Garden city: A synthetic dataset and sandbox environment for analysis of pre-processing algorithms for GPS human mobility data
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866918013827022848 |
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| author | Li, Thomas H. Barreras, Francisco |
| author_facet | Li, Thomas H. Barreras, Francisco |
| contents | Human mobility datasets have seen increasing adoption in the past decade, enabling diverse applications that leverage the high precision of measured trajectories relative to other human mobility datasets. However, there are concerns about whether the high sparsity in some commercial datasets can introduce errors due to lack of robustness in processing algorithms, which could compromise the validity of downstream results. The scarcity of "ground-truth" data makes it particularly challenging to evaluate and calibrate these algorithms. To overcome these limitations and allow for an intermediate form of validation of common processing algorithms, we propose a synthetic trajectory simulator and sandbox environment meant to replicate the features of commercial datasets that could cause errors in such algorithms, and which can be used to compare algorithm outputs with "ground-truth" synthetic trajectories and mobility diaries. Our code is open-source and is publicly available alongside tutorial notebooks and sample datasets generated with it. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_00913 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Garden city: A synthetic dataset and sandbox environment for analysis of pre-processing algorithms for GPS human mobility data Li, Thomas H. Barreras, Francisco Social and Information Networks Human mobility datasets have seen increasing adoption in the past decade, enabling diverse applications that leverage the high precision of measured trajectories relative to other human mobility datasets. However, there are concerns about whether the high sparsity in some commercial datasets can introduce errors due to lack of robustness in processing algorithms, which could compromise the validity of downstream results. The scarcity of "ground-truth" data makes it particularly challenging to evaluate and calibrate these algorithms. To overcome these limitations and allow for an intermediate form of validation of common processing algorithms, we propose a synthetic trajectory simulator and sandbox environment meant to replicate the features of commercial datasets that could cause errors in such algorithms, and which can be used to compare algorithm outputs with "ground-truth" synthetic trajectories and mobility diaries. Our code is open-source and is publicly available alongside tutorial notebooks and sample datasets generated with it. |
| title | Garden city: A synthetic dataset and sandbox environment for analysis of pre-processing algorithms for GPS human mobility data |
| topic | Social and Information Networks |
| url | https://arxiv.org/abs/2412.00913 |