Impact of high-speed railway construction on spatial relationships in the Guanzhong Plain urban agglomeration

The Guanzhong Plain urban agglomeration is a response to the Belt and Road Initiative in Northwest China that aims to promote regional development.The direct impact of high-speed railway construction is to shorten the spatial-temporal distance among regions,improve the accessibility of regional transportation,and promote socioeconomic linkages.From the perspective of accessibility,this study analyzes the impact of high-speed railway construction on the spatial pattems and county-level economic relationships of the Guanzhong Plain urban agglom-eration.The results show that the construction of high-speed railway significantly improves regional accessibility,increases the potential for urban economic development,and gradually narrows the gaps in economic potential among cities.The construction of high-speed railway has increased the intensity of extenal economic relations among numerous counties in the Guanzhong Plain urban agglomeration,and most of the areas with increased connections are located in the direction of routes extension.The development of the internal economic network of the Guanzhong Plain urban agglomeration is unbalanced,and a complex network is gradually emerging with a few large cities at the core,but the construction of high-speed railway is changing the struicture of the economic network.In general,a certain degree of intrinsic coupling exists between regional accessibility change and the evolution of economic relations caused by high-speed railway,reflecting the requirements of the regional overall development strategy.

Application of rough set-based analysis to extract spatial relationship indicator rules： An example of land use in Pearl River Delta

Spatial relations,reflecting the complex association between geographical phenomena and environments,are very important in the solution of geographical issues. Different spatial relations can be expressed by indicators which are useful for the analysis of geographical issues. Urbanization,an important geographical issue,is considered in this paper. The spatial relationship indicators concerning urbanization are expressed with a decision table. Thereafter,the spatial relationship indicator rules are extracted based on the application of rough set theory. The extraction process of spatial relationship indicator rules is illustrated with data from the urban and rural areas of Shenzhen and Hong Kong,located in the Pearl River Delta. Land use vector data of 1995 and 2000 are used. The extracted spatial relationship indicator rules of 1995 are used to identify the urban and rural areas in Zhongshan,Zhuhai and Macao. The identification accuracy is approximately 96.3%. Similar procedures are used to extract the spatial relationship indicator rules of 2000 for the urban and rural areas in Zhongshan,Zhuhai and Macao. An identification accuracy of about 83.6% is obtained.

Detecting visually salient scene areas and deriving their relative spatial relations from continuous street-view panoramas

A salient scene is an area within an image that contains visual elements that stand out from surrounding areas.They are important for distinguishing landmarks in first-person-view(FPV)applications and determining spatial relations in images.The relative spatial relation between salient scenes acts as a visual guide that is easily accepted and understood by users in FPV applications.However,current digitally navigable maps and location-based services fall short of providing information on visual spatial relations for users.This shortcoming has a critical influence on the popularity and innovation of FPV applications.This paper addresses the issue by proposing a method for detecting visually salient scene areas(SSAs)and deriving their relative spatial relationships from continuous panoramas.This method includes three critical steps.First,an SSA detection approach is introduced by fusing region-based saliency derived from super-pixel segmentation and the frequency-tuned saliency model.The method focuses on a segmented landmark area in a panorama.Secondly,a street-view-oriented SSA generation method is introduced by matching and merging the visual SSAs from continuous panoramas.Thirdly,a continuous geotagged panorama-based referencing approach is introduced to derive the relative spatial relationships of SSAs from continuous panoramas.This information includes the relative azimuth,elevation angle,and the relative distance.Experiment results show that the error for the SSA relative azimuth angle is approximately±6°(with an average error of 2.67°),and the SSA relative elevation angle is approximately±4°(with an average error of 1.32°)when using Baidu street-view panoramas.These results demonstrate the feasibility of the proposed approach.The method proposed in this study can facilitate the development of FPV applications such as augmented reality(AR)and pedestrian navigation using proper spatial relation.

Accurate Indoor Navigation System Using Human-Item Spatial Relation

Indoor navigation has received much attention by both industry and academia in recent years. To locate users, a number of existing methods use various localization algorithms in combination with an indoor map, which require expensive infrastructures deployed in advance. In this study, we propose the use of existing indoor objects with attached RFID tags and a reader to navigate users to their destinations, without the need for any additional hardware. The key insight upon which our proposal is based is that a person＇s movement has an impact on the frequency shift values collected from indoor objects when they near a tag. We leverage this local human-item spatial relation to infer the user＇s position and then navigate the user to the desired destination step by step. We implement a prototype navigation system, called Roll Caller, and conduct a comprehensive range of experiments to examine its performance.

Predicting impact forces on pipelines from deep-sea fluidized slides:A comprehensive review of key factors

Deep-sea pipelines play a pivotal role in seabed mineral resource development,global energy and resource supply provision,network communication,and environmental protection.However,the placement of these pipelines on the seabed surface exposes them to potential risks arising from the complex deep-sea hydrodynamic and geological environment,particularly submarine slides.Historical incidents have highlighted the substantial damage to pipelines due to slides.Specifically,deep-sea fluidized slides(in a debris/mud flow or turbidity current physical state),characterized by high speed,pose a significant threat.Accurately assessing the impact forces exerted on pipelines by fluidized submarine slides is crucial for ensuring pipeline safety.This study aimed to provide a comprehensive overview of recent advancements in understanding pipeline impact forces caused by fluidized deep-sea slides,thereby identifying key factors and corresponding mechanisms that influence pipeline impact forces.These factors include the velocity,density,and shear behavior of deep-sea fluidized slides,as well as the geometry,stiffness,self-weight,and mechanical model of pipelines.Additionally,the interface contact conditions and spatial relations were examined within the context of deep-sea slides and their interactions with pipelines.Building upon a thorough review of these achievements,future directions were proposed for assessing and characterizing the key factors affecting slide impact loading on pipelines.A comprehensive understanding of these results is essential for the sustainable development of deep-sea pipeline projects associated with seabed resource development and the implementation of disaster prevention measures.

Deep learning models for spatial relation extraction in text

Spatial relation extraction is the process of identifying geographic entities from text and determining their corresponding spatial relations.Traditional spatial relation extraction mainly uses rule-based pattern matching,supervised learning-based or unsupervised learning-based methods.However,these methods suffer from poor time-sensitive,high labor cost and high dependence on large-scale data.With the development of pre-trained language models greatly alleviating the shortcomings of traditional methods,supervised learning methods incorporating pre-trained language models have become the mainstream relation extraction methods.Pipeline extraction and joint extraction,as the two most dominant ideas of relation extraction,both have obtained good performance on different datasets,and whether to share the contextual information of entities and relations is the main differences between the two ideas.In this paper,we compare the performance of two ideas oriented to spatial relation extraction based on Chinese corpus data in the field of geography and verify which method based on pre-trained language models is more suitable for Chinese spatial relation extraction.We fine-tuned the hyperparameters of the two models to optimize the extraction accuracy before the comparison experiments.The results of the comparison experiments show that pipeline extraction performs better than joint extraction of spatial relation extraction for Chinese text data with sentence granularity,because different tasks have different focus on contextual information,and it is difficult to take account into the needs of both tasks by sharing contextual information.In addition,we further compare the performance of the two models with the rule-based template approach in extracting topological,directional and distance relations,summarize the shortcomings of this experiment and provide an outlook for future work.

Formula for calculating spatial similarity degrees between point clouds on multi-scale maps taking map scale change as the only independent variable

The degree of spatial similarity plays an important role in map generalization, yet there has been no quantitative research into it. To fill this gap, this study first defines map scale change and spatial similarity degree/relation in multi-scale map spaces and then proposes a model for calculating the degree of spatial similarity between a point cloud at one scale and its gener- alized counterpart at another scale. After validation, the new model features 16 points with map scale change as the x coordinate and the degree of spatial similarity as the y coordinate. Finally, using an application for curve fitting, the model achieves an empirical formula that can calculate the degree of spatial similarity using map scale change as the sole independent variable, and vice versa. This formula can be used to automate algorithms for point feature generalization and to determine when to terminate them during the generalization.

Few-Shot Named Entity Recognition with the Integration of Spatial Features

The few-shot named entity recognition(NER)task aims to train a robust model in the source domain and transfer it to the target domain with very few annotated data.Currently,some approaches rely on the prototypical network for NER.However,these approaches often overlook the spatial relations in the span boundary matrix because entity words tend to depend more on adjacent words.We propose using a multidimensional convolution module to address this limitation to capture short-distance spatial dependencies.Additionally,we uti-lize an improved prototypical network and assign different weights to different samples that belong to the same class,thereby enhancing the performance of the few-shot NER task.Further experimental analysis demonstrates that our approach has significantly improved over baseline models across multiple datasets.

Theoretical Models of Space and Its Philosophical Category

Diverse concepts of space developed in history of natural philosophy,mathematics,physics,and other natural or cultural studies form theoretical models of spatial relations,given in human’s experience.Their diversity is due not only to the multiplicity of philosophical and methodological approaches to the concept of space,but also to the variety of ways,in which spatial relationships can be organized.This variety gives a possibility to distinct autonomous spaces of different types with diverse sets of properties as well as separate spaces with their own ordinal,metrical,and sequential structures.Particularly,various ways of space semiotization in culture generate different types of autonomous and separate spaces:written texts,maps,pictures,chessboards,etc.In the same time,all particular notions of space are included in a general logical class.Its volume and content are covered by the philosophical category of space.Such general category cannot be reduced to mathematical,physical,or other concepts of space elaborated in particular sciences,however,it serves as a philosophical basis for their comparison.

Supporting Information Extraction from Visual Documents

Visual Information Extraction (VIE) is a technique that enables users to perform information extraction from visual documents driven by the visual appearance and the spatial relations occurring among the elements in the document. In particular, the extractions are expressed through a query language similar to the well known SQL. To further reduce the human effort in the extraction task, in this paper we present a fully formalized assistance mechanism that helps users in the interactive formulation of the queries.

Ontological exploration of geospatial objects in context

Structured study of spatial objects and their relationships leads to a better cognition of the geospatial information and creates the concept of context at a higher level of abstraction.This study is aimed at providing a comprehensive definition of the context for geospatial objects.A combination of binary qualitative spatial relationships(i.e.direction,distance,and topological relations)among the members of a set of spatial objects will be used accordingly.In addition,by incorporating the general concept of context,obtained from either static data(attributes in a database)or dynamic data(sensors),the compact context of spatial objects will be introduced.Our framework for presentation of the involved knowledge and conception about the objects in context is also explored using ontology and description logic because of powerful conceptualization of relationships,either spatial or non-spatial,integrally.For this purpose,the hierarchies of main structure and object properties are formed at first.The constraint and characteristics of classes,such as subclasses,equivalent classes,cardinality etc.,and object properties,such as being functional,transitive,symmetric,asymmetric,inverse functional,disjoint etc.,are discovered and presented in more detail using web ontology language in description logic mode.The implementation is then performed in the framework of semantic web and extensible markup language syntaxes.The method ultimately facilitates,spatial reasoning by effective querying in a semantic framework taking pellet reasoner and SPARQL(a recursive acronym for SPARQL Protocol and RDF Query Language).

空间、感知与关系嵌入:论数字空间媒介化过程中的技术中介效应

在新兴空间集合体广泛扩散的当下,媒介自身的中介属性被不断强化,人类对于空间环境的驾驭同时受到数字技术的中介。通过理解技术的新变化及其支撑的新应用服务来拓宽对数字空间媒介的认知,是一个值得持续研究的重要议题。本研究以数字技术作为切入点,将英国地理学家多琳·梅西的空间观作为理论切入点,强调从关系入手理解数字空间的必要性和重要性。在此基础上,本研究以高德地图App为例,试图进一步阐释"关系"对于数字空间媒介实践的重要意义,尝试从认识论层面解读技术在驱动数字空间成为媒介时所起的关键作用,以期更全面地揭示作为空间关系性载体的数字空间媒介所附着的多重技术中介效应。