Spatial Evolution and Locational Determinants of High-tech Industries in Beijing

Using datasets on high-tech industries in Beijing as empirical studies, this paper attempts to interpret spatial shift of high-tech manufacturing firms and to examine the main determinants that have had the greatest effect on this spatial evolution. We aimed at merging these two aspects by using firm level databases in 1996 and 2010. To explain spatial change of the high-tech firms in Beijing, the Kernel density estimation method was used for hotspot analysis and detection by comparing their locations in 1996 and 2010, through which spatial features and their temporal changes could be approximately plotted. Furthermore, to provide quantitative results, Ripley′s K-function was used as an instrument to reveal spatial shift and the dispersion distance of high-tech manufacturing firms in Beijing. By employing a negative binominal regression model, we evaluated the main determinants that have significantly affected the spatial evolution of high-tech manufacturing firms and compared differential influence of these locational factors on overall high-tech firms and each sub-sectors. The empirical analysis shows that high-tech industries in Beijing, in general, have evident agglomeration characteristics, and that the hotspot has shifted from the central city to suburban areas. In combination with the Ripley index, this study concludes that high-tech firms are now more scattered in metropolitan areas of Beijing as compared with 1996. The results of regression model indicate that the firms′ locational decisions are significantly influenced by the spatial planning and regulation policies of the municipal government. In addition, market processes involving transportation accessibility and agglomeration economy have been found to be important in explaining the dynamics of locational variation of high-tech manufacturing firms in Beijing. Research into how markets and the government interact to determine the location of high-tech manufacturing production will be helpful for policymakers to enact effective policies toward a more efficient urban spatial structure.

A Study on the Determinants of Location Choice in Inward Foreign Direct Investment （FDI）： Focusing on the Incheon Free Economic Zone

Inward foreign direct investment （FDI） is expected to grow further by virtue of economic globalization. A thorough understanding of the locational determinants of inward FDI will be conducive to enhanced efficiency in attracting direct and SOC-related investments from foreign entities. This study analyzes 51 cases of inward direct foreign investment made in the Incheon free economic zone （IFEZ） from 2002 to 2009 to determine the factors influencing FDI volume, the relevance of locations and the correlation between investment size and location. First, the relationship between the loeational determinants of FDI and the total investment size （total expected project cost） is analyzed. Second, the relationship between the locational determinants of FDI and the FDI is analyzed. Third, the relationship between the locational determinants of FDI and the location choice is analyzed. The results indicate the determinants that influence locations and investment size of FDI entities; whether these factors exercise influence in the zone; and the factors that have relatively significant effects. Ultimately, based on the analytical findings, a few implications for policy and practice are derived.

Exploring the Influencing Factors of FDI in China’s Internal Regions:Evidence From Taiwan

This study aims to explore the influencing factors of the location determinants of Taiwan’s foreign direct investments(FDIs)in China’s internal regions.This study proposes three models to examine the influencing factors of Taiwan’s FDIs in China by combining the location advantages of the host country,the economy and politics of the home country,and network relationship in bilateral and regional trade agreements:First,analyze the location distribution of Taiwan’s FDIs in China’s six regions;Second,analyze the location determinants of Taiwan’s main sectors’(manufacturing and service)FDIs in China;Third,analyze the location determinants of Taiwan’s high-tech industries’FDIs in China’s six regions.The estimated results show that bilateral trade agreement seems to have positive effects on Taiwan’s FDIs in China,while regional trade agreement seems to have negative effects.In Taiwan’s political form,the ruling party with characteristics of political defense against China seems to have negative effects on most of Taiwan’s industries(except manufacturing)and the overall FDIs in China.In terms of China’s relevant economic factors,the main determinants of Taiwan’s high-tech industries’FDIs in the major regions of Eastern and South Central of China are infrastructure and high-level human capital resources.China’s GDP growth and increase in unit labor cost have negative effects on FDIs in most regions of China.This implies that the advantage of low wages in China Factory may gradually decline.This study provides some references and implications for future research and policy makers for the analysis of relevant influencing factors of FDI.

Object Tracking and Tracing:Hidden Semi-Markov Model Based Probabilistic Location Determination

The enhancement of radio frequency identification(RFID) technology to track and trace objects has attracted a lot of attention from the healthcare and the supply chain industry.However,RFID systems do not always function reliably under complex and variable deployment environment.In many cases,RFID systems provide only probabilistic observations of object states.Thus,an approach to predict,record and track real world object states based upon probabilistic RFID observations is required.Hidden Markov model(HMM) has been used in the field of probabilistic location determination.But the inherent duration probability density of a state in HMM is exponential,which may be inappropriate for modeling of object location transitions.Hence,in this paper,we put forward a hidden semi-Markov model(HSMM) based approach for probabilistic location determination. We evaluated its performance comparing with that of the HMM-based approach.The results show that the HSMM-based approach provides a more accurate determination of real world object states based on observation data.