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空间宗教与社会研究 - 数据、方法、技术与研究方向 Spatial Explorer of Religions and Society - Data, Methodology, Technology and Research Agenda 密西根大学中国信息研究中心 鲍曙明 University of.

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Presentation on theme: "空间宗教与社会研究 - 数据、方法、技术与研究方向 Spatial Explorer of Religions and Society - Data, Methodology, Technology and Research Agenda 密西根大学中国信息研究中心 鲍曙明 University of."— Presentation transcript:

1 空间宗教与社会研究 - 数据、方法、技术与研究方向 Spatial Explorer of Religions and Society - Data, Methodology, Technology and Research Agenda 密西根大学中国信息研究中心 鲍曙明 University of Michigan China Data Center Shuming Bao

2 New Development of Religions in China New ClustersNew FashionNew Trends 贾庄土地庙落成仪式

3 影响地方神信仰差异的主要因素 The Primary Factors for Spatial Differences in Local Religions 影响地方神信仰差异的主要因素 朱海滨:《祭祀政策与民间信仰变迁 - 近世浙江民間信仰研究》 上海:复旦大学出版社, 2008 自然灾害 产业形态 交通与地理 乡土意识 迁入移民 巫、道文化  宗教与环境  宗教与经济  宗教与与地理  宗教与文化  宗教与移民  宗教与宗教 。。。。  宗教与历史  宗教与政治  宗教与管理

4 Spatial Analysis of Religions  Identify the spatial patterns of religious, demographic and socioeconomic distribution.  Identify the spatial interactions (linkages) between religious and other aspects of the society.  Evaluate the impacts of religions on future development of the society.

5 Demand for Spatial Analysis  How can we use the data from different sources, time, and formats?  What is the spatial patterns of data distribution?  How the spatial patterns changes over the time?  How the observations are interacted over the time and space?  How different factors are interacted each other over the time and space ?

6 Topics 1.Data 2.Methodology 3.Technology 4.Applications 5.Research Agenda 6.Future Directions

7 I. Data Census (population, economy,…) Government Statistics Enterprises database Remote Sensing Data Market Database Information infrastructure for China Studies Custom Database Financial Database Geography Environment Household Surveys

8 Statistical Data Statistical Database: Monthly Statistics National Statistics Provincial Statistics City Statistics County Statistics Monthly Industrial Data Yearly Industrial Data Statistics on Map Statistical Yearbooks Census Database: Population Census 1982 Population Census 1990 Population Survey 1995, 2005 Province Census 2000 County Census 2000 Economic Census 2004

9 The Census Data of China Population Census: 1953, 1964, 1982, 1990, 2000 Economic Census: Industrial Census (1995) Basic Unit Census (2001) Economic Census (2004)

10 Spatial Data of China  2000 China Township Population Census Data with GIS Maps  2000 China County Population Census Data with GIS Maps (Version III)  2000 China Province Population Census Data with GIS Maps  2000 China Grid Population Census Data with Township Boundaries  China Historical Province Population Census Data with GIS Maps (1953, 1964, 1982, 1990, 2000)  China Historical County Population Census Data with GIS Maps (1953, 1964, 1982, 1990, 2000)  China 1995 Industrial Census Data with GIS Maps  China 2001 Basic Unit Census Data with GIS Maps  China 2004 Economic Census Data with GIS Maps  China City Statistical Indicators with Maps (1996-)  Geographic Layers (rivers, lakes, roads, highways, railways)

11 Local Gazetteers and Journal Databases

12 New Releases

13 II. Methodology

14 Tests on spatial patterns:  Tests on spatial non-stationarity  Tests on spatial autocorrelation Data-driven approaches (Exploratory Spatial Data Analysis)  Global Statistics  Local statistics Model-driven approaches  Spatial linear and non-linear models  Space-temporal models Spatial Statistics

15 Difference between Conventional Statistics and Spatial Statistics Con. statistics Spatial statistics Data: Time-series data Spatial data (cross-sectional) Relationship: Time ( y t-1, y t, y t+1 ) Topology ( y i-1, y i, y i+1 ) Process: {Z(t), t  T} {Z(s;t), s  D(t), t  T} Model: Y =  WY +  t = 1, 2, 3, … w i,j = 1 if i is adjacent to j  - time-series  - spatial autocorrelation autocorrelation

16  Criteria: theoretical and empirical Accessibility ( roads, rivers, railways, airlines and Internet ) Economic linkage ( commuter flows, migrations, trade flows ) Social linkage ( college admission, language ) Locational linkage ( neighborhood, geographical distance )  Methodology: Binary matrix Row standardized matrix Weight function (wij=f(x,y..)) Defining Spatial Linkage (Weights)

17 Theoretical Variogram: Experimental Variogram: where N(h k )={(i,j): x i -x i _ =h}, |N(h k )| is the number of distinct elements of N(h k ), or. Nugget Nugget - represent micro-scale variation or measurement error. Its estimated by  (0). Sill Sill - represent the variance of the random field lim h   (h). Range Range - the distance at which data are no longer autocorrelated. Identifying Spatial Trend

18 Moran I (Z value) is positive: observations tend to be similar; negative: observations tend to be dissimilar; approximately zero: observations are arranged randomly over space. Geary C: large C value (>>1): observations tend to be dissimilar; small C value (<<1) indicates that they tend to be similar. Moran I: Geary C: Identifying Spatial Autocorrelation

19 Local Moran: Local Geary: significant and negative if location i is associated with relatively low values in surrounding locations; significant and positive if location i is associated with relatively high values of the surrounding locations. significant and small Local Geary (t<0) suggests a positive spatial association (similarity); significant and large Local Geary (t>0) suggests a negative spatial association (dissimilarity). Identifying Local Spatial Associations

20 Spatial Regression Spatially autoregressive model Spatial moving average model Semi-parametric model where W 1 and W 2 are spatial weight matrices,  ~ N(0,  ).

21 空间数据分析理论与方法研究领域 空间平衡和非平衡理论 (Spatial equilibrium theories and unequilibrium theories) 探索性数据分析 (Exploratory spatial data analysis) 空间数据抽样 (Spatial sampling) 地理统计学 (Geostatistics, such as multivariates variogram and kriging) 局部统计分析 (Local spatial statistics) 空间聚类分析 (Spatial cluster analysis) 空间数据显示 (Spatial data visualization - dynamic visualization, statistical graphics, multi-dimensional data visualization)

22 III. Technology

23 Functions for Spatial Analysis  compare the data of different times  compare the data at different locations  aggregate the data by spatial ranges  aggregate the data by different levels of administrative units  integrate the data from different sources  conduct spatial data analysis without GIS skills  extract the data efficiently  make the reports for ready analysis and publications  get the local/community information for survey data analysis  detect outliers, spatial patterns and trends

24 Spatial Intelligence: Spatial Intelligence: An Integration of Information, Methodology and Knowledge supported by Spatial Technology DATA RSGIS Information + Methodology + knowledge Technology

25 New Development: China Geo-Explorer Spatial Intelligence for Space-Time Data Integration and Analysis New Development: China Geo-Explorer Spatial Intelligence for Space-Time Data Integration and Analysis Statistics Charts GIS Census Maps Tables Reports

26 China Geo-Explorer - Web Based Platform for Spatial Intelligence Objectives:  To provide a web-based platform for data integration and spatial analysis  To facilitate multidisciplinary studies and applications by brining new methodology  To promote knowledge sharing with new technologyFeatures:  Efficient data integration for spatial and non-spatial data  Quick and accurate location analysis and spatial assessment  Identify spatial patterns and trends  Generate time-saving, easy-to-use, and pre-formatted reports as well as customized reports  Dynamic maps for visual analysis  Applications: proximity analysis, buffer analysis, cluster analysis, simulation and projections,….

27 2000 Population Census with Maps 2000 Population Variables: Basic Structure Minority Age Structure Household Education Fertility Death Marriage Migration Housing Occupation Spatial Hierarchical Structure: Province | Prefecture | County | Township | Sq km Grid

28 2004 Economic Census Data with ZIP Maps Employee 1 1-19 20-49 50-99 100-499 500-999 1000-4999 5000-29999 30000-49999 50000+ Revenue (10,000 Yuan) 0-30 30---50 50---100 100--300 300--500 500--1000 1000-3000 3000-5000 5000-10000 10000-30000 30000-50000 50000-100000 100000-150000 150000-200000 200000 and over Industries Ownerships Revenue Employee …

29 Land Use ( sq km Grid ) Land Use Classification 土地利用分类 Cultivated Land 耕地 Forest 林地 Grassland 草地 Water 水域 Construction 城乡、工矿、居民用地 Bare Land 未利用土地

30 Flex WebGIS Mashup Integration GeoGlobeGoogle Map WMS ServerWFS Server Observations Raster DataTime-Series DataVector Data Statistical Data Server User Other Data …. Data Selection ReportsMapsCharts System Architecture

31 Featured Functions Data Selection 数据选择 (政府统计、人口普查、经济普查、工业普查、单位普查、土地、环境) By administrative units (province, city, county, township) By groups By location (X&Y) and spatial range (km or miles) By time-series statistics (province, city and county) By establishments (province, city, county and ZIP) Reporting 报告 Summary report 汇总报告 Comparison report 比较报告 Original data report 原始数据报表 Spatial Analysis 空间分析 Graphic analysis 图形分析 (直方图, 散点图,多变量图) ESDA spatial statistics 探索性空间统计分析 OLS regression 普通回归 Spatial Regression 空间回归 Export 输出 Data tables (Excel) 数据表格 Reports (Excel, Word, PDF) 分析报告 GIS files (Shape) 数字地图 Maps (PDF) 打印地图

32 Selection by Administrative Units 行政区选择 Select by administrative units Select by map

33 Selection by Groups 分组选择

34 Selection by Locations 按空间位置选择 Select by X & Y coordinates Select by locations on map

35 Selection by Establishments 单位选择

36 Province and City Statistics 政府统计

37 Export of Space-Time Series Data 时空数据分析与输出

38 Exploratory Spatial Data Analysis 空间统计分析

39 Graphic Analysis : Histogram

40 Graphic Analysis : Scatter Plot

41 Graphic Analysis : Multi-Plots

42 Gravity Model for Space-time Data

43 Multiple Outputs 多种报告输出

44 Customized Reports 自定义报告输出

45 Output : Establishments

46 Output : GIS Maps

47 Integration with local Gazetteers

48 IV. 应用研究  Spatial Distribution of Regions  Spatial Structure of Religions  Spatial Trends of Religious Development  Spatial Religion and Population  Spatial Religion and Economy  Spatial Religion and Environment  Spatial Religion and Social Development  Policy Impacts on Religious Development  Location Selection of Religious Sites  Household/Field Surveys

49 Case Study: 宗教与社会空间研究 Case Study: 宗教与社会空间研究 Elevation Reports Charts Web Tool Religious Sites Religious Clusters

50 Case Study: 宗教与社会调查空间分析

51 VI. Research Agenda for Spatial Religions - A New Discipline for Religions in Space and Time -Spatial theory: demand and supply (macro and micro, national, regional and local levels) -Spatial data visualization and exploratory data analysis (ESDA) of religions -Identifying the spatial structure of religions -Identifying the spatial patterns of religious distribution -Identifying outliers, spatial trends and spatial regimes (one or multiple religions) -Spatial model analysis for the complex system -Identifying interactions among different religions -Identifying interactions between religion, population, economy, culture and environment, …. -Location selection of religious sites -Local functions of religious service -Spatial range of religious service -Spatial religion and policies -The internal governance of religions -The local governance of religions

52 What is New for Spatial Religions ? - build a bridge for humanities and social sciences -Add new dimension of time and space to religious studies -Expansion from humanities to social sciences -Expansion from one dimension to multiple dimensions - 宗教研究在时空上的扩展 - 人文学向社会科学的扩展 - 单维向多维研究空间的扩展

53 空间宗教的宏观、中观和微观分析: - 宏观分析:宗教空间分布模式与变化趋势;空间宗教与经济、 技术、政策的发展联系 - 中观分析:空间宗教分布扩散与区域发展联系;某一宗教信仰 的空间分布与扩散 - 微观分析:地方宗教点选址决策分析、空间布局、空间服务范 围 空间宗教的方法研究: - 空间宗教分布与发展的可视化 - 空间宗教探索性数据分析 - 空间宗教数据建模分析 - 空间宗教变化模拟与趋势预测 空间宗教的理论分析: - 宗教的需求与供应的空间平衡分析 - 不同宗教信仰的管理模式与扩散机制 - 地方宗教、文化及社区体系的生态系统研究 空间宗教的研究议题

54 VI. Future Directions 未来方向 数据库扩展(地理、环境、资源、人口、经济、历史 、人文与社科研究资料库) 遥感影像集成分析 用户数据集成分析 个性化与专门化的应用平台 虚拟环境管理 系统模拟与预测 模型库 统计库 图形库

55 Establishing the Spatial Information Network for Religious Studies “ 空间宗教信息研究 ” 合作网

56  可以量化的研究  可以规范化的研究  可以复制的研究  可以积累的研究  可以开放的研究  可以扩展的研究  人人可以从事的研究 What We Can Do with the Network?

57 Contact Shuming Bao China Data Center University of Michigan Suite 301, 1007 E Huron St. Ann Arbor, MI 48104-1690 USA Tel: (734)647-9610 Email: Web:

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