In 2025, Canberra, the capital city of Australia, had the highest Human Development Index (HDI) in the Asia-Pacific region, with a score of ****. In contrast, India's Hyderabad had an HDI score of roughly **** in the same year. HDI provides a human-centered overview of development based on an individual's longevity and wellness, knowledge, and decent living standards.

The average for 2023 based on 184 countries was 0.744 points. The highest value was in Iceland: 0.972 points and the lowest value was in South Africa: 0.388 points. The indicator is available from 1980 to 2023. Below is a chart for all countries where data are available.

In 2023 Zurich was both the leading smart city based on the IMD smart city index as well as the city with the highest human development index score, making it one of the premier places on earth to live in. Notable exceptions to the HDI to IMD index score were Beijing, Dubai, and Abu Dhabi. Beijing is a notable outlier because although it ranked 12th on the digital smart cities ranking it was nearly 90 points lower than Zurich on the HDI score. This is compared to Munich, Germany, which was the 20th digital city but had a HDI score of 950. Smart tech is watching. CCTV cameras powered by artificial intelligence have become a significant growing market in the modern city. These are predominantly residential, with half the market catering to residential applications of CCTV cameras. However, commercial and business-related CCTV cameras have also seen significant growth, with the market reaching over 800 million U.S. dollars in 2023. Digital cities need data and data needs infrastructure. The leading issue with AI infrastructure is data management. AI is a strong influence on how digital cities work and requires a considerable amount of infrastructure to be effective. Storage of AI software is a minor concern, accounting for less than 10 percent of challenges globally in 2023.

Korea HDI: Row House: 6 Metropolitan Cities data was reported at 88.951 Score in Jun 2018. This records a decrease from the previous number of 89.585 Score for May 2018. Korea HDI: Row House: 6 Metropolitan Cities data is updated monthly, averaging 93.286 Score from Jul 2012 (Median) to Jun 2018, with 72 observations. The data reached an all-time high of 97.582 Score in Aug 2017 and a record low of 80.167 Score in Jul 2012. Korea HDI: Row House: 6 Metropolitan Cities data remains active status in CEIC and is reported by Korea Appraisal Board. The data is categorized under Global Database’s Korea – Table KR.EB053: Housing Demand Index.

Korea HDI: 6 Metropolitan Cities data was reported at 88.134 Score in Oct 2018. This records an increase from the previous number of 87.314 Score for Sep 2018. Korea HDI: 6 Metropolitan Cities data is updated monthly, averaging 96.366 Score from Jul 2012 (Median) to Oct 2018, with 76 observations. The data reached an all-time high of 104.349 Score in Nov 2015 and a record low of 78.210 Score in Jul 2012. Korea HDI: 6 Metropolitan Cities data remains active status in CEIC and is reported by Korea Appraisal Board. The data is categorized under Global Database’s Korea – Table KR.EB053: Housing Demand Index.

Korea HDI: Apt: 6 Metropolitan Cities data was reported at 84.489 Score in Oct 2018. This records an increase from the previous number of 83.276 Score for Sep 2018. Korea HDI: Apt: 6 Metropolitan Cities data is updated monthly, averaging 96.437 Score from Jul 2012 (Median) to Oct 2018, with 76 observations. The data reached an all-time high of 108.237 Score in Nov 2015 and a record low of 76.771 Score in Jul 2012. Korea HDI: Apt: 6 Metropolitan Cities data remains active status in CEIC and is reported by Korea Appraisal Board. The data is categorized under Global Database’s Korea – Table KR.EB053: Housing Demand Index.

Korea HDI: Detached House: 6 Metropolitan Cities data was reported at 101.605 Score in Jun 2018. This records an increase from the previous number of 100.905 Score for May 2018. Korea HDI: Detached House: 6 Metropolitan Cities data is updated monthly, averaging 95.280 Score from Jul 2012 (Median) to Jun 2018, with 72 observations. The data reached an all-time high of 109.307 Score in Aug 2017 and a record low of 88.250 Score in Jan 2013. Korea HDI: Detached House: 6 Metropolitan Cities data remains active status in CEIC and is reported by Korea Appraisal Board. The data is categorized under Global Database’s Korea – Table KR.EB053: Housing Demand Index.

In 2021, the Human Development Index (HDI) score for the Netherlands was ***** on a scale from * to *. Utrecht had the highest HDI score among Dutch provinces with a score of *****, while Friesland had the lowest HDI score with *****.

Median (minimum and maximum) of the proportion of the Family Health Strategy (ESF), Health Program of Community Agents (PACS), population, Gross Domestic Product (GDP) and the Human Development Index (HDI) in the cities of São Paulo according to the time.

Korea HDI: Jeonse: 6 Metropolitan Cities data was reported at 84.887 Score in Nov 2018. This records a decrease from the previous number of 85.535 Score for Oct 2018. Korea HDI: Jeonse: 6 Metropolitan Cities data is updated monthly, averaging 106.100 Score from Jul 2012 (Median) to Nov 2018, with 77 observations. The data reached an all-time high of 114.149 Score in Oct 2013 and a record low of 84.556 Score in Aug 2018. Korea HDI: Jeonse: 6 Metropolitan Cities data remains active status in CEIC and is reported by Korea Appraisal Board. The data is categorized under Global Database’s South Korea – Table KR.EB054: Housing Demand Index: Jeonse.

HDI: Jeonse: Detached House: 6 Metropolitan Cities data was reported at 91.687 Score in Jun 2018. This records a decrease from the previous number of 91.934 Score for May 2018. HDI: Jeonse: Detached House: 6 Metropolitan Cities data is updated monthly, averaging 98.911 Score from Jul 2012 (Median) to Jun 2018, with 72 observations. The data reached an all-time high of 101.500 Score in Oct 2012 and a record low of 91.687 Score in Jun 2018. HDI: Jeonse: Detached House: 6 Metropolitan Cities data remains active status in CEIC and is reported by Korea Appraisal Board. The data is categorized under Global Database’s Korea – Table KR.EB054: Housing Demand Index: Jeonse.

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."

In 2025, Seoul was ranked **** among smart cities worldwide according to multiple indicators covering existing infrastructure, technological services, and categories under the Human Development Index (HDI). This was **** places higher than in the previous year. The capital city of South Korea has risen in global smart city rankings almost every survey year since 2019.

Median (minimum and maximum) number of hospitalizations for heart failure and strokes for 10,000 inhabitants in the cities of São Paulo due time.

In 2023, Busan climbed down to **** in rankings among smart cities worldwide according to multiple indicators covering existing infrastructure, technological services, and categories under the Human Development Index (HDI). The South Korean coastal city had been ** ranks higher just three years prior.

In 2024, Sudan was ranked as the most miserable country in the world, with a misery index score of 374.8. Argentina ranked second with an index score of 195.9. Quality of life around the worldThe misery index was created by the economist Arthur Okun in the 1960s. The index is calculated by adding the unemployment rate, the lending rate and the inflation rate minus percent change of GDP per capita. Another famous tool used for the comparison of development of countries around the world is the Human Development Index, which takes into account such factors as life expectancy at birth, literacy rate, education level and gross national income (GNI) per capita. Better economic conditions correlate with higher quality of life Economic conditions affect the life expectancy, which is much higher in the wealthiest regions. With a life expectancy of 85 years, Liechtenstein led the ranking of countries with the highest life expectancy in 2023. On the other hand, Nigeria was the country with the lowest life expectancy, where men were expected to live 55 years as of 2024. The Global Liveability Index ranks the quality of life in cities around the world, basing on political, social, economic and environmental aspects, such as personal safety and health, education and transport services and other public services. In 2024, Vienna was ranked as the city with the highest quality of life worldwide.

The statistic shows the total population in Canada from 2020 to 2024, with projections up until 2030. In 2024, the total population in Canada amounted to about 41.14 million inhabitants. Population of Canada Canada ranks second among the largest countries in the world in terms of area size, right behind Russia, despite having a relatively low total population. The reason for this is that most of Canada remains uninhabited due to inhospitable conditions. Approximately 90 percent of all Canadians live within about 160 km of the U.S. border because of better living conditions and larger cities. On a year to year basis, Canada’s total population has continued to increase, although not dramatically. Population growth as of 2012 has amounted to its highest values in the past decade, reaching a peak in 2009, but was unstable and constantly fluctuating. Simultaneously, Canada’s fertility rate dropped slightly between 2009 and 2011, after experiencing a decade high birth rate in 2008. Standard of living in Canada has remained stable and has kept the country as one of the top 20 countries with the highest Human Development Index rating. The Human Development Index (HDI) measures quality of life based on several indicators, such as life expectancy at birth, literacy rate, education levels and gross national income per capita. Canada has a relatively high life expectancy compared to many other international countries, earning a spot in the top 20 countries and beating out countries such as the United States and the UK. From an economic standpoint, Canada has been slowly recovering from the 2008 financial crisis. Unemployment has gradually decreased, after reaching a decade high in 2009. Additionally, GDP has dramatically increased since 2009 and is expected to continue to increase for the next several years.