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.

Esri story maps are an exciting and popular feature of the ArcGIS platform that combine maps, photos, text, and other media, in a single interactive application. Any topic or project that includes a map can be a story map. In this seminar, you will learn about Esri application templates that simplify story map creation and require no coding. The presenters will discuss how to choose the best template for a project and the steps to create a compelling story map from a template.

This template is in Mature Support. Esri offers several other crowdsourcing and data collection apps. Story Map Crowdsource is a configurable application that lets you set up a Story Map that anyone can contribute to. Use it to engage a specific or general audience and collect their pictures and captions on any topic that interests you. Participants can log in with their social media account or ArcGIS account. When you configure a Crowdsource story, an interactive builder makes it easy to create your story and optionally review and approve contributions before they appear on the map.Use CasesStory Map Crowdsource can be used to create a crowdsourced map of photos related to any topic, event, or cause. The submissions can be all from a single neighborhood or from all over the world. Here are some examples:National Park MemoriesEsri 2016 User ConferenceGIS DayHonoring our VeteransUrban Food MovementConfigurable OptionsThe following aspects of a Story Map Crowdsource app can be configured using the Builder:Title, cover image, cover message, header logo and click-through link, button labels, social sharing options, and home map viewAuthentication services participants can use to sign inWhether new contributions are being acceptedWhether new contributions appear on the map immediately or only after the author approves themSupported DevicesThis application is responsively designed to support use in browsers on desktops, mobile phones, and tablets.Data RequirementsStory Map Crowdsource does not require you to provide any geographic content, but a web map and feature service are created for your story in your account when the Builder is launched. An ArcGIS account with Publisher permissions is required to create a Crowdsource story.Get Started This application can be created in the following ways:Click the Create a Web App button on this page (sign in required)Click the Download button to access the source code. Do this if you want to host the app on your own server and optionally customize it to add features or change styling.For more information, see this FAQ and these blog posts..

Enjoy the map story maps created by many LOJIC agencies.

The objective of this project is to create a story map that highlights and narrates the history of flooding in Chicago. It will map hydrology, rainfall patterns, floods, and income levels. It will draw from this how different socioeconomic regions and neighborhoods differ in flood response and action. This project will be used to justify a possible green bond for flooding mitigation in low income houses in Chicago.

This layer shows workers' place of residence by commute length. 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 commuters whose commute is 90 minutes or more. 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): B08303Data 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.

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).

Created for the on-going maintenance of public art assets and for promotional use of the City of Salem's art collection

The Cape Cod Commission is designing a regional growth policy for the Cape with the goals of creating compact, vibrant, walkable centers in which Cape residents and visitors can live, work, and play. The Geodesign process has been used to identify existing centers of activity across the region that will be the major focus of future growth or redevelopment. GIS analysis was used on parcel data to identify clusters of ideal community characteristics, including civic activity, business activity, and physical form, resulting in a map which identifies both regional and local centers of activity.

Using a teacher created StoryMaps, students will explore the idea of the Demographic Transition Model.This StoryMap activity accompanies the NCGE webinar on March 29, 2023.

DO NOT DELETE OR MODIFY THIS ITEM. This item is managed by the ArcGIS Hub application. Create your own initiative by combining existing applications with a custom site.

Baton Rouge's unique past has shaped the city that we live in today. The layout of the city's streets, the arrangement of prominent government and religious structures, the clustering of businesses, the distribution of residential neighborhoods, and the placement of parks and schools all speak to the long term processes of urban growth. Society invests tremendous effort in creating its urban centers and citizens develop attachments to those places. It is the investment of human effort that stimulates a sense of place and allows individuals to develop strong feelings about their home city. Sense of place is constantly reinforced by contact with the common, everyday landscapes that surround us. In Baton Rouge, the two principal university campuses, the state government complex, along with various historic neighborhoods and structures all stand as perpetual reminders of the city's past. Many familiar and, at the same time, unique landscape features of Baton Rouge shape our sense of place.

Geoscience Australia’s Historical Aerial Photography Program currently involves scanning and georeferencing old flight diagrams to enable the digitising and positioning of historical aerial photographs for easy discovery and download. Accurate digital mapping of GA’s aerial photography collection will make catalogue searches easier and the collection more accessible to the public. This story map presents an interactive history of aerial photography, a background of aerial photography in Australia, historical aerial photography use cases and scenarios, and a background on Geoscience Australia's program to digitise flight diagrams and create a catalogue of aerial photographs.

The dataset presents a list of laboratories set up in the humanities, digital humanities, and media studies within universities across the world in 1983-2018. The data are collected and organized in an interactive map designed in the digital StoryMapJS tool, creating a valuable visible representation of the laboratory concept from a geographical and historical perspective. Based on the interactive map, I analyze the history of the laboratory in the humanities within a global context from the 1980s to 2018. The dataset includes 214 laboratories.

Data collection

I identified laboratories by using different resources such as universities’ websites, articles, and research projects. Besides, I sent a questionnaire to the most relevant networks in October 2018 to identify even more labs created in (digital) humanities and media studies at universities.

Data organization

I collected data about each lab based on its website and other resources. I extracted the following data: year established, year ended (if applicable), lab’s name, university, city, country, affiliation and location (if provided), disciplines and keywords (based on labs’ statements and projects and aiming to situate a lab), selected projects (if provided), purpose (a short quotation of a lab’s statement published on its website), website, and geographical latitude and longitude. I organized all the data in chronological order according to year established in Google Sheets. Next, I used StoryMapJS, a free tool designed by the Northwestern University’s Knight Lab, to map my data.

Published on Jul 25, 2012Unique stories are being created and shared using ArcGIS Online from Esri. Explore the world of web maps, create your own, and discover stories with a cloud-based, collaborative system.Learn more at http://www.esri.com/arcgisonlineView story maps at http://storymaps.esri.com/home/

In Spring of 2019, DCOP staff took various pictures of Cherry Blossoms throughout the city. The OP Data Analysis and Visualization team created a story map featuring these photos and maps highlighting the areas where the photos were collected.

This application is created to allow public to access and apply for authorization under Section 14(4) of the Manitoba Water Resources Administration Act. This application allows public to access and apply for authorization under section 14(4) of Manitoba Water Resources Administration Act. The map within the application contains lines and polygons showing the locations of provincial waterways, such as drains, dikes, diversions, detention basins, floodways, dams and reservoirs. The datasets were created by digitizing high-resolution imagery of the water features and by digitizing the titled Water Control Work plans associated with the features for drains which have been designated as provincial waterways by Order-in-Council. Manitoba Municipal Boundaries and Manitoba Property Assessment information are also displayed within the map for reference purposes.

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 chemical soil variable organic carbon density (ocd) which measures carbon mass in proportion to volume of soil (mass divided by volume.)From Agriculture Victoria: Soil carbon provides a source of nutrients through mineralisation, helps to aggregate soil particles (structure) to provide resilience to physical degradation, increases microbial activity, increases water storage and availability to plants, and protects soil from erosion.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 organic carbon density 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: Organic carbon density in kg/m³Cell 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 ocd were used to create this layer. You may access organic carbon density values 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.

You use your Pepeha to introduce yourself. An important part of your pepeha is the location of your maunga (mountain) and awa (river, lake, water body). In this lesson we are going to take this information and place it on a map, associate images with this location and use digital technology to represent you pepeha.If you don't have a pepeha use https://pepeha.nz/ to create your pepeha.