This dataset tracks annual average expenditure per student from 1995 to 2021 for Shoreline Unified School District

Historical Dataset of Shoreline Unified School District is provided by PublicSchoolReview and contain statistics on metrics:Comparison of Diversity Score Trends,Total Revenues Trends,Total Expenditure Trends,Average Revenue Per Student Trends,Average Expenditure Per Student Trends,Reading and Language Arts Proficiency Trends,Math Proficiency Trends,Science Proficiency Trends,Graduation Rate Trends,Overall School District Rank Trends,American Indian Student Percentage Comparison Over Years (2003-2023),Asian Student Percentage Comparison Over Years (1993-2023),Hispanic Student Percentage Comparison Over Years (1991-2023),Black Student Percentage Comparison Over Years (1988-2013),White Student Percentage Comparison Over Years (1991-2023),Native Hawaiian or Pacific Islander Student Percentage Comparison Over Years (2010-2015),Two or More Races Student Percentage Comparison Over Years (2011-2023),Comparison of Students By Grade Trends

Financial overview and grant giving statistics of Shoreline Public Schools Foundation

[Metadata] Description: Location of public shoreline access ways on Oahu as of 2008. June 2024: Hawaii Statewide GIS Program staff removed extraneous fields that had been added as part of a 2016 GIS database conversion and were no longer needed. For additional information, please refer to metadata at https://files.hawaii.gov/dbedt/op/gis/data/shoreline_public_access_oah.pdf or contact Hawaii Statewide GIS Program, Office of Planning, State of Hawaii; PO Box 2359, Honolulu, Hi. 96804; (808) 587-2846; email: gis@hawaii.gov; Website: https://planning.hawaii.gov/gis.

This dataset tracks annual hispanic student percentage from 1991 to 2023 for Shoreline School District vs. Washington

This is a detailed GIS database of public access locations (point features) along coastal shorelines. It contains a rich variety of information such as amenities (boat launches, toilets, ADA accessible, etc.) and activities (tidepooling, hiking, shellfishing, etc.) that are available at each access point.The information was collected using a GPS in the field between 2008-2010 and is updated as resources allow.For more information, contact Christina Kellum, Washington State Department of Ecology GIS Manager, gis@ecy.wa.gov.

This hosted feature layer has been published in RI State Plane Feet NAD 83.Public access points to the shoreline of Narragansett Bay and Rhode Island coastal waters to parks, beaches, refuge areas, boat ramps, marinas and other areas open to the pubic managed by federal, state, and municipal government, private organizations with interests in land preservation and protection, and rights-of-way that have been designated by the RI Coastal Resource Management Council

This dataset tracks annual total expenditure from 1990 to 2021 for Shoreline School District

About this dataset

Context

The dataset presents the distribution of median household income among distinct age brackets of householders in Shoreline. Based on the latest 2019-2023 5-Year Estimates from the American Community Survey, it displays how income varies among householders of different ages in Shoreline. It showcases how household incomes typically rise as the head of the household gets older. The dataset can be utilized to gain insights into age-based household income trends and explore the variations in incomes across households.

Key observations: Insights from 2023

In terms of income distribution across age cohorts, in Shoreline, householders within the 25 to 44 years age group have the highest median household income at $144,667, followed by those in the 45 to 64 years age group with an income of $128,879. Meanwhile householders within the under 25 years age group report the second lowest median household income of $91,607. Notably, householders within the 65 years and over age group, had the lowest median household income at $74,575.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Age groups classifications include:

• Under 25 years
• 25 to 44 years
• 45 to 64 years
• 65 years and over

Variables / Data Columns

• Age Of The Head Of Household: This column presents the age of the head of household
• Median Household Income: Median household income, in 2023 inflation-adjusted dollars for the specific age group

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

This dataset is a part of the main dataset for Shoreline median household income by age. You can refer the same here

The Shoreline Public Access Project is a geographic information systems (GIS) project to identify the location, length, and degree of public access to Washington State's marine shoreline. Before the project, it was unknown how much of Washington's 3068 miles of shoreline was public. The information was scattered throughout various government agencies and the data quality was variable. Through the Shoreline Public Access Project, the best available information has been summarized into a single data set, used to answer questions about our shoreline's ownership and public accessibility.The purpose of the Shoreline Public Access Project is: 1) to combine various sources of shoreline data into an organized and comprehensive database 2) to create a more accurate dataset of publicly accessible shoreline. The ultimate purpose of this data is to give shoreline managers and planners another tool to assist them in making important shoreline decisions.For more information, contact Christina Kellum, Washington State Department of Ecology GIS Manager, gis@ecy.wa.gov.

About this dataset

Context

The dataset tabulates the population of Shoreline by gender, including both male and female populations. This dataset can be utilized to understand the population distribution of Shoreline across both sexes and to determine which sex constitutes the majority.

Key observations

There is a slight majority of male population, with 50.05% of total population being male. Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

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

Scope of gender :

Please note that American Community Survey asks a question about the respondents current sex, but not about gender, sexual orientation, or sex at birth. The question is intended to capture data for biological sex, not gender. Respondents are supposed to respond with the answer as either of Male or Female. Our research and this dataset mirrors the data reported as Male and Female for gender distribution analysis. No further analysis is done on the data reported from the Census Bureau.

Variables / Data Columns

• Gender: This column displays the Gender (Male / Female)
• Population: The population of the gender in the Shoreline is shown in this column.
• % of Total Population: This column displays the percentage distribution of each gender as a proportion of Shoreline total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

This dataset is a part of the main dataset for Shoreline Population by Race & Ethnicity. You can refer the same here

This dataset tracks annual diversity score from 1991 to 2023 for Shoreline School District vs. Washington

There are critical needs for a nationwide compilation of reliable shoreline data. To meet these needs, the USGS has produced a comprehensive database of digital vector shorelines by compiling shoreline positions from pre-existing historical shoreline databases and by generating historical and modern shoreline data. Historical shoreline positions serve as easily understood features that can be used to describe the movement of beaches through time. These data are used to calculate rates of shoreline change for the U.S. Geological Survey's (USGS) National Assessment of Shoreline Change Project. Each shoreline may represent a compilation of data from one or more sources for one or more dates provided by one or more agencies. Details regarding source are provided in the 'Data Quality Information' section of the individual shoreline metadata report. To make this shoreline data more accessible to the public and other agencies, the USGS created this web service. This web service was created utilizing ESRI ArcServer. Vector shoreline layers were collected, organized by state, and symbology made consistent among similar data sets. This service meets open geospatial consortium standards.

The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This data release includes two new mean high water (MHW) shorelines extracted from lidar data collected in 2010 and 2017-2018. Previously published historical shorelines for South Carolina (Kratzmann and others, 2017) were combined with the new lidar shorelines to calculate long-term (up to 166 years) and short-term (up to 18 years) rates of change. Files associated with the long-term and short-term rates are appended with "LT" and "ST", respectively. A proxy-datum bias reference line that accounts for the positional difference in a proxy shoreline (e.g. High Water Line (HWL) shoreline) and a datum shoreline (e.g. MHW shoreline) is also included in this release.

The Shoreline Public Access Project is a geographic information systems (GIS) project to identify the location, length, and degree of public access to Washington State's marine shoreline. Before the project, it was unknown how much of Washington's 3066 miles of shoreline was public. The information was scattered throughout various government agencies and the data quality was variable. Through the Shoreline Public Access Project, the best available information has been summarized into a single data set, used to answer questions about our shoreline's ownership and public accessibility.For more information, contact Christina Kellum, Washington State Department of Ecology GIS Manager, gis@ecy.wa.gov.

The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This data release includes one new mean high water (MHW) shoreline extracted from lidar data collected in 2017 for the entire coastal region of North Carolina which is divided into four subregions: northern North Carolina (NCnorth), central North Carolina (NCcentral), southern North Carolina (NCsouth), and western North Carolina (NCwest). Previously published historical shorelines for North Carolina (Kratzmann and others, 2017) were combined with the new lidar shoreline to calculate long-term (up to 169 years) and short-term (up to 20 years) rates of change. Files associated with the long-term and short-term rates are appended with "LT" and "ST", respectively. A proxy-datum bias reference line that accounts for the positional difference in a proxy shoreline (e.g. High Water Line (HWL) shoreline) and a datum shoreline (e.g. MHW shoreline) is also included in this release.

The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change. This data release includes two new mean high water (MHW) shorelines extracted from lidar data collected in 2010 and 2017-2018. Previously published historical shorelines for South Carolina (Kratzmann and others, 2017) were combined with the new lidar shorelines to calculate long-term (up to 166 years) and short-term (up to 18 years) rates of change. Files associated with the long-term and short-term rates are appended with "LT" and "ST", respectively. A proxy-datum bias reference line that accounts for the positional difference in a proxy shoreline (e.g. High Water Line (HWL) shoreline) and a datum shoreline (e.g. Mean High Water (MHW) shoreline) is also included in this release.

The U.S. Geological Survey (USGS) has compiled national shoreline data for more than 20 years to document coastal change and serve the needs of research, management, and the public. Maintaining a record of historical shoreline positions is an effective method to monitor national shoreline evolution over time, enabling scientists to identify areas most susceptible to erosion or accretion. These data can help coastal managers and planners understand which areas of the coast are vulnerable to change.

This data release includes a compilation of previously published historical shoreline positions for Virginia spanning 148 years (1849-1997), and two new mean high water (MHW) shorelines extracted from lidar data collected in 2010 and 2017. These data provide a standardized shoreline database for the state. This release includes both long-term (up to 168 years) and short term (~20 years) rates. Files associated with the long-term and short-term rates are appended with 'LT' and 'ST', respectively. A proxy-datum bias reference line that accounts for the positional difference in a proxy shoreline (e.g. High Water Line (HWL) shoreline) and a datum shoreline (e.g. MHW shoreline) is also included in this release.

About this dataset

Context

The dataset tabulates the Shoreline population by age cohorts (Children: Under 18 years; Working population: 18-64 years; Senior population: 65 years or more). It lists the population in each age cohort group along with its percentage relative to the total population of Shoreline. The dataset can be utilized to understand the population distribution across children, working population and senior population for dependency ratio, housing requirements, ageing, migration patterns etc.

Key observations

The largest age group was 18 to 64 years with a poulation of 36,268 (61.18% of the total population). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Content

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

Age cohorts:

• Under 18 years
• 18 to 64 years
• 65 years and over

Variables / Data Columns

• Age Group: This column displays the age cohort for the Shoreline population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
• Population: The population for the age cohort in Shoreline is shown in the following column.
• Percent of Total Population: The population as a percent of total population of the Shoreline is shown in the following column.

Good to know

Margin of Error

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

Custom data

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

Inspiration

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

Recommended for further research

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

Measurements by volunteer scientists using participatory science methods in combination with high resolution remote sensing can improve our ability to monitor water quality changes in highly vulnerable and economically valuable nearshore and estuarine habitats. In the Chesapeake Bay (USA), tidal tributaries are a focus of watershed and shoreline management efforts to improve water quality. The Chesapeake Water Watch program seeks to enhance the monitoring of tributaries by developing and testing methods for volunteer scientists to easily measure chlorophyll, turbidity, and colored dissolved organic matter (CDOM) to inform Bay stakeholders and improve algorithms for analogous remote sensing (RS) products. In the program, trained volunteers have measured surface turbidity using a smartphone app, HydroColor, calibrated with a photographer’s gray card. In vivo chlorophyll and CDOM fluorescence were assessed in surface samples with hand-held fluorometers (Aquafluor) located at sample processing “hubs” where volunteers drop off samples for same day processing. In validation samples, HydroColor turbidity and Aquafluor in vivo chlorophyll and CDOM fluorescence were linear estimators of standard analytical measures of turbidity, chlorophyll and CDOM, respectively, with R2 values ranging from 0.65 to 0.85. Updates implemented in a new version (v2) of HydroColor improved the precision of estimates. These methods are being used for both repeat sampling at fixed sites of interest and ad-hoc “blitzes” to synoptically sample tributaries all around the Bay in coordination with satellite overpasses. All data is accessible on a public database (serc.fieldscope.org) and can be a resource to monitor long-term trends in the tidal tributaries as well as detect and diagnose causes of events of concern such as algal blooms and storm-induced reductions in water clarity.