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.

This collection of images depict Boston, Massachusetts, with particular emphasis on Dorchester Avenue. Some of the images contain photographs of the area, while others detail Dorchester Avenue's history using a timeline. The images are associated with chapters 1 through 4 of the PLAN South Boston Dorchester Avenue report, which contains the history, current conditions, outreach initiatives, goals, and objectives of a proposed plan to create a new mixed-use urban district in Boston, Massachusetts.These images are intended for use in the Storify a planning report tutorial, which details the process of creating a story in ArcGIS StoryMaps for the plan. The story includes maps and a scene that showcase the proposed district. The plan itself was created by the Boston Planning & Development Agency (BPDA).

A story map co-produced by Esri and IUCN featuring a selection of threatened species from the Red List. This is a custom story map that doesn't use one of the Story Map app templates.

Welcome to ArcGIS StoryMaps! This guide will walk you through the basic skills that you need to present information effectively and accessibly in ArcGIS StoryMaps. Our instructions here can pair with coursework across different academic disciplines and are adaptable to various course and grade levels.This is a 6-part exercise that will cover the main functions that the platform can offer. Part 1 gives and overview of setting up and designing a StoryMap, as well as adding text of various sizes and images with credits. Parts 2-4 walk through different options for presenting images and maps with accompanying written content - building slideshows, sidecars, and swipes, in that order. Part 5, the map tour, and Part 6, the timeline, look at two features of StoryMaps that are respectively more rooted in attention to space and time.

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.

NOTICE TO PROVISIONAL 2023 LAND USE DATA USERS: Please note that on December 6, 2024 the Department of Water Resources (DWR) published the Provisional 2023 Statewide Crop Mapping dataset. The link for the shapefile format of the data mistakenly linked to the wrong dataset. The link was updated with the appropriate data on January 27, 2025. If you downloaded the Provisional 2023 Statewide Crop Mapping dataset in shapefile format between December 6, 2024 and January 27, we encourage you to redownload the data. The Map Service and Geodatabase formats were correct as posted on December 06, 2024.

Thank you for your interest in DWR land use datasets.

The California Department of Water Resources (DWR) has been collecting land use data throughout the state and using it to develop agricultural water use estimates for statewide and regional planning purposes, including water use projections, water use efficiency evaluations, groundwater model developments, climate change mitigation and adaptations, and water transfers. These data are essential for regional analysis and decision making, which has become increasingly important as DWR and other state agencies seek to address resource management issues, regulatory compliances, environmental impacts, ecosystem services, urban and economic development, and other issues. Increased availability of digital satellite imagery, aerial photography, and new analytical tools make remote sensing-based land use surveys possible at a field scale that is comparable to that of DWR’s historical on the ground field surveys. Current technologies allow accurate large-scale crop and land use identifications to be performed at desired time increments and make possible more frequent and comprehensive statewide land use information. Responding to this need, DWR sought expertise and support for identifying crop types and other land uses and quantifying crop acreages statewide using remotely sensed imagery and associated analytical techniques. Currently, Statewide Crop Maps are available for the Water Years 2014, 2016, 2018- 2022 and PROVISIONALLY for 2023.

Historic County Land Use Surveys spanning 1986 - 2015 may also be accessed using the CADWR Land Use Data Viewer: https://gis.water.ca.gov/app/CADWRLandUseViewer.

For Regional Land Use Surveys follow: https://data.cnra.ca.gov/dataset/region-land-use-surveys.

For County Land Use Surveys follow: https://data.cnra.ca.gov/dataset/county-land-use-surveys.

For a collection of ArcGIS Web Applications that provide information on the DWR Land Use Program and our data products in various formats, visit the DWR Land Use Gallery: https://storymaps.arcgis.com/collections/dd14ceff7d754e85ab9c7ec84fb8790a.

Recommended citation for DWR land use data: California Department of Water Resources. (Water Year for the data). Statewide Crop Mapping—California Natural Resources Agency Open Data. Retrieved “Month Day, YEAR,” from https://data.cnra.ca.gov/dataset/statewide-crop-mapping.

For many of us, urban areas are the first thing that comes to mind when we think of spaces that have been altered by people. But, as it turns out, these mental images aren't very representative of our overall land use. In the second chapter of our Living in the Age of Humans series, the Esri Story Maps team takes a closer look at the ways Homo sapiens have modified Earth's limited land, and what implications this use has for our future.Data:NASA Blue Marble, July 2004Esri World ImageryESA CCI-LC Land Cover (2015)CIESIN Global Croplands, v1 (2000)CIESIN Global Pastures, v1 (2000)WheatMaizeRiceSoybeansForest Loss**The documentation below is in reference to this items placement in the NM Supply Chain Data Hub. The documentation is of use to understanding the source of this item, and how to reproduce it for updates**Title: The Living LandItem Type: Web Mapping Application Storymap URLSummary: A look at how humans use the Earth's limited land space.Notes: Prepared by: Uploaded by EMcRae_NMCDCSource: Copy of this original map product: https://nmcdc.maps.arcgis.com/home/item.html?id=d29065c5443f4d008e7d7e181e54b05dFeature Service: https://nmcdc.maps.arcgis.com/home/item.html?id=8a16839162554968ac6d2cf0513bcefaUID: 26Data Requested: Ag CensusMethod of Acquisition: Living AtlasDate Acquired: 5/2022Priority rank as Identified in 2022 (scale of 1 being the highest priority, to 11 being the lowest priority): 8Tags: PENDING

Welcome to ArcGIS StoryMaps! This template will guide you through the basic skills that you need to present information effectively and accessibly in StoryMaps. Our instructions here can pair with coursework across different academic disciplines and are adaptable to various course and grade levels.This is a 6-part exercise that will cover the main functions that the platform can offer. Part 1 gives and overview of setting up and designing a StoryMap, as well as adding text of various sizes and images with credits. Parts 2-4 walk through different options for presenting images and maps with accompanying written content - slideshows, sidecars, and swipes, in that order. Part 5, the map tour, and Part 6, the timeline, look at two features of StoryMaps that are respectively more rooted in attention to space and time.

Soil is the foundation of life on earth. More living things by weight live in the soil than upon it. It determines what crops we can grow, what structures we can build, what forests can take root.This layer contains the physical soil variable percent clay (clay).Within the subset of soil that is smaller than 2mm in size, also known as the fine earth portion, clay is defined as particles that are smaller than 0.002mm, making them only visible in an electron microscope. Clay soils contain low amounts of air, and water drains through them very slowly.This layer is a general, medium scale global predictive soil layer suitable for global mapping and decision support. In many places samples of soils do not exist so this map represents a prediction of what is most likely in that location. The predictions are made in six depth ranges by soilgrids.org, funded by ISRIC based in Wageningen, Netherlands.Each 250m pixel contains a value predicted for that area by soilgrids.org from best available data worldwide. Data for percent clay are provided at six depth ranges from the surface to 2 meters below the surface. Each variable and depth range may be accessed in the layer's multidimensional properties.Dataset SummaryPhenomenon Mapped: Proportion of clay particles (< 0.002 mm) in the fine earth fraction in g/100g (%)Cell Size: 250 metersPixel Type: 32 bit float, converted from online data that is 16 Bit Unsigned IntegerCoordinate System: Web Mercator Auxiliary Sphere, projected via nearest neighbor from goode's homolosine land (250m)Extent: World land area except AntarcticaVisible Scale: All scales are visibleNumber of Columns and Rows: 160300, 100498Source: Soilgrids.orgPublication Date: May 2020Data from the soilgrids.org mean predictions for clay were used to create this layer. You may access the percent clay in one of six depth ranges. To select one choose the depth variable in the multidimensional selector in your map client.Mean depth (cm)Actual depth range of data-2.50-5cm depth range-105-15cm depth range-22.515-30cm depth range-4530-60cm depth range-8060-100cm depth range-150100-200cm depth rangeWhat can you do with this Layer?This layer is suitable for both visualization and analysis across the ArcGIS system. This layer can be combined with your data and other layers from the ArcGIS Living Atlas of the World in ArcGIS Online and ArcGIS Pro to create powerful web maps that can be used alone or in a story map or other application.Because this layer is part of the ArcGIS Living Atlas of the World it is easy to add to your map: In ArcGIS Online, you can add this layer to a map by selecting Add then Browse Living Atlas Layers. A window will open. Type "world soils soilgrids" in the search box and browse to the layer. Select the layer then click Add to Map. In ArcGIS Pro, open a map and select Add Data from the Map Tab. Select Data at the top of the drop down menu. The Add Data dialog box will open on the left side of the box, expand Portal if necessary, then select Living Atlas. Type "world soils soilgrids" in the search box, browse to the layer then click OK.In ArcGIS Pro you can use the built-in raster functions or create your own to create custom extracts of the data. Imagery layers provide fast, powerful inputs to geoprocessing tools, models, or Python scripts in Pro.Online you can filter the layer to show subsets of the data using the filter button and the layer's built-in raster functions.This layer is part of the Living Atlas of the World that provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.More information about soilgrids layersAnswers to many questions may be found at soilgrids.org (ISRIC) frequently asked questions (faq) page about the data.To make this layer, Esri reprojected the expected value of ISRIC soil grids from soilgrids' source projection (goode's land WKID 54052) to web mercator projection, nearest neighbor, to facilitate online mapping. The resolution in web mercator projection is the same as the original projection, 250m. But keep in mind that the original dataset has been reprojected to make this web mercator version.This multidimensional soil collection serves the mean or expected value for each soil variable as calculated by soilgrids.org. For all other distributions of the soil variable, be sure to download the data directly from soilgrids.org. The data are available in VRT format and may be converted to other image formats within ArcGIS Pro.Accessing this layer's companion uncertainty layerBecause data quality varies worldwide, the uncertainty of the predicted value varies worldwide. A companion uncertainty layer exists for this layer which you can use to qualify the values you see in this map for analysis. Choose a variable and depth in the multidimensional settings of your map client to access the companion uncertainty layer.

Note: To download this raster dataset, go to ArcGIS Open Data Set and click the download button, and under additional resources select any of the download options. Data can also be downloaded from the FSGeodata Clearinghouse.More information about rangeland productivity and the effects of drought are available in this StoryMap; additional drought and rangeland products from the Office of Sustainability and Climate are available in our Climate Gallery.Time enabled image service showing estimates of annual production of rangeland vegetation.Production data were generated using the Normalized Difference Vegetation Index (NDVI) from the Thematic Mapper Suite from 1984 to 2023 at 250 m resolution. The NDVI is converted to production estimates using two regression formulas depending on the level of the NDVI; there is one equation for lower values (and thus lower production values) and one for higher values. This raster dataset yields estimates of annual production of rangeland vegetation and should be useful for understanding trends and variability in forage resources. These results were then converted to Z-scores for easier comparison of annual relative productivity in coterminous U.S. rangelands, and for rapid display in online time-enabled applications. This Z-scores dataset as well as the raw lbs/acre data that the Z-scores were derived from can be downloaded from: https://data.fs.usda.gov/geodata/rastergateway/rangelands/index.phpMore information about rangeland productivity and the effects of drought are available in this story map.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService For complete information, please visit https://data.gov.

Note: To download this raster dataset, go to ArcGIS Open Data Set and click the download button, and under additional resources select raster download option; the data can also be downloaded directly from the FSGeodata Clearinghouse. To summarize this dataset by U.S. Forest Service Lands, see the Drought Summary Tool. You can also explore cumulative drought and moisture changes from this StoryMap; additional drought products from the Office of Sustainability and Climate are available in our Climate Gallery and the OSC Drought page.

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.

All stories happen somewhere. Place relates to an audience where things happened, which in turn can explain why or how things happened the way that they did. To help storytellers add such crucial context to their stories, express maps were one of the first features incorporated when ArcGIS StoryMaps was rolled out in July of 2019. Storytellers of all cartographic experience levels can populate an express map with features, pop-ups, text labels, and more, injecting slick, effective, interactive cartography into any story.On top of that, express maps also serve as a "Trojan horse" of sorts that allows storytellers to create interactive image experiences as well as maps. Thanks to a capability added in August, 2024, you can now upload an image to serve as the base of an express map. This means that you can apply the same drawing, pop-up, and annotation tools to that image as you can to a map.

Tree canopy was derived from high-resolution remotely sensed data -- 2018 NAIP and 2019 LiDAR. Object-based image analysis techniques (OBIA) were employed to extract potential tree canopy and trees using the best available remotely sensed and vector GIS datasets. OBIA systems work by grouping pixels into meaningful objects based on their spectral and spatial properties, while taking into account boundaries imposed by existing vector datasets. Within the OBIA environment a rule-based expert system was designed to effectively mimic the process of manual image analysis by incorporating the elements of image interpretation (color/tone, texture, pattern, location, size, and shape) into the classification process. A series of morphological procedures were employed to insure that the end product is both accurate and cartographically pleasing. Following the automated OBIA mapping a detailed manual review of the dataset was carried out at a scale of 1:2000 and all observable errors were corrected.

Location data, images, some historical information and maps of Historical Breweries in Toronto. This work and data compilation were inspired by the following book. St. John, Jordan. Lost Breweries of Toronto. Charleston, SC: History Press, 2014. Further Images and maps used in the accompanying Story Maps Workshop can also be found on flickr at https://flic.kr/s/aHsmkrMS9g and https://flic.kr/s/aHskDv5WiU

Spatial Data (ESRI shapefiles and geoTIFFs) underpinning maps appearing in Cooper Creek Flood Modelling StoryMap

Spatial data used to present results of flood inundation modelling scenarios, based on future climate and gas industry activities, developed through stakeholder engagement workshops.

This project delivered outputs from targeted flood modelling scenarios developed in response to on-going engagement with stakeholders in the Cooper Geological Bioregional Assessment (GBA) region using interactive ESRI ArcGIS Online StoryMaps.

The outputs include changes to flood risk and flood characteristics under future climate scenarios and from gas industry developments, such as floodplain infrastructure, diversions or extraction activities.

Lineage: Refer to GBA Data collection on data.gov (Related Link: Hydrodynamic Model of the Cooper Creek Flooplain)

The Cooper Geological Bioregional Assessment (GBA) region has a large floodplain that floods frequently. This dataset contains model setups and validation results of a 2D hydrodynamic model (MIKE21FM), and all inputs and outputs for selected historical flood events (one set each for the Queensland side and South Australian side of the Cooper Creek). The calibrated hydrodynamic model can be used to evaluate how flood characteristics may change under future scenarios (development and future climate change). The model setup includes model mesh and all related setup files, inputs include rainfall, potential evapotranspiration, LiDAR DEM, river and water holes bathymetry, classified Landsat images for the flood events used, soil characteristics, vegetation characteristics, observed streamflow at the upstream gauges and ungauged inflows along the model domain boundaries estimated using a regionally calibrated rainfall-runoff model. Outputs from hydrodynamic modelling include daily estimates of spatial flood extents across the modelling domain, and water depth and velocities for each mesh element for all the historical flood events used. The full details about the modelling (model setups, inputs and outputs) are described in detail in this Cooper flood modelling report.

Esri Story Maps let you combine authoritative maps with narrative text, images, and multimedia content. They make it easy to harness the power of maps and geography to tell your brief.

Note: To download this raster dataset, go to ArcGIS Open Data Set and click the download button, and under additional resources select any of the download options. Data can also be downloaded from the FSGeodata Clearinghouse.More information about rangeland productivity and the effects of drought are available in this StoryMap; additional drought and rangeland products from the Office of Sustainability and Climate are available in our Climate Gallery. Time enabled image service showing estimates of annual production of rangeland vegetation.Production data were generated using the Normalized Difference Vegetation Index (NDVI) from the Thematic Mapper Suite from 1984 to 2023 at 250 m resolution. The NDVI is converted to production estimates using two regression formulas depending on the level of the NDVI; there is one equation for lower values (and thus lower production values) and one for higher values. This raster dataset yields estimates of annual production of rangeland vegetation and should be useful for understanding trends and variability in forage resources. This raw lbs/acre data that the Z-scores were derived from as well as the Z-scores dataset can be downloaded from: https://data.fs.usda.gov/geodata/rastergateway/rangelands/index.phpMore information about rangeland productivity and the effects of drought are available in this story map.

Explore Public Art Discover new and existing public art as part of the City of Kingston Arts Walk, a self-guided tour across the city, accessible by walking and/or by bicycle, car, and public transportation. The Arts Walk highlights City-owned public art – permanent and temporary – developed through the City of Kingston’s Public Art Program, as well as existing public art as part of the Civic Collection.The Arts Walk includes public art along Kingston’s waterfront from Lake Ontario Park to Douglas Fluhrer Park, in the downtown core and Williamsville up to Victoria Park, and in suburban and rural areas including at the INVISTA Centre and YGK Airport. Public Art MapThe City's public art collection is now available online and accessible from your computer, tablet and mobile device. Printed Arts Walk postcards can also be picked up at various locations including City Hall. Scroll through below to view images, locations and artwork information. Photographs by Chris Miner with support from Suleimy Rios-Aguilar.