Mobilkit: A Python Toolkit for Urban Resilience and Disaster Risk Management Analytics using High Frequency Human Mobility Data
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2021
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| _version_ | 1866917601819492352 |
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| author | Ubaldi, Enrico Yabe, Takahiro Jones, Nicholas K. W. Khan, Maham Faisal Ukkusuri, Satish V. Di Clemente, Riccardo Strano, Emanuele |
| author_facet | Ubaldi, Enrico Yabe, Takahiro Jones, Nicholas K. W. Khan, Maham Faisal Ukkusuri, Satish V. Di Clemente, Riccardo Strano, Emanuele |
| contents | Increasingly available high-frequency location datasets derived from smartphones provide unprecedented insight into trajectories of human mobility. These datasets can play a significant and growing role in informing preparedness and response to natural disasters. However, limited tools exist to enable rapid analytics using mobility data, and tend not to be tailored specifically for disaster risk management. We present an open-source, Python-based toolkit designed to conduct replicable and scalable post-disaster analytics using GPS location data. Privacy, system capabilities, and potential expansions of \textit{Mobilkit} are discussed. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2107_14297 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Mobilkit: A Python Toolkit for Urban Resilience and Disaster Risk Management Analytics using High Frequency Human Mobility Data Ubaldi, Enrico Yabe, Takahiro Jones, Nicholas K. W. Khan, Maham Faisal Ukkusuri, Satish V. Di Clemente, Riccardo Strano, Emanuele Computers and Society Social and Information Networks Physics and Society J.2 Increasingly available high-frequency location datasets derived from smartphones provide unprecedented insight into trajectories of human mobility. These datasets can play a significant and growing role in informing preparedness and response to natural disasters. However, limited tools exist to enable rapid analytics using mobility data, and tend not to be tailored specifically for disaster risk management. We present an open-source, Python-based toolkit designed to conduct replicable and scalable post-disaster analytics using GPS location data. Privacy, system capabilities, and potential expansions of \textit{Mobilkit} are discussed. |
| title | Mobilkit: A Python Toolkit for Urban Resilience and Disaster Risk Management Analytics using High Frequency Human Mobility Data |
| topic | Computers and Society Social and Information Networks Physics and Society J.2 |
| url | https://arxiv.org/abs/2107.14297 |