Optimal structure of GIS data for Optioneer

We share best practices on how to structure data to get the most value out of Optioneer.

Adam Anyszewski avatar
Written by Adam Anyszewski
Updated over a week ago

Quick definitions of the most important concepts

  • Data layer

    • Collection of features that can be treated in the same way.

    • Examples: listed buildings, woodlands, single-track railways etc.

  • Dataset (type)

    • Collection of related data layers, relevant to a specific aspect of optioneering.

    • Examples: existing infrastructure, environmental constraints, geology etc.

  • Dataset (file)

    • A specific file containing data layers which gets uploaded to Optioneer.

    • Examples: LinearFeatures_England_v3.gpkg, EnvConst_TexasSouth_FinalFinal_v2.gpkg etc.

Dataset types required to run Optioneer

Design rules in Optioneer are designed to run with specific dataset types. You can upload your own files and associate them with required types. This means that your can update the datasets throughout the project, if assumptions change, or new information becomes available.

Dataset types have specific requirements to ensure that dataset files are interchangeable.

For example, a design rule that helps Optioneer find an optimal path among constraints requires a dataset type called 'constraints' while a design rule that detects and evaluates crossings of linear features needs a dataset type called 'linear features'.

These dataset types are always required in a project:

  • Elevation

    • Offshore: bathymetry

  • Constraints

  • Linear Features

These dataset types can be used in most projects:

  • Slope

  • Environmental Constraints

  • Geology

    • Offshore: seabed geology

  • Land Parcels

    • Offshore: usually not present in offshore projects

  • Linear features to follow or stay close to

    • Access roads

    • Energy corridors

In order to use minimum functionality of Optioneer, you need three separate datasets.

In order to use full functionality of Optioneer, you need six separate datasets (or five offshore, as 'land parcels' are irrelevant).

How to structure data for Optioneer?

The core principles are:

  • use as few layers as possible

    • Multiple layers that share features, have overlapping features, represent the same features divided by authority etc. should be merged into one layer, if possible. You will then save yourself time by configuring Optioneer for all of these shared features at once.

    • Example: regional roads data held by various regional authorities can be treated as a single data layer called 'regional roads'

  • use as many layers as required

    • If there is any reason you'd like to treat layers separately, separate them in ArcGIS / QGIS, name distinctively and package as separate layers. You will then give yourself a possibility of configuring Optioneer for all individual feature layers separately.

    • Example: regional roads data held by various regional authorities should be treated separately because authorities have different definitions of road types and some of the authorities are harder to deal with than others.

  • only include layers relevant for Optioneer in the datasets

    • if you want Optioneer to take a layer into account, include it in one of the datasets. If the data is for informative purposes only, it should be kept in a separate dataset (can be uploaded to Optioneer and viewed on the map).

Example: it is good to see administrative boundaries on the map but there is no aspect of routing that depends on it.

Structuring data in practice - examples

We will walk you through the process of structuring data for Optioneer. The example is simplified but realistic and it illustrates the most important concepts well.

We cover four dataset types:

  • environmental constraints - natural areas and surface water areas

  • constraints - buildings and functional sites

  • linear features - roads and rivers

  • elevation - well, it's just elevation

At the end of the process, we should have four, well-structured datasets which can be ingested into Optioneer.

Please note:

  • The example shows raw data, data processed into layers and eventually into a dataset that can be ingested into Optioneer.

  • Elevation / hillshade serves as the background.

Environmental constraints

Raw data:

Processed layers:

Dataset put together:


Raw data (no processing required):

Dataset put together:

Linear features

Raw data:

Processed layers:

Dataset put together:

Combining datasets in Optioneer

We now have four well-structured datasets.

Once they are uploaded to Optioneer, you will be immediately able to associate various aspects of routing, design and costing logic to them and use this data.

Here is how the datasets will look like in Optioneer. You can manipulate datasets, change layer colours, visibilities etc. using the GIS panel. Learn more about it here.

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