se-jaeger / urban-technologies-berlin

Simulates the water mass flows in Berlin after strong precipitation. Uses exclusively open data sources.

Simulation of Heavy Rain in Berlin

Simulates the water mass flows in Berlin after strong precipitation. Uses exclusively data form the Berlin Open Data Portal.

Thank You!

Many thanks to my fellow student and friend chrisschroer for the offline discussions.

Description

This 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.

Please have a look at the slides of the internal given presentation. A simulation of the rainfalls in 2017 are here available.

Data and Licence

This repository contains two datasets, and variants thereof, of the Berlin Open Data Portal and one combining both.

The data can be found in the directory: data/preprocessed

Dataset: Versiegelung 2016 (Ausgabe 2017)

Nutzungsbedingungen: 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)"

Original Data: https://fbinter.stadt-berlin.de/fb/berlin/service_intern.jsp?id=sach_nutz2015_nutzsa@senstadt&type=WFS

Changes: The file format is geojson and columns are omitted

Dataset: ATKIS® DGM − Digitales Geländemodell

Nutzungsbedingungen: 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"

Original Data: https://fbinter.stadt-berlin.de/fb/berlin/service_intern.jsp?id=a_dgm@senstadt&type=FEED

Tool to download and create this data set is provided in https://github.com/se-jaeger/berlin-gelaendemodelle-downloader

Dataset: Sealing and Ground Level of Berlin

Combines the two above mentioned datasets and is redistributed under the same licence.

Nutzungsbedingungen: 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"

Create the Dataset

Download this repository, install the package and unse the CLI.

git clone [e-mail]:se-jaeger/urban-technologies-berlin.git
cd urban-technologies-berlin

# create and activate environment if you like

python setup.py install
utberlin create-dataset --download --compress 5 # compress from 1x1 pixels into 5x5 pixels

For problems with the Rtree package, try to manually install spatialindex with brew: brew install spatialindex

Information about the Scaffold

Installation

In order to set up the necessary environment:

1. create an environment urban-technologies-berlin with the help of [conda],

   conda env create -f environment.yaml
   

2. activate the new environment with

   conda activate urban-technologies-berlin
   

3. install urban-technologies-berlin with:

   python setup.py install # or `develop`
   

Optional and needed only once after git clone:

4. install several [pre-commit] git hooks with:

   pre-commit install
   

   and checkout the configuration under .pre-commit-config.yaml. The -n, --no-verify flag of git commit can be used to deactivate pre-commit hooks temporarily.

5. install [nbstripout] git hooks to remove the output cells of committed notebooks with:

   nbstripout --install --attributes notebooks/.gitattributes
   

   This is useful to avoid large diffs due to plots in your notebooks. A simple nbstripout --uninstall will revert these changes.

Then take a look into the scripts and notebooks folders.

Dependency Management & Reproducibility

1. Always keep your abstract (unpinned) dependencies updated in environment.yaml and eventually in setup.cfg if you want to ship and install your package via pip later on.
2. Create concrete dependencies as environment.lock.yaml for the exact reproduction of your environment with:

   conda env export -n urban-technologies-berlin -f environment.lock.yaml
   

   For multi-OS development, consider using --no-builds during the export.
3. Update your current environment with respect to a new environment.lock.yaml using:

   conda env update -f environment.lock.yaml --prune
   

Project Organization

├── AUTHORS.rst             <- List of developers and maintainers.
├── CHANGELOG.rst           <- Changelog to keep track of new features and fixes.
├── LICENSE.txt             <- License as chosen on the command-line.
├── README.md               <- The top-level README for developers.
├── configs                 <- Directory for configurations of model & application.
├── data
│   ├── external            <- Data from third party sources.
│   ├── interim             <- Intermediate data that has been transformed.
│   ├── processed           <- The final, canonical data sets for modeling.
│   └── raw                 <- The original, immutable data dump.
├── docs                    <- Directory for Sphinx documentation in rst or md.
├── environment.yaml        <- The conda environment file for reproducibility.
├── models                  <- Trained and serialized models, model predictions,
│                              or model summaries.
├── notebooks               <- Jupyter notebooks. Naming convention is a number (for
│                              ordering), the creator's initials and a description,
│                              e.g. `1.0-fw-initial-data-exploration`.
├── references              <- Data dictionaries, manuals, and all other materials.
├── reports                 <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures             <- Generated plots and figures for reports.
├── scripts                 <- Analysis and production scripts which import the
│                              actual PYTHON_PKG, e.g. train_model.
├── setup.cfg               <- Declarative configuration of your project.
├── setup.py                <- Use `python setup.py develop` to install for development or
|                              or create a distribution with `python setup.py bdist_wheel`.
├── src
│   └── dsproject_demo      <- Actual Python package where the main functionality goes.
├── tests                   <- Unit tests which can be run with `py.test`.
├── .coveragerc             <- Configuration for coverage reports of unit tests.
├── .isort.cfg              <- Configuration for git hook that sorts imports.
└── .pre-commit-config.yaml <- Configuration of pre-commit git hooks.

Note

This project has been set up using PyScaffold 3.2.3. For details and usage information on PyScaffold see https://pyscaffold.org/.