Spatiotemporal Analysis Method Research Articles

Evaluating the spatiotemporal dynamics of ecosystem service supply-demand risk from the perspective of service flow to support regional ecosystem management: A case study of yangtze river delta urban agglomeration

The inappropriate spatio-temporal distribution of natural capital during rapid urbanization has increased ecosystem service supply-demand (ESSD) risks, posing great challenges to the sustainable growth of human well-being, especially in urban agglomerations. Although methods focusing on the spatial match or temporal dynamics of ESSD in risk assessment have been established, a comprehensive understanding of the spatial dynamics underlying ecosystem service flows (ESF) is still missing. Due to the spatiotemporal heterogeneity of various ESSD risks within urban agglomerations, incorporating these elements into a unified spatial planning framework is still difficult. This study integrated the spatio-temporal analysis method of ESSD considering ESF and the spatial clustering method to propose an ESSD risk assessment and management framework: evaluating ES supply, demand and flow, incorporating ESF to quantify ESSD across various timeframes, assessing ESSD risks based on the current status and dynamic trends, and using Self-Organizing Map to identify optimal ESSD risk bundles (ESSDR_Bs). The results showed high ESSD risks of high temperature regulation, nitrogen purification, phosphorus purification, and food production in Yangtze River Delta urban agglomeration. While the ESSD risks of biodiversity conservation, carbon sequestration, soil retention, water yield, and tourism culture were relatively low, a concerning trend of decreasing surpluses was observed in general. In addition, six ESSDR_Bs were identified, and differentiated ecological management strategies were proposed for each bundle. This study provided a novel perspective for efficiently understanding and regulating the ESSD risks in urban agglomerations.

Dynamic wire-pull rubber diaphragm for rapid formation and spatio-temporal investigation of multi-shape free-standing liquid films

Quantitative manufacturing and characterization of thin films are relevant in different scientific and industrial fields. Especially, for the studies of fluid dynamics, the experimental verification of theoretical models creates the need of a free-standing liquid film formation. Driven by the lack of fast and accurate liquid film forming technology, we present a novel device able to fabricate thin free-standing liquid films, which is based on a dynamic and adjustable diaphragm, the opening of which allows for the rapid formation of a self-supported liquid film confined in a wire-pull rubber band. The quantitative thickness mapping of the film formation and its time-resolved evolution are performed by digital holography in real-time. This builds up a strategy that allows for the accurate liquid film formation with controllable size and volume. As the results, multi-shape and multi-material thin films under controllable and stable conditions are successfully formed, including free-standing water, soapy water and glycerol films. A label-free spatio-temporal analysis method for flow dynamics is proposed: The full-field time series of film thickness is reassembled in a new coordinate system composed of radius, time, and thickness. Such spatio-temporal analysis reveals details of leveling and drainage processes. To the best of our knowledge, this is the first full-field quantitative thickness analysis of multi-shape and multi-material thin films. The proposed Dynamic Wire-pull Rubber Diaphragm (DWRD) together with the digital holography add-on offer characterization abilities with an unprecedented level of details allowing for a deeper understanding of the underlying thin film dynamics that can be useful in several different scientific and technological fields as biotechnology, life science, and nanotechnology.

Land Use, Built-Up, and Vegetation Index in North Halmahera Regency through Spatio-Temporal Analysis

Monitoring land use, buildings, and vegetation index of ecotourism areas in North Halmahera can support planning space utilization in urban areas for tourist areas as the concept of land use management and urban planning. This study offers ideas for analyzing the distribution of buildings, vegetation index, and land use in the mangrove ecotourism area of North Halmahera Regency using the spatio-temporal analysis method. The spatio-temporal analysis method comprises several stages: data selection, preprocessing, data integration, spatial analysis, temporal analysis, spatio-temporal analysis, data visualization, interpretation and understanding, and data visualization. The results of this study show that changes in the livelihood strategy of local people, from farmers and fishermen to traders, also affect land use patterns, from agricultural activities to economic activities, which triggers an increase in the number of buildings for production activities to product distribution. The implications of these findings on ecotourism development programs and policies and infrastructure development in the North Halmahera Regency are to consider community livelihoods and space or land use behavior in ecotourism areas based on vegetation, soil, and building index values. Thus, the intensification of building distribution and changes in vegetation index values from 2013-2023 reflect changes in people's livelihood strategies from agrarian activities to trade and from fishermen's activities to tourism transportation service providers.

Coal consumption and carbon emission reductions in BRICS countries.

The primary energy consumption structure of BRICS countries is dominated by fossil energy, particularly coal. Coal consumption in BRICS countries is a major driver underlying increased carbon emissions. Therefore, this study developed a spatiotemporal decoupling mode and incorporated factors related to coal consumption-induced carbon emissions into a spatiotemporal decoupling analysis method to provide differentiated and targeted policies for energy restructuring and emission reduction targets in BRICS countries. Moreover, a temporal-spatial decomposition logarithmic mean Divisia index model was developed using the spatiotemporal decoupling index method. The model is based on CO2 emissions generated by coal consumption in BRICS countries, with a primary focus on data from Brazil, Russia, South Africa, India, and China. The findings reveal distinct spatiotemporal distributions and driving effects of coal consumption and carbon dioxide emissions across various countries. Factors such as CO2 emission intensity, coal consumption intensity, economic output per capita, and population structure exerted either positive or negative effects on the distributional effect of the carbon emission-economic output per capita association in BRICS countries. Additionally, country-level heterogeneity in the influence of the distributional effects of CO2 emissions was observed within each BRICS country. Thus, different policies are needed to achieve carbon emission reduction targets in different countries.

A Spatiotemporal Hierarchical Analysis Method for Urban Traffic Congestion Optimization Based on Calculation of Road Carrying Capacity in Spatial Grids

Traffic congestion is a globally widespread problem that causes significant economic losses, delays, and environmental impacts. Monitoring traffic conditions and analyzing congestion factors are the first, challenging steps in optimizing traffic congestion, one of the main causes of which is regional spatiotemporal imbalance. In this article, we propose an improved spatiotemporal hierarchical analysis method whose steps include calculating road carrying capacity based on geospatial data, extracting vehicle information from remote sensing images to reflect instantaneous traffic demand, and analyzing the spatiotemporal matching degree between roads and vehicles in theory and in practice. First, we defined and calculated the ratio of carrying capacity in a regional road network using a nine-cell-grid model composed of nested grids of different sizes. By the conservation law of flow, we determined unbalanced areas in the road network configuration using the ratio of the carrying capacity of the central cell to that of the nine grid cells. Then, we designed a spatiotemporal analysis method for traffic congestion using real-time traffic data as the dependent variables and five selected spatial indicators relative to the spatial grids as the independent variables. The proposed spatiotemporal analysis method was applied to Chengdu, a typical provincial capital city in China. The relationships among regional traffic, impact factors, and spatial heterogeneity were analyzed. The proposed method effectively integrates GIS, remote sensing, and deep learning technologies. It was further demonstrated that our method is reliable and effective and enhances the coordination of congested areas by virtue of a fast calculation speed and an efficient local balance adjustment.

Spatiotemporal Analysis of Urban Heat Islands and Vegetation Cover Using Emerging Hotspot Analysis in a Humid Subtropical Climate

Research on the temporal and spatial changes of the urban heat island effect can help us better understand how urbanization, climate change, and the environment are interconnected. This study uses a spatiotemporal analysis method that couples the Emerging Hot Spot Analysis (EHSA) technique with the Mann–Kendall technique. The method is applied to determine the intensity of the heat island effect in humid subtropical climates over time and space. The data used in this research include thermal bands, red band (RED) and near-infrared band (NIR), and Landsat 7 and 8 satellites, which were selected from 2000 to 2022 for the city of Sari, an Iranian city on the Caspian Sea. Pre-processed spectral bands from the ‘Google Earth Engine’ database were used to estimate the land surface temperature. The land surface temperature difference between the urban environment and the outer buffer (1500 m) was modeled and simulated. The results of this paper show the accuracy and novelty of using Emerging Hotspot Analysis to evaluate the effect of vegetation cover on the urban heat island intensity. Based on the Normalized Difference Vegetation Index (NDVI), the city’s land surface temperature increased by approximately 0.30 °C between 2011 and 2022 compared to 2001 to 2010. However, the intensity of the urban heat island decreased during the study period, with r = −0.42, so an average −0.031 °C/decade decrease has been experienced. The methodology can be transferred to other cities to evaluate the role of urban green spaces in reducing heat stress and to estimate the heat budget based on historical observations.

A multi-factor spatio-temporal correlation analysis method for PV development potential estimation

The impact of wind speed on PV power generation is influenced by the monsoon and the complex topography and environmental factors found in mountainous areas. This complexity can reduce the correlation between PV power and irradiance. To better understand the impact of wind speed, we conducted an analysis in various topographical regions of the Hengduan Mountain in China. Our findings show that high wind speeds can enhance PV utilization, particularly in plateau and hill regions. This is due to the blocking and lifting effect of the north-south oriented mountains on the east-west wind, as well as the channeling effect on the north-south wind. To improve the efficiency of utilizing solar energy, it is important to consider the spatial distribution of wind speed when developing PV. Correlation analysis of multi-factors on a spatial and temporal scale can also offer insight. Furthermore, planning the layout of PV systems with meteorological resources in mind can enhance PV utilization in complex mountainous terrain.

Spatiotemporal evolution of healthcare service capacity at township health centers in China.

This study analyzes the efficiency, spatiotemporal evolution, and influencing factors of provincial township health centers' healthcare service capacity in China. It utilizes an unexpected output super-efficiency slacks-based measure (SBM) model, exploratory spatiotemporal data analysis methods, and a quantile regression model. The results show that the healthcare service capacity of township health centers is better in provinces with a larger proportion of hierarchical diagnoses and treatments pilot projects in cities, and the regional efficiency trend is ordered central > eastern > western > northeastern. The healthcare service capacity of provincial township health centers mainly shows significant spatial correlation and a spatiotemporal distribution pattern of "high agglomeration, low differentiation." Rural population density and per capita GDP significantly improve the healthcare service capacity of township health centers, while local governments' healthcare and health expenditure increases the healthcare service capacity of township health centers in certain quantiles. The urbanization rate and per capita disposable income inhibit the improvement of the healthcare service capacity of township health centers in certain quantiles. The provinces should accelerate the promotion of hierarchical diagnoses and treatment pilot projects in cities and establish national cooperative development models to promote public health.

Developing a spatio-temporal interactions model for car crashes using a novel data-driven AHP-TOPSIS

We have proposed a data-driven method for spatio-temporal analysis of car crashes based on the Multi Criteria Decision Making (MCDM) procedure in the Zanjan city, NW Iran which are recorded in the period 2019–2020. A combination of Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is used in this paper to accurately identify the spatio-temporal interactions exist in car crashes. A data-driven AHP-TOPSIS procedure is arranged based on assigning proper weights to the time series related to the peak of accidents. On the other hand, for spatial analysis, the Kernel Density Estimation method was used to create the continuous-value maps of different traffic accidents and then classified using Natural Breaks Classifier. In fact, the proposed methodology can be used to identify car crash hotspots by considering spatio-temporal interactions as well as addressing exaggerated weightings arising from knowledge-driven modeling. By using the spatio-temporal interaction maps in which the location and time of crashes are considered, simultaneously, it is possible to provide a new scientific strategy for identifying car crash hotspots which can lead to better traffic management, improved allocation of resources, and enhanced prevention regulations.

Spatio-temporal pattern evolution of China’s provincial tourism efficiency and development level based on DEA-MI model

The spatial differences of efficiency and development level of regional tourism are evident, and the dynamic grasp of their spatiotemporal evolution characteristics and coupling coordination relationship is important to promote high-quality sustainable tourism development. Herein, we measured the tourism development level of 31 provincial units in China during 2000–2020, introduced the data envelopment analysis-based Malmquist productivity index to determine tourism efficiency, used exploratory spatiotemporal data analysis methods to explore the spatiotemporal dynamic characteristics of regional tourism differences and spatial structure, constructed a coupled coordination model of tourism efficiency and development level and analyzed their coupling excellence and synergistic consistency. The results were as follows: (1) The spatial differences of comprehensive tourism efficiency from 31 provinces were evident, and the average situation showed high and low distribution characteristics in the east and west, respectively. The interannual changes showed a fluctuating downward trend, with the scale efficiency playing a supporting role for comprehensive efficiency and technical efficiency playing an influencing and restraining role. (2) Less fluctuation existed in the local spatial structure of tourism efficiency and development level; the direction of dependence was more stable, tourism development level was slightly more volatile, and the spatial dependency direction changes were similar. The tourism efficiency of local structure competition posture was stronger than that of collaboration, with the tourism development level collaboration integration being stronger. (3) The local spatial structure of tourism development level was more stable, the relative position of provincial units was more difficult to change, tourism efficiency of local spatial structure was unstable, and provincial units exhibited a greater possibility of change. (4) Tourism efficiency and the scale of the overall coupling degree and coupling coordination gradually improved, with similar spatiotemporal values. The overall coupling degree and coupling coordination degree of tourism efficiency and development level gradually improved, with similar spatiotemporal heterogeneity and volatility of local evolution. The area with a higher value coupling coordination degree spread slower and more widely.

The effect of tree growth disturbances inertia on dendrogeomorphic spatio-temporal analysis of landslides: A case study

Tree-ring analysis is currently a frequently used absolute dating method for spatio-temporal analysis of landslide movements. However, like all absolute dating methods, this approach has various limitations. One possible limiting factor is the inertia of tree growth responses to the influence of landslide movements. When landslide movements occur with a small recurrence interval (months or early years), trees with an inertial response to an older event may not be able to record a younger event. One possible growth response of trees to a landslide movement with the absence of inertia is a change in the size of the vessel lumen area (VLA; anatomical response) in deciduous trees. Thus, this study aimed to quantify the effect of the absence of VLA inertia on the spatio-temporal analysis of two landslide events of known occurrence with a short time between their occurrence (2010 and 2013). Ninety-six individuals of European beech (Fagus sylvatica L.) were sampled and two types of landslide signals (changes in tree-ring eccentricity and changes in VLA size) were extracted from their tree-ring series. The effect of the absence of inertia was evident in the younger event, with 171.2% more trees responding anatomically than through tree-ring eccentricity. In the case of the spatial extent analysis of the younger landslide event, 29.0% more was detected as an active area through anatomical changes (VLA) compared to the tree-ring eccentricity analysis. The use of growth disturbances without inertia could in the future reduce the negative general property of tree-ring-based chronologies of landslide movements, which are considered minimal when using classical growth disturbances with inertia. The implementation of anatomical responses could lead to a more effective use of dendrogeomorphic data in landslide hazard assessment.

Event pattern analysis: the point density around the appearance and disappearance of points

ABSTRACT Point pattern analysis is a fundamental analytical tool in various academic fields related to spatial information science. Basic but important patterns discussed in existing studies are spatially clustered and dispersed points. Analysis of these static patterns aims to reveal their underlying structure, i.e. why and how they are formed. However, to answer this research question, the direct tracking of the pattern formation process is more effective, though we have not yet fully conducted such analysis along the temporal axis. To fill the research gap, this paper develops a new method of spatiotemporal analysis. We focus on the point density around the appearance and disappearance of points. A research question is whether points appear/disappear in dense or sparse space. Extending Ripley’s K-function, we develop four measures statistically evaluating the point density. We applied the proposed method to the analysis of the competition among convenience store chains in Shibuya-ku, Tokyo.

Study on the coupling coordination development of China's multidimensional digital economy and industrial carbon emission efficiency.

Exploring the coupling coordination between China's digital economy (DE) and industrial carbon emission efficiency (ICEE) is of great significance for achieving sustainable development goals. In the study, a multidimensional indicator system was established to evaluate DE, and spatiotemporal analysis and network analysis methods were used to reveal the dynamic evolution characteristics of DE and ICEE. The coupling coordination model and convergence model were adopted to explore the development trend of coupling coordination between DE and ICEE. The results show that the ICEE and DE in various provinces of China exhibit obvious spatial heterogeneity and spillover effects. Currently, the coupling coordination degree between the development of China's DE and ICEE has reached the level of primary coordination or above. The coupling coordination degree between DE and ICEE in the eastern, central, and northeastern regions has reached an intermediate level or above, with the highest degree in the eastern region. The fluctuation of China's ICEE has consistent σ-convergence and β-convergence, and the convergence effect is higher with the introduction of the DE than without it. The condition β-convergence result indicates that underdeveloped regions can narrow the gap between their ICEE and that of developed regions by utilizing their resource endowments, industrial structure, human capital, and other conditions, improving emission reduction measures and policies. This study provides a certain reference for the green and low-carbon development of industry in China and other developing countries in the digital economy era.

Mixing enhancement of a compressible jet over a convex wall

A highly compressive effect would suppress the mixing of the shear layer in a convex wall jet. The spanwise distributed protrusions at the nozzle lip are employed to achieve mixing enhancement in this study. The mixing characteristics and enhancement mechanisms are numerically investigated by the delayed detached-eddy simulation method based on the two-equation shear-stress transport model. A widely applicable flow spatiotemporal analysis method, called proper orthogonal decomposition (POD), is used to gain further insight into the dynamical behaviours of the flow instability mode. The results reveal that the centrifugal effect maintains and amplifies the initial perturbations induced by the spanwise distributed heterogeneities, resulting in forced streamwise vortices. The instabilities induced by the streamwise vortices significantly increase the growth rate of the jet half-width and the shear layer vorticity thickness. The spanwise wavelength of the streamwise vortices is consistent with the spanwise distributed forced excitation. In addition, the spanwise meandering motion of the streamwise vortices is observed, which is usually associated with the streamwise travelling wave. This is further confirmed by the POD analysis of the spanwise velocity fluctuation in both stream-radial and stream-span sections. Also, the spatial distributions of the POD modes with the highest energy provide information on the secondary instability modes. Both sinuous and varicose types of disturbances are observed in the unforced jet, whereas the forced jet seems to be dominated by the sinuous type instability, which is more easily excited than the varicose type instability. Moreover, the turbulence intensity in the forced jet is also significantly enhanced as expected due to the earlier and stronger streamwise vortices and associated instabilities. The enhanced turbulent characteristics of the highly compressible condition tend to be isotropic, whereas in the unforced jet, it is anisotropic due to the strong compressibility suppressing the spanwise turbulent fluctuations.

Investigation of Risk Factors Associated with the African Swine Fever Outbreaks in the Nizhny Novgorod Region of Russia, 2011–2022

African swine fever (ASF) is a transboundary viral disease that affects both domestic pigs and wild boar, causing enormous damage to the pig production. Numerous studies are devoted to elucidating the role of domestic and wild pigs in ASF outbreaks. The identification of the risk factors contributing to the spread of disease in previously unaffected regions is of special interest. We conducted a retrospective analysis of sporadic ASF outbreaks in the Nizhny Novgorod region of the Russian Federation from 2011 to 2022 among both domestic pig and wild boar populations. Methods of spatiotemporal cluster analysis, mathematical modeling with logistic and negative binomial regressions, as well as the cross-correlation analysis of time series, were used to identify the most significant factors associated with the ASF outbreaks’ presence and intensity. Regression analysis did not reveal a statistically significant correlation between ASF outbreaks in wild boar and their population density over the entire study period. However, the spatial and temporal coincidence between outbreaks and increased wild boar density was observed at the beginning of the ASF epidemic in the region. We identified the significant environmental and sociodemographic factors contributing to the spread of ASF in both domestic pig and wild boar populations. The number of small-scale farms (backyards) and summary road length were determined as the main factors contributing to the spread of ASF among domestic pigs in the Nizhny Novgorod region. The regression model for ASF outbreaks in the wild boar population revealed the presence of water bodies, number of hunting farms, and the occurrence of ASF outbreaks in the domestic pig population to be the main explanatory factors. A statistically significant coincidence was identified between the monthly volume of pork products imported to the region from the rest of Russia and the occurrence of ASF outbreaks in both domestic and wild populations. Thus, the identified spatiotemporal patterns may be useful to determine areas with an elevated risk of ASF outbreaks emergence for better resource management, disease control, and prevention. The importance of intensified monitoring and control measures in the pig products supply chain is strongly needed. The obtained models can be applied not only by the veterinary service of the Nizhny Novgorod region but to other regions with similar environmental and sociodemographic conditions.

The Spatiotemporal Change in Land Cover and Discrepancies within Different Countries on the Qinghai–Tibet Plateau over a Recent 30-Year Period

The Qinghai–Tibet Plateau is a unique global natural geographical unit with a high altitude and fragile ecology, where land cover change has been affecting its regional ecological security and sustainable development. The plateau covers nine countries with different variations in climate change and human activities, which cause significant spatial variations in its land cover change. This paper uses land cover data to reveal the spatiotemporal characteristics and multi-country differences in land cover change on the Qinghai–Tibet Plateau from 1992 to 2020 by applying spatiotemporal characteristic analysis and mapping methods. The results show that grassland (65.70%), bare land (15.56%), and woodland (10.25%) are the main types of land cover on the plateau, accounting for 91.51% of the total area. The interconversion of bare land and grassland was dominant in 1992–2020, with an area share of 67.83%. The turning point year of grassland change occurred in 2015. The grassland area increased by 6312 km2 in 1992–2015, while it decreased by 14,646 km2 in the plateau in 2015–2020. The areas of woodland, cropland, and water increased by 2.77%, 5.85%, and 7.57%, respectively, and the area of built-up land increased from 299.17 km2 to 1206.29 km2. Overall, a warming and wetting trend of the climate in the Qinghai–Tibet Plateau has driven the expansion of natural vegetation and water in the central plateau within China, with its natural vegetation area increasing by 0.11%. However, the development of regional warming and drying caused local grassland degradation, where Kyrgyzstan and India within the Qinghai–Tibet Plateau experienced the most prominent vegetation degradation. Human activities are relatively frequent in the Qinghai–Tibet Plateau within China and India, causing higher growth rates of built-up land and cropland than in other countries. The establishment of reserves and effective forest management policies have led to significant increases in woodland areas in China and Nepal within the plateau, while weak forest management and limited investment in forest conservation have led to high rates of deforestation in India and Myanmar. Accelerated temperature rises and regional differences in precipitation are the main drivers of large-scale land cover change on the plateau and differences in human activities and land use policies are responsible for the dramatic and diverse localized land cover change.

Investigating Whether the Ensemble Average of Multi-Global-Climate-Models Can Necessarily Better Project Seasonal Drought Conditions in China

Global drought patterns are substantially impacted by climate change, with far-reaching implications for socioeconomic and ecological systems. Existing global climate models (GCMs) are unable to accurately project precipitation and drought characteristics, particularly in countries or regions with complex topography and significant seasonal variability, such as China. Consequently, the purpose of this study is to assess the efficacy of GCMs, and their multi-model ensemble mean, as well as to investigate the seasonal drought characteristics in China using precipitation data from CMIP6 under various “possible future” scenarios. This study selected five GCMs with historical (1961–2014) and future (2015–2100) periods, namely CNRM-CM6-1, GFDL-ESM4, MPI-ESM1-2-HR, MPI-ESM1-2-LR, and NorESM2-MM, as well as their ensemble mean ENS-CGMMN. Based on the China Daily Precipitation Analysis Product (CPAP) as the reference precipitation, the performance of these models is evaluated using the DISO index and the quantile mapping (QM) method for calibration, as well as seasonal-scale drought using the standardized precipitation index (SPI) and spatiotemporal variability analysis methods. In comparison to other climate models and the ensemble mean, the calibrated MPI-ESM1-2-HR model can more precisely describe the actual precipitation conditions at the seasonal scale. Under four scenarios, China’s climate will shift from arid to moist in the future period (2015–2100) (SSP126, SSP245, SSP370, and SSP585). Autumn and summer will see a considerable increase in China’s moisture levels. During the autumn, winter, and spring, the moisture will generally increase in the northern subregions of China, including the Qinghai-Tibet Plateau (QTP), Xinjiang (XJ), Northwest (NW), Northeast (NE), and North China (NC). Dryness will decrease in southern subregions, such as the Southwest (SW) and South China (SC). In contrast to these three seasons, summer in XJ exhibits a distinct trend of aridity, especially in the SSP245 scenario, whereas the NE, NC, and SC exhibit a distinct trend of moisture. To be more specific, the aridity changes in subregions during various seasons under different future climate scenarios vary significantly. This study’s findings can provide significant support for future research on climate change and drought, which can help improve the accuracy of future climate projections and serve as a reference for drought risk management and policy formulation.

A Novel Integrated Spatiotemporal-Variable Model of Landscape Changes in Traditional Villages in the Jinshaan Gorge, Yellow River Basin

Spatiotemporal studies of landscape pattern evolution in traditional villages are beneficial for addressing complex urbanization and global climate change. Using the traditional villages of Jiaxian and Linxian in the Jinshaan Gorge of the Yellow River Basin, this study employed a three-dimensional (3D) analysis involving three spatial scales (macro, meso, and micro), temporal scales (past, present, and future), and variables (humanity, society, and nature) based on the methods of spatiotemporal data analysis (SDA), geographic information system, remote sensing, and landscape pattern index (LPI) by Fragstats. On the macro scale, a significant turning point in ecological conservation awareness was indicated by LPI and SDA. Urban and rural construction land continuously increased because of urbanization. Plowland, grassland, and woodland were the main influencing factors in the evolution of rural settlements, with a 0.42% cumulative transformation rate. On the meso scale, the interactions and mutual promotion of mountain and aquatic environments, aquatic facilities, agricultural production, and cultural heritage have shaped the socioeconomic dimensions of evolution. On the micro scale, with urbanization, some traditional humanistic spaces have lost their original functions. A novel spatiotemporal-variable quantitative model explored the spatiotemporal evolution characteristics of human–land coupling, which can be used for the sustainable development of river basins worldwide.

Spatial and temporal variation of vegetation NPP and analysis of influencing factors in Heilongjiang Province, China

Vegetation net primary productivity (NPP) plays an important role in investigating carbon cycle and NPP provided by forest constitutes the main component of terrestrial vegetation NPP. Since late 1990s, a series of policies for protecting forest have been implemented in Heilongjiang Province, China, directly leading to the continuous change of forest area and growth environment. As a result, analysis of spatiotemporal characteristics of NPP after the implementation of protection policies was conducted in this study, and influencing factors leading to the distribution and spatiotemporal heterogeneity of NPP was also compared quantitatively, aiming to reveal the changing characteristics and influencing factors of NPP in a large area over a longer time span. In this study, multi-source data including MOD17A3 NPP product, meteorological data, topographic data and land-use data was selected, and then the spatiotemporal characteristics of NPP from 2001 to 2020 were analyzed through commonly-used spatiotemporal analysis indices and methods. Finally, influencing factors leading to the distribution and spatiotemporal heterogeneity of NPP in the study area were quantitatively compared and analyzed. Results show that the total amount of NPP in Heilongjiang Province has a fluctuating trend with an increasing rate of 23.47% over the past twenty years, and the increase/decrease scenarios of NPP usually can be found in forest regions with high forest coverage and urban regions experiencing dramatic urbanization processes, respectively. Meanwhile, this study also finds that there is no absolute dominant factor can be responsible for the difference in spatial distribution of NPP, but the conversion between forest and other land-use types are the main factors leading to the spatiotemporal heterogeneity of NPP in the study area. The conclusions in this study may provide guiding significance for formulating forestry protection policies in forest regions and designing land-use plans in the future, aiming to achieve the balance between ecological protection and urban development.

Spatiotemporal Evolution and Cause Analysis of Innovation Ecosystem Niche Fitness: A Case Study of the Yellow River Basin

In this study, we explore the evolution and formation mechanism of innovation ecosystem niche fitness from three perspectives: theoretical analysis, model construction, and empirical testing. Based on the niche theory, a theoretical framework for the innovation ecosystem is constructed. Spatiotemporal analysis and qualitative comparison methods are employed to estimate the innovation niche fitness of nine provinces located in the Yellow River Basin, and their spatiotemporal characteristics and differences in terms of formation mechanism differences are then explored. The results show that: (1) temporally, from 2000 to 2017, the innovation niche fitness in the Yellow River Basin experienced minor fluctuations. However, since 2017, there has been a considerable increase. Spatially, the midstream and downstream regions, particularly in the provinces of Shandong, Henan, and Sichuan, have a higher innovation fitness. Conversely, the upstream regions of Qinghai, Ningxia, Gansu, and Inner Mongolia display a lower fitness because of the lack of advantages in innovation elements. (2) The innovation niche fitness is heavily influenced by several factors, including the number of industrial enterprises above designated size, R&D personnel, higher education institutions, scientific research institutions, expenditure for technical renovation, sales revenue of new products, total expenditure on R&D, and the total retail sales of consumer goods. (3) Four mechanisms explain high innovation niche fitness, each of which corresponds to different regions. The formation mechanisms of low innovation niche fitness can be categorized into two paths, which have an asymmetric relationship with the formation mechanisms of high innovation niche fitness. Therefore, provinces and regions should focus on these advantages to enhance the innovation niche fitness. Our research will serve as a theoretical reference to enhance the overall innovation capacity of the Yellow River Basin in the future.