Spatial Flow Research Articles

A hypergraph-based hybrid graph convolutional network for intracity human activity intensity prediction and geographic relationship interpretation

Human activity intensity prediction, i.e., estimating the dynamic population distribution, is crucial to many location-based applications, particularly intelligent transportation systems and urban planning. Geographical artificial intelligence (GeoAI) is an emerging field that has contributed many new modeling approaches for spatiotemporal phenomena, especially spatiotemporal graph neural networks (ST-GNNs). However, there are two challenges to the prediction of human activity intensity. On the one hand, accurate predictions require modeling not only pairwise spatial relationships, e.g., distance and spatial flow but also complex high-order relationships, such as clusters and groups. On the other hand, it is not appropriate to incorporate prior knowledge on the spatial structure of data with subjective assumptions using graphs with predefined topologies or weights. To mitigate such challenges, a novel deep learning-based model was proposed, namely, the Hypergraph-based Hybrid Graph Convolutional Network (HyGCN). In our study, high-order relationships are modeled by hypergraph convolutions, and pairwise relationships are captured in graph convolutions. A spatial fusion layer is added to fuse the pairwise and high-order relationships. Then, adaptive graph and hypergraph generation methods are proposed to interpret edge weights and understand the learned geographic relationships in accordance with principles. After systematic experiments on large-scale mobility data, our results demonstrate that HyGCN outperforms state-of-the-art models. The learned geospatial knowledge can be disentangled and explained from the perspectives of geographic principles. This study offers new insights for not only revisiting geographic laws in GeoAI but also utilizing spatiotemporal knowledge in deep learning models.

Heat exposure assessment based on high-resolution spatio-temporal data of population dynamics and temperature variations

Urban heat waves pose a significant risk to the health and safety of city dwellers, with urbanization potentially amplifying the health impact of extreme heat. Accurate assessments of population heat exposure hinge on the interplay between temperature, population spatial dynamics, and the epidemiological effects of temperature on health. Yet, many past studies have over-simplified the matter by assuming static populations, leading to substantial inaccuracies in heat exposure assessments. To address these issues, this study integrates dynamic population data, fluctuating temperature, and the exposure-response relationship between temperature and health to construct an advanced heat exposure assessment framework predicated on a population dynamic model. We analyzed urban heat island characteristics, population dynamics, and heat exposure during heat wave conditions in Beijing, a major city in China. Our findings highlight significant intra-day population movement between urban and suburban areas during heat wave conditions, with spatial population flow patterns showing clear scale-dependent characteristics. These population flow dynamics intensify heat exposure levels, and the disparity between dynamic population-weighted temperature and average temperature is most pronounced at night. Our research provides a more comprehensive understanding of real urban population heat exposure levels and can furnish city administrators with more scientifically rigorous evidence.

KEKUATAN SPASIAL KLUSTER PERKOTAAN DI KABUPATEN SEMARANG

Semarang Regency has 19 sub-districts with diverse regional growth. Several areas have experienced development into urban areas, such as Bawen District, East Ungaran District, West Ungaran District, Bergas District, Pringapus District, and Ambarawa District. Each sub-district has urban areas with different levels of influence. This research aims to identify urban clusters that are formed in the five sub-districts that are the focus of the study and provide an assessment of the urban areas that have the greatest influence. The research method used is quantitative descriptive. Secondary data collection is carried out through documentation techniques, namely by recording and studying statistical data relevant to the problem being discussed. Urban clusters in West Ungaran and East Ungaran Districts reciprocally provide the greatest spatial influence. The spatial interaction between the Bawen and Ambarawa urban clusters is in second place. The smallest spatial flow occurs between urban clusters in Bergas District and East Ungaran District. It is hoped that this research can provide input to the regional government of Semarang Regency to increase social interaction between Bergas District and Ungaran Timur District, through investment activities and development of several areas for industrial activities or other activities that can increase the attractiveness of the two clusters in these two sub-districts

Effects of increasing complexity in biogeochemistry and hydrology on variability of total phosphorus concentration in models of a low flow subtropical wetland

In south Florida, treatment wetlands known as stormwater treatment areas (STAs) are used to reduce total phosphorus (P) concentrations of agricultural and urban stormwater runoff before discharging into the Everglades Protection Area. The remobilization of P from immobile forms stored in soils and litter has the potential to negatively affect STA treatment efficiency. Phosphorus biogeochemical dynamics in one of the best performing Everglades STA flow-ways, STA-2 FW1, were modeled mechanistically and empirically using diverse streams of data (flow, total P in water, soil, floc, vegetation). We evaluated the effect of P wetland uptake and hydrologic transport on STA-2 FW1's simulated outflow P concentrations by juxtaposing varying complexities of biogeochemistry (1–2 vs 13 wetland P pools) and hydrology (uniform vs. nonuniform flow) models. The uniform flow model considers a constant velocity with each time step while velocity in the nonuniform flow model is a stochastic ensemble mimicking spatial flow variability. When coupled with uniform or nonuniform flow, the variability of water column P concentration in the most complex model was significantly different from the two simple biogeochemistry models, although it was lower compared to data in all models. Regardless of the biogeochemistry model, uniform versus nonuniform transport did not affect simulated outflow P concentration. Significant interaction effects between the choices of biogeochemistry and hydrology models were observed. The most complex model included specific compartments for vegetation, floc and soil and reproduced data trends of more P accumulated in upstream areas that decreases with increasing distance away from inflow. Modeling biotic mechanisms and tracking P in vegetation and sediments, confirms the importance of including biogeochemistry in understanding and predicting outflow P concentrations in STA-2 FW1. This modeling tool could be used to guide future STA data collection needs and inform Everglades STA performance optimization efforts.

Nested numerical simulation of multi-scale flow characteristics around monopile foundation of offshore wind farm at Weihai coastal water, China

When constructing an offshore wind farm under tidal current conditions, flow field characteristics without structures are often used for design. However, the presence of structure will greatly affect the flow field around structures. Therefore, it is important to study the flow characteristics around the wind farm foundations under the tidal flow conditions to ensure safe construction and operation. Due to the spatial-temporal scale differences of different physical processes, it is difficult to apply the existing numerical models to accurately simulate the flow characteristics around the offshore monopile. Therefore, this study will link the large-scale TELEMAC with the small-scale OpenFOAM code to establish a multi-scale spatial nesting model to consider the temporal and spatial flow characteristics of real ocean environmental conditions. The nested model is validated using the measurements. The validated model is then applied to conduct some parametric study to evaluate the effect of the wind speed and direction and tidal flow on the flow characteristics around the monopile. Results reveal that the tidal flow velocity could be significantly suppressed by strong opposite wind and vice versa. Tidal flow separation occurring at the lee side of the pile generates the so-called von Karman vortex street downstream of the pile.

China-US grain trade shapes the spatial genetic pattern of common ragweed in East China cities

Common ragweed is an invasive alien species causing severe allergies in urban residents. Understanding its urban invasion pathways is crucial for effective control. However, knowledge is limited, with most studies focusing on agricultural and natural areas, and occurrence record-based studies exhibiting uncertainties. We address this gap through a study in East China cities, combining population genetics and occurrence records. Leaf samples from 37 urban common ragweed populations across 15 cities are collected. Genomic and chloroplast DNA extraction facilitate analysis of spatial genetic patterns and gene flows. Additionally, international grain trade data is examined to trace invasion sources. Results indicate spatial genetic patterns impacted by multiple introductions over time. We infer the modern grain trade between the United States and China as the primary invasion pathway. Also, cities act as transportation hubs and ports of grain importation might disperse common ragweed to urban areas. Invasive species control should account for cities as potential landing and spread hubs of common ragweed.

Analyzing the Spatial Interaction Characteristics of Urban Area Shared Bicycle Systems: A Case Study of Beijing’s Central Area

Shared bicycle systems play a crucial role in promoting sustainable urban transportation, addressing challenges such as traffic congestion and air pollution. Understanding the spatiotemporal patterns of shared bike usage is essential for optimizing bike-sharing infrastructure and improving transportation planning. In this study, we analyzed 2.4 million records of shared bicycle data to explore the spatial distribution, interaction patterns, and flow dynamics within Beijing’s urban central area. We found that bike distribution peaks during commuting hours, particularly in central regions with employment centers. Complex networks are an important method for studying travel flows. Through a spatial interaction network, we identified key streets with high node strength and popularity, often concentrated in central areas. They experience heavy shared bicycle use during peak hours due to their employment-centric location. Conversely, peripheral areas see increased usage in the evenings, reflecting distinct commuting patterns. The morning exhibits higher positive central values compared to the evening, while negative values show the opposite trend. Based on these findings, we recommend enhancing bike infrastructure in high-density areas with bike lanes and ample shared bikes during peak hours. Implementing mixed-use zoning policies in the central region can reduce traffic congestion. Expanding shared bike services to peripheral regions can promote equitable access. This research underscores the importance of considering spatial and temporal factors in urban transportation planning. Future work should incorporate additional data sources, explore environmental impacts, and analyze usage in different seasons and special events, further contributing to sustainable urban mobility development.

Climate smart agriculture for sustainable productivity and healthy landscapes

In rural areas of developing countries, more than 70% of the population still depends on agriculture. However, economic crises, unscientific land allocation and climate change issues have hindered attempted gains in agricultural productivity and related rural development outcomes. Technology-driven breakthrough has usually pushed agriculture to the brink of another development that can affect not only plant diversity and yield, but also climatological and socio-economic outcomes. The concept of sustainable agriculture has become increasingly popularized as research and farming communities believe that productivity with environmental and social consequences need to be judiciously balanced. Agriculture of developed nations has practically benefitted of this concept worldwide during the 1990 s. In order to be successful worldwide, extensive research must be conducted not only for large-scale farms in developed nations but also for small-scale farms in developing nations. Climate smart agriculture conserves biodiversity by adopting coherent, multifunctional, and multi-cropping approach through design oriented spatial planning and flow among them. Here, a spatiotemporal data driven paradigm for sensing monitoring and directing agricultural ecosystem has been proposed to optimize the use of climate smart circular agricultural system.

COMPARATIVE ANALYSIS OF SOFTWARE SYSTEMS FOR HYDRAULIC TURBINE FLOW SIMULATION

A comprehensive review of modern software complexes used for calculating spatial flow in hydraulic turbine flow parts was conducted. The widely used software system, Ansys, was analyzed. An overview of Ansys was provided, including its history, popularity within the CFD community, key features, and capabilities for analyzing the flow parts of hydraulic turbines. The preprocessing tools, solver parameters, post-processing functions, and visualization capabilities of Ansys were described. The advantages and limitations of using Ansys for calculating spatial flow in hydraulic turbine flow parts were analyzed. The open-source CFD software complex, OpenFOAM, was discussed. The main functions and capabilities of the OpenFOAM program were described. Information about solver libraries, meshing capabilities, advantages, and limitations for analyzing hydraulic turbines was presented, along with insights into the support from the scientific community and resources available to OpenFOAM users. SolidWorks FlowSimulation, which integrates with SolidWorks software, was examined. The unique features of SolidWorks FlowSimulation for analyzing spatial flow in hydraulic turbines were highlighted. The possibilities of CAD integration and the advantages of accurate geometric models were discussed. The capabilities of parametric analysis were explored, and the advantages and limitations of using SolidWorks FlowSimulation for calculating spatial flow in hydraulic turbine flow parts were analyzed. A comparison of the three software complexes was conducted based on their capabilities, ease of use, accuracy, computational resources required, and cost. An assessment of the advantages and disadvantages of each program was provided, along with recommendations for choosing the most suitable program based on specific use cases, objectives, and user requirements. This article serves as a valuable resource for engineers, designers, and researchers seeking insights into the available software systems for analyzing hydraulic turbine flow parts. It enables them to make informed decisions in selecting the most suitable software system based on their specific requirements, ultimately contributing to the optimization of hydraulic turbine performance and efficiency.

The Spatiotemporal Pattern Evolution and Driving Force of Tourism Information Flow in the Chengdu–Chongqing City Cluster

In recent years, the tourism industry has developed rapidly. However, traditional tourism information has the disadvantages of slow response speed and limited information content, which cannot reflect the evolution trend of spatial and temporal patterns of tourism information in time. Here, based on the Baidu Index, we construct an evaluation framework to analyse the spatial and temporal flow of tourism information in the Chengdu–Chongqing urban cluster from 2011 to 2021. Then, we analyse the urban links between different network levels from the evolution pattern. Finally, we use the geodetector model to analyse its driving mechanism. The results show that Chengdu and Chongqing are the most active cities in the study area in terms of tourism information. The unbalanced development of tourism information between Chengdu and Chongqing and other cities in the region gradually deepens during the period 2011–2019 (polarization effect), but the unbalanced development moderates after 2019. On the other hand, cities in the middle of the Chengdu–Chongqing cluster always have weak agglomeration effects of tourism information. Cities with high tourism information outflow rates in the Chengdu–Chongqing city cluster are mainly concentrated around Chengdu. The average outflow rate of Deyang is the highest, at 27.8%. Cities with low tourist information outflow rates are primarily located in the west, central and south. Ya’an is the city with the lowest outflow rate, with an average of −62.2%. Specifically, Chengdu is the dominant and most radiantly influential city. The tourism information of the Chengdu–Chongqing urban cluster shows a radial network with Chengdu and Chongqing as the core. The driving force analysis shows that the push factor of tourist source, such as the number of people buying pension insurance, is the core driving mechanism, while the pull factor of destination, such as the park green area, and resistance factors such as psychological distance, are in the secondary position. In general, this paper uses Internet tourism data to expand the traditional tourism information research of the Chengdu–Chongqing urban cluster, which can better respond to the changes and needs of the tourism market and provide reference for the spatial optimization of tourism destinations.

Migratory patterns of two major influenza virus host species on tropical islands.

Animal migration is a major driver of infectious agent dispersal. Duck and seabird migrations, for instance, play a key role in the spatial transmission dynamics and gene flow of avian influenza viruses (AIV), worldwide. On tropical islands, brown and lesser noddies (Anous stolidus and Anous tenuirostris) may be important AIV hosts, but the lack of knowledge on their migratory behaviour limits our understanding of virus circulation in island networks. Here we show that high connectivity between islands generated by non-breeding dispersive behaviours may be a major driver in the spread and the maintenance of AIV among tropical islands of the western Indian Ocean. Tracking data highlight two types of dispersive behaviours during the non-breeding season: birds either staying in the vicinity of their breeding ground (on Bird Island, Seychelles), or moving to and roosting on other islands in the western Indian Ocean. Migrant birds used a wide range of roosting places from the Tanzanian coasts to the Maldives archipelago and Tromelin Island. Epidemiological data confirm that brown and lesser noddies are major hosts for AIV, although significant variations of seroprevalence between species suggest that other biological and ecological drivers could be involved in virus infection and transmission dynamics.

Objective Boundary Generation for Gross Target Volume and Organs at Risk Using 3D Multi-Modal Medical Images.

Accurate delineation of Gross Target Volume (GTV) and Organs at Risk (OARs) in medical images is an essential but challenging step in radiotherapy. Deep-learning based automated delineation methods, which learn from manual annotations, are currently prevalent in academic research. However, the limited resolution of medical images and the fuzzy boundaries of lesions and organs present a challenge to the precision of manual annotations. By leveraging the complementary information from multi-modal medical images, this study proposed a novel method to generate objective boundaries of GTV and OARs. We present a novel method of objective boundary generation, inspired by image matting primarily used for 2D RGB natural images, to process 3D grayscale medical images. The proposed method has the following advantages. 1) It allows for flexible input modalities and assigns weights to each modality according to their relative significance when computing information flows in the matting algorithm. 2) It computes 3D spatial information flow among voxels, which has more advantages over its 2D counterpart. 3) It has a closed-form solution that generates deterministic results. To evaluate the characteristics of the generated boundaries, patients with stage I nasopharyngeal carcinoma (NPC) were studied, with CT images and multi-modal MR images (T1, T1C, T2) aligned using deformable registration. Region of Interests (ROIs), i.e., GTV and parotid gland, were used, with a rough trimap marking extremely few foreground voxels, many background voxels, and a large unknown region. The proposed algorithm leverages the connection between each voxel and its nearest neighbors in the feature space, to propagate the opacity information. We evaluated the results by employing both qualitative and quantitative methods. Using qualitative evaluation, experienced clinicians confirmed that the results were in agreement with the input data, especially for areas where borders were visible in most modalities (e.g., between air and tumor). For more challenging regions, where boundaries were unclear in the images, the results displayed fine-grained opacity transitions indicating the confidence of each voxel belonging to the ROI. When compared to the delineations made by clinicians, we found our results are usually more compact. We define a precision metric that evaluates the ratio of the matted foreground inside clinicians' delineations versus the entire matted foreground. Using a threshold of 0.4, our binarized result scored 0.95 for GTV and 0.92 for parotid gland. The proposed method demonstrated satisfactory results on challenging ROIs. The objective boundaries generated by this method have advantages in many aspects, including improvement of delineation protocols, enhancement of manual annotation consistency, and increase of deep-learning based automated delineation accuracy.

Lagrangian investigation of the interface dynamics in single-mode Rayleigh–Taylor instability

We apply Lagrangian particle tracking to the two-dimensional single-mode Rayleigh–Taylor (RT) instability to study the dynamical evolution of fluid interface. At the onset of the nonlinear RT stage, we select three ensembles of tracer particles located at the bubble tip, at the spike tip, and inside the spiral of the mushroom structure, which cover most of the interfacial region as the instability develops. Conditional statistics performed on the three sets of particles and over different RT evolution stages, such as the trajectory curvature, velocity, and acceleration, reveals the temporal and spatial flow patterns characterizing the single-mode RT growth. The probability density functions of tracer particle velocity and trajectory curvature exhibit scalings compatible with local flow topology, such as the swirling motion of the spiral particles. Large-scale anisotropy of RT interfacial flows, measured by the ratio of horizontal to vertical kinetic energy, also varies for different particle ensembles arising from the differing evolution patterns of the particle acceleration. In addition, we provide direct evidence to connect the RT bubble re-acceleration to its interaction with the transported fluid from the spike side, due to the shear driven Kelvin–Helmholtz instability. Furthermore, we reveal that the secondary RT instability inside the spiral, which destabilizes the spiraling motion and induces complex flow structures, is generated by the centrifugal acceleration.

Flow-Data-Based Global Spatial Autocorrelation Measurements for Evaluating Spatial Interactions

Spatial autocorrelation analysis is essential for understanding the distribution patterns of spatial flow data. Existing methods focus mainly on the origins and destinations of flow units and the relationships between them. These methods measure the autocorrelation of gravity or the positional and directional autocorrelations of flow units that are treated as objects. However, the intrinsic complexity of actual flow data necessitates the consideration of not only gravity, positional, and directional autocorrelations but also the autocorrelations of the variables of interest. This study proposes a global spatial autocorrelation method to measure the variables of interest of flow data. This method mainly consists of three steps. First, the proximity constraints of the origin and destination of a flow unit are defined to ensure similarity of flow units in terms of direction, distance, and position. This undertaking aims to determine the neighborhood of flow units and generate their adjacent matrices. Second, a spatial autocorrelation measurement model for flow data is constructed on the basis of the adjacent matrix generated. Artificial data sets are also employed to test the validity of the model. Finally, the proposed method is applied to the flow data analysis of population migration in central and eastern China to prove the practical application value of the model. The proposed method is universal and can be generalized to the global spatial autocorrelation analysis of any type of flow data.

Coherent Flow Structures Linked to the Impulse Criterion for Incipient Motion of Coarse Sediment

Incipient motion has been a topic of investigation by researchers, engineers and scientists for more than a century. The main approach for studying sediment entrainment has been the static approach that uses temporal and spatial averaged flow parameters like bed shear stress and stream power to link them indirectly to sediment entrainment. Recent research outputs have shed light on the important role of turbulent fluctuations in the sediment transport process. It is suggested that the approach of using temporal and spatial averaged parameters fails to account for the dynamic and probabilistic nature of the entrainment process, as inherited by flow turbulence. This has led to the introduction of the only dynamic criteria in the literature for studying sediment entrainment, namely the impulse and energy criteria. These criteria take into account both the magnitude and duration of the turbulent flow event used for assessing the conditions that can result in sediment entrainment. In light of this, this work aims to assess whether there is a trend in terms of the type of flow structures that occur in sequence before and after the occurrences of the flow impulses that have resulted in the coarse particle’s entrainment. To achieve this, we conducted a well-controlled laboratory experiment to investigate the incipient motion of a 7 cm diameter instrumented particle. Five runs of the experiment were performed at flowrates close to the threshold of motion. The instrumented particle was equipped with micro-electro-mechanical sensors (MEMS) to accurately measure its inertial dynamics and detect motion. The sensors recorded entrainment events, and these events were stochastically linked to the impulses occurring for the tested flow conditions. Quadrant analysis was used to investigate the type of flow structures that occurred before, during and after the occurrence of quadrant events with an impulse above the critical impulse. The findings herein associate coarse particle entrainments with energetic impulses linked primarily to sweep events (Q4) and secondarily, sequence of sweeps (Q4) and ejections (Q1).

A space-time flow LISA approach for panel flow data

Spatial flow data represent meaningful spatial interaction (SI) phenomena between geographic regions that are often highly dynamic. However, most existing flow analytical methods are cross-sectional, and there is a lack of methods to measure spatiotemporal autocorrelation of flow data. To fill this gap, we proposed a new localized spatial statistical method called Space-Time Flow LISA. The method design is a combination of two existing method families, namely space-time LISA and Spatial Flow LISA. A critical component of the method is the space-time weight matrix of flow data that blends pairwise spatial and temporal connectivities. We design three versions of the matrix, namely contemporaneous, lagged, and hybrid. We evaluate the method using both synthetic data and a case study of U.S. interstate migration from 2005 to 2017. The method is found to have high efficacy in finding spatiotemporal local autocorrelation patterns. Unlike the Spatial Flow LISA that tends to detect short-distance migration corridor havens (‘HH’ flows) and long-distance migration corridor deserts (‘LL’ flows), the Space-Time Flow LISA is less impeded by the distance between flow origin and destination, as they can pick up local patterns that are less spatially explicit but temporally dependent. In addition, the new method is able to detect time-sensitive patterns such as the outmigration from Louisiana forced by Hurricane Katrina in 2005. By integrating spatial, temporal, and attributive associations into a one-step analysis, the proposed Space-Time Flow LISA can illustrate the spatiotemporal structure of flow phenomena well, and reveal dynamic distribution changes over time.

Study on classifying particle size and spatial internal flow field of T-shaped tooth structure in a disturbing rotary centrifugal air classifier

In the present study, three perturbed rotor structures were designed to investigate the performance of a new perturbed rotary centrifugal airflow classifier through experiments and numerical simulations. A model for the maximum and minimum classification particle size was established on the basis of the mechanical analysis of the blade. Pressure distribution and flow patterns at different positions in the Z-X and Z-Y planes of three structures under different rotational frequencies were analyzed, as well as the classification performance. The results show that in the rotor with different tooth structures operating at a critical rotational frequency, the internal flow field have abnormal pressure distribution, where the smaller density and velocity of the gas–solid phase reduce particle motion velocity, centrifugal force, and collision opportunities, forming particle sidewalls near the screen surface, thereby deteriorating the classification effect. The classification performance of the three structures under different operating parameters shows that when the rotation frequency exceeds 24 Hz, feed rate is 0.3 kg/s, and classifier inclination angle is −4°, the highest macroscopic and microscopic classification efficiency of the single-row tooth structure is 94.21% and 94.23%, respectively. The study can provide a reference to improve the flow field and classification performance of air classifiers in industrial applications.

Water security assessment and driving mechanism in the ecosystem service flow condition.

Water resources are the foundation of human survival and development. Thus, it is essential to quantify the relationship between water supply and demand and assess water security to ensure sustainable use of water resources. In this study, we quantified the relationship between water supply and demand in the Yiluo River Basin (YRB) using the InVEST model, constructed a spatial flow model of water provision service at the sub-watershed scale, and analyzed the water security index (WSI) under static and dynamic conditions. Subsequently, we used geographic detectors to identify the explanatory power of the main influencing factors of the WSI. The results showed that (1) from 2005 to 2018, the water supply decreased continuously and the water demand first increased and then decreased in the YRB; (2) the spatial pattern of WSI showed that it was high in the southwest and low in the northeast, wherein the highest and lowest WSI values occurred in 2005 and 2018, respectively. From 2005 to 2018, the mean values of the static and dynamic WSI decreased from 0.77 to 0.60 and 1.56 to 1.20, respectively. (3) The precipitation factor from the supply dimension and the gross domestic product (GDP) factor from the demand dimension had the strongest explanatory power for the static WSI. For the dynamic WSI, the flow quantity of the water provision service flow had the strongest explanatory power. This study provided a reference for governments to formulate sustainable water resource management.

Supply-demand relationship and spatial flow of urban cultural ecosystem services: The case of Shenzhen, China

Cultural ecosystem services (CES) have garnered significant attention due to their capacity to comprehensively evaluate the influence of ecosystem degradation on human well-being. It is very important to evaluate the spatial coordination relationship between the supply and demand of urban CES and explore its spatial flow mechanism for guiding the sustainable development of urban regions. However, research on the relationship between supply and demand of urban CES and their spatial flows is still in the conceptual exploration stage, and its quantitative assessment methods are not yet mature. Taking Shenzhen as an example, this study uses the Social Values for Ecosystem Services model to evaluate the supply of CES based on environmental background and survey statistics; Uses the topic model of natural language processing, quantify the demand of CES based on social media data. The Bivariate Local Indicators of Spatial Association index is utilized to examine the correlation between supply and demand, while a crowd activity extraction method based on mobile phone signaling data is developed to simulate the spatial flow pattern of CES. The results show that: (1) The supply of urban CES shows obvious spatial differentiation; There are differences in the perception of the needs of various CES. Among them, the beneficiaries have higher needs for Aesthetic and Recreation. (2) The relationship between supply and demand of urban CES shows different degrees of spatial mismatch, with the proportion of the supply-demand matching area to the total area being less than 20%, and the main type of supply-demand mismatch being low supply-high demand. (3) All kinds of CES flow from the center of the study area to the east, among which the flow of recreation and Aesthetic is more active, with the highest flow of 189.7 and 135.3, respectively. Overall, the coordination between various types of CES in the region is still insufficient. We should encourage the establishment and improvement of a diversified transportation system connecting the central urban area and other regions while enhancing the condition of the local ecological conditions, to improve the capacity of urban ring roads and rail transit, to promote the sustainable development of the regional ecological environment and social economy, and to enhance human well-being.

A general meta‐ecosystem model to predict ecosystem functions at landscape extents

The integration of ecosystem processes over large spatial extents is critical to predicting whether and how local and global changes may impact biodiversity and ecosystem functions. Yet, there remains an important gap in meta‐ecosystem models to predict multiple functions (e.g. carbon sequestration, elemental cycling, trophic efficiency) across ecosystem types (e.g. terrestrial‐aquatic, benthic‐pelagic). We derive a flexible meta‐ecosystem model to predict ecosystem functions at landscape extents by integrating the spatial dimension of natural systems as spatial networks of different habitat types connected by cross‐ecosystem flows of materials and organisms. We partition the physical connectedness of ecosystems from the spatial flow rates of materials and organisms, allowing the representation of all types of connectivity across ecosystem boundaries. Through simulating a forest‐lake‐stream meta‐ecosystem, our model illustrates that even if spatial flows induced significant local losses of nutrients, differences in local ecosystem efficiencies could lead to increased secondary production at regional scale. This emergent result, which we dub the ‘cross‐ecosystem efficiency hypothesis', emphasizes the importance of integrating ecosystem diversity and complementarity in meta‐ecosystem models to generate empirically testable hypotheses for ecosystem functions.