CN111833224A - Urban main and auxiliary center boundary identification method based on population grid data - Google Patents
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Abstract
The invention discloses a city main and auxiliary center boundary identification method based on population grid data, which comprises the steps of firstly, acquiring city population grid data, importing ArcGIS software to preprocess the city population grid data, and generating vector data of city population distribution; secondly, local spatial autocorrelation analysis is carried out on the urban population distribution vector data generated in the last step, and an area formed by population grids with high-high attribute values is selected as a potential urban center; and thirdly, according to continuity and scale requirements which the city center range should have, screening areas meeting the requirements from the potential city centers identified in the last step to serve as the city centers, wherein the area with the largest population number is the main center, and the rest are auxiliary centers. The method identifies the boundaries of the urban main and auxiliary centers by means of the free open population grid data, has the characteristics of low cost, easiness in operation and high speed, and is favorable for scientifically and reasonably demarcating the urban center boundaries in urban planning.
Description
Technical Field
The invention relates to the technical field of urban planning data analysis, in particular to a method for identifying urban main and auxiliary center boundaries based on population grid data.
Background
Along with the rapid promotion of the urbanization process of China, the urban space is continuously expanded outwards, and a series of urban diseases such as traffic jam, sharply reduced cultivated land and open space, environmental pollution and the like appear. Under the background, the research of urban space development mode is highly concerned in the urban research field at home and abroad, and a space structure mode beneficial to urban sustainable development is hopefully found, so that the recognition of urban multiple centers, economic performance, influence research on resident life, traffic, environment and the like become the focus of attention of scholars. The recognition of the urban multi-center is the premise and the basis for developing the urban multi-center space structure research, and the improvement of the urban multi-center recognition method has important significance for further developing the urban multi-center space structure research. From the perspective of morphological multicenter, a multicenter city is defined as a city that is made up of a set of city centers that have a relatively uniform distribution of importance. Under the definition, city center identification becomes the core of multi-center city measurement, and at present, two methods, namely parametric and nonparametric methods, are formed for city center identification.
The parametric method is to identify the population center of a city by setting absolute or relative threshold values of measure indexes (such as population density and employment density). The method has two limitations, namely, the method has larger randomness when the index threshold value is set, and the method is not beneficial to comparative research of different cities. The nonparametric method is to identify the city center by methods such as local weighted regression, spatial statistics, kernel density analysis, spatial clustering and the like. The non-parameter method overcomes some defects of the parameter method, considers the interaction between the city center and the surrounding area, and simultaneously avoids the randomness of setting the threshold value subjectively. However, the non-parametric method often needs to have more detailed city basic data, needs to pre-define CBD and city center, and has the disadvantages of high data acquisition difficulty and complex operation.
In recent years, with the continuous development of high-resolution population data availability and spatial analysis technology, new ideas and methods are provided for city center identification. The high-resolution population data breaks through the spatial scale restriction of the traditional statistical data taking county and city as statistical units, provides data support for exploring city centers, and the development of the spatial analysis technology provides possibility for overcoming the randomness of a parameter method and the complexity problem of a nonparametric method, thereby being beneficial to better identifying city multiple centers.
Disclosure of Invention
The invention aims to solve the technical problem of providing a method for identifying the boundaries of main and auxiliary urban centers based on population grid data, identifying the boundaries of the main and auxiliary urban centers by means of the free open population grid data, having the characteristics of low cost, easy operation and high speed, and being beneficial to scientifically and reasonably demarcating the boundaries of the urban centers in urban planning.
In order to solve the technical problem, the invention provides a method for identifying the boundaries of main and auxiliary cities based on population grid data, which comprises the following steps:
(1) extracting population grid data of a researched city from a free open global population grid data set such as LandScan, WorldPop and the like;
(2) importing the urban population grid data acquired in the step (1) into ArcGIS software for preprocessing to generate vector data of urban population distribution for further analysis;
(3) performing local spatial autocorrelation analysis on the urban population distribution vector data generated in the step (2), acquiring spatial clustering characteristics of urban population distribution, screening out an area formed by population grids with high-high (HH) attribute values as a potential urban center, and taking the boundary of the formed area as a potential urban center boundary;
(4) further selecting the potential city centers screened in the step (3) by combining the continuity and scale requirements of the city centers, wherein the elimination range is discontinuous, the area is less than 2 square kilometers, or the population scale is less than 5 ten thousand potential city centers;
(5) and (4) aiming at the city center selected in the step (4), selecting the center with the largest population scale as a main city center, taking the rest centers as auxiliary city centers, and taking the corresponding boundaries as the boundaries of the main city center and the auxiliary city center.
Preferably, in the step (1), the step of extracting the population grid data of the city under study from the free open global population grid data set such as LandScan and WorldPop specifically comprises the following steps:
(11) selecting a proper population grid database according to the requirement of research precision, downloading and importing ArcGIS software;
(12) and cutting the downloaded population grid database according to the city range boundary to obtain city population grid data corresponding to the city boundary.
Preferably, in the step (2), the step of preprocessing the city population grid data acquired in the step (1) to generate vector data of city population distribution for further analysis specifically includes the following steps:
(21) converting the geographic coordinates into projection coordinates by the city population grid data acquired in the step (12) through a projection grid tool;
(22) converting the city population grid data converted in the step (21) into vector point data through a grid point conversion tool;
(23) and (4) creating a fishing net which is consistent with the pixel size and the coordinate of the urban population raster data through a fishing net creating tool, and performing spatial link with the vector point data obtained through conversion in the step (22) to obtain the vector data of the urban population distribution.
Preferably, in the step (3), the local spatial autocorrelation analysis is performed on the city population distribution vector data generated in the step (2), so as to obtain the spatial clustering feature of the city population distribution, and a region composed of population grids with high-high (HH) attribute values is screened out as a potential city center, and a boundary of the composed region as a potential city center boundary specifically includes the following steps:
(31) according to a clustering and abnormal value analysis (LISA) tool in a spatial statistic tool in ArcGIS software, local spatial autocorrelation analysis is carried out on urban population vector data by using an inverse distance weighting method to form attribute grids such as high-high (HH), high-low (HL), low-high (LH), low-low (LL) and the like;
(32) according to ArcGIS software, a region composed of high-high (HH) attribute grids is screened as a potential city center, and the boundary of the composed region is used as a potential city center boundary.
Preferably, in the step (4), the potential city center selected in the step (3) is further selected according to the continuity and scale requirements that the city center should have, and the potential city center with a discontinuous removal range and an area less than 2 square kilometers or with a population size less than 5 ten thousand people specifically includes the following steps:
(41) according to continuity and scale requirements of a city center, combining practical experience of city planning, and taking people with an area not less than 2 square kilometers and a population scale not less than 5 ten thousand as a scale threshold of the city center;
(42) in ArcGIS software, according to the determined scale threshold value of the city center, the region which has a continuous range, is not less than 2 square kilometers in area and has the population scale not less than 5 ten thousand persons is selected from the potential city centers screened in the step (32) and is used as the city center.
Preferably, in the step (5), the center with the largest population size is selected as the city main center, and the rest centers are specifically selected as the city subsidiary centers as: and (4) according to the city center selected in the step (42), selecting the region boundary with the largest population scale as a main city center boundary, and using the boundaries of the rest regions as auxiliary city center boundaries.
The invention has the beneficial effects that: the method identifies the boundaries of the urban main and auxiliary centers by means of the free open population grid data, has the characteristics of low cost, easiness in operation and high speed, and is favorable for scientifically and reasonably demarcating the urban center boundaries in urban planning.
Drawings
FIG. 1 is a schematic flow chart of the method of the present invention.
Fig. 2 is a schematic diagram of the grid data of Nanjing City population according to the present invention.
Fig. 3 is a schematic diagram of the vector data of the population distribution in Nanjing City according to the present invention.
Fig. 4 is a schematic diagram of the result of the local autocorrelation analysis of the population distribution in Nanjing City according to the present invention.
Fig. 5 is a schematic diagram of the boundary of potential city centers in Nanjing City of the invention.
Fig. 6 is a schematic diagram of the boundary between the main center and the auxiliary center in Nanjing City of the invention.
Detailed Description
As shown in fig. 1, a method for identifying the boundaries of major and minor cities based on population grid data includes the following steps:
(1) extracting population grid data of a researched city from a free open global population grid data set such as LandScan, WorldPop and the like;
(2) importing the urban population grid data acquired in the step (1) into ArcGIS software for preprocessing to generate vector data of urban population distribution for further analysis;
(3) performing local spatial autocorrelation analysis on the urban population distribution vector data generated in the step (2), acquiring spatial clustering characteristics of urban population distribution, screening out an area formed by population grids with high-high (HH) attribute values as a potential urban center, and taking the boundary of the formed area as a potential urban center boundary;
(4) further selecting the potential city centers screened in the step (3) by combining the continuity and scale requirements of the city centers, wherein the elimination range is discontinuous, the area is less than 2 square kilometers, or the population scale is less than 5 ten thousand potential city centers;
(5) and (4) aiming at the city center selected in the step (4), selecting the center with the largest population scale as a main city center, taking the rest centers as auxiliary city centers, and taking the corresponding boundaries as the boundaries of the main city center and the auxiliary city center.
The technical scheme of the invention is described in detail by combining a method case of urban major-minor center boundary identification based on population grid data in Nanjing urban domain range (about 6587 square kilometers in area and about 840 million resident people) and accompanying drawings, and the invention comprises the following steps:
A. the population grid data of the researched city is extracted from a free open global population grid data set such as LandScan, WorldPop and the like.
A1. Downloading a LandScan global population grid data set according to the requirement of research precision of 1 km-1 km, and importing ArcGIS software;
A2. and cutting the downloaded population grid database according to the Nanjing city range boundary to acquire Nanjing city population grid data corresponding to the Nanjing city boundary, as shown in FIG. 2.
B. And preprocessing the acquired Nanjing city population grid data to acquire vector data of Nanjing city population distribution.
B1. Converting the geographic coordinates into projection coordinates by the Nanjing city population grid data acquired in the step A2 through projection grid data;
B2. converting the Nanjing city population grid data converted in the step B1 into vector point data through a grid point conversion tool;
B3. and (3) creating a fishing net which is consistent with the pixel size and the coordinate of the urban population raster data through a fishing net creating tool, and performing spatial link with vector point data obtained by conversion in B2 to obtain vector data of Nanjing urban population distribution, as shown in FIG. 3.
C. And C, carrying out local autocorrelation analysis on the Nanjing city population distribution vector data generated in the step B, obtaining the spatial clustering characteristics of the Nanjing city population distribution, screening a region formed by population grids with high-high (HH) attribute values as a potential Nanjing city center, and taking the boundary of the formed region as the potential Nanjing city center boundary.
C1. According to a clustering and abnormal value analysis (LISA) tool in a spatial statistic tool in the ArcGIS software, local spatial autocorrelation analysis is performed on Nanjing city population vector data by using an inverse distance weight method to form a high-high (HH), high-low (HL), low-high (LH), low-low (LL) and other attribute grids as shown in FIG. 4;
C2. according to the ArcGIS software, a region composed of the high-high (HH) attribute grid is screened as a potential city center of Nanjing City, and the boundary of the composed region is used as a potential city center boundary of Nanjing City, as shown in FIG. 5.
D. C, further selecting potential city centers in Nanjing city screened in the step C according to the continuity and scale requirements of the city centers, wherein the potential city centers are discontinuous in removal range, less than 2 square kilometers in area or less than 5 ten thousand people in population scale;
D1. according to the continuity and scale requirements of the city center, combining practical experience of city planning, and taking people with the area not less than 2 square kilometers and the population scale not less than 5 ten thousand as the scale threshold of the city center of Nanjing;
D2. in ArcGIS software, according to the determined scale threshold value of the urban center of Nanjing city, a continuous area with the area not less than 2 square kilometers and the population scale not less than 5 ten thousand persons is selected as the potential population center of Nanjing city.
E. And D, selecting the regional boundary with the largest population scale as the main central boundary of the Nanjing city according to the potential city center selected in the step D, and using the boundaries of the rest regions as the auxiliary central boundaries of the Nanjing city, as shown in FIG. 6.
Claims (6)
1. A city major-minor center boundary identification method based on population grid data is characterized by comprising the following steps:
(1) extracting population grid data of the researched city from the global population grid data set;
(2) importing the urban population grid data acquired in the step (1) into ArcGIS software for preprocessing to generate vector data of urban population distribution for further analysis;
(3) performing local spatial autocorrelation analysis on the urban population distribution vector data generated in the step (2), acquiring spatial clustering characteristics of urban population distribution, screening out an area formed by population grids with high-high attribute values as a potential urban center, and taking the boundary of the formed area as a potential urban center boundary;
(4) further selecting the potential city centers screened in the step (3) by combining the continuity and scale requirements of the city centers, wherein the elimination range is discontinuous, the area is less than 2 square kilometers, or the population scale is less than 5 ten thousand potential city centers;
(5) and (4) aiming at the city center selected in the step (4), selecting the center with the largest population scale as a main city center, taking the rest centers as auxiliary city centers, and taking the corresponding boundaries as the boundaries of the main city center and the auxiliary city center.
2. The method for identifying the boundaries of the major and minor centres of a city based on demographic grid data as claimed in claim 1, wherein the step (1) of extracting the demographic grid data of the city under study from the global demographic grid data set specifically comprises the steps of:
(11) selecting a proper population grid database according to the requirement of research precision, downloading and importing ArcGIS software;
(12) and cutting the downloaded population grid database according to the city range boundary to obtain city population grid data corresponding to the city boundary.
3. The method for identifying the boundaries of the major and minor cities based on the population grid data as claimed in claim 1, wherein in the step (2), the step of preprocessing the city population grid data obtained in the step (1) to generate the vector data of the city population distribution for further analysis comprises the following steps:
(21) converting the geographic coordinates into projection coordinates by the city population grid data acquired in the step (12) through a projection grid tool;
(22) converting the city population grid data converted in the step (21) into vector point data through a grid point conversion tool;
(23) and (4) creating a fishing net which is consistent with the pixel size and the coordinate of the urban population raster data through a fishing net creating tool, and performing spatial link with the vector point data obtained through conversion in the step (22) to obtain the vector data of the urban population distribution.
4. The method for identifying the boundaries between the major centers and the minor centers of cities based on the population grid data as claimed in claim 1, wherein in the step (3), the local spatial autocorrelation analysis is performed on the vector data of the population distribution of the cities generated in the step (2), the spatial clustering characteristics of the population distribution of the cities are obtained, the regions composed of the population grids with high-high attribute values are screened out as the potential city centers, and the boundary of the composed regions as the boundary of the potential city centers specifically comprises the following steps:
(31) according to a clustering and abnormal value analysis tool in a spatial statistic tool in ArcGIS software, local spatial autocorrelation analysis is carried out on urban population vector data by using an inverse distance weighting method to form high-high, high-low, low-high and low-low attribute grids;
(32) according to ArcGIS software, screening areas formed by high-high attribute grids as potential city centers, and using the boundaries of the formed areas as potential city center boundaries.
5. The method for identifying urban major-minor center boundaries based on demographic grid data as claimed in claim 1, wherein in step (4), the potential urban centers selected in step (3) are further selected according to the continuity and scalability requirements that the urban centers should have, and the potential urban centers with discontinuous removal range, area less than 2 square kilometers or population size less than 5 ten thousand people specifically comprise the following steps:
(41) according to continuity and scale requirements of a city center, combining practical experience of city planning, and taking people with an area not less than 2 square kilometers and a population scale not less than 5 ten thousand as a scale threshold of the city center;
(42) in ArcGIS software, according to the determined scale threshold value of the city center, the region which has a continuous range, is not less than 2 square kilometers in area and has the population scale not less than 5 ten thousand persons is selected from the potential city centers screened in the step (32) and is used as the city center.
6. The method for identifying urban major-minor center boundaries based on demographic grid data as claimed in claim 1, wherein in step (5), for the urban center selected in step (4), the center with the largest population size is selected as the urban major center, and the specific examples of the remaining centers as the urban minor centers are as follows: and (4) according to the city center selected in the step (42), selecting the region boundary with the largest population scale as a main city center boundary, and using the boundaries of the rest regions as auxiliary city center boundaries.
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| CN112686507A (en) * | 2020-12-18 | 2021-04-20 | 天津大学 | Metropolitan area multi-center index evaluation method based on WorldPop data |
| CN112905911A (en) * | 2021-03-29 | 2021-06-04 | 东南大学 | Method for identifying urban innovation space range |
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