Urban economic planning is the hidden champion behind the blooming cities — a subtle but perfect blend of infrastructure, policy, and people. For long time, I used traditional economic models and assumption to handle this complex field. However, then the emergence of data science opened the news doors wide. The data science just didn’t offer new tools to estimate the traditional economic models, but entirely reshaped the framework to see the urban economics. Here, I would like to discuss the five key data science techniques which entirely transformed my approach towards urban economic planning.
To be honest — everybody at some time in life has wished for crystal ball or magical sphere to forecast the future. It is the predictive analytics which can offer the similar services of predicting the future. It utilizes the historical data to predict anything from housing demand to public transport requirements. Originally, I was bit doubtful of the capabilities of the algorithm that it can foresee the decades. But then I saw it working.
Using predictive model on past time-series data on urban growth and economic, I could simulate the multiple scenarios that were just a guess previously. For instance, I used the predictive modelling to anticipate the economic impact of expansion of public-transit-line towards the undeserved neighboring areas. The model not only confirmed the quantifiable positive impact but also helped measuring the potential increase in local economic activity and rise in property prices within next five years. … Read Full Article
