scholarly journals LCBRG: A lane-level road cluster mining algorithm with bidirectional region growing

2021 ◽  
Vol 13 (1) ◽  
pp. 835-850
Author(s):  
Xianyong Gong ◽  
Fang Wu ◽  
Ruixing Xing ◽  
Jiawei Du ◽  
Chengyi Liu
Keyword(s):  
Spatial Data ◽  
Complex Shape ◽  
Cluster Algorithm ◽  
Region Growing ◽  
Spatial Data Mining ◽  
Recognition Method ◽  
Mining Algorithm ◽  
Cartographic Generalization ◽  
Spatial Alignment ◽  
Cluster Recognition

Abstract Lane-level road cluster is a most representative phenomenon in road networks and is vital to spatial data mining, cartographic generalization, and data integration. In this article, a lane-level road cluster recognition method was proposed. First, the conception of lane-level road cluster and our motivation were addressed and the spatial characteristics were given. Second, a region growing cluster algorithm was defined to recognize lane-level road clusters, where constraints including distance and orientation were used. A novel moving distance (MD) metric was proposed to measure the distance of two lines, which can effectively handle the non-uniformly distributed vertexes, heterogeneous length, inharmonious spatial alignment, and complex shape. Experiments demonstrated that the proposed method can effectively recognize lane-level road clusters with the agreement to human spatial cognition.

scholarly journals THE IMPLEMENTATION OF HESITANT FUZZY SPATIAL CO-LOCATION PATTERN MINING ALGORITHM BASED ON PYTHON

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 763-767
Author(s):  
Z. W. Liu ◽  
B. Wei ◽  
C. L. Kang ◽  
J. W. Jiang
Keyword(s):  
Spatial Data ◽  
Pattern Mining ◽  
Score Function ◽  
Participation Rate ◽  
Spatial Data Mining ◽  
Hesitant Fuzzy Set ◽  
Location Pattern ◽  
Mining Algorithm ◽  
Location Patterns ◽  
Definition Of

Abstract. As one of the important research directions in the spatial data mining, spatial co-location pattern mining aimed at finding the spatial features whose the instances are frequent co-locate in neighbouring domain. With the introduction of fuzzy sets into traditional spatial co-location pattern mining, the research on fuzzy spatial co-location pattern mining has been deepened continuously, which extends traditional spatial co-location pattern mining to deal with fuzzy spatial objects and discover their laws of spatial symbiosis. In this paper, the operation principle of a classical join-based algorithm for mining spatial co-location patterns is briefly described firstly. Then, combining with the definition of classical participation rate and participation degree, a novel hesitant fuzzy spatial co-location pattern mining algorithm is proposed based on the establishment of the hesitant fuzzy participation rate and hesitant fuzzy participation formula according to the characteristics in fusion of hesitant fuzzy set theory, the score function and spatial co-location pattern mining. Finally, the proposed algorithm is written and implemented based on Python language, which uses a NumPy system to the expansion of the open source numerical calculation. The Python program of the proposed algorithm includes the method of computing hesitant fuzzy membership based on score function, the implementation of generating k-order candidate patterns, k-order frequent patterns and k-order table instances. A hesitant fuzzy spatial co-location pattern mining experiment is carried out and the experimental results show that the proposed and implemented algorithm is effective and feasible.

scholarly journals Development of Multilayer-Based Map Matching to Enhance Performance in Large Truck Fleet Dispatching

2021 ◽  
Vol 10 (2) ◽  
pp. 79
Author(s):  
Ching-Yun Mu ◽  
Tien-Yin Chou ◽  
Thanh Van Hoang ◽  
Pin Kung ◽  
Yao-Min Fang ◽  
...  
Keyword(s):  
Data Structures ◽  
Spatial Data ◽  
Spatial Information ◽  
Data Retrieval ◽  
Spatial Data Mining ◽  
Map Matching ◽  
Matching Algorithm ◽  
Table Method ◽  
Look Up Table ◽  
The Look

Spatial information technology has been widely used for vehicles in general and for fleet management. Many studies have focused on improving vehicle positioning accuracy, although few studies have focused on efficiency improvements for managing large truck fleets in the context of the current complex network of roads. Therefore, this paper proposes a multilayer-based map matching algorithm with different spatial data structures to deal rapidly with large amounts of coordinate data. Using the dimension reduction technique, the geodesic coordinates can be transformed into plane coordinates. This study provides multiple layer grouping combinations to deal with complex road networks. We integrated these techniques and employed a puncture method to process the geometric computation with spatial data-mining approaches. We constructed a spatial division index and combined this with the puncture method, which improves the efficiency of the system and can enhance data retrieval efficiency for large truck fleet dispatching. This paper also used a multilayer-based map matching algorithm with raster data structures. Comparing the results revealed that the look-up table method offers the best outcome. The proposed multilayer-based map matching algorithm using the look-up table method is suited to obtaining competitive performance in identifying efficiency improvements for large truck fleet dispatching.

scholarly journals Utilizing MapReduce to Improve Probe-Car Track Data Mining

2018 ◽  
Vol 7 (7) ◽  
pp. 287 ◽  
Author(s):  
Li Zheng ◽  
Meng Sun ◽  
Yuejun Luo ◽  
Xiangbo Song ◽  
Chaowei Yang ◽  
...  
Keyword(s):  
Data Mining ◽  
Traffic Safety ◽  
Spatial Data ◽  
Road Traffic ◽  
Spatial Data Mining ◽  
Tracking Data ◽  
Mining System ◽  
Computational Performance ◽  
True Path ◽  
Probe Car

With the rapidly increasing popularization of the automobile, challenges and greater demands have come to the fore, including traffic congestion, energy crises, traffic safety, and environmental pollution. To address these challenges and demands, enhanced data support and advanced data collection methods are crucial and highly in need. A probe-car serves as an important and effective way to obtain real-time urban road traffic status in the international Intelligent Transportation System (ITS), and probe-car technology provides the corresponding solution through advanced navigation data, offering more possibilities to address the above problems. In addition, massive spatial data-mining technologies associated with probe-car tracking data have emerged. This paper discusses the major problems of spatial data-mining technologies for probe-car tracking data, such as true path restoration and the close correlation of spatial data. To address the road-matching issue in massive probe-car tracking data caused by the strong correlation combining road topology with map matching, this paper presents a MapReduce-based technology in the second spatial data model. The experimental results demonstrate that by implementing the proposed spatial data-mining system on distributed parallel computing, the computational performance was effectively improved by five times and the hardware requirements were significantly reduced.

scholarly journals SPASIAL DATA MINING MENGGUNAKAN MODEL SAR-KRIGING

2011 ◽  
Vol 5 (3) ◽  
pp. 52
Author(s):  
Atje Setiawan ◽  
Rudi Rosadi
Keyword(s):  
Data Mining ◽  
Elementary School ◽  
Spatial Data ◽  
Spatial Data Mining ◽  
Education Quality ◽  
Percentage Error ◽  
Kriging Method ◽  
National Education ◽  
Software Application ◽  
Average Percentage

The region of Indonesia is very sparse and it has a variation condition in social, economic and culture, so the problem in education quality at many locations is an interesting topic to be studied. Database used in this research is Base Survey of National Education 2003, while a spatial data is presented by district coordinate as a least analysis unit. The aim of this research is to study and to apply spatial data mining to predict education quality at elementary and junior high schools using SAR-Kriging method which combines an expansion SAR and Kriging method. Spatial data mining process has three stages. preprocessing, process of data mining, and post processing.For processing data and checking model, we built software application of Spatial Data Mining using SAR-Kriging method. An application is used to predict education quality at unsample locations at some cities at DIY Province.  The result shows that SAR-Kriging method for some cities at DIY for elementary school has an average percentage error 6.43%. We can conclude that for elementary school, SAR-Kriging method can be used as a fitted model. Keywords—  Expansion SAR, SAR-Kriging, quality education

Spatial data mining to support pandemic preparedness

2006 ◽  
Vol 8 (1) ◽  
pp. 80-82 ◽  
Author(s):  
Chris Bailey-Kellogg ◽  
Naren Ramakrishnan ◽  
Madhav V. Marathe
Keyword(s):  
Data Mining ◽  
Spatial Data ◽  
Spatial Data Mining ◽  
Pandemic Preparedness
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