城市规模和产业结构 效用 人口 纺织城市 金融城市
人口 纺织城市 金融城市 城市规模和产业结构 效用
.空间统计学原理 美间绑学应用 环境研究中空间地理位置等因素日益增长的的重要性 ●空间时间变化比较 景观变化 空间分析技术发展GS,RS&GPS ●对更高级和复杂分析方法的要求
II. 空间统计学原理 ⚫ 环境研究中空间地理位置等因素日益增长的的重要性 ⚫ 空间-时间变化比较 ⚫ 景观变化 ⚫ 空间分析技术发展 (GIS, RS & GPS) ⚫ 对更高级和复杂分析方法的要求 关于空间统计学及应用的研究动因
Why Spatial is Special? a Why spatial data is different from non-spatial data?(spatial neighborhood a Statistical property for spatial data Spatial dependence(autocorrelation) Heterogeneity Spatial trend(non-stationarity) u Sensitive to spatial boundaries and spatial unit (Country, County, Tract)Elevation, Major cities and Lat/ Long grid
Why spatial data is different from non-spatial data ? (spatial neighborhood) Statistical property for spatial data: Spatial dependence (autocorrelation) Heterogeneity Spatial trend (non-stationarity) Sensitive to spatial boundaries and spatial unit (Country, County, Tract) Elevation, Major cities and Lat / Long grid Why Spatial is Special?
空海分折题 o Is there any spatial cluster over space? Are spatial observations distributed randomly over space ? o Are spatial observations correlated (autocorrelation)? o Is there any spatial outlier? Is there any spatial trend? o What is the interaction(statistically and theoretically) between different variables? o How to predict an unknown spatial value at a specific location
空间分析问题 ⚫ Is there any spatial cluster over space? ⚫ Are spatial observations distributed randomly over space? ⚫ Are spatial observations correlated (autocorrelation)? ⚫ Is there any spatial outlier? ⚫ Is there any spatial trend? ⚫ What is the interaction (statistically and theoretically) between different variables? ⚫ How to predict an unknown spatial value at a specific location
常规统计学与空间统计学的差异 ●Data Time-series data Spatial data(cross-sectional Relationship Time V y, yu+1 Topology (vi-, yi, yi+,) Process 区z(t),te∈T 区z(s;1),S∈D(t),t∈T Model YI=pY-1+Er Y=pwr+a t=1.2、3 Wii=1 if i is adjacent to j p-time-series p-spatial autocorrelation autocorrelation
常规统计学与空间统计学的差异 常规统计学 空间统计学 ⚫ 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 Yt = Yt − 1+t