Premium B2C Consumer Database - 269+ Million US Records

Supercharge your B2C marketing campaigns with comprehensive consumer database, featuring over 269 million verified US consumer records. Our 20+ year data expertise delivers higher quality and more extensive coverage than competitors.

Core Database Statistics

Consumer Records: Over 269 million

Email Addresses: Over 160 million (verified and deliverable)

Phone Numbers: Over 76 million (mobile and landline)

Mailing Addresses: Over 116,000,000 (NCOA processed)

Geographic Coverage: Complete US (all 50 states)

Compliance Status: CCPA compliant with consent management

Targeting Categories Available

Demographics: Age ranges, education levels, occupation types, household composition, marital status, presence of children, income brackets, and gender (where legally permitted)

Geographic: Nationwide, state-level, MSA (Metropolitan Service Area), zip code radius, city, county, and SCF range targeting options

Property & Dwelling: Home ownership status, estimated home value, years in residence, property type (single-family, condo, apartment), and dwelling characteristics

Financial Indicators: Income levels, investment activity, mortgage information, credit indicators, and wealth markers for premium audience targeting

Lifestyle & Interests: Purchase history, donation patterns, political preferences, health interests, recreational activities, and hobby-based targeting

Behavioral Data: Shopping preferences, brand affinities, online activity patterns, and purchase timing behaviors

Multi-Channel Campaign Applications

Deploy across all major marketing channels:

Email marketing and automation

Social media advertising

Search and display advertising (Google, YouTube)

Direct mail and print campaigns

Telemarketing and SMS campaigns

Programmatic advertising platforms

Data Quality & Sources

Our consumer data aggregates from multiple verified sources:

Public records and government databases

Opt-in subscription services and registrations

Purchase transaction data from retail partners

Survey participation and research studies

Online behavioral data (privacy compliant)

Technical Delivery Options

File Formats: CSV, Excel, JSON, XML formats available

Delivery Methods: Secure FTP, API integration, direct download

Processing: Real-time NCOA, email validation, phone verification

Custom Selections: 1,000+ selectable demographic and behavioral attributes

Minimum Orders: Flexible based on targeting complexity

Unique Value Propositions

Dual Spouse Targeting: Reach both household decision-makers for maximum impact

Cross-Platform Integration: Seamless deployment to major ad platforms

Real-Time Updates: Monthly data refreshes ensure maximum accuracy

Advanced Segmentation: Combine multiple targeting criteria for precision campaigns

Compliance Management: Built-in opt-out and suppression list management

Ideal Customer Profiles

E-commerce retailers seeking customer acquisition

Financial services companies targeting specific demographics

Healthcare organizations with compliant marketing needs

Automotive dealers and service providers

Home improvement and real estate professionals

Insurance companies and agents

Subscription services and SaaS providers

Performance Optimization Features

Lookalike Modeling: Create audiences similar to your best customers

Predictive Scoring: Identify high-value prospects using AI algorithms

Campaign Attribution: Track performance across multiple touchpoints

A/B Testing Support: Split audiences for campaign optimization

Suppression Management: Automatic opt-out and DNC compliance

Pricing & Volume Options

Flexible pricing structures accommodate businesses of all sizes:

Pay-per-record for small campaigns

Volume discounts for large deployments

Subscription models for ongoing campaigns

Custom enterprise pricing for high-volume users

Data Compliance & Privacy

VIA.tools maintains industry-leading compliance standards:

CCPA (California Consumer Privacy Act) compliant

CAN-SPAM Act adherence for email marketing

TCPA compliance for phone and SMS campaigns

Regular privacy audits and data governance reviews

Transparent opt-out and data deletion processes

Getting Started

Our data specialists work with you to:

1. Define your target audience criteria

2. Recommend optimal data selections

3. Provide sample data for testing

4. Configure delivery methods and formats

5. Implement ongoing campaign optimization

Why We Lead the Industry

With over two decades of data industry experience, we combine extensive database coverage with advanced targeting capabilities. Our commitment to data quality, compliance, and customer success has made us the preferred choice for businesses seeking superior B2C marketing performance.

Contact our team to discuss your specific targeting requirements and receive custom pricing for your marketing objectives.

BackgroundOne of the greatest obstacles facing efforts to address quality of care in low and middle income countries is the absence of relevant and reliable data. This article proposes a methodology for creating a single “Quality Index” (QI) representing quality of maternal and neonatal health care based upon data collected as part of the Demographic and Health Survey (DHS) program.MethodsUsing the 2012 Indonesian Demographic and Health Survey dataset, indicators of quality of care were identified based on the recommended guidelines outlined in the WHO Integrated Management of Pregnancy and Childbirth. Two sets of indicators were created; one set only including indicators available in the standard DHS questionnaire and the other including all indicators identified in the Indonesian dataset. For each indicator set composite indices were created using Principal Components Analysis and a modified form of Equal Weighting. These indices were tested for internal coherence and robustness, as well as their comparability with each other. Finally a single QI was chosen to explore the variation in index scores across a number of known equity markers in Indonesia including wealth, urban rural status and geographical region.ResultsThe process of creating quality indexes from standard DHS data was proven to be feasible, and initial results from Indonesia indicate particular disparities in the quality of care received by the poor as well as those living in outlying regions.ConclusionsThe QI represents an important step forward in efforts to understand, measure and improve quality of MNCH care in developing countries.

"Enrollment counts are based on the October 31 Audited Register for the 2017-18 to 2019-20 school years. To account for the delay in the start of the school year, enrollment counts are based on the November 13 Audited Register for 2020-21 and the November 12 Audited Register for 2021-22. * Please note that October 31 (and November 12-13) enrollment is not audited for charter schools or Pre-K Early Education Centers (NYCEECs). Charter schools are required to submit enrollment as of BEDS Day, the first Wednesday in October, to the New York State Department of Education." Enrollment counts in the Demographic Snapshot will likely exceed operational enrollment counts due to the fact that long-term absence (LTA) students are excluded for funding purposes. Data on students with disabilities, English Language Learners, students' povery status, and students' Economic Need Value are as of the June 30 for each school year except in 2021-22. Data on SWDs, ELLs, Poverty, and ENI in the 2021-22 school year are as of March 7, 2022. 3-K and Pre-K enrollment totals include students in both full-day and half-day programs. Four-year-old students enrolled in Family Childcare Centers are categorized as 3K students for the purposes of this report. All schools listed are as of the 2021-22 school year. Schools closed before 2021-22 are not included in the school level tab but are included in the data for citywide, borough, and district. Programs and Pre-K NYC Early Education Centers (NYCEECs) are not included on the school-level tab. Due to missing demographic information in rare cases at the time of the enrollment snapshot, demographic categories do not always add up to citywide totals. Students with disabilities are defined as any child receiving an Individualized Education Program (IEP) as of the end of the school year (or March 7 for 2021-22). NYC DOE "Poverty" counts are based on the number of students with families who have qualified for free or reduced price lunch, or are eligible for Human Resources Administration (HRA) benefits. In previous years, the poverty indicator also included students enrolled in a Universal Meal School (USM), where all students automatically qualified, with the exception of middle schools, D75 schools and Pre-K centers. In 2017-18, all students in NYC schools became eligible for free lunch. In order to better reflect free and reduced price lunch status, the poverty indicator does not include student USM status, and retroactively applies this rule to previous years. "The school’s Economic Need Index is the average of its students’ Economic Need Values. The Economic Need Index (ENI) estimates the percentage of students facing economic hardship. The 2014-15 school year is the first year we provide ENI estimates. The metric is calculated as follows: * The student’s Economic Need Value is 1.0 if: o The student is eligible for public assistance from the NYC Human Resources Administration (HRA); o The student lived in temporary housing in the past four years; or o The student is in high school, has a home language other than English, and entered the NYC DOE for the first time within the last four years. * Otherwise, the student’s Economic Need Value is based on the percentage of families (with school-age children) in the student’s census tract whose income is below the poverty level, as estimated by the American Community Survey 5-Year estimate (2020 ACS estimates were used in calculations for 2021-22 ENI). The student’s Economic Need Value equals this percentage divided by 100.

Due to differences in the timing of when student demographic, address and census data were pulled, ENI values may vary, slightly, from the ENI values reported in the School Quality Reports.

In previous years, student census tract data was based on students’ addresses at the time of ENI calculation. Beginning in 2018-19, census tract data is based on students’ addresses as of the Audited Register date of the given school year.

In previous years, the most recent new entry date was used for students with multiple entry dates into the NYCDOE. Beginning in 2018-19, students’ earliest entry date is used in ENI calculations.

Beginning in 2018-19, students missing ENI data are imputed with the average ENI at their school. " In order to maintain student privacy, schools with % Poverty and ENI values below 5% or above 95% have had their exact values for each category replaced with "Below 5%" and "Above 95%", respectively. Before the start of the 2017-18 school year, the New York State Education Department implemented a new data matching process that refined the methods to identify families eligible for free lunch. This new matching system provides a more efficient and accurate process for matching students across a range of forms that families already complete. This new matching process yielded an increase in the number of students directly certified for free lunch (in other words, matched to another government program) and therefore increased the direct certification rate. As such, the increase in the percent of students in poverty and the Economic Need Index for the 2017-18 school year and later reflects this new matching process, which allows the City to better identify students eligible for free lunch. Approximately 25% of charter schools in NYC do not use NYC DOE School Food to provide meal services. The NYC DOE Office of School Food does not collect documentation on students’ eligibility for Free or Reduced Price Lunch from schools that do not utilize NYC DOE School Food. As a result, the Poverty figures may be understated for approximately 25% of charter schools. New York State Education Department begins administering assessments to be identified as an English Language Learner (ELL) in Kindergarten, but students in Pre-K are still included in the denominator for the ELL calculations. Also, Pre-K NYC Early Education Centers do not use NYC DOE School Food to provide meal services, but are included in the denominator for Poverty calculations.

Welcome to Apiscrapy, your ultimate destination for comprehensive location-based intelligence. As an AI-driven web scraping and automation platform, Apiscrapy excels in converting raw web data into polished, ready-to-use data APIs. With a unique capability to collect Google Address Data, Google Address API, Google Location API, Google Map, and Google Location Data with 100% accuracy, we redefine possibilities in location intelligence.

Key Features:

Unparalleled Data Variety: Apiscrapy offers a diverse range of address-related datasets, including Google Address Data and Google Location Data. Whether you seek B2B address data or detailed insights for various industries, we cover it all.

Integration with Google Address API: Seamlessly integrate our datasets with the powerful Google Address API. This collaboration ensures not just accessibility but a robust combination that amplifies the precision of your location-based insights.

Business Location Precision: Experience a new level of precision in business decision-making with our address data. Apiscrapy delivers accurate and up-to-date business locations, enhancing your strategic planning and expansion efforts.

Tailored B2B Marketing: Customize your B2B marketing strategies with precision using our detailed B2B address data. Target specific geographic areas, refine your approach, and maximize the impact of your marketing efforts.

Use Cases:

Location-Based Services: Companies use Google Address Data to provide location-based services such as navigation, local search, and location-aware advertisements.

Logistics and Transportation: Logistics companies utilize Google Address Data for route optimization, fleet management, and delivery tracking.

E-commerce: Online retailers integrate address autocomplete features powered by Google Address Data to simplify the checkout process and ensure accurate delivery addresses.

Real Estate: Real estate agents and property websites leverage Google Address Data to provide accurate property listings, neighborhood information, and proximity to amenities.

Urban Planning and Development: City planners and developers utilize Google Address Data to analyze population density, traffic patterns, and infrastructure needs for urban planning and development projects.

Market Analysis: Businesses use Google Address Data for market analysis, including identifying target demographics, analyzing competitor locations, and selecting optimal locations for new stores or offices.

Geographic Information Systems (GIS): GIS professionals use Google Address Data as a foundational layer for mapping and spatial analysis in fields such as environmental science, public health, and natural resource management.

Government Services: Government agencies utilize Google Address Data for census enumeration, voter registration, tax assessment, and planning public infrastructure projects.

Tourism and Hospitality: Travel agencies, hotels, and tourism websites incorporate Google Address Data to provide location-based recommendations, itinerary planning, and booking services for travelers.

Discover the difference with Apiscrapy – where accuracy meets diversity in address-related datasets, including Google Address Data, Google Address API, Google Location API, and more. Redefine your approach to location intelligence and make data-driven decisions with confidence. Revolutionize your business strategies today!

The table RI- Demographic Data is part of the dataset Demographic Data, available at https://columbia.redivis.com/datasets/fh74-90v3ge9m2. It contains 734919 rows across 699 variables.

These Demographic Data are U.S. Census American Community Survey Data, from the 2014 5-year set. Data Driven Detroit calculated densities (Per Sq Mile) by dividing the population by the ALAND10 field, which is the census land area field, in square meters.

Annual Resident Population Estimates by Age Group, Sex, Race, and Hispanic Origin: April 1, 2010 to July 1, 2018 // Source: U.S. Census Bureau, Population Division // The contents of this file are released on a rolling basis from December through June. // Note: 'In combination' means in combination with one or more other races. The sum of the five race-in-combination groups adds to more than the total population because individuals may report more than one race. Hispanic origin is considered an ethnicity, not a race. Hispanics may be of any race. Responses of 'Some Other Race' from the 2010 Census are modified. This results in differences between the population for specific race categories shown for the 2010 Census population in this file versus those in the original 2010 Census data. For more information, see https://www2.census.gov/programs-surveys/popest/technical-documentation/methodology/modified-race-summary-file-method/mrsf2010.pdf. // The estimates are based on the 2010 Census and reflect changes to the April 1, 2010 population due to the Count Question Resolution program and geographic program revisions. // For detailed information about the methods used to create the population estimates, see https://www.census.gov/programs-surveys/popest/technical-documentation/methodology.html. // Each year, the Census Bureau's Population Estimates Program (PEP) utilizes current data on births, deaths, and migration to calculate population change since the most recent decennial census, and produces a time series of estimates of population. The annual time series of estimates begins with the most recent decennial census data and extends to the vintage year. The vintage year (e.g., V2017) refers to the final year of the time series. The reference date for all estimates is July 1, unless otherwise specified. With each new issue of estimates, the Census Bureau revises estimates for years back to the last census. As each vintage of estimates includes all years since the most recent decennial census, the latest vintage of data available supersedes all previously produced estimates for those dates. The Population Estimates Program provides additional information including historical and intercensal estimates, evaluation estimates, demographic analysis, and research papers on its website: https://www.census.gov/programs-surveys/popest.html.

This layer shows the age statistics in Tucson by neighborhood, aggregated from block level data, between 2010-2019. For questions, contact GIS_IT@tucsonaz.gov. The data shown is from Esri's 2019 Updated Demographic estimates.Esri's U.S. Updated Demographic (2019/2024) Data - Population, age, income, sex, race, home value, and marital status are among the variables included in the database. Each year, Esri's Data Development team employs its proven methodologies to update more than 2,000 demographic variables for a variety of U.S. geographies.Additional Esri Resources:Esri DemographicsU.S. 2019/2024 Esri Updated DemographicsEssential demographic vocabularyPermitted use of this data is covered in the DATA section of the Esri Master Agreement (E204CW) and these supplemental terms.

Abstract

The 2002 Vietnam Demographic and Health Survey (VNDHS 2002) is a nationally representative sample survey of 5,665 ever-married women age 15-49 selected from 205 sample points (clusters) throughout Vietnam. It provides information on levels of fertility, family planning knowledge and use, infant and child mortality, and indicators of maternal and child health. The survey included a Community/ Health Facility Questionnaire that was implemented in each of the sample clusters.

The survey was designed to measure change in reproductive health indicators over the five years since the VNDHS 1997, especially in the 18 provinces that were targeted in the Population and Family Health Project of the Committee for Population, Family and Children. Consequently, all provinces were separated into “project” and “nonproject” groups to permit separate estimates for each. Data collection for the survey took place from 1 October to 21 December 2002.

The Vietnam Demographic and Health Survey 2002 (VNDHS 2002) was the third DHS in Vietnam, with prior surveys implemented in 1988 and 1997. The VNDHS 2002 was carried out in the framework of the activities of the Population and Family Health Project of the Committee for Population, Family and Children (previously the National Committee for Population and Family Planning).

The main objectives of the VNDHS 2002 were to collect up-to-date information on family planning, childhood mortality, and health issues such as breastfeeding practices, pregnancy care, vaccination of children, treatment of common childhood illnesses, and HIV/AIDS, as well as utilization of health and family planning services. The primary objectives of the survey were to estimate changes in family planning use in comparison with the results of the VNDHS 1997, especially on issues in the scope of the project of the Committee for Population, Family and Children.

VNDHS 2002 data confirm the pattern of rapidly declining fertility that was observed in the VNDHS 1997. It also shows a sharp decline in child mortality, as well as a modest increase in contraceptive use. Differences between project and non-project provinces are generally small.

Geographic coverage

The 2002 Vietnam Demographic and Health Survey (VNDHS 2002) is a nationally representative sample survey. The VNDHS 1997 was designed to provide separate estimates for the whole country, urban and rural areas, for 18 project provinces and the remaining nonproject provinces as well. Project provinces refer to 18 focus provinces targeted for the strengthening of their primary health care systems by the Government's Population and Family Health Project to be implemented over a period of seven years, from 1996 to 2002 (At the outset of this project there were 15 focus provinces, which became 18 by the creation of 3 new provinces from the initial set of 15). These provinces were selected according to criteria based on relatively low health and family planning status, no substantial family planning donor presence, and regional spread. These criteria resulted in the selection of the country's poorer provinces. Nine of these provinces have significant proportions of ethnic minorities among their population.

Analysis unit

• Household
• Women age 15-49

Universe

The population covered by the 2002 VNDHS is defined as the universe of all women age 15-49 in Vietnam.

Kind of data

Sample survey data

Sampling procedure

The sample for the VNDHS 2002 was based on that used in the VNDHS 1997, which in turn was a subsample of the 1996 Multi-Round Demographic Survey (MRS), a semi-annual survey of about 243,000 households undertaken regularly by GSO. The MRS sample consisted of 1,590 sample areas known as enumeration areas (EAs) spread throughout the 53 provinces/cities of Vietnam, with 30 EAs in each province. On average, an EA comprises about 150 households. For the VNDHS 1997, a subsample of 205 EAs was selected, with 26 households in each urban EA and 39 households for each rural EA. A total of 7,150 households was selected for the survey. The VNDHS 1997 was designed to provide separate estimates for the whole country, urban and rural areas, for 18 project provinces and the remaining nonproject provinces as well. Because the main objective of the VNDHS 2002 was to measure change in reproductive health indicators over the five years since the VNDHS 1997, the sample design for the VNDHS 2002 was as similar as possible to that of the VNDHS 1997.

Although it would have been ideal to have returned to the same households or at least the same sample points as were selected for the VNDHS 1997, several factors made this undesirable. Revisiting the same households would have held the sample artificially rigid over time and would not allow for newly formed households. This would have conflicted with the other major survey objective, which was to provide up-to-date, representative data for the whole of Vietnam. Revisiting the same sample points that were covered in 1997 was complicated by the fact that the country had conducted a population census in 1999, which allowed for a more representative sample frame.

In order to balance the two main objectives of measuring change and providing representative data, it was decided to select enumeration areas from the 1999 Population Census, but to cover the same communes that were sampled in the VNDHS 1997 and attempt to obtain a sample point as close as possible to that selected in 1997. Consequently, the VNDHS 2002 sample also consisted of 205 sample points and reflects the oversampling in the 20 provinces that fall in the World Bank-supported Population and Family Health Project. The sample was designed to produce about 7,000 completed household interviews and 5,600 completed interviews with ever-married women age 15-49.

Mode of data collection

Face-to-face

Research instrument

As in the VNDHS 1997, three types of questionnaires were used in the 2002 survey: the Household Questionnaire, the Individual Woman's Questionnaire, and the Community/Health Facility Questionnaire. The first two questionnaires were based on the DHS Model A Questionnaire, with additions and modifications made during an ORC Macro staff visit in July 2002. The questionnaires were pretested in two clusters in Hanoi (one in a rural area and another in an urban area). After the pretest and consultation with ORC Macro, the drafts were revised for use in the main survey.

a) The Household Questionnaire was used to enumerate all usual members and visitors in selected households and to collect information on age, sex, education, marital status, and relationship to the head of household. The main purpose of the Household Questionnaire was to identify persons who were eligible for individual interview (i.e. ever-married women age 15-49). In addition, the Household Questionnaire collected information on characteristics of the household such as water source, type of toilet facilities, material used for the floor and roof, and ownership of various durable goods.

b) The Individual Questionnaire was used to collect information on ever-married women aged 15-49 in surveyed households. These women were interviewed on the following topics:
- Respondent's background characteristics (education, residential history, etc.); - Reproductive history; - Contraceptive knowledge and use;
- Antenatal and delivery care; - Infant feeding practices; - Child immunization; - Fertility preferences and attitudes about family planning; - Husband's background characteristics; - Women's work information; and - Knowledge of AIDS.

c) The Community/Health Facility Questionnaire was used to collect information on all communes in which the interviewed women lived and on services offered at the nearest health stations. The Community/Health Facility Questionnaire consisted of four sections. The first two sections collected information from community informants on some characteristics such as the major economic activities of residents, distance from people's residence to civic services and the location of the nearest sources of health care. The last two sections involved visiting the nearest commune health centers and intercommune health centers, if these centers were located within 30 kilometers from the surveyed cluster. For each visited health center, information was collected on the type of health services offered and the number of days services were offered per week; the number of assigned staff and their training; medical equipment and medicines available at the time of the visit.

Cleaning operations

The first stage of data editing was implemented by the field editors soon after each interview. Field editors and team leaders checked the completeness and consistency of all items in the questionnaires. The completed questionnaires were sent to the GSO headquarters in Hanoi by post for data processing. The editing staff of the GSO first checked the questionnaires for completeness. The data were then entered into microcomputers and edited using a software program specially developed for the DHS program, the Census and Survey Processing System, or CSPro. Data were verified on a 100 percent basis, i.e., the data were entered separately twice and the two results were compared and corrected. The data processing and editing staff of the GSO were trained and supervised for two weeks by a data processing specialist from ORC Macro. Office editing and processing activities were initiated immediately after the beginning of the fieldwork and were completed in late December 2002.

Response rate

The results of the household and individual

This product will include topics such as age, sex, race, Hispanic or Latino origin, household type, family type, relationship to householder, group quarters population, housing occupancy and housing tenure. Some tables will be iterated by race and ethnicity.

The table HI- Demographic Data is part of the dataset Demographic Data, available at https://columbia.redivis.com/datasets/fh74-90v3ge9m2. It contains 767560 rows across 699 variables.

The table MN- Demographic Data is part of the dataset Demographic Data, available at https://columbia.redivis.com/datasets/fh74-90v3ge9m2. It contains 3514445 rows across 699 variables.

Medical Service Study Areas (MSSAs)As defined by California's Office of Statewide Health Planning and Development (OSHPD) in 2013, "MSSAs are sub-city and sub-county geographical units used to organize and display population, demographic and physician data" (Source). Each census tract in CA is assigned to a given MSSA. The most recent MSSA dataset (2014) was used. Spatial data are available via OSHPD at the California Open Data Portal. This information may be useful in studying health equity.Definitions:Race/Ethnicity: Race/ethnicity is categorized as: All races/ethnicities, Non-Hispanic (NH) White, NH Black, Asian/Pacific Islander, or Hispanic. "All races" includes all of the above, as well as other and unknown race/ethnicity and American Indian/Alaska Native. The latter two groups are not reported separately due to small numbers for many cancer sites.Racial/Ethnic Composition: Distribution of residents' race/ethnicity (e.g., % Hispanic, % non-Hispanic White, % non-Hispanic Black, % non-Hispanic Asian/Pacific Islander). (Source: US Census, 2010.)Rural: Percent of residents who reside in blocks that are designated as rural. (Source: US Census, 2010.)Foreign Born: Percent of residents who were born outside the United States. (Source: American Community Survey, 2008-2012.)Socioeconomic Status (Neighborhood Level): A composite measure of seven indicator variables created by principal component analysis; indicators include: education, blue-collar job, unemployment, household income, poverty, rent, and house value. Quintiles based on state distribution, with quintile 1 being the lowest SES and 5 being the highest. (Source: American Community Survey, 2008-2012.)Spatial extent: CaliforniaSpatial Unit: MSSACreated: n/aUpdated: n/aSource: California Health MapsContact Email: gbacr@ucsf.eduSource Link: https://www.californiahealthmaps.org/?areatype=mssa&address=&sex=Both&site=AllSite&race=&year=05yr&overlays=none&choropleth=Obesity

Socio-Economic Index of 7 variables overlayed to compare with the physical blight index- Education, Median Household Income, Renter Occupied, Single Parent Households, Population Density, Poverty Rate, and Unemployment Rate. This map was used to help question what socio-economic factors correlate with the observance of blighted areas in order to better create strategic decisions on how to best prevent blight.By using this dataset you acknowledge the following:Kansas Open Records Act StatementThe Kansas Open Records Act provides in K.S.A. 45-230 that "no person shall knowingly sell, give or receive, for the purpose of selling or offering for sale, any property or service to persons listed therein, any list of names and addresses contained in, or derived from public records..." Violation of this law may subject the violator to a civil penalty of $500.00 for each violation. Violators will be reported for prosecution.By accessing this site, the user makes the following certification pursuant to K.S.A. 45-220(c)(2): "The requester does not intend to, and will not: (A) Use any list of names or addresses contained in or derived from the records or information for the purpose of selling or offering for sale any property or service to any person listed or to any person who resides at any address listed; or (B) sell, give or otherwise make available to any person any list of names or addresses contained in or derived from the records or information for the purpose of allowing that person to sell or offer for sale any property or service to any person listed or to any person who resides at any address listed."

The table FL- Demographic Data is part of the dataset Demographic Data, available at https://columbia.redivis.com/datasets/fh74-90v3ge9m2. It contains 14609762 rows across 699 variables.

Data fields are listed below. Sourced from ESRI's Enrich tool, current as of 2024. NAMEGEOTYPETotalPopMedianAgeMalePopMalePopPctMale_0_4Male_0_4PctMale_5_9Male_5_9PctMale_10_14Male_10_14PctMale_15_19Male_15_19PctMale_20_24Male_20_24PctMale_25_29Male_25_29PctMale_30_34Male_30_34PctMale_35_39Male_35_39PctMale_40_44Male_40_44PctMale_45_49Male_45_49PctMale_50_54Male_50_54PctMale_55_59Male_55_59PctMale_60_Male_60_PctFemalePopFemalePopPctFemale_0_4Female_0_4PctFemale_5_9Female_5_9PctFemale_10_14Female_10_14PctFemale_15_19Female_15_19PctFemale_20_24Female_20_24PctFemale_25_29Female_25_29PctFemale_30_34Female_30_34PctFemale_35_39Female_35_39PctFemale_40_44Female_40_44PctFemale_45_49Female_45_49PctFemale_50_54Female_50_54PctFemale_55_59Female_55_59PctFemale_60_Female_60_PctTotalHHOwnerOccupyOwnerOccupyPctRenterOccupyRenterOccupyPctTotalHUVacantHUVacantHUPctWhitePopWhitePopPctBlackPopBlackPopPctAsianPopAsianPopPctIndianPopIndianPopPctPacificPopPacificPopPctOtherRaceOtherRactPctHispanicPopHispanicPopPctEdu_HS_NoDiplomaEdu_HS_DiplomaEdu_GEDEdu_College_NoDegreeEdu_AssociateDegreeEdu_BachelorDegreeEdu_GraduateDegreeAverageHHSizeHHBelowPoveryMedianHHIncomeMedianHValueMedianAge_1HU_50_HU_50_PctHU_20_49HU_20_49PctHU_10_19HU_10_19PctHU_5_9HU_5_9PctHU_3_4HU_3_4PctHU_2HU_2PctHU_1_AttachedHU_1_AttachedPctHU_1_DetachedHU_1_DetachedPctWorkHomeOtherMeansWalkedBicyclePublicTransDroveAloneCarpooledShape_LengthShape_Area

demographic_statistics_cities

This dataset contains information about the demographics of all US cities and census-designated places with a population greater or equal to 65,000. This data comes from the US Census Bureau's 2015 American Community Survey. This product uses the Census Bureau Data API but is not endorsed or certified by the Census Bureau.

The 2006-07 Pakistan Demographic and Health Survey (PDHS) was undertaken to address the monitoring and evaluation needs of maternal and child health and family planning programmes. The survey was designed with the broad objective to provide policymakers, primarily in the Ministries of Population Welfare and Health, with information to improve programmatic interventions based on empirical evidence. The aim is to provide reliable estimates of the maternal mortality ratio (MMR) at the national level and a variety of other health and population indicators at national, urban-rural, and provincial levels. The 2006-07 Pakistan Demographic and Health Survey (PDHS) is the fifth in a series of demographic surveys conducted by the National Institute of Population Studies (NIPS) since 1990. However, the PDHS 2006-07 is the second survey conducted as part of the worldwide Demographic andHealth Surveys programme. The survey was conducted under the aegis of the Ministry of Population Welfare and implemented by the National Institute of Population Studies. Other collaborating institutions include the Federal Bureau of Statistics, the Aga Khan University, and the National Committee for Maternal and Neonatal Health. Technical support was provided by Macro International Inc. and financial support was provided by the United States Agency for International Development (USAID). The United Nations Population Fund (UNFPA) and United Nations Children's Fund (UNICEF) provided logistical support for monitoring the fieldwork for the PDHS. The 2006-07 PDHS supplements and complements the information collected through the censuses and demographic surveys conducted by the Federal Bureau of Statistics. It updates the available information on population and health issues, and provides guidance in planning, implementing, monitoring and evaluating health and population programmes in Pakistan. Some of the findings of the PDHS may seem at variance with data compiled by other sources. This may be due to differences in methodology, reference period, wording of questions and subsequent interpretation. This fact may be kept in mind while analyzing and comparing PDHS data with other sources. The results of the survey assist in the monitoring of the progress made towards meeting the Millennium Development Goals (MDGs). The 2006-07 PDHS includes topics related to fertility levels and determinants, family planning, fertility preferences, infant, child and maternal mortality and their causes, maternal and child health, immunization and nutritional status of mothers and children, knowledge of HIV/AIDS, and malaria. The 2006-07 PDHS also includes direct estimation of maternal mortality and its causes at the national level for the first time in Pakistan. The survey provides all other estimates for national, provincial and urban-rural domains. This being the fifth survey of its kind, there is considerable trend information on reproductive health, fertility and family planning over the past one and a half decades. More specifically, PDHS had the following objectives: Collect quality data on fertility levels and preference, family planning knowledge and use, childhood—and especially neonatal—mortality levels and awareness regarding HIV/ AIDS and other indicators relevant to the Millennium Development Goals and the Poverty Reduction Strategy Paper; Produce a reliable national estimate of the MMR for Pakistan, as well as information on the direct and indirect causes of maternal deaths using verbal autopsy instruments; Investigate factors that impact on maternal and neonatal morbidity and mortality (i.e., antenatal and delivery care, treatment of pregnancy complications, and postnatal care); Improve the capacity of relevant organizations to implement surveys and analyze and disseminate survey findings.

Abstract

In the early-mid 1990s, Albania entered a new phase of major changes, moving from a totalitarian to a democratic system and shifting gradually to the free market economy. This process led, naturally, to changes in various demographic and health characteristics of the Albanian society.

The 2008-09 Albania Demographic and Health Survey (ADHS) is a nationally representative study aimed at collecting and providing information on population, demographic, and health characteristics of the country. Population-based studies of this magnitude are a major undertaking that provide information on important indicators which measure the progress of a country.

The ADHS results help provide the necessary information to assess, measure, and evaluate the existing programs in the country. They also provide crucial information to policy-makers when drafting new policies and strategies related to the health sector and health services in Albania.

The information collected in the 2008-09 Albania Demographic and Health Survey will be used not only by local decision-makers and programme managers, but also by partners and foreign donors involved in various development areas in Albania, as well as by academic institutions to do further analysis with the collected data.

The 2008-09 Albania Demographic and Health Survey (ADHS) was implemented by the Institute of Statistics (INSTAT) and the Institute of Public Health (IPH), of the Ministry of Health. ICF Macro provided technical assistance to the ADHS through funding from the United Nations Children’s Fund (UNICEF) and the United State Agency for International Development (USAID)-funded MEASURE DHS programme. Local costs of the survey were supported by USAID, the Swiss Cooperation Office in Albania (SCO-A), UNICEF, the United Nations Population Fund (UNFPA), and the World Health Organization (WHO).

Data collection was conducted from 28 October, 2008 to 26 April, 2009 using a nationally representative sample of almost 9,000 households. All women age 15-49 in these households and all men age 15-49 in half of the households were eligible to be individually interviewed. In addition to the data collected through interviews with these women and men, capillary blood samples were collected from all children age 6-59 months and all eligible women and men age 15-49 for anaemia testing. All children under five years of age and eligible women and men age 15-49 were weighed and measured to assess their nutritional status. Finally, blood pressure (BP) was measured for eligible women and men in the households selected for the men’s interview to estimate the prevalence of hypertension in the adult population.

The 2008-09 ADHS is designed to provide data to monitor the population and health situation in Albania. Specifically, the 2008-09 ADHS collected information on fertility levels, marriage, sexual activity, fertility preferences, knowledge and use of family planning methods, breastfeeding practices, nutritional status of women and young children, childhood mortality, maternal and child health, and awareness and behaviour regarding AIDS and other sexually transmitted infections. Additional features of the 2008-09 ADHS include the collection of information on migration (out-migration, returning migrants and internal migration), haemoglobin testing to detect the presence of anaemia, blood pressure (BP) measurements among the adult population, and questions related to accessibility and affordability of health services. The information collected in the 2008-09 ADHS provides updated estimates of an array of demographic and health indicators that will assist in the development of appropriate policies and programmes to address the most important health issues in Albania.

Geographic coverage

National

Analysis unit

• Household
• Children under five years
• Women age 15-49
• Men age 15-49

Universe

All women age 15-49 in the total sample of households, and all men age 15-49 in the subsample of half of the households, who were either usual residents of the households or visitors present in the household on the night before the survey were eligible to be interviewed.

Kind of data

Sample survey data

Sampling procedure

The 2008-09 Albania Demographic and Health Survey is based on a representative probability sample of almost 9,000 households. This sample was selected in such a manner as to allow separate urban and rural, as well as regional-level estimates for key population and health indicators, e.g., fertility, contraceptive prevalence, and infant mortality for children under five.

The 2008-09 ADHS utilized a two-stage sample design. The first stage involved selection of a sample of primary sampling units (PSUs) from the PSUs used for the 2008 Living Standards Measurement Study (LSMS). In total, 450 PSUs were selected for the ADHS sample, including 245 urban PSUs and 205 rural PSUs, covering 4 geographic domains-mountains, central, coastal, and urban Tirana. A listing of each of the selected PSUs was carried out in preparation for the LSMS. The ADHS survey selected 20 households from the updated household listing in each PSU, excluding those households selected for the LSMS. In two PSUs, numbers 27 (13 households) and 172 (17 households), there were less than 20 households in the re-listed PSU-all households were selected in those cases. In a further 6 PSUs there were less than 20 households after the LSMS households were excluded. In these PSUs some of the households from the LSMS sample were included to bring the number of households selected up to 20. After selection of the households, the sample selection forms were printed and the list of selected households was adapted for use in a Personal Digital Assistant (PDA).

All women age 15-49 in the total sample of households, and all men age 15-49 in the subsample of half of the households, who were either usual residents of the households or visitors present in the household on the night before the survey were eligible to be interviewed.

Note: See detailed description of sample implementation in APPENDIX A of the survey final report.

Mode of data collection

Face-to-face [f2f]

Research instrument

Three questionnaires were used for the 2008-09 ADHS: the Household Questionnaire, the Women’s Questionnaire and the Men’s Questionnaire. The content of these questionnaires was based on model questionnaires developed by the MEASURE DHS programme.

Consultations with partners were held in Tirana to obtain input from various national and international experts on a broad array of issues. Based on these consultations, the DHS model questionnaires were modified to reflect issues relevant in Albania concerning population, women and children’s health, family planning, and other health issues. After approval of the final content by the Steering and the Technical Committees, the questionnaires were translated from English into Albanian.

The Household Questionnaire was used to list all the usual members and visitors in the selected households and to identify women and men who were eligible for the individual interview. Basic information was collected on the characteristics of each person listed, including their age, sex, education, and relationship to the head of the household. In addition, a separate listing and basic information on former household members who had emigrated abroad was collected. The Household Questionnaire also collected information on characteristics of the household’s dwelling unit, such as the source of water, type of toilet facilities, materials used for the floor and roof of the house, and ownership of various durable goods. A module was included to obtain information about methods used in the household for disciplining children; the information was gathered concerning one selected child in the age range 2-14 years. Finally, height and weight measurements, and the results of haemoglobin measurements for consenting women and men age 15-49 years and children age 6 to 59 months were recorded in the Household Questionnaire. The haemoglobin testing procedures are described in detail in the next section.

The Women’s Questionnaire was used to collect information from all women age 15-49. These women were asked questions on the following topics: - Background characteristics (education, residential history, media exposure, etc.); - Reproductive history; - Knowledge and use of family planning methods; - Fertility preferences; - Antenatal and delivery care; - Breastfeeding and infant feeding practices; - Vaccinations and childhood illnesses; - Marriage and sexual activity; - Woman’s work and husband’s background characteristics; - Infant and child feeding practices; - Childhood mortality; and - Awareness and behaviour about AIDS and other sexually transmitted infections (STIs).

The Women’s Questionnaire had a number of important additions not present in the DHS model questionnaire. First, the BP readings were taken for all women age 15-49 that lived in the households selected for the men’s survey. Secondly, a vaccination module was added for each child under the age of five years to be completed at the local health clinic or centre. As indicated by the 2005 MICS survey findings and according to child health experts, immunization information in Albania is more frequently kept at the health clinics or centres than on an immunization card or child health book in the mother’s possession. The purpose of this module was, therefore, to collect information on immunizations from the local health clinics or centres in addition to that collected during the woman’s interview. The vaccination module provides better quality immunization indicators because