Spatial Index Research Articles

Local Walsh-average regression for spatial autoregression single index varying coefficient models

. In real life, a large number of variables are spatially correlated in adjacent regions in various fields, such as finance, sociology, ecology, and geographic information systems. And the factors affecting these variables often have both linear and non linear relationships with them, how to accurately depict the relationship between them with a spatial regression model is a significant work. In order to further accurately describe the relationship between explanatory variables and response variables, we establish a spatial autoregressive single index varying coefficient model. The least squares method is the main method of model estimation but it is not robust enough when the parameter distribution is not normal. We present a new estimation method based on local Walsh average-regression. Under some general assumptions, we establish the asymptotic properties of the parametric and non parametric estimators. We further verify the accuracy and efficiency of the estimators under the condition of non normal error distribution through numerical experiments. In addition, the robustness of the proposed method is verified when the spatial weight matrix is interfered and when the bandwidth is changed within a certain range. Finally, we demonstrate the accuracy of forecasting by our proposed model compared with traditional spatial autoregressive model through real data analysis.

Dependence of Europe's most threatened mammals on movement to adapt to climate change.

Current rates of climate change and gloomy climate projections confront managers and conservation planners with the need to integrate climate change into already complex decision-making processes. Predicting and prioritizing climatically stable areas and the areas likely to facilitate adaptive species' range adjustments are important stages in maximizing conservation outcomes and rationalizing future land management. I determined, for the most threatened European terrestrial mammal species, the spatial adaptive trajectories (SATs) of highest expected persistence up to 2080. I devised simple spatial network indices for evaluation of species in those SATs: total persistence; proportion of SATs that offer in situ adaptation (i.e., stable refugia); number of SATs converging in a site; and relationship between SAT convergence and persistence and protected areas, the Natura 2000 and Emerald networks, and areas of low human disturbance. I compared the performance of high-persistence SATs with a scenario in which each species remained in the areas with the best climatic conditions in the baseline period. The 1000 most persistence SATs for each of the 39 species covered one fifth of Europe. The areas with the largest adaptive potential (i.e., high persistence, stability, and SAT convergence) did not always overlap for all the species. Predominantly, these regions were located in southwestern Europe, Central Europe, and Scandinavia, with some occurrences in Eastern Europe. For most species, persistence in the most climatically suitable areas during the baseline period was lower than within SATs, underscoring their reliance on adaptive movements. Importantly, conservation areas (particularly protected areas) covered only minor fractions of species persistence among SATs, and hubs of spatial climate adaptation (i.e., areas of high SAT convergence) were seriously underrepresented in most conservation areas. These results highlight the need to perform analyses on spatial species' dynamics under climate change.

Effect of inlet flow turbulence on hydro–acoustic coupling and flame–vortex interactions in a premixed dump combustor

The present work examines the effect of inlet flow turbulence on the flame–vortex modulations, hydro–acoustic coupling and recirculation zone dynamics at the onset of instability. The flow turbulence intensity varied using a custom-designed turbulence generator with different slot-width blockage plates placed upstream to the nominal flame-stabilization zone. The high-speed recordings of the CH*/OH* chemiluminescence and particle image velocimetry are used to deduce the relation between the flame–acoustic, flame–vortex and hydrodynamic–acoustic features during the unsteady combustion. The bifurcation analysis map revealed the effect of high inlet flow turbulence on postponing the onset of instability to higher inlet flow parameters. The initial observations showed potential changes in the acoustic behaviour and dynamical state of premixed turbulent combustion with an increase in inlet flow turbulence. The spatial Rayleigh index map illustrates a significant change in the acoustic driving region at high inlet flow turbulence based on the flame stabilization, heat release zone and flame–acoustic modulation in the shear layer and recirculation zone. The velocity spectral analysis and dynamic mode decomposition (DMD) spectrum suggested a correlation between the acoustic modulation and hydrodynamic instabilities, resulting in higher heat release rate oscillations. The high inlet flow turbulence modulates the vertical flapping motion of the flame front and flame roll-up in the recirculation region as evident by DMD spectrum and spatial modes. The flame–vortex dynamics during the dynamic transition events showed that the high inlet flow turbulence influenced the vortex shedding along the shear layer and recirculation zone dynamics. At low turbulence intensity, the vortex, in turn, supports the bulk flame movement through the induced velocity, which interacts with the free stream to create regions of low velocity. In contrast, the vortex in the shear layer and flame resides along the shear layer at higher turbulence. The paper concludes that at higher inlet flow turbulence, the recirculating flame reduces the hydro–acoustically modulated flow velocity fluctuations, which significantly affect the upstream flame propagation propensity.

The Association Between a Neighborhood Adverse Childhood Experiences Index and Body Mass Index Among New York City Youth.

Background: The role of neighborhood factors in the association between adverse childhood experiences (ACEs) and body mass index (BMI) has not been widely studied. A neighborhood ACEs index (NAI) captures neighborhood environment factors associated with ACE exposure. This study examined associations between BMI and an NAI among New York City (NYC) youth. An exploratory objective examined the NAI geographic distribution across NYC neighborhoods. Methods: Data for students attending NYC public general education schools in kindergarten-12th grade from 2006-2017 (n = 1,753,867) were linked to 25 geospatial datasets capturing neighborhood characteristics for every census tract in NYC. Multivariable hierarchical linear regression tested associations between BMI and the NAI; analyses also were conducted by young (<8 years), school age (8-12 years), and adolescent (>12 years) subgroups. In addition, NAI was mapped by census tract, and local Moran's I identified clusters of high and low NAI neighborhoods. Results: Higher BMI was associated with higher NAI across all sex and age groups, with largest magnitude of associations for girls (medium NAI vs. low NAI: unstandardized β = 0.112 (SE 0.008), standardized β [effect size]=0.097, p < 0.001; high NAI vs. low NAI: unstandardized β = 0.195 (SE 0.008), standardized β = 0.178, p < 0.001) and adolescents (medium NAI vs. low NAI: unstandardized β = 0.189 (SE 0.014), standardized β = 0.161, p < 0.001, high NAI vs. low NAI: unstandardized β = 0.364 (SE 0.015), standardized β = 0.334, p < 0.001 for adolescent girls; medium NAI vs. low NAI: unstandardized β = 0.122 (SE 0.014), standardized β = 0.095, p < 0.001, high NAI vs. low NAI: unstandardized β = 0.217 (SE 0.015), standardized β = 0.187, p < 0.001 for adolescent boys). Each borough of NYC included clusters of neighborhoods with higher and lower NAI exposure, although clusters varied in size and patterns of geographic dispersion across boroughs. Conclusions: A spatial index capturing neighborhood environment factors associated with ACE exposure is associated with higher BMI among NYC youth. Findings complement prior literature about relationships between neighborhood environment and obesity risk, existing research documenting ACE-obesity associations, and the potential for neighborhood factors to be a source of adversity. Collectively, evidence suggests that trauma-informed place-based obesity reduction efforts merit further exploration as potential means to interrupt ACE-obesity associations.

Effect of Curcuma longa Supplement on Cognitive Performance in Swiss Albino Mice

Dementia is an age-related mental disorder and a characteristic symptom of various neurodegenerative disorders. Hyperglycemia affects areas of the brain crucial for learning and memory processes, potentially leading to cognitive impairments in individuals with diabetes. Curcuma longa (C. longa) contains many pharmacological and chemically important compounds with many beneficial effects. This study aimed to evaluate the effect of Curcuma longa on cognitive function in Swiss albino mice. A total of sixteen (16) mice of both sexes weighing between 24 – 30 grams were used for the study. The mice were divided into four groups of four mice each (N=4). Group I served as control and received 10 ml/kg distilled water; groups II, III and IV were given 5%, 10% and 20 % of Curcuma longa for 14 days respectively. Y maze and novel object recognition task were used to assess spatial working, long-term and recognition memories respectively. We observed that the 5% C. longa (77.60 ± 10.15%) group showed significant (p &lt; 0.05) improvement in percentage alternation compared to the control group (64.40 ± 5.99%). We also found out that the 5% C. longa supplemented group showed a significant (p &lt; 0.05) increase in both long-term memory (-14.08 ± 3.26) and discriminative index (-0.26 ± 0.07) when compared to control group (-31.55 ± 2.65) and (-0.33 ± 0.07) respectively. Thus, C. longa supplement at 5% improves spatial working memory, long-term memory and discriminative index of Swiss albino mice.

Multi-Agent Reinforcement Learning for Stand Structure Collaborative Optimization of Pinus yunnanensis Secondary Forests

This study aims to investigate the potential and advantages of multi-agent reinforcement learning (MARL) in forest management, offering innovative insights and methodologies for achieving sustainable management of forest ecosystems. Focusing on the Pinus yunnanensis secondary forests in Southwest China, we formulated the objective function and constraints based on both spatial and non-spatial structural indices of the forest stand structure (FSS). The value of the objective function (VOF) served as an indicator for assessing FSS. Leveraging the random selection method (RSM) to select harvested trees, we propose the replanting foreground index (RFI) to enhance replanting optimization. The decision-making processes involved in selection harvest optimization and replanting were modeled as actions within MARL. Through iterative trial-and-error and collaborative strategies, MARL optimized agent actions and collaboration to address the collaborative optimization problem of FSS. We conducted optimization experiments for selection felling and replanting across four circular sample plots, comparing MARL with traditional combinatorial optimization (TCO) and single-agent reinforcement learning (SARL). The findings illustrate the superior practical efficacy of MARL in collaborative optimization of FSS. Specifically, replanting optimization based on RFI outperformed the classical maximum Delaunay generator area method (MDGAM). Across different plots (P1, P2, P3, and P4), MARL consistently improved the maximum VOFs by 54.87%, 88.86%, 41.34%, and 22.55%, respectively, surpassing those of the TCO (38.81%, 70.04%, 41.23%, and 18.73%) and SARL (54.38%, 70.04%, 41.23%, and 18.73%) schemes. The RFI demonstrated superior performance in replanting optimization experiments, emphasizing the importance of considering neighboring trees’ influence on growth space and replanting potential. Following selective logging and replanting adjustments, the FSS of each sample site exhibited varying degrees of improvement. MARL consistently achieved maximum VOFs across different sites, underscoring its superior performance in collaborative optimization of logging and replanting within FSS. This study presents a novel approach to optimizing FSS, contributing to the sustainable management of Pinus yunnanensis secondary forests in southwestern China.

Assessment of river recreation safety using hydrodynamic model and fuzzy logic: A spatial river recreational index approach

As demands for river recreational activities increases, assessing their safety has become essential to prevent accidents. The hydraulic conditions of the river critically influence the safety of in-water activities, such as sailing, paddling, and boating. Localized hazardous areas can emerge due to the spatial variability of hydraulic phenomena. This potential risk necessitates providing information about safe zones. Therefore, this study proposes a spatial river recreational index (SRRI) to assess the safety of river recreational activities over river spaces based on hydraulic factors. We reproduce the spatial distribution of the hydraulic parameters under various discharge conditions using a 3D hydrodynamic model and then estimate the SRRI by integrating all membership degrees and weights of these parameters using fuzzy synthetic evaluation (FSE). The application of the SRRI in the confluence of the Nakdong-Guemho River, South Korea, reveals that each hydraulic parameter contributes differently to safety levels, depending on discharge and morphological conditions. Specifically, the flow direction substantially decreases safety near the river confluence, whereas the water depth plays an important role in the meandering reach of the Nakdong River. Under high-flow conditions, velocity becomes a critical factor, especially for nonpowered activities (sailing and paddling/wading). The SRRI indicates that sailing is unsafe in the main flow zone and near the river confluence due to high sensitivity to discharge changes. In contrast, paddling/wading and leisure boating are less sensitive to discharge changes, allowing these activities to be partly allowable even under high-flow conditions, except in the deep-water zones of meandering reach. These results suggest that the SRRI can assist water recreational activity users in safely engaging in river recreational activities by providing spatial safety information based on various hydraulic conditions.

Spatiotemporal Analysis of Food Production-Demand Mismatch in China and Implications for Agricultural Structural Adjustment.

Based on the food equivalent unit (FEU), this article analyzed Chinese food consumption patterns, spatial mismatch, and production potential to explore agricultural reform strategies. Assessing production-demand mismatch involved the spatial mismatch model, drawing data from statistical yearbooks. Calculations of food production potential utilized the CASA model and the Thornthwaite Memorial model, with net primary productivity (NPP) derived from remote sensing data as indicators. The results showed that livestock product consumption is on the rise, and the spatial mismatch index for herbivorous livestock products was the largest, ranging from 22.81 to 98.12 in 2019. The mismatched degree distribution of rations and food-consuming livestock products showed a trend of increasing on both sides, with the Hu Huanyong line as the center line. Production factors played a predominant role in food production-to-demand mismatch. Climatic productivity and actual productivity decreased from the southeast to northwest in space in 2019, and human activities significantly impacted productivity. When grassland agriculture is pursued as the adjustment orientation, the production potential can reach up to 4540.76 × 107 kg·FEU. Moreover, a grassland agriculture plan was devised, prioritizing its development in the developed southern regions.

Spatiotemporal evolution of landscape stability in World Heritage Karst Sites: a case study of Shibing Karst and Libo-Huanjiang Karst

Landscape stability is a paramount concern within the field of landscape ecology. Indices of landscape patterns not only facilitate an effective analysis of land use transformations but also delve into the mechanisms of landscape disturbances across various spatial and temporal dimensions. Utilizing land use data spanning from 2014 to 2022 for the Shibing and Libo-Huanjiang South China Karst (SCK) World Heritage Sites (WHSs), this study delves into the landscape dynamics of these areas over the past 8 years. This investigation employs landscape pattern indices and a moving window technique to construct a landscape stability evaluation model, incorporating indices such as the Contagion Index, Patch Density, and Total Edge Contrast. Moreover, the study employs Moran's I, a spatial autocorrelation index, to scrutinize the shifts in geographical heterogeneity of landscape stability within the said period. The findings reveal: (1) Between 2014 and 2022, the landscape patterns of the Shibing and Libo-Huanjiang WHSs have undergone significant transformations, with Woodland emerging as the predominant landscape type and its area exhibiting an upward trend in recent years. (2) The level of fragmentation within the research area has decreased, landscape diversity has diminished, and the aggregation index has risen, according to the landscape pattern indices from 2014 to 2022, indicating that conservation measures have significantly influenced the evolution of landscape patterns. (3) Throughout most of the study period, landscape stability predominantly remained at a relatively stable level, albeit with noticeable fluctuations in years heavily impacted by human activities. (4) The areas of high-high agglomeration and the hotspots of the heritage sites were primarily concentrated within the core zones of these sites, suggesting focused areas of conservation and landscape integrity.

Unveiling Spatial Immune Cell Profile in Kidney Allograft Rejections Using 36-plex Immunofluorescence Imaging.

Kidney allograft rejections are orchestrated by a variety of immune cells. Because of the complex histopathologic features, accurate pathological diagnosis poses challenges even for expert pathologists. The objective of this study was to unveil novel spatial indices associated with transplant rejection by using a spatial bioinformatic approach using 36-plex immunofluorescence image data. The image obtained from 11 T cell-mediated rejection (TCMR) and 12 antibody-mediated rejection (AMR) samples were segmented into 753 737 single cells using DeepCell's Mesmer algorithm. These cells were categorized into 13 distinct cell types through unsupervised clustering based on their biomarker expression profiles. Cell neighborhood analysis allowed us to stratify kidney tissue into 8 distinct neighborhood components consisting of unique cell type enrichment profiles. In contrast to TCMR samples, AMR samples exhibited a higher frequency of neighborhood components that were characterized by an enrichment of CD31+ endothelial cells. Although the overall frequency of CD68+ macrophages in AMR samples was not significantly high, CD68+ macrophages within endothelial cell-rich lesions exhibited a significantly higher frequency in AMR samples than TCMR samples. Furthermore, the frequency of interactions between CD31+ cells and CD68+ cells was significantly increased in AMR samples, implying the pivotal role of macrophages in AMR pathogenesis. Importantly, patients demonstrating a high frequency of CD31:CD68 interactions experienced significantly poorer outcomes in terms of chronic AMR progression. Collectively, these data indicate the potential of spatial bioinformatic as a valuable tool for aiding in pathological diagnosis and for uncovering new insights into the mechanisms underlying transplant rejection.

Magnetic Particle Imaging-Guided Thermal Simulations for Magnetic Particle Hyperthermia.

Magnetic particle hyperthermia (MPH) enables the direct heating of solid tumors with alternating magnetic fields (AMFs). One challenge with MPH is the unknown particle distribution in tissue after injection. Magnetic particle imaging (MPI) can measure the nanoparticle content and distribution in tissue after delivery. The objective of this study was to develop a clinically translatable protocol that incorporates MPI data into finite element calculations for simulating tissue temperatures during MPH. To verify the protocol, we conducted MPH experiments in tumor-bearing mouse cadavers. Five 8-10-week-old female BALB/c mice bearing subcutaneous 4T1 tumors were anesthetized and received intratumor injections of Synomag®-S90 nanoparticles. Immediately following injection, the mice were euthanized and imaged, and the tumors were heated with an AMF. We used the Mimics Innovation Suite to create a 3D mesh of the tumor from micro-computerized tomography data and spatial index MPI to generate a scaled heating function for the heat transfer calculations. The processed imaging data were incorporated into a finite element solver, COMSOL Multiphysics®. The upper and lower bounds of the simulated tumor temperatures for all five cadavers demonstrated agreement with the experimental temperature measurements, thus verifying the protocol. These results demonstrate the utility of MPI to guide predictive thermal calculations for MPH treatment planning.

Response of Stand Spatial Structure to Nitrogen Addition in Deciduous Broad-Leaved Forest in Jigong Mountain

Significant influences on tree growth and forest functionality are attributed to nitrogen (N) addition. However, limited research has been conducted on the effects of N addition on forest spatial structure. In this study, we examined the effects of different N addition methods and concentrations on the stand spatial structure of a deciduous broad-leaved forest over the period 2012 to 2017. Five N addition treatments were implemented: CK (control group without N addition), CN25 (low N concentration added to the canopy), CN50 (high N concentration added to the canopy), UN25 (low N concentration added to the understory), and UN50 (high N concentration added to the understory). The results showed a moderate influence of N addition (CN25, CN50, UN25, UN50) on optimizing the stand spatial structure. CN25, CN50, and UN25 increased the mean values of the mingling degree (M) and neighborhood comparison (U), while decreasing the mean value of the uniform angle index (W), although these effects were not significant. Enhancements in the average value of the crowding degree (C) and comprehensive spatial structure index (CSSI) between 2012 and 2017 were found in all five treatments, demonstrating statistical significance. Assessing the distribution of the stand spatial structure index, CN25, CN50, and UN25 increased the proportion of M at an intensity (M = 0.75) and extreme intensity (M = 1), while decreasing the proportion at zero intensity (M = 0), weak intensity (M = 0.25), and moderate intensity (M = 0.5). A decrease in the proportion of trees was noted when U = 0 (excluding UN50), with no discernible pattern found in the frequency distribution of other values. CN50 and UN25 increased the proportion of W at a moderate level (W = 0.5), while CN25 and UN50 reduced it. No clear pattern was detected in the frequency distributions of other values. All five treatments increased the proportion of C at the maximum level (C = 1), while decreasing the proportions at levels of 0, 0.25, and 0.5 in 2017. Intriguingly, nitrogen addition treatments appeared to optimize the stand spatial structure to some extent and stimulated the growth of trees with larger diameters. Nevertheless, the short duration of the data collection period, spanning only five years, may have influenced the significance of the outcomes, underlining the requirement for extended studies. Conclusively, N deposition adjusted and enhanced the stand spatial structure to various degrees within the research region, providing valuable insights for further optimization of forest management.

An integrated model chain for diagnosing and predicting conflicts between production-living-ecological space in lake network regions: A case of the Dongting Lake region, China

The disorderly expansion of human production-living construction activities into aquatic ecological sensitive areas has triggered severe production-living-ecological space (PLES) conflicts. However, research on the risk-triggering mechanisms of PLES conflicts in lake network regions and multidimensional scenario simulation techniques is relatively lacking. To address these issues, this study constructs an integrated model chain by coupling the Markov-GMOP-PLUS model to achieve a quantified diagnosis and simulated regulation of spatial conflict strength index (SCSI) under sustainable development goals. Applying it to the Dongting Lake region reveals that: (1) The overall PLES exhibited a distribution pattern where “ecological space encompasses production space, with living space interspersed within”. The category conversion mainly reflected the encroachment of agricultural production space on the plain water ecological space, with a conversion area of up to 899.48 km2. (2) The distinctive “high in the north, low in the south” PLES conflict pattern was closely aligned with the land use structure and water system distribution, with conflict hotspots persistently clustering in the core lake network regions amid rapid urbanization. Among these, the growth rate of severe conflicts in the last ten years was 3.86 times that of the previous ten years. (3) The sustainable development scenario showcased the most effective conflict mitigation by curtailing production-living space expansion and ecological space encroachment. This scenario reversed the growth trend in conflict high-value zones and the reduction trend in low-value zones, with change rates of −3.30 % and 7.05 %, respectively, compared to 2020. This study provides a scientific basis for formulating sustainable land-use policies in lake network regions.

Inference for Dependent Data with Learned Clusters

Abstract This paper presents and analyzes an approach to cluster-based inference for dependent data. The primary setting considered here is with spatially indexed data in which the dependence structure of observed random variables is characterized by a known, observed dissimilarity measure over spatial indices. Observations are partitioned into clusters with the use of an unsupervised clustering algorithm applied to the dissimilarity measure. Once the partition into clusters is learned, a cluster-based inference procedure is applied to a statistical hypothesis testing procedure. The procedure proposed in the paper allows the number of clusters to depend on the data, which gives researchers a principled method for choosing an appropriate clustering level. The paper gives conditions under which the proposed procedure asymptotically attains correct size. A simulation study shows that the proposed procedure attains near nominal size in finite samples in a variety of statistical testing problems with dependent data.

Destigmatizing urban villages by examining their attractiveness: Quantification evidence from Shenzhen

While existing social and political research has qualitatively discussed the stigmatization of urban villages (UVs), there's a lack of quantitative support. This study innovatively attempts to provide quantitative evidence, employing a spatial perspective for the destigmatization of UVs. We integrated population trajectory, GDP data, employment and residential points of interest, and developed a spatial gravity model to derive the spatial attractiveness index. Concurrently, we selected environmental, spatial structure, and housing economic indicators to construct a regression analysis. Our findings revealed that urban villagers exhibit a preference for shorter daily commutes, underscoring the positive role of UVs in promoting job-housing balance and providing diverse services. The high-frequency mobility of villagers accelerates the categorization of UVs and enhances intra-area circulation. We further discussed the interaction between UV renewal and its destigmatization. To counter spatial stigmatization, enhancing the spatial quality and infrastructure of UVs, as well as promoting diversified land use can reduce the public's stereotypical impressions. Moreover, prioritizing affordable housing and equitable distribution of facilities, along with fostering synergies between urban capital and UVs, can facilitate the destigmatization process. Stigmatization in informal settlements is a widespread issue. Our quantitative approaches, as well as targeted renovation and policy recommendations, can serve as a blueprint for addressing similar challenges in informal settlements globally.

Contemporary Trends in the Spatial Extent of Common Riverine Fish Species in Australia’s Murray–Darling Basin

As one of the world’s most regulated river basins, the semi-arid Murray–Darling Basin (MDB) in south-eastern Australia is considered at high ecological risk, with substantial declines in native fish populations already identified and climate change threats looming. This places great importance on the collection and use of data to document population trends over large spatial extents, inform management decisions, and provide baselines from which change can be measured. We used two medium-term data sets (10 MDB basin-wide fish surveys from 2004–2022) covering the 23 catchments and 68 sub-catchments of the MDB to investigate trends in the distribution of common riverine species at the entire basin scale. Fifteen native species were analysed for changes in their contemporary range, and whilst short-term changes were identified, all species showed no significant continuous trend over the study period. We further analysed the native species extent relative to their historic records, with bony herring and golden perch occurring in 78% and 68% of their historic river kilometres, respectively, whereas southern pygmy perch, northern river blackfish, silver perch, mountain galaxias, and freshwater catfish were all estimated to occur in less than 10% of their historic extent. Six established non-native species were also analysed and were very consistent in extent over the years, suggesting that they are near the available limits of expansion of their invasion. We provide effect sizes for the spatial extent index which can be used as baselines for future studies, especially those aiming to monitor changes in the spatial extent and population status of native species, or changes in the spatial extent of new or existing non-native species.

Optimising port arrival statistics: Enhancing timeliness through Automatic Identification System (AIS) data

Today, there is a greater demand to produce more timely official statistics at a more granular level. National Statistical Institutes (NSIs) are more and more looking to novel data sources to meet this demand. This paper focuses on the use of one such source to compile more timely and detailed official statistics on port visits. The data source used is sourced from the Automatic Identification System (AIS) used by ships to transmit their position at sea. The primary purpose of AIS is maritime safety. While some experimental statistics have been compiled using this data, this paper evaluates the potential of AIS as a data source to compile official statistics with respect to port visits. The paper presents a novel method called “Stationary Marine Broadcast Method” (SMBM) to estimate the number of port visits using AIS data. The paper also describes how the H3 Index, a spatial index originally developed by Uber, is added to each transmission in the data source. While the paper concludes that the AIS based estimates won’t immediately replace the official statistics, it does recommend a pathway to using AIS-based estimates as the basis for official port statistics in the future.

Northeast Atlantic species distribution shifts over the last two decades.

Marine species are widely shifting their distributions in response to global changes and it is commonly expected they will move northward and to greater depths to reach cooler, less disturbed habitats. However, local manifestations of global changes, anthropogenic pressures, and species characteristics may lead to unanticipated and varied responses by individual species. In this regard, the Celtic-Biscay Shelf is a particularly interesting study system because it has historically been heavily fished and occurs at the interface between two distinct biogeographic provinces, its community thus comprised of species with diverse thermal preferenda. In the context of rapidly warming temperatures and intense fishery exploitation, we investigated the distribution shifts of 93 taxa (65 Actinopteri, 10 Elasmobranchii, 11 Cephalopoda, 5 Malacostraca, and 2 Bivalvia), which were sampled annually from 1997 to 2020 during a scientific bottom trawl survey. We used a set of 11 complementary spatial indices to quantify taxon distribution shifts over time. Then, we explored the relative effect of taxon abundance, fishing pressure, and climatic conditions on taxon's distribution shift when a significant shift was detected. We observed that 56% of the taxa significantly shifted. Not all taxa will necessarily shift northward and to deeper areas, as it is often expected. Two opposite patterns were identified: taxa either moving deeper and to the southeast, or moving closer to the surface and to the northwest. The main explanatory factors were climate change (short- and long-term temperatures) and taxon abundance. Fishing pressure was the third, but still significant, explanatory factor of taxa of greater commercial importance. Our research highlights that taxa are displaying complex distribution shifts in response to the combined anthropogenic disturbances and underscores the need to conduct regional studies to better understand these responses at the ecosystem scale to develop more suitable management plans and policies.

Random Data Distribution for Efficient Parallel Point Cloud Processing

Abstract. Current point cloud data management systems and formats are heavily specialized and targeted solely towards visualization purposes and fail to address the diverse needs of progressive point cloud workflows like for example semantic segmentation using machine learning. We therefore propose a distributed data infrastructure for dynamic point cloud data management that can support interactive real-time visualization at scale while simultaneously serving as a platform for analytical tasks. By introducing random data distribution, we show that simple query fragmentation and efficient and effective parallelism at scale are possible. At the same time, arbitrary queries in space and time can be efficiently run over the infrastructure including query semantics which returns only a random sample of the query results or preferred points based on an importance dimension calculated, for example, from a local point density information as commonly done in point cloud visualization. To cope with the unknown amount of user-specific attributes and to support even multiple ways of deciding the importance of a given point (ground point removal, coverage of space, random subset) the system is designed to support all of them transparently as multidimensional range queries backed by spatial indices.

Enhanced Multi-Beam Echo Sounder Simulation through Distance-Aided and Height-Aided Sound Ray Marching Algorithms

The study proposes two innovative algorithms in the field of multi-beam echo sounder (MBES) simulation: distance-aided sound ray marching (DASRM) and height-aided sound ray marching (HASRM). These algorithms aim to enhance the efficiency and accuracy of MBES simulations, particularly when dealing with long-distance propagation and real-time processing limitations. DASRM addresses issues related to simulation accuracy by efficiently utilizing the KD-tree for spatial indexing and intersection detection instead of the signed distance field (SDF). Building upon the further analysis of DASRM, HASRM is proposed, which improves the search strategy for ray intersections and utilizes a height field pyramid for sampling and retrieval, thereby reducing memory usage while enhancing indexing efficiency. The experimental results demonstrate that both algorithms significantly outperform traditional methods in terms of simulation time, with HASRM exhibiting particular advantages in parallel computing due to its data structure and improved strategies. Additionally, DASRM is well suited for applications requiring complex scene construction, while HASRM proves especially effective in simulating MBES with a focus on underwater terrain due to its effectiveness in handling large incident angles and long-distance propagation.