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This project leverages large-scale GPS mobility data to study human activity and urban space usage in response to atypical events. Using the Urban Space Usage Index, a normalized measure of relative human presence derived from anonymized mobile device location pings, the project quantifies deviations from typical mobility patterns and examines how cities respond to a range of shocks, from sudden natural disasters to planned public events and slow-onset climate stressors.
Case studies span two countries and four distinct event types: the 2023 Turkey-Syria earthquakes, Republic Day celebrations in Istanbul, heatwaves in Metro Manila, and monsoon-driven flooding in Manila amplified by Typhoon Doksuri.
The analysis is based on the Veraset Movement dataset, provided as part of the Mobility Data collection from the Development Data Partnership. The dataset consists of anonymized, high-frequency GPS pings collected through a network of mobile applications and SDKs. Each record includes geographic coordinates, a UTC timestamp, and an anonymized device identifier.
Mobility observations are spatially aggregated using the Uber H3 hierarchical spatial index at resolutions 7 and 8 (average cell areas of ~5 km² and ~0.74 km², respectively).
The analytical framework follows three steps:
1. Define a measure. The Urban Space Usage Index (I) is defined as the daily share of total active users visiting each H3 hexagon, normalizing for day-to-day fluctuations in overall data volume:
I(h, d) = U(h, d) / U(d)
where U(h, d) is the number of unique users in hexagon h on day d, and U(d) is the total number of active users on that day.
2. Quantify deviations. Deviations from typical conditions are measured through Z-scores computed relative to a stable baseline period:
Z(h, d) = (I(h, d) − μ(h)) / σ(h)
3. Interpret deviations. Z-scores are analyzed temporally and spatially, and stratified by land-use category and functional layer (POI-based), enabling characterization of where and how urban activity changes in response to events.
Full methodological details are available in the Methodological Framework and Spatial Characterization of Urban Units notebooks.
| Country | Area of Interest | Resolution |
|---|---|---|
| Philippines | Metro Manila | H3 resolution 8 (~0.74 km²) |
| Turkey | Istanbul | H3 resolution 8 (~0.74 km²) |
| Turkey | 11 earthquake-affected provinces | H3 resolution 8 (~0.74 km²) |
A key design feature of this project is that the four case studies deliberately span four distinct event typologies, each presenting different challenges for mobility analysis and policy response:
| Event | Location | Typology | Period |
|---|---|---|---|
| Republic Day | Istanbul, Turkey | Planned public event | Oct 2023 |
| 2023 Turkey-Syria Earthquake | Southern Turkey (11 provinces) | Sudden, unpredictable natural disaster | Feb 2023 |
| Flooding (Typhoon Doksuri) | Metro Manila, Philippines | Foreseeable natural disaster (typhoon-driven) | Jul 2023 |
| Heatwaves | Metro Manila, Philippines | Slow-onset climate shock | Apr 2023 |
This typology-driven structure allows the framework to be tested across events with fundamentally different warning horizons, impact profiles, and behavioral responses:
- Planned events (Republic Day) produce sharp, predictable spikes in activity that amplify existing spatial patterns city-wide.
- Sudden disasters (earthquake) generate delayed but extreme anomalies driven by emergency response, displacement, and humanitarian operations, with no anticipatory behavioral signal.
- Foreseeable disasters (typhoon-driven flooding) show a characteristic two-phase pattern: a pre-event increase in activity consistent with anticipatory behaviors (stocking, relocation), followed by a sharp collapse during peak impact.
- Slow-onset shocks (heatwaves) produce weaker aggregate signals but reveal systematic spatial and functional redistributions of activity, with people shifting toward climate-controlled or shaded environments rather than reducing mobility altogether.
Prior to analysis, comprehensive Exploratory Data Analysis and Quality Assessments (EDA+QA) were conducted for each country dataset, documenting temporal coverage, spatial distribution, regime shifts, and user-level heterogeneity.
| Report | Key findings |
|---|---|
| EDA+QA Turkey | ~1.1B GPS points, 18.9M users; 96.7% temporal coverage; three anomalous regimes identified |
| EDA+QA Metro Manila | ~4.4B GPS points, 27.2M users; 96.6% temporal coverage; structural break on 10 July 2023 |
- Python 3.8 or higher
- Jupyter Lab for running notebooks
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Clone the repository:
git clone https://github.com/worldbank/eca-resilience.git cd eca-resilience -
Create and activate the conda environment:
conda env create -f environment.yml conda activate eca-resilience
For detailed documentation and analysis notebooks, visit the project documentation.
For questions, feedback, or contributions, please contact the Development Data Partnership at datapartnership@worldbank.org.
You can also open an issue in the GitHub repository.
This project is licensed under the MIT License together with the World Bank IGO Rider. The Rider is purely procedural: it reserves all privileges and immunities enjoyed by the World Bank, without adding restrictions to the MIT permissions. Please review both files before using, distributing or contributing.
This project maintains a Code of Conduct to ensure an inclusive and respectful environment for everyone. Please adhere to it in all interactions within our community.