Geographical Detector Model Research Articles

Contrasting characteristics and drivers of dry and warm snow droughts in China's largest inland river basin

Study regionThe largest inland river basin in China (the Tarim River Basin) Study focusThis study introduces a novel classification scheme for dry and warm snow droughts with focus on their duration, severity, and intensity. The Nonparametric Standardized SWE Index (NSWEI) and the Standardized Precipitation Index (SPI) were employed to assess these drought types. The optimal parameters-based geographical detector (OPGD) model and machine learning (including random forest model and Shapley-XGBoost algorithm) were applied to reveal the spatial and time dynamic driving forces of droughts. New hydrological insights for the regionsResults indicated that warm-type droughts occurred more frequently and with greater intensity, exhibiting a larger spatial coverage compared to dry-type droughts, which can be attributed to warm-type droughts dominated by temperature and vapor pressure deficit, whereas dry-type are predominantly influenced by relative humidity and solar radiation. Moreover, the dynamics of snow droughts exhibited a contrasting pattern between the south (experiencing an upward trend) and the north (experiencing a descending trend). In the south, dry-type droughts were exacerbated by the strength of North Pacific Oscillation (PDO)-precipitation coupling, while warm-type droughts are influenced by the strength of North Atlantic Oscillation (NAO)-precipitation coupling and El Niño Southern Oscillation (ENSO)-precipitation.

Spatial patterns and driving forces of urban vegetation greenness in China: A case study comprising 289 cities

Urban vegetation in China has changed substantially in recent decades due to rapid urbanization and dramatic climate change. Nevertheless, the spatial differentiation of greenness among major cities of China and its evolution process and drivers are still poorly understood. This study examined the spatial patterns of vegetation greenness across 289 cities in China in 2000, 2005, 2010, 2015, and 2018 by using spatial autocorrelation analysis on the Normalized Difference Vegetation Index (NDVI); then, the influencing factors were analyzed by using the optimal parameters-based geographical detector (OPGD) model and 18 natural and anthropogenic indicators. The findings demonstrated a noticeable rise in the overall greenness of the selected cities during 2000–2018. The cities in northwest China and east China exhibited the rapidest and slowest greening, respectively, among the six sub-regions. A significant positive spatial correlation was detected between the greenness of the 289 cities in different periods, but the correlation strength weakened over time. The hot and very hot spots in southern and eastern China gradually shifted to the southwest. While the spatial pattern of urban greenness in China is primarily influenced by wind speed (WS) and precipitation (PRE), the interaction between PRE and gross domestic product (GDP) has the highest explanatory power. The explanatory power of most natural factors decreased and, conversely, the influence of anthropogenic factors generally increased. These findings emphasize the variations in the influence strength of multiple factors on urban greenness pattern, which should be taken into account to understand and adapt to the changing urban ecosystem.

Spatial stratified heterogeneity analysis of field scale permafrost in Northeast China based on optimal parameters-based geographical detector.

Affected by global warming, the permafrost in Northeast China (NEC) has been continuously degrading in recent years. Many researchers have focused on the spatial and temporal distribution characteristics of permafrost in NEC, however, few studies have delved into the field scale. In this study, based on the Optimal Parameters-based Geographical Detector (OPGD) model and Receiver Operating Characteristic (ROC) test, the spatial stratified heterogeneity of permafrost distribution and the indicating performance of environmental variables on permafrost in NEC at the field scale were analyzed. Permafrost spatial distribution data were obtained from the Engineering Geological Investigation Reports (EGIR) of six highways located in NEC and a total of 19 environmental variables related to heat transfer, vegetation, soil, topography, moisture, and ecology were selected. The H-factors (variables with the highest contribution in factor detector results and interaction detector results): slope position (γ), surface frost number (SFN), elevation (DEM), topographic diversity (TD), and annual snow cover days (ASCD) were found to be the major contributors to the distribution of permafrost at the field scale. Among them, γ has the highest contribution and is a special explanatory variable for permafrost. In most cases, interaction can improve the impact of variables, especially the interaction between H-factors. The risk of permafrost decreases with the increase of TD, RN, and SBD, and increases with the increase of SFN. The performance of SFN to indicate permafrost distribution was found to be the best among all variables (AUC = 0.7063). There is spatial heterogeneity in the distribution of permafrost on highways in different spatial locations. This study summarized the numerical and spatial location between permafrost and different environmental variables at the field scale, and many results were found to be informative for environmental studies and engineering construction in NEC.

Spatial Heterogeneity and the Increasing Trend of Vegetation and Their Driving Mechanisms in the Mountainous Area of Haihe River Basin

In addition to serving as North China’s water supply and ecological barrier, the mountainous area of the Haihe River basin (MHRB) is a crucial location for the application of ecological engineering. Vegetation is an important component in the ecological conservation and eco-hydrological progress of the MHRB. A better understanding of regional vegetation growth can be achieved by a thorough investigation of vegetation indicators. In this research, the leaf area index (LAI) and gross primary productivity (GPP) were chosen as vegetation indicators. The characteristics and driving forces of the spatiotemporal variations of LAI and GPP in the MHRB were explored through Sen’s slope, the Mann–Kendall test, the optimal parameter-based geographical detector model, and correlation analysis. From 2001 to 2018, the annual LAI and GPP increased significantly on the regional scale. The areas with significantly increased vegetation accounted for more than 81% of the MHRB. Land use was the most influential element for the spatial heterogeneity of LAI and GPP, and the humidity index was the most crucial one among climate indicators. Non-linear enhancement or bivariate enhancement was discovered between any two factors, and the strongest interaction was from land use and humidity index. The lowest vegetation cover was found in dry regions with annual precipitation below 407 mm and the humidity index under 0.41; while in both forests and large undulating mountains, higher LAI and GPP were observed. About 87% of the significantly increased vegetation was found in areas with unaltered land use. The increase in vegetation in the MHRB from 2001 to 2018 was promoted by the increased precipitation and humidity index and the reduced vapor pressure deficit. The sensitivity of GPP to climate change was stronger than that of LAI. These findings can serve as a theoretical guide for the application of ecological engineering and ecological preservation in the MHRB.

Elucidating the response mechanisms of hydrological and sediment connectivity to the river network structure, vegetation, and topographic features in the Jinghe River Basin

AbstractThe management of basin water and soil resources greatly benefits from investigating the spatial changes and primary determinants of hydrological and sediment connectivity from perspectives of topographic features, vegetation characteristics, structural features of river networks. However, quantifying the effects of influencing factors and their interactions on connectivity is still a challenge in the field of studies of surface processes. To address this challenge, we applied the geographical detector model (GDM) and random forest (RF) to quantify the relative importance and explanatory power of topographic factors, vegetation features, and river network structure on the hydrological connectivity and sediment connectivity and to clarify the interaction mechanism of various factors in the Jinghe River Basin. Results indicate that from 2005 to 2020, the mean value of hydrological connectivity (ICH) witnessed a decreasing trend from −5.47 to −5.58. Similarly, the mean value of sediment connectivity (ICS) declined over the same period from −9.74 to −10.05, with high values registered in valleys and low values in plains. The river density (D) and hydrological weight factor (normalized SCS‐CN) exhibited a greater spatial explanatory power on ICH than other parameters, reaching 0.149 and 0.182, respectively. The vegetation factor (0.299) and sediment weight factor (0.410) manifested considerably more influence over ICS. Spatial integration between relevant elements can enhance our understanding of basin connectivity. Interactions between vegetation, topography, and river network structure provide more explanatory power compared to interactions among same‐type features. Ultimately, this study offers a theoretical template for understanding the management of soil and water resources in the Jinghe River Basin, as well as the spatial variability in soil erosion.

Dynamic Evaluation of Water Resources Management Performance in the Yangtze River Economic Belt

The evaluation of water resources management performance (WRMP) can provide guidance for water resources management. This paper constructs a scientific WRMP evaluation index system based on “water resources–water environment–water ecology”. Secondly, the game variable weight matter–element extension model is appropriately introduced to dynamically evaluate the WRMP level of the provinces (cities) in the YREB from 2012 to 2021, and Arcgis is used to analyze the spatial and temporal variations in the performance level of each sub-system. Lastly, a geographical detector model is used to explore the main factors influencing the WRMP in the Yangtze River Economic Balt (YREB). The main findings are as follows: (1) The overall provincial WRMP level in the YREB has been improving from 2012 to 2021, and the performance of water resource utilization (WRU) and water environment treatment (WET) are high in the east and low in the west, while the performance of water ecological protection (WEP) shows a trend of continuous improvement. (2) Compared with the model without variable weight modification, the game variable weight matter–element extension model can reflect the influence of the measured value of the index on the evaluation result as much as possible. (3) The top eight factors that have a greater impact on the WRMP level are the industrial water conservation rate, water resource development and utilization rate, water resource sustainability index, sewage diameter ratio, urban water penetration rate, industrial wastewater treatment completion rate, ecological construction and protection of the year to complete the investment in GDP, and the water ecological carrying capacity growth rate. The interaction types of each influence factor are nonlinear enhancement and two-factor enhancement.

Enhancing urban flood resilience: A coupling coordinated evaluation and geographical factor analysis under SES-PSR framework

Urban flooding has emerged as a significant urban issue in cities worldwide, with China being particularly affected. To effectively manage and mitigate urban floods, a holistic examination of the interaction between urban subsystems is required to improve urban flood resilience. However, the interactions and mechanisms between urban subsystems under flood disaster haven't been addressed adequately in previous studies. Therefore, this paper established a conceptual framework for illustrating the interactions between urban natural-ecological and social-economic subsystem considering urban pressure, state, and response within flood cycle. The objective is to investigate the coupling coordination degree (CCD) between these subsystems and identify the driving factors with a geographical detector model, and the cities in Yangtze River Delta are selected as an empirical example. The findings reveal an overall upward trend towards coordination for the whole area with notable variability among the cities. The resilience of the state dimension emerges as a crucial aspect in determining the CCD of the urban flood resilience of the area. Key driving factors for the coordinated development of urban flood resilience are identified as air pollution, global warming, technological innovation, governance power, financial strength, and urbanization. Based on the findings and the interactions among the driving factors, this paper presents potential implications that can serve as effective guidance and offer insights for policymakers, planners, and researchers in their efforts to enhance urban flood resilience for sustainable development in the future.

Geospatial analysis of spatial distribution, patterns, and relationships of health status in the belt and road initiative

The Health Silk Road plays a crucial role in the Belt and Road Initiative, and comprehending the health status within the participating countries is fundamental for fostering cooperation in public health. This paper collected five health indicators to represent the health status of the Belt and Road countries. Employing spatial statistics, the spatial patterns of health indicators and the associations with influencing factors were investigated. The utilized spatial statistics encompass spatial autocorrelation methods, geographical detector and spatial lag model. The results revealed obvious disparities and significant positive spatial autocorrelation of health indicators within the Belt and Road countries. Specifically, countries in Sub-Saharan Africa exhibited significant clustering of limited health indicators, while countries in Europe and Central Asia demonstrated significant clustering of robust health indicators. Furthermore, the health indicators exhibited significant spatial heterogeneity and association with the influencing factors. Universal health coverage, household air pollution, and the prevalence of undernourishment emerge as influential factors affecting health indicators. Overall, our findings highlighted complex influencing factors that contributed to the profound health inequalities across the Belt and Road countries. These factors should be duly considered in public health collaborations within the Belt and Road Initiative.

Quantitative analysis of fractional vegetation cover in southern Sichuan urban agglomeration using optimal parameter geographic detector model, China

Vegetation is a main part of ecosystems and an essential indicator for monitoring changes in terrestrial ecosystems. It is crucial for us to discover the temporal and spatial features and potential drivers of vegetation change to promote regional ecological environment protection and management. However, it can be difficult to pinpoint the causes of vegetation change when considering both human activity and climate change. We used trend and stability method to study the temporal and spatial patterns of vegetation evolution in the South Sichuan Urban Agglomeration (SSUA) from 2001 to 2021 with the Google Earth Engine (GEE) platform. An optimal parameter-based geographical detector (OPGD) model was applied to optimize the spatial scale and zoning effect of geographic data, effectively solving the problem of spatial data heterogeneity. It compensates for the inadequacies of conventional approaches that neglect the modifiable areal units problem (MAUP), and improving the science and accuracy of quantitative analysis and identification of vegetation drivers. We studied demonstrate that (1) During the last 21 years, Fractional Vegetation Cover (FVC) has generally been in good condition, with a multi-year average FVC greater than 0.4 of 71.74 %, and the vegetation is significantly characterized by a low fluctuation of 78.16 %. However, there is a significant trend of vegetation degradation, accounting for 8.89 %, mainly in the main urban areas of Neijiang and Zizhong County, Lu County of Luzhou City, Gao County of Yibin City, and other areas with rapid urbanization. In general, FVC is low in the built-up areas of towns and along transportation roads, while the mountainous and agricultural areas have a high level of vegetation cover. (2) The OPGD model detection showed the optimal spatial scale of vegetation cover in this study region was 2 km. Optimal discrete parameter combinations for slope, elevation, temperature and GDP are quantile breaks with 9 intervals, which contribute to improved scientific accuracy and precision in studies of vegetation change and its drivers. (3) The explanatory power of urbanization rate, land use type, slope, GDP, population density, and average annual precipitation were all above 20 % and were the main drivers of vegetation change. Moreover, any two factors interacted in a nonlinear enhancement and a bi-variable enhancement, increasing the impact on vegetation spatial variation. When the slope is 26.9°∼87.4°, the elevation is 967 m ∼ 4207 m, the average annual temperature is 0.18 °C ∼ 13.6 °C, the average annual precipitation is 328 mm ∼ 439 mm, the GDP is 4.07 ∼ 5.23 million yuan km−2, the population density is 12.7 ∼ 21.1 people/km2, the urbanization rate is 33.4 %∼37.7 %, and the land-use type is forest land, the FVC value is the highest and suitable for vegetation growth. The study showed that using the OPGD model to detect the zoning effects and spatial scale of the explanatory variables solves for the shortcomings of the previous methods for variable regional units and discrete methods, may more precisely explore the features of temporal and spatial changes in the vegetation and the driving mechanisms, offers scientific references for environmental conservation and long-term economic growth in the region.

Monitoring and evaluation of vegetation restoration in the Ebinur Lake Wetland National Nature Reserve under lockdown protection.

For a long time, human activities have been prohibited in ecologically protected areas in the Ebinur Lake Wetland National Nature Reserve (ELWNNR). The implementation of total closure is one of the main methods for ecological protection. For arid zones, there is a lack of in-depth research on whether this measure contributes to ecological restoration in the reserve. The Normalized Difference Vegetation Index (NDVI) is considered to be the best indicator for ecological monitoring and has a key role to play in assessing the ecological impacts of total closure. In this study, we used Sentinel-2, Landsat-8, and Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing data to select optimal data and utilized Sen slope estimation, Mann-Kendall statistical tests, and the geographical detector model to quantitatively analyze the normalized difference vegetation index (NDVI) dynamics and its driving factors. Results were as follows: (1) The vegetation distribution of the Ebinur Lake Wetland National Nature Reserve (ELWNNR) had obvious spatial heterogeneity, showing low distribution in the middle and high distribution in the surroundings. The correlation coefficients of Landsat-8 and MODIS, Sentinel-2 and MODIS, and Sentinel-2 and Landsat-8 were 0.952, 0.842, and 0.861, respectively. The NDVI calculated from MODIS remote sensing data was higher than the value calculated by Landsat-8 and Sentinel-2 remote sensing images, and Landsat-8 remote sensing data were the most suitable data. (2) NDVI indicated more degraded areas on the whole, but the ecological recovery was obvious in the localized areas where anthropogenic closure was implemented. The ecological environment change was the result of the joint action of man and nature. Man-made intervention will change the local ecological environment, but the overall ecological environment change was still dominated by natural environmental factors. (3) Factors affecting the distribution of NDVI in descending order were as follows: precipitation > evapotranspiration > land use type > elevation > vegetation type > soil type > soil erosion > slope > temperature > slope direction. Precipitation was the main driver of vegetation change in ELWNNR. The synergistic effect of the factors showed two-factor enhancement and nonlinear enhancement, and the combined effect of the driving factors would increase the influence on NDVI.

Spatial differentiation and formation mechanism of ecological sensitivity in large river basins: A case study of the Yellow River Basin, China

Conducting ecological sensitivity assessment is a crucial approach for pinpointing regional ecological environmental issues, which can offer a scientific basis to aid in the prevention of regional ecological degradation and ensuring regional ecological security. With the protection and high-quality development of the Yellow River Basin (YRB) at the level of a national strategy in China, there is a critical need for a comprehensive assessment of the YRB’s ecological sensitivity. To this end, this study aimed to construct a new ecological sensitivity evaluation index system from three perspectives: natural ecosystem, natural-social ecosystem, and social ecosystem. The ecological sensitivity index was quantified using the entire-array-polygon indicator method, and the spatiotemporal changes of ecological sensitivity in the YRB and its sub-basins were analyzed from 2000 to 2020. The optimal parameters-based geographical detector model was employed to delve into the mechanisms driving the spatial variation in ecological sensitivity. We found that (1) from 2000 to 2020, ecological sensitivity in the YRB exhibited a bimodal trend. Spatially, it tended to be low in the western and eastern parts while it was relatively high in the southern and central regions. (2) The YRB displayed notable clustering effects in terms of ecological sensitivity. The source area showed both “high-high” and “low-low” groupings, the upstream area showed a predominance of “low-low” clusters, while the midstream area and downstream area were demonstrated by concentrated “high-high” clusters. However, the clustering effect in downstream gradually weakened over time. (3) The ecological sensitivity of the YRB was primarily driven by factors like shifts in climate change and soil erosion. There were also marked differences in spatial heterogeneity of ecological sensitivity across different sub-basins. Moreover, the influence of interactive factors on the YRB's ecological sensitivity and its sub-basins was enhanced. The research findings enhance our understanding of ecological issues faced by the YRB and establish a foundation for developing strategies to protect and manage its environmental ecosystem effectively.

Revealing disparities in different types of park visits based on cellphone signaling data in Guangzhou, China

Urban parks play a crucial role in promoting the urban ecological environment and the health and well-being of dwellers. However, existing research on park visits and drivers has largely ignored the classification of parks. Using the four-level park system in Guangzhou as a case, this study first measured park visits based on cellphone signaling data. Then, the independent and interactive influences of driving factors on the visits of four types of parks were investigated and compared comprehensively based on the geographical detector model. The factor detector model preliminarily distinguished the functional and role differences of various park types. Nature and urban parks are more functional, and community and pocket parks mainly provide nearby residents with convenient relaxation spaces. The interaction detector further revealed the disparities in park visit drivers between four types of parks. The most significant finding is that nearby recreational facility is the key to the use of natural and urban parks, while the determining factor for the visits of community and pocket parks is the surrounding population. Based on these findings, the study recommends tailored strategies for each type of park, to promote effective management and increased utilization. In particular, the study highlights the importance of understanding the differences between park types and developing customized strategies to maximize the benefits of urban parks and foster a healthy and sustainable urban environment.

Identifying regional eco-environment quality and its influencing factors: A case study of an ecological civilization pilot zone in China

Jiangxi Province is one of the first ecological civilization pilot zones (ECPZ) in China. The assessment of eco-environment quality (EQ) assessment and its future trends are of great significance to the maintenance of ecological security in Jiangxi Province and the construction of other pilot zones in China. Here, we propose an extended remote sensing ecological index (ERSEI), and then analyze its spatiotemporal distribution features and trends; we combine this with the optimal parameters-based geographical detector (OPGD) model to quantify the influencing factors, and further explore the future trends and prospects of the ERSEI. Our results show that: (1) Compared with the remote sensing ecological index (RSEI), the ERSEI can more effectively reflect regional differences in EQ. (2) Areas with an excellent ERSEI are mainly located in the important ecological barrier areas of Jiangxi Province, whereas areas with poor or worst ERSEI values correspond to the spatial distribution of the Nanchang metropolitan circle, town clusters, and urban development axes. (3) The ERSEI in Jiangxi Province improved in 72.50% of the study area from 2001 to 2021. (4) Slope, population density, and elevation are the key factors affecting the ERSEI, with the interaction between slope and population density explaining 79.9% of the variance in the ERSEI spatial distribution. (5) The areas predicted to see future rises and falls in the ERSEI in Jiangxi Province at 56.55 × 103 and 96.24 × 103 km2, respectively. These research results can provide support for Jiangxi Province to achieve sustainable development and ecological civilization construction, while also providing an ERSEI that effectively identifies regional differences and acts as a new reference to further EQ research.

Spatiotemporal Analysis of Soil Moisture Variability and Its Driving Factor

Soil moisture (SM), as a crucial input variable of land surface processes, plays a pivotal role in the global hydrological cycle. The aim of this paper is to examine the spatiotemporal variability in SM in the Heihe River Basin using all-weather land surface temperature (LST) and reanalysis land surface data. Initially, we downscaled and generated daily 1 km all-weather SM data (2020) for the Heihe River Basin. Subsequently, we investigated the spatial and temporal patterns of SM using geostatistical and time stability methods. The driving forces of the monthly SM were studied using the optimal parameter-based geographical detector (OPGD) model. The results indicate that the monthly mean values of the downscaled SM data range from 0.115 to 0.146, with a consistently lower SM content and suitable temporal stability throughout the year. Geostatistical analysis revealed that months with a higher SM level exhibit larger random errors and higher variability. Driving analysis based on the factor detector demonstrated that in months with a lower SM level, the q values of each driving factor are relatively small, and the primary driving factors are land cover and elevation. Conversely, in months with a higher SM level, the q values for each driving factor are larger, and the primary driving factors are the normalized difference vegetation index and LST. Furthermore, interaction detector analysis suggested that the spatiotemporal variation in SM is not influenced by a single driving factor but is the result of the interaction among multiple driving factors, with most interactions enhancing the combined effect of two factors.

Spatial-temporal differentiation characteristics and driving factors of China's energy eco-efficiency based on geographical detector model

As China is striving to build a modernization in which people and nature live in harmony, evaluating energy eco-efficiency (EEE) is an important part of achieving a balanced development of socio-economic construction and ecological civilization. From the perspective of the “energy-economy-ecology-society” system, this study included social welfare elements and PM2.5 into the EEE evaluation index, and the EEE of 30 provinces in China from 2000 to 2019 were examined more comprehensively. The distribution differences and sources of EEE were measured by Trend analysis and the Dagum Gini coefficient. A unique analytical method that reveals the drivers of spatial heterogeneity, the Geographical Detector model (Geodetector), was used to better account for the explanatory power of the drivers and their interactions. The results show that China's EEE has a weak “N"-shaped trend in time series, with significant spatial heterogeneity, and the contribution of this imbalance is ranked as inter-regional (39.83%) > hypervariable density (31.95%) > inter-regional (28.22%). The core factors that dominate the spatial-temporal differentiation in the East, Center and West are energy consumption structure (0.688), environmental regulation (0.414), and the level of urbanization (0.548), respectively. The factor interactions are all greater than the explanatory power of factor independent effects on EEE divergence, and the strongest interaction is between Environmental Regulation and Level of educational development in 2010(0.91). The interactions have regional characteristics, and targeted EEE enhancement paths should be selected for different regions.

Response of net water productivity to climate and edaphic moisture in wheat-maize rotation system

Comprehending the mechanisms underlying the response of water productivity to changing climate is essential for developing effective strategies that can maintain sustainable agricultural productivity and ecological stability amidst water scarcity and climate change. This study aimed to clarify the effects of climate and associated biologic and abiotic factors on the net water productivity (NWP) in the winter wheat—summer maize rotation system within the North China Plain (NCP), through integrating spatial-temporal pattern analysis, the Optimal Parameter-based Geographical Detector model, Random Forest, and Structural Equation Modeling. Despite climate warming and water scarcity, the NWP in the vast majority of rotation systems within the NCP exhibited an increasing trend between 2000 and 2019, which was anticipated to persist in the future. From 2000–2019, the impact of climate on the NWP exerted a fluctuating diminishment, besides the influence of WS. It is essential to add adaptive strategies that can cope with WS fluctuation in future management, to better tackle climate variability. Apart from direct effects, meteorological factors indirectly induced NWP variation primarily by influencing edaphic moisture dynamic and crop growth factors. In the paths underlying the effect of climate change on NWP, positive and negative effects coexisted. Edaphic moisture within the 40–100 cm layer played an important role in affecting NWP throughout rotation systems. To sustainably improve NWP, it was advisable to adopt deeper-rooting crop varieties in conjunction with moderate deficit irrigation and improving topsoil water retention capability. Overall, we incorporated soil and crop factors into the framework of climate impact research, providing a systematic elucidation for the mechanisms through which climate change affects NWP. Additionally, the conclusions were based on a continuous spatiotemporal framework that accounted for the geospatial heterogeneity of variables, thereby giving conclusions ample regional universality.

Land use/cover and land degradation across the Eurasian steppe: Dynamics, patterns and driving factors

Despite the ecological and socio-economic importance of Eurasian steppe, the land use/cover change, land degradation and the threats facing this precious ecosystem still have not been comprehensively understood. Taking advantages of the land use/cover change monitoring platform (FROM-GLC Plus), this study developed the annual land use/cover maps during 2000–2022, and the land use/cover change, especially the change of grassland, was further analyzed. The grassland area exhibited a net increase, predominantly transformed from cropland, forest, and bareland, accounting for 17.64 %, 31.91 %, and 45.60 %, respectively. To monitor land degradation, we adopted the framework suggested by the United Nations Convention to Combat Desertification (UNCCD). According to the monitoring result, grassland constituted the highest proportion of degraded land (39.82 %). This may due to its dominance in the Eurasian steppe's land use/cover, as the extent of grassland degradation (1.92 %) was lower than the overall land degradation level (2.83 %) across the region. To offer tailored and sustainable development recommendations, we quantified the driving factors behind land dynamics using the geographical detector model and convergent cross mapping (CCM), considering both spatial and temporal dimensions. Environmental and socio-economic factors, such as precipitation, temperature, urbanization, mining and grazing intensity, etc., were integrated into the analysis. We found that urbanization, cropland and moisture distribution emerged as key drivers influencing land degradation's spatial distribution in the Eurasian steppe, while temperature variations between years impacted vegetation changes. This research thus provides a deeper understanding of the region's land dynamics, enhancing comprehensive monitoring of the Eurasian steppe's land dynamics. Moreover, it serves as a foundation for policymakers and land managers to devise conservation strategies and sustainable development initiatives for this critical ecosystem.

Spatiotemporal Heterogeneity of Water Conservation Function and Its Driving Factors in the Upper Yangtze River Basin

The water conservation function (WCF), as one of the most critical ecosystem services, has an important impact on the ecological sustainability of a region. Accurately characterizing the spatiotemporal heterogeneity of WCF and further exploring its driving factors are of great significance for river basin management. Here, the WCF of the upper Yangtze River basin (UYRB) from 1991 to 2020 was calculated using the water yield module in the Integrated Valuation of Ecosystem Service and Tradeoffs (InVEST) model. Also, we innovatively applied emerging hot spot analysis (EHSA), which could describe the location and pattern of historical changes more accurately, to investigate the spatiotemporal heterogeneity and evolution of WCF. Based on the Geographical Detector Model (GDM), the main driving factors of WCF and their interactions were revealed. The results showed the following: (1) the WCF in the UYRB experienced a temporal increase at a growth rate of 1.48 mm/a, while remarkable differences were observed across the change rates of sub-watersheds. (2) The spatial variation of the WCF showed a gradual increase from northwest to southeast. Interestingly, the Jinshajing River upstream (JSJU) source area with a low WCF showed an increasing trend (with diminishing cold spots). On the contrary, the downstream regions of the JSJU watershed (with intensifying cold spots) underwent a weakening WCF. (3) Among all driving factors, precipitation (q = 0.701) exhibited the most remarkable prominent impact on the spatial heterogeneity of the WCF. Additionally, the interaction of factors exhibited more explanatory power than each factor alone, such as precipitation and saturated soil hydraulic conductivity (q = 0.840). This research study is beneficial to water resource management and provides a theoretical basis for ecological restoration.

Analysis of temporal and spatial evolution characteristics and influencing factors of land use transformation in Hebei Province from the perspective of supply and demand

This study focuses on the counties and districts of Hebei Province as the research unit. It adopts a supply-demand perspective to analyze the spatial-temporal evolution characteristics of farmland utilization transformation in the province, investigate the coordination of this transformation, and explore the influencing factors. The weight of indicators is calculated using the entropy weighting method and Analytic Hierarchy Process The comprehensive evaluation model is then applied to calculate the supply-demand transformation index of farmland utilization in Hebei Province for the years 2005, 2010, 2015, and 2020. Furthermore, the spatial-temporal evolution characteristics are analyzed using the kernel density estimation method. The coupling coordination degree model is selected to explain the relationship between the supply-demand transformation of farmland utilization. Finally, the influencing factors are analyzed using the geographical detector model. The research findings are as follows: 1) The supply-demand transformation index of farmland utilization in Hebei Province has shown an increasing trend during the study period. The standard deviation of the supply transformation index has increased over time, while the demand transformation index has increased at a faster rate. High-density supply transformation is concentrated in the southeast, particularly in the eastern part of Shijiazhuang. Conversely, the northwest exhibits a low-density supply transformation. High-density demand transformation is observed in urban areas across the province, with a significant expansion from 2010 to 2015. 2) There is a strong correlation between the supply and demand transformation of farmland utilization. The coupling coordination degree has gradually improved from 2005 to 2015, transitioning from rapid to stable growth. The level of coupling coordination has shifted from imbalance to coordination. The mountainous areas in the northwest of Hebei Province exhibit relatively lower coupling coordination degrees, while the plains in the southeast demonstrate higher levels. 3) The supply transformation of farmland utilization is closely correlated with the natural environment, particularly elevation and topography. On the other hand, the demand transformation is closely associated with socio-economic development, with a scarcity of supply driving an increase in the demand transformation index. Industrial developed areas show a higher intensity of demand for farmland utilization. 4) To ensure the sustainable utilization of farmland while meeting food production needs, it is crucial to enhance contiguous farmland and mechanization levels, promote the integration of agriculture and tourism, establish an ecological barrier around Beijing-Tianjin, and optimize the ecological compensation mechanism for farmland. The above findings provide valuable insights into farmland utilization transformation and suggest important strategies for its sustainable development.

Observed Equity and Driving Factors of Automated External Defibrillators: A Case Study Using WeChat Applet Data

Out-of-hospital cardiac arrest (OHCA) causes a high mortality rate each year, which is a threat to human well-being and health. An automated external defibrillator (AED) is an effective device for heart attack-related diseases and is a panacea to save OHCA. Most relevant literature focuses on the spatial distribution, accessibility, and configuration optimization of AED devices, which all belong to the characteristics of the spatial distribution of AED devices. Still, there is a lack of discussion on related potential influencing factors. In addition, analysis of AED facilities involving multiple city comparisons is less considered. In this study, data on AED facilities in two major cities in China were obtained through the WeChat applet. Then, the AED equity at the city and block scales and its socioeconomic factors were analyzed using the Gini coefficient, Lorenz curve, and optimal parameters-based geo-graphical detector (OPGD) model. Results show that the number of AEDs in Shenzhen was about eight-times that of in Guangzhou. The distribution of AEDs in Shenzhen was more equitable with a global Gini of 0.347, higher than that in Guangzhou with a global Gini of 0.504. As for the determinants of AED equity, residential density was the most significant determinant in both Guangzhou and Shenzhen from the perspective of individual effects on AED equity. Differently, due to the aging population in Guangzhou, the proportion of the elderly in blocks was influential to local AED equity. The local economic development level was crucial to local AED equity in Shenzhen. The results of the interaction detector model illustrate that relatively equitable AED distributions were found in the high-density residential areas with a balance of employment and housing, high-aging residential areas, and high-mobility residential areas in Guangzhou. The area with a high level of local economic development, dense population, and large mobility was the area with a relatively equitable distribution of AEDs in Shenzhen. The results of this paper are conducive to understanding the equity of AEDs and its socio-economic determinants, providing scientific reference for the optimization and management of AEDs.