ActivitySpace Tools¶

ActivitySpace Tools is a Python library for modeling individual activity spaces and analyzing human mobility patterns using geospatial data.

The library provides tools for computing distance-based mobility metrics, generating activity space geometries, modeling exposure surfaces, and analyzing spatial properties of activity spaces.

The package was developed primarily for research applications in:

Human mobility
Urban analytics
Environmental exposure
GIScience
Transport geography
Spatial behavior analysis

The library is particularly well suited for analyzing mobility data collected through participatory mapping and Public Participation GIS (PPGIS) surveys, where individuals report locations of daily activities and experiences in geographic space.

Scientific Background¶

The methods implemented in ActivitySpace Tools originate from peer-reviewed research on human activity spaces, environmental exposure, and mobility behavior.

The conceptual and methodological foundations of these tools were developed in earlier research on spatial units of analysis and activity space modeling (Hasanzadeh, 2019) and subsequent journal publications.

Activity space conceptualization¶

Hasanzadeh, K., Laatikainen, T., & Kyttä, M. (2018).*A place-based model of local activity spaces: individual place exposure and characteristics.* Journal of Geographical Systems, 20(3), 227–252. https://doi.org/10.1007/s10109-017-0264-z

This work discusses how activity spaces can be conceptualized beyond static residential neighborhoods and introduces approaches for representing individualized spatial behavior using mobility data.

Environmental exposure and activity spaces¶

Laatikainen, T., Hasanzadeh, K., & Kyttä, M. (2018).*Capturing exposure in environmental health research: challenges and opportunities of different activity space models.* International Journal of Health Geographics, 17(1), 29. https://doi.org/10.1186/s12942-018-0149-5

This research demonstrates how daily mobility patterns influence the environmental conditions individuals are exposed to.

Dynamic home range modeling¶

Hasanzadeh, K., Broberg, A., & Kyttä, M. (2017). *Where is my neighborhood? A dynamic individual-based definition of home ranges and implementation of multiple evaluation criteria.*Applied Geography, 84, 1–10. https://doi.org/10.1016/j.apgeog.2017.04.006

This work proposes an individual-based approach for defining residential spatial contexts and introduces the dynamic home range model.

Individualized Residential Exposure Model (IREM)¶

Hasanzadeh, K. (2019). Spatial units of analysis: are there better ways? An empirical framework for use of individualized activity space models in environmental health promotion research. Doctoral dissertation, Aalto University. https://urn.fi/URN:ISBN:978-952-60-8519-7

This dissertation introduces conceptual and methodological foundations for individualized activity space models and proposes new spatial units for studying human–environment relationships.

Early GIS implementation¶

Hasanzadeh, K. (2018). *IASM: individualized activity space modeler.*SoftwareX, 7, 138–142. https://doi.org/10.1016/j.softx.2018.04.005

IASM represents an earlier implementation of activity space modeling tools developed for ArcGIS. The present ActivitySpace Tools library extends these ideas into an open Python ecosystem.

Features¶

ActivitySpace Tools currently provides four main modules.

Spider model¶

Computes distance-to-home metrics for activity locations.

Useful for studying:

travel distances
mobility behavior
spatial reach of daily activities

Home Range model¶

Generates activity space polygons based on home locations and visited destinations.

These polygons approximate the spatial extent of an individual’s daily activity area.

IREM model¶

The Individualized Residential Exposure Model (IREM) produces raster exposure surfaces representing how individuals experience the spatial environment during daily mobility.

Inputs include:

home locations
activity points
travel routes

Analytics tools¶

Additional functions for analyzing activity spaces and exposure surfaces:

raster exposure summaries
geometry metrics
raster-to-polygon conversion
exposure statistics

Basic Example¶

import geopandas as gpd
from activityspace.spider import add_distance_to_home

poi = gpd.read_file("eep.shp")
home = gpd.read_file("Home.shp")

result = add_distance_to_home(
    poi=poi,
    home=home,
    uniqueID="uid"
)

print(result.head())

Data Requirements¶

Typical workflows require three spatial datasets.

Home locations¶

Point dataset representing individuals’ home locations.

Example fields:

uid
geometry

Activity locations (POIs)¶

Point dataset representing visited destinations.

Example fields:

uid
DESTid
<weight_column>     (name configured via poi_weight_col; optional)
<travelMode_column> (name configured via travel_mode_col; optional)
geometry

Routes¶

Line dataset representing travel paths between home and destinations.

Example fields:

uid
DESTid
geometry

Example Workflow¶

A typical workflow using ActivitySpace Tools:

Compute distance-to-home metrics (Spider model)
Generate activity space polygons (Home Range model)
Model exposure surfaces (IREM model)
Summarize exposure statistics
Analyze geometry of activity spaces
Convert exposure rasters to polygons

Conceptually the workflow looks like:

Home points
↓
Activity points
↓
Routes
↓
IREM exposure surfaces
↓
Activity space analysis

Dependencies¶

The library depends on commonly used geospatial Python libraries:

geopandas
pandas
numpy
shapely
scipy
rasterio
pyproj

Author¶

Kamyar Hasanzadeh University of Helsinki

Citation¶

If you use this library in academic work, please cite both the software and the associated scientific publications.

Suggested citation:

Hasanzadeh, K. (2026).activity-space-tools: Python library for modeling individual
activity spaces. Zenodo. https://doi.org/10.5281/zenodo.19036426

License¶

MIT License