Analysing attractiveness of places in a region is beneficial to support urban planning and policy making. However, the attractiveness of a place is a subjective and high-level concept which is difficult to quantify. The existing methods rely on traditional surveys which may require high cost and have low scalability. This thesis attempts to quantify attractiveness of a place in a more efficient way and develop a model which can automatically predict attractiveness based on Street-View data (i.e. from Google Street View). As a study case, 800 Google Street View images from 200 locations in Amsterdam have been extracted, and their attractiveness perceptions have been evaluated via crowd-sourcing to get the ground-truth information. The other attributes which are presumed to have a relationship with attractiveness are also assessed, such as familiarity, uniqueness, friendliness, pleasure, arousal, and dominance. The research and analysis revealed several insights related to the attractiveness of places. Attractive perception when seeing a place is positively correlated with perception of uniqueness, friendliness, pleasure, and dominance. Moreover, pleasure is possibly multi-collinear with attractiveness. It was also found that attractiveness perception has low spatial auto-correlation, which means that nearby places do not necessarily have similar attractiveness. Some visual features related to attractiveness were also investigated. The result indicated that scenes related to roads and residential buildings are less attractive, meanwhile, scenes related to greenery, blue sky, and water environment are more attractive. A Convolutional Neural Network (CNN) model has been developed via machine learning approach which could automatically predict attractiveness perception of a place based on its representing Google Street View image. The developed model achieved 55.9% accuracy and RMSE of 0.70 to predict attractiveness in 5 ordinal values.