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Sensors and Materials, Vol. 35, No. 3 (2023) 855–870
MYU Tokyo
S & M 3214

Study of Urban Expansion and Urban Connection Characteristicsof the Beijing–Tianjin–Hebei Region
Cai Cai,1,2,3 Huimin Tian,1,2 Zeyu Li,1,2 Zongxia Xu,1,2
Yi Wan,1,2 Yutao Guo,1,2 Yingchun Tao,1,2,3 and Bogang Yang1,2,3*
1Beijing Institute of Surveying and Mapping, No. 15 Yangfangdian Road, Haidian District, Beijing 100038, China
2Beijing Key Laboratory of Urban Spatial Information Engineering,No. 15 Yangfangdian Road, Haidian District, Beijing 100038, China

3Analysis and Application of Urban Spatial Big Data in Bogang Yang Staff Innovation Studio,

No. 15 Yangfangdian Road, Haidian District, Beijing 100038, China(Received October 29, 2022; accepted January 25, 2023)

Keywords: urban expansion, urban connection, nighttime light data, Beijing–Tianjin–Hebei region, urban
agglomeration

It is of great significance for coordinated development to analyze the urban expansion process
and urban connection characteristics of urban agglomeration. We studied urban expansion
extraction and urban connection analysis methods for urban agglomeration based on remotesensing satellite sensors of thematic mapper (TM), enhanced thematic mapper (ETM+),
operational land imager (OLI) and visible infrared imaging radiometer suite (VIIRS), and urban
area extraction and multidata analysis technologies. Using the relevant materials of domestic
land cover data and nighttime light data combined with population and economic data, weanalyzed urban agglomeration characteristics of the Beijing–Tianjin–Hebei region, which is oneof the most important urban agglomeration areas in China. The expansion characteristics of the
Beijing–Tianjin–Hebei region from 2000 to 2021, as well as urban connection characteristics andtheir changes among the cities, and the three axes of Beijing–Tianjin, Beijing–Baoding–Shijiazhuang, and Beijing–Tangshan–Qinhuangdao were studied. The results show that: (1) the
Beijing–Tianjin–Hebei region expanded largely during 2000–2021 and the comprehensive
development gap narrowed. (2) A development trend has occurred around Beijing marked by anobvious border construction feature. The expansion speed of the capital metropolitan area wasfaster in the east and north and weaker in the southwest. (3) The connection of the threedevelopment axes was significantly strengthened since 2017, with two cores of Beijing andTianjin. Some problems of uneven urban connection and uncoordinated development were notedin the Beijing–Tianjin–Hebei region and some suggestions have been addressed.

1.

Introduction

Urbanization is part of the natural evolution of non-agricultural industries and rural
populations converging into cities and towns with the development of industrialization. It is an*Corresponding author: e-mail: bogangy@126.com

https://doi.org/10.18494/SAM4206

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

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objective trend of human social development and an important symbol of nationalmodernization.(1) With the development of remote sensing satellite sensors such as thematic
mapper (TM), enhanced thematic mapper (ETM+), operational land imager (OLI), and visible
infrared imaging radiometer suite (VIIRS), and information extraction technologies, urban area
expansion can be extracted and analyzed at a regional scale. With the proposal of the Beijing–
Tianjin–Hebei coordinated development strategy, more and more attention has been paid to
Beijing–Tianjin–Hebei urban agglomeration. Thus, it is crucial to quantify and analyze theurban expansion process of this region.
In recent years, many scholars have conducted studies on the expansion characteristics ofdifferent cities and the analysis of connections between cities. Research on urban expansion has
focused on the spatiotemporal characteristics of the expansion,(2–4) driving force analysis,(5–7)
and predictive models.(8) Huang et al.(2) studied the method of monitoring urban expansion using
multisource nighttime light data, optimized the method of selecting fitting samples in the
integration process of multisource nighttime light data, and carried out monitoring of urban
expansion in the Chengdu–Chongqing area over the last 15 years. Xia et al.(6) used a series ofLandsat remote sensing images as data sources, identified urban construction land, combined
this with a series of urban expansion evaluation indicators and socio–economic statistical data,
and analyzed the temporal and spatial characteristics and driving forces of urban expansion in
Hefei in the past 20 years. On the basis of the Artificial Neural Network–Agent Based Model–
Cellular Automata (ANN–ABM–CA) coupling model, Tao et al.(8) simulated the expansion of
the main urban area of Wuhan from 2005 to 2015 by considering the decision–making behaviorof agents at the macro and micro levels and combining that with 10 driving forces of urban
expansion. The research on urban spatial connections primarily includes three areas: research on
the strength and direction of urban spatial connections,(9,10) research on the analysis of regional
urban functions using an urban flow intensity model,(11) and research on the analysis of regional
spatial connections based on a combination of these two areas.(12) On the basis of the revised
gravity model, Li and Xiang(10) measured the strength of spatial connections between Xi’an andother central cities in central and western China and other cities with respect to their urban
agglomeration and compared and analyzed the gap in and causes of spatial connection ability
between Xi’an and other central cities in central China. An et al.(12) made comprehensive use ofa gravity model, a fracture point model, and an urban flow intensity model to analyze the overallcharacteristics of the spatial linkages of cities within the Beijing–Tianjin–Hebei urban
agglomeration.
In summary, although many studies have been carried out on urban spatial expansion andurban connection, few studies have integrated these two concepts and analyzed the connectionof the Beijing–Tianjin–Hebei urban agglomeration from the perspective of an axis ofdevelopment. On the basis of domestic land cover data, nighttime light data, and population andeconomic data, we analyzed the expansion characteristics of the Beijing–Tianjin–Hebei urban
agglomeration from 2000 to 2021 as well as the characteristics of the connections between thethree axes of Beijing–Tianjin, Beijing–Baoding–Shijiazhuang, and Beijing–Tangshan–
Qinhuangdao. We studied urban area extraction technologies on the basis of data from remotesensing satellite sensors of TM, ETM+, OLI, and VIIRS, geographic information system (GIS)Sensors and Materials, Vol. 35, No. 3 (2023)

857

analysis, and multidata analysis technologies to reveal the rules of changes in urban expansion
and existing problems in the current development of the Beijing–Tianjin–Hebei region. We thenoffer some administrative suggestions for strengthening the spatial connections and promoting
the coordinated development of the Beijing–Tianjin–Hebei urban agglomeration.

2

Study Area and Materials

2.1

Study area

We selected the Beijing–Tianjin–Hebei urban agglomeration as our study area. This region
consists of Beijing, Tianjin, and Hebei Province and has a total area of 218000 km2. It is locatedin the northern part of the North China Plain, adjacent to Taihang Mountain in the west, Yanshan
Mountain in the north, and the Bohai Sea in the east. The region enjoys a warm temperatecontinental monsoon climate.(5) The Beijing–Tianjin–Hebei urban agglomeration, together with
the Pearl River Delta urban agglomeration and the Yangtze River Delta urban agglomeration, is
one of the three major urban agglomerations, is the center of social and economic developmentin the North of China, and is the bellwether of the Bohai Sea economic circle.(13)2.2

Materials(1)	Domestic land cover data
GlobeLand30 is a global geographic information public product provided by China.
Generated from Landsat data, GlobeLand30 contains information from 2000, 2010, and 2020,
and includes 10 first–level land cover categories:(14) artificial surfaces, cultivated land,
woodland, grassland, shrub land, wetland, water bodies, tundra, bare land, glaciers, andpermanent snow cover. The artificial surfaces are mainly cement, asphalt, and other hardened
surfaces, and cultivated land consists of agricultural planting surfaces containing all cash crops.
(2)	Nighttime light data
Annual composite National Polar–orbiting Partnership–Visible Infrared Imaging Radiometer
Suite (NPP–VIIRS) nighttime light data were used in the study; these data excluded the effects
of lightning, moonlight exposure, cloud coverage, and random noise. The annual nighttime lightdata of the Beijing–Tianjin–Hebei area from 2012 to 2021 were used for spatiotemporal analysis.
(3)	Population and economic data
GDP and permanent resident population data were obtained from the national economic andsocial development bulletins and statistical yearbooks of cities in the Beijing–Tianjin–Hebei
region.

3.

Methods

3.1

Urban expansion analysis

Urban expansion analysis methods used in this study include urban area extraction, urban
expansion rate, expansion intensity, average light intensity, light growth rate, and total lightintensity variation coefficient.

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Sensors and Materials, Vol. 35, No. 3 (2023)(1)	Urban area extraction
The category of artificial surface in GlobeLand30 was used as the urban area. By registration,
cutting, and threshold extraction of the nighttime light data with GlobeLand30, results for theurban area in the years 2000, 2010, and 2012–2021 were obtained.
(2)	Urban expansion rate
Urban expansion rate is the ratio of the difference between the area of artificial surface
during the period of interest and the immediately previous period to the area of the immediately
previous period.
(3)	Urban expansion intensity
Urban expansion intensity is the ratio of the difference between the area of artificial surfaceof the period of interest and the previous period to the total area being studied.
(4)	Average light intensity
The average light intensity of a region is calculated by multiplying the light brightness value
by the area over which the light brightness was determined and dividing that product by the total
area of the region. The equation is as follows:
DN maxDN mean =∑ DN = DN DNi × ni ,
i

(1)

min

Nj

where DNmean represents the average value of the light brightness, DNmin represents theminimum value of light brightness, DNmax represents the maximum value of light brightness, ni
represents the number of pixels whose brightness is DNi , and Nj represents the total number ofpixels.
(5)	Light growth rate
The regional light growth rate is calculated by subtracting the average light intensity of thecurrent period from the average light intensity of the previous period and then dividing thatdifference by the average light intensity of the previous period. The equation is as follows:
RDN _ m =DN mean _ m − DN mean _ n
DN mean _ n,(2)

where R DN_m represents the light growth rate, and DNmean_m and DNmean_n represent the average
light brightness of year m and n, respectively.
(6)	Variation coefficient of light intensity
The variation coefficient of regional total light intensity is obtained by dividing the standard
deviation of the regional total lighting by the average value of the regional total lighting. The
equation is as follows:=
TNL

DN max∑DNi = DN min

DNi × ni ,(3)

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859

CV (TNL ) =S (TNL )M (TNL ),(4)

where TNL represents the total light intensity, DNi represents the light brightness, ni represents
the number of pixels whose brightness is DNi, S(TNL) is the standard deviation of the total lightintensity, and M(TNL) is the average value of the total light intensity.
3.2

Correlation analysis

Previous studies(15,16) have proved that nighttime light correlates strongly with GDP and
population. A regression analysis was carried out with the average nighttime light intensityof 13 cities in Beijing, Tianjin, and Hebei Province from 2012–2021 and the regional grosseconomic product and population.
3.3

Analysis of inter-city connection strength(1)	Inter-city connection strength
The inter-city connection strength is calculated using the city scale index and the actual
travel distance between cities. The city scale index is calculated using the city light intensity,
which represents the intensity of human activities in the cities, and the inter–city travel
distance represents the accessibility between the cities by road. The equations for the gravity
model and action potential model of inter–city connection strength are as follows:(17) n A
Scalei = I × B = ∑ ( N k × Sk )  × i , k = m
 A0

(5)

where Scalei is the city scale index; N k is the gray value of the Kth pixel in the city. The term
S k is the total number of pixels with the grayscale value of the Kth pixel in the city, m is theinitial threshold point (m = min = 1), n is the end threshold point (n = max), Ai is the area of
the grayscale value of i pixel of the city between the initial threshold and the end threshold,and A0 is the total area of all cities that meet the grayscale threshold range of the pixels.
=
VCSI ij K

Scaleia Scalebj
Dijc( i ≠ j; i ∈ [1, p ]; j ∈ [1, q ]) ,(6)

where VCSIij is the connection strength index between city i and city j. The value K is the
proportion coefficient; Scalei and Scalej are the regional size indices of the starting anddestination cities, respectively. The values a and b are the potential indices of urban factor
flow generating internal aggregation and external diffusion; Dij is the actual travel distance
between city i and city j, and c is the damping coefficient of the distance effect, which isreflected as the distance decay rate of the interregional attraction force of cities.

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Sensors and Materials, Vol. 35, No. 3 (2023)(2) Total urban connection strength
The total connection strength refers to the sum of the connection strengths between the cityof interest and the other cities.
The actual travel time between two city centers was adopted as the distance between cities,
and the shortest time required by high-speed rail or highway was selected for the calculation
using Baidu map route planning.
Owing to the large gap in economic development levels and accessibility of various cities,
significant differences arise in the connection strength among the cities. The value of connection
strength is divided into four levels: first-, second-, third-, and fourth-level connection degrees by
the ninth, eighth, seventh, and sixth power of 10, respectively.

4.

Results

4.1

Spatiotemporal characteristics of urban expansion of the Beijing–Tianjin–Hebei
regionFigures 1 and 2 show urban expansion statistics and spatial distribution in the Beijing–
Tianjin–Hebei region. In terms of urban expansion scale and rate, compared with the 2000–
2010 period, the expansion rate of Beijing, Tianjin, and Hebei increased by 42.52% in thepast 10 years, which is nearly 2.7 times that of the 2000–2010 period. Beijing is graduallystabilizing, Hebei is in the stage of rapid expansion, and Tianjin is in the stage of decelerating
expansion. The east and south areas of the Beijing–Tianjin–Hebei region show stronger urban
expansion than the areas of other directions.
The nighttime light is highly correlated with GDP and population. For example, Fig. 3(a)shows a strong linear correlation between nighttime light intensity and GDP; the coefficient ofdetermination (R2) is above 0.7. Therefore, nighttime light data can be used to comprehensively
reflect the development of urban space.
From the perspective of the brightness of nighttime light, the growth rate of cities around
Beijing is relatively faster than those around other cities. Langfang and Baoding have shown the

(a)

(b)

Fig. 1. (Color online) (a) Surface area and (b) expansion rate of artificial land in Beijing, Tianjin, and Hebei from2000 to 2020.

Sensors and Materials, Vol. 35, No. 3 (2023)

Fig. 2.

(Color online) Urban expansion map of the Beijing–Tianjin–Hebei region from 2000 to 2021.

(a)Fig. 3.

861(b)(Color online) Correlation between (a) nighttime light and GDP and (b) nighttime light and population.

most significant average growth in the past 10 years, exceeding those of Beijing and Tianjin (Fig.
4). In addition, the overall brightness of Beijing has increased, especially in key areas such as the
Yanqing Winter Olympic Zone and Daxing Airport in the south. A small number of areas, suchas the cities of Handan and Xingtai, showed negative growth of light, which may be due to the
reduction of human activities in the southern urban area caused by the reduction of overcapacity
in key industries, production reduction in industrial areas, and demolition of buildings.
The coefficient of variation of regional nighttime light can reflect the synergy of development
within the region, and a decrease in the coefficient means a decrease in the gap between thecomprehensive development of areas within the region. In the past 10 years, the overall
development gap within the Beijing–Tianjin–Hebei region has been narrowed. The developmentgap between cities increased from 2012 to 2016, and the coefficient of variation of total lightintensity decreased steadily after 2016 (Fig. 5). This variation reflects that the implementation of
the Beijing–Tianjin–Hebei coordinated development strategy has enhanced the driving role of
the region’s urban agglomeration, and that the spatial heterogeneity has gradually weakened,narrowing the gap between the cities.

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(a)

(b)

Fig. 4. (Color online) Spatial distribution of (a) nighttime light growth rate and (b) average growth rate ofnighttime light by city in the Beijing–Tianjin–Hebei area from 2012 to 2021.

Fig. 5.

4.2

Coefficient of variation of total light intensity in the Beijing–Tianjin–Hebei region from 2012 to 2021.

Characteristics of expansion around Beijing and in the capital metropolitan area

As shown in Fig. 4, the growth rate of cities around Beijing was faster owing to urban
expansion as reflected in measurements of nighttime light brightness. For the border area
between Beijing and the surrounding provinces and cities (in the buffer zones 1, 2, and 5 km
around Beijing) as shown in Fig. 6, there is an obvious feature arising from construction at
the borders.
The capital metropolitan area, which is 50 km from Tian’anmen (the center point of
Beijing) showed the highest expansion intensity and showed a circle-like expansion trend. In
the recent 10 years, the capital metropolitan area has been expanding in directions east andnorth, showing a spindle-like structure [Fig. 7(a)]. From 2000 to 2010, the intensity of the
expansion of the capital metropolitan area was higher in the southeast but lower in thenorthwest. From 2010 to 2020, the intensity in the northeast and north was high, whereas it
was low in the west.

Sensors and Materials, Vol. 35, No. 3 (2023)

Fig. 6.

863(Color online) A spatial distribution map for the buffer zones 1, 2, and 5 km around Beijing in 2020.

(a)

(b)

Fig. 7. (Color online) The intensity of expansion of the metropolitan area (a) in all directions and (b) as shown in
expanded spatial distribution maps of artificial surface in the capital metropolitan area (years 2000, 2010, and 2020
from top to bottom).

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Sensors and Materials, Vol. 35, No. 3 (2023)

In the metropolitan area, three counties in the north of Langfang (Sanhe, Dachang HuiAutonomous County, and Xianghe County), Gu’an, Guangyang, and Zhuozhou in Hebei
have a strong momentum of expansion, which indicates the overall role of integration anddevelopment in the capital metropolitan area. Among them, Gu’an County’s expansion ratein the past 10 years was about 10 times that in 2000–2010, and Guangyang District’s
expansion rate doubled in the past 10 years.
4.3

Analysis of urban connection in the Beijing–Tianjin–Hebei region

Beijing and Tianjin show the spatial agglomeration mode of dual-core and multicenter
development, and the dual-core status is very stable. Relying on its unique regional
advantages in the Beijing–Tianjin corridor, Langfang has experienced rapid urban expansion.
Tianjin displays a tendency to expand to the northeast and southwest, and the size of thecities of Langfang and Tangshan has been expanded. The northeast region formed a coastal
cluster development area with Tianjin as the core. Different degrees of urban clusters havebeen formed in the southwest region, with Shijiazhuang as the core city. A large gap still
exists between Baoding, Shijiazhuang, and Tangshan and Beijing and Tianjin, but thedevelopment trend is obvious.
Compared with 2012, the total urban connection value of the Beijing–Tianjin–Hebei
region increased significantly in 2021 (Table 1). The number of cities whose total connection
value exceeded 150 increased from 0 in 2012 to 9, and the number of city pairs with firstlevel connections increased from 0 to 8. After 2017 [the year of the release of the “Beijing
Urban Master Plan (2016–2035)”], the number of city pairs with first-level connections
increased from only Beijing and Tianjin to 8 city pairs (Fig. 8).
Driven by the Beijing–Tianjin axis, Beijing–Baoding–Shijiazhuang axis, and Beijing–
Tangshan–Qinhuangdao axis, the development trend of artificial surfaces in surrounding

Table 1
Ranking of the values of total urban connection in 2012 and 2021.
2012
No
City
No
City
Total connection value (107)1
Beijing
120.43
1
Beijing2
Tianjin
108.682
Tianjin3
Tangshan
47.22
3
Langfang
4Handan37.56
4
Tangshan5
Shijiazhuang
34.08
5
Baoding
6
Xingtai
28.55
6Cangzhou7
Cangzhou
27.66
7
Shijiazhuang
8Langfang
24.81
8
Handan9
Baoding
24.74
9
Xingtai
10
Qinhuangdao
10.97
10Qinhuangdao
11Zhangjiakou
9.64
11
Hengshui12
Hengshui
9.44
12Zhangjiakou
13
Chengde7.14
13
Chengde2021
Total connection value (107)1300.45
1062.66
451.54440.25424.01405.87352.10245.48211.28108.41
107.6595.66
95.06

Sensors and Materials, Vol. 35, No. 3 (2023)

(a)

865

(b)

(c)

Fig. 8. (Color online) Connection strength level and total connection value between Beijing, Tianjin, and Hebei;
number of city pairs with first-level connection strength. (a) 0 in 2012, (b) 1 (Beijing–Tianjin) in 2017, and(c) 8 (Beijing–Tianjin, Beijing–Baoding, Beijing–Cangzhou, Beijing–Langfang, Beijing–Tangshan, Tianjin–
Baoding, Tianjin–Cangzhou, Tianjin–Tangshan) in 2021.

cities is obvious. The average light intensity of each development axis city increased year by
year from 2012 to 2021, and the average light intensity of Beijing was much higher than thatof other cities. Beijing has the highest driving effect on the Beijing–Tianjin axis, and in 2021,
it reached the first level of connection with other cities. In 2021, the connection strength
between the cities of the Beijing–Baoding–Shijiazhuang axis reached the second level orabove. Among them, Baoding is the city most influenced by Beijing on the axis, and thestrength of the Beijing–Baoding connection has reached the first level. In 2021, theconnection strength between cities in the Beijing–Tangshan–Qinhuangdao axis reached thesecond level or above, within which the connection level between Beijing and Langfang andbetween Beijing and Tangshan reached the first level.

5.

Discussion

5.1

Urban spatial expansion and population growth of Beijing–Tianjin–Hebei region

On the whole, the urban spatial expansion of the Beijing–Tianjin–Hebei region correlated
well with the growth of the population of permanent residents during 2000–2010, but theurban spatial expansion was significantly faster than the population growth during 2010–2020. In the past 10 years, the growth rate of the permanent resident population of Beijing,
Tianjin, and Hebei was 4.09%, and the urban space expansion rate was 48.52% (Table 2).
The urban space expansion speeds of Beijing, Hebei, and Tianjin are about 5 times, 33 times,
and 4 times the population growth rate, respectively.

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Sensors and Materials, Vol. 35, No. 3 (2023)

Table 2Comparison of urban spatial expansion and rate of population change in Beijing–Tianjin–Hebei region in the recent
20 years.
Rate of change in 2000–2010 (%)
Rate of change in 2010–2020 (%)
Population
Urban space
Population
Urban space
Beijing–Tianjin–Hebei region
15.87
18.05
4.09
48.52
Beijing
44.54
40.92
11.63
55.52
Tianjin
31.37
46.57
7.17
30.14
Hebei
7.75
11.77
1.48
50.08
Note: The urban space here is represented by the actual artificial surface area.

5.2

Spatial structure changes in the Beijing–Tianjin–Hebei region

The urban space, agricultural space, and ecological space of the Beijing–Tianjin–Hebei
region were statistically analyzed. On the whole, the urban space of this region gradually
expanded, and the agricultural space gradually was reduced (Fig. 9). The ecological space ofBeijing decreased slightly from 2000 to 2010, but increased from 2010 to 2020, indicatingthat the effects of promoting ecological construction and green development gradually
emerged after 2010. The ecological space of Hebei and Tianjin decreased slightly. The
coordinated governance of ecological space needs to be strengthened.
5.3

Uneven connectivity and uncoordinated development of the cities of Beijing, Tianjin,
and Hebei

The urban connections in 2021 between Beijing, Tianjin, and Hebei (Fig. 8) show thecharacteristics of a double core of Beijing and Tianjin, and uneven relationship between
cities. In the Beijing–Tianjin–Hebei region, Beijing and Tianjin are double centers,
accounting for the dominant position in the degrees of connection. The total urban
connection value of Beijing and Tianjin is more than 1000 × 107, far exceeding other cities.
The total urban connection value of Shijiazhuang is even lower than that of Langtang andChangzhou. In addition, except for Beijing and Tianjin, the connection between other cities
is weak, and the role of node cities is lacking. On the development axes of Beijing–Tianjin,
Beijing–Baoding–Shijiazhuang, and Beijing–Tangshan– Qinhuangdao, the intercity
connection strength is the strongest near Beijing and gradually declines outward, and thenorthern cities are weaker than the central and southern cities. The transportation connection
between the northern cities and the central and southern regions is insufficient, and theconnection degree is weak. Some cities, such as Zhangjiakou, Hengshan, Qinhuangdao, andChengde, have relatively weak connection levels with other cities, and the total connection
value is relatively small.
In recent years, the capital metropolitan area has expanded rapidly toward the east andnorth. From the perspective of the surrounding area of Beijing, although the development of
the northwest area of Beijing–Tianjin–Hebei has accelerated in recent years, it is still
relatively weak compared with the northeast, southwest, and southeast.

Sensors and Materials, Vol. 35, No. 3 (2023)

(a)

867

(b)

(c)

Fig. 9. (Color online) Spatial distribution map of land surface in the Beijing–Tianjin–Hebei region. (a) 2000, (b)2010, and (c) 2020.

6.

Conclusions and Suggestions

We studied urban area extraction technologies based on data from remote sensing satellite
sensors of TM, ETM+, OLI, and VIIRS, GIS analysis, multidata analysis technologies for urban
expansion extraction, and urban connection analysis of urban agglomeration. Using the relevantmaterials, including land cover product, nighttime light data, as well as socioeconomic data,urban expansion and urban connection characteristics of the Beijing–Tianjin–Hebei region
during 2000–2021 were analyzed. The methods in this paper can be used for other urban
agglomeration analyses.
6.1

Conclusions

We conclude that the urban expansion and urban connection characteristics of the Beijing–
Tianjin–Hebei region are as follows:(1)	The urban space of the Beijing–Tianjin–Hebei region continues to expand. The urban
expansion trend around Beijing is significant, and the expansion speed in the east and north isfast, while the southwest and southeast are still weak in comparison.
(2)	The connections between the Beijing–Tianjin, Beijing–Baoding–Shijiazhuang, and Beijing–
Tangshan–Qinhuangdao development axes have been significantly strengthened since 2017,reflecting the remarkable achievements of coordinated development in the Beijing–Tianjin–
Hebei region. Urban connections in the Beijing–Tianjin–Hebei region has two cores, namely,
Beijing and Tianjin, leading to the development of the surrounding areas, and the connectionin the southern areas is weaker than elsewhere, because of the absence of nodal cities.

868

Sensors and Materials, Vol. 35, No. 3 (2023)(3)	On the whole, the urban space of the Beijing–Tianjin–Hebei region is gradually expanding,while the agricultural space is gradually shrinking. The ecological space in Hebei and Tianjin
showed a small reduction; thus, the collaborative governance of ecological spaces needs to bestrengthened.
6.2

Administrative suggestions(1)	The northwest area around Beijing can be further promoted by driving the effect of theWinter Olympics on regional development. Taking advantage of the road system for theWinter Olympics, those areas can actively undertake the transfer of high-quality elements
and industries from Beijing and cultivate snow- and ice-related industries.
(2)	Regarding the problems of uneven urban connection and uncoordinated development, both
accelerating the cultivation of key node cities (e.g., Shijiazhuang) and promoting coordinatedregional development are necessary.
(3)	
To optimize the spatial pattern, land use efficiency and the coordination of ecologicalplanning and management in the Beijing–Tianjin–Hebei region should be strengthened.
We studied methods of urban expansion extraction and urban connection analysis basedon multisensor data for urban agglomeration, and the urban expansion and connection
characteristics of the Beijing–Tianjin–Hebei region were analyzed. Other world-class urban
agglomerations can be studied using these methods and, by comparing different urban
agglomeration characteristics, more useful information can be provided for urban
agglomeration development, and additional suggestions for the Beijing–Tianjin–Hebei region tobuild a world-class urban agglomeration can be offered.

Acknowledgments
This study was funded by Beijing Key Laboratory of Urban Spatial Information Engineering,No. 2020204.

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About the Authors
Cai Cai received her B.S. degree from Wuhan University, China, in 2010 and
her Ph.D. degree from Peking University, China, in 2016. From 2014 to 2015,
she was a research scholar at Columbia University, USA. Since 2016, she has
been working at the Beijing Institute of Surveying and Mapping and as a
researcher at the Beijing Key Laboratory of Urban Spatial Information
Engineering, and since 2018, she has been a senior engineer. Her researchinterests are in urban spatial analysis and urban remote sensing.
(caicai361448336@126.com)
Huimin Tian studied for her master’s degree in cartography and geographic
information engineering at China University of Mining and Technology(Beijing) from 2017 to 2020. Since 2020, she has been working at the Beijing
Institute of Surveying and Mapping, where she became a junior engineer. Her
research interest is in urban spatial analysis. (tianhuimin_0123@163.com)

Zeyu Li received her master’s degree from Liaoning Technical University,
China, in 2019. From 2017 to 2019, she studied at the Chinese Academy ofSurveying and Mapping, Beijing, China. Since 2019, she has been working at
the Beijing Institute of Surveying and Mapping, and since 2021, she has been a
middle-level engineer. Her research interests are in urban spatial analysis.
(1479781035@qq.com)
Zongxia Xu received her master’s degree in Geographic Information
Engineering from Capital Normal University, China, in 2019. Since 2019, she
has been working at the Beijing Institute of Surveying and Mapping, and since2021, she has been an engineer. Her research interests are in remote sensing
image interpretation. (xuzongxia123@163.com)

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Sensors and Materials, Vol. 35, No. 3 (2023)

Yi Wan received her master’s degree from Beijing Normal University, China,
in 2021. She has been working at the Beijing Institute of Surveying and
Mapping and as a researcher at the Beijing Key Laboratory of Urban Spatial
Information Engineering. Her research interests are in urban remote sensing
and data analysis. (wanyi0203@foxmail.com)

Yutao Guo received her B.S. degree from Beijing Union University, China, in
2020. Since 2020 she has been working at the Beijing Institute of Surveying
and Mapping. Since 2021, she has been an assistant engineer. Her researchinterests are in data analysis and deep learning in remote sensing.
(2318543567@qq.com)

Yingchun Tao received her B.S. degree in 2004, M.S. degree in 2007, andPh.D. degree in 2019 from Peking University, China. Since 2007, she has beenworking at the Beijing Institute of Surveying and Mapping and as a researcher
at the Beijing Key Laboratory of Urban Spatial Information Engineering,where she has been a professor-level senior engineer since 2018. Her researchinterest is in urban spatial analysis. (taoyc@bism.cn)
Bogang Yang received his Ph.D. degree from Beijing Forestry University,
China, in 2006, and was engaged in postdoctoral work at Peking University
from 2007 to 2009. Since 1983, he has been working at the Beijing Institute ofSurveying and Mapping, where he has been a professor-level senior engineer
since 2004. He is the Director of the Beijing Key Laboratory of Urban Spatial
Information Engineering. His research interests are in surveying and mapping
technologies, smart city construction, and urban spatial analysis.
(bogangy@126.com)