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Sci Rep
Sci Rep
Scientific Reports
2045-2322
Nature Publishing Group UK London

39232100
71297
10.1038/s41598-024-71297-3
Article
Study on the influencing factors of the evolution of space pattern based on principal component analysis in Duolun County
Aruhan aruhan@imau.edu.cn

12
Liu Dongchang 12
1 https://ror.org/015d0jq83 grid.411638.9 0000 0004 1756 9607 College of Desert Control Science and Engineering, Inner Mongolia Agricultural University, Hohhot, 010011 Inner Mongolia China
2 https://ror.org/015d0jq83 grid.411638.9 0000 0004 1756 9607 Key Laboratory of State Forest Administration for Desert Ecosystem Protection and Restoration, Inner Mongolia Agricultural University, Hohhot, 010011 Inner Mongolia China
4 9 2024
4 9 2024
2024
14 2056729 4 2024
27 8 2024
© The Author(s) 2024
2024
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In order to explore the influencing factors of spatial and temporal evolution of production-living-ecological space in Beijing Tianjin sandstorm source area, the remote sensing images, natural environment and socio-economic data of Duolun County in Inner Mongolia from 2000 to 2020 were selected, and the spatial auto-correlation model and principal component analysis model were used to analyze the spatial pattern evolution and influencing factors of production-living-ecological space. The results show that: (1) the function of production space decreases slightly, and the degree of spatial agglomeration decreases; (2) The function of living space rose slightly, and its spatial agglomeration degree showed an upward trend; (3) The ecological spatial function showed a slow upward trend, and its spatial agglomeration degree increased; (4) The spatial pattern of production-living-ecological space is characterized by “high in the southwest and low in the northeast”; (5) Precipitation has the greatest impact on the spatial evolution of the production-living-ecological space. The distance from the main residential areas, per capita GDP, the distance from the main roads and the distance from the main waters have strong explanatory power on the spatial evolution of the production-living-ecological space.

Keywords

Production-living-ecological space
Influencing factors
Principal component analysis
Duolun County in Inner Mongolia
Subject terms

Environmental social sciences
Environmental economics
Socioeconomic scenarios
Sustainability
http://dx.doi.org/10.13039/501100004763 Natural Science Foundation of Inner Mongolia Autonomous Region 2020MS04012 issue-copyright-statement© Springer Nature Limited 2024
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pmcIntroduction

As a complex system and place of political, economic, social and cultural activities in a country or region1, territorial space is the material carrier, foundation and support of human social and economic development2. The evolution of its temporal and spatial pattern is the result of the joint action of natural geographical environment and social and economic development. Its function is to reflect the regional man land relationship and constitute an important theoretical basis for the practical framework of land spatial planning3. In recent years, with the accelerating process of urbanization and industrialization, the elements of urban and rural areas continue to promote and integrate with each other4, and a series of problems such as the chaotic layout of production, life and ecological space in rural areas5, frequent space occupation6. The problem of ecological degradation is very serious in the global scope6. The contradiction between people and land is increasingly prominent, and the contradiction between regional production space, living space and ecological space is increasingly intensified7. The increasingly fierce conflicts among the production-living-ecological spaces have led to poverty and more serious ecological and environmental problems, which have seriously affected the sustainable development of regional economy.The coordinated development of land and space is a necessary condition for regional sustainable development, and the study of the evolution of land and space pattern is the logical premise of coordinating spatial conflicts. At present, the research on the spatial pattern of production-living-ecological spacehas attracted extensive attention of scholars, and the research on production-living-ecological spaceis mainly concentrated at the national level 8. Provincial (or district) level9. county level10, space-time pattern11, coupling and coordination12, etc., pay little attention to the village level space. The research on production-living-ecological space is still in the stage of theoretical exploration, and there are still great differences in the cognition of production-living-ecological space13. The research on the village production-living-ecological space is still in the stage of theoretical exploration, and there are still great differences in the cognition of the village production-living-ecological space. The Beijing Tianjin sandstorm source control project is an ecological project launched and implemented to improve and optimize the ecological environment of Beijing Tianjin and its surrounding areas and reduce the harm of sandstorm. As an important ecological barrier in northern China, the Beijing Tianjin sandstorm source control area is a national demonstration area for ecological restoration and environmental improvement and a key area for collaborative ecological environment control in Beijing Tianjin Hebei, highlighting the importance of the Beijing Tianjin sandstorm source control project14. Therefore, this paper takes Duolun County in Beijing Tianjin sandstorm source area as the research area, selects remote sensing images, natural environment and socio-economic data, uses spatial autocorrelation model and Geographically weighted Principal Component Analysis (GWPCA), analyzes the evolution and influencing factors of the regional production-living-ecological space pattern, and discusses the evolution law of the spatial-temporal pattern of production-living-ecological space, so as to protect and utilize the original resources, optimize the rural landscape space and human settlements, balance the production-living-ecological space structure, provide judgment basis for the effective use and rational development of rural land, and provide scientific guidance for the orderly development of rural areas.

Results

Features of the evolution of temporal and spatial patterns in the production-live-ecological space

Characteristics of the evolution of production space patterns

The production space function of Duolun County in Inner Mongolia is higher in the southwest than in the Northeast (Fig. 1). The high-value areas and high-value areas of production space function are concentrated in Dabeigou town and Xigangou town in the southwest of Duolun County and Duolunnaor town in the middle. The median and low value areas of production space are concentrated in Luanyuan town in the southeast and the northern area of CaiMuShan Township in the northwest, where the terrain is relatively flat and the desertification land area is large. The suitability of production activities in these areas is poor, and the production space area is small, so they belong to animal husbandry areas. The production space is mainly in the middle value area, and the area occupied by low value area, high value area and high value area is lower than that of low and medium value area. The production space function index decreased from 2.185 in 2000 to 1.225 in 2010, and increased to 2.184 in 2020.Fig. 1 Functional spatial distribution map of production space in Duolun County from 2000 to 2020.

Characteristics of the evolution of living space patterns

From 2000 to 2020, the high-value areas and high-value areas of living space function in Duolun County, Inner Mongolia, were mainly concentrated in Duolun Nur town and its surrounding areas in the middle of the county (Fig. 2). The proportion of high value area and high value area is very low. The median area of living space function is gradually distributed from the southwest and central areas to the surrounding areas. The low value areas are distributed in the whole county, and the proportion of their area decreases from 98.44 to 94.13%. The area of low value area of living space function decreased year by year, and the proportion of median area, high value area and high value area increased year by year. The area proportion of high-value areas is very small, and the overall level of life function is relatively low. The function index of living space decreased from 0.079 in 2000 to 0.073 in 2005, and slowly increased to 0.094 in 2020. The proportion of living space area is small, but its expansion trend is very obvious, which is concentrated in duolunnur town in the middle and its surrounding areas.Fig. 2 Functional spatial distribution map of living space in Duolun County from 2000 to 2020.

Characteristics of the evolution of ecological space patterns

From 2000 to 2020, the high-value areas of ecological spatial functions in Duolun County were successively distributed in CaiMuShan Township in the north, luanyuan town in the southeast and xigangou Township in the Southwest (Fig. 3), with an obvious trend of agglomeration and distribution in space, and its area increased from 39.91 to 66.24%. The high value areas of ecological spatial functions are concentrated in the central and eastern areas of the county, and their area is reduced from 45.61 to 27.34%. The median area of ecological spatial function is scattered throughout the county, and the area of the median area is reduced from 13.79 to 6.28%. The low value area of ecological spatial function is distributed in the central area of Duolun County, and its area is reduced from 0.69 to 0.14%. The functional index of living space decreased from 4.21 in 2000 to 3.80 in 2005, and slowly increased to 4.48 in 2020.Fig. 3 Ecological spatial functional spatial distribution map of Duolun County from 2000 to 2020.

Features of evolution of spatial patterns of production-life-ecology

From 2000 to 2020, the high-value area of the comprehensive function of production-living-ecological space in Duolun County was scattered from Dabeigou Township in the southwest to the central area of the county (Fig. 4), and then turned to the centralized distribution in the southwest in the next 10 years, gradually forming a continuous distribution trend in spatial distribution, and its area proportion increased from 12.37 to 33.62%. The high value area of the comprehensive function of the production-living-ecological spaces is distributed in the central and western regions of the county, and its area has increased from 17.09 to 25.99%. The median area of production-living-ecological function was concentrated in the eastern part of the county, and its area decreased from 54.30 to 40.17%. The low value areas are concentrated in the central and western regions of Duolun County and gradually concentrated in the central area of the county seat, with the area reduced from 16.25 to 0.22%. The comprehensive function index of production-living-ecological space dropped from 1.671 in 2000 to 1.280 in 2005, and slowly rose to 1.849 in 2020.Fig. 4 Distribution map of comprehensive functional space of production-living-ecological space from 2000 to 2020.

Spatial auto-correlation analysis of the evolution of production-living-ecological space

Global spatial correlation analysis of the evolution of production-living-ecological space

Using Stata 17 software and the production space, living space, ecological space and production-living-ecological space data of 65 administrative villages in Duolun County, Inner Mongolia from 2000 to 2020, the global Moran's I and Z values of production-living-ecological space were calculated by taking administrative villages as units (Table 1).Table 1 Global spatial auto-correlation test for the mean function of the production-living-ecological space.

Type	2000	2005	2010	2015	2020	
I	z	I	z	I	z	I	z	I	z	
Production space	0.420	5.47	0.545	7.05	0.386	5.05	0.488	6.37	0.539	7.00	
Living space	0.270	5.79	0.375	7.09	0.390	6.82	0.200	5.46	0.375	7.09	
Ecological space	0.282	3.96	0.244	3.43	0.449	6.33	0.318	4.77	0.298	4.60	
Production-living-ecological space	0.405	5.26	0.435	5.65	0.332	5.05	0.347	4.56	0.435	5.65	

It can be seen that the Z-score value of Moran index of the comprehensive functions of production space, living space, ecological space and production-living-ecological space is greater than 2.58, and it shows an upward trend, indicating that the reliability level of production-living-ecological space is rising at the significance level of 1%. The P values of Moran's I in the production-living-ecological space of Duolun County are all less than 0.10, indicating that there are significant spatial auto-correlation characteristics. Since Moran's I is positive and showing an upward trend, it shows that there is an obvious positive spatial spillover effect of production-living-ecological spatial function in Duolun County, Inner Mongolia, and it is showing an increasing trend.

From 2000 to 2020, the Moran index of production space in Duolun County was high, and showed an upward trend, indicating that the spatial dependence was enhanced. The Moran index of living space in Duolun County is low, and shows an upward trend, indicating that the spatial dependence of living space is low, and the spatial dependence of living function is significantly enhanced (Supplementary Information). The Moran index of ecological space in Duolun county is the lowest, showing a weak upward trend, indicating that the spatial dependence of ecological space is low, and its spatial dependence is increasing. The Moran index of production-living-ecological spatial function showed an upward trend, and its spatial dependence was increasing.

Local auto-correlation analysis of the evolution of the triple birth space

Local auto-correlation analysis of production space evolution:the production space of Duolun County in Inner Mongolia is 91% to 100% with varying degrees of spatial agglomeration (Fig. 5). The number of administrative villages in the high concentration area has increased by 2, mainly distributed in the southwest. The number of low-level administrative villages has not changed, and they are concentrated in the central and northern areas of the county. The production space, with Dabeigou town in the southwest of the county as the core, forms a hot spot gathering area in Dalian (Fig. 6), and the number of administrative villages in the hot spot area has increased from 9 to 19. The number of administrative villages in sub hot spots increased from 5 to 29. The non significant areas are concentrated in the northern and central regions, and the number of administrative villages is reduced from 36 to 4. The sub cold spot area and cold spot area are concentrated in the northeast, and the number of administrative villages is reduced from 15 to 13.Fig. 5 LISA agglomeration map of production space in Duolun County from 2000 to 2020.

Fig. 6 Hot spot distribution map of production space in Duolun County from 2000 to 2020.

Local auto-correlation analysis of living space evolution:from 2000 to 2020, 88 to 97% of the living function space units in Duolun County, Inner Mongolia, had different degrees of spatial agglomeration (Fig. 7). The high concentration area of living space formed a agglomeration effect in duolunnur town in the central area of the county, and the number of administrative villages increased by 4, with duolunnur town in the central area of the county expanding outward. The low and low concentration areas are mainly distributed in the north of CaiMuShan Township in the north of Duolun County, luanyuan town in the South and xigangou Township in the southwest. Their living functions are low, showing a trend of fragmentation and concentration. The living space in Duolun County forms a large gathering area centered on Duolun Nur town (Fig. 8), and the living space in other areas presents a discrete distribution. The number of administrative villages in hot spots and sub hot spots increased from 5 to 6, showing a weak growth trend.Fig. 7 LISA agglomeration map of living space in Duolun County from 2000 to 2020.

Fig. 8 Hot spot distribution map of living space in Duolun County from 2000 to 2020.

Local auto-correlation analysis of ecological space evolution:95 to 90% of the spatial units of the ecological space in Duolun County, Inner Mongolia, have different degrees of spatial agglomeration (Fig. 9). The number of administrative villages in the high-altitude cluster area increased by 4, and the high-altitude cluster area is mainly distributed in grassland and forest areas. The number of administrative villages with low concentration increased by 2, which were concentrated in the southwest and central regions. The number of non significant administrative villages decreased by 6. From 2000 to 2020, the hot spots in the ecological space of Duolun County were mainly distributed in grassland and forest areas (Fig. 10), and the number of administrative villages increased by 4. The number of administrative villages in sub hot spots increased by 45. In 2000, the contiguous distribution of non significant regions changed to centralized distribution in the central region, and the number of administrative villages was 48. In 2020, the number of administrative villages in non significant districts will be 2. The sub cold spot area is sporadically distributed in the central and southwest regions, and the number of administrative villages has decreased by 1. The cold spot area is mainly around duolunnur town in the middle and Dabeigou Township in the southwest, and the number of administrative villages has not changed.Fig. 9 LISA agglomeration map of ecological space in Duolun County from 2000 to 2020.

Fig. 10 Hot spot distribution map of ecological space in Duolun County from 2000 to 2020.

Local auto-correlation analysis of the evolution of the comprehensive function of production-living-ecological space:from 2000 to 2020, 94 to 98% of the comprehensive functional units of production-living-ecological space in Duolun County, Inner Mongolia, had different degrees of spatial agglomeration (Fig. 11). The number of administrative villages in the high concentration area increased by 2, mainly distributed in the grassland and forest areas in the southwest. The number of administrative villages with low concentration decreased by 2 and concentrated in the central region. The number of non significant administrative villages has not changed.Fig. 11 LISA agglomeration map of production-living-ecological space in Duolun County from 2000 to 2020.

From 2000 to 2020, the hot spots of production-living-ecological space in Duolun County were mainly distributed in the grassland and forest areas in the Southwest (Fig. 12), and the number of administrative villages increased by 12. The number of administrative villages in the sub hotspot area increased by 13. The distribution area of the non significant area changed greatly, and the number of administrative villages decreased by 27. The sub cold spot area is sporadically distributed in the central and eastern regions, and the number of administrative villages has not changed. The cold spot area is mainly in the middle and eastern part of the Duolun line, and the number of administrative villages is less than 3.Fig. 12 Hot spot distribution map of the production-living-ecological space in Duolun County from 2000 to 2020.

Analysis on the influencing factors of the evolution of production-life-ecology spatial pattern

The evolution of production-living-ecological spatial pattern is affected by natural geography, socio-economic and policy planning. The policy planning factors are difficult to quantify, so qualitative analysis is carried out. According to the existing research (Du et al., 2020), combined with the actual situation of land and space development in Duolun County, natural factors such as precipitation (X1), sunshine hours (X2), surface temperature (X3), annual average temperature (X4), average altitude (X5), average topographic relief (X6) and per capita cultivated land area (X7), per capita grassland area (X8), urban industrial and mining land scale (X9), vegetation coverage (X10) and population density (X11), grain and soybean production (X12), livestock carrying capacity (X13), primary industry (X14), secondary industry (X15), tertiary industry (X16), per capita GDP (X17), distance from main roads (X18), distance from main roads taking the human factors such as the distance from the residential area (X19) and the distance from the main water area (X20) and other human factors as dependent variables, and using Pearson correlation coefficient, the index which has a great relationship with the function of production-living-ecological space is selected, and the influencing factors of production-living-ecological space are analyzed by using principal component analysis method.

Factors affecting the evolution of production space pattern

It can be seen from Fig. 13, from 2000 to 2020, the first principal components are precipitation, surface temperature, sunshine hours, annual average temperature and other natural conditions, and the score coefficients are relatively large, with obvious changes, indicating that precipitation, surface temperature, sunshine hours, annual average temperature and other natural factors have the most obvious impact on production conditions. The score coefficient increases from southeast to northwest. The second principal component is the secondary industry and the distance from the main roads, and the score coefficient is high in the north and low in the south. The third principal component is the urban industrial and mining land scale and. The score coefficient of the secondary industry and the distance from the main roads and the distance from the main water areas is high in the north and low in the south. The third principal component is the distance from the main water area, the distance from the main water area, and the score coefficient is lower in the north than in the south. The comprehensive function of production-living-ecological space is lower in the southeast and northwest.Fig. 13 Principal component score distribution of production space function.

Factors affecting the evolution of living space pattern

It can be seen from Fig. 14 that the first principal component affecting the function of production space from 2000 to 2020 is the score coefficient of natural conditions such as precipitation, surface temperature, sunshine hours, average annual temperature and average altitude, which decreases from southeast to northwest, indicating that natural factors such as precipitation, surface temperature and sunshine hours have the most obvious impact on living conditions.. The score coefficient of the first principal component in 2000 was −1.34–1.89, the score coefficient of the first principal component in 2005 was −0.740–0.830, the score coefficient of the first principal component in 2010 was −1.164–2.246, the score coefficient of the first principal component in 2015 was −1.10–1.91, and the score coefficient of the first principal component in 2020 was −0.89–2.578. It was found that the score coefficient increased year by year, and the impact of natural conditions such as precipitation, surface temperature, sunshine hours and annual average temperature on living conditions was more and more significant. The second principal component is socio-economic factors such as the secondary industry and the distance from the main roads, and the score coefficient is high in the north and low in the south. The score coefficient of the second principal component was −0.068–0.797 in 2000, −0.271–0.530 in 2005, 0.123–0.467 in 2010, −0.046–0.608 in 2015 and 0.078–1.105 in 2020. It can be seen that the score coefficient of the second principal component of living space is low, and gradually increases, with a small increase. The score coefficients of the third principal component and the fourth principal component are very low, and their influencing factors have little impact on living space.Fig. 14 Principal component score distribution of living space function.

Factors affecting the evolution of ecological space pattern

It can be seen from Fig. 15 that the first principal component in 2000 was precipitation, surface temperature, annual average temperature, sunshine hours and other factors, with score coefficients of 0.986, −0.98, −0.977, −0.941 respectively, that is, precipitation had a great positive effect on ecological space, while surface temperature, annual average temperature, sunshine hours and other factors had a great negative effect on human activities. The first principal component in 2005 is the surface temperature and annual average temperature, and their score coefficients are −0.951 and −0.93 respectively, which means that the surface temperature and annual average temperature have a significant negative effect on ecological space. The first principal component in 2010 is precipitation, surface temperature, annual average temperature and other factors, and their score coefficients are 0.892, −0.874, −0.852 respectively. The first principal component in 2015 is sunshine hours, per capita GDP, precipitation and other factors, and their score coefficients are 0.973, 0.968 and 0.957, respectively. The first principal component in 2020 is precipitation, with a score coefficient of 0.912. It can be seen that the first principal component affecting the ecological spatial function from 2000 to 2015 is natural conditions such as precipitation, surface temperature, sunshine hours, annual average temperature, and average altitude. The coefficient decreases from southeast to northwest, indicating that the impact of natural factors on ecological space is weakening year by year. The explanatory power of the first principal component in 2020 is high in the north but difficult to low.Fig. 15 Principal component score distribution of ecological space function.

Factors affecting the evolution of production-living-ecological space pattern

It can be seen from Fig. 16 that the first principal component of the comprehensive function of the production-living-ecological space in Duolun County in 2000, 2010 and 2020 was mainly natural factors such as precipitation, surface temperature and average temperature. The first principal component of the comprehensive function of the production-living-ecological space in 2005 and 2015 was mainly sunshine hours, surface temperature and average temperature. In addition to the negative effect of precipitation in 2000 on the comprehensive function of the production-living-ecological space, the annual average of 2005–2020 has a positive effect. Precipitation has a positive impact on the “three growth” function in more than 90% of the area in Duolun County, and only the river banks in the north of the county and the low-lying land in the South have a negative effect.Fig. 16 Principal component score distribution of production-living-ecological space function.

Discussion

The Beijing Tianjin sandstorm source area is an ecological construction project management area divided by the state to improve the ecological environment and reduce sandstorm disasters in the Beijing Tianjin area15. Duolun County in Inner Mongolia is a typical ecologically fragile area in Beijing Tianjin sandstorm source area16 and an important ecological barrier in Beijing Tianjin Hebei region17. Selecting remote sensing images and using spatial statistical model18, this paper analyzes the spatial and temporal evolution of production-living-ecological space and the influence of its driving factors19. The results show that the production spatial function index shows an increasing trend, which is related to the increase in the number of livestock and the output value of the primary industry20. The growth of living space function index is not obvious, which, together with the implementation of the ecological immigration project, has transformed a large area of rural construction land into woodland and grassland, but the use of abandoned farmland and the increase of grain production per unit area have improved the function of living space. The ecological spatial function index increased significantly, which was related to the weakening of drought21 and the growth of vegetation cover22. Since 2000, Duolun County has implemented projects such as “governance of Beijing Tianjin sandstorm source area”, “returning farmland to forest and Grassland” and “ecological migration”, the forest area in Duolun County has increased from 34666.67 to 150000 hm2. The forest coverage in Duolun County has increased from 6.79% in 2000 to 37.90% at present23. The forest and grass vegetation coverage in the project area has increased from less than 30% in 2000 to more than 85% at present24. And successfully realized the historic change from “looking for green in sand” to “looking for sand in green”, which has increased the area of forest land and grassland. In addition, the average temperature, precipitation and other conditions are conducive to vegetation restoration, so that the vegetation coverage can be rapidly restored and the ecological spatial function can be improved. In 2000, 2005 and 2020, the area of land desertification and potential land desertification accounted for 25.37, 16.89 and 9.47%25 of the total land area of Duolun County, respectively. The area of land desertification showed a decreasing trend. The production space of Duolun County in Inner Mongolia is decreasing, while the ecological space and living space are increasing, with obvious differences in the evolution of different spaces. The center of gravity of production space, ecological space and living space all migrated from northeast to southwest. The overall migration direction of the center of gravity of production space and ecological space was southwest, while the overall migration direction of the center of gravity of living space was northeast. The high-value areas of production space are concentrated in the southern region. The reason is that the southern part of Duolun county is an agricultural area with extensive distribution of cultivated land and prominent agricultural production functions. The living space is concentrated in the central region, and the gathering area is very stable. The central region has a large proportion of construction land and cultivated land, low vegetation coverage, low ecological function, but strong living function. The ecological space is concentrated in the northeast, which may be related to the large proportion of grassland area in the northeast. The evolution of production-living-ecological spatial pattern is affected by natural and socio-economic factors, showing significant differences in different functional areas. The influence degree of each influencing factor on the evolution of production-living-ecological space has different heterogeneity characteristics in time and space. The increase of vegetation coverage in Duolun County reduced the degree of soil erosion, improved the function of production space, and improved the function of living space. The expansion of cultivated land area and the increase of grain production have reduced the function of ecological space. Social and economic factors have more influence on the evolution of production-living-ecological space than natural environmental factors.

Conclusions

From 2000 to 2020, the production space function of Duolun County in Inner Mongolia declined slightly, mainly in the median area; The function of living space rose slightly, mainly in low value areas; The function of ecological space rose slowly, mainly in high value areas; The production-living-ecological comprehensive spatial function index shows a slow upward trend, mainly in the median area, with the spatial pattern characteristics of "high in the southwest and low in the northeast".

The functions of production space, living space, ecological space and the comprehensive space of three kinds of life all show obvious positive enhancement characteristics, and there are significant high high aggregation or low low aggregation areas.

Population density has the greatest impact on the evolution of production space, and the distance from the main residential areas has a greater explanatory power on the evolution of living space; Population density has great explanatory power on the evolution of ecological space; Population density, distance from major residential areas, per capita GDP, and the secondary industry have more explanatory power on the evolution of production-living-ecological space.

Materials and methods

Study area

Duolun County in Inner Mongolia is located in the southern edge of Hunshandake sandy land, which belongs to the south of Beijing Tianjin Wind Sand Park, with a total land area of 3863.69 km2. The terrain is high around, low in the middle, high in the South and low in the north. The landform is dominated by low mountains and hills, and the soil types are mainly gray cinnamon soil, chernozem, chestnut soil and aeolian sandy soil. The gray cinnamon soil is mainly distributed in the forest area in the southwest of the county, chernozem is mainly distributed in the vertical belt of the southern mountains, chestnut soil is widely distributed in the whole county, and aeolian sandy soil is mainly distributed in the northeast and the south of the county. The main vegetation types are forest grassland vegetation and dry grassland vegetation.

Data sources

Five periods of remote sensing images, digital elevation model, DEM and socio-economic data, 1:10000 topographic map and field survey data from 2000 to 2020 were selected. The remote sensing image is from the website of the United States Geological Survey (https://glovis.usgs.gov) It is the Landsat TM with a spatial resolution of 30 m in three phases in 2000, 2005 and 2010 and the Landsat 8 oli with a spatial resolution of 15 m in two phases in 2015 and 2020. The digital elevation data comes from the geospatial data cloud platform (https://www.gscloud.cn) The spatial resolution is 30 m.

Research methods

Production-living-ecological space recognition

According to the natural geographical environment of Duolun County in Inner Mongolia and the third national land use classification system, the land use of Duolun county is divided into 29 secondary land types, including paddy field, irrigated land, dry land, orchard, tea garden, other garden land, arbor forest land, shrub forest land, other forest land, natural pasture land, artificial pasture land, other grassland, mining land, urban residential land, rural residential land, railway land, highway land, rural roads, river water surface, lake water surface, reservoir water surface, pit water surface, inland beach, ditch, hydraulic construction land, facility agricultural land, saline alkali land, sand land, bare land, etc. The land use vectorization data were extracted by arcgis10.8 software. According to Liu Jilai26 production-living-ecological space classification system and assignment rules, obtain the production-living-ecological space score grid map. By superimposing the spatial score distribution grid map of production-living-ecological space in Duolun County with the vector data of administrative villages, the average value of the comprehensive functions of production space, living space, ecological space and production-living-ecological space of each administrative village is obtained. Using the natural break point classification method in arcgis10.8 software, the functions of production space, living space, ecological space and production-living-ecological space are divided into four levels: low value area, medium value area, high value area and high value area (Figs. 1, 2, 3, 4).

Exploratory spatial data analysis

Due to the different natural environment, social and economic conditions among the village units in Duolun County, Inner Mongolia, the distribution of the three biological functions may be different, and these functional differences may affect the spatial auto-correlation of the village units themselves and adjacent village units. Therefore, the spatial auto-correlation model27 should be used to analyze the spatial aggregation of village production-living-ecological space. Spatial auto-correlation model includes global spatial auto-correlation and local spatial auto-correlation. First, we test whether there is spatial correlation in the village production-living-ecological space. If there is spatial correlation28, we use the global Moran’s I index to analyze the local spatial correlation.

Geographically weighted principal component analysis

There are many driving factors that affect the evolution of production-living-ecological space and the distribution of driving factors is complex. In this study, the principal component analysis method was used to find the comprehensive driving factors to reduce the number and keep the original information as much as possible, which were used as the independent variables of the geographically weighted regression (GWR) for regression operation. This combination model can eliminate the multicollinearity problem caused by the correlation between driving factors, and can well explain the spatial effect of each principal component. Firstly, the Pearson correlation coefficient model is used to standardize the selected driving factors, and the correlation coefficient matrix and cumulative contribution rate of driving factors are obtained. The principal components whose cumulative contribution rate is greater than 80% are taken for the next step of regression analysis. In order to avoid multicollinearity in regression analysis, variance expansion factor was used for multicollinearity diagnosis. It is generally believed that when the Vif value is greater than 4, there is a serious multicollinearity problem. Using the ordinary least squares (OLS), find out the index with VIF value greater than 4. Finally, the geographical weighted regression model is used to analyze the spatial effect between the driving factors.

Supplementary Information

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Supplementary Information 12.

Supplementary Information 13.

Supplementary Information 14.

Supplementary Information 15.

Supplementary Information 16.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-024-71297-3.

Acknowledgements

We would like to thank the Natural Science Foundation of Inner Mongolia Autonomous Region for providing us with financial support.

Author contributions

D.L.and A.R. conceptualised the study. D.L.and A.R. collected the data. A.R. prepared the GIS maps. A.R. analysed the data and prepared tables and figures. D.L.and A.R. reviewed and edited the manuscript. All authors approved the final version of the manuscript to be published. All authors reviewed the manuscript.

Funding

This funding was supported by Natural Science Foundation of Inner Mongolia Autonomous Region, 2020MS04012.

Data availability

Data are available upon reasonable request, please contact the corresponding author.

Competing interests

The authors declare no competing interests.

Publisher's note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
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