CN113553391A - Port abdominal region dividing method based on GIS technology - Google Patents
Port abdominal region dividing method based on GIS technology Download PDFInfo
- Publication number
- CN113553391A CN113553391A CN202110881885.0A CN202110881885A CN113553391A CN 113553391 A CN113553391 A CN 113553391A CN 202110881885 A CN202110881885 A CN 202110881885A CN 113553391 A CN113553391 A CN 113553391A
- Authority
- CN
- China
- Prior art keywords
- port
- region
- transportation
- cost
- abdominal region
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 230000003187 abdominal effect Effects 0.000 title claims abstract description 105
- 238000000034 method Methods 0.000 title claims abstract description 47
- 238000005516 engineering process Methods 0.000 title claims abstract description 23
- 238000004458 analytical method Methods 0.000 claims abstract description 22
- 238000000513 principal component analysis Methods 0.000 claims abstract description 20
- 238000003012 network analysis Methods 0.000 claims abstract description 19
- 239000011159 matrix material Substances 0.000 claims description 23
- 238000004364 calculation method Methods 0.000 claims description 17
- 238000010276 construction Methods 0.000 claims description 13
- 238000011161 development Methods 0.000 claims description 9
- 230000002860 competitive effect Effects 0.000 claims description 8
- 238000012545 processing Methods 0.000 claims description 7
- 238000012216 screening Methods 0.000 claims description 5
- 230000035699 permeability Effects 0.000 claims description 4
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 4
- 238000006243 chemical reaction Methods 0.000 claims description 2
- FGXWKSZFVQUSTL-UHFFFAOYSA-N domperidone Chemical compound C12=CC=CC=C2NC(=O)N1CCCN(CC1)CCC1N1C2=CC=C(Cl)C=C2NC1=O FGXWKSZFVQUSTL-UHFFFAOYSA-N 0.000 claims description 2
- 238000011160 research Methods 0.000 description 15
- 238000011156 evaluation Methods 0.000 description 9
- 210000001015 abdomen Anatomy 0.000 description 7
- 230000001186 cumulative effect Effects 0.000 description 7
- 238000010586 diagram Methods 0.000 description 5
- 238000012986 modification Methods 0.000 description 3
- 230000004048 modification Effects 0.000 description 3
- 238000009826 distribution Methods 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- WEFHSZAZNMEWKJ-KEDVMYETSA-N (6Z,8E)-undeca-6,8,10-trien-2-one (6E,8E)-undeca-6,8,10-trien-2-one (6Z,8E)-undeca-6,8,10-trien-3-one (6E,8E)-undeca-6,8,10-trien-3-one (6Z,8E)-undeca-6,8,10-trien-4-one (6E,8E)-undeca-6,8,10-trien-4-one Chemical compound CCCC(=O)C\C=C\C=C\C=C.CCCC(=O)C\C=C/C=C/C=C.CCC(=O)CC\C=C\C=C\C=C.CCC(=O)CC\C=C/C=C/C=C.CC(=O)CCC\C=C\C=C\C=C.CC(=O)CCC\C=C/C=C/C=C WEFHSZAZNMEWKJ-KEDVMYETSA-N 0.000 description 1
- 241000116713 Ferula gummosa Species 0.000 description 1
- 229920013688 Karilon Polymers 0.000 description 1
- 240000007594 Oryza sativa Species 0.000 description 1
- 235000007164 Oryza sativa Nutrition 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 239000004864 galbanum Substances 0.000 description 1
- PCHJSUWPFVWCPO-UHFFFAOYSA-N gold Chemical compound [Au] PCHJSUWPFVWCPO-UHFFFAOYSA-N 0.000 description 1
- 239000010931 gold Substances 0.000 description 1
- 229910052737 gold Inorganic materials 0.000 description 1
- 230000008676 import Effects 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 230000003211 malignant effect Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 238000012847 principal component analysis method Methods 0.000 description 1
- 230000001737 promoting effect Effects 0.000 description 1
- 230000005855 radiation Effects 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 238000007634 remodeling Methods 0.000 description 1
- 238000013468 resource allocation Methods 0.000 description 1
- 235000009566 rice Nutrition 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/29—Geographical information databases
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/213—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods
- G06F18/2135—Feature extraction, e.g. by transforming the feature space; Summarisation; Mappings, e.g. subspace methods based on approximation criteria, e.g. principal component analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
-
- G06Q50/40—
Abstract
The invention discloses a GIS technology-based port abdominal region dividing method, which comprises the following steps: acquiring relevant index data of all ports in a target area; PCA principal component analysis is carried out on the related index data, and the port intensity A of each port is calculatedj(ii) a Respectively constructing a grid analysis layer and a network analysis layer of multi-type intermodal transportation in a road mode in a target area, and comprehensively obtaining a transportation cost distance D from any region i to any port jij(ii) a The port strength A of each portjAnd a transportation cost distance D from any region i to any port jijSubstituting the probability into a Huff model divided based on the abdominal region to obtain the probability that the owner of the area i selects to the port j under the optimal condition of the transportation cost;and substituting the probability into a Cullinane competition abdominal region model, and outputting the division result of each port abdominal region. The method realizes the refined and modernized division of the port abdominal region, and has strong practicability and wide application.
Description
Technical Field
The invention relates to the technical field of port planning, in particular to a GIS (geographic information system) technology-based port abdominal region dividing method.
Background
Nowadays, a port group as a portal of the import and export of regional trade is a basic supporting and remodeling force of the spatial structure of a region and even a country, and has an important promoting effect on the development of regional economy. The development of the port group inevitably brings competition of the source of goods of ports in the region, and the essence is competition of economy and abdominal land. Under the background, the method for determining the abdominal development status, the abdominal range and the abdominal competition relationship of the port provides important support for strategic decision of port investors, and provides important reference for reasonable planning and construction of port groups, reduction of port malignant competition and optimization of capital and social resource allocation.
The existing port abdominal region division theory is based on the radiation angle of the influence of the port on the region, and scientifically defines the abdominal region range to a certain extent. However, with the continuous update of the functions of the ports and the complication of the influence factors of abdominal region division, the fierce competition of the ports puts forward more refined requirements for the definition of the abdominal region range; the continuous improvement of multi-mode logistics systems such as highway-railway combined transportation, river-sea combined transportation and the like also has new important influence on the division of port abdominal areas; the characteristic of high concentration of ports in the port group domain leads to the generation of competition abdominal areas with complex competition relations.
Therefore, on the basis of the existing port abdominal region division method, how to provide a port abdominal region division method based on the GIS technology to meet the modernized port group abdominal region division requirement and realize more refined port abdominal region division becomes a problem to be solved by technical personnel in the field.
Disclosure of Invention
In view of the above problems, the present invention provides a method for dividing harbour belly based on GIS technology, which solves at least some of the above technical problems, and can realize refined and modernized harbour belly division.
The embodiment of the invention provides a GIS technology-based port abdominal region dividing method, which comprises the following steps:
s1, acquiring relevant index data of all ports in the target area; carrying out PCA principal component analysis on the related index data, and calculating the port intensity A of each portj；
S2, respectively constructing a grid analysis layer and a multi-type intermodal network analysis layer in the road mode in the target area, and comprehensively obtaining the transportation cost distance D from any region i to any port jij；
S3, comparing the port strength A of each portjAnd said transportation cost distance D from any region i to any port jijSubstituting the data into a Huff model based on abdominal region division to obtain the data of the owner of the region i to the port j under the condition of optimal transportation costProbability;
and S4, substituting the probability that the owner of the area i selects the port j under the optimal transportation cost condition into the Cullinane competition abdominal region model, and outputting the division result of each port abdominal region.
Further, the relevant index data in step S1 includes: primary index data and secondary index data; the primary index data includes: port infrastructure conditions and regional trade development level data; the secondary index data includes: cargo throughput, berth number, berth water depth, port informatization level, logistics performance level, resource abundance, trade inclusion degree and government support force data.
Further, in step S1, performing PCA principal component analysis on the related index data to calculate port intensity a of each portjThe method comprises the following steps:
s101, establishing a port intensity matrix according to the relevant index data:
F＝B×C×E (1)
in the formula (1), B is a normalized related index data matrix; c is a component score coefficient matrix; e is a variance contribution rate matrix of the extracted principal component;
s102, carrying out non-negative standardization processing on the port intensity matrix to obtain the port intensity A after standardization of each portj：
In the formula (2), FjIntensity for any port j before standardization; (F)j)minIs the minimum value of all port intensities before standardization; (F)j)maxIs the maximum of all port intensities before standardization.
Further, in step S2, the constructing of the grid analysis layer in the road mode includes:
s201, acquiring road network data in the target area; the road network data includes: traffic network, logistics node, port and administrative division data;
s202, creating a vector layer according to the road network data, and rasterizing the vector layer to obtain a unit raster image;
s203, carrying out cost assignment on the unit grid map, completing construction of a grid analysis layer in a road mode, and obtaining an accumulated cost distance in the road mode.
Further, in step S2, the constructing of the network analysis layer of the multimodal transport includes:
s204, constructing a network data set according to the transportation network and the logistics nodes obtained in the step S201; the transportation network comprises a road transportation mode, a railway transportation mode and a waterway transportation mode;
s205, setting the same transportation mode as the same connectivity group, and setting a plurality of connectivity attributes at the logistics node;
s206, generating an OD cost matrix according to the connectivity group, calculating the network cost distance from the logistics node to the port, and completing the construction of a network analysis layer of the multi-mode intermodal transport;
and S207, obtaining the accumulated cost distance in the multi-mode intermodal mode according to the network analysis layer of the multi-mode intermodal.
Further, in step S203, the unit grid map is subjected to cost assignment, and in step S206, the calculation formula for calculating the network cost distance from the logistics node to the port is:
in the formula (3), value is a unit cost distance in the highway mode; m is the logistics cost of transporting unit freight volume in unit time under different road transportation modes; v is the speed of the truck passing on the current grid.
Further, in the step S2, a transportation cost distance D from any region i to any port j is obtainedijThe method comprises the following steps:
the accumulated cost distance in the road mode obtained in the step S203, andadding the accumulated cost distances in the multimodal transportation mode obtained in the step S207, and outputting the total cost of the goods transportation; screening out the minimum value from the total freight transportation cost to obtain the transportation cost distance D from the region i to the port jij。
Further, in step S3, the Huff model based on the abdominal region division is:
in the formula (4), PijProbability of choosing to port j for the owner of region i; u shapeijSelecting a utility to port j for a shipper in region i; a. thejThe port strength of each port; dijThe distance of the transportation cost from any region i to any port j; n is the number of ports in the target area; beta is the distance attenuation coefficient.
Further, the step S4 includes:
s401, extracting the probability maximum value (P) from the owner of the area i to the port j according to the probability from the owner of the area i to the port j under the condition of the optimal transportation costi)max；
S402, according to the probability maximum value (P)i)maxCalculating the relative difference z of the probability of the shipper selecting port j in the area iij：
In the formula (5), PijProbability of choosing to port j for the owner of region i; (P)i)maxSelecting the probability maximum value from the region i to the port j for the owner;
s403, selecting a relative difference z of the probability of the port j according to the owner of the goods in the area iijAnd comparing the competitive permeability f of the port, and outputting the division result of the exclusive abdominal land and the competitive abdominal land of the port.
Further, the step S403 includes:
s4031, if zijIf the number of the selected ports j is less than or equal to f, the difference of the utility of the selected ports j in the region i is smaller than that of the selected ports with the maximum probability, and the region i belongs to the range of the abdominal region of the ports j;
s4032, the abdominal region areas are re-divided according to the membership relationship between the region i obtained in the step S4031 and different abdominal regions of all the ports.
The technical scheme provided by the embodiment of the invention has the beneficial effects that at least:
the embodiment of the invention provides a GIS technology-based port abdominal region dividing method, which comprises the following steps: acquiring relevant index data of all ports in a target area; PCA principal component analysis is carried out on the related index data, and the port intensity A of each port is calculatedj(ii) a Respectively constructing a grid analysis layer and a network analysis layer of multi-type intermodal transportation in a road mode in a target area, and comprehensively obtaining a transportation cost distance D from any region i to any port jij(ii) a The port strength A of each portjAnd a transportation cost distance D from any region i to any port jijSubstituting the probability into a Huff model divided based on the abdominal region to obtain the probability that the owner of the area i selects to the port j under the optimal condition of the transportation cost; and (3) substituting the probability that the owner of the area i selects the port j under the optimal transportation cost condition into the Cullinane competition abdominal region model, and outputting the division result of each port abdominal region. The harbor abdominal region dividing method realizes refined and modernized harbor abdominal region division under the background of multimodal transport and abdominal region competition, and has strong practicability and wide application.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
fig. 1 is a flowchart of a method for dividing a port abdominal region based on a GIS technique according to an embodiment of the present invention;
fig. 2 is a schematic flow chart of mode 1 according to an embodiment of the present invention;
FIG. 3 is a schematic flow chart of mode 2 according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of a port strength calculation result according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of basic geographic data of multimodal transportation according to an embodiment of the present invention;
FIG. 6 is a schematic diagram of vector data rasterization provided by an embodiment of the present invention;
fig. 7 is a schematic diagram of an analysis layer of a multimodal transport network according to an embodiment of the present invention;
8-12 are graphs of a grid of cumulative cost distances in the context of multimodal transport provided by embodiments of the present invention;
fig. 13-17 are grid charts of probability distributions of cargo owners selecting specific ports in various regions within the background area of multimodal transportation according to the embodiment of the present invention;
fig. 18 is a schematic diagram of a final result of the abdominal division of the port according to the embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
The embodiment of the invention provides a GIS technology-based port abdominal region dividing method, which comprises the following steps of:
s1, acquiring relevant index data of all ports in the target area; PCA for related index dataAnalyzing the principal components, and calculating the port intensity A of each portj；
S2, respectively constructing a grid analysis layer and a multi-type intermodal network analysis layer in a road mode in a target area, and comprehensively obtaining a transportation cost distance D from any region i to any port jij；
S3, comparing the port strength A of each portjAnd a transportation cost distance D from any region i to any port jijSubstituting the probability into a Huff model divided based on the abdominal region to obtain the probability that the owner of the area i selects to the port j under the optimal condition of the transportation cost;
and S4, substituting the probability that the owner of the area i selects the port j under the condition of the optimal transportation cost into the Cullinane competition abdominal region model, and outputting the division result of each port abdominal region.
Specifically, in step S1, a comprehensive port strength evaluation index system is constructed; and acquiring related index data in the target area according to the constructed port strength comprehensive evaluation index system. Wherein the relevant index data includes: primary index data and secondary index data; the primary index data includes: port infrastructure conditions and regional trade development level data; the secondary index data includes: cargo throughput, berth number, berth water depth, port informatization level, logistics performance level, resource abundance, trade inclusion degree and government support force data.
Specifically, step S2 includes:
acquiring road network data in a target area; creating a vector layer according to road network data, performing projection processing under a WGS1984 Mercator projection coordinate system, and rasterizing the vector layer to obtain a unit grid map; and carrying out cost assignment on the unit grid map, completing construction of a grid analysis layer in the road mode, and obtaining the accumulated cost distance in the road mode. Wherein, road network data includes: transportation network, logistics node, port and administrative district data.
And performing OD matrix analysis according to the acquired transportation network and logistics nodes, calculating the network cost distance, completing the construction of a network analysis layer of the multi-type intermodal transportation, and obtaining the accumulated cost distance in the multi-type intermodal transportation mode.
Adding the obtained accumulated cost distance in the highway mode and the accumulated cost distance in the multi-mode intermodal transportation mode to output the total cost of freight transportation; screening out the minimum value from the total freight transportation cost to obtain the transportation cost distance D from any region i to any port jij。
In the embodiment, a refined and modern port abdominal region dividing technology is realized through a GIS technology, a modern port abdominal region dividing method which meets the refined requirements of investment decision makers under the background of multimodal transport and abdominal region competition is established, and the method is high in practicability and wide in applicability.
A specific example is provided below to illustrate the above steps in detail:
specifically, in step S1, the constructed comprehensive port strength evaluation index system and data sources are shown in table 1:
TABLE 1 comprehensive evaluation index system for port strength
Further, in step S1, acquiring relevant index data in the target area according to the constructed port strength comprehensive evaluation index system; carrying out Principal Component Analysis (PCA) on the related index data, and calculating the port intensity A of each portjThe method specifically comprises the following steps:
acquiring related index data from the constructed comprehensive port strength evaluation index system; standardizing the obtained index data to eliminate the influence of different dimensions on the calculation result; and then importing the standardized index data into an SPSS platform for PCA principal component analysis, and establishing a port intensity matrix:
F＝B×C×E (1)
in the formula (1), B is a standardized index data matrix; c is a component score coefficient matrix; e is the variance contribution rate matrix of the extracted principal component.
Carrying out nonnegative standardization processing on the port intensity matrix according to a 'maximum-minimum' mode to obtain the port intensity A after standardization of each portj：
In the formula (2), FjIntensity for any port j before standardization; (F)j)minIs the minimum value of all port intensities before standardization; (F)j)maxIs the maximum of all port intensities before standardization. A. thejI.e. the comprehensive development level of port j.
Specifically, in step S2, road network data in the target area is acquired; creating a vector layer according to road network data, performing projection processing under a WGS1984 Mercator projection coordinate system, and rasterizing the vector layer to obtain a unit grid map; and carrying out cost assignment on the unit grid map, completing construction of a grid analysis layer in the road mode, and obtaining the accumulated cost distance in the road mode. The method specifically comprises the following steps:
firstly, obtaining road network data through websites such as native Earth: researching latest hierarchical road network, administrative division and port shape format data of a region, and the method specifically comprises the following steps: the method comprises the steps that traffic transportation networks, logistics nodes, ports and administrative divisions are conducted, road network data are imported into ArcGIS software to create a vector layer, and projection processing is conducted under a WGS1984 Mercator projection coordinate system; and rasterizing the vector layer by an ArcGIS vector-to-grid tool. According to different road grades, a reclassification tool is adopted to respectively assign the cost of the goods through the unit grids to obtain a cost grid map, namely an ArcGIS grid analysis map layer in a road mode, and the accumulated cost distance in the road mode is obtained. Because freight transportation has the characteristic of sensitive expense, the logistics cost is mainly considered based on the expense cost, and therefore, the calculation formula for carrying out cost assignment on the unit grid graph is as follows:
in the formula (3), value is a unit cost distance in the highway mode; m is the logistics cost of transporting unit freight volume in unit time under different road transportation modes; v is the speed of the truck passing on the current grid. Since all parameters of the Huff model are compared only in magnitude with relative values, the dimensions of M and v can be determined from the case-specific.
Specifically, in step S2, according to the acquired transportation network and logistics node, performing OD matrix analysis, calculating a network cost distance, completing construction of a network analysis layer of the multimodal transportation, and obtaining an accumulated cost distance in the multimodal transportation mode. In fact, the process of creating the ArcGIS network analysis layer of multimodal transport (grid graph of cumulative cost distance in the context of multimodal transport) specifically includes:
in the ArcGIS network analysis module, a network data set is constructed based on a multimodal transport network (roads, railways and waterways) and specific logistics nodes according to road network data acquired from websites such as Natural Earth. Different modes of transportation are set to different connectivity groups and multiple connectivity attributes are set at the logistics node so that connectivity is established only at the logistics node between different modes. And calculating the network cost distance from the logistics node to the port by generating an OD cost matrix. And (3) setting the transportation cost (namely network impedance) of the unit goods passing through the unit distance according to the formula (3), namely completing the creation of the ArcGIS network analysis layer of the multi-mode intermodal transportation, and obtaining the accumulated cost distance in the multi-mode intermodal transportation mode.
Further, in step S4, the cumulative cost distance in the road mode obtained in step S2 and the cumulative cost distance in the multimodal transportation mode obtained in step S3 are added to output the total cost of freight transportation. The goods in a specific region can arrive at a specific port in 2 modes according to whether the goods are accessed to the multimodal transportation network or not.
Alternatively, referring to fig. 2, in a mode 1, a case where goods in a specific region are accessed to a multimodal transportation network to reach a specific port. Under the transportation condition of the mode 1, the initial point goods reach a logistics node (a water-free port) through a road grid system (ArcGIS grid analysis layer in the road mode) to complete reloading, and a multi-type intermodal network system (ArcGIS network analysis layer in the multi-type intermodal) is accessed at the logistics node to transport to a specific port. And under the condition of neglecting the reloading time, the freight transportation cost of the mode 1 consists of two parts, namely the accumulated cost distance in the highway mode and the accumulated cost distance in the multimodal transport mode, and the accumulated network costs in the two modes are added to obtain the total freight transportation cost of all the grids reaching the specific port through the specific logistics node.
Alternatively, referring to fig. 3, in case of mode 2, the cargo in a specific region arrives at a specific port without accessing the multimodal transportation network. The mode 2 does not access a multimodal transport network (an ArcGIS network analysis layer of multimodal transport), and the initial point goods directly reach a specific port through a road grid system (the ArcGIS grid analysis layer in the road mode). And in the mode 2, the accumulated cost distance in the road mode is directly calculated without participating in multi-mode intermodal network transportation, and the accumulated cost distance in the road mode is the total cost of cargo transportation.
Specifically, the accumulated cost distance in the highway mode is used for generating a cost grid graph of logistics nodes and ports through an ArcGIS cost distance tool (Dijkstra algorithm). And calculating the network cost distance from the logistics node to the port by generating an OD cost matrix according to the accumulated cost distance in the multimodal transport mode.
Further, in step S2, the minimum value is selected from the total freight transportation costs, and the transportation cost distance D from any region i to any port j is obtainediiThe method specifically comprises the following steps:
comparing the total cost of cargo transportation to a specific port through different regions by using a pixel statistical data tool, screening out the minimum value, obtaining a minimum cost distance grid graph of each grid selection port in the region, and obtaining the transportation cost distance D from any region i to any port jij。
Further, in step S3, the port intensity obtained in step S1 and the transportation cost distance D from any region i to any port j obtained in step S2 are comparedijSubstituting the model into a Huff model based on abdominal region division to obtain the region under the optimal transportation cost conditioni, i.e. the probability of the owner of any grid area inland to select a specific port under the optimal conditions for transportation costs. The basic formula of the Huff model divided from the abdominal region is as follows:
in the formula (4), PijProbability of choosing to port j for the owner of region i; u shapeijSelecting a utility to port j for a shipper in region i; a. thejThe port strength of each port; dijThe distance of the transportation cost from any region i to any port j; n is the number of ports in the target area; beta is the distance attenuation coefficient.
Specifically, step S4, substituting the probability that the owner of the area i selects to the port j under the optimal transportation cost condition obtained in step S3 into the Cullinane competition abdominal region model, and outputting the division result of each port abdominal region specifically includes:
s401, extracting the probability maximum value (P) from the owner of the area i to the port j according to the probability from the owner of the area i to the port j under the optimal transportation cost condition obtained in the step S3i)max；
S402, according to the probability maximum value (P)i)maxCalculating the relative difference z of the probability of the shipper selecting port j in the area iij：
In the formula (5), PijProbability of choosing to port j for the owner of region i; (P)i)maxSelecting the probability maximum value from the region i to the port j for the owner;
s403, selecting relative difference z of port j probability according to cargo owners in region iijAnd comparing the competitive permeability f of the port, and outputting the division result of the exclusive abdominal land and the competitive abdominal land of the port. f describes the minimum transit cost difference between the port and the inland region.
Further, step S403 includes:
s4031, if zijIf the utility difference is larger than f, the difference of the utility of the port j selected by the region i is larger than that of the port with the maximum probability, and the region i does not belong to the range of the abdominal region of the port j; if z isijIf the number of the selected ports j is less than or equal to f, the difference of the utility of the selected ports j in the region i is smaller than that of the selected ports with the maximum probability, and the region i belongs to the range of the abdominal region of the ports j;
and S4032, repartitioning the abdominal region according to the membership relationship between the region i obtained in the S4031 and different abdominal regions of all the ports.
The rule for repartitioning the abdominal region is: defining the area i which is only divided into a port abdominal area range as an exclusive abdominal area of the port j; for a region i having two or more harbor abdominal belongings, the competitive abdominal region of these harbors is defined.
Finally, by means of a merging tool of the ArcGIS platform, a port directory (the relation between a port j and a region i: i is the exclusive abdominal region or the exclusive abdominal region of j) of each region participating in abdominal region competition can be determined, and division of the exclusive-competitive abdominal region of the port group is achieved.
The following description provides a port abdominal region division method based on the GIS technology by taking an example of a practical specific application scenario, and develops detailed descriptions:
the GIS technology-based port abdominal region dividing method provided by the embodiment is applied to the Guineau bay port group abdominal region division, and the implementation steps are as follows:
determining the research scope
According to the comprehensive strength of the port, the research port range of abdominal division is screened out from 25 main ports of the Guinea gulf by a PCA principal component analysis method, and the method specifically comprises the following steps: there are 5 major ports in total for abi-port (koretvara), temabou (garna), lomavi (dorgo), raersian (nigeria), and duala (karman).
The range of abdominal studies includes the guinea bay coastal area and the western africa, mid africa, part of the inland area, where: the guinea bay coastal areas include: liberia, cotterwaw, garna, dorgo, bening, nigeria, karilon, equatorial guinea, galbanum 9 countries; the inland region includes: marie, Bukenawa, Nigerl, chazac, Zhongfei, Congo (gold), Congo (Bu) 7 countries, totaling 16 countries.
Second, port intensity calculation
According to the calculation method, a port strength comprehensive evaluation index system is constructed, and the port strength A of each port is calculated by adopting a Principal Component Analysis (PCA)j. The specific calculation process is as follows:
1. basic data
According to the constructed comprehensive port strength evaluation index system, quantitative index data is acquired through official websites of organizations such as world banks, African development banks, various port and harbour administration and the like, and qualitative indexes are scored by on-site research and reference to local data provided by an outside enterprise. The relevant data of the economic and trade conditions of the area where the port is located is taken as a research unit by the country, all data are taken as a statistical benchmark year in 2017, and the specific assignment result of the index is shown in the table 2:
TABLE 2 Port Strength index evaluation System assignments
Note:
1. the resource abundance, the informatization level and the government support force are assigned in a range of 0-9;
2. the logistics performance level source is published data (0-5 points) of the world bank in 2017, and the higher the score is, the higher the logistics efficiency is;
3. the trade acceptance is represented by the customs procedure burden in the published data of 2017 of world bank (0-7 points), and the higher the score is, the higher the customs efficiency of the country where the port is located is, the higher the acceptance of the trade across the border is.
Further, the index data is imported into an SPSS platform, the platform automatically standardizes the data and then performs PCA principal component analysis, and the calculation results are shown in tables 3 to 5:
TABLE 3 KMO and Batterit sphericity test results
TABLE 4 component variance contribution ratio calculation results
TABLE 5 principal component score coefficient matrix
| 1 | 2 | 3 | |
| Cargo throughput (ten thousand) | .236 | .066 | -.026 |
| Number of berths | .224 | .082 | -.375 |
| Depth of berth (rice) | .153 | .036 | .061 |
| Logistics performance level | .145 | -.501 | -.004 |
| Abundance of resources | -.004 | .642 | .042 |
| Volume of trade | .109 | -.027 | .868 |
| Level of informatization | .236 | .014 | -.219 |
| Government support | .222 | .160 | .173 |
Analysis tables 3-5 show that the factors have large correlation and are suitable for principal component analysis; calculating the port intensity A of each port according to the formula (1) and the formula (2) of the methodjThe results are shown in the figure4, and (2) is as follows:
it can be seen that abilet, timamang harbor, lagos harbor and lomamerican harbor perform prominently in the calculation results of all harbors, and thus are included in the research harbor range divided in the abdominal region; also, dualahong was also included in the abdominal division because it performed best in the central africa region of the gulf of guinea. In conclusion, confirm that the harbour intensity value A of the harbour scope of research and each harbour is divided to the department of the mouth and abdomen of the gulf harbor in GuineajAs shown in table 6:
TABLE 6 stress Port Strength values
| Key port | Abirand | Special horse | Luomei | | Duala |
| A | |||||
| j | 100 | 95.26 | 87.84 | 90.49 | 66.60 |
Third, cost distance calculation
1. Source of underlying data
This section requires the use of three types of data, transportation network (Polyline), Point of interest (Point) and administrative district (Polygon) in the area under study. In order to ensure the data quality, data in a form of 10m resolution shape of a native Earth official website version4.1.0 (published in 2018) is adopted. The Road network data in the Road mode are divided into Major Highway, Road and Unknown, and the Major Highway and the Road are divided into a primary Road and the Unknown is researched for a secondary Road by comprehensively considering the actual condition of a research area; in other modes, according to research results of institutions such as JICA, the national economic consultancy agency NATHAN (hereinafter referred to as NATHAN) and the like, railway infrastructure development in the West Africa region is relatively lagged, only the Pagdou-Abira allows the utilization rate of railway transportation channels to be relatively high, and the railway transportation cost in most regions is not obvious compared with road transportation. Except for the Gannewalt lake watershed, the proportion of water transportation in the current research area is very small. Therefore, only one railway transportation channel of the Vagatou-Abira is considered in the railway mode, the Vagatou and the Abira are used as the non-harbor logistics node for network analysis, and the waterway transportation mode is not considered. Specific road network and logistics node data are shown in fig. 5.
2. Road vector data rasterization and cost assignment
The vector data were imported into ArcGIS software and projected under the WGS1984 Mercator projection coordinate system, dividing the region of interest into grids of about 407356 sides 5km in length. And (4) carrying out cost assignment on the grids according to the formula (3) by using a reclassification tool according to different traffic conditions in the highway mode. V in the definition formula is the passing speed of the truck on the current grid, and the values obtained after researching and reporting the logistics system of NATHAN about West Africa and Mizhong Africa are shown in the table 7:
TABLE 7 grid traffic speed values
| Grade | First-level highway | Second-level road | Nodata |
| V(km/h) | 50 | 30 | 5 |
The cost grid graph obtained after assignment is shown in fig. 6.
3. Road model cost distance calculation
Based on the cost grid map obtained by the calculation, the accumulated cost distance from each grid to each port in the research area is respectively calculated by using a cost distance tool in ArcGIS space analysis, and an accumulated cost distance distribution grid map is obtained. The value of each grid in the graph is the cumulative cost distance of the grid to a particular port in highway mode.
4. Multimodal transport cost distance calculation
(1) Network data set construction
And constructing a network data set by the multimodal transportation network and the logistics nodes. Setting network connectivity according to the method and setting network pass impedance according to the formula (1): the transportation cost of the current western and non-railway transportation per unit time is about 80% of the transportation cost of the highway after conversion is found by referring to related research reports of organizations such as NATHAN and the like. M is because only the proportional relationship of the two will affect the calculation resultjCan simply fetch Mroad＝1，Mrail0.8. The transport speed v of the railway network is 40km/h, and the value of the road transport speed is the same as the content. And after all the parameters are set, the construction of the network data set is completed, and the method is shown in fig. 7.
(2) Grid-network joint analysis
The logistics node and research port are respectively used as a starting point and a final point by using an adding position tool in ArcGISAnd adding the points into a network data set, and performing OD matrix analysis. The grid graph of the cumulative cost distance in the background of the multimodal transport is obtained according to the method, and the cumulative cost distance in the multimodal transport mode is obtained as shown in fig. 8 to 12. And obtaining the total cost of freight transportation according to the accumulated cost distance from the road mode to the specific port and the accumulated cost distance in the multimodal transport mode. Screening out the minimum value from the total freight transportation cost to obtain the transportation cost distance D from any region i to any port jij。
Division of abdomen and abdomen
The model parameters (the port intensity A of each port) obtained by the calculation are usedj(ii) a Transportation cost distance D from any region i to any port jij) The distance attenuation coefficient beta is introduced into a Huff model (formula 4), wherein the value of the distance attenuation coefficient beta is obtained by taking beta as 2 by referring to the research results of scholars such as Xiao Li and Shipeki, etc. By using the grid calculator in the ArcGIS map algebraic dataset, probability maps of the owners of goods in each region of the multimodal transportation background to select specific ports can be obtained, as shown in fig. 13-17.
According to the optimized harbor abdominal region dividing method based on the Cullinane competition abdominal region model, a pixel statistical tool in ArcGIS local analysis is adopted to extract a maximum value grid map from a harbor selection probability grid map (extracting the probability maximum value (P) from the owner of goods in the region i to the harbor ji)max) And then, carrying out condition judgment on the port selection probability grid map, and reassigning the grids:
if z isijIf f is greater than f, the corresponding grid is assigned to be 0, and the region i does not belong to the abdominal region range of the port j; if z isijF is less than or equal to f, the corresponding grid is assigned to be 1, and the region i belongs to the range of the belly of the port j. Wherein z isijSelecting relative differences in the probabilities of ports j for owners of region i; f is the port competition permeability; the grid is the computing unit for region i.
And forming a grid map of the abdominal region range of each port according to the assignment rule. And generating a unique value grid according to different values of the grid map of the abdominal region of each port in a specific grid region (namely different abdominal membership of the specific region to each port, wherein the specific region specifically refers to any grid in the research range) by using a merging tool. The attribute information of each unique value grid is the grid value information of each port in the grid region. For example, if the number of research ports is 2, a unique value grid with attribute values of 00, 01, 10, and 11 in four categories is generated, where the numbers 0 and 1 are the result of the grid assignment after the condition determination, as shown in table 8.
Table 8 combined grid attribute values when the number of ports is 2(n is 2)
Further, unique value grids of different categories are represented by different color blocks, so that different color blocks represent different port abdominal competition relationships, and data are vectorized after filtering and other generalization processing is carried out, so that a final exclusive abdominal and competitive abdominal division result is obtained.
Dividing the scope of exclusive-competitive belly by taking f as 0.2. As a result of the abdominal region division, referring to fig. 18, the abdominal region assignment corresponding to the number region in the figure is shown in table 9.
TABLE 9 results of abdominal region division (numbers in the table correspond to FIG. 18)
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.
Claims (10)
1. A GIS technology-based port abdominal region dividing method is characterized by comprising the following steps:
s1, acquiring relevant index data of all ports in the target area; carrying out PCA principal component analysis on the related index data, and calculating the port strength of each portDegree Aj；
S2, respectively constructing a grid analysis layer and a multi-type intermodal network analysis layer in the road mode in the target area, and comprehensively obtaining the transportation cost distance D from any region i to any port jij；
S3, comparing the port strength A of each portjAnd said transportation cost distance D from any region i to any port jijSubstituting the probability into a Huff model divided based on the abdominal region to obtain the probability that the owner of the area i selects to the port j under the optimal condition of the transportation cost;
and S4, substituting the probability that the owner of the area i selects the port j under the optimal transportation cost condition into the Cullinane competition abdominal region model, and outputting the division result of each port abdominal region.
2. The GIS technology-based port abdominal region dividing method as claimed in claim 1, wherein the relevant index data in the step S1 includes: primary index data and secondary index data; the primary index data includes: port infrastructure conditions and regional trade development level data; the secondary index data includes: cargo throughput, berth number, berth water depth, port informatization level, logistics performance level, resource abundance, trade inclusion degree and government support force data.
3. The GIS-technology-based port abdominal region dividing method as claimed in claim 2, wherein in step S1, PCA principal component analysis is performed on the related index data to calculate port intensity A of each portjThe method comprises the following steps:
s101, establishing a port intensity matrix according to the relevant index data:
F＝B×C×E (1)
in the formula (1), B is a normalized related index data matrix; c is a component score coefficient matrix; e is a variance contribution rate matrix of the extracted principal component;
s102, carrying out non-negative standardization processing on the port intensity matrix to obtain each port standardHarbour strength A after conversionj：
In the formula (2), FjIntensity for any port j before standardization; (F)j)minIs the minimum value of all port intensities before standardization; (F)j)maxIs the maximum of all port intensities before standardization.
4. The GIS technology-based port abdominal region dividing method as claimed in claim 1, wherein in step S2, the construction of the raster analysis layer in the road mode includes:
s201, acquiring road network data in the target area; the road network data includes: traffic network, logistics node, port and administrative division data;
s202, creating a vector layer according to the road network data, and rasterizing the vector layer to obtain a unit raster image;
s203, carrying out cost assignment on the unit grid map, completing construction of a grid analysis layer in a road mode, and obtaining an accumulated cost distance in the road mode.
5. The GIS technology-based port abdominal region dividing method as claimed in claim 4, wherein in step S2, the construction of the network analysis map layer of multimodal transportation includes:
s204, constructing a network data set according to the transportation network and the logistics nodes obtained in the step S201; the transportation network comprises a road transportation mode, a railway transportation mode and a waterway transportation mode;
s205, setting the same transportation mode as the same connectivity group, and setting a plurality of connectivity attributes at the logistics node;
s206, generating an OD cost matrix according to the connectivity group, calculating the network cost distance from the logistics node to the port, and completing the construction of a network analysis layer of the multi-mode intermodal transport;
and S207, obtaining the accumulated cost distance in the multi-mode intermodal mode according to the network analysis layer of the multi-mode intermodal.
6. The GIS technology-based port abdominal region division method as claimed in claim 5, wherein in step S203, the unit grid map is subjected to cost assignment, and in step S206, the calculation formula for calculating the network cost distance from the logistics node to the port is:
in the formula (3), value is a unit cost distance in the highway mode; m is the logistics cost of transporting unit freight volume in unit time under different road transportation modes; v is the speed of the truck passing on the current grid.
7. The GIS technology-based port abdominal region dividing method as claimed in claim 1, wherein in step S2, the transportation cost distance D from any region i to any port j is obtainedijThe method comprises the following steps:
adding the accumulated cost distance in the road mode obtained in the step S203 and the accumulated cost distance in the multimodal transportation mode obtained in the step S207, and outputting the total cost of the freight transportation; screening out the minimum value from the total freight transportation cost to obtain the transportation cost distance D from the region i to the port jij。
8. The GIS technology-based port abdominal region dividing method as claimed in claim 1, wherein in step S3, the Huff model based on the abdominal region division is:
in the formula (4), PijOf region iProbability of owner selecting to port j; u shapeijSelecting a utility to port j for a shipper in region i; a. thejThe port strength of each port; dijThe distance of the transportation cost from any region i to any port j; n is the number of ports in the target area; beta is the distance attenuation coefficient.
9. The GIS technology-based port abdominal region dividing method according to claim 1, wherein the step S4 includes:
s401, extracting the probability maximum value (P) from the owner of the area i to the port j according to the probability from the owner of the area i to the port j under the condition of the optimal transportation costi)max；
S402, according to the probability maximum value (P)i)maxCalculating the relative difference z of the probability of the shipper selecting port j in the area iij：
In the formula (5), PijProbability of choosing to port j for the owner of region i; (P)i)maxSelecting the probability maximum value from the region i to the port j for the owner;
s403, selecting a relative difference z of the probability of the port j according to the owner of the goods in the area iijAnd comparing the competitive permeability f of the port, and outputting the division result of the exclusive abdominal land and the competitive abdominal land of the port.
10. The GIS technology-based port abdominal region dividing method as claimed in claim 9, wherein the step S403 includes:
s4031, if zijIf the number of the selected ports j is less than or equal to f, the difference of the utility of the selected ports j in the region i is smaller than that of the selected ports with the maximum probability, and the region i belongs to the range of the abdominal region of the ports j;
s4032, the abdominal region areas are re-divided according to the membership relationship between the region i obtained in the step S4031 and different abdominal regions of all the ports.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110881885.0A CN113553391A (en) | 2021-08-02 | 2021-08-02 | Port abdominal region dividing method based on GIS technology |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110881885.0A CN113553391A (en) | 2021-08-02 | 2021-08-02 | Port abdominal region dividing method based on GIS technology |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113553391A true CN113553391A (en) | 2021-10-26 |
Family
ID=78133547
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110881885.0A Pending CN113553391A (en) | 2021-08-02 | 2021-08-02 | Port abdominal region dividing method based on GIS technology |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113553391A (en) |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CA2525997A1 (en) * | 2005-05-11 | 2006-11-11 | Optosecurity Inc. | Method and system for screening containers |
| CN101436345A (en) * | 2008-12-19 | 2009-05-20 | 天津市市政工程设计研究院 | System for forecasting harbor district road traffic requirement based on TransCAD macroscopic artificial platform |
| CN101976501A (en) * | 2010-10-29 | 2011-02-16 | 天津市市政工程设计研究院 | Principal component analysis and neural network based port road safety prediction method |
| CN102005135A (en) * | 2010-12-09 | 2011-04-06 | 上海海事大学 | Genetic algorithm-based support vector regression shipping traffic flow prediction method |
| CN102663579A (en) * | 2012-04-27 | 2012-09-12 | 同济大学 | Origin-destination (OD) estimation method for traffic demand of port roadway based on freight processes |
| CN103886394A (en) * | 2014-04-04 | 2014-06-25 | 天津市市政工程设计研究院 | Method for traffic impact evaluation after function adjustment of production land for port logistics |
| CN110400053A (en) * | 2019-06-28 | 2019-11-01 | 宁波市气象台 | A kind of method of harbour Meteorological Services performance evaluation |
| CN110673183A (en) * | 2019-09-24 | 2020-01-10 | 南通润邦重机有限公司 | Container identification and positioning method combined with GPS/INS |
-
2021
- 2021-08-02 CN CN202110881885.0A patent/CN113553391A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CA2525997A1 (en) * | 2005-05-11 | 2006-11-11 | Optosecurity Inc. | Method and system for screening containers |
| CN101436345A (en) * | 2008-12-19 | 2009-05-20 | 天津市市政工程设计研究院 | System for forecasting harbor district road traffic requirement based on TransCAD macroscopic artificial platform |
| CN101976501A (en) * | 2010-10-29 | 2011-02-16 | 天津市市政工程设计研究院 | Principal component analysis and neural network based port road safety prediction method |
| CN102005135A (en) * | 2010-12-09 | 2011-04-06 | 上海海事大学 | Genetic algorithm-based support vector regression shipping traffic flow prediction method |
| CN102663579A (en) * | 2012-04-27 | 2012-09-12 | 同济大学 | Origin-destination (OD) estimation method for traffic demand of port roadway based on freight processes |
| CN103886394A (en) * | 2014-04-04 | 2014-06-25 | 天津市市政工程设计研究院 | Method for traffic impact evaluation after function adjustment of production land for port logistics |
| CN110400053A (en) * | 2019-06-28 | 2019-11-01 | 宁波市气象台 | A kind of method of harbour Meteorological Services performance evaluation |
| CN110673183A (en) * | 2019-09-24 | 2020-01-10 | 南通润邦重机有限公司 | Container identification and positioning method combined with GPS/INS |
Non-Patent Citations (4)
| Title |
|---|
| WENYUAN WANG等: "improved gauss plume model for port sub-category hinterland division", CICTP 2020, 9 December 2020 (2020-12-09), pages 1 - 10 * |
| 王杰等: "基于GIS的港口腹地划分模型", 水运工程, no. 10, 25 October 2014 (2014-10-25), pages 91 - 96 * |
| 金一;韩增林;郭建科;王绍博;: "大连港和营口港空间效应变化预测分析――基于太平湾港口建设", 资源开发与市场, vol. 32, no. 09, 15 September 2016 (2016-09-15), pages 1083 - 1087 * |
| 黄昶生;王刚;周备;: "区域港口群协同发展研究――以山东半岛为例", 河南科学, vol. 38, no. 02, 15 February 2020 (2020-02-15), pages 311 - 320 * |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Gigović et al. | Application of the GIS-DANP-MABAC multi-criteria model for selecting the location of wind farms: A case study of Vojvodina, Serbia | |
| Laxe et al. | Sustainability and the Spanish port system. Analysis of the relationship between economic and environmental indicators | |
| Wu et al. | Identifying different types of urban land use dynamics using Point-of-interest (POI) and Random Forest algorithm: The case of Huizhou, China | |
| Shahparvari et al. | A GIS-LP integrated approach for the logistics hub location problem | |
| Li et al. | Bringing conservation priorities into urban growth simulation: An integrated model and applied case study of Hangzhou, China | |
| Budarova et al. | Information technologies for monitoring the territory of subsoil use | |
| Kumar et al. | Mixed integer linear programming approaches for land use planning that limit urban sprawl | |
| CN107506433A (en) | Urban development space general layout Scene Simulation system | |
| Es et al. | Assessing the logistics activities aspect of economic and social development | |
| CN109118004A (en) | A kind of engineer construction addressing Suitable Area prediction technique | |
| Guo et al. | Large-scale and refined green space identification-based sustainable urban renewal mode assessment | |
| Zhang et al. | Establishing an evaluation index system of Coastal Port shoreline resources utilization by objective indicators | |
| Jing et al. | A hierarchical spatial unit partitioning approach for fine‐grained urban functional region identification | |
| Ortega et al. | The influence of spatial data allocation procedures on accessibility results: The case of high-speed rail networks | |
| Cheng et al. | Updating conventional soil maps by mining soil–environment relationships from individual soil polygons | |
| Crols et al. | Downdating high-resolution population density maps using sealed surface cover time series | |
| Lin et al. | Project risk management | |
| Önden et al. | Green energy source storage location analysis based on GIS and fuzzy Einstein based ordinal priority approach | |
| CN113553391A (en) | Port abdominal region dividing method based on GIS technology | |
| Wei | Urban land and sustainable development | |
| Jayasinghe et al. | Exploration of expansion patterns and prediction of urban growth for Colombo City, Sri Lanka | |
| Beukes et al. | Quantifying the contextual influences on road design | |
| Yao | Application of GIS remote sensing information integration in eco-environmental quality monitoring | |
| Tsiotas et al. | Understanding peripherality in a multidimensional geographical, socioeconomic, and institutional context: Evidence from Greece | |
| Izzo et al. | Classification of urban functional zones through deep learning |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |