We recommend walking everyone through this process in class. The first step for all students is to create an ArcGIS account. Unlike you, students will create a personal account via storymaps.arcgis.com.Once your students have accounts follow the procedure below.Navigate to the Instructional StoryMap you've created for your course, copy the URL, and share it with your students.Have students click the star in the top right of the instructional StoryMap to add it to their favorites.Students should follow the first steps in the instructional StoryMap to create, name, and publish the first part of their StoryMap. Make sure students are sharing to "Everyone (Public)." From here on out, students can sign in at storymaps.arcgis.com and find their StoryMap by clicking Content and the instructional StoryMap by clicking favorites.

Now your students can start editing their templates! It is up to you when, where, and how this happens. We HIGHLY recommend students draft text and collect sources outside of StoryMaps (in Word, GoogleDocs, etc.) and copy material into StoryMaps as they are ready to turn in each part of the template. This protects against browser crashes, accidental edits by instructors or other students, and other inevitable mishaps in the digital space. As written, the template requests different kinds and numbers of sources for each part. You can find a quick list of what students will need to have to hand for each section on the Research Activities and Assignments page .

The Story Map Basic application is a simple map viewer with a minimalist user interface. Apart from the title bar, an optional legend, and a configurable search box the map fills the screen. Use this app to let your map speak for itself. Your users can click features on the map to get more information in pop-ups. The Story Map Basic application puts all the emphasis on your map, so it works best when your map has great cartography and tells a clear story.You can create a Basic story map by sharing a web map as an application from the map viewer. You can also click the 'Create a Web App' button on this page to create a story map with this application. Optionally, the application source code can be downloaded for further customization and hosted on your own web server.For more information about the Story Map Basic application, a step-by-step tutorial, and a gallery of examples, please see this page on the Esri Story Maps website.

Instructions on how to make an ArcGIS map, add georeferenced points, adjust appearances , configure pop up boxes, upload images and sharing a map. Introduces students to ArcGIS mapping. Students learn how to organize and upload designated places onto an ArcGIS map. Students learn how to configure pop-up boxes for each designated place and populate them with information they have uncovered. Students learn how to add images to their designated places on their maps. Once completed, students learn how to import into other media i.e. StoryMaps, Word documents to tell a bigger story about the places on the map.

The interactive map creation tools market is experiencing robust growth, driven by increasing demand for visually engaging data representation across diverse sectors. The market's value is estimated at $2 billion in 2025, exhibiting a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This growth is fueled by several factors, including the rising adoption of location-based services, the proliferation of readily available geographic data, and the growing need for effective data visualization in business intelligence and marketing. The individual user segment currently holds a significant share, but corporate adoption is rapidly expanding, propelled by the need for sophisticated map-based analytics and internal communication. Furthermore, the paid use segment is anticipated to grow more quickly than the free use segment, reflecting the willingness of businesses and organizations to invest in advanced features and functionalities. This trend is further amplified by the increasing integration of interactive maps into various platforms, such as business intelligence dashboards and website content. Geographic expansion is also a significant growth driver. North America and Europe currently dominate the market, but the Asia-Pacific region is showing significant promise due to rapid technological advancements and increasing internet penetration. Competitive pressures remain high, with established players such as Google, Mapbox, and ArcGIS StoryMaps vying for market share alongside innovative startups offering specialized solutions. The market's restraints are primarily focused on the complexities of data integration and the technical expertise required for effective map creation. However, ongoing developments in user-friendly interfaces and readily available data integration tools are mitigating these challenges. The future of the interactive map creation tools market promises even greater innovation, fueled by developments in augmented reality (AR), virtual reality (VR), and 3D visualization technologies. We expect to see the emergence of more sophisticated tools catering to niche requirements, further driving market segmentation and specialization. Continued investment in research and development will also play a crucial role in pushing the boundaries of what's possible with interactive map creation. The market presents opportunities for companies to develop tools which combine data analytics and interactive map design.

The datasets used for this manuscript were derived from multiple sources: Denver Public Health, Esri, Google, and SafeGraph. Any reuse or redistribution of the datasets are subjected to the restrictions of the data providers: Denver Public Health, Esri, Google, and SafeGraph and should consult relevant parties for permissions.1. COVID-19 case dataset were retrieved from Denver Public Health (Link: https://storymaps.arcgis.com/stories/50dbb5e7dfb6495292b71b7d8df56d0a )2. Point of Interests (POIs) data were retrieved from Esri and SafeGraph (Link: https://coronavirus-disasterresponse.hub.arcgis.com/datasets/6c8c635b1ea94001a52bf28179d1e32b/data?selectedAttribute=naics_code) and verified with Google Places Service (Link: https://developers.google.com/maps/documentation/javascript/reference/places-service)3. The activity risk information is accessible from Texas Medical Association (TMA) (Link: https://www.texmed.org/TexasMedicineDetail.aspx?id=54216 )The datasets for risk assessment and mapping are included in a geodatabase. Per SafeGraph data sharing guidelines, raw data cannot be shared publicly. To view the content of the geodatabase, users should have installed ArcGIS Pro 2.7. The geodatabase includes the following:1. POI. Major attributes are locations, name, and daily popularity.2. Denver neighborhood with weekly COVID-19 cases and computed regional risk levels.3. Simulated four travel logs with anchor points provided. Each is a separate point layer.

This story map describes and demonstrates how OpenStreetMap (OSM) data is accessible in ArcGIS, and how ArcGIS users can help to improve OSM with their GIS data. Learn the various ways in which you can access OSM data for your work, and how you can share data to be used in OSM.OpenStreetMap is a free, editable map of the world built by a community of mappers that contribute and maintain geospatial data about our world. It includes a worldwide database that is maintained by over 8 million registered users, with millions of map changes each day. Esri provides access to OSM data to ArcGIS users in multiple ways, including hosted vector tiles, feature layers, and scene layers.This story map shows several examples of how you can access OSM data in your work, and how ArcGIS organizations (e.g. cities, counties, states, nations) can share data they maintain (e.g. buildings, addresses, roads) to be used in OSM. The story illustrates the open data pipeline between ArcGIS and OSM, where open data created and published with ArcGIS can flow to OpenStreetMap and then OSM data flows back again to ArcGIS.

Purpose: This is the 2019 Hurricanes Crowdsourced Photos Public Feature Layer View. This is a live publicly accessible layer for the Crowdsource Story Map accessible here: This layer cannot be edited, it is view only. ShareHidden Field: 0 = Needs Review, 1 = Already Reviewed, 2 = Hidden (not available in this public view).Audience: GIS Staff and Technologists who would like to add this layer to their own web maps and apps. If you need access to this layer in other formats, see the Open Data link. Please send us an email at triage@publicsafetygis.org to tell us if you are going to use this layer and if you have any questions or need assistance with this layer.Need to download the photos? See this technical support article.

USGS and FEMA collaborated to collect data on residences in six of the twenty disaster-declared parishes in Louisiana during the August 8–11 floods in 2016. The National Map Corps (TNMCorps), a volunteer program run the National Geospatial Program of the U.S. Geological Survey, were invited to help with this task. From August 30 to September 30, 2016, Corps editors created and classified data points for each building within the six parishes. FEMA used this information to find which buildings in the parishes were residences, and to estimate by how many feet underwater the houses were. This data was then used to help determine the amount of assistance provided to homeowners in these parishes.The data points were summarized into 5 km hexagons (a process called hexbinning) to provide an easier visualization of the number of points collected by TNMCorps. Lighter colors indicate more buildings per hexagon. Per-hexagon numbers range from 1 to 1800.Map also includes flood extent estimates from FEMA and GOSEP (Louisiana Governer's Office of Homeland Security & Emergency Management). Parish outlines derived from U.S. Census TIGER data.

This layer shows population broken down by race and Hispanic origin. This is shown by tract, county, and state centroids. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the predominant race living within an area, and the total population in that area. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B03002Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. 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. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). 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 erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

The OCS GE (large-scale land cover) repository is a vector database describing land cover and land use for the entire metropolitan area and the overseas departments and regions (DROM). Its model has two dimensions: it separates land use from land use (see precise nomenclature). The geometric accuracy of the OCSGE is metric and is based on the RGE®. Its information comes mainly from aerial photographs updated every three years, and it therefore has a temporal consistency with them. Successive vintages make it possible to quantify and qualify changes in the soil surface. By 2025, the new generation GE SCO – financed by the Directorate-General for Planning, Housing and Nature (DGALN) of the Ministry of Ecological Transition and Territorial Cohesion – will offer complete coverage of the national territory in at least two vintages. It is a core data layer, which can be enriched if necessary at refined thematic levels to take into account local specificities and respond to specific needs. As part of the artificialization observatory, IGN is carrying out work that optimizes the current production chain in order to achieve the greatest possible automation. The aim is to meet two objectives: • a significant reduction in costs, minimising the share of human photo-interpretation, • and a significant reduction in production times making it possible to produce 1/3 of France every year under cruising conditions. This is done by using artificial intelligence (AI) processes such as ‘deep learning’. The GE SCO production chain is based on an infrastructure with significant computing capacities and storage space to host and process large data. This database will be crucial for monitoring artificialisation and achieving net zero artificialisation by 2050, as set out in the ‘Climate and Resilience’ law of 22 August 2021. Further information is available on the Artificialisation Portal. The BD OCS GE N-G is based on the national nomenclature recommended by the National Geographical Information Council (CNIG), developed by the Centre d’études et d’expertise sur les risques, l’environnement, la mobilité et l’aménagement (CEREMA), under the supervision of the Ministry for Ecology, Sustainable Development and Energy (MEDDE.DGALN). The BD OCS GE complies with the national production recommendations that accompany the national nomenclature. It is based on an intermediate data layer, the national reference system for the main networks constituting the ‘National Ossature’. It has four main functions: • ensure geographical continuity between territories; • ensure a reference geometry; • partition territories homogeneously; • ensure spatial cohesion between territorial scales. OPenIG makes available in shapefile format the OCS GE comic for Hérault and the associated documentation. In addition, the IGN provides a user guide designed to facilitate the understanding and use of GE SCO data, presented here at the level of the national pillar. The first section provides a detailed description of the GE OCS and its characteristics, the second presents how these data can be exploited by geomaticians using QGIS software. Each party may be consulted independently. Here is the link to access this guide: https://storymaps.arcgis.com/stories/193550c4e4af4f92845201d74ca8a002

This layer shows disability status by sex and age group. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the percentage of elderly (65+) with a disability. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B18101Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. 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. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). 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 erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

The Midwest Landscape Initiative (MLI) is a collaboration of fish and wildlife organizations that identifies shared conservation priorities to develop solutions for healthy, functioning ecosystems in the Midwest. One product stewarded by the Midwest Landscape Initiative is the Midwest Conservation Blueprint, a base map of priority lands and waters for conservation across the Midwest. The blueprint prioritization was created using more than 20 cross-cutting societal and environmental values (i.e., indicators), including biodiversity, access to drinking water, and public recreation, among many others.This layer illustrates the approximate boundaries of a 2023 America the Beautiful Challenge project which used the Midwest Conservation Blueprint to support its goals. These boundaries are estimated and are for viewing purposes only. This layer is an input for a web map which is highlighted on a StoryMap that is used to share successes of the Midwest Landscape Initiative. It is not recommended to view this layer on its own, but rather as part of the StoryMap, which can be found here: https://storymaps.arcgis.com/stories/15a30ebafbce4f44a1135750bf22105a.

General Accessibility Creative Commons All data products available from the data hub are provided on an 'as is' basis. The City of Sydney (City) makes no warranty, representation or guarantee of any type as to any errors and omissions, or as to the content, accuracy, timeliness, completeness or fitness for any particular purpose or use of any data product available from the data hub. If you find any information that you believe may be inaccurate, please email the City. In addition, please note that the data products available from the data hub are not intended to constitute advice and must not be used as a substitute for professional advice. The City may modify the data products available from the data hub and/or discontinue providing any or all of data products at any time and for any reason, without notice. Accordingly, the City recommends that you regularly check the data hub to ensure that the latest version of data products is used. The City recommends that when accessing data sets, you use APIs. We are committed to making our website as accessible and user-friendly as possible. Web Content Accessibility Guidelines (WCAG) cover a wide set of recommendations to make websites accessible. For more information on WCAG please visit https://www.w3.org/TR/WCAG21/ . This site is built using Esri's ArcGIS Hubs template, and their Accessibility status report is available online at https://hub.arcgis.com/pages/a11y. We create the maps and stories on this site using ArcGIS templates, each template having accessibility features. Examples include Instant Apps, Story maps, and Webapp builder. If you would like to request alternative formats for data products on this site please email the City. We encourage developers using our data to deliver maps and applications with consideration to accessibility for all. Design elements can include colour, contrast, symbol size and style, font size and style, basemap style, alternate text for images, and captions for video and audio. Alternative content such as static maps may sometimes be required. Unless otherwise stated, data products available from the data hub are published under Creative Commons licences. Creative Commons licences include terms and conditions about how licensed data products may be used, shared and/or adapted. Depending on the applicable licence, licensed data products may or may not be used for commercial purposes. The applicable Creative Commons licence for specific data is specified in the "Licence" section of the data description. By accessing, sharing and/or adapting licensed data products, you are deemed to have accepted the terms and conditions of the applicable Creative Common licence. For more information about Creative Commons licences, please visit https://creativecommons.org.au/ and https://creativecommons.org/faq/ If you believe that the applicable Creative Commons licence for the data product that you wish to use is overly restrictive for how you would like to use the data product, please email the City. Contact If you have a question, comments, or requests for interactive maps and data, we would love to hear from you. Council business For information on rates, development applications, strategies, reports and other council business, see the City of Sydney's main website.

ArcGIS Online is a cloud-based mapping and analysis solution. Use it to make maps, analyze data, and to share and collaborate. ... Your data and maps are stored in a secure and private infrastructure.

This story map was produced by Esri's story maps team using the Side Accordion application template. For more information visit the story maps website.

This dataset was created using a combination of the 1968 Johnson and Higgins Plat Map, the 1979 Alaska Packers Association Plat Map of the Cannery in South Naknek, Alaska. The historic function off each building was provided by the Cannery Project. This data was creating using ESRI's World Base Map for interpretive purposes and accuracy is not guaranteed.Used in the URC StoryMap: https://storymaps.arcgis.com/stories/9bdd1b0c0659466e823f9b8bd39764b0The corresponding NPS DataStore on Integrated Resource Management Applications (IRMA) reference is DataStore - WASO STLPG Division NN Cannery Features 2020

The Living Atlas of the World is a rapidly growing part of the ArcGIS Platform, providing users with easy access to a rich set of high-quality, ready-to-use content published by Esri and the ArcGIS user community. This content is accessible through a variety of ArcGIS applications, such as the Living Atlas of the World. We would like to encourage you to become an active participant in our worldwide Living Atlas Community.This Living Atlas Contributor app enables you to prepare and nominate items for inclusion in the Living Atlas. You can sign in to the Contributor app with your ArcGIS account and use the app to review the completeness of items that you have created (such as web maps, map layers, or web mapping applications) and determine if they ready to share with others. For items that you believe are appropriate to include in the Living Atlas of the World, you can use the app to nominate these items for review by our Living Atlas Curators. Items that are accepted will be featured in the Living Atlas of the World and other applications.If you like, you can simply use the Living Atlas Contributor app to review items that you have created (e.g. web maps, story maps, map layers) for completeness before you share them with others. The Living Atlas Contributor app helps check for completeness for several elements of the best practices for sharing.

The Midwest Landscape Initiative (MLI) is a collaboration of fish and wildlife organizations that identifies shared conservation priorities to develop solutions for healthy, functioning ecosystems in the Midwest. One product stewarded by the Midwest Landscape Initiative is the Midwest Conservation Blueprint, a base map of priority lands and waters for conservation across the Midwest. The blueprint prioritization was created using more than 20 cross-cutting societal and environmental values (i.e., indicators), including biodiversity, access to drinking water, and public recreation, among many others.This layer illustrates the approximate boundary of the Northern Forest biome as determined by the U.S. Fish and Wildlife Service for 2024 Inflation Reduction Act spending. Within this boundary, the Midwest Landscape Initiative and Northeast Association of Fish and Wildlife Agencies Landscape Conservation Committee collaborated to select projects for IRA funding. The Midwest Conservation Blueprint and Nature's Network Conservation Design were considered as criteria during the funding process. These boundaries are estimated and are for viewing purposes only. This layer is an input for a web map which is highlighted on a StoryMap that is used to share successes of the Midwest Landscape Initiative. It is not recommended to view this layer on its own, but rather as part of the StoryMap, which can be found here: https://storymaps.arcgis.com/stories/15a30ebafbce4f44a1135750bf22105a.