Geospatial Dataset of GNSS Anomalies and Political Violence Events

Geospatial Dataset of GNSS Anomalies and Political Violence Events Overview The Geospatial Dataset of GNSS Anomalies and Political Violence Events 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 three 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. Monthly_GNSS_Anomalies_and_ACLED-2023-V9.csv Description: Contains aggregated monthly data for each grid cell, combining GNSS anomalies and ACLED political violence events. Summarizes aircraft traffic, anomaly counts, and conflict activity at a monthly resolution. Number of Records: 25,770 Purpose: Enables temporal trend analysis and spatial correlation studies between GNSS interference and political violence, using reduced data volume suitable for modeling and visualization. Data Fields: Daily_GNSS_Anomalies_and_ACLED-2023-V1.csv and Daily_GNSS_Anomalies_and_ACLED-2023-V2.csv 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 Fields: Monthly_GNSS_Anomalies_and_ACLED-2023-V9.csv The file contains monthly aggregated GNSS anomaly and ACLED event data per grid cell. The structure and meaning of each field are detailed below: grid_id Description: Unique identifier for a grid cell on Earth measuring 0.5° latitude by 0.5° longitude. Format: String combining latitude and longitude (e.g., -0.5_-79.0). year_month Description: Month and year of the aggregated data. Format: String in Mon-YY format (e.g., Jan-23). geometry Description: Polygon coordinates of the grid cell in Well-Known Text (WKT) format. Format: POLYGON((longitude latitude, ...))(e.g., POLYGON((-79.0 -0.5, -78.5 -0.5, -78.5 0.0, -79.0 0.0, -79.0 -0.5))). flights Description: Total number of aircraft flights that passed through the grid cell during the month. Format: Integer (e.g., 1230). GPS_jumps Description: Total number of GNSS "jump" anomalies (possible spoofing events) in the grid cell during the month. Format: Integer (e.g., 13). GPS_gaps Description: Total number of GNSS "gap" anomalies, indicating interruptions in aircraft routes, during the month. Format: Integer (e.g., 0). event_id_cnty Description: Semicolon-separated list of ACLED event IDs associated with the grid cell during the month. Format: String (e.g., ECU3151;ECU3158;ECU3150). disorder_type Description: Semicolon-separated list of disorder types (e.g., "Political violence", "Demonstrations") reported by ACLED in that grid cell during the month. Format: String. event_type Description: Semicolon-separated list of high-level ACLED event types (e.g., "Riots", "Protests"). Format: String. sub_event_type Description: Semicolon-separated list of detailed subtypes of ACLED events (e.g., "Mob violence", "Armed clash"). Format: String. acled_count Description: Total number of ACLED conflict events in the grid cell during the month. Format: Integer (e.g., 2). acled_flag Description: Conflict presence indicator: 1 if any ACLED event occurred in the grid cell during the month, otherwise 0. Format: Integer (0 or 1). gaps_density Description: Monthly density of GNSS gaps, calculated as GPS_gaps / flights. Format: Decimal (e.g., 0.0). jumps_density Description: Monthly density of GNSS jumps, calculated as GPS_jumps / flights. Format: Decimal (e.g., 0.0106). 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.GNSS异常与政治暴力事件地理空间数据集 ## 数据集概述 本数据集整合了航空器飞行信息、全球导航卫星系统（GNSS）异常数据，以及来自武装冲突地点与事件数据项目（Armed Conflict Location & Event Data Project，简称ACLED）的政治暴力事件数据。 ## 数据集文件 本数据集包含三个CSV文件： ### Daily_GNSS_Anomalies_and_ACLED-2023-V1.csv - 描述：包含2023年所有存在航空器通行的网格单元与对应日期的记录 - 记录数：6,777,228 - 用途：完整呈现航空器移动轨迹及关联数据，包括未发生任何GNSS异常的网格单元 ### Daily_GNSS_Anomalies_and_ACLED-2023-V2.csv - 描述：V1版本的过滤子集，仅包含报告存在GNSS异常（跳变或中断）的网格单元与日期 - 记录数：718,237 - 用途：聚焦存在GNSS异常的区域与时段，用于定向分析 ### Monthly_GNSS_Anomalies_and_ACLED-2023-V9.csv - 描述：包含每个网格单元的月度聚合数据，整合了GNSS异常与ACLED政治暴力事件信息，以月度分辨率汇总航空器通行量、异常计数与冲突活动情况 - 记录数：25,770 - 用途：支持GNSS干扰与政治暴力之间的时空趋势分析与相关性研究，采用缩减后的数据体量，适配建模与可视化需求 ## 数据字段 ### 日度数据集（Daily_GNSS_Anomalies_and_ACLED-2023-V1.csv与Daily_GNSS_Anomalies_and_ACLED-2023-V2.csv） 1. grid_id：地球表面0.5纬度×0.5经度网格单元的唯一标识符，格式为经纬度拼接的字符串（示例：-10.0_-36.0） 2. day：记录数据的日期，格式为YYYY-MM-DD（示例：2023-03-28） 3. geometry：网格单元的多边形坐标，采用熟知文本（Well-Known Text，简称WKT）格式，格式为`POLYGON((经度 纬度, ...))`（示例：POLYGON((-36.0 -10.0, -35.5 -10.0, -35.5 -9.5, -36.0 -9.5, -36.0 -10.0))） 4. flights：当日通行该网格单元的航空器架次，格式为整数（示例：28） 5. GPS_jumps：当日该网格单元内报告的GNSS跳变异常（可能为欺骗攻击事件）数量，格式为整数（示例：1） 6. GPS_gaps：当日该网格单元内报告的GNSS中断异常（表示航空器航线出现中断）数量，格式为整数（示例：0） 7. gaps_density：GNSS中断异常密度，计算方式为中断数量除以航空器架次，格式为小数（示例：0） 8. jumps_density：GNSS跳变异常密度，计算方式为跳变数量除以航空器架次，格式为小数（示例：0.035714286） 9. event_id_cnty：当日该网格单元内政治暴力事件对应的ACLED事件ID，格式为字符串（示例：BRA69267） 10. disorder_type：ACLED分类的动乱类型（示例："政治暴力"），格式为字符串 11. event_type：ACLED定义的事件大类（示例："针对平民的暴力行为"），格式为字符串 12. sub_event_type：ACLED分类的事件具体子类型（示例："袭击"），格式为字符串 13. acled_count：当日该网格单元内的ACLED事件总数，格式为整数（示例：1） 14. acled_flag：当日该网格单元内ACLED事件存在标识（0表示无事件，1表示存在至少一起事件），格式为整数（0或1） ### 月度数据集（Monthly_GNSS_Anomalies_and_ACLED-2023-V9.csv） 本文件包含每个网格单元的月度聚合GNSS异常与ACLED事件数据，各字段含义如下： 1. grid_id：地球表面0.5纬度×0.5经度网格单元的唯一标识符，格式为经纬度拼接的字符串（示例：-0.5_-79.0） 2. year_month：聚合数据的年月，格式为Mon-YY（示例：Jan-23） 3. geometry：网格单元的多边形坐标，采用熟知文本（Well-Known Text，简称WKT）格式，格式为`POLYGON((经度 纬度, ...))`（示例：POLYGON((-79.0 -0.5, -78.5 -0.5, -78.5 0.0, -79.0 0.0, -79.0 -0.5))） 4. flights：月度内通行该网格单元的总航空器架次，格式为整数（示例：1230） 5. GPS_jumps：月度内该网格单元内的GNSS跳变异常（可能为欺骗攻击事件）总数量，格式为整数（示例：13） 6. GPS_gaps：月度内该网格单元内的GNSS中断异常（表示航空器航线出现中断）总数量，格式为整数（示例：0） 7. event_id_cnty：月度内该网格单元关联的ACLED事件ID列表，以分号分隔，格式为字符串（示例：ECU3151;ECU3158;ECU3150） 8. disorder_type：月度内ACLED报告的该网格单元动乱类型列表，以分号分隔（示例："政治暴力","示威活动"），格式为字符串 9. event_type：月度内ACLED报告的该网格单元事件大类列表，以分号分隔（示例："骚乱","抗议活动"），格式为字符串 10. sub_event_type：月度内ACLED报告的该网格单元事件具体子类型列表，以分号分隔（示例："暴民暴力","武装冲突"），格式为字符串 11. acled_count：月度内该网格单元内的ACLED冲突事件总数量，格式为整数（示例：2） 12. acled_flag：月度冲突存在标识：若该网格单元内发生至少一起ACLED事件则为1，否则为0，格式为整数（0或1） 13. gaps_density：月度GNSS中断异常密度，计算方式为GPS_gaps / flights，格式为小数（示例：0.0） 14. jumps_density：月度GNSS跳变异常密度，计算方式为GPS_jumps / flights，格式为小数（示例：0.0106） ## 数据来源 ### GNSS异常数据 通过开放天空网络（OpenSky Network）的Trino数据库获取的自动相关监视广播（Automatic Dependent Surveillance-Broadcast，简称ADS-B）消息计算得到。GNSS异常包括"跳变"（潜在欺骗攻击事件）与"中断"（航空器航线数据中断）两类。 ### 政治暴力事件数据 源自ACLED数据库，该数据库收录了全球范围内政治暴力与抗议事件的详细信息。 ## 时空覆盖范围 ### 时间覆盖 2023年1月1日至2023年12月31日，日度记录可为时序分析提供精细的时间粒度。 ### 空间覆盖 全球范围覆盖，采用0.5纬度×0.5经度的网格单元，每个网格单元代表地球表面一片区域，便于开展空间分析。 ## 应用场景 ### 安全分析 评估GNSS异常与政治暴力事件之间的潜在相关性，识别GNSS欺骗或信号干扰风险升高的区域。 ### 研发应用 开发基于GNSS异常预测社会政治事件的模型，研究政治不稳定对航空安全的影响。 ### 政策与决策支持 为航空管理部门与政策制定者提供需强化导航安全措施的区域信息，支持冲突分析与监测工作。