Analysis of GPS Trajectories Mapping on Shape Files Using Spatial Computing Approaches

2020 ◽  
pp. 91-100
Author(s):  
Saravjeet Singh ◽  
Jaiteg Singh
Keyword(s):  
Spatial Computing ◽  
Gps Trajectories

scholarly journals StencilFlow: Mapping Large Stencil Programs to Distributed Spatial Computing Systems

2021 ◽  
Author(s):  
Johannes de Fine Licht ◽  
Andreas Kuster ◽  
Tiziano De Matteis ◽  
Tal Ben-Nun ◽  
Dominic Hofer ◽  
...  
Keyword(s):  
Computing Systems ◽  
Spatial Computing

scholarly journals Extracting Stops from Spatio-Temporal Trajectories within Dynamic Contextual Features

2021 ◽  
Vol 13 (2) ◽  
pp. 690
Author(s):  
Tao Wu ◽  
Huiqing Shen ◽  
Jianxin Qin ◽  
Longgang Xiang
Keyword(s):  
Moving Objects ◽  
Major Effect ◽  
Location Based Services ◽  
Trajectory Data ◽  
The Road ◽  
Contextual Features ◽  
Novel Approach ◽  
Gps Trajectories ◽  
Time Distance ◽  
Spatio Temporal

Identifying stops from GPS trajectories is one of the main concerns in the study of moving objects and has a major effect on a wide variety of location-based services and applications. Although the spatial and non-spatial characteristics of trajectories have been widely investigated for the identification of stops, few studies have concentrated on the impacts of the contextual features, which are also connected to the road network and nearby Points of Interest (POIs). In order to obtain more precise stop information from moving objects, this paper proposes and implements a novel approach that represents a spatio-temproal dynamics relationship between stopping behaviors and geospatial elements to detect stops. The relationship between the candidate stops based on the standard time–distance threshold approach and the surrounding environmental elements are integrated in a complex way (the mobility context cube) to extract stop features and precisely derive stops using the classifier classification. The methodology presented is designed to reduce the error rate of detection of stops in the work of trajectory data mining. It turns out that 26 features can contribute to recognizing stop behaviors from trajectory data. Additionally, experiments on a real-world trajectory dataset further demonstrate the effectiveness of the proposed approach in improving the accuracy of identifying stops from trajectories.

A distributed spatial computing prototype system in grid environment

2010 ◽  
Vol 53 (S1) ◽  
pp. 25-32 ◽  
Author(s):  
Lun Wu ◽  
MengLong Yan ◽  
Yong Gao ◽  
ZhenZhen Yang ◽  
Yong Zhao ◽  
...  
Keyword(s):  
Prototype System ◽  
Grid Environment ◽  
Spatial Computing

Visual Analysis of Multiple Route Choices Based on General GPS Trajectories

2017 ◽  
Vol 3 (2) ◽  
pp. 234-247 ◽  
Author(s):  
Min Lu ◽  
Chufan Lai ◽  
Tangzhi Ye ◽  
Jie Liang ◽  
Xiaoru Yuan
Keyword(s):  
Visual Analysis ◽  
Gps Trajectories ◽  
Route Choices

Data analysis and mining of traffic features based on taxi GPS trajectories: A case study in Beijing

2019 ◽  
pp. e5332 ◽  
Author(s):  
Chun Liu ◽  
Shuangyan Wang ◽  
Salvatore Cuomo ◽  
Gang Mei
Keyword(s):  
Data Analysis ◽  
Gps Trajectories
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