What are the GIS spatial data types?

1. Vector data structures, including: simple data structures, topological data structures, surface data structures.

Raster data structure, including: raster matrix structure, tour coding structure, quadtree data structure, octree and sixteen fork tree structure.

2. (1) Application of spatial clustering methods in the analysis of highway disease-intensive areas.

The disease of highway pavement is always denser in some sections and more sparse in some sections. It is important to find out the area with dense disease for the maintenance decision. Spatial clustering can analyze the clustering of spatial objects, and apply cluster analysis to explore the disease-intensive areas of highways, develop maintenance countermeasures, and save human, material and financial resources.

(2) The application of cluster analysis in the spatial zoning of urban economy

The zoning of urban economy involves many elements, relying on only experience and professional knowledge to do qualitative classification is far from enough, often with subjectivity and arbitrariness. In order to find out the comparative advantages and gaps between several cities, and to provide reference for the relevant policy organizations in the formulation of policies, the Q-type cluster analysis is adopted for the optimal segmentation of the eight major elements of the comprehensive competitiveness of cities, and the classification is carried out according to the evaluation coefficients.1Q-type cluster analysis Cluster Analysis is a method to study the "clustering of things into groups", and it is also a method to study the "clustering of things into groups", and it is a method to study the "clustering of things into groups". Cluster Analysis (Cluster Analysis) is a method to study the "clustering of things", some people in China call it group analysis, point group analysis, cluster analysis, etc. The basic idea is to find out from a number of samples of a number of observation indicators between samples or indicators of the degree of similarity between the degree of similarity (affinity) of the statistical quantities, constituting a symmetric similarity matrix, and based on this, further search for the samples.