Search Results (657)

Risk zone delineation and mobility behavior control constitute critical measures in pandemic containment. Numerous studies utilize static demographic data or dynamic mobility data to calculate the high–risk zones present in cities; however, these studies fail to concurrently consider activity and mobility patterns of populations in both space and time, which results in many studies only being able to employ static geostatistical analytical methods, neglecting the transmission risks associated with human mobility. This study utilized the mobile phone signaling data of Shenzhen residents from 2019 to 2020 and developed a CP tensor decomposition algorithm to decompose the long-sequence spatiotemporal trajectory data to detect high risk zones in terms of detecting overlapped community structures. Tensor decomposition algorithms revealed community structures in 2020 and the overlapping regions among these communities. Based on the overlap in spatial distribution and the similarity in temporal rhythms of these communities, we identified regions with spatiotemporal co-location as high–risk zones. Furthermore, we calculated the degree of population mixing in these areas to indicate the level of risk. These areas could potentially lead to rapid virus spread across communities. The research findings address the shortcomings of currently used static geographic statistical methods in delineating risk zones, and emphasize the critical importance of integrating spatial and temporal dimensions within behavioral big data analytics. Future research should consider utilizing non-aggregated individual trajectories to construct tensors, enabling the inclusion of individual and environmental attributes. Full article

The spatial genes of rural settlements show a lot of different traditional settlement traits, which makes them a great starting point for studying rural spatial morphology. However, qualitative and macro-regional statistical indicators are usually used to find and extract rural settlement spatial genes. Taking Yubei Village in the Nanxi River Basin as an example, this study combined remote sensing images, real-time drone mapping, GIS (geographic information system), and space syntax, extracted 12 key indicators from five dimensions (landform and water features (environment), boundary morphology, spatial structure, street scale, and building scale), and quantitatively “decoded” the spatial genes of the settlement. The results showed that (1) the settlement is a “three mountains and one water” pattern, with cultivated land accounting for 37.4% and forest land accounting for 34.3% of the area within the 500 m buffer zone, while the landscape spatial diversity index (LSDI) is 0.708. (2) The boundary morphology is compact and agglomerated, and locally complex but overall orderly, with an aspect ratio of 1.04, a comprehensive morphological index of 1.53, and a comprehensive fractal dimension of 1.31. (3) The settlement is a “clan core–radial lane” network: the global integration degree of the axis to the holy hall is the highest (0.707), and the local integration degree R3 peak of the six-room ancestral hall reaches 2.255. Most lane widths are concentrated between 1.2 and 2.8 m, and the eaves are mostly higher than 4 m, forming a typical “narrow lanes and high houses” water town streetscape. (4) The architectural style is a combination of black bricks and gray tiles, gable roofs and horsehead walls, and “I”-shaped planes (63.95%). This study ultimately constructed a settlement space gene map and digital library, providing a replicable quantitative process for the diagnosis of Jiangnan water town settlements and heritage protection planning. Full article

With the rapid development of heterogeneous satellite networks integrating geostationary earth orbit (GEO) and low earth orbit (LEO) satellite systems, along with the significant growth in the number of satellite users, it is essential to consider frequency compatibility and coexistence between GEO and LEO systems, as well as to design effective system resource allocation strategies to achieve efficient utilization of system resources. However, existing beam-hopping (BH) resource allocation algorithms in LEO systems primarily focus on beam scheduling within a single time slot, lacking unified beam management across the entire BH cycle, resulting in low beam-resource utilization. Moreover, existing algorithms often employ iterative optimization across multiple resource dimensions, leading to high computational complexity and imposing stringent requirements on satellite on-board processing capabilities. In this paper, we propose a BH-based beam scheduling and resource allocation framework. The proposed framework first employs geographic isolation to protect the GEO system from the interference of the LEO system and subsequently optimizes beam partitioning over the entire BH cycle, time-slot beam scheduling, and frequency and power resource allocation for users within the LEO system. The proposed scheme achieves frequency coexistence between the GEO and LEO satellite systems and performs joint optimization of system resources across four dimensions—time, space, frequency, and power—with reduced complexity and a progressive optimization framework. Simulation results demonstrate that the proposed framework achieves effective suppression of both intra-system and inter-system interference via geographic isolation, while enabling globally efficient and dynamic beam scheduling across the entire BH cycle. Furthermore, by integrating the user-level frequency and power allocation algorithm, the scheme significantly enhances the total system throughput. The proposed progressive optimization framework offers a promising direction for achieving globally optimal and computationally tractable resource management in future satellite networks. Full article

Evaluating the economic value of carbon sinks is fundamental to advancing carbon market mechanisms and supporting sustainable regional development. This study focuses on Fujian Province in China, aiming to assess the spatiotemporal evolution of carbon sink value and analyze the influence of socio-economic drivers. Carbon sink values from 2000 to 2020 were estimated using Net Ecosystem Productivity (NEP) simulation combined with the carbon market valuation method. Eleven socio-economic variables were selected through correlation and multicollinearity testing, and their impacts were examined using Geographically and Temporally Weighted Regression (GTWR) at the county level. The results indicate that the total carbon sink value in Fujian declined from CNY 3.212 billion in 2000 to CNY 2.837 billion in 2020, showing a spatial pattern of higher values in the southern region and lower values in the north. GTWR analysis reveals spatiotemporal heterogeneity in the effects of socio-economic factors. For example, the influence of urbanization and retail sales of consumer goods shifts direction over time, while the effects of industrial structure, population, road, and fixed asset investment vary across space. This study emphasizes the necessity of incorporating spatial and temporal dynamics into carbon sink valuation. The findings suggest that northern areas of Fujian should prioritize ecological restoration, rapidly urbanizing regions should adopt green development strategies, and counties guided by investment and consumption should focus on sustainable development pathways to maintain and enhance carbon sink capacity. Full article

Oral narratives play a crucial role in shaping the historical consciousness of Indigenous communities in Northeast India, where history writing is a relatively recent phenomenon. Among the Mizos, Nagas, Khasis, Kuki-Chins, and other Indigenous tribes of Northeast India, including the Bodos, the Garos, the Dimasas, or the Karbis of Assam, much of what is considered written history emerged during British colonial rule. Native historians later continued it in postcolonial India. However, written history, especially when based on fragmented colonial records, includes interpretive gaps. In such contexts, oral traditions provide complementary, and frequently, more authoritative frameworks rooted in cultural memory and collective transmission. Oral narratives, including ritual poetry, folk songs, myths, and folktales, serve as vital mediums for reconstructing the past. Scholars such as Jan Vansina view oral narratives as essential for understanding the histories of societies without written records, while Paul Thompson sees them as both a discovery and a recovery of cultural memory. Romila Thapar argues that narratives become indicative of perspectives and conditions in societies of the past, functioning as a palimpsest with multiple layers of meaning accruing over generations as they are recreated or reiterated over time. The folk narratives of the Mizos and Angami Nagas not only recount their origins and historical migrations, but also map significant geographical and cultural landmarks, such as Khezakheno and Lungterok in Nagaland, Rounglevaisuo in Manipur, and Chhinlung or Rih Dil on the Mizoram–Myanmar border. These narratives constitute a cultural understanding of the past, aligning with Greg Dening’s concept of “public knowledge of the past,” which is “culturally shared.” Additionally, as Linda Tuhiwai Smith posits, such stories, as embodiments of the past, and of socio-cultural practices of communities, create spaces of resistance and reappropriation of Indigenous identities even as they reiterate the marginalization of these communities. This paper deploys these ideas to examine how oral narratives can be used to decolonize grand narratives of history, enabling Indigenous peoples, such as the Mizos and the Angamis in North East India, to reaffirm their positionalities within the postcolonial nation. Full article

Material stocks (MS) serve as essential physical foundations for socio–economic systems, reflecting the accumulation, transformation, and consumption of resources over time and space. Positioned at the intersection of environmental and socio–economic systems, MS are increasingly recognized as leverage points for advancing sustainability. However, there is currently a lack of comprehensive overview, making it difficult to fully capture the latest developments and cutting–edge research. We adopt a systems perspective to conduct a comprehensive bibliometric and thematic review of 602 scholarly publications on MS research. The results showed that MS research encompasses has three development periods: preliminary exploration (before 2007), rapid development (2007–2016), and expansion and deepening (after 2016). MS research continues to deepen, gathering multiple teams and differentiating into diverse topics. MS research has evolved from simple accounting to intersection with socio–economic, resources, and environmental systems, and shifted from relying on statistical data to integrating high–spatio–temporal–resolution geographic big data. MS research is shifting from problem revelation to problem solving, constantly achieving new developments and improvements. In the future, it is still necessary to refine MS spatio–temporal distribution, reveal MS’s evolution mechanism, establish standardized databases, strengthen interaction with other systems, enhance problem–solving abilities, and provide powerful guidance for the formulation of dematerialization and decarbonization policies to achieve sustainable development. Full article

Contrary to what is represented in geospatial databases, places are dynamic and shaped by events. Point clustering analysis commonly assumes events occur in an empty space and therefore ignores geospatial features where events take place. This research introduces relational density, a novel concept redefining density as relative to the spatial structure of geospatial features rather than an absolute measure. Building on this, we developed Space-Time Plume, a new algorithm for detecting and tracking evolving event clusters as smoke plumes in space and time, representing dynamic places. Unlike conventional density-based methods, Space-Time Plume dynamically adapts spatial reachability based on the underlying spatial structure and other zone-based parameters across multiple temporal intervals to capture hierarchical plume dynamics. The algorithm tracks plume progression, identifies spatiotemporal relationships, and reveals the emergence, evolution, and disappearance of event-driven places. A case study of crime events in Dallas, Texas, USA, demonstrates the algorithm’s performance and its capacity to represent and compute criminogenic places. We further enhance metaball rendering with Perlin noise to visualize plume structures and their spatiotemporal evolution. A comparative analysis with ST-DBSCAN shows Space-Time Plume’s competitive computational efficiency and ability to represent dynamic places with richer geographic insights. Full article

The geographic knowledge graph offers a structured framework for mining and discovering spatiotemporal knowledge, which is of great significance for understanding geographic dynamics. However, existing geographic knowledge graphs still encounter significant challenges in comprehensive expression of spatiotemporal elements and understanding the intricate relationships and dynamic evolution among geographic entities, space, and time. Therefore, a Spatiotemporal Evolution Hierarchical Representation Graph (STEHRG) is proposed, which consists of three layers: a spatiotemporal ontology layer, a spatiotemporal evolution layer, and a feature situation layer. The STEHRG characterizes the multidimensional state transitions of spatiotemporal entities across various scales and abstraction levels, enabling a comprehensive representation of geographic spatiotemporal evolution. Additionally, this paper introduces a graph data structure-based approach for managing the state features of spatiotemporal entities and their lifecycle dependencies. Finally, through comparative experiments with existing knowledge graphs (GeoKG, GEKG, and STOKG), the results indicate that the STEHRG has significant advantages in accuracy, completeness, and reproducibility. Full article

Urban park vitality, a key indicator of public space performance, has garnered significant research attention. However, existing studies often neglect the temporal variability in vitality patterns, thus failing to accurately reflect actual park performance and limiting their relevance for strategic urban planning and sustainable resource allocation. This study constructs a “temporal behavior–spatial attributes–park typology” framework using high-precision (50 m) mobile signaling data to capture hourly vitality fluctuations in 59 parks of Hangzhou’s Gongshu District. Using dynamic time-warping-optimized K-means clustering, we identify three vitality types—Morning-Exercise-Dominated, All-Day-Balanced, and Evening-Aggregation-Dominated—revealing distinct weekday/weekend usage rhythms linked to park typology (e.g., community vs. comprehensive parks). Geographical Detector analysis shows that vitality correlates with spatial attributes in time-specific ways; weekend morning vitality is driven by park size and surrounding POI density, while weekday evening vitality depends on interactions between facility density and residential population. These findings highlight how transportation accessibility and commercial amenities shape temporal vitality, informing time-sensitive strategies such as extended evening hours for suburban parks and targeted facility upgrades in residential areas. By bridging vitality patterns with strategic planning demands, the study advances the understanding of how sustainable park management can optimize resource efficiency and enhance public space equity, offering insights for urban green infrastructure planning in other regions. Full article

The “hand-waving sacrifice” is a large-scale sacrificial ceremony with more than 2000 years of history. It was passed down from ancient times by the Tujia ethnic group living in the Wuling Corridor of China, and it integrates religion, sacrifice, dance, drama, and other cultural forms. It primarily consists of two parts: ritual content (inviting gods, offering sacrifices to gods, dancing a hand-waving dance, etc.) and the architectural space that hosts the ritual (hand-waving hall), which together constitute Tujia’s most sacred ritual space and the most representative art and culture symbol. Nonetheless, in existing studies, the hand-waving sacrifice ritual, hand-waving hall architectural space, and hand-waving dance art are often separated as independent research objects, and little attention is paid to the coupling mechanism of the mutual construction of space and ritual in the process of historical development. Moreover, with the acceleration of modernization, the current survival context of the hand-waving sacrifice has undergone drastic changes. On the one hand, the intangible cultural heritage protection policy and the wave of tourism development have pushed it into the public eye and the cultural consumption system. On the other hand, the changes in the social structure of traditional villages have led to the dissolution of the sacredness of ritual space. Therefore, using the interaction of “space-ritual” as a prompt, this research first uses GIS technology to visualize the spatial geographical distribution characteristics and diachronic evolution process of hand-waving halls in six historical periods and then specifically analyzes the sacred construction of hand-waving hall architecture for the hand-waving sacrifice ritual space throughout history, as well as the changing mechanism of the continuous secularization of the hand-waving sacrifice space in contemporary society. Overall, this study reveals a unique path for non-literate ethnic groups to achieve the intergenerational transmission of cultural memory through the collusion of material symbols and physical art practices, as well as the possibility of embedding the hand-waving sacrifice ritual into contemporary spatial practice through symbolic translation and functional extension in the context of social function inheritance and variation. Finally, this study has specific inspirational and reference value for exploring how the traditional culture and art of ethnic minorities can maintain resilience against the tide of modernization. Full article

The events of 2020 reached a fever pitch with the May 25th murder of George Floyd, but earlier on the same morning, a chance encounter between dogwalker Amy Cooper and birding enthusiast Christian Cooper also laid bare enduring social relations. As video footage of the encounter spread across social media, it sparked both public outrage and discourse regarding Black nature enthusiasts. Employing a historical-interpretive method informed by conversation analysis and guided by “whiteness as property,” I assemble news articles, social media posts, and video footage to analyze the events in Central Park and their aftermath. To unsettle existing paradigms regarding who we imagine are entitled to the great outdoors, I identify potential collaborative partners across scales who can further the goals of education, recruitment, and visibility for Black nature enthusiasts and professionals. I demonstrate how expanding environmental justice to include anti-Black racial violence allows us to recognize that the specter of lynching defies geographic boundaries, diffusing across space and time, occasionally coalescing to defend white privilege and historic racial orders. Full article

Extensive scientific evidence underscores the importance of identifying spatiotemporal patterns for investigating urban dynamics. The recent proliferation of location-based social networks (LBSNs) facilitates the measurement of urban rhythms through geotemporal information, providing deeper insights into the underlying causes of urban vibrancy. This study presents a methodology for analyzing the spatiotemporal use of cities and identifying occupancy patterns taking into consideration urban form and function. The analysis relies on data obtained from Google Popular Times (GPT), transforming the relative occupancy of a large number of points of interest (POI) classified into five categories, for estimating the number of people aggregated within urban nodes during a typical day. As a result, this research assesses the utility of this data source for evaluating the changing dynamics of a city across both space and time. The methodology employs geographic information system (GIS) tools and artificial intelligence techniques. The results demonstrate that by analyzing geotemporal data, we can classify urban nodes according to their hourly activity patterns. These patterns, in turn, relate to city form and urban activities, showing a certain spatial concentration. This research contributes to the growing body of knowledge on machine learning (ML) methods for spatiotemporal modeling, laying the groundwork for future studies that can further explore the complexity of urban phenomena. Full article

As China steadily advances its “dual carbon” strategy, understanding the factors influencing carbon emission efficiency (CEE) is crucial for promoting high-quality urban development. This study examines Western Valley cities (WVCs), which play a key role in regional development and exhibit a distinct spatial structure. Using a super-efficiency slacks-based measure (SBM) model and economic and social panel data, we measured CEE and analyzed its spatiotemporal evolution. A geographically and temporally weighted regression (GTWR) was then applied to assess the spatiotemporal heterogeneity of influencing factors. Our findings revealed that the overall CEE of these cities remains relatively low, with a complex pattern of change. While efficiency levels in northern, southern, and central cities have gradually increased, there are notable differences in the quantity and spatial distribution of cities with high, relatively high, relatively low, and low efficiency over time. Additionally, the positive effects of technological investment, road density, population density, and per capita gross domestic product on CEE follow an increasing trend, whereas the negative impacts of energy intensity, green space ratio, secondary industry proportion, land use scale, and gas consumption gradually weaken. Additionally, the magnitude and direction of these effects vary significantly across northern, central, and southern cities. These findings provide important theoretical and practical insights for region-specific strategies aimed at reducing emissions and improving efficiency in WVCs. Full article

In dynamic environments with continuous variability, such as those affected by typhoons, ship path planning must account for both navigational safety and the maneuvering characteristics of the vessel. However, current methods often struggle to accurately capture the continuous evolution of dynamic obstacles and generally lack adaptive exploration mechanisms. Consequently, the planned routes tend to be suboptimal or incompatible with the ship’s maneuvering constraints. To address this challenge, this study proposes a Space–Time Integrated Q-Learning (STIQ-Learning) algorithm for dynamic path planning under typhoon conditions. The algorithm is built upon the following key innovations: (1) Spatio-Temporal Environment Modeling: The hazardous area affected by the typhoon is decomposed into temporally and spatially dynamic obstacles. A grid-based spatio-temporal environment model is constructed by integrating forecast data on typhoon wind radii and wave heights. This enables a precise representation of the typhoon’s dynamic evolution process and the surrounding maritime risk environment. (2) Optimization of State Space and Reward Mechanism: A time dimension is incorporated to expand the state space, while a composite reward function is designed by combining three sub-reward terms: target proximity, trajectory smoothness, and heading correction. These components jointly guide the learning agent to generate navigation paths that are both safe and consistent with the maneuverability characteristics of the vessel. (3) Priority-Based Adaptive Exploration Strategy: A prioritized action selection mechanism is introduced based on collision feedback, and the exploration factor $ϵ$ is dynamically adjusted throughout the learning process. This strategy enhances the efficiency of early exploration and effectively balances the trade-off between exploration and exploitation. Simulation experiments were conducted using real-world scenarios derived from Typhoons Pulasan and Gamei in 2024. The results demonstrate that in open-sea environments, the proposed STIQ-Learning algorithm achieves reductions in path length of 14.4% and 22.3% compared to the D* and Rapidly exploring Random Trees (RRT) algorithms, respectively. In more complex maritime environments featuring geographic constraints such as islands, STIQ-Learning reductions of 2.1%, 20.7%, and 10.6% relative to the DFQL, D*, and RRT algorithms, respectively. Furthermore, the proposed method consistently avoids the hazardous wind zones associated with typhoons throughout the entire planning process, while maintaining wave heights along the generated routes within the vessel’s safety limits. Full article

The analysis of ecosystem service value (ESV) dynamics across space and time, along with their driving factors, is essential for informed ecosystem service administration and policy development. The Shandong Peninsula Urban Agglomeration (SPUA) is an important, highly efficient eco-economic zone in China. Leveraging land use/land cover datasets covering the period 2000–2020, spatial autocorrelation analysis and geographical detector were used to examine the spatial distribution characteristics and driving forces of the ESV. The results indicated the following: (1) From 2000 to 2020, the ESV of SPUA exhibited an overall trend of “increase—decrease—increase”. Cropland, forest, and water bodies were the primary sources of ESV, with significant variations in the changes of ESV across different land-use types. (2) As the spatial scale increased, the level of spatial autocorrelation of the per-unit ESV gradually decreased, and no spatial autocorrelation was observed at the urban scale. Analysis revealed that the clustering trend was more pronounced at the township scale, and its stability over the years was higher than that at the county scale. (3) The per-unit ESV was driven primarily by socio-economic factors, and the relative importance of these driving forces was minimally affected by the spatial scale, indicating a certain degree of similarity across different scales. (4) The spatial distribution pattern of per-unit ESV was not driven by a single factor but by the interaction of multiple factors. These interactions were significantly influenced by spatial scale, with more complex interaction effects observed at the county scale. Slope, in particular, played a crucial role in the interactions. This research contributes valuable scientific knowledge for developing environmental conservation frameworks in the SPUA while supporting the region’s sustainable growth initiatives. Full article