CN116797045A - Urban pattern analysis method for night lamplight fusion multi-source data - Google Patents
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Abstract
The application provides a method for analyzing town pattern of night lamplight fusion multi-source data, which comprises the following steps: step S1: constructing a multi-source space-time database; step S2: establishing a new space observation unit; step S3: calculating town quality indexes; step S4: establishing a town group i; step S5: calculating the total mass of the group townsThe method comprises the steps of carrying out a first treatment on the surface of the Step S6: calculating ecological sustainable development coefficient of groupThe method comprises the steps of carrying out a first treatment on the surface of the Step S7: calculating urban and rural overall capacity coefficient of groupThe method comprises the steps of carrying out a first treatment on the surface of the Step S8: calculating city function service coefficient of groupThe method comprises the steps of carrying out a first treatment on the surface of the Step S9: calculating the alternating resistance coefficient of the groupThe method comprises the steps of carrying out a first treatment on the surface of the Step S10: calculating group distanceThe method comprises the steps of carrying out a first treatment on the surface of the Step S11: measuring attractive forces between town groupsThe method comprises the steps of carrying out a first treatment on the surface of the Step S12: and evaluating the novel urbanization level change condition. By the technical scheme, the novel urban describing method of the high-precision, high-definition and spatial region can be realized, short plates and advantages of regional urban development are analyzed, and important reference basis can be provided for relevant decisions of related departments.
Description
Technical Field
The application relates to the technical field of urbanization evaluation and simulation, in particular to a method for analyzing patterns of township by fusing multi-source data with night light.
Background
The existing novel urbanization evaluation method mainly focuses on the development of statistical data and investigation data on each layer, the evaluation range which is divided into statistical units by administrative areas cannot be broken through all the time, the data is easily limited by subjective judgment of data collectors, and meanwhile, the data has strong hysteresis, so that timeliness and accuracy of the data are difficult to ensure, and the uniformity of a research range is also difficult to ensure.
Disclosure of Invention
Therefore, the application aims to provide the analysis method for the urban structure pattern of the night light fusion multisource data, which realizes the novel urban depiction method for the high-precision, high-definition and spatial areas, further analyzes the short plates and advantages of regional urban development and can provide important reference for relevant decisions of related departments.
In order to achieve the above purpose, the application adopts the following technical scheme: a method for analyzing town pattern of night light fusion multisource data comprises the following steps:
step S1: the night light is fused with the multi-source data to construct a multi-source space-time database;
step S2: based on night light data, combining multisource auxiliary data, adopting a multiscale space segmentation algorithm, breaking administrative area boundaries, and establishing a new space observation unit;
step S3: extracting night lamplight, impermeable water, land utilization type and mobile phone signaling population data, and calculating town quality indexes;
step S4: combining the remote sensing image base map and land utilization data, determining a town quality index threshold according to a town boundary, extracting an observation unit higher than the threshold, and combining the observation unit into a township group i according to spatial position proximity;
step S5: calculating the total mass M of the group towns based on the town mass index and the area of the township group i ；
Step S6: extracting data such as land utilization type, biological resource consumption, energy resource consumption, water resource consumption and the like, and calculating group ecological sustainable development coefficient B ij ；
Step S7: extracting urban and rural population overall arrangement capacity, economic overall arrangement capacity, social overall arrangement capacity and life overall arrangement capacity, and calculating urban and rural overall arrangement capacity coefficient C of the group ij ；
Step S8: based on poi data and road network data, extracting convenience facilities, public service facility positions and road network density, and calculating group city function service coefficients E ij ；
Step S9: calculating the alternating resistance coefficient of the group according to the traffic speed of the urban road network
Step S10: calculating group distance d according to the position of the center of gravity of the town group ij ；
Step S11: comprehensive group urbanization total mass, ecological sustainable development coefficient, urban and rural overall capacity coefficient, urban function service coefficient, alternating resistance coefficient and group distance, and is brought into a urbanization gravitation model to measure attractive force F between town groups ij ；
Step S12: and setting future development scenes by combining with actual development conditions, observing the urban mass, ecological sustainable development, urban and rural overall capacity, economic attractiveness and group attraction change conditions of the group, and evaluating novel urban level change conditions.
In a preferred embodiment, in step S3, the core formula is as follows:
wherein I is town quality index, light is night light brightness, landc is through building land occupation index, imper is water impermeable surface index, pop is mobile phone signaling population data, and the data are substituted into the formula after normalization.
In a preferred embodiment, in step S5, the core formula is as follows:
wherein I is j For town mass index of grid j inside the cluster, n represents the number of grids in the cluster, M i The total mass was township for group i.
In a preferred embodiment, in step S6, the core formula is as follows:
B i ＝ec/(ec+ef)
ec＝β j ×R j ×Y j
wherein B is ij For the ecological sustainable development coefficient between the town groups i and j, B i The ecological sustainable development index of the group i is ec, the ecological bearing capacity of the average person, and ef is the ecological footprint of the average person; aa i The average human organism production area converted for i consumption projects, i being the type of consumed projects and input; c i Is the average consumption of i kinds of consumer products, p i Average capacity for i consumer products; beta i For the i-th ecological productive land area of people's average, R i As an equalizing factor, Y i The ratio of regional yield to world average yield was calculated as yield factor.
In a preferred embodiment, in step S7, the core formula is as follows:
C i ＝k i β i
wherein C is ij For urban and rural overall capacity coefficient between town groups i and j, C i For i group urban and rural overall capacity index, k i For coefficient weight, determining weight by entropy method or analytic hierarchy process, beta i Capacity factors are comprehensively formulated for cities and countryside.
In a preferred embodiment, in step S8, the core formula is as follows:
E i ＝k i δ i
wherein E is ij Service coefficients, E, for urban functions between urbanized groups i and j i Is i group cityMarket function service index, E i 、E j Urban functional service levels, k, for town groups i, j, respectively i For coefficient weights, the weights may be determined using entropy or analytic hierarchy process,serve the city functions.
In a preferred embodiment, in step S9, the core formula is as follows:
in the method, in the process of the application,is the urban alternating current resistance coefficient, v ij Is the average speed between cities.
In a preferred embodiment, in step S11, the core formula is as follows:
wherein F is ij For the spatial link strength between town groups i and j, i.e. the magnitude of the attractive force between groups, B ij To be the sustainable development coefficient of ecology, C ij For urban and rural overall capacity coefficient, E ij The coefficients are served for the functions of the city,is the urban alternating current resistance coefficient d ij For the distance of the center of gravity of the regions of town groups i and j, M i For the total mass of towns of the i town group, M j And the total mass of towns of the j town groups.
In a preferred embodiment, in step S12, the future scenario is reasonably set according to the new urban urbanization development stage and the future planning direction by modifying parameters, including the reference scenario, the planning construction scenario, the urban construction scenario of the county important carrier, the urban and rural element accelerated flow scenario, and the urban and rural fusion development scenario.
In a preferred embodiment, specifically:
the reference scene is the condition of predicting future population and economic development index change by adopting a linear regression and Markov model according to the historical development trend;
planning and constructing a scene to be a reference country and earth space overall planning related index, and determining the area and population number of a future town area;
the county region is an important carrier, and the urban construction scene starts from the county region economy, so that the economic strength index of part of county region is improved according to local conditions;
the urban and rural element accelerating flow scene is used for strengthening urban and rural traffic connection and reducing urban and rural element flow resistance;
the urban and rural fusion development scenario is an index for improving urban and rural overall planning capability and improves urban and rural fusion level.
Compared with the prior art, the application has the following beneficial effects: the application develops a measure method of urban mass and urban space attraction based on multisource space-time data such as night lamplight, impermeable water surface, mobile phone signaling and the like on the basis of an gravitation model, and breaks urban research constraint of administrative division boundaries by taking urban groups as analysis units, thereby realizing novel urban development depiction with high precision and high definition. On the basis, future change trend and spatial pattern of towns are predicted, and effective advice for building novel towns is provided, so that the method has strong practical significance for promoting novel towns in various areas, solving the situation of unbalanced development among various areas and promoting the coordinated development of the areas.
Drawings
Fig. 1 is a functional block diagram of a preferred embodiment of the present application.
Detailed Description
The application is further illustrated below with reference to examples.
It should be noted that the following detailed description is illustrative and is intended to provide further explanation of the application. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of exemplary embodiments according to the present application; as used herein, the singular is also intended to include the plural unless the context clearly indicates otherwise, and furthermore, it is to be understood that the terms "comprises" and/or "comprising" when used in this specification are taken to specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof.
A method for analyzing town pattern of night light fusion multisource data comprises the following steps:
step S1: the night light is fused with the multi-source data to construct a multi-source space-time database;
step S2: based on night light data, combining multisource auxiliary data, adopting a multiscale space segmentation algorithm, breaking administrative area boundaries, and establishing a new space observation unit;
step S3: extracting night lamplight, impermeable water, land utilization type and mobile phone signaling population data, and calculating town quality index;
step S4: combining the remote sensing image base map and land utilization data, determining a town quality index threshold according to a town boundary, extracting an observation unit higher than the threshold, and combining the observation unit into a township group i according to spatial position proximity;
step S5: calculating the total mass M of the group towns based on the town mass index and the area of the township group i 。
Step S6: extracting data such as land utilization type, biological resource consumption, energy resource consumption, water resource consumption and the like, and calculating group ecological sustainable development coefficient B ij ；
Step S7: extracting urban and rural population overall arrangement capacity, economic overall arrangement capacity, social overall arrangement capacity and life overall arrangement capacity, and calculating urban and rural overall arrangement capacity coefficient C of the group ij ；
Step S8: based on poi data and road network data, extracting convenience facilities, public service facility positions and road network density, and calculating group city function service systemNumber E ij ；
Step S9: calculating the alternating resistance coefficient of the group according to the traffic speed of the urban road network
Step S10: calculating group distance d according to the position of the center of gravity of the town group ij ；
Step S11: comprehensive group urbanization total mass, ecological sustainable development coefficient, urban and rural overall capacity coefficient, urban function service coefficient, alternating resistance coefficient and group distance, and is brought into a urbanization gravitation model to measure attractive force F between town groups ij ；
Step S12: by combining with the actual development situation, reasonably setting future development situations, observing changes in aspects of mass of towns, ecological sustainable development, urban and rural overall planning capability, economic attractiveness, group attractiveness and the like of groups, and evaluating the changes of novel towns;
according to the situation and the change condition of the novel township pattern, the economic input-output ratio is calculated, and the effective proposal for constructing the novel township is provided.
Further, in step S3, the core formula is as follows:
wherein I is town quality index, light is night light brightness, landc is through building land occupation index, imper is water impermeable surface index, pop is mobile phone signaling population data, and the data are substituted into the formula after normalization.
Further, in step S5, the core formula is as follows:
wherein I is j For town mass index of grid j inside the cluster, n represents the number of grids in the cluster, M i The total mass was township for group i.
Further, in step S6, the core formula is as follows:
B i ＝ec/(ec+ef)
ec＝β j ×R j ×Y j
wherein B is ij For the ecological sustainable development coefficient between the town groups i and j, B i And the ecological sustainable development index of the group i is ec, the ecological bearing capacity of the average person, and ef is the ecological footprint of the average person. aa i The average human organism production area converted for i consumption projects, i being the type of consumed projects and input; c i Is the average consumption of i kinds of consumer products, p i Is the average capacity of i consumer products. Beta i For the i-th ecological productive land area of people's average, R i As an equalizing factor, Y i As a yield factor (calculating the ratio of regional yield to world average yield);
further, in S7, the core formula is as follows:
C i ＝k i β i
wherein C is ij For urban and rural overall capacity coefficient between town groups i and j, C i For i group urban and rural overall capacity index, k i For coefficient weight, the weight, beta can be determined by entropy method or analytic hierarchy process i For urban and rural overall capacity factors, the factor evaluation system is shown in table 1:
TABLE 1 urban and rural overall capacity assessment system
Further, in S8, the core formula is as follows:
E i ＝k i δ i
wherein E is ij Service coefficients, E, for urban functions between urbanized groups i and j i Serve index for i group urban functions, E i 、E j Urban functional service levels, k, for town groups i, j, respectively i For coefficient weights, the weights may be determined using entropy or analytic hierarchy process,the factor evaluation system for the city function service factors is shown in table 2.
Table 2 City functional service assessment System
Further, in S9, the core formula is as follows:
in the method, in the process of the application,is the urban alternating current resistance coefficient, v ij Is the average speed between cities.
Further, in S11, the core formula is as follows:
wherein F is ij For the spatial link strength between town groups i and j, i.e. the magnitude of the attractive force between groups, B ij To be the sustainable development coefficient of ecology, C ij For urban and rural overall capacity coefficient, E ij The coefficients are served for the functions of the city,is the urban alternating current resistance coefficient d ij For the distance of the center of gravity of the regions of town groups i and j, M i For the total mass of towns of the i town group, M j And the total mass of towns of the j town groups. The data are subjected to normalization processing and then substituted into a formula for calculation.
Further, in S12, according to the new urban development stage and the future planning direction, the future scenario is reasonably set by modifying parameters, including the scenarios of benchmark scenario, planning construction scenario, urban construction scenario where county is an important carrier, urban and rural element accelerated flow scenario, urban and rural fusion development scenario, and the like.
Reference scenario: and predicting future population and economic development index change conditions by adopting a linear regression and Markov model according to the historical development trend.
Planning a construction scene: and determining the area, population number and the like of a future town area by referring to the relevant indexes of the global planning of the homeland space.
County is a town construction scenario with important carriers: starting from the county economy, the economic strength index of part of county is improved according to local conditions.
Urban and rural element accelerated flow scenario: strengthening urban and rural traffic connection and reducing urban and rural element flow resistance.
Urban and rural fusion development scenario: the urban and rural overall capacity index is improved, and the urban and rural integration level is improved.
Claims (10)
1. The urban pattern analysis method for night lamplight fusion multi-source data is characterized by comprising the following steps of:
step S1: the night light is fused with the multi-source data to construct a multi-source space-time database;
step S2: based on night light data, combining multisource auxiliary data, adopting a multiscale space segmentation algorithm, breaking administrative area boundaries, and establishing a new space observation unit;
step S3: extracting night lamplight, impermeable water, land utilization type and mobile phone signaling population data, and calculating town quality indexes;
step S4: combining the remote sensing image base map and land utilization data, determining a town quality index threshold according to a town boundary, extracting an observation unit higher than the threshold, and combining the observation unit into a township group i according to spatial position proximity;
step S5: calculating the total mass M of the group towns based on the town mass index and the area of the township group i ；
Step S6: extracting data such as land utilization type, biological resource consumption, energy resource consumption, water resource consumption and the like, and calculating group ecological sustainable development coefficient B ij ；
Step S7: extracting urban and rural population overall arrangement capacity, economic overall arrangement capacity, social overall arrangement capacity and life overall arrangement capacity, and calculating urban and rural overall arrangement capacity coefficient C of the group ij ；
Step S8: based on poi data and road network data, extracting convenience facilities, public service facility positions and road network density, and calculating group city function service coefficients E ij ；
Step S9: calculating the alternating resistance coefficient of the group according to the traffic speed of the urban road network
Step S10: calculating group distance d according to the position of the center of gravity of the town group ij ；
Step S11: comprehensive group urbanization total mass, ecological sustainable development coefficient, urban and rural overall capacity coefficient, urban function service coefficient, alternating resistance coefficient and group distance, and is brought into a urbanization gravitation model to measure attractive force F between town groups ij ；
Step S12: and setting future development scenes by combining with actual development conditions, observing the urban mass, ecological sustainable development, urban and rural overall capacity, economic attractiveness and group attraction change conditions of the group, and evaluating novel urban level change conditions.
2. The method for analyzing patterns of towns of night light fusion multisource data according to claim 1, wherein the method comprises the steps of
In step S3, the core formula is as follows:
wherein I is town quality index, light is night light brightness, landc is through building land occupation index, imper is water impermeable surface index, pop is mobile phone signaling population data, and the data are substituted into the formula after normalization.
3. The method for analyzing patterns of night light fusion multisource data according to claim 1, wherein in step S5, the core formula is as follows:
wherein I is j For town mass index of grid j inside the cluster, n represents the number of grids in the cluster, M i The total mass was township for group i.
4. The method for analyzing patterns of night light fusion multisource data according to claim 1, wherein in step S6, the core formula is as follows:
B i ＝ec/(ec+ef)
ec＝β j ×R j ×Y j
wherein B is ij For the ecological sustainable development coefficient between the town groups i and j, B i The ecological sustainable development index of the group i is ec, the ecological bearing capacity of the average person, and ef is the ecological footprint of the average person; aa i The average human organism production area converted for i consumption projects, i being the type of consumed projects and input; c i Is the average consumption of i kinds of consumer products, p i Average capacity for i consumer products; beta i For the i-th ecological productive land area of people's average, R i As an equalizing factor, Y i The ratio of regional yield to world average yield was calculated as yield factor.
5. The method for analyzing patterns of night light fusion multisource data according to claim 1, wherein in step S7, the core formula is as follows:
C i ＝k i β i
wherein C is ij For urban and rural overall capacity coefficient between town groups i and j, C i For i group urban and rural overall capacity index, k i For coefficient weight, determining weight by entropy method or analytic hierarchy process, beta i Capacity factors are comprehensively formulated for cities and countryside.
6. The method for analyzing patterns of night light fusion multisource data according to claim 1, wherein in step S8, the core formula is as follows:
E i ＝k i δ i
wherein E is ij Service coefficients, E, for urban functions between urbanized groups i and j i Serve index for i group urban functions, E i 、E j Urban functional service levels, k, for town groups i, j, respectively i For coefficient weights, the weights may be determined using entropy or analytic hierarchy process,serve the city functions.
7. The method for analyzing patterns of night light fusion multisource data according to claim 1, wherein in step S9, the core formula is as follows:
in the method, in the process of the application,is the urban alternating current resistance coefficient, v ij Is the average speed between cities.
8. The method for analyzing patterns of night light fusion multisource data according to claim 1, wherein in step S11, the core formula is as follows:
wherein F is ij For the spatial link strength between town groups i and j, i.e. the magnitude of the attractive force between groups, B ij To be the sustainable development coefficient of ecology, C ij For urban and rural overall capacity coefficient, E ij The coefficients are served for the functions of the city,is the urban alternating current resistance coefficient d ij For the distance of the center of gravity of the regions of town groups i and j, M i For the total mass of towns of the i town group, M j And the total mass of towns of the j town groups.
9. The method for analyzing patterns of towns of night light fusion multisource data according to claim 1, wherein in step S12, future situations including benchmark situations, planning construction situations, county-area important carrier towns construction situations, urban and rural element accelerated flow situations, urban and rural fusion development situations are reasonably set according to new urban towns development stage and future planning direction modification parameters.
10. The method for analyzing patterns of towns of night light fusion multisource data according to claim 9, wherein the method is characterized in that:
the reference scene is the condition of predicting future population and economic development index change by adopting a linear regression and Markov model according to the historical development trend;
planning and constructing a scene to be a reference country and earth space overall planning related index, and determining the area and population number of a future town area;
the county region is an important carrier, and the urban construction scene starts from the county region economy, so that the economic strength index of part of county region is improved according to local conditions;
the urban and rural element accelerating flow scene is used for strengthening urban and rural traffic connection and reducing urban and rural element flow resistance; the urban and rural fusion development scenario is an index for improving urban and rural overall planning capability and improves urban and rural fusion level.
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