Regional Innovation and Air Passenger Network

City competitiveness is an important subject to study since it determines the productivity and prosperity of (Cann, O., 2018). There is also a strand of thought that competitiveness reveals the quality of life (Rogerson, 1999). Later on, Ni, Kamiya, et al. (2017) suggest a comprehensive conception in the recent report of global urban competitiveness through six fundamental pillars: enterprise quality, local elements, local demand, software environment, hardware environment, global connection. Furthermore, they emphasize that innovation in technology will drive global urban competitiveness. Therefore, in this thesis research, I perceive urban competitiveness from the perspective of regional innovation capability. Moreover, the air transportation network also plays an important role in urban competitiveness since the air transportation facilitates physical interaction of firms and market (Mukkala and Tervo, 2013). First, the availability of air infrastructure is one of the locational factors for firms to place subsidiary offices or production facilities for business expansion purposes. Secondly, air passenger flow allows knowledge transfer that leads to the development of innovation. Lastly, Kalayci and Yanginlar (2016) indicate a long-term positive relationship between air transportation, FDI, and economic growth. Hence, it is important to investigate the correlation between air transportation network and the cities competitiveness. In this thesis research, I would like to discuss to what extent air transportation network affect regional innovation and how agglomeration influence the relation. I observe the relation between air transportation network and regional innovation of the United States (US) in Metropolitan Statistical Area (MSA) level in the year 2012. Moreover, I conduct three analysis to develop the discussion in this thesis research. First, I employ descriptive statistical analysis to explain the distribution of innovation. Secondly, I convey network analysis to define the status of each MSA in air transportation network by generating three network measurement, according to the social network approach by Freeman (1978): degree centrality, betweenness centrality, and closeness centrality. Lastly, I conduct correlation analysis to reveal the relationship between regional innovation and region's status in air transportation network (measured by degree centrality), and to find out how firms’ agglomeration influences the relation. All in all, there are several findings resulted from this study. First, the distribution of regional innovation in the United States in 2012 is highly skewed, with two centers of concentration which are the north-east cluster and west cluster. Secondly, as the network analysis result, New York-Northern New Jersey-Long Island (NY-NJ-PA) appears as the MSA with the highest degree of centrality, while, Anchorage (AK) is the MSA with the highest betweenness centrality. Unexpectedly, South Bend-Mishawaka (IN-MI) appears as the MSA with the highest closeness centrality. Third, the relation between regional innovation and region status in air passenger network is statistically significant and shows the positive correlation. Lastly, agglomeration level is also a significant factor which influences the regional innovation. However, the interaction between air passenger and agglomeration levels weaken the influence of air passenger network on regional innovation.