Geography Curriculum: Introduction to Spatial Analysis
2011, 31 (9):
In geography and related disciplines, spatial analysis is an important research tool or method, which has attracted increasing attention in geography and many other subjects, such as ecology, public health, environments, crimes, economics, etc. Based on the concepts, contents of spatial analysis, considering the different development stages and characteristics of spatial analysis since the 1960s, this article studies more than 40 colleges, organizations or universities' syllabus of spatial analysis at home and abroad. Together with the teaching practice of spatial analysis, the authors suggest the "Introduction to Spatial Analysis" course syllabus and the related specific contents, such as the detailed contents, the selection of practice software, class hours, etc. Accordingly, some advice on the teaching of spatial analysis in China are taken: 1) The specific syllabus should include: the introduction (including types and characteristics of spatial/attribute data, spatial autocorrelation, modifiable areal unit problem, edge effects, ecological fallacy), analysis based on maps (including overlay analysis, buffer analysis, Voronoi graph, thiessen polygon, network analysis, optimal path analysis, flow analysis and modeling, location-allocation analysis), geometry analysis (including centrography, distance/area measure, etc.), exploratory spatial data analysis (including visualization, scatter plot, stem-leaf plot, histogram, box plot), spatial point pattern analysis (including completely spatial randomness, quadrat analysis, kernel density estimation, nearest neighbor method, G function, F function, K/L function, Monte Carlo simulation, and some related test methods), area objects and spatial autocorrelation (including spatial weight matrix, global statistics, local statistics, joint count statistics, Moran's I, Geary's C and Getis'G/Gi*, local indicators of spatial association), spatial interpolation and geostatistics (including first law of geography, surface modeling, stochastic/determine modeling, inverse distance weighted interpolation, multinomial interpolation, spline interpolation, regionalized variable, semivariogram, ordinary Kriging, simple Kriging, and other Kriging methods), and new approaches, e.g. neural network (NN), evolutionary computation, cellular automata (CA), agent-based modeling (ABM), etc. in spatial analysis. 2) For the practice software, more than one type of software should be used in the class, in which ArcGIS, and free software GeoDa and R are recommend. Some other software about NN, CA, etc. may also be introduced, if time permitting. And 3) the class hours and practice hours are suggested that all the time should be at least 66 h, of which at least 12 h are practice hours with computer.
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