Spatial Accuracy Evaluation for Mobile Phone Location Data With Consideration of Geographical Context

Song, Xiaoqing; Long, Yi; Ling, Zhenhao; Rossiter, David G.; Liu, Fengyuan; Jian, Wei

doi:10.1109/access.2020.3043317

Cited by 6 publications

(4 citation statements)

References 34 publications

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“…There have recently been studies on simulating the spatial distribution of MPL data positioning bias based on some geographical elements (Song, Long, et al, 2020). This is helpful in analyzing the uncertainty of human mobility results based on MPL data.…”

Section: Discussion and Con Clus I On Smentioning

confidence: 99%

“…The Czech Republic also had similar characteristics: the positioning accuracy in the regional center was high (1.3 km), but in the rural area it was as low as 6 km. Song, Long, et al (2020) found that the regions with high-level spatial accuracy are primarily scattered in urban areas where the building density is high.…”

Section: Introductionmentioning

confidence: 99%

“…To the best of our knowledge, no studies have explored the spatial distribution trends of the positioning uncertainty in MPL data and the influence mechanism of geographical factors on the spatial distribution of the uncertainty of MPL data. These studies are of great significance for predicting and simulating MPL data positioning accuracy and further evaluating the availability and applicability of the data (Song, Long, et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

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Influence of geographical determinants on the spatial distribution of positioning uncertainties in mobile phone location data

Song

Ling

Wang

et al. 2021

Transactions in GIS

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The increasing use of mobile phone location (MPL) data in mobility research has provided many insights into people's travel behaviors. Despite these achievements, the spatial distribution of MPL data positioning uncertainties and their influence mechanism are rarely discussed. In this research, we investigate the influence of geographical determinants on the spatial distribution of the positioning uncertainties in MPL data. First, we discuss the spatial distribution trends in the positioning uncertainties of MPL data. Then we apply multiple linear regression and geographical detector (GeoDetector) models to explore the influence mechanism on the spatial distribution of the positioning uncertainties.By applying these methods to MPL data sets from a major operator in Nanjing city, we find a spatial aggregation phenomenon in the positioning uncertainties. Elevation contributes most to the spatial distribution of the positioning uncertainties. Furthermore, the influencing power of geographical factors on the spatial distribution of positioning uncertainties is nonlinearly enhanced after an interaction.

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Section: Discussion and Con Clus I On Smentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Influence of geographical determinants on the spatial distribution of positioning uncertainties in mobile phone location data

Song

Ling

Wang

et al. 2021

Transactions in GIS

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“…The study further elucidated how spatial data quality issues were tackled within the Mobile GIS context, in accordance with internationally recognized geoinformatics standards like ISO and Open Geospatial Consortium (OGC) standards. Furthermore, in another study by Song et al a linear evaluation model utilizing Geographical Weighted Regression (GWR) and a nonlinear evaluation model based on random forest (RF) were developed [16]. These models were employed to quantitatively assess the relationship between geographical factors and the positioning bias of mobile phone locations.…”

Section: Related Workmentioning

confidence: 99%

Quality In-Use of Mobile Geographic Information Systems for Data Collection

Fhel,

Idri

2023

IJACSA

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Mobile Geographic Information Systems (GIS) plays a vital role in data collection, offering diverse functionalities for spatial data handling. Despite advancements, accurately determining the usage environment during development remains challenging. This study uses machine learning and natural language processing to automatically classify user reviews based on the ISO 25010 quality-in-use model. Motivated by the challenge of gauging user experience during development, stakeholders analyze user reviews for insights. An experimental study compares Support Vector Machine (SVM), Random Forest, Logistic Regression, and Naive Bayes classifiers, revealing superior performance by SVM and Random Forest, particularly in efficiency evaluation. Findings underscore the efficacy of SVM in classifying user reviews, emphasizing its effectiveness in evaluating efficiency within mobile GIS applications. Moreover, it provides valuable insights for stakeholders, contributing to the enhancement of software quality of mobile GIS apps.

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Assessing veracity of big data: An in-depth evaluation process from the comparison of Mobile phone traces and groundtruth data in traffic monitoring

Nalin,

Vignali,

Lantieri

et al. 2024

Journal of Transport Geography

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Spatial Accuracy Evaluation for Mobile Phone Location Data With Consideration of Geographical Context

Cited by 6 publications

References 34 publications

Influence of geographical determinants on the spatial distribution of positioning uncertainties in mobile phone location data

Influence of geographical determinants on the spatial distribution of positioning uncertainties in mobile phone location data

Quality In-Use of Mobile Geographic Information Systems for Data Collection

Assessing veracity of big data: An in-depth evaluation process from the comparison of Mobile phone traces and groundtruth data in traffic monitoring

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