Knowledge Spillover Structure within Shanghai Districts: A Spatial Analysis of Aggregation and Correlation

DOI

10.2991/icetis-13.2013.38How to use a DOI?

Keywords

Knowledge Spillover; Spatial Autocorrelation; Gravity Model; Principle Component Analysis

Abstract

According to previous empirical researches in developing nations, study of knowledge spillover effect within urban districts is scarce. Based on the spatial autocorrelation and block analysis, this paper researched on intra-Shanghai knowledge spillover structure with enterprise-level data, by calculating the extent of knowledge aggregation and correlation with methods of local Geary’s C statistics and Concor algorithm. The results of aggregation analysis and correlation analysis highlight that knowledge stocks volume within different Shanghai districts are various, which indicates that knowledge spillover effects in Shanghai are unbalanced. According to unbalanced knowledge spillover effects, Shanghai could be divided into four regions. For each region, this paper finally outlines a few scenarios to enhance knowledge spillover effect

Copyright

© 2013, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Anyu Yu
AU  - Jie Ma
PY  - 2013/06
DA  - 2013/06
TI  - Knowledge Spillover Structure within Shanghai Districts: A Spatial Analysis of Aggregation and Correlation
BT  - Proceedings of the 2013 the International Conference on Education Technology and Information System (ICETIS 2013)
PB  - Atlantis Press
SP  - 170
EP  - 175
SN  - 1951-6851
UR  - https://doi.org/10.2991/icetis-13.2013.38
DO  - 10.2991/icetis-13.2013.38
ID  - Yu2013/06
ER  -