Event Timeline
Comment Actions
Use cases:
- I want to be able to compare high level occupation trends between different countries, by grouping subsets of occupation categories like football players, dancers as sports people
- I want to choose specific women based on high level occupation fields like Women scientists that should automatically encompass researchers and other subcategories so that I don’t have to write Sparql queries. Also, health care professionals (optometrist, audiologists, nurses, doctors) and STEM category.
Comment Actions
Occupation Total Total with gender Females % Females Gap Males % Males Others % Others
athlete 802,302 796,715 117,866 14.794 % 678,780 85.197 % 69 0.009 %
STEPS to implement back-end generation:
- Create a test, manually comparing denelezh and humaniki the athlete type Q2066131 for the closest available dates. Denelezh:2020-09-21, Humaniki: 2020-11-30
Occupation Total Total with gender Females % Females Gap Males % Males Others % Others
athlete 802,302 796,715 117,866 14.794 % 678,780 85.197 % 69 0.009 %
- update config and config reader to generate occupcation-parent with depth 1
- Update _get_dim_join_from_dim_prop so that occupation join table is a subqueried table with parent and occupation to specified depth. May require retooling of _get_dim_join_from_dim_prop
- Backfill the occupation metrics for humaniki already processed dumps (Deploy backend to production.)
- Front-end integration in advanced search only. Deploy frontend