CN111833224B - Urban main and auxiliary center boundary identification method based on population raster data - Google Patents
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Abstract
The invention discloses a city primary and secondary center boundary identification method based on population raster data, which comprises the steps of firstly, obtaining city population raster data, importing ArcGIS software to preprocess the city population raster data, and generating vector data of city population distribution; secondly, carrying out local space autocorrelation analysis on the urban population distribution vector data generated in the last step, and selecting an area consisting of population grids with high-high attribute values as a potential urban center; and thirdly, according to the requirements of continuity and scale of the urban center range, selecting the areas meeting the requirements from the potential urban centers identified in the last step as the urban centers, wherein the areas with the largest population numbers are the main centers, and the rest are the auxiliary centers. The method and the system rely on the free open population raster data to identify the boundaries of the main and auxiliary centers of the city, have the characteristics of low cost, easy operation and high speed, and are favorable for scientifically and reasonably defining the boundaries of the city center in city planning.
Description
Technical Field
The invention relates to the technical field of urban planning data analysis, in particular to a method for identifying boundaries of main centers and auxiliary centers of cities based on population raster data.
Background
The research of urban space development modes is highly focused in the field of urban research at home and abroad, and a space structure mode which is favorable for urban sustainable development is hoped to be found, so that the research of urban multi-center identification, economic performance and influence on resident life, traffic, environment and the like becomes the focus of attention of students. The recognition of the urban multi-center is a premise and a foundation for developing the urban multi-center space structure research, and the improvement of the recognition method of the urban multi-center is of great significance for further developing the urban multi-center space structure research. From a morphological multicenter perspective, a multicenter city is defined as a city consisting of a set of city centers with relatively uniform importance distributions. Under this definition, the identification of city centers is the core of multi-center city measures, and two methods, parametric and non-parametric, are currently formed with respect to the identification of city centers.
The parametric method is to identify the population center of a city by setting absolute or relative thresholds of measure indexes (such as population density and employment density). The method has two limitations, namely, the method has larger randomness when setting index threshold values, and is not beneficial to the comparison research of different cities. The nonparametric method is to identify the city center by means of local weighted regression, space statistics, nuclear density analysis, space clustering and the like. The nonparametric method overcomes some defects of the parametric method, considers the interaction between the urban center and the surrounding area, and avoids the randomness of subjectively setting the threshold. However, the nonparametric method often needs to have more detailed city basic data, needs to define CBD and city center in advance, 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 space analysis technology, new ideas and methods are provided for the identification of city centers. The high-resolution population data breaks through the space scale constraint of the traditional statistical data taking county and city as statistical units, provides data support for exploring city centers, and provides possibility for overcoming the randomness of a parameter method and the complexity of an nonparametric method by the development of a space analysis technology, so that the urban multi-center identification is facilitated.
Disclosure of Invention
The invention aims to solve the technical problems of providing the urban main and auxiliary center boundary identification method based on the population raster data, which is used for identifying the boundaries of the urban main and auxiliary centers by relying on the free open population raster data, has the characteristics of low cost, easy operation and high speed, and is beneficial to scientifically and reasonably defining the urban center boundary in urban planning.
In order to solve the technical problems, the invention provides a city primary and secondary center boundary identification method based on population raster data, which comprises the following steps:
(1) Extracting population raster data of the researched city from a free open global population raster data set such as LandScan, worldPop;
(2) Importing the urban population raster data obtained in the step (1) into ArcGIS software for preprocessing to generate vector data of urban population distribution for further analysis;
(3) Carrying out local spatial autocorrelation analysis on the urban population distribution vector data generated in the step (2), obtaining spatial clustering characteristics of urban population distribution, screening out an area formed by a population grid with a high-high (HH) attribute value as a potential urban center, and taking the boundary of the formed area as a potential urban center boundary;
(4) The potential city centers screened in the step (3) are further selected by combining the continuity and scale requirements of the city centers, and the rejection range is discontinuous, the area of the region is lower than 2 square kilometers or the population scale is lower than 5 ten thousand people;
(5) Aiming at the city center selected in the step (4), the center with the largest population rule is selected as a city main center, the rest centers are taken as city auxiliary centers, and the corresponding boundary is taken as the boundary of the city main auxiliary center.
Preferably, in the step (1), the step of extracting the population raster data of the city under study from the free open global population raster data set of LandScan, worldPop or the like specifically includes 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 raster database according to the city range boundary to obtain city population raster data corresponding to the city boundary.
Preferably, in step (2), the urban population raster data obtained in step (1) is preprocessed, and the generation of vector data of urban population distribution for further analysis specifically includes the following steps:
(21) Converting the geographic coordinates into projection coordinates by the urban population raster data obtained in the step (12) through a projection raster tool;
(22) Converting the urban population raster data converted in the step (21) into vector point data through a raster point tool;
(23) And (3) creating a fishing net with the pixel size and coordinates consistent with those of urban population raster data through a fishing net creating tool, and performing spatial linking with the vector point data obtained by conversion in the step (22) to obtain vector data of urban population distribution.
Preferably, in step (3), local spatial autocorrelation analysis is performed on the urban population distribution vector data generated in step (2), spatial clustering features of urban population distribution are obtained, and an area composed of a population grid with a high-high (HH) attribute value is screened out as a potential urban center, and a boundary of the composed area is used as a potential urban center boundary, specifically including the following steps:
(31) According to a clustering and outlier analysis (LISA) tool in a space statistics tool in ArcGIS software, carrying out local space autocorrelation analysis 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, the region formed by the high-high (HH) attribute grid is screened as a potential city center, and the boundary of the formed region is used as a potential city center boundary.
Preferably, in the step (4), in combination with the requirements of continuity and scale that the city center should have, the potential city center screened in the step (3) is further selected, and the potential city center with discontinuous rejection range, area less than 2 square kilometers or population scale less than 5 ten thousand people specifically includes the following steps:
(41) According to the requirements of continuity and scale of the city center, combining the practical experience of city planning, taking the area of not less than 2 square kilometers and the population scale of not less than 5 ten thousand people as the scale threshold of the city center;
(42) In ArcGIS software, according to the scale threshold value of the determined city center, selecting an area which is continuous in range and has an area of not less than 2 square kilometers and a population scale of not less than 5 ten thousand people from the potential city centers screened in the step (32) as the city center.
Preferably, in step (5), regarding the city center selected in step (4), the center with the largest population pattern is selected as the city main center, and the rest centers are selected as city auxiliary centers, specifically: and (3) selecting the regional boundary with the largest population rule as a main central boundary of the city and the boundaries of the other regions as auxiliary central boundaries of the city according to the city center selected in the step (42).
The beneficial effects of the invention are as follows: the method and the system rely on the free open population raster data to identify the boundaries of the main and auxiliary centers of the city, have the characteristics of low cost, easy operation and high speed, and are favorable for scientifically and reasonably defining the boundaries of the city center in city planning.
Drawings
FIG. 1 is a schematic flow chart of the method of the present invention.
Fig. 2 is a diagram of the grid data of the population of the south Beijing city according to the present invention.
Fig. 3 is a schematic diagram of population distribution vector data of the south Beijing city according to the present invention.
Fig. 4 is a schematic diagram of a result of local autocorrelation analysis of a population distribution in the south Beijing city according to the present invention.
Fig. 5 is a schematic diagram of a potential city center boundary in the south-Beijing city of the present invention.
Fig. 6 is a schematic diagram of a major-minor center boundary of the tokyo city of the present invention.
Detailed Description
As shown in fig. 1, a city primary and secondary center boundary identification method based on population raster data includes the following steps:
(1) Extracting population raster data of the researched city from a free open global population raster data set such as LandScan, worldPop;
(2) Importing the urban population raster data obtained in the step (1) into ArcGIS software for preprocessing to generate vector data of urban population distribution for further analysis;
(3) Carrying out local spatial autocorrelation analysis on the urban population distribution vector data generated in the step (2), obtaining spatial clustering characteristics of urban population distribution, screening out an area formed by a population grid with a high-high (HH) attribute value as a potential urban center, and taking the boundary of the formed area as a potential urban center boundary;
(4) The potential city centers screened in the step (3) are further selected by combining the continuity and scale requirements of the city centers, and the rejection range is discontinuous, the area of the region is lower than 2 square kilometers or the population scale is lower than 5 ten thousand people;
(5) Aiming at the city center selected in the step (4), the center with the largest population rule is selected as a city main center, the rest centers are taken as city auxiliary centers, and the corresponding boundary is taken as the boundary of the city main auxiliary center.
The following will describe the technical solution of the present invention in detail with reference to the method case and the accompanying drawings for identifying the main and auxiliary center boundaries of cities based on raster data of population in the range of the urban area of south Beijing (about 6587 square kilometers in area, about 840 ten thousand people in resident population), and the present invention comprises the following steps:
A. population raster data of the city under study is extracted from a free open global population raster data set of LandScan, worldPop, etc.
A1. According to the requirement of 1km x 1km of research precision, downloading a LandScan global population grid data set, and importing ArcGIS software;
A2. and cutting the downloaded population grid database according to the border of the Nanjing city range to obtain the population grid data of the Nanjing city corresponding to the border of the Nanjing city, as shown in figure 2.
B. And preprocessing the acquired grid data of the population of the Nanjing city to obtain vector data of the population distribution of the Nanjing city.
B1. Converting geographic coordinates into projection coordinates by the grid data of the population of the Nanjing city obtained in the step A2 through the projection grid data;
B2. b1, converting the grid data of the Nanjing city population converted in the step B1 into vector point data through a grid turning point tool;
B3. and creating a fishing net with the pixel size and coordinates consistent with those of urban population raster data through a fishing net creating tool, and performing spatial linking with vector point data obtained through conversion in the B2 to obtain vector data of the urban population distribution of Nanjing, as shown in figure 3.
C. And C, carrying out local autocorrelation analysis on the population distribution vector data of the Nanjing cities generated in the step B, obtaining spatial clustering characteristics of population distribution of the Nanjing cities, screening an area formed by a population grid with a high-high (HH) attribute value as a potential Nanjing city center, and taking the boundary of the formed area as a potential Nanjing city center boundary.
C1. According to a clustering and outlier analysis (LISA) tool in a space statistics tool in ArcGIS software, performing local space autocorrelation analysis on population vector data of the Nanjing city by using an inverse distance weighting 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 ArcGIS software, the region formed by the high-high (HH) attribute grid is screened as the potential city center of Nanjing, and the boundary of the formed region is used as the potential city center boundary of Nanjing, as shown in fig. 5.
D. C, further selecting the potential city centers of Nanjing city screened in the step C according to the requirements of continuity and scale of the city centers, wherein the rejection range is discontinuous, and the area of the potential city centers is lower than 2 square kilometers or the population scale is lower than 5 ten thousand people;
D1. according to the continuity and scale requirements of the urban center, combining the practical experience of urban planning, taking the area of not less than 2 square kilometers and the population scale of not less than 5 ten thousand people as the scale threshold of the urban center of Nanjing;
D2. in ArcGIS software, according to the determined scale threshold of the urban center of Nanjing city, selecting a continuous area with the area not smaller than 2 square kilometers and the population scale not smaller than 5 ten thousand people as the potential population center of Nanjing city.
E. And D, selecting the boundary of the region with the largest population rule as the main center boundary of the Nanjing city and the boundaries of the other regions as the auxiliary center boundary of the Nanjing city according to the potential city center selected in the step D, as shown in figure 6.
Claims (5)
1. A city primary and secondary center boundary identification method based on population raster data is characterized by comprising the following steps:
(1) Extracting population raster data of the city under study from the global population raster data set;
(2) Importing the urban population raster data obtained in the step (1) into ArcGIS software for preprocessing to generate vector data of urban population distribution for further analysis;
(3) Carrying out local space autocorrelation analysis on the urban population distribution vector data generated in the step (2), obtaining space clustering characteristics of urban population distribution, screening out areas consisting of population grids with high-high attribute values as potential urban centers, and taking boundaries of the areas as potential urban center boundaries; the method specifically comprises the following steps:
(31) According to clustering and outlier analysis tools in a space statistics tool in ArcGIS software, carrying out local space autocorrelation analysis on urban population vector data by using an inverse distance weighting method to form a high-high, high-low, low-high and low-low attribute grid;
(32) According to ArcGIS software, screening a region formed by a high-high attribute grid as a potential city center, wherein the boundary of the formed region is used as a potential city center boundary;
(4) The potential city centers screened in the step (3) are further selected by combining the continuity and scale requirements of the city centers, and the rejection range is discontinuous, the area of the region is lower than 2 square kilometers or the population scale is lower than 5 ten thousand people;
(5) Aiming at the city center selected in the step (4), the center with the largest population rule is selected as a city main center, the rest centers are taken as city auxiliary centers, and the corresponding boundary is taken as the boundary of the city main auxiliary center.
2. The method for identifying primary and secondary center boundaries of a city based on population raster data as set forth in claim 1, wherein in step (1), extracting population raster data of a city under study from a global population raster data set comprises the steps of:
(11) According to the requirement of research precision, selecting a population grid database, downloading and importing ArcGIS software;
(12) And cutting the downloaded population raster database according to the city range boundary to obtain city population raster data corresponding to the city boundary.
3. The method for identifying urban primary and secondary center boundaries based on population raster data according to claim 1, wherein in step (2), the urban population raster data obtained in step (1) is preprocessed, and the generation of vector data of urban population distribution for further analysis specifically comprises the following steps:
(21) Converting the geographic coordinates into projection coordinates by the urban population raster data obtained in the step (12) through a projection raster tool;
(22) Converting the urban population raster data converted in the step (21) into vector point data through a raster point tool;
(23) And (3) creating a fishing net with the pixel size and coordinates consistent with those of urban population raster data through a fishing net creating tool, and performing spatial linking with the vector point data obtained by conversion in the step (22) to obtain vector data of urban population distribution.
4. The method for identifying city primary and secondary center boundaries based on population raster data according to claim 1, wherein in step (4), in combination with requirements of continuity and scale that city centers should have, the method for further selecting the potential city centers selected in step (3), specifically includes the following steps:
(41) According to the requirements of continuity and scale of the city center, combining the practical experience of city planning, taking the area of not less than 2 square kilometers and the population scale of not less than 5 ten thousand people as the scale threshold of the city center;
(42) In ArcGIS software, according to the scale threshold value of the determined city center, selecting an area which is continuous in range and has an area of not less than 2 square kilometers and a population scale of not less than 5 ten thousand people from the potential city centers screened in the step (32) as the city center.
5. The method for identifying city primary and secondary center boundaries based on population raster data according to claim 1, wherein in step (5), regarding the city center selected in step (4), the center with the largest population rule is selected as the city primary center, and the remaining centers are specifically selected as city secondary centers: and (3) selecting the regional boundary with the largest population rule as a main central boundary of the city and the boundaries of the other regions as auxiliary central boundaries of the city according to the city center selected in the step (42).
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