1. 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

2. Introduction to the Data BGIS ECAI: Atlas of Chinese Religion China Data Center

3. 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.

4. 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

5. Methods Data sampling Exploratory Spatial Data Analysis (ESDA) Regression analysis Historical approach Quantitative and qualitative research

6. 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

7. 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

8. China Temple Distribution

10. Moran’s I I=0.3951 P-value =0.0015 with 9999 random permutation Positive spatial autocorrelation HHLH LLHL

11. Local Indicator of Spatial Autocorrelation (LISA) Capture local spatial clustering (Anselin 1995) Provinces that are statistically significant

12. 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

13. Regression Results R-square 0.69 Coefficientt-statisticp-value Constant-780.995-2.0850.048 Population0.0220.4580.651 Income0.1464.1930.000+ Roads (km)0.0010.4200.678 Museum11.3454.0640.000+ Internet users-1.129-2.2410.035 HS_above-33.920-4.3500.000+ Population (10,000) Income (yuan) Internet users (10,000) HS_above (%)

14. 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.

15. 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.

16. 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.)

17. 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