The Best Management Practices Statistical Estimator (BMPSE) version 1.2.0 was developed by the U.S. Geological Survey (USGS), in cooperation with the Federal Highway Administration (FHWA) Office of Project Delivery and Environmental Review to provide planning-level information about the performance of structural best management practices for decision makers, planners, and highway engineers to assess and mitigate possible adverse effects of highway and urban runoff on the Nation's receiving waters (Granato 2013, 2014; Granato and others, 2021). The BMPSE was assembled by using a Microsoft Access® database application to facilitate calculation of BMP performance statistics. Granato (2014) developed quantitative methods to estimate values of the trapezoidal-distribution statistics, correlation coefficients, and the minimum irreducible concentration (MIC) from available data. Granato (2014) developed the BMPSE to hold and process data from the International Stormwater Best Management Practices Database (BMPDB, www.bmpdatabase.org). Version 1.0 of the BMPSE contained a subset of the data from the 2012 version of the BMPDB; the current version of the BMPSE (1.2.0) contains a subset of the data from the December 2019 version of the BMPDB. Selected data from the BMPDB were screened for import into the BMPSE in consultation with Jane Clary, the data manager for the BMPDB. Modifications included identifying water quality constituents, making measurement units consistent, identifying paired inflow and outflow values, and converting BMPDB water quality values set as half the detection limit back to the detection limit. Total polycyclic aromatic hydrocarbons (PAH) values were added to the BMPSE from BMPDB data; they were calculated from individual PAH measurements at sites with enough data to calculate totals. The BMPSE tool can sort and rank the data, calculate plotting positions, calculate initial estimates, and calculate potential correlations to facilitate the distribution-fitting process (Granato, 2014). For water-quality ratio analysis the BMPSE generates the input files and the list of filenames for each constituent within the Graphical User Interface (GUI). The BMPSE calculates the Spearman’s rho (ρ) and Kendall’s tau (τ) correlation coefficients with their respective 95-percent confidence limits and the probability that each correlation coefficient value is not significantly different from zero by using standard methods (Granato, 2014). If the 95-percent confidence limit values are of the same sign, then the correlation coefficient is statistically different from zero. For hydrograph extension, the BMPSE calculates ρ and τ between the inflow volume and the hydrograph-extension values (Granato, 2014). For volume reduction, the BMPSE calculates ρ and τ between the inflow volume and the ratio of outflow to inflow volumes (Granato, 2014). For water-quality treatment, the BMPSE calculates ρ and τ between the inflow concentrations and the ratio of outflow to inflow concentrations (Granato, 2014; 2020). The BMPSE also calculates ρ between the inflow and the outflow concentrations when a water-quality treatment analysis is done. The current version (1.2.0) of the BMPSE also has the option to calculate urban-runoff quality statistics from inflows to BMPs by using computer code developed for the Highway Runoff Database (Granato and Cazenas, 2009;Granato, 2019). Granato, G.E., 2013, Stochastic empirical loading and dilution model (SELDM) version 1.0.0: U.S. Geological Survey Techniques and Methods, book 4, chap. C3, 112 p., CD-ROM https://pubs.usgs.gov/tm/04/c03 Granato, G.E., 2014, Statistics for stochastic modeling of volume reduction, hydrograph extension, and water-quality treatment by structural stormwater runoff best management practices (BMPs): U.S. Geological Survey Scientific Investigations Report 2014–5037, 37 p., http://dx.doi.org/10.3133/sir20145037. Granato, G.E., 2019, Highway-Runoff Database (HRDB) Version 1.1.0: U.S. Geological Survey data release, https://doi.org/10.5066/P94VL32J. Granato, G.E., and Cazenas, P.A., 2009, Highway-Runoff Database (HRDB Version 1.0)--A data warehouse and preprocessor for the stochastic empirical loading and dilution model: Washington, D.C., U.S. Department of Transportation, Federal Highway Administration, FHWA-HEP-09-004, 57 p. https://pubs.usgs.gov/sir/2009/5269/disc_content_100a_web/FHWA-HEP-09-004.pdf Granato, G.E., Spaetzel, A.B., and Medalie, L., 2021, Statistical methods for simulating structural stormwater runoff best management practices (BMPs) with the stochastic empirical loading and dilution model (SELDM): U.S. Geological Survey Scientific Investigations Report 2020–5136, 41 p., https://doi.org/10.3133/sir20205136

NUTS2 boundaries generalised to 20m.The Nomenclature of Territorial Units for Statistics (NUTS) were drawn up by Eurostat in order to define territorial units for the production of regional statistics across the European Union. The NUTS classification has been used in EU legislation since 1988, but it was only in 2003 that the EU Member States, the European Parliament and the Commission established the NUTS regions within a legal framework (Regulation (EC) No 1059/2003).The Irish NUTS 3 regions comprise the eight Regional Authorities established under the Local Government Act, 1991 (Regional Authorities) (Establishment) Order, 1993 which came into operation on January 1st 1994. The NUTS 2 regions, which were proposed by Government and agreed to by Eurostat in 1999, are groupings of the Regional Authorities.This dataset is provided by Tailte Éireann .hidden { display: none }

INSPIRE dataset for Statistical Units theme represents information about the current Statistical grid ETRS89 of the Republic of Lithuania and current area of statistical administrative units. This statistical grid has three different cell sizes: 1x1 km, 10x10 km and 100x100km. Statistical grid cells have unique codes. These codes allow the creation of links between INSPIRE data themes, such as Population Distribution – Demography and Statistical Unit themes. This dataset represents the current area of statistical administrative units: 1) NUTS1 (boundaries and area of Lithuania), 2) NUTS2 (boundaries and area of Capital region and Central and Western Lithuania region), 3) NUTS3 (boundaries and areas of counties), 4) LAU (boundaries and areas of districts). Statistical administrative units have unique codes.

The Small Area Boundaries were created with the following credentials. National boundary dataset. Consistent sub-divisions of an ED. Created not to cross some natural features. Defined area with a minimum number of GeoDirectory building address points. Defined area initially created with minimum of 65 – approx. average of around 90 residential address points. Generated using two bespoke algorithms which incorporated the ED and Townland boundaries, ortho-photography, large scale vector data and GeoDirectory data. Before the 2011 census they were split in relation to motorways and dual carriageways. After the census some boundaries were merged and other divided to maintain privacy of the residential area occupants. They are available as generalised and non generalised boundary sets in the ITM projection.This dataset is provided by Tailte Éireann .hidden { display: none }

CSO Electoral Divisions - National Statistical Boundaries - 2022 - Generalised 100mCSO EDs are a statistical geography that aligns closely with the official ED boundary. EDs are comprised of whole Small Areas. In a small number of cases, CSO EDs do not align with the official boundary, where EDs are amalgamated to ensure statistical confidentiality or where a change was required to ensure better CSO ED/CSO alignment.Creation of generalised versions of Published ED boundaries. Using Douglas-Peucker algorithm with tolerances of 20m, 50m, 100m and writes out features classes. Uses topology and option of preserving common boundaries to ensure output does not generalise differently on common boundaries.Update Notice: 4th August 2023: ED and LEA attributes changed on 15 SAs. As a result of the changes to SAs, CSO ED has one additional ED and the number of CSO EDs is 3420 and there is a change to 2 CSO LEAs. The ED and LEAs impacted are

ED 2ae19629-1d37-13a3-e055-000000000001 renamed to DALKEY-COLIEMORE

ED 94b26e15-6ed2-44c2-a0b0-207c369a2da8 SHANKILL-RATHSALLAGH added

LEA 40aece0e-a19d-4e78-af9d-e129f5557496 DÚN LAOGHAIRE redrawn

LEA d65ef6e7-75e6-49d9-bda9-d4690e8f68dc KILLINEY-SHANKILL redrawn

NUTS2 boundaries ungeneralised. The Nomenclature of Territorial Units for Statistics (NUTS) were drawn up by Eurostat in order to define territorial units for the production of regional statistics across the European Union. The NUTS classification has been used in EU legislation since 1988, but it was only in 2003 that the EU Member States, the European Parliament and the Commission established the NUTS regions within a legal framework (Regulation (EC) No 1059/2003).The Irish NUTS 3 regions comprise the eight Regional Authorities established under the Local Government Act, 1991 (Regional Authorities) (Establishment) Order, 1993 which came into operation on January 1st 1994. The NUTS 2 regions, which were proposed by Government and agreed to by Eurostat in 1999, are groupings of the Regional Authorities.This dataset is provided by Tailte Éireann .hidden { display: none }

The gap between genomics and phenomics is narrowing. The rate at which it is narrowing, however, is being slowed by improper statistical comparison of methods. Quantification using Pearson’s correlation coefficient (r) is commonly used to assess method quality, but it is an often misleading statistic for this purpose as it is unable to provide information about the relative quality of two methods. Using r can both erroneously discount methods that are inherently more precise and validate methods that are less accurate. These errors occur because of logical flaws inherent in the use of r when comparing methods, not as a problem of limited sample size or the unavoidable possibility of a type I error. A popular alternative to using r is to measure the limits of agreement (LOA). However both r and LOA fail to identify which instrument is more or less variable than the other and can lead to incorrect conclusions about method quality. An alternative approach, comparing variances of methods, requires repeated measurements of the same subject, but avoids incorrect conclusions. Variance comparison is arguably the most important component of method validation and, thus, when repeated measurements are possible, variance comparison provides considerable value to these studies. Statistical tests to compare variances presented here are well established, easy to interpret and ubiquitously available. The widespread use of r has potentially led to numerous incorrect conclusions about method quality, hampering development, and the approach described here would be useful to advance high throughput phenotyping methods but can also extend into any branch of science. The adoption of the statistical techniques outlined in this paper will help speed the adoption of new high throughput phenotyping techniques by indicating when one should reject a new method, outright replace an old method or conditionally use a new method.

Dataset and statistical analysis for the paper "Robust Control Chart Application in Semiconductor Manufacturing Process" submitted to ICoAIMS 2023: 4th International Conference on Applied & Industrial Mathematics and Statistics 2023.

The Port Statistical Areas dataset was updated on June 05, 2025 from the United States Army Corp of Engineers (USACE) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). USACE works with port authorities from across the United States to develop the statistical port boundaries through an iterative and collaborative process. Port boundary information is prepared by USACE to increase transparency on public waterborne commerce statistic reporting, as well as to modernize how the data type is stored, analyzed, and reported. A Port Statistical Area (PSA) is a region with formally justified shared economic interests and collective reliance on infrastructure related to waterborne movements of commodities that is formally recognized by legislative enactments of state, county, or city governments. PSAs generally contain groups of county legislation for the sole purpose of statistical reporting. Through GIS mapping, legislative boundaries, and stakeholder collaboration, PSAs often serve as the primary unit for aggregating and reporting commerce statistics for broader geographical areas. Per Engineering Regulation 1130-2-520, the U.S. Army Corps of Engineers' Navigation Data Center is responsible to collect, compile, publish, and disseminate waterborne commerce statistics. This task has subsequently been charged to the Waterborne Commerce Statistics Center to perform. Performance of this work is in accordance with the Rivers and Harbors Appropriation Act of 1922. Included in this work is the definition of a port area. A port area is defined in Engineering Pamphlet 1130-2-520 as: (1) Port limits defined by legislative enactments of state, county, or city governments. (2) The corporate limits of a municipality. The USACE enterprise-wide port and port statistical area feature classes per EP 1130-2-520 are organized in SDSFIE 4.0.2 format. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/2ngc-4984

Breakdown of Namur into statistical sectors (Statbel) This dataset is used on the Portal "Statistics of the 46 districts of Namur", OPENDATA Observatory tab of the municipality of Namur.

Women and Men in Spain: Gross monthly salary of the main job. Lower limits and average values of each decile. Annual. National.

Statistical Area 1 2023 update

SA1 2023 is the first major update of the geography since it was first created in 2018. The update is to ensure SA1s are relevant and meet criteria before each five-yearly population and dwelling census. SA1 2023 contains 3,251 new SA1s. Updates were made to reflect real world changes including new subdivisions and motorways, improve the delineation of urban rural and other statistical areas and to ensure they meet population criteria by reducing the number of SA1s with small or large populations.

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Description

This dataset is the definitive version of the annually released statistical area 1 (SA1) boundaries as at 1 January 2023, as defined by Stats NZ. This version contains 33,164 SA1s (33,148 digitised and 16 with empty or null geometries (non-digitised).

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SA1 is an output geography that allows the release of more low-level data than is available at the meshblock level. Built by joining meshblocks, SA1s have an ideal size range of 100–200 residents, and a maximum population of about 500. This is to minimise suppression of population data in multivariate statistics tables.

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The SA1 should:

form a contiguous cluster of one or more meshblocks,

be either urban, rural, or water in character,

be small enough to:

• allow flexibility for aggregation to other statistical geographies,

• allow users to aggregate areas into their own defined communities of interest,

form a nested hierarchy with statistical output geographies and administrative boundaries. It must:

• be built from meshblocks,

• either define or aggregate to define SA2s, urban rural areas, territorial authorities, and regional councils.

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SA1s generally have a population of 100–200 residents, with some exceptions:

• SA1s with nil or nominal resident populations are created to represent remote mainland areas, unpopulated islands, inland water, inlets, or oceanic areas.

• Some SA1s in remote rural areas and urban industrial or business areas have fewer than 100 residents.

• Some SA1s that contain apartment blocks, retirement villages, and large non-residential facilities (prisons, boarding schools, etc) have more than 500 residents.

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SA1 numbering

SA1s are not named. SA1 codes have seven digits starting with a 7 and are numbered approximately north to south. Non-digitised codes start with 79.

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As new SA1s are created, they are given the next available numeric code. If the composition of an SA1 changes through splitting or amalgamating different meshblocks, the SA1 is given a new code. The previous code no longer exists within that version and future versions of the SA1 classification.

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Digitised and non-digitised SA1s

The digital geographic boundaries are defined and maintained by Stats NZ.

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Aggregated from meshblocks, SA1s cover the land area of New Zealand, the water area to the 12-mile limit, the Chatham Islands, Kermadec Islands, sub-Antarctic islands, off-shore oil rigs, and Ross Dependency. The following 16 SA1s are held in non-digitised form.

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7999901; New Zealand Economic Zone, 7999902; Oceanic Kermadec Islands,7999903; Kermadec Islands, 7999904; Oceanic Oil Rig Taranaki,7999905; Oceanic Campbell Island, 7999906; Campbell Island, 7999907; Oceanic Oil Rig Southland, 7999908; Oceanic Auckland Islands, 7999909; Auckland Islands, 7999910; Oceanic Bounty Islands, 7999911; Bounty Islands, 7999912; Oceanic Snares Islands, 7999913; Snares Islands, 7999914; Oceanic Antipodes Islands, 7999915; Antipodes Islands, 7999916; Ross Dependency.

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For more information please refer to the Statistical standard for geographic areas 2023.

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Generalised version

This generalised version has been simplified for rapid drawing and is designed for thematic or web mapping purposes.

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Digital data

Digital boundary data became freely available on 1 July 2007.

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To download geographic classifications in table formats such as CSV please use Ariā

Small Areas were designed as the lowest level of geography for the dissemination of statistics and generally comprise either complete or part of townlands or neighbourhoods. Small Areas were created by The National Institute of Regional and Spatial Analysis (NIRSA) on behalf of the Tailte Éireann (TE) in consultation with CSO.Small Areas generally comprise between 80 and 120 dwellings and nest within CSO Electoral Divisions.The Small Area boundaries have been amended based on Census 2022 population data.Generalised data: provided for information only.Update Notice: 4th August 2023: Attribution changed for ED and LEA attributes. An implication of this is CSO ED increase in count from 3419 to 3420 and CSO LEA boundary changes. ED and LEAs impacted are

LEA 40aece0e-a19d-4e78-af9d-e129f5557496 DÚN LAOGHAIRE redrawn

LEA d65ef6e7-75e6-49d9-bda9-d4690e8f68dc KILLINEY-SHANKILL redrawn

ED 2ae19629-1d37-13a3-e055-000000000001 renamed to DALKEY-COLIEMORE

ED 2ae19629-1e18-13a3-e055-000000000001 SHANKILL-RATHSALLAGH

SA by GUIDS Impacted:

('4c07d11e-166e-851d-e053-ca3ca8c0ca7f','4c07d11e-30f0-851d-e053-ca3ca8c0ca7f','4c07d11e-30b0-851d-e053-ca3ca8c0ca7f','4c07d11e-30a0-851d-e053-ca3ca8c0ca7f', '4c07d11e-30e2-851d-e053-ca3ca8c0ca7f','4c07d11e-30e3-851d-e053-ca3ca8c0ca7f','4c07d11e-309d-851d-e053-ca3ca8c0ca7f','4c07d11e-30bd-851d-e053-ca3ca8c0ca7f', '4c07d11e-34fc-851d-e053-ca3ca8c0ca7f','4c07d11e-353b-851d-e053-ca3ca8c0ca7f')

('4c07d11e-2b05-851d-e053-ca3ca8c0ca7f','4c07d11e-2bcd-851d-e053-ca3ca8c0ca7f','4c07d11e-3337-851d-e053-ca3ca8c0ca7f','4c07d11e-2bcb-851d-e053-ca3ca8c0ca7f', '4c07d11e-16cf-851d-e053-ca3ca8c0ca7f')

SA_PUB2022,SA_GEOGID_2022 updated for the following SA_GUID_2022 values

4c07d11e-0aa3-851d-e053-ca3ca8c0ca7f 4c07d11d-f918-851d-e053-ca3ca8c0ca7f 4c07d11e-034c-851d-e053-ca3ca8c0ca7f 4c07d11e-1042-851d-e053-ca3ca8c0ca7f 4c07d11e-25c8-851d-e053-ca3ca8c0ca7f

Statistical areas, subareas and divisions are used globally for the purpose of reporting fishery statistics. CCAMLR's Convention Area in the Southern Ocean is divided, for statistical purposes, into Area 48 (Atlantic Antarctic) between 70oW and 30oE, Area 58 (Indian Ocean Antarctic) between 30o and 150oE, and Area 88 (Pacific Antarctic) between 150oE and 70oW. These areas, which are further subdivided into subareas and divisions, are managed by CCAMLR. A global register of statistical areas, subareas and divisions is maintained by FAO http://www.fao.org/fishery/area/search/en. CCAMLR Secretariat (2013)

The 2006 Second Edition TIGER/Line files are an extract of selected geographic and cartographic information from the Census TIGER database. The geographic coverage for a single TIGER/Line file is a county or statistical equivalent entity, with the coverage area based on the latest available governmental unit boundaries. The Census TIGER database represents a seamless national file with no overlaps or gaps between parts. However, each county-based TIGER/Line file is designed to stand alone as an independent data set or the files can be combined to cover the whole Nation. The 2006 Second Edition TIGER/Line files consist of line segments representing physical features and governmental and statistical boundaries. This shapefile represents the current Core Based Statistical Areas in the 2006 TIGER Second Edition dataset for Torrance County, NM.

NUTS3 boundaries generalised to 50m. The Nomenclature of Territorial Units for Statistics (NUTS) were drawn up by Eurostat in order to define territorial units for the production of regional statistics across the European Union. The NUTS classification has been used in EU legislation since 1988, but it was only in 2003 that the EU Member States, the European Parliament and the Commission established the NUTS regions within a legal framework (Regulation (EC) No 1059/2003).

Mid-point and 95% confidence limits for community size at increasing probability levels of critical scalar stress, as estimated by the Logistic Regression model.