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Spatial relationships are core components in the design and definition of spatial queries. A spatial relationship determines how two or more spatial objects are related or connected in space. Hence, given a spatial dataset, users can retrieve spatial objects in a given relationship with a search object. Different interpretations of spatial relationships are conceivable, leading to different types of relationships. The main types are (i) topological relationships (e.g. overlap, meet, inside), (ii) metric relationships (e.g. nearest neighbors), and (iii) direction relationships (e.g. cardinal directions). Although spatial information retrieval has been extensively studied in the literature, it is unclear which types of spatial queries can be defined using spatial relationships. In this article, we introduce a taxonomy for naming, describing, and classifying types of spatial queries frequently found in the literature. This taxonomy is based on the types of spatial relationships that are employed by spatial queries. By using this taxonomy, we discuss the intuitive descriptions, formal definitions, and possible implementation techniques of several types of spatial queries. The discussions lead to the identification of correspondences between types of spatial queries. Further, we identify challenges and open research topics in the spatial information retrieval area.

Bill Hillier’s space syntax method and theory enables us to describe the spatial properties of a sustainable city. Empirical testing of the space syntax method over time has confirmed the capacity and innovativeness of analyzing spatial relationships with the purpose of understanding and explaining the socio-spatial organization of built environments. However, the conceptual framework of space syntax elements is scattered around in various academic writings. This article, therefore, gives a holistic and compact overview of the various concepts that are used in space syntax, from its basic elements to various analytical techniques and theories. To achieve this compact overview, we reviewed all space syntax literature accessible since the 1970s for finding core references to various concepts used in space syntax. Following a short description of its foundation and evolution through the work of Bill Hillier, we explain its basic concepts and measures in the form of an extended glossary. Explanations are enriched with various space syntax analyses and scenario testing on various scales that were applied to the city of Rotterdam in the Netherlands. We conclude with a discussion about the advantages and limitations of space syntax and about how this method adds value to the creation of sustainable cities.

Attention is rapidly directed to stimuli associated with rewards in past experience, independent of current task goals and physical salience of stimuli. However, despite the robust attentional priority given to reward-associated features, studies often indicate negligible priority toward previously rewarded locations. Here, we propose a relational account of value-driven attention, a mechanism that relies on spatial relationship between items to achieve value-guided selections. In three experiments ( N  = 124), participants were trained to associate specific locations with rewards (e.g., high-reward: top-left; low-reward: top-right). They then performed an orientation-discrimination task where the target’s absolute location (top-left or top-right) or spatial relationship (“left of” or “right of”) had previously predicted reward. Performance was superior when the target’s spatial relationship matched high-reward than low-reward, irrespective of absolute locations. Conversely, the impact of reward was absent when the target matched the absolute location but not the spatial relationship associated with high reward. Our findings challenge the default assumption of location specificity in value-driven attention, demonstrating a generalizable mechanism that humans adopted to integrate value and spatial information into priority maps for adaptive behavior.

Although the deep semantic segmentation network (DSSN) has been widely used in remote sensing (RS) image semantic segmentation, it still does not fully mind the spatial relationship cues between objects when extracting deep visual features through convolutional filters and pooling layers. In fact, the spatial distribution between objects from different classes has a strong correlation characteristic. For example, buildings tend to be close to roads. In view of the strong appearance extraction ability of DSSN and the powerful topological relationship modeling capability of the graph convolutional neural network (GCN), a DSSN-GCN framework, which combines the advantages of DSSN and GCN, is proposed in this paper for RS image semantic segmentation. To lift the appearance extraction ability, this paper proposes a new DSSN called the attention residual U-shaped network (AttResUNet), which leverages residual blocks to encode feature maps and the attention module to refine the features. As far as GCN, the graph is built, where graph nodes are denoted by the superpixels and the graph weight is calculated by considering the spectral information and spatial information of the nodes. The AttResUNet is trained to extract the high-level features to initialize the graph nodes. Then the GCN combines features and spatial relationships between nodes to conduct classification. It is worth noting that the usage of spatial relationship knowledge boosts the performance and robustness of the classification module. In addition, benefiting from modeling GCN on the superpixel level, the boundaries of objects are restored to a certain extent and there are less pixel-level noises in the final classification result. Extensive experiments on two publicly open datasets show that DSSN-GCN model outperforms the competitive baseline (i.e., the DSSN model) and the DSSN-GCN when adopting AttResUNet achieves the best performance, which demonstrates the advance of our method.

This article discusses spatial optimization in geography, focusing on contributions of geographers in explicit geographical contexts. An overview of spatial optimization is given, as well as illustrative examples. Many of the individuals contributing to this area of the discipline are identified, demonstrating the breadth of academic institutions spanning the globe where spatial optimization is represented in the research and curriculum of geographers. The article provides a characterization of what a spatial optimization problem is, but also properties, relationships, and challenges behind this. The ultimate purpose of this article is to highlight the spatial optimization subspecialty within geography and in doing so, highlight the need for continued spatial model development and application in the discipline. Further, there is also a need for research focused on techniques to solve spatial optimization problems, particularly in the context of geographic information systems.

Landslides are complex geological phenomena that occurred caused of one or more conditioning factors. It can be difficult to analyse the landslide occurrence phenomena and produce landslide susceptibility mapping. However, selecting an appropriate contributing factor for the landslide model will generate valuable landslide susceptibility mapping. This paper assesses the potentiality and suitability of landslide conditioning factors for evaluating relationship landslide spatial analysis. Ten landslides contributing factors including elevation, slope, aspect, curvature, Topography Wetness Index (TWI), lithology, soil series, distance to drainage, land use, and rainfall were selected Multicollinearity analysis. Next, to analyse the landslide spatial relationship between its conditioning factors, Geographical Information System (GIS) approach ware used. The results showed the most distribution landslides occurred on the elevation range from 13.62 to 463.90 m, the slope between 16º to 25º, northeast direction for aspect, convex surface for curvature, 11 to 15 index TWI, within 200 m distance to the river, acid intrusive for lithology, and Steep land for soil series. Langat River Basin substantial rainfall, exceeding 2100 mm annually, exacerbates slope instability and contributes significantly to landslide frequency. By doing this, the present study contributes to identified appropriate conditioning factors and it is very important in future studies to develop landslide susceptibility analysis at Langat River Basin. The insights presented are invaluable for policymakers, land use planners, and disaster management agencies in implementing proactive measures to reduce the impact of landslides and enhance the region’s resilience to geological hazards.

The past few decades have witnessed unprecedented global urbanisation, with direct or indirect impacts on global cultural heritage sites. Research on the spatial relationship between cultural heritage sites and urban areas has provided a new perspective for understanding the impact processes between them, which have previously been discussed at the regional scale. In this article, we analyse the spatial relationship between world cultural heritage sites and neighbouring towns through systematic observations at the global scale and attempt to model change processes and identify impact mechanisms. We adopt spatial analysis and spatial statistics to analyse the changing characteristics of the spatial relationship between world cultural heritage sites and neighbouring towns from 1990 to 2018 and to analyse the impact processes at different spatial and temporal scales by combining indicators, such as income levels and urbanisation rates, at the national scale. The results show that 8.52% of world cultural heritage sites have been incorporated into urban areas over the aforementioned 28 years, with a certain aggregation in the spatial distribution of these sites, and that the growth rate can be divided into three phases, including two periods of rapid growth. The spatial relationship between towns and the 523 world cultural heritage sites that were previously located outside towns has not yet changed substantially, but the distances between most of the towns and these sites have been decreasing, with 81% of the world cultural heritage sites having a variation in distance from the corresponding town of 7.60 km or less. We also analysed the variation in distance between cultural heritage sites and neighbouring towns and found a relationship with indicators, such as the income level and urbanisation rate of the country to which a site belongs. Among the indicators, variation in national urbanisation rates most greatly affected the distance between heritage sites and towns. This study shows that world cultural heritage sites are affected by urbanisation and that particular attention should be given to the relationship between cultural heritage sites and neighbouring towns, especially in countries undergoing rapid urbanisation, so that the authenticity and integrity of cultural heritage are not compromised. This article provides a basis for development plans and policies in urban design, especially those that are sensitive to cultural heritage, and may also provide ideas and references for heritage conservation against the background of urbanisation.

Construction workers of long tunnel projects are confronted with numerous safety hazards such as fall from height (FFH) and object strike due to the harsh jobsite environment, limited space, and complex working conditions. And the absence of protective guardrails is identified as the primary cause of falling accidents from height. In order to automatically detect the safety protection status of working-at-high workers, a computer vision-based recognition method for working-at-high operation safety protection according to target detection and spatial relationship was proposed in this study. Firstly, the Cycle-consistent Generative Adversarial Networks (CycleGAN) was used to preprocess construction site images to enhance the image quality. Secondly, a YOLOv8 model integrated with the coordinate attention (CA) module was established to rapidly detect targets such as workers, trolleys, and guardrails in the tunnel. Furthermore, an identification method for working-at-high operation safety protection is proposed based on the detected targets and their spatial relationships. Finally, a case study was conducted, revealing that the model achieves an accuracy and recall rate of 95.89% and 97.22%, respectively, in identifying the safety protection status of working-at-high workers. The result indicates that the proposed method provides a new way for intelligent identification of working-at-high operation safety protection and assisting on-site management personnel to prevent the risk of FFH in the tunnel.

Spatial scenes, as fundamental units of geospatial cognition, encompass rich objects and spatial relationships, and their generation techniques hold significant application value in disaster simulation and emergency drills, delayed spatial reconstruction and analysis, and other fields. However, existing studies still face limitations in modeling complex spatial relationships during scene generation, leading to insufficient semantic consistency and geographical accuracy. The advancement of Geospatial Artificial Intelligence (GeoAI) offers a new technical pathway for the intelligent modeling of spatial scenes. Against this backdrop, we propose SceneDiffusion, a scene generation model embedded with spatial constraints, and construct a geospatial scene dataset incorporating spatial relationship descriptions and geographic semantics, aiming to enhance the understanding and modeling capabilities of GeoAI models for spatial information. Specifically, SceneDiffusion employs a spatial scene representation framework to uniformly characterize objects and their topological, directional, and distance relationships, enhances the interactive modeling of objects and relationships through a Spatial relationship Attention-aware Graph (SAG) module, and finally generates high-quality scene images conforming to geographic semantics using a Layout information-guided Conditional Diffusion (LCD) module. Both qualitative and quantitative experiments demonstrate the superiority of SceneDiffusion, achieving a 56.6% reduction in FID and a 35.3% improvement in SSIM compared to baseline methods. Ablation studies confirm the importance of multi-relational modeling with attention mechanisms. By generating scenes that satisfy spatial distribution constraints, this work provides technical support for applications such as emergency scene simulation and virtual scene construction, while also offering insights for theoretical research and methodological innovation in GeoAI.

Historical and Cultural Towns (Villages) (HCTVs) are the important parts in the recordation of the traditional culture, folk customs and architectural art in China. However, with the rapid development of the economy and rural urbanization, these towns and villages are faced with a series of problems, such as traditional features lost, traditional architecture destroyed and the lack of a long-term comprehensive and effective plan for scientific conservation measures. As a result, these historic heritages and their surroundings are in a serious crisis and will be destroyed to a certain extent. This paper chose 799 HCTVs as objects of this study, which were published by the Department of Housing Construction. The distribution features and mechanism have been analyzed with thesupport of GIS technology. In addition, the spatial correlation between elevation, stream, transportation, traditional culture and language was also explained through spatial overlay analysis. Through the statistical comparison and cluster analysis, it explored natural and human factors with the influence of HCTV distribution. The purpose is to help us make selection more reasonable and offer a reference of development of regional tourism for the future. The results showed that: (1) The spatial distribution of HCTVs clearly varies among different regions of China, and four concentrated typical areas are found in the Shanxi-Hebei-Henan border area, southeast coastal zone, Sichuan-Chongqing-Guizhou border area and Hunan-Guangxi border area. (2) The distribution characteristics have a peculiar style in the countryside and have a trend of cluster around a geographic line (e.g., a traffic line, a river) and a small town. (3) The HCTV has different characteristics in spatial form, river system distribution, regional culture and transportation system. The majority of them are distributed along the river and are cultural centers, traffic hubs and birthplace of civilizations in history. (4) Natural geographical environment, current population distribution patterns, level of regional economic development, accumulation of historic and cultural heritages, as well as rules and standards in the definition of HCTV, are the main factors affecting the spatial distribution. The purpose of this paper is to help us select more reasonable criteria and rules in the process of HCTV selection.