Spatiotemporal Dynamic Evolution of Population and Economy in

Chongqing

LiYun Su

1,a

, MingLiang Yin

1,b

and FengLan Li

2,c

College of Science, Chongqing University of Technology, Chongqing, China

Library, Chongqing University of Technology, Chongqing, China

Keywords: Population Distribution, Economic Layout, Gravity Shift, Spatial Autocorrelation.

Abstract: Population change and economic development are closely related and interact. The coordinated development

of population and economy has increasingly become a hot topic in modern society. Based on the data of

resident population and GDP of 38 districts and counties in Chongqing from 2010 to 2019, this paper uses

ArcGIS and GeoDa, adopts gravity center method, geographic concentration, inconsistency index, spatial

autocorrelation and other research methods to systematically analyze the change of gravity center of

population and economy in Chongqing, the spatial distribution and agglomeration degree of population and

economy, the coordinated development of population and economy, and the spatial correlation of population

and economy. The results show that: (1) Chongqing's population and economic center of gravity show signs

of migration to the main urban area of Chongqing; (2)The geographical concentration of population and

economy shows that the population and economy of Chongqing are mainly concentrated in the main urban

area and its surrounding counties; (3) The inconsistency index between population and economy shows that

population agglomeration and economic agglomeration are coordinated in half districts and counties of

Chongqing; (4)The spatial autocorrelation analysis shows that the spatial distribution of Chongqing's

population and economy has a relatively obvious positive spatial correlation, and areas with similar population

and economic coordination tend to congregate in space.

1 INTRODUCTION

Population and economy are two key factors that

determine whether a region can develop rapidly.

They interact with each other. The growth,

agglomeration and flow of population can affect the

economic development of a region to a great extent.

Whether the economy of a region is prosperous,

whether the economy is developing rapidly, and

whether the market is prosperous will in turn play an

important role in the distribution and change of

population. The interaction, connection and

restriction between population and regional economy

is an important topic in the study of regional social

development. Population and economy are also two

important indicators reflecting the differences

between regions. Studying the relationship between

population and regional economy is helpful to

promote the development of regional economy and

society, construct a good pattern of regional

economic development, and realize the coordinated

development between regions. To explore the

relationship between regional population growth and

regional economic growth is of great practical

significance for the population development and

economic development of a region.

In recent years, many scholars at home and abroad

have done a lot of research on the relationship

between population and economy. Wesley E and

Klaus P analyzed the role of population growth in

economic growth (Wesley, 2017, Peterson, 2017,

Klaus, 2014); Oliver M studied the impact of

population aging on Japanese economy and the role

of population aging in economic growth (Mikiko,

2015); Luca S analyzed the economic problems

caused by population growth and the relationship

between population growth and economic crisis

(Luca, 2016, Luca, 2018). Domestic research on the

population and economy of provinces is mainly based

on the analysis of the dynamic evolution of

population and economy in time and space. (Huang

2016, Zhao 2016), (Liu, 2017, Zhang, 2017, Liu,

2017) and (Xu, 2013, Tang, 2013, Wei, 2013)

analyzed the change of population and economic

634

Su, L., Yin, M. and Li, F.

Spatiotemporal Dynamic Evolution of Population and Economy in Chongqing.

DOI: 10.5220/0011202000003440

In Proceedings of the International Conference on Big Data Economy and Digital Management (BDEDM 2022), pages 634-647

ISBN: 978-989-758-593-7

gravity center in Guizhou Province, Liaoning

Province and Xi’an city by using regional population

and economic gravity center; (Cai, 2016, Lu, 2016,

Hua, 2016), (Ren, 2016, Zhu, 2016, Shou, 2016) and

(Wang, 2013, Qin, 2013) analyzed the coupling

relationship between population and economy in

Guangxi Province, Ningxia urban agglomeration

along the Yellow River and Shanxi Province by using

geographic concentration and inconsistency index;

(Bin 2018, Tang 2018, Li 2018), (Liu, 2013, Zheng,

2013) and (Ding, 2014, Gao, 2014, Gao, 2014) used

exploratory spatial data analysis and other methods to

study the spatial correlation between population and

economy in Hunan Province, Henan Province and

Qinghai Tibet Plateau; (Lian, 2018, Wu, 2018) and

(Zhang, 2018, Run, 2018) used the growth elasticity

of population and economy to analyze the impact of

economic growth on population size in northeast

China and Yangtze River Delta; Based on Cobb

Douglas function, (Chen, 2018, Li, 2018, Yao, 2018)

analyzed the impact of population agglomeration on

China’s urban economic growth. Some scholars have

also studied the mutual development of population

and economy in Chongqing (Guan, 2017, Tan, 2017,

Zhang, 2017, Zhou, 2011, Tu, 2011, Lu, 2011, Liu,

2013, Su, 2013, Guan, 2013), but some of them have

been studied for a long time, and there are few studies

on the change of Chongqing’s population and

economic gravity center.

This paper mainly uses the method of barycenter,

geographic concentration, inconsistency index and

spatial autocorrelation, and uses Moran statistics,

Moran scatter diagram and LISA cluster diagram,

analyzes the change of the center of gravity of

population and economy in Chongqing, the spatial

distribution and agglomeration degree of population

and economy in different districts and counties, the

coordinated development of population and

economy, and the spatial correlation of population

and economy, and reveals the aggregation and

coupling law of population and economy in

Chongqing in recent years, which has practical

significance for the coordinated development of

population and economy in all districts and counties

of Chongqing.

2 RESEARCH METHODS AND

DATA SOURCES

2.1 Research Methods

2.1.1 Barycentre Method

The center of gravity of population and economy is

the point to maintain regional balance and

coordination. Suppose that a region is composed of

subunits, then the calculation formula of the

barycenter coordinate

(,)QXY

is as follows:

()

ii ii

rx ry

QXY X Y





== =









(1)

In equation (1),

(,)QXY

represents the center of

gravity coordinates of Chongqing's population or

GDP;

()

represents the longitude and latitude

coordinates of the geometric centers of all regions in

Chongqing;

r represents a certain attribute value of

the statistical unit, that is, population or GDP.

2.1.2 Geographical Concentration

Geographical concentration is the data reflecting the

concentration degree of a certain factor in a certain

area, including population geographical

concentration and economic geographical

concentration, reflecting the spatial distribution of

population and economy.

pop

pop pop



(2)

GDP

GDP GDP



(3)

In formulas (2) and (3),

pop

and

GDP

respectively represent the geographical concentration

of population and economy in the

th region,

pop

and

GDP

respectively represent the population and

GDP in the

th region,

pop



and

GDP



respectively represent the total population and total

economy in Chongqing, and

S and



respectively

represent the area of the

th region and the total area

of Chongqing.

Spatiotemporal Dynamic Evolution of Population and Economy in Chongqing

635

2.1.3 Inconsistency Index of Population and

Economy

The inconsistency index of population and economy

is an important parameter to examine whether the

distribution of population and economy is balanced in

a region. The calculation formula is:

pop GDP

IR R=

(4)

Where

is the inconsistency index, the closer

to 1, the more coordinated the population and

economic development of a region; the smaller

is,

the stronger the spatial aggregation ability of a

region's economy is; the greater

is, the greater the

agglomeration degree of population is.

2.1.4 Global Spatial Autocorrelation

We can use global Moran statistics to analyze the

autocorrelation and spatial agglomeration of

population and economy in Chongqing. Spatial

agglomeration means that the population and

economy between adjacent regions do not exist

independently, but show typical characteristics of

spatial spillover and spatial diffusion (Yang, 2021,

Zhang, 2021, Zhang, 2021). According to Moran's

research in 1948 (Moran, 1948), the calculation

formula is as follows:

()( )

ij i j

wx xx x

−−



(5)

Where

is the global Moran statistics,

and

represent the population or GDP of district

and

district

respectively,

is the adjacent weight of

spatial units. In the spatial correlation analysis part of

this paper, the queen adjacency matrix is used as the

spatial weight matrix.

=( )

Sxx

−



is the

variance of population or GDP. The value range of

is between -1 and 1, greater than 0 means that the

spatial distribution of population or GDP is positively

correlated; less than 0 means that the population or

GDP is negatively correlated in space; equal to 0

means that the population or GDP is not spatially

related.

2.1.5 Local Spatial Autocorrelation

The method of local spatial autocorrelation can be

used to analyze the spatial difference degree and

significance of population or economy between an

area and its adjacent areas (Anselin, 1995). The

definition of local Moran statistics is as follows:

ii ijj

zwz



(6)

Among them,

and

are the deviation of the

number of permanent residents or GDP of the

and

area from the mean, that is,

()

zxx=−

()

zxx=−

, if

I >

, it means that there is a

positive correlation between the population or

economy of the adjacent districts and counties;

I <

, it means that there is a negative correlation. Moran

scatter plot depicts the relationship between the

population or economy of each region and its spatial

lag term, and makes a visual two-dimensional display

of it, reflecting the spatial autocorrelation of the

investigated variables in the local region. Moran

scatter are divided into four types: high-high, low-

low, low-high and high-low. The point falling into a

high-high or low-low region indicates that the

population or economic agglomeration degree of a

region is high (low) and the population or economic

agglomeration degree of its adjacent regions is high

(low), with small spatial difference, which belongs to

spatial positive correlation; the point falling into a

low-high or high-low region indicates that the

population or economic agglomeration degree of a

region is low (high) and the population or economic

agglomeration degree of its adjacent regions is high

(low), large spatial difference, belonging to spatial

negative correlation.

2.2 Interpretation of Data

The resident population and GDP data of Chongqing

from 2010 to 2019 are all from Chongqing statistical

yearbook (Chongqing Statistics Bureau). Before

analyzing the data, the relevant data are standardized.

3 RESULTS AND DISCUSSION

3.1 Analysis on the Migration of

Gravity Center of Population and

Economy in Chongqing

3.1.1 Population Distribution Gravity

Center and Migration Change in

Chongqing

According to the resident population data of various

regions of Chongqing from 2010 to 2019, the

BDEDM 2022 - The International Conference on Big Data Economy and Digital Management

636

population distribution center of gravity (107.2088

E°

-107.2898

E°

, 29.8694

N°

-29.8984

N°

) and

geometric center coordinates (107.8744

E°

, 30.0572

N°

) are calculated, and the migration map of

population distribution center of gravity in

Chongqing is generated (Fig. 1)

Figure 1: Migration map of population distribution gravity

center in Chongqing.

From the migration map of population

distribution center of gravity, it can be seen that from

2010 to 2019, Chongqing’s population center of

gravity is distributed in Changshou District, which

has a large deviation from the geometric center of

Chongqing. In the past ten years, the population

gravity center of Chongqing has been distributed in

the southwest of the geometric center of Chongqing,

and it has moved from northeast to southwest, and is

farther and farther away from the geometric center of

Chongqing, and keep the trend of migration to the

southwest. This may be related to the good economic

development and high population attraction of the

main urban area in the southwest of Chongqing

geometric center. In 2010, the total population of

Chongqing main urban area is 7.4576 million,

accounting for 25.85% of the total population; in

2019, the total population of Chongqing main urban

area is 8.8439 million, accounting for 28.31% of the

total population of Chongqing. In the past ten years,

the proportion of population in the main urban area

has increased by nearly 3 percentage points, which is

enough to see the trend of population gravity center

moving to the southwest.

3.1.2 The Gravity Center of Economic

Distribution and Its Change in

Chongqing

According to the GDP data of each district and county

in Chongqing from 2010 to 2019, the annual

economic distribution gravity center (106.9095

E°

106.9345

E°

, 29.7498

N°

-29.7660

N°

) from 2010

to 2019 is calculated, and the economic distribution

gravity center migration map of Chongqing is

generated (Fig 2).

Figure 2: Chongqing economic distribution center of

gravity transfer map.

It can be seen from the transfer chart of economic

gravity center that the economic gravity center of

Chongqing is distributed in Yubei District from 2010

to 2019, which is quite different from the geometric

center of Chongqing. In the past ten years,

Chongqing’s economic center of gravity is located in

the southwest of Chongqing’s geometric center. From

2010 to 2013, Chongqing’s economic center of

gravity moved from south to north. From 2013 to

Spatiotemporal Dynamic Evolution of Population and Economy in Chongqing

637

2019, Chongqing’s economic center of gravity

moved from northeast to southwest. From 2010 to

2013, Yubei District’s GDP increased greatly. In

2010, Yubei District’s GDP accounted for 7.14% of

Chongqing’s GDP, while in 2013, Yubei district’s

GDP accounted for 8.1%. Over the past few years, it

has increased by 1 percentage point, which is the

largest increase among all districts and counties in

Chongqing. Therefore, from 2010 to 2013,

Chongqing’s economic gravity center has been

moving northward. From 2013, the proportion of

GDP in Yubei District began to decrease, and the

GDP of Hechuan, Rongchang, Tongliang and other

districts and counties in southwest Chongqing began

to increase significantly. From 2017 to 2019, the

proportion of GDP in Hechuan, Rongchang,

Tongliang increased by 0.63%, 0.53% and 0.53%

respectively. Therefore, from 2013, the economic

distribution gravity center of Chongqing began to

move to the southwest.

3.2 Geographical Concentration

Analysis of Chongqing’s Population

and Economy

3.2.1 Analysis of Geographical

Concentration of Population in

Chongqing

Based on the data of resident population in 2010 and

2019, the geographic concentration index of

Chongqing’s population can be calculated according

to equation (2). The geographic concentration can be

divided into five grades. The higher the level is, the

more concentrated the population is. The

classification results are shown in Table 1.

Table 1: Classification of geographical concentration of

population in Chongqing in 2010 and 2019.

Geo

hical concentration of

ulation

Grades 2010 2019

Level 1

Kaizhou、Chengkou、

Wuxi、Yunyang、

Fengjie、Wushan、

Shizhu、Fengdu、

Nanchuan、Wulong、

Pengshui、Qianjiang、

You y an g、Xiushan

Kaizhou、Chengkou、

Wuxi、Yunyang、

Fengjie、Wushan、

Shizhu、Fengdu、

Nanchuan、Wulong、

Pengshui、Qianjiang、

You y an g、Xiushan

Level 2

Tongnan、Hechuan、

Tongliang、Dazu、

Jiangjin、Banan、

Qijiang、Fuling、

Changshou、Dianjiang、

Liangping、Zhongxian、

Wanzhou

Tongnan、Hechuan、

Tongliang、Dazu、

Jiangjin、Banan、

Qijiang、Fuling、

Changshou、

Dianjiang、Liangping、

Zhongxian、Wanzhou、

Rongchang、Yongchuan

Level 3 Rongchang、Yongchuan、 Bishan、Beibei、Yu be i

Bishan、Beibei、Yu be i

Level 4

Shapingba、Dadukou、

Jiulongpo、Jiangbei、

Nan’an

Shapingba、Dadukou、

Jiulongpo、Jiangbei、

Nan’an

Level 5 Yuzhon

Yuzhon

Figure 3: Geographical concentration distribution of

Chongqing’s population in 2010(a) and 2019(b).

Fig 3 shows the distribution of population

geographical concentration in Chongqing in 2010 and

2019. As can be seen from Fig 3, the population

geographical concentration of Chongqing in 2010

showed a pattern of high in the West and low in the

East. The population geographical concentration

index decreases from the main city to the surrounding

districts and counties. In terms of the degree of

agglomeration, the main urban area has the most

dense population, among which Yuzhong has the

highest degree of population agglomeration, and the

population geographical concentration index is 75.97,

which is much larger than other regions. It is the only

one of the level 5 of agglomeration areas. The

population of the Nan’an and Jiangbei are also highly

concentrated, with an index greater than 7.18, which

are all areas of the fourth aggregation level. Some

areas in the north and west of the main urban area,

such as Bishan and Yongchuan, also have high

population density, with an index between 1.57 and

2.65. They belong to the third aggregation level.

Some areas south of the main urban area have

relatively small population aggregation, such as

Qijiang. Their indexes are between 0.84 and 1.57,

which is the second aggregation level area. However,

some areas in southeast and northeast Chongqing,

which are far away from the main urban area, have a

low degree of population aggregation, and the index

is below 0.84. By 2019, the population geographical

concentration of Chongqing has not changed much.

Only the population concentration in Rongchang and

Yongchuan has decreased, and the population

concentration in the main urban area is still very high.

Generally speaking, from 2010 to 2019, the main

urban area has always been the most densely

populated place in Chongqing, the degree of

aggregation is increasing year by year, and the area

with high population concentration tends to narrow.

The population geographical concentration in

Northeast and Southeast Chongqing remains at a low

BDEDM 2022 - The International Conference on Big Data Economy and Digital Management

638

level, and the population concentration is gradually

weakening.

3.2.2 Analysis of Economic Geographical

Concentration in Chongqing

Based on the GDP data of Chongqing in 2010 and

2019, the economic geographical concentration index

in 2010 and 2019 is calculated. According to the

economic geographical concentration index, each

district and county is divided into five grades. The

higher the grade, the more concentrated the economy

is. The classification results are shown in Table 2.

Table 2: Classification of economic geographical

concentration degree of Chongqing in 2010 and 2019.

Economic

hical concentration

Grades

2010 2019

Level 1

Kaizhou、Chengkou、

Wuxi、Yunyang、

Fengjie、Wushan、

Qijiang、Shizhu、

Dianjiang、Liangping、

Zhongxian、Tongnan、

Fengdu、Nanchuan、

Wulong、Pengshui、

Qianjiang、Youyang、

Xiushan

Kaizhou、Chengkou、

Wuxi、Yunyang、

Fengjie、Wushan、

Shizhu、Wulong、

Fengdu、Nanchuan、

Xiushan、Yo uy ang 、

Pengshui、Qianjiang、

Zhongxian

Level 2

Hechuan、Banan、

Tongliang、Dazu、

Jiangjin、Fuling、

Changshou、Bishan、

Wanzho u、Rongchang、

Yongchuan

Banan、Wanzhou、

Changshou、Dazu、

Jiangjin、Dianjiang、

Liangping、Tongnan、

Hechuan、Tongliang、

Fuling、Qijiang

Level 3

Beibei、Yu be i

Bishan、Beibei、

Yub ei 、Rongchang、

Yon

chuan

Level 4

Shapingba、Dadukou、

Jiulongpo、Jiangbei、

Nan’an

Shapingba、Dadukou、

Jiulongpo、Jiangbei、

Nan’an

Level 5

Yuzhon

Figure 4: Distribution of economic geographical

concentration of Chongqing in 2010(a) and 2019(b).

Fig 4 shows the distribution of economic

geographical concentration of Chongqing in 2010

and 2019. In terms of the degree of agglomeration, in

2010, the economic distribution in the main urban

area was the most intensive, among which Yuzhong

District had the highest degree of economic

concentration, and the economic geographical

concentration index was 239.44, which was the area

with the strongest degree of economic agglomeration.

The index of Jiangbei, Nan’an and other regions is

greater than 13.70, belonging to the fourth level of

economic agglomeration. The index of Yubei and

Beibei are 4.06 and 3.16 respectively, belonging to

the third-class economic agglomeration area. Bishan,

Tongliang and other regional around the main urban

area index are between 0.81 and 1.95, belonging to

the second level economic agglomeration area. The

index of Southeast Chongqing and Northeast

Chongqing, which are far away from the main urban

area, are less than 0.81, belonging to the area with the

weakest degree of economic agglomeration. By 2019,

Yuzhong District will still maintain the highest

degree of economic agglomeration, and the degree of

economic agglomeration in Bishan, Rongchang and

other regions has increased. On the whole, the

economic geographical concentration of Chongqing,

like the population geographical concentration,

shows an unbalanced pattern of high in the West and

low in the East. The trend of economic geographical

concentration decreasing from the main city to the

surrounding districts and counties is very obvious.

The main urban area has always been the most

concentrated place of economic agglomeration in

Chongqing, and the degree of economic

agglomeration is becoming stronger and stronger.

However, in the area far away from the main urban

area with sparse population and vast area, the

concentration of economic geography is very small.

It can be seen that the economic development of

Chongqing is mainly concentrated in the main urban

area and its surrounding areas, while the economic

development of some districts and counties on the

edge of Chongqing is relatively slow, and the

economic gap is gradually expanding.

3.3 Analysis on the Inconsistent Index

of Population and Economy in

Chongqing

In order to analyze whether the distribution of

population and economy in various regions of

Chongqing is consistent, According to formula (4),

the inconsistency index of population and economy

in Chongqing in 2010 and 2019 can be calculated, and

the coordinated development of population and

economy in various regions can be divided into three

types: first, second and third, which respectively

represent that the degree of population agglomeration

is less than the degree of economic agglomeration,

the degree of population agglomeration is consistent

with the degree of economic agglomeration, and the

Spatiotemporal Dynamic Evolution of Population and Economy in Chongqing

639

degree of population agglomeration is greater than

the degree of economic agglomeration.

Figure 5: Spatial consistency pattern of population and

economic distribution in Chongqing in 2010(a) and

2019(b).

As can be seen from Figure 5, the areas where

population agglomeration was greater than economic

agglomeration in 2010 were mainly concentrated in

Northeast Chongqing and Southeast Chongqing. The

degree of population and economic agglomeration in

Changshou, Wanzhou and other regions is relatively

consistent. In the main urban area, Bishan and Fuling,

there is a trend that population agglomeration lags

behind economic agglomeration. By 2019,

population agglomeration and economic

agglomeration in Tongnan, Hechuan and other

regions tend to be coordinated. The degree of

population agglomeration in most areas of Southeast

and Northeast Chongqing is still higher than that of

economic agglomeration. In general, only some areas

of the main urban area have higher economic

agglomeration than population agglomeration, and

most other areas have greater population

agglomeration than economic agglomeration or

population agglomeration is consistent with

economic agglomeration. From 2010 to 2019, the

spatial differentiation pattern of the relationship

between population and economic distribution in

Chongqing has changed significantly, indicating that

the economy of Chongqing has developed rapidly in

recent years, and the GDP of many regions has

increased rapidly, which has promoted the

concentration of economy, thus promoting the

coordinated development of population and economy

in places with excessive population agglomeration.

3.4 Spatial Autocorrelation Analysis of

Population and Economy in

Chongqing

3.4.1 Spatial Autocorrelation Analysis of

Chongqing’s Population

In order to effectively measure the spatial correlation

of Chongqing’s population distribution, the global

Moran statistics of Chongqing’s population from

2010 to 2019 are calculated and tested, as shown in

Table 3.

Table 3: Global Moran statistics of Chongqing’s

population.

Z 2.

In Table 3, I

values are greater than zero,

indicating that the population distribution in

Chongqing has a spatial positive correlation globally.

The Moran scatter map of Chongqing’s population in

2010, 2013, 2016 and 2019 is drawn, as shown in Fig

Figure 6: Moran scatter of Chongqing population in

2010(a), 2013(b), 2016(c), 2019(d).

As can be seen from Fig 6, the global Moran

statistical value is greater than zero, indicating that

there is a significant global spatial positive

correlation between the population in various regions

of Chongqing, and the population basically presents

the characteristics of relatively stable spatial

agglomeration, that is, the population in the

surrounding areas is also more concentrated, where

the population is more concentrated, and vice versa.

But the global Moran statistics is low, which indicates

that the agglomeration characteristics of population

BDEDM 2022 - The International Conference on Big Data Economy and Digital Management

640

distribution in Chongqing is not very strong and the

correlation is relatively weak. From the Moran scatter

diagram, we can see that most districts and counties

in Chongqing are located in the first, second and third

quadrants, and the distribution is relatively average,

which indicates that the distribution of population

does not show a very obvious high-high distribution

or low-low distribution. It further shows that although

there is a positive spatial autocorrelation between the

population in various regions of Chongqing, the

agglomeration trend is not strong and the positive

correlation is weak. In Fig 6, since 2010, the global

Moran statistics of the population in all districts and

counties of Chongqing has been gradually increasing,

which indicates that the spatial agglomeration of

Chongqing’s population is becoming more and more

significant, and the polarization effect is increasing.

Lisa agglomeration map can be used to analyze

the local spatial agglomeration characteristics of

Chongqing population. Fig 7 shows the local spatial

agglomeration of population in different years.

Figure 7. Local spatial autocorrelation agglomeration map

of Chongqing’s population in 2010(a), 2013(b), 2016(c),

2019(d).

It can be seen from Fig 7 that the population

distribution of Chongqing has no obvious spatial

agglomeration characteristics, and only a few areas

belong to high-high type and low-low type. In

general, the distribution pattern of population spatial

agglomeration in Chongqing has changed to some

extent. The number of high-high type areas of

population spatial distribution has been increasing,

from one in 2010 to seven in 2019; similarly, the

number of low-high type areas of population spatial

distribution is also increasing, from three in 2010 to

five in 2019. It can be seen that the agglomeration

characteristics of Chongqing’s population spatial

distribution are more and more strong, especially in

the main urban area. Six of the seven high-high type

areas are in the main urban area, which indicates that

the population growth of the main urban area is fast

and the population is relatively dense in recent ten

years. With the economic development, the main

urban area can attract more people to immigrate.

3.4.2 Spatial Autocorrelation Analysis of

Chongqing’s Economy

In order to effectively measure the spatial correlation

of Chongqing’s economic distribution, Moran

statistics of Chongqing’s economy are calculated, as

shown in Table 4. It can be seen from table 4 that the

Moran statistics of the economies of all districts in

Chongqing from 2010 to 2019 passed the significance

test at the confidence level of 0.01, and the global

Moran statistics are greater than zero, indicating that

the economic distribution of Chongqing has obvious

positive spatial correlation in the world.

Table 4: Global Moran statistics of Chongqing’s economy.

Z 3.

In Fig 8, the GDP of most districts and counties in

Chongqing is distributed in the first quadrant and the

third quadrant, indicating that the GDP distribution of

each district and county presents a relatively obvious

high-high or low-low distribution.

Spatiotemporal Dynamic Evolution of Population and Economy in Chongqing

641

Figure 8. Moran scatter diagram of Chongqing’s economy

in 2010(a), 2013(b), 2016(c), 2019(d).

From 2010 to 2013, the overall Moran index of

Chongqing’s GDP has slightly decreased, but

Chongqing’s GDP is increasing every year. The

reason for the decrease of Moran index may be that

the regional economic development policies have a

certain impact on the differences of economic spatial

distribution. Since 2013, the overall Moran index of

the economy of Chongqing has been gradually

increasing, which indicates that the spatial

agglomeration of Chongqing’s economy is becoming

more and more significant, and the polarization effect

is increasing.

Figure 9: Local spatial autocorrelation agglomeration map

of Chongqing’s economy in 2010(a), 2013(b), 2016(c),

2019(d).

Fig 9 is the LISA spatial agglomeration map of

economy in 2010, 2013, 2016 and 2019. It can be

seen from Fig 9 that the spatial agglomeration

characteristics of Chongqing economy are obvious,

showing the basic pattern of high-high agglomeration

in the main urban area and its surrounding areas, and

low-low agglomeration in northeast Chongqing.

Among them, the districts and counties with

significant high-high type are Yubei, Jiangbei,

Yuzhong and Nan’an in the main urban area,

Hechuan, Changshou in the north of the main urban

area, and Jiangjin, Yongchuan in the south of the

main urban area. The main urban area is the economic

center of Chongqing, with rapid economic

development and strong spatial agglomeration. The

significant low-low types of districts and counties are

mainly Chengkou, Wuxi, Wushan and Fengjie in

northeast Chongqing and Qianjiang in southeast

Chongqing. Most of these areas are mountainous

areas. Due to the poor geographical conditions, the

economic development of these areas is relatively

slow, and the overall level belongs to the less

developed areas of the city. The significant low-high

type areas are Dadukou district and Tongliang district

BDEDM 2022 - The International Conference on Big Data Economy and Digital Management

642

in the west of the main urban area, which shows that

there is a relatively large spatial difference between

the economic development of these two areas and the

surrounding districts and counties, and there is a

relatively strong negative spatial correlation.

Although Dadukou District belongs to the main urban

area, the population of Dadukou District is the least

among the nine main urban areas, and the GDP is also

the least among the nine main urban areas, which is

several times lower than the other eight areas.

Therefore, although Dadukou District belongs to the

main urban area, the economic spatial distribution of

Dadukou District does not show obvious high-high

distribution, but low-high distribution. In general, the

change of economic spatial agglomeration

characteristics of Chongqing’s districts and counties

is not particularly large. From 2010 to 2019, the

number of high-high type areas has remained stable

at 12. Moreover, the basic pattern of high-high

agglomeration in the main urban area and its

surrounding areas and low-low agglomeration in the

northeast of Chongqing has not changed much, which

indicates that the change of economic agglomeration

characteristics in various regions of Chongqing is

basically stable.

3.4.3 Spatial Autocorrelation Analysis of

Coordinated Development of

Population and Economy in

Chongqing

Based on the inconsistent index data of Chongqing in

2010, 2013, 2016 and 2019, this paper analyzes the

spatial correlation between the coordinated

development of population and economy in

Chongqing. Use 10eode to draw the global Moran

scatter diagram, as shown in Fig 10.

As can be seen from Fig 10, the global Moran

autocorrelation coefficient of the inconsistency index

within four years is significantly greater than zero,

and most areas are located in the first quadrant and

the third quadrant. The results show that there is a

significant positive correlation between the

population and economic disharmony index in

Chongqing, showing obvious high-high distribution

and low-low distribution. The regions with similar

degree of population and economic coordination tend

to gather in spatial distribution. Since 2013, the

Moran value has been rising, which indicates that the

spatial agglomeration degree of areas with similar

population and economic coordination in Chongqing

has become higher. In order to more intuitively reflect

the difference and change process of the

inconsistency index of local population and economic

spatial distribution in Chongqing, the Lisa cluster

diagram of the inconsistency index of population and

economy in four years can be drawn for analysis.

Spatiotemporal Dynamic Evolution of Population and Economy in Chongqing

643

Figure 10: Moran scatter of inconsistency index of

Chongqing in 2010(a), 2013(b), 2016(c), 2019(d).

Figure 11: LISA cluster diagram of the inconsistency index

in 2010(a), 2013(b), 2016(c), 2019(d).

Fig 11 is the LISA clustering map of the

inconsistency index of population and economy. It

can be seen from the map that in 2010, the main urban

area and its surrounding areas were low-low

agglomeration areas in the spatial distribution of

population and economic inconsistency index;

Wushan, Wuxi, Chengkou in northeast Chongqing

are the regions with high-high agglomeration type of

inconsistency index; Wanzhou and Qianjiang belong

to low-high agglomeration type; Hechuan belongs to

high-low agglomeration type. In 2019, the spatial

agglomeration characteristics of Chongqing’s

population and economic inconsistency index did not

change much. Generally speaking, the regions with

low-low distribution of the inconsistency index are

mainly concentrated in the main urban areas with

better economic development and some areas around

the main urban areas. The regions with obvious high-

high distribution of inconsistency index are mainly

concentrated in the remote areas and counties of

northeast Chongqing where the economic

development conditions are poor. It can be seen that

Chongqing’s population and economic development

situation has obvious regional imbalance. The main

urban area and its surrounding areas have a very good

development foundation and conditions, and they are

also in a relatively obvious advantage in the

BDEDM 2022 - The International Conference on Big Data Economy and Digital Management

644

economic competition of various regions in

Chongqing, which promotes the economic

agglomeration of the main urban area and its

surrounding counties to be higher than the population

agglomeration, and the inconsistency index of

population and economy is significantly less than 1.

Because of the terrain, the economic development of

northeast Chongqing is relatively slow, and the

natural resources and labor resources are not rich

enough, which leads to the backward economy. The

economic agglomeration lags behind the population

agglomeration, and the Inconsistency index of

population and economy is significantly greater than

4 CONCLUSIONS

Through the analysis of the development pattern and

spatial relationship between population and economic

distribution in Chongqing, the following conclusions

can be drawn: (1) The population and economic

center of gravity has moved to the main urban area;

(2)The population and economy are mainly

concentrated in the main urban area and its

surrounding counties; (3)Population agglomeration

and economic agglomeration are coordinated in half

of Chongqing; (4)The spatial distribution of

Chongqing's population and economy has a relatively

obvious positive spatial correlation.

First of all, from the perspective of the gravity

center and migration route of Chongqing's population

and economy, the gravity center of Chongqing's

population and economy is located in the southwest

of Chongqing's geometric center, which is consistent

with the fact that the main urban area of Chongqing

is located in the southwest of Chongqing. From 2010

to 2019, Chongqing's population gravity center has

been located in Changshou District, and it is moving

to the southwest every year. The economic center of

gravity has always been located in Yubei district.

From 2010 to 2013, the economic center of gravity

has the trend of moving northward, but from 2013,

the economic center of gravity has gradually moved

to the southwest. On the whole, the population and

economic center of Chongqing is gradually moving

to the main urban area of Chongqing.

Secondly, from the spatial agglomeration degree

and distribution of Chongqing's population and

economy, the geographical concentration degree of

population and economic show that the spatial

distribution of Chongqing's population and economy

presents a significant unbalanced distribution pattern

of high agglomeration degree in "one circle" and low

agglomeration degree in "two wings". "One circle"

refers to the "one hour economic circle" in the main

urban area and surrounding areas of Chongqing, and

"two wings" refers to the remote areas in the

southeast and northeast of Chongqing from the main

urban area. The main urban area of Chongqing is the

political, economic, cultural and financial center of

the city. Due to the terrain, the main urban area of

Chongqing and its surrounding areas are mainly low

mountains and hills. It is rich in natural resources and

has strong carrying capacity of resources and

environment, which can attract more foreign

population to the main urban area for development

and further promote the economic development of the

main urban area. As a result, the population and

economy of the main urban area are highly

concentrated, and the distribution density of

population and economy is also very high. The "two

wings" of southeast Chongqing and northeast

Chongqing are located in Wuling mountain area and

Daba mountain area, with steep and complex terrain,

low environmental carrying capacity of resources,

and poor economic development, which leads to the

outflow of population. Therefore, the degree of

population and economy aggregation in this part of

the area is not obvious, and the distribution density of

population and economy is relatively low.

Thirdly, from the perspective of the coordination

of Chongqing's population and economic

development, with the economic growth of all

districts and counties in Chongqing, there are some

changes in the coordinated development of

Chongqing's population and economy from 2010 to

2019. For example, Tongnan and Hechuan to the

north of the main urban area and Qijiang to the south

of the main urban area have changed from the area

where population concentration is ahead of economic

concentration to the area where population

concentration and economic concentration are

coordinated. On the whole, population agglomeration

and economic agglomeration are coordinated in half

of Chongqing, while only a few areas have economic

agglomeration ahead of population agglomeration.

Finally, from the perspective of spatial correlation

of Chongqing’s population and economy, the Moran

global scatter diagram shows that the spatial

distribution of Chongqing’s population and economy

shows a relatively obvious positive correlation, and

the correlation is becoming stronger and stronger.

LISA cluster diagram shows that the spatial

agglomeration of Chongqing's population and

economy in the "one circle" presents an obvious high-

high distribution, and the low-low distribution is

mainly concentrated in the "two wings" area. The

Spatiotemporal Dynamic Evolution of Population and Economy in Chongqing

645

global Moran scatter diagram and LISA cluster

diagram of the inconsistency index of population and

economic distribution in Chongqing show that the

inconsistency index has a significant positive spatial

correlation, and regions with similar population and

economic coordination tend to cluster in space. The

specific performance is that the low-low

agglomeration distribution of population and

economic inconsistency index is always in the main

urban area and some surrounding areas, while the

high-high spatial agglomeration distribution area is

always in northeast Chongqing, and has not changed

much in the past decade.

The coupling development of population and

economy is a complex and continuous evolution

process. Due to the limitation of data source and

quality, it is obviously limited to conduct in-depth

research on the population and economic

development of Chongqing only based on the

population and GDP data of Chongqing from 2010 to

2019. However, in view of the certain characteristics

of population migration and economic development

in Chongqing, although this paper is a preliminary

study, based on the research ideas and conclusions of

this paper, it can provide an important basis for

further in-depth analysis in the future. In future

research, in-depth analysis and research can be

carried out from the following aspects: (1) Select the

data with longer time span and the latest data to

deeply grasp the development law and latest

development of population and economic in

Chongqing; (2)Deeply analyze the impact of the

differences of human capital structure and age

structure on the migration of gravity center of

population and economic growth in Chongqing; (3)

Construct the index of influencing factors of GDP,

and analyze the influencing factors of economic

differences among regions in Chongqing and the

spatial spillover effect of economic development.

ACKNOWLEDGMENTS

This research was funded by Humanities and Social

Sciences Research Project of Chongqing Municipal

Commission of Education of China in 2020

(20SKGH174)

REFERENCES

Anselin L. (1995) Local indicators of spatial association -

LISA. Geographical Analysis, 27: 93-115.

Bin J.Y, Tang X.B, Li M. (2018) On the Spatial Coupling

Distribution Between Population and Economic

Development in the Counties of Hunan Province.

Journal of Natural Science of Hunan Normal

University, 41: 1-8.

Cai Z.J, Lu Y, Hua C. (2016) Spatial and Temporal

Evolution of County Population and Economic

Agglomeration in Guangxi. Geospatial Information,

14: 13-16.

Chen L, Li X, Yao Y. (2018) Effects of population

agglomeration on urban economic growth in China.

Acta Geographica Sinica, 73: 1107-1120.

Chongqing Statistics Bureau, Chongqing Statistical

Yearbook http://www.cqdata.gov.cn/.

Ding X.H, Gao X.Y, Gao X.C. (2014) On the Spatial

Pattern and Coupling between the Population and

Economy of Qinghai—Tibetan Plateau from 2000 to

2010. Journal of Tibet University, 29: 34-42.

Guan D.J, Tan J, Zhang M.J. (2017) Spatial Coupling

Distribution of population and Economic Development

In Chongqing. Human Geography, 2017(02): 122-128.

Huang D.K, Zhao Y.L. (2016) Spatial-temporal evolution

of county population and economic disparities in

Guizhou province. Journal of Guizhou Normal

University (Natural Sciences), 34: 16-25.

Klaus P. (2014) The non-monotonous impact of population

growth on economic prosperity. Economics

Letters,2014,124(1).

Lian X.M, Wu J.H. (2018) Dynamics of Spatial Pattern

between Population and Economies in Northeast

China[J]. Population Journal,2018(01):45-55.

Liu G.C, Zhang H, Liu Z.C. (2017) A Study on the Spatial

and Temporal Evolution of Population Focus and

Economic Center of Gravity. Jounrnal of Harbin

University of Commerce, 2017(05): 58-67.

Liu X.M, Su W.C, Guan D.J. (2013) On Spatial Coupling

Distribution between Population and Economic in the

Three Gorges Reservoir Area of Chongqing. Journal of

Chongqing Normal University (Natural Science), 30:

37-43.

Liu Z.B, Zheng L.X. (2013) The Analysis of Distribution

of Population and Economy for Henan Province.

Journal of Anyang Teachers College, 2013(5): 73-79.

Luca S. (2016) From sprawl to compactness and back:

population dynamics (1848–2011) and the economic

structure of a Mediterranean city.

GeoJournal,2016,81(2).

Luca S. (2018) Population growth and the economic crisis:

understanding latent patterns of change in Greece,

2002–2016. Letters in Spatial and Resource

Sciences,2018,11(2).

Mikiko O. (2015) Population ageing and economic growth

in Japan. International Journal of Sociology and Social

Policy,2015,35(11/12).

P. A. P. Moran. (1948) The Interpretation of Statistical

Maps. Journal of the Royal Statistical Society. Series B

(Methodological), 10: 243-251.

Ren K.L, Zhu Z.L, Shou M.T. (2016) Study on the Space-

time Coupling between Population and Economic in

BDEDM 2022 - The International Conference on Big Data Economy and Digital Management

646

Urban Agglomerations along the Yellow River in

Ningxi. Modern Urban Research, 2016(10): 91-96.

Wang G.X, Qin Z.Q. (2013) Study on the Change of the

Spatial Relation between Population and Economy in

Shanxi Province. Economic Geography, 33: 29-35.

Wesley E, Peterson F. (2017) The Role of Population in

Economic Growth[J]. SAGE Open,2017,7(4).

Xu S, Tang N, Wei D. (2013) The evolution of population

gravity center and economy gravity center in Xian

based on GIS. Journal of Northwest University (Natural

Science Edition), 46: 985-989.

Yang G.M, Zhang F, Zhang F.T. (2021) Spatiotemporal

changes in efficiency and influencing factors of China’s

industrial carbon emissions. Environmental Science

and Pollution Research, 28: 36288–36302.

Zhang J, Run D.S. (2018) Study on the Evolution Law and

Driving Factors of the Synergistic Evolution of

Population and Economy in the Yangtze River Delta.

Modern Economic Research, 2018(12): 84-92.

Zhou Y, Tu J.J, Lu D.B. (2011) Study On The Relationship

Between Population And Economic Spatial

Distribution And Its Dynamic In Chongqing[J].

Economic Geography,11: 1781-1785.

Spatiotemporal Dynamic Evolution of Population and Economy in Chongqing

647