CN111833224B - Urban main and auxiliary center boundary identification method based on population raster data - Google Patents

Abstract

The invention discloses a method for identifying the boundaries of urban main and secondary centers based on population raster data. First, the urban population raster data is obtained, and imported into ArcGIS software to preprocess it to generate vector data of urban population distribution; secondly, The urban population distribution vector data generated in the previous step was analyzed for local spatial autocorrelation, and areas composed of population grids with high-high attribute values were selected as potential urban centers; again, according to the continuity and scale that the urban center should have Requirements: select areas that meet the requirements from the potential urban centers identified in the previous step as urban centers. The area with the largest population is the main center, and the rest are sub-centers. The present invention relies on free and open population raster data to identify the boundaries of the city's main and sub-centers. It has the characteristics of low cost, easy operation and fast speed, and is conducive to the scientific and reasonable delineation of the city center boundary in urban planning.

Description

A method for identifying the boundaries of urban main and sub-centers based on population raster data

Technical field

The invention relates to the technical field of urban planning data analysis, in particular to a method for identifying the boundaries of urban main and secondary centers based on population raster data.

Background technique

The field of urban research at home and abroad pays great attention to the study of urban spatial development models, hoping to find spatial structure models that are conducive to sustainable urban development. Therefore, scholars have become scholars in the identification of urban polycenters, economic performance, and their impact on residents' lives, transportation, and the environment. focus of attention. Among them, the identification of urban polycenters is the premise and basis for carrying out research on urban polycenter spatial structures. Improving the identification method of urban polycenters is of great significance for further research on urban polycentric spatial structures. From the perspective of morphological polycentricity, a polycentric city is defined as a city composed of a group of urban centers whose importance is relatively evenly distributed. Under this definition, the identification of urban centers becomes the core of polycentric urban measurement. Currently, there are two methods for identifying urban centers, parametric and non-parametric.

The parametric method identifies the city's population center by setting absolute or relative thresholds for measurement indicators (such as population density and employment density). This method has two limitations. First, it is highly arbitrary when setting indicator thresholds. Second, it is not conducive to comparative research in different cities. The non-parametric method identifies urban centers through local weighted regression, spatial statistics, kernel density analysis and spatial clustering. The non-parametric method overcomes some of the shortcomings of the parametric method, taking into account the interaction between the city center and the surrounding areas, while also avoiding the arbitrariness of subjectively setting thresholds. However, the non-parametric method often requires relatively detailed basic urban data and pre-demarcation of the CBD and city center, which has the disadvantages of high difficulty in data acquisition and complex operations.

In recent years, the availability of high-resolution population data and the continuous development of spatial analysis technology have provided new ideas and methods for the identification of urban centers. High-resolution population data breaks through the spatial scale constraints of traditional statistical data, which uses counties and cities as statistical units, and provides data support for exploring urban centers. The development of spatial analysis technology also overcomes the arbitrariness of parametric methods and the complexity of non-parametric methods. The problem provides the possibility to better identify urban polycenters.

Contents of the invention

The technical problem to be solved by the present invention is to provide a method for identifying the boundaries of the city's main and sub-centers based on population raster data. It relies on free and open population raster data to identify the boundaries of the city's main and sub-centers, which is low-cost and easy to operate. , the characteristics of fast speed are conducive to the scientific and reasonable delineation of urban center boundaries in urban planning.

In order to solve the above technical problems, the present invention provides a method for identifying the boundaries of main and sub-centers of a city based on population raster data, which includes the following steps:

(1) Extract the population raster data of the city under study from free and open global population raster data sets such as LandScan and WorldPop;

(2) Import the urban population raster data obtained in step (1) into ArcGIS software for preprocessing to generate vector data of urban population distribution for further analysis;

(3) Perform local spatial autocorrelation analysis on the urban population distribution vector data generated in step (2), obtain the spatial clustering characteristics of urban population distribution, and screen out the population grid composed of high-high (HH) attribute values. The region serves as a potential city center, and the boundaries of the composed regions serve as potential city center boundaries;

(4) Combined with the continuity and scale requirements that urban centers should have, further select the potential urban centers screened in step (3), and eliminate those with discontinuous scope, regional area less than 2 square kilometers, or population size less than 5 Potential urban center for 10,000 people;

(5) For the urban centers selected in step (4), select the center with the largest population as the city's main center, the remaining centers as the city's sub-centers, and the corresponding boundaries as the boundaries of the city's main and sub-centers.

Preferably, in step (1), extracting the population raster data of the city under study from free and open global population raster data sets such as LandScan and WorldPop specifically includes the following steps:

(11) According to the requirements of research accuracy, select an appropriate population raster database, download and import it into ArcGIS software;

(12) Cut the downloaded population raster database according to the city boundary to obtain urban population raster data corresponding to the city boundary.

Preferably, in step (2), preprocessing the urban population raster data obtained in step (1), and generating vector data of urban population distribution for further analysis specifically includes the following steps:

(21) Convert the urban population raster data obtained in step (12) into projected coordinates using the projected raster tool;

(22) Use the raster to point tool to convert the urban population raster data converted in step (21) into vector point data;

(23) Use the Create Fishing Net tool to create a fishing net with the same pixel size and coordinates as the urban population raster data, and spatially link it with the vector point data converted in step (22) to obtain vector data of urban population distribution.

Preferably, in step (3), perform local spatial autocorrelation analysis on the urban population distribution vector data generated in step (2), obtain the spatial clustering characteristics of urban population distribution, and filter out the high-high (HH) attribute values The area composed of the population grid is used as a potential city center, and the boundaries of the composed area are used as potential city center boundaries. The specific steps include the following steps:

(31) According to the clustering and outlier analysis (LISA) tool in the spatial statistics tool in ArcGIS software, the inverse distance weighting method was used to conduct local spatial autocorrelation analysis on the urban population vector data to form high-high (HH), high -Attribute grids such as low (HL), low-high (LH), low-low (LL);

(32) According to ArcGIS software, areas composed of high-high (HH) attribute grids are screened as potential city centers, and the boundaries of the composed areas are used as potential city center boundaries.

Preferably, in step (4), the potential urban centers screened in step (3) are further selected based on the continuity and scale requirements that urban centers should have, and those with discontinuous scope and area less than 2 square kilometers are eliminated. Or potential urban centers with a population of less than 50,000 people include the following steps:

(41) Based on the continuity and scale requirements that urban centers should have and combined with the practical experience of urban planning, an area of not less than 2 square kilometers and a population of not less than 50,000 people are used as the scale thresholds of urban centers;

(42) In ArcGIS software, according to the determined size threshold of urban centers, select potential urban centers screened in step (32) with a continuous range, an area of not less than 2 square kilometers, and a population of not less than 50,000 people. area as the city center.

Preferably, in step (5), for the urban centers selected in step (4), the center with the largest population is selected as the main city center, and the remaining centers are selected as city sub-centers. Specifically, the city center selected according to step (42) , select the boundary of the area with the largest population as the boundary of the city's main center, and the boundaries of the remaining areas as the boundary of the city's sub-center.

The beneficial effects of the present invention are: the present invention relies on free and open population raster data to identify the boundaries of the main and sub-centers of the city, has the characteristics of low cost, easy operation and fast speed, and is conducive to the scientific and reasonable delineation of cities in urban planning. center border.

Description of the drawings

Figure 1 is a schematic flow chart of the method of the present invention.

Figure 2 is a schematic diagram of Nanjing population raster data according to the present invention.

Figure 3 is a schematic diagram of the population distribution vector data of Nanjing City according to the present invention.

Figure 4 is a schematic diagram of the local autocorrelation analysis results of Nanjing's population distribution according to the present invention.

Figure 5 is a schematic diagram of the potential urban center boundary of Nanjing in the present invention.

Figure 6 is a schematic diagram of the boundaries of Nanjing's main and sub-centers according to the present invention.

Detailed ways

As shown in Figure 1, a method for identifying the boundaries of urban main and sub-centers based on population raster data includes the following steps:

(1) Extract the population raster data of the city under study from free and open global population raster data sets such as LandScan and WorldPop;

(2) Import the urban population raster data obtained in step (1) into ArcGIS software for preprocessing to generate vector data of urban population distribution for further analysis;

(3) Perform local spatial autocorrelation analysis on the urban population distribution vector data generated in step (2), obtain the spatial clustering characteristics of urban population distribution, and screen out the population grid composed of high-high (HH) attribute values. The region serves as a potential city center, and the boundaries of the composed regions serve as potential city center boundaries;

(4) Combined with the continuity and scale requirements that urban centers should have, further select the potential urban centers screened in step (3), and eliminate those with discontinuous scope, regional area less than 2 square kilometers, or population size less than 5 Potential urban center for 10,000 people;

(5) For the urban centers selected in step (4), select the center with the largest population as the city's main center, the remaining centers as the city's sub-centers, and the corresponding boundaries as the boundaries of the city's main and sub-centers.

The technical solution of the present invention will be explained in detail below with reference to the method case and drawings of the boundary identification method of the main and sub-centers of the city based on population raster data within the Nanjing city area (an area of about 6587 square kilometers and a permanent population of about 8.4 million people). The invention includes the following steps:

A. Extract the population raster data of the city under study from free and open global population raster data sets such as LandScan and WorldPop.

A1. Based on the research accuracy of 1km*1km, download the LandScan global population raster data set and import it into ArcGIS software;

A2. Cut the downloaded population raster database according to the boundaries of Nanjing city to obtain the Nanjing urban population raster data corresponding to the boundaries of Nanjing, as shown in Figure 2.

B. Preprocess the obtained Nanjing urban population raster data to obtain vector data of Nanjing urban population distribution.

B1. Convert the Nanjing urban population raster data obtained in step A2 into projected coordinates by projecting the raster data;

B2. Use the raster to point tool to convert the Nanjing urban population raster data converted in step B1 into vector point data;

B3. Use the Create Fishing Net tool to create a fishing net with the same pixel size and coordinates as the urban population raster data, and spatially link it with the vector point data converted in B2 to obtain the vector data of Nanjing's urban population distribution, as shown in Figure 3 .

C. Perform local autocorrelation analysis on the Nanjing urban population distribution vector data generated in step B, obtain the spatial clustering characteristics of Nanjing urban population distribution, and screen areas composed of population grids with high-high (HH) attribute values as potential Nanjing The boundaries of the city center and the composed area serve as the potential boundaries of Nanjing city center.

C1. According to the clustering and outlier analysis (LISA) tool in the spatial statistics tool in ArcGIS software, use the inverse distance weighting method to conduct local spatial autocorrelation analysis on the Nanjing urban population vector data to form high-high (HH), high -Attribute grids such as low (HL), low-high (LH), low-low (LL), etc., as shown in Figure 4;

C2. According to ArcGIS software, the area composed of high-high (HH) attribute grid is screened as the potential city center of Nanjing City, and the boundaries of the formed areas are used as the potential city center boundary of Nanjing City, as shown in Figure 5.

D. Combined with the continuity and scale requirements that urban centers should have, further select the potential urban centers in Nanjing screened in step C, and eliminate those with discontinuous scope, regional area less than 2 square kilometers, or population size less than 50,000 Potential urban centers for people;

D1. Based on the continuity and scale requirements that urban centers should have and combined with the practical experience of urban planning, an area of not less than 2 square kilometers and a population of not less than 50,000 people are set as the scale thresholds for Nanjing’s urban centers;

D2. In ArcGIS software, based on the determined size threshold of Nanjing's urban center, select a continuous area with an area of no less than 2 square kilometers and a population of no less than 50,000 as potential population centers of Nanjing.

E. Based on the potential urban centers selected in step D, select the boundary of the area with the largest population as the main center boundary of Nanjing City, and the boundaries of the remaining areas as the sub-center boundary of Nanjing City, as shown in Figure 6.

Claims (5)

1. A method for identifying the boundaries of urban main and sub-centers based on population raster data, which is characterized by including the following steps: (1) Extract the population raster data of the city under study from the global population raster data set; (2) Import the urban population raster data obtained in step (1) into ArcGIS software for preprocessing to generate vector data of urban population distribution for further analysis; (3) Perform local spatial autocorrelation analysis on the urban population distribution vector data generated in step (2), obtain the spatial clustering characteristics of urban population distribution, and screen out areas composed of population grids with high-high attribute values as potential The city center of the city, the boundaries of the composed areas are used as potential city center boundaries; the specific steps include the following: (31) According to the clustering and outlier analysis tools in the spatial statistics tool in ArcGIS software, the inverse distance weighting method was used to conduct local spatial autocorrelation analysis on the urban population vector data to form high-high, high-low, and low-high , low-low attribute grid; (32) According to ArcGIS software, areas composed of high-high attribute grids are screened as potential city centers, and the boundaries of the composed areas are used as potential city center boundaries; (4) Combined with the continuity and scale requirements that urban centers should have, further select the potential urban centers screened in step (3), and eliminate those with discontinuous scope, regional area less than 2 square kilometers, or population size less than 5 Potential urban center for 10,000 people; (5) For the urban centers selected in step (4), select the center with the largest population as the city's main center, the remaining centers as the city's sub-centers, and the corresponding boundaries as the boundaries of the city's main and sub-centers. 2. The method for identifying the main and secondary center boundaries of a city based on population raster data as claimed in claim 1, characterized in that in step (1), the specific population raster data of the city under study is extracted from the global population raster data set. Includes the following steps: (11) According to the requirements of research accuracy, select the population raster database, download and import it into ArcGIS software; (12) Cut the downloaded population raster database according to the city boundary to obtain urban population raster data corresponding to the city boundary. 3. The urban main and secondary center boundary identification method based on population raster data as claimed in claim 1, characterized in that in step (2), the urban population raster data obtained in step (1) is preprocessed, Generating vector data of urban population distribution for further analysis specifically includes the following steps: (21) Convert the urban population raster data obtained in step (12) into projected coordinates using the projected raster tool; (22) Use the raster to point tool to convert the urban population raster data converted in step (21) into vector point data; (23) Use the Create Fishing Net tool to create a fishing net with the same pixel size and coordinates as the urban population raster data, and spatially link it with the vector point data converted in step (22) to obtain vector data of urban population distribution. 4. The method for identifying the boundaries of urban main and sub-centers based on population raster data as claimed in claim 1, characterized in that in step (4), step (3) is combined with the continuity and scale requirements that the urban center should have. ) for further selection, and the elimination of potential urban centers with discontinuous scope, area less than 2 square kilometers, or population less than 50,000 includes the following steps: (41) Based on the continuity and scale requirements that urban centers should have and combined with the practical experience of urban planning, an area of not less than 2 square kilometers and a population of not less than 50,000 people are used as the scale thresholds of urban centers; (42) In ArcGIS software, according to the determined size threshold of urban centers, select potential urban centers screened in step (32) with a continuous range, an area of not less than 2 square kilometers, and a population of not less than 50,000 people. area as the city center. 5. The method for identifying the boundaries of urban primary and secondary centers based on population raster data as claimed in claim 1, characterized in that in step (5), for the urban center selected in step (4), the city center with the largest population is selected. The center serves as the city's main center, and the remaining centers serve as city sub-centers. Specifically, according to the city center selected in step (42), the regional boundary with the largest population size is selected as the city's main center boundary, and the boundaries of the remaining regions serve as the city's sub-center boundaries.