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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Earth Sciences".

Deadline for manuscript submissions: 10 February 2025 | Viewed by 22770

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Special Issue Editors

Dr. Qiliang Liu

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Guest Editor

Department of Geo-informatics, Central South University, Changsha 410083, Hunan, China
Interests: spatio-temoral clustering; scale; spatial statistics

Dr. Yan Shi

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Guest Editor

Department of Geo-Informatics, Central South University, Changsha 410083, China
Interests: spatio-temporal data mining; spatio-temporal outlier detection
Special Issues, Collections and Topics in MDPI journals

Dr. Zhanjun He

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Guest Editor

School of Geography and Information Engineering, China University of Geosciences, Wuhan 430074, Hubei, China
Interests: spatio-temporal data mining; spatio-temporal association rule mining

Special Issue Information

Dear Colleagues,

It is our pleasure to invite you to contribute to this Special Issue entitled Recent Advances in Geospatial Big Data Mining.

In recent years, geospatial big data has attracted extensive attention from different disciplines. Geospatial big data can be roughly classified into two types, i.e., big earth observation data and big human behavior data. Data mining is essential for revealing valuable spatio–temporal patterns (e.g., clusters, outliers, association rules, etc.) hidden in geospatial big data, which are useful for understanding complex human–land relationships. Over the past two decades, the identification of spatial patterns from geospatial big data has been a popular topic in urban planning, transportation management, epidemiology, environmental science, and criminology. Geospatial big data has some unique characteristics, e.g., fine spatio-temporal granularity, wide spatio-temporal scope, rich information on human–land relationships, high spatio-temporal bias, and low spatio-temporal precision. Correspondingly, geospatial big data requires specially designed data mining methods given its unique characteristics. Geospatial big data mining is facing new opportunities and challenges.

In this Special Issue, we invite submissions exploring cutting-edge research and recent advances in the field of geospatial big data mining. Both theoretical and experimental studies are welcome.

Dr. Qiliang Liu
Dr. Yan Shi
Dr. Zhanjun He
Guest Editors

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Keywords

geospatial big data
spatio-temporal clustering
spatio-temporal outlier detection
spatio-temporal association
spatio-temporal prediction
human mobility
urban computing
LiDAR Data

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Published Papers (11 papers)

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Research

27 pages, 12093 KiB

Open AccessArticle

Spatio-Temporal Heterogeneous Ensemble Learning Method for Predicting Land Subsidence

by Bin Zhao, Gusheng Wu, Junjie Li, Qianhong Wu and Min Deng

Appl. Sci. 2024, 14(18), 8330; https://doi.org/10.3390/app14188330 - 16 Sep 2024

Viewed by 668

Abstract

The prediction of land subsidence is of significant value for the early warning and prevention of geological disasters. Although numerous land subsidence prediction methods are currently available, two obstacles still exist: (i) spatio-temporal heterogeneity of land subsidence is not well considered, and (ii) the prediction performance of individual models is unsatisfactory when the data do not meet their assumptions. To address these issues, we developed a spatio-temporal heterogeneous ensemble learning method for predicting land subsidence. Firstly, a two-stage hybrid spatio-temporal clustering method was proposed to divide the dataset into internally homogeneous spatio-temporal clusters. Secondly, within each spatio-temporal cluster, an ensemble learning strategy was employed to combine one time series prediction model and three spatio-temporal prediction models to reduce the prediction uncertainty of an individual model. Experiments on a land subsidence dataset from Cangzhou, China, show that the prediction accuracy of the proposed method is significantly higher than that of four individual prediction models. Full article

(This article belongs to the Special Issue Recent Advances in Geospatial Big Data Mining)

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13 pages, 1845 KiB

Open AccessArticle

Refinement and Computation Method for Line/Body Topological Relationships

by Xiaoguang Zhou, Xiaohan Wang, Dongyang Hou, Qiankun Kang and Nawaz Ali

Appl. Sci. 2024, 14(8), 3474; https://doi.org/10.3390/app14083474 - 20 Apr 2024

Viewed by 976

Abstract

Three-dimensional topological relationships serve as a theoretical foundation for quality control, update processing, and spatial analysis of three-dimensional spatial data in real-world three-dimensional GIS. The existing 3D topological relationship models are all basic relationship models that cannot distinguish the refined topological relationship between the line and the body with multiple intersections. In this study, we develop a 3D refined topological relationship description framework that draws from the two-dimensional refined topological relationship model, defines the unit intersection between the line and the body based on manifold topology, and proposes a method for describing the unit intersections between the line and the body considering Euler numbers and adjacency types. In total, 23 basic types between the line and the body are deduced. An example is provided to illustrate the distinguished refined topological relationship between the line and the body with multiple intersections. Subsequently, an algorithm for determining the basic type of line/body is developed. Finally, a line/body refined topological relationship computation prototype system is developed using the Nef polyhedron model, C++ language, and an open-source geometric algorithm library, and the effectiveness of our method is verified using actual building and pedestrian data. Full article

(This article belongs to the Special Issue Recent Advances in Geospatial Big Data Mining)

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24 pages, 8453 KiB

Open AccessArticle

Spatial Patterns and Multi-Dimensional Impact Analysis of Urban Street Quality Perception under Multi-Source Data: A Case Study of Wuchang District in Wuhan, China

by Tianyue Li, Hong Xu and Haozun Sun

Appl. Sci. 2023, 13(21), 11740; https://doi.org/10.3390/app132111740 - 26 Oct 2023

Cited by 3 | Viewed by 2912

Abstract

The human spatial perception of urban streets has a high complexity and traditional research methods often focus on access surveys of human perception. Urban streets serve as both a direct conduit for pedestrians’ impressions of a city and a reflection of the spatial quality of that city. Street-view images can provide a large amount of primary data for the image semantic segmentation technique. Deep learning techniques were used in this study to collect the boring, beautiful, depressing, lively, safe, and wealthy perception scores of street spaces based on these images. Then, the spatial pattern of urban street-space quality perception was analyzed by global Moran’s I and GIS hotspot analyses. The findings demonstrate that various urban facilities affect street quality perception in different ways and that the strength of an influencing factor’s influence varies depending on its geographical location. The results of the influencing factors reveal the difference in the degree of influence of positive and negative influencing factors on various perceptions of the visual dimension of pedestrians. The primary contribution of this study is that it reduces the potential bias of a single data source by using multi-dimensional impact analysis to explain the relationship between urban street perception and urban facilities and visual elements. The study’s findings offer direction for high-quality urban development as well as advice for urban planning and enhanced design. Full article

(This article belongs to the Special Issue Recent Advances in Geospatial Big Data Mining)

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22 pages, 4116 KiB

Open AccessArticle

A Formation Mechanism of Spatial Distribution Pattern of Industrial Clusters under Flow Space

by Yan Shi, Yan Wu, Bingrong Chen, Da Wang and Min Deng

Appl. Sci. 2023, 13(11), 6704; https://doi.org/10.3390/app13116704 - 31 May 2023

Cited by 2 | Viewed by 1380

Abstract

This study focuses on analyzing the spatial distribution pattern and formation mechanisms of urban industrial clusters and aims to address the mismatch between industrial clusters and resource distribution. Firstly, the spatial distribution pattern of industrial clusters is analyzed using the kernel density estimation approach. Subsequently, a multi-layered model of interactive driving factors is constructed to analyze the functional types within the multi-layered network space. Lastly, a spatial weighted regression analysis model, considering the intensity of flow space, is developed to explore the intrinsic formation mechanisms of industrial agglomeration. The experimental results indicate the following: (1) There is a trend of industrial agglomeration in the Yangtze River Delta region, primarily concentrated in cities such as Shanghai, Nanjing, Hefei, Jinhua, and Taizhou. (2) The impact of spatial interaction factors on industrial agglomeration development is significant, and the analysis of interaction networks reflects the strength of interactive influencing factors to a certain extent. (3) The regression analysis model, which incorporates interactive information considering flow space intensity, better aligns with the study of the actual mechanisms behind industrial agglomeration in physical space. Full article

(This article belongs to the Special Issue Recent Advances in Geospatial Big Data Mining)

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18 pages, 12618 KiB

Open AccessArticle

Use of Land Gravity Data in Small Areas to Support Structural Geology, a Case Study in Eskişehir Basin, Turkey

by Emir Balkan and Muammer Tün

Appl. Sci. 2023, 13(4), 2286; https://doi.org/10.3390/app13042286 - 10 Feb 2023

Viewed by 2310

Abstract

Various researchers have contributed to the literature on the locations and lengths of existing faults in the Eskişehir Basin, Turkey. However, the majority of the literature on the subject bases its results on fault indications observed on the surface, for example, surface ruptures. In addition, studies using geophysical methods in order to reveal buried faults have also fallen short regarding depth compared to gravity. In order to have a better understanding, the gravity method was applied with a total of 448 gravity measurements on five parallel lines in the north–south direction of the study area, which also includes the urban area of the Eskişehir Basin. Considering the neotectonics of the Eskişehir basin, the measurement lines were chosen to perpendicularly cut the east–west extending faults of the Eskişehir fault zone. For the first time in the literature, a detailed Bouguer gravity anomaly map has been obtained for the Eskişehir Basin using land gravity measurements. The edge detection Horizontal Gradient Magnitude (HGM) and Euler Deconvolution (ED) methods were applied to obtained Bouguer anomaly data. Both of these use spatial analysis of Bouguer gravity anomalies. An HGM map shows the presence of maximum amplitude areas in the south and north of the study, and these areas were found to be compatible with the known faults in the literature. ED solutions also support HGM maximums. The relationship between the lineaments obtained from the edge detections and the seismicity of the region were examined. It can be seen that the results obtained from both the HGM and ED edge detection methods are highly compatible with each other, and highly related to the structural geology of the region. Although great agreement with the faults in the literature was determined by both methods, only the ED method showed a number of newly found faults in the area. In addition, the locations of the known faults in the region were supported by the geo-physical gravity method for the first time. Full article

(This article belongs to the Special Issue Recent Advances in Geospatial Big Data Mining)

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19 pages, 7817 KiB

Open AccessArticle

Geospatial Evaluation of Cropping Pattern and Cropping Intensity Using Multi Temporal Harmonized Product of Sentinel-2 Dataset on Google Earth Engine

by Sonia, Tathagata Ghosh, Amel Gacem, Taghreed Alsufyani, M. Mujahid Alam, Krishna Kumar Yadav, Mohammed Amanullah and Marina M. S. Cabral-Pinto

Appl. Sci. 2022, 12(24), 12583; https://doi.org/10.3390/app122412583 - 8 Dec 2022

Cited by 4 | Viewed by 3112

Abstract

Due to the declining land resources over the past few decades, the intensification of land uses has played a significant role in balancing the ever-increasing demand for food in developing nations such as India. To optimize agricultural land uses, one of the crucial indicators is cropping intensity, which measures the number of times a single parcel of land is farmed. Therefore, it is imperative to create a timely and accurate cropping intensity map so that landowners and agricultural planners can use it to determine the best course of action for the present and for the future. In the present study, we have developed an algorithm on Google Earth Engine (GEE) to depict cropping patterns and further fused it with a GIS environment to depict cropping intensity in the arid western plain zone of Rajasthan, India. A high-resolution multi-temporal harmonized product of the Sentinel-2 dataset was incorporated for depicting the growth cycle of crops for the year 2020–2021 using the greenest pixel composites. Kharif and Rabi accounted for 73.44% and 26.56% of the total cultivated area, respectively. Only 7.42% was under the double-cropped area to the total cultivated area. The overall accuracy of the classified image was 90%. For the Kharif crop, the accuracy was 95%, while for Rabi and the double-cropped region, the accuracy was 88%, with a kappa coefficient of 0.784. The present study was able to depict the seasonal plantation system in arid arable land with higher accuracy. The proposed work can be used to monitor cropping patterns and cost-effectively show cropping intensities. Full article

(This article belongs to the Special Issue Recent Advances in Geospatial Big Data Mining)

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18 pages, 6875 KiB

Open AccessArticle

Research on the Spatiotemporal Evolution of the Patterns of Expressed Inferiority in Different Groups of Occupations and Education Stages

by Xi Kuai, Lin Li and Yu Liu

Appl. Sci. 2022, 12(22), 11735; https://doi.org/10.3390/app122211735 - 18 Nov 2022

Viewed by 1606

Abstract

Inferiority is a complex emotion of helplessness and self-deprecation. A lack of timely and effective treatment may cause serious consequences to people who experience inferiority. People with different occupational and educational backgrounds display different patterns of inferiority. Due to privacy issues, individuals who experience inferiority are often reluctant to seek face-to-face help. However, they often spontaneously share their feelings on social media, so social media can provide a large number of data on inferiority. Based on the data from Sina Weibo, the largest social media in China, this study explores the groups that are most affected by inferiority and reveals the spatiotemporal patterns of inferiority groups with different occupational and educational backgrounds based on the data from Sina Weibo, the largest social media in China. In this study, the Weibo data on inferiority-related topics published in 288 Chinese cities from 2012 to 2017 were collected, and the geospatial locations of the posts were extracted. The spatial variation of inferiority was analyzed, and the influence of the inferiority of people in different occupations and at education stages was examined. The results show that science and technology personnel, college students, and manufacturing workers are the groups most strongly affected by inferiority, and the expressed inferiority in the three groups show significant spatiotemporal non-stationarity. Excessive evaluation pressure increases the rate of inferiority among researchers and technicians, and inferiority among college students is increasing every year. In most areas in China, the increase in the density of manufacturing employees increases the risk of inferiority among these individuals. The findings of this study can help relevant organizations to better understand the regional distribution of inferiority and provide references for these organizations to develop regional treatment interventions for inferiority. Full article

(This article belongs to the Special Issue Recent Advances in Geospatial Big Data Mining)

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15 pages, 3236 KiB

Open AccessArticle

Building Function Recognition Using the Semi-Supervised Classification

by Xuejing Xie, Yawen Liu, Yongyang Xu, Zhanjun He, Xueye Chen, Xiaoyun Zheng and Zhong Xie

Appl. Sci. 2022, 12(19), 9900; https://doi.org/10.3390/app12199900 - 1 Oct 2022

Cited by 8 | Viewed by 1917

Abstract

The functional classification of buildings is important for creating and managing urban zones and assisting government departments. Building function recognition is incredibly valuable for wide applications ranging from the determination of energy demand. By aiming at the topic of urban function classification, a semi-supervised graph structure network combined unified message passing model was introduced. The data of this model include spatial location distribution of buildings, building characteristics and the information mined from points of interesting (POIs). In order to extract the context information, each building was regarded as a graph node. Building characteristics and corresponding POIs information were embedded to mine the building function by the graph convolutional neural network. When training the model, several node labels in the graph were masked, and then these labels were predicted by the trained model so that this work could take full advantage of the node label and the feature information of all nodes in both the training and prediction stages. Quasi-experiments proved that the proposed method for building function classification using multi-source data enables the model to capture more meaningful information with limited labels, and it achieves better function classification results. Full article

(This article belongs to the Special Issue Recent Advances in Geospatial Big Data Mining)

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20 pages, 41985 KiB

Open AccessArticle

Detecting Dynamic Communities in Vehicle Movements Using Ant Colony Optimization

by Qiliang Liu, Sancheng Zhu, Meihua Chen and Wenkai Liu

Appl. Sci. 2022, 12(15), 7608; https://doi.org/10.3390/app12157608 - 28 Jul 2022

Cited by 2 | Viewed by 1299

Abstract

Detecting dynamic community structure in vehicle movements is helpful for revealing urban structures and human mobility patterns. Despite the fruitful research outcomes of community detection, the discovery of irregular-shaped and statistically significant dynamic communities in vehicle movements is still challenging. To overcome this challenge, we developed an evolutionary ant colony optimization (EACO) method for detecting dynamic communities in vehicle movements. Firstly, a weighted, spatially embedded graph was constructed at each time snapshot. Then, an ant-colony-optimization-based spatial scan statistic was upgraded to identify statistically significant communities at each snapshot by considering the effects of the communities discovered at the previous snapshot. Finally, different rules defined based on the Jaccard coefficient were used to identify the evolution of the communities. Experimental results on both simulated and real-world vehicle movement datasets showed that EACO performs better than three representative dynamic community detection methods: FacetNet (a framework for analyzing communities and evolutions in dynamic networks), DYNMOGA (dynamic multi-objective genetic algorithm), and RWLA (random-walk-based Leiden algorithm). The dynamic communities identified by EACO may be useful for understanding the dynamic organization of urban structures. Full article

(This article belongs to the Special Issue Recent Advances in Geospatial Big Data Mining)

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14 pages, 3928 KiB

Open AccessArticle

Convolutional Neural Network-Based Travel Mode Recognition Based on Multiple Smartphone Sensors

by Lin Guo, Jincai Huang, Wei Ma, Longzhi Sun, Lianjie Zhou, Jianping Pan and Wentao Yang

Appl. Sci. 2022, 12(13), 6511; https://doi.org/10.3390/app12136511 - 27 Jun 2022

Cited by 3 | Viewed by 1796

Abstract

Nowadays, large-scale human mobility has led to increasingly severe traffic congestion in cities, how to accurately identify people’s travel mode has become particularly important for urban traffic planning and management. However, traditional methods are based on telephone interviews or questionnaires, which makes it difficult to obtain accurate and effective data. Nowadays, numbers of smartphones are equipped with various sensors, including accelerometers, gyroscopes, and GPS, providing a novel social sensing data source to detect people’s travel modes. The fusion of multiple sensor data is a promising way for travel mode detection. However, how to use these sensor data to accurately detect travel mode is still a challenging task. In this paper, we presented a light-weight method for travel mode detection based on four types of smartphone sensor data collected from an accelerometer, gyroscope, magnetometer, and barometer, and a prototype application was developed. Then, a novel convolutional neural network (CNN) was designed to identify five representative travel modes (walk, bicycle, bus, car, and metro). We compared the overall performance of the proposed method via different hyperparameters, and the experimental results show that the F value of the proposed method reaches 97%, which verified the effectiveness of the proposed method for travel mode classification. Full article

(This article belongs to the Special Issue Recent Advances in Geospatial Big Data Mining)

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22 pages, 14199 KiB

Open AccessArticle

An Analysis of Food Accessibility of Mountain Cities in China: A Case Study of Chongqing

by Yufeng He, Haixia Pu, Nianhua Liu, Yongchuan Zhang and Yehua Sheng

Appl. Sci. 2022, 12(7), 3236; https://doi.org/10.3390/app12073236 - 22 Mar 2022

Cited by 4 | Viewed by 2962

Abstract

Mountain cities are characterized by undulating terrain, complex road networks, and diverse road facilities, which makes accessing food more difficult than in cities with a flat terrain. This study proposes an enhanced two-step method based on the Baidu map service for the construction of supermarket–market–retail food sales architecture and for calculating food accessibility. The accessibility indices of seven major food categories (grains and oils, fruits, vegetables, seafood, meat, milk, and eggs) were calculated considering the principle of the fairest walking routes in Chongqing. The correlations between food accessibility and house price and house age in Chongqing were explored through local Moran’s analysis and geographically weighted regression. The correlations illustrated the fairness of the distribution of food accessibility in Chongqing among the poor and rich. The experiments showed generally well-developed food accessibility in the main urban areas of Chongqing. However, accessibility to fresh fruits and vegetables lagged in newly built urban areas. Full article

(This article belongs to the Special Issue Recent Advances in Geospatial Big Data Mining)

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