The National Center for Education Statistics surveyed 12,330 U.S. adults ages 16 to 74 living in households from 2012 to 2017 for the Program for the International Assessment of Adult Competencies (PIAAC), an international study involving over 35 countries. Using small area estimation models (SAE), indirect estimates of literacy and numeracy proficiency have been produced for all U.S. states and counties. By using PIAAC survey data in conjunction with data from the American Community Survey, the Skills Map data provides reliable estimates of adult literacy and numeracy skills in all 50 states, all 3,141 counties, and the District of Columbia.

The indirect estimates provided in this data were created using a sophisticated statistical method generally referred to as small area estimation (SAE). SAE is a model-dependent approach that produces indirect estimates for areas where survey data is inadequate for direct estimation. SAE models assume that counties with similar demographics would have similar estimates of skills. An estimate for a county then “borrows strength” across related small areas through auxiliary information to produce reliable indirect estimates for small areas. The models rely on covariates available at the small areas, and PIAAC survey data. In the absence of any other proficiency assessment data for individual states and counties, the estimates provide a general picture of proficiency for all states and counties. For technical details on the SAE approach applied to PIAAC, see section 5 of the State and County Estimation Methodology Report.

The U.S. state indirect estimates reported in this data are not directly comparable with the direct estimates for PIAAC countries that are reported by the Organization for Economic Cooperation and Development (OECD). Specifically, the U.S. state indirect estimates (1) represent modeled estimates for adults ages 16-74 whereas the OECD’s direct estimates for participating countries represent estimates for adults ages 16-65, (2) include data for “literacy-related nonresponse” (i.e., adults whose English language skills were too low to participate in the study) whereas the OECD’s direct estimates for countries exclude these data, and (3) are based on three combined data collections (2012/2014/2017) whereas OECD’s direct estimates are based on a single data collection.Please visit the Skills Map to learn more about this data.All information contained in this file is in the public domain. Data users are advised to review NCES program documentation and feature class metadata to understand the limitations and appropriate use of these data.

Female literacy rate in Nigeria is among the highest in West Africa. However, the difference between male and female literacy rates are substantial in both urban and rural areas. As of 2018, the rate among men living in rural areas of Nigeria reached roughly 60 percent, whereas female literacy rates in the same areas was 35 percent. The gap in urban Nigeria amounted to about ten percentage points.

In West Africa, the highest female literacy rates were registered in Cabo Verde and Ghana, while Nigeria ranked third.

The U.S. PIAAC Skills Map provides estimates of adult literacy and numeracy proficiency in all U.S. states and counties, based on small area estimation applied to data from U.S. PIAAC Cycle I (2012-2017). The estimates from the Skills Map were published in an Excel format available from within the Skills Map's interactive webpage. This project includes the Skills Map estimates as well as the user guide and methodological reports published with the Skills Map.

Abstract Information Literacy arises from the concern with the training in research and the use of technologies by future professionals. In the health area, the use of scientific information grounds the decision-making process, because the search results may potentially be transformed into action. The aim of this study is to map the information literacy of health graduates in the use of sources of information for scientific research purposes, based on standards of information literacy for higher education of the Association of College and Research Libraries. The research is a descriptive type cross-sectional study with a quantitative and qualitative approach. Three hundred and eighteen students enrolled in the Institute of Health and Society of a University in the State of São Paulo participated in undergraduate courses in Physical Education, Physiotherapy, Nutrition, Occupational Therapy, Psychology and Social Work. It was found that the students have difficulties in establishing the need for information, low knowledge when it comes to accessing the databases available, difficulties in assessing the quality of the sources used, need to discuss the ethical use of information and they are unaware of the concept of information literacy. In conclusion, the subject needs to be explored by researchers, as well as be addressed in the training process at universities.

This map shows the predominant highest level of education for the population age 25+ in the United States. This is shown by state, county, and census tracts throughout the US. Click on a feature to learn more about the breakdown of population by their highest level of education.The categories are grouped as:Less than High SchoolHigh SchoolAssociate's DegreeSome CollegeBachelor's Degree or HigherThe data shown is current-year American Community Survey (ACS) data from the US Census. The data is updated each year when the ACS releases its new 5-year estimates. For more information about the data layer used in this map, visit this page.To learn more about when the ACS releases data updates, click here.

This Public Schools feature dataset is composed of all Public elementary and secondary education in the United States as defined by the Common Core of Data, National Center for Education Statistics, US Department of Education. This includes all Kindergarten through 12th grade schools as tracked by the Common Core of Data. Included in this dataset are the military schools abroad and referenced in the city field with an APO or FPO address. Also referenced in the state field with the abbreviation AE. Please note that the APO and FPO schoolpoints are located at 0,0. This feature class contains all MEDS/MEDS+ as approved by NGA. For each field the 'Not Avaliable' and 'NULL' designations are used to indicate that the data for the particular record and field is currently unavaliable and will be populated when and if that data becomes avaliable.

According to exit polling in *** key states of the 2024 presidential election in the United States, almost ********** of voters who had never attended college reported voting for Donald Trump. In comparison, a similar share of voters with ******** degrees reported voting for Kamala Harris.

This map shows the percentage of people age 25+ whose highest education level is some college. This is shown by state, county, and census tracts throughout the US. Zoom to any city to see the pattern there, or use one of the bookmarks to explore different areas.Some college education means that the individual has some college credits, but no degree. For more information from the Census Bureau, click here.The pop-up is configured to show the overall breakdown of educational attainment for the population 25+. The data shown is current-year American Community Survey (ACS) data from the US Census Bureau. The data is updated each year when the ACS releases its new 5-year estimates. For more information about the data, visit this page.To learn more about when the ACS releases data updates, click here.

The 2020-2021 School Learning Modalities dataset provides weekly estimates of school learning modality (including in-person, remote, or hybrid learning) for U.S. K-12 public and independent charter school districts for the 2020-2021 school year, from August 2020 – June 2021.

These data were modeled using multiple sources of input data (see below) to infer the most likely learning modality of a school district for a given week. These data should be considered district-level estimates and may not always reflect true learning modality, particularly for districts in which data are unavailable. If a district reports multiple modality types within the same week, the modality offered for the majority of those days is reflected in the weekly estimate. All school district metadata are sourced from the https://nces.ed.gov/ccd/files.asp#Fiscal:2,LevelId:5,SchoolYearId:35,Page:1">National Center for Educational Statistics (NCES) for 2020-2021.

School learning modality types are defined as follows:

• In-Person: All schools within the district offer face-to-face instruction 5 days per week to all students at all available grade levels.
• Remote: Schools within the district do not offer face-to-face instruction; all learning is conducted online/remotely to all students at all available grade levels.
• Hybrid: Schools within the district offer a combination of in-person and remote learning; face-to-face instruction is offered less than 5 days per week, or only to a subset of students.

Data Information

• School learning modality data provided here are model estimates using combined input data and are not guaranteed to be 100% accurate. This learning modality dataset was generated by combining data from four different sources: Burbio [1], MCH Strategic Data [2], the AEI/Return to Learn Tracker [3], and state dashboards [4-20]. These data were combined using a Hidden Markov model which infers the sequence of learning modalities (In-Person, Hybrid, or Remote) for each district that is most likely to produce the modalities reported by these sources. This model was trained using data from the 2020-2021 school year. Metadata describing the location, number of schools and number of students in each district comes from NCES [21].
• You can read more about the model in the CDC MMWR: https://www.cdc.gov/mmwr/volumes/70/wr/mm7039e2.htm" target="_blank">COVID-19–Related School Closures and Learning Modality Changes — United States, August 1–September 17, 2021.
• The metrics listed for each school learning modality reflect totals by district and the number of enrolled students per district for which data are available. School districts represented here exclude private schools and include the following NCES subtypes:
  • Public school district that is NOT a component of a supervisory union
  • Public school district that is a component of a supervisory union
  • Independent charter district
• “BI” in the state column refers to school districts funded by the Bureau of Indian Education.

Technical Notes

• Data from September 1, 2020 to June 25, 2021 correspond to the 2020-2021 school year. During this timeframe, all four sources of data were available. Inferred modalities with a probability below 0.75 were deemed inconclusive and were omitted.
• Data for the month of July may show “In Person” status although most school districts are effectively closed during this time for summer break. Users may wish to exclude July data from use for this reason where applicable.

Sources

1. K-12 School Opening Tracker. Burbio 2021; https://cai.burbio.com/school-opening-tracker/. June 2021.
2. COVID-19 IMPACT: School District Operational Status. MCH Strategic Data: 2021; https://www.mchdata.com/covid19/schoolclosings. (30 June 2021, date last accessed).
3. Return to Learn Tracker. American Enterprise Institute 2021; https://www.returntolearntracker.net/. (30 June 2021, date last accessed).
4. Colorado School District Reopening Plans. Colorado Department of Education 2021; https://docs.google.com/spreadsheets/d/e/2PACX-1vQipdjO8QWhilhhJ4bX0FBebnHEzK1G3LEDQbE_S-xRvs2t0oHNm--acHwMRFmL9uKw4cXcOUqy1V66/pubhtml. (30 June 2021, date last accessed).
5. Connecticut Report Cards. Connecticut State Department of Education 2021; http://edsight.ct.gov/SASPortal/main.do. (30 June 2021, date last accessed).
6. COVID Planning Tool. Hawaii Department of Education 2021; https://public.tableau.com/views/COVIDPlanningTool-Embed700px/SimpleDashboard. (30 June 2021, date last accessed).
7. Idaho School District Status. Idaho State Department of Education 2021; https://ioem.maps.arcgis.com/apps/opsdashboard/index.html#/379299e56f234fa89cff3c8c6acf3ac8. (30 June 2021, date last accessed).
8. How Illinois Districts Are Providing Instruction: Virtual, In-Person, and Blended Learning. Illinois State Board of Education 2021; https://isp.maps.arcgis.com/apps/opsdashboard/index.html#/fceeacb37da04de4b237ed941dd7d5c4. (30 June 2021, date last accessed).
9. Digital Learning Model Schools. Louisiana Department of Education 2021; https://analytics.la.gov/t/DOE/views/DigitalLearningModel/SchoolLevelModeofLearning. (30 June 2021, date last accessed).
10. Safe Learning Model Dashboard. Minnesota Department of Education 2021; https://analytics.education.state.mn.us/t/MDEPublic/views/MN_Safe_Learning_Model/Dashboard. (30 June 2021, date last accessed).
11. Coronavirus (COVID-19) information. Missouri Department of Elementary & Secondary Education 2021; https://dese.mo.gov/communications/coronavirus-covid-19-information. (30 June 2021, date last accessed).
12. North Carolina School Dashboard. North Carolina Department of Public Instruction 2021. https://docs.google.com/spreadsheets/d/1We8gDpa4Do5NR83Nf8niGE_YxzLDf-KZh-tVWifStxE/edit#gid=0. (30 June 2021, date last accessed).
13. Ring the Bell: Return to In-Person Learning. New Mexico Public Education Department 2021; https://webnew.ped.state.nm.us/reentry-district-and-school-guidance/. (30 June 2021, date last accessed).
14. Reset and Restart Education. Ohio Department of Education 2021; http://education.ohio.gov/Topics/Reset-and-Restart. (30 June 2021, date last accessed).
15. 2020-21 School Status. Oregon Department of Education 2021; https://www.oregon.gov/ode/students-and-family/healthsafety/Pages/2020-21-School-Status.aspx. (30 June 2021, date last accessed).
16. Current Operational Status. South Carolina Department of Education 2021; https://ed.sc.gov/districts-schools/schools/district-and-school-closures/operational-status/#blk2102. (30 June 2021, date last accessed).
17. Tennessee Department of Education COVID-19 District Information. Tennessee Department of Education 2021; https://districtinformation.tnedu.gov/covid-information/search/-1. (30 June 2021, date last accessed).
18. State Snapshot: Virginia School Operational Status. Virginia Department of Education 2021; https://www.doe.virginia.gov/support/health_medical/office/reopen-status.shtml. (30 June 2021, date last accessed).
19. Current Reopening Status of Vermont's K-12 Public Schools. Vermont Department of Financial Regulation 2021; https://dfr.vermont.gov/about-us/covid-19/school-reopening#public-school. (30 June 2021, date last accessed).
20. School Reopening Data. Washington Office of Superintendent of Public Instruction 2021; https://www.k12.wa.us/about-ospi/press-releases/novel-coronavirus-covid-19-guidance-resources/school-reopening-data. (30 June 2021, date last accessed).
21. Public school districts. National Center for Education Statistics 2021; https://nces.ed.gov/ccd/districtsearch/. (30 June 2021, date last accessed).

A project of the EdLab Group, Communities Connect Network (CCN) is a statewide coalition of public and private organizations working to make Washington State a leader in “digital inclusion” – the movement to ensure that all individuals have access and the skills to use the Internet and information technologies. CCN brings together community technology providers with state and local governments, corporate, philanthropic, and individual supporters to ensure that all residents are able to fully participate in Washington’s increasingly technology-rich society. Most recently, CCN is the recipient of federal Department of Commerce NTIA Broadband Technology Opportunities Program (BTOP) funding for public computing centers. The Communities Connect Network Project works to improve broadband adoption rates, workforce preparation, digital literacy, access to education, justice resources, and training at 22 sites in seven counties around Washington State. Partners include non-profits, libraries, community centers and courthouses. See BTOP Computer Labs for a complete list of partners.

This layer serves as the authoritative geographic data source for all school district area boundaries in California. School districts are single purpose governmental units that operate schools and provide public educational services to residents within geographically defined areas. Agencies considered school districts that do not use geographically defined service areas to determine enrollment are excluded from this data set. In order to view districts represented as point locations, please see the "California School District Offices" layer. The school districts in this layer are enriched with additional district-level attribute information from the California Department of Education's data collections. These data elements add meaningful statistical and descriptive information that can be visualized and analyzed on a map and used to advance education research or inform decision making.School districts are categorized as either elementary (primary), high (secondary) or unified based on the general grade range of the schools operated by the district. Elementary school districts provide education to the lower grade/age levels and the high school districts provide education to the upper grade/age levels while unified school districts provide education to all grade/age levels in their service areas. Boundaries for the elementary, high and unified school district layers are combined into a single file. The resulting composite layer includes areas of overlapping boundaries since elementary and high school districts each serve a different grade range of students within the same territory. The 'DistrictType' field can be used to filter and display districts separately by type.Boundary lines are maintained by the California Department of Education (CDE) and are effective in the 2023-24 academic year . The CDE works collaboratively with the US Census Bureau to update and maintain boundary information as part of the federal School District Review Program (SDRP). The Census Bureau uses these school district boundaries to develop annual estimates of children in poverty to help the U.S. Department of Education determine the annual allocation of Title I funding to states and school districts. The National Center for Education Statistics (NCES) also uses the school district boundaries to develop a broad collection of district-level demographic estimates from the Census Bureau’s American Community Survey (ACS).The school district enrollment and demographic information are based on student enrollment counts collected on Fall Census Day (first Wednesday in October) in the 2023-24 academic year. These data elements are collected by the CDE through the California Longitudinal Achievement System (CALPADS) and can be accessed as publicly downloadable files from the Data & Statistics web page on the CDE website https://www.cde.ca.gov/ds.

The National Center for Education Statistics’ (NCES) Education Demographic and Geographic Estimates (EDGE) program develops annually updated school district boundary composite files that include public elementary, secondary, and unified school district boundaries clipped to the U.S. shoreline. School districts are special-purpose governments and administrative units designed by state and local officials to organize and provide public education for local residents. District boundaries are collected for NCES by the U.S. Census Bureau to support educational research and program administration, and the boundaries are essential for constructing district-level estimates of the number of children in poverty.The Census Bureau’s School District Review Program (SDRP) (https://www.census.gov/programs-surveys/sdrp.html) obtains the boundaries, names, and grade ranges from state officials, and integrates these updates into Census TIGER. Census TIGER boundaries include legal maritime buffers for coastal areas by default, but the NCES composite file removes these buffers to facilitate broader use and cleaner cartographic representation. The inputs for this data layer were developed from Census TIGER/Line 2024 and represent boundaries reported for the 2023-2024 school year. For more information about NCES school district boundary data, see: https://nces.ed.gov/programs/edge/Geographic/DistrictBoundaries.Collections are available for the following years:SY 2022-23 TL 23SY 2021-22 TL 22SY 2020-21 TL 21SY 2019-20 TL 20SY 2018-19 TL 19SY 2017-18 TL 18SY 2015-16 TL 17SY 2015-16 TL 16SY 2013-14 TL 15SY 2013-14 TL 14All information contained in this file is in the public domain. Data users are advised to review NCES program documentation and feature class metadata to understand the limitations and appropriate use of these data.

This map shows the predominant language(s) spoken by people who have limited English speaking ability. This is shown using American Community Survey data from the US Census Bureau by state, county, and tract.There are 12 different language/language groupings: SpanishFrench, Haitian, or CajunKoreanChinese (including Mandarin and Cantonese)VietnameseTagalog (including Filipino)ArabicGerman or other West GermanicRussian, Polish, or other SlavicOther Indo-European (such as Italian or Portuguese)Other Asian and Pacific Island (such as Japanese or Hmong)Other and unspecified (such as Navajo or Hebrew).This map also uses a feature effect to identify the counties with either 10,000 or 5% of the population having limited English ability. According to the Voting Rights Act, "localities where there are more than 10,000 or over 5 percent of the total voting age citizens in a single political subdivision (usually a county, but a township or municipality in some states) who are members of a single language minority group, have depressed literacy rates, and do not speak English very well" are required to "provide [voting materials] in the language of the applicable minority group as well as in the English language".This map uses these hosted feature layers containing the most recent American Community Survey data. These layers are part of ArcGIS Living Atlas, and are updated every year when the American Community Survey releases new estimates, so values in the map always reflect the newest data available.

Context

This Dataset is added as response to task2 in the Paani foundation Watercup Challenge dataset. https://www.kaggle.com/bombatkarvivek/paani-foundations-satyamev-jayate-water-cup.

Content

It contains population data collected in 2001 and 2011. It also contains area of each district in Maharashtra State and their literacy rates. Also included outline map of the state.

Inspiration

This dataset is created in support for the datset Paani foundation Watercup Challenge https://www.kaggle.com/bombatkarvivek/paani-foundations-satyamev-jayate-water-cup. Which supports the movement of Paani foundation by spreading awareness by creating interactive visualizations and data analysis.

BackgroundLandscaping studies related to public health education in India do not exclusively focus on the most common Masters of Public Health (MPH) program. The field of public health faces challenges due to the absence of a professional council, resulting in fragmented documentation of these programs. This study was undertaken to map all MPH programs offered across various institutes in India in terms of their geographic distribution, accreditation status, and administration patterns.MethodologyAn exhaustive internet search using various keywords was conducted to identify all MPH programs offered in India. Websites were explored for their details. A data extraction tool was developed for recording demographic and other data. Information was extracted from these websites as per the tool and collated in a matrix. Geographic coordinates obtained from Google Maps, and QGIS software facilitated map generation.ResultsThe search identified 116 general and 13 MPH programs with specializations offered by different universities and institutes across India. India is divided into six zones, and the distribution of MPH programs in these zones is as follows, central zone has 20 programs; the east zone has 11; the north zone has 35; the north-east zone has 07; the south zone has 26; and the west zone has 17 MPH programs. While 107 are university grants commission (UGC) approved universities and institutes, only 46 MPH programs are conducted by both UGC approved and National Assessment and Accreditation Council (NAAC) accredited universities and institutes. Five universities are categorized as central universities; 22 are deemed universities; 51 are private universities; and 29 are state universities. Nine are considered institutions of national importance by the UGC, and four institutions are recognized as institutions of eminence. All general MPH programs span 2 years and are administered under various faculties, with only 27 programs being conducted within dedicated schools or centers of public health.ConclusionThe MPH programs in India show considerable diversity in their geographic distribution, accreditation status, and administration pattern.

This web map displays the California Department of Education's (CDE) core set of geographic data layers. This content represents the authoritative source for all statewide public school site locations and school district service areas boundaries for the 2018-19 academic year. The map also includes school and district layers enriched with student demographic and performance information from the California Department of Education's data collections. These data elements add meaningful statistical and descriptive information that can be visualized and analyzed on a map and used to advance education research or inform decision making.

NOTE: This Feature Service Supersedes All Previous EditionsTABULAR UPDATES ONLY NO BOUNDARY CHANGESThis feature service depicts the boundaries of the nine Educational Service Districts in Washington State. Educational Service Districts are nine regional educational support agencies partnering with the Office of Superintendent of Public Instruction (OSPI) to provide essential services for school districts and communities and to help OSPI implement legislatively-supported education initiatives. This service also adds the ESD office locations.Republish to remove the year from the naming convention

This map shows schools, school districts, and population density throughout the US. Click on the map to learn more about the school districts and schools within an area. A few things you can learn within this map:How many public/private schools fall within the district?What type of population density lives within this district? Socioeconomic factors about the Census Tracts which fall within the district:School enrollment of under 19 by grade Children living below the poverty level Children with no internet at home Children without a working parentRace/ethnicity breakdown of the population within the districtFor more information about the data sources:Socioeconomic factors:The American Community Survey (ACS) helps us understand the population in the US. This app uses the 5-year estimates, and the data is updated annually when the U.S. Census Bureau releases the newest estimates. For detailed metadata, visit the links in the bullet points above. Current School Districts layer:The National Center for Education Statistics’ (NCES) Education Demographic and Geographic Estimate (EDGE) program develops annually updated school district boundary composite files that include public elementary, secondary, and unified school district boundaries clipped to the U.S. shoreline. School districts are single-purpose administrative units designed by state and local officials to organize and provide public education for local residents. District boundaries are collected for NCES by the U.S. Census Bureau to support educational research and program administration, and the boundaries are essential for constructing district-level estimates of the number of children in poverty.The Census Bureau’s School District Boundary Review program (SDRP) (https://www.census.gov/programs-surveys/sdrp.html) obtains the boundaries, names, and grade ranges from state officials, and integrates these updates into Census TIGER. Census TIGER boundaries include legal maritime buffers for coastal areas by default, but the NCES composite file removes these buffers to facilitate broader use and cleaner cartographic representation. The NCES EDGE program collaborates with the U.S. Census Bureau’s Education Demographic, Geographic, and Economic Statistics (EDGE) Branch to develop the composite school district files. The inputs for this data layer were developed from Census TIGER/Line and represent the most current boundaries available. For more information about NCES school district boundary data, see https://nces.ed.gov/programs/edge/Geographic/DistrictBoundaries.Private Schools layer:This Private Schools feature dataset is composed of private elementary and secondary education facilities in the United States as defined by the Private School Survey (PSS, https://nces.ed.gov/surveys/pss/), National Center for Education Statistics (NCES, https://nces.ed.gov), US Department of Education for the 2017-2018 school year. This includes all prekindergarten through 12th grade schools as tracked by the PSS. This feature class contains all MEDS/MEDS+ as approved by NGA. Complete field and attribute information is available in the ”Entities and Attributes” metadata section. Geographical coverage is depicted in the thumbnail above and detailed in the Place Keyword section of the metadata. This release includes the addition of 2675 new records, modifications to the spatial location and/or attribution of 19836 records, the removal of 254 records no longer applicable. Additionally, 10,870 records were removed that previously had a STATUS value of 2 (Unknown; not represented in the most recent PSS data) and duplicate records identified by ORNL.Public Schools layer:This Public Schools feature dataset is composed of all Public elementary and secondary education facilities in the United States as defined by the Common Core of Data (CCD, https://nces.ed.gov/ccd/ ), National Center for Education Statistics (NCES, https://nces.ed.gov ), US Department of Education for the 2017-2018 school year. This includes all Kindergarten through 12th grade schools as tracked by the Common Core of Data. Included in this dataset are military schools in US territories and referenced in the city field with an APO or FPO address. DOD schools represented in the NCES data that are outside of the United States or US territories have been omitted. This feature class contains all MEDS/MEDS+ as approved by NGA. Complete field and attribute information is available in the ”Entities and Attributes” metadata section. Geographical coverage is depicted in the thumbnail above and detailed in the Place Keyword section of the metadata. This release includes the addition of 3065 new records, modifications to the spatial location and/or attribution of 99,287 records, and removal of 2996 records not present in the NCES CCD data.WorldPop Populated Foorprint layer:This layer represents an estimate of the footprint of human settlement in 2020. It is intended as a fast-drawing cartographic layer to augment base maps and to focus a map reader's attention on the location of human population. This layer is not intended for analysis.This layer was derived from the 2020 slice of the WorldPop Population Density 2000-2020 100m and 1km layers. WorldPop modeled this population footprint based on imagery datasets and population data from national statistical organizations and the United Nations. Zooming in to very large scales will often show discrepancies between reality and this or any model. Like all data sources imagery and population counts are subject to many types of error, thus this gridded footprint contains errors of omission and commission. The imagery base maps available in ArcGIS Online were not used in WorldPop's model. Imagery only informs the model of characteristics that indicate a potential for settlement, and cannot intrinsically indicate whether any or how many people live in a building.

Table from the American Community Survey (ACS) 5-year series on education enrollment and attainment related topics for City of Seattle Council Districts, Comprehensive Plan Growth Areas and Community Reporting Areas. Table includes B14007/B14002 School Enrollment, B15003 Educational Attainment. Data is pulled from block group tables for the most recent ACS vintage and summarized to the neighborhoods based on block group assignment.