Since its beginning in 1965 as a part of the War on Poverty, Head Start's goal has been to boost the school readiness of low income children. Based on a "whole child" model, the program provides comprehensive services that include preschool education; medical, dental, and mental health care; nutrition services; and efforts to help parents foster their child's development. Head Start services are designed to be responsive to each child's and family's ethnic, cultural, and linguistic heritage. In the 1998 reauthorization of Head Start, Congress mandated that the United States Department of Health and Human Services determine, on a national level, the impact of Head Start on the children it serves. This legislative mandate required that the impact study address two main research questions: What difference does Head Start make to key outcomes of development and learning (and in particular, the multiple domains of school readiness) for low-income children? What difference does Head Start make to parental practices that contribute to children's school readiness? Under what circumstances does Head Start achieve the greatest impact? What works for which children? What Head Start services are most related to impact? The Head Start Impact Study addresses these questions by reporting on the impacts of Head Start on children and families during the children's preschool, kindergarten, and first grade years. It was conducted with a nationally representative sample of nearly 5,000 three- and four-year old preschool children across 84 nationally representative grantee/delegate agencies in communities where there are more eligible children and families than can be served by the program. The children participating were randomly assigned to either a treatment group (which had access to Head Start services) or a comparison group (which did not have access to Head Start services, but could receive other community resources). Data collection began in the fall of 2002 and ended in spring 2006, following children through the spring of their first grade year. Baseline data were collected through parent interviews and child assessments in fall 2002. The annual spring data collection included child assessments, parent interviews, teacher surveys, and teacher-child ratings. In addition, during the preschool years only, data collection included classroom and family day care observations, center director interviews, care provider interviews, and care provider-child ratings. The study examined differences in outcomes in several domains related to school readiness: children's cognitive, social-emotional, health, and parenting outcomes (e.g., reading to the child, use of spanking and time out, exposing children to cultural enrichment activities, safety practices, parent-child relationships). It also examined whether impacts differed based on characteristics of the children and their families, including the child's pre-academic skills at the beginning of the study; the child's primary language; whether the child has special needs; the mother's race/ethnicity; the primary caregiver's level of depressive symptoms; household risk; and urban or rural location. The Head Start Impact Study differs from other evaluations of early childhood programs in that it: represents children from the majority of Head Start programs, represents a scaled-up federal program, represents the full range of quality within the national program, employs a randomized control design, the strongest design for testing impacts, examines all domains of children's school readiness, as well as parenting outcomes, follows children through their early years of elementary school, and compares children who have access to Head Start to a control group that includes many children in center-based and other forms of early childhood education programs. The Third Grade Follow-up to the Head Start Impact Study builds upon the existing randomized control design in the Head Start Impact Study (HSIS) in order to determine the longer-term impact of the Head Start program on the well-being of children and families through the end of third grade. The data collection for the Third Grade Follow-up to the Head Start Impact Study was conducted during the spring of the children's third grade year (2007 and 2008). In addition to the child assessments, parent interviews, teacher surveys, and teacher-ch

Analysis of ‘Head Start Program Provider’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/804f237e-4616-4563-8b02-7df3f997460b on 26 January 2022.

--- Dataset description provided by original source is as follows ---

The Detroit early childhood interactive map contains data relating to early childhood and education. It is meant to help stakeholders better understand the early childhood landscape better.

--- Original source retains full ownership of the source dataset ---

Analysis of ‘Head Start Enrollment’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/d0e16c1f-0ad4-4c4a-a45d-5b3d45d66c8e on 26 January 2022.

--- Dataset description provided by original source is as follows ---

Increase the percentage of eligible children enrolled in Head Start from 61.3% in 2013 to 64% by 2018.

--- Original source retains full ownership of the source dataset ---

Analysis of ‘Head Start Locations 2016’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/60e9452b-bc0b-4cd4-935e-fea2442c4ace on 26 January 2022.

--- Dataset description provided by original source is as follows ---

The Office of Head Start within the U.S. Department of Health and Human Services, provided location information for all Early Head Start and Head Start centers in the State of Michigan. Data was obtained for the Education section of Little Caesar's Arena District Needs Assessment.

--- Original source retains full ownership of the source dataset ---

Analysis of ‘IFF Early Childhood Gap Analysis 2017’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/c5eac5b5-6a92-47e8-8bb6-bf0826aa7eac on 26 January 2022.

--- Dataset description provided by original source is as follows ---

IFF analysis on early childhood educational settings and availabilty, paid for by the Kellogg Foundation, 2017. Report: http://kresge.org/sites/default/files/library/iff-detroit-report-final.pdf . IFF studied the supply of childcare, including Great Start, Head Start, and Early Head Start program funding and availability (slots). They compared supply to demand by estimating the number of 0-5 year olds and those who would qualify for those programs. They used Detroit master planning areas as their unit of analysis and ranked each area in terms of need.

--- Original source retains full ownership of the source dataset ---

Analysis of ‘Child Care Locations (Licensed or Registered)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/39b427aa-d941-410c-bea3-32e661590cd4 on 26 January 2022.

--- Dataset description provided by original source is as follows ---

Locations of all Licensed and / or Registered childcare providers in Southeast Michigan from June 2014. Provided by the United Way of Southeast Michigan's Regional Resource Center, a partner of the Early Childhood Investment Corporation. Contains information on whether a provider is a Head Start or Great Start Readiness provider, as well as the quality rating from Great Start to Quality if a provider participates. This data was exported to Data Driven Detroit by the United Way for SE Michigan. Data Driven Detroit added fields indicating whether a location was physically in Detroit, and also a single field indicating whether a provider paricipated in either Head Start, Great Start or Early Head Start.

--- Original source retains full ownership of the source dataset ---

IFF analysis on early childhood educational settings and availabilty, paid for by the Kellogg Foundation, 2017. Report: http://kresge.org/sites/default/files/library/iff-detroit-report-final.pdf . IFF studied the supply of childcare, including Great Start, Head Start, and Early Head Start program funding and availability (slots). They compared supply to demand by estimating the number of 0-5 year olds and those who would qualify for those programs. They used Detroit master planning areas as their unit of analysis and ranked each area in terms of need.

Analysis of ‘State Head Start Spaces by Town SFY2019’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/3a3c2edc-6967-4b8d-899e-3ceb01530f43 on 26 January 2022.

--- Dataset description provided by original source is as follows ---

State Head Start funded spaces by Town SFY2019. 19 Head Start Grantee/Delegates; 320 Spaces; 44 Program Sites

--- Original source retains full ownership of the source dataset ---

Abstract (en): This administrative dataset provides descriptive information about the families and children served through the federal Child Care and Development Fund (CCDF). CCDF dollars are provided to states, territories, and tribes to provide assistance to low-income families receiving or in transition from temporary public assistance, to obtain quality child care so they can work, or depending on their state's policy, to attend training or receive education. The Personal Responsibility and Work Opportunity Act of 1996 requires states and territories to collect information on all family units receiving assistance through the CCDF and to submit monthly case-level data to the Office of Child Care. States are permitted to report case-level data for the entire population, or a sample of the population, under approved sampling guidelines. The Summary Records file contains monthly state-level summary information including the number of families served. The Family Records file contains family-level data including single parent status of the head of household, monthly co-payment amount, date on which child care assistance began, reasons for care (e.g., employment, training/education, protective services, etc.), income used to determine eligibility, source of income, and the family size on which eligibility is based. The Child Records file contains child-level data including ethnicity, race, gender, and date of birth. The Setting Records file contains information about the type of child care setting, the total amount paid to the provider, and the total number of hours of care received by the child. The Pooling Factor file provides state-level data on the percentage of child care funds that is provided through the CCDF, the federal Head Start region the grantee (state) is in and is monitored by, and the state FIPS code for the grantee. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Performed consistency checks.; Standardized missing values.; Checked for undocumented or out-of-range codes.. Datasets:DS0: Study-Level FilesDS1: Summary RecordsDS2: Family RecordsDS3: Child RecordsDS4: Setting RecordsDS5: Pooling FactorDS6: Adjusted Child Records File (Online Analysis Only)DS7: Unadjusted Child Records File (Online Analysis Only)DS8: Adjusted Family Records File (Online Analysis Only)DS9: Unadjusted Family Records File (Online Analysis Only) Children and families receiving assistance through the Child Care and Development Fund (CCDF), through their state, territory, or tribe. This sample dataset consists of monthly data provided by states that reported sample data and states that reported full population data, as well as any territory data received. Sampling of the data from states reporting full population data was done in accordance with Technical Bulletin #5, Appendix II: Annual Sampling Plan, Example A The month with the lowest caseload was selected for determining the sampling rate so that at least 200 samples were selected for each month. Additional information on the development of this sample dataset is provided in the accompanying technical documentation.

Quadrant provides Insightful, accurate, and reliable mobile location data.

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We conduct stringent evaluations on data providers to ensure authenticity and quality. Our proprietary algorithms detect, and cleanse corrupted and duplicated data points – allowing you to leverage our datasets rapidly with minimal processing or cleaning. During the ingestion process, our proprietary Data Filtering Algorithms remove events based on a number of both qualitative factors, as well as latency and other integrity variables to provide more efficient data delivery. The deduplicating algorithm focuses on a combination of four important attributes: Device ID, Latitude, Longitude, and Timestamp. This algorithm scours our data and identifies rows that contain the same combination of these four attributes. Post-identification, it retains a single copy and eliminates duplicate values to ensure our customers only receive complete and unique datasets.

We actively identify overlapping values at the provider level to determine the value each offers. Our data science team has developed a sophisticated overlap analysis model that helps us maintain a high-quality data feed by qualifying providers based on unique data values rather than volumes alone – measures that provide significant benefit to our end-use partners.

Quadrant mobility data contains all standard attributes such as Device ID, Latitude, Longitude, Timestamp, Horizontal Accuracy, and IP Address, and non-standard attributes such as Geohash and H3. In addition, we have historical data available back through 2022.

Through our in-house data science team, we offer sophisticated technical documentation, location data algorithms, and queries that help data buyers get a head start on their analyses. Our goal is to provide you with data that is “fit for purpose”.

Quadrant provides Insightful, accurate, and reliable mobile location data.

Our privacy-first mobile location data unveils hidden patterns and opportunities, provides actionable insights, and fuels data-driven decision-making at the world's biggest companies.

These companies rely on our privacy-first Mobile Location and Points-of-Interest Data to unveil hidden patterns and opportunities, provide actionable insights, and fuel data-driven decision-making. They build better AI models, uncover business insights, and enable location-based services using our robust and reliable real-world data.

We conduct stringent evaluations on data providers to ensure authenticity and quality. Our proprietary algorithms detect, and cleanse corrupted and duplicated data points – allowing you to leverage our datasets rapidly with minimal processing or cleaning. During the ingestion process, our proprietary Data Filtering Algorithms remove events based on a number of both qualitative factors, as well as latency and other integrity variables to provide more efficient data delivery. The deduplicating algorithm focuses on a combination of four important attributes: Device ID, Latitude, Longitude, and Timestamp. This algorithm scours our data and identifies rows that contain the same combination of these four attributes. Post-identification, it retains a single copy and eliminates duplicate values to ensure our customers only receive complete and unique datasets.

We actively identify overlapping values at the provider level to determine the value each offers. Our data science team has developed a sophisticated overlap analysis model that helps us maintain a high-quality data feed by qualifying providers based on unique data values rather than volumes alone – measures that provide significant benefit to our end-use partners.

Quadrant mobility data contains all standard attributes such as Device ID, Latitude, Longitude, Timestamp, Horizontal Accuracy, and IP Address, and non-standard attributes such as Geohash and H3. In addition, we have historical data available back through 2022.

Through our in-house data science team, we offer sophisticated technical documentation, location data algorithms, and queries that help data buyers get a head start on their analyses. Our goal is to provide you with data that is “fit for purpose”.

Quadrant provides Insightful, accurate, and reliable mobile location data.

Our privacy-first mobile location data unveils hidden patterns and opportunities, provides actionable insights, and fuels data-driven decision-making at the world's biggest companies.

These companies rely on our privacy-first Mobile Location and Points-of-Interest Data to unveil hidden patterns and opportunities, provide actionable insights, and fuel data-driven decision-making. They build better AI models, uncover business insights, and enable location-based services using our robust and reliable real-world data.

We conduct stringent evaluations on data providers to ensure authenticity and quality. Our proprietary algorithms detect, and cleanse corrupted and duplicated data points – allowing you to leverage our datasets rapidly with minimal processing or cleaning. During the ingestion process, our proprietary Data Filtering Algorithms remove events based on a number of both qualitative factors, as well as latency and other integrity variables to provide more efficient data delivery. The deduplicating algorithm focuses on a combination of four important attributes: Device ID, Latitude, Longitude, and Timestamp. This algorithm scours our data and identifies rows that contain the same combination of these four attributes. Post-identification, it retains a single copy and eliminates duplicate values to ensure our customers only receive complete and unique datasets.

We actively identify overlapping values at the provider level to determine the value each offers. Our data science team has developed a sophisticated overlap analysis model that helps us maintain a high-quality data feed by qualifying providers based on unique data values rather than volumes alone – measures that provide significant benefit to our end-use partners.

Quadrant mobility data contains all standard attributes such as Device ID, Latitude, Longitude, Timestamp, Horizontal Accuracy, and IP Address, and non-standard attributes such as Geohash and H3. In addition, we have historical data available back through 2022.

Through our in-house data science team, we offer sophisticated technical documentation, location data algorithms, and queries that help data buyers get a head start on their analyses. Our goal is to provide you with data that is “fit for purpose”.

This is a compilation of several data sets related to the Galveston Texas Seaturtle Headstart program. Most notable is the Kemp's ridley headstart program [1978-1992] which captive reared up to 2,000 Kemp's ridley hatchlings per year until they were large enough to receive up to 4 tags, then they were released into the wild in the Gulf of Mexico. Morphometric measurements such as carapace length, carapace width, body depth and weight are taken to track growth and health. This data package also contains data from the seawater intake pump. Water quality data is recorded during the intake of seawater through the intake lines. This data package also contains a summary of bacteria found in the turtle tanks. In order to maintain healthy reared sea turtle swabs were taken and tested for several different bacteria.

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Quadrant provides Insightful, accurate, and reliable mobile location data.

Our privacy-first mobile location data unveils hidden patterns and opportunities, provides actionable insights, and fuels data-driven decision-making at the world's biggest companies.

These companies rely on our privacy-first Mobile Location and Points-of-Interest Data to unveil hidden patterns and opportunities, provide actionable insights, and fuel data-driven decision-making. They build better AI models, uncover business insights, and enable location-based services using our robust and reliable real-world data.

We conduct stringent evaluations on data providers to ensure authenticity and quality. Our proprietary algorithms detect, and cleanse corrupted and duplicated data points – allowing you to leverage our datasets rapidly with minimal processing or cleaning. During the ingestion process, our proprietary Data Filtering Algorithms remove events based on a number of both qualitative factors, as well as latency and other integrity variables to provide more efficient data delivery. The deduplicating algorithm focuses on a combination of four important attributes: Device ID, Latitude, Longitude, and Timestamp. This algorithm scours our data and identifies rows that contain the same combination of these four attributes. Post-identification, it retains a single copy and eliminates duplicate values to ensure our customers only receive complete and unique datasets.

We actively identify overlapping values at the provider level to determine the value each offers. Our data science team has developed a sophisticated overlap analysis model that helps us maintain a high-quality data feed by qualifying providers based on unique data values rather than volumes alone – measures that provide significant benefit to our end-use partners.

Quadrant mobility data contains all standard attributes such as Device ID, Latitude, Longitude, Timestamp, Horizontal Accuracy, and IP Address, and non-standard attributes such as Geohash and H3. In addition, we have historical data available back through 2022.

Through our in-house data science team, we offer sophisticated technical documentation, location data algorithms, and queries that help data buyers get a head start on their analyses. Our goal is to provide you with data that is “fit for purpose”.

Quadrant provides Insightful, accurate, and reliable mobile location data.

Our privacy-first mobile location data unveils hidden patterns and opportunities, provides actionable insights, and fuels data-driven decision-making at the world's biggest companies.

These companies rely on our privacy-first Mobile Location and Points-of-Interest Data to unveil hidden patterns and opportunities, provide actionable insights, and fuel data-driven decision-making. They build better AI models, uncover business insights, and enable location-based services using our robust and reliable real-world data.

We conduct stringent evaluations on data providers to ensure authenticity and quality. Our proprietary algorithms detect, and cleanse corrupted and duplicated data points – allowing you to leverage our datasets rapidly with minimal processing or cleaning. During the ingestion process, our proprietary Data Filtering Algorithms remove events based on a number of both qualitative factors, as well as latency and other integrity variables to provide more efficient data delivery. The deduplicating algorithm focuses on a combination of four important attributes: Device ID, Latitude, Longitude, and Timestamp. This algorithm scours our data and identifies rows that contain the same combination of these four attributes. Post-identification, it retains a single copy and eliminates duplicate values to ensure our customers only receive complete and unique datasets.

We actively identify overlapping values at the provider level to determine the value each offers. Our data science team has developed a sophisticated overlap analysis model that helps us maintain a high-quality data feed by qualifying providers based on unique data values rather than volumes alone – measures that provide significant benefit to our end-use partners.

Quadrant mobility data contains all standard attributes such as Device ID, Latitude, Longitude, Timestamp, Horizontal Accuracy, and IP Address, and non-standard attributes such as Geohash and H3. In addition, we have historical data available back through 2022.

Through our in-house data science team, we offer sophisticated technical documentation, location data algorithms, and queries that help data buyers get a head start on their analyses. Our goal is to provide you with data that is “fit for purpose”.

Abstract (en): The Early Childhood Longitudinal Study, Kindergarten Class of 1998-1999 (ECLS-K) focuses on children's early school experiences beginning with kindergarten through fifth grade. It is a nationally representative sample that collects information from children, their families, their teachers, and their schools. ECLS-K provides data about the effects of a wide range of family, school, community, and individual variables on children's development, early learning, and early performance in school. This data collection contains the wave of data collected in the spring of third grade (2002). The third-grade data collection includes information about the diversity of the study children, the schools they attended, and their academic progress in the years following kindergarten. Other variables include child gender, child race, family background, childcare, childcare arrangements, food security, hours per week in child care, socioeconomic status, household income, highest level of education for parents and students, parents' employment status, teachers' evaluation practice, and usefulness of different activities in the classroom. ICPSR data undergo a confidentiality review and are altered when necessary to limit the risk of disclosure. ICPSR also routinely creates ready-to-go data files along with setups in the major statistical software formats as well as standard codebooks to accompany the data. In addition to these procedures, ICPSR performed the following processing steps for this data collection: Checked for undocumented or out-of-range codes.. Datasets:DS0: Study-Level FilesDS1: Third Grade Child DataDS2: Round 5 Base Weights and Adjustment FactorsDS3: Cross-Round Weight StatusDS4: Third Grade Child Data: C45CW0 - child assessment data for both spring-first grade and spring-third grade (Online Analysis Only)DS5: Third Grade Child Data: C45PW0 - parent interview data for both spring-first grade and spring-third grade (Online Analysis Only)DS6: Third Grade Child Data: C245CW0 - child assessment data for spring-kindergarten and spring-first grade and spring-third grade (Online Analysis Only)DS7: Third Grade Child Data: C245PW0 - parent interview data for spring-kindergarten and spring-first grade and spring-third grade (Online Analysis Only)DS8: Third Grade Child Data: C1_5FC0 - child assessment data for four rounds of data collections (Online Analysis Only)DS9: Third Grade Child Data: C1_5FP0 - parent interview data for four rounds of data collection (Online Analysis Only)DS10: Third Grade Child Data: C1_5SC0 - child assessment data for all five rounds of data collection (Online Analysis Only)DS11: Third Grade Child Data: C1_5SP0 - parent interview data for all five rounds of data collection (Online Analysis Only)DS12: Third Grade Child Data: C5PW0 - parent interview data for third grade (Online Analysis Only)DS13: Third Grade Child Data: C5CPTW0 - child assessment data for third grade combined with third grade parent interview data and third grade teacher data (Online Analysis Only) Children and their families, teachers, and schools in the United States. ECLS-K utilized a multistage probability sample design to select a nationally representative sample of children attending kindergarten in 1998-1999. The Third Grade sample consisted of all children who were base year respondents and children who were brought into the sample in spring-first-grade wave through the sample freshening procedure described in section 4.3 of the manual and their families, teachers, and schools. The first-grade data collection targeted base-year respondents, in which a case was considered responding if there was a completed child assessment or parent interview in fall- or spring-kindergarten. While all base-year respondents were eligible for the spring-first-grade data collection, the effort for fall-first-grade was limited to a 30-percent subsample. The spring student sample was freshened to include current first graders who had not been enrolled in kindergarten in 1998-1999 and, therefore, had no chance of being included in the ECLS-K base-year kindergarten sample. For both fall- and spring-first grade, only a subsample of students who had transferred from their kindergarten school was followed. The third-grade data collection targeted base-year respondents and children sampled in first grade through the freshening operation. As in the first-grade data collection, only a subsample of students who had transferred...

Abstract

The aim of the trial is to evaluate a community based early education and development program launched by the Department of Non-Formal Education Ministry of National Education. The program was developed in collaboration with the World Bank with a total budget of US$127,000,000 and targets an estimated 738,000 children aged 0 to 6 living in approximately 6,000 poor communities (dusuns). The aim of the program is to increase access to early childhood services with the secondary aim of improving school readiness.

Geographic coverage

Village level (310 villages in 9 of 34 provinces).

Analysis unit

Child/student Caregiver Heads of villages Health provider Education service provider

Universe

The survey included children aged 1 to 4 years in 2009, caregivers, random sample of classmates, heads of villages, health service providers, and education service providers.

Kind of data

Sample survey data

Sampling procedure

The trial was a pragmatic cluster (by village) randomized controlled trial with an additional matched control group. Sampling included 310 villages in the following 9 districts:

1. Bengkulu, Bengkulu Province
2. Sarolangun, Jambi Province
3. Lampung Timur, Lampung Province
4. Majalengka, Jawa Barat Province
5. Rembang, Jawa Tengah Province
6. Kulon Progo, DI Yogyakarta Province
7. Ketapang, Kalimantan Barat Province
8. Sidenreng Rappang, Sulawesi Selatan Province
9. Lombok Tengah, Nusa Tenggara Barat Province

It was planned that batch 1 would receive the first block grants at the start of the project. Block grants for batch 2 and batch 3 were to follow after nine and eighteen months respectively. A selection of villages was randomly allocated to either batch 1 or batch 3 (within each district), this sampling feature was used during the evaluation design.

Of the 310 villages, 100 were originally allocated to the intervention arm, 20 were originally allocated to a nine month delay staggered start, 100 were originally allocated to an 18 month delay staggered start, and 90 villages were allocated to a matched control group (no intervention).

A panel survey cohort methodology, collecting data several times from the same respondent during a specified time, was used. The analysis identifies children based on their age in baseline (2009) which consists of two cohorts: the first cohort consists of children aged 12 to 23 months and the second consists of children aged 48 to 59 months.

Sampling deviation

In December 2008, the team uncovered that five districts had not complied with the village level randomization. Specifically, this meant that these districts took action to implement the project in an order that was different to what was originally agreed to with MoNE and the World Bank in 2006. The impact evaluation team decided to drop the survey work in the district (Gorontalo). Therefore, the sample of 30 villages that were originally allocated to Gorontalo were replaced by villages in East Lombok and all 60 project villages in East Lombok were included in the study sample, thus increasing the total sample size by 10 villages, to 310.

Mode of data collection

Face-to-face

Research instrument

There are six types of questionnaires: 1. Village Head (Book A) 2. Household (Book B) 3. Child (Book C) 4. Caregiver (Book D) 5. Village Midwife (Book E) 6. Posyandu Cadre (Book F)

Cleaning operations

Data entry, double entry, and data cleaning was done by Entry Data Team.

Table S1Patient informationS1_Table Patient information.xlsx

Quadrant provides Insightful, accurate, and reliable mobile location data.

Our privacy-first mobile location data unveils hidden patterns and opportunities, provides actionable insights, and fuels data-driven decision-making at the world's biggest companies.

These companies rely on our privacy-first Mobile Location and Points-of-Interest Data to unveil hidden patterns and opportunities, provide actionable insights, and fuel data-driven decision-making. They build better AI models, uncover business insights, and enable location-based services using our robust and reliable real-world data.

We conduct stringent evaluations on data providers to ensure authenticity and quality. Our proprietary algorithms detect, and cleanse corrupted and duplicated data points – allowing you to leverage our datasets rapidly with minimal processing or cleaning. During the ingestion process, our proprietary Data Filtering Algorithms remove events based on a number of both qualitative factors, as well as latency and other integrity variables to provide more efficient data delivery. The deduplicating algorithm focuses on a combination of four important attributes: Device ID, Latitude, Longitude, and Timestamp. This algorithm scours our data and identifies rows that contain the same combination of these four attributes. Post-identification, it retains a single copy and eliminates duplicate values to ensure our customers only receive complete and unique datasets.

We actively identify overlapping values at the provider level to determine the value each offers. Our data science team has developed a sophisticated overlap analysis model that helps us maintain a high-quality data feed by qualifying providers based on unique data values rather than volumes alone – measures that provide significant benefit to our end-use partners.

Quadrant mobility data contains all standard attributes such as Device ID, Latitude, Longitude, Timestamp, Horizontal Accuracy, and IP Address, and non-standard attributes such as Geohash and H3. In addition, we have historical data available back through 2022.

Through our in-house data science team, we offer sophisticated technical documentation, location data algorithms, and queries that help data buyers get a head start on their analyses. Our goal is to provide you with data that is “fit for purpose”.