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Mobilkit: A Python Toolkit for Urban Resilience and Disaster Risk Management Analytics using High Frequency Human Mobility Data [article]

Enrico Ubaldi, Takahiro Yabe, Nicholas K. W. Jones, Maham Faisal Khan, Satish V. Ukkusuri, Riccardo Di Clemente, Emanuele Strano
<span title="2021-09-16">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>

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https://web.archive.org/web/20210921190007/https://arxiv.org/pdf/2107.14297v2.pdf

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<span title="2021-07-29">2021</span> <span class="release-stage" >pre-print</span>
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2107.14297v1">arXiv:2107.14297v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/u57zcgb27fd5fag4m37ptpsvjy">fatcat:u57zcgb27fd5fag4m37ptpsvjy</a> </span>
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.  ...  In this work, we present a toolkit designed to conduct replicable and scalable analyses for DRM and resilient urban planning utilizing large human mobility datasets in the form of raw (disaggregated) GPS  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2107.14297v2">arXiv:2107.14297v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/6pmgqtgfzbdizaj63wfomcvxri">fatcat:6pmgqtgfzbdizaj63wfomcvxri</a> </span>
<span class="ui label basic orange small" title="Open Access">Open Access</span> <span class="ui label basic blue small">Multiple Versions</span>
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Citation

Enrico Ubaldi, Takahiro Yabe, Nicholas K. W. Jones, Maham Faisal Khan, Satish V. Ukkusuri, Riccardo Di Clemente, Emanuele Strano. "Mobilkit: A Python Toolkit for Urban Resilience and Disaster Risk Management Analytics using High Frequency Human Mobility Data." arXiv (2021)

Mobile Phone Location Data for Disasters: A Review from Natural Hazards and Epidemics [article]

Takahiro Yabe, Nicholas K W Jones, P Suresh C Rao, Marta C Gonzalez, Satish V Ukkusuri
<span title="2021-08-05">2021</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>

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https://web.archive.org/web/20210815000704/https://arxiv.org/pdf/2108.02849v1.pdf

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The COVID-19 pandemic has spurred the use of mobile phone location data for pandemic and disaster response.  ...  Mobile phone location data have enabled us to observe, estimate, and model human mobility dynamics at an unprecedented spatio-temporal granularity and scale.  ...  U. is partly funded by NSF Grant No. 1638311 CRISP Type 2/Collaborative Research: Critical Transitions in the Resilience and Recovery of Interdependent Social and Physical Networks.  ... 
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Citation

Takahiro Yabe, Nicholas K W Jones, P Suresh C Rao, Marta C Gonzalez, Satish V Ukkusuri. "Mobile Phone Location Data for Disasters: A Review from Natural Hazards and Epidemics." arXiv (2021)