Financial Services of Libya

This dataset presents a range of data items sourced from a wide variety of collections, both Australian Bureau of Statistics (ABS) and non-ABS. The data is derived from the 22 November 2023 release of Data by region (DBR) . Individual data items present the latest reference year data available on DBR. This layer presents data by Local Government Areas (LGA), 2021. The Economy and industry theme is based on groupings of data within Data by region (DBR). Concepts, sources and methods for each dataset can be found on the Data by region methodology page. The Economy and industry theme includes: Agricultural commodities Building approvals Counts of Australian businesses, including entries and exits Gross value of agricultural production Industry of employment (Census) Residential property prices Personal insolvencies Registered motor vehicles When analysing these statistics, care needs to be taken as time periods, definitions, methodologies, scope, and coverage can differ across collections. Some data values have been randomly adjusted or suppressed to avoid the release of confidential data. In some cases small cells have been randomly altered to zero. Care should be taken when interpreting cells with small numbers or zeros. Made possible by the Digital Atlas of Australia The Digital Atlas of Australia is an Australian Government initiative being led by Geoscience Australia. It brings together trusted datasets from across government in an interactive, secure, and easy-to-use geospatial platform. The Australian Bureau of Statistics (ABS) is working in partnership with Geoscience Australia to establish a set of web services to make ABS data available in the Digital Atlas. Contact the Australian Bureau of Statistics (ABS) If you have questions, feedback or would like to receive updates about this web service, please email geography@abs.gov.au. For information about how the ABS manages any personal information you provide view the ABS privacy policy. Data and geography references Source data publication: Data by region (DBR) Geographic boundary information: Australian Statistical Geography Standard (ASGS) Edition 3 Further information: Data by region methodology, reference period 2011-23 Source: Australian Bureau of Statistics (ABS)

As part of A Place to Grow: Growth Plan for the Greater Golden Horseshoe, the Minister released a new policy framework for protecting employment areas that are critical to the region’s economy. These changes provide flexibility by allowing municipalities to consider employment area conversions prior to the municipal comprehensive review while ensuring protections are in place to protect key employment areas as needed. To ensure important employment areas are not converted during this window, the province introduced a policy that allows the Minister to identify provincially significant employment zoneswhich are employment areas that cannot be converted prior to the municipal comprehensive review without a more comprehensive assessment approved by the province. Regional Intensification Corridors are major transportation and growth corridors where higher-density, mixed-use development is encouraged to support transit investment, efficient infrastructure use, and the creation of complete communities. These corridors play a key role in accommodating population and employment growth within the existing urban area. This data is intended to be used in conjuction with other related mapping layers for this schedule, and may be impacted by other schedules, with consideration to the text of Brampton Plan.

Visió general Aquest servei conté el detall mensual de les retribucions, indemnitzacions i dietes associades als càrrecs electes municipals. Recull informació econòmica vinculada als representants polítics locals i permet consultar els imports assignats per període, tipologia i persona beneficiària. Contingut del servei Regidors i regidores identificats amb el grup municipal corresponent. Tipus de percepció, amb la descripció del concepte retributiu o indemnitzatori. Imports per mes i any, amb ordenació temporal de les dades. Identificació de l’ens al qual s’associen els registres. Àmbit i ús El servei està orientat al seguiment i la transparència de la despesa vinculada als càrrecs electes municipals. És útil per a consultes de fiscalització, anàlisi econòmica i explotació en aplicacions i visors GIS amb accés a dades administratives locals. Font i disponibilitat Les dades provenen dels Serveis Econòmics i s’actualitzen trimestralment. - Font: Serveis Econòmics - Actualització: trimestral - Formats:

Budgeted and actual expenses divided by fun, department, business unit, and account with details for each fiscal year.  This dataset is updated annually. Source Link: https://www.nashville.gov/departments/finance/management-and-budget/citizens-guide-budgetMetadata Document: Metro Budget to Actual Expenses (FY2010 - Present) Metadata.pdfContact Data Owner: opendata@nashville.gov

Employment, Commuting, Occupation, Income, Health Insurance, Poverty, and more. This service is updated annually with American Community Survey (ACS) 5-year data. Contact: District of Columbia, Office of Planning. Email: planning@dc.gov. Geography: District-wide. Current Vintage: 2019-2023. ACS Table(s): DP03. Data downloaded from: Census Bureau's API for American Community Survey. Date of API call: January 2, 2025. National Figures: data.census.gov. Please cite the Census and ACS when using this data. Data Note from the Census: Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables. Data Processing Notes: This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Boundaries come from the US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2020 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page. Data processed using R statistical package and ArcGIS Desktop. Margin of Error was not included in this layer but is available from the Census Bureau. Contact the Office of Planning for more information about obtaining Margin of Error values.

Employment, Commuting, Occupation, Income, Health Insurance, Poverty, and more. This service is updated annually with American Community Survey (ACS) 5-year data. Contact: District of Columbia, Office of Planning. Email: planning@dc.gov. Geography: Census Tracts. Current Vintage: 2019-2023. ACS Table(s): DP03. Data downloaded from: Census Bureau's API for American Community Survey. Date of API call: January 2, 2025. National Figures: data.census.gov. Please cite the Census and ACS when using this data. Data Note from the Census: Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables. Data Processing Notes: This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Boundaries come from the US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2020 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page. Data processed using R statistical package and ArcGIS Desktop. Margin of Error was not included in this layer but is available from the Census Bureau. Contact the Office of Planning for more information about obtaining Margin of Error values.