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Initial Readings of the Data About Contemporary Chinese Buddhist Monasteries. Jiang Wu 吴疆 Department of East Asian Studies Daoqin Tong 童道琴 School of Geography & Development The University of Arizona. Introduction to the Data. BGIS ECAI: Atlas of Chinese Religion China Data Center.
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Initial Readings of the Data About Contemporary Chinese Buddhist Monasteries Jiang Wu 吴疆 Department of East Asian Studies Daoqin Tong 童道琴 School of Geography & Development The University of Arizona
Introduction to the Data • BGIS • ECAI: Atlas of Chinese Religion • China Data Center
Assumptions of Chinese Monasteries • Buddhist monasteries are fundamentally independent and local institutions. • They are one of the types of local institution which has been allowed to grow in China. • Temple building activities are largely spontaneous endeavors undertaken by local communities • Thus, temple building can be retreated as an index to social and cultural development.
Purpose of this Study • Changing the paradigm in the study of Buddhism • From sectarian-based model to monastery-or place-based study • Identify various social, cultural, economic factors (viables) and their relationships to temple building • Identify patterns in the growth of Buddhism through history • Understand the transformation of Chinese society
Methods • Data sampling • Exploratory Spatial Data Analysis (ESDA) • Regression analysis • Historical approach • Quantitative and qualitative research
Exploratory Spatial Data Analysis (ESDA) • Allow users to describe and visualize spatial distributions, discover patters of association, clusters, etc. • Explore the properties of datasets without the need for formal model building • We believe that the temple distribution is not random • Spatial autocorrelation
Spatial Autocorrelation • Refers to the coincidence of attribute similarity and locational similarity (Anselin 1988) • Moran’s I (Anselin 1995) • Provides the degree of linear association between values observed at different locations • Positive vs. negative
Moran’s I • I=0.3951 • P-value =0.0015 with 9999 random permutation • Positive spatial autocorrelation LH HH LL HL
Local Indicator of Spatial Autocorrelation (LISA) • Capture local spatial clustering (Anselin 1995) • Provinces that are statistically significant
Factors to Explain Variability in Temple Distribution • Linear regression • Dependent/Response variable (Y) • number of temples in a province • Independent/Explanatory variables (X’s) • Population • Income • Rural/urban • media (TV, newspaper, internet users) • Ethnicity • Education • Transportation
Regression Results • R-square 0.69 • Population (10,000) • Income (yuan) • Internet users (10,000) • HS_above (%)
Statistically Insignificant • Population: Population does not contribute significantly to the variality of temple distribution. • Roads (km): transportation does not have correlations with temple distribution. • Interpretation: Chinese population is huge and transportation has been well-developed. Thus they have minimum impact.
Positive Correlation • Income: Higher income level tends to boost the number of temples. • Museum: the existence of museum indicates the existence of more temples • Interpretation: Economic growth stimulates the growth of Buddhist institutions. • As cultural indicators, museums and monasteries have similar role in local society as they require local investment. (Note: some temples might have been appropriated as museums.
Negative Correlations • Internet users: The area where the number of internet users increases may have negative impact on the distribution of Buddhist institutions. • HS_above: people with above high-school education may have negative impact on the building of Buddhist institutions. • Interpretation: Higher education may discourage the development of Buddhist institutions. (Not necessarily Buddhism as a whole.)
Future works • Narrow the scales to country level • Seeking continuities with data in Tang and Qing • Incorporating William Skinner’s Macro-region theory more effectively • Conducting residual analysis to identify the defects in the original data collection