I'm using Google bigquery to analyze the GDELT GKG 2.0 dataset and would like to better understand how to query based on themes (or V2Themes). The docs mention a 'Category List' spreadsheet but so far I've been unsuccessful in finding that list.
the following asesome blog mentions that you can use World Bank Taxonomy among others to narrow down your search. My objective is to find all items that mention "droughts / too little water" ,all items that mention "floods / too much water" and all items that mention " poor quality / too dirty water" that have a geographical match on a sub-country level.
So far I've been able to get a list of distinct themes but this is non-extensive and I don't get the hierarchy / structure of it.
SELECT
DISTINCT theme
FROM (
SELECT
GKGRECORDID,
locations,
REGEXP_EXTRACT(themes,r'(^.[^,]+)') AS theme,
CAST(REGEXP_EXTRACT(locations,r'^(?:[^#]*#){0}([^#]*)') AS NUMERIC) AS location_type,
REGEXP_EXTRACT(locations,r'^(?:[^#]*#){1}([^#]*)') AS location_fullname,
REGEXP_EXTRACT(locations,r'^(?:[^#]*#){2}([^#]*)') AS location_countrycode,
REGEXP_EXTRACT(locations,r'^(?:[^#]*#){3}([^#]*)') AS location_adm1code,
REGEXP_EXTRACT(locations,r'^(?:[^#]*#){4}([^#]*)') AS location_adm2code,
REGEXP_EXTRACT(locations,r'^(?:[^#]*#){5}([^#]*)') AS location_latitude,
REGEXP_EXTRACT(locations,r'^(?:[^#]*#){6}([^#]*)') AS location_longitude,
REGEXP_EXTRACT(locations,r'^(?:[^#]*#){7}([^#]*)') AS location_featureid,
REGEXP_EXTRACT(locations,r'^(?:[^#]*#){8}([^#]*)') AS location_characteroffset,
DocumentIdentifier
FROM
`gdelt-bq.gdeltv2.gkg_partitioned`,
UNNEST(SPLIT(V2Locations,';')) AS locations,
UNNEST(SPLIT(V2Themes,';')) AS themes
WHERE
_PARTITIONTIME >= "2018-08-20 00:00:00"
AND _PARTITIONTIME < "2018-08-21 00:00:00" )
WHERE
(location_type = 5
OR location_type = 4
OR location_type = 2) --WorldState, WorldCity or US State
ORDER BY
theme
And a list of water related themes I've been able to find so far (sample, not exhaustive):
CRISISLEX_C06_WATER_SANITATION
ENV_WATERWAYS
HUMAN_RIGHTS_ABUSES_WATERBOARD
HUMAN_RIGHTS_ABUSES_WATERBOARDED
HUMAN_RIGHTS_ABUSES_WATERBOARDING
NATURAL_DISASTER_FLOODWATER
NATURAL_DISASTER_FLOODWATERS
NATURAL_DISASTER_FLOOD_WATER
NATURAL_DISASTER_FLOOD_WATERS
NATURAL_DISASTER_HIGH_WATER
NATURAL_DISASTER_HIGH_WATERS
NATURAL_DISASTER_WATER_LEVEL
TAX_AIDGROUPS_WATERAID
TAX_DISEASE_WATERBORNE_DISEASE
TAX_DISEASE_WATERBORNE_DISEASES
TAX_FNCACT_WATERBOY
TAX_FNCACT_WATERMAN
TAX_FNCACT_WATERMEN
TAX_FNCACT_WATER_BOY
TAX_WEAPONS_WATER_CANNON
TAX_WEAPONS_WATER_CANNONS
TAX_WORLDBIRDS_WATERFOWL
TAX_WORLDMAMMALS_WATER_BUFFALO
UNGP_CLEAN_WATER_SANITATION
WATER_SECURITY
WB_1000_WATER_MANAGEMENT_STRUCTURES
WB_1021_WATER_LAW
WB_1063_WATER_ALLOCATION_AND_WATER_SUPPLY
WB_1064_WATER_DEMAND_MANAGEMENT
WB_1199_WATER_SUPPLY_AND_SANITATION
WB_1215_WATER_QUALITY_STANDARDS
WB_137_WATER
WB_138_WATER_SUPPLY
WB_139_SANITATION_AND_WASTEWATER
WB_140_AGRICULTURAL_WATER_MANAGEMENT
WB_141_WATER_RESOURCES_MANAGEMENT
WB_143_RURAL_WATER
WB_144_URBAN_WATER
WB_1462_WATER_SANITATION_AND_HYGIENE
WB_149_WASTEWATER_TREATMENT_AND_DISPOSAL
WB_150_WASTEWATER_REUSE
WB_155_WATERSHED_MANAGEMENT
WB_156_GROUNDWATER_MANAGEMENT
WB_159_TRANSBOUNDARY_WATER
WB_1729_URBAN_WATER_FINANCIAL_SUSTAINABILITY
WB_1731_NON_REVENUE_WATER
WB_1778_FRESHWATER_ECOSYSTEMS
WB_1790_INTERNATIONAL_WATERWAYS
WB_1798_WATER_POLLUTION
WB_1805_WATERWAYS
WB_1998_WATER_ECONOMICS
WB_2008_WATER_TREATMENT
WB_2009_WATER_QUALITY_MONITORING
WB_2971_WATER_PRICING
WB_2981_DRINKING_WATER_QUALITY_STANDARDS
WB_2992_FRESHWATER_FISHERIES
WB_427_WATER_ALLOCATION_AND_WATER_ECONOMICS
Supported by Google Jigsaw, the GDELT Project monitors the world's broadcast, print, and web news from nearly every corner of every country in over 100 languages and identifies the people, locations, organizations, themes, sources, emotions, counts, quotes, images and events driving our global society every second of ...
The GDELT 2.0 Events table now has 70 million records and the GDELT 2.0 Event Mentions table has 224 million rows. The GDELT 1.0 Events table now contains 342 million records.
Global Database of Events, Language and Tone (GDELT) data quality note.
The vision of the GDELT Project is to codify the entire planet into a computable format using all available open information sources that provides a new platform for understanding the global world.
While this link is provided as a theme listing:
http://data.gdeltproject.org/documentation/GDELT-Global_Knowledge_Graph_CategoryList.xlsx
...it is far from complete (perhaps just the original theme list?). I just pulled a single day's worth of GKG, and there are tons of themes not on the list of 283 themes in that spreadsheet.
GKG documentation located at https://blog.gdeltproject.org/world-bank-group-topical-taxonomy-now-in-gkg/ points to a World Bank Taxonomy located at http://pubdocs.worldbank.org/en/275841490966525495/Theme-Taxonomy-and-definitions.pdf. The GKG post implies this World Bank taxonomy has been rolled into the GKG theme list.
This is presented as a complete listing of World Bank Taxonomy themes. Unfortunately, I've found numerous World Bank themes in GKG that aren't in this publication. The union of these two lists represents a portion of GKG themes, but it definitely isn't all of them.
Here is the list of GKG Themes:
http://data.gdeltproject.org/documentation/GDELT-Global_Knowledge_Graph_CategoryList.xlsx
If anyone needs this, I have added a list of all themes in the GKG v1 in the timeperiod from 1/1/2017-31/12/2020 which are at least present in 10 or more articles for that particular day: Themes.parquet
It consists of 17639 unique themes with the count per day. Looks like this:
The complete numbers for that 4 year dataset is 36 713 385 unique actors, 50 845 unique themes as well as 26 389 528 unique organizations. These numbers are not filtered for different spellings for the same entity, and hence Donald Trump and Donald J. Trump will count as two separate actors.
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