DOI: 10.5281/zenodo.14199648
title: Geospatial Dataset of GNSS Anomalies and Political Violence Events (2023)

description: Geospatial Dataset of GNSS Anomalies and Political Violence Events (2023)

Overview

The Geospatial Dataset of GNSS Anomalies and Political Violence Events (2023) is a collection of data that integrates aircraft flight information, GNSS (Global Navigation Satellite System) anomalies, and political violence events from the ACLED (Armed Conflict Location & Event Data Project) database.

Dataset Files

The dataset consists of two CSV files:



Daily_GNSS_Anomalies_and_ACLED-2023-V1.csv



Description: Contains all grids and dates that had aircraft traffic during 2023.

Number of Records: 6,777,228

Purpose: Provides a complete view of aircraft movements and associated data, including grids without any GNSS anomalies.


Daily_GNSS_Anomalies_and_ACLED-2023-V2.csv



Description: A filtered version of V1, including only the grids and dates where GNSS anomalies (jumps or gaps) were reported.

Number of Records: 718,237

Purpose: Focuses on areas and times with GNSS anomalies for targeted analysis.



Data Fields

Both files share the same set of fields, which are detailed below:



grid_id



Description: Unique identifier for a grid cell on Earth measuring 0.5 degrees latitude by 0.5 degrees longitude.

Format: String combining latitude and longitude (e.g., -10.0_-36.0).


day



Description: Date of the recorded data.

Format: YYYY-MM-DD (e.g., 2023-03-28).


geometry



Description: Polygon coordinates of the grid cell in Well-Known Text (WKT) format.

Format: POLYGON((longitude latitude, ...)) (e.g., POLYGON((-36.0 -10.0, -35.5 -10.0, -35.5 -9.5, -36.0 -9.5, -36.0 -10.0))).


flights



Description: Number of aircraft flights that passed through the grid on that day.

Format: Integer (e.g., 28).


GPS_jumps



Description: Number of reported GNSS "jump" anomalies (possible spoofing incidents) in the grid on that day.

Format: Integer (e.g., 1).


GPS_gaps



Description: Number of reported GNSS "gap" anomalies, indicating gaps in aircraft routes, in the grid on that day.

Format: Integer (e.g., 0).


gaps_density



Description: Density of GNSS gaps, calculated as the number of gaps divided by the number of flights.

Format: Decimal (e.g., 0).


jumps_density



Description: Density of GNSS jumps, calculated as the number of jumps divided by the number of flights.

Format: Decimal (e.g., 0.035714286).


event_id_cnty



Description: ACLED event ID corresponding to political violence events in the grid on that day.

Format: String (e.g., BRA69267).


disorder_type



Description: Type of disorder as classified by ACLED (e.g., "Political violence").

Format: String.


event_type



Description: General category of the event according to ACLED (e.g., "Violence against civilians").

Format: String.


sub_event_type



Description: Specific subtype of the event as per ACLED classification (e.g., "Attack").

Format: String.


acled_count



Description: Number of ACLED events in the grid on that day.

Format: Integer (e.g., 1).


acled_flag



Description: Indicator of ACLED event presence in the grid on that day (0 for no events, 1 for one or more events).

Format: Integer (0 or 1).



Data Sources



GNSS Anomalies Data:



Calculated from ADS-B (Automatic Dependent Surveillance-Broadcast) messages obtained via the OpenSky Network's Trino database.

GNSS anomalies include "jumps" (potential spoofing incidents) and "gaps" (interruptions in aircraft route data).


Political Violence Events Data:



Sourced from the ACLED database, which provides detailed information on political violence and protest events worldwide.



Temporal and Spatial Coverage



Temporal Coverage:



From January 1, 2023, to December 31, 2023.

Daily records provide temporal granularity for time-series analysis.


Spatial Coverage:



Global coverage with grid cells measuring 0.5 degrees latitude by 0.5 degrees longitude.

Each grid cell represents an area on Earth's surface, facilitating spatial analysis.



Usage and Applications



Security Analysis:



Assess potential correlations between GNSS anomalies and political violence events.

Identify regions with increased risk of GNSS spoofing or signal disruption.


Research and Development:



Develop models to predict socio-political events based on GNSS anomalies.

Study the impact of political instability on aviation safety.


Policy and Decision Making:



Inform aviation authorities and policymakers about regions requiring enhanced navigation security measures.

Support conflict analysis and monitoring efforts.