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Abstract
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.
Highlights
Identification of urban structure is one of the most important tasks of Urban Geography
One of its key components is the identification of urban centers, or urban activity centers (UACs), which is necessary for urban planning, regulation of traffic flows, and other practical matters
This study focuses on Tokyo1, one of the largest cities in the world by the population of the metropolitan area (Demographia 2017)
Summary
Identification of urban structure is one of the most important tasks of Urban Geography. The majority of studies aimed at identifying urban structure analyze census data, economic statistics, or employment patterns. Our work is devoted to the development of a methodology based on the use of social media data, location of points of interest (POI) and their characteristics. In this research we use the term ‘urban activity centers’ to refer to the areas of the city with the concentration of urban activity and points producing it, to a higher measure, in comparison to their surroundings. This study focuses on Tokyo, one of the largest cities in the world by the population of the metropolitan area (Demographia 2017). Having developed over the course of several centuries, Tokyo formed a complicated sociospatial structure: it is a polycentric urban system which consists of several urban cores interconnected by energy, human, traffic and information flows between them
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