roel/pydov

A Python package to retrieve data from Databank Ondergrond Vlaanderen (DOV)

PyDOV

A Python package to query and download data from Databank Ondergrond Vlaanderen (DOV).

• Free software: MIT license
• Documentation: https://pydov.readthedocs.io.

Introduction

The pydov package is a community effort and everyone is welcome to contribute. It is hosted on GitHub and development is coordinated by Databank Ondergrond Vlaanderen (DOV). DOV aggregates data about soil, subsoil and groundwater of Flanders and makes them publicly available. Interactive and human-readable extraction and querying of the data is provided by a web application, whereas the focus of this package is to support machine-based extraction and conversion of the data. The pacakge aims to support a set of complementary use cases, for example:

• integrate DOV data in larger data processing pipelines
• support the reproducibility and/or repeatability of research studies
• integrate the data in third-party applications

The machine-based availability of the data can potentially serve a diverse community of researchers, consultants, modelers, and students. As performant and proper functioning of DOV data processing is of interest to the variety of users, we believe that a community-based effort to develop and maintain these functionalities as an open source package provides the optimal development traject.

Scope of the package

The pydov provides in the first place a convenient wrapper around the XML export of the DOV Verkenner and related applications, in combination with the available DOV WMS/WFS webservices. By combining the information of these web services, different data request use cases can be automated.

The central elements of the package are:

1. search, i.e. check the available information to download and atrributes to query
2. download_, i.e. extraction part: downloading data based on a single station or a list of stations. The user extracts data using the available filters. An important filter is the location, which can be expressed using different spatial entities, coordinates, boundingbox, aquifer, polygon,...
3. subset_*, i.e. filter part: this should provide some straightforward functions to further filter the downloaded data.
4. to_***, i.e. conversion part: the data is stored or exported to a new file-format that could be useful for the user. to_csv/ to_excel are straight forward examples, but more advanced and domain-specific export functionalities are envisioned, e.g. to_modflow(), to_menyanthes(), to_swap()