Latest Contributions by TamaraGrant1

Ready to take your maps to the next level? Vector tiles enable dynamic cartography and provide the flexibility to create your own basemap style. The new ArcGIS Vector Tile Style Editor makes it easy to test different styles until you find one that works well with your brand and design. It even provides suggestions to help you discover combinations you never considered. We will show you how to use the tool and provide some creative examples. The ArcGIS Living Atlas of the World team continually enhances and expands its set of vector basemaps. During the webinar we’ll share our localization plans that are already underway—vector basemaps were just released in six languages across eight basemap styles. We will also walk you through a gallery of creative map styles. These unique map styles were developed by our cartographers and incorporate new map features and labels. These are ready to use and available in ArcGIS Living Atlas of the World. Register to join this webinar. ... View more

by Bern Szukalski The Thomas Fire The Thomas Fire was a devastating wildfire of historic proportions that burned through parts of California’s Ventura and Santa Barbara Counties. The fire started on December 4, 2017, north of Santa Paula and near the Thomas Aquinas College, the fire’s namesake. Driven by strong Santa Ana winds that persisted for nearly two weeks, and a large amount of available fuel, the fire grew rapidly. By it’s official end on January 12, 2018, the fire had become the largest in California history (since superceded by the Mendocino Complex Fire). The fire destroyed over a thousand structures, with hundreds more damaged. It generated over $2.18 billion in damages, injuring one firefighter and claiming the life of another, along with one resident who died in an auto accident while fleeing the area. The destruction didn’t stop with the fire, as post-fire rains created massive mud flows that claimed 21 lives, destroyed 129 residences, and damaged over 300 more. See the Thomas Fire on Wikipediafor more information.   The Thomas Fire map In this tutorial you will learn more about the fire using ArcGIS  analysis tools and content available from the ArcGIS Living Atlas of the World to author a map showing the  fire perimeter, learn more about the population and demographics of the impacted area, and view satellite imagery before, during, and after the fire. You will use the following steps: Find the fire location Add and save the fire perimeter Geoenrich the perimeter, adding population and other demographic information Add Sentinel-2 imagery for dates prior to the fire, during the fire, and after the fire was fully contained. Adjust the imagery to reveal specific details   Find the fire location Step 1 – Use Search to find the location. The first step in making the map is to navigate to the fire location. Open a new web map, use search and enter Santa Paula, California. You can also use Thomas Aquinas College, Santa Barbara County, or Ventura County as the search string.   Add the Thomas Fire perimeter The USA Wildfire Activity layer from the Living Atlas displays current and inactive fire locations and perimeters. From your web map, Browse the Living Atlas to find the USA Wildfire Activity layer, then add it to your map. Step 1 – Click Add, then Browse Living Atlas Layers. Step 2 – Search for “wildfire” to locate USA Wildfire Activity, then click + to add the layer to your map. Step 3 – Display the Thomas Fire perimeter. By default, the  USA Wildfire Activity layer has the active fires locations and perimeters toggled on. Since the Thomas Fire is no longer active, you will turn the default active fire sublayers off, and turn the inactive perimeters on. Click the layer title to reveal the sublayers, and toggle the layers as shown below. Zooming out, you can view the extent of the Thomas Fire.   Create a copy of the perimeter Inactive fire locations and perimeters are eventually phased out from the service, so you will create a copy of the perimeter to ensure it will always be available in your map. You will use the Analysis tools to create a new feature layer from the existing Thomas Fire perimeter. Step 1a – Open the Analysis tools. Step 1b – Open the Find Locations group. Step 1c – Click Find Existing Locations. Step 2 – Complete the tool steps (numbered in blue) as follows. In tool step 1, choose the USA Wildfire Activity-Inactive Perimeter sublayer. Step 3 – In tool step 2, construct the attribute query expression to find the Thomas Fire perimeter. The expression looks like this: Step 4 – Complete the tool inputs for Find Existing Locations by entering a Result layer name in tool step 3, then click Run Analysis.   Enrich the fire perimeter polygon How may people lived in the burn area? How many households were impacted? What is the population like? These questions can be answered by using geoenrichment tools that tap into Living Atlas data to add attributes describing these characteristics. These data are added based upon the area of the polygon using apportionment, the full details of the implementation and methodology can be viewed in the Data Apportionment documentation. Step 1 – Open the Analysis tools, and choose Enrich Layer. Step 2 – Complete the tool steps in the Enrich Layer tool. First, select the layer you created in Step 8 in tool step 1. Step 3 – In tool step 2, click Select Variables to open the Data Browser. Step 3 – Choose the following attributes to enrich the fire perimeter: 2018 Total Population (Esri) 2018 Total Households (Esri) 2018 Median Household Income (Esri) 2018 Dominant Tapestry Segment Name (Esri) Begin by selecting a category: Then choose the attributes. To go back to the categories, click the category box in the upper left: Step 4 – After selecting the attributes, complete the tool steps as shown below, then click Run Analysis.   Add Sentinel-2 imagery Sentinel-2 is multispectral, multitemporal global imagery obtained via two satellites placed in the same orbit by the European Space Agency, and phased 180 degrees from each other. Every location on the planet is revisited every 5 days. This Living Atlas imagery layer pulls content directly from the source Sentinel-2 archive on Amazon Web Services (AWS), and is updated daily. The layer can be easily added to web maps (and scenes), and rendered in different ways to highlight specific characteristics. In addition, the imagery can be filtered by date. Step 1 – Add the Sentinel-2 imagery layer from the Living Atlas. This is similar to what you did in Step 3, but search for Sentinel-2 instead. Step 2 – Remove the default filter on Sentinel-2. The default filter on the Sentinel-2 layer will display the most recent and clearest imagery available. You will want to search for all imagery around the dates of the fire, even smoke-filled imagery, so remove the filter so all imagery will be available. After adding the layer, click the Filter icon to display the current filter, and remove it. Step 3 – Click More Options (…) and choose the Image Filter. You will use this filter to search for available imagery around the dates of the fire. Step 4 –  Select the imagery at the desired dates. Adjust the time slider around the dates of the fire. The available imagery will be listed. Hover over each to display the coverage footprint in yellow on the map. Identify the date, then select the images that cover the fire perimeter. Repeat the process to create layers for before, during, and afterwards. Note that to cover the entire fire perimeter you will have to select two images, then click Add To Map combining both into one layer. When moving on to the next date, remember to uncheck the previously selected layers. The fire started December 4, 2017, and officially ended January 12, 2018. Using the image filter to add Sentinel-2 imagery for the following dates: October 23, 2017 (before the fire began) December 7, 2017 (during the fire) January 16, 2018 (after the fire)   Adjust the imagery display To glean more information from multispectral imagery like Sentinel-2, you can choose a renderer that highlights specific characteristics of the imagery. For example, you can choose renderers that highlight healthy vegetation, geology, moist areas, and more. The default renderer is Natural Color with DRA (Dynamic Range Adjustment). In this section you will apply the following renderers to the imagery layers you have added to your map: October 23, 2017 (before the fire began) – use the default Natural Color with DRA. December 7, 2017 (during the fire) – use Short-wave Infrared to highlight actively burning areas and hotspots. January 16, 2018 (after the fire) – use Normalized Burn Ratio to highlight the burned areas. Step 1 – From the layer options, choose Image Display. Step 2 – From the list of renderers, choose the one that you want, then click Apply. Repeat the steps for the other layer, and save your map when finished.   Summary and results Using content from the ArcGIS Living Atlas of the World and ArcGIS  capabilities, you have authored a map that delivers enhanced information about the Thomas Fire.  Demographic attributes have been added to the fire perimeter delivering information about the impacted households and enabling us to learn more about those that lived there.  Sentinel-2 layers of different vintages have been added, and specific spectral characteristics of the imagery have been rendered to highlight active burn areas, and post-fire impacts. These are reviewed below. Geoenriched fire perimeter After configuring the layer pop-up, clicking the perimeter provides more information about the population. The total number of acres was in the original perimeter, but geoenrichment has added the population, number of households, median age, median household income, and dominant Tapestry segment (Exurbanites). The dominant segment is a link that opens detailed documentation about Exurbanites, helping us learn more about the characteristics of the impacted residents. Pre-fire Sentinal-2 imagery December 23, 2017 Using the default Natural Color DRA renderer, this image show the normal conditions and appearance for the burn area. During-fire Sentinel-2 imagery December 7, 2017 Captured a few days after the fire started, and using the Short-wave Infrared renderer, this layer allows us to peer through the smoke and clearly see the active burn areas. Post-fire Sentinal-2 imagery January 16, 2018 This layer uses the Normalized Burn Area renderer to highlight the burned areas in black. View the map Click the image below to view the map authored via this tutorial. Since the map contains subscription content, you will be prompted to sign in when the map is opened.   More information For more information, see the following: Use the analysis tools Find Locations Data Enrichment USA Wildfire Activity item pages Sentinel-2 Views item pages ArcGIS Living Atlas of the World ... View more

by Andy Skinner This is an update of a blog and a sequence of story maps written in 2017 looking at the mechanics of customizing our vector basemaps. It includes updated links, and details on working with the new Vector Style Editor released in early 2018. If you prefer to go straight to the Story maps, this link takes you to the first of them: Pt 1: The Basics WEBINAR OPPORTUNITY:  Personalize your maps with vector basemaps Ready to take your maps to the next level? Vector tiles enable dynamic cartography and provide the flexibility to create your own basemap style. In our September 6, 2018 webinar, we will show you how to use the new ArcGIS Vector Tile Style Editor to personalize your maps. We’ll also walk you through an inspiring gallery of creative map styles that incorporate new map features and labels. Register for the webinar here! ***** Vector Tile Layers can be used to create multi-scale maps that are efficient, high-resolution and customizable, and this includes Vector Basemaps. Esri’s Living Atlas Content Team has created vector tile versions of most of our traditional basemaps (plus some new ones), and these work well in themselves … But have you ever had that moment where you thought ‘This basemap would work really well, if only …’? What are Vector Basemaps? Our vector basemaps originate as a Vector Tile Package, built in and generated by ArcGIS Pro, then published with a generic style. It establishes the maximum level of content available and a defined scale range for each layer of information. Separate style documents are created in JSON Code for each of our basemaps, although they do not necessarily use all of this content, or the available scale range. The Vector Tile Package A vector tile package contains the vector tiles, and various formatting documents. Of interest to us is the Resources Folder, which includes:   Fonts – Any font that is used in the ArcGIS Pro map. Sprites – PNG Raster images created for special effects, such as textured patterns and point symbols. In most circumstances, once these have been exported from Pro, they are not editable. However, we have created alternatives for our different core maps. Styles – A root.json file that establishes the symbolization of the map detail. This is editable, and it is the key to the discussion here. We can make changes to this code to affect color, line weight, label size/font, and scale range … either directly, or through an editing app. How do I work with them? The Vector Basemaps Reference Document, is a downloadable PDF describing how our maps are structured. As the maps themselves have become more versatile (and we are trying to improve them all the time), our layer structure has become more complex. This document will help you to find your way through. It may be a little confusing at first, but the more you work with it, the more it will make sense. Don’t feel you have to learn it before you start work though (It’s a ‘reference’ document!). We provide two editing apps to help you on your way (Note that both are in beta, so details could change without warning). Both require you to sign-in to an ArcGIS.com organization. The first is new as of 2018. The ArcGIS Vector Tile Style Editor is an intuitive app that leads you through most of the simpler changes you can make to the map style. For most tasks this will make life much easier for you. The JSON Code Editor has been available for a while. It pairs the JSON style with a preview map as an easy way for you to work directly with the code and see the results. If you are confident in your ability to work with code, the JSON style file gives you access to some more sophisticated options. It is a requirement if you want to add data layers to the style. For more information, these links take you to the sequence of story maps. Use these to get a better understanding of how our basemaps are structured, and how to exploit them using these two apps: Pt 1: The Basics Pt 2: Colors Pt 3: Lines Pt 4: Labels ... View more

by Shane Matthews The ArcGIS World Geocoding Service helps you find and display global addresses on a map with a high degree of accuracy. Its global address dataset includes data from commercial sources, all levels of government, and many reputable mapping organizations. Esri works with its global distributors to include local data suited for each region for an unrivaled user experience. International reference data ensures that ArcGIS World Geocoding Service offers consistent and authoritative geocoding results. Community Addresses Our Community Maps contributors have been asking how they can help improve Esri’s World Geocoding Service. Many of our contributors are managing progressive communities, where new residential and commercial areas have been constructed. Some are enhancing their city or county 911 system that uses ArcGIS  mapping as an essential component. The Community Maps Program uses best available address datasets to support ArcGIS World Geocoding Service. Included addresses represent comprehensive and accurate locations for many countries, and are available to search against and provide the most accurate geocoding results. You can now contribute point and polygon address data to enhance the geocoding experience of your users. Community Addresses Accepted content Comprehensive and Accurate Point and Polygon Addressing Data located at Primary entrance points to a building or unit (preferred) Rooftop points – central location on rooftop of a building (2) Offset from the street at a location where a vehicle would arrive at the address (3) Offset from the street at a location in front of the building or parcel (4) Parcel centroids (5) Should include complete address attributes: House number Apartment or Unit Information (if available) Building Name (if available) Complete Street Name Administrative addressing information such as City or State Postal Information (recommended) How do I share my Community Addresses? Easy! Click on the image below to download the latest version of the Community Maps Data Prep Tools, we have included a new tool that prepares address data! There are instructions to help you along the way. Just email the Community Maps Team (communitymaps@esri.com) if you have questions. Community Maps Data Prep Tools If you are a registered Community Maps Program contributor just log into your account, select ‘My Account’ and select Addresses under the ‘Change Registered Layers’ section. Not registered with the Community Maps Program? Get it done here! Related Blogs: It’s back to school with a Community Maps Data Prep Tools Update! What’s New in Community Maps (August 2018) ... View more

by Shane Matthews Well, it’s back to school season and with that comes hoarding supplies, buying books, and collecting tools to help get through it all. We wanted to make sure our contributors had the tools they needed by providing a Version 3.0 Release of the Community Maps Data Prep Tools. These easy-to-use tools assist contributors with preparing basemap layers for submission, standardize content, speed up data integration – what’s not to like about that? So, you can retire the former CM DataPrepTools Version 2.0! Let’s breakdown the update… Changes in Version 3.0 Release The tools work in both ArcMap (10.4.1 and higher) and ArcGIS Pro (2.2.1). Tool parameters (i.e., the query statements) are automatically saved to a parameters file, and the tools remember your previous settings. This means you can easily re-run a tool without needing to enter the query statements each time. You can also save the parameters and re-use them when you re-run the tools for your next data contribution. The Road Centerline Tool has been simplified and combined into a single tool (no more Part1 and Part2). There is a new tool to support contributors who want to provide Address points or polygons. The output layer is used by the Esri Geocoding Team to improve accuracy in the ArcGIS World Geocoding Service – as opposed to these being features added to the Esri basemaps. Community Maps Data Preparation Tools – Part 1 This video provides an introduction to the Community Maps Data Prep Tools. It demonstrates how to download the tools, explores the resources that are included in the download, shows you how to begin by running the Setup Tool, and gives an example of running the Building Footprint Tool. Click the image below to view the video. Community Maps Data Prep Tools Part 1 - Introducing the Tools in ArcMap Community Maps Data Preparation Tools – Part 2 When you run a tool in Version 3.x of the Community Maps Data Prep Tools, your queries and other settings are saved in a Parameters file. Storing this information makes it incredibly easy to re-run the tool without re-entering all of these queries. This video provides an explanation of how to Save and re-use your query parameters in the Community Maps Data Prep Tools. It demonstrates where the parameters are saved, how they are used, and how to save and retrieve them for your next data contribution. Click the image below to view the video. Community Maps Data Prep Tools Part 2 - Saving and Using Tool Parameters Contributor App Updates This video highlights new capabilities of the Community Maps Contributor App. Among other things, the big news here is that this latest release includes the ability to provide Road Centerline data in the form of a Service, which was requested the most from our contributors. Click the image below to view the video. What's New in the Community Maps Contributor App Community Maps Data Prep Tools OK, you’ve been patient, so here they are. Click the image below to download the new tools now and get started on that basemap layer submission! If you have not yet registered with Esri’s Community Maps Program, you can get that done here. Have questions? Email the Community Maps Team (communitymaps@esri.com). Community Maps Data Prep Tools Related blog: What’s New in Community Maps (August 2018) ... View more

by Dan Pisut It’s rare for hurricanes to threaten the Hawaiian Islands. And it’s even more rare for one to threaten Honolulu, which is nestled in the middle of the chain on Oahu. Hurricane Lane, however, is tracking along the coast and is generating hurricane conditions for many of the islands.   Hurricane Lane Forecast Map Forecast #31a for Hurricane Lane on August 22, 2018. Note: this is a static dataset used for display purposes and is not the Live Feed. The forecast will change but this map will not.   Turns out that the Active Hurricanes layer in the Living Atlas of the World was missing a wind field found in other products from the National Hurricane Center. Why? Well…it has to do with how different components of the National Hurricane Center are responsible for different portions of the Atlantic and Pacific Basins, and they have different websites, data servers, and notification mechanisms. And when there are no storms (typically) to check if the data is there, you don’t know its missing. Kind of like the age-old question, “If a tree falls in a forest and no one is around to hear it, does it make a sound?” Only here, the tree is a major hurricane near the Hawaiian Islands. The good news is that Central Pacific storms now have the forecast wind probability fields. This layer shows the chance that winds of a certain speed will impact an area. The 64 knot layer is probably the most useful, as that shows hurricane force winds. The layer does not display by default, so you’ll need to expand the options and check the appropriate boxes.   Happy Probabilistic Wind Forecasting! For comments or questions about this blog, please visit our GeoNet. ... View more

by Shane Matthews Through the Community Maps Program organizations contribute their local geographic content which is published and freely-hosted by Esri. Everything from basemap layers such as parks and trees, to imagery and stream gauge data can be contributed. Detailed large-scale basemap layers and high-resolution imagery shared to the ArcGIS Living Atlas of the World are what set Basemaps in ArcGIS apart from other mapping APIs. When I speak with local governments, regional council of governments, universities and other like organizations that provide their local authoritative content through the Community Maps Program, I tell them to let the basemaps on ArcGIS  truly reflect their communities. I encourage them to empower themselves and contribute to a trusted foundation that helps them accomplish their work, helps them create better web maps and applications, and deliver better resources to their citizens. Below are a few examples of how these maps can be transformed when ‘local-knowledge geography’ is added. The left panel in the images that follow illustrate content supplied through the Community Maps Program. Click on the images for the best view. Bardstown, KY Bothell, WA Gillette-Campbell County, WY Pasco, WA Dover, DE Rutherford County, TN Umatilla, OR Latest Release This month 48 communities have shared new and updated map layers in support of Esri’s expanding suite of high-performance basemaps and imagery services. Map layers include aerial photography, boundaries, buildings, owner parcels, parks, points of interest, trees, and similar large-scale features that enhance our foundational information sets for the world to use. Tour our newest communities by clicking the Story Map below. What's New in Community Maps (August 2018) How does my organization contribute? It’s easy! The Community Maps Program works with authoritative GIS data contributions to build the ArcGIS Living Atlas of the World consisting of reference and thematic maps covering a wide variety of topics. Community Maps Program contributors participate by sharing data to one or more of the following categories. You can begin contributing by registering here! Upcoming Webinar Personalize your maps with vector basemaps Ready to take your maps to the next level? Vector tiles enable dynamic cartography and provide the flexibility to create your own basemap style. In our September 6, 2018 webinar, we will show you how to use the new ArcGIS Vector Tile Style Editor to personalize your maps. We’ll also walk you through an inspiring gallery of creative map styles that incorporate new map features and labels. Register for the webinar here! ... View more

by Diana Lavery Did you know that Census’ American Community Survey data come with margins of error? Did you also know that you can easily incorporate these values into your web maps to help display the accuracy of the data? What are Margins of Error? If you’ve worked with some of our feature layers in the ArcGIS Living Atlas contain data from the U.S. Census Bureau’s American Community Survey (ACS), you have probably seen fields called “margin of error.” Ever wonder what those are? First and foremost, remember that the ACS is based on a sample, just like any other survey. When I think of “samples,” I first think of ice cream. A sample of ice cream at the ice cream shop gives us an estimate of the characteristics of the whole tub of ice cream. Do all samples have the exact same amount of chocolate chips? Of course not. Does a sample from the top of a tub of ice cream taste the same as a sample from the bottom of the tub? Maybe, maybe not. Which sample is a better approximation of the taste of the entire tub? Photo source: Yelp’s page for The Hop in Reno, NV. Similarly, a survey’s sample gives us estimates of the characteristics of the whole population. We can measure how good those estimates are. Margins of error are an indicator of the reliability of the estimate, an upper- and lower-bound of a range that Census has given us. The estimate is simply the midpoint of the range, or “confidence interval.” For example, this feature layer of disability status by sex by age gives us an estimate for Florida Tract 120990064.02 of women age 75+ with a disability of 361, and a Margin of Error (MOE) of 158. This tells us that the Census Bureau is 90% confident that the true count of women age 75+ with a disability in that tract is between 203 (361-158) and 519 (361+158). 361 is the midpoint of that range. In general, the confidence interval gets larger as your population gets smaller. Your population could get smaller geographically (the range for the estimate of Los Angeles county’s population will be much smaller than the range for the estimate of a tract’s population), or demographically (the range for the estimate of same-sex married couples will be larger than the range for the estimate of opposite-sex married couples). Why bother using the Margin of Error at all? By incorporating the margins of error into our maps, data analysts and GIS analysts who inform decision makers can show the full picture of the data in their information products and reports. The Census Bureau has given its data users a gift: a measurement of how accurate each and every estimate is that they produce. Other large-scale surveys publish confidence intervals as well, such as many political polls. This blog post presents some options for incorporating the margins of error into your web maps when mapping ACS data. Like with all map making, the option you choose depends on your audience! Option 1 is easiest to communicate to lay audiences who just want the high-level information. Option 2 is best for those who like to see all available data. Option 3 is for ACS power-users who are familiar with margins of error. Option 1: Suppress Unreliable Estimates Suppress Using the Reliability Flags from Geoenrichment It’s often helpful to consider the ratio of the range to the estimate. If the confidence interval or range for a given estimate is very small in relation to the estimate, the estimate is more precise, but if the range is large, the estimate can be imprecise or unreliable. Some analysts have a strict requirement of a 10 percent cutoff threshold, meaning if an estimate has a confidence interval that is 10 percent or more of the estimate’s value, then they consider it somewhat unreliable. Others have a more lax requirement of 15 percent. Those in the middle might use 12 percent. Esri’s demographics team created reliability flags for demographic estimates available through GeoEnrichment, and a 12-percent cutoff point is used to identify estimates with high reliability. For example, I added a layer of census tracts from the Living Atlas to my map, and I filtered to only show tracts in Arizona. Then I enriched this layer to add the number of households with any retirement income. GeoEnrichment added three fields to my layer’s tabular data: one with the prefix “ACS” which is the estimate, one with the prefix “MOE” which is the margin of error, and one with the prefix “REL” which is Esri’s reliability flag. The reliability flag field take values of 1, 2, or 3: high reliability, medium reliability, or low reliability. I want to filter out census tracts whose estimate of households with any retirement income is highly questionable, so I’ll only map features with a value of 1 or 2. In other words, I want to suppress any features with a reliability flag of 3. First apply a filter such as the one below: Be sure the dropdown menu at the top is set to “any” of the following expressions” rather than “all of the following expressions” since we want the records whose reliability flag is either 1 or 2. I notice that not all tracts have the default orange symbol given. Using the original fields, I can verify that – sure enough – the ones that don’t have a symbol are the ones that had a reliability flag of 3. Now I can change the style of my features, and configure the pop-ups as I want. As long as the filter is applied, only the records with high or medium reliability will display. Suppress Using Your Own Threshold for the MOE If you did not obtain your ACS data using geoenrichment and do not have the reliability flags, you can construct your own by using your own cutoff for the MOE. For example, the disability by age by race layer mentioned earlier only has fields for the estimates and the MOEs. Depending on how strict you want to be in deeming an estimate “unreliable,” choose a cutoff point of somewhere around 8 to 15 percent for the range-to-estimate ratio. A cutoff of 8 percent would be very strict, a cutoff of 10-12 percent would be fairly strict, and a cutoff of 15 percent would be less strict, only removing the most unreliable estimates. Create an Arcade Expression when styling the map and configuring the pop-up and you can customize your suppression logic. Option 2: Map all the data and display the range in the pop-ups When configuring your pop-up, use Attribute Expressions to create an upper bound and a lower bound on the fly with Arcade.  For example, my upper bound expression is simply the estimate (count field) plus the margin of error (MOE field): Then insert these expressions into a custom attribute display: You will then get the pop-up displayed below: Remember, estimates of zero still have an MOE!  Write this into your Arcade Expression for the lower bound: This way you will not get any negative numbers displaying as your lower bound.  For example, the following pop-up displays a range of 0 to 13, not -13 to 13. Other examples of items in the Living Atlas that display these ranges in the pop-ups are an app of various occupations vulnerable to extreme heat and a web map of detailed Asian American ethnicities. Need to combine a few fields for your web map and are curious how to compute the margins of error for calculated fields?  The Census Bureau has many resources on this very problem. I used their guidance on approximating the MOEs of combined estimates in the maps of occupations vulnerable to extreme heat, in which I had to add the male and female counts for each occupation. Option 3: Use Transparency to Show the Estimate’s Reliability I want to show how reliable those estimates are of elderly women with a disability by tract in a way that doesn’t require the person viewing this map click on every single pop-up.  One effective way to do this is by using transparency to visually indicate reliability.  In the Change Style options, we can vary the transparency based on an attribute’s values. We can select the attribute we’d like to use, or in our case, we’ll add a new expression. The expression dialogue box appears for us to name and type in our expression. We want to create an expression that shows the range as a percent of the estimate. Also, we can subtract it from 1 so that the smallest MOE percentages will appear darker (less transparent) : Then back to setting the transparency based on attribute values: I’d like to use a cutoff of 10% (.1), so I’ll have to click “Zoom in” to see that section of the histogram better: Now those tracts with poor estimates are more transparent than those with more reliable estimates.  The transparency appears in the legend underneath the proportional symbols.  Configure your pop-up as discussed above, and you get the following map: Many Living Atlas Layers Contain Margins of Error If you’re working with American Community Survey data in ArcGIS , always check the Living Atlas first, to see if the variables you want have already been published as part of feature layer.  An ever-increasing number of layers in the ArcGIS Living Atlas contain data from the American Community Survey, and include the margins of error as fields.  Some example topics are household income, educational attainment, ancestry and ethnicity, veteran characteristics, health insurance, language spoken at home, transportation to work, housing unit characteristics, and poverty, just to name a few! Now that you know some options for displaying the margins of error, I look forward to seeing how you choose to incorporate the MOEs in your own web maps!   For more information about the American Community Survey, see the Esri White Paper or the Census Bureau’s ACS Handbook for Data Users. ... View more

by Dan Pisut and Keith VanGraafeiland Ever dip your foot into the ocean and think, “This water is perfect…I bet it’s about 82 degrees.” Well, there’s one way to be sure for any place around the world: satellite sea surface temperature data.   Besides hunting for prime beach spots, SST is a key climate and weather measurement used for weather prediction, ocean forecasts, tropical cyclone forecasts, and in coastal applications such as fisheries, pollution monitoring and tourism. El Niño and La Niña are two examples of climate events, which are forecasted and monitored using sea surface temperature maps.    A daily updated SST analysis is available in the Living Atlas (with an archive going back to 2008). By default, time animation is enabled. But if you disable time in the Properties, you can take advantage of the layer’s multidimensional settings using definition queries on the time to return just that particular layer. This selection works in ArcGIS Desktop or . In addition to time analysis, this layer can be used for visualization  in web maps and in ArcGIS Desktop.  Two server-side processing template options can be used to help in these visualizations: cartographic vs analytic renderer, and convert Celsius to Fahrenheit. Convert C to F is pretty self-explanatory. The cartographic render presents the map as RGB values stretched from 0-255, which displays efficiently for fast visualization. The analytic renderer provides the map as its range of temperature values (-2 to 34.5). Using the analytic renderer means you can change the display of the data (e.g., range, color, etc). In ArcGIS Pro, access these options from the Processing Templates in the layer Properties. Or in ArcGIS , you’ll click on Image Display to access the processing templates. Since I’m iridisophobic, I use this render option to quickly change the color palette…maybe to something like my favorite “hurricane formation zone” color palette with a break at 26 o C, or a luminance-controlled palette from dark purple to bright yellow. Additionally, many other analyses can be performed using this layer, including: Apply contours using Raster Functions Chain together several operations using the Raster Function Editor and Model Builder. Generate summary statistics Plot a graph of temperatures In other words, use the archive of SST as if it were local on your desktop. Combine it with other layers from the Living Atlas or your local data to explore the Earth-system or author beautiful maps. You can also quickly browse SST data in the Daily Sea Surface Temperaturetime aware application.  Have questions or comments about this blog? Post them in our GeoNet. ... View more

by Dan Pisut The Living Atlas derives its name from the idea that it is an ever-changing collection of resources from around the world. While some layers update more than others, one collection is updated in an automated manner as soon as the source data is available. We call these the “Live Feeds.” They are not only some of the most popular resources in the Living Atlas, but are also relied upon by millions of users to provide reliable information for weather, natural disaster, and environmental applications.   Here’s a quick breakdown of some of the Live Feeds services, along with other near real-time data available in the Living Atlas from Esri and its partners. Also, check out this blog for ideas on how to build customized maps from layers in the Living Atlas. Weather Feeds Weather events evolve rapidly, and decision support tools require reliable, authoritative data. We generate our weather-related Live Feeds from the official U.S. and global analyses from the NOAA National Weather Service. These feeds are scripted to update as soon as the NWS issues a new alert, guidance or data product. Item Name Source Update Frequency Short-Term Weather Warnings NOAA National Weather Service 1 minute Weather Watches and Warnings NOAA National Weather Service 5 minutes Current Weather Conditions NOAA National Weather Service 1 hour Storm Reports NOAA National Weather Service 1 hour National forecast models NOAA National Weather Service 6 hours Smoke Forecast NOAA National Weather Service 6 hours National Water Model NOAA National Weather Service 6 hours While not generated by the Esri Live Feeds methods, NOAA also contributes real-time GOES satellite imagery and NEXRAD radar mosaics that can be merged with any of these layers. Disaster Feeds Like the weather feeds, disaster-related services are aggregated from official sources. Earthquake data from the U.S. Geological Survey PAGER program updates in real-time, and has a rolling archive based on intensity (i.e., more intense events are kept longer in the service). Hurricane forecasts, issued by the National Hurricane Center and the Joint Typhoon Warning Center, are typically updated every 6 hours. However, under special circumstances, more frequent advisories may be issued. The 15 minute update frequency will catch any of these updates. The Flooding Map is a query of the Live Stream Gauges layer (see Earth Observations Feeds below), displaying only the stream gauges undergoing flooding conditions. Wildfire mapping has a few caveats that are described in this blog. Item Name Source Update Frequency Recent Earthquakes USGS 5 minutes Active Hurricanes NOAA National Weather Service 15 minutes Recent Hurricanes NOAA National Weather Service 15 minutes USA Wildfire Activity NASA 15 minutes Flooding Map Esri 1 hour   Earth Observation Feeds A variety of Earth systems variables are available in the Living Atlas in near real-time. Some of these are feature services, while others are time enabled image services that include raster functions and templates for use  or in ArcGIS Pro. Item Name Source Update Frequency MODIS Thermal Activity NASA 15 minutes Stream Gauges Esri 1 hour HYCOM ocean model (beta) HYCOM 1 day Sea Surface Temperature NOAA 1 day Current Drought Conditions NOAA 1 week Global hydrology analysis NASA, NOAA 1 month Multispectral Imagery Feeds   While not technically associated with the Esri Live Feeds, these satellite imagery products can provide near real-time situational awareness. The multispectral band combinations and raster functions can be used for a variety of applications. Item Name Source Update Frequency Sentinel-2 ESA, Amazon Web Services 1 day; 5 day revisit Landsat USGS, Amazon Web Services 1 day; 16 day revisit Additional Resources One of the great values of the Living Atlas is that it’s more than just data layers – it includes web maps, apps, Story Maps, and resources such as this blog. Here’s a few that relate to the Live Feeds. I’ll try to update this as more resources become available (or people tell me about them). Apps Severe Weather Public Information Map Hurricanes Public Information Map Wildfire Public Information Map Flooding Public Information Map Earthquakes Public Information Map Esri Drought Tracker Stylized fire and smoke app   Blogs and How-To Blog on wildfire: Mapping the Inferno Blog on weather: Weather Just the Way You Want It Blog: Mapping earthquakes Blog: Using Arcade expressions to calculate new fields Story Map: Configuring Hurricane Apps Story Map: Configuring Wildfire Apps   Webinars Webinars on using Living Atlas for disaster management Webinar on using weather and climate data in ArcGIS ... View more

by Molly Zurn Market opportunity maps show where demand for a product or service exceeds supply. They can help you find the best locations to start or expand your business. Using ArcGIS  and demographic data from ArcGIS Living Atlas of the World, you can create your own market opportunity map. This article explains how. Where are the best locations for opening new stores? As a data analyst for a clothing retail company, you might consider this question. Perhaps you’re considering expanding your business into the Manhattan borough of New York City. One way to identify business opportunity is to look at current sales and potential sales, based on household spending, in the clothing industry. The higher the potential, the more opportunity, or demand, there could be for your company’s products. Where can you find reliable data that compares retail sales and customer spending in your industry? And how can you visualize the data to see business opportunities? Using ArcGIS  Map Viewer and demographic data from ArcGIS Living Atlas of the World, you can create your own market opportunity map, allowing you to identify the best locations for your new stores. Here’s how you do it in 3 main steps. Step 1: Find your location on a map Begin by opening a new map of Manhattan in ArcGIS  Map Viewer. a. Sign in to ArcGIS  with your ArcGIS account and click Map. You’ll need privileges to use premium content to make the market opportunity map. If you don’t have these privileges, contact your administrator or join the Learn ArcGIS organizationfor a free 60-day membership. b. In the map search box, type Manhattan, NY, USA and click the Search button (or choose from the suggested addresses). c. Zoom out and pan the map until you can see most or all of the southern part of Manhattan Island. Step 2: Add a Living Atlas layer Next, you’ll add data related to clothing store supply and demand from ArcGIS Living Atlas of the World. a. Click Add > Browse Living Atlas Layers. b. In the search box, type 2018 USA Clothing Store Market Opportunity, press Enter, and add the layer to the map. c. In the search window, click the Back arrow to return to the Contents pane. The layer you added has many features, symbolized as either brown or green. Green areas are places where the demand for clothing stores exceeds supply, while brown areas are places where supply exceeds demand. You’re only interested in green areas where demand exceeds supply. d. Click any green feature to open its pop-up and explore the data. The pop-up shows the number of stores in the area, as well as total sales (supply) and sales potential (demand). Based on these numbers, each feature also has a market opportunity number, also known as a leakage/surplus factor, on a scale from 100 to -100. Positive numbers mean demand exceeds supply, while negative numbers indicate the opposite. A factor of 100 means there are no stores in the area, so demand exceeds supply by 100 percent. Step 3: Filter the layer You’ll create a filter to see where demand significantly exceeds supply –  features that have a leakage/surplus factor of at least 20. a. In the Contents pane, click the 2018 USA Clothing/Accessory Stores Market Opportunity layer to expand it. b. Point to the Block Group category and click Filter. Tip: If block group is grayed out, zoom in to your map until the block group category is active (no longer grayed out). c. In the filter, for the first box, choose 2017 Leakage/Surplus Factor: Clothing/Accessory Stores (NAICS 4431). For the second box, choose is at least. For the third box, type 20. d. Click Apply Filter. Your map now displays only census blocks where demand significantly exceeds supply. e. To help see the market opportunity data, change the basemap to Dark Gray Canvas. In 3 steps, you made a market opportunity map. You can use it to help find the best locations for your new stores. Where are the best locations? You’ll want consider a variety of factors, including areas with high potential demand. Based on your map, the eastern part of Manhattan Island is a good area to explore. Now that you’ve completed some basic data exploration, you may be interested in trying these related learning resources. Make a demographic map in 5 minutes Use spreadsheet data to find potential customers near a new salon Run a site suitability analysis to find potential hurricane shelters Publish data of reported coyote activity ... View more

by Dan Pisut Its fire season here in California! (Actually, a recent New York Times article quotes the deputy chief of CalFire as saying that it is always fire season in California.) Whether you live in The Golden State or not, there are a variety of resources available in the Living Atlas to map, analyze, and understand wildfires and fire risk in the U.S. and across the world. Many Earth-observing satellites contain sensors capable of detecting the infrared energy released by fires. Not only can the hotspots be located, but areas of burned land can also be identified based both on their thermal characteristics and visible appearance. Along with intelligence on the ground, these data are used to plot the location and spread of fires around the world. Satellite image of the Southern California fires from 2017. Pinpointing Potential Fires The most basic fire data are the locations of hotspots and typically come from geostationary and polar-orbiting satellite with frequent revisit times (i.e., less than a day), such as the NOAA GOES, NOAA-20, or NASA Aqua and Terra satellites. These data are a count of thermal pixels. But many things can generate a hotspot, including controlled burns, oil and gas rigs, volcanoes, etc. This kind of data must be used with some caution since there are many features that are not wildfires. However, it is still extremely useful at monitoring known fire events or fire-prone areas. In the Living Atlas, the MODIS Thermal Activity layer provides daily updated global hotspot locations. Data from two NASA satellites are combined in this layer: Aqua (“A” in the table) and Terra (“T”). Each can be filtered or queried in ArcGIS  or Desktop. Since Aqua is in the “afternoon orbit,” when wildfires are typically at their peak, I prefer to use this source since it reduces the number of redundant features – the two satellite orbits are only 3-hours apart. This map shows the Aqua thermal activity data, using the newly released Firefly symbologyin ArcGIS . I use the Counts and Amounts option with the FRP (Fire Radiative Power) attribute, which measures the energy released by each hotspot. Notice the “false positives” in Kuwait associated with the flames atop oil wells. Want to use Firefly in ArcGIS Pro? Check this out.   U.S. Active Fire Data In the U.S., the USA WIldfire Activity layer provides a more quality controlled version of the data. It shows only wildfires submitted to the USGS by fire agencies, as opposed to all of the other events that can cause an automated satellite-based hotspot detection. However, since this layer relies on human analysis, sometimes it doesn’t update as frequently as the MODIS hotspots. The layer also contains the perimeter of the fire area, which IMHO is the most interesting feature. Both current (active) and older (inactive) fires are included. But why not combine the best attributes of both datasets? In this map, the Firefly effect is used on the % Contained attribute in the Active Fire Report layer. Fires that are less contained are larger and colored more intensely. While the Active Perimeter layer displays at all scales, the Active Fire Report layer uses the Set Visibility Range option. It disappears at closer scales and the MODIS Thermal Activity layer appears, again using the FRP attribute.   Peering Through the Clouds While the weather-focused satellites from NOAA and NASA provide high temporal resolution fire data, really detailed analysis of the fire impact is often left to moderate resolution multispectral imaging satellites such as Landsat 8 and Sentinel-2, or commercial high-resolution satellites. Here we can see the benefits of the multispectral capabilities of the Sentinel-2 satellite, now available in the Living Atlas. The Thomas Fire outside Los Angeles imaged using Sentinel-2 multispectral imagery from 12/5/2017. Sentinel-2’s infrared sensitivity (Channel 12; 2.19 micron band) provides the ability to identify areas of active fires, much like NOAA-20 or Aqua/Terra, but at 20m resolution. If you’re using the Sentinel-2 Views layer in ArcGIS , go into the Image Display options. Pull down the Render options and select Short-wave Infrared with DRA. This particular RGB combination relies more on thermal than visible channels, penetrating through clouds to see active fire areas. The Short-wave Infrared RGB combination is also available in the Sentinel Explorer app.   In addition to visualizing active fire areas, multispectral imagery is also effective at assessing burn scars. Besides the ecosystem impact, denuded vegetation along sloped areas can lead to landslides, especially when combined with heavy rains. Smoke Impacts The flames of an intense fire span across a few dozen miles, but the smoke emitted from a fire can seriously affect the air quality of areas hundreds of miles downwind. NOAA’s smoke forecast models rely on understanding both the vegetation of an area along with the heat/energy of a fire – which is where the Fire Radiative Power (FRP) attribute from above comes in again. Higher temperatures or FRP can burn more types of materials, creating more smoke. NOAA smoke dispersion model. In the Living Atlas, the National Weather Service Smoke Forecast layer can be merged with any of the fire location layers, or other forecast data such as wind speeds. In fact, check out this blog and app from Michael Dangermond to see an example.   New, Now and Next The Living Atlas team is currently working on a few updates to our data layers. Adding the VIIRS thermal activity data.  The VIIRS sensor is the more modern version of MODIS, and is flown on NOAA’s latest polar-orbiting satellites. The sensor is a huge improvement over MODIS, providing 375m per pixel resolution, and it has multiple channels that can detect fires. By comparison, the thermal channels on MODIS are around 1km per pixel. You can see the improvement in this swipe app I built to compare the data from MODIS with VIIRS for the first day of the Thomas Fire that occurred outside Los Angeles in 2017.   Improvements to the Smoke Forecast We will be updating many of the Live Feeds datasets, including the National Weather Service Smoke Forecast. Besides improving some of the data classifications, we’ll also be adding in the Air Quality Index attribute that should provide a more meaningful impact of the smoke on populations. Additional Blogs and Tutorials We’ll be digging in a little deeper to the layers referenced above to show how you can use them for more meaningful impact analysis on populations and habitats. ... View more

by Michael Dangermond Years of drought and soaring temperatures have made much of Western North America a tinderbox. As expected, fire season has come upon us strongly and suddenly. How can we keep up with the rapid spread of these devastating fires? A new fire map assembles the most important and timely information available about these devastating fires, mashing up satellite fire detections, fire perimeters, and a smoke forecast. Fire Map Fire detections in this map come from two sources, the MODIS satellites with 1km resolution, and the Suomi NPP VIIRS satellite with 375m resolution. The MODIS satellite service may be found in the Living Atlas, detections are updated once or twice daily. The VIIRS satellite provides 7 times the density of information due to its higher resolution, and at this time they are only a WMS service. That means the VIIRS detections appear only as small red dots, with no information in the popup window when you click on the points. Also of interest to North Americans is the National Weather Service Smoke Forecast, updated every 24 hours. The NDGD smoke forecast plays as an animation inside the map. Watch the time indicator at the bottom of the map, it shows future local time as forecasted particulate levels are displayed. ... View more