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        "description": "[![PyPI version](https://lbsn.vgiscience.org/lbsntransform/pypi.svg?&kill_cache=2)](https://pypi.org/project/lbsntransform/) [![pylint](https://lbsn.vgiscience.org/lbsntransform/pylint.svg)](https://gitlab.vgiscience.de/lbsn/lbsntransform) [![pipeline](https://lbsn.vgiscience.org/lbsntransform/pipeline.svg?&kill_cache=2)](https://gitlab.vgiscience.de/lbsn/lbsntransform) [![Documentation](https://lbsn.vgiscience.org/lbsntransform/documentation.svg)](https://lbsn.vgiscience.org/lbsntransform/docs/)\n\n# LBSNTransform\n\nA python package that uses the [common location based social network (LBSN) data structure][lbsnstructure] \n(ProtoBuf) to import, transform and export Social Media data such as Twitter and Flickr.\n\n![Illustration of functions](https://lbsn.vgiscience.org/lbsntransform/docs/inputoutput.svg)\n\n## Motivation\n\nThe goal is to provide a common interface to handle Social Media Data, \nwithout the need to individually adapt to the myriad API endpoints available. \nAs an example, consider the ProtoBuf spec [lbsn.Post][lbsnpost], which can be a Tweet on Twitter, \na Photo shared on Flickr, or a post on Reddit. However, all of these objects share\na common set of attributes, which is reflected in the lbsnstructure.\n\nThe tool is based on a 4-Facet conceptual framework for LBSN, introduced in a paper \nby [Dunkel et al. (2018)](https://www.tandfonline.com/doi/full/10.1080/13658816.2018.1546390). \n\nThe GDPR directly requests Social Media Network operators to allow \nusers to transfer accounts and data in-between services.\nWhile there are attempts by Google, Facebook etc. (e.g. see the [data-transfer-project][data-transfer-project]), \nthis is not currently possible. With the lbsnstructure, a primary motivation is to systematically \ncharacterize LBSN data aspects in a common, cross-network data scheme that enables privacy-by-design \nfor connected software, data handling and database design.\n\n## Description\n\nThis tool enables data import from a Postgres database, JSON, or CSV and export to CSV, [LBSN ProtoBuf][lbsnstructure] \nor the [hll][hlldb] and [raw][rawdb] versions of the LBSN prepared Postgres Databases.\nThe tool will map Social Media endpoints (e.g. Twitter tweets) to a common [LBSN Interchange Structure][lbsnstructure] \nformat in ProtoBuf. LBSNTransform can be used using the command line (CLI) or imported to other Python projects with \n`import lbsntransform`, for on-the-fly conversion.\n\n## Quick Start\n\nThe recommended way to install lbsntransform, for both Linux and Windows, \nis through the conda package manager.\n\n1. Create a conda env using `environment.yml`\n\nFirst, create an environment with the dependencies for lbsntransform using\nthe [environment.yml][environment.yml] that is provided in the root of the repository.\n\n```bash\ngit clone https://github.com/Sieboldianus/lbsntransform.git\ncd lbsntransform\n# not necessary, but recommended:\nconda config --env --set channel_priority strict\nconda env create -f environment.yml\n```\n\n2. Install lbsntransform without dependencies\n\nAfterwards, install lbsntransform using pip, without dependencies.\n\n```bash\nconda activate lbsntransform\npip install lbsntransform --no-deps --upgrade\n# or locally, from the latest commits on master\n# pip install . --no-deps --upgrade\n```\n\n3. Import data using a mapping\n\nFor each data source, a mapping must be provided that\ndefines how data is mapped to the [lbsnstructure][lbsnstructure].\n\nThe default mapping is [lbsnraw][lbsnraw].\n\nAdditional mappings can be dynamically loaded from a folder.\n\nWe have provided two [example mappings][mappings] for the [Flickr YFCC100M dataset][yfcc100m] (CSV)\nand Twitter (json).\n\nFor example, to import the first 1000 records from json data from Twitter to the \n[lbsn raw database][rawdb], clone [field_mapping_twitter.py][field_mapping_twitter] \nto a local folder `./resources/mappings/`, startup the Docker [rawdb][rawdb] container,\nand use:\n\n```shell\nlbsntransform --origin 3 \\\n              --mappings_path ./resources/mappings/ \\\n              --file_input \\\n              --file_type \"json\" \\\n              --mappings_path ./resources/mappings/ \\\n              --dbpassword_output \"sample-key\" \\\n              --dbuser_output \"postgres\" \\\n              --dbserveraddress_output \"127.0.0.1:5432\" \\\n              --dbname_output \"rawdb\" \\\n              --dbformat_output \"lbsn\" \\\n              --transferlimit 1000\n```\n\n.. with the above input args, the the tool will:  \n- read local json from `./01_Input/`\n- and store lbsn records to the [lbsn rawdb][rawdb].\n\nVice versa, to import data directly to the privacy-aware\nversion of lbsnstructure, called [hlldb][hlldb], startup the\nDocker container, and use:\n\n```shell\nlbsntransform --origin 3 \\\n              --mappings_path ./resources/mappings/ \\\n              --file_input \\\n              --file_type \"json\" \\\n              --mappings_path ./resources/mappings/ \\\n              --dbpassword_output \"sample-key\" \\\n              --dbuser_output \"postgres\" \\\n              --dbserveraddress_output \"127.0.0.1:25432\" \\\n              --dbname_output \"hlldb\" \\\n              --dbformat_output \"hll\" \\\n              --dbpassword_hllworker \"sample-key\" \\\n              --dbuser_hllworker \"postgres\" \\\n              --dbserveraddress_hllworker \"127.0.0.1:25432\" \\\n              --dbname_hllworker \"hlldb\" \\\n              --include_lbsn_objects \"origin,post\" \\\n              --include_lbsn_bases hashtag,place,date,community \\\n              --transferlimit 1000\n```\n\n.. with the above input args, the the tool will:  \n- read local json from `./01_Input/`  \n- and store lbsn records to the privacy-aware [lbsn hlldb][hlldb]  \n- by converting only lbsn objects of type [origin][lbsnorigin] and [post][lbsnpost]  \n- and updating the HyperLogLog (HLL) target tables `hashtag`, `place`, `date` and `community`  \n\nA full list of possible input and output args is available in the \n[documentation](https://lbsn.vgiscience.org/lbsntransform/docs/).\n\n## Built With\n\n* [lbsnstructure](https://pypi.org/project/lbsnstructure/) - A common language independend and cross-network social-media datascheme\n* [protobuf](https://github.com/google/protobuf) - Google's data interchange format\n* [psycopg2](https://github.com/psycopg/psycopg2) - Python-PostgreSQL Database Adapter\n* [ppygis3](https://github.com/AlexImmer/ppygis3) - A PPyGIS port for Python\n* [shapely](https://github.com/Toblerity/Shapely) - Geometric objects processing in Python\n* [emoji](https://github.com/carpedm20/emoji/) - Emoji handling in Python\n\n## Authors\n\n* **Alexander Dunkel** - Initial work\n\nSee also the list of [contributors](/../graphs/master).\n\n## License\n\nThis project is licensed under the GNU GPLv3 or any higher - \nsee the [LICENSE.md](LICENSE.md) file for details.\n\n[lbsnstructure]: https://lbsn.vgiscience.org/structure/\n[lbsnpost]: https://lbsn.vgiscience.org/structure/#post\n[lbsnorigin]: https://lbsn.vgiscience.org/structure/#origin\n[data-transfer-project]: https://datatransferproject.dev/\n[rawdb]: https://gitlab.vgiscience.de/lbsn/databases/rawdb\n[hlldb]: https://gitlab.vgiscience.de/lbsn/databases/hlldb\n[lbsnraw]: lbsntransform/input/mappings/field_mapping_lbsn.py\n[mappings]: resources/mappings\n[field_mapping_twitter]: resources/mappings/field_mapping_twitter.py\n[yfcc100m]: http://projects.dfki.uni-kl.de/yfcc100m/\n\n\n",
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