Comprehensive population and demographic data for Rini Math Village

This dataset gives the complete list of all 205,822 exceptional Dehn fillings on the 1-cusped hyperbolic 3-manifolds that have ideal triangulations with at most 9 ideal tetrahedra.

Wastewater-based epidemiology is a promising public health tool that can yield a more representative view of the population than case reporting. However, only about 80% of the U.S. population is connected to public sewers, and the characteristics of populations missed by wastewater-based epidemiology are unclear. To address this gap, we used publicly available datasets to assess sewer connectivity in the U.S. by location, demographic groups, and economic groups. Data from the U.S. Census’ American Housing Survey revealed that sewer connectivity was lower than average when the head of household was American Indian and Alaskan Native, White, non-Hispanic, older, and for larger households and those with higher income, but smaller geographic scales revealed local variations from this national connectivity pattern. For example, data from the U.S. Environmental Protection Agency showed that sewer connectivity was positively correlated with income in Minnesota, Florida, and California. Data from the U.S. Census’ American Community Survey and Environmental Protection Agency also revealed geographic areas with low sewer connectivity, such as Alaska, the Navajo Nation, Minnesota, Michigan, and Florida. However, with the exception of the U.S. Census data, there were inconsistencies across datasets. Using mathematical modeling to assess the impact of wastewater sampling inequities on inferences about epidemic trajectory at a local scale, we found that in some situations, even weak connections between communities may allow wastewater monitoring in one community to serve as a reliable proxy for an interacting community with no wastewater monitoring, when cases are widespread. A systematic, rigorous assessment of sewer connectivity will be important for ensuring an equitable and informed implementation of wastewater-based epidemiology as a public health monitoring system.

Comprehensive population and demographic data for Math Usarsanda Village

Investigate rates of population growth and decline with US Census data. THE GEOINQUIRIES™ COLLECTION FOR MATHEMATICShttp://www.esri.com/geoinquiriesThe GeoInquiry™ collection for Mathematics contains 15 free, standards-based activities that correspond and extend spatial concepts found in course textbooks frequently used in introductory algebra or geometry classes. The activities use a common inquiry-based instructional model, require only 15 minutes to deliver, and are device/laptop agnostic. Each activity includes an ArcGIS Online map but requires no login or installation. The activities harmonize with the Common Core math national curriculum standards. Activities include:· Rates & Proportions: A lost beach· D=R x T· Linear rate of change: Steady growth· How much rain? Linear equations· Rates of population change· Distance and midpoint· The coordinate plane· Euclidean vs Non-Euclidean· Area and perimeter at the mall· Measuring crop circles· Area of complex figures· Similar triangles· Perpendicular bisectors· Centers of triangles· Volume of pyramids

Teachers, GeoMentors, and school administrators can learn more at http://www.esri.com/geoinquiries.

Third grade English Language Arts (ELA) and Math test results for the 2016-2017 school year by census tract for the state of Michigan. Data Driven Detroit obtained these datasets from MI School Data, for the State of the Detroit Child tool in July 2017. Test results were originally obtained on a school level and aggregated to census tract by Data Driven Detroit. Student data was suppressed when less than five students were tested per school.Click here for metadata (descriptions of the fields).

This view filters on US Census variables for field of bachelor degree reported for Mesa and other local comparison city's and Maricopa County. The American Community Survey (ACS) 1-year Estimates are source of information. For grouping of Majors Into Broad and Detailed Fields, see Appendix A at https://www2.census.gov/library/publications/2012/acs/acs-18.pdf

Science and Engineering include: Computers, mathematics, and statistics Biological, agricultural, and environmental sciences Physical and related science Psychology Social sciences Engineering Multidisciplinary studies

Science and engineering related includes nursing, architecture, math teacher

Arts, Humanities and other include: Literature and languages Liberal arts and history Visual and performing arts Communications

Correlation of the percentage of a Florida county subdivision not connected to septic tanks with different demographic or economic variables.

Comprehensive population and demographic data for Math Adam Village

Compares percent distribution of STEM (science, technology, engineering and math and computer science) and BHASE (non-STEM) fields of study between census divisions.

Comprehensive population and demographic data for Math Marsua Village

CONTEXT

Practice Scenario: The UIW School of Engineering wants to recruit more students into their program. They will recruit students with great math scores. Also, to increase the chances of recruitment,​ the department will look for students who qualify for financial aid. Students who qualify for financial aid more than likely come from low socio-economic backgrounds. One way to indicate this is to view how much federal revenue a school district receives through its state. High federal revenue for a school indicates that a large portion of the student base comes from low incomes families.

The question we wish to ask is as follows: Name the school districts across the nation where their Child Nutrition Programs(c25) are federally funded between the amounts $30,000 and $50,000. And where the average math score for the school districts corresponding state is greater than or equal to the nations average score of 282.

The SQL query below in 'Top5MathTarget.sql' can be used to answer this question in MySQL. To execute this process, one would need to install MySQL to their local system and load the attached datasets below from Kaggle into their MySQL schema. The SQL query below will then join the separate tables on various key identifiers.

DATA SOURCE Data is sourced from The U.S Census Bureau and The Nations Report Card (using the NAEP Data Explorer).

Finance: https://www.census.gov/programs-surveys/school-finances/data/tables.html

Math Scores: https://www.nationsreportcard.gov/ndecore/xplore/NDE

COLUMN NOTES

All data comes from the school year 2017. Individual schools are not represented, only school districts within each state.

FEDERAL FINANCE DATA DEFINITIONS

t_fed_rev: Total federal revenue through the state to each school district.

C14- Federal revenue through the state- Title 1 (no child left behind act).

C25- Federal revenue through the state- Child Nutrition Act.

Title 1 is a program implemented in schools to help raise academic achievement ​for all students. The program is available to schools where at least 40% of the students come from low inccom​​e families.

Child Nutrition Programs ensure the children are getting the food they need to grow and learn. Schools with high federal revenue to these programs indicate students that also come from low income​ families.

MATH SCORES DATA DEFINITIONS

Note: Mathematics, Grade 8, 2017, All Students (Total)

average_scale_score - The state's average score for eighth graders taking the NAEP math exam.

All population characteristics in the table were identical for the synthetic microdata and the American Community Survey data.

Comprehensive population and demographic data for Sital Math Village

Context

The United States Census Bureau conducts annual surveys to assess the finances of elementary and high schools. This data has been programmatically organized here in two files; one for school districts (districts.csv) and one for states (states.csv).

Also included is a summary of data from the NAEP (National Assessment of Educational Progress), contained in naep.csv.

Content

districts.csv A comma-separated spreadsheet containing revenues and expenditures for all U.S. school districts, 1992-2016.

STATE,ENROLL,NAME,YRDATA,TOTALREV,TFEDREV,TSTREV,TLOCREV,TOTALEXP,TCURINST,TCURSSVC,TCURONON,TCAPOUT
Alabama,9609,AUTAUGA COUNTY SCHOOL DISTRICT,2016,80867,7447,53842,19578,76672,43843,23941,6401,1506
Alabama,30931,BALDWIN COUNTY SCHOOL DISTRICT,2016,338236,23710,145180,169346,299880,164977,97231,19439,9749
Alabama,912,BARBOUR COUNTY SCHOOL DISTRICT,2016,10116,2342,5434,2340,10070,4907,3896,975,110

states.csv A comma-separated spreadsheet containing state summaries of revenues and expenditures, organized by year.

STATE,YEAR,ENROLL,TOTAL_REVENUE,FEDERAL_REVENUE,STATE_REVENUE,LOCAL_REVENUE,TOTAL_EXPENDITURE,INSTRUCTION_EXPENDITURE,SUPPORT_SERVICES_EXPENDITURE,OTHER_EXPENDITURE,CAPITAL_OUTLAY_EXPENDITURE
Alabama,1992,,2678885,304177,1659028,715680,2653798,1481703,735036,,174053
Alaska,1992,,1049591,106780,720711,222100,972488,498362,350902,,37451
Arizona,1992,,3258079,297888,1369815,1590376,3401580,1435908,1007732,,609114

naep.csv A comma-seperated spreadsheet containing state performance on mathematics and reading tests, for 4th and 8th grade on a selection of years.

YEAR,STATE,AVG_SCORE,TEST_SUBJECT,TEST_YEAR
2017,Alabama,232.170687741509,Mathematics,4
2017,Alaska,230.456277558902,Mathematics,4
2017,Arizona,234.435788152091,Mathematics,4

Be warned, some data will be NaN's (most notably, the 1992 records contain no data for enrollment).

Data was created from the spreadsheets in elsec.zip (taken from the U.S. Census Bureau site) using chew_data.py and state_summary.py. Column names are documented in school15doc.pdf.

Sources

https://www.census.gov/programs-surveys/school-finances/data/tables.html

https://www.nationsreportcard.gov/ndecore/landing

Changelog

[v 0.2] Added data from 1993-2001. Data is now harvested from the main spreadsheets instead of the summary spreadsheets. Data by school district is now available.

[v 0.3] Added 1992 data. Added enrollment data for all years except 1992 (unavailable).

[v 0.4] Straightening a few things out as I play with the data in my own kernel. Changed "program_other_expenditure" to "other_expenditure" and fixed chew_data.py to properly pull that information. Removed "non-elsec" funding and "program_current_expenditure" columns.

[v 0.5] Added 2016 data. My limited testing says that it worked, but I should probably keep an eye out for possible issues.

[v 0.6] Major code refactoring. Changed filenames to be a little more intuitive. Added a main function. Added NAEP data.

This is a hybrid gridded dataset of demographic data for China from 1979 to 2100, given as 21 five-year age groups of population divided by gender every year at a 0.5-degree grid resolution.

The historical period (1979-2020) part of this dataset combines the NASA SEDAC Gridded Population of the World version 4 (GPWv4, UN WPP-Adjusted Population Count) with gridded population from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP, Histsoc gridded population data).

The projection (2010-2100) part of this dataset is resampled directly from Chen et al.’s data published in Scientific Data.

This dataset includes 31 provincial administrative districts of China, including 22 provinces, 5 autonomous regions, and 4 municipalities directly under the control of the central government (Taiwan, Hong Kong, and Macao were excluded due to missing data).

Method - demographic fractions by age and gender in 1979-2020

Age- and gender-specific demographic data by grid cell for each province in China are derived by combining historical demographic data in 1979-2020 with the national population census data provided by the National Statistics Bureau of China.

To combine the national population census data with the historical demographics, we constructed the provincial fractions of demographic in each age groups and each gender according to the fourth, fifth and sixth national population census, which cover the year of 1979-1990, 1991-2000 and 2001-2020, respectively. The provincial fractions can be computed as:

\(\begin{align*} \begin{split} f_{year,province,age,gender}= \left \{ \begin{array}{lr} POP_{1990,province,age,gender}^{4^{th}census}/POP_{1990,province}^{4^{th}census} & 1979\le\mathrm{year}\le1990\\ POP_{2000,province,age,gender}^{5^{th}census}/POP_{2000,province}^{5^{th}census} & 1991\le\mathrm{year}\le2000\\ POP_{2010,province,age,gender}^{6^{th}census}/POP_{2010,province}^{6^{th}census}, & 2001\le\mathrm{year}\le2020 \end{array} \right. \end{split} \end{align*}\)

Where:

- \( f_{\mathrm{year,province,age,gender}}\)is the fraction of population for a given age, a given gender in each province from the national census from 1979-2020.

- \(\mathrm{PO}\mathrm{P}_{\mathrm{year,province,age,gender}}^{X^{\mathrm{th}}\mathrm{census} }\) is the total population for a given age, a given gender in each province from the X^th national census.

- \(\mathrm{PO}\mathrm{P}_{\mathrm{year,province}}^{X^{\mathrm{th}}\mathrm{census} }\) is the total population for all ages and both genders in each province from the X^th national census.

Method - demographic totals by age and gender in 1979-2020

The yearly grid population for 1979-1999 are from ISIMIP Histsoc gridded population data, and for 2000-2020 are from the GPWv4 demographic data adjusted by the UN WPP (UN WPP-Adjusted Population Count, v4.11, https://beta.sedac.ciesin.columbia.edu/data/set/gpw-v4-population-count-adjusted-to-2015-unwpp-country-totals-rev11), which combines the spatial distribution of demographics from GPWv4 with the temporal trends from the UN WPP to improve accuracy. These two gridded time series are simply joined at the cut-over date to give a single dataset - historical demographic data covering 1979-2020.

Next, historical demographic data are mapped onto the grid scale to obtain provincial data by using gridded provincial code lookup data and name lookup table. The age- and gender-specific fraction were multiplied by the historical demographic data at the provincial level to obtain the total population by age and gender for per grid cell for china in 1979-2020.

Method - demographic totals and fractions by age and gender in 2010-2100

The grid population count data in 2010-2100 under different shared socioeconomic pathway (SSP) scenarios are drawn from Chen et al. published in Scientific Data with a resolution of 1km (~ 0.008333 degree). We resampled the data to 0.5 degree by aggregating the population count together to obtain the future population data per cell.

This previously published dataset also provided age- and gender-specific population of each provinces, so we calculated the fraction of each age and gender group at provincial level. Then, we multiply the fractions with grid population count to get the total population per age group per cell for each gender.

Note that the projected population data from Chen’s dataset covers 2010-2020, while the historical population in our dataset also covers 2010-2020. The two datasets of that same period may vary because the original population data come from different sources and are calculated based on different methods.

Disclaimer

This dataset is a hybrid of different datasets with independent methodologies. Spatial or temporal consistency across dataset boundaries cannot be guaranteed.

Compares percent distribution of STEM (science, technology, engineering and math and computer science) and BHASE (non-STEM) fields of study between census divisions.

Analysis unit

Persons and households

UNITS IDENTIFIED: - Dwellings: no - Vacant Units: no - Households: yes - Individuals: yes - Group quarters: no

UNIT DESCRIPTIONS: - Dwellings: Dwellings generally have one or more than one room or floor, outer walls of concrete or mud wall with raw materials, a roof and are used by people for dwelling purposes. - Households: A household is a group of people living together based on the same source of income who take their meals in the same kitchen. In a household there might be only one person or many person, relatives or not relatives as well. The main basis for identifying household members is the shared income and kitchen concept. - Group quarters: Group quarters are considered instituational households such as jails, orphanages, mental hospitals, army and police barracks, cantonments, foster homes, hostels, old age homes, or rehabilitation centers.

Universe

All individuals residing within the kingdom

Kind of data

Population and Housing Census [hh/popcen]

Sampling procedure

MICRODATA SOURCE: Central Bureau of Statistics

SAMPLE SIZE (person records): 3238842.

SAMPLE DESIGN: Systematic sampling is used to draw sample households from all areas except in 6 districts (Rasuwa , Mugu, Humla, Dolpa, Mustang and Manang) and 52 (out of 58) municipalities, which were instead fully enumerated. In this sampling, the first serial number of house is randomly selected by the supervisor, and other numbers are selected by systematically mathematical procedure. The sampling rate is 1:8 for all ward/sub wards. IPUMS subsampled fully enumerated geographies to achieve approximately the equivalent of a 1:8 sample.

Mode of data collection

Face-to-face [f2f]

Research instrument

There are two forms used. Schedule 1 (Short form) is used to collect the information of all households and individuals. Schedule 2 (Long form) is used to collect information of the households and individuals from the sampled households.

Details on STEM (science, technology, engineering and math and computer science) and BHASE (non-STEM) fields of study by highest certificate, diploma or degree, age and gender for census divisions and municipalities.

Comprehensive population and demographic data for Math Toi Village