Mapping changes of residence with passive mobile positioning data: the case of Estonia

Kamenjuk, Pilleriine; Aasa, Anto; Sellin, Jaanus

doi:10.1080/13658816.2017.1295308

Cited by 15 publications

(5 citation statements)

References 60 publications

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“…Analysis with these data has provided insights on a wide variety of social phenomena and socio-spatial processes, including crisis situations. Examples include, e.g., analysis on population mobility and commuting ( Ahas et al., 2015 ; Järv et al., 2012 ), detecting functional economic regions ( Novak et al., 2013 ; OECD, 2020 ), the provision and accessibility to state services ( Järv et al., 2018 ), identifying migration flows ( Kamenjuk et al., 2017 ) and cross-border mobility ( Silm et al., 2020a ), analyzing (in)equity between population groups and spatial segregation ( Mooses et al., 2016 ; Shelton et al., 2015 ; Silm et al., 2018 ), supporting transport solutions ( Positium, 2019 ) and environmental management ( Heikinheimo et al., 2020 ; Poom et al., 2017 ), characterizing tourist behavior ( Campagna et al., 2015 ; Raun et al., 2016; Saluveer et al., 2020 ), or reflecting the lived experiences of people in case of disruptions ( Shelton et al., 2014 ). Much of this research is conducted in countries where access to mobile Big Data has been relatively easy.…”

Section: Pre-covid-19 Mobile Big Data Researchmentioning

confidence: 99%

COVID-19 is spatial: Ensuring that mobile Big Data is used for social good

et al. 2020

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The mobility restrictions related to COVID-19 pandemic have resulted in the biggest disruption to individual mobilities in modern times. The crisis is clearly spatial in nature, and examining the geographical aspect is important in understanding the broad implications of the pandemic. The avalanche of mobile Big Data makes it possible to study the spatial effects of the crisis with spatiotemporal detail at the national and global scales. However, the current crisis also highlights serious limitations in the readiness to take the advantage of mobile Big Data for social good, both within and beyond the interests of health sector. We propose two strategical pathways for the future use of mobile Big Data for societal impact assessment, addressing access to both raw mobile Big Data as well as aggregated data products. Both pathways require careful considerations of privacy issues, harmonized and transparent methodologies, and attention to the representativeness, reliability and continuity of data. The goal is to be better prepared to use mobile Big Data in future crises.

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Section: Pre-covid-19 Mobile Big Data Researchmentioning

confidence: 99%

COVID-19 is spatial: Ensuring that mobile Big Data is used for social good

et al. 2020

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“…Moreover, the spatial interaction analysis of flow data has attracted various research attention (Li et al 2017; Zhu et al 2018). Examples include travel chain pattern mining based on tourist behavior data (Gu et al 2019; Li et al 2018; Wang et al 2019), characteristic analysis for population migration at a large scale (Bailey, Kapetanios, and Pesaran 2016; Ma et al 2019), semantic OD flow construction and its spatial interaction pattern detection (Hu et al 2017; Zhang, Zhou, and Huang 2020), and behavior pattern analysis of residential mobility in inner cities (Kamenjuk, Aasa, and Sellin 2017; Jin et al 2020). Recently, a spatial autocorrelation model based on multivariate analysis has been proposed (Anselin 2019, 2020), and some scholars have introduced this idea into OD flow data (e.g., BiFlowLISA model, which is developed to measure the spatial association for bivariate aggregated flow data (Tao 2020)).…”

Section: Literature Reviewmentioning

confidence: 99%

Detecting Colocation Flow Patterns in the Geographical Interaction Data

Zhang¹,

Zhou

Tang³

et al. 2021

Geographical Analysis

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The detection of colocation pattern is an important and widely used method to analyze the spatial associations of geographical objects and events. Existing studies primarily focus on discovering colocation patterns and association rules based on point data. A broad range of flow data types, such as population flow, logistics, and information flow, have emerged in recent years. However, colocation patterns and association rules based on flow data are difficult to detect because of their complex structure. This work proposes a colocation pattern detection and spatial association rule discovery approach that treats origin‐destination (OD) flow as Boolean spatial features, while considering the spatial proximity of the origins and destinations of OD flows and its direction similarity. The effectiveness of this approach is verified by an artificial data set. Finally, this work analyzes the data of tourists who are traveling from different countries or regions to diverse cities in China. It also proves the application value of the proposed approach, which has general applicability to the mining of colocation patterns and association rules from any type of OD flow data.

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“…The accuracy of spatial location information depends on the structure of the mobile tower network. Passive mobile positioning data have been used in demographic research describing the spatial and temporal mobility of people in urban settings [74,75] and patterns in tourism [72]. Additionally, all mobile phones have a subscriber identity module, commonly known as a SIM card.…”

Section: Gps and Phone Datamentioning

confidence: 99%

Blue Water Visitor Monitoring Potential: A Literature Review and Alternative Proposal

Andrew

Burns

Schwarzmann

et al. 2021

Water

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This review presents a summary of existing visitor monitoring methods and relevant studies in land and marine-based areas, with a focus on the application to unique aquatic settings. Various opportunities and challenges exist with respect to the use of each method in different marine settings. These methods differ in terms of the complexity, costs, level of accuracy, and detailed information they provide. Furthermore, the feasibility of applying these methods also depends on the site attributes of a marine area. Since each marine area varies in geographical scale and environmental and social conditions, some methods will be more appropriate or perform more successfully than others in a particular location. Therefore, the consideration of these methods should be part of a proposed alternative process, focused on adaptive monitoring that scales to address visitor ebbs and flows in these aquatic areas. The proposed alternative seeks to develop consensus around quantitative goals for visitor monitoring and estimating techniques in marine settings, using a customizable mix of methods and techniques. This alternative effort progresses to subsequent tasks and discussions, and recommendations are made considering the feasibility and confidence of using these methods in particular marine settings and future pilot sites.

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Mapping changes of residence with passive mobile positioning data: the case of Estonia

Cited by 15 publications

References 60 publications

COVID-19 is spatial: Ensuring that mobile Big Data is used for social good

COVID-19 is spatial: Ensuring that mobile Big Data is used for social good

Detecting Colocation Flow Patterns in the Geographical Interaction Data

Blue Water Visitor Monitoring Potential: A Literature Review and Alternative Proposal

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