Estimating the attractiveness of the city for skilled workers using jobs-housing matching, spatial data and NLP techniques.

This paper proposes a new method for assessing the attractiveness of a city for different types of workers depending on their skills and industry. The method uses job vacancy data to obtain information on the qualifications required for each type of worker and to determine the wage levels in different industries. It then calculates the value of an urban livability index, which reflects the spatial and financial correspondence between jobs and housing for each type of worker. It also reflects the connectivity between them by public transport rather than spatial proximity. In this task, transport connectivity affects a worker's quality of life more than the spatial remoteness of the workplace. The value of the indicator can be used to assess the well-being and quality of life in the city for workers with different qualifications. This approach improves the jobs-housing mismatch indicator, by using NLP methods to consider wage differentials across occupations. [ABSTRACT FROM AUTHOR]