What Local Environments Drive Opportunities for Social Events? A New Approach Based on Bayesian Modeling in Dallas, Texas, USA
In-person social events bring people to places, while people and places influence where and what social events occur. Knowing what people do and where they build social relationships gives insights into the distribution and availability of places for social functions. We developed a Bayesian Network...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-03-01
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| Series: | ISPRS International Journal of Geo-Information |
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| Online Access: | https://www.mdpi.com/2220-9964/13/3/81 |
| _version_ | 1797240781473316864 |
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| author | Yalin Yang Yanan Wu May Yuan |
| author_facet | Yalin Yang Yanan Wu May Yuan |
| author_sort | Yalin Yang |
| collection | DOAJ |
| description | In-person social events bring people to places, while people and places influence where and what social events occur. Knowing what people do and where they build social relationships gives insights into the distribution and availability of places for social functions. We developed a Bayesian Network model, integrating points of interest (POIs) and sociodemographic characteristics, to estimate the probabilistic effects of places and people on the presence of social events. A case study in Dallas demonstrated the utility and performance of the model. The Bayesian Network model predicted the presence likelihoods for seven types of social events with an R<sup>2</sup> value around 0.83 (95% confidence interval). For both the presence and absence of social events at locations, the model predictions were within a 20% error for most event types. Furthermore, the model suggested POI, age, education, and population density configurations as important contextual variables for place–event associations across locations. A spatial cluster analysis identified likely multifunctional hotspots for social events (i.e., socially vibrant places). While psychological and cultural factors likely contribute further to local likelihoods of social event occurrences, the proposed conceptually informed geospatial data-science approach elucidated intricate place–people–event relationships and implicates inclusive, participatory places for urban development. |
| first_indexed | 2024-04-24T18:12:53Z |
| format | Article |
| id | doaj.art-d8743cc2389a4091a95da661b8c60334 |
| institution | Directory Open Access Journal |
| issn | 2220-9964 |
| language | English |
| last_indexed | 2024-04-24T18:12:53Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | ISPRS International Journal of Geo-Information |
| spelling | doaj.art-d8743cc2389a4091a95da661b8c603342024-03-27T13:44:53ZengMDPI AGISPRS International Journal of Geo-Information2220-99642024-03-011338110.3390/ijgi13030081What Local Environments Drive Opportunities for Social Events? A New Approach Based on Bayesian Modeling in Dallas, Texas, USAYalin Yang0Yanan Wu1May Yuan2School of Economic, Political and Policy Sciences, The University of Texas at Dallas, Dallas, TX 75081, USASchool of Economic, Political and Policy Sciences, The University of Texas at Dallas, Dallas, TX 75081, USASchool of Economic, Political and Policy Sciences, The University of Texas at Dallas, Dallas, TX 75081, USAIn-person social events bring people to places, while people and places influence where and what social events occur. Knowing what people do and where they build social relationships gives insights into the distribution and availability of places for social functions. We developed a Bayesian Network model, integrating points of interest (POIs) and sociodemographic characteristics, to estimate the probabilistic effects of places and people on the presence of social events. A case study in Dallas demonstrated the utility and performance of the model. The Bayesian Network model predicted the presence likelihoods for seven types of social events with an R<sup>2</sup> value around 0.83 (95% confidence interval). For both the presence and absence of social events at locations, the model predictions were within a 20% error for most event types. Furthermore, the model suggested POI, age, education, and population density configurations as important contextual variables for place–event associations across locations. A spatial cluster analysis identified likely multifunctional hotspots for social events (i.e., socially vibrant places). While psychological and cultural factors likely contribute further to local likelihoods of social event occurrences, the proposed conceptually informed geospatial data-science approach elucidated intricate place–people–event relationships and implicates inclusive, participatory places for urban development.https://www.mdpi.com/2220-9964/13/3/81social eventsPOIsBayesian networkMAUPplace–event relationships |
| spellingShingle | Yalin Yang Yanan Wu May Yuan What Local Environments Drive Opportunities for Social Events? A New Approach Based on Bayesian Modeling in Dallas, Texas, USA ISPRS International Journal of Geo-Information social events POIs Bayesian network MAUP place–event relationships |
| title | What Local Environments Drive Opportunities for Social Events? A New Approach Based on Bayesian Modeling in Dallas, Texas, USA |
| title_full | What Local Environments Drive Opportunities for Social Events? A New Approach Based on Bayesian Modeling in Dallas, Texas, USA |
| title_fullStr | What Local Environments Drive Opportunities for Social Events? A New Approach Based on Bayesian Modeling in Dallas, Texas, USA |
| title_full_unstemmed | What Local Environments Drive Opportunities for Social Events? A New Approach Based on Bayesian Modeling in Dallas, Texas, USA |
| title_short | What Local Environments Drive Opportunities for Social Events? A New Approach Based on Bayesian Modeling in Dallas, Texas, USA |
| title_sort | what local environments drive opportunities for social events a new approach based on bayesian modeling in dallas texas usa |
| topic | social events POIs Bayesian network MAUP place–event relationships |
| url | https://www.mdpi.com/2220-9964/13/3/81 |
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