Characteristics Of Spatial Data Research Articles

Multi-source data based anomaly detection through temporal and spatial characteristics

Anomaly detection is vital in complex distributed systems. However, existing methods did not take into full account the temporal and spatial characteristics of data from multiple sources in the system. That will lead to less accurate of anomaly detection. To address this limitation, in this paper we propose a multi-source data based anomaly detection method through temporal and spatial characteristics. Specifically, we first considered the spatial characteristics. We propose a Transformer encoder to parse log templates and output template vectors, then an attention-based CNN is introduced to obtain the spatial characteristics from traces. Next, we considered the temporal characteristics. We propose Bi-LSTM network to obtain the temporal characteristics of multi-source data of the distributed systems, followed by anomaly detection. Finally, extensive comparative experiments verify the effectiveness of our method, the F1-score reaches 0.859 and improves by 2.14% compared to state-of-the-art methods.

Inversion and forward estimation with process-based models: An investigation into cost functions, uncertainty-based weights and model-data fusion

Process-based models for inversion and forward estimation have supported our understanding of earth, environment, and hazards processes. These methods are often applied on spatial data without accounting for their spatial nature and uncertainty. Using an example of reconstructing past volcanic eruption characteristics and associated tephra fallout from different sets of field observation, we demonstrate the importance of making the best use of data-related uncertainty and spatial information in inversion and forward estimation. We present strategies for: (1) the selection of appropriate cost functions accounting for their behaviour and implied distribution of residuals, (2) the treatment of differential uncertainty when combining multiple data, and (3) the leveraging of both model and data when estimating the spatial distribution of output. Results show that a data-informed choice of cost function and accounting for uncertainty and spatial characteristics of data leads to consistent improvements in model predictive performance for both inversion and forward models.

Landslide Displacement Prediction Based on CEEMDAN Method and CNN–BiLSTM Model

Landslides are a typical geological disaster, and are a great challenge to land use management. However, the traditional landslide displacement model has the defect of ignoring random displacement. In order to solve this situation, this paper proposes a CNN–BiLSTM model that combines a convolutional neural network (CNN) model and a bidirectional long short-term memory network (BiLSTM) model. In this model, the CEEMDAN method is innovatively proposed to decompose landslide displacement. The GRA–MIC fusion correlation calculation method is used to select the factors influencing landslide displacement, and finally the CNN–BiLSTM model is used for prediction. The CNN–BiLSTM model was constructed to extract the temporal and spatial characteristics of data for landslide displacement prediction. Two new concepts that evaluate the state of a landslide and the trend of the landslide are proposed to improve the performance of the prediction model. Then, we discuss the prediction performance of the CNN–BiLSTM model under four different input conditions and compare it with seven other prediction models. The experimental prediction results show that the model proposed in this paper can be popularized and applied in areas with frequent landslides, and provide strong support for disaster prevention and reduction and land use management.

Spatial data trusts: an emerging governance framework for sharing spatial data

ABSTRACT Data Trusts are an important emerging approach to enabling the much wider sharing of data from many different sources and for many different purposes, backed by the confidence of clear and unambiguous data governance. Data Trusts combine the technical infrastructure for sharing data with the governance framework of a legal trust. The concept of a data Trust applied specifically to spatial data offers significant opportunities for new and future applications, addressing some longstanding barriers to data sharing, such as location privacy and data sovereignty. This paper introduces and explores the concept of a ‘spatial data Trust’ by identifying and explaining the key functions and characteristics required to underpin a data Trust for spatial data. The work identifies five key features of spatial data Trusts that demand specific attention and connects these features to a history of relevant work in the field, including spatial data infrastructures (SDIs), location privacy, and spatial data quality. The conclusions identify several key strands of research for the future development of this rapidly emerging framework for spatial data sharing.

The morphing for continuous generalization of linear features based on Dynamic Time Warping distance

Shape morphing has been used to generate arbitrary scale maps in the field of continuous generalization in recent years. The morphing approach consists of two main steps: shape characteristic matching and trajectory interpolation. Most of the shape characteristic matching methods are difficult to consider the local and global characteristics of spatial data at the same time, which will result in distortion of interpolation results. This paper proposes a Dynamic Time Warping (DTW) distance-based morphing method for continuous generalization of linear features. In this method, the DTW distance is employed as shape similarity distance to find the optimal correspondence by minimizing the total cost between each pair of vertices, which considers both the local and global structure of two linear features. Firstly, we build a matrix to record the distance between vertices in two linear features at two different scales. Then we use the DTW algorithm to find the optimal warping path to establish the corresponding relationship between the vertices of two linear features. Finally, the linear interpolation method is used to dynamically generate the geometric shape at any intermediate scale. Experiment results demonstrate that the proposed method can generate continuous and smooth geometric shapes with a gradual transformation effect, and can be used for the continuous generalization of linear features. The generalization results are consistent with map representation rules and human cognition.

Reinforced Two-Stream Fuzzy Neural Networks Architecture Realized With the Aid of One-Dimensional/Two-Dimensional Data Features

A novel structure of reinforced two-stream fuzzy neural networks (TSFNNs) realized with the aid of fuzzy logic and transfer learning method is presented. This architecture consists of a TSFNN and a fusion strategy. TSFNN architecture consists of two combined networks of both fuzzy rules-based radial basis function neural networks (FRBFNN) and convolutional neural networks (CNNs). In the TSFNN architecture, one stream employs the deep CNN to extract the spatial information of images and effectively learn the high-level features and another stream uses the FRBFNN to analyze the distribution of data points over the input space and learn to capture complex relationships in data. In the fusion strategy, the outputs of two streams are concatenated by a softmax function, which normalizes the output to a probability distribution. A transfer learning method is considered to reconstruct new data representation as the inputs of CNN to mine potential spatial features of data. Moreover, L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -norm regularization is used to alleviate the possible overfitting and enhance the generalization ability. The proposed method not only inherits the advantages of FRBFNN and CNN such as global feature extraction ability, good local approximating performance, ability of handling uncertainty by fuzzy logic but also improves the classification performance under the synergy between two-stream architecture and the fusion strategy. Experimental results obtained for a diversity of datasets as well as partial discharge datasets be using in the real life of fault diagnosis and black plastic wastes datasets for recycling confirm the effectiveness of the proposed TSFNN. A comprehensive comparative analysis is covered. This design can simultaneously capture different level information of inputs and easing the insufficient problem of extracting features from a single steam. Especially, we show that the synergistic effect of FRBFNN, CNN, enabling deep learning for generic classification tasks and multipoint crossover, and L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -norm regularization can effectively improve the performance of the TSFNNs.

Spatial data modeling by means of Gibbs–Markov random fields based on a generalized planar rotator model

We introduce a Gibbs–Markov random field for spatial data on Cartesian grids, which is based on the generalized planar rotator (GPR) model. The GPR model generalizes the recently proposed modified planar rotator (MPR) model by including in the Hamiltonian additional terms that better capture realistic features of spatial data, such as smoothness, non-Gaussianity, and geometric anisotropy. In particular, the GPR model can include an infinite number of higher-order harmonics with exponentially vanishing interaction strength, directional dependence of the bilinear interaction term between nearest neighbors, longer-distance neighbor interactions, and two types of an external bias field. Hence, in contrast with the single-parameter MPR, the GPR model features five additional parameters: the number n of higher-order terms and the decay-rate parameter α, the exchange anisotropy parameter Jnn, the further-neighbor interaction coupling Jfn, and the external field (bias) parameters K (or K′). We present numerical tests on various synthetic and real data which demonstrate the effects of the respective terms on the model’s prediction performance, and we discuss these results in connection with the data properties.

Urban public health spatial planning using big data technology and visual communication in IoT.

The planning of urban public health spatial can not only help people's physical and mental health but also help to optimize and protect the urban environment. It is of great significance to study the planning methods of urban public health spatial. The application effect of traditional urban public health spatial planning is poor, in this paper, urban public health spatial planning using big data technology and visual communication in the Internet of Things (IoT) is proposed. First, the urban public health spatial planning architecture is established in IoT, which is divided into the perception layer, the network layer and the application layer; Second, information collection is performed at the perception layer, and big data technology is used at the network layer to simplify spatial model information, automatically sort out spatial data, and establish a public health space evaluation system according to the type and characteristics of spatial data; Finally, the urban public health space is planned based on the health assessment results and the visual communication design concept through the application layer. The results show that when the number of regions reaches 60,000, the maximum time of region merging is 7.86s. The percentage of spatial fitting error is 0.17. The height error of spatial model is 0.31m. The average deviation error of the spatial coordinates is 0.23, which can realize the health planning of different public spaces.

Novel Bootstrap-Based Discriminant NPE Integrated With Orthogonal LPP for Fault Diagnosis

It has become a challenge to identify the discriminant information and the local geometric feature from the complex process data for improving fault diagnosis accuracy. Facing this challenge, this article proposes a novel fault diagnosis methodology based on discriminant neighborhood preserving embedding integrated with orthogonal locality preserving projections (DNPEOLPP). In DNPEOLPP, a novel local discriminant weight is constructed to identify the local geometric feature for dimensionality reduction (DR), so that the label of data is fully utilized. Furthermore, the cosine distance considering the spatial features of data is introduced into the process of neighbor graph construction to extract representative features. The bootstrap-based DNPEOLPP (B-DNPEOLPP) is eventually developed to further address the issue of singular matrix caused by the small number of classes when applying DNPEOLPP for DR. In addition, the Akaike information criterion (AIC) is utilized to identify the suitable order of DR, and AdaBoost.M2 is utilized to classify the type of fault. Finally, the Tennessee Eastman process (TEP) and the grid-connected photovoltaic system (GPVS) are used to validate the performance of B-DNPEOLPP. Through case study analysis, it is demonstrated that B-DNPEOLPP can effectively identify different faults and obtain superior accuracy in fault diagnosis.

Geospatial complex event processing in smart city applications

The extensive deployment of the Internet of things (IoT) devices in urban areas enables smart city systems (e.g., urban traffic detection and air pollution monitoring) to use these devices to discover the affected regions by various events, particularly complex ones. A complex event is inferred from patterns of different individual primitive events according to their spatio-temporal information. Complex event processing (CEP) systems facilitate applications to define their reasoning rules based on event processing languages (EPL), similar to SQL. However, current languages and CEP engines do not efficiently support spatial data characteristics. Geospatial CEP is challenging since applications have diverse definitions for complex events (e.g., air quality index), IoT devices have heterogeneous specifications/capabilities, and complex events may happen concurrently. This paper proposes the Geo-Tesla language that enables smart city applications to define spatial attributes in complex event definitions and reasoning rules. Then, we devise the GeoT-Rex CEP engine that implements geospatial operations that compiles Geo-Tesla language and processes the spatial data. The integration of Geo-Tesla and GeoT-Rex enables applications to process complex events with spatial characteristics in their reasoning rules and identify boundaries of the detected complex event (event footprint). The evaluation results from the proof of concept implementation show that our proposed solution outperforms in identifying the footprint of complex events up to 44% in the largest network size. They also show that the detection percentage in the concurrent appearance of complex events is 40% more on average than a close state-of-the-art baseline.

Deep learning-based discriminant model for wearable sensing gait pattern

&lt;p&gt;In order to effectively improve the accuracy of identifying the gait pattern of wearable sensing data, this paper proposes a new model for deep learning gait mode discrimination that integrates convolutional neural network and long short-term memory neural network, which makes full use of the convolutional neural network to obtain the most local spatial characteristics of data and the long short-term memory neural network to obtain the inherent characteristics of the data, and effectively excavates the hidden high-dimensional, nonlinear, time-space gait characteristics of random wearable sensing timing gait data that are closely related to gait pattern changes, to improve the classification performance of gait mode. The effectiveness of the proposed model in this paper is evaluated using the HAR dataset from University of California UCI database. The experiment results showed that the proposed model in this paper can effectively obtain the time-space gait characteristics embedded in the wearable sensor gait data, and the classification accuracy can reach 91.45%, the precision rate 91.54%, and the recall rate 91.53%, and the classification performance is significantly better than that of the traditional machine learning model, which provides a new solution for accurately identifying the gait mode of wearable sensor data.&lt;/p&gt;

Icing diagnosis model for wind turbine blade based on feature optimization and 1D-convolutional neural network

Wind turbines located in high humidity and high altitude areas are often accompanied by blade icing, which adverses the operating efficiency and even causes safety accidents. Early identification of blade icing will help improve the operating efficiency of the wind turbine. This paper proposes an icing diagnosis method for wind turbine blades based on feature optimization and the one-dimensional convolutional neural network (1D-CNN). First, feature optimization is achieved by feature selection and feature reconstruction. The XGBoost algorithm is used to calculate the importance of each feature and select the features comprehensively that reflect blade icing. Second, the important features related to blade icing are reconstructed by using the deviation principle to extract the deviation information of features accurately when blades ice. Finally, the features screened by XGBoost and the reconstructed features are combined into the final feature set as the input of the 1D-CNN, which takes the temporal and spatial characteristics of data into account, to diagnose the icing state of blades. The method is validated on the data set collected from a real wind farm. The experimental results show that the proposed icing diagnosis method for wind turbine blades is superior to the traditional deep learning methods. It is favorable to improve the efficiency of wind turbine operation and maintenance.

Outward FDI and exports relation: A heterogeneous panel approach dealing with cross-sectional dependence

This paper explores the link between outward foreign direct investment (OFDI) and exports using Spain's flows to the top-50 recipient countries over the period 1995–2019. Methodologically, it addresses two often forgotten features of spatial data (heterogeneity and cross-sectional dependence) by combining a heterogeneous panel approach with the treatment of cross-sectional dependence. Indeed, when addressing cross-sectional dependence, it removes one of its two components (spatial dependence) so that comparing raw and spatially filtered data yields relevant conclusions. Findings show that OFDI has a significant positive effect on exports in the long term, but it is exclusively due to spatial dependence.

Evaluation of Land Use/Land Cover Classification based on Different Bands of Sentinel-2 Satellite Imagery using Neural Networks

Spatial data analytics is an emerging technology. Artificial neural network techniques play a major role in analysing any critical dataset. Integrating remote sensing data with deep neural networks has led a way to several research problems. This paper aims at producing land use land cover map of Bangalore region, Karnataka, India with various band combinations of sentinel satellite imagery obtained from google earth engine. LULC map classes include water, urban, forest, vegetation and openland. Band combinations of satellite images represent different characteristics of spatial data. Hence, several band combinations are used to build LULC maps. Also, classified maps are generated using different neural networks with pixel-based classification approach. Appropriate performance metrics were identified to evaluate the classification results such as Accuracy, Precision, Recall, F1-score and Confusion Matrix. Among neural networks, Convolutional Neural Network technique outperformed with 98.1 % of accuracy and less error rates in confusion matrix considering RGBNIR (4328) band combination of satellite imagery.

Spatiotemporal convolutional LSTM with attention mechanism for monthly rainfall prediction

Climate has always become part of an essential role in human life on earth. Climate change is a scorching topic because of various prediction efforts to predict the future of the earth's climate on human survival. As one institution that deals with climatology in detail, NOAA publishes a journal on the approach used to generate CHIRPS as a spatiotemporal dataset. The data consist of spatial two dimensions and temporal data from 1981 to 2020. The dataset for the experiment contains 480 pictures, which are 480 months or 40 years of data. K-means clustering is used to collect different characteristics of spatial data based on the Within Cluster Sum Square (WCSS) value using the Elbow method. Generalizing four spatial areas of different characteristic data, Authors have proposed a Convolutional Long Short-Term Memory model to carry out and develop by an attention mechanism. A deep learning architecture designed to learn the dependencies of temporal and spatial data exclusively utilizing convolutional layers and recurrent layers. Two issues with convolutional networks' ability to forecast sequences using historical data are addressed by our suggested architecture: (1) break the temporal order throughout the learning process, and (2) demand that the lengths of the input and output sequences be equal. The method's purpose is to improve the prediction for each previous method and having lower error than other models such as GRU, LSTM, and LSTM-AT. Still, every model reported in this paper has characteristics of how they learn the pattern of the data and visualize the error rate and performance in every spatial point. The Convolutional LSTM-AT model achieved MAE of 67.97, with training time around 2.5 minutes.

3D TOPOLOGICAL SUPPORT IN SPATIAL DATABASES: AN OVERVIEW

Abstract. The storage of spatial data that consists of spatial and non-spatial properties requires a database management system that possesses spatial functions that can cater to the spatial characteristics of data. These characteristics include the geometrical shape, topological and positional information. Parallel to how geometries describe the shape of an object, topological information is also an important spatial property which describes how the geometries in a space are related to each other. This information describes the connectivity, containment and adjacencies of spatial objects which are the foundation for more complex analysis such as navigation, data reconstruction, spatial queries and others. However, the topological support provided by spatial databases varies. This paper provided an overview on the current implementations of topological support in spatial databases such as ArcGIS, QGIS, PostgreSQL and others. The native topology in most spatial databases was found to be 2D topology maintained by 2D topology rules with limited representation of 3D topological relationships. Consequently, 3D objects represented by 2D topology had to be decomposed into objects of lower dimensions. Approaches to implement additional topological support for spatial databases included the use of topological data models, data structures, operators, and rules. 3D applications such as 3D cadastre required more detailed representations of topological information which required a more comprehensive 3D topological data model. Nonetheless, comprehensive preservation of topological information also mandates voluminous storage and higher computational efficiency. Thus, the appropriate 3D topological support should be provided in spatial databases to accurately represent 3D objects and meet 3D analysis requirements.

State‐of‐health estimation and remaining useful life for lithium‐ion battery based on deep learning with Bayesian hyperparameter optimization

Lithium-ion battery state-of-health (SOH) estimation and remaining usable life (RUL) prediction are important for battery prognosis and health management. In this article, a framework-combined deep convolution neural network (DCNN) with double-layer long short-term memory (LSTM) is proposed, which is designed for online health prognosis. Based on the raw data obtained during the constant current charging process, the aging characteristics of the battery can be extracted by DCNN to estimate SOH. The estimation results are then sent to the LSTM for the temporal prediction of the RUL. This framework considers both temporal and spatial characteristics of data, and the powerful spatial feature extraction ability of DCNN and the effectiveness of LSTM for time series problems can ensure the high precision of calculation. At the same time, the hyperparameters of the neural networks, which can highly affect the performance of networks, are obtained by Bayesian optimization to ensure the networks run in the best status. The results show that the proposed method can achieve low root mean square error, which are inferior to 0.0061 and 0.0627 for SOH estimation and RUL prediction, respectively.

A Bayesian Approach to Estimate the Spatial Distribution of Crowdsourced Radiation Measurements around Fukushima

Citizen-led movements producing spatio-temporal big data are potential sources of useful information during hazards. Yet, the sampling of crowdsourced data is often opportunistic and the statistical variations in the datasets are not typically assessed. There is a scientific need to understand the characteristics and geostatistical variability of big spatial data from these diverse sources if they are to be used for decision making. Crowdsourced radiation measurements can be visualized as raw, often overlapping, points or processed for an aggregated comparison with traditional sources to confirm patterns of elevated radiation levels. However, crowdsourced data from citizen-led projects do not typically use a spatial sampling method so classical geostatistical techniques may not seamlessly be applied. Standard aggregation and interpolation methods were adapted to represent variance, sampling patterns, and the reliability of modeled trends. Finally, a Bayesian approach was used to model the spatial distribution of crowdsourced radiation measurements around Fukushima and quantify uncertainty introduced by the spatial data characteristics. Bayesian kriging of the crowdsourced data captures hotspots and the probabilistic approach could provide timely contextualized information that can improve situational awareness during hazards. This paper calls for the development of methods and metrics to clearly communicate spatial uncertainty by evaluating data characteristics, representing observational gaps and model error, and providing probabilistic outputs for decision making.

Towards Semantically-Rich Spatial Network Representation Learning via Automated Feature Topic Pairing.

Automated characterization of spatial data is a kind of critical geographical intelligence. As an emerging technique for characterization, spatial Representation Learning (SRL) uses deep neural networks (DNNs) to learn non-linear embedded features of spatial data for characterization. However, SRL extracts features by internal layers of DNNs, and thus suffers from lacking semantic labels. Texts of spatial entities, on the other hand, provide semantic understanding of latent feature labels, but is insensible to deep SRL models. How can we teach a SRL model to discover appropriate topic labels in texts and pair learned features with the labels? This paper formulates a new problem: feature-topic pairing, and proposes a novel Particle Swarm Optimization (PSO) based deep learning framework. Specifically, we formulate the feature-topic pairing problem into an automated alignment task between 1) a latent embedding feature space and 2) a textual semantic topic space. We decompose the alignment of the two spaces into: 1) point-wise alignment, denoting the correlation between a topic distribution and an embedding vector; 2) pair-wise alignment, denoting the consistency between a feature-feature similarity matrix and a topic-topic similarity matrix. We design a PSO based solver to simultaneously select an optimal set of topics and learn corresponding features based on the selected topics. We develop a closed loop algorithm to iterate between 1) minimizing losses of representation reconstruction and feature-topic alignment and 2) searching the best topics. Finally, we present extensive experiments to demonstrate the enhanced performance of our method.

Density-based spatial clustering of applications with noise using Gower distance

Most clustering algorithms considering spatial characteristics of data have been developed based on the geological location of observations. Density-based spatial clustering of applications with noise (DBSCAN) provides arbitrarily shaped clusters grouping a set of observations which are closely packed together and noise detecting outliers which lie alone in low-density regions. A distance measure for DBSCAN is Euclidean distance, which is the standard measure of distance and especially suitable to handle continuous variables. To handle both categorical and continuous variables simultaneously, other measures are required to compute distance for various types of variables. Thus, we propose DBSCAN algorithm using Gower distance. We provide numerical results on spatial and non-spatial setup comparing DBSCAN methods with Euclidean and Gower distance and we apply this method to land price data and migraine treatments data. DBSCAN using Gower distance has a reasonable method and gives comparably stable results.