Beyond the Hypes of Geospatial Big Data: Theories, Methods, Analytics, and Applications

Submission Deadline: 01 January 2019 (closed) View: 148

Guest Editors

Allison Kealy, Professor, Royal Melbourne Institute of Technology (RMIT), Australia
Qianxin Wang, Associate Professor, China University of Mining and Technology (CUMT), China
Shengjie (Patrick) Zhai, Research Professor, University of Nevada, Las Vegas (UNLV), USA

Summary

Geospatial data has always been big data, and it is becoming bigger than ever. Of the explosive 2.5 quintillion (1018) bytes of data that is being generated every day across the globe, a large portion (arguably as much as 80%) of that data are found to be geo-referenced. The academia, industry, and government agencies, on a daily basis, have to confront and address the massive challenges of storing, managing, processing, analyzing, visualizing and verifying such big geospatial data that are yet to overrun the world.

In this special issue, "Beyond the Hypes of Geospatial Big Data: Theories, Methods, Analytics, and Applications", we thus invite researchers and practitioners to present their original and "big" ideas and progress on handling and utilization of spatial and spatio-temporal data. Suggested topics include, but are not limited to:

• Theories and methods of handling structured and unstructured geospatial big data

• Efficient data processing and Intelligent processing method of geospatial big data

• Quality assessment of geospatial big data

• Data-intensive computation model

• Analytics of geospatial big data

• Geospatial big data visualization including virtual and augmented reality

• Innovative applications of geospatial big data

• Data privacy

Keywords

Geospatial Information: Big Data; Intelligent Analysis; Virtual Reality; Data Privacy

Published Papers

Show export options

Open Access

EDITORIAL

Introduction for the Special Issue on Beyond the Hypes of Geospatial Big Data: Theories, Methods, Analytics, and Applications
Qianxin Wang, Allison Kealy, Shengjie Zhai
CMES-Computer Modeling in Engineering & Sciences, Vol.119, No.2, pp. 245-247, 2019, DOI:10.32604/cmes.2019.06589
（This article belongs to the Special Issue: Beyond the Hypes of Geospatial Big Data: Theories, Methods, Analytics, and Applications)
Abstract This article has no abstract. More >

View
4848

Download
2296

Cited by
1
Open Access

ARTICLE

The Quality Assessment of Non-Integer-Hour Data in GPS Broadcast Ephemerides and Its Impact on the Accuracy of Real-Time Kinematic Positioning Over the South China Sea
Zhangzhen Sun, Tianhe Xu, Fan Gao, Chunhua Jiang, Guochang Xu
CMES-Computer Modeling in Engineering & Sciences, Vol.119, No.2, pp. 263-280, 2019, DOI:10.32604/cmes.2019.04425
（This article belongs to the Special Issue: Beyond the Hypes of Geospatial Big Data: Theories, Methods, Analytics, and Applications)
Abstract Abnormal effects in GPS broadcast ephemerides can have a significant effect on real-time navigation and positioning solutions that use the orbit and clock error data provided by GPS broadcast ephemerides. This paper describes three types of non-integer-hour navigation data in GPS broadcast ephemeris data. Compared with GPST integer hour data, we find that there are two types of data blocks for non-integer-hour navigation containing gross errors with different levels of precision, which is reflected in the user range accuracy (URA) of the broadcast ephemeris. These gross errors can cause large deviations when using the GPS… More >

View
4058

Download
2063
Open Access

ARTICLE

LNA Design for Future S Band Satellite Navigation and 4G LTE Applications
Muhammad Arsalan, Falin Wu
CMES-Computer Modeling in Engineering & Sciences, Vol.119, No.2, pp. 249-261, 2019, DOI:10.32604/cmes.2019.04430
（This article belongs to the Special Issue: Beyond the Hypes of Geospatial Big Data: Theories, Methods, Analytics, and Applications)
Abstract A good design of LNA for S band satellite navigation receivers and 4G LTE wireless communication system has been implemented in this paper. Due to increased congestion in the present L band, the S Band frequency from 2483.5-2500 MHz has been allocated for the future satellite navigation systems. For this purpose ATF-34143 amplifier (pHEMT) having high electron mobility and fast switching response has been chosen due to its very low Noise Figure (NF). The amplifier has been designed having bandwidth of 0.8 GHz from 1.8-2.6 GHz. Because of the large bandwidth, the amplifier could serve… More >

View
4653

Download
2217

Cited by
2
Open Access

ARTICLE

RAIM Algorithm Based on Fuzzy Clustering Analysis
Shouzhou Gu, Jinzhong Bei, Chuang Shi, Yaming Dang, Zuoya Zheng, Congcong Cui
CMES-Computer Modeling in Engineering & Sciences, Vol.119, No.2, pp. 281-293, 2019, DOI:10.32604/cmes.2019.04421
（This article belongs to the Special Issue: Beyond the Hypes of Geospatial Big Data: Theories, Methods, Analytics, and Applications)
Abstract With the development of various navigation systems (such as GLONASS, Galileo, BDS), there is a sharp increase in the number of visible satellites. Accordingly, the probability of multiply gross measurements will increase. However, the conventional RAIM methods are difficult to meet the demands of the navigation system. In order to solve the problem of checking and identify multiple gross errors of receiver autonomous integrity monitoring (RAIM), this paper designed full matrix of single point positioning by QR decomposition, and proposed a new RAIM algorithm based on fuzzy clustering analysis with fuzzy c-means (FCM). And on More >

View
4179

Download
1863

Cited by
2
Open Access

ARTICLE

Exploring Urban Population Forecasting and Spatial Distribution Modeling with Artificial Intelligence Technology
Yan Zou, Shaoliang Zhang, Yanhai Min
CMES-Computer Modeling in Engineering & Sciences, Vol.119, No.2, pp. 295-310, 2019, DOI:10.32604/cmes.2019.03873
（This article belongs to the Special Issue: Beyond the Hypes of Geospatial Big Data: Theories, Methods, Analytics, and Applications)
Abstract The high precision population forecasting and spatial distribution modeling are very important for the theory and application of population sociology, city planning and Geo-Informatics. However, the two problems need to be solved for providing the high precision population information. One is how to improve the population forecasting precision of small area (e.g., street scale); another is how to improve the spatial resolution of urban population distribution model. To solve the two problems, some new methods are proposed in this contribution. (1) To improve the precision of small area population forecasting, a new method is developed… More >

View
4362

Download
2118

Cited by
2
Open Access

ARTICLE

Inferring Spatial Distribution Patterns in Web Maps for Land Cover Mapping
Yang Liu, Zeying Lan, Hanfa Xing
CMES-Computer Modeling in Engineering & Sciences, Vol.119, No.2, pp. 311-330, 2019, DOI:10.32604/cmes.2019.04284
（This article belongs to the Special Issue: Beyond the Hypes of Geospatial Big Data: Theories, Methods, Analytics, and Applications)
Abstract Web maps represent an effective source for land cover mapping in capturing human activities. However, due to spatial heterogeneity, previous research has mainly focused on generating land cover maps in partial areas. Inferring spatial distribution patterns in Web maps may provide an alternative perspective on improving map production on a larger scale. This paper represents a novel approach to investigating the spatial distribution in Web maps for land cover mapping. First, linear features from Web maps are utilised to delineate parcels with insufficient Web map data for classification. Then, spatial factors are constructed from point… More >

View
3931

Download
1749
Open Access

ARTICLE

Monitoring Multiple Cropping Index of Henan Province, China Based on MODIS-EVI Time Series Data and Savitzky-Golay Filtering Algorithm
Lihui Wang, Feng Qi, Xin Shen, Jinliang Huang
CMES-Computer Modeling in Engineering & Sciences, Vol.119, No.2, pp. 331-348, 2019, DOI:10.32604/cmes.2019.04268
（This article belongs to the Special Issue: Beyond the Hypes of Geospatial Big Data: Theories, Methods, Analytics, and Applications)
Abstract Multiple cropping index (MCI) is a very important indicator in crop production and agricultural intensification, which represents the utilizing degree of agriculture resources at time scale and the effective utilization situation of arable land. The objective of this paper is monitoring multiple cropping index of Henan province of China according to the time series of MODIS (Moderate-Resolution Imaging Spectroradiometer) EVI (Enhanced Vegetation Index) after Savitzky-Golay filter processing from the year 2006 to 2011.The results revealed that this method could provide an effective way to monitor multiple cropping index, and the method of no additional authentication More >

View
4531

Download
1963

Cited by
6
Open Access

ARTICLE

Frequency Domain Filtering SAR Interferometric Phase Noise Using the Amended Matrix Pencil Model
Y,ong Gao, Shubi Zhang, Kefei Zhang, Shijin Li
CMES-Computer Modeling in Engineering & Sciences, Vol.119, No.2, pp. 349-363, 2019, DOI:10.32604/cmes.2019.03943
（This article belongs to the Special Issue: Beyond the Hypes of Geospatial Big Data: Theories, Methods, Analytics, and Applications)
Abstract Interferometric phase filtering is one of the key steps in interferometric synthetic aperture radar (InSAR/SAR). However, the ideal filtering results are difficult to obtain due to dense fringe and low coherence regions. Moreover, the InSAR/SAR data range is relatively large, so the efficiency of interferential phase filtering is one of the major problems. In this letter, we proposed an interferometric phase filtering method based on an amended matrix pencil and linear window mean filter. The combination of the matrix pencil and the linear mean filter are introduced to the interferometric phase filtering for the first… More >

View
4044

Download
2076

Cited by
1
Open Access

ARTICLE

3D Web Reconstruction of a Fibrous Filter Using Sequential Multi-Focus Images
Lingjie Yu, Guanlin Wang, Chao Zhi, Bugao Xu
CMES-Computer Modeling in Engineering & Sciences, Vol.119, No.2, pp. 365-372, 2019, DOI:10.32604/cmes.2019.04494
（This article belongs to the Special Issue: Beyond the Hypes of Geospatial Big Data: Theories, Methods, Analytics, and Applications)
Abstract A fibrous filtering material is a kind of fiber assembly whose structure exhibits a three-dimensional (3D) network with dense microscopic open channels. The geometrical/morphological attributes, such as orientations, curvatures and compactness, of fibers in the network is the key to the filtration performance of the material. However, most of the previous studies were based on materials’ 2D micro-images, which were unable to accurately measure these important 3D features of a filter’s structure. In this paper, we present an imaging method to reconstruct the 3D structure of a fibrous filter from its optical microscopic images. Firstly, More >

View
4366

Download
1900

Cited by
1
Open Access

ARTICLE

Optimization of Well Position and Sampling Frequency for Groundwater Monitoring and Inverse Identification of Contamination Source Conditions Using Bayes’ Theorem
Shuangsheng Zhang, Hanhu Liu, Jing Qiang, Hongze Gao, Diego Galar, Jing Lin
CMES-Computer Modeling in Engineering & Sciences, Vol.119, No.2, pp. 373-394, 2019, DOI:10.32604/cmes.2019.03825
（This article belongs to the Special Issue: Beyond the Hypes of Geospatial Big Data: Theories, Methods, Analytics, and Applications)
Abstract Coupling Bayes’ Theorem with a two-dimensional (2D) groundwater solute advection-diffusion transport equation allows an inverse model to be established to identify a set of contamination source parameters including source intensity (M ), release location ( X₀ , Y₀) and release time (T₀), based on monitoring well data. To address the issues of insufficient monitoring wells or weak correlation between monitoring data and model parameters, a monitoring well design optimization approach was developed based on the Bayesian formula and information entropy. To demonstrate how the model works, an exemplar problem with an instantaneous release of a contaminant in… More >

View
4224

Download
1900

Cited by
1
Open Access

ARTICLE

A Data-Intensive FLAC^3D Computation Model: Application of Geospatial Big Data to Predict Mining Induced Subsidence
Yaqiang Gong, Guangli Guo
CMES-Computer Modeling in Engineering & Sciences, Vol.119, No.2, pp. 395-408, 2019, DOI:10.32604/cmes.2019.03686
（This article belongs to the Special Issue: Beyond the Hypes of Geospatial Big Data: Theories, Methods, Analytics, and Applications)
Abstract Although big data are widely used in various fields, its application is still rare in the study of mining subsidence prediction (MSP) caused by underground mining. Traditional research in MSP has the problem of oversimplifying geological mining conditions, ignoring the fluctuation of rock layers with space. In the context of geospatial big data, a data-intensive FLAC^3D (Fast Lagrangian Analysis of a Continua in 3 Dimensions) model is proposed in this paper based on borehole logs. In the modeling process, we developed a method to handle geospatial big data and were able to make full use of More >

View
4219

Download
2121

Cited by
4

Beyond the Hypes of Geospatial Big Data: Theories, Methods, Analytics, and Applications

Guest Editors

Summary

Keywords

Published Papers

View

4848

Download

2296

Cited by

1

View

4058

Download

2063

View

4653

Download

2217

Cited by

2

View

4179

Download

1863

Cited by

2

View

4362

Download

2118

Cited by

2

View

3931

Download

1749

View

4531

Download

1963

Cited by

6

View

4044

Download

2076

Cited by

1

View

4366

Download

1900

Cited by

1

View

4224

Download

1900

Cited by

1

View

4219

Download

2121

Cited by

4

Further Information

Guidelines

Follow Us

Join Us

Contact Us

WhatsApp:

Share Link