Firm age class limits.

According to a survey conducted in 2023, ** percent of K-12 teachers at public schools in the United States said that they had decided on their own, without being directed by school or district leaders, to limit discussions about political and social issues in class. In comparison, only ** percent said that they had not decided to limit discussing political and social issues in class.

Spatial analysis and statistical summaries of the Protected Areas Database of the United States (PAD-US) provide land managers and decision makers with a general assessment of management intent for biodiversity protection, natural resource management, and recreation access across the nation. The PAD-US 3.0 Combined Fee, Designation, Easement feature class (with Military Lands and Tribal Areas from the Proclamation and Other Planning Boundaries feature class) was modified to remove overlaps, avoiding overestimation in protected area statistics and to support user needs. A Python scripted process ("PADUS3_0_CreateVectorAnalysisFileScript.zip") associated with this data release prioritized overlapping designations (e.g. Wilderness within a National Forest) based upon their relative biodiversity conservation status (e.g. GAP Status Code 1 over 2), public access values (in the order of Closed, Restricted, Open, Unknown), and geodatabase load order (records are deliberately organized in the PAD-US full inventory with fee owned lands loaded before overlapping management designations, and easements). The Vector Analysis File ("PADUS3_0VectorAnalysisFile_ClipCensus.zip") associated item of PAD-US 3.0 Spatial Analysis and Statistics ( https://doi.org/10.5066/P9KLBB5D ) was clipped to the Census state boundary file to define the extent and serve as a common denominator for statistical summaries. Boundaries of interest to stakeholders (State, Department of the Interior Region, Congressional District, County, EcoRegions I-IV, Urban Areas, Landscape Conservation Cooperative) were incorporated into separate geodatabase feature classes to support various data summaries ("PADUS3_0VectorAnalysisFileOtherExtents_Clip_Census.zip") and Comma-separated Value (CSV) tables ("PADUS3_0SummaryStatistics_TabularData_CSV.zip") summarizing "PADUS3_0VectorAnalysisFileOtherExtents_Clip_Census.zip" are provided as an alternative format and enable users to explore and download summary statistics of interest (Comma-separated Table [CSV], Microsoft Excel Workbook [.XLSX], Portable Document Format [.PDF] Report) from the PAD-US Lands and Inland Water Statistics Dashboard ( https://www.usgs.gov/programs/gap-analysis-project/science/pad-us-statistics ). In addition, a "flattened" version of the PAD-US 3.0 combined file without other extent boundaries ("PADUS3_0VectorAnalysisFile_ClipCensus.zip") allow for other applications that require a representation of overall protection status without overlapping designation boundaries. The "PADUS3_0VectorAnalysis_State_Clip_CENSUS2020" feature class ("PADUS3_0VectorAnalysisFileOtherExtents_Clip_Census.gdb") is the source of the PAD-US 3.0 raster files (associated item of PAD-US 3.0 Spatial Analysis and Statistics, https://doi.org/10.5066/P9KLBB5D ). Note, the PAD-US inventory is now considered functionally complete with the vast majority of land protection types represented in some manner, while work continues to maintain updates and improve data quality (see inventory completeness estimates at: http://www.protectedlands.net/data-stewards/ ). In addition, changes in protected area status between versions of the PAD-US may be attributed to improving the completeness and accuracy of the spatial data more than actual management actions or new acquisitions. USGS provides no legal warranty for the use of this data. While PAD-US is the official aggregation of protected areas ( https://www.fgdc.gov/ngda-reports/NGDA_Datasets.html ), agencies are the best source of their lands data.

This statistic observes the limits posted by the relevant authorities in Europe on the acceptable volumes of pollutants from motorcycles in class II, from the year 1998 to the year 2006, in grams per kilometer. There is a steep decrease in pollutant limit values for carbon monoxide and hydrocarbons across the period of record.

Spatial analysis and statistical summaries of the Protected Areas Database of the United States (PAD-US) provide land managers and decision makers with a general assessment of management intent for biodiversity protection, natural resource management, and outdoor recreation access across the nation. This data release (PAD-US 4.1 Vector Analysis and Summary Statistics) presents results from statistical summaries of the PAD-US 4.1 protection status (by GAP Status Code) and public access status for various land unit boundaries. Summary statistics are also available to explore and download from the PAD-US Statistics Dashboard ( https://www.usgs.gov/programs/gap-analysis-project/science/pad-us-statistics ). The vector GIS analysis file, source data used to summarize statistics for areas of interest to stakeholders (National, State, Department of the Interior Region, Congressional District, County, EcoRegions I-IV, Urban Areas, Landscape Conservation Cooperative), and complete Summary Statistics Tabular Data (CSV) are included in this data release. Raster analysis files are also available for combination with other raster data (PAD-US 4.1 Raster Analysis child item). The PAD-US Combined Fee, Designation, Easement feature class in the Full Inventory Database, with Military Lands and Tribal Areas from the Proclamation and Other Planning Boundaries feature class, was modified to prioritize and remove overlapping management designations, limiting overestimation in protection status or public access statistics and to support user needs for vector and raster analysis data. Analysis files in this data release were clipped to the Census State boundary file to define the extent and fill in areas (largely private land) outside the PAD-US, providing a common denominator for statistical summaries.

Anomaly detection in random fields is an important problem in many applications including the detection of cancerous cells in medicine, obstacles in autonomous driving and cracks in the construction material of buildings. Such anomalies are often visible as areas with different expected values compared to the background noise. Scan statistics based on local means have the potential to detect such local anomalies by enhancing relevant features. We derive limit theorems for a general class of such statistics over M-dependent random fields of arbitrary but fixed dimension. By allowing for a variety of combinations and contrasts of sample means over differently-shaped local windows, this yields a flexible class of scan statistics that can be tailored to the particular application of interest. The latter is demonstrated for crack detection in 2D-images of different types of concrete. Together with a simulation study this indicates the potential of the proposed methodology for the detection of anomalies in a variety of situations.

Financial ratios of farms, by revenue class and quartile boundary, incorporated and unincorporated sectors, Canada. Data are available on an annual basis.

Replication Data for: Grading for Equity? The Limits of Course-Level Interventions in Closing Achievement Gaps in Political Science Education. Published in Journal of Political Science Education. 2025. https://doi.org/10.1080/15512169.2025.2538574

The number of cross-boundary students (CBS) using various land-based boundary control points, with a breakdown by class level

Abstract

This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied.

This product, Australian Statistical Geography Standard (ASGS) Volume 1 - Main Structure and Greater Capital City Statistical Areas (cat no. 1270.0.55.001), is the first in a series of Volumes that will detail the various structures and regions of the ASGS. Its purpose is to outline the conceptual basis of the regions of the Main Structure and the Greater Capital City Statistical Areas and their relationship to each other.

Volume 2 - Indigenous Structure (cat no. 1270.0.55.002), is the second in a series of Volumes that detail the various structures and regions of the ASGS. Its purpose is to outline the conceptual basis for the design of the Indigenous Structure. This product contains several elements including the manual, region names and codes and the digital boundaries.

The Non-ABS Structures bring together those regions which are not defined by the ABS, but which are important to users of ABS statistics. ABS is committed to providing a range of statistics for these areas. They generally represent administrative regions and are approximated by Mesh Blocks (MBs), Statistical Areas Level 1 (SA1) or Statistical Areas Level 2 (SA2). As the Non-ABS Structures represent regions that are subject to ongoing change, the ABS will release a revised publication for ASGS Non-ABS Structures in July each year. The individual structures will only be updated where significant change has occurred in the past year.

Full metadata is available at the feature class level by selecting the 'Description' tab in ArcCatalog.

This dataset contains three Geodatabases:

1. ABS Boundaries 2011

Feature Classes:

a) Greater Capital City Statistical Area polygons for Australia - GCCSA_2011_AUST

b) Mesh Block polygons split into State feature classes - MB_2011_\[STATE\]

c) Statistical Area polygons, Split into Levels 1, 2, 3 and 4 feature classes - SA\[LEVEL\]_2011

d) State Borders for Australia polygons - STE_2011_AUST

1. Indigenous Structures 2011

Feature Classes:

a) Indigenous Areas - Polygons

b) Indigenous Locations - Polygons

c) Indigenous Regions - Polygons

1. Non ABS Boundaries 2011

Feature Classes

a) Australian Drainage Divisions

b) Commonwealth Electoral Divisions

c) Local Government Areas

d) Postal Areas

e) State Electoral Boundaries

f) State Suburb Code

g) Tourism Regions

Dataset History

The Australian Statistical Geography Standard (ASGS) is a hierarchical classification system of geographical regions and consists of a number of interrelated structures. The ASGS brings all the regions for which the Australian Bureau of Statistics (ABS) publishes statistics within the one framework and will be used by the ABS for the collection and dissemination of geographically classified statistics from the 1 July 2011. It provides a common framework of statistical geography and enables the production of statistics which are comparable and can be spatially integrated.

Dataset Citation

Australian Bureau of Statistics (2011) ABS Boundaries 2011. Bioregional Assessment Source Dataset. Viewed 29 September 2017, http://data.bioregionalassessments.gov.au/dataset/8b65c3a4-7010-4a79-8eaa-5621b750347f.

description: Cities, Towns and Villages dataset current as of 2010. City Limits feature class located within the boundary data geodatabase.; abstract: Cities, Towns and Villages dataset current as of 2010. City Limits feature class located within the boundary data geodatabase.

As required by Dallas City Charter, redistricting of elected official districts is required every ten years based on updated Census information. This feature layer represents the archived city council district boundaries as approved by the Dallas City Council on October 5, 2011. This feature class is useful for representing Council District borders as patterned lines in web map applications. This feature class is not authoritative; it is derived from the Enterprise GIS Council District polygon feature class. Boundaries are based upon 2010 Census Block geography and may not conform with other data. Where street boundaries serve as district borders, the entire right of way is assigned to the northern or eastern boundary. Utilizing this file with non-census shapefiles may result in discrepancies between boundaries. To identify appropriate boundaries, please use in conjunction with the 2010 Census edges and block files. Note: External boundaries conflated to match base City Limits GIS data.

A polygon feature class of municipal boundaries within Miami-Dade County, data includes the municipal codes and names.Updated: As Needed The data was created using: Projected Coordinate System: WGS_1984_Web_Mercator_Auxiliary_SphereProjection: Mercator_Auxiliary_Sphere

Spatial analysis and statistical summaries of the Protected Areas Database of the United States (PAD-US) provide land managers and decision makers with a general assessment of management intent for biodiversity protection, natural resource management, and outdoor recreation access across the nation. This data release presents results from statistical summaries of the PAD-US 4.0 protection status (by GAP Status Code) and public access status for various land unit boundaries (PAD-US 4.0 Vector Analysis and Summary Statistics). Summary statistics are also available to explore and download from the PAD-US Statistics Dashboard ( https://www.usgs.gov/programs/gap-analysis-project/science/pad-us-statistics ). The vector GIS analysis file, source data used to summarize statistics for areas of interest to stakeholders (National, State, Department of the Interior Region, Congressional District, County, EcoRegions I-IV, Urban Areas, Landscape Conservation Cooperative), and complete Summary Statistics Tabular Data (CSV) are included in this data release. Raster analysis files are also available for combination with other raster data (PAD-US 4.0 Raster Analysis). The PAD-US Combined Fee, Designation, Easement feature class in the Full Inventory Database, with Military Lands and Tribal Areas from the Proclamation and Other Planning Boundaries feature class, was modified to prioritize and remove overlapping management designations, limiting overestimation in protection status or public access statistics and to support user needs for vector and raster analysis data. Analysis files in this data release were clipped to the Census State boundary file to define the extent and fill in areas (largely private land) outside the PAD-US, providing a common denominator for statistical summaries.

Population by sex and social class by Province. (Census 2022 Theme 9 Table 1 )Census 2022 table 9.1 is population aged 15+ by sex and social class. Attributes include population breakdown by social class and sex. Census 2022 theme 9 is Social Class and Socio-Economic Group. The methodology has changed for SOC and SEG so comparisons cannot be made with 2016 data. See Background Notes - CSO - Central Statistics Officehttps://www.cso.ie/en/releasesandpublications/ep/p-cpp7/census2022profile7-employmentoccupationsandcommuting/backgroundnotes/ Ireland is divided into four provinces - Leinster, Ulster, Munster and Connacht. They do not have any administrative functions and they are relevant for a number of historical, cultural and sporting reasons. The borders of the provinces coincide with the boundaries of counties. Three of the nine counties in Ulster are within the jurisdiction of the State.Coordinate reference system: Irish Transverse Mercator (EPSG 2157). These boundaries are based on 20m generalised boundaries sourced from Tailte Éireann Open Data Portal. Provinces - National Statutory Boundaries - 2019This dataset is provided by Tailte Éireann

AdminVector is the vector data set of Belgian administrative and statistical units. It includes various classes. First class: Belgian statistic sectors as defined by the FPS Economy. Second class: municipal sections, with no unanimous definition. The five following classes correspond to official administrative units as managed by the FPS Finance. Other classes are added to these classes, like border markers or the Belgian maritime zone. The boundaries of the seven first classes are consolidated together in order to keep the topological cohrence of the objects. This data set can be freely downloaded.