The Spatiotemporal Big Data Store Tutorial introduces you the the capabilities of the spatiotemporal big data store in ArcGIS Data Store, available with ArcGIS Enterprise. Observation data can be moving objects, changing attributes of stationary sensors, or both. The spatiotemporal big data store enables archival of high volume observation data, sustains high velocity write throughput, and can run across multiple machines (nodes). Adding additional machines adds capacity, enabling you to store more data, implement longer retention policies of your data, and support higher data write throughput.

After completing this tutorial you will:

Understand the concepts and best practices for working with the spatiotemporal big data store available with ArcGIS Data Store. Have configured the appropriate security settings and certificates on a enterprise server, real-time server, and a data server which are necessary for working with the spatiotemporal big data store. Have learned how to process and archive large amounts of observational data in the spatiotemporal big data store. Have learned how to visualize the observational data that is stored in the spatiotemporal big data store.

Releases

Each release contains a tutorial compatible with the version of GeoEvent Server listed. The release of the component you deploy does not have to match your version of ArcGIS GeoEvent Server, so long as the release of the component is compatible with the version of GeoEvent Server you are using. For example, if the release contains a tutorial for version 10.6; this tutorial is compatible with ArcGIS GeoEvent Server 10.6 and later. Each release contains a Release History document with a compatibility table that illustrates which versions of ArcGIS GeoEvent Server the component is compatible with.

NOTE: The release strategy for ArcGIS GeoEvent Server components delivered in the ArcGIS GeoEvent Server Gallery has been updated. Going forward, a new release will only be created when

  a component has an issue,
  is being enhanced with new capabilities,
  or is not compatible with newer versions of ArcGIS GeoEvent Server.

This strategy makes upgrades of these custom
components easier since you will not have to
upgrade them for every version of ArcGIS GeoEvent Server
unless there is a new release of
the component. The documentation for the
latest release has been
updated and includes instructions for updating
your configuration to align with this strategy.

Latest

Release 4 - February 2, 2017 - Compatible with ArcGIS GeoEvent Server 10.5 and later.

Previous

Release 3 - July 7, 2016 - Compatible with ArcGIS GeoEvent Server 10.4 thru 10.8.

Release 2 - May 17, 2016 - Compatible with ArcGIS GeoEvent Server 10.4 thru 10.8.

Release 1 - March 18, 2016 - Compatible with ArcGIS GeoEvent Server 10.4 thru 10.8.

This is a web map hosted on our enterprise server and shared to ArcGIS Online as a collaboration. The map contains data pertaining to Plan development.

This resource was created by Esri Canada Education and Research. To browse our full collection of higher-education learning resources, please visit https://hed.esri.ca/resourcefinder/.This tutorial introduces you to using Python code in a Jupyter Notebook, an open source web application that enables you to create and share documents that contain rich text, equations and multimedia, alongside executable code and visualization of analysis outputs. The tutorial begins by stepping through the basics of setting up and being productive with Python notebooks. You will be introduced to ArcGIS Notebooks, which are Python Notebooks that are well-integrated within the ArcGIS platform. Finally, you will be guided through a series of ArcGIS Notebooks that illustrate how to create compelling notebooks for data science that integrate your own Python scripts using the ArcGIS API for Python and ArcPy in combination with thousands of open source Python libraries to enhance your analysis and visualization.To download the dataset Labs, click the Open button to the top right. This will automatically download a ZIP file containing all files and data required.You can also clone the tutorial documents and datasets for this GitHub repo: https://github.com/highered-esricanada/arcgis-notebooks-tutorial.git.Software & Solutions Used: Required: This tutorial was last tested on August 27th, 2024, using ArcGIS Pro 3.3. If you're using a different version of ArcGIS Pro, you may encounter different functionality and results.Recommended: ArcGIS Online subscription account with permissions to use advanced Notebooks and GeoEnrichmentOptional: Notebook Server for ArcGIS Enterprise 11.3+Time to Complete: 2 h (excludes processing time)File Size: 196 MBDate Created: January 2022Last Updated: August 27, 2024

Geoportal Server is a standards-based, open source product that enables discovery and use of geospatial resources including data and services.

With the Geoportal Server you can:

• Improve the efficiency and effectiveness of geospatial activities within your enterprise and across organizations.
• Support collaboration and cooperation among departments and organizations by facilitating the sharing of geospatial resources regardless of the GIS platform.
• Gain an enterprise-level awareness of disparate geospatial data, Web services, and activities.
• Leverage existing geospatial resources so your organization doesn't duplicate those resources or the effort to create them.
• Ensure the use of approved, high-quality datasets.
• Reduce the time users spend trying to find relevant, usable geospatial resources.

Learn state-of-the-art skills to build compelling, useful, and fun Web GIS apps easily, with no programming experience required.Building on the foundation of the previous three editions, Getting to Know Web GIS, fourth edition,features the latest advances in Esri’s entire Web GIS platform, from the cloud server side to the client side.Discover and apply what’s new in ArcGIS Online, ArcGIS Enterprise, Map Viewer, Esri StoryMaps, Web AppBuilder, ArcGIS Survey123, and more.Learn about recent Web GIS products such as ArcGIS Experience Builder, ArcGIS Indoors, and ArcGIS QuickCapture. Understand updates in mobile GIS such as ArcGIS Collector and AuGeo, and then build your own web apps.Further your knowledge and skills with detailed sections and chapters on ArcGIS Dashboards, ArcGIS Analytics for the Internet of Things, online spatial analysis, image services, 3D web scenes, ArcGIS API for JavaScript, and best practices in Web GIS.Each chapter is written for immediate productivity with a good balance of principles and hands-on exercises and includes:A conceptual discussion section to give you the big picture and principles,A detailed tutorial section with step-by-step instructions,A Q/A section to answer common questions,An assignment section to reinforce your comprehension, andA list of resources with more information.Ideal for classroom lab work and on-the-job training for GIS students, instructors, GIS analysts, managers, web developers, and other professionals, Getting to Know Web GIS, fourth edition, uses a holistic approach to systematically teach the breadth of the Esri Geospatial Cloud.AUDIENCEProfessional and scholarly. College/higher education. General/trade.AUTHOR BIOPinde Fu leads the ArcGIS Platform Engineering team at Esri Professional Services and teaches at universities including Harvard University Extension School. His specialties include web and mobile GIS technologies and applications in various industries. Several of his projects have won specialachievement awards. Fu is the lead author of Web GIS: Principles and Applications (Esri Press, 2010).Pub Date: Print: 7/21/2020 Digital: 6/16/2020 Format: Trade paperISBN: Print: 9781589485921 Digital: 9781589485938 Trim: 7.5 x 9 in.Price: Print: $94.99 USD Digital: $94.99 USD Pages: 490TABLE OF CONTENTSPrefaceForeword1 Get started with Web GIS2 Hosted feature layers and storytelling with GIS3 Web AppBuilder for ArcGIS and ArcGIS Experience Builder4 Mobile GIS5 Tile layers and on-premises Web GIS6 Spatial temporal data and real-time GIS7 3D web scenes8 Spatial analysis and geoprocessing9 Image service and online raster analysis10 Web GIS programming with ArcGIS API for JavaScriptPinde Fu | Interview with Esri Press | 2020-07-10 | 15:56 | Link.

The size of the Italy Geospatial Analytics market was valued at USD XXX Million in 2023 and is projected to reach USD XXX Million by 2032, with an expected CAGR of 8.17% during the forecast period.Geospatial analytics is the act of applying geospatial data to understand spatial data patterns, relation, and trends. The method utilizes numerous types of sources ranging from satellite imaging, GPS signals, and sensor-generated data in constructing interactive maps as well as different forms of visualization. Geospatial analytics becomes a utility across most industries from urban planning and agriculture, to transportation, to environmental monitoring. It can, for instance, optimize the routes for the transportation of products, monitor environmental pollution, and assess the impacts of climate changes along the coasts. The industry is driven by increased government spending on infrastructure construction, growing interest in precision agriculture, and the wide adoption of high-tech solutions such as artificial intelligence and machine learning in the geospatial world. Recent developments include: March 2023: The Italian space agency and NASA have collaborated to build and launch the Multi-Angle Imager for Aerosols mission, an effort to investigate the health impacts of tiny airborne particles polluting the cities through analyzing data by collecting data from the satellite-based observatories, which would fuel the demand for geospatial analytics market in the country., January 2023: EDB, an open-source database service provider in Italy, announced its partnership with Esri to certify EDB Postgres Advanced Server with Esri ArcGIS Pro and Esri ArcGIS Enterprise, which work together to form Esri's Geospatial analytic solutions, operating in many countries, including Italy. After this partnership, users can connect their EDB Postgres Advanced Server to explore, visualize and analyze their geospatial data and share their work with an Esri ArcGIS Enterprise portal. In addition, EDB customers, especially those in the public sector, can use their database with Esri ArcGIS software to transform their data into something that improves workflows and processes and shapes policies and engagement within their communities.. Key drivers for this market are: Increase in the number of Smart Cities in The Country, The Implementation of analytics Software in the Country's Public Transportation. Potential restraints include: High Costs and Operational Concerns, Lack of Standardization for Data Integration. Notable trends are: The Increase in the Number of Smart Cities in The Country Fuels the Market Growth.

The establishment of a BES Multi-User Geodatabase (BES-MUG) allows for the storage, management, and distribution of geospatial data associated with the Baltimore Ecosystem Study. At present, BES data is distributed over the internet via the BES website. While having geospatial data available for download is a vast improvement over having the data housed at individual research institutions, it still suffers from some limitations. BES-MUG overcomes these limitations; improving the quality of the geospatial data available to BES researches, thereby leading to more informed decision-making.

   BES-MUG builds on Environmental Systems Research Institute's (ESRI) ArcGIS and ArcSDE technology. ESRI was selected because its geospatial software offers robust capabilities. ArcGIS is implemented agency-wide within the USDA and is the predominant geospatial software package used by collaborating institutions.


   Commercially available enterprise database packages (DB2, Oracle, SQL) provide an efficient means to store, manage, and share large datasets. However, standard database capabilities are limited with respect to geographic datasets because they lack the ability to deal with complex spatial relationships. By using ESRI's ArcSDE (Spatial Database Engine) in conjunction with database software, geospatial data can be handled much more effectively through the implementation of the Geodatabase model. Through ArcSDE and the Geodatabase model the database's capabilities are expanded, allowing for multiuser editing, intelligent feature types, and the establishment of rules and relationships. ArcSDE also allows users to connect to the database using ArcGIS software without being burdened by the intricacies of the database itself.


   For an example of how BES-MUG will help improve the quality and timeless of BES geospatial data consider a census block group layer that is in need of updating. Rather than the researcher downloading the dataset, editing it, and resubmitting to through ORS, access rules will allow the authorized user to edit the dataset over the network. Established rules will ensure that the attribute and topological integrity is maintained, so that key fields are not left blank and that the block group boundaries stay within tract boundaries. Metadata will automatically be updated showing who edited the dataset and when they did in the event any questions arise.


   Currently, a functioning prototype Multi-User Database has been developed for BES at the University of Vermont Spatial Analysis Lab, using Arc SDE and IBM's DB2 Enterprise Database as a back end architecture. This database, which is currently only accessible to those on the UVM campus network, will shortly be migrated to a Linux server where it will be accessible for database connections over the Internet. Passwords can then be handed out to all interested researchers on the project, who will be able to make a database connection through the Geographic Information Systems software interface on their desktop computer. 


   This database will include a very large number of thematic layers. Those layers are currently divided into biophysical, socio-economic and imagery categories. Biophysical includes data on topography, soils, forest cover, habitat areas, hydrology and toxics. Socio-economics includes political and administrative boundaries, transportation and infrastructure networks, property data, census data, household survey data, parks, protected areas, land use/land cover, zoning, public health and historic land use change. Imagery includes a variety of aerial and satellite imagery.


   See the readme: http://96.56.36.108/geodatabase_SAL/readme.txt


   See the file listing: http://96.56.36.108/geodatabase_SAL/diroutput.txt

This file contains the digital vector boundaries for Local Enterprise Partnerships, in England, as at December 2022.The boundaries available are: (BUC) Ultra generalised (500m) - clipped to the coastline (Mean High Water mark).Contains both Ordnance Survey and ONS Intellectual Property Rights.

REST URL of Feature Server – https://services1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/rest/services/Local_Enterprise_Partnerships_December_2022_EN_BUC/FeatureServerREST URL of WFS Server –https://dservices1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/services/Local_Enterprise_Partnerships_December_2022_EN_BUC/WFSServer?service=wfs&request=getcapabilitiesREST URL of Map Server –https://services1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/rest/services/Local_Enterprise_Partnerships_December_2022_EN_BUC/MapServer

This file contains the digital vector boundaries for Local Enterprise Partnerships, in England, as at December 2022.The boundaries available are: (BFC) Full resolution - clipped to the coastline (Mean High Water mark).Contains both Ordnance Survey and ONS Intellectual Property Rights. Version 2 - To account for name changes. E37000011 Gloucestershire changed its name to GFirst on the 31st December 2022E37000045 Derby, Derbyshire, Nottingham and Nottinghamshire has changed its name to D2N2 on the 31st December 2022E37000051 London has changed it’s name to The London Economic Action Partnership on the 31st December 2022E37000053 Oxfordshire has changed it’s name to OxLEP on the 31st December 2022E37000054 Sheffield City Region has changed it’s name to South Yorkshire on the 1st December 2022E37000059 Greater Cambridge and Greater Peterborough has changed it’s name to The Business Board on the 31st December 2022REST URL of Feature Access Service – https://services1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/rest/services/LEP_DEC_2022_EN_BFC_V2/FeatureServer

REST URL of WFS Server – https://dservices1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/services/Local_Enterprise_Partnerships_December_2022_EN_BFC/WFSServer?service=wfs&request=getcapabilities REST URL of Map Server – https://services1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/rest/services/Local_Enterprise_Partnerships_December_2022_EN_BFC_Tile/MapServer

The Italian geospatial analytics market, valued at €260 million in 2025, is poised for robust growth, exhibiting a Compound Annual Growth Rate (CAGR) of 8.17% from 2025 to 2033. This expansion is driven by increasing adoption of precision agriculture techniques, the burgeoning need for efficient infrastructure management within utilities and communication sectors, and rising demand for advanced analytics in defense and intelligence applications. Furthermore, the Italian government's focus on smart city initiatives and the expanding digitalization across various sectors, including healthcare and real estate, significantly contribute to market growth. The market segmentation reveals a strong demand across diverse verticals, with agriculture, utilities, and defense exhibiting substantial growth potential. The prevalent use of surface analysis techniques reflects a focus on immediate application needs, while the growing adoption of network and geo-visualization analytics indicates a shift toward more sophisticated and insightful data interpretation. Leading players such as ESRI, Hexagon AB, and Trimble Geospatial are actively contributing to market development by providing cutting-edge software and services. Competition is likely to intensify as smaller, specialized companies emerge, offering niche solutions and catering to the evolving demands of different market segments. The forecast period (2025-2033) anticipates substantial market expansion, largely attributed to continued technological advancements, particularly in AI and machine learning, enhancing the analytical capabilities of geospatial data. However, challenges exist, potentially including data security concerns, the need for skilled professionals to interpret complex analytical results, and the high initial investment required for advanced geospatial technology implementation. Nevertheless, the long-term outlook for the Italian geospatial analytics market remains positive, driven by sustained government investment in digital infrastructure and the increasing awareness of the value proposition offered by sophisticated geospatial analysis across various sectors. The market's trajectory suggests a significant opportunity for both established and emerging players in the years to come. Recent developments include: March 2023: The Italian space agency and NASA have collaborated to build and launch the Multi-Angle Imager for Aerosols mission, an effort to investigate the health impacts of tiny airborne particles polluting the cities through analyzing data by collecting data from the satellite-based observatories, which would fuel the demand for geospatial analytics market in the country., January 2023: EDB, an open-source database service provider in Italy, announced its partnership with Esri to certify EDB Postgres Advanced Server with Esri ArcGIS Pro and Esri ArcGIS Enterprise, which work together to form Esri's Geospatial analytic solutions, operating in many countries, including Italy. After this partnership, users can connect their EDB Postgres Advanced Server to explore, visualize and analyze their geospatial data and share their work with an Esri ArcGIS Enterprise portal. In addition, EDB customers, especially those in the public sector, can use their database with Esri ArcGIS software to transform their data into something that improves workflows and processes and shapes policies and engagement within their communities.. Key drivers for this market are: Increase in the number of Smart Cities in The Country, The Implementation of analytics Software in the Country's Public Transportation. Potential restraints include: Increase in the number of Smart Cities in The Country, The Implementation of analytics Software in the Country's Public Transportation. Notable trends are: The Increase in the Number of Smart Cities in The Country Fuels the Market Growth.

Time Aware is a configurable app template that enables you visualize time enabled layers in a web map using a time slider. This is useful for displaying changes in data over time. Use CasesBuild a stand alone app that presents data changing through time.Build a time aware app and embed it within a story map journal or story map series to include time animation within your story.Configurable OptionsChoose a title, logo, and color scheme.Configure the ability for feature and location search.Customize the color and date time format of the time slider.Enable a legend, scalebar, share dialog, or about window.Supported DevicesThis application is responsively designed to support use in browsers on desktops, mobile phones, and tablets.Data RequirementsThis requires time aware data, to learn more see the configure time help topic. An existing time aware feature service can be consumed from this application, however in order to create your own time aware feature service you will either need ArcGIS Enterprise or an ArcGIS Online subscription.Get Started This application can be created in the following ways:Click the Create a Web App button on this pageShare a map and choose to Create a Web AppOn the Content page, click Create - App - From Template Click the Download button to access the source code. Do this if you want to host the app on your own server and optionally customize it to add features or change styling.

The boundaries available are: (BGC) Generalised (20m) - clipped to the coastline (Mean High Water mark).Contains both Ordnance Survey and ONS Intellectual Property Rights. Version 2 - To account for name changes. E37000011 Gloucestershire changed its name to GFirst on the 31st December 2022E37000045 Derby, Derbyshire, Nottingham and Nottinghamshire has changed its name to D2N2 on the 31st December 2022E37000051 London has changed it’s name to The London Economic Action Partnership on the 31st December 2022E37000053 Oxfordshire has changed it’s name to OxLEP on the 31st December 2022E37000054 Sheffield City Region has changed it’s name to South Yorkshire on the 1st December 2022E37000059 Greater Cambridge and Greater Peterborough has changed it’s name to The Business Board on the 31st December 2022 REST URL of Feature Access Service – https://services1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/rest/services/LEP_DEC_2022_EN_BGC_V2/FeatureServer REST URL of WFS Server – https://dservices1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/services/LEP_DEC_2022_EN_BGC_V2/WFSServer?service=wfs&request=getcapabilities REST URL of Map Server – https://services1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/rest/services/LEP_DEC_2022_EN_BGC_V2/MapServer

The DWR Enterprise image server has hundreds of image services, but there is no interface for searching or querying the server. The image server index contains footprints of the geographic extent of each available image service, as well as relevant attributes that describe the image service. There are also related tables for most types of image services that contain information specific to that type of data, such as specification numbers for design drawings or beam types for bathymetry data.

The boundaries available are: (BFE) Full resolution - extent of the realm (usually this is the Mean Low Water mark but in some cases boundaries extend beyond this to include off shore islands).Contains both Ordnance Survey and ONS Intellectual Property Rights. Version 2 - To account for name changes. E37000011 Gloucestershire changed its name to GFirst on the 31st December 2022E37000045 Derby, Derbyshire, Nottingham and Nottinghamshire has changed its name to D2N2 on the 31st December 2022E37000051 London has changed it’s name to The London Economic Action Partnership on the 31st December 2022E37000053 Oxfordshire has changed it’s name to OxLEP on the 31st December 2022E37000054 Sheffield City Region has changed it’s name to South Yorkshire on the 1st December 2022E37000059 Greater Cambridge and Greater Peterborough has changed it’s name to The Business Board on the 31st December 2022 REST URL of Feature Access Service – https://services1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/rest/services/LEP_DEC_2022_EN_BFE_V2/FeatureServer REST URL of WFS Server – https://dservices1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/services/LEP_DEC_2022_EN_BFE_V2/WFSServer?service=wfs&request=getcapabilities REST URL of Map Server – https://services1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/rest/services/LEP_DEC_2022_EN_BFE_V2/MapServer

This file contains the digital vector boundaries for Local Enterprise Partnerships, in England, as at December 2022.The boundaries available are: (BFE) Full resolution - extent of the realm (usually this is the Mean Low Water mark but in some cases boundaries extend beyond this to include off shore islands).Contains both Ordnance Survey and ONS Intellectual Property Rights. Version 2 - To account for name changes. E37000011 Gloucestershire changed its name to GFirst on the 31st December 2022E37000045 Derby, Derbyshire, Nottingham and Nottinghamshire has changed its name to D2N2 on the 31st December 2022E37000051 London has changed it’s name to The London Economic Action Partnership on the 31st December 2022E37000053 Oxfordshire has changed it’s name to OxLEP on the 31st December 2022E37000054 Sheffield City Region has changed it’s name to South Yorkshire on the 1st December 2022E37000059 Greater Cambridge and Greater Peterborough has changed it’s name to The Business Board on the 31st December 2022

REST URL of Feature Access Service – https://services1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/rest/services/LEP_DEC_2022_EN_BFE_V2/FeatureServer

REST URL of WFS Server – https://dservices1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/services/LEP_DEC_2022_EN_BFE_V2/WFSServer?service=wfs&request=getcapabilities

REST URL of Map Server – https://services1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/rest/services/LEP_DEC_2022_EN_BFE_V2/MapServer

Swimming pools are important for property tax assessment because they impact the value of the property. Tax assessors at local government agencies often rely on expensive and infrequent surveys, leading to assessment inaccuracies. Finding pools that are not on the assessment roll (such as those recently constructed) is valuable to assessors and will ultimately mean additional revenue for the community.This deep learning model helps automate the task of finding pools from high resolution satellite imagery. This model can also benefit swimming pool maintenance companies and help redirect their marketing efforts. Public health and mosquito control agencies can also use this model to detect pools and drive field activity and mitigation efforts.Licensing requirementsArcGIS Desktop – ArcGIS Image Analyst extension for ArcGIS ProArcGIS Enterprise – ArcGIS Image Server with raster analytics configuredArcGIS Online – ArcGIS Image for ArcGIS OnlineUsing the modelFollow the guide to use the model. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.Note: Deep learning is computationally intensive, and a powerful GPU is recommended to process large datasets.Input8-bit, 3-band high resolution (5-7.5 centimeters) imageryOutputFeature class containing bounding boxes depicting pool locations with class BuiltinPool | PopupPoolApplicable geographiesThe model is expected to work well in the New Zealand.Model architectureThe model uses the MMDetection model architecture implemented using ArcGIS Pro Arcpy.Accuracy metricsThe model has an average precision score of 0.95.1 BuiltInPool2PopupPoolSample resultsHere are a few results from the model.(Post processing are recommended to filter out False Positive Object. If the confidence are below certain threshold e.g 5%)To learn how to use this model, see this story

The California Irrigation Management Information System (CIMIS) currently manages over 145 active weather stations throughout the state. Archived data is also available for 85 additional stations that have been disconnected from the network for various reasons. CIMIS stations provide hourly records of solar radiation, precipitation, air temperature, air humidity, and wind speed. Most of the CIMIS stations produce estimates of reference evapotranspiration (ETo) for the station location and their immediate surroundings, often in agricultural areas. The Department of Water Resources operates CIMIS as a free resource to help California to manage water resources more efficiently. CIMIS weather stations collect weather data on a minute-by-minute basis. Hourly data reflects the previous hour's 60 minutes of readings. Hourly and daily values are calculated and stored in the dataloggers. A computer at the DWR headquarters in Sacramento calls every station starting at midnight Pacific Standard Time (PST) and retrieves data at predetermined time intervals. At the time of this writing, CIMIS data is retrieved from the stations every hour. When there is a communication problem between the polling server and any given station, the server skips that station and calls the next station in the list. After all other stations have reported, the polling server again polls the station with the communication problem. The interrogation continues into the next day until all of the station data have been transmitted. CIMIS data processing involves checking the accuracy of the measured weather data for quality, calculating reference evapotranspiration (ETo/ETr) and other intermediate parameters, flagging measured and calculated parameters, and storing the data in the CIMIS database. Evapotranspiration (ET) is a loss of water to the atmosphere by the combined processes of evaporation from soil and plant surfaces and transpiration from plants. Reference evapotranspiration is ET from standardized grass or alfalfa surfaces over which the weather stations are sitting. The standardization of grass or alfalfa surfaces for a weather station is required because ET varies depending on plant (type, density, height) and soil factors and it is difficult, if not impossible, to measure weather parameters under all sets of conditions. Irrigators have to use crop factors, known as crop coefficients (Kc), to convert ET from the standardized reference surfaces into an actual evapotranspiration (ETc) by a specific crop. For more information go to https://cimis.water.ca.gov/. The associated data are considered DWR enterprise GIS data, which meet all appropriate requirements of the DWR Spatial Data Standards, specifically the DWR Spatial Data Standard version 3.3, dated April 13, 2022. DWR makes no warranties or guarantees —either expressed or implied — as to the completeness, accuracy, or correctness of the data. DWR neither accepts nor assumes liability arising from or for any incorrect, incomplete, or misleading subject data. Comments, problems, improvements, updates, or suggestions should be forwarded to GIS@water.ca.gov.

This data table shows COVID-19 vaccination rates by race and ethnicity in Maricopa County zip codes.Data Source: Maricopa County GIS Open Data weekly count of COVID-19 vaccinations. The data were reformatted from the source data to accommodate dashboard configuration. Dates: Updated data shows publishing dates which represents values from the previous calendar week (Sunday through Saturday). For more details on data reporting, please see the Maricopa County COVID-19 data reporting notes at https://www.maricopa.gov/5460/Coronavirus-Disease-2019.The Maricopa County Department of Public Health (MCDPH) releases the COVID-19 vaccination rate per 100,000 of the vaccine eligible population and COVID-19 vaccination rate per 100,000 of the total population for each zip code and city in Maricopa County at ~12:00 PM weekly on Wednesdays via the Maricopa County GIS Open Data website (https://data-maricopa.opendata.arcgis.com/). More information about the data is available on the Maricopa County COVID-19 Vaccine Data page (https://www.maricopa.gov/5671/Public-Vaccine-Data#dashboard).Additional InformationSource: Maricopa County Department of Public Health (MCDPH) through Maricopa County GIS Open Data weekly count of COVID-19 vaccinationsContact (author): n/aContact E-Mail (author): n/aContact (maintainer): City of Tempe Open Data TeamContact E-Mail (maintainer): data@tempe.govData Source Type: TablePreparation Method: Data are exposed via ArcGIS Server and its REST API.Publish Frequency: WeeklyPublish Method: Data are downloaded each week once Maricopa County GIS Open Data updates its public API. Data are transformed and appended to a table in Tempe’s Enterprise GIS.Data Dictionary