Abstract
Nowadays, there are a mature set of tools and techniques for data analytics, which help Data Scientists to extract knowledge from raw heterogeneous data. Nonetheless, there is still a lack of spatiotemporal historical dataset allowing to study everyday life phenomena, such as vehicular congestion, press influence, the effect of politicians comments on stock exchange markets, the relation between food prices evolution and temperatures or rainfall, social structure resilience against extreme climate events, among others. Unfortunately, few datasets are combining from different sources of urban data to carry out studies of phenomena occurring in cities (i.e., Urban Analytics). To solve this problem, we have implemented a Web crawler platform for gathering a different kind of available public datasets.
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Notes
- 1.
Legislative Decree 1353: “Decreto legislativo que crea la autoridad nacional de transparencia y acceso a la información pública, fortalece el régimen de protección de datos personales y la regulación de la gestión de intereses”.
- 2.
Sistema Nacional de Información Ambiental: sinia.minam.gob.pe/.
- 3.
Weibo website: tw.weibo.com.
- 4.
London city dashboard: citydashboard.org/london.
- 5.
Scrapy: scrapy.org.
- 6.
BITMAP Urban Analytics: bitmap.com.pe/urbands.html.
- 7.
Indico API: https://indico.io/.
- 8.
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Nunez-del-Prado, M., Esposito, B., Luna, A., Morzan, J. (2018). Privacy-Aware Data Gathering for Urban Analytics. In: Lossio-Ventura, J., Alatrista-Salas, H. (eds) Information Management and Big Data. SIMBig 2017. Communications in Computer and Information Science, vol 795. Springer, Cham. https://doi.org/10.1007/978-3-319-90596-9_5
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