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

This page contains the help documentation for the GIS Open Data Portal. Refer to https://gisdata-csj.opendata.arcgis.com/pages/help.

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.

Bulk exports, in file-geodatabase format, of data that is shared via the VT EGC (Enterprise GIS Consortium) Geospatial Data Exchange Protocol.

ArcGIS Location Tracking Privacy Best Practices (Esri Whitepaper).This document contains relevant information that helps guide IT managers, GIS administrators, andprivacy and security team members in deploying cloud and enterprise GIS in a manner that helps complywith privacy regulations, such as GDPR, for location tracking services._Communities around the world are taking strides in mitigating the threat that COVID-19 (coronavirus) poses. Geography and location analysis have a crucial role in better understanding this evolving pandemic.When you need help quickly, Esri can provide data, software, configurable applications, and technical support for your emergency GIS operations. Use GIS to rapidly access and visualize mission-critical information. Get the information you need quickly, in a way that’s easy to understand, to make better decisions during a crisis.Esri’s Disaster Response Program (DRP) assists with disasters worldwide as part of our corporate citizenship. We support response and relief efforts with GIS technology and expertise.More information...

The State of Delaware Enterprise GIS (FirstMap) system serves as a centralized repository for commonly used GIS data layers including, but not limited to, the framework layers used in base maps and aerial imagery. The repository supports inter-agency data sharing, facilitates data collection and updates, and through automated replication simplifies the process to find the most recent authoritative geospatial data for the state.

COMPLETED 2010. The data was converted from the most recent (2010) versions of the adopted plans, which can be found at https://cms3.tucsonaz.gov/planning/plans/ Supplemental Information: In March 2010, Pima Association of Governments (PAG), in cooperation with the City of Tucson (City), initiated the Planned Land Use Data Conversion Project. This 9-month effort involved evaluating mapped land use designations and selected spatially explicit policies for nearly 50 of the City's adopted neighborhood, area, and subregional plans and converting the information into a Geographic Information System (GIS) format. Further documentation for this file can be obtained from the City of Tucson Planning and Development Services Department or Pima Association of Governments Technical Services. A brief summary report was provided, as requested, to the City of Tucson which highlights some of the key issues found during the conversion process (e.g., lack of mapping and terminology consistency among plans). The feature class "Plan_boundaries" represents the boundaries of the adopted plans. The feature class "Plan_mapped_land_use" represents the land use designations as they are mapped in the adopted plans. Some information was gathered that is implicit based on the land use designation or zones (see field descriptions below). Since this information is not explicitly stated in the plans, it should only be viewed by City staff for general planning purposes. The feature class "Plan_selected_policies" represents the spatially explicit policies that were fairly straightforward to map. Since these policies are not represented in adopted maps, this feature class should only be viewed by City staff for general planning purposes only. 2010 - created by Jamison Brown, working as an independent contractor for Pima Association of Governments, created this file in 2010 by digitizing boundaries as depicted (i.e. for the mapped land use) or described in the plans (i.e. for the narrative policies). In most cases, this involved tracing based on parcel (paregion) or street center line (stnetall) feature classes. Snapping was used to provide line coincidence. For some map conversions, freehand sketches were drawn to mimick the freehand sketches in the adopted plan. Field descriptions Field descriptions for the "Plan_boundaries" feature class: Plan_Name: Plan name Plan_Type: Plan type (e.g., Neighborhood Plan) Plan_Num: Plan number ADOPT_DATE: Date of Plan adoption IMPORTANT: A disclaimer about the data as it is unofficial. URL: Uniform Resource Locator Field descriptions for the "Plan_mapped_land_use" feature class: Plan_Name: Plan name Plan_Type: Plan type (e.g., Neighborhood Plan) Plan_Num: Plan number LU_DES: Land use designation (e.g., Low density residential) LISTED_ALLOWABLE_ZONES: Allowable zones as listed in the Plan LISTED_RAC_MIN: Minimum residences per acre (if applicable), as listed in the Plan LISTED_RAC_TARGET: Target residences per acre (if applicable), as listed in the Plan LISTED_RAC_MAX: Maximum residences per acre (if applicable), as listed in the Plan LISTED_FAR_MIN: Minimum Floor Area Ratio (if applicable), as listed in the Plan LISTED_FAR_TARGET: Target Floor Area Ratio (if applicable), as listed in the Plan LISTED_FAR_MAX: Maximum Floor Area Ratio (if applicable), as listed in the Plan BUILDING_HEIGHT_MAX Building height maximum (ft.) if determined by Plan policy IMPORTANT: A disclaimer about the data as it is unofficial. URL: Uniform Resource Locator IMPLIED_ALLOWABLE_ZONES: Implied (not listed in the Plan) allowable zones IMPLIED_RAC_MIN: Implied (not listed in the Plan) minimum residences per acre (if applicable) IMPLIED_RAC_TARGET: Implied (not listed in the Plan) target residences per acre (if applicable) IMPLIED_RAC_MAX: Implied (not listed in the Plan) maximum residences per acre (if applicable) IMPLIED_FAR_MIN: Implied (not listed in the Plan) minimum Floor Area Ratio (if applicable) IMPLIED_FAR_TARGET: Implied (not listed in the Plan) target Floor Area Ratio (if applicable) IMPLIED_FAR_MAX: Implied (not listed in the Plan) maximum Floor Area Ratio (if applicable) IMPLIED_LU_CATEGORY: Implied (not listed in the Plan) general land use category. General categories used include residential, office, commercial, industrial, and other.PurposeLorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.Dataset ClassificationLevel 0 - OpenKnown UsesThis layer is intended to be used in the City of Tucson's Open Data portal and not for regular use in ArcGIS Online, ArcGIS Enterprise or other web applications.Known ErrorsLorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.Data ContactJohn BeallCity of Tucson Development Services520-791-5550John.Beall@tucsonaz.govUpdate FrequencyLorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua.

This is a video demonstrating how to create an offline map in ArcGIS Pro. Steps:Start with creating a vector tile package (.vtpk) from vector data.Add the vector tile package on top of other relevant data in a basemap view. The other data can be a raster image or any of the Esri's default basemaps.Add the basemap into another map view. In this map, you can add other operational layers on top of the basemap.Create a mobile map package (.mmpk) from the multi-layered map.The mobile map package can then be shared through ArcGIS Enterprise portal or manually copied to mobile devices.Author: Irvan Salim - Solution Engineer from Esri IndonesiaCopyright © 2020 Esri Indonesia. All rights reserved.

The District of Columbia’s Data program is pleased to offer the enterprise GIS geodatabase archive. This product offers a historic snapshot of the DC GIS data. The data consists of the GIS mapping layers and are exported as shapefiles. The date format in the named is "Archive_YYYYMMDD."

Status: COMPLETED 2010. The data was converted from the most recent (2010) versions of the adopted plans, which can be found at http://cms3.tucsonaz.gov/planning/plans/Contact: John Beall, City of Tucson Development Services, 520-791-5550, John.Beall@tucsonaz.gov. Jamie Brown, City of Tucson, 520-837-6934. Jamie.Brown@tucsonaz.govIntended Use: For mappingSupplemental Information: In March 2010, Pima Association of Governments (PAG), in cooperation with the City of Tucson (City), initiated the Planned Land Use Data Conversion Project. This 9-month effort involved evaluating mapped land use designations and selected spatially explicit policies for nearly 50 of the City's adopted neighborhood, area, and subregional plans and converting the information into a Geographic Information System (GIS) format. Further documentation for this file can be obtained from the City of Tucson Planning and Development Services Department or Pima Association of Governments Technical Services. A brief summary report was provided, as requested, to the City of Tucson which highlights some of the key issues found during the conversion process (e.g., lack of mapping and terminology consistency among plans). The feature class "Plan_boundaries" represents the boundaries of the adopted plans. The feature class "Plan_mapped_land_use" represents the land use designations as they are mapped in the adopted plans. Some information was gathered that is implicit based on the land use designation or zones (see field descriptions below). Since this information is not explicitly stated in the plans, it should only be viewed by City staff for general planning purposes. The feature class "Plan_selected_policies" represents the spatially explicit policies that were fairly straightforward to map. Since these policies are not represented in adopted maps, this feature class should only be viewed by City staff for general planning purposes only. 2010 - created by Jamison Brown, working as an independent contractor for Pima Association of Governments, created this file in 2010 by digitizing boundaries as depicted (i.e. for the mapped land use) or described in the plans (i.e. for the narrative policies). In most cases, this involved tracing based on parcel (paregion) or street center line (stnetall) feature classes. Snapping was used to provide line coincidence. For some map conversions, freehand sketches were drawn to mimick the freehand sketches in the adopted plan. Field descriptions Field descriptions for the "Plan_boundaries" feature class: Plan_Name: Plan name Plan_Type: Plan type (e.g., Neighborhood Plan) Plan_Num: Plan number ADOPT_DATE: Date of Plan adoption IMPORTANT: A disclaimer about the data as it is unofficial. URL: Uniform Resource Locator Field descriptions for the "Plan_mapped_land_use" feature class: Plan_Name: Plan name Plan_Type: Plan type (e.g., Neighborhood Plan) Plan_Num: Plan number LU_DES: Land use designation (e.g., Low density residential) LISTED_ALLOWABLE_ZONES: Allowable zones as listed in the Plan LISTED_RAC_MIN: Minimum residences per acre (if applicable), as listed in the Plan LISTED_RAC_TARGET: Target residences per acre (if applicable), as listed in the Plan LISTED_RAC_MAX: Maximum residences per acre (if applicable), as listed in the Plan LISTED_FAR_MIN: Minimum Floor Area Ratio (if applicable), as listed in the Plan LISTED_FAR_TARGET: Target Floor Area Ratio (if applicable), as listed in the Plan LISTED_FAR_MAX: Maximum Floor Area Ratio (if applicable), as listed in the Plan BUILDING_HEIGHT_MAX Building height maximum (ft.) if determined by Plan policy IMPORTANT: A disclaimer about the data as it is unofficial. URL: Uniform Resource Locator IMPLIED_ALLOWABLE_ZONES: Implied (not listed in the Plan) allowable zones IMPLIED_RAC_MIN: Implied (not listed in the Plan) minimum residences per acre (if applicable) IMPLIED_RAC_TARGET: Implied (not listed in the Plan) target residences per acre (if applicable) IMPLIED_RAC_MAX: Implied (not listed in the Plan) maximum residences per acre (if applicable) IMPLIED_FAR_MIN: Implied (not listed in the Plan) minimum Floor Area Ratio (if applicable) IMPLIED_FAR_TARGET: Implied (not listed in the Plan) target Floor Area Ratio (if applicable) IMPLIED_FAR_MAX: Implied (not listed in the Plan) maximum Floor Area Ratio (if applicable) IMPLIED_LU_CATEGORY: Implied (not listed in the Plan) general land use category. General categories used include residential, office, commercial, industrial, and other.Usage: This layer is intended to be used in the City of Tucson's Open Data portal and not for regular use in ArcGIS Online, ArcGIS Enterprise or other web applications.Link to Open Data item: https://gisdata.tucsonaz.gov/datasets/redevelopment-plans-open-data

This dataset represents a water shortage vulnerability analysis performed by DWR using modified PLSS sections pulled from the Well Completion Report PLSS Section Summaries. The attribute table includes water shortage vulnerability indicators and scores from an analysis done by CA Department of Water Resources, joined to modified PLSS sections. Several relevant summary statistics from the Well Completion Reports are included in this table as well. This data is from the 2024 analysis.

Water Code Division 6 Part 2.55 Section 8 Chapter 10 (Assembly Bill 1668) effectively requires California Department of Water Resources (DWR), in consultation with other agencies and an advisory group, to identify small water suppliers and “rural communities” that are at risk of drought and water shortage. Following legislation passed in 2021 and signed by Governor Gavin Newsom, the Water Code Division 6, Section 10609.50 through 10609.80 (Senate Bill 552 of 2021) effectively requires the California Department of Water Resources to update the scoring and tool periodically in partnership with the State Water Board and other state agencies. This document describes the indicators, datasets, and methods used to construct this deliverable.  This is a statewide effort to systematically and holistically consider water shortage vulnerability statewide of rural communities, focusing on domestic wells and state small water systems serving between 4 and 14 connections. The indicators and scoring methodology will be revised as better data become available and stake-holders evaluate the performance of the indicators, datasets used, and aggregation and ranking method used to aggregate and rank vulnerability scores. Additionally, the scoring system should be adaptive, meaning that our understanding of what contributes to risk and vulnerability of drought and water shortage may evolve. This understanding may especially be informed by experiences gained while navigating responses to future droughts.”

A spatial analysis was performed on the 2020 Census Block Groups, modified PLSS sections, and small water system service areas using a variety of input datasets related to drought vulnerability and water shortage risk and vulnerability. These indicator values were subsequently rescaled and summed for a final vulnerability score for the sections and small water system service areas. The 2020 Census Block Groups were joined with ACS data to represent the social vulnerability of communities, which is relevant to drought risk tolerance and resources. These three feature datasets contain the units of analysis (modified PLSS sections, block groups, small water systems service areas) with the model indicators for vulnerability in the attribute table. Model indicators are calculated for each unit of analysis according to the Vulnerability Scoring documents provided by Julia Ekstrom (Division of Regional Assistance).

All three feature classes are DWR analysis zones that are based off existing GIS datasets. The spatial data for the sections feature class is extracted from the Well Completion Reports PLSS sections to be aligned with the work and analysis that SGMA is doing. These are not true PLSS sections, but a version of the projected section lines in areas where there are gaps in PLSS. The spatial data for the Census block group feature class is downloaded from the Census. ACS (American Communities Survey) data is joined by block group, and statistics calculated by DWR have been added to the attribute table. The spatial data for the small water systems feature class was extracted from the State Water Resources Control Board (SWRCB) SABL dataset, using a definition query to filter for active water systems with 3000 connections or less. None of these datasets are intended to be the authoritative datasets for representing PLSS sections, Census block groups, or water service areas. The spatial data of these feature classes is used as units of analysis for the spatial analysis performed by DWR.

These datasets are intended to be authoritative datasets of the scoring tools required from DWR according to Senate Bill 552. Please refer to the Drought and Water Shortage Vulnerability Scoring: California's Domestic Wells and State Smalls Systems documentation for more information on indicators and scoring. These estimated indicator scores may sometimes be calculated in several different ways, or may have been calculated from data that has since be updated. Counts of domestic wells may be calculated in different ways. In order to align with DWR SGMO's (State Groundwater Management Office) California Groundwater Live dashboards, domestic wells were calculated using the same query. This includes all domestic wells in the Well Completion Reports dataset that are completed after 12/31/1976, and have a 'RecordType' of 'WellCompletion/New/Production or Monitoring/NA'.

Please refer to the Well Completion Reports metadata for more information. 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 3.4, dated September 14, 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.

Priority Areas for Environmental Conservation is a composite of multiple primary source datasets. As a result, there is no formal data dictionary available for this layer. For those seeking more information regarding the constituent datasets that comprise this layer, it is advised that reference be made to the primary sources. These primary sources may contain their own data dictionaries, metadata, or other relevant documentation that can provide an in-depth comprehension of the data.

Links to recordings of the Integrated Services Program and 9-1-1 & Geospatial Services Bureau webinar series, including NG9-1-1 GIS topics such as: data preparation; data provisioning and maintenance; boundary best practices; and extract, transform, and load (ETL). Offerings include:Topic: Virginia Next Generation 9-1-1 Dashboard and Resources Update Description: Virginia recently updated the NG9-1-1 Dashboard with some new tabs and information sources and continues to develop new resources to assist the GIS data work. This webinar provides an overview of changes, a demonstration of new functionality, and a guide to finding and using new resources that will benefit Virginia public safety and GIS personnel with roles in their NG9-1-1 projects. Wednesday 16 June 2021. Recording available at: https://vimeo.com/566133775Topic: Emergency Service Boundary GIS Data Layers and Functions in your NG9-1-1 PSAP Description: Law, Fire, and Emergency Medical Service (EMS) Emergency Service Boundary (ESB) polygons are required elements of the NENA NG9-1-1 GIS data model stack that indicate which agency is responsible for primary response. While this requirement must be met in your Virginia NG9-1-1 deployment with AT&T and Intrado, there are quite a few ways you could choose to implement these polygons. PSAPs and their GIS support must work together to understand how this information will come into a NG9-1-1 i3 PSAP and how it will replace traditional ESN information in order to make good choices while implementing these layers. This webinar discusses:the function of ESNs in your legacy 9-1-1 environment, the role of ESBs in NG9-1-1, and how ESB information appears in your NG9-1-1 PSAP. Wednesday, 22 July 2020. Recording available at: https://vimeo.com/441073056#t=360sTopic: "The GIS Folks Handle That": What PSAP Professionals Need to Know about the GIS Project Phase of Next Generation 9-1-1 DeploymentDescription: Next Generation 9-1-1 (NG9-1-1) brings together the worlds of emergency communication and spatial data and mapping. While it may be tempting for PSAPs to outsource cares and concerns about road centerlines and GIS data provisioning to 'the GIS folks', GIS staff are crucial to the future of emergency call routing and location validation. Data required by NG9-1-1 usually builds on data that GIS staff already know and use for other purposes, so the transition requires them to learn more about PSAP operations and uses of core data. The goal of this webinar is to help the PSAP and GIS worlds come together by explaining the role of the GIS Project in the Virginia NG9-1-1 Deployment Steps, exploring how GIS professionals view NG9-1-1 deployment as a project, and fostering a mutual understanding of how GIS will drive NG9-1-1. 29 January 2020. Recording available at: https://vimeo.com/showcase/9791882/video/761225474Topic: Getting Your GIS Data from Here to There: Processes and Best Practices for Extract, Transform and Load (ETL) Description: During the fall of 2019, VITA-ISP staff delivered workshops on "Tools and Techniques for Managing the Growing Role of GIS in Enterprise Software." This session presents information from the workshops related to the process of extracting, transforming, and loading data (ETL), best practices for ETL, and methods for data schema comparison and field mapping as a webinar. These techniques and skills assist GIS staff with their growing role in Next Generation 9-1-1 but also apply to many other projects involving the integration and maintenance of GIS data. 19 February 2020. Recording available at: https://vimeo.com/showcase/9791882/video/761225007Topic: NG9-1-1 GIS Data Provisioning and MaintenanceDescription: VITA ISP pleased to announce an upcoming webinar about the NG9-1-1 GIS Data Provisioning and Maintenance document provided by Judy Doldorf, GISP with the Fairfax County Department of Information Technology and RAC member. This document was developed by members of the NG9-1-1 GIS workgroup within the VITA Regional Advisory Council (RAC) and is intended to provide guidance to local GIS and PSAP authorities on the GIS datasets and associated GIS to MSAG/ALI validation and synchronization required for NG9-1-1 services. The document also provides guidance on geospatial call routing readiness and the short- and long-term GIS data maintenance workflow procedures. In addition, some perspective and insight from the Fairfax County experience in GIS data preparation for the AT&T and West solution will be discussed in this webinar. 31 July 2019. Recording available at: https://vimeo.com/showcase/9791882/video/761224774Topic: NG9-1-1 Deployment DashboardDescription: I invite you to join us for a webinar that will provide an overview of our NG9-1-1 Deployment Dashboard and information about other online ISP resources. The ISP website has been long criticized for being difficult to use and find information. The addition of the Dashboard and other changes to the website are our attempt to address some of these concerns and provide an easier way to find information especially as we undertake NG9-1-1 deployment. The Dashboard includes a status map of all Virginia PSAPs as it relates to the deployment of NG9-1-1, including the total amount of funding requested by the localities and awards approved by the 9-1-1 Services Board. During this webinar, Lyle Hornbaker, Regional Coordinator for Region 5, will navigate through the dashboard and provide tips on how to more effectively utilize the ISP website. 12 June 2019. Recording not currently available. Please see the Virginia Next Generation 9-1-1 Dashboard and Resources Update webinar recording from 16 June 2021. Topic: PSAP Boundary Development Tools and Process RecommendationDescription: This webinar will be presented by Geospatial Program Manager Matt Gerike and VGIN Coordinator Joe Sewash. With the release of the PSAP boundary development tools and PSAP boundary segment compilation guidelines on the VGIN Clearinghouse in March, this webinar demonstrates the development tools, explains the process model, and discusses methods, tools, and resources available for you as you work to complete PSAP boundary segments with your neighbors. 15 May 2019. Recording available at: https://www.youtube.com/watch?v=kI-1DkUQF9Q&feature=youtu.beTopic: NG9-1-1 Data Preparation - Utilizing VITA's GIS Data Report Card ToolDescription: This webinar, presented by VGIN Coordinator Joe Sewash, Geospatial Program Manager Matt Gerike, and Geospatial Analyst Kenny Brevard will provide an overview of the first version of the tools that were released on March 25, 2019. These tools will allow localities to validate their GIS data against the report card rules, the MSAG and ALI checks used in previous report cards, and the analysis listed in the NG9-1-1 migration proposal document. We will also discuss the purpose of the tools, input requirements, initial configuration, how to run them, and how to make sense of your results. 10 April 2019. Recording available at: https://vimeo.com/showcase/9791882/video/761224495Topic: NG9-1-1 PSAP Boundary Best Practice WebinarDescription: During the months of November and December, VITA ISP staff hosted regional training sessions about best practices for PSAP boundaries as they relate to NG9-1-1. These sessions were well attended and very interactive, therefore we feel the need to do a recap and allow those that may have missed the training to attend a makeup session. 30 January 2019. Recording not currently available. Please see the PSAP Boundary Development Tools and Process Recommendation webinar recording from 15 May 2019.Topic: NG9-1-1 GIS Overview for ContractorsDescription: The Commonwealth of Virginia has started its migration to next generation 9-1-1 (NG9-1-1). This migration means that there will be a much greater reliance on geographic information (GIS) to locate and route 9-1-1 calls. VITA ISP has conducted an assessment of current local GIS data and provided each locality with a report. Some of the data from this report has also been included in the localities migration proposal, which identifies what data issues need to be resolved before the locality can migrate to NG9-1-1. Several localities in Virginia utilize a contractor to maintain their GIS data. This webinar is intended for those contractors to review the data in the report, what is included in the migration proposal and how they may be called on to assist the localities they serve. It will still ultimately be up to each locality to determine whether they engage a contractor for assistance, but it is important for the contractor community to understand what is happening and have an opportunity to ask questions about the intent and goals. This webinar will provide such an opportunity. 22 August 2018. Recording not currently available. Please contact us at NG911GIS@vdem.virginia.gov if you are interested in this content.

This dataset is a compilation of road centerline data from Minnesota suppliers that have opted-in for their road centerline data to be included in this dataset.

It includes the following 43 suppliers that have opted-in to share their data openly as of the publication date of this dataset: Aitkin County, Anoka County, Benton County, Carver County, Cass County, Chippewa County, Chisago County, Clay County, Cook County, Dakota County, Douglas County, Fillmore County, Hennepin County, Houston County, Isanti County, Itasca County, Koochinching County, Lac qui Parle County, Lake County, Le Sueur County, Lyon County, Marshall County, McLeod County, Morrison County, Mower County, Murray County, Otter Tail County, Pipestone County, Pope County, Polk County, Ramsey County, Renville County, Rock County, Saint Louis County, Scott County, Sherburne County, Stearns, Stevens County, Waseca County, Washington County, Wright County, and Yellow Medicine County.

The two sources of road centerline data are the Minnesota Next Generation 9-1-1 (NG9-1-1) Program, in collaboration with local data suppliers, and the MetroGIS Road Centerlines (Geospatial Advisory Council Schema) which is on the Minnesota Geospatial Commons:

The Minnesota NG9-1-1 Program enterprise database provides the data outside of the Metro Region which is provide by the suppliers. The data have been aggregated into a single dataset which implements the MN NG9-1-1 GIS Data Model (https://ng911gis-minnesota.hub.arcgis.com/documents/79beb1f9bde84e84a0fa9b74950f7589/about ).

Only data which have meet the requirements for supporting NG9-1-1 are in the statewide aggregate GIS data. MnGeo extracts the available data, applies domain translations, and transforms it to UTM Zone 15 to comply with the GAC road centerline attribute schema: https://www.mngeo.state.mn.us/committee/standards/roadcenterline/index.html.

The MetroGIS Road Centerlines data was created by a joint collaborative project involving the technical and managerial GIS staff from the the Metropolitan Counties (Anoka, Carver, Chisago, Dakota, Hennepin, Isanti, Ramsey, Scott, Sherburne, and Washington), the Metropolitan Emergency Services Board, MetroGIS and the Metropolitan Council. The data are pulled from the Minnesota Geospatial Commons: https://gisdata.mn.gov/dataset/us-mn-state-metrogis-trans-road-centerlines-gac

‘Supplier’ is a term used throughout this document. A supplier will typically be a county, but it could also be a public safety answering point (PSAP), region, or tribal nation. The supplier is the agency which provides the individual datasets for the aggregated dataset. The trans_road_centerlines_open_metadata feature layer will contain the geometry/shape of the supplier boundaries, supplier name, supplier type, and feature count.

Aggregation Process:
1. Extract NG9-1-1 data from the Department of Public Safety (DPS) Enterprise database.
2. Download the latest MetroGIS data from the Geospatial Commons.
3. Extract, Translate, and Load (ETL) the DPS data to the GAC schema.
4. Combine NG9-1-1 data with MetroGIS data.
5. Filter the data for the Opt-In Open data counties

Laatste update: 07 december 2023Een locator kan helpen om een specifieke locatie op de kaart te vinden, of om een tabel met adressen of plaatsen om te zetten naar punten op de kaart. Het kan gebruikt worden in meerdere onderdelen van het ArcGIS-platform. Een locator bevat de referentiedata die gebruikt wordt om te geocoderen, en daarnaast ook indices, regels, configuratie en regionale kennis over de weergave van adressen. Daarom kan een locator een beter zoekresultaat geven dan een eenvoudige database query.Een locator kan worden gemaakt binnen ArcGIS Pro (zie Create a locator—ArcGIS Pro | Documentation) en lokaal worden gebruikt. Het voordeel van het ArcGIS-platform is dat een locator ook op eenvoudige wijze kan worden gedeeld binnen de organisatie of met het publiek. Een locator kan gedeeld worden als een service in ArcGIS Enterprise. De locator kan up to date gehouden worden, zonder dat de eindgebruiker hinder ondervindt van eventuele updates.

A PDF map showing the local enterprise partnerships (LEPs) in England as at December 2022. (File Size - 227 KB)

This document is an Elevation Certificate that represents the given Address listed in the title of the document. Some may vary in appearance due to age and documentation updates.

The CDO annual report documents accomplishments and the Enterprise Dataset Inventory results of the previous calendar year. It assess current progress and shares the District's progress with Open Data and the Data Policy. Since last year’s report, I was privileged to spend most of my work time serving as the Interim Chief Technology Officer of the District of Columbia, as well as Chief Data Officer. OCTO is a unique and special agency, with a breadth of responsibility that runs from complex statewide applications for sophisticated customers like the Department of Motor Vehicles to hyper-local hands-on tech support in the public schools. That acknowledged, I’d like to thank Mayor Muriel Bowser for trusting me to run the agency that has shaped much of my career, and thanks also to our newly appointed CTO, Lindsey Parker, for diving right into data and open government efforts and supporting our team.

The Los Angeles County departments and organization uses the Esri HUB site(s) as a platform to provide access to information related to various components of their open data. Hosted documentation, upload a file which supported by the following potential file types: Microsoft Excel (.xls, .xlsx), Microsoft PowerPoint (.ppt, .pptx), Microsoft Word (.doc, .docx), PDF (.pdf), and Microsoft Visio Document (.vsd) or others. This site, Countywide Address Management System (CAMS) HUB Site and associated pages are used to share information about specific topics, projects, and plans related to the Countywide Address Management System (CAMS). The content, items, groups, etc. are all critical to ensure the access to Open Data the contained information should serve as a bridge between the Los Angeles County enterprise GIS organization and the community it serves (ie EVERYONE). potential information available added for context.

The Los Angeles County departments and organization uses the Esri HUB site(s) as a platform to provide open access to information related to various component of their open data.Hosted documentation, upload a file which supported by the following potential file types: Microsoft Excel (.xls, .xlsx), Microsoft PowerPoint (.ppt, .pptx), Microsoft Word (.doc, .docx), PDF (.pdf), and Microsoft Visio Document (.vsd) or others. This site, Countywide Address Management System (CAMS) HUB Site and associated pages are used to share information about specific topics, projects, and plans related to the Countywide Address Management System (CAMS). The content, items, groups, etc. are all critical to ensure the access to Open Data the contained information should serve as a bridge between the Los Angeles County enterprise GIS organization and the community it serves (ie EVERYONE). potential information available added for context.