Spatial analysis

Introduction: Spatial analysis originated from the measurement revolution of geography and regional science in the 1960s, in the initial stage, it mainly applied quantitative (mainly statistical) analysis methods to analyze the spatial distribution patterns of points, lines and planes. Later, more emphasis is placed on the characteristics of geospatial itself, the spatial decision process and the spatial and temporal evolution process of complex spatial systems. In fact, since the map has been in place, people have been conducting various types of spatial analysis consciously or unconsciously. For example, measuring the distance, azimuth, and area between geographic elements on a map, and even using maps for tactical research and strategic decision-making, are examples of people using maps for spatial analysis, while the latter is essentially at a higher level. Space Analysis.

GIS integrates the latest multi-disciplinary technologies, such as relational database management, efficient graphics algorithms, interpolation, zoning and network analysis, providing powerful tools for spatial analysis, making advanced spatial analysis tasks difficult and complex in the past. Most GIS software currently has spatial analysis capabilities. Spatial analysis has long been one of the core functions of geographic information systems, which unique extraction, representation and transmission functions of geographic information (especially implicit information) are the main functional features of geographic information systems that are different from general information systems.

Spatial analysis is a general term for technologies related to the analysis of spatial data. According to the different nature of the data, it can be divided into: (1) analysis operation based on spatial graphics data; (2) data operation based on non-spatial attributes; (3) joint operation of spatial and non-spatial data.The basis of spatial analysis is geospatial database, the means of its application include various geometric logic operations, mathematical statistics analysis, algebraic operations and other mathematical means, the ultimate goal is to solve the practical problems involved in geospatial space, and transport geospatial information, especially implicit information, to aid decision making.

This chapter introduces the basic functions of spatial analysis in GIS, including spatial query and measurement, buffer analysis, overlay analysis, path analysis, spatial interpolation, statistical classification analysis, etc., and describes the related algorithms and calculation formulas.

• Spatial query and measurement
  • Spatial query
  • Spatial computation
    • Geometric measurement
    • Shape measurement
    • Centroid measurement
    • Distance measurement
• Space transformation
• Re classification
• Buffer analysis
• Superposition analysis
  • Visual information overlay
  • Point and polygon overlay
  • Line and polygon overlay
  • Point-in-polygon overlay
  • Raster overlay
• Network analysis
  • Network data structure
  • Main network analysis function
    • Path analysis
    • Dijkstra algorithm for calculating the shortest path
    • Resource allocation
• Spatial interpolation
  • Concept and theory of spatial interpolation
  • Data source of spatial interpolation
  • Spatial interpolation method
    • Global interpolation method
    • Local interpolation method
• Spatial statistical classification analysis
  • Principal component analysis (PCA)
  • Analytic hierarchy process
  • Systematic clustering analysis
  • Discriminant analysis