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Rotterdam Urban Big Data Thesis Award 2020 - Urban Big Data Rotterdam
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Rotterdam Urban Big Data Thesis Award 2020


Thesis award

 
 



The Urban Big Data Knowledge Lab aims to promote knowledge about and development of urban big data policies. To this purpose, it yearly awards a prize for the best thesis on the subject by Master students from Erasmus University Rotterdam and Bachelor Students from Rotterdam University of Applied Sciences. The winner receives €1.500.

Based on criteria such as originality, practical relevance and methodological rigor the thesis by Ties Hagdorn was selected as the winners of our 2020 Thesis Award. Congratulations, Ties!

You can find Ties's full thesis here. The abstract of the thesis reads as follows:

Providing housing for an increasing number of citizens is a challenging task for cities across the world in the face of global urbanisation. A metropolis that is currently dealing with a housing crisis, is London. Increased demand for housing outpaced supply, driving up the cost of houses 600% over the past 25 years. Understanding the drivers of public opinion on a housing crisis is valuable because it aids governmental bodies to undertake actions on the most critical components of a crisis and provides insights into how the public opinion may develop. This research analyses 96 million tweets sent from London from 2012 to 2018 as a novel technique to construct a representation of London’s opinion on the housing crisis. Along with Twitter data, various housing-related variables served as input for two regression models to estimate the relationship between shifts in the London housing market and changes in tone and size of the public opinion on the housing market. The results show that the London housing market has become an increasingly discussed topic on Twitter and that the tone of the housing tweets has become more negative over the years. The regression models prove that housing cost, housing demand, annual income and the rate of homelessness have significant effects on both the tone and number of housing tweets sent from London.