Test Data and Example Workflow

This section describes the small example dataset used to demonstrate and validate the functionality of the ActivitySpace Tools library.

The dataset is derived from anonymized Public Participation GIS (PPGIS) data originally collected in Oulu, Finland. To protect privacy, the data have been simplified and reduced to a minimal example suitable for testing.

The dataset represents mobility information for two individuals and includes a total of eight activity markings.

Files

Home.shp

Point dataset representing the home locations of individuals.

Attributes include:

uid
geometry

Each individual has one home location.

eep.shp

Point dataset representing Everyday Errand Points (EEPs) or destinations visited by individuals.

These points originate from PPGIS survey responses where participants marked locations they frequently visit.

Attributes include:

uid
DESTid
weight
travelMode
geometry

The example dataset includes eight activity points in total.

routes.shp

Line dataset representing precomputed shortest routes between each individual’s home location and their activity points.

Routes were calculated beforehand using a routing algorithm on a road network.

Attributes include:

uid
DESTid
geometry

Each route connects a home location with a corresponding activity point.

Test Workflow

The file run_tests.py demonstrates a complete workflow using ActivitySpace Tools.

The script performs the following steps:

  1. Load spatial datasets

    • home locations

    • activity locations (EEPs)

    • travel routes

  2. Compute distance-to-home metrics

    The Spider model calculates the distance between each activity location and the individual’s home.

  3. Generate activity space polygons

    The Home Range model constructs an activity space polygon for each individual using home locations and activity points.

  4. Generate exposure surfaces

    The IREM (Individualized Residential Exposure Model) produces raster exposure surfaces based on:

    • home locations

    • activity locations

    • travel routes

  5. Summarize raster exposure

    Exposure values are summarized for each individual.

  6. Compute geometry metrics

    Geometric properties of the resulting activity space polygons are calculated.

  7. Convert exposure rasters to polygons

    Raster exposure surfaces can be converted into polygons using percentile thresholds.

Purpose

These files are intended only for:

  • testing library functionality

  • demonstrating example workflows

  • providing reproducible examples

The dataset is intentionally small so that the entire workflow can be executed quickly.