ම ය ම නව ස වර ධන දර ශකය අන ව රටවල ල ය ස ත වක World map of countries by Human Development Index categories in increments

2024 te yayınlanan 2022 verilerine göre iGE dünya haritası 0 950 0 900 0 950 0 850 0 899 0 800 0 849 0 750 0 799 0 700 0

This dataset is extracted from https://en.wikipedia.org/wiki/Human_Development_Index. Context: There s a story behind every dataset and heres your opportunity to share yours.Content: What s inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too. Acknowledgements:We wouldn t be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.Inspiration: Your data will be in front of the world s largest data science community. What questions do you want to see answered?

This project contains data for the paper: Lemmerich, Florian, Diego Sáez-Trumper, Robert West, and Leila Zia. "Why the World Reads Wikipedia: Beyond English Speakers." Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining (WSDM). ACM, 2019.From the abstract:As one of the Web's primary multilingual knowledge sources, Wikipedia is read by millions of people across the globe every day. Despite this global readership, little is known about why users read Wikipedia's various language editions. To bridge this gap, we conduct a comparative study by combining a large-scale survey of Wikipedia readers across 14 language editions with a log-based analysis of user activity. We proceed in three steps. First, we analyze the survey results to compare the prevalence of Wikipedia use cases across languages, discovering commonalities, but also substantial differences, among Wikipedia languages with respect to their usage. Second, we match survey responses to the respondents' traces in Wikipedia's server logs to characterize behavioral patterns associated with specific use cases, finding that distinctive patterns consistently mark certain use cases across language editions. Third, we show that certain Wikipedia use cases are more common in countries with certain socio-economic characteristics; e.g., in-depth reading of Wikipedia articles is substantially more common in countries with a low Human Development Index. These findings advance our understanding of reader motivations and behaviors across Wikipedia languages and have implications for Wikipedia editors and developers of Wikipedia and other Web technologies.

   The data is described in the README as well as here on this meta page.For more information, see the:* wiki page* blogpost* paper* mirror of data

Context

This dataset contains the locations found in the Kiva datasets included in an administrative or geographical region. You can also find poverty data about this region. This facilitates answering some of the tough questions about a region's poverty.

Content

In the interest of preserving the original names and spelling for the locations/countries/regions all the data is in Excel format and has no preview (I think only the Kaggle recommended file types have preview - if anyone can show me how to do this for an xlsx file, it will be greatly appreciated)

The Tables datasets contain the most recent analysis of the MPI on countries and regions. These datasets are updated regularly. In unique regions_names_from_google_api you will find 3 levels of inclusion for every geocode provided in Kiva datasets. (village/town, administrative region, sub-national region - which can be administrative or geographical). These are the results from the Google API Geocoding process.

Files:

• all_kiva_loans.csv

Dropped multiple columns, kept all the rows from loans.csv with names, tags, descriptions and got a csv file of 390MB instead of 2.13 GB. Basically is a simplified version of loans.csv (originally included in the analysis by beluga)

• country_stats.csv

1. population source: https://en.wikipedia.org/wiki/List_of_countries_by_population_(United_Nations)
2. population_below_poverty_line: Percentage
3. hdi: Human Development Index
4. life_expectancy: Life expectancy at birth
5. expected_years_of_schooling: Expected years of schooling
6. mean_years_of_schooling: Mean years of schooling
7. gni: Gross national income (GNI) per capita This dataset was originally created by beluga.

• all_loan_theme_merged_with_geo_mpi_regions.xlsx

This is the loan_themes_by_region left joined with Tables_5.3_Contribution_of_Deprivations. (all the original entries from loan_themes and only the entries that match from Tables_5; for the regions that lack MPI data, you will find Nan)

These are the columns in the database:

1. Partner ID
2. Field Partner
3. Name
4. sector
5. Loan Theme ID
6. Loan Theme Type
7. Country
8. forkiva
9. number
10. amount
11. geo
12. rural_pct
13. City
14. Administrative region
15. Sub-national region
16. ISO
17. World region
18. Population Share of the Region (%)
19. region MPI
20. Education (%)
21. Health (%)
22. Living standards (%)
23. Schooling (%)
24. Child school attendance (%)
25. Child Mortality (%)
26. Nutrition (%)
27. Electricity (%)
28. Improved sanitation (%)
29. Drinking water (%)
30. Floor (%)
31. Cooking fuel (%)
32. Asset ownership (%)

• mpi_on_regions.xlsx

Matched the loans in loan_themes_by_region with the regions that have info regarding MPI. This dataset brings together the amount invested in a region and the biggest problems the said region has to deal with. It is a join between the loan_themes_by_region provided by Kiva and Tables 5.3 Contribution_of_Deprivations.

It is a subset of the all_loan_theme_merged_with_geo_mpi_regions.xlsx, which contains only the entries that I could match with poverty decomposition data. It has the same columns.

• Tables_5_SubNational_Decomposition_MPI_2017-18.xlsx

Multidimensional poverty index decomposition for over 1000 regions part of 79 countries.

Table 5.3: Contribution of deprivations to the MPI, by sub-national regions
This table shows which dimensions and indicators contribute most to a region's MPI, which is useful for understanding the major source(s) of deprivation in a sub-national region.

Source: http://ophi.org.uk/multidimensional-poverty-index/global-mpi-2016/

• Tables_7_MPI_estimations_country_levels.xlsx

MPI decomposition for 120 countries.

Table 7 All Published MPI Results since 2010
The table presents an archive of all MPI estimations published over the past 5 years, together with MPI, H, A and censored headcount ratios. For comparisons over time please use Table 6, which is strictly harmonised. The full set of data tables for each year published (Column A), is found on the 'data tables' page under 'Archive'.

The data in this file is shown in interactive plots on Oxford Poverty and Human Development Initiative website. http://www.dataforall.org/dashboard/ophi/index.php/

• unique_regions_from_kiva_loan_themes.xlsx

These are all the regions corresponding to the geocodes found in Kiva's loan_themes_by_region. There are 718 unique entries, that you can join with any database from Kiva that has either a coordinates or region column.
Columns:

• geo: pair of Lat, Lon (from loan_themes_by_region)

• City: name of the city (has the most NaN's)

• Administrative region: first level of administrative inclusion for the city/location; (the equivalent of county for US)

• Sub-national region: second level of administrative inclusion for the geo pair. (like state for US)

• Country: name of the country

Acknowledgements

Thanks to Shane Lynn for the batch geocoding and to Joseph Deferio for reverse geocoding:

https://www.shanelynn.ie/batch-geocoding-in-python-with-google-geocoding-api/

https://github.com/jdeferio/Reverse_Geocode

The MPI datasets you can find on the Oxford website (http://ophi.org.uk/) under Research.

"Citation: Alkire, S. and Kanagaratnam, U. (2018)

“Multidimensional Poverty Index Winter 2017-18: Brief methodological note and results.” Oxford Poverty and Human Development Initiative, University of Oxford, OPHI Methodological Notes 45."

HDI aşağıdaki anlamlara gelebilir insani Gelişme Endeksi ingilizce Human Development Index HDI Arena Almanya nın Hannove

Description of the data and file structure

These directories contain the code, aggregated data, and preprocessing scripts to re-create the figures in "Architectural styles of curiosity in global Wikipedia readership"

Files and variables

File: Archive.zip

Figures: Publically usable illustrations are available here.

Description: Contains analysis, data, preprocessing, results, and utils folders. 14 directories, 25 files.

|-- analysis
| |-- KNOT_analysis.R <- analyzes laboratory data
| |-- analyze_1000-networks.ipynb <- analyzes naturalistic data
| |-- analyze_1000-networks_comparison-knot-rw_clean.ipynb <- compares datasets and nulls
| |-- analyze_KNOT_networks.ipynb <- analyzes laboratory data
| |-- analyze_forward_flow.ipynb <- calculates forward flow
| |-- forest_plots.R <- correlations wtih sociodemographic variables
| |-- topic_analysis.R <- analysis of topic and information diversity
| `-- worldmap.R <- visualization of geographical data sources
|-- data
| |-- laboratory_data <- variables for laboratory browsing and survey data
| |-- mobile_app_data <- aggregated data for network structure and topic (rows are individuals)
| |-- pretrained_embeddings <- fastText word embeddings
| |-- spatial_navigation <- data from Sea Hero Quest
| |-- surveys <- data from nationally aggregated sociodemographic surveys
| `-- wikispeedia <- data from WikiSpeedia game
|-- preprocessing
| |-- data_knowledge-networks_generate-subsample_clean.ipynb <- processes mobile app data
| |-- data_knowledge-networks_metrics-combined_clean.ipynb <- calculates network metrics
| |-- data_knowledge-networks_rw_get-data.ipynb <- calculates null networks
| `-- data_knowledge-networks_sessions-app_cleaned.ipynb <- processes individual browsing
|-- requirements.txt
|-- results
| |-- UMAP <- data used to generate network embedding (rows are individuals)
| `-- figs <- code for generated figure on forward flow
`-- utils
|-- plot_knowledge-networks_network-comparison.ipynb <- visualizations of network comparisons
|-- plot_knowledge-networks_network-metrics_distance.ipynb <- visualizations of distance between datasets
|-- plot_knowledge-networks_summary-stats.ipynb <- visualizations of summary stats
|-- utils_embedding.py <- get word and document embeddings
|-- utils_filtration_metrics.py <- higher-order topology functions (unused)
|-- utils_gt.py <- graph-tool functions
|-- utils_network.py <- functions to make networks from series of article IDs
|-- utils_network_metrics.py <- network metrics
|-- utils_networkx.py <- networkx functions
|-- utils_rw.py <- functions to generate random walks and null models
`-- utils_tokenizer.py <- functions for processing embeddings.

Code/software

See requirements.txt

Access information

Other publicly accessible locations of the data:

* https://gitlab.wikimedia.org/repos/research/curiosity

Additional data was derived from the following sources:

* [Human Development Index]
* [World Happiness Report]
* [WikiSpeedia]
* [FastText]
* [Sea Hero Quest]
* [Knowledge Networks Over Time Study]