The Diagnosis of Development Drivers of Major European Metropolises: A Perspective from Urban Agglomeration

Author: XIE BenhaoUpload date: 2019-05-09
The study intends to study the development momentum of an individual city from a perspective of urban agglomerations with promotion from gravity model, urban network and Internet big data tools.
Country: ChinaProfessional Area : Intelligent PlanningKeywords : Urban development drivers, Urban dynamics, Regional collaboration, Urban gravity model, Urban network, Internet search index

The relative studies and theories of urbanization and researches on the driving factors of urban development are crucial for the sustainable development of the future city. The researches on urban development dynamics has been carried out for 50 years since the birth of urban dynamics. The model judgment of urban dynamic development process and the qualitative analysis of urban development elements have emerged in the past half century, but there are still vulnerabilities that need to be filled related to its selection of research objects and research indicators, furthermore, no cutting-edge research tools have been included into the above-mentioned studies until now.

Based on the theoretical study and literature review related to urban dynamics and development drivers, the paper proposes a new perspective for the definition and diagnosis of urban development driving forces, and proposes the three major dynamic elements of population, technology and capital as the core of urban development drivers. The evaluation on the driving factors of urban development is not limited to the comparison of the gross amount. With the trend of global development nowadays, urban studies should be improved to the perspective of regional development. The urban development drivers are not only the absolute value of the accumulation of indicators, the element transfer and connection intensity of which within the region represents the status of the city in regional development and provides a new dimension to measure the dynamic development of the city as well.

As a result, based on the sorting information of theoretical researches, the paper uses Metropolises on Europe continent (within the perspective on the European urban agglomeration level) as the research subject, and introduces relevant methods of urban spatial connection strength to conduct empirical research. The first phase of the study compared the development of dynamic factors in all sample cities horizontally. In the second stage, by modifying and improving the traditional gravity model, the research measures the inter-city strengths of the three major dynamic factors based on multiple indicators separately and then evaluated the capacity of sample cities on the dynamic strength of the three types of factors, resulting in the preliminary analysis that Paris, as a super city in the area of population and capital accumulation, far exceeds the cohesive power of other cities. While in the field of science and technology, the scientific strengths of Stuttgart and Munich as well as Vienna prove the unparalleled technological impetus from the German-speaking area in the development of knowledge economy in Europe. In the third stage, the three major factor indicators are integrated to calculate the development power of the sample cities, and then concludes with the overall dynamic intensity between the sample cities, where the core positions of Paris and the six German-speaking cities are fully demonstrated. On the combination of the urban network method, the study arrives at the diagnosis conclusion of urban development power of these metropolises in the European urban agglomeration. The study finally used Google Trends' Internet search data to test the correlation between urban development dynamics to certify the rationality of the diagnostic results.

Cites have never played such an important role as it is now, so the research aims to demonstrate the essential driving factors for urban development in the exploration of future cities, and to combine traditional statistical methods with the latest big data tools. Researching on European metropolises within urban agglomeration as an empirical process, it presents new discovery ideas and diagnostic methods for the research field of urban development, anticipating keeping pace with the development of the times and benefiting researches on urban planning and development dynamics in the era of artificial intelligence.