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Sensors and Materials, Vol. 35, No. 3 (2023) 871–887
MYU Tokyo
S & M 3215

Commuting Characteristics of Peripheral Clusters
around a Central City Based on Mobile Phone Signaling Data
—A Case Study of Tianjin, China
Zifei Liu, Peng Guo,* Yanling Sun,** Junrong Li, and Xinrui Pei
School of Geography and Environmental Science, Tianjin Normal University,
No. 393 Binshui West Road, Xiqing District, Tianjin 300387, China(Received October 29, 2022; accepted February 14, 2023)

Keywords: commuting, peripheral clusters, job–housing separation, central city, mobile phone signaling
data, Tianjin

Commuting has been the focus of research on urban spatial structure in recent years. Owing
to the lack of information about employment locations in Chinese census data, commuting
research has typically relied on traditional data sources such as surveys and questionnaires. With
the advancement of communication and geographic information technology, new ideas andtechnical means for commuting research have emerged. The peripheral clusters of a central city
are an essential part of urban functions and are critical areas that affect residents’ commuting.
We present a framework for analyzing the commuting characteristics of peripheral clusters
around a central city based on mobile phone signaling data. We used the peripheral clusters
around the central city of Tianjin as the research area for a case study based on this framework.
The results show that the proportion of commuting within peripheral clusters around the central
city of Tianjin is significant, and the overall commuting situation is excellent. Most peripheral
clusters around the central city of Tianjin are in a relatively balanced state of employment andresidence. The Xinli Cluster and Airport Logistics Park Cluster have slightly fewer residences.
The Dongli Lake Cluster has a moderate shortage of job opportunities and a slight separation
between job and housing availability.

1.

Introduction

Commuting refers to the movement of people between residences and employment locationsin an urban area.(1) It is inextricably linked to the spatial layout, land use planning, transportation
structure, and residents’ social life. Commuting characteristics are critical in determining therationality and livability of an urban area. Moreover, the differences in commuting characteristics
within various urban spatial distributions and built environments become the focus ofcommuting research, which usually involves the study of the spatial distribution of houses andsites of employment, commuting time, and commuting mode.(2) Thorough research into thecommuting characteristics of urban residents can shed light on the internal workings of organic*Corresponding author: e-mail: pguo@tjnu.edu.cn**Corresponding author: e-mail: sunyanling@tjnu.edu.cn

https://doi.org/10.18494/SAM4207

ISSN 0914-4935 © MYU K.K.
https://myukk.org/

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urban development, streamline land resource distribution, boost the efficiency of urban
operations, and guide the pursuit of high-quality urban growth.(3)The current focus of scholars who study commuting characteristics is the investigation ofresidents’ commuting time, distance, efficiency, transportation mode, route planning, and otheraspects, as well as the analysis of the factors influencing commuting characteristics. Somescholars use the ratio of commuting time spent at work to describe commuting features.(4–7)
From the perspective of improving the urban traffic system to alleviate traffic congestion andrationally optimizing the urban spatial layout, scholars have used various methods such as the
structural equation model, the multinomial logit regression model, the discrete choice model,
and the multi-level mixed effect model to systematically and thoroughly discuss the spatial
characteristics of residents’ commuting, commuting time, commuting distance, commuting
mode, carbon emissions due to commuting, and factors influencing these characteristics, and put
forward policy suggestions for optimizing the urban spatial structure and encouraging lowcarbon transportation.(8–10) The most common research method is to select specific employment
locations or residences for case analysis or comparative analysis.
Owing to the absence of information on employment locations in Chinese census data,
scholars initially relied on traditional data such as questionnaires and activity logs, which were
inefficient, offered tiny sample sizes, and were sluggish with respect to updating data.(11) In
today’s era of big data, the rapid development of information technology provides new ideas andtechnical means for analyzing commuting characteristics. Datasets that included relatively
specific spatial-temporal attributes and had high accuracy, such as bus swipe card data, media
check-in data, and micro-blog user data, had been used earlier. However, the drawback of thesedatasets is that their scope limited the research to card users and micro-blog users.(12–14) The
emergence of mobile phone signaling data brings a new opportunity to study residents’commuting characteristics, which can portray residents’ spatial distribution and movement more
realistically and effectively through real-time positioning information from mobile phones. The
broad spatial and temporal coverage, high rate of ownership, and large quantity of mobile
signaling data are important data supplements for studying commuting characteristics.(15–18)
Even though numerous researchers have studied commuting characteristics and produced
outstanding results, most of their attention has been focused on mega-cities such as New York,Tokyo, Beijing, and Shanghai. More research is needed on the peripheral clusters around central
cities. Peripheral clusters around central cities are the outlying regions that can meet the basic
living needs of residents and support the migration of various functions from the central city.(19)The establishment of peripheral clusters can effectively control the irrational expansion of acentral city and relieve the pressure of population and employment, which is an essential strategy
for coordinating regional development, help in the optimization of a city’s spatial layout, andassist in realizing sustainable urban development.(20) However, the lack of synchronization
between places of employment and residences in the construction of peripheral clusters has
resulted in a large number of “sleeping cities” and “empty cities,” exacerbating the imbalance
between available jobs and housing, increasing the commuting costs of residents in peripheral
clusters, and impacting their daily lives. Analyzing the commuting characteristics of peripheral
clusters around a central city is necessary for optimizing the spatial layout of an area, enhancing

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the efficiency of resource utilization within an area, and attaining high-quality urban
development. As a result, we propose a framework for analyzing the commuting characteristics
of peripheral clusters around a central city based on mobile phone signaling data. We used
Tianjin’s peripheral clusters in a case study to provide a reference for the improvement of
peripheral cluster planning in Tianjin.

2.

Data and Methods

2.1

Study area

The study area was the peripheral cluster around the central city of Tianjin. According to the
document “Tianjin Urban Master Plan (2005–2020),”(21) Tianjin has developed as a four-tiertown system consisting of primary and secondary centers, new towns, central towns, andgeneral towns to promote urbanization and steadily raise the level of urbanization. As the urban
functions of Tianjin continued to spread beyond the central area, residential areas and industries
in the former central urban districts were gradually relocated to the suburbs. In locations along
the outer ring outside the main city center, peripheral clusters of various scales and purposes
were developed, including Xiqing New Town and 9 clusters in Dasi, Shuanggang, Xinli, Airport
Logistics Park, Dongli Lake, Dabizhuang, Xiaodian, Shuangjie, and Qingshuang (Fig. 1). The
building of the Xiqing New Town and the 9 clusters were primarily completed by 2017.

Fig. 1. (Color online) Layout structure of “central city + peripheral clusters” for the central city of Tianjin.
[Source: Tianjin Urban Master Plan (2005–2020)]

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Methods

Anonymized mobile phone signaling data from China Mobile operators in Tianjin between
Oct. 18 and Nov. 23, 2017, were used in this study. Specifically, anonymized user identification(ID) numbers, time, base station ID numbers, and signaling types were included. During theactive period, mobile phones were used to connect with the mobile signal base station, andinformation was recorded approximately every 4 min between the hours of 6:00 and 22:00. Eachmobile phone produced nearly 131–171 signals per day on average. According to statistics, by theend of 2017, China Mobile subscribers accounted for 62.4% of all mobile subscribers.
Furthermore, there was no significant difference in the spatial distribution of users in choosing
communication operators. To a certain extent, the data reflected the commuting characteristics
of residents. On the basis of the information and the technical framework shown in Fig. 2, ananalysis was carried out.
2.2.1 Identification of locations of employment and housing
The adjacent time intervals generated by each signaling data are not fixed, and there is apossibility of recording time longer than 1 h or shorter than 1 min. As a result, we used thecumulative time method proposed by Niu and Xie to analyze signaling data from Oct. 18 throughNov. 23, 2017, to identify locations of mobile phone subscribers and eliminate the irregularities

Fig. 2.

(Color online) Diagram of the technical framework.

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in the signaling recording time.(22) The locations where users spent the most cumulative time
during the day from 9:00 to 18:00 were labeled as employment locations, while the locations
where they spent the most cumulative time during the night from 20:00 to 8:00 were labeled as
residences. Finally, in the regional scope of the peripheral clusters in the central city of Tianjin,
approximately 341000 residences and 385000 employment locations were identified. Comparing
the number of residents in each block identified by mobile phone data with the data from thesixth nationwide population census showed that the two types of data have a high consistency inspatial distribution. Although mobile phone records and identification rules have an impact on
the results of the identification of the resident population, the method of using mobile phonesignaling data to identify the resident population is still considered feasible.
2.2.2 Origin destination (OD) analysis
The OD survey refers to a survey of traffic volume between an origin and a destination.
When using the mobile phone signaling data to identify the locations of residences andemployment of mobile phone users, the spatial mapping relationship between the base station
cell and each traffic cell was obtained, and an OD table of mobile phone users’ travel wasconstructed.(23) By analyzing the OD travel of mobile phone users, both the commuting flow andtype of mobile phone users were determined. The balance between their places of employment
and residences, referred to as the job–housing balance, was calculated as a result.
2.2.3 Analysis of commuting between sites of employment and residences
The ratio of the number of jobs to the number of residents in an area and a self-sufficiency
index were chosen to measure the spatial relationship between sites of employment andresidences. On the quantitative level, this ratio represents the balanced relationship between thenumber of jobs and the number of residents in a particular area; on the qualitative level, the selfsufficiency index represents the degree of independence of employment locations and residences
in a particular area.
(1)	Job–housing ratio. The job–housing ratio is the ratio of the number of jobs in an area to thenumber of residents in that same area.(24) The specific calculation formula is
Zi =

Ai
,
Bi

(1)

	where Zi is the job–housing ratio in region I, Ai is the number of jobs in region i, and Bi is thenumber of residents in region i. As reported by Cervero,(25) a balanced job–housing
relationship in a region is indicated by a ratio ranging from 0.8 to 1.2. Therefore, the range of
0.8–1.2 was used as a standard for a balanced job–housing ratio in this study.
(2)	Self-sufficiency index. The self-sufficiency index reflects the potential of job–housingbalance in a particular region. Xu and Wu modified Thomas’s “independence index” andproposed a self-sufficiency index.(26) The specific calculation is876

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Cfi =

Yi
,
Avg (Yi )

(2)

Rsi =

Ni
,
Avg ( Ni )

(3)

Esi =

Bi
,
Avg ( Bi )(4)	where Yi is the ratio of people who work and live in region i to the total number of people
living there, Ni is the ratio of people who live in region i but do not work to the total numberof people who live there, and Bi is the ratio of people who work but do not live in region i to
the total number of people who work there.
The self-sufficiency index (Cfi) measures the level of integration between jobs and housing ina region. A higher Cfi value indicates a higher degree of integration and a weaker separation
between jobs and housing. On the other hand, a higher value of the residence independence
index (Rsi) suggests greater housing independence in the region, with a higher proportion ofpeople who work elsewhere and limited employment opportunities. The employment
independence index (Esi) reflects the employment independence of a region, with a higher value
indicating a higher proportion of people who live elsewhere, more abundant employment
opportunities, but a shortage of housing.

3.

Analysis of Results

3.1

Residence and employment

3.1.1 Population distribution of residences and employment
Table 1 shows the resident population, the number of employed people in the population, andthe residential density and employment density of the clusters in the peripheral clusters around
Tianjin’s central city identified through mobile phone signaling data. From the resident
population, the number of residents in Xiqing New Town is much larger than in other clusters,
followed by the Airport Logistics Park Cluster and the Dasi Cluster; the number of residents
identified in Xiaodian, Shuangjie, Shuanggang, and Xinli clusters is similar; the Qingshuang
and Dabizhuang clusters have a small number of residents, and the Dongli Lake Cluster has thesmallest number of residents. Considering the number employed people, Xiqing New Town is
still far more populated than other clusters, and the Dongli Lake Cluster is the least populated.
The difference in ranking in terms of the number of residents is that, while the Xinli Cluster’s
residents are not as numerous as those in the Shuanggang Cluster, the Dongli Economic
Development Area within the Xinli Cluster may cause the number of employed people in the
Xinli Cluster identified by mobile phones to exceed that of the Shuanggang Cluster.

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Table 1
Number and density of employment and residents in peripheral clusters in the central city of Tianjin.
Resident
Employment
Density ofDensity ofCluster area
Cluster namepopulation
population
residences
employment(km 2)(person)(person)(person/km 2) (person/km 2)
Xiqing New Town
131526139241
90.00
1461.38
1547.10
Airport Logistics Park Cluster
52946
73080
111.96472.92652.76
Dasi Cluster
35666
39278
19.91
1791.78
1973.23
Xiaodian Cluster
22745
26077
23.54
966.271107.83
Shuangjie Cluster
22320
23683
22.45
994.371055.10
Shuanggang Cluster
21001
21588
11.03
1903.33
1956.53
Xinli Cluster
18575
25025
14.29
1299.85
1751.21
Qingshuang Cluster
14545
16391
15.93
913.061028.94
Dabizhuang Cluster
13732
14225
23.02
596.59618.01
Dongli Lake Cluster
8053
6497
11.81
681.91550.15

Because of the large area of the cluster, Xiqing New Town has the most people and a relatively
high density of residences. Furthermore, Zhangjiawo Town, Yangliuqing Town, Zhongbei Town,
and other densely populated areas are part of the Xiqing New Town. The high density ofresidences and employment in the Airport Logistics Park Cluster is also related to the large sizeof the cluster, although the density of residences and employment is very low. The Dasi Cluster
and the Shuanggang Cluster have a relatively high number and density of residences andemployment, which is related to the fact that the Dasi Cluster has two economic and technologicaldevelopment zones, and the Shuanggang Cluster has the Shuanggang Industrial Zone. The
number and density of residences and employment in the Dongli Lake Cluster are low due to thedevelopment of tourism in the vicinity of Dongli Lake, which has leisure and entertainment
venues such as resorts and Happy Valley.
The kernel density analysis of the resident population distribution (Fig. 3) and the employment
population distribution (Fig. 4) identified by mobile phone signaling data in the peripheral
clusters of Tianjin’s central city reveal that the resident population distribution is relatively
concentrated in each cluster, while the employment population distribution is relatively dispersedand balanced. Areas with a resident population density of more than 5000 people per km2 areroughly located in Zhongbei Town, Yangliuqing Town and Zhangjiawo Town of Xiqing New
Town, Beichen Science and Technology Park of Shuangjie Cluster, Jinzhong Street of
Dabizhuang Cluster, and Tianjin Airport Economic Zone of Airport Logistics Park Cluster. The
pattern of residential concentration shows a decline in population density in concentric circles
radiating from the center. Meanwhile, the distribution of the employed population is moredispersed and balanced across the region. However, there are still certain areas with a relatively
high concentration of employed individuals, such as the high-tech zones in Xiqing New Town,
Yangliuqing Town, the Airport Economic Zone in the Airport Logistics Park Cluster, the Xinli
Street in the Xinli Cluster, and the Dasi Town in the Dasi Cluster.

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Fig. 3. (Color online) Kernel density of the resident population distribution in peripheral clusters in the central cityof Tianjin.

Fig. 4. (Color online) Kernel density of the distribution of the population of employed people in peripheral clusters
in the central city of Tianjin.

3.1.2 Job–housing balance analysis
The table describing the balance between jobs and residential housing for the peripheral
clusters in the central city of Tianjin (Table 2) shows that most of the clusters are in a balanced
state in terms of the number of residents and jobs, but the ratio of jobs to residences in the
Airport Logistics Park Cluster and Xinli Cluster exceeds the maximum value of 1.2 in terms of
the balance between jobs and the number of residents, indicating that the number of jobs in thesetwo clusters is greater than the number of residents. From the perspective of the self-sufficiency

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Table 2
The assessment of the balance between jobs and housing in the peripheral clusters in the central city of Tianjin.
Jobs–housing
Self-sufficiency
Residence
Employment
Cluster name
ratio
index
independence index independence index
Xiqing New Town
1.06
1.05
0.77
0.74
Airport Logistics Park Cluster
1.38
1.08
0.65
1.40
Dasi Cluster
1.10
0.96
1.18
1.13
Xiaodian Cluster
1.15
1.09
0.63
0.89
Shuangjie Cluster
1.06
1.00
1.00
0.91
Shuanggang Cluster
1.03
0.92
1.35
1.06
Xinli Cluster
1.35
1.03
0.88
1.46
Qingshuang Cluster
1.13
1.01
0.95
1.04
Dabizhuang Cluster
1.04
1.00
1.00
0.84
Dongli Lake Cluster
0.81
0.86
1.59
0.52

index, the Xiaodian Cluster, the Airport Logistics Park Cluster, and Xiqing New Town haverelatively high self-sufficiency indexes, indicating a well-balanced relationship between jobs andresidential housing. On the other hand, the Dongli Lake Cluster has a lower self-sufficiency
index of 0.86, suggesting a less balanced job–housing relationship. In terms of the residence
independence index, the Dongli Lake Cluster and the Shuanggang Cluster have higher residence
independence, implying that these areas have adequate housing and a high proportion ofresidents who commute for employment. Meanwhile, the Xinli Cluster and the Airport Logistics
Park Cluster have higher employment independence index values, which indicate that theseclusters have a higher availability of employment opportunities and a higher proportion ofpeople who commute from outside the region.
3.2

Commuting space characteristics

We obtained the travel OD table of users within each cluster using mobile phone signaling
data to identify the locations of employment sites and residences. Furthermore, the employment
whereabouts of the residents and the commuting directions of employees can be obtained byanalyzing the travel OD of mobile phone users. On the basis of this, we examined the commuting
characteristics of residents in the peripheral clusters in Tianjin’s central city from four
perspectives: distribution of employment sites of the residents, distribution of residential sites
with respect to employment, commuting type, and commuting distance.
3.2.1 Employment site distribution of residents
The kernel density of the distribution of employment sites of residents in peripheral clustersof Tianjin’s central city (Fig. 5) shows that majority of the residents in peripheral clusters in thecentral city of Tianjin are employed in a central street or a specific town within the cluster, suchas Shuangjie Town in Shuangjie Cluster, Jinzhong Street in Dabizhuang Cluster, Xinli Street in
Xinli Cluster, Shuanggang Town in Shuanggang Cluster, Dasi Town in Dasi Cluster, andHuaming Street in Dongli Lake Cluster. The vast majority of residents in each cluster congregate

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(a)

(b)

(c)

(d)

(e)

(f)

Fig. 5. (Color online) Distribution of places of employment of residents in the peripheral clusters of the central cityof Tianjin: (a) Xiqing New Town, (b) Airport Logistics Park Cluster, (c) Dasi Cluster, (d) Xiaodian Cluster, (e)Shuangjie Cluster, and (f) Shuanggang Cluster.

on these streets or in these towns. In addition, some clusters have jobs more evenly distributed
among several towns or streets, such as Zhangjiawo Town, Yangliuqing Town, and Zhongbei
Town in Xiqing New Town, Qingguang Town and Shuangkou Town in Qingshuang Cluster,
Yixingbu Town and Xiaodian Town in Xiaodian Cluster, and Huaming Street, Xinli Street, andJunliangcheng Street in the Airport Logistics Park Cluster.

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(g)

(h)

(i)

(j)

Fig. 5. (Continued) (Color online) Distribution of places of employment of residents in the peripheral clusters of
the central city of Tianjin: (g) Xinli Cluster, (h) Qingshuang Cluster, (i) Dabizhuang Cluster, and (j) Dongli Lake
Cluster.

3.2.2 Distribution of employees with respect to residence
The kernel density of distribution of residential areas of employees in peripheral clusters ofTianjin’s central city (Fig. 6) indicates that the majority of the residences of employees in these
peripheral clusters are also primarily concentrated within their respective clusters. Analysis andcomparison revealed that the distribution of the residential locations of the employees and the
distribution of their employment sites are largely the same within each cluster. The streets withthe highest concentrations of employees also have the highest concentrations of residents. The
consistency of spatial distribution between workplace and residence is relatively high.
3.3

Commuting types

The types of commuting are classified into internal and external commuting according to therelationship between locations of employment and residences in each peripheral cluster. Internal
commuting refers to living and working in the same cluster, whereas external commuting refers
to working in a cluster but not living there. The ratio of commuting types for each cluster is882

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(a)

(b)

(c)

(d)

(e)

(f)

Fig. 6. (Color online) Distribution of residential areas of employees in peripheral clusters of the central city of
Tianjin: (a) Xiqing New Town, (b) Airport Logistics Park Cluster, (c) Dasi Cluster, (d) Xiaodian Cluster, (e)Shuangjie Cluster, and (f) Shuanggang Cluster.

shown in Table 3. The internal commuting ratio within a peripheral cluster is ranked similarly tothe self-sufficiency index. The highest commuting ratios are found in the Xiaodian and Airport
Logistics Park clusters, while the lowest ratio is in the Dongli Lake Cluster. The ranking of theexternal commuting ratio corresponds to the ranking of the employment independence index.

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(g)

(h)

(i)

(j)

Fig. 6. (Continued) (Color online) Distribution of residential areas of employees in peripheral clusters of thecentral city of Tianjin: (g) Xinli Cluster, (h) Qingshuang Cluster, (i) Dabizhuang Cluster, and (j) Dongli Lake
Cluster.

Table 3
Proportion of commuting types in peripheral clusters in the central city of Tianjin.
Cluster nameInternal commuting (%) External commuting (%)
Xiqing New Town
85
19
Airport Logistics Park Cluster
88
37
Dasi Cluster
78
30
Xiaodian Cluster
88
23
Shuangjie Cluster
81
24
Shuanggang Cluster
74
28
Xinli Cluster
83
38
Qingshuang Cluster
82
27
Dabizhuang Cluster
81
22
Dongli Lake Cluster
70
14

The Xinli Cluster and Airport Logistics Park Cluster ranked highest, with the Dongli Lake
Cluster ranking lowest. The internal commuting ratios of the peripheral clusters in Tianjin’scentral city are all 70% or higher. The other peripheral clusters, except the Airport Logistics

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Park and Xinli clusters, all have 30% or lower external commuting ratios. Except for the Airport
Logistics Park Cluster and Xinli Cluster, which have a higher proportion of external workers, theother peripheral clusters in Tianjin central city maintain a balance between jobs and residents,
consistent with the results of commuting characteristics obtained from the analysis of the ratio ofjobs to residents.
3.4

Commuting distanceThe commuting distance for the peripheral clusters in the central city of Tianjin (Table 4)shows that the average commuting distance within each cluster is relatively short, and mostclusters have an internal commuting distance of 300 m or less. The internal commuting distanceexceeds 500 m in Xiqing New Town and Airport Logistics Park Cluster because of the large areaof the clusters. The commuting distance from the inside out of each cluster is quite different, andthe commuting distance from the inside out of Xiqing New Town, Airport Logistics Park
Cluster, Shuangjie Cluster, and Dongli Lake Cluster exceeds 10 km. The commuting distanceback from the outside of each cluster is roughly equivalent to the commuting distance from theinside out. The average commuting distance of residents in each cluster is reasonable for mostclusters and lies between distances of 1 km and 2.5 km, but the average commuting distance ofresidents in the Dongli Lake Cluster is more than 4 km, which is related to the fact that Dongli
Lake Cluster has a small number of jobs and the commuting distance from the inside to theoutside is long. The attractiveness of employment is closely related to the commuting distance tolocations of employment. If the average commuting distance to employment locations in aparticular region is significantly greater than the average commuting distance of residents, theattractiveness of employment in the area is higher. According to Table 4, the Airport Logistics
Park Cluster and the Xinli Cluster are very attractive in terms of employment, while the Dongli
Lake Cluster is less attractive.

Table 4
Commuting distance in peripheral clusters in the central city of Tianjin (unit: m).

Cluster name
Xiqing New Town
Airport Logistics Park Cluster
Dasi Cluster
Xiaodian Cluster
Shuangjie Cluster
Shuanggang Cluster
Xinli Cluster
Qingshuang Cluster
Dabizhuang Cluster
Dongli Lake Cluster

Internal
commutingdistance

Commutingdistance frominside out

Commutingdistance backfrom outside

Averagecommutingdistance ofresidents

582
501
242
290
282
173
219
128
140
156

11357
11871
7498
8628
11357
8814
9489
6629
7553
14339

10360
13480
9974
8986
10616
7789
8870
6785
6256
13186

2171
1909
1870
1296
2402
2393
1777
1304
1559
4450

Averagecommutingdistance of
employment
locations
2486
5241
3119
2316
2741
2284
3528
1946
1482
1925

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4.

885

Discussion

Analyzing the commuting characteristics of peripheral clusters in a central city can help
improve the efficiency of urban operations and play a positive role in the planning andconstruction of the city. The framework proposed in this study analyzes the commuting
characteristics of peripheral clusters in a central city using mobile phone signaling data. The
sample size based on mobile phone data is large, so it reflects the commuting characteristics
quantitatively. The results of identifying the locations of employment sites and residences bymobile phone data are consistent with the census data. We compared our results of the case study
on the peripheral clusters in the central city of Tianjin with Qiqi Xiao’s research on the
distribution of jobs and housing in Tianjin.(27) The latter research results pointed out that the
distributions of residential agglomeration areas and employment agglomeration areas in Tianjin
are highly similar, and the distributions of employment sites and residences are relatively
balanced. The internal commuting characteristics of areas such as Dongli District and Xiqing
District are more prominent, with many residents commuting within the same cluster. We also
compared our results with those from the study on the relationship among commuting,
employment, and housing in Tianjin by Baidu Huiyan and Taupd Joint Innovation Lab. The latter
research results pointed out that the number of jobs in places such as the Airport Development
Zone is relatively high, which are mainly located in industrial clusters, while the Dongli Lake
Cluster has a modest shortage of jobs.(28) These results are basically consistent with the results ofthis study, which suggests that it is feasible and reasonable to use mobile phone signaling data to
analyze the commuting characteristics of peripheral clusters around a central city.
According to our results, the overall situation involving jobs and housing in the peripheral
clusters around the central city of Tianjin is excellent, but there is still much potential for
improvement. The Xinli Cluster and the Airport Logistics Park Cluster have high employment
rates and cannot meet the housing needs of employees. We suggest that these two clusters
strengthen their construction of housing and infrastructure, improve living conditions, and solve
the housing problems of the employees. There are few jobs in the Dongli Lake Cluster, and theaverage commuting distance of residents is relatively long. The cluster’s industrial structure andthe long commuting distance of the residents should be improved, the development of industries
other than tourism and vacationing should be emphasized, and the number of jobs should be
increased to provide more job options closer to residential areas for the residents of the Dongli
Lake Cluster. Improvements in the study of these issues could also be made. For example, theindividual characteristics of mobile phone user data should be taken into account when studying
the commuting characteristics of peripheral clusters around a central city. Because of limitationsin the data, the users’ age range was not taken into consideration in this work, so we failed toseparate the employed residential group from the unemployed residential group. We also did notinclude the gender, occupation, and income level of users and did not thoroughly analyze thereasons for the differences in commuting characteristics of the residents. We also did not
examine factors influencing the commuting characteristics of each cluster. These aspects should
be considered in future work.

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Conclusions

We proposed a framework for analyzing the commuting characteristics of peripheral clusters
based on mobile phone signaling data and took the peripheral clusters in the central city of
Tianjin as the study area to conduct an analysis. The main conclusions are as follows.
(1)	The overall commuting situation in the peripheral clusters around the central city of Tianjin
is excellent. Most of the clusters have a high proportion of internal commuting, and theaverage commuting distance is relatively short.
(2)	The relationship between the number of jobs and the number of residents in a particular area
can be characterized as balanced in most peripheral clusters in the central city of Tianjin.
However, some clusters may experience slight imbalances in either housing or employment,
leading to a slight separation of the job–housing distribution.
These research results may help optimize the spaces for employment sites and housing in the
peripheral clusters around the central city of Tianjin, save commuting costs, and improve thecommuting efficiency of residents, thus alleviating urban traffic conflicts and improving urban
operation efficiency. We have identified aspects that need to be improved in the planning anddevelopment process of peripheral clusters around the central city of Tianjin and have suggested
improvements to provide references for the planning of peripheral clusters around the central
city of Tianjin.

Acknowledgments
This work was supported by the 2021 Open Project of Key Laboratory of Intelligent
Transportation Industry of Urban Public Transport (2021-APTS-02) and the National Natural
Science Foundation of China (Grant Nos. 41001022 and 41971306).

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About the Authors
Zifei Liu received her B.S. degree from Tianjin Normal University, China, in 2021. She iscurrently working on her M.S. degree at Tianjin Normal University. Her research interests are inclimate change and urban regional development. (liuzifei_0801@163.com)
Peng Guo received his B.S. and M.S. degrees from Southwest Agricultural University in China
in 2001 and 2004, and his Ph.D. degree from Wuhan University, China, in 2008. Since 2008, hehas been at Tianjin Normal University, China, and currently serves as an associate professor. His
research interests are in the analysis of geographic big data. (pguo@tjnu.edu.cn)
Yanling Sun received her B.S., M.S., and Ph.D. degrees from Southwest Agricultural University,
China, in 2001, 2003, and 2006, respectively. From July 2006 to May 2008, she was a
postdoctoral fellow at the Key Laboratory of Regional Climate-Environment for East Asia,
Institute of Atmospheric Physics, CAS. Since 2008, she has been at Tianjin Normal University
and currently serves as an associate professor. Her research interests are in remote sensing ofresources and environment and global change. (sunyanling@tjnu.edu.cn)
Junrong Li received her B.S. degree from Taiyuan Normal University, China, in 2020. She iscurrently working on her M.S. degree at Tianjin Normal University, China. Her researchinterests are in climate change and urban regional development.(lijunrong98@163.com)
Xinrui Pei received her B.S. degree from Tianjin Normal University, China, in 2020. She iscurrently working on her M.S. degree at Tianjin Normal University. Her research interests are inclimate change and urban regional development. (pei_xinrui@163.com)