A Generalization Of Geographic Conditions Maps Constrained By Both Spatial And Semantic Scales

High precision,huge volume and rich semantics are the common characteristics of geographic conditions data(GCD),which reflects the spatial distribution of the natural elements and human elements on earth,as well as their interrelations.GCD is a basic data source for map products and application services.In order to meet the needs of multi-level services,multi-domain applications,multi-granularity expressions,it is a necessity to produce multi-scale versions of the data through scale transformation.Map generalization of GCD belongs to the category of thematic map generalization.Compared with the general map generalization which aims to realize the simplification of spatial features,the generalization of GCD should concentrate on the generalization of semantic features.Considerations on the spatial and semantic constraints become the main requirements for the GCD generalization,and they run through the whole generalization process including generalization rules making,generalization algorithm design,and generalization quality evaluation.The first census of Chinese geographic conditions was completed in 2015,and the study of thematic map generalization is still in infancy.Combing the needs of actual work,the paper focus on the research of generalization of geographic conditions maps.Combing the needs of actual work,we focus on the research on generalization of GCD.Based on the existing theories and methods of map generalization,the paper expands and improves the theories and methods of thematic map generalization according to GCD features and generalization demands.The construction of generalization rules,the design of generalization algorithms and the corresponding quality evaluation methods are studied,which provide the technical supports for the rapid,efficient and accurate transformation for GCD.The main contents of this paper are as follows:(1)The theories and methods of thematic map generalization for the scale transformation of GCD are summarized,and some map generalization strategies by combing the spatial and semantic scales on the conceptual level are established.Thus,the multiple constraints of map generalization of GCD are proposed and the characteristics of map generalization of GCD are summarized.(2)Map generalization rules model under the constraints of spatial and semantic features are constructed,including process control rules,element sequencing rules,adjacent proximity calculation rules,and nine-tuple operator decision rules.The process control rules consisting of a series of operations such as merging,dimensionality reduction,subdivision and edge simplification is proposed.The element sequencing rules are established based on semantic importance.For adjacent proximity calculation rules,a semantic proximity computing model is proposed based on the combination of semantic hierarchy and ordered quantities,which solves the problem of quantizing semantic differences between horizontal different types and vertical semantic levels in the semantic hierarchy tree.Furthermore,a generalization neighborhood computing model under different spatial and semantic relationships is proposed.For the generalization operator decision-making rules,generalization rules base on the nine-tuple model are proposed through considering the characteristic differences of geographic conditions in different regions of China.According to these rules,the generalization of GCD can be realized by adaptively considering the area,shape,width,semantic attribute,spatial position and other characteristic information of the elements.In addition,it considers the spatial and semantic relationships among the adjacent polygons and the contextual characteristics during the operations of merging,subdivision and elimination.(3)Algorithms of polygon scale transformation are proposed,which consider spatial simplification,area balance and semantic consistency.During the generalization of GCD,area of each feature type needs to keep area balance as far as possible.For solving this difficult problem,several effective transformation algorithms are proposed by considering the area positive transformation and negative transformation.For the long and narrow polygons generalization,the detection methods based on rolling ball and simplex objects are proposed,and the advantages and disadvantages of two methods are compared.A subdivision algorithm for the dimensionality reduction of long and narrow polygons based on semantic-proximity weight is designed.For the small area patches generalization,a comprehensive treatment strategy is established under different spatial and semantic conditions,which includes aggregation of nonadjacent patches,exaggeration,changing points,elimination and tessellation.For the simplification of boundary-shared lines among different patches,method of boundary simplification for natural elements based on the envelope circle and method of boundary simplification for artificial elements based on the least-square method are designed.(4)The quality evaluation indexes and methods for the generalization of geographic conditions maps are proposed,which take into account preserving both the spatial and semantic features.In terms of evaluation for the structural characteristics maintenance,the area balance of elements,the stability of spatial layout,the preservation of distribution characteristics,and the dominance of pattern are utilized to establish the quality evaluation model.With regard to the evaluations for the semantic characteristics maintenance,the semantic accuracy,the semantic consistency,the integrity of semantic type and the similarity of semantic structure are utilized to establish the quality evaluation model.(5)Based on the above results,a experimental system for generalization of geographic conditions maps is developed combined with engineering application requirements.The software design architecture,technology development mode,the processing flow of generalization are expounded.Furthermore,some examples of geographic conditions maps with different scales are displayed.This research broadens the extension of map generalization,and expands the generalization for thematic maps beneficially,especially in the scale transformation of semantic feature data.It is of great practical significance to study generalization methods of geographic conditions maps combining with practical engineering needs.However,there is still further work to be done,such as the design of intelligent generalization algorithms and the efficiency improving in automatic operations.