A simple and direct method to analyse the influences of sampling fractions on modelling intra-city human mobility

Jiang, Jincheng; Li, Qingquan; Tu, Wei; Shaw, Shih‐Lung; Yang, Yue

doi:10.1080/13658816.2018.1552964

Cited by 14 publications

(12 citation statements)

References 38 publications

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“… 34 , 35 , 36 , 37 , 38 During the COVID-19 pandemic, both individual-level and aggregated-level human mobility patterns have been found useful in pandemic modeling and digital contact tracing. 6 , 13 , 39 , 40 However, technical challenges (eg, location uncertainty), socioeconomic and sampling bias, 41 , 42 , 43 , 44 privacy and ethical concerns have been expressed by national and international societies. 45 , 46 , 47 , 48 Moving forward, research efforts should continue exploring the balance of using such human mobility data at different geographic scales for public health and social good while preserving individual privacy and rights.…”

Section: Discussionmentioning

confidence: 99%

Association of Mobile Phone Location Data Indications of Travel and Stay-at-Home Mandates With COVID-19 Infection Rates in the US

et al. 2020

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Key Points Question Did human mobility patterns change during stay-at-home orders and were the mobility changes associated with the coronavirus disease 2019 (COVID-19) curve? Findings This cross-sectional study using anonymous location data from more than 45 million mobile phones found that median travel distance decreased and stay-at-home time increased across the nation, although there was geographic variation. State-specific empirical doubling time of total COVID-19 cases increased (ie, the spread reduced) significantly after stay-at-home orders were put in place. Meaning These findings suggest that stay-at-home social distancing mandates were associated with the reduced spread of COVID-19 when they were followed.

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Section: Discussionmentioning

confidence: 99%

Association of Mobile Phone Location Data Indications of Travel and Stay-at-Home Mandates With COVID-19 Infection Rates in the US

et al. 2020

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“…Given that our analysis is based on comparisons between two identified groups over the two periods, the two groups should have similar demographics. Evidence shows that 2% sampling rate is sufficient in modeling intra-city human mobility patterns (e.g., travel distance and travel time) using public transit smart card data [27]. Therefore, we consider the identified two groups with more than 20% sampling rates are demographically and statistically similar in this study.…”

Section: Introductionmentioning

confidence: 99%

Urban commuting dynamics in response to public transit upgrades: A big data approach

Gao

Zhou

et al. 2019

PLoS ONE

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Public transit, especially urban rail systems, plays a vital role in shaping commuting patterns. Compared with census data and survey data, large-scale and real-time big data can track the impacts of urban policy implementations at finer spatial and temporal scales. Therefore, this study proposed a multi-level analytical framework using transit smartcard data to examine urban commuting dynamics in response to rail transit upgrades. The study area was Shenzhen, one of the most highly urbanized and densely populated cities in China, which provides the opportunity to examine the effects of rail transit upgrades on commuting patterns in a rapidly developing urban context. Changes in commuting patterns were examined at three levels: city, region, and individual. At the city level, we considered the average commuting time, commuting speed, and commuting distance across the whole city. At the region level, we analyzed changes in the job accessibility of residential zones. Finally, this study evaluated the potential effects of rail transit upgrades on the jobs-housing relationship at the individual level. Difference-in-difference models were used for causal inference between rail transit upgrades and commuting patterns. In the very short term, the opening of new rail transit lines resulted in no significant changes in overall commuting patterns across the whole city; however, two effects of rail transit upgrades on commuting patterns were identified. First, rail transit upgrades enhanced regional connectivity between residential zones and employment centers, thus improving job accessibility. Second, rail transit improvement increased the commuting distances of individuals and contributed to the separation of workplaces and residences. This study provides meaningful insights into the effects of rail transit upgrades on commuting patterns.

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“…Mathematical modeling is used to build theoretical models that reflect real problems; then, solving the model can yield results that are also the solution to the real problem. In the big data era, scholars in the field of GIScience use rich social media resources [63] and mathematical methods to model complex GIScience problems [64][65] and explore the patterns and motivations of human activities at multi-spatiotemporal scales [66][67][68].…”

Section: Theory and Methodsmentioning

confidence: 99%

Measuring multi-spatiotemporal scale tourist destination popularity based on text granular computing

Chi¹,

Li²,

Zhao³

et al. 2020

PLoS ONE

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User-generated content (UGC) is an important data source for tourism GIScience research. However, no effective approach exists for identifying hidden spatiotemporal patterns within multi-scale unstructured UGC. Therefore, we developed an algorithm to measure the tourist destination popularity (TDP) based on a multi-spatiotemporal text granular computing model, called TDPMTGC. To accurately granulate the spatial and temporal information of tourism text, tourism text data granules are used to represent landscape objects. These granules are unified objects that possess multiple attributes, such as spatial and temporal dimensions. The multi-spatiotemporal scales are characterized by the multi-hierarchical structure of granular computing, and transformations of granular layers and data granule size are achieved by scale selection in the spatial and temporal dimensions. Therefore, all scales between the spatial and temporal dimension are related, which allows for the comparability of the data granules of all spatial-spatial, temporal-temporal and spatial-temporal layers. This approach achieves a quantitative description and comparison of the popularity value of granules between adjacent scales and cross-scales. Therefore, the TDP with multispatiotemporal scales can be deduced and calculated in a systematic framework. We first introduce the conceptual framework of TDPMTGC to construct a quantitative measurement model of TDP at multi-spatiotemporal scales. Then, we present a dataset construction approach to support multi-spatiotemporal scale granular reorganization. Finally, TDPMTGC is derived to describe both the TDP at a single spatial or temporal scale and the patterns and processes of the TDP at multi-spatiotemporal scales. A case study from Jiuzhaigou shows that the TDP derived using TDPMTGC is consistent with the conclusions of existing studies. More importantly, TDPMTGC provides additional detailed characteristics, such as the contributions of different scenic spots in a tourist route or scenic area, the monthly anomalies and daily contributions of TDP in a specific year, the distinct weakening of tourist route scale in tourist cognition, and the daily variations of TDP during in-season and off-season times. This is the first time that a granular computing model has been introduced to tourism GIScience that provides a feasible scheme for reorganizing large-scale unstructured text

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A simple and direct method to analyse the influences of sampling fractions on modelling intra-city human mobility

Cited by 14 publications

References 38 publications

Association of Mobile Phone Location Data Indications of Travel and Stay-at-Home Mandates With COVID-19 Infection Rates in the US

Association of Mobile Phone Location Data Indications of Travel and Stay-at-Home Mandates With COVID-19 Infection Rates in the US

Urban commuting dynamics in response to public transit upgrades: A big data approach

Measuring multi-spatiotemporal scale tourist destination popularity based on text granular computing

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