Smart City Gnosys
Smart city article details
| Title | Using Digital Trace Data To Identify Regions And Cities |
|---|---|
| ID_Doc | 60533 |
| Authors | Brelsford C.; Thakur G.; Arthur R.; Williams H. |
| Year | 2019 |
| Published | Proceedings of the 2nd ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities, ARIC 2019 |
| DOI | http://dx.doi.org/10.1145/3356395.3365539 |
| Abstract | A greater understanding of human dynamics as they play out in both physical space and through inter-personal communication is vital for the design and development of intelligent and resilient cities. Physical context provides insight into the space-time distribution of population and their activity patterns, while interpersonal communication can now be measured at the population scale through digital interactions. In this work, we propose a novel method to discover these dynamics. We use a dataset of 72 million tweets to develop a spatially embedded network of communication, and then use community detection algorithms to explore regional and urban delineation in the United States. We compare these results to US census regions and economic and infrastructural networks. We find that the broad spatial delineation of communities and sub-communities is consistent with United States regions, states, and major metropolitan areas. We describe how these methods could be extended to generate a measure of social regions that can be consistently applied anywhere there is a sufficiently rich data source. A deeper understanding of urban social structure measured by spatially embedded communication networks can enable a better understanding of the interactions between urban social and physical contexts. This, in turn, may enable urban managers and policy makers to identify strategies for supporting urban resilience. © 2019 Association for Computing Machinery. |
| Author Keywords | Cities; Communities; Digital Trace Data; Networks; Twitter |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
21407  | 0.887 | Mazzamurro M.; Wu Y.; Guo W. | Dynamic Spatial Cluster Process Model Of Geo-Tagged Tweets In London | 5th IEEE International Smart Cities Conference, ISC2 2019 (2019) |
| 25526 | 0.883 | Liu J.; Yuan Y. | Exploring Dynamic Urban Mobility Patterns From Traffic Flow Data Using Community Detection | Annals of GIS, 30, 4 (2024) |
| 51970 | 0.876 | Nolasco-Cirugeda A.; García-Mayor C. | Social Dynamics In Cities: Analysis Through Lbsn Data | Procedia Computer Science, 207 (2022) |
| 11332 | 0.87 | Chiesa G.; Boffa M.; Lanza C.; Baldoni V.; Fabiani F.; Ravera A. | Automatic Identification Of Urban Functions Via Social Mining | Cities, 137 (2023) |
| 46603 | 0.862 | Doorley R.; Noyman A.; Xiong Z.; Alonso L.; Grignard A.; Larson K. | Revurb: Understanding Urban Activity And Human Dynamics Through Point Process Modelling Of Telecoms Data | 2019 Smart Cities Symposium Prague, SCSP 2019 - Proceedings (2019) |
| 49304 | 0.861 | Blasi S.; Gobbo E.; Sedita S.R. | Smart Cities And Citizen Engagement: Evidence From Twitter Data Analysis On Italian Municipalities | Journal of Urban Management, 11, 2 (2022) |
| 26968 | 0.861 | Monachesi P. | Fostering Sustainable Urban Futures Through Twitter Public Space | Urban Sustainability, Part F3685 (2023) |
| 60393 | 0.86 | Ferreira D.L.; Nunes B.A.A.; Campos C.A.V.; Obraczka K. | User Community Identification Through Fine-Grained Mobility Records For Smart City Applications | IEEE Transactions on Intelligent Transportation Systems, 23, 5 (2022) |
| 54707 | 0.859 | Gkontzis A.F.; Kotsiantis S.; Feretzakis G.; Verykios V.S. | Temporal Dynamics Of Citizen-Reported Urban Challenges: A Comprehensive Time Series Analysis | Big Data and Cognitive Computing, 8, 3 (2024) |
| 40140 | 0.859 | de Oliveira T.H.M.; Painho M. | Open Geospatial Data Contribution Towards Sentiment Analysis Within The Human Dimension Of Smart Cities | Lecture Notes in Intelligent Transportation and Infrastructure, Part F1384 (2021) |