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2018

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)

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)

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.

 

layers in active hurricane live feeds

Happy Probabilistic Wind Forecasting!

 

For comments or questions about this blog, please visit our GeoNet.

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

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!

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.

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 .

 

properties option

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.

 

processing templates in ArcGIS Pro

Or in ArcGIS , you’ll click on Image Display to access the processing templates.

 

processing templates in ArcGIS

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 26oC, or a luminance-controlled palette from dark purple to bright yellow.

 

GIF of two SST color palettes

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

 

SST histogram graph

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.

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 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 NameSourceUpdate Frequency
Short-Term Weather WarningsNOAA National Weather Service1 minute
Weather Watches and WarningsNOAA National Weather Service5 minutes
Current Weather ConditionsNOAA National Weather Service1 hour
Storm ReportsNOAA National Weather Service1 hour
National forecast modelsNOAA National Weather Service6 hours
Smoke ForecastNOAA National Weather Service6 hours
National Water ModelNOAA National Weather Service6 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

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 NameSourceUpdate Frequency
Recent EarthquakesUSGS5 minutes
Active HurricanesNOAA National Weather Service15 minutes
Recent HurricanesNOAA National Weather Service15 minutes
USA Wildfire ActivityNASA15 minutes
Flooding MapEsri1 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 NameSourceUpdate Frequency
MODIS Thermal ActivityNASA15 minutes
Stream GaugesEsri1 hour
HYCOM ocean model (beta)HYCOM1 day
Sea Surface TemperatureNOAA1 day
Current Drought ConditionsNOAA1 week
Global hydrology analysisNASA, NOAA1 month
satellite

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 NameSourceUpdate Frequency
Sentinel-2ESA, Amazon Web Services1 day; 5 day revisit
LandsatUSGS, Amazon Web Services1 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

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.

 

The Map option opens Map Viewer

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.

 

Manhattan New York

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.

 

Add Living Atlas layer

c. In the search window, click the Back arrow to return to the Contents pane.

 

Clothing store opportunity

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.

 

Pop-up about market opportunity

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.

 

Apply a filter to the block group layer

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.

 

filter-expression

d. Click Apply Filter. Your map now displays only census blocks where demand significantly exceeds supply.

 

Filtered store layer

e. To help see the market opportunity data, change the basemap to Dark Gray Canvas.

 

Map with Dark Gray Canvas basemap

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.