Year in Madrid as described through the analysis of geotagged Twitter data (Record no. 11684)
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| fixed length control field | 02435nab a2200301 4500 |
| 005 - DATE & TIME | |
| control field | 20210602160647.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 210602b ||||| |||| 00| 0 eng d |
| 100 ## - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Travis R Meyer, |
| 245 ## - TITLE STATEMENT | |
| Title | Year in Madrid as described through the analysis of geotagged Twitter data |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) | |
| Name of publisher, distributor, etc | Sage, |
| Date of publication, distribution, etc | 2019. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Pages | Vol 46, Issue 9, 2019(1724-1740 p.) |
| 520 ## - SUMMARY, ETC. | |
| Summary, etc | Gaining a complete picture of the activity in a city using vast data sources is challenging yet potentially very valuable. One such source of data is Twitter which generates millions of short spatio-temporally localized messages that, as a collection, have information on city regions and many forms of city activity. The quantity of data, however, necessitates summarization in a way that makes consumption by an observer efficient, accurate, and comprehensive. We present a two-step process for analyzing geotagged twitter data within a localized urban environment. The first step involves an efficient form of latent Dirichlet allocation, using an expectation maximization, for topic content summarization of the text information in the tweets. The second step involves spatial and temporal analysis of information within each topic using two complimentary metrics. These proposed metrics characterize the distributional properties of tweets in time and space for all topics. We integrate the second step into a graphical user interface that enables the user to adeptly navigate through the space of hundreds of topics. We present results of a case study of the city of Madrid, Spain, for the year 2011 in which both large-scale protests and elections occurred. Our data analysis methods identify these important events, as well as other classes of more mundane routine activity and their associated locations in Madrid. |
| 650 ## - Subject | |
| Subject | Urban sensing, |
| 650 ## - Subject | |
| Subject | topic model, |
| 650 ## - Subject | |
| Subject | latent Dirichlet allocation, |
| 650 ## - Subject | |
| Subject | spatial analysis |
| 700 ## - Added Entry Personal Name | |
| Added Entry Personal Name | Balagué, Daniel |
| 700 ## - Added Entry Personal Name | |
| Added Entry Personal Name | Camacho-Collados, Miguel |
| 700 ## - Added Entry Personal Name | |
| Added Entry Personal Name | Li, Hao |
| 700 ## - Added Entry Personal Name | |
| Added Entry Personal Name | Khuu, Katie |
| 700 ## - Added Entry Personal Name | |
| Added Entry Personal Name | Brantingham, P Jeffrey |
| 700 ## - Added Entry Personal Name | |
| Added Entry Personal Name | Bertozzi, Andrea L |
| 773 0# - HOST ITEM ENTRY | |
| Host Biblionumber | 11590 |
| Host Itemnumber | 15512 |
| Place, publisher, and date of publication | Sage 2019. |
| Title | Environment and Planning B: Urban Analytics and City Science |
| 856 ## - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | https://doi.org/10.1177/2399808318764123 |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
| Koha item type | Articles |
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| 942 ## - ADDED ENTRY ELEMENTS (KOHA) | |
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