Podaris allows you to perform advanced geospatial analysis on the areas serviced by your networks by generating queries related to your data layers.
Isochrone queries can help you understand how travel-time in your networks relates to the distribution of entities within a dataset. This feature can help you answer questions such as:
- How many people live within 10 mins of stops along a bus route?
- How many more low-income units does Scenario A provide connectivity to compared to Scenario B?
- If I move our office to location X, how many employees can reach it in 15 mins? 30 mins? 45 mins?
In this tutorial, we will use the isochrone query tool to explore this in relation to rental developments in the Santa Clara area.
Exploring reach and travel time from a specific point
- Having constructed your transport network, import the dataset you wish to query, as per the instructions on datasets.
- Click to open the isochrone panel.
- If you have selected you will be able to explore accessibility either or a specific point on your map, at a time designated by the respective service day and departure/arrival time.
- Click a point on your map to set it as either a start or destination point.
- Click on the QUERIES section of the isochrone tool panel to load the Isochrone Query editor.
- Having given our query a name, we're going to take a specific attribute from our dataset - in this case, total_affo, a number representing the amount of affordable units associated with that entity.
- Click OKAY and then to generate a list of affordable units within a range of travel times.
In constructing a query, you are required to specify several values:
- Name: This will be displayed on the travel time chart.
- Total per entity/From Attribute: Whether this query applies to each entity in a dataset, or to a specific attribute.
- Weight: A multiplier of either your total per entity or attribute value.
- Aggregation: Sum/average/minimum/maximum.
With each amendment to your query, you are required to click on the isochrone tool panel before you can see your changes.
Filtering Dataset Entries
With dataset filtering, you can create complex filters using logical operations on the attributes of your data layers. Once you have added more than one rule with the button, you can apply either an or boolean operator between them.
Rules can also be nested in groups with the button.
All dataset entries visible when filtering is derived from the dataset's attributes with the exception of Layer (which can be used to reference the entire layer by name) and Geometry Type.