Analysis Of Spatial Structure Research Articles

Research on a Multi-Scale Clustering Method for Buildings Taking into Account Visual Cognition

Building clustering is a key problem that needs to be solved in the realization of the automatic synthesis of large-scale maps. The selection of different feature and spatial distance calculation methods has a great impact on the clustering results, and the need to manually select appropriate feature and distance metrics leads to the problem of not being able to fully consider the complexity and diversity of buildings. In this paper, we propose a multi-scale clustering method for buildings that takes visual perception into account using the Gestalt principle to simulate how humans classify buildings through visual perception. Moreover, by analyzing the spatial features and texture attributes of buildings, a visual distance is designed to be used as a condition for building classification to assess the similarity between buildings, solving the complexity of manually selecting feature vectors and spatial distances and realizing the adaptive selection of features. Through experimental validation at different scales (macro, meso and micro), the present method is able to achieve the accurate clustering of buildings, and a frequency threshold of 91% is found, which is able to determine the optimal clustering results. The experimental results show that the proposed method can not only fully consider the complexity and diversity of buildings but also effectively support the understanding and analysis of urban spatial structure and provide a scientific decision-making basis for urban planning and management.

Spatial-structural analysis of macroeconomic factors' impact on carbon emissions in East Africa: a spatial econometric panel study.

Despite the abundance of research on reducing carbon emissions, there is a significant gap in understanding the influence of macroeconomic factors on carbon dioxide (CO2) emissions from a spatial-structural perspective. This study aims to contribute to the literature by investigating the impact of macroeconomic factors on carbon dioxide emissions in six East African countries between 1989 and 2020. Using spatial econometric panel models, the study analyzed spatial dependence among the variables. The empirical findings indicate that gross domestic product (GDP) per capita and electricity consumption have positive direct and indirect effects on carbon emissions, while fuel prices and exports have negative direct effects, but positive spillover effects on neighboring countries. Imports have a positive impact on local economies, but negative spillover effects. Additionally, the urban population has no significant impact on the environment. These findings provide important policy implications for optimizing spatial growth patterns and achieving a low-carbon economy in East African countries.

BESO and SESO: Comparative Analysis of Spatial Structures Considering Self-Weight and Structural Reliability

A comparative analysis between Bidirectional Evolutionary Structural Optimization (BESO) and Smoothing-ESO (SESO), simultaneously considering Reliability-Based Topology Optimization (RBTO) and the structure’s self-weight in the case of three-dimensional elasticity, is presented in this study. Due to the important role of the existence of uncertainties in making the structural design more realistic, geometry, volumetric fraction, modulus of elasticity, compliance, and loading are considered random variables with normal probability distribution. When adopting the First Order Reliability Method (FORM), the failure probability is calculated based on the reliability index. Furthermore, considering the influence of self-weight in problems involving large structures in civil engineering, especially in the case of bridges, this makes the optimal configuration more reliable for design. A series of examples are covered to validate the methods presented, showing their efficiency and robustness.

Predicting the Integrated Fire Resistance of Wildland–Urban Interface Plant Communities by Spatial Structure Analysis Learning for Shanghai, China

Abstract: Fire is a prevalent hazard that poses a significant risk to public safety and societal progress. The continuous expansion of densely populated urban areas, exacerbated by global warming and the increasing intensification of urban heat islands, has led to a notable increase in the frequency and severity of fires worldwide. Incorporating measures to withstand different types of calamities has always been a crucial aspect of urban infrastructure. Well-designed plant communities play a pivotal role as a component of green space systems in addressing climate-related challenges, effectively mitigating the occurrence and spread of fires. This study conducted field research on 21 sites in the green belt around Shanghai, China, quantifying tree morphological indexes and coordinate positions. The spatial structure attributes of different plant communities were analyzed by principal component analysis, CRITIC weighting approach, and stepwise regression analysis to build a comprehensive fire resistance prediction model. Through this research, the relationship between community spatial structures and fire resistance was explored. A systematic construction of a prediction model based on community spatial structures for fire resistance was undertaken, and the fire resistance performance could be quickly judged by easily measured tree morphological indexes, providing valuable insights for the dynamic prediction of fire resistance. According to the evaluation and ranking conducted by the prediction model, the Celtis sinensis, Sapindus saponaria, Osmanthus fragrans, Koelreuteria paniculata, and Distylium racemosum + Populus euramericana ‘I-214’ communities exhibited a high level of fire resistance. On the other hand, the Koelreuteria bipinnata + Ligustrum lucidum, Ginkgo biloba + Camphora officinarum + Ligustrum lucidum, and Ligustrum lucidum + Sapindus saponaria communities obtained lower scores and were positioned lower in the ranking. It is emphasized that the integration of monitoring and regulation is essential to ensure the ecological integrity and well-being of green areas in the Wildland–Urban Interface.

Micro-Scale Analysis and Optimization of Rural Settlement Spatial Patterns: A Case Study of Huanglong Town, Dayu County

Optimizing the spatial patterns of rural settlements is crucial for rural development and revitalization. Enhancing the internal spatial configuration of these settlements necessitates a comprehensive understanding of their micro-scale spatial characteristics. This study develops evaluation indicators and methodologies to quantify rural settlement spatial patterns by analyzing their multidimensional aspects. The research utilizes Huanglong Town in Dayu County, Jiangxi Province, as a case study for exploring micro-scale spatial patterns and proposing corresponding optimization models. The research employs remote sensing image processing and GIS spatial analysis to collect data on the study area. The results indicate that rural settlements in Huanglong Town generally form clustered patterns with moderate spatial structure intensity and order. Notably, spatial heterogeneity is observed across the northern mountainous area, the central plain and low hilly region, and the southern hilly area. Based on these findings, the study categorizes rural settlements in Huanglong Town into four optimization models: stable improvement, internal potential exploitation, controlled expansion, and relocation and withdrawal. Each model is associated with differentiated optimization strategies. By integrating analyses of spatial form, structure, and order, this study reveals the intrinsic spatial characteristics of rural settlements, offering a systematic approach to guide their spatial optimization.

Kinematics and spatial structure analysis of TBM gunite robot based on D–H parameter method

In modern tunnel construction, TBM (Tunnel Boring Machine) plays a very important role. In response to the needs of tunnel wall reinforcement and TBM automated construction for tunnel construction, a shotcrete mechanism mounted on the TBM is designed. In order to evaluate the kinematic performance of the mechanism, this paper studies the forward and inverse kinematics and spatial architecture of the TBM shotcrete robot. Firstly, based on the D–H parameter method, the number of joints and links is determined and structural analysis is performed to obtain the robot's forward kinematics equation, achieving the mapping between joint space and pose space. Then, by determining the joint variables, the mapping of the end tool in Cartesian space is achieved. Finally, based on the Monte Carlo random sampling method, the workspace of the robot is constructed, and its reachability and flexibility within the robot workspace are evaluated. The performance of the device is verified by building a prototype, which meets the requirements well. Through the research in this paper, it can provide theoretical basis and guidance for the design and control of the shotcrete robot.

How to achieve differentiated urban mitigation? Evidence from the spatial differences in aggregate embodied carbon intensity

A better understanding of the spatial differences in aggregate carbon intensity (ACI) among cities is useful in achieving differentiated urban mitigation. In this study, we investigated the aggregate embodied carbon intensities and spatial differences in the four municipalities in China during 2007–2017 using an input-output (I–O) model at both the aggregate and final demand category levels. The results indicated that the spatial differences were distinct and gradually increased at both levels. In addition, a spatial structural decomposition analysis was utilized to examine the main factors that contributed to these differences. The energy intensity effect was confirmed as the dominant factor, while the structural effects played an increasingly significant role, particularly the Leontief production structure effect. Finally, policy implications associated with the differentiated CO2 peaking paths were proposed.

Spatial and component structure analysis of the inclusive circular economy: SGICE

Modern statistics and scientific research consider the circular economy rather one-sidedly, essentially as recycling and reuse of resources. The article proposes methodology (concept) of the global inclusive circular economy, which can be considered as a complex multidimensional system, the main components of which are the economic, sociological, ecological and circular aspects of the country's life. To achieve this goal, the GNU regression, econometrics and time-series library was used – an applied software package for econometric modeling, a part of the GNU project. Accordingly, we will define the global inclusive circular economy as the SGICE (Global Inclusive Circular Economy) system, characterized by the vector of functions. For the most exhaustive consideration of the entire range of opportunities of the global inclusive circular economy, the study developed and accordingly analyzed the integrated index of the development of the global inclusive circular economy (IGICE) by ecological, economic, social and circular components with isolated weakly correlated indicators. The ranking of countries was carried out separately by the components of the index, on the basis of which circular cores were distinguished: the social component (Belgium, the Czech Republic, USA, China, France, Greece, Austria, Australia), the ecological component (Japan, Denmark), the economic component (Germany, China), which became the basis for the model of formation of global inclusive circular chains. The conducted cluster analysis based on the component indicators of the index of the global inclusive circular economy confirmed the formation of a large circular gap (gap) in the ecological and circular components. This indicator is introduced for the first time and makes it possible to comprehensively analyze the country and highlight additional effects that arise from the moment of implementation of circular business projects and the inclusion of the country (on the basis of firms, corporations) in global circular chains of added value. After all, the main problematic aspects are the illegal trade in waste and growing smuggling, which cause serious negative social consequences and actualize the inclusive component in the justification of the global inclusive circular economy paradigm. Waste reduction combined with wise use of resources has the potential to address the gap resulting from the scarcity of natural resources and a growing global population or consumption. The formation of circular trade will contribute to: the determination of priority materials for trade and the required level of processing capacity, coordination of material quality standards, promoting demand for used goods and secondary raw materials, removing unnecessary regulatory barriers and avoiding environmentally harmful activities such as non-compliance, poor regulation and informal recovery.

Solar radiation visibility testing algorithm for precise shadow distribution for spatial steel structural thermal analysis under sunlight

As a large-span spatial steel structure, the radio telescope, characterized by its extensive span and numerous structural components, is sensitive to temperature gradients and susceptible to localized deformations induced by non-uniform temperature filed under sunlight, thereby affecting the operational performance. In order to calculate the shadow distribution of the structure, this study proposes a solar radiation visibility testing algorithm based on the depth buffer. To validate the accuracy of the proposed algorithm, the shadow distribution of a shading device is calculated employing the new algorithm, theoretical methods, and Ladybug tools. The results show that the shadow distribution obtained by the three methods is consistent. The thermal numerical simulation of the grid structure using the direct solar radiation calculated by the proposed algorithm is compared with the experiment results, and the error is below 5 %. Taking a radio telescope as an engineering application, the temperature distribution and pointing accuracy under solar are calculated. 9:00 and 16:00 have the worst pointing accuracy, while 11:00–13:00 has the best pointing accuracy. This study can provide a reference for the calculation of temperature field of spatial steel structure which is sensitive to sunlight temperature.

Narrative and Space: Reconstruction of Heritage Network for South Guangdong Ancient Post Road

The ancient post road in Southern Guangdong is a channel of communication inside and outside Lingnan in different historical periods. It is also known as a corridor of population migration, a channel of commodity exchange and an ancient road of civilization and culture. In view of the limitations of focusing on space while ignoring narrative in the previous research of ancient post road, the analysis method of narrative spatial structure was introduced herein. Based on the four main functions of the ancient post road in Southern Guangdong, including postal exchange, military war, commerce and trade, and official travel, the holistic and systematic narrative space study of the ancient post road in Southern Guangdong was carried out in order to construct a relatively complete narrative space structure of the heritage corridor network. Research showed that the four functional narrative points all rely on corridors. Its spatial structure took Guangzhou as the main core, with Leizhou and Yangjiang in western Guangdong and Chaozhou in eastern Guangdong as the sub-core. A waterway-based land as the auxiliary network system was formed; The post road shifted from the original military function to the economic function, experienced the internal communication with the Central Plains to the coastal areas, and then to the world through the Maritime Silk Road. It became the epitome of the historical development process of Lingnan region. The narrative spatial structure analysis method can deeply explore the historical characteristics and cultural connotation of the heritage corridor, and promote the overall cognition of the post road. The analysis results have certain guiding significance for the construction and protection of the heritage corridor network of the ancient post road in Southern Guangdong. It can also provide a reference for other scholars to carry out the research on similar heritage corridors.

Material Selection for Restoration of Genetic Diversity of Abies koreana on Mt. Jirisan in South Korea

A strategy is required for selecting appropriate materials for the restoration of Abies koreana on Mt. Jirisan, where the habitat of A. koreana is continuously shrinking. The current study aimed to analyze the genetic characteristics of A. koreana in three subpopulations (Banyabong, Byeoksoryeng, and Cheonwangbong) on Mt. Jirisan using 10 nuclear simple sequence repeat (nSSR) markers and calculate the sampling distance for each subpopulation for avoiding genetically similar samples. Based on the calculated sampling distance, we proposed the size of a sample containing more than 95% of the alleles at a frequency greater than 0.05. AMOVA showed that the difference in genetic variation across subpopulations of A. koreana on Mt. Jirisan was small, approximately 3% of the total. Spatial genetic structure analysis results suggested that it would be appropriate to collect samples of the Banyabong subpopulation at intervals of 10 m or more, when sampling A. koreana, whereas for the Byeoksoryeong and Cheonwangbong subpopulations, samples should be collected at intervals of 20 m or more. Results of random sampling of 5 to 30 individuals indicated that, by applying a 10 m distance within the Banyabong subpopulation, more than 95% of the total alleles with a frequency ≥ 0.05 were secured when more than 25 individuals were extracted. Therefore, as a restoration strategy for A. koreana on Mt. Jirisan, we proposed the collection of more than 25 samples, keeping 10 m distance within the Banyabong subpopulation, which has a relatively high genetic diversity.

Algorithm for Point Cloud Dust Filtering of LiDAR for Autonomous Vehicles in Mining Area

With the continuous development of the transformation of the “smart mine” in the mineral industry, the use of sensors in autonomous trucks has become very common. However, the driving of trucks causes the point cloud collected by through Light Detection and Ranging (LiDAR) to contain dust points, leading to a significant decline in its detection performance, which makes it easy for vehicles to have failures at the perceptual level. In order to solve this problem, this study proposes a LiDAR point cloud denoising method for the quantitative analysis of laser reflection intensity and spatial structure. This method uses laser reflectivity as the benchmark template, constructs the initial confidence level template and initially screens out the sparse dust point cloud. The results are analyzed through the Euclidean distance of adjacent points, and the confidence level in the corresponding template is reduced for rescreening. The experimental results show that our method can significantly filter dust point cloud particles while retaining the rich environmental information of data. The computational load caused by filtering is far lower than that of other methods, and the overall operation efficiency of the system has no significant delay.

Discovering invariant spatial features in electron energy loss spectroscopy images on the mesoscopic and atomic levels

Over the last two decades, Electron Energy Loss Spectroscopy (EELS) imaging with a scanning transmission electron microscope has emerged as a technique of choice for visualizing complex chemical, electronic, plasmonic, and phononic phenomena in complex materials and structures. The availability of the EELS data necessitates the development of methods to analyze multidimensional data sets with complex spatial and energy structures. Traditionally, the analysis of these data sets has been based on analysis of individual spectra, one at a time, whereas the spatial structure and correlations between individual spatial pixels containing the relevant information of the physics of underpinning processes have generally been ignored and analyzed only via the visualization as 2D maps. Here, we develop a machine learning-based approach and workflows for the analysis of spatial structures in 3D EELS data sets using a combination of dimensionality reduction and multichannel rotationally invariant variational autoencoders. This approach is illustrated for the analysis of both the plasmonic phenomena in a system of nanowires and in the core excitations in functional oxides using low loss and core-loss EELS, respectively. The code developed in this manuscript is open sourced and freely available and provided as a Jupyter notebook for the interested reader.

Lifetime seismic fragility analysis of long-span spatial structures considering the wind-induced fatigue effect

Long-span spatial structures are susceptible to cumulative fatigue damage over their lifespan due to prolonged exposure to wind loads. The degradation in material properties resulting from fatigue can impact the structure's performance to resist earthquakes. However, previous studies and current design specifications do not address the issue of wind-induced fatigue in long-span spatial structures, making it difficult to evaluate the multihazard risk of the facilities under the combined action of fatigue and earthquakes. In this study, an assessment framework for the lifetime multihazard fragility of long-span spatial structures subjected to fatigue and earthquakes is developed to address this issue. The innovation of the proposed wind-induced fatigue analysis is that the influence of wind direction can be considered. Multihazard fragility surfaces are established with a mixed exponential model to assess the seismic fragility of the structure with different service times. The proposed method is applied to the seismic analysis of a typical single-layer Kiewitt-8 reticulated dome (SK8RD) to verify its feasibility. The results demonstrate that after 47 years of service, the ultimate bearing capacity of SK8RD decreases by 45.5% due to the wind-induced fatigue, while its collapse probability increases by 25.3% under the seismic action. Neglecting wind direction randomness and focusing solely on a single direction can lead to an overestimation of wind fatigue damage by more than twofold. Utilizing the probabilistic multihazard demand model based on the mixed exponential function enhances the regression accuracy by 12.9%, facilitating a more precise assessment of multihazard fragility for the SK8RD subjected to seismic and long-term wind fatigue. The proposed framework offers a valuable tool for evaluating multihazard risk considering the wind-induced fatigue for in-service long-span spatial structures.

Simultaneous monitoring of the limited area ionosphere with the use of GPS and ionosonde

The importance of this study lies in its contribution to the field of ionospheric science, particularly in the monitoring and prediction of ionospheric parameters. This study focuses on comparing two techniques for spatio-temporal kriging of critical ionospheric parameters, namely the F2 layer critical frequency (foF2) and Total Electron Content (TEC), under both quiet and disturbed ionospheric conditions. The first technique, Ordinary Kriging (OK), relies solely on foF2 data, while the second technique, Kriging with External Drift (KED), incorporates the dynamic relationship between foF2 and TEC data to improve the interpolation process. This study demonstrates how KED, which utilizes dynamic secondary information, offers advantages over OK. By simultaneously monitoring the ionosphere from a single ionosonde station and collecting data from visible GPS satellites, KED enhances the accuracy of spatial structural analysis, enabling more precise estimations of ionospheric characteristics across interpolation grid nodes. The research highlights that the non-uniform distribution of ionosonde stations, especially in regions with limited foF2 data, can benefit from KED's incorporation of TEC data. Additionally, the study reveals the potential for improving TEC maps by assimilating foF2 data located at the periphery of the observation area. Furthermore, the paper discusses the determination of semivariogram structures for both foF2 and TEC data, emphasizing the spatial correlations between data points. These findings contribute to a better understanding of ionospheric behavior and the potential for enhanced ionospheric modeling.

Buckling and post buckling analysis of spatial thin film structures under shearing based on perturbation method

Spatial thin film structures exhibit high sensitivity to shear deformation, often exhibiting wrinkling phenomena under minimal shear loads. In this study, we used a combined experimental and theoretical approach to investigate the wrinkling and post-buckling behaviors of thin films subjected to shear forces. Initially, we designed and fabricated a high-precision experimental apparatus, which, in conjunction with the MTS Exceed 40 series universal testing machine, was a versatile experimental platform for evaluating the responses of thin films to shear. Subsequently, we extended the classical Föppl–von Kármán nonlinear plate model to capture the large deformation behaviors of thin films under shear. This development led to the formulation of governing equations that describe the shear-induced deformation of the films. We introduced two boundary conditions to characterize these deformations: one in which the non-loaded edge was free (unconstrained), and another in which the non-loaded edge was constrained (analogous to a frame constraining a painting). To solve the governing equations, we treated the incremental shear displacement angle of the thin film as a small parameter and devised a numerical method grounded in perturbation theory. We further formulated an implicit difference method of arbitrary order accuracy to enhance the precision and stability of the numerical solution. Our experimental and theoretical analyses revealed a comprehensive buckling pathway for thin films under shear, characterized by an initial wrinkling configuration (Config. 1), followed by a transition to a secondary wrinkling state (Config. 2), and culminating in a wrinkling splitting pattern (Config. 3). We also determined that by increasing the pre-stretching of thin films, their resistance to wrinkling along the entire buckling pathway could be significantly enhanced.

A Novel Method for Spatial Structural Kinematic Analysis

Kinematic analysis is the first task that needs to be solved for further real-life problems such as machine dynamics and durability, machine balancing, and optimization, chemistry, etc. Currently, there are many methods to analyze the kinematic problem, for instance: vector graphics method, vector-analytic method, or Dennavit-Hartenberg-Craig matrix method. Although each method has its advantages, they have mostly encountered difficulties in establishing equations associated with complicated spatial structures. This research proposed a novel method, entitled “'perpendicular projection method” to solve the problem of spatial structural kinematic analysis. This is an approximate solution method that combines the use of generalized coordinates and direction cosine matrices. With this method, the problem of kinematic analysis of spatial structures will be solved simply and easily by establishing connection equations as well as simulating the motion of spatial structures. This method was then applied in conducting the kinematic analysis of a spatial mechanism with 1 rotating joint (R), 1 ball joint (S), 1 translational joint (P), and one cylindrical joint (C). The calculated results, including the velocity, acceleration of generalized coordinates, angular velocity, angular acceleration of the links, velocity, and acceleration of the center of mass of the links, showed the efficiency of the proposed method.

Spatial Structure Analysis in Regional Development Context at Samosir Regency

Regional inequality or disparity is a natural consequence of development and an unavoidable stage. This happens because of differences in population, natural resource potential and geographical conditions in each region. This research aims to analyze public service centers, inter-district interactions, and settlement patterns in the context of regional development in Samosir Regency. The study uses a descriptive method with a quantitative approach to analyze the centers of public services, inter-district interactions, and settlement patterns among districts in Samosir Regency using the tools of centrality index analysis, gravity model, and nearest neighbor analysis. The analysis results show that the public service facilities in Samosir Regency in 2021 include 517 units of educational facilities, 442 units of healthcare facilities, 561 units of religious facilities, and 2,418 units of economic facilities, with the service center located in Pangururan District with a total availability of 1,081 units, which has the highest centrality value of 966.63. The largest inter-district interactions occur in Palipi District with a total interaction of 11,671,532.31, while the smallest interaction occurs in Simanindo District with a total interaction of 749,250.15. The settlement pattern in Samosir Regency consists of 1 district with a random settlement pattern and 8 other districts with clustered settlement patterns. In the development of Samosir Regency, the influence of district interactions, service centers, and settlement patterns are important factors to be considered.Keywords: District, Service Center, Interaction, Settlement Pattern

Study of Biopharmaceutical Agricultural Development in Karanganyar Regency

Agriculture in Karanganyar Regency is not considered a primary sector in the Solo Raya area; however, it possesses untapped potential that needs to be developed. Medicinal plants, or bio-pharmaceutical commodities, are among the prominent potentials, with Karanganyar Regency ranking among the top 10 highest producers in Central Java Province and having seven sub-districts as centres for medicinal plant cultivation. Ginger, turmeric, and galangal are outstanding commodities in the Agricultural Region of Karanganyar, identified as potential and key commodities based on linkage system analysis, agropolitan subsystem analysis, spatial structure analysis, LQ and shiftshare analysis, policy analysis, and land carrying capacity analysis. The analysis reveals the presence of potential and challenges as driving and inhibiting factors. Nevertheless, the bio-pharmaceutical agriculture sector faces several issues, such as inadequate support in terms of facilities and infrastructure, as well as imbalances in inter-regional infrastructure conditions that impact marketing disparities. Additionally, monitoring and reporting of agricultural sector development programs are not yet optimal. Therefore, further analysis and studies are needed to develop the agricultural sector, particularly in the bio-pharmaceutical commodities domain.

Seismic Response Analysis of Dome-Shaped Large Spatial Structures According to TMD Installation

Seismic Response Analysis of Dome-Shaped Large Spatial Structures According to TMD Installation