Uncovering and estimating complementarity in urban lives
Abstract We typically think of the demand volume for a business in a city as a function of basic characteristics, such as the type of business, the quality of the product or service offered and its pricing. In addition, factors related to the urban environment, such as population density and accessi...
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SpringerOpen
2025-02-01
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| Series: | EPJ Data Science |
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| Online Access: | https://doi.org/10.1140/epjds/s13688-025-00527-z |
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| author | Xin Liu Konstantinos Pelechrinis |
| author_facet | Xin Liu Konstantinos Pelechrinis |
| author_sort | Xin Liu |
| collection | DOAJ |
| description | Abstract We typically think of the demand volume for a business in a city as a function of basic characteristics, such as the type of business, the quality of the product or service offered and its pricing. In addition, factors related to the urban environment, such as population density and accessibility are also crucial and have been considered in the literature. However, these considerations have typically been at the macro level. In this work we are interested in exploring the complementarity between specific (pairs) of venues. Simply put, venue B is complementary to venue A, if customers are more probable to visit venue B after being at venue A. This can increase the traffic for a business beyond the demand expected from the aforementioned factors, and it has been largely ignored in the literature. In this study we take a simulation-based approach to estimate this complementarity. We perform our simulations and analysis on two different spatial levels, namely, the venue level, as well as, the urban area level (e.g., zip code, neighborhood, etc.). The estimated complementarity provides insights for business owners and urban planners that can allow them to satisfy more demand, which consequently can increase the revenue for the businesses, but also can create more convenient urban navigation for city dwellers. |
| format | Article |
| id | doaj-art-66b5ea696a4a4aac8d4f72ddefd61e7e |
| institution | Kabale University |
| issn | 2193-1127 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | EPJ Data Science |
| spelling | doaj-art-66b5ea696a4a4aac8d4f72ddefd61e7e2025-02-09T12:24:53ZengSpringerOpenEPJ Data Science2193-11272025-02-0114112810.1140/epjds/s13688-025-00527-zUncovering and estimating complementarity in urban livesXin Liu0Konstantinos Pelechrinis1School of Computing and Information, Department of Informatics and Networked Systems, University of PittsburghSchool of Computing and Information, Department of Informatics and Networked Systems, University of PittsburghAbstract We typically think of the demand volume for a business in a city as a function of basic characteristics, such as the type of business, the quality of the product or service offered and its pricing. In addition, factors related to the urban environment, such as population density and accessibility are also crucial and have been considered in the literature. However, these considerations have typically been at the macro level. In this work we are interested in exploring the complementarity between specific (pairs) of venues. Simply put, venue B is complementary to venue A, if customers are more probable to visit venue B after being at venue A. This can increase the traffic for a business beyond the demand expected from the aforementioned factors, and it has been largely ignored in the literature. In this study we take a simulation-based approach to estimate this complementarity. We perform our simulations and analysis on two different spatial levels, namely, the venue level, as well as, the urban area level (e.g., zip code, neighborhood, etc.). The estimated complementarity provides insights for business owners and urban planners that can allow them to satisfy more demand, which consequently can increase the revenue for the businesses, but also can create more convenient urban navigation for city dwellers.https://doi.org/10.1140/epjds/s13688-025-00527-zComplementarityUrban computingLocal businessMixed-effects modelGraph neural network |
| spellingShingle | Xin Liu Konstantinos Pelechrinis Uncovering and estimating complementarity in urban lives EPJ Data Science Complementarity Urban computing Local business Mixed-effects model Graph neural network |
| title | Uncovering and estimating complementarity in urban lives |
| title_full | Uncovering and estimating complementarity in urban lives |
| title_fullStr | Uncovering and estimating complementarity in urban lives |
| title_full_unstemmed | Uncovering and estimating complementarity in urban lives |
| title_short | Uncovering and estimating complementarity in urban lives |
| title_sort | uncovering and estimating complementarity in urban lives |
| topic | Complementarity Urban computing Local business Mixed-effects model Graph neural network |
| url | https://doi.org/10.1140/epjds/s13688-025-00527-z |
| work_keys_str_mv | AT xinliu uncoveringandestimatingcomplementarityinurbanlives AT konstantinospelechrinis uncoveringandestimatingcomplementarityinurbanlives |