Novel model for predicting individuals’ movements in dynamic regions of interest

Shen, Xiaoqi; Shi, Wenzhong; Chen, Pengfei; Liu, Zhewei; Wang, Lukang

doi:10.1080/15481603.2022.2026637

Cited by 6 publications

(6 citation statements)

References 48 publications

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“…Coupling with the spatial autocorrelation of hazards, human mobility law related to decay effect (i.e., “individuals are more likely to visit nearby regions”) is possibly another cause for accentuated traveling-induced hazard exposures. , The human mobility patterns are greatly influenced by the urban environment and transportation. ,− Given that people tend to frequent places that are closer to their residence, their mobility patterns essentially increase their dwell time in high-hazard environments if they live in high-risk areas. If their home and nearby regions share similar hazard characteristics due to spatial autocorrelation, their likelihood of exposure increases with each trip they make within this radius.…”

Section: Discussionmentioning

confidence: 99%

“… 45 , 46 The human mobility patterns are greatly influenced by the urban environment and transportation. 30 , 47 − 49 Given that people tend to frequent places that are closer to their residence, their mobility patterns essentially increase their dwell time in high-hazard environments if they live in high-risk areas. If their home and nearby regions share similar hazard characteristics due to spatial autocorrelation, their likelihood of exposure increases with each trip they make within this radius.…”

Section: Discussionmentioning

confidence: 99%

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Beyond Residence: A Mobility-based Approach for Improved Evaluation of Human Exposure to Environmental Hazards

Liu,

Mostafavi

2023

Environ. Sci. Technol.

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Standard environmental hazard exposure assessment methods have been primarily based on residential places, neglecting individuals’ hazard exposures due to activities outside home neighborhood and underestimating peoples’ overall hazard exposures. To address this limitation, this study proposes a novel mobility-based index for the hazard exposure evaluation. Using large-scale human mobility data, we quantify the extent of population dwell time in high environmental hazard places in 239 US counties for three environmental hazards. We explore how human mobility extends the reach of environmental hazards and leads to the emergence of latent exposure for populations living outside high-hazard areas. Notably, neglect of mobility can lead to over 10% underestimation of hazard exposures. The interplay of spatial clustering in high-hazard regions and human movement trends creates “environmental hazard traps.” Poor and ethnic minority residents disproportionately face multiple types of environmental hazards. This data-driven evidence supports the severity of these injustices. We also studied latent exposure arising from visits outside residents’ home areas, revealing millions of the population having 5 to 10% of daily activities occur in high-exposure zones. Despite living in perceived safe areas, human mobility could expose millions of residents to different hazards. These findings provide crucial insights for targeted policies to mitigate these severe environmental injustices.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Beyond Residence: A Mobility-based Approach for Improved Evaluation of Human Exposure to Environmental Hazards

Liu,

Mostafavi

2023

Environ. Sci. Technol.

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“…Therefore, a better description of human activities is obtained. There are also other studies exploring individuals' activity areas based on clustering algorithms [4,5,26,67]. The data used in these studies for each clustering processing was an individual's activity records.…”

Section: Discussionmentioning

confidence: 99%

Extracting Human Activity Areas from Large-Scale Spatial Data with Varying Densities

Shen

Shi

Liu

et al. 2022

IJGI

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Human activity area extraction, a popular research topic, refers to mining meaningful location clusters from raw activity data. However, varying densities of large-scale spatial data create a challenge for existing extraction methods. This research proposes a novel area extraction framework (ELV) aimed at tackling the challenge by using clustering with an adaptive distance parameter and a re-segmentation strategy with noise recovery. Firstly, a distance parameter was adaptively calculated to cluster high-density points, which can reduce the uncertainty introduced by human subjective factors. Secondly, the remaining points were assigned according to the spatial characteristics of the clustered points for a more reasonable judgment of noise points. Then, to face the varying density problem, a re-segmentation strategy was designed to segment the appropriate clusters into low- and high-density clusters. Lastly, the noise points produced in the re-segmentation step were recovered to reduce unnecessary noise. Compared with other algorithms, ELV showed better performance on real-life datasets and reached 0.42 on the Silhouette coefficient (SC) indicator, with an improvement of more than 16.67%. ELV ensures reliable clustering results, especially when the density differences of the activity points are large, and can be valuable in some applications, such as location prediction and recommendation.

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“…With geography's transformation into a data-driven discipline an increasing number of studies are conducted on large-scale and complex datasets at multiple geographic scales (Miller and Goodchild 2015). For example, studying global volunteered geographic information (VGI) or other user-generated datasets can be used to infer the home location of users (citizens) (Heikinheimo et al 2022), to predict human mobility (Shen et al 2022) or to analyze global citizen sentiment during the COVID-19 pandemic (Okango and Mwambi 2022), to name but a few. Unlike local or regional studies, global datasets have the possibility to include data on Null Island (see Section 3.2 for examples).…”

Section: Geospatial Technologymentioning

confidence: 99%

'I think I discovered a military base in the middle of the ocean' -- Null Island, the most real of fictional places

Juhasz,

Mooney

2022

Preprint

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This paper explores Null Island, a fictional place located at 0°latitude and 0°longitude in the WGS84 geographic coordinate system. Null Island is erroneously associated with large amounts of geographic data in a wide variety of location-based services, place databases, social media and web-based maps. While it was originally considered a joke within the geospatial community, this article will demonstrate implications of its existence, both technological and social in nature, promoting Null Island as a fundamental issue of geographic information that requires more widespread awareness. The article summarizes error sources that lead to data being associated with Null Island. We identify four evolutionary phases which help explain how this fictional place evolved and established itself as an entity reaching beyond the geospatial profession to the point of being discovered by the visual arts and the general population. After providing an accurate account of data that can be found at (0, 0), geospatial, technological and social implications of Null Island are discussed. Guidelines to avoid misplacing data to Null Island are provided. Since data will likely continue to appear at this location, our contribution is aimed at both GIScientists and the general population to promote awareness of this error source.

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Novel model for predicting individuals’ movements in dynamic regions of interest

Cited by 6 publications

References 48 publications

Beyond Residence: A Mobility-based Approach for Improved Evaluation of Human Exposure to Environmental Hazards

Beyond Residence: A Mobility-based Approach for Improved Evaluation of Human Exposure to Environmental Hazards

Extracting Human Activity Areas from Large-Scale Spatial Data with Varying Densities

'I think I discovered a military base in the middle of the ocean' -- Null Island, the most real of fictional places

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