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., 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 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 ------------- .. code-block:: python 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 (name configured via poi_weight_col; optional) (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: 1. Compute distance-to-home metrics (Spider model) 2. Generate activity space polygons (Home Range model) 3. Model exposure surfaces (IREM model) 4. Summarize exposure statistics 5. Analyze geometry of activity spaces 6. 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 Copyright (c) 2026 Kamyar Hasanzadeh .. toctree:: :maxdepth: 2 :caption: Documentation installation usage test .. toctree:: :maxdepth: 2 :caption: Modules modules/spider_module modules/home_range_module modules/irem_module modules/analytics_module