The Center for Spatial Intelligence and Learning

- Spatial Explorer of Religions and Society- Data, Methodology, Technology and Research Agenda

University of Michigan China Data Center Shuming Bao1New Development of Religions in ChinaNew Clusters

New Fashion

New Trends

The Primary Factors for Spatial Differences in Local Religions-2008Spatial 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.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 ?

55TopicsDataMethodologyTechnologyApplicationsResearch AgendaFuture DirectionsI. DataCensus (population, economy,)Government StatisticsEnterprises databaseRemote Sensing DataMarket DatabaseInformation infrastructure for China StudiesCustom DatabaseFinancial DatabaseGeographyEnvironmentHousehold SurveysStatistical 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 20048The Census Data of ChinaPopulation Census:1953, 1964, 1982, 1990, 2000

Economic Census:Industrial Census (1995)Basic Unit Census (2001)Economic Census (2004)Spatial Data of China2000 China Township Population Census Data with GIS Maps2000 China County Population Census Data with GIS Maps (Version III)2000 China Province Population Census Data with GIS Maps2000 China Grid Population Census Data with Township BoundariesChina 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 MapsChina 2001 Basic Unit Census Data with GIS MapsChina 2004 Economic Census Data with GIS MapsChina City Statistical Indicators with Maps (1996-)Geographic Layers (rivers, lakes, roads, highways, railways)

10Local Gazetteers and Journal Databases

New Releases

II. MethodologyTests on spatial patterns:Tests on spatial non-stationarityTests on spatial autocorrelation

Data-driven approaches (Exploratory Spatial Data Analysis)Global StatisticsLocal statistics

Model-driven approachesSpatial linear and non-linear modelsSpace-temporal modelsSpatial StatisticsDifference between Conventional Statistics and Spatial Statistics Con. statistics Spatial statistics

Data: Time-series data Spatial data (cross-sectional)Relationship: Time (yt-1, yt, yt+1) Topology (yi-1, yi, yi+1) Process: {Z(t), tT} {Z(s;t), sD(t), tT} Model: Y = WY + t = 1, 2, 3, wi,j = 1 if i is adjacent to j - time-series - spatial autocorrelation autocorrelation

Criteria: theoretical and empiricalAccessibility (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 matrixRow standardized matrix Weight function (wij=f(x,y..))

Defining Spatial Linkage (Weights)Theoretical Variogram:

Experimental Variogram:

where N(hk)={(i,j): xi-xi_=h}, |N(hk)| is the number of distinct elements of N(hk), or .

Nugget - represent micro-scale variation or measurement error. Its estimated by (0).Sill - represent the variance of the random field limh(h).Range - the distance at which data are no longer autocorrelated.

Identifying Spatial Trend

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 (