4
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      A new commercial boundary dataset for metropolitan areas in the USA and Canada, built from open data

      data-paper
      1 , , 2 , 3
      Scientific Data
      Nature Publishing Group UK
      Geography, Society, Business

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          The purpose of this study is to define the geographic boundaries of commercial areas by creating a consistent definition, combining various commercial area types, including downtowns, retail centres, financial districts, and other employment subcentres. Our research involved the collection of office, retail and job density data from 69 metropolitan regions across USA and Canada. Using this data, we conducted an unsupervised image segmentation model and clustering methods to identify distinctive commercial geographic boundaries. As a result, we identified 23,751 commercial areas, providing a detailed perspective on the commercial landscape of metropolitan areas in the USA and Canada. In addition, the generated boundaries were successfully validated through comparison with previously established commerce-related boundaries. The output of this study has implications for urban and regional planning and economic development, delivering valuable insights into the overall commercial geography in the region. The commercial boundary and used codes are freely available on the School of Cities Github, and users can reuse, reproduce and modify the boundaries.

          Related collections

          Most cited references20

          • Record: found
          • Abstract: not found
          • Article: not found

          OSMnx: New methods for acquiring, constructing, analyzing, and visualizing complex street networks

            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found
            Is Open Access

            Assessing Completeness and Spatial Error of Features in Volunteered Geographic Information

              Bookmark
              • Record: found
              • Abstract: not found
              • Article: not found

              Unsupervised Learning of Image Segmentation Based on Differentiable Feature Clustering

                Bookmark

                Author and article information

                Contributors
                byeonghwa.jeong@utoronto.ca
                Journal
                Sci Data
                Sci Data
                Scientific Data
                Nature Publishing Group UK (London )
                2052-4463
                24 April 2024
                24 April 2024
                2024
                : 11
                : 422
                Affiliations
                [1 ]Postdoctoral Fellow, School of Cities, University of Toronto, ( https://ror.org/03dbr7087) Toronto, Canada
                [2 ]Lead, Data Visualization, School of Cities, University of Toronto, ( https://ror.org/03dbr7087) Toronto, Canada
                [3 ]Director, School of Cities and Professor, Geography & Planning, University of Toronto, ( https://ror.org/03dbr7087) Toronto, Canada
                Author information
                http://orcid.org/0009-0009-8643-2640
                http://orcid.org/0000-0001-9575-7169
                http://orcid.org/0000-0002-4417-4251
                Article
                3275
                10.1038/s41597-024-03275-3
                11043369
                38658658
                34f0fc6d-153a-49c0-a5ba-e26dbdbc9086
                © The Author(s) 2024

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 30 November 2023
                : 17 April 2024
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100003579, University of Toronto (UofT);
                Categories
                Data Descriptor
                Custom metadata
                © Springer Nature Limited 2024

                geography,society,business
                geography, society, business

                Comments

                Comment on this article