Method for measuring complexity of mountain road distribution under support of weighted joint entropy

Abstract

The invention discloses a method for measuring the complexity of mountain road distribution under the support of weighted joint entropy, and the method comprises the following steps: firstly, carrying out the division of space units based on a mountain region range, and obtaining the road distribution data in each unit through space intersection; secondly, calculating length information of each road section in different units, and defining a direction measurement rule to complete calculation of direction information of each road section; thirdly, calculating the road density in each unit, and performing overall normalization on the road density values of all the units to obtain a density weight; fourthly, calculating the joint entropy of the road based on the length sequence and direction sequence data corresponding to all the road sections in each space unit; and 5, based on the density weight and the joint entropy in each grid unit, calculating a weighted joint entropy to realize the measurement of the road distribution complexity. According to the method, the road scale and the road structure are respectively represented by using the density and the joint entropy for meeting the requirements of mountain traffic development, the concept and the method of weighted joint entropy are introduced, and the length-direction joint entropy of density weighting is provided to realize the measurement of the complexity of mountain area road distribution. Actual demonstration shows that the method can fully combine scale information and structure information of road distribution, and calculates the weighted joint entropy to realize measurement of the spatial distribution complexity of the mountain road. The invention provides a more visual and accurate road distribution complexity measurement method for the aspects of mountain traffic layout and planning, mountain economic development, mountain traffic vulnerability evaluation and the like.

Description

Technical Field

[0001] The present invention relates to the fields of geographic information technology, traffic network management and planning, and in particular to a method for measuring the complexity of mountain road distribution under the support of weighted joint entropy. Background Art

[0002] Mountains are the most common landform type on Earth. For example, in China, mountainous areas account for more than 60% of the country's land area. In addition, mountainous areas are also home to a large population, and mountain transportation plays a very important role in the transportation of mountain populations. Therefore, improving and optimizing the spatial layout of mountain transportation networks is of great significance for improving the travel of mountain populations and promoting economic development in mountainous areas. Among them, the measurement of mountain road distribution complexity is an important aspect of clarifying the current status of mountain transportation, and can fully support the reasonable spatial layout and optimization of mountain transportation networks.

[0003] The widespread use of open source geographic information technology can provide complete and accurate spatial distribution data of traffic road networks, providing data support for the study of the spatial structure of mountain traffic road networks at a large scale. At the same time, geographic information spatial analysis techniques, such as spatial intersection and spatial statistics, can effectively extract the spatial distribution characteristics of traffic road network data. On this basis, combined with data analysis techniques in computer science, mathematics and other disciplines, such as information entropy, it can effectively realize the in-depth detection and analysis of the spatial distribution characteristics of traffic road networks. Existing methods mainly measure the complexity of road distribution from a single aspect of the road, such as length information or direction information, and lack a method that can simultaneously use multiple aspects of road distribution information to measure and analyze the complexity of road distribution. Based on this, this study adopts the idea of ​​joint entropy to construct a method system for extracting spatial distribution characteristics and measuring complexity of mountain roads. Summary of the invention

[0004] The present invention is aimed at the management and optimization of traffic road networks in mountainous areas, and provides a method for measuring the complexity of road distribution in mountainous areas under the support of weighted joint entropy. The technical problems solved include the calculation of road distribution weights in mountainous sub-units and the calculation of road distribution joint entropy, and finally a weighted joint entropy is constructed to realize the measurement of road network distribution complexity.

[0005] To achieve the above object, the present invention adopts the following technical solutions:

[0006] A method for measuring the complexity of road distribution in mountainous areas supported by weighted joint entropy includes five parts: division of mountainous spatial units and extraction of road network data of different units, length calculation of all road sections in different units and definition and calculation of directions, calculation of road density and weight generation in different units, calculation of joint entropy of length and direction of road sections in different units, calculation of weighted joint entropy of road density of all units and measurement of road distribution complexity.

[0007] The steps of mountain spatial unit division and road network data extraction of different units are as follows:

[0008] S1: According to the spatial boundaries of the mountainous area, different spatial division schemes are formulated to generate basic units and complete the division of mountainous spatial units;

[0009] S2: extracting the road distribution data corresponding to each spatial unit through the operation of superimposing and intersecting the road data and the spatial unit data;

[0010] The length calculation and direction definition and calculation of all road sections in different units are as follows:

[0011] S3: For all road sections in each unit, calculate the length of the road section;

[0012] S4: Taking the east-west direction as the reference direction, according to the relationship between the direction of the road section and the reference direction, the counterclockwise angle between the reference direction and the direction of the road section is defined as the measurement value of the direction of the road section;

[0013] S4: for all road segments in each unit, calculate the direction of the road segment according to the above definition;

[0014] The road density calculation and weight generation in different units are as follows:

[0015] S6: Calculate the total length of the roads in each unit and the total area of ​​the unit, and further calculate the road density in each unit;

[0016] S7: Summarize the road densities of all units, and map the density values ​​to the interval of 0 to 1 through normalization processing. The normalized road density value is the weight;

[0017] The joint entropy calculation in the length direction of the road sections in different units is as follows:

[0018] S8: Matching the section length information and the section direction information in each unit according to the section number, and regrouping the section length sequence and the direction sequence according to fixed intervals;

[0019] S9: Based on the grouped section length sequence and direction sequence, the joint entropy of the two variables is calculated to obtain the joint entropy of the section length and direction;

[0020] The calculation of the weighted joint entropy of all unit road densities and the measurement of road distribution complexity are as follows:

[0021] S10: Multiply the road density weight in each unit by the joint entropy in the length direction to obtain the weighted joint entropy of road density, which can illustrate the complexity of road distribution in the unit. The higher the weighted joint entropy, the more complex the road network distribution. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only schematic diagrams of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying creative work.

[0023] Figure 1 Invention technology flow chart;

[0024] Figure 2 A schematic diagram of direction definition and calculation provided by the present invention;

[0025] Figure 3 The road network and spatial unit distribution map provided by the present invention;

[0026] Figure 4 The frequency distribution histogram of the joint entropy provided by the present invention;

[0027] Figure 5 The weighted joint entropy spatial distribution diagram provided by the present invention; DETAILED DESCRIPTION

[0028] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0029] The following is the division of mountain space units and the extraction of road network data in different units:

[0030] Step 1: According to the spatial distribution of mountainous areas, different plans are formulated for spatial division to generate basic spatial units, such as administrative boundaries, regular quadrilaterals, and regular hexagons.

[0031] Step 2: Spatially intersect the spatial units within the mountainous area with the road distribution data to obtain the road distribution data corresponding to different grids.

[0032] The following is the length calculation of all road segments in different units and the direction definition and calculation part:

[0033] Step 1: For each road vector line feature data corresponding to each spatial unit, the road between two vertex points in the line feature is regarded as a road segment, and the length of each road segment is calculated respectively.

[0034] Step 2: Take the east-west direction as the reference direction. According to the intersection relationship between the direction of the road section and the reference direction, define the counterclockwise angle between the reference direction and the direction of the road section as the measurement value of the direction of the road section. The definition of direction is as shown in the attached figure. Figure 2 shown.

[0035] Step 3: For each road vector line feature data corresponding to each spatial unit, the road between two vertex points in the line feature is regarded as a road segment, and the direction measurement value of each road segment is calculated respectively.

[0036] The following is the calculation of road density and weight generation in different units:

[0037] Step 1: Calculate the road density in each grid based on the length information of all road sections in each grid and the area of ​​the spatial unit. The calculation formula is as follows:

[0038]

[0039] Among them, RD k represents the road density in the kth unit, represents the length of the i-th road segment in the k-th unit, A k represents the total area of ​​the kth unit.

[0040] Step 2: Since the road density in some local areas may be abnormally high, in order to ensure normal subsequent calculations, the sum of the average density and twice the density standard deviation is set as the threshold. The road density corresponding to the spatial units with density higher than this threshold is reset to this threshold to ensure that the subsequent calculation process is not affected by abnormally high values.

[0041] Step 3: Summarize the road density of all units in the mountain area, and then map the road density value to the range of 0 to 1 through normalization. The normalization formula is as follows:

[0042]

[0043] Among them, W k is the road density weight corresponding to the kth unit, min(RD) represents the minimum value of all road density values, and max(RD) represents the maximum value of all road density values.

[0044] The following is the joint entropy calculation part of the road length direction in different units:

[0045] Step 1: Group all the length values ​​and direction values ​​of each unit according to fixed intervals to facilitate the calculation of the joint probability distribution of this part of the statistics. The interval of the length grouping is recorded as ΔL, and the interval of the angle grouping is recorded as ΔS.

[0046] Step 2: Based on the regrouped length sequence L and direction sequence S, calculate the joint information entropy of length and direction, which can reflect the common information of these two road features. The calculation formula is as follows:

[0047]

[0048] Among them, H(L,S) k represents the joint entropy of the k-th unit road length and direction, L k With S k They represent the length sequence and direction sequence after regrouping respectively. p(l,s) represents the joint probability distribution of L and S within the spatial unit, that is, the probability that the two take different values ​​at the same time. The joint probability of a specific combination is obtained by counting the combinations and dividing them by the total amount.

[0049] The following is the calculation of the weighted joint entropy of all unit road densities and the measurement of road distribution complexity:

[0050] Step 1: Calculate the weighted joint entropy for each spatial unit’s road density weight and the joint entropy of the road section length and direction. The calculation formula is as follows:

[0051] WH k =W k ×H(L,S) k

[0052] Among them, WH k represents the weighted joint entropy corresponding to the kth unit, W k is the road density weight corresponding to the kth unit, H(L,S) k represents the joint entropy of the length and direction of the k-th unit road.

[0053] Step 2: The weighted joint entropy corresponding to each unit is added as the corresponding field in turn and displayed in spatial mapping, which can be used to measure the spatial distribution characteristics of the complexity of mountain road distribution.

[0054] Finally, the Qinling-Dabie Mountain region in China was selected as a case study area. The traffic network was derived from Open StreetMap. The method of this patent was used to measure and analyze the complexity of road distribution. The spatial unit was divided into regular quadrilaterals with a size of 10km×10km. The interval ΔL of the length grouping was set to 10m, and the interval ΔS of the angle grouping was set to 3 degrees. Figure 3The distribution of road networks and spatial units in the case area is shown in the Appendix. Figure 4 The frequency distribution histogram of the joint entropy of all cells in the case area is shown. Figure 5 The spatial distribution map of weighted joint entropy in the case area is shown, which can measure the spatial regularity of the complexity of road distribution in the area.

[0055] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined by the scheme may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown in the scheme, but will conform to the widest scope consistent with the principles and novel features disclosed in the present invention.

Claims

1. A method for measuring the complexity of road distribution in mountainous areas supported by weighted joint entropy, including the division of mountainous spatial units and the extraction of road network data in different units, the calculation of the lengths of all road sections in different units and the definition and calculation of their directions, the calculation of road density and weight generation in different units, the calculation of joint entropy of length and direction of road sections in different units, the calculation of weighted joint entropy of road density in all units and the measurement of road distribution complexity. The steps of mountain spatial unit division and road network data extraction of different units are as follows: S1: According to the spatial boundaries of the mountainous area, different spatial division schemes are formulated to generate basic units and complete the division of mountainous spatial units; S2: extracting the road distribution data corresponding to each spatial unit through the operation of superimposing and intersecting the road data and the spatial unit data; The length calculation and direction definition and calculation of all road sections in different units are as follows: S3: For all road sections in each unit, calculate the length of the road section; S4: Taking the east-west direction as the reference direction, according to the relationship between the direction of the road section and the reference direction, the counterclockwise angle between the reference direction and the direction of the road section is defined as the measurement value of the direction of the road section; S4: for all road segments in each unit, calculate the direction of the road segment according to the above definition; The road density calculation and weight generation in different units are as follows: S6: Calculate the total length of the roads in each unit and the total area of ​​the unit, and further calculate the road density in each unit; S7: Summarize the road densities of all units, and map the density values ​​to the interval of 0 to 1 through normalization processing. The normalized road density value is the weight; The joint entropy calculation in the length direction of the road sections in different units is as follows: S8: Matching the section length information and the section direction information in each unit according to the section number, and regrouping the section length sequence and the direction sequence according to fixed intervals; S9: Based on the grouped section length sequence and direction sequence, the joint entropy of the two variables is calculated to obtain the joint entropy of the section length and direction; The calculation of the weighted joint entropy of all unit road densities and the measurement of road distribution complexity are as follows: S10: Multiply the road density weight in each unit by the joint entropy in the length direction to obtain the weighted joint entropy of road density, which can illustrate the complexity of road distribution in the unit. The higher the weighted joint entropy, the more complex the road network distribution.

2. The road joint entropy calculation according to claim 1 is characterized in that, in steps S8 to S9, the length information and direction information of the road section are taken into consideration at the same time, and the joint entropy is calculated based on the combination of length and direction, thereby realizing the measurement of the total amount of information contained in the two types of information, length and direction.

3. The weighted joint entropy calculation and road complexity measurement according to claim 1 has the advantage of using density as a weight coefficient to measure the scale of the road, and using the joint entropy of length and direction to measure the structure of the road. It comprehensively measures the complexity of road distribution from two aspects: road scale and road structure, and supports spatial analysis of road distribution complexity.

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