Spatial-temporal Differentiation Research Articles

Study on Spatial-Temporal Differentiation of Water Resources Carrying Capacity Evaluation in Liaoning Province

Study on Spatial-Temporal Differentiation of Water Resources Carrying Capacity Evaluation in Liaoning Province

Spatial-Temporal Differentiation of Soil Biochemical Parameters and Their Relationship with Nitrogen Resources during the Vegetation Period of Selected Crops

Understanding the spatial–temporal variability of soil enzymatic activity and its relationship with nitrogen (N) resources in the soil and crop yield is crucial in rational management practices of mineral fertilization. The scarcity of comprehensive studies on geostatic analyses of agricultural soils and plant yields, which would take into account both temporal and spatial variability, was the reason for undertaking this research. The aim of this study was to determine the spatial and temporal variability of the activity of soil enzymes, such as acid (PAC) and alkaline (PAL) phosphatases, urease (URE) and protease (PROT), the content of N-NH4 (ammonium ions), N-NO3 (nitrate ions), phosphorus (P), pH, moisture, as well as crop yield on a conventionally managed farmland of 40 ha. During the two-year experiment, soil samples were collected from 37 measurement points. Wheat was the first tested crop, followed by oilseed rape. It was shown that all the tested soil parameters showed temporal and spatial variability, and a significant number of them were significantly higher in July. The creation of raster maps showing the distribution of the tested parameters allowed for the observation of the considerable activity of PAC, PAL, URE, and PROT, as well as a high application of N-NO3 in the southern part of the field during the growth of the plants. The statistical analysis revealed a negative interaction between the N-NH4 and N-NO3 and the urease in the soil under the cultivation of plants. The pH and the percentage of moisture in the soil also had higher values in the south of the field. This pointed to the existence of separate production zones in the south-central part of the field, characterized by a higher yield of wheat and rape. On the basis of the conducted research, it was unequivocally stated that the values of enzymatic and chemical parameters of the soil were reflected in the size of the yield obtained, which allows conclusions to be drawn with respect to the rational management of N in the production process, laying the foundations for precision agriculture.

Spatiotemporal Distribution and Driving Force Analysis of the Ecosystem Service Value: A Typical Case Study of the Coastal Zone, Eastern China

Identifying and assessing the drivers of change in ecosystem service value (ESV) is critical for integrated management and human well-being in coastal zone areas. This paper took a typical coastal zone in eastern China as the research object. Based on five periods of remote sensing monitoring data of land use status from 2000 to 2020, the ESV of Yancheng was estimated by adopting the equivalence factor method. Spatial statistical analysis and GeoDetector were applied to reveal the heterogeneous features of ESV and its driving mechanism. The results suggested that: (1) From 2000–2020, each land use type in the study area changed to different degrees, among which the most drastic change was in the construction land, which increased continuously by 962.69 km2, the cultivated land area decreased continuously by 784.1 km2, and the area of water body decreased by 163.34 km2. (2) ESV experienced a process of increasing and then decreasing, from 547.84 × 108 yuan to 570.86 × 108 yuan and then decreasing to 507.62 × 108 yuan, with farmland ecosystems having the largest ESV, accounting for more than 60%. Regulatory services were the core function of ecosystem services, accounting for more than 60%. (3) There was significant spatial-temporal differentiation in ESV, with extremely low ESV expanding in rapidly developing urbanized areas. The ESV distribution exhibited significant spatial autocorrelation and local spatial clustering, with the gravity center showing a general trend from north to southwest. (4) The ESV spatial and temporal evolutionary characteristics were the result of a multifactorial integration of land use, topography, socio-economics, and climate factors. The explanatory power of each factor in descending order was LDI > DEM > POP > GDP > RAI > TEM, and any two-factor interaction had higher explanatory power than the single factor.

Spatial-temporal differentiation and driving factors of ecological resilience in the Yellow River Basin, China

Ecological problems are considered to be the key constraint to high-quality development in the Yellow River Basin (YRB), so accurately grasping and understanding the spatial–temporal differentiation characteristics and driving factors of ecological resilience (ER) in the YRB is crucial for the sustainable development of the YRB. This study develops a three-dimensional ER evaluation index system comprising resistance (RE), adaptability (AD), and recovery (RC), measures the ER of 76 cities in the YRB using the entropy method, and investigates the spatial–temporal evolutionary characteristics and drivers of ER in the YRB using the Dagum Gini coefficient (DGC), Kernel density estimation (KDE) and geographic detector methods (GDM). The results show that the spatial differences in ER of the YRB showed a narrowing trend. The primary cause of the overall differences was the interregional differences. There is no significant polarization of ER in the basin as a whole, and the differentiation between the three regions is gradually weakening. Economic development (ED) is a key factor influencing the spatial differences in ER in the YRB and downstream regions. Average annual precipitation (AP) and adaptability (AD) are the most important drivers affecting ER in the upper and middle reaches, respectively. The combined impact of multiple factors on the spatial differences of ER in the YRB is significantly more pronounced than that of individual factors. This study provides empirical evidence and decision-making reference to comprehensively grasp the current ER development characteristics in the YRB, and to develop and implement differentiated regional ecological conservation strategies.

Investigating the Spatial-Temporal Variation of Pre-Trip Searching in an Urban Agglomeration

Search engines have been the primary tool for online information search before traveling. Timely detection and the control of peak tourist flows in scenic areas prevent safety hazards and the overconsumption of tourism resources due to excessive tourist clustering. This study focuses on the spatial-temporal interactions between the pre-trip stage and the after-arrival stage to investigate online information search behavior. Big data obtained from mobile roaming and search engines provide precise data on daytime and city scales, which enabled this paper to examine the relationship between daily tourist arrivals and their pre-trip searching from 40 cities within the Yangtze River Delta urban agglomeration. This study had several original results. First, tourists generally search for tourist information 2–8 days before arriving at destinations, while tourist volume and SVI from source cities show distance attenuation. Second, SVI is a precursor to changes in tourist volume. The precursory time rises with the increase of traffic time spatially. Third, we validated a VAR model and improved its accuracy by constructing it based on the spatial-temporal differentiation of search features. These findings would enhance the management and preservation of tourism resources and promote the sustainable development of tourism destinations.

THE SPATIAL-TEMPORAL DIFFERENTIATION IN AIR POLLUTION AND INDUSTRY STRUCTURE — EVIDENCE FROM THE METROPOLITAN AREA OF BEIJING–TIANJIN–HEBEI URBAN IN CHINA

In developing countries, environmental problems are often intertwined with industrial spatial distribution and migration. This paper is the first in this field to apply the standard deviational ellipse (SDE) method to the analysis of spatial agglomeration and spatiotemporal evolution of air pollution in 13 cities in the Beijing–Tianjin–Hebei region (BTH). According to the industrial classification, each industry could be further divided into five sub-industries: labor-intensive, capital-intensive, technology-intensive, pollution-intensive, and the mining industry. The discussion includes an exploration of the relationship between environmental quality and the spatial distribution of different sub-industries, along with their dynamic changes in migration. This paper also describes the innovative use of the geographically weighted regression (GWR) to study the impact of the whole industry and sub-industries on the environment. The main conclusions of this paper are as follows: (i) Both air pollution and the industry have obvious spatial agglomeration characteristics. (ii) The spatial difference coefficients between the secondary industry and environmental pollution and between various sub-industries and environmental pollution are small, and there is spatial consistency. (iii) Capital-intensive industries represent a primary source of air pollution. In addition to the slowdown of the tertiary industry, the contribution of various types of sub-industries to environmental pollution displays an increasing trend.

Spatial-temporal differentiation of coupling coordination degree for green finance and green innovation efficiency: a case study in China.

Continued investment in finance and innovation is beneficial to economic development, and the joining of green system can accelerate the process of economic recovery from environmental distress. To better enhance the relationship of green finance and green innovation, it is vital to demonstrate the synergy between the two thoroughly. Thirty provinces in China are selected to examine the coupling coordination relationship between the two, specifically testing the spatial aggregation and evolutionary differences in the coupling coordination by adopting the coupling coordination degree (CCD) model, spatial autocorrelation, and kernel density estimation. Conclusions of the paper show that green finance is calculated by the EW-TOPSIS method, and the overall score of provinces is low. Using super-SBM model to evaluate green innovation, the uneven distribution of efficiency is obvious, although it is gradually increasing. The CCD in most provinces is in low-level or basic coordination, with significant regional heterogeneity. The global Moran's index becomes gradually evident with time. The local Moran scatter diagram presents a downward trend from east to west, but with more L-L aggregation provinces emerging in 2020. The center of the national kernel density curve gradually shifts to the right, indicating that the national overall synergy level is improving. Deepening the understanding of the empirical results facilitates the formulation of reasonable policies that fit the four major regions.

Spatial–temporal differentiation and driving factors of water resources green efficiency in the Huaihe River Basin

Abstract Considering the regional economic and social characteristics and high-quality development goals, this paper improves the calculation method of green efficiency of water resources and adopts the SBM-DEA model to calculate the green efficiency of water resources in 35 cities of four provinces in the Huaihe River Basin. The results show that (1) the green efficiency of water resources in the Huaihe River Basin decreased first and then increased from 2011 to 2020. (2) In terms of spatial distribution, the provincial-level divisions of green efficiency of water resources in the Huaihe River Basin from high to low are Shandong, Henan, Jiangsu, and Anhui. The elliptical area of green efficiency of water resources keeps expanding, and the efficiency value radiates outward along ‘Yangzhou–Zhengzhou’. (3) At the level of total-factor productivity decomposition, TC (technological change) has a more noticeable impact on factors than EC (efficiency change). TC scale technology changes mainly come from the improvement of MATC (magnitude technical change). (4) Industrial output value has a positive driving effect on the green efficiency of water resources. Government participation, resource endowment, and economic growth rate reflect China's current incongruity between economic development and water resources green efficiency improvement.

Spatial-Temporal Differentiation and Spatial Interaction Effect Analysis of Residents’ Consumption Capacity and Consumption Upgrading in China

To investigate Chinese residents’ consumption capacity and consumption upgrading, this study uses the Moran index, the Dagum Gini coefficient method, and the SPVAR model to analyze the spatial-temporal differentiation and interactions between the two; the entropy method is employed to measure the development level of both factors. As per the results, from 2008 to 2020, the levels of consumption capacity and consumption upgrading have increased rapidly and are distinctly unbalanced, but there is a clear trend of convergence between the developments in each province, and both show spatial agglomeration, mainly in the “high–high” and “low–low” clusters. The “high–high” clusters are primarily concentrated in the more economically developed eastern coastal and central fast-rising regions, with inter-regional differences being the main reason for the regional differences between the two. In terms of spatial interaction, the two are Granger causalities, with positive feedback effects that promote each other, and have a “center–periphery” spatial spillover effect, where the center generates spatial spillover and also suffers reverse shocks from the periphery, while the shock effect is limited by inter-provincial economic ties and geographical distance.

Spatial–Temporal Differentiation of Housing Burden of Urban Floating Population and Migration in China

Housing costs, including rent, have become a significant economic burden for millions of floating population families in urban China, affecting their living standards and influencing migration decision-making. Using data from the China Migrants Dynamic Survey (CMDS) in 2012 and 2017, this study analyzes the spatial patterns of rent, family income, and the rent-to-income ratio among different regions, cities, and groups. Results show that rent and the rent-to-income ratio have an inverse correlation with the regional economy, with lower ratios observed in eastern coastal areas compared to the central and western regions, especially the northeast. High-level cities record higher incomes, higher rents, and higher rent-to-income ratios, and groups with higher educational levels and occupational characteristics exhibit higher affordability. Rent plays a role in the flow and changes of the floating population, and the housing burden has become a key constraint for long-term residence or migration. Developed provinces and municipalities in the eastern region and high-level cities remain major destinations for migrants, but rising house prices impede permanent settlement through commercial house purchases. The government should consider migrants’ demands for housing and increase the supply of subsidized housing, such as public rental housing, for the floating population.

Spatial-temporal differentiation pattern and influencing factors of high-quality development in counties: A case of Sichuan, China

High-quality development (HQD) is a development model proposed by the Chinese government in the regional development process, with the five major development concepts of innovation, green, coordination, openness and sharing as its core. It is an expansion of sustainable development, incorporating the connotation of green development and emphasizing the joint promotion and coordinated development of the five subsystems. This study takes the counties in Sichuan, China, as the case area, constructs an evaluation index system based on the five development concepts, conducts research on the endogenous coordination of the HQD system through the Coupling Coordination Degree model. On this basis, its spatial–temporal differentiation characteristics are characterized by Getis-Ord Gi*, and the key influencing factors of the regional HQD are identified by Geographical Detector. The results indicated that the HQD coupling degree in Sichuan Province was in a highly coupling state, but the coupling coordination degree was at the primary coordination state, and the level of HQD was relatively low. Moreover, a spatial characteristic of “high east and low west” was showed in the HQD coupling coordination degree of Sichuan Province. Taking Chengdu City as the agglomeration center, it showed obvious agglomeration effect, and the HQD coupling coordination degree in the areas around Chengdu were significantly higher than in other areas. Furthermore, the permanent populations per unit area, the road mileage per capita, the number of employees and the proportion of fiscal expenditure to GDP had significant effects on HQD. The experience of the case area can be a reference for other similar regions globally to promote their high-quality economic and environmental development.

Analysis of Spatial-Temporal Differentiation and Influencing Factors of Ecosystem Services in Resource-Based Cities in Semiarid Regions

The spatial-temporal differentiation characteristics and driving mechanisms of ecosystem services are of great significance for optimizing the pattern of land spatial protection and realizing regional sustainable development. Existing studies seldom consider the segmental influence mechanism of various influencing factors on different levels of ecosystem service value (ESV). Therefore, this paper analyzes the temporal and spatial differentiation evolution characteristics of ESV in semiarid regions through an improved ESV evaluation model. The spatial panel quantile regression (SPQR) model was introduced to explore the relationship between various types of influencing factors and ESV in different intervals. The results showed the following: (1) The changes in ESV in Baotou City from 2000 to 2018 tended to be stable, but the spatial differentiation of ESV intensified. The aggregation feature of the low-ESV region is significant and gradually expanding. (2) Precipitation was the dominant factor increasing the ESV in each interval, and temperature had a significant negative impact on the low-ESV area. (3) Higher land use integrity accelerates the decline of ESV in the surrounding areas of built-up areas. The high-ESV area was more sensitive to the intensity of human activity. The direction of human activities should be effectively controlled, and the structure of comprehensive land use should be optimized to enhance the service function of regional ecosystems. This research provides new thinking for the ecological restoration zoning of regional territorial spatial planning and the sustainable development of resource-based cities.

Understanding the structure and determinants of economic linkage network: The case of three major city clusters in Yangtze River Economic belt

The economic linkages between cities within the Yangtze River Economic Belt city cluster are important for precisely implementing the Yangtze River Economic Belt strategy. Accordingly, this study used the revised gravity model, social network method, and spatial Durbin model to analyze the economic linkage strength, spatial-temporal differentiation, and driving factors of the three major city cluster in the Yangtze River Economic Belt from 2006–2020. The results confirm that the Chengdu-Chongqing urban agglomeration has formed a network structure with Chengdu and Chongqing as the two poles; the urban agglomeration in the middle reaches of the Yangtze River has formed a point-axis development pattern of Wuhan, Changsha and Nanchang. The Yangtze River Delta urban agglomeration formed a multi-center radiation and sub-network development pattern. Additionally, the characteristics of network structure significantly affected the flow of factors, the level of economic development was the basis for strengthening communication and cooperation between cities. Meanwhile, the advanced industrial structures played an important role in promoting industrial linkages, and human capital exerts an obvious spatial spillover effect. Therefore, these three major urban agglomerations should implement differentiated development strategies and enhance the synergy of their linkage networks, thus promoting high-quality development.

Spatial-temporal Differentiation and Driving Factors of Industrial Ecological Efficiency in the Yellow River Basin

Ecological protection and high-quality development of the Yellow River Basin are major national strategies, and industrial ecology is an effective way to maintain the integrity of important ecosystems, rational resource allocation and take into account the actual situation of the region while ensuring sustained and stable economic growth. Taking the Yellow River Basin as the research area and industrial ecology as the research object, this paper discusses the spatial differences in industrial ecological efficiency and the influencing factors that lead to them, aiming to provide reference for the comparative analysis of regions and the development direction of each region. In this paper, the industrial ecological efficiency of 59 cities and prefectures in the Yellow River Basin in 2001~2020 was calculated by constructing a super-efficient SBM model. The double fixed effect regression model based on Driscoll-Kraay standard error and geographic detector model were used to explore the spatial heterogeneity and driving factors of industrial ecological efficiency in the study area, and how these driving factors affected the spatial distribution of industrial ecological efficiency. The results show that: 1. The spatial differentiation characteristics of industrial ecological efficiency in the study area are significant, showing a spatial pattern of decreasing diffusion from provincial capital cities as high-value points. 2. Technological innovation, environmental control, industrial structure and opening up all have an impact on the improvement of industrial ecological efficiency, and will affect the spatial distribution of industrial ecological efficiency due to its own spatial distribution. 3. The influence of each driving factor on industrial ecological efficiency and spatial heterogeneity is different. 4. The influence of the interaction between factors on industrial ecological efficiency is better than that of single factor.

Spatial-Temporal Differentiation and Causes of Enterprise Network Evolution in Guangdong Coastal Economic Cities

Spatial-Temporal Differentiation and Causes of Enterprise Network Evolution in Guangdong Coastal Economic Cities

Spatial-temporal differentiation of the water footprint of agricultural crop growing in the Sluch river catchment area under the conditions of climate change

Spatial-temporal differentiation of the water footprint of agricultural crop growing in the Sluch river catchment area under the conditions of climate change

Spatial-Temporal Evolution Characteristics of Agricultural Intensive Management and Its Influence on Agricultural Non-Point Source Pollution in China

The influencing mechanism of agricultural non-point source pollution under intensive agricultural management is complicated. This paper adopted provincial panel data from 2008 to 2020 to estimate the level of agricultural intensive management, the agricultural chemical oxygen demand (COD), total nitrogen (TN) and total phosphorus (TP) emissions and emission intensity of agricultural non-point source pollution in different regions of China and analyze the spatial-temporal differentiation characteristics. Moreover, the mediation effect model and spatial spillover effect model were adopted to further explore the influence mechanism of agricultural intensive management on agricultural non-point source pollution. The results show that (1) The total emissions and emission intensity of agricultural non-point source pollution both showed an increasing trend, and these areas with high levels of agricultural non-point source pollution are roughly consistent with those areas with high-level of agricultural intensive management. (2) At the overall level, there were mediating effects of natural ecology, agricultural land management, planting and rearing structure and pollution control investment between the relationship of agricultural intensive management and agricultural non-point source pollution, among which agricultural land management was the largest. Additionally, there was significant spatial heterogeneity in the influencing mechanism of agricultural intensive management on non-point source pollution. (3) There were significant spatial agglomeration characteristics in both agricultural intensive management and agricultural non-point source pollution, which showed a fluctuating trend of “rise-decline-rise-decline”. (4) Agricultural intensive management has a significant positive spatial spillover effect on COD, TN and TP emissions of agricultural non-point source pollution. However, environmental regulation could cause agricultural non-point source pollution to be transferred nearby. Scientific understanding of the spatio-temporal differentiation characteristics and influencing mechanism of agricultural non-point source pollution under the agricultural intensive management model is conducive to providing reference for policy regulation.

Spatial-temporal differentiation and driving factors of vegetation coverage in Ningxia Helan Mountain based on geodetector

Monitoring the regional changes in vegetation coverage and analyzing its driving factors are beneficial to realizing the sustainable development of ecological environment. Based on Landsat 5/8 remote sensing images from 1989 to 2021, vegetation coverage of Helan Mountain in Ningxia was estimated by pixel dichotomy model. In addition, the influence of 10 factors, including environmental factors and human factors, on the spatial-temporal variations of vegetation coverage was quantified by geodetector. The results showed that average vegetation coverage was 35.8% in the study area from 1989 to 2021. On the temporal scale, it showed an increasing trend, with an average increasing rate of 0.043·(10 a)-1. On the spatial scale, vegetation coverage presented a distribution characteristic of decreasing from southwest to northeast. 58.1% of vegetation coverage in the study area would continue to improve in the future, but 30.7% of vegetation would have the potential risk of degradation. Precipitation was the dominant environmental factor driving the distribution of vegetation. Compared with single factor, the interaction between environmental factors and human factors had a stronger impact on vegetation coverage, while the interaction between precipitation and other factors played a leading role.

Spatial-temporal differentiation theorems

Let \((X, \mathcal{B}, \mu, T)\) be a dynamical system where X is a compact metric space with Borel \(\sigma\)-algebra \(\mathcal{B}\), and \(\mu\) is a probability measure that is ergodic with respect to the homeomorphism \(T \colon X \to X\). We study the following differentiation problem: Given \(f \in C(X)\) and \(F_k \in \mathcal{B}\), where \(\mu(F_k) > 0\) and \(\mu(F_k) \to 0\), when can we say that $$\lim_{k \to \infty} \frac{\int_{F_k} ( \frac{1}{k} \sum_{i = 0}^{k - 1} T^i f ) \, \, \mathrm{d} \mu}{\mu(F_k)} = \int f \, \mathrm{d} \mu \ ?$$ We establish some sufficient conditions for these sequences to converge.

The Impact of Urbanization on Carbon Emissions and Spatial–Temporal Differentiation Based on Meta-Analysis in China

Urbanization and carbon emissions have received more attention in the context of “double carbon”, but the existing research on the relationship between the two has not reached a consistent conclusion. By introducing meta-regression analysis, based on the existing research results, this paper retests the relationship between urbanization and carbon emissions, and discusses the reasons for the differences in previous research results from the perspectives of publication bias and research heterogeneity. The results show that (1) urbanization in China has a strong positive impact on carbon emissions and meets the inverted U-shaped Kuznets curve relationship, and (2) there are significant temporal and spatial differences in the impact of urbanization on carbon emissions in China. Temporally, the positive impact is the strongest in the first decade of the 21st century, and in regional terms, the positive impact is the strongest in the eastern region. In addition, the selection of indicators will not interfere with the measurement of the impact of urbanization on carbon emissions.