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

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.