Constructing Queries

Podaris enables you to create queries and associate them with your isochrones and selections.

Queries in Podaris

Queries in Podaris can be created in order to filter items selected with the select tool as well as helping to answer demographic questions when created within the isochrone tool.

The isochrone and select tool query editors have have properties that are specific to their function and you can find specific details on each below:

Both tools however, share a means of filtering results.

Filtering Entities

With filtering, you can create complex filters using logical operations on the attributes of your layers. Once you have added more than one rule with the add rule button, you can apply either an and or or boolean operator between them.

Rules can also be nested in groups with the add group button.

What attributes can be filtered?

When working with isochrone queries, you can filter:

  • Dataset attributes: any attribute created or imported as part of a dataset
  • Layer: a dataset layer
  • Project: either the current project or transport and service data added into your current project via the Networks panel.

When constructing queries with the select tool you can filter by the following attributes:

  • Station
    • Name
    • ID
    • Kind: station, waypoint or depot
    • Berths
    • Has Alerts
  • Transport Route
    • Name
    • Short Name
    • ID
    • Mode
    • Agency
    • Number of Patterns
    • Number of Trips
  • Layer: either transport of dataset layer
  • Project: either the current project or transport and service data added into your current project via the Networks panel
  • Dataset Feature
    • Geometry Type: point, linestring or polygon

Working with operators

When working with datasets, the operators available to you when filtering entities will depend on the data type of the attribute you have selected.

For example, Column1 contains string values, allowing for the contains operator to be used in order to find instances of the specified string (in this case 'residents') within Column1 of that dataset's entities.

string operators

Column2 contains number types, meaning that you may use operators such as less than and greater than to filter your selection.

number operators

Column3 is of boolean type and therefore only allows filtering according to whether the entities value is true or false.