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As Location-Based Services (LBSs) rapidly develop, indoor localization technology is garnering significant attention as a critical component. Smartphones have become tools for indoor localization due to their highly integrated sensors, fast-evolving computational capabilities, and widespread user adoption. With the rapid advancement of smartphones, methods for smartphone-based indoor localization have increasingly attracted attention. Although there are reviews on indoor localization, there is still a lack of systematic reviews focused on smartphone-based indoor localization methods. In particular, existing reviews have not systematically analyzed smartphone-based indoor localization methods or considered the combination of smartphone sensor data with prior knowledge of the indoor environment to enhance localization performance. In this study, through systematic retrieval and analysis, the existing research was first categorized into three types to dissect the strengths and weaknesses based on the types of data sources integrated, i.e., single sensor data sources, multi-sensor data fusion, and the combination of spatial context with sensor data. Then, four key issues are discussed and the research gaps in this field are summarized. Finally, a comprehensive conclusion is provided. This paper offers a systematic reference for research and technological applications related to smartphone-based indoor localization methods. [ABSTRACT FROM AUTHOR]

Vehicle detection is a very important part in intelligent transportation system. In order to improve the detection speed without sacrificing the accuracy, this paper propose an improved Faster R-CNN algorithm based on frame difference and spatiotemporal context to realize real-time detection of vehicles. We improve the training and testing speed of Faster R-CNN by improving the RPN module. Different from the original Faster R-CNN's anchor based strategy, the inter frame difference in this paper is mainly used to extract the region of interest of the target. And we introduce spatiotemporal context information to assist our detection. Among them, the spatial context is formed by adding the association information outside the target area to enhance the expression of target information and improve the accuracy of target detection, while the anchor filtering of the original Faster R-CNN can be carried out by integrating the temporal context information, so as to improve the detection efficiency. This improved RPN has strong pertinence to the detection of moving vehicles. This strategy not only makes our branch network parallelly process with the original Faster R-CNN, but also avoids the extra time consumption caused by the addition of algorithm. More importantly, it can be simply added to the existing Faster R-CNN based application system without algorithm adjustment or network retraining. Experimental results show that the proposed method has high detection efficiency and low sensitivity to background changes. [ABSTRACT FROM AUTHOR]

This study explores the dynamics of online community entrepreneurship, using a rural grocery store as a case study. I examine the role of social media in community entrepreneurship, revealing insights into the interplay between social interactions, spatial factors, and the entrepreneurial process. The case account outlines a process where online communities turn ideas into actions and shape local environments. Social media acts as a platform for collective effort, resource allocation, and coordination, challenging traditional notions of localized entrepreneurship. Three key themes emerge: social media facilitates communication and collaboration; collective agency empowers online communities for change; and the dynamic nature of context blurs the boundaries between local and online. I introduce the concept of 'online community entrepreneurship', which connects participants based on shared interests, promoting social goals and asynchronous engagement. This research enriches an understanding of community entrepreneurship by highlighting the transformative impact of online communities. [ABSTRACT FROM AUTHOR]

Mobile Crowd Sensing (MCS) has become a promising new data perception paradigm. It is to be able to easily submit the wrong or untrusted data for the malicious attackers in such an environment. This greatly affects the normal operation of the MCS system and the authenticity of task results. Therefore, ensuring the reliability of data is becoming a key research direction in MCS, especially for real-time application scenarios. For this purpose, we propose a multi-dimensional framework for improving data reliability, named MDF. It integrates three dimensions of temporal, spatial context and sensing measurement. Through a series of experiments, it is demonstrated that MDF outperforms existing methods.

Time series analysis of medium-resolution multispectral satellite imagery is critical to investigate forest disturbance dynamics at the landscape scale. In particular, the spatial, temporal, and radiometric consistency of Landsat time series data provides unprecedented insight into past disturbances that occurred over the last four decades. Several Landsat time series-based algorithms have been developed to automate the detection of forest disturbances. However, automated detection of non-stand-replacing disturbances based on Landsat time series remains a challenging task due to the difficulty of effectively separating them from spectral noise. Here, we present the High-dimensional detection of Landscape Dynamics (HILANDYN) algorithm, which exploits spatial and spectral information provided by Landsat time series to detect forest disturbance dynamics retrospectively. A novel and unsupervised procedure for changepoint detection in high-dimensional time series allows HILANDYN to perform the temporal segmentation of inter-annual time series into linear trends. The algorithm embeds a noise filter to remove spurious changepoints caused by residual spectral noise in the time series. We tested HILANDYN to detect disturbances that occurred in the forests of the European Alps over a period of 39 years, i.e. between 1984 and 2022, and evaluated its accuracy using a validation dataset of 3000 plots randomly located inside and outside the disturbed patches. We compared HILANDYN with the Bayesian Estimator of Abrupt change, Seasonality, and Trend (BEAST), which is a well-established and high-performing time series-based algorithm for changepoint detection. The quantitative results highlighted that the number of bands, i.e. original Landsat bands and spectral indices, included in the high-dimensional time series and the threshold controlling the significance of changepoints strongly influenced the user’s accuracy (UA). Conversely, changes in the combinations of bands primarily affected the producer’s accuracy (PA). HILANDYN achieved an F1 score of 0.801, which increased to 0.833 when we activated the noise filter, allowing the algorithm to balance UA (83.1%) and PA (83.5%). The qualitative results showed that disturbed forest patches detected by HILANDYN were characterized by a high spatio-temporal consistency, regardless of the disturbance severity. Furthermore, our algorithm was able to detect forest patches associated with secondary disturbances, such as salvage logging, that occur in close succession with respect to the primary event. The comparison with BEAST evidenced a similar sensitivity of the algorithms to non-stand-replacing events, as both achieved comparable PA. However, BEAST struggled to balance UA and PA when using a single parameter set, achieving a maximum F1 score of 0.717. Moreover, the computational efficiency of BEAST in processing high-dimensional time series was very limited due to its univariate nature based on the Bayesian approach. The adaptability of HILANDYN to detect a wide range of disturbance severities using a single parameter set and its computational efficiency in handling high-dimensional time series promotes its scalability to large study areas characterized by heterogeneous ecological conditions.

Third Places (TPs), defined by Oldenburg in suburban American context are social informal gathering spaces, except home and workplace. Major literature focused on how TPs create social capital and cohesion within communities, largely benefitting socially, psychologically and even economically. Over the years, the TP concept is contested to globally transform from its initial theme of communitarianism to present dominant forms of neo-liberal consumerism. This posed significant threat to the inherent idea of affordable sociability rooted in characteristic plainness and neutral ground of TP. Moreover, exploring the spatial patterns and the influence of geographical variations on the nature of TP across urban landscapes is largely understudied. This study thus aimed to develop a consolidated understanding of the spatial dynamics of TPs in Indian context. The paper presented a city level, empirical based quantitative study based on online questionnaire survey. Commercial TPs are found to be most preferred. Research findings showed that the types of TP significantly affect the individual’s preferred travel distance. Also, one’s behavioural attributes as a whole significantly affect the catchment of the TPs, influenced by spatial variations. Hotspot analysis demonstrated three significant spatial concentrations across the city based on distance-based clustering. It showed heterogenous agglomerating tendencies of TP. [ABSTRACT FROM AUTHOR]

Accurately identifying small objects in high-resolution aerial images presents a complex and crucial task in the field of small object detection on unmanned aerial vehicles (UAVs). This task is challenging due to variations in UAV flight altitude, differences in object scales, as well as factors like flight speed and motion blur. To enhance the detection efficacy of small targets in drone aerial imagery, we propose an enhanced You Only Look Once version 7 (YOLOv7) algorithm based on multi-scale spatial context. We build the MSC-YOLO model, which incorporates an additional prediction head, denoted as P2, to improve adaptability for small objects. We replace conventional downsampling with a Spatial-to-Depth Convolutional Combination (CSPDC) module to mitigate the loss of intricate feature details related to small objects. Furthermore, we propose a Spatial Context Pyramid with Multi-Scale Attention (SCPMA) module, which captures spatial and channel-dependent features of small targets acrossmultiple scales. This module enhances the perception of spatial contextual features and the utilization of multiscale feature information. On the Visdrone2023 and UAVDT datasets, MSC-YOLO achieves remarkable results, outperforming the baseline method YOLOv7 by 3.0% in terms of mean average precision (mAP). The MSCYOLO algorithm proposed in this paper has demonstrated satisfactory performance in detecting small targets in UAV aerial photography, providing strong support for practical applications. [ABSTRACT FROM AUTHOR]

Crafting is a common activity in many vernacular settlements. In North Aceh in Indonesia, crafting of the Rapai Pase musical instrument is a popular activity. This research explores the influence of the spatial context on the techniques of crafting the Rapai Pase musical instruments. It seeks to understand how the spatial contexts affect the local knowledge of the people in crafting the Rapai Pase musical instrument, and its process of making. It employs a qualitative ethnographic perspective to gain a deep understanding of how the community adaptations to the local environment influences knowledge and practices in the crafting of this traditional musical instrument. Data collection involves observations at Rapai Pase musical instrument production sites, interviews with the local artisans and craftsmen, analysis of documents related to local wisdom and the practice of crafting. It reveals that the influence of spatial context on the practice of crafting Rapai Pase musical instruments can be understood from three main aspects: First, geographical factors affect the availability of raw materials and manufacturing techniques, with natural topography playing a crucial role in shaping these instruments. Second, local history and culture, especially the history of the Samudera Pasai Kingdom based on Islam provide unique colors in the process of crafting Rapai Pase musical instruments. The concept of "habluminal'alam" also contributes to shaping the local knowledge related to crafting. Third, the practice of crafting Rapai Pase is confined not only to technical processes but also relates closely to its social and physical environmental contexts. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Extracting building from high-resolution (HR) remote sensing imagery (RSI) serves a variety of areas, such as smart city, environment management, and emergency disaster services. Previous building extraction methods primarily focus on pixel-level and superpixel-level features, which do not fully utilize the superpixel-level spatial context, leaving room for performance improvement. To bridge the gap, this study incorporates spatial context of both pixels and superpixels for building extraction of HR RSI. Specifically, the proposed method develops a trainable superpixel segmentation module to segment HR RSI into superpixels by fusing pixel features and pixel-level context. And a superpixel-level context aggregation module is devised to incorporate the multiple-scale spatial context of superpixels to extract buildings. Experiments on public challenging datasets show that our method is superior to the state-of-the-art baselines in accuracy, with better building boundaries and higher integrity. This study explores a new approach for HR RSI building extraction by introducing spatial context of superpixels, and a methodological reference for the HR RSI interpretation tasks.

As Location-Based Services (LBSs) rapidly develop, indoor localization technology is garnering significant attention as a critical component. Smartphones have become tools for indoor localization due to their highly integrated sensors, fast-evolving computational capabilities, and widespread user adoption. With the rapid advancement of smartphones, methods for smartphone-based indoor localization have increasingly attracted attention. Although there are reviews on indoor localization, there is still a lack of systematic reviews focused on smartphone-based indoor localization methods. In particular, existing reviews have not systematically analyzed smartphone-based indoor localization methods or considered the combination of smartphone sensor data with prior knowledge of the indoor environment to enhance localization performance. In this study, through systematic retrieval and analysis, the existing research was first categorized into three types to dissect the strengths and weaknesses based on the types of data sources integrated, i.e., single sensor data sources, multi-sensor data fusion, and the combination of spatial context with sensor data. Then, four key issues are discussed and the research gaps in this field are summarized. Finally, a comprehensive conclusion is provided. This paper offers a systematic reference for research and technological applications related to smartphone-based indoor localization methods.

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. [ABSTRACT FROM AUTHOR]

This paper studies the entrepreneurial phenomenon on a remote island to assess the effects of the spatial location on the entrepreneurial process. A qualitative approach was adopted to conduct this research, through a multiple-case study of 8 entrepreneurs from the island of Madeira, an autonomous Portuguese region in the Atlantic. The primary goal of this study is to characterize the entrepreneurial process in remote islands. This study adopts Bygrave's (2009) definition and model of the entrepreneurial process. The findings show that the geographical environment on remote islands influences most phases of the entrepreneurial process. The identification of business opportunities will vary greatly between sectors of activity. Additionally, island-based entrepreneurs were more motivated by push factors, which suggests that the entrepreneurial landscape in remote islands will be populated by necessity entrepreneurs. Moreover, findings also demonstrate that remote island entrepreneurs are more preoccupied with firm survival than they are with growth. Finally, this study also discusses the impact of advances in information technologies on the entrepreneurial process on small and remote islands. [ABSTRACT FROM AUTHOR]

Semantic overlapping community detection is an important research hotspot in social networks. However, most of the existing methods ignore the fusion of multi-semantic attributes of users and variability of topological features under the spatial context. Motivated by this, we propose a semantic overlapping community detection method with embedding multi-dimensional relationships and spatial context. Firstly, on the basis of modeling users' Microblogs by LDA, we further extract the semantic topic information. Two core topic matrices are captured to quantify users' interest preferences. And the structure of social network is optimized by a new index named Network Constructability in line with user interest preference. Secondly, taking the network topology embedded with implicit semantic information as the core, the seed nodes are generated by leveraging Entropy to fuse explicit information. Finally, under the spatial context, Friend Probability and the Preference Matching Degree are utilized to deeply search the corresponding neighbor nodes in the process of detection. The effectiveness of the method is verified on the real datasets of social networks. The experimental results show that the method has obvious advantages and can detect the high-quality semantic overlapping communities with spatial context in real social networks. [ABSTRACT FROM AUTHOR]

Remote sensing images (RSI) of urban regions often exhibit shadows cast by buildings and other objects, which may result in imprecise analysis and interpretation. Therefore, shadow detection plays a significant role in RSI scene understanding. Current approaches have paid little attention to the presence of pseudo shadows and their confounding effects on detection results. We tackle these questions in the spatial context of orientation-awareness, effectively capturing the intricate relationships between shadows and ground objects. Here, we introduce a novel deep learning network named the Orientation-Aware and Multi-Scale Feature Fusion Network (OAMSFNet) due to noise reduction considerations. The proposed OAMSFNet comprises Shadow Aware Feature Encoder (SFE), Orientation-Aware Context Module (OCM), and Multi-Scale Feature Pyramid (MFP). Furthermore, a multi-scale feature fusion algorithm has been devised to enhance the detection and segmentation capabilities of the model in shadow regions. Finally, a comparative study was conducted on the Aerial Imagery dataset for Shadow Detection (AISD) in both quantitative and qualitative aspects. The experimental results show that our method outperforms state-of-the-art methods while maintaining a lightweight model design, indicating that our approach exhibits remarkable accuracy and stability in excellent agreement with predictions. OAMSFNet achieved an average F-score of 89.85%, surpassing the straightforward semantic segmentation model SegNet by 9.94%, and demonstrated remarkable efficiency enhancements, with FLOPs reduced to as low as 11.48 G, resulting in efficiency improvements of 66.7%-96.5% compared to other shadow detection models. [ABSTRACT FROM AUTHOR]

Accurate traffic speed forecasting is a prerequisite for anticipating future traffic status and increasing the resilience of intelligent transportation systems. However, most studies ignore the involvement of context information ubiquitously distributed over the urban environment to boost speed prediction. The diversity and complexity of context information also hinder incorporating it into traffic forecasting. Therefore, this study proposes a multimodal context-based graph convolutional neural network (MCGCN) model to fuse context data into traffic speed prediction, including spatial and temporal contexts. The proposed model comprises three modules, ie (a) hierarchical spatial embedding to learn spatial representations by organizing spatial contexts from different dimensions, (b) multivariate temporal modeling to learn temporal representations by capturing dependencies of multivariate temporal contexts and (c) attention-based multimodal fusion to integrate traffic speed with the spatial and temporal context representations for multi-step speed prediction. We conduct extensive experiments in Singapore. Compared to the baseline model (spatial-temporal graph convolutional network, STGCN), our results demonstrate the importance of multimodal contexts with the mean-absolute-error improvement of 0.29 km/h, 0.45 km/h and 0.89 km/h in 30-min, 60-min and 120-min speed prediction, respectively. We also explore how different contexts affect traffic speed forecasting, providing references for stakeholders to understand the relationship between context information and transportation systems. [ABSTRACT FROM AUTHOR]

Examining the similarity of event environments or surroundings—more precisely, settings—provides additional insight in analyzing event sequences, as it provides information about the context and potential common factors that may have influenced them. This article proposes a new similarity measure for event setting sequences, which involve the space and time in which events occur. While similarity measures for spatiotemporal event sequences have been studied, the settings and setting sequences have not yet been studied. While modeling event setting sequences, we consider spatial and temporal scales to define the bounds of the setting and incorporate dynamic variables alongside static variables. Using a matrix-based representation and an extended Jaccard index, we developed new similarity measures that allow for the use of all variable data types. We successfully used these similarity measures coupled with other multivariate statistical analysis approaches in a case study involving setting sequences and pollution event sequences associated with the same monitoring stations, which validate the hypothesis that more similar spatial-temporal settings or setting sequences may generate more similar events or event sequences. In conclusion, the developed similarity measures have wide application beyond the case study to other disciplinary contexts and geographical settings. They offer researchers a powerful tool for understanding different factors and their dynamics corresponding to occurrences of spatiotemporal event sequences.

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The utilization of multimodal data from multisensors (e.g., hyperspectral and light detection ranging (LiDAR) data) to classify ground objects has been an important topic in remote sensing interpretation. However, complex background leads to the difficulty in extracting context relationships; at the same time, redundancy and noise among multimodal data bring great challenges to accurate classification. In this letter, we propose a novel spatial context and de-redundant fusion network (SCDNet) to fuse hyperspectral and LiDAR data for land cover classification. Specifically, a multiscale attention fusion module (MSAF) is developed in the feature extraction stage, which adaptively fuses global and local information of different scales to obtain a more accurate spatial context. In the feature fusion stage, a fusion module based on gated mechanism is proposed, which can remove the redundant information of multimode data and obtain discriminative fusion features. We design a series of comparisons and ablation experiments on the Houston2013 dataset and Trento dataset, and the results demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

The art of Pakistan is under constant evolution. This research aims to study the changing perceptions of art in Pakistan, where the idea behind the artwork supersedes the act of creation. The article intends to investigate the definition of art with respect to the found object art, also known as ready-made art. It is a revolutionary form of artistic expression, which challenges the conventional practice of creativity by presenting everyday objects assembled within a gallery or museum. The pioneer of found object art Marcel Duchamp, a Modern Western artist, defines art as an idea, rather than the process of making. Building upon Duchamp’s legacy, this article examines the paradigm shift in Pakistani art from artmaking to art-assembling. The found object art by contemporary Pakistani artists, Huma Mulji, Adeela Suleman, Ehsan ul Haq, and Mohsin Shafi, depicts how the identity and connotation of these objects transform according to the spatial context, provoking diverse responses from the viewers. This qualitative research presents an analytical study of found object art by Duchamp and aforementioned Pakistani artists. The images of artworks, collected from museum visit, books, and online sources, help examine the innovative approach of these artists, which stimulates viewer’s curiosity, prompting them to question the very essence of art. The research demonstrates the significance of found objects in expanding the boundaries of creative expression, art definition, and viewer’s experience, besides broadening the perception of art in Pakistan. [ABSTRACT FROM AUTHOR]

Examining the similarity of event environments or surroundings—more precisely, settings—provides additional insight in analyzing event sequences, as it provides information about the context and potential common factors that may have influenced them. This article proposes a new similarity measure for event setting sequences, which involve the space and time in which events occur. While similarity measures for spatiotemporal event sequences have been studied, the settings and setting sequences have not yet been studied. While modeling event setting sequences, we consider spatial and temporal scales to define the bounds of the setting and incorporate dynamic variables alongside static variables. Using a matrix-based representation and an extended Jaccard index, we developed new similarity measures that allow for the use of all variable data types. We successfully used these similarity measures coupled with other multivariate statistical analysis approaches in a case study involving setting sequences and pollution event sequences associated with the same monitoring stations, which validate the hypothesis that more similar spatial-temporal settings or setting sequences may generate more similar events or event sequences. In conclusion, the developed similarity measures have wide application beyond the case study to other disciplinary contexts and geographical settings. They offer researchers a powerful tool for understanding different factors and their dynamics corresponding to occurrences of spatiotemporal event sequences. [ABSTRACT FROM AUTHOR]

Youth entrepreneurship is an increasingly prominent aspect of entrepreneurship support policies, but there is surprisingly little relevant research-based empirical evidence. This research gap is particularly noticeable when it comes to the personal and contextual factors that steer young people's decision to start a business. Using statistically representative survey data from the Global Entrepreneurship Monitor for Germany, we apply logit regressions to determine the influence of 10 independent variables on the likelihood of starting a business. We distinguish between 18–24-year-olds and 25–64-year-olds as well as between founders and non-founders. Self-efficacy in entrepreneurial skills, fear of failure and gender are the strongest influencing variables for the person-related factors and knowledge of other founders for the contextual factors. For younger people, the formal level of education and the perception of local entrepreneurial opportunities do not play a role in the decision to start a business, whereas they are very important for older people. Our results suggest that start-up promotion policies should explicitly address the empirically proven factors of youth entrepreneurship instead of a 'one size fits all' policy for new businesses, regardless of the age of the founders. [ABSTRACT FROM AUTHOR]

In recent years, multi-person pose forecasting has gained significant attention due to its potential applications in various fields such as computer vision, robotics, sports analysis, and human-robot interaction. In this paper, we propose a novel deep learning model for multi-person pose forecasting called MPFSIR (multi-person pose forecasting and social interaction recognition) that achieves comparable results with state-of-the-art models, but with up to 30 times fewer parameters. In addition, the model includes a social interaction prediction component to model and predict interactions between individuals. We evaluate our model on three benchmark datasets: 3DPW, CMU-Mocap, and MuPoTS-3D, compare it with state-of-the-art methods, and provide an ablation study to analyze the impact of the different model components. Experimental results show the effectiveness of MPFSIR in accurately predicting future poses and capturing social interactions. Furthermore, we introduce the metric MW-MPJPE to evaluate the performance of pose forecasting, which focuses on motion dynamics. Overall, our results highlight the potential of MPFSIR for predicting the poses of multiple people and understanding social dynamics in complex scenes and in various practical applications, especially where computational resources are limited. The code is available at https://github.com/RomeoSajina/MPFSIR.

This article explores the role of neighbourhoods as a spatial context for peer relationships among adolescents. We examine the correlations between neighbourhood composition and places suitable for young people for friendship intimacy and peer belonging. We hypothesise that favourable demographic and social neighbourhood compositions, knowledge, and use of places suitable for young people, as well as the spatial appropriation of such places, promote peer relationships. The present study carries out empirical testing of the spatial hypotheses with survey data from adolescents (N = 3225) in two German cities with 30 neighbourhoods. Our results show that neighbourhood composition is not related to peer relationships. Nevertheless, knowledge of safe places suitable for adolescents, as well as the appropriation of unsupervised (hang out) places, correlate with peer relationships. Interestingly, there are divergent results for 7th and 9th graders that can be explained by the developmental stages of the adolescents.

Recent literature recognises the importance of situating social networks in spatial contexts to better understand how space, place, and social networks interact and are co‐constituted. Despite this call, the mainstream literature in social network analysis pays relatively little attention to spatial dimensions of social networks and remains largely disconnected from the vast body of research on spatial networks in geography and cognate fields. This thematic issue is one step towards advancing this research agenda by examining how such an approach relates to issues of social inclusion and social participation. It includes a selection of studies that focus on the relation between space and social networks across a wide variety of research fields and contexts. Contributions use original, often mixed‐method approaches and multiple perspectives for capturing the role of space and people’s experience of place in network formation through physical, cultural, and geographical dimensions. We conclude this editorial by briefly suggesting areas for future research.

Animal survival necessitates adaptive behaviors in volatile environmental contexts. Virtual reality (VR) technology is instrumental to study the neural mechanisms underlying behaviors modulated by environmental context by simulating the real world with maximized control of contextual elements. Yet current VR tools for rodents have limited flexibility and performance (e.g., frame rate) for context-dependent cognitive research. Here, we describe a high-performance VR platform with which to study contextual behaviors immersed in editable virtual contexts. This platform was assembled from modular hardware and custom-written software with flexibility and upgradability. Using this platform, we trained mice to perform context-dependent cognitive tasks with rules ranging from discrimination to delayed-sample-to-match while recording from thousands of hippocampal place cells. By precise manipulations of context elements, we found that the context recognition was intact with partial context elements, but impaired by exchanges of context elements. Collectively, our work establishes a configurable VR platform with which to investigate context-dependent cognition with large-scale neural recording. [ABSTRACT FROM AUTHOR]

Simple Summary: The exotic cabbage seedpod weevil (CSW), Ceutorhynchus obstrictus, is a major pest of canola crops. This insect is mainly regulated in Europe by the parasitoid Trichomalus perfectus, a natural enemy also accidentally introduced in eastern Canada since 2009. The objective of the present study was to evaluate how the landscape influenced the CSW infestation and abundance and the parasitism of T. perfectus. Results from six years in eight Quebec regions show that the CSW was positively affected by roads and cereal crops. Regarding T. perfectus, the parasitism was variable (from about 5 to almost 25%) and positively influenced by landscape diversity, crop edge density, hay/pastures and soybean crops. These results will help determine the potential of an eventual introduction of T. perfectus in western Canada, where most of the canola is produced, and the optimal landscape composition and configuration needed to succeed. The cabbage seedpod weevil (CSW), Ceutorhynchus obstrictus, an exotic pest accidentally introduced in North America in 1931, spread all over this continent and is now a major pest of canola crops. One of its main natural enemies in Europe, Trichomalus perfectus, was observed in eastern Canada in 2009. This study aimed to evaluate the landscape influence on CSW infestation and abundance and on T. perfectus parasitism in Quebec to understand the optimal conditions to potentially release this parasitoid in the Canadian Prairies. Field research was conducted in 19 to 28 canola fields per year, from 2015 to 2020, among eight Quebec regions. CSW was sampled by sweep net during canola blooming and parasitoids by collecting canola pods kept in emergence boxes until adults emerge. Infestation and parasitism calculations were based on pod emergence holes. For analysis, 20 landscape predictors were considered. Results show that CSW infestation and abundance increased if there were more roads and cereal crops in the landscapes. Meanwhile, T. perfectus parasitism decreased when hedgerows length and distance from water were longer. However, it increased when landscape diversity and average crop perimeter-to-area ratio were higher, and along with more hay/pastures and soybean crops. This study's results highlight that these four landscape predictors could provide more resources and overwintering areas, promoting greater efficiency of T. perfectus to control the CSW. [ABSTRACT FROM AUTHOR]

The purpose of this study is to provide a discussion ground that can answer the question, “as a contemporary theme in transformation of space perception, what is the meaning of architecture in the art of the 2000s”. As the scope, works of leading figures of 2000s art were examined through literature review and it was aimed to obtain an original concept set by evaluating them in the context of art-space relationship. The methodology is divided into three sections. The first section is literature research which is based on the determination of the theory about the artworks. The second section is based on the demonstrations of the relationships between the spatial themes through “knowledge discovery in databases”. In the third section, the groups are emphasized through “relative frequencies of the spatial themes”. Accordingly, when the artworks are evaluated in terms of spatial features, it is seen that narratives are related with war, peace, anarchy, individuality, imprisonment, separation, loneliness, uncanny, anxiety, curiosity, desire, lethargy, memory, boredom, consumer culture, ambiguity, habit, conflict and revel. As a result, the image of space occurs while being positioned together with interior and exterior, official and informal, ordinary and extraordinary, dramatic and analytical where dualities formed by contrasts are dissolved.

This study investigates the effect of firm performance on corporate social responsibility (CSR) in a specific spatial context. The results for a sample of 1,557 listed companies in China suggest that a firm's CSR performance level is influenced by that of nearby firms. This study also confirms the indirect link between financial and CSR performance through the mediating role of institutional and executive shareholding rates. In addition, the empirical evidence in this study not only supports the spatial context-sensitive thesis but, more importantly, proposes a spatiotemporal context-sensitive thesis. It provides strong empirical support for the true relative value of the spatiotemporal context affecting CSR performance, which yields important theoretical, methodological, and policy implications. [ABSTRACT FROM AUTHOR]

The purpose of this study is to provide a discussion ground that can answer the question, “as a contemporary theme in transformation of space perception, what is the meaning of architecture in the art of the 2000s”. As the scope, works of leading figures of 2000s art were examined through literature review and it was aimed to obtain an original concept set by evaluating them in the context of art-space relationship. The methodology is divided into three sections. The first section is literature research which is based on the determination of the theory about the artworks. The second section is based on the demonstrations of the relationships between the spatial themes through “knowledge discovery in databases”. In the third section, the groups are emphasized through “relative frequencies of the spatial themes”. Accordingly, when the artworks are evaluated in terms of spatial features, it is seen that narratives are related with war, peace, anarchy, individuality, imprisonment, separation, loneliness, uncanny, anxiety, curiosity, desire, lethargy, memory, boredom, consumer culture, ambiguity, habit, conflict and revel. As a result, the image of space occurs while being positioned together with interior and exterior, official and informal, ordinary and extraordinary, dramatic and analytical where dualities formed by contrasts are dissolved. [ABSTRACT FROM AUTHOR]

Contextual information has been shown to be powerful for semantic segmentation. This work proposes a novel Context-based Tandem Network (CTNet) by interactively exploring the spatial contextual information and the channel contextual information, which can discover the semantic context for semantic segmentation. Specifically, the Spatial Contextual Module (SCM) is leveraged to uncover the spatial contextual dependency between pixels by exploring the correlation between pixels and categories. Meanwhile, the Channel Contextual Module (CCM) is introduced to learn the semantic features including the semantic feature maps and class-specific features by modeling the long-term semantic dependence between channels. The learned semantic features are utilized as the prior knowledge to guide the learning of SCM, which can make SCM obtain more accurate long-range spatial dependency. Finally, to further improve the performance of the learned representations for semantic segmentation, the results of the two context modules are adaptively integrated to achieve better results. Extensive experiments are conducted on four widely-used datasets, i.e., PASCAL-Context, Cityscapes, ADE20K and PASCAL VOC2012. The results demonstrate the superior performance of the proposed CTNet by comparison with several state-of-the-art methods. The source code and models are available at https://github.com/syp2ysy/CTNet. [ABSTRACT FROM AUTHOR]

We explore the experiences of LGBT* ethnic minority entrepreneurs, their changing locations and their entrepreneurial activities. Using a unique mixed-method approach which collected empirical data from Germany and the Netherlands, the paper combines an ethnographic fieldwork of intersectional entrepreneurs, community activists and policy-makers with an original survey with LGBT* customers. Our findings contribute to understanding of intersectionality by revealing the role played by the contextualized embeddedness of intersectional entrepreneurs at the different geographic scales of supranational, national, regional and inter and intra-urban. While such embeddedness frames the challenges they face, it also provides opportunities for intersectional entrepreneurs. Using a multi-scalar perspective, this paper delivers a spatially contextual perspective of entrepreneurial diversity and provides a framework to analyse the complex issues and contexts with which intersectional entrepreneurs are both confronted and embedded within. This paper contributes to refining the spatial context of entrepreneurship which has gained attention in recent studies of entrepreneurship and regional development. The paper responds to a call for gender entrepreneurship scholars to contribute to understanding of intersectional entrepreneurship. Finally, this study goes beyond the binary view of female migrant entrepreneurship by adopting a more gender diverse lens which considers the experiences of LGBT* entrepreneurs from ethnic minorities. [ABSTRACT FROM AUTHOR]

Mobile Crowd Sensing (MCS) has become a promising new data perception paradigm. It is to be able to easily submit the wrong or untrusted data for the malicious attackers in such an environment. This greatly affects the normal operation of the MCS system and the authenticity of task results. Therefore, ensuring the reliability of data is becoming a key research direction in MCS, especially for real-time application scenarios. For this purpose, we propose a multi-dimensional framework for improving data reliability, named MDF. It integrates three dimensions of temporal, spatial context and sensing measurement. Through a series of experiments, it is demonstrated that MDF outperforms existing methods. [ABSTRACT FROM AUTHOR]

Transforming user check-in data into graph structure data is a popular and powerful way to analyze users' behaviors in the field of recommendation. Graph-based deep learning methods such as graph embeddings and graph neural networks have shown promising performance on the task of point-of-interest recommendation in recent years. Despite effectiveness, existing methods fail to capture deep graph structural information, leading the suboptimal representations. In addition, they lack the ability of learning the influences of both global preference and user preference on the check-in behavior. To address the aforementioned issues, we propose a general framework based on preference-aware graph diffusion, named PGD. We first construct two types of graphs to represent the global preference and user preference. Then, we apply a graph diffusion process to capture the structural information of the generated graphs, resulting in weighted adjacency matrices. Finally, graph neural network-based backbones are introduced to learn the representations of users and POIs on weighted adjacency matrices. A learnable aggregation module is developed to learn the final representations from global preference and user preference adaptively. Extensive experiments on four real-world datasets demonstrate the superiority of PGD on POI recommendation, compared with themainstream graph-based deep learning methods. [ABSTRACT FROM AUTHOR]

Youth entrepreneurship is an increasingly prominent aspect of entrepreneurship support policies, but there is surprisingly little relevant research-based empirical evidence. This research gap is particularly noticeable when it comes to the personal and contextual factors that steer young people’s decision to start a business. Using statistically representative survey data from the Global Entrepreneurship Monitor for Germany, we apply logit regressions to determine the influence of 10 independent variables on the likelihood of starting a business. We distinguish between 18–24-year-olds and 25–64-year-olds as well as between founders and non-founders. Self-efficacy in entrepreneurial skills, fear of failure and gender are the strongest influencing variables for the person-related factors and knowledge of other founders for the contextual factors. For younger people, the formal level of education and the perception of local entrepreneurial opportunities do not play a role in the decision to start a business, whereas they are very important for older people. Our results suggest that start-up promotion policies should explicitly address the empirically proven factors of youth entrepreneurship instead of a ‘one size fits all’ policy for new businesses, regardless of the age of the founders.

This article explores the role of neighbourhoods as a spatial context for peer relationships among adolescents. We examine the correlations between neighbourhood composition and places suitable for young people for friendship intimacy and peer belonging. We hypothesise that favourable demographic and social neighbourhood compositions, knowledge, and use of places suitable for young people, as well as the spatial appropriation of such places, promote peer relationships. The present study carries out empirical testing of the spatial hypotheses with survey data from adolescents (N = 3225) in two German cities with 30 neighbourhoods. Our results show that neighbourhood composition is not related to peer relationships. Nevertheless, knowledge of safe places suitable for adolescents, as well as the appropriation of unsupervised (hang out) places, correlate with peer relationships. Interestingly, there are divergent results for 7th and 9th graders that can be explained by the developmental stages of the adolescents. [ABSTRACT FROM AUTHOR]

Recent literature recognises the importance of situating social networks in spatial contexts to better understand how space, place, and social networks interact and are co‐constituted. Despite this call, the mainstream literature in social network analysis pays relatively little attention to spatial dimensions of social networks and remains largely disconnected from the vast body of research on spatial networks in geography and cognate fields. This thematic issue is one step towards advanc‐ ing this research agenda by examining how such an approach relates to issues of social inclusion and social participation. It includes a selection of studies that focus on the relation between space and social networks across a wide variety of research fields and contexts. Contributions use original, often mixed‐method approaches and multiple perspectives for capturing the role of space and people’s experience of place in network formation through physical, cultural, and geo‐ graphical dimensions. We conclude this editorial by briefly suggesting areas for future research. [ABSTRACT FROM AUTHOR]

Facade parsing aims to decompose a building facade image into semantic regions of the facade objects. Considering each architectural element on a facade as a parameterized rectangle, we formulate the facade parsing task as object detection, allowing overlapping and nesting, which will support structural 3D modeling and editing for further applications. In contrast to general object detection, the spatial arrangement regularity and appearance similarity between the facade elements of the same category provide valuable context for accurate element localization. In this paper, we propose to exploit the spatial arrangement regularity and appearance similarity of facade elements in a detection framework. Our element-arrangement context network (EACNet) consists of two unidirectional attention branches, one to capture the column-context and the other to capture row-context to aggregate element-specific features from multiple instances on the facade. We conduct extensive experiments on four public datasets (ECP, CMP, Graz50, and eTRIMS). The proposed EACNet achieves the highest mIoU (82.1% on ECP, 77.35% on Graz50, and 82.3% on eTRIMS) compared with the state-of-the-art methods. Both the quantitative and qualitative evaluation results demonstrate the effectiveness of our dual unidirectional attention branches to parse facade elements. [ABSTRACT FROM AUTHOR]

The growing interest in street children's research in the field of urban and regional planning has been influenced by the renewed recognition of the importance of the spatial context of social issues. This paper is a recount of the author's fieldwork experience supported by existing theoretical, conceptual and historical literature in dealing with the methodological and ethical issues in research with street children. The author reflected on the definitional problem of the subject matter, the research design, the sampling dilemma, and the sapiential strategies of engaging children and data analytical issues. The imperative of the definitional clarity, the integrity of drawing data from more than one vantage position and the importance of cultural immersion were accentuated. The paper also reinforced the sensitiveness of ethical issues in researching street children. Although the author's recital in this paper contributes to knowledge in street children research in any discipline, it is more beneficial to researchers in urban and regional planning and related academic fields. [ABSTRACT FROM AUTHOR]

Predation strategies are shaped by multiple factors, such as prey type, prey density, and/or abiotic conditions. However, limited research has investigated the role of spatial constraints on post-capture behaviors. Our aim was to assess the impact of spatial context on prey manipulation in the California moray eel (Gymnothorax mordax), a crevice forager with a diverse prey handling repertoire. We first compared the duration engaged in specific manipulation behaviors between spatially "enclosed" and "open" foraging environments, followed by a comparison of tightly enclosed spaces that were scaled to individual moray diameter. We observed that the durations spent engaged in specific manipulation behaviors varied based on spatial context. Despite these changes, total feeding time, which consists of both manipulating and swallowing prey, did not vary across treatments. These results highlight the behavioral flexibility of morays, suggesting that they can adjust prey manipulation strategies for specific spatial contexts without compromising overall feeding duration. We additionally documented two previously undescribed behaviors (tail and body anchoring) which seem to be used in the narrowest treatments for leverage when knotting behavior was not possible due to spatial constraints. This research provides valuable insight into the foraging ecology of this locally abundant apex predator and aids in furthering our understanding of predator-prey interactions in the benthic community within the kelp forest ecosystem. [Display omitted] • Morays are benthic, crevice-dwelling predators with diverse foraging tactics. • Spatial context influenced usage of specific prey manipulation behaviors in morays. • The diameter of the foraging space did not influence overall time spent foraging. • We documented two previously undescribed anchoring behaviors. • A larger behavioral repertoire allows morays to forage effectively across contexts. [ABSTRACT FROM AUTHOR]