Detecting permanent and intermittent purchase hotspots via computational stigmergy
Copyright © 2019 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved The analysis of credit card transactions allows gaining new insights into the spending occurrences and mobility behavior of large numbers of individuals at an unprecedented scale. However, unfolding such s...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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SCITEPRESS - Science and Technology Publications
2021
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| Online Access: | https://hdl.handle.net/1721.1/138026 |
| _version_ | 1826199570158190592 |
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| author | Alfeo, Antonio Cimino, Mario Lepri, Bruno Pentland, Alex Vaglini, Gigliola |
| author_facet | Alfeo, Antonio Cimino, Mario Lepri, Bruno Pentland, Alex Vaglini, Gigliola |
| author_sort | Alfeo, Antonio |
| collection | MIT |
| description | Copyright © 2019 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved The analysis of credit card transactions allows gaining new insights into the spending occurrences and mobility behavior of large numbers of individuals at an unprecedented scale. However, unfolding such spatiotemporal patterns at a community level implies a non-trivial system modeling and parametrization, as well as, a proper representation of the temporal dynamic. In this work we address both those issues by means of a novel computational technique, i.e. computational stigmergy. By using computational stigmergy each sample position is associated with a digital pheromone deposit, which aggregates with other deposits according to their spatiotemporal proximity. By processing transactions data with computational stigmergy, it is possible to identify high-density areas (hotspots) occurring in different time and days, as well as, analyze their consistency over time. Indeed, a hotspot can be permanent, i.e. present throughout the period of observation, or intermittent, i.e. present only in certain time and days due to community level occurrences (e.g. nightlife). Such difference is not only spatial (where the hotspot occurs) and temporal (when the hotspot occurs) but affects also which people visit the hotspot. The proposed approach is tested on a real-world dataset containing the credit card transaction of 60k users between 2014 and 2015. |
| first_indexed | 2024-09-23T11:22:04Z |
| format | Article |
| id | mit-1721.1/138026 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T11:22:04Z |
| publishDate | 2021 |
| publisher | SCITEPRESS - Science and Technology Publications |
| record_format | dspace |
| spelling | mit-1721.1/1380262021-11-10T03:31:00Z Detecting permanent and intermittent purchase hotspots via computational stigmergy Alfeo, Antonio Cimino, Mario Lepri, Bruno Pentland, Alex Vaglini, Gigliola Copyright © 2019 by SCITEPRESS - Science and Technology Publications, Lda. All rights reserved The analysis of credit card transactions allows gaining new insights into the spending occurrences and mobility behavior of large numbers of individuals at an unprecedented scale. However, unfolding such spatiotemporal patterns at a community level implies a non-trivial system modeling and parametrization, as well as, a proper representation of the temporal dynamic. In this work we address both those issues by means of a novel computational technique, i.e. computational stigmergy. By using computational stigmergy each sample position is associated with a digital pheromone deposit, which aggregates with other deposits according to their spatiotemporal proximity. By processing transactions data with computational stigmergy, it is possible to identify high-density areas (hotspots) occurring in different time and days, as well as, analyze their consistency over time. Indeed, a hotspot can be permanent, i.e. present throughout the period of observation, or intermittent, i.e. present only in certain time and days due to community level occurrences (e.g. nightlife). Such difference is not only spatial (where the hotspot occurs) and temporal (when the hotspot occurs) but affects also which people visit the hotspot. The proposed approach is tested on a real-world dataset containing the credit card transaction of 60k users between 2014 and 2015. 2021-11-09T18:47:46Z 2021-11-09T18:47:46Z 2019 2019-07-26T17:49:23Z Article http://purl.org/eprint/type/ConferencePaper https://hdl.handle.net/1721.1/138026 Alfeo, Antonio, Cimino, Mario, Lepri, Bruno, Pentland, Alex and Vaglini, Gigliola. 2019. "Detecting permanent and intermittent purchase hotspots via computational stigmergy." en 10.5220/0007581308220829 Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf SCITEPRESS - Science and Technology Publications arXiv |
| spellingShingle | Alfeo, Antonio Cimino, Mario Lepri, Bruno Pentland, Alex Vaglini, Gigliola Detecting permanent and intermittent purchase hotspots via computational stigmergy |
| title | Detecting permanent and intermittent purchase hotspots via computational stigmergy |
| title_full | Detecting permanent and intermittent purchase hotspots via computational stigmergy |
| title_fullStr | Detecting permanent and intermittent purchase hotspots via computational stigmergy |
| title_full_unstemmed | Detecting permanent and intermittent purchase hotspots via computational stigmergy |
| title_short | Detecting permanent and intermittent purchase hotspots via computational stigmergy |
| title_sort | detecting permanent and intermittent purchase hotspots via computational stigmergy |
| url | https://hdl.handle.net/1721.1/138026 |
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