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Street-view imagery has been densely covering cities. They provide a close-up perspective of the urban physical environment, allowing a comprehensive perception and understanding of cities. There has been a significant amount of effort to represent the urban physical environment based on street view imagery, and this representation has been utilized to study the relationships between the physical environment, human dynamics, and socioeconomic environments. However, there are two key challenges in representing the urban physical environment of streets based on street-view images for downstream tasks. First, current research mainly focuses on the proportions of visual elements within the scene, neglecting the spatial adjacency between them. Second, the spatial dependency and spatial interaction between streets have not been adequately accounted for. These limitations hinder the effective representation and understanding of urban streets. To address these challenges, we propose a dynamic graph representation framework based on dual spatial semantics. At the intra-street level, we consider the spatial adjacency relationships of visual elements. Our method dynamically parses visual elements within the scene, achieving context-specific representations. At the inter-street level, we construct two spatial weight matrices by integrating the spatial dependency and the spatial interaction relationships. It could account for the hybrid spatial relationships between streets comprehensively, enhancing the model's ability to represent human dynamics and socioeconomic status. Furthermore, aside from these two modules, we also provide a spatial interpretability analysis tool for downstream tasks. A case study of our research framework shows that our method improves vehicle speed and flow estimation by 2.4% and 6.4%, respectively. This not only indicates that street-view imagery provides rich information about urban transportation but also offers a more accurate and reliable data-driven framework for urban studies. The code is available at: (https://github.com/yemanzhongting/HybridGraph). • A framework for street representation with street-view imagery considering multi-level spatial effects • A dynamic weighting module incorporates the spatial adjacency relationships of visual elements at the intra-street level. • A hybrid graph neural network fuses the spatial dependencies graph and spatial interactions graph at the inter-street level. [ABSTRACT FROM AUTHOR]

This paper analyzes city system dynamics, based on a theoretical framework relating interaction potentials to agglomeration economies and density externalities. It employs new historical time series data on population size of cities in Sweden over two centuries (1810–2010) and introduces two schematic growth factors: (i) the intra-city potential and (ii) the extra-city potential located in in rings encircling each city. The first factor is measured by each city's population size, while the second is a vector of distance-discounted population size for each of a city's urban rings. In this way, we can explain a city's growth as a function of its interaction potential inside the city, as well as inside the first, second hand third ring. A robust finding is that cities with large ring potentials follow different development paths than those with small ring potentials. We also find clear evidence of structural change between the two centuries 1810–1910 and 1910–2010. In the first period, city growth is positively impacted by the size of the intra-city potential, whereas the same potential dampens or reduces the growth in the second period. Moreover, the ring potentials outside the city tend to switch from having negative growth stimulation in the first period to having positive stimulation in the second period. [ABSTRACT FROM AUTHOR]

This editorial, introducing the nine papers comprising this issue of Spatial Economic Analysis (SEA), shows that novel methodologies applied to spatial data allow for a better understanding of the location phenomena at different spatial and sectoral scales. Global processes also have local specificities, which should be investigated, but, in parallel, local processes might hide a component of global structure in the spatial data that can drive estimation results, as the first paper shows. The papers in this issue henceforth present novel analyses, which enable the study of phenomena whose extent is usually considered to be global showing their spatial relevance, such as the location of energy facilities, job polarisation and the effects of devaluation on trade. Other aspects such as the Okun's law, the efficiency of firms, and the market for liquid petroleum, the performance of start-ups and the happiness of people are studied and shown to depend on local characteristics and interactions. Furthermore, the issue introduces a number of novel techniques, such as spatial stochastic frontier estimations, new decompositions and mixed spatial analysis of variance (MS-ANOVA) models. [ABSTRACT FROM AUTHOR]

RGB-Thermal semantic segmentation is important in widespread applications in adverse illumination conditions, such as autonomous driving and robotic sensing. However, most existing methods ignore the feature differences between the two modalities and do not effectively exploit and handle the features at different levels. In this paper, we present a novel multimodal feature fusion network named HEFANet, which effectively enhances the interaction and fusion of features. Concretely, we propose a Cross-layer and Cross-modal Feature Descriptor module (CCFD) to mitigate differences between different multimodal data and to mine the valuable and correlated features of cross-layers. To effectively fuse multimodal features at different levels, we propose a Multi-modal Interleaved Sparse Self-Attention module (MISSA) to aggregate rich spatial semantic information in the earlier layers. Then, we propose the Spatial Interaction and Channel Selection module (SICS) in the last layer to enhance the representation of rich contextual features and highlight important information by channel communication interactions for optimal sparse feature aggregation selectively. Extensive experiments were carried out on three publicly available datasets (MFNet, PST900, and FMB), and achieved new state-of-the-art results. The code and results are available at https://github.com/shenzw21/HEFANet. [ABSTRACT FROM AUTHOR]

Urban land use is central to urban planning. With the emergence of urban big data and advances in deep learning methods, several studies have leveraged graph convolutional networks (GCNs) with local functional characteristics from points of interest data and spatial features from flow data to infer urban land use. However, these studies cannot distinguish spatial interaction and spatial dependence in terms of conceptualization and modeling mechanisms and overlook the inadequacy of GCNs in modeling spatial interaction. This study proposes a novel framework—a heterogeneous graph convolutional network (HGCN)—to explicitly account for the spatial demand and supply components embedded in spatial interaction data. Several experiments, including 19 different models and datasets from Shenzhen and London, were conducted to validate the proposed framework and its generalizability within the same and different spatial contexts. The HGCN can distinguish heterogeneous mechanisms in supply- and demand-related modalities of spatial interactions, incorporating both spatial interaction and spatial dependence for urban land-use inference. Empowered by HGCN, we found that spatial interaction features play a distinctively crucial role in urban land-use inference compared to local attributes and spatial dependence features. In addition, our findings highlight the superiority of HGCN-based models in boosting performance and enhancing model transferability. [ABSTRACT FROM AUTHOR]

This paper examines empirically cross‐fertilization in the productivity growth of banks between a state and its neighbouring and non‐neighbouring states (i) before (i.e. 1971–1977) the interstate multibank holding company (IMBHC) deregulations and (ii) during (i.e. 1982–1995) the IMBHC deregulations, which, through cross‐border bank M&As mainly among neighbouring states, could inject new blood, awaken the market for corporate control and enhance cross‐fertilization in bank performance among neighbouring states. Further, the 1978–1981 period offers a natural experiment to examine Baumol's Contestable Markets Hypothesis (CMH). The legislature of Maine made the first IMBHC deregulatory move in 1978. There was no reciprocity until New York and Alaska made their moves in 1982. Under CMH, Maine's move should inject a competitive spirit and alter bank performance for better across all—neighbouring or non‐neighbouring – banking markets during this period. Theoretically, Kane's regulatory equilibrium framework provides guidance to address these matters and Tiebout's people vote with their feet framework extends and supplements this guidance. Empirically, FDIC's annual banking data, aggregated at the state level, constitute the main input in computing the productivity growth indices for each of the 48 contiguous sample states between 1971 and 1995. Estimations of a novel spatially driven fixed effects model that uses these indices produce empirical results. The empirical model exploits the proximity of one sample state to its neighbouring states while also embracing a set of randomly chosen non‐neighbouring states as a control sample. Results show that cross‐fertilization in bank performance, observed among neighbouring states before the introduction of the IMBHC deregulations during 1971–1977, gets stronger in response to the dynamically evolving IMBHC deregulations during 1982–1995 and that improvements in banks' productivity growth during 1978–1981 support Baumol's CMH. Overall, our results demonstrate the importance and influence of cross‐fertilization, as a matter of proximity of subjects, on banks' performance and suggest promise for future research that embraces the spatial dimension of banking markets and data. [ABSTRACT FROM AUTHOR]

We propose spatial difference-in-differences (DID) models that are able to incorporate treatment effect spillovers through modeling spatial interactions in the response and spatial correlations in treatment status among individuals. We first explore the ways in which combinations of spatial interaction and spatial correlation bias the conventional DID estimator, and then we develop spatial DID models, estimators, and specification tests that allow for a flexible order of local spatial structures. We consider both simultaneous and dynamic treatment. The local spatial DID models with a flexible order of spatial structure allow for different types of heterogeneity in the treatment effects. Monte Carlo simulations support our discussions of the bias in the conventional DID model under spatial interaction and correlation, and demonstrate the finite sample performance of our proposed models, estimators, and tests. [ABSTRACT FROM AUTHOR]

East Bogor Regency plays a crucial role in the region's economy, contributing 19.09% of the total Regional Domestic Product (PDRB) in 2022. This area has witnessed rapid development, driven by improved accessibility between sub-districts and the resulting increase in built-up land. This study aims to: (1) assess the accessibility index of urban areas in East Bogor and (2) analyze land cover/land use changes from 1998 to 2023. The research utilizes Hansen's model to analyze spatial interaction, assuming that activity centers, facilities, and accessibility influence land cover changes. Additionally, a weighted scalogram approach integrated with hexagonal grid spatial analysis is employed to evaluate the urban area development index. Correlation analysis is then performed to examine the relationship between land cover change and the accessibility index. This study reveals significant finding of differences in accessibility and land cover across the analyzed villages. Three sub-districts have the highest accessibility index, while two have the lowest. Cileungsi exhibits a very high hexagonal grid classification, while Sukamakmur has a low classification. The remaining three sub-districts fall into the very low category. The correlation coefficient between land cover and accessibility is 0.33, indicating a weak relationship. This study highlights the significant land cover changes occurring in East Bogor over the past 25 years, potentially influenced by the varying accessibility levels of different sub-districts. [ABSTRACT FROM AUTHOR]

Existing regionalization methods tend to be either spatial attribute- or spatial interaction-based, while real-world tasks usually involve both considerations to satisfy multiple objectives simultaneously. In this research, we propose Multilayer Network Community Detection and Kernel Extension (MNCD-KE), a two-step regionalization framework, as a feasible solution for such tasks. First, spatial attributes are embedded into attributes of nodes in a spatial interaction-defined multilayer network, and the kernel and marginal parts of the regions are determined by giving the membership value of the regionalization units to network communities. Second, the final result is obtained through a kernel extension process considering geographical constraints, including spatial contiguity, size balance, morphological regularity, and existing boundary consistency of the regions. Empirical experiments show that the proposed method yields outcomes that, in maintaining comparable performances with most baseline algorithms with either 'attribute' or 'interaction' objectives as measured by the respective criteria, simultaneously meet the dual objectives with results intuitively comprehensible. Its low computing costs and parameter adjustment flexibility make the proposed framework a convenient approach for real-world multi-objective regionalization tasks. We conclude the research with discussions on the boundary conditions for the framework to work and their relevance to city science theories, along with practical implications. [ABSTRACT FROM AUTHOR]

Traditional architectural space design often relies on fixed display boards, paper guides, and static displays. Viewers can only passively receive information and lack opportunities to interact with the displayed content, making it difficult to generate deep participation and personalized experiences. This article applied new media images into the interactive design of architectural spaces, transforming and innovating traditional architectural spaces, and providing audiences with a richer and more attractive spatial experience. The navigation service was set as the architectural space design goal, and analyzed using the Leifeng Tower building in Hangzhou as an example. Building Information Modeling (BIM) was used to digitally model the Leifeng Tower building, accurately simulating the building structure, spatial layout, and landscape information. By adding virtual information annotations and navigation routes, tourists were provided with a more diverse and colorful architectural space experience. The experimental results showed that among 400 tourists, the highest number of tourists using augmented reality navigation was 204, and the average satisfaction score of tourists with augmented reality navigation reached 90. Moreover, presenting the guide content in a multimodal form can better enhance the spatial experience of tourists. The use of augmented reality in architectural spatial interaction design can enrich tourists’ perception and experience in the architectural space, enabling them to interact with the building in a more vivid and interactive way.

Abstract A variety of complex socioeconomic phenomena, for example, migration, commuting, and trade can be abstracted by spatial interaction networks, where nodes represent geographic locations and weighted edges convey the interaction and its strength. However, obtaining fine-grained spatial interaction data is very challenging in practice due to limitations in collection methods and costs, so spatial interaction data such as transportation data and trade data are often only available at a coarse scale. Here, we propose a gravity downscaling (GD) method based on readily accessible socioeconomic data and the gravity law to infer fine-grained interactions from coarse-grained data. GD assumes that interactions of different spatial scales are governed by the similar gravity law and thus can transfer the parameters estimated from coarse-grained regions to fine-grained regions. Results show that GD has an average improvement of 24.6% in Mean Absolute Percentage Error over alternative downscaling methods (i.e., the areal-weighted method and machine learning models) across datasets with different spatial scales and in various regions. Using simple assumptions, GD enables accurate downscaling of spatial interactions, making it applicable to a wide range of fields, including human mobility, transportation, and trade.

Object detection is a critical technology for the intelligent analytical processing of images captured by drones. The objects usually come in various scales and can be extremely small. Existing detection methods are inherently based on pyramid hierarchy architectures to extract multi-scale features and provide better feature representation for small objects. Nevertheless, they inevitably dilute the representation of details in low-level features during top-down feature fusion and are totally unconcerned with whether the fused feature fits the objects of specific scales within a layer. Moreover, the pyramid can only implicitly fuse the spatial context, which makes the fused features cannot receive fine spatial location information for object localization. In this work, we propose an effective boundary-aware network with attention refinement and spatial interaction to tackle the above challenges. Specifically, we first present a highly effective yet simple boundary-aware detection head (BAH), which directly guides representation learning of object structure semantics in the prediction layer to preserve object-related boundary semantics. Additionally, the attentional feature parallel fusion (AFPF) module offers multi-scale feature encoding capability in a parallel triple fusion fashion and adaptively selects features appropriate for objects of certain scales. Furthermore, we design a spatial interactive module (SIM) to preserve fine spatial detail through cross-spatial feature association. Extensive experiments prove that the proposed network significantly outperforms the state-of-the-art methods, in which we achieve 33.1 mAP and 56.5 AP50 on the VisDrone benchmark, 63.4 mAP and 94 AP50 on the NWPU VHR-10 benchmark. The source code will be released. [ABSTRACT FROM AUTHOR]

AbstractUncovering the collective behavior of flows among locations is critical to understanding the structure within an ever-changing spatial network. When a network evolves, there may exist subgraphs within which the internal flows generally follow a rule: the change rates of the flow weight are either collectively high or low. Classic network measures such as degree, clustering, and betweenness can be used to quantify the process of network evolution by profiling the overall characteristics over time. However, it remains challenging to elucidate how a spatial network is evolving without looking at structures where collective changes emerge. To bridge this gap, we introduce the concept of the Collective Flow-Evolutionary Pattern (CFEP) as a mesoscopic description for spatial network evolution. Four types of patterns with distinct features are defined to clarify the collective behaviors of the flow-evolutionary characteristics. We provide an analytical framework that utilizes flow change rates between two snapshots of the spatial network to detect CFEPs as optimized flow evolution (evo-groups). Synthetic experiments are presented to validate the method. A case study of large-scale individual mobile positioning data is conducted in the Twin Cities Metropolitan Area, Minnesota, US to demonstrate how CFEP can effectively understand the evolution of human mobility networks. [ABSTRACT FROM AUTHOR]

The present paper seeks to conceptualise the notion of complementarity with the help of rail-based commodity flow data in one of the most prominent railway towns in India i.e. PanditDeenDayalUpadhyaya Nagar (Mughal Sarai). Referring to the theoretical notion of 'complementarity' and a modified form of 'gravity model' from the 'Family of Spatial Interaction Models', this research puts an intensive discussion forward on the existing nature of spatial interaction of PanditDeenDayalUpadhyaya Nagar (Mughal Sarai) with its complementary areas and a comparison of these areas in terms of their relative complementarity with the town concerned. The resultant outcome in the form of a Complementarity Index, portrays a different, if not entirely contradictory picture from the crude values of material shipments to PanditDeenDayalUpadhyaya Nagar (Mughal Sarai). This analysis thus well-indicates that merely supplying greater amount of commodities of any kind does not necessarily reflect greater complementarity and vice versa. Therefore, a more comprehensive analysis of commodity flows is imperative to have a deeper insight into the notion of spatial interaction. [ABSTRACT FROM AUTHOR]

The rational matching of urban land and population has become an important prerequisite for sustainable urban development. In this paper, the traditional urban land scale elasticity model was improved, and combined with the gravity model, the spatial interaction between land expansion and population change in 618 cities in China during the period 2006–2021 was investigated. The geographical detector method was used to reveal what drives them. The main results were as follows: (1) China's urban land expansion rate was 1.83 times faster than the population growth rate during 2006–2021. After the implementation of the New-type Urbanisation Plan in 2014, the ratio of land expansion rate to population growth rate dropped from 2.46 to 1.12. (2) Among the six interaction types identified, land rapid expansion is the most significant, accounting for 41.59% of urban samples. (3) The geographical detector method found that the indicators of urban development rights such as the level of administrative hierarchy and the ratio of fiscal revenue to fiscal expenditure were the main factors affecting land expansion and that economic indicators such as gross domestic product and employment opportunities dominated population change. Fortunately, the intervention role of urban development rights has declined, and the constraints of market mechanisms, resources and environment have gradually become the dominant factors in urban land expansion and population change. These findings provide a theoretical basis for alleviating the human–land contradiction and achieving sustainable urban development. [ABSTRACT FROM AUTHOR]

A variety of complex socioeconomic phenomena, for example, migration, commuting, and trade can be abstracted by spatial interaction networks, where nodes represent geographic locations and weighted edges convey the interaction and its strength. However, obtaining fine-grained spatial interaction data is very challenging in practice due to limitations in collection methods and costs, so spatial interaction data such as transportation data and trade data are often only available at a coarse scale. Here, we propose a gravity downscaling (GD) method based on readily accessible socioeconomic data and the gravity law to infer fine-grained interactions from coarse-grained data. GD assumes that interactions of different spatial scales are governed by the similar gravity law and thus can transfer the parameters estimated from coarse-grained regions to fine-grained regions. Results show that GD has an average improvement of 24.6% in Mean Absolute Percentage Error over alternative downscaling methods (i.e., the areal-weighted method and machine learning models) across datasets with different spatial scales and in various regions. Using simple assumptions, GD enables accurate downscaling of spatial interactions, making it applicable to a wide range of fields, including human mobility, transportation, and trade. [ABSTRACT FROM AUTHOR]

Redistricting is the process by which electoral district boundaries are drawn so as to capture coherent communities of interest (COIs). While states rely on various proxies for community illustration, such as compactness and municipal split counts, to guide redistricting, recent legal challenges and scholarly works have shown the difficulty of balancing multiple criteria in district plan creation. To address these issues, we propose the use of spatial interaction to directly quantify the degree to which districts capture the underlying COIs. Using large-scale human mobility flow data, we condense spatial interaction community capture for a set of districts into a single number, the interaction ratio (IR), for redistricting plan evaluation. To compare the IR to traditional redistricting criteria (compactness and fairness), we employ a Markov chain-based regionalization algorithm (ReCom) to produce ensembles of valid plans and calculate the degree to which they capture spatial interaction communities. Furthermore, we propose two methods for biasing the ReCom algorithm towards different IR values. We perform a multi-criteria assessment of the space of valid maps, and present the results in an interactive web map. The experiments on Wisconsin congressional districting plans demonstrate the effectiveness of our methods for biasing sampling towards higher or lower IR values. Furthermore, the analysis of the districts produced with these methods suggests that districts with higher IR and compactness values tend to produce district plans that are more proportional with regard to seats allocated to each of the two major parties. [ABSTRACT FROM AUTHOR]

Good city management is essential in mitigating the impact of various crisis events and thus enhances urban resilience. The current zoning system that underlies China's city management system, however, hardly meets the resilient requirements in failing to appropriately and flexibly allocate patrol personnel under normal and various crisis scenarios. We propose an optimization method that gives rise to a resilient city management zoning system by introducing spatial interaction. We realize this through community detection in a spatially embedded patrol passage cost network. Illustrated by a case in Jiangbei District, Ningbo, China, we show that the optimized zoning system has significant advantages in terms of event coverage, response efficiency, and workload balance in the normal scenario as compared to the status quo with up to 3.6 percent, 5.9 percent, and 34.0 percent performance improvement, respectively. Moreover, the method also achieves similar performance improvement in three typical shock scenarios in city management: epidemic, typhoon, and crowd gathering. We conclude the article with discussions on the findings' significance in urban resilience building and their methodological and theoretical implications in applied urban sciences. [ABSTRACT FROM AUTHOR]

The study of spatial social networks has evolved from identifying structures within single networks to analyzing spatial associations between multilayer networks. However, current research primarily focuses on many-to-many flow structures, neglecting the unique advantages of one-to-many flow in characterizing local and global interactions. To bridge this gap, this paper introduces flow sequences and flow structure curves to effectively represent and visualize structures of one-to-many flows. Building on this foundation, a similarity-based comparison strategy to analyze spatial associations of one-to-many flow structures within two different networks from a local perspective is proposed. This method can be utilized to examine associations across diverse scenarios, including between distinct networks, within a single network over different temporal intervals, and between the inflow and outflow of the same network. The effectiveness and robustness of the proposed method were validated using a synthetic dataset. Case studies on migration and attention flows demonstrated its diverse applications and potential. The proposed approach enhances convergent and divergent analysis in multi-semantic spatial networks by offering tools for investigating structural consistency across networks. It emphasizes the influence of individual nodes on the entire network and the reciprocal shaping of local interaction relationships by global patterns.

ABSTRACTSpatial interaction research is particularly important for geographical analyses, as it plays a crucial role in extracting travel patterns. However, previous studies on spatial interactions have not adequately considered regional population variations over time, resulting in insufficiently precise travel predictions. Moreover, the threshold of spatial correlations is difficult to determine. Existing studies have assumed fully connected spatial correlation matrices, which is not realistic. To address these limitations, we proposed the Self-paced Gaussian-Based Graph Convolutional Network (SG-GCN) to automatically estimate the threshold of spatial correlations for travel flow predictions. It incorporates a temporal dimension into spatial relationship matrices to enhance the accuracy of vehicle flow predictions. In particular, Gaussian-based GCN identifies patterns in a time series of regional flows, enabling more precise capturing of spatial relationships while fusing node and edge features. Building on this model, self-paced contrastive learning automatically sets thresholds to determine the presence or absence of spatial relationships. The model's performance was verified through two empirical case studies conducted in New York City, USA, and Ningbo, China, using 2.8 million bicycle-sharing records and 1.25 million taxi trip records, respectively. The proposed model helps delineate mobility patterns in cities of varying scales and with different modes of transportation.

ABSTRACTVarious big geo-data provide a social sensing approach to measure spatial interactions. Existing studies often aggregate individual-level movement trajectories or social ties to obtain the interaction intensity between places, neglecting the detailed meanings (i.e. the semantics) behind spatial interactions. However, such meanings help to understand the relationship between two places, and consequently, the characteristics of both places. We argue that semantics can be extracted from spatial interactions through features of space, time, symmetry, and individual-based statistics. Whereafter the calculation and applications of the features are given. Furthermore, we discuss the construction of spatial interaction networks with semantics, as well as approaches to representing places according to spatial interactions. Finally, we illustrate the potential value of spatial interaction semantics in facilitating decision-making through an example in the context of tourism planning.

Objectives: As the main form of urbanization, the urban agglomeration can greatly affect the urban spatial pattern in China. Methods: Based on the impervious area data of Chang-Zhu-Tan urban agglomeration (CZT-UA), the spatial organization structure, dynamic development pattern and spatially heterogeneous driving mechanism of the expansion of built-up areas in CZT-UA are quantitatively revealed with a collection of measurement methods, e.g., fractal dimension, expansion intensity index, Moran's I, GetisOrd Gi*, principal component analysis and geographically weighted regression. Results: From 2003 to 2018, the spatial structure of CZT-UA shows obvious axial distribution, which generally follows Xiang-jiang River and the transportation network consisting of five vertical and five horizontal trunk lines. The built-up area and expansion speed of Changsha, Zhuzhou and Xiangtan show an upward trend. Compared with Changsha, Zhuzhou and Xiangtan expanded more slowly and the gap is gradually widened. The overall spatial differences in expansion and distribution of the built-up area in CZT-UA are gradually narrowed. The hotspot regions of urban expansion form a kernel in central CZT-UA, which provides driving force to the peripheral areas. Conclusions: The geographically weighted regression model demonstrates that the flow of residents and economy between cities, clear policy guidance from government, convenient transportation network as well as the radiation effect of the central city can jointly attribute to the expansion of built-up areas. However, the importance and effects of each factor varied in different regions of CZT-UA. [ABSTRACT FROM AUTHOR]

Various big geo-data provide a social sensing approach to measure spatial interactions. Existing studies often aggregate individual-level movement trajectories or social ties to obtain the interaction intensity between places, neglecting the detailed meanings (i.e. the semantics) behind spatial interactions. However, such meanings help to understand the relationship between two places, and consequently, the characteristics of both places. We argue that semantics can be extracted from spatial interactions through features of space, time, symmetry, and individual-based statistics. Whereafter the calculation and applications of the features are given. Furthermore, we discuss the construction of spatial interaction networks with semantics, as well as approaches to representing places according to spatial interactions. Finally, we illustrate the potential value of spatial interaction semantics in facilitating decision-making through an example in the context of tourism planning. [ABSTRACT FROM AUTHOR]

The development of geospatial artificial intelligence (GeoAI) systems depends on the ability to learn effective representations of places. To learn accurate place representations from spatial interactions, it is important to extract features that capture both the spatial and non-spatial driving factors. However, existing methods lack a robust interpretation and the explanatory power of the learned representations on spatial factors remains unexplored. Here, we propose an approach to learning place representations from spatial interactions. Our method is inspired by flow allocation, which is the main focus of single-constrained gravity models. We first validate the method on synthetic flows with known driving factors and then apply it to multi-scale real-world flows. Results show that the learned representations can effectively capture features that explain place characteristics, along with the impact of spatial impedance. Our study not only contributes an efficient method to learn place representations from spatial interactions but also offers insights into pre-training procedures in GeoAI. [ABSTRACT FROM AUTHOR]

Space has always been essential within the economy, yet its importance in economics has been downplayed in several ways. This editorial introduces the seven papers comprising this issue of Spatial Economic Analysis (SEA) and shows that while the classics of economics acknowledged the importance of the location of economic activities, for many years the study of space was left to heterodox economics scholars and geographers. This is despite the established tradition of learned societies, such as Regional Science International and the Regional Studies Association, which are placed at the intersection of these fields. Space finally became mainstream in economics again due, on the one hand, to the introduction of the new economic geography some 30 years ago and, on the other, to the fact that several different economic sub-disciplines have come to understand and consider space as essential for the processes they study. This was facilitated by methodological advancements, such as in spatial econometrics. The seven papers in this issue henceforth illustrate some of the situations and approaches which make space relevant to contemporary economic questions. Essential are, in particular, the interactions between different locations and the interactions between individuals and geographical features. [ABSTRACT FROM AUTHOR]

Background: Tumor-associated macrophages (TAMs) constitute a plastic and heterogeneous cell population of the tumor microenvironment (TME) that can regulate tumor proliferation and support resistance to therapy, constituting promising targets for the development of novel anticancer agents. Our previous results suggest that SHP2 plays a crucial role in reprogramming the phenotype of TAMs. Thus, we hypothesized that SHP2+ TAM may predict the treatment efficacy of non-small cell lung cancer NSCLC patients as a biomarker. Methods: We analyzed cancer tissue samples from 79 NSCLC patients using multiplex fluorescence (mIF) staining to visualize various SHP-2+ TAM subpopulations (CD68+SHP2+, CD68+ CD86+, CD68 + 206+, CD68+ CD86+SHP2+, CD68+ CD206+SHP2+) and T cells (CD8+ Granzyme B +) of immune cells. The immune cells proportions were quantified in the tumor regions (Tumor) and stromal regions (Stroma), as well as in the overall tumor microenvironment (Tumor and Stroma, TME). The analysis endpoint was overall survival (OS), correlating them with levels of cell infiltration or effective density. Cox regression was used to evaluate the associations between immune cell subsets infiltration and OS. Correlations between different immune cell subsets were examined by Spearman's tests. Results: In NSCLC, the distribution of different macrophage subsets within the TME, tumor regions, and stroma regions exhibited inconsistency. The proportions of CD68+ SHP2+ TAMs (P < 0.05) were higher in tumor than in stroma. And the high infiltration of CD68+SHP2+ TAMs in tumor areas correlated with poor OS (P < 0.05). We found that the expression level of SHP2 was higher in M2-like macrophages than in M1-like macrophages. The CD68+SHP2+ subset proportion was positively correlated with the CD68+CD206+ subset within TME (P < 0.0001), tumor (P < 0.0001) and stroma (P < 0.0001). Conclusions: The high infiltration of CD68+SHP2+ TAMs predict poor OS in NSCLC. Targeting SHP2 is a potentially effective strategy to inhibit M2-phenotype polarization. And it provides a new thought for SHP2 targeted cancer immunotherapy. [ABSTRACT FROM AUTHOR]

Habitat is fundamental for facilitating various life activities in animals, for instance, snakes procure essential energy for survival and reproduction by selecting ambush microhabitats. While there has been extensive research on the selection of microhabitat for feeding in terrestrial and aquatic snakes, little is known about arboreal snakes. In the present study, we analyzed the ambush microhabitat preferences of Viridovipera stejnegeri, a widely distributed Asian pitviper in China, conducted association analysis between snake microhabitat and prey microhabitat and abundance to determine the ro5le of microhabitat selection in feeding. Employing random forest analysis and habitat selection functions, we further constructed a predictive framework for assessing the probability of ambush site selection by V. stejnegeri. Our results revealed that V. stejnegeri exhibited a distinct microhabitat preference for ambush prey. Among the 13 environmental factors assessed, V. stejnegeri showed pronounced preferences towards 12 of these factors, including climatic factors, geographical factors, and vegetation factors. Furthermore, although the preferences of V. stejnegeri overlapped substantially with those of its prey across multiple habitat factors, food abundance shows no significant association with various habitat factors of V. stejnegeri, and does not have significant predictive effect on habitat selection of V. stejnegeri. Therefore, we infer that V. stejnegeri does not preferentially select microhabitats with the highest food abundance, which does not support the hypothesis that "snakes select habitats based on the spatial distribution of prey abundance." By analyzing the characteristics of vegetation, geography, and climate, we conclude that V. stejnegeri tends to choose microhabitats with better ambush conditions to increase attack success rate, thereby achieving the optimal feeding success rate at the microhabitat scale, which is in line with the predictions of optimal foraging theory. This study provides new insights into the predation ecology and habitat selection of snakes. [ABSTRACT FROM AUTHOR]

Large-scale vegetation restoration has caused complex changes in ecosystem service (i.e., ES) interactions. However, current analysis on the spatial interactions of ESs and their driving mechanisms remains deficient, limiting the adaptive management in vegetation restoration areas. This study focused on a representative restoration area (Yan'an) to analyze the relationships among carbon sequestration, water yield, baseflow regulation, and soil conservation from 1990 to 2020. Employing the bivariate boxplot and spatial autocorrelation methods, we identified the overall changes and spatial patterns of ES interactions. The geographically and temporally weighted regression (i.e., GTWR) model was applied to elucidate the driving factors of these spatial ES interactions. The results indicated the following: (1) Over the past three decades, synergies between carbon sequestration and water yield emerged as the joint results of spatial 'low–low' interactions and 'high–high' interactions between the two ESs, while other ES pairs generally exhibited comparatively weaker synergies, due to their spatial 'low–high' interactions in southern semi-humid areas. (2) In the northern semi-arid areas, both fractional vegetation cover (i.e., FVC) and climatic factors consistently exerted negative influences on all 'low–low' ES interactions, which caused a reduced area in synergies, while in the southern semi-humid areas, FVC suppressed the 'low–high' trade-offs between ESs, indicating the adaptability of grassland restoration efforts. (3) The impact of human activities on ES interactions has increased in the last 10 years, and exhibited positive effects on the 'low–low' ES interactions in northern semi-arid areas. However, the expansion of trade-off between soil conservation and carbon sequestration warrants attention. This study offers important insights into understanding the spatial interactions among carbon, water, and soil-related ESs in drylands. [ABSTRACT FROM AUTHOR]

The question of whether the construction of the highway network is economical and can produce positive economic benefits has been a hot topic of discussion in recent years. Previous scholars have explored the impact from multiple perspectives. Our paper draws the “trade potential” model proposed by Armstrong, based on the universal gravity model and the principle of space interaction, which is different from the traffic accessibility, market potential, and market access used in most of the literature. We argue that it is more appropriate to consider both the size impact and the time distance or trade cost impact of the two cities. The paper constructs a conceptual framework and theoretical model for the impact of highways on regional economic growth, measures the “minimum transit time” of highways between prefecture-level cities in China, and calculates the trade potential of prefecture-level cities. Through corresponding empirical model testing, we have obtained some meaningful conclusions. First published online 17 September 2024

Extracting spatial network structure (SNS) from large-scale origin-destination flow data is an important approach for understanding interregional association patterns and interaction laws. Currently, the extraction of SNS primarily relies on complex network clustering or aggregated statistics with predefined regional constraints. However, these methods often overlook one or more fundamental principles essential for ensuring correctness and accuracy: 1) Aggregation of spatially proximate nodes is necessary when strong interactions exist, whereas separation is preferred in the absence of such interactions. 2) It is crucial to maintain strong interactions between non-spatially proximate nodes. 3) Ultimately, nodes within each group should exhibit spatial continuity. To address these challenges, a multi-hierarchical SNS extraction method is proposed, which focuses on raw node aggregating and generalization, measurement of interaction volume and strength between node groups and strategies for node/edge filtering. The effectiveness and value of the proposed method are demonstrated through a case study using city population migration data. Furthermore, the method provides a general approach for extracting SNSs from any origin-destination flow dataset that includes locations and weights, facilitating effective flow map generalization through aggregation of origin destination (OD) flow data. [ABSTRACT FROM AUTHOR]

In this article, I develop and implement the multiscale geographically weighted negative binomial (MGWNB) model, extending the spatially weighted interaction models by integrating a multiscale framework. This model effectively tackles the multiscale nonstationarity and overdispersion issues found in spatial interaction models. By comparing it with multiscale geographically weighted Poisson regression using simulated data, I demonstrate its superior performance in several aspects, including its capability to estimate the scale of processes, its effectiveness in capturing the spatial heterogeneity, and its ability to produce a better goodness of fit. The application of MGWNB in interprovincial population migration in China, using 2020 Chinese census data, also demonstrates its effectiveness and efficiency, revealing strong multiscale spatial heterogeneity in the migration patterns. [ABSTRACT FROM AUTHOR]

In the new stage of the deep integration of culture and tourism, fully exploring its mechanism of action, spatial connection, and path of influence will be of great significance to the sustainable development of the integration of culture and tourism. In this paper, we analysed the mechanism of integration of culture and tourism with the help of systems theory, introduced the concept of spatial interaction to construct a new measurement model of integration of culture and tourism, and conducted empirical analyses with Chinese provinces as the target. We found that in the last decade, the tourism industry has been dominant, and most of the industrial elements have been clustered towards it. The centre of the spatial interaction network of culture and tourism is located in the area of the "North China Plain—Middle and Lower Yangtze River Plain" and moves towards the southwest over time. The northwestern and northeastern zones have become "desert zones" in the spatial interaction network of culture and tourism. The level of integration of culture and tourism peaked in 2018, with East China having a chronically higher level of integration. In addition, the deep integration of culture and tourism can be promoted through the combination of new development philosophies. [ABSTRACT FROM AUTHOR]

This study aimed to analyze: (1) the causes of spatial interaction between the regencies in West Timor based on horticultural commodities, (2) the existing and optimizing benefits of spatial interactions between the regencies in West Timor based on horticultural commodities. The research was conducted in 5 regencies on Timor Island namely Kupang, TTS, TTU, Belu, and Malaka at wholesale markets which were determined purposive and snowball for traders. Purposive sampling with the criteria of traders who market horticultural products to 4 other districts in West Timor, followed by a snowball to trace traders who provide the intended horticultural products to the destination market. Data analysis uses gravity analysis, and the shortest path multi-object optimization model. The results showed that the variables of cost, population, price, and GDP had a significant effect on spatial interactions; while the transport capacity had no significant effect on spatial interactions. The total benefit of spatial interaction between the regencies in West Timor is IDR 1,055,467,000.-. The benefit of spatial interaction will increase by 65.60% to IDR 1,747,888,918,000.-. Therefore, interactions between regions must pay attention to the number of requests, selling prices, and transportation costs in order to maximize benefits. These findings are useful for policy makers and horticultural traders in determining the number of horticultural commodities marketed at each destination market in 4 districts in West Timor that provide optimum profits. [ABSTRACT FROM AUTHOR]

The manufacturing industry is undergoing rapid evolution, necessitating flexible and adaptable robots. However, configuring such machines requires technical experts, which are hard to find, especially for small and medium enterprises. Therefore, the process needs to be simplified by allowing non-experts to configure robots. During such configuration, one key aspect is the definition of objects' grasping poses. The literature proposes deep learning techniques to compute grasping poses automatically and facilitate the process. Nevertheless, practical implementation for inexperienced factory operators can be challenging, especially if task-specific knowledge and constraints should be considered. To overcome this barrier, we propose an approach that facilitates teaching such poses. Our method, employing a novel user grasp metric, combines the operator's initial grasp guess given by a 3D spatial device with a state-of-the-art deep learning algorithm, thus returning reliable grasping poses but simultaneously close to the operator's initial guess. We compare this approach against commercial grasping pose definition interfaces through a user test involving 28 participants and against state-of-the-art deep learning grasp estimators. The results demonstrate a significant improvement in system usability (+24%) and a reduced workload (-16%). Furthermore, our experiments reveal an increased grasp success rate when utilizing the user grasp metric, surpassing state-of-the-art deep learning grasping estimators. [ABSTRACT FROM AUTHOR]

Precise identification of spatial unit functional features in the city is a pre-condition for urban planning and policy-making. However, inferring unknown attributes of urban spatial units from data mining of spatial interaction remains a challenge in geographic information science. Although neural-network approaches have been widely applied to this field, urban dynamics, spatial semantics, and their relationship with urban functional features have not been deeply discussed. To this end, we proposed semantic-enhanced graph convolutional neural networks (GCNNs) to facilitate the multi-scale embedding of urban spatial units, based on which the identification of urban land use is achieved by leveraging the characteristics of human mobility extracted from the largest mobile phone datasets to date. Given the heterogeneity of multi-modal spatial data, we introduced the combination of a systematic data-alignment method and a generative feature-fusion method for the robust construction of heterogeneous graphs, providing an adaptive solution to improve GCNNs' performance in node-classification tasks. Our work explicitly examined the scale effect on GCNN backbones, for the first time. The results prove that large-scale tasks are more sensitive to the directionality of spatial interaction, and small-scale tasks are more sensitive to the adjacency of spatial interaction. Quantitative experiments conducted in Shenzhen demonstrate the superior performance of our proposed framework compared to state-of-the-art methods. The best accuracy is achieved by the inductive GraphSAGE model at the scale of 250 m, exceeding the baseline by 25.4%. Furthermore, we innovatively explained the role of spatial-interaction factors in the identification of urban land use through the deep learning method. [ABSTRACT FROM AUTHOR]

A transient 3D model was established to investigate the effect of spatial interaction of ultrasounds on the dual-frequency ultrasonic field in magnesium alloy melt. The effects of insertion depth and tip shape of the ultrasonic rods, input pressures and their ratio on the acoustic field distribution were discussed in detail. Additionally, the spacing, angle, and insertion depth of two ultrasonic rods significantly affect the interaction between distinct ultrasounds. As a result, various acoustic pressure distributions and cavitation regions are obtained. The spherical rods mitigate the longitudinal and transversal attenuation of acoustic pressure and expand the cavitation volume by 53.7% and 31.7%, respectively, compared to the plate and conical rods. Increasing the input pressure will enlarge the cavitation region but has no effect on the acoustic pressure distribution pattern. The acoustic pressure ratio significantly affects the pressure distribution and the cavitation region, and the best cavitation effect is obtained at the ratio of 2:1 (P15:P20). [ABSTRACT FROM AUTHOR]

Abstract Habitat is fundamental for facilitating various life activities in animals, for instance, snakes procure essential energy for survival and reproduction by selecting ambush microhabitats. While there has been extensive research on the selection of microhabitat for feeding in terrestrial and aquatic snakes, little is known about arboreal snakes. In the present study, we analyzed the ambush microhabitat preferences of Viridovipera stejnegeri, a widely distributed Asian pitviper in China, conducted association analysis between snake microhabitat and prey microhabitat and abundance to determine the ro5le of microhabitat selection in feeding. Employing random forest analysis and habitat selection functions, we further constructed a predictive framework for assessing the probability of ambush site selection by V. stejnegeri. Our results revealed that V. stejnegeri exhibited a distinct microhabitat preference for ambush prey. Among the 13 environmental factors assessed, V. stejnegeri showed pronounced preferences towards 12 of these factors, including climatic factors, geographical factors, and vegetation factors. Furthermore, although the preferences of V. stejnegeri overlapped substantially with those of its prey across multiple habitat factors, food abundance shows no significant association with various habitat factors of V. stejnegeri, and does not have significant predictive effect on habitat selection of V. stejnegeri. Therefore, we infer that V. stejnegeri does not preferentially select microhabitats with the highest food abundance, which does not support the hypothesis that “snakes select habitats based on the spatial distribution of prey abundance.” By analyzing the characteristics of vegetation, geography, and climate, we conclude that V. stejnegeri tends to choose microhabitats with better ambush conditions to increase attack success rate, thereby achieving the optimal feeding success rate at the microhabitat scale, which is in line with the predictions of optimal foraging theory. This study provides new insights into the predation ecology and habitat selection of snakes.

BackgroundTumor-associated macrophages (TAMs) constitute a plastic and heterogeneous cell population of the tumor microenvironment (TME) that can regulate tumor proliferation and support resistance to therapy, constituting promising targets for the development of novel anticancer agents. Our previous results suggest that SHP2 plays a crucial role in reprogramming the phenotype of TAMs. Thus, we hypothesized that SHP2+ TAM may predict the treatment efficacy of non-small cell lung cancer NSCLC patients as a biomarker.MethodsWe analyzed cancer tissue samples from 79 NSCLC patients using multiplex fluorescence (mIF) staining to visualize various SHP-2+ TAM subpopulations (CD68+SHP2+, CD68+CD86+, CD68 + 206+, CD68+ CD86+SHP2+, CD68+ CD206+SHP2+) and T cells (CD8+ Granzyme B +) of immune cells. The immune cells proportions were quantified in the tumor regions (Tumor) and stromal regions (Stroma), as well as in the overall tumor microenvironment (Tumor and Stroma, TME). The analysis endpoint was overall survival (OS), correlating them with levels of cell infiltration or effective density. Cox regression was used to evaluate the associations between immune cell subsets infiltration and OS. Correlations between different immune cell subsets were examined by Spearman’s tests.ResultsIn NSCLC, the distribution of different macrophage subsets within the TME, tumor regions, and stroma regions exhibited inconsistency. The proportions of CD68+ SHP2+ TAMs (P < 0.05) were higher in tumor than in stroma. And the high infiltration of CD68+SHP2+ TAMs in tumor areas correlated with poor OS (P < 0.05). We found that the expression level of SHP2 was higher in M2-like macrophages than in M1-like macrophages. The CD68+SHP2+ subset proportion was positively correlated with the CD68+CD206+ subset within TME (P < 0.0001), tumor (P < 0.0001) and stroma (P < 0.0001).ConclusionsThe high infiltration of CD68+SHP2+ TAMs predict poor OS in NSCLC. Targeting SHP2 is a potentially effective strategy to inhibit M2-phenotype polarization. And it provides a new thought for SHP2 targeted cancer immunotherapy.

Freeway traffic volume is strongly correlated with the intensity of regional socioeconomic spatial interactions and the road network structure. Although existing studies have proposed indicators of betweenness centrality (BC) integrated into regional spatial interactions, the socio-economic drivers of freeway traffic volume formation have been neglected. More importantly, existing studies have not established a non-linear response relationship among BC, city socio-economic spatial interactions, and road traffic volume, which severely limits the comprehensive quantification of the role of freeway traffic flow drivers. Therefore, this study proposes a freeway traffic volume inference method that integrates spatial interaction to enhance BC. First, the socioeconomic factors of the origin and destination cities are incorporated into the BC indicator to create an enhanced betweenness centrality indicator (ODBC), which quantifies the strength of spatial interactions between cities. Second, a machine learning approach is used to develop the non-linear response relationship between ODBC and freeway traffic flow to accurately infer traffic volume. Finally, utilizing the SHapley additive explanation approach, the role vectors of intercity freeway traffic volume drivers are quantified. Experiments conducted on data from freeway toll stations demonstrate that the proposed method surpasses the baseline method based on BC and weighted by BC considering only the potential destination or origin city attractiveness, with an improvement in R2 of 14%, 4.2%, and 4%, and a maximum reduction in RMSE of 40%, 24.5%, and 26%. The proposed method yields higher accuracy for unknown road segments and is easily interpretable.

Soil life revolves around microorganisms that are crucial for soil ecosystems and health. In megacities, the combined exposure of multiple pollutants exerts a significant impact on the structures and functions of soil microorganisms; however, there is a lack of empirical studies on this topic. Hence, we conducted a study including urban parks in Beijing, China. The results indicate that bacteria were abundant in the soils of Beijing parks, showing the same dominant groups but different rare groups. The dominant groups included Actinobacteria and Proteobacteria. Candidate phyla radiation bacteria, a large evolutionary radiation of bacterial lineages whose members remain mostly uncultivated, were the main specialists. Under the combined exposure of multiple pollutants, the structures of soil microbial communities in different parks were similar. Community change due to pollutants (31%) was greater than that due to natural factors (2.4%). Among multipollutants, organophosphate esters, led by dibutyl phosphate, had the highest influence on microbial abundance and distribution. An increase in dibutyl phosphate concentration decreased the abundance of Firmicutes, while the abundance of Synergistota was increased. The interactions among pollutants affecting the bacteria were different. Bis(2-chloroethyl) phosphate, nickel and benzo[g,h,i]perylene influenced microorganisms by working with organophosphate esters. High-molecular-weight polycyclic aromatic hydrocarbons, such as benzo[a]pyrene and benzo[g,h,i]perylene, mainly acted on the functional genes and thus affected multiple biogeochemical cycles. Benzo[a]anthracene, bis(2-chloroethyl) phosphate, and arsenic were the primary pollutants affecting metabolic pathways. Our research helps to better understand the impacts of urban environmental pollution on soil microorganisms.

The rational matching of urban land and population has become an important prerequisite for sustainable urban development. In this paper, the traditional urban land scale elasticity model was improved, and combined with the gravity model, the spatial interaction between land expansion and population change in 618 cities in China during the period 2006–2021 was investigated. The geographical detector method was used to reveal what drives them. The main results were as follows: (1) China’s urban land expansion rate was 1.83 times faster than the population growth rate during 2006–2021. After the implementation of the New-type Urbanisation Plan in 2014, the ratio of land expansion rate to population growth rate dropped from 2.46 to 1.12. (2) Among the six interaction types identified, land rapid expansion is the most significant, accounting for 41.59% of urban samples. (3) The geographical detector method found that the indicators of urban development rights such as the level of administrative hierarchy and the ratio of fiscal revenue to fiscal expenditure were the main factors affecting land expansion and that economic indicators such as gross domestic product and employment opportunities dominated population change. Fortunately, the intervention role of urban development rights has declined, and the constraints of market mechanisms, resources and environment have gradually become the dominant factors in urban land expansion and population change. These findings provide a theoretical basis for alleviating the human–land contradiction and achieving sustainable urban development.