Historical dataset of population level and growth rate for the Buenos Aires, Argentina metro area from 1950 to 2025.

Estimated Total Population By Sex, Area And Population Density According To Commune
This dataset falls under the category Traffic Generating Parameters Population.
It contains the following data: Estimated total population by sex, area and population density according to commune. City of Buenos Aires. Years 2006/2017
This dataset was scouted on 2022-02-20 as part of a data sourcing project conducted by TUMI. License information might be outdated: Check original source for current licensing. The data can be accessed using the following URL / API Endpoint: https://data.buenosaires.gob.ar/dataset/estructura-poblacion/resource/cdf71939-cbc1-4a04-bcb2-d272e03afa79See URL for data access and license information.

Population Projections
This dataset falls under the category Traffic Generating Parameters Population.
It contains the following data: Population projections by sex and five-year age groups. Autonomous City of Buenos Aires. Years 2010-2040
This dataset was scouted on 2022-02-20 as part of a data sourcing project conducted by TUMI. License information might be outdated: Check original source for current licensing. The data can be accessed using the following URL / API Endpoint: https://data.buenosaires.gob.ar/dataset/estructura-poblacion/resource/6de546ba-8509-484e-bbf7-2be708da8ac8See URL for data access and license information.

Population Structure
This dataset falls under the category Traffic Generating Parameters Population.
It contains the following data: Population structure according to sex and age. Census information (updated every 10 years).
This dataset was scouted on 2022-02-20 as part of a data sourcing project conducted by TUMI. License information might be outdated: Check original source for current licensing. The data can be accessed using the following URL / API Endpoint: https://data.buenosaires.gob.ar/dataset/estructura-poblacion

Population By Sex And Five-Year Age Groups
This dataset falls under the category Traffic Generating Parameters Population.
It contains the following data: Number of population according to sex and five-year age groups.
This dataset was scouted on 2022-02-20 as part of a data sourcing project conducted by TUMI. License information might be outdated: Check original source for current licensing. The data can be accessed using the following URL / API Endpoint: https://data.buenosaires.gob.ar/dataset/estructura-poblacion/resource/f23c7498-996f-42b5-8195-54dca3b301afSee URL for data access and license information.

This dataset shows all 515 departments in Argentina, which correspond to second-level administrative divisions currently used in said country.

The Excel file includes filters for each column.

Column Description

• Department: Name of the department
• Capital: Capital city of the department
• Province: Province the department belongs to
• Map: Map of the department within the province it belongs to
• Population (2022): Population of the department as of 2022
• Area (squared km): Total land area of the department
• Population density (people per sq. km): Population per square kilometer

NOTES - Within the province of Buenos Aires, departments are not referred to as such, but as "partidos". - There are 135 partidos in the province of Buenos Aires, the other 380 second-level administrative divisions correspond to "departamentos" (departments) spread throughout the rest of the country. - The city of Buenos Aires is classified as "ciudad autónoma" (autonomous city), meaning that it is a separate department in itself.

Demographic Structure
This dataset falls under the category Traffic Generating Parameters Population.
It contains the following data: Descriptive statistics on sex and age of the population of the City of Buenos Aires.
This dataset was scouted on 2022-02-20 as part of a data sourcing project conducted by TUMI. License information might be outdated: Check original source for current licensing. The data can be accessed using the following URL / API Endpoint: https://data.buenosaires.gob.ar/dataset/estructura-demografica

This repository contains the datasets required for replicating the results in Abbate et al (forthcoming). The datasets also include per capita income estimates at a 50x50 meter resolution for the years 2013, 2018, and 2022, using satellite images from the Metropolitan Area of Buenos Aires (Argentina) and 2010 census+survey data. The model, based on the EfficientnetV2 architecture, achieved high accuracy in predicting household incomes (Rsquared=0.878), surpassing existing methods in spatial resolution and performance.

Inside the Replication Package folder, the user can replicate the main results from the paper. This includes:

1. Small Area Estimation (SAE) Replication:

   • Argentina Household Survey Data (EPH): Processed microdata for 2010, 2013, 2018, and 2022 (ARG_*_EPHC-S2_*.dta).

   • Argentina Census Microdata: Raw 2010 census microdata (censo2010_fullraw_p.dta).

   • Census Tract Map: Shapefile of 2010 census tracts (radios_eph_with_link.shp).

   • SAE Output: The final small_area_estimates.parquet file containing census tract-level population and estimated income, which serves as labels for the CNN model.

2. CNN-based Income Prediction Replication (Paper Results):

   • CNN Model Income Predictions: Gridded 50x50m income estimates for Buenos Aires for 2013, 2018, and 2022 (income_estimates_*.shp).

   • Normalization Scalars: A CSV file (scalars_ln_pred_inc_mean_trimTrue.csv) to convert the model's log-scale outputs into real income values (2010 PPP-adjusted Argentinian pesos).

   • World Settlement Footprint (WSF): Satellite-based data (WSF2015_v2_-60_-36.tif) used to mask predictions in uninhabited areas.

Key prediction datasets are published in shapefile format, while input data for SAE and other auxiliary files are in formats like .dta, .parquet, .csv, and .tif.

Results can be replicated by connecting these datasets with the scripts available at the GitHub repo linked below.

Important Usage Note: Since the predictions for each 50x50m cell individually present some random variation, we recommend that the results are used by averaging out the estimations for each area of interest (e.g., municipalities, neighborhoods, sections, or census tracts) and not at an individual cell level. As detailed throughout the paper, the aggregated results, even in small areas such as census tracts, predict household incomes with precision.

Furthermore, inside this repository, it is possible to access and use the model’s trained parameters to make predictions about different satellite images.

Data can be visualized by accessing: https://ingresoamba.netlify.app

Census Information By Radio
This dataset falls under the category Traffic Generating Parameters Population.
It contains the following data: Census information of the City, disaggregated by radius.
This dataset was scouted on 2022-02-20 as part of a data sourcing project conducted by TUMI. License information might be outdated: Check original source for current licensing. The data can be accessed using the following URL / API Endpoint: https://data.buenosaires.gob.ar/dataset/informacion-censal-por-radio

Context

For Past a decade, Airbnb has emerged as a great personalized staying option for customers worldwide.This dataset gives the details of Airbnb listings in Buenos Aires as on 24th November 2019

Content

This dataset includes all information about hosts, geographical availability, necessary metrics to make predictions and perform analysis

Acknowledgements

This public dataset was published by Airbnb and the exact source is found here

Inspiration

What can we know about various hosts? What are the major busy areas of Buenos Aires? Which hosts are one of the busiest and why ?

This survey was conducted by the Center for Public Opinion at the University of Buenos Aires, Argentina. The adult population of Metropolitan Area (capital and Buenos Aires) were surveyed.

Please Note: This dataset is part of the historical CISER Data Archive Collection and is also available at the Roper Center for Public Opinion Research at https://doi.org/10.25940/ROPER-31081038. We highly recommend using the Roper Center version as they may make this dataset available in multiple data formats in the future.

Abstract

This file contains data on Gini coefficients, cumulative quintile shares, explanations regarding the basis on which the Gini coefficient was computed, and the source of the information. There are two data-sets, one containing the "high quality" sample and the other one including all the information (of lower quality) that had been collected.

The database was constructed for the production of the following paper:

Deininger, Klaus and Lyn Squire, "A New Data Set Measuring Income Inequality", The World Bank Economic Review, 10(3): 565-91, 1996.

This article presents a new data set on inequality in the distribution of income. The authors explain the criteria they applied in selecting data on Gini coefficients and on individual quintile groups’ income shares. Comparison of the new data set with existing compilations reveals that the data assembled here represent an improvement in quality and a significant expansion in coverage, although differences in the definition of the underlying data might still affect intertemporal and international comparability. Based on this new data set, the authors do not find a systematic link between growth and changes in aggregate inequality. They do find a strong positive relationship between growth and reduction of poverty.

Geographic coverage

In what follows, we provide brief descriptions of main features for individual countries that are included in the data-base. Without being comprehensive, these notes are intended to indicate some of the considerations underlying our decision to include or exclude certain observations.

Argentina Various permanent household surveys, all covering urban centers only, have been regularly conducted since 1972 and are quoted in a wide variety of sources and years, e.g., for 1980 (World Bank 1992), 1985 (Altimir 1994), and 1989 (World Bank 1992). Estimates for 1963, 1965, 1969/70, 1970/71, 1974, 1975, 1980, and 1981 (Altimir 1987) are based only on Greater Buenos Aires. Estimates for 1961, 1963, 1970 (Jain 1975) and for 1970 (van Ginneken 1984) have only limited geographic coverage and do not satisfy our minimum criteria.

Despite the many urban surveys, there are no income distribution data that are representative of the population as a whole. References to national income distribution for the years 1953, 1959, and 1961(CEPAL 1968 in Altimir 1986 ) are based on extrapolation from national accounts and have therefore not been included. Data for 1953 and 1961 from Weisskoff (1970) , from Lecaillon (1984) , and from Cromwell (1977) are also excluded.

Australia Household surveys, the result of which is reported in the statistical yearbook, have been conducted in 1968/9, 1975/6, 1978/9, 1981, 1985, 1986, 1989, and 1990.

Data for 1962 (Cromwell, 1977) and 1966/67 (Sawyer 1976) were excluded as they covered only tax payers. Jain's data for 1970 was excluded because it covered income recipients only. Data from Podder (1972) for 1967/68, from Jain (1975) for the same year, from UN (1985) for 78/79, from Sunders and Hobbes (1993) for 1986 and for 1989 were excluded given the availability of the primary sources. Data from Bishop (1991) for 1981/82, from Buhman (1988) for 1981/82, from Kakwani (1986) for 1975/76, and from Sunders and Hobbes (1993) for 1986 were utilized to test for the effect of different definitions. The values for 1967 used by Persson and Tabellini and Alesina and Rodrik (based on Paukert and Jain) are close to the ones reported in the Statistical Yearbook for 1969.

Austria: In addition to data referring to the employed population (Guger 1989), national household surveys for 1987 and 1991 are included in the LIS data base. As these data do not include income from self-employment, we do not report them in our high quality data-set.

Bahamas Data for Ginis and shares are available for 1973, 1977, 1979, 1986, 1988, 1989, 1991, 1992, and 1993 in government reports on population censuses and household budget surveys, and for 1973 and 1975 from UN (1981). Estimates for 1970 (Jain 1975), 1973, 1975, 1977, and 1979 (Fields 1989) have been excluded given the availability of primary sources.

Bangladesh Data from household surveys for 1973/74, 1976/77, 1977/78, 1981/82, and 1985/86 are available from the Statistical Yearbook, complemented by household-survey based information from Chen (1995) and the World Development Report. Household surveys with rural coverage for 1959, 1960, 1963/64, 1965, 1966/67 and 1968/69, and with urban coverage for 1963/64, 1965, 1966/67, and 1968/69 are also available from the Statistical yearbook. Data for 1963/64 ,1964 and 1966/67, (Jain 1975) are not included due to limited geographic coverage, We also excluded secondary sources for 1973/74, 1976/77, 1981/82 (Fields 1989), 1977 (UN 1981), 1983 (Milanovic 1994), and 1985/86 due to availability of the primary source.

Barbados National household surveys have been conducted in 1951/52 and 1978/79 (Downs, 1988). Estimates based on personal tax returns, reported consistently for 1951-1981 (Holder and Prescott, 1989), had to be excluded as they exclude the non-wage earning population. Jain's figure (used by Alesina and Rodrik) is based on the same source.

Belgium Household surveys with national coverage are available for 1978/79 (UN 1985), and for 1985, 1988, and 1992 (LIS 1995). Earlier data for 1969, 1973, 1975, 1976 and 1977 (UN 1981) refer to taxable households only and are not included.

Bolivia The only survey with national coverage is the 1990 LSMS (World Development Report). Surveys for 1986 and 1989 cover the main cities only (Psacharopoulos et al. 1992) and are therefore not included. Data for 1968 (Cromwell 1977) do not refer to a clear definition and is therefore excluded.

Botswana The only survey with national coverage was conducted in 1985-1986 (Chen et al 1993); surveys in 74/75 and 85/86 included rural areas only (UN 1981). We excluded Gini estimates for 1971/72 that refer to the economically active population only (Jain 1975), as well as 1974/75 and 1985/86 (Valentine 1993) due to lack of national coverage or consistency in definition.

Brazil Data from 1960, 1970, 1974/75, 1976, 1977, 1978, 1980, 1982, 1983, 1985, 1987 and 1989 are available from the statistical yearbook, in addition to data for 1978 (Fields 1987) and for 1979 (Psacharopoulos et al. 1992). Other sources have been excluded as they were either not of national coverage, based on wage earners only, or because a more consistent source was available.

Bulgaria: Data from household surveys are available for 1963-69 (in two year intervals), for 1970-90 (on an annual basis) from the Statistical yearbook and for 1991 - 93 from household surveys by the World Bank (Milanovic and Ying).

Burkina Faso A priority survey has been undertaken in 1995.

Central African Republic: Except for a household survey conducted in 1992, no information was available.

Cameroon The only data are from a 1983/4 household budget survey (World Bank Poverty Assessment).

Canada Gini- and share data for the 1950-61 (in irregular intervals), 1961-81 (biennially), and 1981-91 (annually) are available from official sources (Statistical Yearbook for years before 1971 and Income Distributions by Size in Canada for years since 1973, various issues). All other references seem to be based on these primary sources.

Chad: An estimate for 1958 is available in the literature, and used by Alesina and Rodrik and Persson and Tabellini but was not included due to lack of primary sources.

Chile The first nation-wide survey that included not only employment income was carried out in 1968 (UN 1981). This is complemented by household survey-based data for 1971 (Fields 1989), 1989, and 1994. Other data that refer either only to part of the population or -as in the case of a long series available from World Bank country operations- are not clearly based on primary sources, are excluded.

China Annual household surveys from 1980 to 1992, conducted separately in rural and urban areas, were consolidated by Ying (1995), based on the statistical yearbook. Data from other secondary sources are excluded due to limited geographic and population coverage and data from Chen et al (1993) for 1985 and 1990 have not been included, to maintain consistency of sources..

Colombia The first household survey with national coverage was conducted in 1970 (DANE 1970). In addition, there are data for 1971, 1972, 1974 CEPAL (1986), and for 1978, 1988/89, and 1991 (World Bank Poverty Assessment 1992 and Chen et al. 1995). Data referring to years before 1970 -including the 1964 estimate used in Persson and Tabellini were excluded, as were estimates for the wage earning population only.

Costa Rica Data on Gini coefficients and quintile shares are available for 1961, 1971 (Cespedes 1973),1977 (OPNPE 1982), 1979 (Fields 1989), 1981 (Chen et al 1993), 1983 (Bourguignon and Morrison 1989), 1986 (Sauma-Fiatt 1990), and 1989 (Chen et al 1993). Gini coefficients for 1971 (Gonzalez-Vega and Cespedes in Rottenberg 1993), 1973 and 1985 (Bourguignon and Morrison 1989) cover urban areas only and were excluded.

Cote d'Ivoire: Data based on national-level household surveys (LSMS) are available for 1985, 1986, 1987, 1988, and 1995. Information for the 1970s (Schneider 1991) is based on national accounting information and therefore excluded

Cuba Official information on income distribution is limited. Data from secondary sources are available for 1953, 1962, 1973, and 1978, relying on personal wage income, i.e. excluding the population that is not economically active (Brundenius 1984).

Czech Republic Household surveys for 1993 and 1994 were obtained from Milanovic and Ying. While it is in principle possible to go back further, splitting national level surveys for the former Czechoslovakia into their independent parts, we decided not to do so as the same argument could be used to

This dataset aims to deepen the analysis of cortical gyral and sulcal asymmetry of the entire cerebral cortex of healthy adult individuals by quantifying the gray matter content of the right and left hemispheres in a reference sample from South America. The subjects were sampled from populations scarcely represented in MRI research, which tends to be biased towards groups of European ancestry from Europe and North America. In contrast, the population under study is an admixture of Native American, European, and African components that contributed to a variable extent to their gene pool. Consequently, this study will add to expanding the diversity in brain morphometric data and the construction of more population-representative references. Methods We recruited 175 healthy adult right-handed volunteers of both sexes from the metropolitan area of Buenos Aires (Autonomous city of Buenos Aires and Florencio Varela city) and Bariloche (Argentinian Patagonia), Argentina. 3D T1 volumetric images were obtained in three high-field (3T) scanners: a) Philips Achieva scanner located at the SAMIC El Cruce Hospital (F. Varela, Buenos Aires); b) Siemens Trio scanner located at the Angel Roffo Institute (Autonomous City of Buenos Aires); c) SIGNA PET / MR 3T scanner located in INTECNUS (Bariloche, Río Negro). The parameters of the images at each sampling site were as follows: a) 3D FFE sequence, TE= 3.3 msec, TR= 2300 msec, TI= 900 msec, flip angle= 9°, FOV= 240x240x180, voxel size= 1x1x1 mm3 and 239 slices; b) MP-RAGE, TE= 2.27 msec, TR= 2000 msec, TI= 900 msec, inverted angle= 80, FOV= 250x250, voxel size= 1x1x1 mm3 and 204 slices; c) 3D SPGR (SAG) sequence with an inversion pulse, TE= 2.9 msec, TR= 7.1 msec, TI= 900 msec, flip angle= 9 °, FOV= 256x256x176, voxel size= 1x1x1 mm3 and 176 slices. All the images were processed in the same workstation (with Ubuntu 18.04), using FreeSurfer's “recon-all” algorithm (v 6.0.0.0) (http://surfer.nmr.mgh.harvard.edu), widely used in structural brain studies, and which exhibits a high precision, reliability, and validity. Then the cerebral cortex of each hemisphere was parcellated taking a probabilistic atlas as a reference, in this case, the atlas by Destrieux et al. (2010) was used. Then, in each subject’s native space, the volume and surface area of the gray matter of 74 parcels were obtained.

Estimation of effective population size for all areas using linkage disequilibrium method.