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full scale approximation of covariance functions for large spatial data sets
Gaussian process models have been widely used in spatial statistics but face tremendous computational challenges for very large data sets. The model fitting and spatial prediction of such models typically require O(n3) operations for a data set of size n. Various approximations of the covariance functions have been introduced to reduce the computational cost. However, most existing approximations cannot simultaneously capture both the large‐ and the small‐scale spatial dependence. A new approximation scheme is developed to provide a high quality approximation to the covariance function at both the large and the small spatial scales. The new approximation is the summation of two parts: a reduced rank covariance and a compactly supported covariance obtained by tapering the covariance of the residual of the reduced rank approximation. Whereas the former part mainly captures the large‐scale spatial variation, the latter part captures the small‐scale, local variation that is unexplained by the former part. By combining the reduced rank representation and sparse matrix techniques, our approach allows for efficient computation for maximum likelihood estimation, spatial prediction and Bayesian inference. We illustrate the new approach with simulated and real data sets.
Journal Article
The 3-D global spatial data model : principles and applications
Traditional methods for handling spatial data are encumbered by the assumption of separate origins for horizontal and vertical measurements, but modern measurement systems operate in a 3-D spatial environment. The 3-D Global Spatial Data Model: Principles and Applications, Second Edition maintains a new model for handling digital spatial data, the global spatial data model or GSDM. The GSDM preserves the integrity of three-dimensional spatial data while also providing additional benefits such as simpler equations, worldwide standardization, and the ability to track spatial data accuracy with greater specificity and convenience. This second edition expands to new topics that satisfy a growing need in the GIS, professional surveyor, machine control, and Big Data communities while continuing to embrace the earth center fixed coordinate system as the fundamental point of origin of one, two, and three-dimensional data sets. Ideal for both beginner and advanced levels, this book also provides guidance and insight on how to link to the data collected and stored in legacy systems. -- Provided by publisher.
Book
Geographically weighted regression with the integration of machine learning for spatial prediction
Conventional methods of machine learning have been widely used to generate spatial prediction models because such methods can adaptively learn the mapping relationships among spatial data with limited prior knowledge. However, the direct application of these methods to build a global model without considering spatial heterogeneity cannot accurately describe the local relationships among spatial variables, which might lead to inaccurate predictions. To avoid these shortcomings, we have presented a unified framework for handling spatial heterogeneity by incorporating the geographically weighted scheme into machine learning methods. The proposed framework has the potential to extend the existing models of machine learning for analysing heterogeneous spatial data. Furthermore, geographically weighted support vector regression (GWSVR) has been introduced as an implementation of the proposed framework. Experimental studies on environmental datasets were used to test the ability of model predictions. The results show that the mean absolute percentage error, normalized mean square error, and relative error percentage of the GWSVR model are 0.436, 0.903, and 0.558, respectively, when analysing soil metal chromium (Cr) concentrations and 0.221, 0.287, and 0.206, respectively, when predicting PM2.5 concentrations; these values are lower than those obtained using support vector regression, geographically weighted regression (GWR), and GWR-kriging models. These case studies have proved the validity and feasibility of the proposed framework.
Journal Article
Building geospatial infrastructure
Many visions for geospatial technology have been advanced over the past half century. Initially researchers saw the handling of geospatial data as the major problem to be overcome. The vision of geographic information systems arose as an early international consensus. Later visions included spatial data infrastructure, Digital Earth, and a nervous system for the planet. With accelerating advances in information technology, a new vision is needed that reflects today's focus on open and multimodal access, sharing, engagement, the Web, Big Data, artificial intelligence, and data science. We elaborate on the concept of geospatial infrastructure, and argue that it is essential if geospatial technology is to contribute to the solution of problems facing humanity.
Journal Article
Geographic information systems to spatial data infrastructure : a global perspective
This book draws on author's wealth of knowledge working on numerous projects across many countries. It provides a clear overview of the development of the SDI concept and SDI worldwide implementation and brings a logical chronological approach to the linkage of GIS technology with SDI enabling data. The theory and practice approach help understand that SDI development and implementation is very much a social process of learning by doing. The author masterfully selects main historical developments and updates them with an analytical perspective promoting informed and responsible use of geographic information and geospatial technologies for the benefit of society from local to global scales. -- Provided by publisher.
Book
Spatial effects of carbon emission intensity and regional development in China
Due to the imbalance of technological level and industrial structure in regional economic development, the same carbon source can bring differentiated carbon emission levels in different regions, thus making the carbon emission show significant regional differences. In order to explore the regional differences in China’s provincial carbon emission intensity and the effect of relevant influencing factors, this paper combines EKC model and STIRPAT model to conduct research. Using carbon emission intensity and other influencing factors of China’s 30 provinces ranging from 2005 to 2017 to construct a panel data, the authors use exploratory spatial data analysis and Spatial Durbin Model to study the spatial effect of carbon emission intensity in China’s provincial regions and the impact of different development factors on carbon emission intensity. The results show that from 2005 to 2017, China’s carbon emission intensity gradually declined from east to west and from south to north. The inter-provincial carbon emission intensity of China presents an agglomeration effect in space, and the agglomeration effect gradually weakens with time. In addition, reducing energy intensity can reduce carbon emission intensity to a large extent. By optimizing industrial structure, increasing the degree of foreign trade and promoting financial development, carbon emission intensity can also be inhibited. Therefore, reducing the energy intensity of various industries and establishing inter-regional carbon emission cooperation mechanism will be effective to control the carbon emission intensity.
Journal Article
Spatial analysis for radar remote sensing of tropical forests
\"This book is based on authors' extensive involvement in large Synthetic Aperture Radar (SAR) mapping projects, targeting the health of an important earth ecosystem, the tropical forests. It highlights past achievements, explains the underlying physics that allow the radar practitioners to understand what radars image, and can't yet image, and paves the way for future developments including wavelet-based techniques to estimate tropical forest structural measures combined with InSAR and Lidar techniques. As first book on this topic, this composite approach makes it appealing for students, learning through important case studies ; and for researchers finding new ideas for future studies\"-- Provided by publisher.
Book
Spatial Negative Co-Location Pattern Directional Mining Algorithm with Join-Based Prevalence
It is usually difficult for prevalent negative co-location patterns to be mined and calculated. This paper proposes a join-based prevalent negative co-location mining algorithm, which can quickly and effectively mine all the prevalent negative co-location patterns in spatial data. Firstly, this paper verifies the monotonic nondecreasing property of the negative co-location participation index (PI) value as the size increases. Secondly, using this property, it is deduced that any prevalent negative co-location pattern with size n can be generated by connecting prevalent co-location with size 2 and with an n − 1 size candidate negative co-location pattern or an n − 1 size prevalent positive co-location pattern. Finally, the experiment results demonstrate that while other conditions are fixed, the proposed algorithm has an excellent efficiency level. The algorithm can eliminate the 90% useless negative co-location pattern maximumly and eliminate the useless 40% negative co-location pattern averagely.
Journal Article