A Framework for Interactive Geospatial Map Cleaning using GPS Trajectories

Vementala, Nikhil; Papotti, Paolo; Sarwat, Mohamed

doi:10.1145/3151547.3151551

Cited by 2 publications

(1 citation statement)

References 20 publications

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“…The existing logical-based approaches are reported to consider velocity constraints, reduction of travel distance, and human navigational system [49][50][51]. Nearly, similar problems are also considered when working with outlier-detection based approach where the consideration of driving behavior, statistical process controls, partitioning is carried [56][57][58][59][60][61][62][63]. Table 3 summarizes the research contribution of present times with respect to different parameters to exhibit that all the problems are associated with advantage as well as significant limitation too.…”

Section: Existing Research Trendsmentioning

confidence: 99%

A comprehensive insight towards Pre-processing Methodologies applied on GPS data

Prabha

Kabadi²

2020

IJECE

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Reliability in the utilization of the Global Positioning System (GPS) data demands a higher degree of accuracy with respect to time and positional information required by the user. However, various extrinsic and intrinsic parameters disrupt the data transmission phenomenon from GPS satellite to GPS receiver which always questions the trustworthiness of such data. Therefore, this manuscript offers a comprehensive insight into the data preprocessing methodologies evolved and adopted by present-day researchers. The discussion is carried out with respect to standard methods of data cleaning as well as diversified existing research-based approaches. The review finds that irrespective of a good number of work carried out to address the problem of data cleaning, there are critical loopholes in almost all the existing studies. The paper extracts open end research problems as well as it also offers an evidential insight using use-cases where it is found that still there is a critical need to investigate data cleaning methods.

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Section: Existing Research Trendsmentioning

confidence: 99%

A comprehensive insight towards Pre-processing Methodologies applied on GPS data

Prabha

Kabadi²

2020

IJECE

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(Big) Data in Urban Design Practice: Supporting High-Level Design Tasks Using a Visualization of Human Movement Data from Smartphones

Rout

Willett

2021

Urban Informatics and Future Cities

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We present the SmartCampus visualization tool, representing spatiotemporal data of over 200 student pathways and restpoints on a university campus. Based on our experiences with SmartCampus, we also propose a task-based framework that describes how practicing urban designers (specifically, architects) can use human movement data visualizations in their work. Although extensive amounts of location data are produced daily by smartphones, existing geospatial tools are not customized to specifically support high-level urban design tasks. To help identify opportunities in urban design for visualizing human movement data from devices such as smartphones, we used our SmartCampus prototype to facilitate a series of 3 participatory design sessions (3 participants), a targeted online survey (14 participants), and semi-structured interviews (6 participants) with architectural experts. Our findings showcase the need for location analysis tools tailored to concrete urban design practices, and also highlight opportunities for Smart City researchers interested in developing domain specific, visualization tools.

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A Framework for Interactive Geospatial Map Cleaning using GPS Trajectories

Cited by 2 publications

References 20 publications

A comprehensive insight towards Pre-processing Methodologies applied on GPS data

A comprehensive insight towards Pre-processing Methodologies applied on GPS data

(Big) Data in Urban Design Practice: Supporting High-Level Design Tasks Using a Visualization of Human Movement Data from Smartphones

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