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This article explores the issue of “geography of education” focusing on the pivotal contribution of place to one’s education. The geographic location of schools and the administrative organisation of local authorities that are responsible for state schools in England create sociospatial inequalities that are associated with individual life‐course trajectories and can contribute to the intergenerational transfer of disadvantage. This article focuses on Latvian migrant families for whom better status often can be achieved through being included in the education system of the country. Therefore, the educational achievement of the children who speak Latvian at home but live and attend schools in England is the main focus of this article. The academic attainment of these children is well below not only the national average across all levels of compulsory education but also compared to both monolingual English speakers and all pupils speaking English as an additional language. The article provides evidence that in addition to the sociodemographic individual and family‐level factors geography also plays a significant role in explaining the educational achievement gaps. As the descriptive quantitative analysis of the geographical and educational data indicates, Latvian children are disproportionally present in local authorities where there is a relatively high proportion of low‐quality schools, a higher‐than‐average proportion of individuals with low qualifications and those in low‐qualified jobs.

The National Land Cover Database (NLCD) 2016 provides a suite of data products, including land cover and land cover change of the conterminous United States from 2001 to 2016, at two- to three-year intervals. The development of this product is part of an effort to meet the growing demand for longer temporal duration and more frequent, accurate, and consistent land cover and change information. To accomplish this, we designed a new land cover strategy and developed comprehensive methods, models, and procedures for NLCD 2016 implementation. Major steps in the new procedures consist of data preparation, land cover change detection and classification, theme-based postprocessing, and final integration. Data preparation includes Landsat imagery selection, cloud detection, and cloud filling, as well as compilation and creation of more than 30 national-scale ancillary datasets. Land cover change detection includes single-date water and snow/ice detection algorithms and models, two-date multi-index integrated change detection models, and long-term multi-date change algorithms and models. The land cover classification includes seven-date training data creation and 14-run classifications. Pools of training data for change and no-change areas were created before classification based on integrated information from ancillary data, change-detection results, Landsat spectral and temporal information, and knowledge-based trajectory analysis. In postprocessing, comprehensive models for each land cover theme were developed in a hierarchical order to ensure the spatial and temporal coherence of land cover and land cover changes over 15 years. An initial accuracy assessment on four selected Landsat path/rows classified with this method indicates an overall accuracy of 82.0% at an Anderson Level II classification and 86.6% at the Anderson Level I classification after combining the primary and alternate reference labels. This methodology was used for the operational production of NLCD 2016 for the Conterminous United States, with final produced products available for free download.

Building rooftop extraction has been applied in various fields, such as cartography, urban planning, automatic driving, and intelligent city construction. Automatic building detection and extraction algorithms using high spatial resolution aerial images can provide precise location and geometry information, significantly reducing time, costs, and labor. Recently, deep learning algorithms, especially convolution neural networks (CNNs) and Transformer, have robust local or global feature extraction ability, achieving advanced performance in intelligent interpretation compared with conventional methods. However, buildings often exhibit scale variation, spectral heterogeneity, and similarity with complex geometric shapes. Hence, the building rooftop extraction results exist fragmentation and lack spatial details using these methods. To address these issues, this study developed a multi-scale global perceptron network based on Transformer and CNN using novel encoder-decoders for enhancing contextual representation of buildings. Specifically, an improved multi-head-attention encoder is employed by constructing multi-scale tokens to enhance global semantic correlations. Meanwhile, the context refinement decoder is developed and synergistically uses high-level semantic representation and shallow features to restore spatial details. Overall, quantitative analysis and visual experiments confirmed that the proposed model is more efficient and superior to other state-of-the-art methods, with a 95.18% F1 score on the WHU dataset and a 93.29% F1 score on the Massub dataset.

Semantic segmentation of remote sensing images (RSIs) is pivotal for numerous applications in urban planning, agricultural monitoring, and environmental conservation. However, traditional approaches have primarily emphasized learning within the spatial domain, which frequently leads to less than optimal discrimination of features. Considering the inherent spectral qualities of RSIs, it is essential to bolster these representations by incorporating the spectral context in conjunction with spatial information to improve discriminative capacity. In this paper, we introduce the spectral–spatial context-boosted network (SSCBNet), an innovative network designed to enhance the accuracy semantic segmentation in RSIs. SSCBNet integrates synergetic attention (SYA) layers and cross-fusion modules (CFMs) to harness both spectral and spatial information, addressing the intrinsic complexities of urban and natural landscapes within RSIs. Extensive experiments on the ISPRS Potsdam and LoveDA datasets reveal that SSCBNet surpasses existing state-of-the-art models, achieving remarkable results in F1-scores, overall accuracy (OA), and mean intersection over union (mIoU). Ablation studies confirm the significant contribution of SYA layers and CFMs to the model’s performance, emphasizing the effectiveness of these components in capturing detailed contextual cues.

This study addresses the limited usability of Goryeo celadon data from the Gaeseong Manwoldae Inter-Korean Joint Excavation. The current Digital Archive for the Inter-Korean Joint Excavation of Gaeseong Manwoldae separates artifact attributes from spatial information, hindering integrated analysis of artifact-findspot relationships and spatial distribution. This research utilizes Geographic Information System (GIS) to overcome this fragmentation. The methodology involved creating an Excel Artifact Attribute Table for 1,089 Goryeo celadon sherds with 28 attributes, including production period and technique. High-resolution site drawings were then georeferenced using ArcGIS, and the attribute data was spatially joined to each artifact's precise excavation location using its unique 'registration number' as a key. This process produced the ArcGIS Artifact Spatial Information Map, an integrated dataset linking artifact attributes with their spatial information. In conclusion, this study establishes a new framework for integrated spatial analysis, overcoming the limitations of fragmented data. The findings are expected to enhance the usability of Manwoldae data, contributing to deeper research on the Goryeo royal court.

Recent longitudinal studies of household car ownership have examined factors associated with increases and decreases in car ownership level. The contribution of this panel data analysis is to identify the predictors of different types of car ownership level change (zero to one car, one to two cars and vice versa) and demonstrate that these are quite different in nature. The study develops a large scale data set (n = 19,334), drawing on the first two waves (2009–2011) of the UK Household Longitudinal Study (UKHLS). This has enabled the generation of a comprehensive set of life event and spatial context variables. Changes to composition of households (people arriving and leaving) and to driving licence availability are the strongest predictors of car ownership level changes, followed by employment status and income changes. Households were found to be more likely to relinquish cars in association with an income reduction than they were to acquire cars in association with an income gain. This may be attributed to the economic recession of the time. The effect of having children differs according to car ownership state with it increasing the probability of acquiring a car for non-car owners and increasing the probability of relinquishing a car for two car owners. Sensitivity to spatial context is demonstrated by poorer access to public transport predicting higher probability of a non-car owning household acquiring a car and lower probability of a one-car owning household relinquishing a car. While previous panel studies have had to rely on comparatively small samples, the large scale nature of the UKHLS has provided robust and comprehensive evidence of the factors that determine different car ownership level changes.

Semantic segmentation plays a pivotal role in the intelligent interpretation of remote sensing images (RSIs). However, conventional methods predominantly focus on learning representations within the spatial domain, often resulting in suboptimal discriminative capabilities. Given the intrinsic spectral characteristics of RSIs, it becomes imperative to enhance the discriminative potential of these representations by integrating spectral context alongside spatial information. In this paper, we introduce the spectrum-space collaborative network (SSCNet), which is designed to capture both spectral and spatial dependencies, thereby elevating the quality of semantic segmentation in RSIs. Our innovative approach features a joint spectral–spatial attention module (JSSA) that concurrently employs spectral attention (SpeA) and spatial attention (SpaA). Instead of feature-level aggregation, we propose the fusion of attention maps to gather spectral and spatial contexts from their respective branches. Within SpeA, we calculate the position-wise spectral similarity using the complex spectral Euclidean distance (CSED) of the real and imaginary components of projected feature maps in the frequency domain. To comprehensively calculate both spectral and spatial losses, we introduce edge loss, Dice loss, and cross-entropy loss, subsequently merging them with appropriate weighting. Extensive experiments on the ISPRS Potsdam and LoveDA datasets underscore SSCNet’s superior performance compared with several state-of-the-art methods. Furthermore, an ablation study confirms the efficacy of SpeA.

All experiences preserved within episodic memory contain information on the space and time of events. The hippocampus is the main brain region involved in processing spatial and temporal information for incorporation within episodic memory representations. However, the other brain regions involved in the encoding and retrieval of spatial and temporal information within episodic memory are unclear, because a systematic review of related studies is lacking and the findings are scattered. The present study was designed to integrate the results of functional magnetic resonance imaging and positron emission tomography studies by means of a systematic review and meta-analysis to provide converging evidence. In particular, we focused on identifying the brain regions involved in the retrieval of spatial and temporal information. We identified a spatial retrieval network consisting of the inferior temporal gyrus, parahippocampal gyrus, superior parietal lobule, angular gyrus, and precuneus. Temporal context retrieval was supported by the dorsolateral prefrontal cortex. Thus, the retrieval of spatial and temporal information is supported by different brain regions, highlighting their different natures within episodic memory.

The early and effective diagnosis of Alzheimer’s disease (AD) and mild cognitive impairment (MCI) has received increasing attention in recent years. However, currently available deep learning methods often ignore the contextual spatial information contained in structural MRI images used for early diagnosis and classification of Alzheimer’s disease. This may lead us to miss important structural details by failing to adequately capture the potential connections between each slice and its neighboring slices. This lack of contextual information may cause the accuracy of the network model to suffer, which in turn affects its generalization ability and application in real-life scenarios. To explore deeper the connection between spatial context slices, this research is designed to develop a new network model to effectively detect or predict AD by digging into the deeper spatial contextual structural information. In this paper, we design a spatial context network based on 3D convolutional neural network to learn the multi-level structural features of brain MRI images for AD classification. The experimental results show that the model has good stability, accuracy and generalization ability. Our experimental method had a classification accuracy of 92.6% in the AD/CN comparison, 74.9% in the AD/MCI comparison, and 76.3% in the MCI/CN comparison. In addition, this paper demonstrates the effectiveness of the proposed network model through ablation experiments.

Our visual system relies on memory to store and retrieve goal-relevant structures and information from the environment for the purpose of optimizing the allocation of attention. This concept, referred to as contextual cueing, has been demonstrated using visual search tasks, wherein repeated visual contexts lead to reduced search times compared with random displays. Subsequently, when an unexpected change occurs in the environment, or memory fails, a cognitive expense is incurred as the mind tries to resolve the conflict with the memory of the previous environmental context. How memory resolves these conflicts and is updated is of great interest. Previous studies showed that, without extensive practice, individuals were unable to associate a secondary target location with a previously learned spatial context following the relocation of the initially learned target. Here, we explored variables that could potentially affect contextual learning and relearning, such as display size, crowding, context color, and whether the target switched to a previously occupied or unoccupied location. In a series of four experiments, we find relearning occurring in all instances. Previous research may have suffered from underpowered designs.