A comparison of GPS-TEC with IRI-TEC at low latitudes in China in 2006

Wan, Qingtao; Ma, Guanyi; Li, Jinghua; Wang, Xiaolan; Fan, Jiangtao; Li, Qi; Lu, Weijun

doi:10.1016/j.asr.2016.12.002

Cited by 9 publications

(4 citation statements)

References 11 publications

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“…Guangzhou is considered to be one of the most attractive destinations in China due to its heterogeneous population and job opportunities regarding demographic characteristics [122], socioeconomic status [123,124], and place of origin [28]. Guangzhou, China (longitude from 112°57′ to 114°3′E and latitude from 22°26′ to 23°56′N [125]) is located on the south coast of Pearl River Delta (PRD) with a 14.5 million population [126] and had a total area of 7434.4 km 2 [127]. In 2015, Guangzhou was divided into 11 districts (Baiyun, Conghua, Haizhu, Huadu, Huangpu, Liwan, Nansha, Panyu, and Zengcheng) [128].…”

Section: Dataset and Study Areamentioning

confidence: 99%

Spatiotemporal Analysis to Observe Gender Based Check-In Behavior by Using Social Media Big Data: A Case Study of Guangzhou, China

2019

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In a location-based social network, users socialize with each other by sharing their current location in the form of “check-in,” which allows users to reveal the current places they visit as part of their social interaction. Understanding this human check-in phenomenon in space and time on location based social network (LBSN) datasets, which is also called “check-in behavior,” can archive the day-to-day activity patterns, usage behaviors toward social media, and presents spatiotemporal evidence of users’ daily routines. It also provides a wide range of opportunities to observe (i.e., mobility, urban activities, defining city boundary, and community problems in a city). In representing human check-in behavior, these LBSN datasets do not reflect the real-world events due to certain statistical biases (i.e., gender prejudice, a low frequency in sampling, and location type prejudice). However, LBSN data is primarily considered a supplement to traditional data sources (i.e., survey, census) and can be used to observe human check-in behavior within a city. Different interpretations are used elusively for the term “check-in behavior,” which makes it difficult to identify studies on human check-in behavior based on LBSN using the Weibo dataset. The primary objective of this research is to explore human check-in behavior by male and female users in Guangzhou, China toward using Chinese microblog Sina Weibo (referred to as “Weibo”), which is missing in the existing literature. Kernel density estimation (KDE) is utilized to explore the spatiotemporal distribution geographically and weighted regression (GWR) method was applied to observe the relationship between check-in and districts with a focus on gender during weekdays and weekend. Lastly, the standard deviational ellipse (SDE) analysis is used to systematically analyze the orientation, direction, spatiotemporal expansion trends and the differences in check-in distribution in Guangzhou, China. The results of this study show that LBSN is a reliable source of data to observe human check-in behavior in space and time within a specified geographic area. Furthermore, it shows that female users are more likely to use social media as compared to male users. The human check-in behavior patterns for social media network usage by gender seems to be slightly different during weekdays and weekend.

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Section: Dataset and Study Areamentioning

confidence: 99%

Spatiotemporal Analysis to Observe Gender Based Check-In Behavior by Using Social Media Big Data: A Case Study of Guangzhou, China

2019

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“…Wan et al. (2017) studied a comparison of a GPS‐derived TEC and the IRI‐2012 model at low latitudes in China in 2006 by calculating the deviation between the two models. These works simply compare the difference between two models in China which lack a reliable reference to validate the models.…”

Section: Introductionmentioning

confidence: 99%

“…The IRI model can provide a reliable TEC background field for modeling regional TEC maps in China and adjacent areas (Aa et al., 2015; Qiao et al., 2021) as well as a real‐time regional ionospheric model (Galkin et al., 2021). Due to the lack of ionospheric data in China since the establishment of the IRI model (Wan et al., 2017), it is necessary to assess the performance of the GIMs and IRI model in this region during different ionospheric conditions. This paper presents the assessment of the WHU‐GIM and IRI‐2020 over China during the time from 2008 to 2020.…”

Section: Introductionmentioning

confidence: 99%

“…Luo et al (2014) evaluated the IRI-2012 and GIM TEC at low-latitude and mid-latitude regions of China using 1-year data with medium solar activity. Wan et al (2017) studied a comparison of a GPS-derived TEC and the IRI-2012 model at low latitudes in China in 2006 by calculating the deviation between the two models. These works simply compare the difference between two models in China which lack a reliable reference to validate the models.…”

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confidence: 99%

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A Comparison of a GNSS‐GIM and the IRI‐2020 Model Over China Under Different Ionospheric Conditions

He,

Li,

Zhang

et al. 2023

Space Weather

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The ionosphere is a crucial factor affecting Global Navigation Satellite System positioning. The Global Ionosphere Map (GIM) or the International Reference Ionosphere (IRI) model can be used for regional ionospheric correction. Since southern China is located near the electron density equatorial anomaly, this study evaluates the performance of the Wuhan University GIM (WHU‐GIM) and the IRI‐2020 from 2008 to 2020 over the China region. The comparison indicates that the Total Electron Content (TEC) from IRI‐2020 is lower than that from WHU‐GIM overall, the discrepancy is more obvious in high solar conditions and low‐latitude regions. The differential Slant TEC (dSTEC) during a phase‐arc with about 0.1 TECU accuracy derived from Global Positioning System (GPS) observations is used for model validation, the results show that the accuracies of WHU‐GIM and IRI‐2020 are 3.14 and 4.57 TECU, respectively. The dSTEC error is larger at low latitudes and decreases with increasing latitude. GPS‐derived TEC is taken for reference to evaluate the model reliability. Results show that both models can reproduce the diurnal TEC variations, but IRI‐2020 is more influenced by geomagnetic activities. The TEC correction percentage for IRI‐2020 is about 60%–80% under different ionospheric conditions, while for WHU‐GIM is 80%–90%. The Single‐Frequency Precise Point Positioning is performed with the ionosphere delay corrected by the two models, respectively. The positioning errors show that using IRI‐2020 has a lower accuracy, and the TEC discrepancy of the IRI‐2020 can cause a large bias in the up direction, especially at low‐latitude regions.

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Performance analysis of quiet and disturbed time ionospheric TEC responses from GPS-based observations, IGS-GIM, IRI-2016 and SPIM/IRI-Plas 2017 models over the low latitude Indian region

Reddybattula

Panda

2019

Advances in Space Research

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A comparison of GPS-TEC with IRI-TEC at low latitudes in China in 2006

Cited by 9 publications

References 11 publications

Spatiotemporal Analysis to Observe Gender Based Check-In Behavior by Using Social Media Big Data: A Case Study of Guangzhou, China

Spatiotemporal Analysis to Observe Gender Based Check-In Behavior by Using Social Media Big Data: A Case Study of Guangzhou, China

A Comparison of a GNSS‐GIM and the IRI‐2020 Model Over China Under Different Ionospheric Conditions

Performance analysis of quiet and disturbed time ionospheric TEC responses from GPS-based observations, IGS-GIM, IRI-2016 and SPIM/IRI-Plas 2017 models over the low latitude Indian region

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