# 4Wings API Open In Colab This guide provides detailed instructions on how to use the [gfw-api-python-client](https://github.com/GlobalFishingWatch/gfw-api-python-client) to access the 4Wings API, which is designed for generating reports and statistics on activities within specified regions. This API is particularly useful for creating data visualizations related to fishing effort and other vessel activities. Here is a [Jupyter Notebook](https://github.com/GlobalFishingWatch/gfw-api-python-client/blob/develop/notebooks/usage-guides/4wings-api.ipynb) version of this guide with more usage examples. > **Note:** See the [Data Caveats](https://globalfishingwatch.org/our-apis/documentation#data-caveat) and [Terms of Use](https://globalfishingwatch.org/our-apis/documentation#terms-of-use) pages in the [GFW API documentation](https://globalfishingwatch.org/our-apis/documentation#introduction) for details on GFW data, API licenses, and rate limits. ## Prerequisites - You have installed the `gfw-api-python-client`. Refer to the [Getting Started](../getting-started) guide for installation instructions. ## Getting Started To interact with the 4Wings endpoints, you first need to instantiate the `gfw.Client` and then access the `fourwings` resource: ```python import os import gfwapiclient as gfw access_token = os.environ.get( "GFW_API_ACCESS_TOKEN", "", ) gfw_client = gfw.Client( access_token=access_token, ) ``` The `gfw_client.fourwings` object provides methods to generate reports, retrieve the last generated report, and get global fishing effort statistics. These methods return a `result` object, which offers convenient ways to access the data as Pydantic models using `.data()` or as pandas DataFrames using `.df()`. ## Creating a Fishing Effort Report (`create_fishing_effort_report`) Generates **AIS (Automatic Identification System) apparent fishing effort** reports to visualize fishing activity. [Please check the data caveats here](https://globalfishingwatch.org/our-apis/documentation#apparent-fishing-effort). ```python fishing_effort_report_result = await gfw_client.fourwings.create_fishing_effort_report( spatial_resolution="LOW", temporal_resolution="MONTHLY", group_by="GEARTYPE", start_date="2022-01-01", end_date="2022-05-01", region={ "dataset": "public-eez-areas", "id": "5690", }, ) ``` ### Access the report data as Pydantic models ```python fishing_effort_report_data = fishing_effort_report_result.data() fishing_effort_report_item = fishing_effort_report_data[-1] print(( fishing_effort_report_item.date, fishing_effort_report_item.gear_type, fishing_effort_report_item.hours, fishing_effort_report_item.vessel_ids, fishing_effort_report_item.lat, fishing_effort_report_item.lon, )) ``` **Output:** ``` ('2022-04', 'fishing', 2.705, 1, 52.0, 155.2) ``` ### Access the report data as a DataFrame ```python fishing_effort_report_df = fishing_effort_report_result.df() print(fishing_effort_report_df.info()) print(fishing_effort_report_df[["date", "gear_type", "hours", "vessel_ids", "lat", "lon"]].head()) ``` **Output:** ``` RangeIndex: 41916 entries, 0 to 41915 Data columns (total 20 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 date 41916 non-null object 1 detections 0 non-null object 2 flag 0 non-null object 3 gear_type 41916 non-null object 4 hours 41916 non-null float64 5 vessel_ids 41916 non-null int64 6 vessel_id 0 non-null object 7 vessel_type 0 non-null object 8 entry_timestamp 0 non-null object 9 exit_timestamp 0 non-null object 10 first_transmission_date 0 non-null object 11 last_transmission_date 0 non-null object 12 imo 0 non-null object 13 mmsi 0 non-null object 14 call_sign 0 non-null object 15 dataset 0 non-null object 16 report_dataset 41916 non-null object 17 ship_name 0 non-null object 18 lat 41916 non-null float64 19 lon 41916 non-null float64 dtypes: float64(3), int64(1), object(16) memory usage: 6.4+ MB ``` ## Creating an AIS Presence Report (`create_ais_presence_report`) Generates **AIS (Automatic Identification System) vessel presence** reports to visualize movement patterns of any vessel type. [Please check the data caveats here](https://globalfishingwatch.org/our-apis/documentation#ais-vessel-presence-caveats). > **Disclaimer:** AIS vessel presence is one of the largest datasets available. To prevent timeouts and ensure optimal performance, keep requests manageable: prefer simple, small regions and shorter time ranges (e.g., a few days). ```python ais_presence_report_result = await gfw_client.fourwings.create_ais_presence_report( spatial_resolution="LOW", temporal_resolution="MONTHLY", group_by="GEARTYPE", start_date="2022-01-01", end_date="2022-05-01", region={ "dataset": "public-eez-areas", "id": "5690", }, ) ``` ### Access the report data as Pydantic models ```python ais_presence_report_data = ais_presence_report_result.data() ais_presence_report_item = ais_presence_report_data[-1] print(( ais_presence_report_item.date, ais_presence_report_item.hours, ais_presence_report_item.vessel_ids, ais_presence_report_item.lat, ais_presence_report_item.lon, )) ``` **Output:** ``` ('2022-04', 9.0, 8, 50.4, 160.7) ``` ### Access the report data as a DataFrame ```python ais_presence_report_df = ais_presence_report_result.df() print(ais_presence_report_df.info()) print(ais_presence_report_df[["date", "hours", "vessel_ids", "lat", "lon"]].head()) ``` **Output:** ``` RangeIndex: 144227 entries, 0 to 144226 Data columns (total 20 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 date 144227 non-null object 1 detections 0 non-null object 2 flag 0 non-null object 3 gear_type 144227 non-null object 4 hours 144227 non-null float64 5 vessel_ids 144227 non-null int64 6 vessel_id 0 non-null object 7 vessel_type 0 non-null object 8 entry_timestamp 0 non-null object 9 exit_timestamp 0 non-null object 10 first_transmission_date 0 non-null object 11 last_transmission_date 0 non-null object 12 imo 0 non-null object 13 mmsi 0 non-null object 14 call_sign 0 non-null object 15 dataset 0 non-null object 16 report_dataset 144227 non-null object 17 ship_name 0 non-null object 18 lat 144227 non-null float64 19 lon 144227 non-null float64 dtypes: float64(3), int64(1), object(16) memory usage: 22.0+ MB ``` ## Creating a SAR Presence Report (`create_sar_presence_report`) Generates **SAR (Synthetic-Aperture Radar) vessel detections** reports to identify vessels detected via radar, including non-broadcasting (possible `"dark"`) vessels. [Please check the data caveats here](https://globalfishingwatch.org/our-apis/documentation#sar-vessel-detections-data-caveats). ```python sar_presence_report_result = await gfw_client.fourwings.create_sar_presence_report( spatial_resolution="LOW", temporal_resolution="MONTHLY", group_by="GEARTYPE", start_date="2022-01-01", end_date="2022-05-01", region={ "dataset": "public-eez-areas", "id": "5690", }, ) ``` ### Access the report data as Pydantic models ```python sar_presence_report_data = sar_presence_report_result.data() sar_presence_report_item = sar_presence_report_data[-1] print(( sar_presence_report_item.date, sar_presence_report_item.detections, sar_presence_report_item.vessel_ids, sar_presence_report_item.lat, sar_presence_report_item.lon, )) ``` **Output:** ``` ('2022-04', 1, 1, 46.6, 142.6) ``` ### Access the report data as a DataFrame ```python sar_presence_report_df = sar_presence_report_result.df() print(sar_presence_report_df.info()) print(sar_presence_report_df[["date", "detections", "vessel_ids", "lat", "lon"]].head()) ``` **Output:** ``` RangeIndex: 3300 entries, 0 to 3299 Data columns (total 20 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 date 3300 non-null object 1 detections 3300 non-null int64 2 flag 0 non-null object 3 gear_type 3300 non-null object 4 hours 0 non-null object 5 vessel_ids 3300 non-null int64 6 vessel_id 0 non-null object 7 vessel_type 0 non-null object 8 entry_timestamp 0 non-null object 9 exit_timestamp 0 non-null object 10 first_transmission_date 0 non-null object 11 last_transmission_date 0 non-null object 12 imo 0 non-null object 13 mmsi 0 non-null object 14 call_sign 0 non-null object 15 dataset 0 non-null object 16 report_dataset 3300 non-null object 17 ship_name 0 non-null object 18 lat 3300 non-null float64 19 lon 3300 non-null float64 dtypes: float64(2), int64(2), object(16) memory usage: 515.8+ KB ``` ## Creating a Report (`create_report`) Generates a report for any [supported datasets](https://globalfishingwatch.org/our-apis/documentation#supported-datasets), using fully customizable parameters. [Please check the data caveats here](https://globalfishingwatch.org/our-apis/documentation#data-caveat). ```python report_result = await gfw_client.fourwings.create_report( spatial_resolution="LOW", temporal_resolution="MONTHLY", group_by="GEARTYPE", datasets=["public-global-fishing-effort:latest"], start_date="2022-01-01", end_date="2022-05-01", region={ "dataset": "public-eez-areas", "id": "5690", }, ) ``` ### Access the report data as Pydantic models ```python report_data = report_result.data() report_item = report_data[-1] print(( report_item.date, report_item.gear_type, report_item.hours, report_item.vessel_ids, report_item.lat, report_item.lon, )) print(report_item.model_dump()) ``` **Output:** ``` ('2022-04', 'fishing', 2.705, 1, 52.0, 155.2) ``` ### Access the report data as a DataFrame ```python report_df = report_result.df() print(report_df.info()) print(report_df[["date", "hours", "lat", "lon"]].head()) ``` **Output:** ``` RangeIndex: 41916 entries, 0 to 41915 Data columns (total 20 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 date 41916 non-null object 1 detections 0 non-null object 2 flag 0 non-null object 3 gear_type 41916 non-null object 4 hours 41916 non-null float64 5 vessel_ids 41916 non-null int64 6 vessel_id 0 non-null object 7 vessel_type 0 non-null object 8 entry_timestamp 0 non-null object 9 exit_timestamp 0 non-null object 10 first_transmission_date 0 non-null object 11 last_transmission_date 0 non-null object 12 imo 0 non-null object 13 mmsi 0 non-null object 14 call_sign 0 non-null object 15 dataset 0 non-null object 16 report_dataset 41916 non-null object 17 ship_name 0 non-null object 18 lat 41916 non-null float64 19 lon 41916 non-null float64 dtypes: float64(3), int64(1), object(16) memory usage: 6.4+ MB ``` ## Reference Data The 4Wings API often requires specifying geographic regions. You can use the [Reference Data API](references-data-api) to retrieve the `dataset` and `id` of various regions (e.g., EEZs, MPAs, RFMOs) that can then be used in the `create_report()` method. ## Next Steps Explore the [Usage Guides](index) for other API resources to understand how you can combine the reporting and statistical capabilities of the 4Wings API with vessel information, event data, and more. Check out the following resources: - [Vessels API](vessels-api) - [Events API](events-api) - [Insights API](insights-api) - [Datasets API](datasets-api) - [Reference Data API](references-data-api)