Objectives: Inborn error of immunity (IEI) comprises a broad group of inherited immunological disorders that usually display an overlap in many clinical manifestations challenging their diagnosis. The identification of disease-causing variants comprises the gold-standard approach to ascertain IEI diagnosis. The efforts to increase the availability of clinically relevant genomic data for these disorders constitute an important improvement in the study of rare genetic disorders. This work aims to make available whole-exome sequencing (WES) data of Brazilian patients' suspicion of IEI without a genetic diagnosis. We foresee a broad use of this dataset by the scientific community in order to provide a more accurate diagnosis of IEI disorders. Data description: Twenty singleton unrelated patients treated at four different hospitals in the state of Rio de Janeiro, Brazil were enrolled in our study. Half of the patients were male with mean ages of 9±3, while females were 12±10  years old. The WES was performed in the Illumina NextSeq platform with at least 90% of sequenced bases with a minimum of 30 reads depth. Each sample had an average of 20,274 variants, comprising 116 classified as rare pathogenic or likely pathogenic according to ACMG guidelines. The genotype-phenotype association was impaired by the lack of detailed clinical and laboratory information, besides the unavailability of molecular and functional studies which, comprise the limitations of this study. Overall, the access to clinical exome sequencing data is limited, challenging exploratory analyses and the understanding of genetic mechanisms underlying disorders. Therefore, by making these data available, we aim to increase the number of WES data from Brazilian samples despite contributing to the study of monogenic IEI-disorders.

Abstract

The L2 Voter and Demographic Dataset includes demographic and voter history tables for all 50 states and the District of Columbia. The dataset is built from publicly available government records about voter registration and election participation. These records indicate whether a person voted in an election or not, but they do not record whom that person voted for. Voter registration and election participation data are augmented by demographic information from outside data sources.

Methodology

To create this file, L2 processes registered voter data on an ongoing basis for all 50 states and the District of Columbia, with refreshes of the underlying state voter data typically at least every six months and refreshes of telephone numbers and National Change of Address processing approximately every 30 to 60 days. These data are standardized and enhanced with propriety commercial data and modeling codes and consist of approximately 185,000,000 records nationwide.

Usage

For each state, there are two available tables: demographic and voter history. The demographic and voter tables can be joined on the LALVOTERIDvariable. One can also use the LALVOTERIDvariable to link the L2 Voter and Demographic Dataset with the L2 Consumer Dataset.

In addition, the LALVOTERIDvariable can be used to validate the state. For example, let's look at the LALVOTERID = LALCA3169443. The characters in the fourth and fifth positions of this identifier are 'CA' (California). The second way to validate the state is by using the RESIDENCE_ADDRESSES_STATEvariable, which should have a value of 'CA' (California).

The date appended to each table name represents when the data was last updated. These dates will differ state by state because states update their voter files at different cadences.

The demographic files use 698 consistent variables. For more information about these variables, see 2025-01-10-VM2-File-Layout.xlsx.

The voter history files have different variables depending on the state. The ***2025-07-16-L2-Voter-Dictionaries.tar.gz file contains .csv data dictionaries for each state's demographic and voter files. While the demographic file data dictionaries should mirror the 2025-01-10-VM2-File-Layout.xlsx*** file, the voter file data dictionaries will be unique to each state.

***2025-04-24-National-File-Notes.pdf ***contains L2 Voter and Demographic Dataset ("National File") release notes from 2018 to 2025.

***2025-07-16-L2-Voter-Fill-Rate.tar.gz ***contains .tab files tracking the percent of non-null values for any given field.

Bulk Data Access

Data access is required to view this section.

DataMapping Tool

Data access is required to view this section.

The Bureau of the Census has released Census 2000 Summary File 1 (SF1) 100-Percent data. The file includes the following population items: sex, age, race, Hispanic or Latino origin, household relationship, and household and family characteristics. Housing items include occupancy status and tenure (whether the unit is owner or renter occupied). SF1 does not include information on incomes, poverty status, overcrowded housing or age of housing. These topics will be covered in Summary File 3. Data are available for states, counties, county subdivisions, places, census tracts, block groups, and, where applicable, American Indian and Alaskan Native Areas and Hawaiian Home Lands. The SF1 data are available on the Bureau's web site and may be retrieved from American FactFinder as tables, lists, or maps. Users may also download a set of compressed ASCII files for each state via the Bureau's FTP server. There are over 8000 data items available for each geographic area. The full listing of these data items is available here as a downloadable compressed data base file named TABLES.ZIP. The uncompressed is in FoxPro data base file (dbf) format and may be imported to ACCESS, EXCEL, and other software formats. While all of this information is useful, the Office of Community Planning and Development has downloaded selected information for all states and areas and is making this information available on the CPD web pages. The tables and data items selected are those items used in the CDBG and HOME allocation formulas plus topics most pertinent to the Comprehensive Housing Affordability Strategy (CHAS), the Consolidated Plan, and similar overall economic and community development plans. The information is contained in five compressed (zipped) dbf tables for each state. When uncompressed the tables are ready for use with FoxPro and they can be imported into ACCESS, EXCEL, and other spreadsheet, GIS and database software. The data are at the block group summary level. The first two characters of the file name are the state abbreviation. The next two letters are BG for block group. Each record is labeled with the code and name of the city and county in which it is located so that the data can be summarized to higher-level geography. The last part of the file name describes the contents . The GEO file contains standard Census Bureau geographic identifiers for each block group, such as the metropolitan area code and congressional district code. The only data included in this table is total population and total housing units. POP1 and POP2 contain selected population variables and selected housing items are in the HU file. The MA05 table data is only for use by State CDBG grantees for the reporting of the racial composition of beneficiaries of Area Benefit activities. The complete package for a state consists of the dictionary file named TABLES, and the five data files for the state. The logical record number (LOGRECNO) links the records across tables.

This formatted dataset (AnalysisDatabaseGBD) originates from raw data files from the Institute of Health Metrics and Evaluation (IHME) Global Burden of Disease Study (GBD2017) affiliated with the University of Washington. We are volunteer collaborators with IHME and not employed by IHME or the University of Washington.

The population weighted GBD2017 data are on male and female cohorts ages 15-69 years including noncommunicable diseases (NCDs), body mass index (BMI), cardiovascular disease (CVD), and other health outcomes and associated dietary, metabolic, and other risk factors. The purpose of creating this population-weighted, formatted database is to explore the univariate and multiple regression correlations of health outcomes with risk factors. Our research hypothesis is that we can successfully model NCDs, BMI, CVD, and other health outcomes with their attributable risks.

These Global Burden of disease data relate to the preprint: The EAT-Lancet Commission Planetary Health Diet compared with Institute of Health Metrics and Evaluation Global Burden of Disease Ecological Data Analysis. The data include the following: 1. Analysis database of population weighted GBD2017 data that includes over 40 health risk factors, noncommunicable disease deaths/100k/year of male and female cohorts ages 15-69 years from 195 countries (the primary outcome variable that includes over 100 types of noncommunicable diseases) and over 20 individual noncommunicable diseases (e.g., ischemic heart disease, colon cancer, etc). 2. A text file to import the analysis database into SAS 3. The SAS code to format the analysis database to be used for analytics 4. SAS code for deriving Tables 1, 2, 3 and Supplementary Tables 5 and 6 5. SAS code for deriving the multiple regression formula in Table 4. 6. SAS code for deriving the multiple regression formula in Table 5 7. SAS code for deriving the multiple regression formula in Supplementary Table 7
8. SAS code for deriving the multiple regression formula in Supplementary Table 8 9. The Excel files that accompanied the above SAS code to produce the tables

For questions, please email davidkcundiff@gmail.com. Thanks.

Brazil Population: Residents: South: Rio Grande do Sul: Carlos Barbosa data was reported at 29,409.000 Person in 2018. This records an increase from the previous number of 28,091.000 Person for 2017. Brazil Population: Residents: South: Rio Grande do Sul: Carlos Barbosa data is updated yearly, averaging 22,901.000 Person from Jun 1992 (Median) to 2018, with 24 observations. The data reached an all-time high of 29,409.000 Person in 2018 and a record low of 16,692.000 Person in 1992. Brazil Population: Residents: South: Rio Grande do Sul: Carlos Barbosa data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Brazil Premium Database’s Socio and Demographic – Table BR.GAA054: Population: by Municipality: South: Rio Grande do Sul.

Brazil Population: Residents: Northeast: Rio Grande do Norte: Natal data was reported at 877,640.000 Person in 2018. This records a decrease from the previous number of 885,180.000 Person for 2017. Brazil Population: Residents: Northeast: Rio Grande do Norte: Natal data is updated yearly, averaging 772,060.500 Person from Jun 1992 (Median) to 2018, with 24 observations. The data reached an all-time high of 885,180.000 Person in 2017 and a record low of 619,241.000 Person in 1992. Brazil Population: Residents: Northeast: Rio Grande do Norte: Natal data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Brazil Premium Database’s Socio and Demographic – Table BR.GAA047: Population: by Municipality: Northeast: Rio Grande do Norte.

Brazil Population Census: Female: Northeast: Rio Grande do Norte data was reported at 1,619,140.000 Person in 2010. This records an increase from the previous number of 1,416,829.000 Person for 2000. Brazil Population Census: Female: Northeast: Rio Grande do Norte data is updated yearly, averaging 1,362,972.500 Person from Jul 1991 (Median) to 2010, with 4 observations. The data reached an all-time high of 1,619,140.000 Person in 2010 and a record low of 1,236,849.000 Person in 1991. Brazil Population Census: Female: Northeast: Rio Grande do Norte data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Brazil Premium Database’s Socio and Demographic – Table BR.GAC009: Population Census: by State and Sex.

Brazil Population: Residents: Northeast: Rio Grande do Norte: Age 10 to 14 data was reported at 293.250 Person th in 2015. This records an increase from the previous number of 273.400 Person th for 2014. Brazil Population: Residents: Northeast: Rio Grande do Norte: Age 10 to 14 data is updated yearly, averaging 292.727 Person th from Sep 2001 (Median) to 2015, with 14 observations. The data reached an all-time high of 322.507 Person th in 2008 and a record low of 270.000 Person th in 2012. Brazil Population: Residents: Northeast: Rio Grande do Norte: Age 10 to 14 data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Brazil Premium Database’s Socio and Demographic – Table BR.GAA020: Population: by States and Age: Northeast: Rio Grande do Norte.

Brazil Population: Projection: Residents: Northeast: Rio Grande do Norte: Age 15 to 19 Years data was reported at 195,507.000 Person in 2060. This records a decrease from the previous number of 197,022.000 Person for 2059. Brazil Population: Projection: Residents: Northeast: Rio Grande do Norte: Age 15 to 19 Years data is updated yearly, averaging 238,635.000 Person from Jun 2010 (Median) to 2060, with 51 observations. The data reached an all-time high of 301,247.000 Person in 2010 and a record low of 195,507.000 Person in 2060. Brazil Population: Projection: Residents: Northeast: Rio Grande do Norte: Age 15 to 19 Years data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Brazil Premium Database’s Socio and Demographic – Table BR.GAB024: Population: Projection: by Age: Northeast: Rio Grande do Norte.

Brazil Population: Residents: Northeast: Rio Grande do Norte: Água Nova data was reported at 3,230.000 Person in 2018. This records a decrease from the previous number of 3,260.000 Person for 2017. Brazil Population: Residents: Northeast: Rio Grande do Norte: Água Nova data is updated yearly, averaging 2,888.000 Person from Jun 1992 (Median) to 2018, with 24 observations. The data reached an all-time high of 3,260.000 Person in 2017 and a record low of 2,330.000 Person in 1992. Brazil Population: Residents: Northeast: Rio Grande do Norte: Água Nova data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Brazil Premium Database’s Socio and Demographic – Table BR.GAA047: Population: by Municipality: Northeast: Rio Grande do Norte.

Brazil Population: Residents: South: Rio Grande do Sul: Anta Gorda data was reported at 6,003.000 Person in 2018. This records a decrease from the previous number of 6,210.000 Person for 2017. Brazil Population: Residents: South: Rio Grande do Sul: Anta Gorda data is updated yearly, averaging 6,275.500 Person from Jun 1992 (Median) to 2018, with 24 observations. The data reached an all-time high of 6,927.000 Person in 1992 and a record low of 6,003.000 Person in 2018. Brazil Population: Residents: South: Rio Grande do Sul: Anta Gorda data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Brazil Premium Database’s Socio and Demographic – Table BR.GAA054: Population: by Municipality: South: Rio Grande do Sul.

Population: Residents: Central West: Mato Grosso do Sul: Rio Verde de Mato Grosso data was reported at 19,682.000 Person in 2018. This records an increase from the previous number of 19,569.000 Person for 2017. Population: Residents: Central West: Mato Grosso do Sul: Rio Verde de Mato Grosso data is updated yearly, averaging 19,106.500 Person from Jun 1992 (Median) to 2018, with 24 observations. The data reached an all-time high of 20,402.000 Person in 2006 and a record low of 14,927.000 Person in 1995. Population: Residents: Central West: Mato Grosso do Sul: Rio Verde de Mato Grosso data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Brazil Premium Database’s Socio and Demographic – Table BR.GAA059: Population: by Municipality: Central West: Mato Grosso do Sul.

This map shows population density of the United States. Areas in darker magenta have much higher population per square mile than areas in orange or yellow. Data is from the U.S. Census Bureau’s 2020 Census Demographic and Housing Characteristics. The map's layers contain total population counts by sex, age, and race groups for Nation, State, County, Census Tract, and Block Group in the United States and Puerto Rico. From the Census:"Population density allows for broad comparison of settlement intensity across geographic areas. In the U.S., population density is typically expressed as the number of people per square mile of land area. The U.S. value is calculated by dividing the total U.S. population (316 million in 2013) by the total U.S. land area (3.5 million square miles).When comparing population density values for different geographic areas, then, it is helpful to keep in mind that the values are most useful for small areas, such as neighborhoods. For larger areas (especially at the state or country scale), overall population density values are less likely to provide a meaningful measure of the density levels at which people actually live, but can be useful for comparing settlement intensity across geographies of similar scale." SourceAbout the dataYou can use this map as is and you can also modify it to use other attributes included in its layers. This map's layers contain total population counts by sex, age, and race groups data from the 2020 Census Demographic and Housing Characteristics. This is shown by Nation, State, County, Census Tract, Block Group boundaries. Each geography layer contains a common set of Census counts based on available attributes from the U.S. Census Bureau. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis.Vintage of boundaries and attributes: 2020 Demographic and Housing Characteristics Table(s): P1, H1, H3, P2, P3, P5, P12, P13, P17, PCT12 (Not all lines of these DHC tables are available in this feature layer.)Data downloaded from: U.S. Census Bureau’s data.census.gov siteDate the Data was Downloaded: May 25, 2023Geography Levels included: Nation, State, County, Census Tract, Block GroupNational Figures: included in Nation layer The United States Census Bureau Demographic and Housing Characteristics: 2020 Census Results 2020 Census Data Quality Geography & 2020 Census Technical Documentation Data Table Guide: includes the final list of tables, lowest level of geography by table and table shells for the Demographic Profile and Demographic and Housing Characteristics.News & Updates This map is ready to be used in ArcGIS Pro, ArcGIS Online and its configurable apps, Story Maps, dashboards, Notebooks, Python, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the U.S. Census Bureau when using this data. Data Processing Notes: These 2020 Census boundaries come from the US Census TIGER geodatabases. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For Census tracts and block groups, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract and block group boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2020 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are unchanged and available as attributes within the data table (units are square meters).  The layer contains all US states, Washington D.C., and Puerto Rico. Census tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99). Block groups that fall within the same criteria (Block Group denoted as 0 with no area land) have also been removed.Percentages and derived counts, are calculated values (that can be identified by the "_calc_" stub in the field name). Field alias names were created based on the Table Shells file available from the Data Table Guide for the Demographic Profile and Demographic and Housing Characteristics. Not all lines of all tables listed above are included in this layer. Duplicative counts were dropped. For example, P0030001 was dropped, as it is duplicative of P0010001.To protect the privacy and confidentiality of respondents, their data has been protected using differential privacy techniques by the U.S. Census Bureau.

This dataset contains aggregate data on violent index victimizations at the quarter level of each year (i.e., January – March, April – June, July – September, October – December), from 2001 to the present (1991 to present for Homicides), with a focus on those related to gun violence. Index crimes are 10 crime types selected by the FBI (codes 1-4) for special focus due to their seriousness and frequency. This dataset includes only those index crimes that involve bodily harm or the threat of bodily harm and are reported to the Chicago Police Department (CPD). Each row is aggregated up to victimization type, age group, sex, race, and whether the victimization was domestic-related. Aggregating at the quarter level provides large enough blocks of incidents to protect anonymity while allowing the end user to observe inter-year and intra-year variation. Any row where there were fewer than three incidents during a given quarter has been deleted to help prevent re-identification of victims. For example, if there were three domestic criminal sexual assaults during January to March 2020, all victims associated with those incidents have been removed from this dataset. Human trafficking victimizations have been aggregated separately due to the extremely small number of victimizations.

This dataset includes a " GUNSHOT_INJURY_I " column to indicate whether the victimization involved a shooting, showing either Yes ("Y"), No ("N"), or Unknown ("UKNOWN.") For homicides, injury descriptions are available dating back to 1991, so the "shooting" column will read either "Y" or "N" to indicate whether the homicide was a fatal shooting or not. For non-fatal shootings, data is only available as of 2010. As a result, for any non-fatal shootings that occurred from 2010 to the present, the shooting column will read as “Y.” Non-fatal shooting victims will not be included in this dataset prior to 2010; they will be included in the authorized dataset, but with "UNKNOWN" in the shooting column.

The dataset is refreshed daily, but excludes the most recent complete day to allow CPD time to gather the best available information. Each time the dataset is refreshed, records can change as CPD learns more about each victimization, especially those victimizations that are most recent. The data on the Mayor's Office Violence Reduction Dashboard is updated daily with an approximately 48-hour lag. As cases are passed from the initial reporting officer to the investigating detectives, some recorded data about incidents and victimizations may change once additional information arises. Regularly updated datasets on the City's public portal may change to reflect new or corrected information.

How does this dataset classify victims?

The methodology by which this dataset classifies victims of violent crime differs by victimization type:

Homicide and non-fatal shooting victims: A victimization is considered a homicide victimization or non-fatal shooting victimization depending on its presence in CPD's homicide victims data table or its shooting victims data table. A victimization is considered a homicide only if it is present in CPD's homicide data table, while a victimization is considered a non-fatal shooting only if it is present in CPD's shooting data tables and absent from CPD's homicide data table.

To determine the IUCR code of homicide and non-fatal shooting victimizations, we defer to the incident IUCR code available in CPD's Crimes, 2001-present dataset (available on the City's open data portal). If the IUCR code in CPD's Crimes dataset is inconsistent with the homicide/non-fatal shooting categorization, we defer to CPD's Victims dataset.

For a criminal homicide, the only sensible IUCR codes are 0110 (first-degree murder) or 0130 (second-degree murder). For a non-fatal shooting, a sensible IUCR code must signify a criminal sexual assault, a robbery, or, most commonly, an aggravated battery. In rare instances, the IUCR code in CPD's Crimes and Victims dataset do not align with the homicide/non-fatal shooting categorization:

1. In instances where a homicide victimization does not correspond to an IUCR code 0110 or 0130, we set the IUCR code to "01XX" to indicate that the victimization was a homicide but we do not know whether it was a first-degree murder (IUCR code = 0110) or a second-degree murder (IUCR code = 0130).
2. When a non-fatal shooting victimization does not correspond to an IUCR code that signifies a criminal sexual assault, robbery, or aggravated battery, we enter “UNK” in the IUCR column, “YES” in the GUNSHOT_I column, and “NON-FATAL” in the PRIMARY column to indicate that the victim was non-fatally shot, but the precise IUCR code is unknown.

Other violent crime victims: For other violent crime types, we refer to the IUCR classification that exists in CPD's victim table, with only one exception:

1. When there is an incident that is associated with no victim with a matching IUCR code, we assume that this is an error. Every crime should have at least 1 victim with a matching IUCR code. In these cases, we change the IUCR code to reflect the incident IUCR code because CPD's incident table is considered to be more reliable than the victim table.

Note: All businesses identified as victims in CPD data have been removed from this dataset.

Note: The definition of “homicide” (shooting or otherwise) does not include justifiable homicide or involuntary manslaughter. This dataset also excludes any cases that CPD considers to be “unfounded” or “noncriminal.”

Note: In some instances, the police department's raw incident-level data and victim-level data that were inputs into this dataset do not align on the type of crime that occurred. In those instances, this dataset attempts to correct mismatches between incident and victim specific crime types. When it is not possible to determine which victims are associated with the most recent crime determination, the dataset will show empty cells in the respective demographic fields (age, sex, race, etc.).

Note: The initial reporting officer usually asks victims to report demographic data. If victims are unable to recall, the reporting officer will use their best judgment. “Unknown” can be reported if it is truly unknown.

Brazil Population: Residents: North: Pará: São Miguel do Guamá data was reported at 58,328.000 Person in 2018. This records an increase from the previous number of 57,364.000 Person for 2017. Brazil Population: Residents: North: Pará: São Miguel do Guamá data is updated yearly, averaging 44,675.000 Person from Jun 1992 (Median) to 2018, with 24 observations. The data reached an all-time high of 58,328.000 Person in 2018 and a record low of 33,440.000 Person in 1992. Brazil Population: Residents: North: Pará: São Miguel do Guamá data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Brazil Premium Database’s Socio and Demographic – Table BR.GAA036: Population: by Municipality: North: Pará.

Brazil Population: Residents: Northeast: Rio Grande do Norte: Pedro Avelino data was reported at 6,780.000 Person in 2018. This records a decrease from the previous number of 6,938.000 Person for 2017. Brazil Population: Residents: Northeast: Rio Grande do Norte: Pedro Avelino data is updated yearly, averaging 7,052.000 Person from Jun 1992 (Median) to 2018, with 24 observations. The data reached an all-time high of 12,003.000 Person in 1995 and a record low of 5,463.000 Person in 2006. Brazil Population: Residents: Northeast: Rio Grande do Norte: Pedro Avelino data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Brazil Premium Database’s Socio and Demographic – Table BR.GAA047: Population: by Municipality: Northeast: Rio Grande do Norte.

Brazil Population Census: South: Rio Grande do Sul: Esteio data was reported at 80,755.000 Person in 2010. This records an increase from the previous number of 78,816.000 Person for 2007. Brazil Population Census: South: Rio Grande do Sul: Esteio data is updated yearly, averaging 79,432.000 Person from Jul 1996 (Median) to 2010, with 4 observations. The data reached an all-time high of 80,755.000 Person in 2010 and a record low of 75,233.000 Person in 1996. Brazil Population Census: South: Rio Grande do Sul: Esteio data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Brazil Premium Database’s Socio and Demographic – Table BR.GAC058: Population Census: by Municipality: South: Rio Grande do Sul.

Brazil Population: Non Literate: Northeast: Rio Grande do Norte: 30 Years to 34 Years data was reported at 32,597.000 Person in 2010. This records a decrease from the previous number of 43,945.000 Person for 2000. Brazil Population: Non Literate: Northeast: Rio Grande do Norte: 30 Years to 34 Years data is updated yearly, averaging 43,945.000 Person from Jul 1991 (Median) to 2010, with 3 observations. The data reached an all-time high of 45,792.000 Person in 1991 and a record low of 32,597.000 Person in 2010. Brazil Population: Non Literate: Northeast: Rio Grande do Norte: 30 Years to 34 Years data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Brazil Premium Database’s Socio and Demographic – Table BR.GAD087: Population: Non Literate: by Municipality: Northeast: Rio Grande do Norte.

Brazil Population Census: Central West: Mato Grosso do Sul: Três Lagoas data was reported at 101,791.000 Person in 2010. This records an increase from the previous number of 85,914.000 Person for 2007. Brazil Population Census: Central West: Mato Grosso do Sul: Três Lagoas data is updated yearly, averaging 82,486.500 Person from Jul 1996 (Median) to 2010, with 4 observations. The data reached an all-time high of 101,791.000 Person in 2010 and a record low of 74,797.000 Person in 1996. Brazil Population Census: Central West: Mato Grosso do Sul: Três Lagoas data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Brazil Premium Database’s Socio and Demographic – Table BR.GAC062: Population Census: by Municipality: Central West: Mato Grosso do Sul.

Brazil Population: Projection: Residents: Northeast: Rio Grande do Norte: Age 75 to 79 Years data was reported at 195,757.000 Person in 2060. This records an increase from the previous number of 191,550.000 Person for 2059. Brazil Population: Projection: Residents: Northeast: Rio Grande do Norte: Age 75 to 79 Years data is updated yearly, averaging 100,678.000 Person from Jun 2010 (Median) to 2060, with 51 observations. The data reached an all-time high of 195,757.000 Person in 2060 and a record low of 40,963.000 Person in 2010. Brazil Population: Projection: Residents: Northeast: Rio Grande do Norte: Age 75 to 79 Years data remains active status in CEIC and is reported by Brazilian Institute of Geography and Statistics. The data is categorized under Brazil Premium Database’s Socio and Demographic – Table BR.GAB024: Population: Projection: by Age: Northeast: Rio Grande do Norte.