Clustering Cities over Features Extracted from Multiple Virtual Sensors Measuring Micro-Level Activity Patterns Allows One to Discriminate Large-Scale City Characteristics
The impact of micro-level people’s activities on urban macro-level indicators is a complex question that has been the subject of much interest among researchers and policymakers. Transportation preferences, consumption habits, communication patterns and other individual-level activities can signific...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-05-01
|
| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/23/11/5165 |
| _version_ | 1797596748034605056 |
|---|---|
| author | Ricardo Muñoz-Cancino Sebastián A. Ríos Manuel Graña |
| author_facet | Ricardo Muñoz-Cancino Sebastián A. Ríos Manuel Graña |
| author_sort | Ricardo Muñoz-Cancino |
| collection | DOAJ |
| description | The impact of micro-level people’s activities on urban macro-level indicators is a complex question that has been the subject of much interest among researchers and policymakers. Transportation preferences, consumption habits, communication patterns and other individual-level activities can significantly impact large-scale urban characteristics, such as the potential for innovation generation of the city. Conversely, large-scale urban characteristics can also constrain and determine the activities of their inhabitants. Therefore, understanding the interdependence and mutual reinforcement between micro- and macro-level factors is critical to defining effective public policies. The increasing availability of digital data sources, such as social media and mobile phones, has opened up new opportunities for the quantitative study of this interdependency. This paper aims to detect meaningful city clusters on the basis of a detailed analysis of the spatiotemporal activity patterns for each city. The study is carried out on a worldwide city dataset of spatiotemporal activity patterns obtained from geotagged social media data. Clustering features are obtained from unsupervised topic analyses of activity patterns. Our study compares state-of-the-art clustering models, selecting the model achieving a 2.7% greater Silhouette Score than the next-best model. Three well-separated city clusters are identified. Additionally, the study of the distribution of the City Innovation Index over these three city clusters shows discrimination of low performing from high performing cities relative to innovation. Low performing cities are identified in one well-separated cluster. Therefore, it is possible to correlate micro-scale individual-level activities to large-scale urban characteristics. |
| first_indexed | 2024-03-11T02:57:23Z |
| format | Article |
| id | doaj.art-2292081193af415796f6effbd48c2491 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T02:57:23Z |
| publishDate | 2023-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-2292081193af415796f6effbd48c24912023-11-18T08:33:25ZengMDPI AGSensors1424-82202023-05-012311516510.3390/s23115165Clustering Cities over Features Extracted from Multiple Virtual Sensors Measuring Micro-Level Activity Patterns Allows One to Discriminate Large-Scale City CharacteristicsRicardo Muñoz-Cancino0Sebastián A. Ríos1Manuel Graña2Computational Intelligence Group, University of Basque Country, 20018 San Sebastián, SpainBusiness Intelligence Research Center (CEINE), Department of Industrial Engineering, University of Chile, Beauchef 851, Santiago 8370456, ChileComputational Intelligence Group, University of Basque Country, 20018 San Sebastián, SpainThe impact of micro-level people’s activities on urban macro-level indicators is a complex question that has been the subject of much interest among researchers and policymakers. Transportation preferences, consumption habits, communication patterns and other individual-level activities can significantly impact large-scale urban characteristics, such as the potential for innovation generation of the city. Conversely, large-scale urban characteristics can also constrain and determine the activities of their inhabitants. Therefore, understanding the interdependence and mutual reinforcement between micro- and macro-level factors is critical to defining effective public policies. The increasing availability of digital data sources, such as social media and mobile phones, has opened up new opportunities for the quantitative study of this interdependency. This paper aims to detect meaningful city clusters on the basis of a detailed analysis of the spatiotemporal activity patterns for each city. The study is carried out on a worldwide city dataset of spatiotemporal activity patterns obtained from geotagged social media data. Clustering features are obtained from unsupervised topic analyses of activity patterns. Our study compares state-of-the-art clustering models, selecting the model achieving a 2.7% greater Silhouette Score than the next-best model. Three well-separated city clusters are identified. Additionally, the study of the distribution of the City Innovation Index over these three city clusters shows discrimination of low performing from high performing cities relative to innovation. Low performing cities are identified in one well-separated cluster. Therefore, it is possible to correlate micro-scale individual-level activities to large-scale urban characteristics.https://www.mdpi.com/1424-8220/23/11/5165activity patternsclustering modelsgeotagged dataCity Innovation Index |
| spellingShingle | Ricardo Muñoz-Cancino Sebastián A. Ríos Manuel Graña Clustering Cities over Features Extracted from Multiple Virtual Sensors Measuring Micro-Level Activity Patterns Allows One to Discriminate Large-Scale City Characteristics Sensors activity patterns clustering models geotagged data City Innovation Index |
| title | Clustering Cities over Features Extracted from Multiple Virtual Sensors Measuring Micro-Level Activity Patterns Allows One to Discriminate Large-Scale City Characteristics |
| title_full | Clustering Cities over Features Extracted from Multiple Virtual Sensors Measuring Micro-Level Activity Patterns Allows One to Discriminate Large-Scale City Characteristics |
| title_fullStr | Clustering Cities over Features Extracted from Multiple Virtual Sensors Measuring Micro-Level Activity Patterns Allows One to Discriminate Large-Scale City Characteristics |
| title_full_unstemmed | Clustering Cities over Features Extracted from Multiple Virtual Sensors Measuring Micro-Level Activity Patterns Allows One to Discriminate Large-Scale City Characteristics |
| title_short | Clustering Cities over Features Extracted from Multiple Virtual Sensors Measuring Micro-Level Activity Patterns Allows One to Discriminate Large-Scale City Characteristics |
| title_sort | clustering cities over features extracted from multiple virtual sensors measuring micro level activity patterns allows one to discriminate large scale city characteristics |
| topic | activity patterns clustering models geotagged data City Innovation Index |
| url | https://www.mdpi.com/1424-8220/23/11/5165 |
| work_keys_str_mv | AT ricardomunozcancino clusteringcitiesoverfeaturesextractedfrommultiplevirtualsensorsmeasuringmicrolevelactivitypatternsallowsonetodiscriminatelargescalecitycharacteristics AT sebastianarios clusteringcitiesoverfeaturesextractedfrommultiplevirtualsensorsmeasuringmicrolevelactivitypatternsallowsonetodiscriminatelargescalecitycharacteristics AT manuelgrana clusteringcitiesoverfeaturesextractedfrommultiplevirtualsensorsmeasuringmicrolevelactivitypatternsallowsonetodiscriminatelargescalecitycharacteristics |