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Luo Chen
College of Electronic Science, National University of Defense Technology, Changsha 410073, China

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Journal article
Published: 18 November 2020 in Computers & Geosciences
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Rapid visualization of large-scale spatial vector data is a long-standing challenge in Geographic Information Science. In existing methods, the computation overheads grow rapidly with data volumes, leading to the incapability of providing real-time visualization for large-scale spatial vector data, even with parallel acceleration technologies. To fill the gap, we present HiVision, a display-driven visualization model for large-scale spatial vector data. Different from traditional data-driven methods, the computing units in HiVision are pixels rather than spatial objects to achieve real-time performance, and efficient spatial-index-based strategies are introduced to estimate the topological relationships between pixels and spatial objects. HiVision can maintain exceedingly good performance regardless of the data volume due to the stable pixel number for display. In addition, an optimized parallel computing architecture is proposed in HiVision to ensure the ability of real-time visualization. Experiments show that our approach outperforms traditional methods in rendering speed and visual effects while dealing with large-scale spatial vector data, and can provide interactive visualization of datasets with billion-scale points/segments/edges in real-time with flexible rendering styles. The HiVision code is open-sourced at https://github.com/MemoryMmy/HiVision with an online demonstration.

ACS Style

Mengyu Ma; Ye Wu; Xue Ouyang; Luo Chen; Jun Li; Ning Jing. HiVision: Rapid visualization of large-scale spatial vector data. Computers & Geosciences 2020, 147, 104665 .

AMA Style

Mengyu Ma, Ye Wu, Xue Ouyang, Luo Chen, Jun Li, Ning Jing. HiVision: Rapid visualization of large-scale spatial vector data. Computers & Geosciences. 2020; 147 ():104665.

Chicago/Turabian Style

Mengyu Ma; Ye Wu; Xue Ouyang; Luo Chen; Jun Li; Ning Jing. 2020. "HiVision: Rapid visualization of large-scale spatial vector data." Computers & Geosciences 147, no. : 104665.

Journal article
Published: 07 October 2020 in IEEE Transactions on Big Data
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As a typical big data representative, the information of global Cellular Signal Strength (CSS), defined as the signal power received by mobile phones, plays an important role in geographic flow analysis, because the density of such information can reflect the urbanization variables such as population, gross domestic product, built-up area, electric power consumption, and etc. Despite the importance, the real-time analysis of global CSS distribution remains a challenging problem due to the large data scale. In this paper, a Display-driven Computing (DisDC) technique is designed and applied to provide efficient large scale interactive CSS visualization, generating results by calculating the value of each pixel that directly for display that can dramatically improve system capacity in big data handling. Specifically, we present an efficient CSS measurement algorithm, which introduces spatial indexes and a corresponding query strategy; besides, an optimized parallel computing architecture is proposed to ensure the ability of real-time visualization. Experiments show that our approach obviously outperforms traditional methods and is capable of handling more than 40 million base stations in real-time. Moreover, an online demonstration is provided at https://github.com/MemoryMmy/CSSMap.

ACS Style

Mengyu Ma; Luo Chen; Xue Ouyang; Xiaoran Liu; Jun Li; Ning Jing. Efficient Interactive Global Cellular Signal Strength Visualization. IEEE Transactions on Big Data 2020, PP, 1 -1.

AMA Style

Mengyu Ma, Luo Chen, Xue Ouyang, Xiaoran Liu, Jun Li, Ning Jing. Efficient Interactive Global Cellular Signal Strength Visualization. IEEE Transactions on Big Data. 2020; PP (99):1-1.

Chicago/Turabian Style

Mengyu Ma; Luo Chen; Xue Ouyang; Xiaoran Liu; Jun Li; Ning Jing. 2020. "Efficient Interactive Global Cellular Signal Strength Visualization." IEEE Transactions on Big Data PP, no. 99: 1-1.

Journal article
Published: 16 November 2019 in ISPRS International Journal of Geo-Information
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Measuring the similarity between a pair of trajectories is the basis of many spatiotemporal clustering methods and has wide applications in trajectory pattern mining. However, most measures of trajectory similarity in the literature are based on precise models that ignore the inherent uncertainty in trajectory data recorded by sensors. Traditional computing or mining approaches that assume the preciseness and exactness of trajectories therefore risk underperforming or returning incorrect results. To address the problem, we propose an amended ellipse model which takes both interpolation error and positioning error into account by making use of motion features of trajectory to compute the ellipse’s shape parameters. A specialized similarity measure method considering uncertainty called UTSM based on the model is also proposed. We validate the approach experimentally on both synthetic and real-world data and show that UTSM is not only more robust to noise and outliers but also more tolerant of different sample frequencies and asynchronous sampling of trajectories.

ACS Style

Ning Guo; Shashi Shekhar; Wei Xiong; Luo Chen; Ning Jing. UTSM: A Trajectory Similarity Measure Considering Uncertainty Based on an Amended Ellipse Model. ISPRS International Journal of Geo-Information 2019, 8, 518 .

AMA Style

Ning Guo, Shashi Shekhar, Wei Xiong, Luo Chen, Ning Jing. UTSM: A Trajectory Similarity Measure Considering Uncertainty Based on an Amended Ellipse Model. ISPRS International Journal of Geo-Information. 2019; 8 (11):518.

Chicago/Turabian Style

Ning Guo; Shashi Shekhar; Wei Xiong; Luo Chen; Ning Jing. 2019. "UTSM: A Trajectory Similarity Measure Considering Uncertainty Based on an Amended Ellipse Model." ISPRS International Journal of Geo-Information 8, no. 11: 518.

Journal article
Published: 20 June 2019 in ISPRS International Journal of Geo-Information
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The tremendous advance in information technology has promoted the rapid development of location-based services (LBSs), which play an indispensable role in people’s daily lives. Compared with a traditional LBS based on Point-Of-Interest (POI), which is an isolated location point, an increasing number of demands have concentrated on Region-Of-Interest (ROI) exploration, i.e., geographic regions that contain many POIs and express rich environmental information. The intention behind the POI is to search the geographical regions related to the user’s requirements, which contain some spatial objects, such as POIs and have certain environmental characteristics. In order to achieve effective ROI exploration, we propose an ROI top-k keyword query method that considers the environmental information of the regions. Specifically, the Word2Vec model has been introduced to achieve the distributed representation of POIs and capture their environmental semantics, which are then leveraged to describe the environmental characteristic information of the candidate ROI. Given a keyword query, different query patterns are designed to measure the similarities between the query keyword and the candidate ROIs to find the k candidate ROIs that are most relevant to the query. In the verification step, an evaluation criterion has been developed to test the effectiveness of the distributed representations of POIs. Finally, after generating the POI vectors in high quality, we validated the performance of the proposed ROI top-k query on a large-scale real-life dataset where the experimental results demonstrated the effectiveness of our proposals.

ACS Style

Xiangdian Zhu; Ye Wu; Luo Chen; Ning Jing; Zhu; Wu; Chen; Jing. Spatial Keyword Query of Region-Of-Interest Based on the Distributed Representation of Point-Of-Interest. ISPRS International Journal of Geo-Information 2019, 8, 287 .

AMA Style

Xiangdian Zhu, Ye Wu, Luo Chen, Ning Jing, Zhu, Wu, Chen, Jing. Spatial Keyword Query of Region-Of-Interest Based on the Distributed Representation of Point-Of-Interest. ISPRS International Journal of Geo-Information. 2019; 8 (6):287.

Chicago/Turabian Style

Xiangdian Zhu; Ye Wu; Luo Chen; Ning Jing; Zhu; Wu; Chen; Jing. 2019. "Spatial Keyword Query of Region-Of-Interest Based on the Distributed Representation of Point-Of-Interest." ISPRS International Journal of Geo-Information 8, no. 6: 287.

Journal article
Published: 13 March 2019 in ISPRS International Journal of Geo-Information
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High crowd mobility is a characteristic of transportation hubs such as metro/bus/bike stations in cities worldwide. Forecasting the crowd flow for such places, known as station-level crowd flow forecast (SLCFF) in this paper, would have many benefits, for example traffic management and public safety. Concretely, SLCFF predicts the number of people that will arrive at or depart from stations in a given period. However, one challenge is that the crowd flows across hundreds of stations irregularly scattered throughout a city are affected by complicated spatio-temporal events. Additionally, some external factors such as weather conditions or holidays may change the crowd flow tremendously. In this paper, a spatio-temporal U-shape network model (ST-Unet) for SLCFF is proposed. It is a neural network-based multi-output regression model, handling hundreds of target variables, i.e., all stations’ in and out flows. ST-Unet emphasizes stations’ spatial dependence by integrating the crowd flow information from neighboring stations and the cluster it belongs to after hierarchical clustering. It learns the temporal dependence by modeling the temporal closeness, period, and trend of crowd flows. With proper modifications on the network structure, ST-Unet is easily trained and has reliable convergency. Experiments on four real-world datasets were carried out to verify the proposed method’s performance and the results show that ST-Unet outperforms seven baselines in terms of SLCFF.

ACS Style

Yirong Zhou; Hao Chen; Jun Li; Ye Wu; Jiangjiang Wu; Luo Chen. Large-Scale Station-Level Crowd Flow Forecast with ST-Unet. ISPRS International Journal of Geo-Information 2019, 8, 140 .

AMA Style

Yirong Zhou, Hao Chen, Jun Li, Ye Wu, Jiangjiang Wu, Luo Chen. Large-Scale Station-Level Crowd Flow Forecast with ST-Unet. ISPRS International Journal of Geo-Information. 2019; 8 (3):140.

Chicago/Turabian Style

Yirong Zhou; Hao Chen; Jun Li; Ye Wu; Jiangjiang Wu; Luo Chen. 2019. "Large-Scale Station-Level Crowd Flow Forecast with ST-Unet." ISPRS International Journal of Geo-Information 8, no. 3: 140.

Journal article
Published: 10 January 2019 in ISPRS International Journal of Geo-Information
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Buffer and overlay analysis are fundamental operations which are widely used in Geographic Information Systems (GIS) for resource allocation, land planning, and other relevant fields. Real-time buffer and overlay analysis for large-scale spatial data remains a challenging problem because the computational scales of conventional data-oriented methods expand rapidly with data volumes. In this paper, we present HiBO, a visualization-oriented buffer-overlay analysis model which is less sensitive to data volumes. In HiBO, the core task is to determine the value of pixels for display. Therefore, we introduce an efficient spatial-index-based buffer generation method and an effective set-transformation-based overlay optimization method. Moreover, we propose a fully optimized hybrid-parallel processing architecture to ensure the real-time capability of HiBO. Experiments on real-world datasets show that our approach is capable of handling ten-million-scale spatial data in real time. An online demonstration of HiBO is provided (http://www.higis.org.cn: 8080/hibo).

ACS Style

Mengyu Ma; Ye Wu; Luo Chen; Jun Li; Ning Jing. Interactive and Online Buffer-Overlay Analytics of Large-Scale Spatial Data. ISPRS International Journal of Geo-Information 2019, 8, 21 .

AMA Style

Mengyu Ma, Ye Wu, Luo Chen, Jun Li, Ning Jing. Interactive and Online Buffer-Overlay Analytics of Large-Scale Spatial Data. ISPRS International Journal of Geo-Information. 2019; 8 (1):21.

Chicago/Turabian Style

Mengyu Ma; Ye Wu; Luo Chen; Jun Li; Ning Jing. 2019. "Interactive and Online Buffer-Overlay Analytics of Large-Scale Spatial Data." ISPRS International Journal of Geo-Information 8, no. 1: 21.

Journal article
Published: 01 December 2018 in IEICE Transactions on Information and Systems
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In terms of spatial online aggregation, traditional stand-alone serial methods gradually become limited. Although parallel computing is widely studied nowadays, there scarcely has research conducted on the index-based parallel online aggregation methods, specifically for spatial data. In this letter, a parallel multilevel indexing method is proposed to accelerate spatial online aggregation analyses, which contains two steps. In the first step, a parallel aR tree index is built to accelerate aggregate query locally. In the second step, a multilevel sampling data pyramid structure is built based on the parallel aR tree index, which contribute to the concurrent returned query results with certain confidence degree. Experimental and analytical results verify that the methods are capable of handling billion-scale data.

ACS Style

Luo Chen; Ye Wu; Wei Xiong; Ning Jing. A Multilevel Indexing Method for Approximate Geospatial Aggregation Analysis. IEICE Transactions on Information and Systems 2018, E101.D, 3242 -3245.

AMA Style

Luo Chen, Ye Wu, Wei Xiong, Ning Jing. A Multilevel Indexing Method for Approximate Geospatial Aggregation Analysis. IEICE Transactions on Information and Systems. 2018; E101.D (12):3242-3245.

Chicago/Turabian Style

Luo Chen; Ye Wu; Wei Xiong; Ning Jing. 2018. "A Multilevel Indexing Method for Approximate Geospatial Aggregation Analysis." IEICE Transactions on Information and Systems E101.D, no. 12: 3242-3245.

Journal article
Published: 30 November 2018 in ISPRS International Journal of Geo-Information
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Buffer analysis, a fundamental function in a geographic information system (GIS), identifies areas by the surrounding geographic features within a given distance. Real-time buffer analysis for large-scale spatial data remains a challenging problem since the computational scales of conventional data-oriented methods expand rapidly with increasing data volume. In this paper, we introduce HiBuffer, a visualization-oriented model for real-time buffer analysis. An efficient buffer generation method is proposed which introduces spatial indexes and a corresponding query strategy. Buffer results are organized into a tile-pyramid structure to enable stepless zooming. Moreover, a fully optimized hybrid parallel processing architecture is proposed for the real-time buffer analysis of large-scale spatial data. Experiments using real-world datasets show that our approach can reduce computation time by up to several orders of magnitude while preserving superior visualization effects. Additional experiments were conducted to analyze the influence of spatial data density, buffer radius, and request rate on HiBuffer performance, and the results demonstrate the adaptability and stability of HiBuffer. The parallel scalability of HiBuffer was also tested, showing that HiBuffer achieves high performance of parallel acceleration. Experimental results verify that HiBuffer is capable of handling 10-million-scale data.

ACS Style

Mengyu Ma; Ye Wu; Wenze Luo; Luo Chen; Jun Li; Ning Jing. HiBuffer: Buffer Analysis of 10-Million-Scale Spatial Data in Real Time. ISPRS International Journal of Geo-Information 2018, 7, 467 .

AMA Style

Mengyu Ma, Ye Wu, Wenze Luo, Luo Chen, Jun Li, Ning Jing. HiBuffer: Buffer Analysis of 10-Million-Scale Spatial Data in Real Time. ISPRS International Journal of Geo-Information. 2018; 7 (12):467.

Chicago/Turabian Style

Mengyu Ma; Ye Wu; Wenze Luo; Luo Chen; Jun Li; Ning Jing. 2018. "HiBuffer: Buffer Analysis of 10-Million-Scale Spatial Data in Real Time." ISPRS International Journal of Geo-Information 7, no. 12: 467.

Conference paper
Published: 06 November 2018 in Proceedings of the 7th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data
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ACS Style

Mengyu Ma; Wei Xiong; Luo Chen; Ning Guo; Jun Li; Ning Jing. HiAccess. Proceedings of the 7th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data 2018, 28 -31.

AMA Style

Mengyu Ma, Wei Xiong, Luo Chen, Ning Guo, Jun Li, Ning Jing. HiAccess. Proceedings of the 7th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data. 2018; ():28-31.

Chicago/Turabian Style

Mengyu Ma; Wei Xiong; Luo Chen; Ning Guo; Jun Li; Ning Jing. 2018. "HiAccess." Proceedings of the 7th ACM SIGSPATIAL International Workshop on Analytics for Big Geospatial Data , no. : 28-31.

Journal article
Published: 17 September 2018 in IEEE Access
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Accessibility is an important issue in transport geography, land planning, and many other related fields. Accessibility problems become computationally demanding when involving high-resolution requirements. Using conventional methods, providing high-resolution accessibility analysis for real-time decision support remains a challenge. In this paper, we present a parallel processing model, named HiAccess, to solve the high-resolution accessibility analysis problems in real time. One feature of HiAccess is a fast road network construction method, in which the road network topology is determined by traversing the original road nodes only once. The parallel strategies of HiAccess are fully optimized with few repeated computations. Moreover, a simple, efficient, and highly effective map generalization method is proposed to reduce computation load without an accuracy loss. The flexibility of HiAccess enables it to work well when applied to different accessibility analysis models. To further demonstrate the applicability of HiAccess, a case study of settlement sites selection for poverty alleviation in Xiangxi, Central China, is carried out. The accessibility of jobs, health care, educational resources, and other public facilities are comprehensively analyzed for settlement sites selection. HiAccess demonstrates the striking performance of measuring high-resolution (using $100~\text {m} \times 100~\text {m}$ grids) accessibility of a city (in total over 250k grids, roads with 232k segments, and 40 facilities) in 1 sec without preprocessing, while ArcGIS takes nearly 1 h to achieve a less satisfactory result. In additional experiments, HiAccess is tested on much larger data sets with excellent performance.

ACS Style

Mengyu Ma; Ye Wu; Ning Guo; Luo Chen; Qi Gong; Jun Li. A Parallel Processing Model for Accelerating High-Resolution Geo-Spatial Accessibility Analysis. IEEE Access 2018, 6, 52936 -52952.

AMA Style

Mengyu Ma, Ye Wu, Ning Guo, Luo Chen, Qi Gong, Jun Li. A Parallel Processing Model for Accelerating High-Resolution Geo-Spatial Accessibility Analysis. IEEE Access. 2018; 6 ():52936-52952.

Chicago/Turabian Style

Mengyu Ma; Ye Wu; Ning Guo; Luo Chen; Qi Gong; Jun Li. 2018. "A Parallel Processing Model for Accelerating High-Resolution Geo-Spatial Accessibility Analysis." IEEE Access 6, no. : 52936-52952.

Conference paper
Published: 01 June 2018 in 2018 26th International Conference on Geoinformatics
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With the development of the Internet and the enhanced rendering ability of browser end, electronic map based on the Internet, which is called web map, has been visited and plotted by more and more users. Normally, the representation of the plotting results such as the markers and curves are changed with the map level, and the traditional method is to take a strategy to select the composition points. In this paper, instead, an adaptive sampling method of plotting curves which preserves the characteristic points is proposed and a relationship between map level and the number of sampling points is established based on the Radical Law in cartography. Experiments are conducted to compare the proposed sampling method with others to validate the simplicity and accuracy. The result indicates that the proposed method can realize the sampling of plotting curves on the web map at different levels in terms of simplicity and accuracy for the multi-level representation.

ACS Style

Wenze Luo; Ye Wu; Luo Chen; Ning Jing. Sampling-Based Multi-Level Representation of Plotting Curves on Web Map. 2018 26th International Conference on Geoinformatics 2018, 1 -6.

AMA Style

Wenze Luo, Ye Wu, Luo Chen, Ning Jing. Sampling-Based Multi-Level Representation of Plotting Curves on Web Map. 2018 26th International Conference on Geoinformatics. 2018; ():1-6.

Chicago/Turabian Style

Wenze Luo; Ye Wu; Luo Chen; Ning Jing. 2018. "Sampling-Based Multi-Level Representation of Plotting Curves on Web Map." 2018 26th International Conference on Geoinformatics , no. : 1-6.

Conference paper
Published: 01 June 2018 in 2018 26th International Conference on Geoinformatics
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Accessibility is an important issue in transport geography, land planning and many other related fields. Accessibility problems become computationally demanding when involving high-resolution requirements. Using conventional methods, providing high-resolution accessibility analysis for real time decision support remains a challenge. In this paper, we present a parallel method, named HiAccess, to solve the high-resolution accessibility analysis problems in real time. Pointing at accelerating accessibility analysis, we proposed an extended road network structure. Correspondingly, a fast road network construction method is proposed, in which the road network topology is determined by traversing the original road nodes only once. The parallel strategies of HiAccess are fully optimized through theoretical analysis and experimental comparisons. HiAccess demonstrates the striking performance of measuring high-resolution (using 100 m×100m grids) accessibility of a city (in total over 250k grids, roads with 232k segments, 40 facilities) in 1 second without preprocessing, while ArcGIS takes nearly 1 hour to achieve a less satisfactory result.

ACS Style

Mengyu Ma; Ye Wu; Ning Guo; Luo Chen; Qi Gong; Jun Li. HiAccess: A Parallel Method for Measuring High-Resolution Spatial Accessibility in Real Time. 2018 26th International Conference on Geoinformatics 2018, 1 -6.

AMA Style

Mengyu Ma, Ye Wu, Ning Guo, Luo Chen, Qi Gong, Jun Li. HiAccess: A Parallel Method for Measuring High-Resolution Spatial Accessibility in Real Time. 2018 26th International Conference on Geoinformatics. 2018; ():1-6.

Chicago/Turabian Style

Mengyu Ma; Ye Wu; Ning Guo; Luo Chen; Qi Gong; Jun Li. 2018. "HiAccess: A Parallel Method for Measuring High-Resolution Spatial Accessibility in Real Time." 2018 26th International Conference on Geoinformatics , no. : 1-6.

Conference paper
Published: 01 June 2018 in 2018 26th International Conference on Geoinformatics
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Taxi demand prediction is to predict the number of taxis needed in given zones at a certain time. However, it is becoming much more needed and useful to predict where the passengers would probably go and how many, i.e. their probable destinations and the amount. We call such prediction as refined taxi demand prediction, which seldom researches paid on. This is due to the increasingly popular real-time ride sharing, which is an economical and environment-friendly way to call taxis. Such refined taxi demand prediction would improve service quality and user experience. In this paper, we propose a method named ST-Vec, which maps zones with dense low-dimensional vectors (embedding) sustaining their spatio-temporal relationships of taxi demand, i.e. the more probable destination zones of taxi demand from a given zones, their vectors would be closer. By such method, the spatio-temporal relationships of taxi demand in zones can be measured in terms of geometric distance between the respective vectors. By combining this with multi-outputs support vector regression (MSVR) model, we obtain a robust refined taxi demand prediction model and verify it on the New York City taxi trip dataset.

ACS Style

Yirong Zhou; Ye Wu; Jiangjiang Wu; Luo Chen; Jun Li. Refined Taxi Demand Prediction with ST-Vec. 2018 26th International Conference on Geoinformatics 2018, 1 -6.

AMA Style

Yirong Zhou, Ye Wu, Jiangjiang Wu, Luo Chen, Jun Li. Refined Taxi Demand Prediction with ST-Vec. 2018 26th International Conference on Geoinformatics. 2018; ():1-6.

Chicago/Turabian Style

Yirong Zhou; Ye Wu; Jiangjiang Wu; Luo Chen; Jun Li. 2018. "Refined Taxi Demand Prediction with ST-Vec." 2018 26th International Conference on Geoinformatics , no. : 1-6.

Conference paper
Published: 01 June 2018 in 2018 26th International Conference on Geoinformatics
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We interpret the geographic world as a combination of geographic entities (such as city, road, river). Using these geographic entities as the basic granularity for description, storage and manipulation, therefore, can provide a bridge between conceptual understanding and practical application. However, the issue arises of how to design a data model that is generic enough for the geographic entities when the data can change spatially and temporally. This difficulty is compounded when there is a need for geographic inference. In this paper, we offer a feasible solution by presenting a Spatio-Temporal Data Model of Geographic Entities (STDMGE), which describes each geographic entity as geographic object part and geographic relation part, both of which are well designed to organize the multi-source data (traditional geospatial data, sensor data, relationship data among geographic entities, etc.). Spatial-temporal changes can be accurately expressed and efficiently implemented using this model. Moreover, the formal description of geographic rules and events are incorporated in the data model for the possible future requirements of geographic reasoning. We further illustrate the data model by applying it to the case study of the river data.

ACS Style

Qi Gong; Ning Guo; Wei Xiong; Luo Chen; Ning Jing. A Spatio-Temporal Data Model of Geographic Entities. 2018 26th International Conference on Geoinformatics 2018, 1 -6.

AMA Style

Qi Gong, Ning Guo, Wei Xiong, Luo Chen, Ning Jing. A Spatio-Temporal Data Model of Geographic Entities. 2018 26th International Conference on Geoinformatics. 2018; ():1-6.

Chicago/Turabian Style

Qi Gong; Ning Guo; Wei Xiong; Luo Chen; Ning Jing. 2018. "A Spatio-Temporal Data Model of Geographic Entities." 2018 26th International Conference on Geoinformatics , no. : 1-6.

Article
Published: 15 January 2018 in ISPRS International Journal of Geo-Information
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The buffer generation algorithm is a fundamental function in GIS, identifying areas of a given distance surrounding geographic features. Past research largely focused on buffer generation algorithms generated in a stand-alone environment. Moreover, dissolved buffer generation is data- and computing-intensive. In this scenario, the improvement in the stand-alone environment is limited when considering large-scale mass vector data. Nevertheless, recent parallel dissolved vector buffer algorithms suffer from scalability problems, leaving room for further optimization. At present, the prevailing in-memory cluster-computing framework—Spark—provides promising efficiency for computing-intensive analysis; however, it has seldom been researched for buffer analysis. On this basis, we propose a cluster-computing-oriented parallel dissolved vector buffer generating algorithm, called the HPBM, that contains a Hilbert-space-filling-curve-based data partition method, a data skew and cross-boundary objects processing strategy, and a depth-given tree-like merging method. Experiments are conducted in both stand-alone and cluster environments using real-world vector data that include points and roads. Compared with some existing parallel buffer algorithms, as well as various popular GIS software, the HPBM achieves a performance gain of more than 50%.

ACS Style

Jinxin Shen; Luo Chen; Ye Wu; Ning Jing. Approach to Accelerating Dissolved Vector Buffer Generation in Distributed In-Memory Cluster Architecture. ISPRS International Journal of Geo-Information 2018, 7, 26 .

AMA Style

Jinxin Shen, Luo Chen, Ye Wu, Ning Jing. Approach to Accelerating Dissolved Vector Buffer Generation in Distributed In-Memory Cluster Architecture. ISPRS International Journal of Geo-Information. 2018; 7 (1):26.

Chicago/Turabian Style

Jinxin Shen; Luo Chen; Ye Wu; Ning Jing. 2018. "Approach to Accelerating Dissolved Vector Buffer Generation in Distributed In-Memory Cluster Architecture." ISPRS International Journal of Geo-Information 7, no. 1: 26.

Journal article
Published: 30 October 2017 in ISPRS International Journal of Geo-Information
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The processing and analysis of trajectories are the core of many location-based applications and services, while trajectory similarity is an essential concept regularly used. To address the time-consuming problem of similarity query, an efficient algorithm based on Fréchet distance called Ordered Coverage Judge (OCJ) is proposed, which could realize the filtering query with a given Fréchet distance threshold on large-scale trajectory datasets. The OCJ algorithm can obtain the result set quickly by a two-step operation containing morphological characteristic filtering and ordered coverage judgment. The algorithm is expedient to be implemented in parallel for further increases of speed. Demonstrated by experiments over real trajectory data in a multi-core hardware environment, the new algorithm shows favorable stability and scalability besides its higher efficiency in comparison with traditional serial algorithms and other Fréchet distance algorithms.

ACS Style

Ning Guo; Mengyu Ma; Wei Xiong; Luo Chen; Ning Jing. An Efficient Query Algorithm for Trajectory Similarity Based on Fréchet Distance Threshold. ISPRS International Journal of Geo-Information 2017, 6, 326 .

AMA Style

Ning Guo, Mengyu Ma, Wei Xiong, Luo Chen, Ning Jing. An Efficient Query Algorithm for Trajectory Similarity Based on Fréchet Distance Threshold. ISPRS International Journal of Geo-Information. 2017; 6 (11):326.

Chicago/Turabian Style

Ning Guo; Mengyu Ma; Wei Xiong; Luo Chen; Ning Jing. 2017. "An Efficient Query Algorithm for Trajectory Similarity Based on Fréchet Distance Threshold." ISPRS International Journal of Geo-Information 6, no. 11: 326.

Conference paper
Published: 01 January 2017 in Proceedings of the 2017 International Conference on Electronic Industry and Automation (EIA 2017)
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ACS Style

Zheng Zhao; Luo Chen; Ye Wu; Ning Jing. Spark-Based Iterative Spatial Overlay Analysis Method. Proceedings of the 2017 International Conference on Electronic Industry and Automation (EIA 2017) 2017, 1 .

AMA Style

Zheng Zhao, Luo Chen, Ye Wu, Ning Jing. Spark-Based Iterative Spatial Overlay Analysis Method. Proceedings of the 2017 International Conference on Electronic Industry and Automation (EIA 2017). 2017; ():1.

Chicago/Turabian Style

Zheng Zhao; Luo Chen; Ye Wu; Ning Jing. 2017. "Spark-Based Iterative Spatial Overlay Analysis Method." Proceedings of the 2017 International Conference on Electronic Industry and Automation (EIA 2017) , no. : 1.

Conference paper
Published: 01 January 2017 in Proceedings of the 2017 International Conference on Electronic Industry and Automation (EIA 2017)
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ACS Style

Jieyu Dong; Luo Chen; Mengyu Ma; Ning Jing. Research on Multi-level Gazetteer Services Based on Object Relational Database. Proceedings of the 2017 International Conference on Electronic Industry and Automation (EIA 2017) 2017, 1 .

AMA Style

Jieyu Dong, Luo Chen, Mengyu Ma, Ning Jing. Research on Multi-level Gazetteer Services Based on Object Relational Database. Proceedings of the 2017 International Conference on Electronic Industry and Automation (EIA 2017). 2017; ():1.

Chicago/Turabian Style

Jieyu Dong; Luo Chen; Mengyu Ma; Ning Jing. 2017. "Research on Multi-level Gazetteer Services Based on Object Relational Database." Proceedings of the 2017 International Conference on Electronic Industry and Automation (EIA 2017) , no. : 1.

Conference paper
Published: 01 December 2016 in 2016 International Conference on Industrial Informatics - Computing Technology, Intelligent Technology, Industrial Information Integration (ICIICII)
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Spatial Join Aggregation (SJA) is a time-consuming operation in the spatial database, how to design efficient distributed SJA algorithms is attracting more and more attention. The paper proposed a strategy (RSJA-MR) to process spatial join aggregation in MapReduce based on distributed R-tree, which is used to return results of SJA more efficiently. SJA tasks met independent parallel computation and could easily be expressed in MapReduce. The experiment results show that, RSJA-MR is out perform the non-indexed SJA stratedgy in the time performance.

ACS Style

Chuang Yao; Luo Chen; Ye Wu; Jinxin Shen. Accelerating Spatial Join Aggregation with R-Tree for MapReduce. 2016 International Conference on Industrial Informatics - Computing Technology, Intelligent Technology, Industrial Information Integration (ICIICII) 2016, 1 -5.

AMA Style

Chuang Yao, Luo Chen, Ye Wu, Jinxin Shen. Accelerating Spatial Join Aggregation with R-Tree for MapReduce. 2016 International Conference on Industrial Informatics - Computing Technology, Intelligent Technology, Industrial Information Integration (ICIICII). 2016; ():1-5.

Chicago/Turabian Style

Chuang Yao; Luo Chen; Ye Wu; Jinxin Shen. 2016. "Accelerating Spatial Join Aggregation with R-Tree for MapReduce." 2016 International Conference on Industrial Informatics - Computing Technology, Intelligent Technology, Industrial Information Integration (ICIICII) , no. : 1-5.

Conference paper
Published: 01 November 2016 in 2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI)
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Satellite observation scheduling is a complex combinational optimization problem. Current researches usually adopt intelligent optimization methods to solve it, ignoring the similar historical scheduling cases. In order to improve algorithm performance, case-based learning method is introduced to the scheduling process. Considering the characteristic of the problem, a method of retrieving, matching and revising satellite observing scheduling historical cases is designed. Then, a novel algorithm based on case-based learning and a genetic algorithm is proposed. Finally, some experiments are conducted to validate the correctness and practicability of our algorithm.

ACS Style

Chen Wang; Hao Chen; Baorong Zhai; Jun Li; Luo Chen. Satellite Observing Mission Scheduling Method Based on Case-Based Learning and a Genetic Algorithm. 2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI) 2016, 627 -634.

AMA Style

Chen Wang, Hao Chen, Baorong Zhai, Jun Li, Luo Chen. Satellite Observing Mission Scheduling Method Based on Case-Based Learning and a Genetic Algorithm. 2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI). 2016; ():627-634.

Chicago/Turabian Style

Chen Wang; Hao Chen; Baorong Zhai; Jun Li; Luo Chen. 2016. "Satellite Observing Mission Scheduling Method Based on Case-Based Learning and a Genetic Algorithm." 2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI) , no. : 627-634.