CN115689106A - Method, device and equipment for quantitatively identifying regional space structure of complex network view angle - Google Patents
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Abstract
The invention discloses a method, a device and equipment for quantitatively identifying a regional space structure of a complex network view angle, wherein the method comprises the following steps: collecting multi-source data reflecting urban natural and humanistic development conditions, and preprocessing the multi-source data; constructing a city comprehensive quality evaluation index system reflecting city attraction capacity and radiation capacity; calculating city comprehensive quality and inter-city time cost distance, and constructing a city comprehensive space contact network; according to the urban space contact network, a community discovery algorithm is used for delimiting the range of the regional space unit; and constructing node centrality indexes, identifying the importance of each city in the region and quantitatively measuring the internal space structure of the region. The invention combines the prior knowledge of urban spatial relationship and the like with the advantages of a complex network, and explores a method for objectively and accurately defining the area range and quantitatively measuring the internal spatial structure of the area range on the basis of interdisciplinary discipline.
Description
Technical Field
The invention relates to a method, a device and equipment for quantitatively identifying a regional space structure of a complex network view angle, and belongs to the technical field of quantitative analysis of urban and regional space ranges.
Background
Along with the urbanization development, the urban population and land scale are continuously expanded, the living standard of people is continuously improved, and simultaneously, the great challenge that the urban scale and the development quality are difficult to coordinate is faced. The urban diseases such as unbalanced economic development, conflict of human-ground relationship, deterioration of ecological environment and the like show that the urbanization level exceeding the upper limit of the current self development capability can block the further development of cities. Therefore, a single urban development model cannot adapt to the current global urbanization development trend, and the regional cooperative development becomes a key for relieving global disadvantages and realizing sustainable development. The inter-city cooperation division and the advantage complementation form space units with different scales, and a regional cooperative development mode is promoted. Previous researches show that the spatial structure is the basis of regional development, the regional collaborative development is enhanced, and various aspects such as optimizing the spatial layout need to be started. The rational layout of the urban spatial structure becomes an important condition for promoting the collaborative development of the region, and how to accurately and quantitatively measure the spatial structure becomes an important subject in the research of the collaborative development.
The current regional spatial structure recognition is mainly divided into a static recognition method based on morphological attributes (population aggregation distribution, building density, road network construction, etc.) and a dynamic recognition method based on big data (trajectory data, mobile phone signaling, etc.) reflecting human communication activities. However, under the global process, the economic, traffic and other aspects of the cities are more complicated and diversified, and a plurality of administrative units and administrative hierarchies exist in the region, and cities of different sizes form a spatial organization of the urban network which is more frequently and closely related to each other and has more complicated and diversified connection ways under different spatial scales. The spatial structure is used as an important visual angle for understanding the internal relation of the region, the identification method under a single visual angle has one-sidedness, and the traditional method is gradually difficult to satisfy the nonlinear relation between the sub-systems developed by the analysis region. Meanwhile, the large data related to people are used for defining boundaries which often reflect population activity spaces, and the boundaries have certain difference with the region boundaries with real and static characteristics in planning management, so that the subsequent region space structure identification also has certain influence.
Therefore, how to comprehensively and comprehensively represent the inter-city spatial relationship and quantitatively measure the internal spatial structure of the region by using the advantages of the interdisciplinary disciplines under the background of meeting the requirements of regional planning management is an urgent problem to be solved.
Disclosure of Invention
In order to solve the problems, the invention provides a method, a device and equipment for quantitatively identifying a regional space structure of a complex network view angle, which can accurately and quantitatively measure an internal space structure of a city.
The technical scheme adopted for solving the technical problems is as follows:
in a first aspect, an embodiment of the present invention provides a method for quantitatively identifying a spatial structure of a region of a complex network view, including the following steps:
collecting multi-source data reflecting urban natural and humanistic development conditions, and preprocessing the multi-source data;
constructing a city comprehensive quality evaluation index system reflecting city attractive capacity and radiation capacity;
calculating city comprehensive quality and inter-city time cost distance, and constructing a city comprehensive space contact network;
according to the urban space contact network, a community discovery algorithm is utilized to define the range of the regional space unit;
and constructing node centrality indexes, identifying the importance of each city in the region and quantitatively measuring the internal space structure of the region.
As a possible implementation manner of this embodiment, the multi-source data includes statistical data, surface coverage data, road network data, POI points, night light data, and atmospheric environment data.
As a possible implementation manner of this embodiment, the statistical data is derived from the city-level statistical yearbook and the prefecture-level statistical bulletin, the surface coverage data and the road network data are derived from the result of the general survey data of the geographic national conditions, the POI point is derived from the high-grade map, the night light data is derived from the NPP/VIIRS data, and the atmospheric environment data is derived from the meteorological site data and the CHAP data set.
As a possible implementation manner of this embodiment, the preprocessing multi-source data includes:
converting the spatial coordinate system of the multi-source data into a unified coordinate system;
converting the statistical data into panel data;
carrying out statistical analysis on the vector and the raster data according to the county scale;
and combining the spatial data calculation result and the statistical data and arranging the spatial data calculation result and the statistical data into panel data.
As a possible implementation manner of this embodiment, the preprocessing the multi-source data includes:
performing projection on the spatial data to perform spatial coordinate system conversion, and converting the spatial data into a 2000 national geodetic coordinate system;
processing the surface coverage data and the administrative division space range data, and counting according to the administrative division;
processing the missing values of the grid data, and performing statistical analysis on night light data and atmospheric data;
processing the statistical data into panel data according to district and county units;
crawling POI data of each district and county in a research district based on a web crawler algorithm, wherein the POI data of each district and county comprises data of scientific research institutes, schools, hospitals, public service facilities and leisure and entertainment categories of each district and county;
the driving distance and the required time between county and government points are calculated based on the Goods Path planning API.
As a possible implementation manner of this embodiment, the building of an urban comprehensive quality evaluation index system reflecting urban attractive capacity and urban radiation capacity includes:
selecting indexes from four aspects of economic competitiveness, potential competitiveness, social competitiveness and environmental competitiveness based on multi-source data;
and constructing a city comprehensive quality evaluation index system which is divided into a target layer, a criterion layer and an index layer from top to bottom.
As a possible implementation manner of this embodiment, the preprocessing multi-source data includes:
unifying the multi-source data coordinate system, and merging and arranging the spatial data calculation result and the statistical data into panel data.
As a possible implementation manner of this embodiment, before constructing the urban integrated space contact network, it is necessary to perform a correction process on a gravity coefficient and a distance coefficient in a traditional gravity model, where the correction process includes:
1) And (3) correcting the gravity coefficient in the traditional gravity model:
wherein k is the corrected gravity coefficient; m i And M j The comprehensive quality value of the city i and the city j is obtained;
2) And (3) correcting the distance coefficient in the traditional gravity model:
wherein D is the distance between two cities under the condition of time cost; t is the driving time between two cities; r is the shortest distance between two cities.
As a possible implementation manner of this embodiment, the defining the range of the area space unit by using the community discovery algorithm according to the city space contact network includes:
initializing communities, and regarding each node as a small community;
the nodes move randomly and calculate average coding length expected values L (M) under the condition that the nodes and the communities are combined respectively, the nodes and the communities which can reduce L (M) to the minimum are combined, and the step is repeatedly executed until the variation of L (M) is minimum; the calculation formula of L (M) is as follows:
p i ＝q i +∑ α∈i p α (5)
wherein L (M) represents the average encoding length expected value of the path along which the random walk in the community and the community; q represents the probability of random walks between communities, q i And pi represents the probability of random walks within the community; pi represents the probability m of random walk within a community represents the number of communities; h (Q), H (P) i ) Entropy representing the motion probability of random walk among communities and in communities respectively;
and according to the properties of the Huffman codes, the shortest random walking code length corresponds to the optimal community delineation result, and the shortest code length result is selected as the identification result of the region space unit range.
As a possible implementation manner of this embodiment, the constructing a node centrality index includes:
calculating a centrality index:
in the formula, n is the total number of the city nodes; a is ji Show standThe point is j and the end point is a contact edge of i; w is a ji Representing the contact weight with the starting point being j and the end point being i; a is ij Representing a contact edge with a starting point i and an end point j; w is a ij Is the contact weight which represents the starting point as i and the end point as j;
and
representing weighted in-degree and weighted out-degree of node i, D i The sum of the degrees of representation;
calculating an approximate centrality index:
wherein CC i Is the approximate centrality value of each node, n is the total number of city nodes, d i (i, j) is the shortest distance between nodes i and j;
calculating the intermediate centrality index:
wherein BC i Is the value of the centrality of the intermediary, N, of each node jk Represents the total number of shortest paths from node j to k; n is a radical of hydrogen jk (i) Representing the shortest path number between the node j and the node k through the node i;
calculating the characteristic vector centrality index:
wherein EC is i Is the centrality value of the feature vector of each node, c is the proportionality constant, A is the adjacency matrix, the nodeIf there is a connection to (i, j), then a ij =1, otherwise 0; lambda 1 ,λ 2 ,……,λ n Representing eigenvalues of the adjacency matrix, and each eigenvalue corresponding to a vector x = [ x = 1 ,x 2 ,x 3 ,……x n ]；
Calculating the PageRank index:
wherein PR is i Is the PageRank value for node i; q is a constant term; n is the total number of nodes; j represents the strength of the connection from city i to city j; l is a radical of an alcohol j The total number of all connected edges starting from node i and weighted by the strength of contact;
the importance of each city in the identification area comprises the following steps:
and (3) carrying out comprehensive evaluation on the urban nodes by adopting a principal component analysis method:
Y i ＝β 1 D i +β 2 CC i +β 3 BC i +β 4 EC i +β 5 PR i (16) Wherein Y is i The main component values of the urban node importance comprehensive evaluation values, beta, corresponding to the central degree measurement values are calculated; and constructing a multivariable linear function to analyze and sequence the importance of each node in the region range.
As a possible implementation manner of this embodiment, the quantitative measurement region internal space structure includes:
the comprehensive evaluation value of the importance of the urban nodes is divided into three types by using a natural breakpoint method: the "center type city", "transition type city", and "edge type city" identify the regional unit center city and its internal spatial structure.
In a second aspect, an embodiment of the present invention provides a device for quantitatively identifying a spatial structure of a region from a complex network view, including:
the data acquisition module is used for acquiring multi-source data reflecting the natural and humanistic development conditions of a city and preprocessing the multi-source data;
the evaluation index system building module is used for building a city comprehensive quality evaluation index system reflecting city attraction capacity and radiation capacity;
the contact network construction module is used for calculating the urban comprehensive quality and the inter-urban time cost distance and constructing an urban comprehensive space contact network;
the space unit demarcation module is used for demarcating the range of the area space unit by utilizing a community discovery algorithm according to the urban space contact network;
and the space structure measuring module is used for constructing node centrality indexes, identifying the importance of each city in the region and quantitatively measuring the internal space structure of the region.
In a third aspect, an embodiment of the present invention provides a computer device, including: the computer device comprises a processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, when the computer device runs, the processor and the memory are communicated through the bus, and the processor executes the machine-readable instructions to realize the quantitative identification method of the regional space structure of the complex network view as described in any one of the above.
In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, where the storage medium stores a computer program, and the computer program is configured to execute any one of the above methods for quantitatively identifying a spatial structure of a region from a complex network view.
The technical scheme of the embodiment of the invention has the following beneficial effects:
the invention combines the prior knowledge of urban spatial relation and the like with the advantages of a complex network, and explores a method for objectively and accurately demarcating the area range and quantitatively measuring the internal spatial structure of the area range on the basis of interdisciplinary science.
The method only uses conventional public data, quantitatively evaluates the urban development level from various aspects such as society, economy, traffic, ecological environment and the like based on multi-source data, overcomes the limitation that only single-dimensional quantitative analysis is concerned in the traditional method, can comprehensively measure the inter-city contact strength, and enables the identification result of the internal space structure of the subsequent region to be more accurate.
Compared with the existing method, the method is based on the complex network view angle, emphasizes that the inter-city spatial relation is taken as the judgment basis, avoids the error caused by the subjective selection of the threshold in the traditional method, and has low data cost. Meanwhile, the community discovery algorithm objectivity guarantees high confidence of the result, and large-scale popularization and application can be achieved.
Compared with the traditional regional collaborative research under a static visual angle, the dynamic analysis visual angle of the flow space is adopted, the development level difference of cities in a region can be truly reflected by the invention result, the spatial conditions of the cities and element aggregation and configuration are reflected, the reasonable distribution of development resources such as finance and traffic is facilitated, the related suggestions of regional space planning management are provided in a targeted manner, and the reference is provided for the regional integrated development.
Description of the drawings:
FIG. 1 is a flow diagram illustrating a method for quantitative identification of regional spatial structure for a complex network view according to an example embodiment;
FIG. 2 is a schematic diagram illustrating a device for quantitatively identifying a spatial structure of a region from a complex network view according to an exemplary embodiment;
FIG. 3 is a flow chart of an embodiment of the present invention for quantitative identification of spatial structure of a region;
FIG. 4 is a schematic diagram of the urban comprehensive space connection network in Jingjin Ji area constructed by the method of the present invention;
FIG. 5 is a schematic diagram of the spatial units of the boundary range area of each city circle in Jingjin Ji area extracted by the present invention;
fig. 6 is a schematic diagram of the spatial structure of the area of each city circle in the kyojin Ji area identified quantitatively by the present invention.
Detailed Description
The invention is further illustrated by the following examples in conjunction with the following figures:
in order to clearly explain the technical features of the present invention, the present invention will be explained in detail by the following embodiments and the accompanying drawings. The following disclosure provides many different embodiments, or examples, for implementing different features of the invention. To simplify the disclosure of the present invention, the components and arrangements of specific examples are described below. Moreover, the present disclosure may repeat reference numerals and/or letters in the various examples. This repetition is for the purpose of simplicity and clarity and does not in itself dictate a relationship between the various embodiments and/or configurations discussed. It should be noted that the components illustrated in the figures are not necessarily drawn to scale. Descriptions of well-known components and processing techniques and procedures are omitted so as to not unnecessarily limit the invention.
As shown in fig. 1, a method for quantitatively identifying a spatial structure of a region of a complex network view according to an embodiment of the present invention includes the following steps:
collecting multi-source data reflecting urban natural and humanistic development conditions, and preprocessing the multi-source data;
constructing a city comprehensive quality evaluation index system reflecting city attraction capacity and radiation capacity;
calculating city comprehensive quality and inter-city time cost distance, and constructing a city comprehensive space contact network;
according to the urban space contact network, a community discovery algorithm is utilized to define the range of the regional space unit;
and constructing node centrality indexes, identifying the importance of each city in the region and quantitatively measuring the internal space structure of the region.
As a possible implementation manner of this embodiment, the multi-source data includes statistical data, surface coverage data, road network data, POI points, night light data, and atmospheric environment data.
As a possible implementation manner of this embodiment, the statistical data is derived from the city-level statistical yearbook and the prefecture-level statistical bulletin, the surface coverage data and the road network data are derived from the result of the general survey data of the geographic national conditions, the POI point is derived from the high-grade map, the night light data is derived from the NPP/VIIRS data, and the atmospheric environment data is derived from the meteorological site data and the CHAP data set.
As a possible implementation manner of this embodiment, the preprocessing the multi-source data includes:
converting the spatial coordinate system of the multi-source data into a unified coordinate system;
converting the statistical data into panel data;
carrying out statistical analysis on the vector and the raster data according to the county scale;
and combining the spatial data calculation result and the statistical data and arranging the spatial data calculation result and the statistical data into panel data.
As a possible implementation manner of this embodiment, the preprocessing the multi-source data may further include:
performing projection on the spatial data to perform spatial coordinate system conversion, and converting the spatial data into a 2000 national geodetic coordinate system;
processing the surface coverage data and the administrative division space range data, and counting according to the administrative division;
processing the missing values of the grid data, and performing statistical analysis on night light data and atmospheric data;
processing the statistical data into panel data according to district and county units;
crawling POI data of each district and county in a research district based on a web crawler algorithm, wherein the POI data of each district and county comprises data of scientific research institutes, schools, hospitals, public service facilities and leisure and entertainment categories of each district and county;
the driving distance and the required time between each county government point are calculated based on the Goodpastel Path planning API.
The two pre-processing cores of the multi-source data are to unify a multi-source data coordinate system and combine and arrange a spatial data calculation result and statistical data into panel data.
As a possible implementation manner of this embodiment, the building of an urban comprehensive quality evaluation index system reflecting urban attractive capacity and urban radiation capacity includes:
selecting indexes from four aspects of economic competitiveness, potential competitiveness, social competitiveness and environmental competitiveness based on multi-source data;
and constructing a city comprehensive quality evaluation index system which is divided into a target layer, a criterion layer and an index layer from top to bottom.
As a possible implementation manner of this embodiment, before constructing the urban integrated space contact network, it is necessary to perform a correction process on a gravity coefficient and a distance coefficient in a traditional gravity model, where the correction process includes:
1) And (3) correcting the gravity coefficient in the traditional gravity model:
wherein k is the corrected gravity coefficient; m i And M j The comprehensive quality value of the city i and the city j is obtained;
2) And (3) correcting the distance coefficient in the traditional gravity model:
wherein D is the distance between two cities under the condition of time cost; t is the driving time between two cities; r is the shortest distance between two cities.
As a possible implementation manner of this embodiment, the defining the range of the area space unit by using the community discovery algorithm according to the city space contact network includes:
initializing communities, and regarding each node as a small community;
the nodes move randomly and calculate average coding length expected values L (M) under the condition that the nodes and the communities are combined respectively, the nodes and the communities which can reduce L (M) to the minimum are combined, and the step is repeatedly executed until the variation of L (M) is minimum; the calculation formula of L (M) is as follows:
p i ＝q i +∑ α∈i p α (5)
wherein L (M) represents the average coding length expectation value of the path along which the random walk in the community and the community; q represents the probability of random walks between communities, q i And pi represents the probability of random walks within the community; pi represents the probability m of random walk within a community represents the number of communities; h (Q), H (P) i ) Entropy respectively representing the motion probability of random walk between communities and in communities;
and according to the properties of the Huffman codes, the shortest random walking code length corresponds to the optimal community delineation result, and the shortest code length result is selected as the identification result of the region space unit range.
As a possible implementation manner of this embodiment, the constructing a node centrality index includes:
calculating a centrality index:
in the formula, n is the total number of the city nodes; a is ji Representing a contact edge with a starting point j and an end point i; w is a ji To indicate that the starting point is j and the end point is iA contact weight; a is ij Representing a contact edge with a starting point i and an end point j; w is a ij Is a contact weight representing a starting point i and an end point j;
and
representing weighted in-degree and weighted out-degree of node i, D i The sum of the degrees of representation;
calculating an approximate centrality index:
wherein CC i Is the approximate centrality value of each node, n is the total number of city nodes, d i (i, j) is the shortest distance between nodes i and j;
calculating the intermediate centrality index:
wherein BC i Is the value of the centrality of the intermediary, N, of each node jk Represents the total number of shortest paths from node j to k; n is a radical of jk (i) Representing the shortest path number between the node j and the node k through the node i;
calculating the characteristic vector centrality index:
wherein EC i Is the centrality value of the feature vector of each node, c is the proportionality constant, A is the adjacency matrix, and if there is a connection between the node and (i, j), then a ij =1, otherwise 0; lambda [ alpha ] 1 ,λ 2 ,……,λ n Representing eigenvalues of the adjacency matrix, and each eigenvalue corresponding to a vector x = [ x = 1 ,x 2 ,x 3 ,……x n ]；
Calculating the PageRank index:
wherein PR is i Is the PageRank value for node i; q is a constant term; n is the total number of nodes; j represents the strength of the contact of city i to city j; l is j The total number of all connecting edges taking the node i as a starting point is weighted by the strength of the connection;
the importance of each city in the identification area comprises the following steps:
and (3) carrying out comprehensive evaluation on the urban nodes by adopting a principal component analysis method:
Y i ＝β 1 D i +β 2 CC i +β 3 BC i +β 4 EC i +β 5 PR i (16) Wherein Y is i Beta is a main component value of each centrality measurement value corresponding to the urban node importance comprehensive evaluation value;
and constructing a multivariable linear function to analyze and sequence the importance of each node in the region range.
As a possible implementation manner of this embodiment, the quantitative measurement region internal space structure includes:
the comprehensive evaluation value of the importance of the urban nodes is divided into three types by using a natural breakpoint method: the "center type city", "transition type city" and "edge type city" identify the regional unit center city and its internal space structure.
As shown in fig. 2, an apparatus for quantitatively identifying a spatial structure of a region of a complex network view according to an embodiment of the present invention includes:
the data acquisition module is used for acquiring multi-source data reflecting the natural and humanistic development conditions of a city and preprocessing the multi-source data;
the evaluation index system building module is used for building a city comprehensive quality evaluation index system reflecting the city attractive capacity and the radiation capacity;
the contact network construction module is used for calculating the urban comprehensive quality and the inter-urban time cost distance and constructing an urban comprehensive space contact network;
the space unit delimiting module is used for delimiting the range of the regional space units by using a community discovery algorithm according to the urban space contact network;
and the space structure measurement module is used for constructing node centrality indexes, identifying the importance of each city in the region and quantitatively measuring the internal space structure of the region.
As shown in FIG. 3, the quantitative identification of the spatial structure of the region using the device of the present invention is performed as follows.
Step 1: constructing a city comprehensive quality evaluation index system, and reflecting the city attractive capacity and radiation capacity;
and 2, step: collecting multi-source data reflecting urban natural and humanistic development conditions, and correspondingly calculating index data after preprocessing;
and 3, step 3: calculating the comprehensive quality of each city and the time cost distance between cities, and constructing a city comprehensive space contact network based on an improved gravitation model;
and 4, step 4: according to the urban space contact network, a community discovery algorithm is utilized to define the range of the regional space unit;
and 5: and establishing node centrality indexes to identify the importance of each city in the region and quantitatively measuring the internal space structure of the region.
Preferably, the specific implementation process of step 1 includes the following sub-steps:
step 1.1: according to the principles of data comparability, availability, completeness and the like, selecting indexes from four aspects of economic competitiveness, potential competitiveness, social competitiveness and environmental competitiveness to construct an index system, and dividing the index system into a target layer, a criterion layer and an index layer from top to bottom;
step 1.2: and calculating the weight of each index by using the processed index data, and weighting and summing each index to finally obtain the comprehensive quality value of each city.
Preferably, the specific implementation process of step 2 includes the following sub-steps:
step 2.1: converting a space coordinate system, and unifying a multi-source data coordinate system;
step 2.2: converting the statistical data into panel data;
step 2.3: carrying out statistical analysis on the vector and the grid data according to the county scale;
step 2.4: combining and arranging the spatial data calculation result and the statistical data into panel data;
preferably, the specific implementation process of step 3 includes the following sub-steps:
step 3.1: and (3) correcting the gravity coefficient in the traditional gravity model:
the interaction generated by the development level difference between cities is in an unbalanced state, so the connection strength between the two cities is regarded as directed, and the gravity flow direction of the cities is judged according to the total mass of the cities, which is the total mass of the two cities.
Wherein k is the corrected gravity coefficient; m i And M j The quality value is the combined quality value of the city i and the city j.
Step 3.2: and (3) correcting the distance coefficient in the traditional gravity model:
and (4) representing the distance between two places according to the distance between district and county government points based on the time factor. And (3) crawling the shortest driving distance and the driving time between the counties in batches through a Baidu map API (application program interface), and correcting the distance parameter in the gravity model by using the product of the shortest driving distance and the driving time.
Wherein D is the distance between two cities under the time cost condition; t is the driving time between two cities; r is the shortest distance between two cities.
Step 3.3: carrying out geographic position assignment on the inter-city gravitation according to the coordinate position of each city government point, and converting the inter-city gravitation into a data form of 'starting point-end point';
step 3.4: the ranking is performed according to the connected edge weights.
Preferably, the specific implementation process of step 4 includes the following sub-steps:
step 4.1: initializing communities, and regarding each node as a small community;
and 4.2: the nodes move randomly and average coding length expected values L (M) under the condition that the nodes and the communities are combined are respectively calculated, and the nodes and the communities which can reduce L (M) to the minimum are combined; the calculation formula is as follows:
p i ＝q i +∑ α∈i p α (5)
wherein L (M) represents the average encoding length expected value of the path along which the random walk in the community and the community; q and pi respectively represent the probability of random walk among communities and in communities; m represents the number of communities; h (Q), H (P) i ) Entropy representing the probability of motion between communities and within communities of random walks, respectively.
Step 4.3: repeating the iteration step until the L (M) variation is minimum;
step 4.4: according to the properties of Huffman coding, the shortest random walk coding length corresponds to the optimal community delineation result, namely the transition probability among communities is low, and the transition probability in the communities is high, so that the shortest coding length result is selected as the identification result of the region space unit range;
preferably, the specific implementation process of step 5 includes the following sub-steps:
step 5.1: constructing a central comprehensive evaluation function to realize comprehensive evaluation of each node in the urban spatial contact network;
1) Center of gravity
Degree centrality reflects the degree of contact of the node with other nodes and whether the node is more central in the network than other nodes. In the directed graph, the degree center degree is divided into two concepts of point-in degree and point-out degree, the point-in degree reflects the size of the node influenced by other nodes, the point-out degree reflects the size of the influence capacity of the node on other nodes, and the calculation formula is as follows:
in the formula, n is the total number of the city nodes; a is a ji Representing a contact edge with a starting point j and an end point i; w is a ji Representing the contact weight with the starting point being j and the end point being i; a is ij Representing a contact edge with a starting point i and an end point j; w is a ij Is the contact weight which represents the starting point as i and the end point as j;
and
representing weighted in-degree and weighted out-degree of node i, D i Representing the sum of degrees.
2) Near centrality
Proximity centrality reflects the proximity and reachability between a node and other nodes in the network. The greater the near centrality value, the more central the proof is. The formula is as follows:
wherein CC i Is the approximate centrality value of each node, n is the total number of city nodes, d i (i, j) is the shortest distance between nodes i and j.
3) Center of the medium
The intermediary centrality is an index for describing the importance of nodes by the number of shortest paths passing through a certain node, and can measure the control capability of one city node on peripheral city nodes, wherein the higher the value of the index is, the stronger the control capability is, and the index is in the core position in a network. The calculation formula is as follows:
wherein BC i Is the value of the centrality of the intermediary, N, of each node jk Represents the total number of shortest paths from node j to k; n is a radical of hydrogen jk (i) Represents the number of shortest paths between node j and node k through node i
4) Feature vector centrality
The feature vector centrality judges the importance of the node based on the number and importance of the adjacent nodes, and emphasizes that the importance of a node depends on the number of the adjacent nodes and the importance of the adjacent nodes.
The concrete formula is as follows:
wherein EC is i Is the centrality value of each node feature vector, c is the proportionality constant, A is the adjacency matrix, and if there is a connection between the node and (i, j), then a ij =1, otherwise 0; lambda [ alpha ] 1 ,λ 2 ,……,λ n Representing eigenvalues of the adjacency matrix, and each eigenvalue corresponding to a vector x = [ x = 1 ,x 2 ,x 3 ,……x n ]The relation is formula (14).
5)PageRank
The PageRank not only considers the number of the connections between the nodes, but also considers the importance degree of the nodes, calculates the importance of all the nodes and sorts the nodes according to the weight. On the basis of the centrality of the feature vector, the PageRank method avoids misjudgment on the centrality of adjacent nodes caused by the influence of nodes with high centrality. The formula is as follows:
wherein PR is i Is the PageRank value for node i; q is a constant term, typically 0.85; n is the total number of nodes; j represents the strength of the connection from city i to city j; l is j Is the total number of all connected edges starting from node i and weighted by the strength of the contact.
Step 5.2: comprehensively evaluating the urban nodes by adopting a principal component analysis method, and constructing a multivariate linear function to analyze and sequence the importance of each node in Jingjin Ji area;
Y i ＝β 1 D i +β 2 CC i +β 3 BC i +β 4 EC i +β 5 PR i (16)
wherein Y is i And beta corresponds to the main component value of each centrality measurement value for the comprehensive evaluation value of the importance of the urban nodes.
Step 5.3: the comprehensive evaluation value of the importance of the urban nodes is divided into three types by a natural breakpoint method: the city identification method comprises the steps of 'center type city', 'transition type city' and 'edge type city', so that the identification of the regional space unit center city and the internal structure thereof is realized.
The method provided by the invention is tested by adopting real multi-source data, the results of the area space unit range defining and the internal space structure identification are reasonable, and the method is proved to be correct and effective in theory and feasible in practical application.
The following tests are carried out in Jingjin Ji area, the statistical data are from each city grade statistical yearbook and each county statistical communique, the ground surface coverage data and the road network data are from the result of general survey data of geographic national conditions, the POI point is from a Gode map, the night light data are from NPP/VIIRS data, and the atmospheric environment data are from meteorological site data and CHAP data sets, and the method provided by the invention has the following specific implementation steps:
step 1: processing city quality evaluation data;
preprocessing the collected multi-source data to generate panel data for evaluating the urban development level, and specifically comprising the following steps of:
(1) Space coordinate system conversion, namely projecting space data such as vector data and raster data, and converting the space data into a 2000 national geodetic coordinate system;
(2) Processing the ground surface coverage data and the administrative division space range data by using an intersection tool of ArcGIS, and performing statistics according to the administrative division by using an intersection tabulation tool;
(3) Processing the missing value of the grid data by using a neighborhood analysis tool of ArcGIS, and performing statistical analysis on night light data and atmospheric data by using a partition statistical tool;
(4) Processing the statistical data into panel data according to district and county units;
(5) Crawling POI data of each district and county in a research area based on a web crawler algorithm, wherein the POI data mainly relates to categories of scientific research institutions, schools, hospitals, public service facilities and leisure and entertainment;
(6) And calculating driving distances and required time between county government points based on the Goods path planning API, wherein the driving distances and the required time serve as representatives for measuring connection resistance between counties.
And 2, step: constructing a city comprehensive quality index system;
according to the principles of data comparability, availability, completeness and the like, selecting indexes from four aspects of economic competitiveness, potential competitiveness, social competitiveness and environmental competitiveness to construct an index system;
and (4) utilizing an entropy weight method to determine the weight of the index, and multiplying the weight of the index by the weight of the index to obtain the weighted comprehensive quality value of each city.
And 3, step 3: building a city space contact network;
(1) Calculating a gravity coefficient and a distance coefficient of each city according to formulas (1) and (2), and calculating comprehensive relation among cities after correcting a gravity model;
(2) Carrying out space assignment on the spatial connection among cities according to the longitude and latitude of each district and county government point;
(3) The spatial contact is spatialized by utilizing an ArcGIS coordinate line-turning tool;
(4) And (3) carrying out hierarchical processing on the spatial relations, referring to the spatial unit definitions and related regulations of each region, processing the minimum value according to a defined target, carrying out statistical analysis on the urban relations which are more than 200km if the urban relations are in a urban circle unit, and pruning the whole relation network after counting the values, as shown in fig. 4.
And 4, step 4: defining a region space unit range;
and realizing a community discovery algorithm according to the urban space network construction result, and defining the unit range of the internal space of the region, as shown in fig. 5. Dividing the network into a plurality of sub-networks according to the principle that the connection degree between the nodes is as small as possible and the connection in the community is as large as possible, and mapping the dividing result on a geospatial entity.
(1) Initializing communities, and regarding each node as a small community;
(2) Calculating the average coding length expected value under the condition that the nodes and the communities are combined according to the formulas (3) to (7), and combining the nodes and the communities which can reduce the average coding length expected value to the minimum;
(3) Repeating the iteration step until the variation of the average coding length expected value is minimum;
(4) Carrying out rationality and reliability inspection on the classification result, and analyzing and correcting the country affiliation by combining with actual development and policy guidance;
1) Verification of regional unit rationality and reliability by constructing traffic equal time circle mode
2) Using the ratio of the result of the defined urban circle to the overlapped area of the corresponding isochronous circle after being overlapped as an index for evaluating the accuracy rate of the defined result;
in the formula, MA i Indicates the area of each city delineation result, IC i Indicates the corresponding equal time circle area, ac i And the accuracy of the demarcation result of each city circle is shown.
(5) Judging the situation of the demarcating precision according to the verification result, wherein more than 75% of the situation is the final result, and the precision can be properly reduced for the large-scale area unit;
and 5: identifying a region space structure;
and (3) performing iterative training and classification until all sample classes are determined, completing urban area range extraction, and quantitatively identifying the regional spatial structure of each urban circle in the Jingjin Ji area as shown in fig. 6.
Preferably, the specific implementation process of step 5 includes the following sub-steps:
step 5.1: calculating the corresponding centrality index value of each city;
and step 5.2: calculating the comprehensive centrality of each city by using a principal component analysis method;
step 5.3: the comprehensive evaluation value of the importance of the urban nodes is divided into three types by using a natural breakpoint method: the city recognition method comprises the following steps of 'center type city', 'transition type city' and 'edge type city', so that the goals of recognizing the center city of the region and the internal structure of the center city are achieved.
The embodiment of the invention provides computer equipment, which comprises: the system comprises a processor, a memory and a bus, wherein the memory stores machine readable instructions executable by the processor, when the computer device runs, the processor and the memory are communicated through the bus, and the processor executes the machine readable instructions to realize the method for quantitatively identifying the regional space structure of the complex network view as described in any one of the above.
Specifically, the memory and the processor can be general-purpose memory and processor, and are not limited in particular, and when the processor runs a computer program stored in the memory, the method for quantitatively identifying the spatial structure of the area from the complex network perspective can be performed.
Those skilled in the art will appreciate that the configuration of the computer device is not limiting of the computer device and may include more or fewer components than illustrated, or some components may be combined, or some components may be split, or a different arrangement of components.
In some embodiments, the computer device may further include a touch screen operable to display a graphical user interface (e.g., a launch interface for an application) and receive user operations with respect to the graphical user interface (e.g., launch operations with respect to the application). A particular touch screen may include a display panel and a touch panel. The Display panel may be configured in the form of an LCD (Liquid Crystal Display), an OLED (Organic Light-Emitting Diode), and the like. The touch panel may collect contact or non-contact operations on or near the touch panel by a user and generate preset operation instructions, for example, operations of the user on or near the touch panel using any suitable object or accessory such as a finger, a stylus, etc. In addition, the touch panel may include two parts of a touch detection device and a touch controller. The touch detection device detects the touch direction and gesture of a user, detects signals brought by touch operation and transmits the signals to the touch controller; the touch controller receives the touch information from the touch detection device, converts the touch information into information which can be processed by the processor, sends the information to the processor, and receives and executes a command sent by the processor. In addition, the touch panel may be implemented by various types such as a resistive type, a capacitive type, an infrared ray, a surface acoustic wave, and the like, and may also be implemented by any technology developed in the future. Further, the touch panel may overlay the display panel, a user may operate on or near the touch panel overlaid on the display panel according to a graphical user interface displayed by the display panel, the touch panel detects an operation thereon or nearby and transmits the operation to the processor to determine a user input, and the processor then provides a corresponding visual output on the display panel in response to the user input. In addition, the touch panel and the display panel can be realized as two independent components or can be integrated.
Corresponding to the starting method of the application program, an embodiment of the present invention further provides a computer-readable storage medium, where the storage medium stores a computer program, and the computer program is configured to execute any one of the above methods for quantitatively identifying a spatial structure of a region of a complex network view.
The starting device of the application program provided by the embodiment of the application program can be specific hardware on the device or software or firmware installed on the device. The device provided by the embodiment of the present application has the same implementation principle and technical effect as the foregoing method embodiments, and for the sake of brief description, reference may be made to the corresponding contents in the foregoing method embodiments where no part of the device embodiments is mentioned. It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the foregoing systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, a division of modules is merely a division of logical functions, and an actual implementation may have another division, and for example, a plurality of modules or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of devices or modules through some communication interfaces, and may be in an electrical, mechanical or other form.
Modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical modules, may be located in one place, or may be distributed on a plurality of network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment.
In addition, functional modules in the embodiments provided in the present application may be integrated into one processing module, or each module may exist alone physically, or two or more modules are integrated into one module.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solutions of the present invention and not for limiting the same, and although the present invention is described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: modifications and equivalents may be made to the embodiments of the invention without departing from the spirit and scope of the invention, which is to be covered by the claims.
Claims (10)
1. A quantitative identification method for a regional space structure of a complex network view angle is characterized by comprising the following steps:
collecting multi-source data reflecting urban natural and humanistic development conditions, and preprocessing the multi-source data;
constructing a city comprehensive quality evaluation index system reflecting city attractive capacity and radiation capacity;
calculating city comprehensive quality and inter-city time cost distance, and constructing a city comprehensive space contact network;
according to the urban space contact network, a community discovery algorithm is utilized to define the range of the regional space unit;
and constructing node centrality indexes, identifying the importance of each city in the region and quantitatively measuring the internal space structure of the region.
2. The method for quantitatively identifying the regional spatial structure of a complex network view according to claim 1, wherein the multi-source data comprises statistical data, surface coverage data, road network data, POI points, night light data and atmospheric environment data.
3. The method for quantitatively identifying the regional spatial structure of the complex network view of claim 1, wherein the preprocessing the multi-source data comprises:
converting the spatial coordinate system of the multi-source data into a unified coordinate system;
converting the statistical data into panel data;
carrying out statistical analysis on the vector and the raster data according to the county scale;
and combining the spatial data calculation result and the statistical data and arranging the spatial data calculation result and the statistical data into panel data.
4. The method for quantitatively identifying the regional spatial structure of the complex network view according to claim 1, wherein the constructing of the urban comprehensive quality evaluation index system reflecting the urban attractive capacity and the urban radiative capacity comprises:
selecting indexes from four aspects of economic competitiveness, potential competitiveness, social competitiveness and environmental competitiveness based on multi-source data;
and constructing an urban comprehensive quality evaluation index system which is divided into a target layer, a criterion layer and an index layer from top to bottom.
5. The method for quantitatively identifying the regional space structure of the complex network view according to claim 1, wherein before constructing the urban integrated space connection network, a gravity coefficient and a distance coefficient in a traditional gravity model need to be modified, and the modification process comprises:
1) Correcting a gravity coefficient in a traditional gravity model:
wherein k is the corrected gravity coefficient; m i And M j The comprehensive quality value of the city i and the city j is obtained;
2) And (3) correcting the distance coefficient in the traditional gravity model:
wherein D is the distance between two cities under the condition of time cost; t is the driving time between two cities; r is the shortest distance between two cities.
6. The method for quantitatively identifying the regional space structure of the complex network view according to claim 1, wherein the step of using a community discovery algorithm to define the range of regional space units according to the urban space contact network comprises:
initializing communities, and regarding each node as a small community;
the nodes move randomly and calculate average coding length expected values L (M) under the condition that the nodes and the communities are combined respectively, the nodes and the communities which can reduce L (M) to the minimum are combined, and the step is repeatedly executed until the variation of L (M) is minimum; the calculation formula of L (M) is as follows:
p i ＝q i +∑ α∈i p α (5)
wherein L (M) represents the average encoding length expected value of the path along which the random walk in the community and the community; q represents the probability of random walks between communities, q i And pi represents the probability of random walks within the community; pi represents the probability m of random walk within a community represents the number of communities; h (Q), H (P) i ) Entropy respectively representing the motion probability of random walk between communities and in communities;
and according to the property of the Huffman coding, the shortest random walk coding length corresponds to the optimal community delineation result, and the shortest coding length result is selected as the identification result of the region space unit range.
7. The method for quantitatively identifying the regional spatial structure of the complex network view of any one of claims 1 to 6, wherein the constructing of the node centrality index comprises:
calculating a centrality index:
in the formula, n is the total number of the city nodes; a is ji Representing a contact edge with a starting point j and an end point i; w is a ji Is a contact weight which represents that the starting point is j and the end point is i; a is ij Representing a contact edge with a starting point i and an end point j; w is a ij Is the contact weight which represents the starting point as i and the end point as j;
and
representing weighted in-degree and weighted out-degree of node i, D i The sum of the degrees of representation;
calculating an approximate centrality index:
wherein CC i Is the approximate centrality value of each node, n is the total number of city nodes, d i (i, j) is the shortest distance between nodes i and j;
calculating the intermediate centrality index:
wherein BC i Is the intermediate centrality value, N, of each node jk Represents the total number of shortest paths from node j to k; n is a radical of jk (i) Representing the shortest path number between the node j and the node k through the node i;
calculating the centrality index of the feature vector:
wherein EC i Is the centrality value of the feature vector of each node, c is the proportionality constant, A is the adjacency matrix, and if there is a connection between the node and (i, j), then a ij =1, otherwise 0; lambda [ alpha ] 1 ,λ 2 ,……,λ n Representing the eigenvalues of the adjacency matrix, and each eigenvalue corresponding to a vector x = [ x = 1 ,x 2 ,x 3 ,……x n ]；
Calculating the PageRank index:
wherein PR i Is the PageRank value for node i; q is a constant term; n is the total number of nodes; j represents the strength of the connection from city i to city j; l is j The total number of all connected edges starting from node i and weighted by the strength of contact;
the importance of each city in the identification area comprises the following steps:
and (3) carrying out comprehensive evaluation on the urban nodes by adopting a principal component analysis method:
Y i ＝β 1 D i +β 2 CC i +β 3 BC i +β 4 EC i +β 5 PR i (16)
wherein Y is i Beta is a main component value of each centrality measurement value corresponding to the urban node importance comprehensive evaluation value;
and constructing a multivariable linear function to analyze and sequence the importance of each node in the region range.
8. A device for quantitatively identifying a regional space structure of a complex network view angle is characterized by comprising:
the data acquisition module is used for acquiring multi-source data reflecting the natural and humanistic development conditions of a city and preprocessing the multi-source data;
the evaluation index system building module is used for building a city comprehensive quality evaluation index system reflecting city attraction capacity and radiation capacity;
the contact network construction module is used for calculating city comprehensive quality and inter-city time cost distance and constructing a city comprehensive space contact network;
the space unit demarcation module is used for demarcating the range of the area space unit by utilizing a community discovery algorithm according to the urban space contact network;
and the space structure measurement module is used for constructing node centrality indexes, identifying the importance of each city in the region and quantitatively measuring the internal space structure of the region.
9. A computer device, comprising: a processor, a memory and a bus, wherein the memory stores machine readable instructions executable by the processor, when the computer device runs, the processor and the memory communicate through the bus, and the processor executes the machine readable instructions to realize the method for quantitatively identifying the regional spatial structure of the complex network view according to any one of claims 1 to 7.
10. A computer-readable storage medium, wherein the storage medium stores a computer program for executing the method for quantitatively identifying a spatial structure of a region from a complex network view according to any one of claims 1 to 7.
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