Spatial Distribution Data Research Articles

Examining Geographic Disparities in Fatal Automobile Accidents Across Kuwait Using GIS

Traffic accidents are considered a major public health issue worldwide, causing more than a million deaths annually and roughly five times that many injuries. Studying such accidents from a geographic perspective may help policy makers form better mitigation strategies for reducing accident rates. A Geographic Information System (GIS) is a powerful tool for performing this task due to its ability to visualize the spatial distribution of traffic accident data. Our research focused upon analyzing the geographic disparities in fatal car accidents. We chose to examine data between October 1, 2022 and March 31, 2023 for the state of Kuwait, which has a relatively high fatal accident rate per capita. We used various GIS-based tools in this research, such as average nearest neighborhood, point density, and optimized hot spot analysis. The results showed a significant disparity in accidents between urban and rural areas. Furthermore, an accident rate comparison between winter and summer seasons also revealed seasonal geographic disparities.

High precision aerodynamic heat prediction method based on data augmentation and transfer learning

Data-driven modeling methods have become one of the main technologies for predicting aerodynamic heat in hypersonic conditions. However, due to the limitations of wind tunnel experimental conditions, the spatial distribution of aerothermal wind tunnel experimental data is often sparse, and the sample size is relatively small. Furthermore, there is a lack of direct correlation in the aerodynamic heat distribution data among different shapes of vehicles, which poses challenges for constructing high-performance data-driven aerodynamic heat prediction models. To address these issues, this paper proposes a high-precision aerodynamic heat modeling and prediction method based on data augmentation and transfer learning. First, integrating the concept of data fusion, we propose to enhance the sparse aerothermal wind tunnel experimental data by using deep neural networks and introducing low-precision numerical computation results. Next, based on the close physical correlation between boundary layer outer edge information and wall surface aerodynamic heat, we construct the aerodynamic heat prediction model ED-ResNet using a double-series residual neural network. Finally, by fine-tuning the ED-ResNet model for transfer learning, high-precision predictions of aerothermal wind tunnel experimental results for different shaped vehicles are achieved under small sample conditions. Verification using hypersonic double-ellipsoid, blunt cone, and blunt bicone shows that after data augmentation, the prediction error of the aerodynamic heat prediction model is significantly reduced to 1/3 of that when data augmentation is not used. Moreover, through transfer learning, the model effectively leverages existing hypersonic double-ellipsoid aerothermal wind tunnel experimental data to achieve high-precision predictions of aerodynamic heat distribution for blunt cone and blunt double cone under different incoming flow conditions, with normalized root mean square error(NRMSE) maintained below 10%.

Empirical Bayesian Kriging, a Robust Method for Spatial Data Interpolation of a Large Groundwater Quality Dataset from the Western Netherlands

No single spatial interpolation method reigns supreme for modelling the precise spatial distribution of groundwater quality data. This study addresses this challenge by evaluating and comparing several commonly used geostatistical methods: Local Polynomial Interpolation (LPI), Ordinary Kriging (OK), Simple Kriging (SK), Universal Kriging (UK), and Empirical Bayesian Kriging (EBK). We applied these methods to a vast dataset of 3033 groundwater records encompassing a substantial area (11,100 km2) in the coastal lowlands of the western Netherlands. To our knowledge, no prior research has investigated these interpolation methods in this specific hydrogeological setting, exhibiting a range of groundwater qualities, from fresh to saline, often anoxic, with high natural concentrations of PO4 and NH4. The prediction performance of the interpolation methods was assessed through statistical indicators such as root means square error. The findings indicated that EBK outperforms the other geostatistical methods in forecasting groundwater quality for the five variables considered: Cl, SO4, Fe, PO4, and NH4. In contrast, SK performed worst for the species except for SO4. We recommend not using SK to interpolate groundwater quality species unless the data exhibit low spatial variation, high sample density, or evenly distributed sampling.

Georeferenced X (formerly twitter) data as a proxy of mobility behaviour: case study of Norway

Georeferenced messages on social media represent a powerful data source to gain a different perspective for estimating mobility behaviour, which is still mainly based on travel surveys. These data are openly available, yet few studies have explored their potential. This paper assesses the feasibility of large-scale Twitter data as a proxy of human mobility behaviour to complement traditional travel surveys, and for calibration and validation of transport models. Almost 12 million Tweets from more than 90,000 users were further analysed to detect the trip patterns at municipality level in Norway from 2012 to 2022. Results showed that the mobility patterns changed between 2014 and 2019 for the travel survey, as for 2019 most of the reported trips were short and concentrated in the densely populated areas of the country, where most respondents lived, triggering a lack of information for certain areas. In contrast, Twitter data presented a more stable data source along both years with similar population distribution and average trip length. Although Twitter data have limitations in relation to the socio-demographic information of the users, it could complement the travel survey given the broader spatial and temporal distribution of this large-scale data.

A Review of Methods to Assess Groundwater Vulnerability to Pollution

Groundwater resources that are increasingly being cherished for most socioeconomic development are exposed to varied pollutant sources. Studies have shown that they are vulnerable to various impacts such as climatic change, human impacts and also pollution from seawater intrusion in coastal areas. The susceptibility of a groundwater body to pollution indicates extent to which its quality is at risk of being compromised by contaminants. Assessments of this vulnerability are classified based on scale (site, local, regional) or objective (such as risk management or protection zoning) and also distinguish between source and resource vulnerability maps, as well as specific and intrinsic vulnerability maps. Groundwater vulnerability assessment methods differ based on several factors, including the availability and spatial distribution of quantitative and qualitative data, the objectives and scale of the mapping, the costs of model development, and the particular hydrogeological characteristics of the aquifer under investigation. The National Research Council has classified these methods into three primary categories: process-based methods, statistical methods, and overlay/index methods. Among these, the overlay/index method is widely employed for conducting large-scale assessments of aquifer sensitivity and groundwater vulnerability. It is especially advantageous in developing countries due to the easily accessible data required for its implementation.

Extraction of River Water Bodies Based on ICESat-2 Photon Classification

The accurate extraction of river water bodies is crucial for the utilization of water resources and understanding climate patterns. Compared with traditional methods of extracting rivers using remote sensing imagery, the launch of satellite-based photon-counting LiDAR (ICESat-2) provides a novel approach for river water body extraction. The use of ICESat-2 ATL03 photon data for inland river water body extraction is relatively underexplored and thus warrants investigation. To extract inland river water bodies accurately, this study proposes a method based on the spatial distribution of ATL03 photon data and the elevation variation characteristics of inland river water bodies. The proposed method first applies low-pass filtering to denoised photon data to mitigate the impact of high-frequency signals on data processing. Then, the elevation’s standard deviation of the low-pass-filtered data is calculated via a sliding window, and the photon data are classified on the basis of the standard deviation threshold obtained through Gaussian kernel density estimation. The results revealed that the average overall accuracy (OA) and Kappa coefficient (KC) for the extraction of inland river water bodies across the four study areas were 99.12% and 97.81%, respectively. Compared with the improved RANSAC algorithm and the combined RANSAC and DBSCAN algorithms, the average OA of the proposed method improved by 17.98% and 7.12%, respectively, and the average KC improved by 58.38% and 17.69%, respectively. This study provides a new method for extracting inland river water bodies.

A novel method to select time-varying multivariate time series models for the surveillance of infectious diseases

BackgroundDescribing the transmission dynamics of infectious diseases across different regions is crucial for effective disease surveillance. The multivariate time series (MTS) model has been widely adopted for constructing cross-regional infectious disease transmission networks due to its strengths in interpretability and predictive performance. Nevertheless, the assumption of constant parameters frequently disregards the dynamic shifts in disease transmission rates, thereby compromising the accuracy of early warnings. This study investigated the applicability of time-varying MTS models in multi-regional infectious disease monitoring and explored strategies for model selection.MethodsThis study focused on two prominent time-varying MTS models: the time-varying parameter-stochastic volatility-vector autoregression (TVP-SV-VAR) model and the time-varying VAR model using the generalized additive framework (tvvarGAM), and intended to explore and verify their applicable conditions for the surveillance of infectious diseases. For the first time, this study proposed the time delay coefficient and spatial sparsity indicators for model selection. These indicators quantify the temporal lags and spatial distribution of infectious disease data, respectively. Simulation study adopted from real-world infectious disease surveillance was carried out to compare model performances under various scenarios of spatio-temporal variation as well as random volatility. Meanwhile, we illustrated how the modelling process could help the surveillance of infectious diseases with an application to the influenza-like case in Sichuan Province, China.ResultsWhen the spatio-temporal variation was small (time delay coefficient: 0.1–0.2, spatial sparsity:0.1–0.3), the TVP-SV-VAR model was superior with smaller fitting residuals and standard errors of parameter estimation than those of the tvvarGAM model. In contrast, the tvvarGAM model was preferable when the spatio-temporal variation increased (time delay coefficient: 0.2–0.3, spatial sparsity: 0.6–0.9).ConclusionThis study emphasized the importance of considering spatio-temporal variations when selecting appropriate models for infectious disease surveillance. By incorporating our novel indicators—the time delay coefficient and spatial sparsity—into the model selection process, the study could enhance the accuracy and effectiveness of infectious disease monitoring efforts. This approach was not only valuable in the context of this study, but also has broader implications for improving time-varying MTS analyses in various applications.

Observations from the seafloor: ultra-low-frequency ambient ocean-bottom nodal seismology at the Amendment field

SUMMARY Large-scale ocean-bottom node (OBN) arrays of 1000s of multicomponent instruments deployed over 1000s of square kilometres have been used successfully for active-source seismic exploration activities including full waveform inversion (FWI) at exploration frequencies above about 2.0 Hz. The analysis of concurrently recorded lower-frequency ambient wavefield data, though, is only just beginning. A key long-term objective of such ambient wavefield analyses is to exploit the sensitivity of sub-2.0 Hz energy to build long-wavelength initial elastic models, thus facilitating FWI applications. However, doing so requires a more detailed understanding of ambient wavefield information recorded on the seafloor, the types, frequency structure and effective source distribution of recorded surface-wave modes, the near-seafloor elastic model structure, and the sensitivity of recorded wave modes to subsurface model structure. To this end, we present a wavefield analysis of low- and ultra-low-frequency ambient data (defined as <1.0 and <0.1 Hz, respectively) acquired on 2712 OBN stations in the Amendment Phase 1 survey covering 2750 km2 of the Gulf of Mexico. After applying ambient data conditioning prior to cross-correlation and seismic cross-coherence interferometry workflows, we demonstrate that the resulting virtual shot gather (VSG) volumes contain evidence for surface-wave and guided P-wave mode propagation between the 0.01 and 1.0 Hz that remains coherent to distances of at least 80 km. Evidence for surface-wave scattering from near-surface salt-body structure between 0.35 and 0.85 Hz is also present in a wide spatial distribution of VSG data. Finally, the interferometric VSG volumes clearly show waveform repetition at 20 s intervals in sub-0.3 Hz surface-wave arrivals, a periodicity consistent with the mean active-source shot interval. This suggests that the dominant contribution of surface-wave energy acquired in this VSG frequency band is likely predominantly related to air-gun excitation rather than by naturally occurring energy sources. Overall, these observations may have important consequences for the early stages of initial model building for elastic FWI analysis.

Urban Fire Risk Dynamics and Mitigation Strategies in Shanghai: Integrating Spatial Analysis and Game Theory

In recent decades, the increasing frequency of urban fires, driven by urban functional enhancements and climate change, has posed a growing threat to metropolitan sustainability. This study investigates the temporal and spatial characteristics of fire incidents in Shanghai from 2019 to 2023. Using satellite fire point data and official government records, kernel density analysis and wavelet analysis were employed to analyze the time series and spatial distribution of fire data. Subsequently, eleven primary factors influencing urban fire occurrence were identified, encompassing probability, regional characteristics, and hazard sources. A combined methodology of subjective and objective weights with game theory was used to generate a fire risk assessment at a 1 × 1 km2 grid scale. Furthermore, the spatial distribution characteristics of the assessments were analyzed. The results reveal that the downtown area exhibits the highest intensity of urban fires in terms of spatial domain, with a decreasing intensity towards the suburbs. Temporally, fire frequency demonstrates significant periodicity at an 18a time scale, while clear seasonal fluctuations and periodicity are observed at a 16-22a time scale, with higher occurrences in spring and winter. The study identifies typical aggregation patterns of urban fires, with high-risk centers in downtown Shanghai. Considering the impact of climate change and human activities, high-risk areas may gradually expand to adjacent urban suburbs, presenting a concerning future scenario. By examining the dual attributes of “combustibles and fireproof space” within urban greening systems, this research offers recommendations for the future strategies of disaster prevention and mitigation of green systems in Shanghai.

Interpolation and Synthesis of Sparse Samples in Exoplanet Atmospheric Modeling

This paper highlights methods from geostatistics that are relevant to the interpretation, intercomparison, and synthesis of atmospheric model data, with a specific application to exoplanet atmospheric modeling. Climate models are increasingly used to study theoretical and observational properties of exoplanets, which include a hierarchy of models ranging from fast and idealized models to those that are slower but more comprehensive. Exploring large parameter spaces with computationally expensive models can be accomplished with sparse sampling techniques, but analyzing such sparse samples can pose challenges for conventional interpolation functions. Ordinary kriging is a statistical method for describing the spatial distribution of a data set in terms of the variogram function, which can be used to interpolate sparse samples across any number of dimensions. Variograms themselves may also be useful diagnostic tools for describing the spatial distribution of model data in exoplanet atmospheric model intercomparison projects. Universal kriging is another method that can synthesize data calculated by models of different complexity, which can be used to combine sparse samples of data from slow models with larger samples of data from fast models. Ordinary and universal kriging can also provide a way to synthesize model predictions with sparse samples of exoplanet observations and may have other applications in exoplanet science.

47‐5: Analyzing and Enhancing Display Quality in FRC Algorithm

The FRC algorithm is a commonly employed technique in LCD displays, aimed at enhancing the data bit depth through pixel dithering. However, it can also give rise to certain display issues such as noise, scrolling stripes, and flickering [1]. To address these problems comprehensively, this paper conducts an analysis from both temporal and spatial dimensions. It reveals that screen noise is closely associated with the uniformity of pixel data distribution throughout the entire FRC cycle time, while rolling stripes are linked to the spatial distribution of pixel data in both row and column directions. Furthermore, flickering arises due to variations in the number of FRC pattern pixels actually displayed per frame. The validity of these theories has been verified on an FPGA platform.

Graph convolutional network for lithological classification and mapping using stream sediment geochemical data and geophysical data

Lithological mapping plays a vital role in acquiring a comprehensive understanding of subsurface rocks and geological structures. Traditional lithological mapping relies heavily on manual field surveys, with the accuracy of the mapping greatly constrained by the expertise of mappers, and thus this process is commonly slow and costly. In the present study, the graph convolutional network (GCN) method was proposed for efficient and automatic lithological mapping, using various geological survey data (e.g. geochemical and geophysical data). The GCN is not limited by the spatial distribution of sampling data, and hence it can be directly applied to the integrated resampling data that do not adhere to the regular spatial distribution patterns of Euclidean space. To evaluate the potential application of the GCN method in lithological classification and mapping, a case study was conducted in the Maqin region (China). Geochemical and geophysical data were utilized as the basis for lithological classification, as they can provide valuable information on geochemical compositions and physical properties of geological bodies, respectively. The study process was primarily divided into three parts. First, basic pre-processing was conducted on the geochemical data and the geophysical data, including centred log-ratio transformation and resampling. Subsequently, the resampled points were divided into training and testing sets in the proportion of 5.5:1. In the training set, 20% of the points are randomly selected as the validation set. Ultimately, using each resampled point as a node and the integrated geochemical and geophysical data as features, a GCN model and an undirected graph were constructed based on an adjacency distance of 1 km. The graph was employed by the constructed GCN model for lithological classification. The results reveal that the majority of lithological units can be accurately classified with an overall accuracy of 86%, indicating that the GCN model exhibits good applicability in lithological classification.

Multi-source monitoring data filtering assisted deformation analysis model updating of ultra-high rockfill dam

The deformation monitoring of ultra-high rockfill dams is now characterized by the applications of various monitoring instruments and the comparably dense measurement point deployment. The deformation analysis of rockfill dam can be greatly improved based on the multi-source monitoring data using the model updating technique. However, the model updating of rockfill dam faces challenges such as imbalanced spatial distribution of data, uneven data quality, and information redundancy, which leads to low performance and limits its application. Thus, this study proposes an innovative scheme for deformation analysis model updating, involving multi-source monitoring data filtering and model parameter identification. This scheme reduces the information redundancy, and ensures the finite element analysis (FEA) model of rockfill dam better reflects the actual deformation. The application in the 295 m Lianghekou rockfill dam demonstrates its notably enhancement of FEA accuracy, further improving the safety assessment.

The statistical grid as a unit of observation and analysis: Andalusia, Spanish region case study

Units of observation with reduced dimensions and regular geometry have been increasingly generated in a recent, albeit already steady, trend towards further territorial data disaggregation. In this line of research, the present study reports the results of using the spatial distribution of population data and built-up areas at a high level of territorial data disaggregation with reduced dimensions and with a homogeneous observation unit by applying a regular grid consisting of 250-m square cells. e main objective was to show the results and advantages of working at a high level of spatial data disaggregation. This approach provides a more comprehensive knowledge of the territory and allows for a more accurate analysis of spatial patterns in the different variables under study, thereby enhancing the quality of decision-making processes.

Spatial standardization of taxon occurrence data—a call to action

AbstractThe fossil record is spatiotemporally heterogeneous: taxon occurrence data have patchy spatial distributions, and this patchiness varies through time. Large-scale quantitative paleobiology studies that fail to account for heterogeneous sampling coverage will generate uninformative inferences at best and confidently draw wrong conclusions at worst. Explicitly spatial methods of standardization are necessary for analyses of large-scale fossil datasets, because nonspatial sample standardization, such as diversity rarefaction, is insufficient to reduce the signal of varying spatial coverage through time or between environments and clades. Spatial standardization should control both geographic area and dispersion (spread) of fossil localities. In addition to standardizing the spatial distribution of data, other factors may be standardized, including environmental heterogeneity or the number of publications or field collecting units that report taxon occurrences. Using a case study of published global Paleobiology Database occurrences, we demonstrate strong signals of sampling; without spatial standardization, these sampling signatures could be misattributed to biological processes. We discuss practical issues of implementing spatial standardization via subsampling and present the new R package divvy to improve the accessibility of spatial analysis. The software provides three spatial subsampling approaches, as well as related tools to quantify spatial coverage. After reviewing the theory, practice, and history of equalizing spatial coverage between data comparison groups, we outline priority areas to improve related data collection, analysis, and reporting practices in paleobiology.

Searching for pairs of actively accreting supermassive black holes in the Gaia FPR GravLens catalog

Observational data on dual supermassive black holes (SMBHs) are scarce, even though they should be common according to the current hierarchical formation model for galaxies. We present hereby a method to detect such dual systems, involving an already known quasar, as two luminosity peaks with angular separations smaller than one arcsecond (1″). It is based on the analysis of the spatial distribution of the Gaia data delivered in the Focused Product Release (FPR), that published for the first time detections made by Gaia at the epoch level. This approach relies on the selection of elongated spatial distributions of epoch measurements, due to two clumps of detections, checked against images coming from the PanSTARRS1 and 2MASS surveys. We have analyzed the 3.8 million of quasars flagged as single in the Gaia FPR extra-galactic table and we currently end up with only 8 dual AGN candidates, applying very conservative thresholds. G-band magnitude of each of the two luminosity peaks is provided for these candidates. The mean value of the angular separation between these two components is found to be equal to 0.3″. This demonstrates the ability of Gaia epoch data to detect previously unknown dual sources, which might be compact dual AGN.

An adaptive-size vector tile pyramid construction method considering spatial data distribution density characteristics

Vector tile pyramid is a technology that provides a compact representation of geospatial data. It enables efficient transmission and rendering by storing geographic information in a tile-based format on the server side. Traditional vector tile construction methods divide vector data into a series of tiles of fixed size, such as 256*256 pixels, which leads to numerous empty tiles, imbalanced data distribution between tiles, and diminished internet transmission efficiency. To address these limitations, we propose a novel three-step method for dynamically adjusting tile sizes based on spatial data distribution density during construction. Firstly, we generalize the raw data into a multi-resolution vector dataset encompassing varying levels of detail (LOD). Subsequently, we employ a quadtree-based approach for each level within the multi-resolution dataset to construct adaptive-size vector tiles that align with the specific spatial distribution density characteristics. Finally, we encode these different-size vector tiles using Geohash technology to facilitate spatial indexing and internet transmission. Experimental results validate the effectiveness of our approach, including a notable reduction in the number of generated tiles, a more balanced distribution of data volume among them, and a marked enhancement in internet transmission efficiency.

Analysis model of flood spatiotemporal change characteristics based on k-means clustering algorithm

<p>In order to analyze the spatiotemporal change characteristics of regional flood disaster, analyze the main change characteristics and hydrological response of precipitation distribution in Guangxi, and improve the comprehensive utilization efficiency of water resources, this paper introduces the k-means clustering algorithm to design an analysis model of spatiotemporal change characteristics of flood disaster. First of all, obtain the flood data in Guangxi and the observation data of the national meteorological station within 5 km, complete the collection of the basic data of mountain flood disasters. Based on the collected data, an analysis was conducted on the spatial and temporal distribution of flash floods in the Guangxi region. Secondly, the Kriging spatial interpolation method was used to analyze the spatial distribution of precipitation data in Guangxi. The Mann-Kendall trend test was then employed to examine the trend of precipitation-related statistical parameters over time. Additionally, wavelet theory was applied to analyze the time series of annual precipitation and precipitation with different durations in Guangxi. Subsequently, the k-means clustering algorithm was introduced to construct a model for analyzing the spatiotemporal characteristics of flood changes, determining the concentration and duration of precipitation in different years in the region. Finally, analyze the spatiotemporal change characteristics of flood events in different seasons under various indicators, and realize the analysis of flood spatiotemporal change characteristics. The research results indicate that the Frank Copula function fits the best correlation between annual precipitation and temperature, and can better characterize the correlation between the two. The Frank Copula function has the best fitting effect on the correlation between precipitation and temperature in autumn in Guilin, summer in Nanning, and summer and winter in Beihai. In Guangxi Zhuang Autonomous Region, the annual precipitation shows a gradually decreasing trend, especially at R and P stations. In summary, the Frank Copula function can effectively characterize the correlation and trend of precipitation and temperature in different seasons and regions of Guangxi.</p>

A high-resolution synthesis dataset for multistressor analyses along the US West Coast

Abstract. Global trends of ocean warming, deoxygenation, and acidification are not easily extrapolated to coastal environments. Local factors, including intricate hydrodynamics, high primary productivity, freshwater inputs, and pollution, can exacerbate or attenuate global trends and produce complex mosaics of physiologically stressful or favorable conditions for organisms. In the California Current System (CCS), coastal oceanographic monitoring programs document some of this complexity; however, data fragmentation and limited data availability constrain our understanding of when and where intersecting stressful temperatures, carbonate system conditions, and reduced oxygen availability manifest. Here, we undertake a large data synthesis to compile, format, and quality-control publicly available oceanographic data from the US West Coast to create an accessible database for coastal CCS climate risk mapping, available from the National Centers for Environmental Information (accession 0277984) at https://doi.org/10.25921/2vve-fh39 (Kennedy et al., 2023). With this synthesis, we combine publicly available observations and data contributed by the author team from synoptic oceanographic cruises, autonomous sensors, and shore samples with relevance to coastal ocean acidification and hypoxia (OAH) risk. This large-scale compilation includes 13.7 million observations from 66 sources and spans 1949 to 2020. Here, we discuss the quality and composition of the synthesized dataset, the spatial and temporal distribution of available data, and examples of potential analyses. This dataset will provide a valuable tool for scientists supporting policy- and management-relevant investigations including assessing regional and local climate risk, evaluating the efficacy and completeness of CCS monitoring efforts, and elucidating spatiotemporal scales of coastal oceanographic variability.

Spatial Analysis for Better Marketing Decisions with Special Focus on Consumer Behaviour Patterns

The study explores the pivotal role of Geographic Information Systems (GIS) in shaping marketing decisions, with a special emphasis on consumer behaviour patterns. Distinct studies are scrutinized, spanning diverse applications of GIS in marketing, from spatial consumer behaviour in small towns to the visualization of consumer sales promotions. The analysis encompasses the integration of GIS with methodologies such as spatial point pattern analysis, kernel density estimation, and RFID systems, offering insights into optimal retail site locations, consumer preferences in shopping centers, and the spatial distribution of data. The findings underscore GIS's capacity to enhance decision-making processes, offering a valuable resource for marketers seeking to leverage spatial intelligence for strategic advantage.