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  • 1. Mapping location and co-location of industries at the neighborhood level: A spatial kernel density approach
    Articles and reports: 18-001-X2025001
    Description: This paper brings the analysis of business cluster to a more granular geographic scale by developing a methodology for identifying business clusters at the neighborhood level. The proposed method identifies clusters of businesses at the DB level, which is one of the most granular spatial units of analysis defined by Statistics Canada. The method is developed with an application to four census metropolitan areas (CMAs) of different sizes and for different industry cluster specifications, including simple 2-digit North American Industry Classification System (NAICS) groups as well as industry clusters resulting from groupings of NAICS codes, as defined by Delgado et al. (2014).
    Release date: 2025-10-10
Articles and reports (1)

Articles and reports (1) ((1 result))

  • 1. Mapping location and co-location of industries at the neighborhood level: A spatial kernel density approach
    Articles and reports: 18-001-X2025001
    Description: This paper brings the analysis of business cluster to a more granular geographic scale by developing a methodology for identifying business clusters at the neighborhood level. The proposed method identifies clusters of businesses at the DB level, which is one of the most granular spatial units of analysis defined by Statistics Canada. The method is developed with an application to four census metropolitan areas (CMAs) of different sizes and for different industry cluster specifications, including simple 2-digit North American Industry Classification System (NAICS) groups as well as industry clusters resulting from groupings of NAICS codes, as defined by Delgado et al. (2014).
    Release date: 2025-10-10

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