| id |
ecaade2016_096 |
| authors |
Chen, Nai Chun, Nagakura, Takehiko and Larson, Kent |
| year |
2016 |
| title |
Social Media as Complementary Tool to Evaluate Cities - Data Mining Innovation Districts in Boston |
| source |
Herneoja, Aulikki; Toni Österlund and Piia Markkanen (eds.), Complexity & Simplicity - Proceedings of the 34th eCAADe Conference - Volume 2, University of Oulu, Oulu, Finland, 22-26 August 2016, pp. 447-456 |
| doi |
https://doi.org/10.52842/conf.ecaade.2016.2.447
|
| wos |
WOS:000402064400044 |
| summary |
High tech industries are playing an important role in the economic development in the United States. While some cities are shrinking, the "innovation" cities are growing. The attributes that cause some cities to successfully become innovative is a very relevant 21st century topic and will be investigated here.Previous work conduct city analysis through conventional government GIS or census data but such analyses do not answer questions about the perception of citizens inhabiting the city, and the activities they conduct. The novelty of this current project is to make use of large-scale bottom-up data available from social media. Several social media sources-CrunchBase, Twitter, Yelp, and Flickr- were data mined pertaining to four innovation districts in Boston. We found that the success of innovation districts in Boston were correlated with several important variables: the most successful districts tended to occur near research institutions, in very "mixed use" areas, and were unexpectedly not correlated with land and labor prices, unlike technology districts in the past. Based on our study, we make recommendations for the urban design that cities should put in place to increase the potential for "innovation". |
| keywords |
Smart Cities; Social Media; Innovation District; Spatial Analysis; Data Mining; Natural Language Processing |
| series |
eCAADe |
| email |
|
| full text |
 file.pdf (22,633,182 bytes) |
| references |
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