عنوان مقاله [English]
Expansion of digital technologies and wide social networks has caused a change in the understanding of spatial experience. It has also caused a flow of extensive information from the citizens' viewpoints about their experience of urban spaces. Thus, moving towards modern analyses based on big data can cause a paradigm shift in the methods of measuring the quality of urban spaces. In big data analysis, the search process to reveal hidden patterns and unknown correlations can be used for next decisions. Moreover, due to instantaneous changes in various criteria of urban spaces, they are nowadays considered as a dynamic entity. For this reason, their measurement and evaluation should also be presented in the form of methods that can respond to these instantaneous changes. The present study aims to provide a flexible and dynamic method to measure the quality of Pakington Street in Australia as an example of urban spaces based on big data analysis. This measurement has been used due to changes in the urban space in the short term. The main method used in this study is to use the Kalman algorithm model to obtain the moving average graph of the quality of spaces based on the time variable and the rate of the indicators based on the data obtained from the citizens' participation in the Place Score program. After analyzing the big data for the five indicators of the quality of the urban space, it was found that in Pakington Street, the average rate of the two indicators of view and function and uniqueness in the short term is stable, and from the users' viewpoint, the average of the three indicators of safety, things to do, and care of the space varies in the short term of a day and night.