of the hulls were defined as the popularity and area of urban hotspots.

Results: Spatial distribution of urban hotspots

Figure 5. The spatial distribution of urban hotspots (total of 309)

Results: Spatial distribution of urban hotspots

Using the urban hotspot extraction method designed in this work, a totalof 309 urban hotspots were extracted from the Weibo data of 9 time periods inthe central area of Nanjing.
As can be seen from the above picture, the urban hotspots in Nanjing's
main urban area are mainly distributed in Nanjing Xinjiekou commercialcircle, Hunan Road commercial circle, Confucius Temple scenic spot, Arctic
Pavilion scenic spot, and Nanjing Station square. Moreover, the location,quantity, area, and even popularity of urban hotspots have continuously
changed over time.
In addition, by comparing the results of this case study with the resultsof traditional research and Nanjing urban and rural land layout planning, it isfound that this method can accurately and efficiently identify urban hotspots
and determine their functions.

Results: Functional orientation of urban hotspots

Figure 6. The result of HCA
After the identification of urban hotspots in downtown of Nanjing, the proportion of
7 types of check-in points was counted for the further HCA. As seen in Figure 6, the resultof optimal number calculating showed that the 309 urban hotspots should be classified
into 4 groups. The hierarchy of HCA was shown in Figure 6. The prime result showed
that, most of the urban hotspots were grouped into group 1 and 4, up to 143 and 102.
These two groups of urban hotspots occupied 46.3% and 33.0% of the total, respectively.
Group 3 account for the least number of urban hotspots of 19.

Results: Functional orientation of urban hotspots

Figure 7. The functional orientation of urban hotspots
We implemented a case study in the central urban area of Nanjing and identified 309
urban hotspots in the experimental area during 2014.04.27 to 2015.05.01. In addition, wealso found that these urban hotspots undertook the functions of daily living space, public
service area, tourist attractions and entertainment center.

Results: Dynamic trend of location of urban hotspots

The results showed that:(1) From the perspective of migration
direction, the migration direction of three days (holiday, weekend andworkday) showed completely different characteristics.
(2) In terms of migration path length, t
he shortest migration path length(344.7m) is on April 27, 2014 (weekend), and the longest migration pat
h length (720.8m) is on April 29, 20
14 (weekday).In addition, the migration path of hotspots mass center in holiday is 509.0m.

Figure 8. The migration path of mass centers of urban hotspots

Results: Dynamic trend of amount of urban hotspots

Figure 9. Dynamic trend of amount of urban hotspots(1) In general, during the study period, the urban hotspots in the main urban area of Nanjing
are mostly on holiday (117) and weekend (110), while the number of urban hotspots on
weekday was less than the other two days, only 82.
(2) Specifically, the morning and afternoon of the holiday and the afternoon of the weekend
are the periods with the largest number of urban hotspots.

Results: Dynamic trend of area of urban hotspots

Figure 10. Dynamic trend of area of urban hotspots(1) In general, the average area of urban hotspots in the study period is the largest on weekend,
the second on holiday, and the smallest on weekday, 34442.28m2, 32703.91m2 and30953.80m2, respectively.
(2) Specifically, the average area of urban hotspots in holiday afternoon, weekday morningand weekend afternoon were relatively larger, all over 36000m2. Followed by the weekday
afternoon and weekend night, the average area in these periods were about 35000m2.

Results: Dynamic trend of popularity of urban hotspots

Figure 11. Dynamic trend of popularity of urban hotspots(1) In general, the distribution characteristics of popularity of urban hotspots was close to theirnumber, showing that weekend and holiday were much higher than weekday.
(2) Specifically, the average popularity of urban hotspots on holiday and weekend was
extremely high in the afternoon (the maxima appeared on the afternoon of the weekend,reaching 1777.50) and relatively low in the morning and evening.

Results: Dynamic trend of function of urban hotspots

Figure 12. Dynamic trend of function of urban hotspots

Results: Dynamic trend of function of urban hotspots
Functionally, the overall function structure of urban hotspots showed thecharacteristics of holiday and weekday consistency, and the proportion of urban
hotspots with scenic spots and daily living space functions increases on weekends.
Specifically, in some special urban spaces, the functions of urban hotspots changed
over time:
1. Urban hotspots centered on well-known universities and parks in the city merge
into large urban hotspots of entertainment centers in the afternoon of non-workingdays.
2. The edge of urban large-scale entertainment center urban hotspots will differentiate
and derive some small-scale urban hotspots of daily life spaces.
3. The urban hotspots of scenic spots around the residential area will be converted
into daily life spaces in the weekday morning to undertake the leisure activities of thesurrounding residents.
4. The functions of urban hotspots, such as daily life spaces and entertainment centers,
are frequently transformed from each another.

Results: The driving factors of dynamic trend of urban hotspots

In this study, we selected Pearson
correlation coefficient method toquantitatively analyze the correlation
between prime variables like number(NUM) , area (AREA), popularity(POP) of urban hotspots and built
environment variables likepopulation density (PD), building
density (BD), road density (RD), bus
station density (SD) and distancefrom the city center (CD) in 12
research objects at different dates andperiods.

Figure 13. Verification area of the correlation analysis

Results: The driving factors of dynamic trend of urban hotspots

Figure 14. Correlation matrix in different date(Weekend-Left; Weekday-Middle; Holiday-Right)
The results of correlation analysis further showed that: (1) urban areas with highpopulation density tend to generate more urban hotspots during holidays, while areas withhigh road density tend to generate more urban hotspots during weekends and weekdays. (2)The more the number of public transport stations in urban space, the higher the area andpopularity of urban hotspots. (3) The area with higher building density and the area close to
the center of the city are also the hotspots of the city.

Results: The driving factors of dynamic trend of urban hotspots

Figure 15. Correlation matrix in different time(Morning-Left; Afternoon-Middle; Evening-Right)
The results of the above correlation analysis further show that: (1) It is more difficult tograsp the time of the emergence of urban hotspots than to predict the date. (2) In the threeperiods, there is no strong or significant correlation between the attributes of urban
hotspots and the elements of urban built environment at night.

Conclusions: What had been found throughout this research?
Through the KANN-DBSCAN and SNS data based urban hotspots detecting architecture
designed in this study, the dynamic trend of the urban hotspots in downtown, Nanjing wasdeiminated. The main conclusions are as follows:1.

We designed a method of urban hotspot extraction based on SNS sign in data, which could identify the shape,area, quantity, popularity and function of urban hotspot.

2.

By analyzing the changes of various attributes of urban hotspots in the study period, we found that urban
hotspots had the following dynamic trend: From the perspective of location, the hotspots in the city will
explode to the city's commercial center and tourist attractions along the main road in the afternoon ofnon-working days. In terms of basic attributes, the number, area and popularity of urban hotspots obey
the characteristics of reaching maximum value on weekends and holidays (especially in the afternoon).
Functionally, the overall function structure of urban hotspots showed the characteristics of holiday andweekday consistency, and the proportion of urban hotspots with scenic spots and daily living space functions
increases on weekends.

3.

Through qualitative and quantitative analysis of the driving factors of the dynamic change of urban hotspots, wefound that large-scale population migration (and the commuting mode of residents) in urban space
may be the main cause of urban hotspot displacement. In addition, population density andaccessibility of public transportation facilities are the main influential factors on the number, areaand popularity of urban hotspots. The invisible function of the urban spaces can explain the change of urban
hotspot function.

Q&A