Mapping location and co-location of industries at the neighborhood level Alessandro ALASIA, Dennis HUYNH (Statistics Canada) In Canada, there is limited analysis on industry locations at the neighbourhood level; location and co-location of industries have been assessed primarily at the regional scale which results in an information gap for businesses. Recent evidence suggests that businesses do not just choose a city for their location, they choose specific business districts within a metropolitan area. Recent improvements in the geolocation of business microdata allow to address the information gap. This work, undertaken as part of the Business Data Lab and in collaboration with the Canadian Chamber of Commerce, is a first attempt to map industry locations at the neighbourhood level in major metropolitan areas of Canada. Using establishment-level microdata from the Business Register, we apply spatial kernel density estimations to identify neighbourhoods with high employment/revenue density for selected industries (2-digit NAICS) and industry clusters (grouping of 6-digits NAICS). The geographic delineation of business districts within metropolitan areas is the first step in understanding the evolution of industry location and co-location over time, and assessing local business dynamics at the neighbourhood level. Ultimately, these business districts can be analyzed in combination with additional data sources (e.g., mobility and road traffic) to derive further economic insights. Find out more at https://oe.cd/spl-mtg

1. Presented to the OECD Technical Workshop
November 13, 2023
Dennis Huynh
Data Exploration and Integration Lab
Centre for Special Business Projects
In collaboration with Business Data Lab and Canadian Chamber of Commerce
Mapping location and co-location of
industries at the neighbourhood level

2. Knowing where industries cluster matters
• Understand economic activity in
regions
• Business location selection
2

3. Objectives
• Granular clustering of businesses by industry within
metropolitan areas of Canada
• Supports public and private sector decision making alike
• Public: Plan infrastructure and target provision of specific services to
the business community
• Private: Provide business intelligence on neighbourhoods and support
location decision and investment
3

4. Methodology: an overview
Spatial Kernel Density Estimation (SKDE), inspired by the 2021 Statistics Canada
study, Defining Canada’s Downtown Neighbourhoods: 2016 Boundaries
4

5. Methods
• Spatial Kernel Density Equation:
𝑆𝐾𝐷𝐸 =
1
𝑏𝑎𝑛𝑑𝑤𝑖𝑑𝑡ℎ2
∗ ෍
𝑖=1
𝑛
3
π
∗ 1 −
𝑑𝑖𝑠𝑡ᵢ
𝑏𝑎𝑛𝑑𝑤𝑖𝑑𝑡ℎ
2 2
• Silverman Bandwidth Equation:
0.9 ∗ min 𝑆𝐷,
1
ln 2
∗ 𝐷ₘ ∗ 𝑛−0.2
5

6. Data sources
• Business Register of Statistics Canada
• Each business is assigned an industry code (NAICS)
• Selected industries
• Retail trade (NAICS 44-45)
• Manufacturing (NAICS 31-33)
• Accommodation and food (NAICS 72)
• Industry Clusters based on Porter Classification (Custom group of 4-
digit NAICS)
6

7. Unit of analysis
• Starting with a 50x50 metre grid
• Conflated to Dissemination Block (DB) from SKDE grid
• Within Census Metropolitan Area (CMA) regions
7
DB

8. Distribution of employment by dissemination blocks,
Toronto
8
Employee Density = Number of Employees in DB / Total Number of Employees in CMA

9. Manufacturing Sectors (NAICS 31-33)
9
The project is on-going, this is an illustrative example of results for discussion and feedback
Ottawa Winnipeg

10. Retail Trade Sectors (NAICS 44-45)
10
The project is on-going, this is an illustrative example of results for discussion and feedback
Ottawa Winnipeg

11. Accommodations and Food Sector (NAICS 72)
11
The project is on-going, this is an illustrative example of results for discussion and feedback
Ottawa Winnipeg

12. Ottawa – Industry Clusters
12
The project is on-going, this is an illustrative example of results for discussion and feedback
Distribution and Electronic
Commerce
Financial Services Hospitality and Tourism
• Delgado, Mercedes. , Porter, Michael E., and Stern, Scott (2014) Defining Clusters of Related Industries, National Bureau of Economic Research,
https://www.nber.org/papers/w20375
• Porter, Michael E. (1998) Clusters and the New Economics of Competition, Harvard Business Review

13. Manufacturing Sectors (NAICS 31-33) Zoomed In
13
The project is on-going, this is an illustrative example of results for discussion and feedback
Ottawa Winnipeg
*East Kildonan *NE Downtown

14. Retail Trade Sectors (NAICS 44-45) Zoomed In
14
The project is on-going, this is an illustrative example of results for discussion and feedback
Ottawa Winnipeg

15. Accommodations and Food Sector (NAICS 72) Zoomed In
15
The project is on-going, this is an illustrative example of results for discussion and feedback
Ottawa Winnipeg

16. Next steps
• Expand geographic coverage and number of industries and industry
clusters
• Challenge: Increased processing times
• Fine tune inputs used by the SKDE
• Explore different bandwidth sizes​
• Compare results from different grid cell sizes
16

17. Additional Analysis
• Potential uses of results in other analytical work
• Integration of results with spatial accessibility measures / proximity measures
(also computed at the DB level)
• Analysis of real time mobility, with aggregate mobile device data, in different
business districts (traffic trends, return to work)
• Analysis of businesses trends different business districts
• Analysis of commodity flows/linkages among local districts
17

18. Contact
18
Lead Developer
Dennis Huynh
Data Scientist
dennis.huynh@statcan.gc.ca
Unit Head
Jerome Blanchet
Data Scientist
jerome.blanchet@statcan.gc.ca
Chief
Bjenk Ellefsen
bjenk.ellefsen@statcan.gc.ca

19. Questions?
19