Spatiotemporal Density Research Articles

Google Earth Engine based spatio-temporal analysis of air pollutants before and during the first wave COVID-19 outbreak over Turkey via remote sensing

Air pollution is one of the vital problems for the sustainability of cities and public health. The lockdown caused by the COVID-19 outbreak has become a natural laboratory, enabling to investigate the impact of human/industrial activities on the air pollution. In this study, we investigated the spatio-temporal density of TROPOMI-based nitrogen dioxide (NO2) and sulfur dioxide (SO2) products, and MODIS-derived Aerosol Optical Depth (AOD) from January 2019 to September 2020 (also covering the first wave of the COVID-19) over Turkey using Google Earth Engine (GEE). The results showed a significant decrease in NO2 and AOD, while SO2 unchanged and had slightly higher concentrations in some regions during the lockdown compared to 2019. The relationship between air pollutants and meteorological parameters during the lockdown showed that air temperature and pressure were highly correlated with air pollutants, unlike precipitation and wind speed. Moreover, Purchasing Managers' Index (PMI) data, indicator of economic/industrial activities, also provided poor correlation with air pollutants. TROPOMI-based NO2 and SO2 were compared with station-based pollutants for three sites (suburban, urban, and urban-traffic classes) in Istanbul, revealing 0.83, 0.70 and 0.65 correlation coefficients for NO2, respectively, while SO2 showed no significant correlation. Besides, AOD data were validated using two AERONET sites providing 0.86 and 0.82 correlation coefficients. Overall, the satellite-based data provided significant outcomes for the spatio-temporal evaluation of air quality, especially during the first wave of the COVID-19 lockdown.

Present Tectonic Dynamics of the Geological Structural Setting of the Eastern Part of the Adriatic Region Obtained from Geodetic and Geological Data

The Adriatic microplate has always attracted scientific attention, and various studies on the geodynamics of this area have been performed over the years. With the development of global navigation satellite system (GNSS) technology in the last 30 years, most significant research in this field has used it as the primary source of data on geodynamic movements. However, apart from a few global positioning system (GPS) campaigns conducted in the 1990s, the measurements had a low spatiotemporal density. Therefore, the eastern side of the Adria region or the territory of the Republic of Croatia was usually omitted from the results presented in the various published papers. A study of this literature concluded that the territory of Croatia represents a kind of scientific gap and that denser measurement data from GPS/GNSS stations could be used to supplement the geodynamic picture of the area in question. Thus, GPS/GNSS measurements from 83 stations (geodynamic, reference, and POS’ GPS/GNSS) all over Croatia and neighboring countries for a period of almost 20 years (1994–2013) were collected and processed with Bernese software to obtain a unique database of relative velocities. From the geological perspective, the most important and latest insights on the recent geological structural setting, tectonic movements, most active faults, and relationships and movements of structures were taken into account. It was important to compare the geodetic and geological data, observe the present tectonic dynamics of the geological structural setting, and determine the causes of the obtained directions of movement. The research presented in this paper, based on a combination of geodetic and geological data, was conducted to broaden the current knowledge of the present tectonic dynamics of the geological structural setting of the eastern part of the Adriatic region.

Dynamics of a bifunctional motor under crowded conditions

A simple and inexpensive fabrication of a polymer based bifunctional motor, containing catalytic palladium nanoparticles (Pd NPs) and luminescent zinc quinolate inorganic complex (Zn-QC), is reported. The motor displayed buoyancy driven self-propulsion in aqueous hydrogen peroxide (H2O2) solution, and could be easily tracked within liquid media using its luminescence under UV light. The propulsion speed and the luminescence of the motor were found to be correlated with the solution viscosity and local concentration of macromolecules within the medium. The bifunctional motor was further used to monitor bacterial concentration in solutions and was found to be an excellent probe for simultaneous characterization of spatio-temporal density and viscosity profiles of inhomogeneous solutions and particle assemblies. The motor fabricated is scalable and offers convenient platform for using self-powered catalytic motors in gleaning comprehensive insights into the rheology and density profiles of complex crowded systems, like the cytosol.

Spatiotemporal contact density explains the disparity of COVID-19 spread in urban neighborhoods

The rapid early spread of COVID-19 in the US was experienced very differently by different socioeconomic groups and business industries. In this study, we study aggregate mobility patterns of New York City and Chicago to identify the relationship between the amount of interpersonal contact between people in urban neighborhoods and the disparity in the growth of positive cases among these groups. We introduce an aggregate spatiotemporal contact density index (CDI) to measure the strength of this interpersonal contact using mobility data collected from mobile phones, and combine it with social distancing metrics to show its effect on positive case growth. With the help of structural equations modeling, we find that the effect of CDI on case growth was consistently positive and that it remained consistently higher in lower-income neighborhoods, suggesting a causal path of income on case growth via CDI. Using the CDI, schools and restaurants are identified as high contact density industries, and the estimation suggests that implementing specific mobility restrictions on these point-of-interest categories is most effective. This analysis can be useful in providing insights for government officials targeting specific population groups and businesses to reduce infection spread as reopening efforts continue to expand across the nation.

Evaluating seismic beamforming capabilities of distributed acoustic sensing arrays

Abstract. The versatility and cost efficiency of fibre-optic distributed acoustic sensing (DAS) technologies facilitate geophysical monitoring in environments that were previously inaccessible for instrumentation. Moreover, the spatio-temporal data density permitted by DAS naturally appeals to seismic array processing techniques, such as beamforming for source location. However, the measurement principle of DAS is inherently different from that of conventional seismometers, providing measurements of ground strain rather than ground motion, and so the suitability of traditional seismological methods requires in-depth evaluation. In this study, we evaluate the performance of a DAS array in the task of seismic beamforming, in comparison with a co-located nodal seismometer array. We find that, even though the nodal array achieves excellent performance in localising a regional ML 4.3 earthquake, the DAS array exhibits poor waveform coherence and consequently produces inadequate beamforming results that are dominated by the signatures of shallow scattered waves. We demonstrate that this behaviour is likely inherent to the DAS measurement principle, and so new strategies need to be adopted to tailor array processing techniques to this emerging measurement technology. One strategy demonstrated here is to convert the DAS strain rates to particle velocities by spatial integration using the nodal seismometer recordings as a reference, which dramatically improves waveform coherence and beamforming performance and warrants new types of “hybrid” array design that combine dense DAS arrays with sparse seismometer arrays.

State estimation in turbulent channel flow from limited observations

Abstract

Adaptive Feature Fusion and Spatio-Temporal Background Modeling in KDE Framework for Object Detection and Shadow Removal

Moving cast shadow identification and extraction are the commonly encountered problems in visual surveillance applications. In the video object detection problem, a shadow is a category of the dynamic entity which needs to be removed for accurate detection of moving objects. In this paper, we have proposed a new scheme to detect the moving object in video frames while removing the shadow. The proposed scheme is based on the notion of background modeling and model learning. The background is modeled by the proposed Spatio-Temporal Kernel Density Estimation (ST-KDE) based model, but the model learning takes place in online mode in the fused feature space. In the fusion process, the Local Binary Pattern (LBP) features of the ST-KDE model of a frame are determined and are fused probabilistically with the Gabor features of the corresponding original frame. To achieve accurate background modeling, weights in the probabilistic fusion are adaptively determined from the scene dynamics. Background model learning is pixel-based where model histograms learn the background with every temporal frame. Learning and classification of the fused feature frames happen simultaneously with frame by frame. The classified frame thus obtained contains the object with some residual shadow which is eliminated by entropy map and thresholding. The main novelties of our paper are: (i) ST-KDE based background modeling, (ii) Determination of the weights of the fusion process in an adaptive framework and, (iii) online background model learning in feature space. The proposed modeling approach has been tested successfully with different video sequences considered from CDnet, Lasiesta, ATON-CVRR, SRD, and ISTD databases. The performance metrics of the proposed scheme have been compared with many existing methods and it has been found that the proposed method exhibits improved performance.

Cardiolipin Selectively Binds to the Interface of VsSemiSWEET and Regulates Its Dimerization.

Lipid-regulated oligomerization of membrane proteins plays a critical role in many cell-transduction pathways. However, molecular details of such processes are often hard to define experimentally. Here we reveal the key role of interfacial cardiolipin in regulating the functional dimerization of VsSemiSWEET (one of the smallest transporters) using molecular dynamics simulations. Four binding sites for cardiolipins are identified by calculating the spatiotemporal density distribution of cardiolipins and the free energy surface. Two types of dimerization modes (i.e., arm-to-body and body-to-body) are observed in the assembly process of VsSemiSWEET protomers. Binding of enough cardiolipin molecules at the dimer interface on the cytoplasmic side is found to be crucial in adjusting the monomer-dimer equilibrium and regulating the formation of functional dimers with proper conformation. Our results provide useful information on the relationship between lipid binding and functional dimerization of VsSemiSWEET and are helpful to understand the molecular mechanism of biological function of sugar transporters.

On Some Statistical Properties of the Spatio-Temporal Product Density

We present an extension of the non-parametric edge-corrected Ohser-type kernel estimator for the spatio-temporal product density function. We derive the mean and variance of the estimator and give a closed-form approximation for a spatio-temporal Poisson point process. Asymptotic properties of this second-order characteristic are derived, using an approach based on martingale theory. Taking advantage of the convergence to normality, confidence surfaces under the homogeneous Poisson process are built. A simulation study is presented to compare our approximation for the variance with Monte Carlo estimated values. Finally, we apply the resulting estimator and its properties to analyse the spatio-temporal distribution of the invasive meningococcal disease in the Rhineland Regional Council in Germany.

Efficient Mining of Hotspot Regional Patterns with Multi-Semantic Trajectories

Due to the explosive growth in trajectory data with multi-semantic labels, mining hotspots based on extracted sequential patterns from multi-semantic trajectories are an emerging need in various applications of crowd behavior analysis, such as location-based advertising, business location, and urban planning. However, most existing regional pattern mining methods only focus on temporal continuity, spatial compactness, and a single semantics. Outdated or nonhot regions may be discovered if the timeliness, multi-semantic features, and user activity of trajectory patterns are not taken into consideration. To address this issue, this paper studies a hotspot regional multi-semantic trajectory pattern mining problem, with the aim of identifying the hotspot pattern of a social crowd in the trajectory data. Specifically, 1) we propose a new spatiotemporal density scheme to quantify the frequency and heat of a specific pattern in space, and to discover all regions where movement behaviors occur densely and have high heat; 2) we develop a trajectory mining algorithm, effective hotspot regional multi-semantic trajectory pattern mining (tHRM), which effectively reveals the hot movement patterns in a region that are not necessarily dominant in intensity. Experiments using real trajectory data sets show that tHRM is able to discover regional hotspot regional patterns, which are difficult to discover with state-of-the-art regional pattern mining methods. Furthermore, tHRM runs 3-4 times faster, and the mining quality is an order of magnitude higher than that of the rival methods.

Uncovering temporal changes in Europe\u2019s population density patterns using a data fusion approach

The knowledge of the spatial and temporal distribution of human population is vital for the study of cities, disaster risk management or planning of infrastructure. However, information on the distribution of population is often based on place-of-residence statistics from official sources, thus ignoring the changing population densities resulting from human mobility. Existing assessments of spatio-temporal population are limited in their detail and geographical coverage, and the promising mobile-phone records are hindered by issues concerning availability and consistency. Here, we present a multi-layered dasymetric approach that combines official statistics with geospatial data from emerging sources to produce and validate a European Union-wide dataset of population grids taking into account intraday and monthly population variations at 1 km2 resolution. The results reproduce and systematically quantify known insights concerning the spatio-temporal population density structure of large European cities, whose daytime population we estimate to be, on average, 1.9 times higher than night time in city centers.

Detection of Indoor High-Density Crowds via Wi-Fi Tracking Data.

Accurate detection of locations of indoor high-density crowds is crucial for early warning and emergency rescue during indoor safety accidents. The spatial structure of indoor environments is more complicated than outdoor environments. The locations of indoor high-density crowds are more likely to be the sites of security accidents. Existing detection methods for high-density crowd locations mostly focus on outdoor environments, and relatively few detection methods exist for indoor environments. This study proposes a novel detection framework for high-density indoor crowd locations termed IndoorSRC (Simplification–Reconstruction–Cluster). In this paper, a novel indoor spatiotemporal clustering algorithm called Indoor-STAGNES is proposed to detect the indoor trajectory stay points to simplify indoor movement trajectory. Then, we propose use of a Kalman filter algorithm to reconstruct the indoor trajectory and properly align and resample the data. Finally, an indoor spatiotemporal density clustering algorithm called Indoor-STOPTICS is proposed to detect the locations of high-density crowds in the indoor environment from the reconstructed trajectory. Extensive experiments were conducted using indoor Wi-Fi positioning datasets collected from a shopping mall. The results show that the IndoorSRC framework evidently outperforms the existing baseline method in terms of detection performance.

Association of wild bird densities around poultry farms with the risk of highly pathogenic avian influenza virus subtype H5N8 outbreaks in the Netherlands, 2016.

Highly pathogenic (HP) avian influenza viruses (AIV) can spread globally through migratory birds and cause massive outbreaks in commercial poultry. AIV outbreaks have been associated with proximity to waterbodies, presence of waterfowl or wild bird cases near poultry farms. In this study, we compared densities of selected HPAI high‐risk wild bird species around 7 locations (H farms) infected with HPAIV H5N8 in the Netherlands in 2016–2017 to densities around 21 non‐infected reference farms. Nine reference farms were in low‐lying water‐rich areas (R‐W) and 12 in higher non‐water‐rich areas (R‐NW). Average monthly numbers/km2 of Eurasian wigeons, tufted ducks, Anatidae (ducks, geese and swans) and Laridae (gulls) were calculated between September and April in rings of 0–1, 1–3, 3–6 and 6–10 km around the farms. Linear mixed model analyses showed generally higher bird densities for H and R‐W compared to R‐NW farms between October and March. This was most striking for Eurasian wigeons, with in peak month December 105 (95% CI:17–642) and 40 (7–214) times higher densities around H and R‐W farms, respectively, compared to R‐NW farms. Increased densities around H farms for Eurasian wigeons and Anatidae were more pronounced for distances up to 10 km compared to 0–1 km that mostly consists of the farm yard, which is an unattractive habitat for waterfowl. This distance effect was not observed in gulls, nor in tufted ducks that live on large open waterbodies which are unlikely to be within 0–1 km of farms. This study provides insights into spatio‐temporal density dynamics of HPAI high‐risk birds around farms and their associations with poultry outbreaks. The outcomes indicate that knowledge of environmental and ecological drivers for wild bird presence and abundance may facilitate identification of priority areas for surveillance and biosecurity measures and decisions on establishments of poultry farms to reduce risk of HPAI outbreaks.

Analyzing traffic violation behavior at urban intersections: A spatio-temporal kernel density estimation approach using automated enforcement system data

The Automated Enforcement System (AES) has become the most important traffic enforcement system in China. In this study, a spatio-temporal kernel density estimation (STKDE) model, integrating spatio-temporal statistics and three-dimensional visualization techniques, was applied to reveal the spatial and temporal patterns of traffic violation behavior at urban intersections. The multivariate Gaussian kernel function was selected for space and time density estimation, as it has been shown to be a good arbitrary probability density function for continuous multivariate data. Because the STKDE model builds a space-time cube that adopts different colors of voxels to visualize the density of traffic violations, an optimal bandwidth selector that combines unconstrained pilot bandwidth matrices with a data-driven method was selected for achieving the best visualization result. The raw AES traffic violation data over 200 weekdays from 69 intersections in the city of Wujiang were empirically analyzed. The results show that the STKDE space-time cube made it easier to detect the spatio-temporal patterns of traffic violations than did the traditional hotspots map. An interesting finding was that traffic sign violations and traffic marking violations were primarily concentrated not in regular peak hours, but during the time period of 14:00-16:00, which indicates that these intersections were the most congested during this period. Primarily, the STKDE model identified seven patterns of spatio-temporal traffic violation hotspots and coldspots. These results are important because their prediction of temporal trends of traffic violations may help contribute toward the understanding and improvement of intersection safety problems.

Identifying spatio-temporal patterns of bus bunching in urban networks

The objective of this paper is to identify hot spots of bus bunching events at the network level, both in time and space, using Automatic Vehicle Location (AVL) data from the Athens (Greece) Public Transportation System. A two-step spatio-temporal clustering analysis is employed for identifying localized hot spots in space and time and for refining detected hot spots, based on the nature of bus bunching events. First, the Spatio-Temporal Density Based Scanning Algorithm with Noise (ST-DBSCAN) is applied to distinguish bunching patterns at the network level and subsequently a k++means algorithm is employed to distinguish different types of bunching clusters. Results offer insights on specific time periods and route segments, where bus bunching events are more likely to occur and, also, on how bus bunching clusters change over time. Further, headway deviation analysis reveals the differences in the characteristics of the various bunching event types per line, showing that routes running on shared corridors experience more issues while underlying causes may vary per line. Collectively, results can help guide practice toward more flexible solutions and control strategies. Indeed, depending on the type of spatio-temporal patterns detected, appropriate improvements in service planning and real-time control strategies may be identified in order to mitigate their negative effects and improve quality of service. In light of emerging electric public transport systems, the proposed framework can be also used to determine preventive strategies and improve reliability in affected stops prior to the deployment of charging infrastructure.

Effects of human-induced eutrophication on macroinvertebrate spatiotemporal dynamics in Lake Dianchi, a large shallow plateau lake in China.

The Yungui Plateau lakes, which are characterized by a highly endemic biodiversity, have been suffering severely from anthropogenic intervention in the recent decades. Studies on the response of these biodiversity to human-mediated effects are still limited. Here, we selected the typical Lake Dianchi to investigate the correlation between macroinvertebrate spatiotemporal dynamics and human-induced eutrophication across a 2-year span (2009-2010). A total of 26 taxa were recorded, and the assemblage pattern of the macroinvertebrate community was mainly controlled by the spatiotemporal (region, season, and year) density fluctuations of some pollution-tolerant species (Limnodrilus hoffmeisteri, Tubifex tubifex, Branchiura sowerbyi, and Chironomus plumosus). Taxon richness, total density, biomass, and the abundance of Oligochaeta and Chironomidae decreased from the north to the south of the lake but were much higher in 2009 than in 2010. Moreover, the high densities of total assemblages and oligochaete occurred during spring and/or autumn, whereas that of chironomids was only high during summer. The contributions of important factors varied in different seasons, but the community variations were mainly shaped by eutrophication-related factors (e.g., Chla, N, and P). Variance partitioning analyses showed that aquatic factors were able to explain more community variations than sediment (6.9-36.6 vs. 5.3-14.7%) across seasons, but their interactive effects were negligible. The results of this study will be beneficial for restoring and managing hypereutrophic lakes in the Yungui Plateau and imply the necessity of long-term monitoring in bioassessment projects involving intensively disturbed lakes.

Integrating low-cost air quality sensor networks with fixed and satellite monitoring systems to study ground-level PM2.5

Airborne particulate matter (PM) mass concentrations measured by conventional monitoring stations are reliable data sources for air quality communication, pollution mapping, and exposure estimation. A high spatiotemporal density of monitoring stations can provide a better understanding of PM transport on a regional and global scale and help to reduce exposure misclassification leading to a better assessment of the health impacts associated with PM exposure. However, due to the cost and operational complexities, only a limited number of such PM monitors can be deployed. Apart from conventional measurements, PM mass concentration can be estimated from aerosol optical depth (AOD) data observations by using empirical, semi-empirical or modeling methods, but these datasets are usually compromised by weather conditions and lack of knowledge of aerosol properties. In addition to the above methods, a network of low-cost PM sensors is also a promising approach to increase the measurement density. In this study, we propose an integration of information from multiple measurement approaches. We demonstrate this approach by synergizing the data from 75 monitoring stations, 2,363 AirBox low-cost sensors (the amount of data entries is ∼10 million), and the Terra remote sensing satellite to estimate surface concentrations of PM for Taiwan Main Island during July 14 2018 to Oct 31 in 2018. A machine learning method selects the useful data from the low-cost sensor datasets, and the ordinary Kriging method is used to create a visual daily PM distribution map. The integration of datasets can enhance the overall data quantity and quality, leading to more accurate pollution maps with greater detail. The maps created from these three data sources demonstrate an approximate 30-fold synergistic improvement in the spatial resolution of PM mapping. The Root Mean Square Error (RMSE) of the predicted maps was analyzed through leave-one-out cross-validation, ten-fold cross-validation, and standard data validation. It shows that including low-cost PM sensor data brings in greater detail and largely enhances the spatial distribution while maintaining the pollution mapping characteristics. The approach described here will greatly assist the validation of PM transport models and enhance the accuracy of exposure estimations in future studies.

Імовірнісні моделі менеджменту водними ресурсами на урбанізованих територіях

Gradual global climate change poses new challenges to the mathematical sciences, which are related to forecasting of meteorological conditions, preparing the infrastructure for possible rains, storms, droughts, and other climatic disasters. One of the most common approaches is synthetic regression-probability models, which use the spatio-temporal probability density functions of precipitation level. This approach is applied to the statistics of precipitation in the Kharkiv region, which shows the tendency to a gradual increase in air temperature, high indices of basic water stress, indices of drought and riverside flood threats. Open data on temperature distributions and precipitation were processed using various probability statistics. It is shown that the lognormal distribution most accurately describes the measurement data and allows making more accurate prognoses. Estimates of drought and flood probabilities in Kharkiv region under different scenarios of climate change dynamics have been carried out. The results of the study can be used for management of water resources on urban territories at global climate warming.

Validating Ocean Wind Wave Global Hindcast with Visual Observations from VOS

Joint analysis of wind wave characteristics derived from Voluntary Observing Ships data (VOS) and the third-generation spectral wave model WAVEWATCH III v.5.16 hindcast is presented. Global distributions of significant wave heights and mean periods in both datasets demonstrate good qualitative and quantitative agreement, especially in regions with a high spatiotemporal density of observations. Simulation results and visually observed wind sea and swell show perfectly consistent patterns of directional steadiness. However, wind sea heights in WW3 are overestimated predominantly in stormy regions, while swell heights are globally underestimated. The reasons for these discrepancies in assessment of wave system components are investigated in the framework of various wave regime analyses.

Predicting environmental features by learning spatiotemporal embeddings from social media

Spatiotemporal modelling is an important task for ecology. Social media tags have been found to have great potential to assist in predicting aspects of the natural environment, particularly through the use of machine learning methods. Here we propose a novel spatiotemporal embeddings model, called SPATE, which is able to integrate textual information from the photo-sharing platform Flickr and structured scientific information from more traditional environmental data sources. The proposed model can be used for modelling and predicting a wide variety of ecological features such as species distribution, as well as related phenomena such as climate features. We first propose a new method based on spatiotemporal kernel density estimation to handle the sparsity of Flickr tag distributions over space and time. Then, we efficiently integrate the spatially and temporally smoothed Flickr tags with the structured scientific data into low-dimensional vector space representations. We experimentally show that our model is able to substantially outperform baselines that rely only on Flickr or only on traditional sources.