August 2021
GIS: Helping commercial businesses find new opportunities How is the pandemic affecting consumer behaviour? Where should we build new store locations to maximize profits? How are successful companies using location data combined with buyer information to spot areas of high customer demand? Geospatial tools offer huge analytical opportunities to commercial businesses, allowing them to work with their spatial, demographic and behavioural data all at once. Esri Canada’s partner, Environics, demonstrates how this is possible with August’s map, “Summer 2019 vs 2020 Visitor Change for Toronto Super Regional Malls”. Summer 2019 vs 2020 Visitor Change for Toronto Super Regional Malls A map showing the changes of toronto super regional malls from 2019 to 2020 Month: August 2021Organization: Environics AnalyticsProducts: ArcGIS Pro This map illustrates how the COVID-19 pandemic is impacting consumer behaviour. Using the latest anonymized mobile movement data and leveraging Environics Analytics’ proprietary geofence library, a thematic map was created that illustrates percent change in visitor patronage in the summer of 2020 over the same time in 2019. Neighbourhoods in red illustrate a decline of more than 25% in patronage to one of four malls. Areas in blue highlight neighbourhoods that have actually increased patronage to those malls. These data are updated daily, allowing clients to leverage Esri mapping technology and Environics Analytics’ data when making evidence-based decisions related to staffing, tenant communications and logistics. Retail sales in the Greater Vancouver Area Will my company need to build a new store in a certain postal code? What are the income and education levels of my potential customers there? How far will they have to travel to get to the nearest one of my retail locations? To answer these kinds of questions, key decision makers can see segments of their target market in a single glance using geospatial dashboards, like the example below, built by Paul Voegtle at Esri Canada. The map in the middle shows all the existing Greater Vancouver-based store locations for a fictional company. By selecting a postal code in the right-hand column, stakeholders can view socioeconomic data for each forward sortation area, then compare them with sales per store, total orders and orders per store. Using GIS to uncover business opportunities in retail Before joining Esri as a subject matter expert in retail, Gary Sankary spent 30 years in the retail industry. Now, he writes regular articles for the Esri Blog and WhereNext Magazine on how commercial companies are making use of GIS to gain uncover new busines opportunities, address emergent issues and pinpoint the locations of their customer bases. A screenshot of the header for the linked article, entitled "DICK's Sporting Goods Strengthens Omnichannel through Brick and Mortar Planning". The header features a photo of a DICK’s Sporting Goods brick and mortar location. A screenshot of the article entitled "A Lifelong Learner Brings a Data Science Edge to Fruit of the Loom" with a headshot of Beth Rogers of Fruit of the Loom. Screenshot of the article called "Carhartt Wins in Omnichannel with Customer Focus, Location Intelligence", with a header featuring a young person kayaking. GIS data & resources Yum! Russia Expands KFC with Help of Location Intelligence: An iconic brand excels at market development by using location intelligence to plan new stores, help franchisees plan their investments and create the right dining experience for customers. Human Weather, Customer Changes Revealed by Geospatial Analysis: Geospatial analysis reveals how human weather and customer patterns are changing during the pandemic for big box retailers, restaurants, entertainment venues, malls and other businesses. Dark Stores: How to Choose the Right Location | Location Intelligence: For business locations edging toward unprofitability, executives once had to choose between continuing operations and closing the doors. Now they have a third option, and location analysis is helping them make profitable decisions. Gary Sankary | Author at Esri: Read more blog posts by Gary Sankary, retail subject matter expert at Esri.
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Additional Info
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| Last Updated | March 25, 2026, 02:21 (UTC) |
| Created | March 25, 2026, 02:21 (UTC) |
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Domain / Topic
Domain or topic of the dataset being cataloged.
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Title
Title for the Dataset.
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August 2021 |
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Description
A description of the dataset.
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GIS: Helping commercial businesses find new opportunities How is the pandemic affecting consumer behaviour? Where should we build new store locations to maximize profits? How are successful companies using location data combined with buyer information to spot areas of high customer demand? Geospatial tools offer huge analytical opportunities to commercial businesses, allowing them to work with their spatial, demographic and behavioural data all at once. Esri Canada’s partner, Environics, demonstrates how this is possible with August’s map, “Summer 2019 vs 2020 Visitor Change for Toronto Super Regional Malls”. Summer 2019 vs 2020 Visitor Change for Toronto Super Regional Malls A map showing the changes of toronto super regional malls from 2019 to 2020 Month: August 2021Organization: Environics AnalyticsProducts: ArcGIS Pro This map illustrates how the COVID-19 pandemic is impacting consumer behaviour. Using the latest anonymized mobile movement data and leveraging Environics Analytics’ proprietary geofence library, a thematic map was created that illustrates percent change in visitor patronage in the summer of 2020 over the same time in 2019. Neighbourhoods in red illustrate a decline of more than 25% in patronage to one of four malls. Areas in blue highlight neighbourhoods that have actually increased patronage to those malls. These data are updated daily, allowing clients to leverage Esri mapping technology and Environics Analytics’ data when making evidence-based decisions related to staffing, tenant communications and logistics. Retail sales in the Greater Vancouver Area Will my company need to build a new store in a certain postal code? What are the income and education levels of my potential customers there? How far will they have to travel to get to the nearest one of my retail locations? To answer these kinds of questions, key decision makers can see segments of their target market in a single glance using geospatial dashboards, like the example below, built by Paul Voegtle at Esri Canada. The map in the middle shows all the existing Greater Vancouver-based store locations for a fictional company. By selecting a postal code in the right-hand column, stakeholders can view socioeconomic data for each forward sortation area, then compare them with sales per store, total orders and orders per store. Using GIS to uncover business opportunities in retail Before joining Esri as a subject matter expert in retail, Gary Sankary spent 30 years in the retail industry. Now, he writes regular articles for the Esri Blog and WhereNext Magazine on how commercial companies are making use of GIS to gain uncover new busines opportunities, address emergent issues and pinpoint the locations of their customer bases. A screenshot of the header for the linked article, entitled "DICK's Sporting Goods Strengthens Omnichannel through Brick and Mortar Planning". The header features a photo of a DICK’s Sporting Goods brick and mortar location. A screenshot of the article entitled "A Lifelong Learner Brings a Data Science Edge to Fruit of the Loom" with a headshot of Beth Rogers of Fruit of the Loom. Screenshot of the article called "Carhartt Wins in Omnichannel with Customer Focus, Location Intelligence", with a header featuring a young person kayaking. GIS data & resources Yum! Russia Expands KFC with Help of Location Intelligence: An iconic brand excels at market development by using location intelligence to plan new stores, help franchisees plan their investments and create the right dining experience for customers. Human Weather, Customer Changes Revealed by Geospatial Analysis: Geospatial analysis reveals how human weather and customer patterns are changing during the pandemic for big box retailers, restaurants, entertainment venues, malls and other businesses. Dark Stores: How to Choose the Right Location | Location Intelligence: For business locations edging toward unprofitability, executives once had to choose between continuing operations and closing the doors. Now they have a third option, and location analysis is helping them make profitable decisions. Gary Sankary | Author at Esri: Read more blog posts by Gary Sankary, retail subject matter expert at Esri. |
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Tags / Keywords
Keywords/tags categorizing the dataset.
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Format (CSV, XLS, TXT, PDF, etc)
File format of the dataset.
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Dataset Size
Dataset size in megabytes.
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14169.85 |
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Metadata Identifier
Metadata identifier – can be used as the unique identifier for catalogue entry
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Published Date
Published date of the dataset.
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2020-12-11 |
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Time Period Data Span (start date)
Start date of the data in the dataset.
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Time Period Data Span (end date)
End date of time data in the dataset.
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GeoSpatial Area Data Span
A spatial region or named place the dataset covers.
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Access category
Type of access granted for the dataset (open, closed, service, etc).
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public |
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License
License used to access the dataset.
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License not specified |
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Limits on use
Limits on use of data.
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Location
Location of the dataset.
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https://map-calendar-esricanada.hub.arcgis.com/datasets/EsriCanada::august-2021 |
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Data Service
Data service for accessing a dataset.
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Owner
Owner of the dataset.
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[email protected]_EsriCanada |
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Contact Point
Who to contact regarding access?
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Esri Canada |
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Contact Point Email
The email to contact regarding access?
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[email protected] |
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Publisher
Publisher of the dataset.
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Esri Canada |
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Publisher Email
Email of the publisher.
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Author
Author of the dataset.
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[email protected]_EsriCanada |
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Author Email
Email of the author.
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Accessed At
Date the data and metadata was accessed.
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2021-10-20 |
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Identifier
Unique identifier for the dataset.
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28d9b2ce08bb4c33bc8aabcc899cd4e3 |
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Language
Language(s) of the dataset
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English |
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Link to dataset description
A URL to an external document describing the dataset.
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https://map-calendar-esricanada.hub.arcgis.com/datasets/EsriCanada::august-2021 |
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Persistent Identifier
Data is identified by a persistent identifier.
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Globally Unique Identifier
Data is identified by a persistent and globally unique identifier.
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Contains data about individuals
Does the data hold data about individuals?
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Contains data about identifiable individuals
Does the data hold identifiable data about individual?
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Contains Indigenous Data
Does the data hold data about Indigenous communities?
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Portal Type
Platform type of the source portal.
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Version
Version of the datatset
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None |
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Source
Source of the dataset.
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None |
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Version notes
Version notes about the dataset.
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Is version of another dataset
Link to dataset that it is a version of.
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Other versions
Link to datasets that are versions of it.
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Provenance Text
Provenance Text of the data.
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Esri Canada |
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Provenance URL
Provenance URL of the data.
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Temporal resolution
Describes how granular the date/time data in the dataset is.
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GeoSpatial resolution in meters
Describes how granular (in meters) geospatial data is in the dataset.
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GeoSpatial resolution (in regions)
Describes how granular (in regions) geospatial data is in the dataset.
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Indigenous Community Permission
Who holds the Indigenous Community Permission. Who to contact regarding access to a dataset that has data about Indigenous communities.
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Community Permission
Community permission (who gave permission).
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The Indigenous communities the dataset is about
Indigenous communities from which data is derived.
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Number of data rows
If tabular dataset, total number of rows.
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Number of data columns
If tabular dataset, total number of unique columns.
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Number of data cells
If tabular dataset, total number of cells with data.
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Number of data relations
If RDF dataset, total number of triples.
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Number of entities
If RDF dataset, total number of entities.
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Number of data properties
If RDF dataset, total number of unique properties used by the triples.
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Data quality
Describes the quality of the data in the dataset.
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Metric for data quality
A metric used to measure the quality of the data, such as missing values or invalid formats.
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