Context

OpenAddresses's goal is to connect the digital and physical worlds by sharing geographic coordinates, street names, house numbers and postal codes.

Content

This dataset contains one datafile for each state in the U.S. South region (although some are arguably not in the South).

States included in this dataset:

• Alabama - al.csv
• Arkansas - ar.csv
• Washington D.C. - dc.csv
• Delaware - de.csv
• Florida - fl.csv
• Georgia - ga.csv
• Kentucky - ky.csv
• Louisiana - la.csv
• Maryland - md.csv
• Mississippi - ms.csv
• North Carolina - nc.csv
• Oklahoma - ok.csv
• South carolina - sc.csv
• Tennessee - tn.csv
• Texas - tx.csv
• Virginia - va.csv
• West Virginia - wv.csv

Field descriptions:

• LON - Longitude
• LAT - Latitude
• NUMBER - Street number
• STREET - Street name
• UNIT - Unit or apartment number
• CITY - City name
• DISTRICT - ?
• REGION - ?
• POSTCODE - Postcode or zipcode
• ID - ?
• HASH - ?

Acknowledgements

Data collected around 2017-07-25 by OpenAddresses (http://openaddresses.io).

Address data is essential infrastructure. Street names, house numbers and postal codes, when combined with geographic coordinates, are the hub that connects digital to physical places.

Data licenses can be found in LICENSE.txt.

Data source information can be found at https://github.com/openaddresses/openaddresses/tree/9ea72b079aaff7d322349e4b812eb43eb94d6d93/sources

Inspiration

Use this dataset to create maps in conjunction with other datasets for crime or weather

About this dataset

Context

The dataset tabulates the data for the Texas population pyramid, which represents the Texas population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Texas, is 31.6.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Texas, is 20.0.
• Total dependency ratio for Texas is 51.6.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Texas is 5.0.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group for the Texas population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Texas for the selected age group is shown in the following column.
• Population (Female): The female population in the Texas for the selected age group is shown in the following column.
• Total Population: The total population of the Texas for the selected age group is shown in the following column.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Texas Population by Age. You can refer the same here

This resource is a member of a series. The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) System (MTS). The MTS represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The TIGER/Line shapefiles include both incorporated places (legal entities) and census designated places or CDPs (statistical entities). An incorporated place is established to provide governmental functions for a concentration of people as opposed to a minor civil division (MCD), which generally is created to provide services or administer an area without regard, necessarily, to population. Places always nest within a state but may extend across county and county subdivision boundaries. An incorporated place is usually a city, town, village, or borough, but can have other legal descriptions. CDPs are delineated for the decennial census as the statistical counterparts of incorporated places. CDPs are delineated to provide data for settled concentrations of population that are identifiable by name but are not legally incorporated under the laws of the state in which they are located. The boundaries for CDPs are often defined in partnership with state, local, and/or tribal officials and usually coincide with visible features or the boundary of an adjacent incorporated place or another legal entity. CDP boundaries often change from one decennial census to the next with changes in the settlement pattern and development; a CDP with the same name as in an earlier census does not necessarily have the same boundary. The only population/housing size requirement for CDPs is that they must contain some housing and population. The boundaries of most incorporated places in this shapefile are as of January 1, 2024, as reported through the Census Bureau's Boundary and Annexation Survey (BAS). The boundaries of all CDPs were delineated as part of the Census Bureau's Participant Statistical Areas Program (PSAP) for the 2020 Census, but some CDPs were added or updated through the 2024 BAS as well.

A census tract is a geographic area defined by the U.S. Census Bureau for the purpose of collecting and analyzing demographic data. Typically, a census tract contains a population of about 1,200 to 8,000 people and is designed to reflect homogenous social and economic characteristics. Tracts are used in various statistical analyses and are updated every ten years with the decennial census, allowing for a detailed understanding of population trends, housing, and economic conditions within specific communities. These files do not include demographic data, but they contain geographic entity codes that can be linked to the Census Bureau’s demographic data, available on https://data.census.gov. Terms of Use This product is for informational purposes and may not have been prepared for or be suitable for legal, engineering, or surveying purposes. It does not represent an on-the-ground survey and represents only the approximate relative location of property boundaries. This product has been produced by the US Census for the sole purpose of geographic reference. No warranty is made by the City of Austin regarding specific accuracy or completeness.

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Census tracts are small, relatively permanent statistical subdivisions of a county or equivalent entity, and were defined by local participants as part of the 2020 Census Participant Statistical Areas Program. The Census Bureau delineated the census tracts in situations where no local participant existed or where all the potential participants declined to participate. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of census data and comparison back to previous decennial censuses. Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people. When first delineated, census tracts were designed to be homogeneous with respect to population characteristics, economic status, and living conditions. The spatial size of census tracts varies widely depending on the density of settlement. Physical changes in street patterns caused by highway construction, new development, and so forth, may require boundary revisions. In addition, census tracts occasionally are split due to population growth, or combined as a result of substantial population decline. Census tract boundaries generally follow visible and identifiable features. They may follow legal boundaries such as minor civil division (MCD) or incorporated place boundaries in some States and situations to allow for census tract-to-governmental unit relationships where the governmental boundaries tend to remain unchanged between censuses. State and county boundaries always are census tract boundaries in the standard census geographic hierarchy. In a few rare instances, a census tract may consist of noncontiguous areas. These noncontiguous areas may occur where the census tracts are coextensive with all or parts of legal entities that are themselves noncontiguous. For the 2010 Census, the census tract code range of 9400 through 9499 was enforced for census tracts that include a majority American Indian population according to Census 2000 data and/or their area was primarily covered by federally recognized American Indian reservations and/or off-reservation trust lands; the code range 9800 through 9899 was enforced for those census tracts that contained little or no population and represented a relatively large special land use area such as a National Park, military installation, or a business/industrial park; and the code range 9900 through 9998 was enforced for those census tracts that contained only water area, no land area.

About this dataset

Context

The dataset tabulates the population of Texas by gender across 18 age groups. It lists the male and female population in each age group along with the gender ratio for Texas. The dataset can be utilized to understand the population distribution of Texas by gender and age. For example, using this dataset, we can identify the largest age group for both Men and Women in Texas. Additionally, it can be used to see how the gender ratio changes from birth to senior most age group and male to female ratio across each age group for Texas.

Key observations

Largest age group (population): Male # 10-14 years (1.12 million) | Female # 10-14 years (1.08 million). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis.

Variables / Data Columns

• Age Group: This column displays the age group for the Texas population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Texas is shown in the following column.
• Population (Female): The female population in the Texas is shown in the following column.
• Gender Ratio: Also known as the sex ratio, this column displays the number of males per 100 females in Texas for each age group.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Texas Population by Gender. You can refer the same here

This resource is a member of a series. The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) System (MTS). The MTS represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Census tracts are small, relatively permanent statistical subdivisions of a county or equivalent entity and were defined by local participants as part of the 2020 Census Participant Statistical Areas Program. The Census Bureau delineated the census tracts in situations where no local participant existed or where all the potential participants declined to participate. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of census data and comparison back to previous decennial censuses. Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people. When first delineated, census tracts were designed to be homogeneous with respect to population characteristics, economic status, and living conditions. The spatial size of census tracts varies widely depending on the density of settlement. Physical changes in street patterns caused by highway construction, new development, and so forth, may require boundary revisions. In addition, census tracts occasionally are split due to population growth, or combined because of substantial population decline. Census tract boundaries generally follow visible and identifiable features. They may follow legal boundaries such as minor civil division or incorporated place boundaries in some states and situations to allow for census tract-to-governmental unit relationships where the governmental boundaries tend to remain unchanged between censuses. State and county boundaries always are census tract boundaries in the standard Census Bureau geographic hierarchy. In a few rare instances, a census tract may consist of noncontiguous areas. These noncontiguous areas may occur where the census tracts are coextensive with all or parts of legal entities that are themselves noncontiguous.

This resource is a member of a series. The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) System (MTS). The MTS represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Block groups are clusters of blocks within the same census tract. Each census tract contains at least one block group, and are uniquely numbered within census tracts. Block groups have a valid code range of 0 through 9. They also have the same first digit of their 4-digit census block number from the same decennial census. For example, tabulation blocks numbered 3001, 3002, 3003,.., 3999 within census tract 1210.02 are also within block group 3 within that census tract. Block groups coded 0 are intended to only include water area, no land area, and they are generally in territorial seas, coastal water, and Great Lakes water areas. Block groups generally contain between 600 and 3,000 people. A block group usually covers a contiguous area but never crosses county or census tract boundaries. They may, however, cross the boundaries of other geographic entities like county subdivisions, places, urban areas, voting districts, congressional districts, and American Indian / Alaska Native / Native Hawaiian areas. The block group boundaries in this release are those that were delineated as part of the Census Bureau's Participant Statistical Areas Program (PSAP) for the 2020 Census.

Comprehensive dataset containing 27 verified Human resources businesses in Texas, United States with complete contact information, ratings, reviews, and location data.

1. Context

Capital punishment is one of the controversial human rights issues in the United States. While surfing the Internet for an interesting dataset, I came across this database by Texas Department of Criminal Justice, which comprises of the offenders' last words before execution. Some of the statements are:

"...Young people, listen to your parents; always do what they tell you to do, go to school, learn from your mistakes. Be careful before you sign anything with your name. Never, despite what other people say..." (Ramiro Hernandez, executed on April 9th, 2014)

"First and foremost I'd like to say, "Justice has never advanced by taking a life" by Coretta Scott King. Lastly, to my wife and to my kids, I love y'all forever and always. That's it." (Taichin Preyor, executed on July 27th, 2017)

As I skimmed these lines, I decided to create this dataset.

2. Content

This dataset includes information on criminals executed by Texas Department of Criminal Justice from 1982 to November 8th, 2017. In Furman v Georgia in 1972, the Supreme Court considered a group of consolidated cases, whereby it severely restricted the death penalty. However, like other states, Texas adjusted its legislation to address the Court's concern and once again allow for capital punishment in 1973. Texas adopted execution by lethal injection in 1977 and in 1982, the starting year of this dataset, the first offender was executed by this method.

The dataset consists of 545 observations with 21 variables. They are:
- Execution: The order of execution, numeric.
- LastName: Last name of the offender, character.
- FirstName: First name of the offender, character.
- TDCJNumber: TDCJ Number of the offender, numeric.
- Age: Age of the offender, numeric.
- Race: Race of the offender, categorical : Black, Hispanic, White, Other.
- CountyOfConviction: County of conviction, character.
- AgeWhenReceived: Age of offender when received, numeric.
- EducationLevel: Education level of offender, numeric.
- Native County: Native county of offender, categorical : 0 = Within Texas, 1= Outside Texas.
- PreviousCrime : Whether the offender committed any crime before, categorical: 0= No, 1= Yes.
- Codefendants: Number of co-defendants, numeric.
- NumberVictim: Number of victims, numeric.
- WhiteVictim, HispanicVictim, BlackVictim, VictimOtherRace. FemaleVictim, MaleVictim: Number of victims with specified demographic features, numeric.
- LastStatement: Last statement of offender, character.

3. Acknowledgement

This dataset is derived from the database by Texas Department of Criminal Justice which can be found in this link: http://www.tdcj.state.tx.us/death_row/dr_executed_offenders.html . It can be seen that the original one has fewer than 10 variables and is embedded with some links to sub-datasets, so I manually inputted more variables based on those links.

There are some complications with this dataset. Firstly, the dataset was manually created so mistakes are inevitable, though I have tried my best to minimize them. Secondly, the recording of offender information is not complete and consistent. For example, sometimes the education level of GED is interpreted as 11 years, at other times as 9 or 10 years. "None" and "NA" are used interchangeably, making it hard to distinguish between 0 and NA in the coded variable. The victim's information is often omitted, so I rely on the description of the crime for the names and pronouns to make a judgement of the number of victims and their gender. Finally, the last statements are sometimes recorded in the first person and sometimes in the third, so the word choice might not be original. That being said, I find this dataset meaningful and worth sharing.

4. Inspiration

What are the demographics of the death row inmates? What are the patterns of their last statements? What is the relationship between the two?

Comprehensive dataset containing 29 verified Texas Health and Human Services Commission locations in United States with complete contact information, ratings, reviews, and location data.

Context

Between the years of 1982 and 2017, the state of Texas has executed approximately 543 inmates. During this time, the TDCJ(Texas Department of Criminal Justice) recorded data regarding each execution.

Content

Each row in the data set includes the executed inmate's age, last statement, date of his/her execution, first and last name, race and county. The data was scraped from the TDCJ 's website: here

Acknowledgements

Thank you to the TDCJ for recording this dataset

Inspiration

I would like to see some analysis on the demographics of the prisoner and their last statement(or lack of one). Is age associated with the length of the last statement? Do the demographics of the prisoner have an association with whether or not the prisoner left a last statement? How many times, on average is the word "sorry" used?

The dataset contains the name and county of every licensed barber in Texas.

The data was collected from the Texas Department of Licensing & Regulation then cleaned for data analysis.

I added a FIPS county code column to the dataset for each county in Texas. FIPS is a five-digit Federal Information Processing Standards code which uniquely identifies counties in the United States.

Tabular data includes: - license_type: Type of barber license. - license_number: License number. - license_expiration_date: Date license expires. - first_name: First name. - last_name: Last name. - county: County of residence. - fips: Federal Information Processing Standards code.

This data set contains the job title, departments, annual salaries of UT Austin employees, along with their standard USPS addresses and latitude and longitude of their home locations drawn from publicly available data correct as of 2018. The names have been made anonymous to protect their privacy. This data set can help inform us of the geographical distribution of people with different incomes across the city of Austin, Texas. It also has data on the employee's race and gender, which can help inform the demographics and social circumstances across different incomes.

[Context of the event]

On May 24, 2022, 18-year-old Salvador Ramos fatally shot nineteen students and two teachers and wounded seventeen other people at Robb Elementary School in Uvalde, Texas, United States. Earlier in the day, he shot his grandmother in the forehead at home, severely wounding her. Outside the school, he fired shots for approximately five minutes before entering unobstructed with an AR-15 style rifle through an unlocked side-entrance door. He then shut himself inside two adjoining classrooms, killed nineteen students and two teachers, and remained there for more than an hour before members of the United States Border Patrol Tactical Unit (BORTAC) fatally shot him. The shooting was the third-deadliest school shooting in the United States, after the Virginia Tech shooting in 2007 and the Sandy Hook Elementary School shooting in 2012,[6] and the deadliest in Texas.

Read more here: https://en.wikipedia.org/wiki/Robb_Elementary_School_shooting

[Content] Dataset(s) contain tweets monitoring specific and related hashtags pertaining to the incident.

[Acknowledgements] Thank you to Anaconda Jupyter, Python, Microsoft Azure, and Tweepy for libraries, services, and programming tools.

[Cover photo source] https://tinyurl.com/2p8dykue

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation.

Block Groups (BGs) are clusters of blocks within the same census tract. Each census tract contains at least one BG, and BGs are uniquely numbered within census tracts. BGs have a valid code range of 0 through 9. BGs have the same first digit of their 4-digit census block number from the same decennial census. For example, tabulation blocks numbered 3001, 3002, 3003,.., 3999 within census tract 1210.02 are also within BG 3 within that census tract. BGs coded 0 are intended to only include water area, no land area, and they are generally in territorial seas, coastal water, and Great Lakes water areas. Block groups generally contain between 600 and 3,000 people. A BG usually covers a contiguous area but never crosses county or census tract boundaries. They may, however, cross the boundaries of other geographic entities like county subdivisions, places, urban areas, voting districts, congressional districts, and American Indian / Alaska Native / Native Hawaiian areas.

The BG boundaries in this release are those that were delineated as part of the Census Bureau's Participant Statistical Areas Program (PSAP) for the 2020 Census.

As recommended by the Health and Human Services Commission (HHSC) to ensure consistency across all HHSC agencies, in 2012 DFPS adopted the HHSC methodology on how to categorize race and ethnicity. As a result, data broken down by race and ethnicity in 2012 and after is not directly comparable to race and ethnicity data in 2011 and before. The population totals may not match previously printed DFPS Data Books. Past population estimates are adjusted based on the U.S. Census data as it becomes available. This is important to keep the data in line with current best practices, but may cause some past counts, such as Abuse/Neglect Victims per 1,000 Texas Children, to be recalculated. Population Data Source - Population Estimates and Projections Program, Texas State Data Center, Office of the State Demographer and the Institute for Demographic and Socioeconomic Research, The University of Texas at San Antonio. Current population estimates and projections data as of December 2020. Visit dfps.texas.gov for information on all DFPS programs.

Comprehensive dataset containing 66 verified Men's Wearhouse locations in Texas, United States with complete contact information, ratings, reviews, and location data.

This resource is a member of a series. The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) System (MTS). The MTS represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. Congressional districts are the 435 areas from which people are elected to the U.S. House of Representatives. After the apportionment of congressional seats among the states based on decennial census population counts, each state is responsible for establishing congressional districts for the purpose of electing representatives. Each congressional district is to be as equal in population to all other congressional districts in a state as practicable. The 119th Congress is seated from January 2025 through December 2026. States that had updates between the previous and current session include Alabama, Georgia, Louisiana, New York, and North Carolina. In Connecticut, Illinois, and New Hampshire, the Redistricting Data Program (RDP) participant did not define the congressional districts to cover the entirety of the state or state equivalent area. In the areas with no congressional districts defined, the code "ZZ" has been assigned, which is treated as a single congressional district for purposes of data presentation. The TIGER/Line shapefiles for the District of Columbia, Puerto Rico, and the Island Areas (American Samoa, Guam, the Commonwealth of the Northern Mariana Islands, and the U.S. Virgin Islands) contain a single record for the non-voting delegate district in these areas. The boundaries of all other congressional districts reflect information provided to the Census Bureau by the states by May 31, 2024.

Comprehensive dataset containing 30 verified Men's tailor businesses in Texas, United States with complete contact information, ratings, reviews, and location data.