Identification of urban activity centers is among the most important components of the urban structure study, it is necessary for reasonable planning, regulation of traffic flows and other practical measures. The purpose of this paper is to design a complex method to identify urban activity centers based on different but universal data types. In this study, we used social media data (Twitter) since it guarantees regular updates and does not rely on administrative borders and points of interest database that was considered a 'hard' representation of multifunctional urban activities. A large amount of geotagged tweets was processed by means of statistical modelling (spatial autoregression) and combined with the distribution analysis of points of interest. This allowed to identify the local centers of urban activity within 23 special wards of Tokyo more objectively and precisely than when only based on the social media data. Thereafter, delimitated centers were classified in order to define and describe their main functional and spatial characteristics. As a result of the study, railway transport was identified as the main attraction factor of the urban activity; the modern urban structure of Tokyo was identified and mapped; a new comprehensive method for identification of urban activity centers was developed and five classes of urban activity centers were defined and described.