CN108021623B - Method and system for improving weighting objectivity of map symbols - Google Patents

Method and system for improving weighting objectivity of map symbols Download PDF

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CN108021623B
CN108021623B CN201711166381.0A CN201711166381A CN108021623B CN 108021623 B CN108021623 B CN 108021623B CN 201711166381 A CN201711166381 A CN 201711166381A CN 108021623 B CN108021623 B CN 108021623B
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王红
姚尧
王金云
张贞
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Hubei University
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Abstract

A method for improving weighting objectivity of map symbols comprises the following steps: s1, first-layer classification: dividing several element types of point symbols, linear symbols and surface symbols under the map elements according to the basic element types contained in the map elements; constructing a judgment matrix by combining an Analytic Hierarchy Process (AHP) and constructing a scale table according to the judgment matrix by combining the AHP, and constructing a judgment matrix of a first layer and geometric sense sequencing in first-layer classification by comparing importance degrees between any two elements to obtain a corresponding scale value; s2, second-layer classification: in step S1, based on the first layer classification, the determination matrix of each element under the dot symbol, the determination matrix of each element under the linear symbol, and the determination matrix of each element under the planar symbol are constructed, and the semantically important program ranking of each element under the dot symbol, the linear symbol, and the planar symbol is completed.

Description

Method and system for improving weighting objectivity of map symbols
Technical Field
The invention relates to a method for weighting map symbols, which is used for solving the problem of hierarchical sequencing of map symbols when symbol conflicts occur, in particular to a method and a system for improving the weighting objectivity of the map symbols.
Background
The priority ranking of the map symbols is essentially data summarization based on expert experience rules under certain environments, and has scientificity and objectivity. Starting from the geometrical characteristics of the symbols, if the geometrical types of the symbols are different, the positioning priority is different. According to understanding of the real world, people abstract geographic phenomena into a series of element classes, and in a symbol system, the same elements can be continuously divided into subclasses to construct family inheritance relationships in the real world. The sorting of the element classes is the basis of the quantization of the symbol weight values, and corresponding priority sorting rules and calculation methods are provided according to different characteristics of the element classes and the element subclasses.
The process of weighting map symbols is a process of quantifying indexes by applying a mathematical method, belongs to the structural category of a comprehensive evaluation index system, and generally follows the following steps: the general principle, the scientific principle, the hierarchical principle, the objective principle, the comparability principle, the operability principle and the like. Methods for quantitative variable quantification can be classified into "direct quantification methods" and "indirect quantification methods", and generally, the accuracy of the direct quantification method is higher than that of the indirect quantification method, and in practice, the two methods can also be used in combination.
The Analytic Hierarchy Process (AHP) is a systematic method which takes a complex multi-target decision problem as a system, decomposes a target into a plurality of targets or criteria and further decomposes the targets into a plurality of layers of multi-index (or criteria and constraints), and calculates the single-layer ordering (weight) and the total ordering by a qualitative index fuzzy quantization method to be taken as the target (multi-index) and multi-scheme optimization decision. The symbol system in the priority ordering of the map symbols also has similar step hierarchy membership, and is similar to the structure of grouping according to primary and secondary and dominating relations in the analytic hierarchy process, so that the analytic hierarchy process is applied to the calculation of the symbol weight value.
The existing map symbol system can not combine the properties of the symbol if depending on a method of artificial subjective weighting, and the objectivity of the weighted value is insufficient and the randomness is strong. And by means of hierarchical analysis method with similar structure, the workload of the whole process is huge, the differentiation granularity of the map symbol weight is reduced along with the increase of symbols in subclasses within the normalized given distribution threshold, and the semantic information of the symbols cannot be fully utilized.
Disclosure of Invention
In view of the above, the invention provides a method and a system for improving the weighting objectivity of a map symbol more objectively and reasonably by combining the self semantic and geometric factors of the map symbol.
A method for improving weighting objectivity of map symbols comprises the following steps:
s1, first-layer classification: dividing several element types of point symbols, linear symbols and surface symbols under the map elements according to the basic element types contained in the map elements; constructing a judgment matrix by combining an Analytic Hierarchy Process (AHP) and constructing a scale table according to the judgment matrix by combining the AHP, and constructing a judgment matrix of a first layer and geometric sense sequencing in first-layer classification by comparing importance degrees between any two elements to obtain a corresponding scale value;
s2, second-layer classification: on the basis of the first layer classification in step S1, the construction of the judgment matrix of each element under the dot symbol, the judgment matrix of each element under the linear symbol, and the judgment matrix of each element under the planar symbol is completed, and the semantic important program sorting of each element under the dot symbol, the linear symbol, and the planar symbol is completed;
s3, based on the judgment matrix of each element under the point symbol, the judgment matrix of each element under the linear symbol and the judgment matrix of each element under the planar symbol, the constructed matrix is based on the formula
Figure BDA0001476371810000021
Performing single-layer sequencing and consistency check, jumping to the step S4 if the single-layer sequencing and the consistency check pass, and jumping to the step S2 to reconstruct a judgment matrix if the single-layer sequencing and the consistency check do not pass; lambda represents the maximum characteristic root of the judgment matrix, and n represents the number of elements under different types of symbols;
s4, according to the weight calculation method in the analytic hierarchy process, multiplying the weight of each map element by the corresponding weight of the upper layer, and calculating the weight value of each subdivided map element relative to all map symbols; and according to the formula
Figure BDA0001476371810000022
Performing total hierarchical sequencing and consistency check, and if the total hierarchical sequencing and the consistency check do not pass the check, jumping to the step S1 to reconstruct a judgment matrix; CI represents the consistency check index in S3, and RI represents the average random consistency index;
s5, for some elements still having the second-level classification, using an exponential function y-KxCalculating the model, and determining the weight of the continuously subdivided map symbols; wherein the exponential function y is KxIn the model, k is a total hierarchical ranking weighted value obtained by the element type elements by using an analytic hierarchy process, and x is a user-defined weighted value which is uniformly valued between 0 and 1 according to the number of the elements.
In the method for improving the weighting objectivity of the map symbols,
the geometric meaning of each type of symbols in the first-layer classification is ordered into point symbols, linear symbols and surface symbols.
In the method for improving the weighting objectivity of the map symbols,
the construction method of the judgment matrix of each element under the point symbol is the same as that of the judgment matrix of the first layer; the semantic importance degree of each element under the point symbol is ranked as measurement control > boundary > residential area > traffic > pipeline > water system > landform > soil vegetation.
In the method for improving the weighting objectivity of the map symbols,
the construction method of the judgment matrix of each element under the linear symbol is the same as that of the judgment matrix of the first layer; the semantic importance degrees of the elements under the linear symbols are ranked as the boundary > residential place > traffic > pipeline > water system > landform.
In the method for improving the weighting objectivity of the map symbols,
the construction method of the judgment matrix of each element under the planar symbol is the same as that of the judgment matrix of the first layer; the semantic importance degrees of all elements under the planar symbols are ranked as residential area > water system > landform > soil vegetation.
In the method for improving the weighting objectivity of the map symbols,
the border element types under the linear symbols are subdivided into country level, provincial level, special administrative district, ground level, county level, special area, development area, protection area and natural cultural protection area, and the importance degrees of the border element types are weakened in sequence; according to the same exponential function model, traffic elements under the linear symbols are subdivided into several types of standard rails, narrow rails, high speeds, national roads, provincial roads, special highways, county and rural roads, rail traffic, main roads, secondary main roads, tractor-ploughing roads, rural villages, paths and seasonal roads.
The invention also provides a system for improving the weighting objectivity of the map symbols, which comprises the following units:
the first-layer matrix construction unit is used for first-layer classification: dividing several element types of point symbols, linear symbols and surface symbols under the map elements according to the basic element types contained in the map elements; constructing a judgment matrix by combining an Analytic Hierarchy Process (AHP) and constructing a scale table according to the judgment matrix by combining the AHP, and constructing a judgment matrix of a first layer and geometric sense sequencing in first-layer classification by comparing importance degrees between any two elements to obtain a corresponding scale value;
a second layer matrix construction unit for second layer classification: on the basis of classification of a first layer in a first-layer matrix construction unit, construction of a judgment matrix of each element under a point symbol, a judgment matrix of each element under a linear symbol and a judgment matrix of each element under a planar symbol is finished respectively, and semantic important program sequencing of each element under the point symbol, the linear symbol and the planar symbol is finished respectively;
a first check unit for calculating a formula for the matrix based on the judgment matrix of each element under the dot symbol, the judgment matrix of each element under the linear symbol, and the judgment matrix of each element under the planar symbol
Figure BDA0001476371810000041
Performing single-layer sequencing and consistency check, jumping to a second check unit if the single-layer sequencing and the consistency check pass the check, and jumping to a second-layer matrix construction unit to reconstruct a judgment matrix if the single-layer sequencing and the consistency check do not pass the consistency check; lambda represents the maximum characteristic root of the judgment matrix, and n represents the number of elements under different types of symbols;
the second verification unit is used for multiplying the weight of each map element by the corresponding weight of the upper layer of the map element according to a weight calculation method in the analytic hierarchy process, and calculating the weight value of each subdivided map element relative to all map symbols; and according to the formula
Figure BDA0001476371810000042
Performing total hierarchical sequencing and consistency check, and if the total hierarchical sequencing and consistency check does not pass the check, skipping to a first-layer matrix construction unit to reconstruct a judgment matrix; CI represents the consistency check index in S3, and RI represents the average random consistency index;
weight calculation unitFor some elements still classified in the second layer, an exponential function y is used, KxCalculating the model, and determining the weight of the continuously subdivided map symbols; wherein the exponential function y is KxIn the model, k is a total hierarchical ranking weighted value obtained by the element type elements by using an analytic hierarchy process, and x is a user-defined weighted value which is uniformly valued between 0 and 1 according to the number of the elements.
In the system for improving the weighting objectivity of the map symbols,
the geometric meaning of each type of symbols in the first-layer classification is ordered into point symbols, linear symbols and surface symbols.
In the system for improving the weighting objectivity of the map symbols,
the construction method of the judgment matrix of each element under the point symbol is the same as that of the judgment matrix of the first layer; sequencing semantic importance degrees of all elements under the point symbols into measurement control, a boundary, a residential area, a traffic line, a water system, a landform and a soil vegetation;
the construction method of the judgment matrix of each element under the linear symbol is the same as that of the judgment matrix of the first layer; the semantic importance degrees of all elements under the linear symbols are sorted into a boundary, a residential place, a traffic, a pipeline, a water system and a landform;
the construction method of the judgment matrix of each element under the planar symbol is the same as that of the judgment matrix of the first layer; the semantic importance degrees of all elements under the planar symbols are ranked as residential area > water system > landform > soil vegetation.
In the system for improving the weighting objectivity of the map symbols,
the border element types under the linear symbols are subdivided into country level, provincial level, special administrative district, ground level, county level, special area, development area, protection area and natural cultural protection area, and the importance degrees of the border element types are weakened in sequence; according to the same exponential function model, traffic elements under the linear symbols are subdivided into several types of standard rails, narrow rails, high speeds, national roads, provincial roads, special highways, county and rural roads, rail traffic, main roads, secondary main roads, tractor-ploughing roads, rural villages, paths and seasonal roads.
Compared with the prior art, the method and the system for improving the weighting objectivity of the map symbols have the following beneficial effects: on the basis of analysis of a traditional artificial subjective weighting and hierarchical analysis method, the method introduces concepts of semantic weight and geometric weight, comprehensively evaluates the weight of the map symbol, and weights the map symbol in different levels by calculating a comprehensive index. The weight of each symbol of the first layer and the next layer calculates the occupied proportion of each symbol relative to all map symbols according to an analytic hierarchy process, and an exponential function K is adopted for the map symbols which need to be subdivided continuouslyxDetermining weight of subdivided symbols, uniformly taking x value according to the number of the subdivided symbols by using total hierarchical sorting weight value obtained by an analytic hierarchy process, and calculating K by using a formulax(0<x<1. Uniform value) and calculating the weight of each map symbol to be subdivided. The index function is introduced, so that the weighted value assigned to each symbol can be effectively combined with the geometric weight obtained by the analytic hierarchy process and the semantic quantization weight of the subclass to evaluate the weight value of the subdivided subclass symbol. Because the method combines the self semantics and the geometric factors of the map symbols, the obtained weight value is more objective and reasonable.
Drawings
FIG. 1 is a schematic flow diagram of the present invention;
FIG. 2 is a diagram of a map element classification scheme;
FIG. 3AHP determines a matrix scalar table;
FIG. 4 shows a judgment matrix constructed by first-layer points, lines and planes;
FIG. 5 shows a judgment matrix constructed by each element under the dot symbol;
FIG. 6 shows a judgment matrix constructed by the elements under the line symbol;
FIG. 7 shows a judgment matrix constructed by the elements under the surface symbol;
the final result of each symbol weight in the AHP of fig. 8;
FIG. 9 illustrates boundary line subdivision element weight values;
FIG. 10 traffic segmentation each element weight value.
Detailed Description
As shown in fig. 1 to 10, the present invention constructs a judgment matrix of map symbols based on an analytic hierarchy process, thereby obtaining weight values of the map symbols. Using the exponent y-K for symbol types requiring continued subdivisionxThe function assigns values to the corresponding symbols uniformly.
Some element types, such as dot symbols, linear symbols, and planar symbols, are divided under map elements according to the types of basic elements included in the map elements, as shown in fig. 2. And constructing a judgment matrix by combining a layer analysis method, constructing a scale table according to the judgment matrix in the AHP method, wherein the scale table is shown in figure 3, and constructing the judgment matrix of the first layer by comparing the importance degrees between any two elements to obtain a corresponding scale value, wherein the importance degrees of various symbols in the first layer are ordered into point symbols, linear symbols and planar symbols according to the geometrical significance of the importance degrees, and the construction result of the matrix is shown in figure 4.
Based on the classification of the first layer, the elements are further subdivided under the dotted symbols, as shown in fig. 2. The judgment matrix of each element is constructed according to the importance degree of each element under the point symbol, the construction method of the matrix is the same as that of the judgment matrix constructed at the first layer, a scale table is constructed according to the judgment matrix, and the judgment matrix of each element under the point symbol is finally constructed and shown in figure 5 by combining semantic importance degree sequencing measurement control > boundary > residential area > traffic > pipeline > water system > landform > soil texture vegetation.
Dividing the structure of each element under the linear symbol as shown in figure 2, constructing a judgment matrix of each element, wherein the method is the same as the construction method of the judgment matrix under the dotted symbol, combining the semantic importance degree sequence of each element under the linear symbol, and drawing corresponding values according to the gradually enhanced contrast in the judgment matrix scale table of the landform, namely, the boundary, the residential area, the traffic, the pipeline, the water system and the landform as shown in figure 6.
The structure diagram of each element under the surface symbol is shown in fig. 2, the judgment matrix of each element under the surface symbol is constructed by the same method as the construction method of the judgment matrix under the point symbol and the linear symbol, the semantic importance degree of each element under the linear symbol is ranked as residential area > water system > landform > soil vegetation, and the finally constructed element matrix is shown in fig. 7.
The construction of the judgment matrix in the analytic hierarchy process needs to pass consistency check, so that the judgment matrix of each layer is well established according to a formula
Figure BDA0001476371810000061
And performing single-layer sequencing and consistency check on the constructed matrix, wherein the larger the CI is, the more serious the inconsistency is, and the closer the CI is to 0, the better consistency is achieved. And if the consistency check is not passed, reconstructing a judgment matrix. λ represents the maximum characteristic root of the judgment matrix, and n represents the number of elements under different types of symbols.
According to the weight calculation method in the analytic hierarchy process, the weight of each map element is multiplied by the corresponding weight of the upper layer, the weight value w of each subdivided map element relative to all map symbols is calculated and is shown in figure 8, the total hierarchical ranking and consistency check are carried out, and a consistency ratio formula is introduced
Figure BDA0001476371810000071
When the consistency ratio CR is less than 0.1, the degree of inconsistency of the judgment matrix is considered to be within the allowable range, and satisfactory consistency is obtained, and the consistency test is passed. Its normalized feature vector can be used as a weight vector, otherwise it is reconstructed into a comparison matrix. CI represents the consistency check index in the previous step, and RI represents the average random consistency index.
For some elements still having secondary classification, an exponential function K is usedxThe model performs calculation to determine the weight of the map symbols which are continuously subdivided. Wherein the exponential function y is KxIn the model, k is a total hierarchical ranking weighted value obtained by the element type elements by using an analytic hierarchy process, and x is a user-defined weighted value which is uniformly valued between 0 and 1 according to the number of the elements. For example, the types of the border elements under the linear symbols are further divided into country level, provincial level, special administrative district, district level, county level, special region, development district, protection district, and natural cultural protection district, the importance levels thereof are sequentially reduced, and the classification result is shown in fig. 9. Traffic under linear symbols according to the same exponential function modelThe elements are subdivided into several types of standard rails, narrow rails, high speed, national roads, provincial roads, special highways, county and rural roads, rail traffic, main roads, secondary roads, tractor-ploughing roads, rural villages, paths and seasonal roads, as shown in fig. 10.
It is understood that various other changes and modifications may be made by those skilled in the art based on the technical idea of the present invention, and all such changes and modifications should fall within the protective scope of the claims of the present invention.

Claims (10)

1. A method for improving weighting objectivity of map symbols is characterized by comprising the following steps:
s1, first-layer classification: dividing several element types of point symbols, linear symbols and surface symbols under the map elements according to the basic element types contained in the map elements; constructing a judgment matrix by combining an Analytic Hierarchy Process (AHP) and constructing a scale table according to the judgment matrix by combining the AHP, and constructing a judgment matrix of a first layer and geometric sense sequencing in first-layer classification by comparing importance degrees between any two elements to obtain a corresponding scale value;
s2, second-layer classification: on the basis of the first layer classification in step S1, the construction of the judgment matrix of each element under the dot symbol, the judgment matrix of each element under the linear symbol, and the judgment matrix of each element under the planar symbol is completed, and the semantic important program sorting of each element under the dot symbol, the linear symbol, and the planar symbol is completed;
s3, based on the judgment matrix of each element under the point symbol, the judgment matrix of each element under the linear symbol and the judgment matrix of each element under the planar symbol, the constructed matrix is based on the formula
Figure FDA0001476371800000011
Performing single-layer sequencing and consistency check, jumping to the step S4 if the single-layer sequencing and the consistency check pass, and jumping to the step S2 to reconstruct a judgment matrix if the single-layer sequencing and the consistency check do not pass; lambda represents the maximum characteristic root of the judgment matrix, and n represents the number of elements under different types of symbols;
s4, according to the weight calculation method in the analytic hierarchy process, multiplying the weight of each map element by the corresponding weight of the upper layer, and calculating the weight value of each subdivided map element relative to all map symbols; and according to the formula
Figure FDA0001476371800000012
Performing total hierarchical sequencing and consistency check, and if the total hierarchical sequencing and the consistency check do not pass the check, jumping to the step S1 to reconstruct a judgment matrix; CI represents the consistency check index in S3, and RI represents the average random consistency index;
s5, for some elements still having the second-level classification, using an exponential function y-KxCalculating the model, and determining the weight of the continuously subdivided map symbols; wherein the exponential function y is KxIn the model, k is a total hierarchical ranking weighted value obtained by the element type elements by using an analytic hierarchy process, and x is a user-defined weighted value which is uniformly valued between 0 and 1 according to the number of the elements.
2. The method according to claim 1, wherein the map symbol weighting objectivity is increased,
the geometric meaning of each type of symbols in the first-layer classification is ordered into point symbols, linear symbols and surface symbols.
3. The method according to claim 1, wherein the map symbol weighting objectivity is increased,
the construction method of the judgment matrix of each element under the point symbol is the same as that of the judgment matrix of the first layer; the semantic importance degree of each element under the point symbol is ranked as measurement control > boundary > residential area > traffic > pipeline > water system > landform > soil vegetation.
4. The method according to claim 1, wherein the map symbol weighting objectivity is increased,
the construction method of the judgment matrix of each element under the linear symbol is the same as that of the judgment matrix of the first layer; the semantic importance degrees of the elements under the linear symbols are ranked as the boundary > residential place > traffic > pipeline > water system > landform.
5. The method according to claim 1, wherein the map symbol weighting objectivity is increased,
the construction method of the judgment matrix of each element under the planar symbol is the same as that of the judgment matrix of the first layer; the semantic importance degrees of all elements under the planar symbols are ranked as residential area > water system > landform > soil vegetation.
6. The method according to claim 4, wherein the map symbol weighting objectivity is increased,
the border element types under the linear symbols are subdivided into country level, provincial level, special administrative district, ground level, county level, special area, development area, protection area and natural cultural protection area, and the importance degrees of the border element types are weakened in sequence; according to the same exponential function model, traffic elements under the linear symbols are subdivided into several types of standard rails, narrow rails, high speeds, national roads, provincial roads, special highways, county and rural roads, rail traffic, main roads, secondary main roads, tractor-ploughing roads, rural villages, paths and seasonal roads.
7. A system for improving weighting objectivity of map symbols is characterized by comprising the following units:
the first-layer matrix construction unit is used for first-layer classification: dividing several element types of point symbols, linear symbols and surface symbols under the map elements according to the basic element types contained in the map elements; constructing a judgment matrix by combining an Analytic Hierarchy Process (AHP) and constructing a scale table according to the judgment matrix by combining the AHP, and constructing a judgment matrix of a first layer and geometric sense sequencing in first-layer classification by comparing importance degrees between any two elements to obtain a corresponding scale value;
a second layer matrix construction unit for second layer classification: on the basis of classification of a first layer in a first-layer matrix construction unit, construction of a judgment matrix of each element under a point symbol, a judgment matrix of each element under a linear symbol and a judgment matrix of each element under a planar symbol is finished respectively, and semantic important program sequencing of each element under the point symbol, the linear symbol and the planar symbol is finished respectively;
a first check unit for calculating a formula for the matrix based on the judgment matrix of each element under the dot symbol, the judgment matrix of each element under the linear symbol, and the judgment matrix of each element under the planar symbol
Figure FDA0001476371800000031
Performing single-layer sequencing and consistency check, jumping to a second check unit if the single-layer sequencing and the consistency check pass the check, and jumping to a second-layer matrix construction unit to reconstruct a judgment matrix if the single-layer sequencing and the consistency check do not pass the consistency check;
the second verification unit is used for multiplying the weight of each map element by the corresponding weight of the upper layer of the map element according to a weight calculation method in the analytic hierarchy process, and calculating the weight value of each subdivided map element relative to all map symbols; and according to the formula
Figure FDA0001476371800000032
Performing total hierarchical sequencing and consistency check, and if the total hierarchical sequencing and consistency check does not pass the check, skipping to a first-layer matrix construction unit to reconstruct a judgment matrix;
a weight calculation unit for calculating the weight of the elements still having the second layer classification by using an exponential function y equal to KxCalculating the model, and determining the weight of the continuously subdivided map symbols; wherein, the weight value of the corresponding element on the upper layer of k, and the value of X is evenly selected between 0 and 1 according to the number of the elements.
8. The system according to claim 7, wherein the map symbol weighting objectivity is increased,
the geometric meaning of each type of symbols in the first-layer classification is ordered into point symbols, linear symbols and surface symbols.
9. The system according to claim 7, wherein the map symbol weighting objectivity is increased,
the construction method of the judgment matrix of each element under the point symbol is the same as that of the judgment matrix of the first layer; sequencing semantic importance degrees of all elements under the point symbols into measurement control, a boundary, a residential area, a traffic line, a water system, a landform and a soil vegetation;
the construction method of the judgment matrix of each element under the linear symbol is the same as that of the judgment matrix of the first layer; the semantic importance degrees of all elements under the linear symbols are sorted into a boundary, a residential place, a traffic, a pipeline, a water system and a landform;
the construction method of the judgment matrix of each element under the planar symbol is the same as that of the judgment matrix of the first layer; the semantic importance degrees of all elements under the planar symbols are ranked as residential area > water system > landform > soil vegetation.
10. The system for improving objectivity in weighting a map symbol of claim 9, wherein,
the border element types under the linear symbols are subdivided into country level, provincial level, special administrative district, ground level, county level, special area, development area, protection area and natural cultural protection area, and the importance degrees of the border element types are weakened in sequence; according to the same exponential function model, traffic elements under the linear symbols are subdivided into several types of standard rails, narrow rails, high speeds, national roads, provincial roads, special highways, county and rural roads, rail traffic, main roads, secondary main roads, tractor-ploughing roads, rural villages, paths and seasonal roads.
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