Modeling activity spaces using big geo‐data: Progress and challenges

Yuan, Yihong; Xu, Yang

doi:10.1111/gec3.12663

Cited by 7 publications

(3 citation statements)

References 120 publications

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“…They discovered that among the external activity space indicators, the ROG is less susceptible to outliers. In contrast, among internal activity space indicators, entropy is frequently utilized in prior research to quantify the randomness of user activity patterns, especially when derived from sparse datasets of LBSM data [ 16 ]. Therefore, we applied the ROG to model the external shape and entropy to model the internal structure of an activity space in this study.…”

Section: Methodsmentioning

confidence: 99%

“…Different indicators have been used to model the external morphology (e.g., shape and size) and internal structure (patterns of visiting different points of interest) of an activity space [ 14 , 15 ]. Previous studies show that the radius of gyration (ROG) and entropy are the two commonly used indicators to model the external morphology and internal structure of an activity space, respectively [ 15 , 16 ]. ROG is less susceptible to outliers and entropy can capture the randomness of user activity patterns, which are very crucial for sparse datasets like location-based social media (LBSM) data [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

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The Impact of Scale on Extracting Individual Mobility Patterns from Location-Based Social Media

Bin Asad,

Yuan

2024

Sensors

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Understanding human movement patterns is crucial for comprehending how a city functions. It is also important for city planners and policymakers to create more efficient plans and policies for urban areas. Traditionally, human movement patterns were analyzed using origin–destination surveys, travel diaries, and other methods. Now, these patterns can be identified from various geospatial big data sources, such as mobile phone data, floating car data, and location-based social media (LBSM) data. These extensive datasets primarily identify individual or collective human movement patterns. However, the impact of spatial scale on the analysis of human movement patterns from these large geospatial data sources has not been sufficiently studied. Changes in spatial scale can significantly affect the results when calculating human movement patterns from these data. In this study, we utilized Weibo datasets for three different cities in China including Beijing, Guangzhou, and Shanghai. We aimed to identify the effect of different spatial scales on individual human movement patterns as calculated from LBSM data. For our analysis, we employed two indicators as follows: an external activity space indicator, the radius of gyration (ROG), and an internal activity space indicator, entropy. These indicators were chosen based on previous studies demonstrating their efficiency in analyzing sparse datasets like LBSM data. Additionally, we used two different ranges of spatial scales—10–100 m and 100–3000 m—to illustrate changes in individual activity space at both fine and coarse spatial scales. Our results indicate that although the ROG values show an overall increasing trend and the entropy values show an overall decreasing trend with the increase in spatial scale size, different local factors influence the ROG and entropy values at both finer and coarser scales. These findings will help to comprehend the dynamics of human movement across different scales. Such insights are invaluable for enhancing overall urban mobility and optimizing transportation systems.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Impact of Scale on Extracting Individual Mobility Patterns from Location-Based Social Media

Bin Asad,

Yuan

2024

Sensors

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“…The multiple places at which individuals interact with or are exposed to the food environment can be represented through their 'activity space' (i.e., subsuming people's daily travel patterns and the locations visited [21,22]). While surveys and travel diaries are prone to inaccuracies due to possible recall biases, the use of Global Positioning System(s) (GPS) to define exposure to the food environment is the most accurate method to capture an individual's day-to-day activity space objectively [22][23][24][25][26][27][28][29][30][31]. GPS-based food environment studies capture the out-of-home locations and routes individuals visit, thus more precisely capturing the totality and the duration of exposure to the food environment.…”

Section: Introductionmentioning

confidence: 99%

Global positioning system-based food environment exposures, diet-related, and cardiometabolic health outcomes: a systematic review and research agenda

Siddiqui,

Wei,

Mackenbach

et al. 2024

Int J Health Geogr

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Background Geographic access to food may affect dietary choices and health outcomes, but the strength and direction of associations may depend on the operationalization of exposure measures. We aimed to systematically review the literature on up-to-date evidence on the association between food environment exposures based on Global Positioning System (GPS) and diet-related and cardiometabolic health outcomes. Methods The databases PubMed, Embase.com, APA PsycInfo (via Ebsco), Cinahl (via Ebsco), the Web of Science Core Collection, Scopus, and the International Bibliography of the Social Sciences (via ProQuest) were searched from inception to October 31, 2022. We included studies that measured the activity space through GPS tracking data to identify exposure to food outlets and assessed associations with either diet-related or cardiometabolic health outcomes. Quality assessment was evaluated using the criteria from a modified version of the Newcastle–Ottawa Scale (NOS) for cross-sectional studies. We additionally used four items from a quality assessment tool to specifically assess the quality of GPS measurements. Results Of 2949 studies retrieved, 14 studies fulfilled our inclusion criteria. They were heterogeneous and represent inconsistent evidence. Yet, three studies found associations between food outlets and food purchases, for example, more exposure to junk food outlets was associated with higher odds of junk food purchases. Two studies found associations between greater exposure to fast food outlets and higher fast food consumption and out of three studies that investigated food environment in relation to metabolic outcomes, two studies found that higher exposure to an unhealthy food environment was associated with higher odds of being overweight. Conclusions The current and limited evidence base does not provide strong evidence for consistent associations of GPS-based exposures of the food environment with diet-related and cardiometabolic health outcomes.

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Influence of residential built environment on human mobility in Xining: A mobile phone data perspective

Yang,

Li,

Fang

et al. 2024

Travel Behaviour and Society

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Modeling activity spaces using big geo‐data: Progress and challenges

Cited by 7 publications

References 120 publications

The Impact of Scale on Extracting Individual Mobility Patterns from Location-Based Social Media

The Impact of Scale on Extracting Individual Mobility Patterns from Location-Based Social Media

Global positioning system-based food environment exposures, diet-related, and cardiometabolic health outcomes: a systematic review and research agenda

Influence of residential built environment on human mobility in Xining: A mobile phone data perspective

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