Competitive Cities: Establishing a Classification Model using Data Science-related Jobs


Carlos Orellana Fantoni (ESPOL)
Andrea Mero (ESPOL)
Denisse Orozco (ESPOL)

Session: 1.2A: Development and economics

Abstract: The concept of competitive cities has been spreading greatly over the years; a way to measure the advancement of cities economically speaking using several socio-economic indicators: GDP per capita, personal income and employment rate for most rankings. However, as time goes on and the impact of technology and Data Science-related jobs in the industry is more prevalent, the level at which this aspect is present in a competitive city is unknown. In this study, we aim to establish classification models that can accurately define a competitive city using Data Science-related job offers found for said city in, a job application website. Our results signal the KNN-based model as the best classification method, with a reported accuracy of 0.65 and an AUC of 0.58.