Spatial Elements Research Articles

Identifying firefighters' situation awareness requirements for fire and non-fire emergencies using a goal-directed task analysis

A firefighter's situation awareness (SA) is considered crucial to making effective tactical decisions and actions at the scene. Despite the importance of the firefighter's SA, there have been limited research efforts to understand what cues and information firefighters use to assess ongoing situations and predict future conditions. In addition to fire events, contemporary firefighters respond to an increasing volume of non-fire incidents. Thus, this study aims to identify firefighters' SA during three fire incidents (single house fire, vehicle fire, and passenger aircraft fire) and three non-fire incidents (medical emergency, hazardous materials, and urban search and rescue). A goal-directed task analysis was conducted via focus group discussions with eight career firefighters. Findings indicate that firefighters build their SA by processing various cues from hazards (e.g., fire, ignition source), humans (e.g., occupants, bystanders, drivers, passengers), spatial elements (e.g., building structure, location of hazards), and surrounding conditions (e.g., traffic, weather). Our findings provide insights into SA measurement, SA-oriented work processes, training for SA, and designing technologies to support firefighters' SA during all-hazard responses.

EEG Emotion Recognition based on Multi scale Self Attention Convolutional Networks

A multi-view self-attention module is proposed and paired with a multi-scale convolutional model to builda multi-view self-attention convolutional network for multi-channel EEG emotion recognition. First, timeand frequency domain characteristics are extracted from multi-channel EEG signals, and a three-dimensionalfeature matrix is built using spatial mapping connections. Then, a multi-scale convolutional network extractsthe high-level abstract features from the feature matrix, and a multi-view self-attention network strengthensthe features. Finally, use the multilayer perceptron for sentiment classification. The experimental results revealthat the multi-view self-attention convolutional network can effectively integrate the time domain, frequencydomain, and spatial domain elements of EEG signals using the DEAP public emotion dataset. The multi-viewself-attention module can eliminate superfluous data, apply attention weight to the network to hasten networkconvergence, and enhance model recognition precision.

Attention Block Based on Binary Pooling

Image classification has become highly significant in the field of computer vision due to its wide array of applications. In recent years, Convolutional Neural Networks (CNN) have emerged as potent tools for addressing this task. Attention mechanisms offer an effective approach to enhance the accuracy of image classification. Despite Global Average Pooling (GAP) being a crucial component of traditional attention mechanisms, it only computes the average of spatial elements in each channel, failing to capture the complete range of feature information, resulting in fewer and less expressive features. To address this limitation, we propose a novel pooling operation named “Binary Pooling” and integrate it into the attention block. Binary pooling combines both GAP and Global Max Pooling (GMP), obtaining a more comprehensive feature vector by extracting average and maximum values, thereby enriching the diversity of extracted image features. Furthermore, to further enhance the extraction of image features, dilation operations and pointwise convolutions are applied on the channel-wise. The proposed attention block is simple yet highly effective. Upon integration into ResNet18/50 models, it leads to accuracy improvements of 2.02%/0.63% on ImageNet.

Between the House and the Street Live, Work, and Community Environment in Peripheral Neighbourhoods of Rio de Janeiro

The urban space of Rio de Janeiro's peripheral neighbourhoods, transformed by its residents, is analysed as a reference for thinking about alternatives to housing and the city that have been planned previously. The approach to these built environments and their residents' ways of life aims to recognize, record, and disseminate their forms of production of space, that do not correspond to hegemonic models, but are the result of a daily construction process. The live–work unit has a prominent role in the production of its urban environments that, unlike the planned residential areas and contrary to legislation, is mixed with houses for strictly residential use. Responding to economic, cultural, and social demands, the transformations operated in the houses – understood as 'tactics' (De Certeau, 1984) – promote the dilution of the border between private and public space and create transition spaces, with the quality of 'in-between space' (Her berger, 1996). Allowing and encouraging the mixed use of housing and work is analysed for its potential to qualify the urban space, propitiating income generation and reduction of displacements, issues that are even more pressing after the COVID-19 pandemic. A synthesis of the research fi ndings is presented, de fining spatial categories and constructive elements that constitute a repertoire for the project and for discussing changes in legislation that could help in the increase of live–work units.

Numerical investigation of convergence of Fourier series, poly-nomials, and method of finite elements

Purpose. To compare the efficiency of using finite elements with variable and averaged mechanical and geometric parameters and to investigate the convergence of results obtained by the semi-analytical finite element method (SAFEM) using Fourier series and polynomials with the results obtained by the finite element method (FEM). The methods. The construction and development of an algorithm for studying the stress-strain state of spatial bodies with variable and averaged mechanical and geometric parameters were carried out based on SAFEM. Findings. Solvability indicators of SAFEM were obtained for calculating nodal reactions and stiffness matrix coefficients of finite elements with variable and averaged mechanical and geometric parameters. Numerical convergence studies of results obtained using SAFEM with Fourier series, polynomials, and the finite element method were conducted for a benchmark example, which was the Boussinesq problem for a half-space subjected to a concentrated force. The results indicate that the convergence of the investigated coordinate functions in the considered problem is of the first order. The originality. The obtained solvability indicators of SAFEM for calculating nodal reactions and stiffness matrix coefficients of finite elements with variable and averaged mechanical and geometric parameters allow for the study of various classes of problems. Numerical convergence studies using Fourier series, polynomials, and the finite element method were conducted for the benchmark example. Practical implementation. The practical significance lies in the development of a methodology for determining the stress-strain state of relevant spatial elements of structures with variable and averaged mechanical and geometric parameters subjected to arbitrarily distributed spatial loads.

Research on the Correlations between Spatial Morphological Indices and Carbon Emission during the Operational Stage of Built Environments for Old Communities in Cold Regions

The escalation of the urban population and energy demands has exacerbated the carbon emission intensity at the operational stage of urban old communities. The spatial elements of the built environments comprising building groups, roads and landscape, and the spatial morphology of these elements, are endowed not only with human activities but also impact local microclimates and overall carbon emissions. Nonetheless, little attention has been paid to the correlation mechanism between the spatial morphology of the urban built environments and carbon emissions. In this paper, the aim is to combine carbon emissions simulation and statistical analysis to find the correlation between the spatial morphological indices and carbon emissions and to bridge the gaps. Thus, guided by the principles of urban energy modeling, this research adopts a parametric process of “information model construction–carbon emission simulation–statistical analysis”. First, taking 60 typical samples of an old community in Jinan, China, as objects, morphological indices such as density, texture and layout are analyzed through regression analysis to highlight their impacts on carbon emissions. Then, a carbon emission prediction model based on spatial morphological indices is established and verified. The results show that the floor area ratio (FAR), building coverage ratio (BCR), enclosure degree (ED), shape factor (SF) and average road aspect ratio (AS) have significant impacts on carbon emissions during the operational stage. Among these indices, the FAR and the ED are identified as the pivotal influencers. The findings confirm the important role of spatial morphological design of old communities in cold regions in improving urban carbon reduction potential, and they provide theoretical underpinnings and empirical data as references for urban morphology design formulated within the context of low-carbon objectives.

Research on the Industrial Heritage Community Retrofitting Design Based on Space Network Model of Carbon

The low-carbon retrofitting of industrial heritage communities is an important issue for reducing urban carbon emissions. Previous research on the judgment of heritage elements and carbon emission factors of industrial heritage communities lacked the construction of elements within the space, and the value judgment of conservation and the determination of low-carbon factors lacked a systematic network analysis. Carbon spatial networks as a systematic approach can systematically harmonize the contradictions between “conservation–transformation–low carbon” while considering the spatial and temporal carbon emissions of nodes. This research uses hierarchical analysis to analyze the value of heritage elements and locate them in space, then combine the elements that affect carbon activities in space to form carbon space nodes integrated with heritage conservation elements, and analyze the links between nodes to form a carbon space network. Then, this research uses a carbon spatial network to dissect the structure of carbon emissions, calculate the carbon activity at nodes, and reflect it into a parameterized platform to guide designers. After that, this research selected 16 industrial heritage communities in the severe cold region for the carbon activity measurement of carbon spatial network node elements in the operation stage and conducted a correlation analysis to obtain a correlation matrix model of node elements and carbon activities. Within the constraints of heritage element protection, the results show that the volume ratio, green area ratio, and building density of the carbon spatial network model have the greatest influence on its carbon performance, followed by porosity and road density. Through case simulation verification, the floor area ratio of industrial heritage communities should be controlled between 2.1–2.5, the number of residential building floors should be 7–14, the road network density should be 4.16–4.50%, the green space ratio should be 20–35%, and the porosity should be 35–45%. Taking the three major power road communities in Harbin as an example, retrofit measures were decided by reference to relevant parameter control intervals, resulting in a 21.1% reduction in energy consumption, an approximate 32.7% reduction in carbon emissions, and a 7.3% reduction in the annual percentage of hours in extreme hot and cold environments.

Production Of Spatial Geosite Itinerary Maps as Tourism Destinations

The availability of tourist maps so far has experienced limitations in presenting attribute data in the form of non-spatial characteristics. Tourism is called quality if it can convey the information the community needs. Among them are location attributes, land use, natural and cultural phenomena history, and interrelationships between spaces. Geographical and cultural features are essential to be used in realizing quality tourism through the availability of information on tourism space areas, such as visualization of landscapes and depth of local cultural knowledge. This study used a qualitative method with data collection techniques carried out through interviews, focus group discussions, landscape documentation, surveys, and observations to determine the geographical position of spatial elements. The results of the study produced Itinerary Maps and geosite distribution, including the Saddang River, Buntu Lindo Batu, Lo'ko Malillin, Buntu Lingkobo, Mount Benteng Alla’, Buntu Karua, and Kalosi Arabica Coffee Cultivation. The row of geosites has a chronology and chronology of formation and is related to the social culture of the people in the Fort Alla’ area. The findings from this study are expected to provide exposure to the public, tourists, and the government to preserve natural and cultural heritage to become a reference in implementing sustainable tourism development in Enrekang Regency. It is necessary to develop a digital-based information system that presents spatial attribute data and ethnographic records that can be accessed online. Keyword: Tourism Geography, Ethnography, Benteng Alla’, GIS

Evaluation of Sustainable Development Potential of High-Speed Railway Station Areas Based on “Node-Place-Industry” Model

The development of the HSR station area is the result of the combined effect of the three elements of transport, place, and industry. This study introduces the industrial dimension and constructs the node-place-industry model to empirically analyze the development potential of station areas along the Hunan section of the Beijing–Guangzhou and the Shanghai–Kunming high-speed railway lines. The results show that (1) the development of the three spatial elements of the station area is mostly out of sync, and the node value has the highest fit with the integrated potential value of the station area; (2) there is a significant correlation between the magnitude of the combined potential of the station area and the site location, station class and time of development; (3) according to the results of the cluster analysis, it was found that most of the stations were in a state of disequilibrium, and the main reason was that the functional value of the place did not match with the value of industrial aggregation. In particular, the introduction of the industry dimension extends the NP model and establishes a tessellated analytical framework for station type classification, providing an interesting assessment tool for the sustainable development of transport hub areas.

Probabilistic Regionalization via Evidence Accumulation with Random Spanning Trees as Weak Spatial Representations

Spatial clusters contain biases and artifacts, whether they are defined via statistical algorithms or via expert judgment. Graph‐based partitioning of spatial data and associated heuristics gained popularity due to their scalability but can define suboptimal regions due to algorithmic biases such as chaining. Despite the broad literature on deterministic regionalization methods, approaches that quantify regionalization probability are sparse. In this article, we propose a local method to quantify regionalization probabilities for regions defined via graph‐based cuts and expert‐defined regions. We conceptualize spatial regions as consisting of two types of spatial elements: core and swing. We define three distinct types of regionalization biases that occur in graph‐based methods and showcase the use of the proposed method to capture these types of biases. Additionally, we propose an efficient solution to the probabilistic graph‐based regionalization problem via performing optimal tree cuts along random spanning trees within an evidence accumulation framework. We perform statistical tests on synthetic data to assess resulting probability maps for varying distinctness of underlying regions and regionalization parameters. Lastly, we showcase the application of our method to define probabilistic ecoregions using climatic and remotely sensed vegetation indicators and apply our method to assign probabilities to the expert‐defined Bailey's ecoregions.

Research on Dynamic Response of a Single-Tower Cable-Stayed Bridge with Successive Cable Breaks Based on a 3D Index

Aiming at the problem of cable breakage of an asymmetric single-tower cable-stayed bridge by taking a single-tower cable-stayed bridge that has been in operation for more than 20 years as an engineering example, a spatial finite element model that reflected the actual stress state of the structure was established and a method for solving the time point of cable breakage was proposed. The results show that the strength of the main girder, pylon and cable could meet the stress requirements of the vibration caused by successive cable breaks, and the maximum dynamic displacement of the main girder did not exceed the stiffness limit. Although the breakage of the stay cable caused the stress in the adjacent position and the corresponding position on the opposite side of the tower to increase sharply, the stress fluctuation in other positions was small. The fluctuation of the dynamic stress of the cable tower was relatively small, but the displacement of the tower top changed greatly. The dynamic response of the cables breaking in succession was less than that of simultaneous breaking, and the structural vibration caused by cable breaking was not as severe as that caused by simultaneous breaking.

Opportunities and challenges in the application of single-cell and spatial transcriptomics in plants.

Single-cell and spatial transcriptomics have diverted researchers' attention from the multicellular level to the single-cell level and spatial information. Single-cell transcriptomes provide insights into the transcriptome at the single-cell level, whereas spatial transcriptomes help preserve spatial information. Although these two omics technologies are helpful and mature, further research is needed to ensure their widespread applicability in plant studies. Reviewing recent research on plant single-cell or spatial transcriptomics, we compared the different experimental methods used in various plants. The limitations and challenges are clear for both single-cell and spatial transcriptomic analyses, such as the lack of applicability, spatial information, or high resolution. Subsequently, we put forth further applications, such as cross-species analysis of roots at the single-cell level and the idea that single-cell transcriptome analysis needs to be combined with other omics analyses to achieve superiority over individual omics analyses. Overall, the results of this review suggest that combining single-cell transcriptomics, spatial transcriptomics, and spatial element distribution can provide a promising research direction, particularly for plant research.

Perubahan Pola Ruang Pasca Pandemi dalam Sektor Wisata Khususnya pada Rumah Produktif Perajin Perak Kampung Basen

Title: Changes in Post-Pandemic Spatial Patterns in The Tourism Sector, Case Study: The Productive House of Silver Artisans in Basen Village The Covid-19 pandemic in recent years has brought many changes to various sectors of life. In the tourism sector, the decline in tourist numbers makes the tourist spots change to maintain existence. This research aims to find changes in the spatial elements and the pattern of productive houses after the pandemic and evaluate resident adaptation to the spatial changes. This research uses a qualitative descriptive method, by raising a study case of three silvers-productive houses affected by the Covid-19 pandemic. Data retrieval is made through observation of changes in resident activity patterns and changes in the interior function of productive houses. Research results in analyzing new elements, space patterns, and residents' adaptation. Changes in spatial patterns experienced by productive houses of silver artisans occurred due to changes in residents' livelihoods due to the Covid-19 pandemic. Changes in semi-fixed elements are found in all case studies, as it is the easiest to manage the needs of its residents. This study will provide insight into the spatial pattern of the silver artist's house in Basen Village and evaluate the resident's adaptation to changes in spatial pattern.

Railway Track Fault Detection Using Selective MFCC Features from Acoustic Data.

Railway track faults may lead to railway accidents and cause human and financial loss. Spatial, temporal, and weather elements, and wear and tear, lead to ballast, loose nuts, misalignment, and cracks leading to accidents. Manual inspection of such defects is time-consuming and prone to errors. Automatic inspection provides a fast, reliable, and unbiased solution. However, highly accurate fault detection is challenging due to the lack of public datasets, noisy data, inefficient models, etc. To obtain better performance, this study presents a novel approach that relies on mel frequency cepstral coefficient features from acoustic data. The primary objective of this study is to increase fault detection performance. As well as designing an ensemble model, we utilize selective features using chi-square(chi2) that have high importance with respect to the target class. Extensive experiments were carried out to analyze the efficiency of the proposed approach. The experimental results suggest that using 60 features, 40 original features, and 20 chi2 features produces optimal results both regarding accuracy and computational complexity. A mean accuracy score of 0.99 was obtained using the proposed approach with machine learning models using the collected data. Moreover, this performance was significantly better than that of existing approaches; however, the performance of models may vary in real-world settings.

Geometrically consistent nonlinear plane strain and stress constitutive models: Application to soft-material oscillations

Position-gradient constraints are used in this study to develop new geometrically consistent plane strain and stress constitutive models for the nonlinear large-displacement analysis of soft materials. The position gradients are used to define strain and stress constraints that enter into the formulation of the deformation tensor, strain-energy density function, and new constitutive models. The new approach is applied to three nonlinear hyperelastic constitutive models for incompressible materials: Neo-Hookean, Mooney Rivlin, and Yeoh Models. The underlying geometric plane-stress and plane-strain assumptions are preserved by using the finite elements (FE) of the absolute nodal coordinate formulation (ANCF). Limitations of widely used incompressibility penalty functions that produce stresses at initial stress-free configurations are discussed. Several numerical examples developed using planar ANCF shear-deformable beam and rectangular elements are used to assess the proposed formulation and verify its results using spatial solid elements implemented in commercial FE software. It is demonstrated that the ANCF plane-stress and plane-strain formulations for the incompressible materials predict accurately soft-material oscillations, demonstrating that stiffer behavior can be attributed to material-model inconsistencies and cannot always be attributed to locking.

Assessment of the cultural-natural importance of the scenic spots from a network perspective

A scenic spot within a natural landscape is not only a spatial carrier of biodiversity but also an indispensable spatial element of culture. In the lower reaches of large river basins, natural conservation and cultural development are both important. Taking the scenic spots in the lower reach of the Yellow River as study objects, we used minimum cumulative resistance, circuit theory, and igraph package to build a cultural network, natural network and cultural-natural network to assess the importance of scenic spots based on connectivity. The scenic spots were grouped into four categories (based on the relative importance of habitat connectivity and tourism connectivity) and three optimization scenarios (including the cultural, natural, and cultural-natural approaches) were set to indicate the impact of population, resistance and publicity on the importance of scenic spots. We found that important nodes in each of the networks were clustered in different parts of the region. Only 2 of the 18 nodes belonged to the category in which habitat connectivity is not important and tourism connectivity is important, and 7 nodes belonged to the category in which habitat connectivity is not important and tourism connectivity is not important, which is the largest category. The cultural-natural optimization method and the natural optimization method are respectively the best in terms of the degree of the importance of the value of the optimization objects and the number of the importance of optimization objects. The network-based assessment method we provide can support landscape planning from a cultural-natural perspective and provide a potential ranking for landscape connectivity optimization that considers both ecological connectivity and social connectivity.

A finite element approach to the numerical solutions of Leland’s model

In this paper, finite element method is applied to Leland’s model for numerical simulation of option pricing with transaction costs. Spatial finite element models based on P1 and/or P2 elements are formulated in combination with a Crank–Nicolson-type temporal scheme. The temporal scheme is implemented using the Rannacher approach. Examples with several sets of parameter values are presented and compared with finite difference results in the literature. Spatial–temporal mesh-size ratios are observed for controlling the stability of our method. Our results compare favourably with the finite difference results in the literature for the model.

Robust estimations of areal grain size distribution from geometric surface roughness in a proglacial outwash area

Rapid glacier mass loss in high-alpine catchments exposes unconsolidated sediments to erosion by meltwater-fed mountain streams, with impact on the catchment-scale sediment dynamics. Proglacial outwash areas (OWA) are a key spatial element in this sediment flux cascade, with grain size distributions (GSD) as an integral variable in current and future glacio-fluvial processes and associated effects.In this study, surface grain size estimation methods based on linear correlations between geometric surface roughness and GSD data are adapted to test this new mapping approach for areal GSD patterns and their changes in a proglacial OWA. Surface roughness is computed with adaptive kernel sizes from high-resolution topographic point clouds derived from UAV-based photogrammetry. For model calibration, ground truth data is manually obtained from 30 dry-exposed GSD sampling patches of 1 m2 each. The novelty and potential of this surface roughness regression model lies in its capability to robustly map GSD percentiles at 1-m cell resolution in highly dynamic proglacial environments and inherent data scarcity, drastically reducing the portion of labour-intensive and time-consuming manual surveying.Our optimized linear regression function for the key figure D84, which is widely used in practice, returns a coefficient of determination of 76.4 %. The linear relation found between the D84 of field GSD and the 84th distribution percentile of surface roughness metrics has a slope of α = 6.01, which is consistent with the range of recently published studies using LiDAR-based and photogrammetric surveys. The presented surface roughness regression model provides realistic surficial estimations of areal GSD patterns in a proglacial domain of sediment remobilization. In addition, this work analyses and discusses detected changes in the estimated D84 over a three-month period. A systematic shift is identified for sediment compositions with an initial D84 above 60 mm. This is thought to be an effect of the original image quality and variable lighting conditions between photogrammetric surveys and indicates that best practice should include careful survey planning for most uniform flight settings. Nevertheless, visually verified significant changes in D84 along frequently wet low-flow channels can be detected automatically, and thus the findings demonstrate the feasibility of this GSD monitoring approach for tracking fluvial re-working of surface sediment.

Menoreh Mountain sustainable tourism development: a spatial outlook

Increasing development of geotourism and Geopark raises awareness of more environmental-friendly and sustainable tourism which can be approached using spatial analysis. This paper aims to connect the spatial element with sustainability tourism in Menoreh Mountain and formulate sustainable tourism development. Using Moran’s Index, this study examines the Geo-tourism site’s spatial characteristics and how the development should be done based on that analysis. For the result, 120 sites have been collected through Google Maps data and a field survey for validation. The sites comprise caves, waterfalls, pine forests, summits/peaks, and religious sites. By plotting the density of the tourism destination to a certain village, the Global Moran’s Index for it is 0.318. The Univariate Local Moran’s I show 10 villages in the High-High quadrant, 4 villages in the Low-High quadrant, 3 villages in the High-Low quadrant, and 2 villages in the Low-Low quadrant or cluster. Based on the spatial pattern, it is crucial to focus on the characteristic of each quadrant to establish sustainable tourism comprising economic and geological, and ecological sustainability and strengthen the spatial policy. The characteristics of each cluster also become a basis for developing strategies regarding the administrative division of Menoreh Mountain.

No man is an Island: A spatially explicit approach to measure development resilience

Building resilience is paramount in the development and humanitarian agenda, yet existing resilience measures “are all, at best, imperfect, and at worst, deeply flawed” (Upton, Constenla-Villoslada and Barrett, 2022). This study contributes to improving the conceptualization and the measurement of resilience. Conceptually, it builds on the notion of systemic resilience. Operationally, it explicitly models the spatial structureunderlying human-system interactions and locational effects. To do so, a generalization of the conditional moments of well-being approach (Cissé and Barrett, 2018) is proposed. Our method has relatively low data requirements and is reasonably straightforward to implement. It largely preserves the desirable in-sample features of the parent method while significantly increasing the targeting accuracy. From a policy perspective, incorporating spatial elements into measuring resilience has the potential to improve resource allocation efficiency for development and humanitarian interventions. It also emphasizes the importance of synergizing community-based interventions to complement household-level ones in building resilience.