{"id":24785754,"url":"https://github.com/se-jaeger/urban-technologies-berlin","last_synced_at":"2025-03-24T09:13:40.085Z","repository":{"id":116138446,"uuid":"225551517","full_name":"se-jaeger/urban-technologies-berlin","owner":"se-jaeger","description":"Simulates the water mass flows in Berlin after strong precipitation. 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Uses exclusively data form the [Berlin Open Data Portal](https://daten.berlin.de).\n\n\n## Thank You!\n\nMany thanks to my fellow student and friend [chrisschroer](https://github.com/chrisschroer) for the offline discussions.\n\n\n## Description\n\nThis project carried out as part of the Urban Technology course at the Beuth University of Applied Sciences in Berlin of the masters course Data Science. Please note this.\n\nPlease have a look at the [slides](./reports/slides.pdf) of the internal given presentation. A simulation of the rainfalls in 2017 are [here available](./data/interim/simulation/berlin_17.gif).\n\n\n## Data and Licence\n\nThis repository contains two datasets, and variants thereof, of the [Berlin Open Data Portal](https://daten.berlin.de) and one combining both.\n\nThe data can be found in the directory: [data/preprocessed](data/preprocessed)\n\n\n### Dataset: Versiegelung 2016 (Ausgabe 2017)\n\nNutzungsbedingungen: Für die Nutzung der Daten ist die Datenlizenz Deutschland - Namensnennung - Version 2.0 anzuwenden. Die Lizenz ist über https://www.govdata.de/dl-de/by-2-0 abrufbar. Der Quellenvermerk gemäß (2) der Lizenz lautet \"Umweltatlas Berlin / Versiegelung 2016 (Ausgabe 2017)\"\n\n**Original Data:** https://fbinter.stadt-berlin.de/fb/berlin/service_intern.jsp?id=sach_nutz2015_nutzsa@senstadt\u0026type=WFS\n\n**Changes:** The file format is `geojson` and columns are omitted\n\n\n### Dataset: ATKIS® DGM − Digitales Geländemodell\n\nNutzungsbedingungen: Für die Nutzung der Daten ist die Datenlizenz Deutschland - Namensnennung - Version 2.0 anzuwenden. Die Lizenz ist über https://www.govdata.de/dl-de/by-2-0 abrufbar. Der Quellenvermerk gemäß (2) der Lizenz lautet \"Umweltatlas Berlin / ATKIS® DGM − Digitales Geländemodell\"\n\n**Original Data:** https://fbinter.stadt-berlin.de/fb/berlin/service_intern.jsp?id=a_dgm@senstadt\u0026type=FEED\n\nTool to download and create this data set is provided in https://github.com/se-jaeger/berlin-gelaendemodelle-downloader\n\n\n### Dataset: Sealing and Ground Level of Berlin\n\nCombines the two above mentioned datasets and is redistributed under the same licence.\n\nNutzungsbedingungen: Für die Nutzung der Daten ist die Datenlizenz Deutschland - Namensnennung - Version 2.0 anzuwenden. Die Lizenz ist über https://www.govdata.de/dl-de/by-2-0 abrufbar. Der Quellenvermerk gemäß (2) der Lizenz lautet \"Umweltatlas Berlin / ATKIS® DGM − Sealing and Ground Level of Berlin\"\n\n#### Create the Dataset\n\nDownload this repository, install the package and unse the CLI.\n\n```bash\ngit clone git@github.com:se-jaeger/urban-technologies-berlin.git\ncd urban-technologies-berlin\n\n# create and activate environment if you like\n\npython setup.py install\nutberlin create-dataset --download --compress 5 # compress from 1x1 pixels into 5x5 pixels\n```\n\nFor problems with the `Rtree` package, try to manually install `spatialindex` with brew: `brew install spatialindex`\n\n\n## Information about the Scaffold\n\n### Installation\n\nIn order to set up the necessary environment:\n\n1. create an environment `urban-technologies-berlin` with the help of [conda],\n   ```\n   conda env create -f environment.yaml\n   ```\n2. activate the new environment with\n   ```\n   conda activate urban-technologies-berlin\n   ```\n3. install `urban-technologies-berlin` with:\n   ```\n   python setup.py install # or `develop`\n   ```\n\nOptional and needed only once after `git clone`:\n\n4. install several [pre-commit] git hooks with:\n   ```\n   pre-commit install\n   ```\n   and checkout the configuration under `.pre-commit-config.yaml`.\n   The `-n, --no-verify` flag of `git commit` can be used to deactivate pre-commit hooks temporarily.\n\n5. install [nbstripout] git hooks to remove the output cells of committed notebooks with:\n   ```\n   nbstripout --install --attributes notebooks/.gitattributes\n   ```\n   This is useful to avoid large diffs due to plots in your notebooks.\n   A simple `nbstripout --uninstall` will revert these changes.\n\nThen take a look into the `scripts` and `notebooks` folders.\n\n\n### Dependency Management \u0026 Reproducibility\n\n1. Always keep your abstract (unpinned) dependencies updated in `environment.yaml` and eventually\n   in `setup.cfg` if you want to ship and install your package via `pip` later on.\n2. Create concrete dependencies as `environment.lock.yaml` for the exact reproduction of your\n   environment with:\n   ```\n   conda env export -n urban-technologies-berlin -f environment.lock.yaml\n   ```\n   For multi-OS development, consider using `--no-builds` during the export.\n3. Update your current environment with respect to a new `environment.lock.yaml` using:\n   ```\n   conda env update -f environment.lock.yaml --prune\n   ```\n\n\n### Project Organization\n\n```\n├── AUTHORS.rst             \u003c- List of developers and maintainers.\n├── CHANGELOG.rst           \u003c- Changelog to keep track of new features and fixes.\n├── LICENSE.txt             \u003c- License as chosen on the command-line.\n├── README.md               \u003c- The top-level README for developers.\n├── configs                 \u003c- Directory for configurations of model \u0026 application.\n├── data\n│   ├── external            \u003c- Data from third party sources.\n│   ├── interim             \u003c- Intermediate data that has been transformed.\n│   ├── processed           \u003c- The final, canonical data sets for modeling.\n│   └── raw                 \u003c- The original, immutable data dump.\n├── docs                    \u003c- Directory for Sphinx documentation in rst or md.\n├── environment.yaml        \u003c- The conda environment file for reproducibility.\n├── models                  \u003c- Trained and serialized models, model predictions,\n│                              or model summaries.\n├── notebooks               \u003c- Jupyter notebooks. Naming convention is a number (for\n│                              ordering), the creator's initials and a description,\n│                              e.g. `1.0-fw-initial-data-exploration`.\n├── references              \u003c- Data dictionaries, manuals, and all other materials.\n├── reports                 \u003c- Generated analysis as HTML, PDF, LaTeX, etc.\n│   └── figures             \u003c- Generated plots and figures for reports.\n├── scripts                 \u003c- Analysis and production scripts which import the\n│                              actual PYTHON_PKG, e.g. train_model.\n├── setup.cfg               \u003c- Declarative configuration of your project.\n├── setup.py                \u003c- Use `python setup.py develop` to install for development or\n|                              or create a distribution with `python setup.py bdist_wheel`.\n├── src\n│   └── dsproject_demo      \u003c- Actual Python package where the main functionality goes.\n├── tests                   \u003c- Unit tests which can be run with `py.test`.\n├── .coveragerc             \u003c- Configuration for coverage reports of unit tests.\n├── .isort.cfg              \u003c- Configuration for git hook that sorts imports.\n└── .pre-commit-config.yaml \u003c- Configuration of pre-commit git hooks.\n```\n\n\n## Note\n\nThis project has been set up using PyScaffold 3.2.3. For details and usage\ninformation on PyScaffold see https://pyscaffold.org/.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fse-jaeger%2Furban-technologies-berlin","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fse-jaeger%2Furban-technologies-berlin","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fse-jaeger%2Furban-technologies-berlin/lists"}