An Exploratory Urban Analysis via Big Data Approach: Eindhoven Case Measuring popularity based on POIs, accessibility and perceptual quality parameters

Abstract

The cities are equipped with the data as a result of the individuals' sharings and application usage. This significant amount of data has the potential to reveal relations and support user-centric decision making. The focus of the research is to examine the relational factors of the neighborhoods' popularity by implementing a big data approach to contribute to the problem of urban areas' degradation. This paper presents an exploratory urban analysis for Eindhoven at the neighborhood level by considering variables of popularity: density and diversity of points of interest (POI), accessibility, and perceptual qualities. The multi-sourced data are composed of geotagged photos, the location and types of POIs, travel time data, and survey data. These different datasets are evaluated using BBN (Bayesian Belief Network) to understand the relationships between the parameters. The results showed a positive and relatively high connection between popularity-population change, accessibility by walk-density of POIs, and the feeling of safety-social cohesion. For further studies, this approach can contribute to the decision-making process in urban development, specifically in real estate and tourism development decisions to evaluate the land prices or the hot-spot touristic places.