Package: mobilkit
mobilkitA Python Toolkit for Urban Resilience and Disaster Risk Management Analytics using High Frequency Human Mobility Data.mobilkitprovides a set of tools to analyze mobility traces to assess the users response to extreme events.
Trymobilkitwithout installing it in a MyBinder notebook:Table of contentsDocumentationCollaborate with usInstallationwith pipwith condatest installationTutorialsExamplesQuickstartCitingCredits and contactsDocumentationFull documentation with examples can be found onlinehere, otherwise see the notebooks indocs/examplesfor a step-by-step coverage of the library or the ones inexamples/for a more detailed showcase of the package's capabilities.Collaborate with usmobilkitis an active project and any contribution is welcome.You are encouraged to report any issue or problem encountered while using the software or to seek for support.If you would like to contribute or add functionalities tomobilkit, feel free to fork the project, open an issue and contact us.InstallationInstall with pipStart by creating an environment and install mobilkit there.Create an environmentmobilkitpython3 -m venv mobilkit
 # or, on Windows
 python -m venv c:\path\to\mobilkitActivatesource mobilkit/bin/activate
 # or, on Windows
 c:\path\to\mobilkit\Scripts\activate.batUpdate pippip install --upgrade pipInstallmobilkit(this will also installDaskand all the needed modules)pip install mobilkitOPTIONAL to usemobilkiton the jupyter notebookActivate the virutalenv:source mobilkit/bin/activateInstall jupyter notebook:pip install jupyterRun jupyter notebookjupyter notebook(Optional) install the kernel with a specific name to your existing notebook serversource mobilkit/bin/activate
  pip install ipykernel
  ipython kernel install --user --name=mobilkit_envIf you already havescikit-mobilityinstalled, skip the environment creation and run these commands from the skmob anaconda environment.mobilkitby default will only install core packages needed to run the main functions. There are three optional packages of dipendencies (themobilkit[complete]installs everything):[viz]will installcontextily, needed to visualize map backgrounds in certain viz functions;[doc]will install all the needed packages to build the docs;[skmob]will installscikit-mobilityas well;[locations]will also installinfostopto detect users' typical locations.Install with condaTODOTest the installation> source activate mobilkit
(mobilkit)> python
>>> import mobilkit
>>>ExamplesSeveral notebooks are found in thedocs/examplesfolder, we resume here the most important ones.QuickstartWe show the basic usage and functionalities in themobilkit_tutorial.ipynbnotebook.CitingIf you usemobilkitplease cite us:Enrico Ubaldi, Takahiro Yabe, Nicholas K. W. Jones, Maham Faisal Khan, Satish V. Ukkusuri, Riccardo Di Clemente and Emanuele StranoMobilkit: A Python Toolkit for Urban Resilience and Disaster Risk Management Analytics using High Frequency Human Mobility Data,
2021, KDD 2021 Humanitarian Mapping Workshop,https://arxiv.org/abs/2107.14297Bibtex:@misc{ubaldi2021mobilkit,
    title={Mobilkit: A Python Toolkit for Urban Resilience and Disaster Risk Management Analytics using High Frequency Human Mobility Data},
    author={Enrico Ubaldi and Takahiro Yabe and Nicholas K. W. Jones and Maham Faisal Khan and Satish V. Ukkusuri and Riccardo {Di Clemente} and Emanuele Strano},
    year={2021},
    eprint={2107.14297},
    primaryClass={cs.CY},
    archivePrefix={arXiv},
}Credits and contactsThis code has been developed byMindearth, theGlobal Facility for Disaster Reduction and Recovery(GFDRR) andPurdue University.Funding was provided by the Spanish Fund for Latin America and the Caribbean (SFLAC) under the Disruptive Technologies for Development (DT4D) program.The code is released under the MIT license (see the LICENSE file for details).