Study on the Spatial and Temporal Evolution Characteristics and Driving Factors of the “Production–Living–Ecological Space” in Changfeng County

Abstract

The rapid development of industrialization and urbanization aggravates the tension of human–land relationships, leading to increasingly prominent contradictions and a serious imbalance in the relationship among production–living–ecological space (PLES). The study of county PLES is important for guiding the spatial development and layout optimization of national land as well as promoting the integrated development of urban and rural areas. This can be made more accurate, comprehensive, and visualized by using a land transfer matrix, a land use dynamic attitude, and a barycenter migration model. Research results have shown that the spatial type of Changfeng County was dominated by production space and living space from 2000 to 2020. The production space area decreased the most, to 16.3044 km², and the ecological space area increased by 50.175 km², within which the single dynamic attitude was first positive and then negative, with more drastic spatial changes and the fastest expansion rate. The transfer relationship was mainly based on the transfer of production space out and ecological space in; the longest distance and most obvious change was in the center of gravity of ecological space in the first 10 years of the study period, showing a trend toward the southeastern town of Xiatang. In addition, population, the increase in the proportion of tertiary industry structures, and policy regulation are the dominant factors in changes in the PLES in the county. This study provides a basis and support for the rational use of land resources and the balanced and coordinated development of people and land in Changfeng County, which is currently implementing unbalanced development.

1. Introduction

Following reforms and opening up, the rapid development of urbanization and industrialization has caused disorderly land reclamation, the blind expansion of production and living space scales, the deterioration of the ecological environment, and tension between humans and land. Conflicts involving production–life–ecological space (PLES) are also becoming increasingly prominent [1,2,3]. The relationship between PLES and human beings has become seriously imbalanced due to the uncontrolled development of living space and the tightening of resource constraints. PLES is a classification of land space based on the main functions of land and is the basic paradigm for perceiving the spatial and functional attributes of land [4]. For the development of human society, PLES provides the basic conditions for human activities and is the basic vehicle for human survival and development. Therefore, understanding the transformation between the three dominant functions of PLES [5,6], exploring the driving factors affecting the changes in PLES, and promoting the scientific rationalization of urban planning and land use are crucial. The balanced and coordinated development of the human–land relationship is of great significance for sustainable urban economic and social development [7,8,9].

At present, the conceptual and theoretical framework surrounding PLES has been established, and research focusing on spatial identification and pattern evolution analysis, spatial heterogeneity evaluation research, etc., has been identified. The spatial and temporal evolution of land use is often described using methods including a transfer matrix, a land use dynamic attitude, a barycenter migration model, a geographic probe, a global autocorrelation model, and an ecological and environmental quality index, among others [10]. These are the common analytical methods used by most scholars to study urban land use change. The evolution of the PLES has been driven by the territorial system of human–land relations. The driving analysis shows a good explanation of the relationship between human activities and the evolution of the PLES pattern and future land development patterns [11]. Domestic and international scholars have focused on the ecological and environmental effects [12,13], spatial and temporal evolution, and functional coupling and coordination [14] of the driving factors of the PLES. The study area has mainly been focused on the watershed level, such as the Chaohu basin [3], the national level [15], and the city [16] and town levels, with less attention on the county [17] and village levels [18]. Land use change is the result of complex interactions between human activities (population, socio-economics [19,20], GDP, policies [21], etc.) and natural factors [22] (soils, annual rainfall, average annual temperature, topography, climate, etc.), acting at a wide range of temporal and spatial scales. Human drivers are the main determinants of short-term land use and regional development, and natural factors act on a wide range of temporal and spatial scales [23]. Scholars usually use correlation analysis, multiple linear regression simulation, and principal component analysis to analyze and predict land use development trends and spatial distribution characteristics [24,25], which have implications for urban planning, management, and the conservation of land resources. For example, Guoliang Xu used typical correlation analysis and principal component analysis to study the main driving forces affecting the change in arable land use in Foshan City, and the study showed that population and policy were the main factors affecting regional development [26]. However, the current model and methodology of the PLES drivers are not yet perfect [27]. Therefore, the study of the changes and development trends of the PLES and their driving mechanisms is very important for the optimization of the spatial layout of a country [11]. Balancing and optimizing different functional land use types [28] can provide a scientific basis for the formulation of reasonable land use policies [29,30].

Changfeng County is dominated by agriculture and has underutilized land resources [31]. The development of industrialization and urbanization led to a serious loss of arable land area and changes in the ecological environment function. Around 2000, the policy of returning farmland to forest was fully implemented, which promoted the rapid conversion of surrounding land use [32]. Since 2010, the economic development model has shifted from promoting growth to promoting transformation, and the urbanization process has accelerated. Through the introduction of a series of policies, financial support has been provided to promote the high-quality construction of agricultural modernization [33] and to promote changes in the spatial pattern of urban and rural development [17,21]. However, the water ecosystem still faces serious problems with protection and restoration. By 2020, the rural revitalization strategy will accelerate the modernization of agriculture, the integrated development of urban and rural areas, and the effectiveness of ecological protection and environmental management. However, the already-established water environment control system and networked management and supervision mechanisms need to be further improved and perfected [34]. In the past, urban economic and social development has been based on the central county before gradually expanding outward and driving the development and construction of the surrounding land. However, Changfeng County jumped out of the “county administrative boundary” and fully relied on the drive of the provincial capital city of Hefei to take the lead in developing the southern part of the county [35]. Meanwhile, most scholars studying spatial and temporal evolution and the drivers of county land use often use typical regression analysis methods. In contrast, our study of the spatio-temporal evolution and drivers of the uneven development of counties in the PLES adopted a spatio-temporal perspective and a center of gravity shift model, which are less frequently used in such studies. Based on these, this study intends to use Changfeng County as a typical case to comprehensively analyze and identify the natural, socio-economic, and policy-related drivers of PLES changes from 2000 to 2020 and provide a basis for the optimal allocation of land resources in other, similar counties.

2. Materials and Methods

2.1. Overview of the Study Area

Located between the cities of Hefei, Huainan, and Bengbu, Changfeng County is the core region of central Anhui Province and an important location for Hefei to radiate towards northern Anhui, with strong location advantages (see Figure 1). Changfeng’s central county seat is located in the county’s northernmost town, Shuihu, while the southern towns of Shuangdun and Gangji have enjoyed urban spillover effects and rapid economic development. Changfeng County is located at the northern end of the Jianghuai Mountain Range, with high southeast and low northwest terrain. The land is susceptible to changes due to external factors such as temperature, wind, rain, snow, and running water. The rivers within the county boundary are mostly short and fast-flowing at their source, which has an impact on the biodiversity and ecosystem types of the area. Changfeng County has 10 towns and 4 townships, with a total area of 1841 km² [36], accounting for about 16% of the total area of Hefei. By the end of 2022, the county’s resident population will be 800,000, and its GDP will be CNY 82.383 billion [37]. Changfeng County has plans to build an A1-rated county general aviation airport in the future to enable the transfer of industries from the east. Due to natural conditions and policy planning, there are significant differences in population distribution and economic development across Changfeng County, resulting in uneven regional development.

2.2. Data Sources and Processing

The land use data for 2000, 2010, and 2020 used in this study were obtained from the global surface coverage data GlobeLand30 (Welcome-GlobeLand30) with a resolution of 30 [38] and a data accuracy of over 83%. Administrative boundary data were derived from the National Natural Resources and Geospatial Infrastructure Information Database at http://sgic.net.cn/web/geo/index.html (accessed on 3 June 2023). The statistics used to conduct the driver analysis were obtained from the Anhui Provincial Statistical Yearbook, https://www.ah.gov.cn/ (accessed on 3 June 2023), the national economic and social development bulletins of Changfeng County in different years, http://epaper.routeryun.com/Article/index?aid=6735835 (accessed on 4 June 2023), etc.

Land use data for the years 2000, 2010, and 2020 need to be pre-processed before the classification of surface cover types can be carried out. The image accuracy was first analyzed and corrected in Arcgis 10.2, followed by image color synthesis, mosaic, and cropping. Using these pre-processing steps, raster data on the current land use situation in Changfeng County for three periods was obtained, making the research results more accurate and reliable.

In this study, by considering the current status of land resources in the study area and referring to the GlobeLand30 classification system, six land cover types, namely, cropland, forest land, grassland, wetland, water body, and artificial ground, were selected as secondary land classes. Then, by combining the dominant functions of land use types with the PLES, a classification system of the dominant functions of land use in the PLES was established by using the subsumption classification method, as shown in

Table 1. Classification of land use based on PLES functions.

Classification of Dominant Functions of Land Use in PLES		Land Use Type
Grade I Land Type Entry 2	Secondary Land Type Data
Producing space	Cropland	Paddy fields, irrigated dry land, rain-fed dry land, pasture land, etc.
Ecological space	Forest land	Deciduous broad-leaved forest, evergreen broad-leaved forest, deciduous coniferous forest, etc.
	Grassland	Meadows, savannas, and urban artificial grasslands
	Water body	Rivers, lakes, reservoirs, ponds, etc.
	Wetland	Inland marshes, lake marshes, etc.
Living Space	Artificial ground	Different types of residential land, such as towns and cities, industrial and mining areas, transportation facilities, etc.

.

2.3. Analysis of the Dynamic Change Methodology of the PLES

2.3.1. Land Use Dynamics

The dynamic attitude of a single land use type can clearly and accurately reflect the rate and degree of change of different land use spatial types and is based on a land use transfer matrix that quantifies the quantitative changes of a particular land use type over time. Its calculation equation is as follows [39,40]:

$$\begin{array}{c}\mathrm{K}=\frac{{\mathrm{U}}_{\mathrm{b}}{-\mathrm{U}}_{\mathrm{a}}}{{\mathrm{U}}_{\mathrm{a}}}\times \frac{1}{\mathrm{T}}\times 100\%\end{array},$$

(1)

where K is a land use type’s rate of change; U_a, U_b are areas of each land use type at the start and conclusion of the survey, respectively; and T is the research period.

2.3.2. Land Use Transfer Matrix

The land use transfer matrix is a data analysis tool based on remote sensing imagery and GIS data that reveals the flow and occupancy relationships among land use types. By classifying and cross-comparing land use types in different time periods, the interconversion relationships between land use types in different time periods can be derived [41], which helps people better understand the mechanisms and trends of land use change. The relevant equation [42] is:

$${\mathrm{S}}_{\mathrm{ij}}=\left[\begin{array}{cccc}{\mathrm{S}}_{11}& {\mathrm{S}}_{12}& \dots & {\mathrm{S}}_{1\mathrm{n}}\\ {\mathrm{S}}_{21}& {\mathrm{S}}_{22}& \dots & {\mathrm{S}}_{2\mathrm{n}}\\ \dots & \dots & \dots & \dots \\ {\mathrm{S}}_{\mathrm{n}1}& {\mathrm{S}}_{\mathrm{n}2}& \dots & {\mathrm{S}}_{\mathrm{nn}}\end{array}\right],$$

(2)

where i and j are the land use types at the start and conclusion of the survey, and S stands for the area. n denotes the number of land use types before and after the transfer.

2.3.3. Barycenter Migration Model

The geographical center of gravity is a key indicator reflecting the spatial and temporal distribution characteristics of a geographical element, and its location and movement can directly reflect the distribution trend and evolution pattern of the geographical element in the region. The center of gravity migration model is based on GIS data and calculates the coordinates of the center of gravity of each geographic unit in different time periods to determine the direction and distance of the center of gravity migration. It is widely used in the fields of urban evolution and land use change, which enables us to comprehensively understand the evolutionary trajectory and spatial distribution pattern of geographic elements in the region and grasp the resource allocation in the region. The relevant formula [43] is:

$${\mathrm{x}}_{\mathrm{t}}=\frac{\sum _{\mathrm{i}=1}^{\mathrm{m}}{(\mathrm{C}}_{\mathrm{ti}}{\mathrm{X}}_{\mathrm{i}})}{\sum _{\mathrm{i}=1}^{\mathrm{m}}{\mathrm{C}}_{\mathrm{ti}}}{\mathrm{Y}}_{\mathrm{t}}=\frac{\sum _{\mathrm{i}=1}^{\mathrm{m}}{(\mathrm{C}}_{\mathrm{ti}}{\mathrm{Y}}_{\mathrm{i}})}{\sum _{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{C}}_{\mathrm{ti}}},$$

(3)

where X_i and Y_i stand for the longitude and latitude of the center of a certain type of space in year t; x_t and y_t for the area of the ith region in year t; and m for the overall number of regions included in that type of space.

2.3.4. Interannual Distance Moved by the Barycenter

The interannual movement distance of the center of gravity is based on the center of gravity migration model, which is derived by using accurate GIS data and then calculating the distance between the coordinates of the center of gravity in different time periods. D is the separation between the center of gravity coordinates of the factor under investigation in the year t + 1 and the center of gravity coordinates in the year t. R is a constant with a kilometer value of 111.11. The formula is as follows [43]:

$${\mathrm{D}}_{\left(\mathrm{t}+1-\mathrm{t}\right)}=\mathrm{R}\times \sqrt{{{(\mathrm{X}}_{\mathrm{t}+1}-{\mathrm{X}}_{\mathrm{t}})}^2+{{(\mathrm{Y}}_{\mathrm{t}+1}-{\mathrm{Y}}_{\mathrm{t}})}^2},$$

(4)

where (x_t+1, y_t+1) and (x_t, y_t) denote the coordinates of the center of gravity for different years.

2.3.5. Land Use Drivers

In order to more comprehensively analyze the drivers influencing PLES changes, factors such as urbanization rate, population, and GDP, which are the core indicators for assessing urban scale and economic growth, are chosen for indicator selection; in terms of the environment, the commonly used climate variables of precipitation and temperature factors are selected [44,45]. These drivers are analyzed and compared to better understand the potential drivers of land use change in the region and to guide scientific and rational land use development.

3. Results

3.1. Analysis of the Temporal Evolution of the PLES in Changfeng County

3.1.1. Change in Area and Dynamic Attitude of PLES

Table 2. PLES area and single land use dynamic degree change (km²).

Space Type	2000		2010		2020		2000–2010		2010–2020		2000–2020
	Area (Km2)	Proportion (%)	Area (Km2)	Proportion (%)	Area (Km2)	Proportion (%)	Dynamic Attitude (%)	Change Area (km2)	Dynamic Attitude (%)	Change Area (km2)	Dynamic Attitude (%)	Change Area (km2)
Production Space	1536.3207	83.78	1466.4762	79.97	1501.6887	81.92	−0.45	−69.8445	0.24	35.2125	−0.11	−34.6320
Living Space	274.0500	14.94	287.3493	15.67	257.7456	14.06	0.49	13.2993	−1.03	−29.6037	−0.30	−16.3044
Ecological Space	23.4864	1.28	80.0316	4.37	73.6614	4.02	24.08	56.5452	−0.80	−6.3702	10.68	50.1750

shows the changes in the PLES area ratio and land use attitude from 2000 to 2020 in Changfeng County. As can be seen from the table, from 2000 to 2020, the area of production space shrank from 1536.3207 km² to 1501.6887 km², with a total decrease of 34.6320 km²; the area of living space changed from 274.0500 km² to 257.7456 km², with a total loss of 16.3044 km² and an undetectable change trend. The ecological space, on the other hand, showed a general rising trend from 2000 to 2010 and a minor decline from 2010 to 2020, increasing from 23.4864 km² to 73.6614 km² in 2020, which was an increase of 50.1750 km². Using the land use ratio study, it was determined that between 2000 and 2020, the percentages of production space, dwelling space, and ecological space decreased from 83.78%, 14.94%, and 1.28% to 81.92%, 14.06%, and 4.02%.

The single dynamic attitude of production space, living space, and ecological space has changed from 2000 to 2010 by −0.45%, 0.49%, and 24.08%, respectively, with corresponding area changes of 69.8445 km², 13.2993 km², and 56.5452 km². This shows that the ecological space dynamic has changed the most, and the policy of converting farmland back to forest has been successful. In the years 2010–2020, the single land use dynamic attitude of production space changed to 0.24%, with an area change of 35.2125 km², while the single land use dynamic attitude of living space increased and then decreased from 0.49% to −1.03% in 2000–2010, demonstrating that the downward trend in space and living space has been slowed down thanks to the adoption of a stringent agricultural land preservation and conservation system and strict control of the total amount of urban and rural building land. Additionally, the growth rate of ecological space slowed down, and the single-motion attitude was only −0.80%, which was much lower than all other time periods, as shown in Figure 2.

In summary, in the years 2000–2020, Changfeng County had the largest production space area, which always dominated. Production space had an overall negative single-motion attitude; living space had the trend of increasing and then decreasing; and the single-motion attitude of ecological space changed the most drastically and always maintained an increasing trend.

3.1.2. Structural Shift Changes in the PLES

Production space was the most transformed in the two time periods of 2000–2010 and 2010–2020, with 40.780 km² and 82.617 km² turned into living space, reflecting that the construction and development of Changfeng County led to a large loss of arable land area and land use changes aimed at dynamically balancing production. Land development activities in total space continue to advance, but it is still difficult to offset the overall declining trend. Living space was the second most transformed, with values of 112.95 km² and 103.397 km² in the two periods, respectively, indicating that land use efficiency in Changfeng County has improved. Ecological space was mainly transformed into production space during the 20 years under study, at rates of 4.816 km² and 9.918 km², respectively, which caused the destruction of the ecological environment and rural landscape to a certain extent, as shown in

Table 3. Land use transfer matrix of PLES from 2000 to 2020 (km²).

Times	Space Type	Production Space/km2	Ecological Space/km2	Living Space/km2	Grand Total/km2	Roll-Out/km2
2000–2010	Production Space/km2	1435.176	60.364	40.780	1536.321	101.145
	Ecological Space/km2	4.816	18.396	0.274	23.486	5.090
	Living Space/km2	26.483	1.270	246.296	274.050	27.754
	Grand Total/km2	1466.476	80.032	287.349	1833.857	—
	Roll In/km2	31.300	61.636	41.054	—	133.989
2010–2020	Production Space/km2	1371.016	12.843	82.617	1466.476	95.46
	Ecological Space/km2	19.109	60.192	0.73	80.031	19.839
	Living Space/km2	111.563	1.387	174.398	287.349	112.95
	Grand Total/km2	1501.688	74.422	257.745	1833.857	—
	Roll In/km2	130.672	14.23	83.347	—	228.249
2000–2020	Production Space/km2	1390.277	59.429	86.615	1536.321	146.044
	Ecological Space/km2	9.918	13.091	0.478	23.487	10.396
	Living Space/km2	101.494	1.904	170.653	274.05	103.397
	Grand Total/km2	1501.689	74.423	257.746	1833.857	—
	Roll In/km2	111.412	61.332	87.093	—	259.837

. The change in the trajectory of the PLES shows that 111.563 km² and 170.653 km² of the living space in Changfeng County were converted to production space and 1.387 km² and 1.904 km² to ecological space, as shown in Figure 3.

Figure 4 shows the inter-transfer relationships and the number of transfers of the PLES in Changfeng County for the periods of 2000–2010, 2010–2010, and 2000–2020. As can be seen from the table, during the period 2000–2020, the transfer of production space was the main focus, and the transfer trajectory was reflected in the significant increase in ecological patches in 10 towns (except Luotang Township, Zhuangtomb Township, Yijing Township, and Yangmiao Township); in addition, the increase in living space area was mainly reflected in the transfer of production space to living space patches in Shuangfeng Economic Development Zone and Shuangtun Township in southern Changfeng County.

In conclusion, one of the reasons for the change in the PLES in Changfeng County from the temporal latitude analysis was the impact of the double pressure of urbanization and industrial development. Secondly, the government and society paid attention to the environment and ecology, such as by implementing the measures taken to restrict development and construction, which led to a gradual increase in the area of woodland and water and the gradual protection and restoration of the PLES. Therefore, the change in the PLES was determined by the continuous interaction of the external environment (e.g., urbanization, policies) and internal forces (e.g., society, individuals).

3.2. Spatial Evolution Analysis of Changfeng County

3.2.1. Distribution of the PLES Pattern

The land use classification maps of Changfeng County for the years 2000, 2010, and 2020 were decoded using Arcgis 10.2 and then reclassified to obtain the distribution map of the PLES in Changfeng County, as shown in Figure 5a. According to the topography and characteristics of Changfeng County, the overall pattern of the PLES did not change abruptly during the study period, and most of the townships expanded or shrank on the basis of the original spatial types. The living space is concentrated in the northern-most center of the county town of Shuihu and the southern-most towns of Shuangtun, Gangji, etc. The remaining living space is scattered throughout the county, forming a distribution pattern of “two stars and more points”, as shown in Figure 5b. Production space and ecological space have spread over almost the entire city area, as shown in Figure 5c. These changes occurred mainly because Changfeng County built the Shuangfeng Economic Development Zone and started to take over the industrial transfer from Hefei City, which has a general airport, a railroad, and other traffic and air transportation land, resulting in the development of several towns adjacent to Hefei City rather than the development of the central county.

3.2.2. PLES Center for Barycenter Migration

Using the above formula for shifting the center of gravity of land use, the coordinates of the center of gravity and the degree of change in the center of gravity occurring between 2000 and 2020 were calculated for the different spatial types. The results showed ecological space and living space shifting in the opposite direction to production space, reflecting the focus and development trend of urban development in recent years, as shown in Figure 6a.

The center of gravity of the production space was located in the town of Xiantang, shifting from (117.1915° N, 32.2330° E) to the northwest to (117.1885° N, 32.2403° E), with a cumulative distance of 0.8769 km in 20 years. The center of gravity shifted 0.2908 km and 0.6146 km to the northwest from 2000 to 2010 and 2010 to 2020, respectively, as shown in

Table 4. The barycentric coordinates and moving distance of PLES from 2000 to 2020.

Space Type	2000		2010		2020		2000–2010 Distance/km	2010–2020 Distance/km	2000–2020 Distance/km
	Longitude	Latitude	Longitude	Latitude	Longitude	Latitude
Production Space	117.1915° N	32.2330° E	117.1897° N	32.2349° E	117.1885° N	32.2403° E	0.2908	0.6146	0.8769
Living Space	117.1914° N	32.2393° E	117.1932° N	32.2268° E	117.1979° N	32.2038° E	1.4032	2.6083	4.0100
Ecological Space	117.1826° N	32.2918° E	117.2150° N	32.2588° E	117.2271° N	32.2279° E	5.1385	3.6871	8.6519

, reflecting the trend of the spatially concentrated distribution area of production shrinking to the northwest.

The center of gravity of the living space was also located in the town of Xiantang. It shifted south-east from (117.1914° N, 32.2393° E) to (117.1979° N, 32.2038° E) in the last 20 years, with a cumulative displacement of 4.0100 km. Specifically, the center of gravity shifted 1.4032 km and then 2.6083 km to the south-east in the first and second decades, respectively. This indicates that, in recent years, the construction land in the southern part of the study area, in Shuangfeng Economic Development Zone, Shuangdun Town, and Gangji Town, has been developed to a higher degree and infrastructure development has been accelerated, as shown in Figure 6b.

The center of gravity of the ecological space shifted from Yijing town (117.1826° N, 32.2918° E) to Xiantang town (117.2271° N, 32.2279° E) between 2000 and 2020, moving 8.6519 km to the south-east by 5.1385 km and 3.6871 km in the first and last decades of the study period, respectively. As a result, the longest ecological spatial migration distances and the most significant changes were observed in the first 10 years of the study period. Combined with the spatial pattern distribution map, it can be seen that there was an increase in ecological spatial patches in the central part of the county, mainly due to the influence of the watershed and the improved ecological conditions, as shown in Figure 6c.

The above analysis of the pattern distribution and center of gravity shift shows that the change in the spatial dimension of the PLES was the result of a combination of factors. For example, the promotion of regional strategic development and urbanization, changes in social needs, and the strengthening of national efforts to protect the ecological environment have led to the spatial pattern of the three aspects showing a large degree of construction and development at either end, with the spatial center of gravity of life and production moving to the southeast. Therefore, the spatial pattern distribution and center of gravity migration of any region are closely related to the regional economic, cultural, and social development.

4. Analysis of Driving Factors

PLES is essentially the result of the evolution and differentiation of the relationship between humans and land [43]. Social factors affect land use and are more complex, mainly as a result of anthropogenic factors (human activities), while natural factors are mainly related to changes in precipitation and temperature, which are relatively intensive, effective, and widespread [46]. By analyzing and discussing the main driving factors affecting the development of the PLES, we can better understand the development mechanisms and laws of the PLES and provide a reference basis for urban development strategies and policies.

4.1. Analysis of Socio-Economic Factors

Population is one of the main drivers of urban development, and an increase or decrease in numbers, distribution, and migration influences the extent and type of land use. The total population of Changfeng County has shown a clear downward trend, but the urban population increased significantly from 80,000 in 2000 to 471,300 in 2020, and the urbanization rate increased from 8% in 2000 to 60.1% in 2020, an increase of 52.1%, as shown in Figure 7a.

In addition to population, the economy is another important driver of change in the PLES of Changfeng County. In terms of total economic development, the GDP of Changfeng County was CNY 1.992 billion in 2000 and started to rise linearly in 2005, reaching CNY 65.94 billion in 2020. In terms of regional investment, Changfeng County’s fixed capital investment was CNY 495 million in 2000 and will reach CNY 49.13 billion in 2020. This indicates that the rapid economic development of Changfeng County and the increase in regional fixed investment during the study period led to a change in the structure and area of the PLES. In terms of economic structure, the proportion of primary, secondary, and tertiary industries in the economy of Changfeng County was 53.67%, 24.22%, and 22.11% in 2000, 24.22%, 54.96%, and 40.3% in 2010, and 11.53%, 40.3%, and 48.18% in 2020, respectively, as shown in Figure 7b. By comparing and analyzing the changes in the three industrial structures, we can see that Changfeng County is currently dominated by the secondary industry, and the primary industry accounts for a relatively low percentage. At the same time, the development of new industrialization drives the adjustment of the economic structure and promotes substantial economic growth with intensive and efficient production and living space, which has a certain impact on urbanization and agricultural modernization.

4.2. Analysis of Natural Environmental Factors

Temperature and climate, among other factors, play a role in agricultural and forest use but also have a significant impact on the future distribution of the PLES [47]. For example, areas with a wide and flat topography and relatively simple climate types may change the overall spatial layout of the city when the environment changes. The low altitude and latitude of Changfeng County and the long sunshine hours make it suitable for agricultural development and promote the development and construction of living and productive spaces for people. Usually, an increase in the intensity of urban development and construction leads to an increase in temperature and a decrease in rainfall. The information presented in Figure 8 indicates that the annual rainfall in Changfeng County increased year by year and the temperature decreased, which was most likely related to flooding in the urban area in recent years and the rise in the water level in the Jianghuai basin and the urban industrialization development.

4.3. Analysis of Policy Factors

The model of territorial spatial development is changing from a single production space-oriented model to a model of coordinated development of the PLES [32]. Policies play a macro-regulatory role in urban land use change and differentiation, both facilitating and limiting urban land use change. After 1998, the ecological protection barrier construction strategy and the strategy of returning farmland to forest were implemented; in 2000, it was proposed that attention should be paid to the geospatial distribution of various industries to ensure that the spatial planning of production, living, and ecology can play a better role in the environment as a whole. In 2010, the main functional area plan proposed the establishment of a specific and feasible spatial planning system to delineate and plan the spatial development patterns of production, living, ecology, etc. In 2015, the importance of paying attention to the rationality of urban spatial structure and the management of the PLES was emphasized; in 2018, the layout of territorial spatial planning was further optimized with the aim of increasing coordination and control.

At the local policy level, several documents emphasize ecological protection, production promotion, spatial coordination, and the integration of the development of old and new livelihoods. For example, the Changfeng County Master Plan (2014–2030) identifies two core areas of the county, which are located in the far north and south, forming the spatial structure of the county into two axes, two corridors, and multiple points, influencing the direction of urban expansion as well as changes in land use patterns.

Therefore, the quantitative and structural changes in PLES in Changfeng County require not only bottom-up development but also top-down policy incentives in order to promote the sustainable development of the area.

5. Discussion

This study reveals the spatial and temporal evolution of the PLES in Changfeng County and combines the driving factors from spatial and temporal perspectives with the center of gravity migration model, indicating that the improvement in the ecological environment and the loss of arable land area are important reasons for the conversion of production space and living space to ecological space [12,32]. In addition, this paper provides a quantitative and qualitative analysis of the spatial drivers of urban triple life at the county level for implementing unbalanced development and breaks through the limitation of focusing only on the functional area of land. The study concludes that the southern region close to Hefei City is developing rapidly and that population, secondary industrial structure, and policies are the dominant forces in the spatial changes of triple life [48,49]. Policy documents and other top-level designs play a macro-regulatory role [50]. Therefore, the evolution of the PLES pattern is the result of a combination of regional economic, social development, and policy factors, and the findings of this study are similar to those of other scholars [51,52,53]. At the same time, this paper has made some progress in the methodological selection in the analysis of the dynamic changes of the PLES, based on the study of counties with unbalanced development [54], and further subdivided and deepened the research objects on the evolution of the PLES pattern.

However, there are still some shortcomings in the study. For example, the selected methods are not rich enough, and more methods should be selected to study which factors influence the central counties with insufficient development potential so as to better optimize the spatial planning layout and land use in the future. In addition, differences should be taken into account when formulating ecological protection and restoration policies and spatial planning in the future; the spatial development patterns of cities and towns should be arranged in a targeted manner to promote the balanced and coordinated development of the human–land relationship. Finally, it is not possible to make a precise judgment on the magnitude of the role of each driver in the evolution of the PLES or on how some unmentioned influences affect the evolution of the PLES. Therefore, according to the goals of functional optimization of PLES, coordinated development of urban and rural areas, and rural revitalization proposed by Changfeng County, future research should investigate the evolutionary characteristics and laws from multiple perspectives to make up for the shortcomings of existing studies. The methodology and data selection should be as detailed as possible in the later stage, so as to provide reference and reference significance for the rational layout of the PLES and the coordinated and balanced development of land and space at the county level.

6. Conclusions

In this study, Changfeng County in the Hopewell Industrial Corridor was selected for the interpretation of remote sensing images in 2000, 2010, and 2020. Based on the construction of the PLES land use function dominant classification, the evolution characteristics and driving factors of PLES in Changfeng County were analyzed using the land use dynamic attitude and center of gravity migration model. The conclusions are as follows.

According to the analysis of spatial and temporal changes, we know that production space was the main spatial type in Changfeng County from 2000 to 2020, but the area decreased year by year. This was mainly due to the influence of the dual pressures of urbanization and industrialization, which affected the change in land use in the PLES. Since 2000, the area of ecological space has increased year by year, and the single dynamic attitude has changed significantly; however, the production space and living space have continued to decrease, with the area shrinking by 34.6320 km² and 16.3044 km², respectively. The current expansion rate of living space is slightly moderated compared with 10 years ago.

During the study period, the transfer trajectory of Changfeng County transitioned from mainly production space towards more ecological space due to a variety of factors, including the economic and policy development of the region. This is reflected in the substantial increase in ecological patches in 10 towns (other than Luotang Township, Zhuangtomb Township, Yijing Township, and Yangmiao Township) and the obvious increase in living space in the south, i.e., the area of the Shuangfeng Economic Development Zone, etc. In addition, the center of gravity of production and living space in all three periods was in Xiantang town; the center of gravity of living space migrated from the south-central part of the county to the southeast, with a larger distance; the migration distance of the production space was smaller, at 4.0096 km; and the ecological space’s center of gravity changed from Yijing town to Xiantang town, with obvious changes in migration distance, showing a trend to the southeast.

The increase in the proportion of secondary industry, rainfall, and temperature, as well as national and local policy factors, have influenced the development and construction of living and production spaces in Changfeng County, changing the spatial composition and development needs of the PLES. Through the above conclusions, we are able to posit that the spatial changes of the three aspects in Changfeng County are the result of the joint action of several factors and are inseparable from the economic and social development of the region. Therefore, according to the corresponding conclusions, we can propose corresponding strategies to alleviate the conflicts among PLES and provide development ideas and methods for cities with similar development patterns.