Characteristics Of Spatial Data Research Articles

Spatial analysis for political scientists

AbstractHow does space matter in our analyses? How can we evaluate diffusion of phenomena or interdependence among units? How biased can our analysis be if we do not consider spatial relationships? All the above questions are critical theoretical and empirical issues for political scientists belonging to several subfields from Electoral Studies to Comparative Politics, and also for International Relations. In this special issue on methods, our paper introduces political scientists to conceptualizing interdependence between units and how to empirically model these interdependencies using spatial regression. First, the paper presents the building blocks of any feature of spatial data (points, polygons, and raster) and the task of georeferencing. Second, the paper discusses what a spatial matrix (W) is, its varieties and the assumptions we make when choosing one. Third, the paper introduces how to investigate spatial clustering through visualizations (e.g. maps) as well as statistical tests (e.g. Moran's index). Fourth and finally, the paper explains how to model spatial relationships that are of substantive interest to some of our research questions. We conclude by inviting researchers to carefully consider space in their analysis and to reflect on the need, or the lack thereof, to use spatial models.

A hybrid optimization method of factor screening predicated on GeoDetector and Random Forest for Landslide Susceptibility Mapping

The aim of this study was to develop a hybrid model (Geo-RFE-RF) for Landslide Susceptibility Mapping (LSM) predicated on GeoDetector and Random Forest (RF) using the Recursive Feature Elimination (RFE-RF) method for eliminating redundant and noise factors. At the outset, for the sample of 1522 investigated landslides and 1522 non-landslides, twenty-two factors were chosen as the initial landslide-conditioning factors to construct a spatial database. Subsequently, the GeoDetector and RFE-RF methods were adopted to eliminate the least effective factors, respectively, with the Geo-RFE-RF model being formulated with the combination factors of these two methods. Finally, the performance of the Geo-RFE-RF and RF model with twenty-two initial factors (In-RF) were compared and assessed, and the higher accuracy model was employed to generate a LSM in a case study area, Fengjie County (China). The results indicate that, the Area Under Curve, Accuracy, Precision, and F1 Score of the test dataset is increased by 0.9%, 0.4%, 1.5%, and 0.3%, respectively, under the Geo-RFE-RF model, as compared to the In-RF model. The conditioning factors used to construct the model have been reduced from twenty-two to thirteen, but the predictive ability of the Geo-RFE-RF model performs better, proving the effectiveness of the hybrid model that combines the factors from GeoDetector and RFE-RF methods. This hybrid model not only considers the spatial pattern characteristics of spatial data for screening factors, but the selected factors are also in good agreement with the adopted machine learning method, offering potential use as a general framework for eliminating redundant and noise factors in LSM across the globe.

Supervised-Learning-Based Intelligent Fault Diagnosis for Mechanical Equipment

Recently, anomaly detection for improving the productivity of machinery in industrial environments has drawn considerable attention. As large-scale data collection and processing are becoming easier owing to technological developments, data-based deep-learning technology is being developed to detect anomalies in mechanical equipment operation. This study proposes an ensemble model that combines stacked two-dimensional and one-dimensional convolutional neural networks (CNNs), residual long short-term memory (LSTM), and LSTM based on supervised learning. The model, which is called the SCRLSTM model, can detect abnormal data generated by mechanical equipment. The proposed model can extract the spatial features of data using a CNN model and detect anomalous states in the time-series-based vibration datasets of machinery under various environments through residual LSTM. To verify this model, data augmentation was applied to the original time-series-based mechanical vibration dataset, which had unbalanced samples that lowered the performance of the abnormal anomaly detection model. In addition, an image-based analysis was performed by converting time-series-based raw-signal data to Mel-spectrogram images, thereby achieving better performance in the fault diagnosis system to which data augmentation was applied. The proposed SCRLSTM model shows better performance than other supervised-learning-based models on datasets having different lengths under various conditions. This indicates that the proposed anomaly detection model can be expected to improve the productivity of mechanical equipment in industrial settings.

Identification of dominant features in spatial data

Dominant features of spatial data are connected structures or patterns that emerge from location-based variation and manifest at specific scales or resolutions. To identify dominant features, we propose a sequential application of multiresolution decomposition and variogram function estimation. Multiresolution decomposition separates data into additive components, and in this way enables the recognition of their dominant features. A dedicated multiresolution decomposition method is developed for arbitrary gridded spatial data, where the underlying model includes a precision and spatial-weight matrix to capture spatial correlation. The data are separated into their components by smoothing on different scales, such that larger scales have longer spatial correlation ranges. Moreover, our model can handle missing values, which is often useful in applications. Variogram function estimation can be used to describe properties in spatial data. Such functions are therefore estimated for each component to determine its effective range, which assesses the width-extent of the dominant feature. Finally, Bayesian analysis enables the inference of identified dominant features and to judge whether these are credibly different. The efficient implementation of the method relies mainly on a sparse-matrix data structure and algorithms. By applying the method to simulated data we demonstrate its applicability and theoretical soundness. In disciplines that use spatial data, this method can lead to new insights, as we exemplify by identifying the dominant features in a forest dataset. In that application, the width-extents of the dominant features have an ecological interpretation, namely the species interaction range, and their estimates support the derivation of ecosystem properties such as biodiversity indices.

AN OPTIMIZED SFC APPROACH FOR ND WINDOW QUERYING ON POINT CLOUDS

Abstract. Dramatically increasing collection of point clouds raises an essential demand for highly efficient data management. It can also facilitate modern applications such as robotics and virtual reality. Extensive studies have been performed on point data management and querying, but most of them concentrate on low dimensional spaces. High dimensional data management solutions from computer science have not considered the special features of spatial data; so, they may not be optimal. A Space Filling Curve (SFC) based approach, PlainSFC which is capable of nD point querying has been proposed and tested in low dimensional spaces. However, its efficiency in nD space is still unknown. Besides that, PlainSFC performs poorly on skewed data querying. This paper develops HistSFC which utilizes point distribution information to improve the querying efficiency on skewed data. Then, the paper presents statistical analysis of how PlainSFC and HistSFC perform when dimensionality increases. By experimenting on simulated nD data and real data, we confirmed the patterns deduced: for inhomogeneous data querying, the false positive rate (FPR) of PlainSFC increases drastically as dimensionality goes up. HistSFC alleviates such deterioration to a large extent. Despite performance degeneration in ultra high dimensional spaces, HistSFC can be applied with high efficiency for most spatial applications. The generic theoretical framework developed also allows us to study related topics such as visualization and data transmission in the future.

Identifying local differences with fused-MCP: an apartment rental market case study on geographical segmentation detection

As the variety and quality of spatial data increase in recent times, the potential to analyze local characteristics based on spatial data is getting stronger. Previous spatial analysis methods structuralize the spatial autocorrelation of data by the distances between data observation points and the contiguity of the data-observed regions. It is significant for the estimation of global characteristics of spatial data. However, these approaches are not suitable for identifying local differences from the data since they assume a smooth spatial autocorrelation structure. Generalized fused lasso, which can detect local differences in spatial data, has been proposed in machine learning studies. Its limitation is that the estimated parameters are biased toward zero; however, methods that overcome the limitation have also been proposed. Fused-MCP is one of those methods and is expected to be useful in spatial analyses. This study applies fused-MCP to spatial analyses. As an example of spatial analyses based on fused-MCP, this study analyzes the structure of geographical segmentation of the real estate market in central Tokyo. Fused-MCP is utilized to extract areas where the valuation standard is the same. The results reveal that the geographical segmentation displays hierarchal patterns. Specifically, the market is divided by municipalities, railway lines and stations, and neighborhoods. The case study confirmed the applicability of fused-MCP to spatial analyses.

Геоинформационное обеспечение дисциплины «География населения с основами демографии»

The authors pointed out that the rapid growth of technical supply and the emergence of new information and communication technologies has led to the common usage of new technologies towards the various functions and needs of higher education. A university graduate should have the skills to develop and use information systems for decision support as a basic framework for analyzing unfolding situations, developing informed decisions and as a result — problem-solving. The connecting link for professional skills development appears as a GIS technology usage. GIS provide unique opportunities for its implementation in educational programs putting in touch with forecasting and analysis of the phenomena of the world around us; there are comprehension and allocation of the main causes and factors, possible consequences, with subsequent planning and adoption of strategic decisions of the actions taken. Geoinformation technologies can change the content of activities for both teachers and students. GIS technologies allow to master the methods of data gathering and processing; development of models and forecasts for specific geographical problem-solving. Students, using GIS, unaidedly obtain data, absorb new methods of data processing, receive best practices for working in various fields. The article gives a description and analyzes of the GIS usage for studying the course “Population geography with the demography basics”. The GIS usage for studying the course “Population geography with the demography basics” allows to significantly diversify the methods and forms of laboratory studies, student’s research work. In the course of work with GIS, a geographer comprehends the features of spatial data presented in digital form, analyzes geographic data, presents geographic data in the form of maps, tables, graphs.

IOceanSee: A Novel Scheme for Ocean State Estimation Using 3D Mobile Convolutional Neural Network

Ocean state estimation is a basic problem in the field of ocean engineering. Under the trend of data-driven, the development of intelligent ship decision-making, ocean energy system design and other aspects, are inseparable from the estimation of wave parameters in the ocean area. In recent years, researchers have developed remote sensing technology to monitor ocean waves. However, sensor-based methods all have a key limitation, which is high cost and fault worry. More importantly, one major limitation exists in current research: due to lack of change information and relying on a single feature of spatial data, the final predictive results are inaccurate. Adopting a 3D Convolutional Neural Network is a possible solution to improve the detection accuracy. Unfortunately, it cannot be deployed in the ocean environment due to lack of physical network connections. To resolve these issues, we develop a light-weight version of 3D Convolutional Neural Network, namely a low-cost, high-accuracy detection scheme to foresee ocean wave parameters using a 3D Mobile Convolutional Neural Network technique called iOceanSee in the marine environment. iOceanSee employs a mobile terminal composed of low-cost measuring equipment and non-interference (except light) device-an RGB camera to collect video data in real time. It extracts both space and time features through three-dimensional depthwise separable convolutions. More specifically, iOceanSee is able to capture the encoding motion information from multiple adjacent frames of the video, according to which period and height of the waves being evaluated. Our experimental results conclude that iOceanSee obtains comparable performance to 3D Convolutional Neural Network and outperforms other models in terms of measurement accuracy in the marine environments.

Photovoltaic power forecasting based LSTM-Convolutional Network

The volatile and intermittent nature of solar energy itself presents a significant challenge in integrating it into existing energy systems. Accurate photovoltaic power prediction plays an important role in solving this problem. With the development of deep learning, more and more scholars have applied the deep learning model to time series prediction and achieved very good results. In this paper, a hybrid deep learning model (LSTM-Convolutional Network) is proposed and applied to photovoltaic power prediction. In the proposed hybrid prediction model, the temporal features of the data are extracted first by the long-short term memory network, and then the spatial features of the data are extracted by the convolutional neural network model. In order to show the superior performance of the proposed hybrid prediction model, the prediction results of the hybrid model are compared with those of the single model (long-short term memory network, convolutional neural network) and the hybrid network (Convolutional-LSTM Network) model, and the results of eight error evaluation indexes are given. The results show that the hybrid prediction model has better prediction effect than the single prediction model, and the proposed hybrid model (extract the temporal characteristics of data first, and then extract the spatial characteristics of data) is better than Convolutional-LSTM Network (extract the spatial characteristics of data first, and then extract the temporal characteristics of data).

Importance of spatial predictor variable selection in machine learning applications – Moving from data reproduction to spatial prediction

Machine learning algorithms find frequent application in spatial prediction of biotic and abiotic environmental variables. However, the characteristics of spatial data, especially spatial autocorrelation, are widely ignored. We hypothesize that this is problematic and results in models that can reproduce training data but are unable to make spatial predictions beyond the locations of the training samples. We assume that not only spatial validation strategies but also spatial variable selection is essential for reliable spatial predictions.We introduce two case studies that use remote sensing to predict land cover and the leaf area index for the “Marburg Open Forest”, an open research and education site of Marburg University, Germany. We use the machine learning algorithm Random Forests to train models using non-spatial and spatial cross-validation strategies to understand how spatial variable selection affects the predictions.Our findings confirm that spatial cross-validation is essential in preventing overoptimistic model performance. We further show that highly autocorrelated predictors (such as geolocation variables, e.g. latitude, longitude) can lead to considerable overfitting and result in models that can reproduce the training data but fail in making spatial predictions. The problem becomes apparent in the visual assessment of the spatial predictions that show clear artefacts that can be traced back to a misinterpretation of the spatially autocorrelated predictors by the algorithm. Spatial variable selection could automatically detect and remove such variables that lead to overfitting, resulting in reliable spatial prediction patterns and improved statistical spatial model performance.We conclude that in addition to spatial validation, a spatial variable selection must be considered in spatial prediction models of ecological data to produce reliable results.

A Survey on Spatial Prediction Methods

With the advancement of GPS and remote sensing technologies, large amounts of geospatial data are being collected from various domains, driving the need for effective and efficient prediction methods. Given spatial data samples with explanatory features and targeted responses (categorical or continuous) at a set of locations, the spatial prediction problem aims to learn a model that can predict the response variable based on explanatory features. The problem is important with broad applications in earth science, urban informatics, geosocial media analytics, and public health, but is challenging due to the unique characteristics of spatial data, including spatial autocorrelation, heterogeneity, limited ground truth, and multiple scales and resolutions. This paper provides a systematic review on principles and methods in spatial prediction. We provide a taxonomy of methods categorized by the key challenge they address. For each method, we introduce its underlying assumption, theoretical foundation, and discuss its advantages and disadvantages. We also discuss spatiotemporal extensions of methods. Our goal is to help interdisciplinary domain scientists choose techniques to solve their problems, and more importantly, to help data mining researchers to understand the main principles and methods in spatial prediction and identify future research opportunities.

Exploring essential variables in the settlement selection for small-scale maps using machine learning

Abstract. The decision about removing or maintaining an object while changing detail level requires taking into account many features of the object itself and its surrounding. Automatic generalization is the optimal way to obtain maps at various scales, based on a single spatial database, storing up-to-date information with a high level of spatial accuracy. Researchers agree on the need for fully automating the generalization process (Stoter et al., 2016). Numerous research centres, cartographic agencies as well as commercial companies have undertaken successful attempts of implementing certain generalization solutions (Stoter et al., 2009, 2014, 2016; Regnauld, 2015; Burghardt et al., 2008; Chaundhry and Mackaness, 2008). Nevertheless, an effective and consistent methodology for generalizing small-scale maps has not gained enough attention so far, as most of the conducted research has focused on the acquisition of large-scale maps (Stoter et al., 2016). The presented research aims to fulfil this gap by exploring new variables, which are of the key importance in the automatic settlement selection process at small scales. Addressing this issue is an essential step to propose new algorithms for effective and automatic settlement selection that will contribute to enriching, the sparsely filled small-scale generalization toolbox.The main idea behind this research is using machine learning (ML) for the new variable exploration which can be important in the automatic settlement generalization in small-scales. For automation of the generalization process, cartographic knowledge has to be collected and formalized. So far, a few approaches based on the use of ML have already been proposed. One of the first attempts to determine generalization parameters with the use of ML was performed by Weibel et al. (1995). The learning material was the observation of cartographers manual work. Also, Mustière tried to identify the optimal sequence of the generalization operators for the roads using ML (1998). A different approach was presented by Sester (2000). The goal was to extract the cartographic knowledge from spatial data characteristics, especially from the attributes and geometric properties of objects, regularities and repetitive patterns that govern object selection with the use of decision trees. Lagrange et al. (2000), Balboa and López (2008) also used ML techniques, namely neural networks to generalize line objects. Recently, Sester et al. (2018) proposed the application of deep learning for the task of building generalization. As noticed by Sester et al. (2018), these ideas, although interesting, remained proofs of concepts only. Moreover, they concerned topographic databases and large-scale maps. Promising results of automatic settlement selection in small scales was reported by Karsznia and Weibel (2018). To improve the settlement selection process, they have used data enrichment and ML. Thanks to classification models based on the decision trees, they explored new variables that are decisive in the settlement selection process. However, they have also concluded that there is probably still more “deep knowledge” to be discovered, possibly linked to further variables that were not included in their research. Thus the motivation for this research is to fulfil this research gap and look for additional, essential variables governing settlement selection in small scales.

Spectrum Analysis and Convolutional Neural Network for Automatic Modulation Recognition

Recent convolutional neural networks (CNNs)-based image processing methods have proven that CNNs are good at extracting features of spatial data. In this letter, we present a CNN-based modulation recognition framework for the detection of radio signals in communication systems. Since the frequency variation with time is the most important distinction among radio signals with different modulation types, we transform 1-D radio signals into spectrogram images using the short-time discrete Fourier transform. Furthermore, we analyze statistical features of the radio signals and use a Gaussian filter to reduce noise. We compare the proposed CNN framework with two existing methods from literature in terms of recognition accuracy and computational complexity. The experiments show that the proposed CNN architecture with spectrogram images as signal representation achieves better recognition accuracy than existing deep learning-based methods.

A spatial heterogeneity-based rough set extension for spatial data

ABSTRACTWhen classical rough set (CRS) theory is used to analyze spatial data, there is an underlying assumption that objects in the universe are completely randomly distributed over space. However, this assumption conflicts with the actual situation of spatial data. Generally, spatial heterogeneity and spatial autocorrelation are two important characteristics of spatial data. These two characteristics are important information sources for improving the modeling accuracy of spatial data. This paper extends CRS theory by introducing spatial heterogeneity and spatial autocorrelation. This new extension adds spatial adjacency information into the information table. Many fundamental concepts in CRS theory, such as the indiscernibility relation, equivalent classes, and lower and upper approximations, are improved by adding spatial adjacency information into these concepts. Based on these fundamental concepts, a new reduct and an improved rule matching method are proposed. The new reduct incorporates spatial heterogeneity in selecting the feature subset which can preserve the local discriminant power of all features, and the new rule matching method uses spatial autocorrelation to improve the classification ability of rough set-based classifiers. Experimental results show that the proposed extension significantly increased classification or segmentation accuracy, and the spatial reduct required much less time than classical reduct.

Methodological Aspects of Building Information System for Monitoring of Landslide Hazards

The aim of the current paper is to present some methodological aspects of building monitoring information system for landslides. Having regard the importance of data in functioning of the monitoring system a special attention is given to the landslide factors, determining the weights of different factors and methods for making a landslide susceptibility map which is a base for selecting monitoring points and assessment of landslide hazard. The paper is not about the monitoring equipment and technical issues of building monitoring system. It concern landslide factor analysis and characteristics of spatial data in geographic information system (GIS) environment used for assessment of landslide susceptibility as a part of monitoring system. Based on ArcGIS Spatial analyst tools (ESRI Inc.) two overlay techniques – fuzzy logic and weighted sum are suggested for making a landslide susceptibility map. Application of GIS allows processing of a large amount of data and easily updating of information. It helps to better communicating the information about landslide hazards to the public by the relevant visualisation of the spatial data and also facilitate decision makers in case of natural hazard.

Low-quality multivariate spatio-temporal serial data preprocessing

As the accumulation of spatio-temporal data, the low-quality problems of multivariate spatio-temporal data become clear and mainly present that numerous missing data, high noise of time series and great different spatial scale of spatiotemporal data. Aimed at the low-quality problems of multivariate spatio-temporal series, we propose three methods to process them, firstly, using improved non-local means (NLM) algorithm and nonlinear regression analysis method to achieve incomplete data imputation; secondly, using NLM algorithm to deal with Gaussian white noise in time series; thirdly, using the Gaussian pyramid method to zoom the spatial scale for multivariate spatial data. Besides, we compare with traditional methods using quantitative evaluation indices to measure the performance, including K-nearest and wavelet threshold. The experimental simulations indicate that the interpolating accuracy of the two proposed algorithms are higher than K-nearest neighbor algorithm and the effect of denoising using NLM algorithm is obviously better than wavelet threshold method. The experiment results further indicate that the Gaussian pyramid method effectively achieves spatial scale transformation of multivariate spatial data and keeps the local detail characteristics of spatial data with better visual expression effect.

Optimum allocation for pre-hospital resource with big data analysis

Objective To This article mainly takes the Shenzhen Baoan people's Hospital pre-hospital department as the observation object, analyses the spacial site data distribution of pre-hospital, to realize optimum allocation for pre-hospital resource. Methods By means of the intelligentized ambulance, the site data of the pre-hospital is obtaineed, so is the pre-hospital hot map. With the hot map, the special pre-hospital characteristics is researched. Results The three spatial data characteristics of pre-hospital medical are proposed, i. e., non-uniform, relative stability, individual difference. Conclusions These three characteristics provide us with a new method and a new basis for decision making. In this paper, based on the pre-hospital characteristic, a modeling method of non-uniform relative stability model is proposed, and the optimized distribution of hospital site is discussed by applying planning mathematics. Key words: Pre-hospital medical; FMC position; Non-uniform; Relatively stable; Optimum planning; Big data

An Adaptive Sweep-Circle Spatial Clustering Algorithm Based on Gestalt

An adaptive spatial clustering (ASC) algorithm is proposed in this present study, which employs sweep-circle techniques and a dynamic threshold setting based on the Gestalt theory to detect spatial clusters. The proposed algorithm can automatically discover clusters in one pass, rather than through the modification of the initial model (for example, a minimal spanning tree, Delaunay triangulation, or Voronoi diagram). It can quickly identify arbitrarily-shaped clusters while adapting efficiently to non-homogeneous density characteristics of spatial data, without the need for prior knowledge or parameters. The proposed algorithm is also ideal for use in data streaming technology with dynamic characteristics flowing in the form of spatial clustering in large data sets.

Bayesian Analysis of Censored Spatial Data Based on a Non-Gaussian Model

In this paper, we suggest using a skew Gaussian-log Gaussian model for the analysis of spatial censored data from a Bayesian point of view. This approach furnishes an extension of the skew log Gaussian model to accommodate to both skewness and heavy tails and also censored data. All of the characteristics mentioned are three pervasive features of spatial data. We utilize data augmentation method and Markov chain Monte Carlo (MCMC) algorithms to do posterior calculations. The methodology is illustrated using simulated data, as well as applying it to a real data set. Keywords: Censored data, data augmentation, non-Gaussian spatial models, outlier, unified skew Gaussian.

A Fractional Cartesian Composition Model for Semi-Spatial Comparative Visualization Design.

The study of spatial data ensembles leads to substantial visualization challenges in a variety of applications. In this paper, we present a model for comparative visualization that supports the design of according ensemble visualization solutions by partial automation. We focus on applications, where the user is interested in preserving selected spatial data characteristics of the data as much as possible-even when many ensemble members should be jointly studied using comparative visualization. In our model, we separate the design challenge into a minimal set of user-specified parameters and an optimization component for the automatic configuration of the remaining design variables. We provide an illustrated formal description of our model and exemplify our approach in the context of several application examples from different domains in order to demonstrate its generality within the class of comparative visualization problems for spatial data ensembles.