Feature layer generated from running the Aggregate Points solutions. Points from Public_311_2022 were aggregated to Minneapolis_Neighborhoods

Feature layer generated from running the Aggregate Points solutions. Points from mini_gsi_0015 were aggregated to minigent - copy

Feature layer generated from running the Aggregate Points solutions. Points from GSI Project Funding 2000-2020 were aggregated to minigent - copy

Feature layer generated from running the Aggregate Points solutions. Points from Tax Account Points filtered by Residential / commercial properties were aggregated to Bins 0.3 and 0.8 miles with Stats for CityTaxValue, TotalTaxValue, LandValue and ImpVal

This dataset presents an aggregation of the power consumption at 30 minutes in Wh for delivery points with a power below 36kVA. The number of delivery points concerned per electricity distribution system operator is also indicated. The geographical mesh is the regional mesh. The data are from resesda (formerly URM), Strasbourg Electricité Réseaux and Enedis. These data are published in compliance with the rules relating to the protection of Commercially Sensitive Information. A question about the dataset? A use case to share with other users? The Forum of open data experts electricity and gas is here for that!

The Uniform Appraisal Dataset (UAD) Aggregate Statistics Data File and Dashboards are the nation’s first publicly available datasets of aggregate statistics on appraisal records, giving the public new access to a broad set of data points and trends found in appraisal reports. The UAD Aggregate Statistics for Enterprise Single-Family, Enterprise Condominium, and Federal Housing Administration (FHA) Single-Family appraisals may be grouped by neighborhood characteristics, property characteristics and different geographic levels.DocumentationOverview (10/28/2024)Data Dictionary (10/28/2024)Data File Version History and Suppression Rates (12/18/2024)Dashboard Guide (2/3/2025)UAD Aggregate Statistics DashboardsThe UAD Aggregate Statistics Dashboards are the visual front end of the UAD Aggregate Statistics Data File. The Dashboards are designed to provide easy access to customized maps and charts for all levels of users. Access the UAD Aggregate Statistics Dashboards here.UAD Aggregate Statistics DatasetsNotes:Some of the data files are relatively large in size and will not open correctly in certain software packages, such as Microsoft Excel. All the files can be opened and used in data analytics software such as SAS, Python, or R.All CSV files are zipped.

The dataset on aggregate extractions in the European seas was created in 2014 by AZTI for the European Marine Observation and Data Network (EMODnet). It is the result of the aggregation and harmonization of datasets provided by several sources from all across Europe. It is available for viewing and download on EMODnet web portal (Human Activities, https://emodnet.ec.europa.eu/en/human-activities). The dataset contains points representing aggregate extraction sites, by year (although some data are indicated by a period of years), in the following countries: Belgium, Denmark, Finland, France, Germany, Ireland, Italy, Lithuania, Poland, Portugal, Spain, Sweden, The Netherlands and United Kingdom. Where available, each point has the following attributes: Id (Identifier), Position Info (e.g.: Estimated, Original, Polygon centroid of dredging area, Estimated polygon centroid of dredging area), Country, Sea basin, Sea, Name of the extraction area, Area of activity (km2), Year (the year when the extraction took place; when a time period is available, the first year of the period is indicated), Permitted Amount (m3) (permitted amount of material to be extracted, in m3), Permitted Amount (t) (permitted amount of material to be extracted, in tonnes), Requested Amount (m3) (requested amount of material to be extracted, in m3), Requested Amount (t) (requested amount of material to be extracted, in tonnes), Extracted Amount (m3) (extracted amount of material, in m3), Extracted Amount (t) (extracted amount of material, in tonnes), Extraction Type (Marine sediment extraction), Purpose (e.g.: Commercial, Others, N/A), End Use (e.g.: Beach nourishment, Construction, Reclamation fill, N/A), Material type (e.g.: sand, gravel, maerl), Notes, Link to Web Sources. In 2018, a feature on areas for aggregate extractions was included. It contains polygons representing areas of seabed licensed for exploration or extraction of aggregates, in the following countries: Belgium, Denmark, Estonia, Finland, France, Germany, Italy, Lithuania, Poland, Portugal, Russia, Spain, Sweden, The Netherlands and United Kingdom. Where available, each polygon has the following attributes: Id (Identifier), Area code, Area name, Country, Sea basin, Sea, Starting year (the year when the license starts), End year (the year when the license ends), Site Type (exploration area, extraction area, extraction area (in use)), License status (Active, not active, expired, unknown), Material type (e.g.: sand, gravel, maerl), Notes, Distance to coast (in metres), Link to Web Sources. In the 2024 update, extraction data until 2023 and new areas have been included.

“Aggregates” is the term geologists use to describe rocks used for building and construction purposes. Aggregate Potential Mapping aims to identify areas where aggregate is most likely to be found.It is a vector dataset. Vector data portray the world using points, lines, and polygons (areas). The data is shown as points and polygonsPlease read the metadata lineage for each layer for further information.

Belgium Business Survey: Seasonally Adjusted (sa): Aggregate data was reported at -14.700 % Point in Apr 2025. This records an increase from the previous number of -15.100 % Point for Mar 2025. Belgium Business Survey: Seasonally Adjusted (sa): Aggregate data is updated monthly, averaging -7.000 % Point from Jan 1980 (Median) to Apr 2025, with 544 observations. The data reached an all-time high of 10.100 % Point in Jul 2021 and a record low of -36.100 % Point in Apr 2020. Belgium Business Survey: Seasonally Adjusted (sa): Aggregate data remains active status in CEIC and is reported by National Bank of Belgium. The data is categorized under Global Database’s Belgium – Table BE.S001: Business Survey: Seasonally Adjusted. [COVID-19-IMPACT]

The aggregate deposits presented here comprise near-shore deposits of non-metallic detrital minerals and calcium carbonate. They occur both on beaches and deeper seabed areas. Marine aggregate deposits are principally extracted for use in the construction industry. Concentrated into their present occurrences by hydrodynamic processes, aggregates may have originally been deposited by mechanisms such as river or glacial deposition.

The Shared Savings Program County-level Aggregate Expenditure and Risk Score Data on Assignable Beneficiaries Public Use File (PUF) for the Medicare Shared Savings Program (Shared Savings Program) provides aggregate data consisting of per capita Parts A and B FFS expenditures, average CMS-HCC prospective risk scores, average demographic risk scores and total person-years for Shared Savings Program assignable beneficiaries by Medicare enrollment type (End Stage Renal Disease (ESRD), disabled, aged/dual eligible, aged/non-dual eligible). DISCLAIMER: This information is current as of the last update. Changes to Shared Savings Program Accountable Care Organization (ACO) information occur periodically. Each Shared Savings Program ACO has the most up-to-date information about their organization. Consider contacting the Shared Savings Program ACO for the latest information. Contact information is available in the ACO PUF and the ACO Participants PUF.

The method to create the Wind Resource Area datasets is to:Query Power Plant point locations from the California Energy Commission, California Power Plants data set by operational status and capacity greater than or equal to 2 MW at each facility from the Quarterly Fuel and Energy Report, CEC-1304A. Plants tracked include those of at least 1 MW, which are considered of commercial size. A polygon was generated around the resulting operational, commercial wind facilities using the Aggregate Points geoprocessing tool with an aggregation distance of 15 survey miles. A 5 mile spatial buffer was added to the resulting polygons. The buffer does not represent information regarding environmental analysis. It is used only to depict plant concentration regions.

Traffic volume data from automatic permanent counting stations on federal trunk roads are made available to BASt by the Federal Motorway GmbH and the federal states in a uniform data format. These raw data available to BASt are hourly data and have not been checked for plausibility by BASt, nor have data preparations taken place. Thus, the files may contain, among other things, data gaps, incomplete measuring cross-sections, incorrect directions and implausible lane arrangements. In addition, time shifts, incorrect vehicle type distinctions, format errors and incorrect numerical values may be present.

The hourly data are available as raw data in the BASt stock band format for traffic volume data as ANSI dataset. Based on this raw data, aggregated hourly raw data were created for directional values. The general information on the respective automatic continuous counters is provided as monthly CSV files together with the direction-aggregated raw data in the zip files. Both the hourly data and the metadata only reflect the current status at the time of provision. In general, no guarantee can be given by BASt for completeness and quality at this stage of data collection. There is a complete disclaimer. BASt assumes no liability for damages resulting from the use of the information provided. A monthly update is planned.

The results based on the plausible and finally prepared hourly data as well as the associated hourly data from 2003 onwards are also made available by BASt:

Automatic counting points on motorways and federal roads
Dataset description:

Dataset description for directional traffic volume data (PDF)
Each file can contain several million records. A correspondingly powerful editing software is recommended.

Query Power Plant point locations from the California Energy Commission, California Power Plants data set by operational status and capacity greater than or equal to 2 MW at each facility from the Quarterly Fuel and Energy Report, CEC-1304A. Plants tracked include those of at least 1 MW, which are considered of commercial size. A polygon was generated around the resulting operational, commercial wind facilities using the Aggregate Points geoprocessing tool with an aggregation distance of 15 survey miles. A 5 mile spatial buffer was added to the resulting polygons. The buffer does not represent information regarding environmental analysis. It is used only to depict plant concentration regions.

Monthly Aggregate Ranking of the top 5 most popular WiFi Access Points in the CBR Free WiFi Network. Monthly Aggregate Ranking of the top 5 most popular WiFi Access Points in the CBR Free WiFi Network.

Dataset from Nanyang Polytechnic. For more information, visit https://data.gov.sg/datasets/d_eb7bb85a49e021e63f9cb7b54497a400/view

Although by looking at just the point locations of where the bombing took place in London on the first night of The Blitz you can get an idea of where was most intensely hit. In this exercise you are going to do some analysis to calculate the number of bombs that fell in 1km hexagon areas of London that more clearly shows the intensity of bombing across areas of London that will look something like this:In this exercise you will:Use the Aggregate Points tool in ArcGIS Online to calculate the number of bombs that fell in 1km hexagon areas of London that more clearly shows the intensity of bombing across areas of London in a hex mapEdit the symbology of this layer to give it a 3D effectAdd a custom basemap to make the intensity map stand out more

This dataset presents an aggregation of power generation within 30 minutes in Wh for injection points (any power level). The number of injection points concerned per electricity distribution system operator is also indicated. The geographical mesh is the regional mesh. The data are from resesda (formerly URM), Strasbourg Electricité Réseaux and Enedis. These data are published in compliance with the rules relating to the protection of Commercially Sensitive Information. A question about the dataset? A use case to share with other users?The Forum of open data experts electricity and gas is here for that! The geographical mesh is the regional mesh. The data are from resesda (formerly URM), Strasbourg Electricité Réseaux and Enedis. These data are published in compliance with the rules relating to the protection of Commercially Sensitive Information. A question about the dataset? A use case to share with other users? The Forum of open data experts electricity and gas is here for that!

Artificial dataset of addresses of COVID-19 cases in Paris. The dataset was created to test geomasking techniques to be used on the real data collected by the French health administration. The dataset was used in the paper "Geographically Masking Addresses to Study COVID-19 Clusters" by Walid Houfaf-Khoufaf and Guillaume Touya. The dataset contains the following files:

• roads_paris_IGN.shp contains the road lines from IGN France in Paris;
• buildings_paris_IGN.shp contains the building polygons from IGN France in Paris (useful to aggregate points to building groups);
• faces.shp contains the blocks built from the roads (useful to aggregate points to blocks);
• ban_75.shp contains all the address points in Paris from the open BAN database.
• artificial_COVID_cases.csv contains the artificial COVID cases generated from 3 months in 2020 in the Paris area.

Feature layer generated from running the Aggregate Points solutions. Points from COVID-19 were aggregated to FSA