Spatio-temporal Dimensions Research Articles

Robust Reverberation Suppression Method Based on Alternating Projections

By leveraging the high correlation between multi-ping echo data, low-rank and sparse decomposition methods are applied for reverberation suppression. Previous methods typically perform decomposition on the vectorized multi-ping echograph, which is obtained by stacking beamforming outputs from all directions in the same column. However, when the multi-ping correlation of beamforming outputs from different directions varies significantly due to the time-varying nature of the underwater acoustic channel, it becomes challenging to precisely capture the variations of the reverberation background. As a result, the performance of reverberation suppression is degraded. To alleviate this issue, we attempt to decompose the matrix formed by multi-ping beamforming outputs in different directions individually. The accelerated alternating projections method is used to estimate the steady reverberation for moving target detection. By exploiting the differences in spatio-temporal dimensions between moving targets and reverberation fluctuations, a weighted spatio-temporal density method with adaptive thresholding is used to further extract the target echoes. Field data were utilized to validate the effectiveness of the proposed method, and the experimental results demonstrated its superior robustness in an unstable reverberation-limited environment, maintaining an accurate estimation of steady reverberation.

Harmonizing Brain Function

As brain diseases become more prevalent, early assessment of brain function is increasingly vital. This study introduces a piano sight-reading data collection and processing system, along with a quantitative evaluation method for brain health based on spatiotemporal dimensions. The system demonstrated effective data storage and analysis, with speed and ability scores improving over time. At the highest difficulty (D), the right hand outperformed the left in both speed (7% advantage) and ability (8.6% higher score). Over time, Speed scores peaked at 90 for the left hand and 73 for the right, while ability scores reached 96 and 92, respectively. The method showed high sensitivity (92%) and specificity (90%), with positive and negative predictive values of 90.2% and 91.8%, confirming its reliability for detecting cognitive improvement and decline. These findings highlight the potential of musical training for early cognitive assessment and brain health promotion.

CSLens: Towards Better Deploying Charging Stations via Visual Analytics -- A Coupled Networks Perspective.

In recent years, the global adoption of electric vehicles (EVs) has surged, prompting a corresponding rise in the installation of charging stations. This proliferation has underscored the importance of expediting the deployment of charging infrastructure. Both academia and industry have thus devoted to addressing the charging station location problem (CSLP) to streamline this process. However, prevailing algorithms addressing CSLP are hampered by restrictive assumptions and computational overhead, leading to a dearth of comprehensive evaluations in the spatiotemporal dimensions. Consequently, their practical viability is restricted. Moreover, the placement of charging stations exerts a significant impact on both the road network and the power grid, which necessitates the evaluation of the potential post-deployment impacts on these interconnected networks holistically. In this study, we propose CSLens, a visual analytics system designed to inform charging station deployment decisions through the lens of coupled transportation and power networks. CSLens offers multiple visualizations and interactive features, empowering users to delve into the existing charging station layout, explore alternative deployment solutions, and assess the ensuring impact. To validate the efficacy of CSLens, we conducted two case studies and engaged in interviews with domain experts. Through these efforts, we substantiated the usability and practical utility of CSLens in enhancing the decision-making process surrounding charging station deployment. Our findings underscore CSLens's potential to serve as a valuable asset in navigating the complexities of charging infrastructure planning.

Biodiversity Change in Cultural Landscapes-The Rural Hotspot Hypothesis.

A dramatic decrease of biodiversity is currently questioning human-environment interactions that have shaped ecosystems over thousands of years. In old cultural landscapes of Central and East European (CEE) countries, a vast species decline has been reported for various taxa although intensive land cultivation has been reduced in favor of agroecological transformation, nature conservation and sustainable land management in the past 30 years. Thus, in the recent history, agricultural intensification cannot solely be discussed as the major driver controlling biodiversity. In cultural landscapes, we state that drivers and pressures mainly emerge from the backyards of rural settlements that act as interconnected rural hotspots and therefore form an ecological metapopulation in which small-scale backyard habitats are capable of preserving and exchanging species pools of the historical cultural landscape. We further argue that shifting sociocultural norms significantly affecting the survival of source populations in rural hotspots and drastically limit their dispersal pathways, which triggers the degradation of the rural metapopulation in recent times. Pressures of cultivation shift, landscape decoupling, structural homogenization, and use of technology and agrochemicals are identified as backyard ecological drivers negatively affecting biodiversity preservation, particularly in the surrounding rural landscape. Spatiotemporal dimensions of backyard pressures involving material fluxes, species exchange and retention, alternation of site conditions, and local genetic adaptation are delineated for different backyard features, including building structures, gardens, lawns, and paved grounds. Finally, we propose a future research agenda to quantify effects and trends of rural hotspots and followed patterns of altered species dynamics. We give an example on the use of satellite time series to remotely map rural backyard habitats and reveal significant spatiotemporal trends induced by small-scale human behavior that may lead to a new socioecological perception and stimulate actions to shape ecological dynamics emerging from the backyards of human settlements.

Assessment of fish habitat suitability in the wake of elevated pollution of a tropical river in India.

Evaluating fish habitat suitability (FHS) is of utmost importance in the present times, as rising pollution and ecological stress have led to the deterioration of their habitat and population. The present study has evaluated ecological water quality using the comprehensive pollution index (CPI), eutrophication index (EI), and organic pollution index (OPI). Fish community structure has been assessed based on tropic level score (TLC) and habitat orientation score (HOS). Moreover, FHS has been evaluated using the habitat suitability index (HSI). These assessments have been conducted in the spatio-temporal dimension. This study demonstrates that the ecological water quality is significantly poorer during the monsoon (MON) season, according to the CPI. However, it was at its lowest during the pre-monson (PRM) season in 2014-2015 and the post-monson (POM) season in 2022-2023, as indicated by the OPI and EI. The water is significantly polluted downstream of the Durgapur barrage, particularly after the Tamla Nallah confluence. According to the CPI, 72.73% of water samples during the MON season were highly contaminated in 2014-2015 and in 2022-2023. Practically water was badly polluted in all seasons according to the OPI and EI. The fish community composition indicates that the river is currently in a precarious state, and the ecosystems inside the river have undergone alterations in certain areas. According to the HSI, the Damodar River exhibits diminishing appropriateness for fish habitat during PRM season in certain areas, while other seasons show no suitability for fish habitat. This investigation will be highly beneficial for mitigating pollution and restoring rivers. It is crucial to uphold and conserve freshwater biodiversity and the ecosystem services provided by the river.

Spatial and Temporal Analysis of Urban Crime Factors in Kaunas City, Lithuania

Crime is a negative phenomenon that can highly influence the economic and social state of an area and is relevant to every country in the world. Crimes are recognized to be one of the main factors that disrupt a healthy society, as it can cause a disruption of its state, by leaving the limitations of insecurity, anxiety, and mental distress among the citizens. This article aims to investigate spatio-temporal dimensions which affect the urban crime level in the city of Kaunas. The research question – to what extent do population density, age and unemployment rate affect the Urban Crime in the city of Kaunas, Lithuania (2008–2021)? The mapping of crime prevalence has demonstrated that population density in different parts of the city has a direct correlation with the amount of crime. However, this relationship weakens as one moves away from the central parts of the city. Also, crimes are the most prominent in the middle of the city of Kaunas and become rarer when moving out. The relationship between mean age of population and crime prevalence showed a bell-shaped curve, and therefore the hypothesis that there is an association between age structure of population and crime prevalence is partially supported. However, due to lack of data, the relationship between the age structure of the population and the crime prevalence in that area could not be found statistically reliable.

A robust adaptive meta-sample generation method for few-shot time series prediction

The research and exploration of time series prediction (TSP) have attracted much attention recently. Researchers can achieve effective TSP based on the deep learning model and a large amount of data. However, when sufficient high-quality data are not available, the performance of prediction models based on deep learning techniques may degrade. Therefore, this paper focuses on few-shot time series prediction (FTSP) and plans to combine meta-learning and generative models to alleviate the problems caused by insufficient training data. When using meta-learning techniques to process FTSP tasks, researchers set the meta-parameter in model-agnostic meta-learning (MAML) as a meta-sample and construct meta-sample generation methods based on advanced generative modeling theory to achieve better uncertainty coding. The existing meta-sample generation methods in FTSP scenes have an inherent limitation: With the increase of the complexity of prediction tasks, samples based on Gaussian distribution may be sensitive to noise and outliers in the meta-learning environment and lack of uncertainty expression, thus affecting the robustness and accuracy of prediction. Therefore, this paper proposes an adaptive sample generation method called JLSG-Diffusion. Based on the Jensen constraint framework and Laplace modeling theory, this method constructs a sample adapter with reasonable adaptive steps and fast convergence for specific tasks. The advantage is to realize fast adaptive convergence of samples to new tasks at lower cost, effectively control the overall generalization error, and improve the robustness and non-Gaussian generalization of sample posterior reasoning. Moreover, the meta sampler of JLSG-Diffusion embeds meta-learning from the implicit probability measure level of Denoising Diffusion Probabilistic Models (DDPM), which makes the meta-sample distribution directly establish a function mapping with the new task and effectively quantifies the uncertainty of spatiotemporal dimension. Experimental results on three real datasets prove the efficiency and effectiveness of JLSG-Diffusion. Compared with the benchmark methods, the prediction model combined with JLSG-Diffusion shows better accuracy.

Monitoring the Urban Heat Island Phenomenon in Pontianak City, West Kalimantan, using Google Earth Engine

The urban heat island (UHI) phenomenon can cause the land surface temperature (LST) in the city center to be higher than in the surrounding area. The population of Pontianak City increases yearly, resulting in land cover changes and LST distribution. This research aims to monitor the UHI phenomenon by identifying the temporal distribution of LST in Pontianak City. This research analyses the Landsat 8 thermal band, which is converted into LST value based on the normalised difference vegetation index (NDVI). The land cover classification was conducted using a random forest algorithm and acquisition of confusion matrix classification accuracy value. This research utilizes the Google Earth Engine (GEE) platform because of its ability to process satellite data continuously and has data with a sizeable spatiotemporal dimension. The results show a strong relationship between urban development and LST distribution. Based on the land cover classification in 2014 and 2019, there is a significant change in the composition of the surface, namely, built-up land has increased, while open space and vegetation land have decreased. Time series analysis also shows that the average LST of built-up land, open space, and vegetation are 28.10℃, 22.80℃, and 21.56℃, respectively. The results of monitoring the UHI phenomenon can help in better urban spatial planning, especially in increasing green space, because it is proven effective in reducing land surface temperature.

A Rotating Tidal Current Controller and Energy Router Siting and Capacitation Method Considering Spatio-Temporal Distribution

As the proportion of new energy access increases year by year, the resulting energy imbalance and voltage/trend distribution complexity of the distribution network system in the spatio-temporal dimension become more and more prominent. The joint introduction of electromagnetic rotary power flow controller (RPFC) and energy router (ER) can improve the high proportion of new active distribution network (ADN) consumption and power supply reliability from both spatial and temporal dimensions. To this end, the paper proposes an ADN expansion planning method considering RPFC and ER access. A two-layer planning model for RPFC and ER based on spatio-temporal characteristics is established, with the upper model being the siting and capacity-setting layer, which takes the investment and construction cost of RPFC and ER as the optimization objective, and the lower model being the optimal operation layer, which takes the lowest operating cost of the distribution network as the objective. The planning model is solved by a hybrid optimization algorithm with improved particle swarm and second-order cone planning. The proposed planning model and solving algorithm are validated with the IEEE33 node example, and the results show that the joint access of RPFC and ER can effectively improve the spatial-temporal distribution of voltage in the distribution network and has the lowest equivalent annual value investment and operation cost.

Spatio-temporal changes in China's mainland shorelines over 30 years using Landsat time series data (1990–2019)

Abstract. Continuous monitoring of shoreline dynamics is essential to understanding the drivers of shoreline changes and evolution. A long-term shoreline dataset can describe the dynamic changes in the spatio-temporal dimension and provide information on the influence of anthropogenic activities and natural factors on coastal areas. This study, conducted on the Google Earth Engine (GEE) platform, analyzed the spatio-temporal evolution characteristics of China's shorelines, including those of Hainan and Taiwan, from 1990 to 2019 using a long time series of Landsat Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and Operational Land Imager (OLI) images. First, we constructed a time series of the modified normalized difference water index (MNDWI) with high-quality reconstruction by the harmonic analysis of time series (HANTS) algorithm. Second, the Otsu algorithm was used to separate the land and water of coastal areas based on MNDWI value at high-tide levels. Finally, a 30-year shoreline dataset was generated and a shoreline change analysis was conducted to characterize length change, area change, and rate of change. We concluded the following: (1) China's shoreline has shown an increasing trend in the past 30 years, with varying growth patterns across regions; the total shoreline length increased from 24 905.55 km in 1990 to 25 391.34 km in 2019, with a total increase greater than 485.78 km, a rate of increase of 1.95 %, and an average annual increasing rate of 0.07 %. (2) The most visible expansion has taken place in Tianjin, Hangzhou Bay, and Zhuhai for the three economically developed regions of the Bohai Bay–Yellow River estuary zone (BHB-YREZ), the Yangtze River estuary–Hangzhou Bay zone (YRE-HZBZ), and the Pearl River estuary zone (PREZ), respectively. (3) The statistics of shoreline change rate for the three economically developed regions show that the average end point rates (EPRs) were 43.59, 39.10, and 13.42 m yr−1, and the average linear regression rates (LRRs) were 57.40, 43.85, and 10.11 m yr−1, respectively. This study presents an innovative and up-to-date dataset and comprehensive information on the status of China's shoreline from 1990 to 2019, contributing to related research and policy implementation, especially in support of sustainable development (https://doi.org/10.57760/sciencedb.16228, Yang et al., 2024).

Rhythms of recycling: reconfiguring apartment waste practices in London and Melbourne

ABSTRACT Taking the example of recycling in apartments, this paper argues that paying heed to the rhythms of urban waste can provide insights into how the waste burden can be more justly distributed. Housing in London and Melbourne, our two case study sites, is an essential locus for the domestic waste regime, and apartments are anomalies that disrupt it. Overall, recycling rates are lower in apartments, and comingled stream contamination rates where they exist are relatively higher. Rather than assuming this is a product of household behavior, we take the starting point that socio-material dynamics are at play that make apartments and apartment living incompatible with dominant waste regimes. We construct and apply a practice-based spatio-temporal rhythmic framework, in which recycling is analyzed as a social practice and apartments as the site of the social, leading to two main insights. First, the spatio-temporal dimensions of more than material and more than waste relationships add to the insights that practice theory can provide on the importance of mundane urban domestic processes of waste management by pointing to unevenness. Second, spatio-temporal rhythms analysis also assists in identifying possibilities for purposive attempts to intervene at various scales of waste governance.

A Novel Centralized Allocation Data Envelopment Analysis Model for Carbon Emission Allocation Under a Heterogeneous Abatement Cost: Application Within the Chinese Industrial Sector

This paper presents a mathematical approach to analyzing carbon abatement costs and the allocation of carbon emission allowances in China’s industrial sectors. We utilize input–output data from 30 Chinese provinces between 2009 and 2018 to estimate carbon abatement costs by applying the slack-based measure (SBM) efficiency model and its dual form. The SBM model captures inefficiencies and offers a rigorous framework for measuring abatement costs. Using these costs, we develop a centralized allocation data envelopment analysis (DEA) model, which maximizes sectoral benefits through optimal reallocation. This DEA model is formalized as a linear programming problem, with the aim of determining the efficient allocations of carbon allowances while maintaining the system’s economic productivity. Furthermore, we construct intertemporal, interregional, and spatiotemporal allocation DEA models to examine the dynamics of carbon emission allowance allocation over time, space, and combined spatiotemporal dimensions. These models offer insights into the efficiency of carbon markets under varying conditions. Our proposed new mathematical formulations reveal optimal allocation strategies that can balance emission reductions with industrial productivity. This study also provides novel mathematical frameworks for analyzing the carbon allowance distribution and contributions to both the theory and application of mathematical optimization in environmental policy design. Our findings reveal that China’s industrial carbon abatement costs exhibit significant interprovincial and regional differences. Developed provinces with higher levels of industrial development have higher carbon abatement costs, while provinces with less-developed industrial sectors have lower costs. Under the interregional allocation scenario of carbon emission allowances that consider abatement costs, developed provinces have smaller industrial carbon emission reductions, whereas less-developed provinces have larger reductions. In the intertemporal allocation scenario, provinces with larger industrial economies face greater emission reduction tasks. Under the combined interregional and intertemporal allocation scenario, industrial sectors in coastal developed provinces have lower carbon emission reductions, while those in inland less-developed provinces have higher reductions, mirroring the spatial allocation results of carbon emission allowances.

Modeling spatiotemporal dynamics of Amblyomma americanum questing activity in the central Great Plains.

Ticks represent important vectors of a number of bacterial and viral disease agents, owing to their hematophagous nature and their questing behavior (the process in which they seek new hosts). Questing activity is notably seasonal with spatiotemporal dynamics that needs to be understood in detail as part of mediating and mitigating tick-borne disease risk. Models of the geography of tick questing activity developed to date, however, have ignored the temporal dimensions of that behavior; more fundamentally, they have often not considered the sampling underlying available occurrence data. Here, we have addressed these shortfalls for Amblyomma americanum, the most commonly encountered tick in the central Great Plains, via (1) detailed, longitudinal sampling to characterize the spatiotemporal dimensions of tick questing activity; (2) randomization tests to establish in which environmental dimensions a species is manifesting selective use; and (3) modeling methods that include both presence data and absence data, taking fullest advantage of the information available in the data resource. The outcome was a detailed picture of geographic and temporal variation in suitability for the species through the two-year course of this study. Such models that take full advantage of available information will be crucial in understanding the risk of tick-borne disease into the future.

Beyond Permanent Residences: Measuring Place Attachment in Tempo-Local Housing Arrangements

Current societal shifts, including increased mobility, advances in information technologies, and diverse lifestyles, are driving postmodern housing arrangements that prioritize mobility and flexibility. The traditional view of housing as fixed and stable is evolving to encompass temporary forms, including multi-locality, which are often underestimated due to measurement challenges. Recognizing and integrating the potential of transient populations into urban societies is crucial to enhancing community cohesion and reducing social fragmentation. Therefore, the development of rootedness, emotional connection, and spatial identity should be analyzed in the context of temporary housing practices. Data collected through a representative survey in two major German urban regions in 2023 enable the validation of a four-dimensional place attachment scale, which includes the dimensions of place identity, place dependence, ambient bonding, and social bonding. Each dimension includes distinct elements of place-making constructions. The classification of four tempo-local housing types serves as the foundation for measuring how the temporal and spatial structure of housing arrangements influences emotional attachment to one’s place of residence. Interestingly, spatial mobility, when paired with temporal stability in each location, can cultivate deep connections, revealing an often-overlooked potential within this group. The results emphasize the significance of incorporating spatio-temporal dimensions of housing to foster inclusive, comprehensive, and diverse urban development.

Jane Jacobs's urban vitality focusing on three-facet criteria and its confluence with urban physical complexity

Urban vitality is crucial for sustainability, contributing to social cohesion, information exchange, and overall quality of life. Understanding and quantifying urban vitality is essential for effective planning and policymaking, yet it remains a complex task. Previous studies have primarily focused on single-dimensional measures of urban vitality, overlooking the multifaceted nature emphasized by Jane Jacobs, including spatiotemporal dimensions. This study revisits Jacobs's urban vitality, focusing on three key indicators of intensity, diversity, and consistency of urban vitality. Additionally, we explore the relationships between urban complexity and urban vitality. We employ variety, uniformity, and connectivity metrics to assess urban complexity, which examines the physical structures of buildings, streets, and blocks. Linear regression and Maximal Information-based Nonparametric Exploration statistics for non-linearity analysis are used to determine the associations between urban complexity and urban vitality. Our findings reveal nuanced details of the relationships. Notably, variety and connectivity variables have more significant influences on urban vitality than those related to uniformity. Furthermore, the analysis highlights the differential impacts of complexity variables on different types of vitality. These insights provide valuable guidance for planners and policymakers, facilitating the design of sustainable and vibrant cities by considering optimal complexity measures of built environment that enhance urban vitality.

Understanding the impact of COVID-19 on mobility behavior of public transport passengers: the case of Metropolitan Area of Porto

Public transport systems worldwide experienced significant declines in usage during the COVID-19 pandemic due to lockdowns and work-from-home mandates. While numerous studies have examined these phenomena, there is still a need for empirical evidence that not only documents what occurred but also provides actionable insights for future transport planning. This study aims to enhance understanding of public transport passengers’ mobility behaviors during different stages of the pandemic, using the Metropolitan Area of Porto, Portugal, as a case study. Automated Fare Collection data from 2020 were analyzed and compared with data from the pre-pandemic year of 2019. The analysis included temporal, spatial, spatio-temporal, and sociodemographic dimensions. Key patterns and trends identified include a rapid recovery of ridership post-restriction easing, homogenized daily travel patterns, varied impacts on different transport modes, and significant shifts in demographic travel behaviors. These findings highlight the resilience of public transport demand and suggest that adaptive scheduling, enhanced safety measures, targeted support for vulnerable groups, promotion of off-peak travel, investment in bus infrastructure, and encouragement of multi-modal transport are essential strategies. Implementing these strategies can help improve public transport planning and mitigate the adverse effects of future crises.

European eel (Anguilla anguilla) survival modeling based on a 22-year capture-mark-recapture survey of a Mediterranean subpopulation.

Since the 1980s, the European eel (Anguilla anguilla) has declined by over 90% in recruitment across its European and North African distribution area. This diadromous fish spawns at sea and migrates into continental waters, where it grows for three to more than 30 years, depending on habitat conditions and location. During their growth, different habitat use tactics can locally influence the life-history traits of eels, including their survival rates. Thus, the spatio-temporal dimension of this species is crucial for management. Based on a rare Mediterranean long-term survey of more than 20 years (2001-2022) in an artificial drainage canal connected to a vast brackish lagoon (the Vaccarès lagoon), we aimed to study the dynamics of one subpopulation's life-history traits. We used Bayesian multistate capture-mark-recapture (CMR) models to assess the temporal variability in survival and abundance at both seasonal and inter-annual scales, considering life-stage structure. High survival rates and low detection probabilities were found for the undifferentiated and female yellow stages. In contrast, female silver eels exhibited lower survival rates and higher capture probabilities. Estimating detection probabilities and survival rates enabled accurate assessment of relative abundance across different life stages and time periods. Our findings indicated a substantial decrease in the abundance of undifferentiated and female yellow eels in the early 2000s, whereas the abundance of female silver eels remained consistently low yet stable throughout the study period. Considering the life stage seemed essential to study the dynamics of the eel during its continental growing period. The present results will provide key elements to propose and implement suitable sustainable environmental management strategies for eel conservation.

Spatio-temporal effects and influence mechanism of digital technology on tourism efficiency in Chinese provinces

Digital technology serves as a new industrial driving force, playing a crucial role in promoting the efficiency of regional tourism. To explore the inherent logic between digital technology and tourism development, the study employed the DEA-BCC model and the entropy-weighted TOPSIS method to measure the tourism efficiency and digital technology development level of 31 provinces (municipalities and districts) in China from 2011 to 2022. The evolutionary characteristics of the relationship between digital technology and tourism efficiency were analyzed from the spatio-temporal dimension, and empirical tests were carried out using the spatial error model (SEM) and the spatial lag model (SLM) to explore the mechanism of the impact of digital technology on tourism efficiency. The results show that: during the study period, the overall trend of digital technology development and tourism efficiency is upward, but there is a certain degree of spatial mismatch between the two; digital technology is not only conducive to the improvement of tourism efficiency in the province, but also acts on neighboring provinces through spatial spillover effects, mechanism of action tests show that digital technology can positively moderate the effects of tourism economic growth and tourism industry structure on tourism efficiency; in western and northeast China, the positive effect of digital technology on tourism efficiency is more obvious. The conclusions provide a new perspective for understanding and analyzing the development of provincial tourism in China, as well as a reference for the rational use of digital technology to promote tourism development.

Trajectory Privacy-Protection Mechanism Based on Multidimensional Spatial–Temporal Prediction

The popularity of global GPS location services and location-enabled personal terminal applications has contributed to the rapid growth of location-based social networks. Users can access social networks at anytime and anywhere to obtain services in the relevant location. While accessing services is convenient, there is a potential risk of leaking users’ private information. In data processing, the discovery of issues and the generation of optimal solutions constitute a symmetrical process. Therefore, this paper proposes a symmetry–trajectory differential privacy-protection mechanism based on multi-dimensional prediction (TPPM-MP). Firstly, the temporal attention mechanism is designed to extract spatiotemporal features of trajectories from different spatiotemporal dimensions and perform trajectory-sensitive prediction. Secondly, class-prevalence-based weights are assigned to sensitive regions. Finally, the privacy budget is assigned based on the sensitive weights, and noise conforming to localized differential privacy is added. Validated on real datasets, the proposed method in this paper enhanced usability by 22% and 37% on the same dataset compared with other methods mentioned, while providing equivalent privacy protection.

Bidirectional Transmission Mapping of Architectural Styles of Tibetan Buddhist Temples in China from the 7th to the 18th Century

Architecture is the stone book of history, and the evolution of architectural styles showcases a non-verbal history constructed through images. As an important part of China’s historical and cultural heritage, the architectural forms and styles of Tibetan Buddhist temples were initially modeled on Tang dynasty temple architecture and gradually evolved into the most significant architectural types in regions such as Tibet and Qinghai in China. Religious architecture has also played a significant role in shaping regional cultural landscapes. Existing research on Tibetan Buddhist temples is primarily focused on qualitative studies of individual temple buildings. This research takes the spatiotemporal evolution of architectural styles of Tibetan Buddhist temples as an entry point and, for the first time, employs ArcGIS technology to visualize the spatial and geographical distribution of Tibetan Buddhist temples from the 7th to the 18th century, establishing a comprehensive academic vision that encompasses both historical stratification and cross-regional spatial correlations. By analyzing the cultural symbolic features embodied in the construction styles of Tibetan Buddhist temples and the visual characteristics reflected in their decorative arts, we propose two spatiotemporal dimensions for the formation and transmission of Tibetan Buddhist temple architectural styles: “Westward Transmission” and “Eastward Diffusion”. Firstly, from the 7th to the 9th centuries, the architectural style and construction techniques of Tang dynasty Buddhist temples were transmitted westward along the Tang–Tibet ancient road, integrating with local Tibetan elements to form the Tubo architectural style, which was further refined into the “Sino–Tibetan Combined Style” with strong visual characteristics around the 13th century. Subsequently, along with the spread of Tibetan Buddhism, this temple architectural style underwent an eastward diffusion from the 13th to the 18th century, reaching regions, such as Sichuan, Qinghai, Gansu, Inner Mongolia, Hebei, and Beijing, presenting a spatial gradient from west to east in the geographical dimension. On this basis, in this research, we construct a historical evolution mapping of Tibetan Buddhist temple architectural styles based on bidirectional transmission, attempting to elucidate that the intrinsic driving forces are religious and the cultural identity that guided the bidirectional transmission mechanism of these architectural styles under the historical context of the formation and dissemination of Tibetan Buddhism from the 7th to the 18th century.