Proximity Relationships Research Articles

Spatiotemporal patterns of commercial activities in Exarchia-Neapoli area.

The focus of this paper is the analysis of spatio-temporal patterns of commercial activities in the area of Exarchia-Neapoli, in Athens, during the last decade. A significant downturn during the financial crisis has left its mark on commerce along with all other sectors of the economy in Greece. However, there is poor geographical analysis up to this date, depicting the changes in spatial concentration and dispersion of shops, flourishing and decaying sectors and their spatial footprint. The main purpose of this study is to reveal these transformations in a central mixed-use neighborhood of Athens. The type of commercial activity of ground floor stores and offices in the area is recorded at three different points in time (2009, 2014, 2019). After the collection and categorization of data, spatial analysis was carried out using geostatistical indicators such as mean center and standard deviational ellipse and spatial patterns were determined through nearest neighbor analysis. Τhe analysis over space and time reveals trends and patterns of clustering and avoidance respectively, both within commercial uses and between different ones. Furthermore, their correlation with the centrality of the network (space syntax analysis) reveals not only proximity relationships, but also their strength and resilience over time. Finally, we discuss our findings in relation to the dynamics of commerce in the Exarchia area and propose scaled-up research opportunities.

A Network-Based Analysis of Disease Modules From a Taxonomic Perspective.

Human-curated diseaseontologies are widely used for diagnostic evaluation, treatment and data comparisons over time, and clinical decision support. The classification principles underlying these ontologies are guided by the analysis of observable pathological similarities between disorders, often based on anatomical or histological principles. Although, thanks to recent advances in molecular biology, disease ontologies are slowly changing to integrate the etiological and genetic origins of diseases, nosology still reflects this "reductionist" perspective. Proximity relationships of disease modules (hereafter DMs) in the human interactome network are now increasingly used in diagnostics, to identify pathobiologically similar diseases and to support drug repurposing and discovery. On the other hand, similarity relations induced from structural proximity of DMs also have several limitations, such as incomplete knowledge of disease-gene relationships and reliability of clinical trials to assess their validity. The purpose of the study described in this paper is to shed more light on disease similarities by analyzing the relationship between categorical proximity of diseases in human-curated ontologies and structural proximity of the related DMs in the interactome. We propose a method (and related algorithms) to automatically induce a hierarchical structure from proximity relations between DMs, and to compare this structure with a human-curated disease taxonomy. We demonstrate that the proposed method allows to systematically analyze commonalities and differences among structural and categorical similarity of human diseases, help refine and extend human disease classification systems, and identify promising network areas where new disease-gene interactions can be discovered.

Spatial Patterns, Utility, and Limitations of Volunteered Reports of Urban Homeless Campsites in Portland, Oregon

Homelessness is a complex and diverse social issue that affects many urban areas in the United States. Robust knowledge of how and where homeless individuals subsist is essential for an assessment of the granular spatiotemporal context of homelessness in a given locale. Common means for surveying homeless rest sites are resource intensive and conducted infrequently, which warrants investigations into complementary approaches. In this article, we examine the spatial patterns of homeless campsites in Portland, Oregon, and their relationship to urban features based on a three-year data set of reports volunteered by members of the public. Our quantitative analysis employs a combination of spatial analysis, statistical methods, and cartographic mapping to determine prevailing spatial patterns and proximity relationships. In that context, we assess the characteristics of the volunteered data in terms of their suitability for capturing expected local patterns of homeless campsites. The data indicate concentrations of reported campsites and significant spatial proximity relationships between campsite locations, zoning, transit stops, and homelessness support services. Our findings evidence the utility of the volunteered data for the analysis of urban homeless campsite locations on an annual basis. We further discuss the limitations of the data and provide suggestions for improvements and future research.

‘Local foods’ as trustworthy food: geographical proximity, social areas and interpersonal relationships

‘Local foods’ as trustworthy food: geographical proximity, social areas and interpersonal relationships

Relationship Between Lower Third Molars and Mandibular Alveolar Canal through Cone Beam CT Scans

Background: Third molar extractions are considered one of the most common procedures in oral surgery. Well-known complications of these procedures are lesions to the inferior alveolar nerve because of its proximity to the roots of third molars. Hence, it is important to making an adequate assessment of this relationship to avoid complications. Purpose: To determine the relationship between the inferior alveolar canal (IAC) retained lower third molars, and that relationship with angulation, class and type of lower third molars through cone-beam computed tomographies. Methods: A descriptive and transversal study was conducted with 73 tomographies to 113 lower third molars that were analyzed in the axial, occlusal, and sagittal planes (p = 0.05). Results: 54 % of third molars were in contact with the IAC and more frequently in its lower location (45.9 %) (p = 0.000). Proximity relationships were more common on the left side with a vertical angulation (40.6 %), type A (37.5 %), and class II (75 %) (p = 0.015). On the right side, a mesioangular angulation (34.5 %), type A (48.3 %) (p = 0.004), and class II (58.6 %) (p = 0.000) were the most common. Conclusion: A close proximity relationship of the IAC with the lower third molars was found using cone beam computed tomography, and it is recommended to conduct a careful diagnosis before a surgical procedure.

When COVID-19 Is the Invader and Internal Communication Is the Hero: Understanding the Influence of Internal Communication on Individual Performance and Evaluating the Mediating Role of Perceived Support

Objective: The main objective of this study was to evaluate the impact of internal communication (IC) of organisations on the quality of the individual–organisation relationship, specifically with regard to the effects generated with respect to individual performance (IP) in a pandemic framework. In this sense, the study intends to evaluate the effects of IC on the employee’s IP, having as a mediator perceived organisational support (POS). Methodology: To achieve the aforementioned objectives, a cross-sectional quantitative study was prepared, data for which were collected during a period of confinement that took place between 9 February and 15 March 2021. A total of 340 individuals of both sexes participated in the study. Genders were 67.6% female and 32.4% male, with ages ranging from 25 years to over 61 years, from all districts of Portugal and the Islands. Results: The main results obtained showed that IC was positively and significantly correlated with POS and also with IP, and that there was also a total mediation effect of POS in the relationship between IC and IP. Practical implications: These results seem to support the need for organisations to invest in their internal communication practices as a way of stimulating strong and fruitful relationships between workers and the organisation. Internal communication seems to be a relevant indicator for the management of proximity relationships with workers, especially in adverse contexts, like the ones experienced during the pandemic crisis. Well-developed internal communication supports and practices seem to be a valid path towards developing bonds leading to improved performance.

Graph embedding via multi-scale graph representations

Graph embedding provides an effective way to encode graph nodes as continuous vector representations in a low-dimensional space. The high-order proximities based graph embedding methods can preserve global structures, but the high-order proximities based objectives typically imply non-convex optimization and high computational complexity. In contrast, the low-order proximities based graph embedding methods evade the definition and optimization of complex high-order proximities based objectives, but cannot capture global structures. Furthermore, numerous graph embedding methods that only consider the proximity relationships among nodes ignore to capture global structure from the perspective of subgraphs. Motivated by this, we propose a novel graph embedding framework via multi-scale graph representations, named MSGE. MSGE first determines multiple subgraphs of different scales based on random walk. At each scale, MSGE generates a new graph by creating edges among the subgraphs which are treated as supernodes. Thus, the generated multi-scale graphs can approximate rich global structure of the original graph. MSGE then employs any existing graph embedding method as a black box to learn subgraph (i.e., supernode) embeddings on each generated graph. Subsequently, a first-order proximity based embedding fusion method is devised to yield node embeddings of the original graph via the learned multi-scale subgraph embeddings. Finally, we apply MSGE on four classical graph embedding methods and extensive experimental results demonstrate that our framework can generate embeddings of better quality and significantly outperform the original graph embedding methods on visualization, node classification and community detection tasks.

A spatial case-based reasoning method for regional landslide risk assessment

Various machine learning methods have been applied to study regional landslide risk assessment problems, but most of them mainly consider the influencing factors associated with landslide occurrences and ignore the spatial features. Therefore, by combining the advantages of traditional case-based reasoning (CBR) and by fully mining the spatial features, this paper proposes a novel spatial case-based reasoning method for landslide risk assessment. In this method, the spatial proximity and spatial topological relationships were extracted as spatial features, and the influencing factors associated with landslide occurrences were selected as attribute features. Then, the integrated expression of the spatial and attribute features of a landslide case was further constructed. Finally, attribute similarity and spatial similarity were used for joint reasoning. This study then carried out experiments in Lushan, China. Upon comparison of the three CBR models, the proposed integrated CBR outperformed the other models. Furthermore, an optimized support vector machine and a deep convolutional neural network were selected as experimental methods in the control group. The results show that the proposed integrated CBR has better performance. In conclusion, the proposed integrated CBR can be effectively applied to regional landslide risk evaluation and other similar geospatial problems.

To Be with or Not: Emotional Causality and Afrikaans Prepositional met and van Constructions

The objective of this article is to consider the Afrikaans prepositions met (with) and van (of) in relation to emotional causality from a cognitive linguistics perspective. The inquiry opens with the question of how a cognitive linguistics analysis that proceeds from reference points can capture the patterned use of the Afrikaans preposition met and van in relation to other aspects of proximity relationships, specifically the causal experience of emotion and companionship. Although met can manifest a precise merged point in time and space, for concomitant causation concerning responses to emotions in Afrikaans, a construction such as bewe van vrees/angs (tremble with fear/anxiety) employs the preposition van, as would be the case in other causal constructions of the same form, such as bewe van die koue (tremble with cold), and so emphasises intersections between companionship, containment, causality, circumstances, and possession.

MHieR-encoder: Modelling the high-frequency changes across stocks

Temporal dependency and mutual impact are two major aspects of the qualitative analysis of stock prices. Modeling the dynamic and complex nature of both the timeline and the mutual impact network is a challenging task for stock price prediction. Furthermore, if we want to capture the high-frequency changes of stock price, the time series will become extremely long, leading to a practical challenge in terms of modeling. To address these challenges, this paper proposes a novel memory-based hierarchical recurrent neural encoder (MHieR-encoder) to embed the time series of stock price into a new representation that preserves (i) the sequential dependency of the time series and (ii) the proximity relationships across stocks in the impact network. The hierarchical structure of the proposed model can easily capture the long-range dependence from an extremely long time series, including in terms of high-frequency prices. Moreover, to capture the dynamic mutual impact across stocks, the intermediate results of the impact network will be stored in the memory module to support further exploration at the training stage. The method is validated using an extremely long time series composed of the one-minute prices derived from the all the Chinese stocks. The results show that MHieR-encoder outperforms all the 8 baselines in the bull market, bear market, calm bull market and calm bear market, and significantly improves the accuracy to 52.2% in a three-class prediction: rising, falling and flat.

Visualizing the 3D genome in situ

3D Genomics The conformation of the genome within the cell changes depending on cell state, such that being able to visualize genome structure can identify cis and trans interactions among regulatory genetic elements. Payne et al. have developed an unbiased genome-sequencing technique in single cells in situ that can infer the chromatin structure by imaging. They were able to identify sequences at subnuclei locations to analyze the proximity relationships among genetic elements within and across chromosomes in single cells. Using this technique, they could detect chromosome territories and distinctions between different types of repetitive sequences and chromosomal features. This method can map and image genomic coordinates with submicrometer resolution in intact single cells. Science , this issue p. [eaay3446][1] [1]: /lookup/doi/10.1126/science.aay3446

Verification of Error-Increasing Factors by Sensor Response-Based Localization Technology Through Real Device Experiments

A sensor response-based localization technology that uses the non-geotagged responses of environmental object detection sensors to estimate the positions of sensors in a defined observation area was proposed in a previous study. That method is based on the concept that the proximity relationships among sensors obtained from the closeness of the sensor response times or collected data similarities. However, since that study was limited to simulation-based experiments, this study extends the scheme to experiments involving real devices, simulated environments, and ideal environments to ascertain the accuracy of localization estimates produced using real devices. We also aimed at extracting the problems that must be resolved before the proposed technology can be put into practical use. Even if clock synchronization between the sensors was impossible, the proximity relationships among them could be estimated using before-after detection time relationships, thus confirming that sensor locations could be estimated based on these proximity relationships. In a real device experiment using 20 sensors indoors at 20.25 m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , we confirmed that the average localization error was 0.84 m. Based on those results and other findings resulting from this study, we determined that the primary factor related to increased sensor localization errors was the number of incorrectly constructed sensor pairs. The results of this study are expected to facilitate the realization of physical world sensing using sensor response-based localization technology.

In situ genome sequencing resolves DNA sequence and structure in intact biological samples.

Understanding genome organization requires integration of DNA sequence and three-dimensional spatial context; however, existing genome-wide methods lack either base pair sequence resolution or direct spatial localization. Here, we describe in situ genome sequencing (IGS), a method for simultaneously sequencing and imaging genomes within intact biological samples. We applied IGS to human fibroblasts and early mouse embryos, spatially localizing thousands of genomic loci in individual nuclei. Using these data, we characterized parent-specific changes in genome structure across embryonic stages, revealed single-cell chromatin domains in zygotes, and uncovered epigenetic memory of global chromosome positioning within individual embryos. These results demonstrate how IGS can directly connect sequence and structure across length scales from single base pairs to whole organisms.

A novel dual-domain clustering algorithm for inhomogeneous spatial point event

PurposeThe spatial and non-spatial attributes are the two important characteristics of a spatial point, which belong to the two different attribute domains in many Geographic Information Systems applications. The dual clustering algorithms take into account both spatial and non-spatial attributes, where a cluster has not only high proximity in spatial domain but also high similarity in non-spatial domain. In a geographical dataset, traditional dual spatial clustering algorithms discover homogeneous spatially adjacent clusters suffering from the between-cluster inhomogeneity where those spatial points are described in non-spatial domain. To overcome this limitation, a novel dual-domain clustering algorithm (DDCA) is proposed by considering both spatial proximity and attribute similarity with the presence of inhomogeneity.Design/methodology/approachIn this algorithm, Delaunay triangulation with edge length constraints is first employed to construct spatial proximity relationships amongst objects. Then, a clustering strategy based on statistical change detection is designed to obtain clusters with similar attributes.FindingsThe effectiveness and practicability of the proposed algorithm are illustrated by experiments on both simulated datasets and real spatial events. It is found that the proposed algorithm can adaptively and accurately detect clusters with spatial proximity and similar non-spatial attributes under the consideration of inhomogeneity.Originality/valueTraditional dual spatial clustering algorithms discover homogeneous spatially adjacent clusters suffering from the between-cluster inhomogeneity where those spatial points are described in non-spatial domain. The research here is a contribution to developing a dual spatial clustering method considering both spatial proximity and attribute similarity with the presence of inhomogeneity. The detection of these clusters is useful to understand the local patterns of geographical phenomena, such as land use classification, spatial patterns research and big geo-data analysis.

A linguagem da Matemática no contexto escolar: um mapeamento das produções científicas brasileiras

O objetivo deste artigo é apresentar um mapeamento da produção científica no Brasil, a partir das teses e dissertações que contemplaram a linguagem da matemática. Para o levantamento das pesquisas foram consideradas as produções disponibilizadas no Catálogo de Teses e Dissertações da Capes e na Biblioteca Digital Brasileira de Teses e Dissertações – BDTD. A orientação metodológica segue os pressupostos da pesquisa qualitativa e consistiram na identificação das produções, classificação, organização e análise. Esse processo resultou em 21 produções que foram, cuidadosamente, agrupadas e analisadas conforme sua relação de proximidade. Logo, ficou evidenciado que a linguagem da matemática no contexto escolar tem sido abordada como: um recurso didático para comunicar ideias matemáticas através da Literatura; bem como revela o papel da linguagem na comunicação dos conceitos; e como enfoque didático pedagógico na Performance Matemática Digital.

A dual spatial clustering method in the presence of heterogeneity and noise

AbstractThe detection of spatial clusters, taking into account both spatial proximity and attribute similarity, plays a vital role in spatial data analysis. Although several dual clustering methods are currently available in the literature, most of them have detected homogeneous spatially adjacent clusters suffering from between‐cluster inhomogeneity and noise, where those spatial points have been described in the attribute domain. This article aims to accommodate both spatial proximity and attribute similarity with the presence of heterogeneity and noise. In this algorithm, Delaunay triangulation with edge‐length constraints, with consideration of arbitrary geometrical shapes, different densities, and spatial noise, is first utilized to construct spatial proximity relationships among points. Then, a clustering strategy employing information entropy is designed to identify clusters having similar attributes. The attribute clustering can adaptively and accurately detect clusters under the consideration of heterogeneity and noise. The efficacy and practicability of the proposed algorithm are illustrated by experiments employing both simulated datasets and real spatial point events.

MGAT: Multi-view Graph Attention Networks

Multi-view graph embedding is aimed at learning low-dimensional representations of nodes that capture various relationships in a multi-view network, where each view represents a type of relationship among nodes. Multitudes of existing graph embedding approaches concentrate on single-view networks, that can only characterize one simple type of proximity relationships among objects. However, most of the real-world complex systems possess multiple types of relationships among entities. In this paper, a novel approach of graph embedding for multi-view networks is proposed, named Multi-view Graph Attention Networks (MGAT). We explore an attention-based architecture for learning node representations from each single view, the network parameters of which are constrained by a novel regularization term. In order to collaboratively integrate multiple types of relationships in different views, a view-focused attention method is explored to aggregate the view-wise node representations. We evaluate the proposed algorithm on several real-world datasets, and it demonstrates that the proposed approach outperforms existing state-of-the-art baselines.

El valor del lugar en las relaciones de inclusión y exclusión en un colegio rural agrupado. Un estudio etnográfico

En un contexto marcado por la transformación neoliberal de los servicios educativos y cuando la inclusión se presenta como un valor de referencia para el desarrollo sostenible, la finalidad de este artículo es contribuir a la comprensión del papel activo que juegan los lugares y las interacciones que se generan en ellos para la formación de experiencias de segregación o de inclusión en el entorno de la escuela rural. El objetivo es identificar cuáles son las experiencias de inclusión o exclusión entre los miembros de un Colegio Rural Agrupado (CRA). El estudio etnográfico realizado en una escuela adscrita a un CRA de la Comunidad de Aragón (España), a través de la observación participante, conversaciones informales y análisis de documentos, muestra cómo las interacciones generadas en torno a estructuras como la escuela y el CRA difieren respecto a las relaciones de proximidad entre las familias y el alumnado de una parte y los docentes de otra. Finalmente, se discuten las tensiones en relación con las experiencias vividas en las escuelas rurales, subrayando la necesidad de que las políticas educativas atiendan a las particularidades de las distintas escuelas.

Inter-individual Distance in Different Captive Packs of Iberian Wolf (Canis lupus signatus): Management Applications

ABSTRACT There is a tendency for the wolves of a pack to stay together assuming that there is a social bond between the pack members. A rough estimate of the strength of this bond can be obtained by measuring the distances between pack members. The aim of this study was to determine the inter-individual distance by calculating the Euclidian distance to determine the Iberian wolf cohesion in different pack compositions. The study subjects were seven captive Iberian wolves (Canis lupus signatus) housed at the Barcelona Zoo. Multifocal sampling methods were used for data collection, and instantaneous scans were made at 15-min during different sessions. Proximity relationships among these wolves in different pack composition were visualized using two-dimensional scaling analysis. The pair leader without offspring and the breeding pair with their offspring were significantly closer than the rest of the dyads which combinations were equidistant. During the pregnancy period, the breeding pair was significantly further than during the juvenile period. More studies in field and captivity by using Euclidian distance are necessary to improve social husbandry techniques, animal welfare and conservation programs.

Spatial Reliability Assessment of Social Media Mining Techniques with Regard to Disaster Domain-Based Filtering

The data generated by social media such as Twitter are classified as big data and the usability of those data can provide a wide range of resources to various study areas including disaster management, tourism, political science, and health. However, apart from the acquisition of the data, the reliability and accuracy when it comes to using it concern scientists in terms of whether or not the use of social media data (SMD) can lead to incorrect and unreliable inferences. There have been many studies on the analyses of SMD in order to investigate their reliability, accuracy, or credibility, but that have not dealt with the filtering techniques applied to with the data before creating the results or after their acquisition. This study provides a methodology for detecting the accuracy and reliability of the filtering techniques for SMD and then a spatial similarity index that analyzes spatial intersections, proximity, and size, and compares them. Finally, we offer a comparison that shows the best combination of filtering techniques and similarity indices to create event maps of SMD by using the Getis-Ord Gi* technique. The steps of this study can be summarized as follows: an investigation of domain-based text filtering techniques for dealing with sentiment lexicons, machine learning-based sentiment analyses on reliability, and developing intermediate codes specific to domain-based studies; then, by using various similarity indices, the determination of the spatial reliability and accuracy of maps of the filtered social media data. The study offers the best combination of filtering, mapping, and spatial accuracy investigation methods for social media data, especially in the case of emergencies, where urgent spatial information is required. As a result, a new similarity index based on the spatial intersection, spatial size, and proximity relationships is introduced to determine the spatial accuracy of the fine-filtered SMD. The motivation for this research is to develop the ability to create an incidence map shortly after a disaster event such as a bombing. However, the proposed methodology can also be used for various domains such as concerts, elections, natural disasters, marketing, etc.