Spatial Analysis Tools Research Articles

Seq-Scope: repurposing Illumina sequencing flow cells for high-resolution spatial transcriptomics.

Spatial transcriptomics technologies aim to advance gene expression studies by profiling the entire transcriptome with intact spatial information from a single histological slide. However, the application of spatial transcriptomics is limited by low resolution, limited transcript coverage, complex procedures, poor scalability and high costs of initial setup and/or individual experiments. Seq-Scope repurposes the Illumina sequencing platform for high-resolution, high-content spatial transcriptome analysis, overcoming these limitations. It offers submicrometer resolution, high capture efficiency, rapid turnaround time and precise annotation of histopathology at a much lower cost than commercial alternatives. This protocol details the implementation of Seq-Scope with an Illumina NovaSeq 6000 sequencing flow cell, allowing the profiling of multiple tissue sections in an area of 7 mm × 7 mm or larger. We describe the preparation of a fresh-frozen tissue section for both histological imaging and sequencing library preparation and provide a streamlined computational pipeline with comprehensive instructions to integrate histological and transcriptomic data for high-resolution spatial analysis. This includes the use of conventional software tools for single-cell and spatial analysis, as well as our recently developed segmentation-free method for analyzing spatial data at submicrometer resolution. Aside from array production and sequencing, which can be done in batches, tissue processing, library preparation and running the computational pipeline can be completed within 3 days by researchers with experience in molecular biology, histology and basic Unix skills. Given its adaptability across various biological tissues, Seq-Scope establishes itself as an invaluable tool for researchers in molecular biology and histology.

Discharge characteristics of silicon-based DC Helium microplasmas: comparison between Through Silicon Via and closed cavity type micro-reactors

Abstract This paper explores the microfabrication process of plasma reactors, from the production of micro-reactors to the addition of Through Silicon Vias (TSV) using proprietary etching techniques and on site cleanroom facilities. The investigation focuses on both newly fabricated closed and open-cavity Micro-Hollow Cathode Discharge (MHCD) reactors, with particular emphasis on electrical and optical diagnostics to characterize their plasma properties. All experiments, including those on closed-cavity reactors, were specifically conducted for this study under identical conditions to ensure a rigorous comparison between reactor types. When electrical diagnostics were carried out in helium at various pressures, interesting phenomena such as the evolution of voltage breakdown were observed. These diagnostics also provided new insights into the impact of surface electrode and overall geometry properties and pressure on reactor performance. 
Additional investigation into instability mechanisms revealed self-pulsing oscillations, with different reactor types having different oscillation amplitudes for similar operating conditions. Optical emission spectroscopy (OES) emerged as a strong tool for spatial plasma analysis, revealing consistent gas temperature distributions across reactor diameters. The Inglis-Teller series provided an estimate of the upper limit of electron density, while the Stark broadening analysis of Hα offered valuable insights into electron density variations, particularly in the self-pulsing regime.

Prioritization of important plant areas for conservation of frailejones (Espeletiinae, Asteraceae) in the Northern Andes

AbstractThe tropical Andes region harbors areas that are highly significant for biodiversity at both global and local scales. However, despite the severe conservation threats that this region has to confront, conservation initiatives are limited, by funding and because of the need for more information to decide which areas are priorities for conservation. Identifying Important Plant Areas (IPAs) offers a valuable methodology for establishing conservation priorities, a particularly complex task in a mega-biodiverse region such as the northern Andean tropics. Due to its iconic recognition and conservation value, this study focused on the Espeletiinae subtribe (Asteraceae) as a model group; hence, the distribution data for 138 species was compiled from 5560 georeferenced records. Using the IPA methodology, we divided the study area, this is, the distribution area of the subtribe in the Andean tropics, into 220 Units of Analysis (UA) represented by 10 × 10 km plots. Refined species’ distribution areas, incorporating richness, threatened species, and ecosystem-based richness distributions, were analyzed using newly generated maps to evaluate the conservation value of each UA. Our analysis identified 176 UAs with some level of relevance using sub-criterion cA1 (with 59 species) and 51 UAs using sub-criterion cB (with 76 species). Integrating both criteria, we classified 11 UAs as high-priority, 62 as medium-priority, and 147 as low-priority, highlighting the IPAs that require focused conservation efforts. Two identified high-priority IPAs are located in Venezuela and nine in Colombia, predominantly associated with the Eastern Cordillera. These areas concentrate between 7 and 12 species. They are mainly linked to the páramo complexes of Colombia and the Sierra Nevada in Venezuela, primarily within Cool Temperate Moist Grassland ecosystems on mountains. Our results provide a spatial planning procedure and analytical tool for decision-makers to guide conservation management and actions across northern Andes.

An Areal Approach to Spatial Accessibility Analysis

Place‐based spatial accessibility quantifies the distribution of access to goods and services across space. The Two‐Step Floating Catchment Area (2SFCA) family of methods have become a default tool for spatial accessibility analysis in part due to their intuitive approach and interpretability. This family of methods relies on calculating catchment areas around supply locations to estimate the area and population that may utilize them. However, these “catchment areas” are generally defined by origin‐destination matrices of travel‐time, giving us point‐to‐point distances and not polygons with actual area. This means that population geographies (census tracts, blocks, etc.) are binarily included or excluded, with no room for partial inclusion. When using nongranular data, which is often the case due to data privacy restrictions, this has the potential to cause significant errors in accessibility measurements. In this article, we propose Areal 2SFCA: a new approach that considers the area of overlap between travel‐time polygons and population geographies. We demonstrate the effectiveness of the Areal 2SFCA method using a case study that compares the Enhanced Two‐Step Floating Catchment Area (E2SFCA) and Areal E2SFCA for the state of Illinois in the USA using multiple population granularities.

ML Approaches for the Study of Significant Heritage Contexts: An Application on Coastal Landscapes in Sardinia

Remote Sensing (RS) and Geographic Information Science (GIS) techniques are powerful tools for spatial data collection, analysis, management, and digitization within cultural heritage frameworks. Despite their capabilities, challenges remain in automating data semantic classification for conservation purposes. To address this, leveraging airborne Light Detection And Ranging (LiDAR) point clouds, complex spatial analyses, and automated data structuring is crucial for supporting heritage preservation and knowledge processes. In this context, the present contribution investigates the latest Artificial Intelligence (AI) technologies for automating existing LiDAR data structuring, focusing on the case study of Sardinia coastlines. Moreover, the study preliminary addresses automation challenges in the perspective of historical defensive landscapes mapping. Since historical defensive architectures and landscapes are characterized by several challenging complexities—including their association with dark periods in recent history and chronological stratification—their digitization and preservation are highly multidisciplinary issues. This research aims to improve data structuring automation in these large heritage contexts with a multiscale approach by applying Machine Learning (ML) techniques to low-scale 3D Airborne Laser Scanning (ALS) point clouds. The study thus develops a predictive Deep Learning Model (DLM) for the semantic segmentation of sparse point clouds (<10 pts/m2), adaptable to large landscape heritage contexts and heterogeneous data scales. Additionally, a preliminary investigation into object-detection methods has been conducted to map specific fortification artifacts efficiently.

Designing breezeways to enhance wind environments in high-density cities: A comprehensive analysis of ten morphological parameters

To advance urban breezeway designs, this paper presents a pioneering and comprehensive study of breezeway morphological parameters. Ten parameters, selected through extensive literature review, include coverage ratio, porosity, line density, sinuosity, rotation angle, width, length, average height, height variation, and aspect ratio. Regression analysis, utilizing over 200 data points collected from wind tunnel experiments in Hong Kong, established correlations between these parameters and pedestrian-level wind velocity ratio (VRpoint). Results reveal that among the 2D parameters, width, length, line density, and coverage ratio exhibit the strongest correlations with VRpoint, while aspect ratio and porosity emerge as significant factors among the 3D parameters. Notably, simple 2D parameters, coverage ratio and width, effectively substitute for their 3D counterparts, porosity and aspect ratio, in high-density urban areas. Furthermore, the results highlight the parameters’ relative contributions to urban ventilation. From a street-level perspective, VRpoint is primarily influenced by configurations of street segments (width, 80%) and street intersections (rotation angle, 20%). From a neighborhood-level perspective, permeability (coverage ratio, 35%), fragmentation (line density, 30%), and roughness (average height, 35%) are critical factors. Illustrative examples are provided to facilitate the translation of findings into spatial analysis tools and design guidelines to assist planners and decision-makers in sustainable developments.

GraphCVAE: Uncovering cell heterogeneity and therapeutic target discovery through residual and contrastive learning.

Advancements in Spatial Transcriptomics (ST) technologies in recent years have transformed the analysis of tissue structure and function within spatial contexts. However, accurately identifying spatial domains remains challenging due to data sparsity and noise. Traditional clustering methods often fail to capture spatial dependencies, while spatial clustering methods struggle with batch effects and data integration. We introduce GraphCVAE, a model designed to enhance spatial domain identification by integrating spatial and morphological information, correcting batch effects, and managing heterogeneous data. GraphCVAE employs a multi-layer Graph Convolutional Network (GCN) and a variational autoencoder to improve the representation and integration of spatial information. Through contrastive learning, the model captures subtle differences between cell types and states. Extensive testing on various ST datasets demonstrates GraphCVAE's robustness and biological contributions. In the dorsolateral prefrontal cortex (DLPFC) dataset, it accurately delineates cortical layer boundaries. In glioblastoma, GraphCVAE reveals critical therapeutic targets such as TF and NFIB. In colorectal cancer, it explores the role of the extracellular matrix in colorectal cancer. The model's performance metrics consistently surpass existing methods, validating its effectiveness. GraphCVAE's advanced visualization capabilities further highlight its precision in resolving spatial structures, making it a powerful tool for spatial transcriptomics analysis and offering new insights into disease studies.

Model for Estimating Soil Chemical Properties with RGB Drone Images

Precision agriculture optimizes crop management by providing accurate data on soil chemical properties, thereby improving agricultural productivity and sustainability. This study aims to develop models to estimate soil chemical properties, such as pH, electrical conductivity (EC), and organic matter (OM), by analyzing drone-captured RGB images. The methodology included photogrammetric flights with a DJI Phantom 4 Pro drone equipped with a 20 Mpx camera and simultaneous sampling, laboratory analysis and on-site measurements, with Royal Eijkelkamp EC meter set voor grond multiparameter sensors and pH meter set for soil and water. The aerial images were processed with the PIX4Dmapper software, to generate the orthophoto and spectral bands. With the resulting orthophoto of 1.6 cm/pixel, eight spectral indices were calculated, using the spatial analysis tools of ArcGIS software. The in situ results showed an average pH value of 5.83, indicating a slightly acidic soil, and an EC of 1.09 dS/m, suggesting a soil with a low concentration of dissolved salts. Laboratory analyses showed a medium-high content of OM, with an average of 5.19 %. A strong correlation was found between OM and pH_index with coefficients of determination R2=0.55, while moderate correlations were also observed between pH with pH_index and EC with sal_index6 with coefficients of determination R2=-0.39 and R2=0.42 respectively. The aforementioned results allowed the generation of two models for the estimation of these variables from RGB images.

Heritage Tourism Development Should Take Care of Industrial Heritage Protection: A Study on the Development Strategy of Industrial Heritage Tourism in Nanjing

The tourism development of industrial heritage is an effective way to activate cultural heritage and can provide new solutions for the renewal and protection of industrial heritage. This study focuses on the industrial heritage in Nanjing, aiming to explore its spatial distribution pattern, tourism development strategy, and sustainable development model. This study adopts a combination of quantitative and qualitative research methods. First, relevant information on 93 sites of industrial heritage type historic buildings in Nanjing is collected. Secondly, ArcGIS was used to visualize the evolution of industrial buildings and the spatial distribution of industrial heritage type historic buildings. Finally, the spatial analysis tools of ArcGIS and the accessibility analysis method in space syntax theory are superimposed to comprehensively analyze the spatial distribution pattern and traffic accessibility characteristics of Nanjing’s industrial heritage. The research results propose a specific plan to promote the value transformation of industrial heritage through tourism: based on the spatial distribution characteristics of Nanjing’s industrial heritage along the water system and traffic arteries, a tourist route of “multi-point, two-axis, one-center” is planned; the tourism development strategy of “point protrusion, linear links, and surface darning” is implemented; and a sustainable development model under the guidance of low-carbon environmental protection goals is explored. This study provides a reference for the protective development of industrial heritage and the expansion of tourism and opens up a new perspective for the regeneration and planning of other urban heritage.

Spatial Epidemiology and Its Role in Prevention and Control of Swine Viral Disease

Spatial epidemiology offers a comprehensive framework for analyzing the spatial distribution and transmission of diseases, leveraging advanced technical tools and software, including Geographic Information Systems (GISs), remote sensing technology, statistical and mathematical software, and spatial analysis tools. Despite its increasing application to swine viral diseases (SVDs), certain challenges arise from its interdisciplinary nature. To support novices, frontline veterinarians, and public health policymakers in navigating its complexities, we provide a comprehensive overview of the common applications of spatial epidemiology in SVD. These applications are classified into four categories based on their objectives: visualizing and elucidating spatiotemporal distribution patterns, identifying risk factors, risk mapping, and tracing the spatiotemporal evolution of pathogens. We further elucidate the technical methods, software, and considerations necessary to accomplish these objectives. Additionally, we address critical issues such as the ecological fallacy and hypothesis generation in geographic correlation analysis. Finally, we explore the future prospects of spatial epidemiology in SVD within the One Health framework, offering a valuable reference for researchers engaged in the spatial analysis of SVD and other epidemics.

The Extent of Agricultural Land Cover Change: The Case of Bautista, Pangasinan

Urban Expansion in Bautista periodically alters its land cover, significantly diminishing agricultural land in favor of built-up areas. Therefore, a comprehensive investigation of past and future land cover changes is essential for sustainable development. The main objective of this research is to determine the extent of agricultural land cover change and its spatial pattern from 2018 to 2022 and in the year 2027 in Bautista, Pangasinan. This study employed a descriptive research approach integrating time series data, spatial analysis, and simulation tools to evaluate the dynamic changes in agricultural land cover. The extent of agricultural land cover change decreased from 3,480.95 hectares in 2018 to 3,417.04 hectares in 2022, primarily near road networks and within the identified urban barangays of the municipality. The change exhibited a clustered distribution, as per the summary report from the Average Nearest Neighbor using the Geographic Information System. In contrast, built-up land cover change expanded by 60.68 hectares from 2018 to 2022, following an increasing trend. Furthermore, the projected agricultural land cover change is expected to decrease continuously from 3,417.039 hectares in 2022 to 3,406.875 hectares in 2027. The results indicate a conversion of agricultural land to built-up areas between 2018 and 2022 and a further decrease in 2027, reflecting a shift from agriculture to man-made structures. This research underscores the importance of monitoring land conversions and provides a basis for sustainable solutions to address the growing demand for urbanization without compromising the future extent of agricultural land cover in Bautista, Pangasinan.

Environmental Fragility in Commercial Forest Plantations Aiming at Sustainable Management

Objective: The objective of this study is to investigate the environmental fragility of two landscapes in which there is a predominance of commercial forest plantations, with the aim of analyzing this fragility through multicriterial evaluation, focusing on the planning of forest roads. Theoretical Framework: The theory of environmental fragility, multi-criteria evaluation methods, and principles of landscape management stand out, providing a solid basis for understanding the context of the investigation. These frameworks are crucial for analyzing the impact of forest roads on environmental variables such as water dynamics, sediment production, and soil erodibility. The integration of these theories allows for a comprehensive assessment of environmental fragility, essential for effective forest planning and road maintenance in forested areas Method: The methodology adopted involves a multi-criteria evaluation approach to assess environmental fragility in forest landscapes. Key criteria, including slope, distance from water bodies, soil erodibility, and topographic humidity, were integrated to create fragility maps. Data were collected through geographical and environmental analysis, processed with spatial analysis tools, and supplemented by principal component analysis to identify influential criteria. This approach enabled a detailed assessment of forest roads' environmental impact, supporting informed decisions in road planning and landscape management. Results and Discussion: The results revealed notable variations in environmental fragility. The analysis of forest roads highlighted their varying impact on environmental fragility. These results are framed within the context of environmental fragility and multi-criteria evaluation, emphasizing the role of criteria such as slope, proximity to water bodies, soil erodibility, and topographic humidity in assessing landscape degradation. The study underscores the need for careful forest road planning to mitigate environmental impacts and promotes the use of comprehensive evaluation methods in sustainable landscape management. Research Implications: The practical and theoretical implications of this research are discussed, providing insights into how the results can be applied or influence practices in the field of forest management and environmental conservation. These implications could encompass forest road planning, sustainable landscape management, and the development of environmental policies aimed at mitigating the impact of infrastructure on natural ecosystems.The study's findings emphasize the need for incorporating multi-criteria evaluation methods into forest planning processes to identify and mitigate areas of high environmental fragility. This approach can guide decision-makers in prioritizing areas for road maintenance, rehabilitation, or even rerouting to minimize ecological damage. Furthermore, the research underscores the importance of integrating environmental fragility assessments into broader forest management strategies, enhancing the sustainability of commercial forestry operations while preserving essential ecosystem services. Originality/Value: This study contributes to the literature by introducing an innovative application of multi-criteria evaluation in assessing environmental fragility within forest landscapes, specifically in the context of forest road planning. The research combines various environmental criteria into a comprehensive fragility map, offering a nuanced understanding of landscape vulnerability that is less commonly addressed in existing studies. Notably, this methodology focused on forest roads has not been previously explored in the literature, highlighting the unique contribution of this research to the field.The relevance and value of this research are evidenced by its potential to influence forest management practices, particularly in the sustainable planning and maintenance of forest roads.

Mining, farming, and diplomacy. Understanding the human landscape of Bronze Age Sardinia (Italy) through geospatial analysis

Human agency on landscape modification and land use is often seen in terms of socio-economic opportunities vs. natural constraints. In the study of prehistoric cultures this is both a strong source of information about sustenance strategies and community behaviours, and a subject potentially easy to analyse within a limited set of physical and social parameters. The recent advancements in the use of spatial analysis tools in landscape archaeology allow to obtain ever more precise models. However, studies that compare at the same time the geological landscape and social elements are very scarce. We used Point Pattern Analysis and Modelling to investigate megalithic structures (nuraghes) in Bronze Age southwestern Sardinia (Italy) and identify correlations between their spatial patterns and a set of covariates encompassing both environmental (i.e. topography and geological resources) and cultural factors. The models which best represent pattern distribution come from the combination of covariates from both groups.The models highlight a close distance from known ore deposits and show a clear dependence of Nuragic populations to ore extraction and metallurgy. The availability of fertile soils with moderate permeability and moderately low pH is also significant, as well as a preference to prominent locations with a positive correlation with the Topographic Position Index and the Convexity Index. From a cultural standpoint, we observed a consistent aggregation of simple nuraghes around complex nuraghes at mid-short distances. The occurrence of polycentric patterns can be explained either by the former emerging from the presence of the latter or vice versa, and is typically associated with a loosely stratified social structure devoid of strong hierarchies. These results underscore the efficacy of spatial analysis in disentangling and juxtaposing the physical and social factors influencing the distribution of past culture, and offer new insight on the development of Bronze Age societies in their geographical context.

Enhancing Digital Twins with Human Movement Data: A Comparative Study of Lidar-Based Tracking Methods

Digitals twins, used to represent dynamic environments, require accurate tracking of human movement to enhance their real-world application. This paper contributes to the field by systematically evaluating and comparing pre-existing tracking methods to identify strengths, weaknesses and practical applications within digital twin frameworks. The purpose of this study is to assess the efficacy of existing human movement tracking techniques for digital twins in real world environments, with the goal of improving spatial analysis and interaction within these virtual modes. We compare three approaches using indoor-mounted lidar sensors: (1) a frame-by-frame method deep learning model with convolutional neural networks (CNNs), (2) custom algorithms developed using OpenCV, and (3) the off-the-shelf lidar perception software package Percept version 1.6.3. Of these, the deep learning method performed best (F1 = 0.88), followed by Percept (F1 = 0.61), and finally the custom algorithms using OpenCV (F1 = 0.58). Each method had particular strengths and weaknesses, with OpenCV-based approaches that use frame comparison vulnerable to signal instability that is manifested as “flickering” in the dataset. Subsequent analysis of the spatial distribution of error revealed that both the custom algorithms and Percept took longer to acquire an identification, resulting in increased error near doorways. Percept software excelled in scenarios involving stationary individuals. These findings highlight the importance of selecting appropriate tracking methods for specific use. Future work will focus on model optimization, alternative data logging techniques, and innovative approaches to mitigate computational challenges, paving the way for more sophisticated and accessible spatial analysis tools. Integrating complementary sensor types and strategies, such as radar, audio levels, indoor positioning systems (IPSs), and wi-fi data, could further improve detection accuracy and validation while maintaining privacy.

Implementing geographic information systems in academic paper search: A framework and case study: GISAPS

The Geographic Information Systems Academic Publication Search (GISAPS) project pioneers the integration of spatial data into academic research through a novel online platform. Utilizing Web Map Service (WMS) and advanced search functionalities, GISAPS enables efficient access and analysis of georeferenced academic publications. This paper outlines the development and capabilities of GISAPS, including experimental WMS layer integration for enhanced data visualization. Key contributions include facilitating interdisciplinary research, improving spatial data accessibility, and promoting open science through its open-source framework. GISAPS's implications for academia and the research community are significant, offering new avenues for data-driven research and education. Future enhancements aim to incorporate real-time data feeds, advanced spatial analysis tools, and mobile accessibility, ensuring GISAPS remains at the forefront of GIS technology in academic settings.

Spatial patterns of maternal and neonatal continuum of care use and its correlations with women’s empowerment

BackgroundThe continuum of care (CoC) in maternal health refers to the continuity of individual reproductive health care across the antenatal, intrapartum, and postnatal periods. The CoC is an indicator of the quality of maternal and newborn health outcomes and women’s empowerment is crucial to improving maternal and neonatal health service access and utilisation.ObjectiveTo examine the spatial patterns of continuum of care use for maternal and neonatal health services and its correlation with women’s empowerment.MethodsWe analysed data from the Ethiopian Demographic and Health Surveys (EDHS) of 2011 and 2016. All women aged 15–49 who had live births in the preceding five years of the DHS surveys were included in the analysis. We measured the continuum of care using the modified co-coverage index (CoCI), which consisted of six indicators. Women’s empowerment was assessed using a validated survey-based Women’s Empowerment (SWPER) index. We used the Getis-Ord-Gi* spatial analysis tool to portray locations with clusters of CoC service use and spatial correlations between CoC use and women empowerment.ResultsNone of the newborn-mother pairs in the 2011 survey received the entire continuum of care and only 2.5% of newborn-mother pairs received the full range of continuum of care services in the 2016 survey. In 2016, 6.9% of mother-newborn pairs received the basic CoC services (four or more antenatal care [ANC] visits, skilled birth attendance [SBA], and postnatal care [PNC]), and no mother-newborn pair received all three services at the same time in 2011. The Amhara, Afar, and Somali regional states had the least CoC service use in both surveys. There was a positive spatial correlation between CoC use and women’s empowerment domains.ConclusionOur analysis showed that the use of four or more ANC visits, SBS, newborn PNC, Bacillus Calmette-Guérin (BCG) vaccine uptake, and tetanus toxoid protection at birth were low in Ethiopia. Women empowerment domains were found to have a positive spatial correlation with CoC services use. To improve and preserve continuity of care, it is critical to leverage every maternal health facility encounter to encourage sustained service usage at each step of the continuum. Government policies should prioritise women’s empowerment and raise public awareness of maternity services.

Estimating Evacuation Distance, Time, and Range to Shelters Through Network Analysis

This study employed the QGIS spatial information analysis tool to conduct network analyses through the O-D Matrix and ISO-Area methods. These network analyses calculated travel distances and evacuation times from flooded residential buildings (evacuation start points) to shelters (evacuation end points) in the event of a reservoir collapse. Feasibility of evacuation from each residential building was assessed by comparing the evacuation time with the initial flood wave arrival and peak flood level occurrence times. Walking speeds were set based on population characteristics: 1 m/s for children and the elderly and 2 m/s for adults (fast walking). Using a walking speed of 1 m/s, evacuation zones were established at 300-meter intervals, representing a 5-minute walking distance for each shelter. The resident population per building was estimated to determine the number of evacuees, and shelter capacity was calculated based on the required space per person.

Changes in river morphology and influencing factors in the upper Yellow River over the past 25 years

Changes to the morphology of the upper Yellow River (UYR) had various impacts on the surrounding ecology and society, as well as the entire basin. However, low-spatial-resolution imagery (e.g., MODIS, AVHRR) cannot capture sufficient spatial details for monitoring complex water bodies, while high-spatial-resolution imagery (e.g., SPOT, Quickbird, Ikonos) lacks spatial coverage and the revisit frequency necessary for large-scale water body monitoring. To address these limitations, this study utilized the Google Earth Engine (GEE) and ArcGIS spatial analysis tools, applied pan-sharpening to downscale Landsat imagery of the study area from 1999 to 2023, performed river extraction, and calculated the spatiotemporal changes in river morphology in the UYR using river morphological parameters (i.e. area, channel width, centerline length, sinuosity index, lateral migration rate, channel stability). The Automated Water Extraction Index (AWEIsh) effectively characterized the study area, and pan-sharpening technology improved the extraction accuracy of small water bodies. Finally, the overall accuracy and Kappa coefficient were 0.993 and 0.985, respectively. Over the past 25 years, the area and average width of the entire reach of the UYR changed significantly, with the maximum value being 1.3 times the minimum value, whereas the centerline length and sinuosity index showed no apparent changes, and the lateral migration rate varied minimally, with the average annual movement ranging from 4.67 m to 10.18 m. In typical river segments without human activity, although single-channel reaches exhibited stronger stability than multi-channel reaches, natural factors (i.e. annual precipitation, annual runoff, annual sediment discharge) had a noticeable impact on the morphology of both single-channel and multi-channel reaches. Large-scale cascade hydropower development in the UYR has significantly impacted river morphology over a short period. Meanwhile, in river sections unaffected by human activities, the changes occurred gradually. This study provides support for better understanding complex river morphologies at large regional and long-term scales and a scientific basis for water resource management and sustainable development in the UYR.

Analyzing network flows of used EEE and e‐waste with platform data: Adding reuse into the EPR system for WEEE recycling in China

AbstractChina has established a nationwide formal e‐waste disassembly system based on the extended producer responsibility principle. However, the system mainly focuses on material recycling while excluding the reuse of used electrical and electronic equipment (used EEE) due to the complexity of flows and transactions during the reuse stage. Recently, emerging online platforms have played an increasingly crucial role in the value chain of reuse and recycling, significantly improving the visibility of flows in these activities. This paper aims to depict the spatial structure of the used EEE and e‐waste flow networks using a multi‐scale analysis framework. Using spatial analysis tools in complex network analysis, we characterize cross‐city reuse flows in China. A clear regional pattern of a hierarchical reuse network of notable regional hubs in certain regions in China is revealed in this analysis. The role of regulation is demonstrated in the comparison of the spatial flows in reuse and recycling. In conclusion, it is proposed that reuse should be included and emphasized in the management of waste electrical and electronic equipment in China to maximize the value of circularity.

Research on the construction of intangible cultural heritage corridors in the Yellow River Basin based on geographic information system (GIS) technology and the minimum cumulative resistance (MCR) model

Objectively and accurately identifying the spatial structure and protection scope of intangible cultural heritage and constructing intangible cultural heritage corridors are crucial for the comprehensive systematic protection of intangible cultural heritage and the synergistic development of the region. However, the current research on intangible cultural heritage is limited to the protection and development of intangible cultural heritage in specific locations or specific areas. Thus, systematic and holistic research perspectives are relatively limited. Therefore, this study employs geographic information system spatial analysis and the minimum cumulative resistance model to construct an intangible cultural heritage corridor in the Yellow River Basin. This study aims to establish a systematic protection method and framework for intangible cultural heritage. The results show the following: (1) The intangible cultural heritage in the Yellow River Basin has a large-scale centralized distribution and small-scale scattered distribution, which provides an important spatial basis for the construction of intangible cultural heritage corridors. (2) Overall, intangible cultural heritage corridors can be more effectively constructed in the eastern region of the Yellow River Basin than in the western region, with 84.6% of the area being suitable and 15.4% being unsuitable. (3) Based on the suitability analysis, the "18 + N" corridor system of intangible cultural heritage in the Yellow River Basin, distributed across the eastern, central and southern regions, is constructed. The major corridor has a suitable width of 60–100 km, a total length of 11,935 km, and an area of 625,976 km2–919,942 km2, and can connect 634–711 intangible cultural heritage sites in series. On this basis, this study proposes a multilevel construction system for intangible cultural heritage corridors in the Yellow River Basin that integrates the "network structure–spatial scope" and "element–axis–region" levels. This approach culminates in a pattern of intangible cultural heritage protection and development in the Yellow River Basin characterized by "connecting points into lines, distributing in groups, and linking regions." This study reveals that combining geographic information system spatial analysis tools with a minimum cumulative resistance model effectively identifies potential heritage corridor networks and clarifies the hierarchical relationships of heritage element protection in the study area. This approach provides a reference model for the comprehensive protection and systematic development of intangible cultural heritage in the Yellow River Basin. Furthermore, the effectiveness and universality of this framework make it applicable to the protection and development of other similar international heritage areas.