Internal edge area identification method, device, electronic device and storage medium

Abstract

The invention discloses an internal marginal area identification method, device, electronic device and storage medium, belonging to the field of geographic information processing technology, and the method comprises the steps of: clarifying three theoretical models of internal marginal areas and converting them into four-dimensional characteristic indicators of internal marginal areas; obtaining data information of a target area and integrating multi-source data reflecting the real development level of the city, and pre-processing the multi-source data; performing general processing on the pre-processed multi-source data, respectively using four-dimensional internal marginal area identification methods to identify the internal marginal areas, and obtaining preliminary internal marginal areas; combining the four-dimensional internal marginal area identification method with a spatial overlay analysis method, and comprehensively identifying the internal marginal areas of the target area based on the preliminary internal marginal areas. The invention identifies internal marginal areas based on multi-source data, and overcomes the problem of inconsistent data collection standards, panel data or abnormal values ​​leading to unstable results in previous studies.

Description

Technical Field

[0001] The invention relates to an internal edge area recognition method, device, electronic equipment and storage medium, belonging to the technical field of geographic information processing. Background Art

[0002] With the rapid economic development and the continuous acceleration of urbanization, the high concentration of production factors and the disorderly expansion of urban scale have caused many urban development problems, which are manifested in the increasingly prominent problems of overloaded population size, unbalanced industrial structure, low-level homogeneous competition among cities, and the phenomenon of unbalanced and insufficient regional development. Therefore, in the process of coordinated development of urban agglomerations, how to scientifically identify and effectively solve the problem of "internal marginalization" is of great significance to achieving high-quality regional development.

[0003] Classic theories of urban and regional development, including core-periphery theory, central place theory, multi-core theory, growth pole theory and "point-axis" theory, reveal the characteristics of regional resource concentration, service level distribution, multi-core structure, economic polarization effect and point-axis linkage development. By combing these theories, three types of models for internal marginal areas are constructed: first, enclaves with low economic potential, which are characterized by a much lower level of economic development in the region than in the surrounding areas; second, areas with low public service penetration, which are difficult to obtain public services due to their remote geographical location; third, areas with a lack of connection and decline, which gradually decline due to insufficient connection with the surrounding areas. These theories and models jointly analyze the imbalance and complexity of regional development and provide a scientific basis for coordinated development and policy optimization.

[0004] At present, the identification of internal marginal areas is mainly divided into static identification methods based on statistical data (regional economic development, population distribution, etc.) and geographic spatial data (transportation, land use, etc.) and dynamic identification methods based on big data reflecting human communication activities (mobile phone signaling, trajectory data, etc.). However, with the acceleration of China's urbanization process, the connection between cities has shown dynamic characteristics, and the development of modern information technology has greatly reduced the temporal and spatial barriers between cities, enabling resources, information and people flow between cities to flow more efficiently. As the core growth pole of future economic development, the identification and research of internal marginal areas of urban agglomerations have become particularly important. Traditional methods are mostly based on static data analysis, which makes it difficult to capture the dynamic evolution of internal marginal areas and potential risks. Dynamic evaluation and long-term monitoring can reveal the evolution of internal marginal areas. Scientific construction of indicators and improvement of localization can improve the applicability and objectivity of identification methods, which has a certain impact on promoting regional coordinated development and promoting urban-rural integration.

[0005] Therefore, by comprehensively considering multiple dimensions and fully identifying the spatial characteristics of internal marginal areas from multiple perspectives, we can comprehensively characterize the spatial distribution, characteristics and evolution laws of internal marginal areas, which will provide more scientific theoretical support and practical paths for theoretical research and policy formulation of regional coordinated development. Summary of the invention

[0006] In order to solve the above problems, the present invention proposes a method, device, electronic device and storage medium for identifying internal edge areas, which can accurately identify and analyze internal edge areas in urban agglomerations.

[0007] The technical solution adopted by the present invention to solve the technical problem is:

[0008] In a first aspect, an embodiment of the present invention provides a method for identifying an inner edge area, comprising the following steps:

[0009] The three theoretical models of the inner marginal areas are clarified and transformed into the four-dimensional characteristic indicators of the inner marginal areas. The three theoretical models of the inner marginal areas include the theoretical model of the enclave with low economic potential, the theoretical model of the area with low public service penetration, and the theoretical model of the area with decline due to lack of connection. The four dimensions of the inner marginal areas include the dimension of travel time to the regional center, the dimension of economic development potential, the dimension of accessibility of public service facilities, and the dimension of regional development status.

[0010] Obtain data information of the target area and integrate multi-source data that reflects the real development level of the city, and pre-process the multi-source data;

[0011] The pre-processed multi-source data are generally processed, and the four-dimensional characteristic indicators of the internal edge area are respectively identified by using the four-dimensional internal edge area identification methods to identify the internal edge area, and the preliminary internal edge area is obtained;

[0012] Combining the four-dimensional internal edge area identification method with the spatial overlay analysis method, the internal edge areas of the target area are comprehensively identified based on the preliminary internal edge areas.

[0013] As a possible implementation of this embodiment, the four dimensional characteristic indicators of the internal edge area include:

[0014] Long time to regional center: It is a characteristic indicator of the travel time dimension to the regional center, used to determine whether the travel time to the regional center of the target area is higher than that of its neighboring areas;

[0015] Low economic development potential: It is a characteristic indicator of the economic development potential dimension, which is used to calculate the economic development potential of the target area and compare it with the surrounding areas to identify the internal marginal areas;

[0016] Difficulty in obtaining public services: This is a characteristic indicator of the accessibility dimension of public service facilities, which is used to calculate the travel time to the nearest public service facility and determine the difficulty of obtaining public services;

[0017] Development recession: It is a characteristic indicator of the regional development status dimension, which is used to identify regions where per capita GDP, regional gross product, public budget revenue and expenditure have declined.

[0018] As a possible implementation method of this embodiment, the multi-source data includes administrative boundary data, government point data, night light data, public service data, socio-economic statistics data, DEM data, temperature data, precipitation data, city boundary data and mobile phone signaling data.

[0019] As a possible implementation of this embodiment, the preprocessing of multi-source data includes:

[0020] Use linear interpolation method to supplement the missing data;

[0021] Convert socioeconomic data into panel data;

[0022] Clean and reclassify the POI data, including hospitals, pharmacies, banks, primary schools, middle schools, supermarkets, convenience stores, cinemas, railway stations, and bus stations;

[0023] Transform the spatial coordinate system of multi-source data, convert it into a unified coordinate system and crop it

[0024] As a possible implementation of this embodiment, the general processing of the preprocessed multi-source data includes:

[0025] Use the quartile method to divide internal marginal areas and potential internal marginal areas:

[0026] (1)

[0027] in, is the exact value of the quartile, , the first quartile , second quartile and the third quartile Representing 25%, 50%, and 75% of all statistics respectively; is the lower limit of the quartile interval; n is the total number of observations, is the cumulative absolute frequency of the previous interval; is the absolute frequency of the quartile interval; is the width of the interquartile interval;

[0028] Area identification is performed on the grid scale, and after time standardization, small strips representing inconvenient transportation are formed;

[0029] Set 5km 2 The area threshold is used to remove areas smaller than 5 km 2 The small strips of area make the spatial pattern clearer;

[0030] Merge adjacent grid cells or those less than 5 km apart into continuous areas, smooth the boundaries of the merged areas, and remove grid cells with an area less than 5 km. 2 Small strip-shaped areas.

[0031] As a possible implementation of this embodiment, the four-dimensional internal edge region identification method includes:

[0032] 1) Method for identifying internal edge areas in the travel time dimension to the regional center:

[0033] County-level government points were selected as regional research centers;

[0034] Using the path planning of Baidu API, we calculated the time from the grid unit to the regional center and the average level of the county where it is located and the neighboring counties, and finally calculated the difference:

[0035] (2),

[0036] in, T is the travel time from each grid cell to the nearest regional center, is the average level of the county where the grid unit is located and its neighboring counties, is the difference between the grid unit and the average level of the county where the grid unit is located and its neighboring counties;

[0037] Use the four-point division Areas with a value < 0 will be above the second quartile The grid of is defined as the inner edge area, that is, the area with a longer time to reach the center of the area compared with the neighboring areas is defined as the inner edge area;

[0038] Delete, merge and smooth the boundaries of grid cells adjacent to the internal edge areas;

[0039] 2) Methods for identifying internal marginal areas in terms of economic development potential:

[0040] Calculate the night light class kinetic energy index of the grid cell and the difference between the grid cell and the average value:

[0041] (3),

[0042] (4),

[0043] (5),

[0044] in, It is the kinetic energy of regional night lighting. is the total brightness of the night light; is the increase multiple of the total brightness of night lights, is the difference between the grid unit and the average level of the county where the grid unit is located and its neighboring counties. is the average level of the county where the grid unit is located and its neighboring counties;

[0045] Use the four-point division >0 The area will be above the second quartile The grids of which are areas with low economic potential are defined as inner marginal areas;

[0046] Delete, merge and smooth the boundaries of grid cells adjacent to the internal edge areas;

[0047] 3) Methods for identifying internal marginal areas in terms of accessibility of public service facilities:

[0048] Calculate the travel time from each grid cell to the nearest public service facility through Baidu API’s path planning;

[0049] Compare the travel time to the nearest public service for each grid cell to the average travel time for its county and neighboring counties, and calculate the difference:

[0050] (6),

[0051] in, G is the travel time from the grid unit to the nearest public service facility, is the average level of the county where the grid unit is located and its neighboring counties, is the difference between the grid unit and the average level of the county where the grid unit is located and its neighboring counties;

[0052] Use the four-point division Areas with a value < 0 will be above the second quartile The grid of areas with difficulty in accessing public services or medical services or educational services is defined as the inner marginal areas;

[0053] Delete, merge and smooth the boundaries of grid cells adjacent to the internal edge areas;

[0054] 4) Method for identifying internal marginal areas in the dimension of regional development status:

[0055] According to different variables, corresponding thresholds are set: population density is 50% lower than the average of neighboring counties, GDP is 85% lower than the average of neighboring counties, and the proportion of public budget investment is lower than that of neighboring counties;

[0056] According to the set thresholds, compare population density, GDP and the proportion of public budget investment, and determine the changing trends of total population, per capita GDP and the proportion of public budget investment;

[0057] Variables that are below the set threshold and show an overall downward trend are identified as internal marginal areas.

[0058] As a possible implementation of this embodiment, the internal edge region identification method combining four dimensions with the spatial overlay analysis method, based on the preliminary internal edge region, comprehensively identifies the internal edge region of the target region, including:

[0059] Assign the value of non-inner edge areas within the target area to 0, and the value of inner edge areas to 1;

[0060] By setting different coding rules, the types of internal marginal areas are divided;

[0061] The preliminary internal edge areas obtained by the internal edge area identification method in four dimensions are subjected to spatial overlay analysis to obtain the internal edge areas of the target area.

[0062] As a possible implementation of this embodiment, the encoding rule includes:

[0063] The thousands digit is coded as "1" to indicate the inner edge area formed by the longer travel time to the regional center;

[0064] The hundredth digit is coded as “1” to indicate an inner marginal area formed due to low economic development potential;

[0065] The ten-digit number is coded as “1” to indicate an inner marginal area formed by the difficulty in accessing public service facilities;

[0066] The single digit is coded as “1” to indicate an inner fringe area formed due to development decline.

[0067] In a second aspect, an embodiment of the present invention provides a device for identifying an inner edge region, comprising:

[0068] A characteristic index determination module is used to clarify three theoretical models of internal marginal areas and transform them into four dimensional characteristic indicators of internal marginal areas. The three theoretical models of internal marginal areas include a theoretical model of enclaves with low economic potential, a theoretical model of areas with low public service penetration, and a theoretical model of areas with decline due to lack of connection. The four dimensions of internal marginal areas include a travel time dimension to the regional center, an economic development potential dimension, a public service facility accessibility dimension, and a regional development status dimension.

[0069] The multi-source data acquisition module is used to obtain data information of the target area and integrate the multi-source data reflecting the real development level of the city, and pre-process the multi-source data;

[0070] The four-dimensional recognition module is used to perform general processing on the pre-processed multi-source data, and to identify the internal edge areas using the four-dimensional internal edge area recognition methods for the four-dimensional characteristic indicators of the internal edge areas, so as to obtain preliminary internal edge areas;

[0071] The target area comprehensive identification module is used to combine the internal edge area identification method of four dimensions with the spatial overlay analysis method to comprehensively identify the internal edge areas of the target area based on the preliminary internal edge areas.

[0072] In a third aspect, an embodiment of the present invention provides an electronic device, comprising a processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor communicates with the memory via the bus, and the processor executes the machine-readable instructions to perform the steps of any internal edge area identification method as described above.

[0073] In a fourth aspect, an embodiment of the present invention provides a storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of any of the above-mentioned methods for identifying internal edge areas are executed.

[0074] The beneficial effects of the technical solution of the embodiment of the present invention are as follows:

[0075] A method for identifying an internal marginal region of the technical solution of an embodiment of the present invention comprises the following steps: clarifying three theoretical models of internal marginal regions and converting them into four-dimensional characteristic indicators of internal marginal regions; obtaining data information of the target region and integrating multi-source data reflecting the real development level of the city, and preprocessing the multi-source data; performing general processing on the preprocessed multi-source data, and using four-dimensional internal marginal region identification methods to identify the internal marginal regions for the four-dimensional characteristic indicators of the internal marginal regions, respectively, to obtain preliminary internal marginal regions; combining the four-dimensional internal marginal region identification methods with the spatial overlay analysis method, and comprehensively identifying the internal marginal regions of the target region based on the preliminary internal marginal regions. The present invention adopts a dynamic perspective to analyze inter-regional development, comprehensively considers different dimensions of urban development, and identifies the internal marginal regions in urban agglomerations from multiple aspects such as society, economy, regional center accessibility, and population density based on multi-source data, thereby overcoming the problems of inconsistent data collection standards, panel data or abnormal values ​​leading to unstable results in previous studies.

[0076] Compared with existing methods, the present invention reflects the comprehensive development status of the region based on multiple dimensional perspectives, thereby improving the evaluation accuracy of coordinated development and avoiding the defects of traditional methods that are too single and lack a dynamic perspective. At the same time, the present invention ensures the comprehensiveness, scientificity, fairness and dynamism of the evaluation system, and provides a more reliable and objective regional development evaluation through multi-angle and multi-level analysis.

[0077] Different from the traditional single-dimensional static analysis, the present invention uses a comprehensive analysis from a multi-dimensional perspective. The results of the invention can reflect the real changing trends of the region in different time and space dimensions, reveal potential problems in urban development, such as spatial unevenness and functional zoning adjustments, and identify details and dynamic changes that may be overlooked by traditional methods, thereby providing a basis for more accurate decision-making and planning. BRIEF DESCRIPTION OF THE DRAWINGS

[0078] Figure 1 is a flow chart of a method for identifying an internal edge area according to an exemplary embodiment;

[0079] Figure 2 is a schematic structural diagram of a device for identifying an inner edge region according to an exemplary embodiment;

[0080] Figure 3 A schematic diagram of the inner edge area of ​​a certain urban agglomeration with the characteristic of a long time to reach the regional center is identified by using the method of the present invention;

[0081] Figure 4 A schematic diagram of the inner edge area of ​​a certain urban agglomeration with the characteristic of low economic development potential is identified by using the method of the present invention;

[0082] Figure 5 A schematic diagram of an inner edge area of ​​a certain urban agglomeration with the characteristic of difficulty in obtaining public service facilities is identified by using the method of the present invention;

[0083] Figure 6 A schematic diagram of an inner edge area of ​​a certain urban agglomeration with a characteristic of development decline is identified by using the method of the present invention;

[0084] Figure 7 The method of the present invention is used to comprehensively identify four characteristics to identify the internal edge areas of a certain urban agglomeration. DETAILED DESCRIPTION

[0085] In order to more clearly illustrate the technical features of the solution of the present invention, the present invention is described in detail below through specific implementation methods and in conjunction with the accompanying drawings.

[0086] like Figure 1 As shown, an embodiment of the present invention provides a method for identifying an internal edge area, comprising the following steps:

[0087] The three theoretical models of the inner marginal areas are clarified and transformed into the four-dimensional characteristic indicators of the inner marginal areas. The three theoretical models of the inner marginal areas include the theoretical model of the enclave with low economic potential, the theoretical model of the area with low public service penetration, and the theoretical model of the area with decline due to lack of connection. The four dimensions of the inner marginal areas include the dimension of travel time to the regional center, the dimension of economic development potential, the dimension of accessibility of public service facilities, and the dimension of regional development status.

[0088] Obtain data information of the target area and integrate multi-source data that reflects the real development level of the city, and pre-process the multi-source data;

[0089] The pre-processed multi-source data are generally processed, and the four-dimensional characteristic indicators of the internal edge area are respectively identified by using the four-dimensional internal edge area identification methods to identify the internal edge area, and the preliminary internal edge area is obtained;

[0090] Combining the four-dimensional internal edge area identification method with the spatial overlay analysis method, the internal edge areas of the target area are comprehensively identified based on the preliminary internal edge areas.

[0091] As a possible implementation of this embodiment, the four dimensional characteristic indicators of the internal edge area include:

[0092] Long time to regional center: It is a characteristic indicator of the travel time dimension to the regional center, used to determine whether the travel time to the regional center of the target area is higher than that of its neighboring areas;

[0093] Low economic development potential: It is a characteristic indicator of the economic development potential dimension, which is used to calculate the economic development potential of the target area and compare it with the surrounding areas to identify the internal marginal areas;

[0094] Difficulty in obtaining public services: This is a characteristic indicator of the accessibility of public service facilities, which is used to calculate the travel time to the nearest public service facility and determine the difficulty of obtaining public services. Excellent public service facilities can ensure the quality of life of the people in the region, increase the region's ability to attract production factors, and reduce population outflow;

[0095] Development recession: It is a characteristic indicator of regional development status, which is used to identify regions where GDP per capita, regional GDP, public budget revenue and expenditure are declining. Even if the first three characteristic indicators are not met, the region may still experience a spiral of development recession due to external shocks.

[0096] As a possible implementation method of this embodiment, the multi-source data includes administrative boundary data, government point data, night light data, public service data, socio-economic statistics data, DEM data, temperature data, precipitation data, city boundary data and mobile phone signaling data.

[0097] As a possible implementation method of this embodiment, the administrative boundary data and city boundary data are derived from the national geographic information resource directory service system, the government point data and public service data are derived from the geographic national conditions monitoring results and interest point data of the Ministry of Natural Resources, the night light data are derived from NPP / VIIRS data, the socio-economic statistical data are derived from the municipal statistical yearbooks and district and county statistical bulletins, the DEM data are derived from the geospatial data cloud, the temperature data and precipitation data are derived from the National Qinghai-Tibet Plateau Science Data Center, and the mobile phone signaling data are derived from mobile communication operators, such as China Unicom.

[0098] As a possible implementation of this embodiment, the preprocessing of multi-source data includes:

[0099] Use linear interpolation method to supplement the missing data;

[0100] Convert socioeconomic data into panel data;

[0101] Clean and reclassify POI data, including hospitals, pharmacies, banks, primary schools, middle schools, supermarkets, convenience stores, cinemas, railway stations, and bus stations. POI data comes from map software, such as Amap.

[0102] Transform the spatial coordinate system of multi-source data, convert it into a unified coordinate system and crop it

[0103] As a possible implementation method of this embodiment, the preprocessing of multi-source data further includes:

[0104] Consult the statistical yearbooks of cities, districts and counties or use linear interpolation methods to supplement missing socioeconomic data;

[0105] The collected socioeconomic data were converted into panel data using Stata based on county-level administrative division codes;

[0106] Crawl POI data in the study area based on web crawler algorithm;

[0107] Combined with the geographical conditions monitoring results of the Ministry of Natural Resources, the POI data was cleaned and reclassified into: medical service facilities, financial service facilities, educational service facilities, retail service facilities, cultural service facilities and transportation service facilities;

[0108] Arcmap 10.7 was used to convert the .nc format data of temperature and precipitation into raster format and calculate the mean;

[0109] The multi-source data were projected, the coordinate system was converted to WGS_1984, and uniformly clipped according to the scope of the study area.

[0110] As a possible implementation of this embodiment, the general processing of the preprocessed multi-source data includes:

[0111] Use the quartile method to divide internal marginal areas and potential internal marginal areas:

[0112] (1)

[0113] in, is the exact value of the quartile, , the first quartile , second quartile and the third quartile Representing 25%, 50%, and 75% of all statistics respectively; is the lower limit of the quartile interval; n is the total number of observations, is the cumulative absolute frequency of the previous interval; is the absolute frequency of the quartile interval; is the width of the interquartile range; the first, second, and third quartiles represent 25%, 50%, and 75% of all statistical data, respectively. Quartiles are represented by uppercase Q and the quartile index is represented so that the first quartile is , the second quartile is , the third quartile is ;

[0114] Area identification is performed on the grid scale, and after time standardization, small strips representing inconvenient transportation are formed;

[0115] Set 5km 2 The area threshold is used to remove areas smaller than 5 km 2 The small strips of area make the spatial pattern clearer;

[0116] Merge adjacent grid cells or those less than 5 km apart into continuous areas, smooth the boundaries of the merged areas, and remove grid cells with an area less than 5 km. 2 Small strip-shaped areas.

[0117] As a possible implementation of this embodiment, the four-dimensional internal edge region identification method includes:

[0118] 1) Method for identifying internal edge areas in the travel time dimension to the regional center:

[0119] County-level government points were selected as regional research centers;

[0120] Using the path planning of Baidu API, we calculated the time from the grid unit to the regional center and the average level of the county where it is located and the neighboring counties, and finally calculated the difference:

[0121] (2),

[0122] in, T is the travel time from each grid cell to the nearest regional center, is the average level of the county where the grid unit is located and its neighboring counties, is the difference between the grid unit and the average level of the county where the grid unit is located and its neighboring counties;

[0123] Use the four-point division Areas with a value < 0 will be above the second quartile The grid of is defined as the inner edge area, that is, the area with a longer time to reach the center of the area compared with the neighboring areas is defined as the inner edge area;

[0124] Delete, merge and smooth the boundaries of grid cells adjacent to the internal edge areas;

[0125] 2) Methods for identifying internal marginal areas in terms of economic development potential:

[0126] Calculate the night light class kinetic energy index of the grid cell and the difference between the grid cell and the average value:

[0127] (3),

[0128] (4),

[0129] (5),

[0130] in, It is the kinetic energy of regional night lighting. is the total brightness of the night light; is the increase multiple of the total brightness of night lights, is the difference between the grid unit and the average level of the county where the grid unit is located and its neighboring counties. is the average level of the county where the grid unit is located and its neighboring counties;

[0131] Use the four-point division >0 The area will be above the second quartile The grids of which are areas with low economic potential are defined as inner marginal areas;

[0132] Delete, merge and smooth the boundaries of grid cells adjacent to the internal edge areas;

[0133] 3) Methods for identifying internal marginal areas in terms of accessibility of public service facilities:

[0134] Calculate the travel time from each grid cell to the nearest public service facility through Baidu API’s path planning;

[0135] Compare the travel time to the nearest public service for each grid cell to the average travel time for its county and neighboring counties, and calculate the difference:

[0136] (6),

[0137] in, G is the travel time from the grid unit to the nearest public service facility, is the average level of the county where the grid unit is located and its neighboring counties, is the difference between the grid unit and the average level of the county where the grid unit is located and its neighboring counties;

[0138] Use the four-point division Areas with a value < 0 will be above the second quartile The grid of areas with difficulty in accessing public services or medical services or educational services is defined as the inner marginal areas;

[0139] Delete, merge and smooth the boundaries of grid cells adjacent to the internal edge areas;

[0140] 4) Method for identifying internal marginal areas in the dimension of regional development status:

[0141] According to different variables, corresponding thresholds are set: population density is 50% lower than the average of neighboring counties, GDP is 85% lower than the average of neighboring counties, and the proportion of public budget investment is lower than that of neighboring counties;

[0142] According to the set thresholds, compare population density, GDP and the proportion of public budget investment, and determine the changing trends of total population, per capita GDP and the proportion of public budget investment;

[0143] Variables that are below the set threshold and show an overall downward trend are identified as internal marginal areas.

[0144] As a possible implementation of this embodiment, the internal edge region identification method combining four dimensions with the spatial overlay analysis method, based on the preliminary internal edge region, comprehensively identifies the internal edge region of the target region, including:

[0145] Assign the non-inner edge areas within the target area a value of 0, and the inner edge areas a value of 1;

[0146] By setting different coding rules, the types of internal marginal areas are divided;

[0147] The preliminary internal edge areas obtained by the internal edge area identification method in four dimensions are subjected to spatial overlay analysis to obtain the internal edge areas of the target area.

[0148] As a possible implementation of this embodiment, the encoding rule includes:

[0149] The thousands digit is coded as "1" to indicate the inner edge area formed by the longer travel time to the regional center;

[0150] The hundredth digit is coded as “1” to indicate an inner marginal area formed due to low economic development potential;

[0151] The ten-digit number is coded as “1” to indicate an inner marginal area formed by the difficulty in accessing public service facilities;

[0152] The single digit is coded as “1” to indicate an inner fringe area formed due to development decline.

[0153] like Figure 2 As shown, an embodiment of the present invention provides an internal edge area recognition device, comprising:

[0154] A characteristic index determination module is used to clarify three theoretical models of internal marginal areas and transform them into four dimensional characteristic indicators of internal marginal areas. The three theoretical models of internal marginal areas include a theoretical model of enclaves with low economic potential, a theoretical model of areas with low public service penetration, and a theoretical model of areas with decline due to lack of connection. The four dimensions of internal marginal areas include a travel time dimension to the regional center, an economic development potential dimension, a public service facility accessibility dimension, and a regional development status dimension.

[0155] The multi-source data acquisition module is used to obtain data information of the target area and integrate the multi-source data reflecting the real development level of the city, and pre-process the multi-source data;

[0156] The four-dimensional recognition module is used to perform general processing on the pre-processed multi-source data, and to identify the internal edge areas using the four-dimensional internal edge area recognition methods for the four-dimensional characteristic indicators of the internal edge areas, so as to obtain preliminary internal edge areas;

[0157] The target area comprehensive identification module is used to combine the internal edge area identification method of four dimensions with the spatial overlay analysis method to comprehensively identify the internal edge areas of the target area based on the preliminary internal edge areas.

[0158] The process of using the method of the present invention to identify the internal edge area is as follows.

[0159] Step 1: Clarify the three theoretical concepts of the inner periphery and sort them into four operational judgment dimensions.

[0160] Step 1.1: Based on several urban development theories, further clarify the theoretical concept of internal fringe areas;

[0161] Step 1.2: Sort out the theoretical concepts of internal marginal areas and derive four operational judgment dimensions: time to regional center, economic development potential, availability of public services, and development status.

[0162] Step 2: Collect multi-source data that reflects the actual development level of the city and perform preprocessing.

[0163] Step 2.1: Use linear interpolation method to supplement the missing data;

[0164] Step 2.2: Convert socioeconomic data into panel data;

[0165] Step 2.3: Clean the POI data and reclassify them;

[0166] Step 2.4: Transform the spatial coordinate system of the multi-source data into a unified coordinate system and perform cropping.

[0167] Step 3: Use OD analysis, night light kinetic energy calculation, and statistical yearbook data processing to obtain characteristic indicators of four dimensions for extracting internal edge areas.

[0168] Step 3.1: Select county-level government points as regional research centers;

[0169] Step 3.2: Use Baidu API's path planning to calculate the time from the grid unit to the center of the area T ;

[0170] Step 3.3: Calculate the night light kinetic energy index of the grid cell. The formula is as follows:

[0171] (3),

[0172] (4),

[0173] in, It is the kinetic energy of regional night lighting. is the total brightness of the night light; It is the increase multiple of the total brightness of night lights;

[0174] Step 3.4: Calculate the travel time G from the grid cell to the nearest public service facility through the path planning of Baidu API;

[0175] Step 3.5: Set corresponding thresholds based on different variables, including: population density lower than 50% of the average of neighboring counties, GDP lower than 85% of the average of neighboring counties, and public budget investment ratio lower than that of neighboring counties;

[0176] Step 3.6: Based on the set thresholds, compare population density, GDP, and the proportion of public budget investment, and determine the changing trends of total population, per capita GDP, and the proportion of public budget investment.

[0177] Step 4: Comprehensively determine and identify internal edge areas using general processing methods.

[0178] Step 4.1: Use the route planning of Baidu API to calculate the average time from the county where the grid cell is located and the neighboring counties to the regional center, and then calculate the difference. The formula is as follows:

[0179] (2),

[0180] in, T is the travel time from each grid cell to the nearest regional center, is the average level of the county where the grid unit is located and its neighboring counties, is the difference between the grid unit and the average level of the county where the grid unit is located and its neighboring counties;

[0181] Step 4.2: Calculate the difference between the nighttime light kinetic energy index of the grid cell and the average value. The formula is as follows:

[0182] (5),

[0183] in, It is the kinetic energy of regional night lighting. is the difference between the grid unit and the average level of the county where the grid unit is located and its neighboring counties. is the average level of the county where the grid unit is located and its neighboring counties;

[0184] Step 4.3: Use the route planning of Baidu API to calculate the average travel time from the county where the grid cell is located and the neighboring counties to the nearest public service facilities, and then calculate the difference. The formula is as follows:

[0185] (6),

[0186] in, G is the travel time from the grid unit to the nearest public service facility, is the average level of the county where the grid unit is located and its neighboring counties, is the difference between the grid unit and the average level of the county where the grid unit is located and its neighboring counties;

[0187] Step 4.4: Use the four-division method to divide the internal edge areas and potential internal edge areas. The formula is as follows:

[0188] (1),

[0189] in, is the exact value of the quartile, is the lower limit of the quartile range. n is the total number of observations, is the cumulative absolute frequency of the previous interval. is the absolute frequency of the quartile interval. is the width of the interquartile range. The first, second, and third quartiles represent 25%, 50%, and 75% of all statistical data, respectively. Quartiles are represented by uppercase Q and the quartile index is represented so that the first quartile is , the second quartile is , the third quartile is ;

[0190] Step 4.5: Divide the part where the difference between the travel time of the grid cell to the regional center and to the nearest public service facility and the average level of the county where the grid cell is located and its neighboring counties is less than zero or the difference between the night light kinetic energy index of the grid cell and the average value is greater than zero, and divide the part above The grid is defined as the inner edge area;

[0191] Step 4.6: Identify regions at the grid scale and after time standardization, small strips of inconvenient transportation areas appear;

[0192] Step 4.7: Set up 5km 2 The area threshold is used to remove areas smaller than 5 km 2 The small strips of area make the spatial pattern clearer;

[0193] Step 4.8: Merge adjacent grid cells or grid cells that are less than 5 km apart into continuous areas, and perform boundary smoothing on the merged areas, eliminating grid cells with an area less than 5 km. 2 Small strip-shaped areas;

[0194] Step 4.9: Assign a value of 0 to non-interior edge areas and a value of 1 to interior edge areas, set different coding rules, classify the types of interior edge areas, and spatially superimpose the interior edge areas identified by the four features.

[0195] The following experiment is conducted on a certain region. The administrative boundary data and city boundary data are from the National Geographic Information Resource Directory Service System, the government point and public service data are from the geographical national conditions monitoring results and point of interest data of the Ministry of Natural Resources, the point of interest (POI) data are from Amap, the night light data are from NPP / VIIRS data, the social and economic statistics are from the city-level statistical yearbooks and the district and county statistical bulletins, the DEM data are from the geospatial data cloud, the temperature data and precipitation data are from the National Tibetan Plateau Science Data Center, and the mobile phone signaling data are from China Unicom. The specific implementation steps of the method described in the present invention are as follows.

[0196] Step 1: Identify four dimensions for identifying inner edge areas:

[0197] 1) Clarify three theoretical models of internal marginal areas, namely, the theoretical model of enclaves with low economic potential, the theoretical model of areas with low public service penetration, and the theoretical model of areas with decline due to lack of connection;

[0198] 2) Extract the key features of the three theoretical models of internal marginal areas and transform the extracted features into corresponding indicators.

[0199] Step 2: Data processing of urban development level evaluation:

[0200] The collected multi-source data are preprocessed to generate panel data for urban development level evaluation. The specific steps are as follows:

[0201] 1) Consult the statistical yearbooks of cities, districts and counties or use linear interpolation methods to supplement the missing data;

[0202] 2) Using Stata and county-level administrative division codes, the collected socioeconomic data were converted into panel data;

[0203] 3) Based on the web crawler algorithm, crawl the POI data in the study area, mainly including hospitals, pharmacies, banks, primary schools, middle schools, supermarkets, convenience stores, cinemas, railway stations, and bus stations;

[0204] 4) Combined with the geographical conditions monitoring results of the Ministry of Natural Resources, the POI data was cleaned and reclassified into: medical service facilities, financial service facilities, educational service facilities, retail service facilities, cultural service facilities and transportation service facilities;

[0205] 5) Use Arcmap 10.7 to convert the .nc format data of temperature and precipitation into raster format and calculate the mean;

[0206] 6) The multi-source data were projected, the coordinate system was converted to WGS 1984, and the clipping tool of ArcGIS 10.7 was used to perform uniform clipping according to the scope of the study area.

[0207] Step 3: Calculate the characteristic index to identify the internal edge area:

[0208] 1) Based on the city size, number of cities and urban distribution, county-level government points were selected as the regional centers of the study;

[0209] 2) Use the Create Raster tool in ArcGIS 10.7 and set the distance to 1 km. 2 The grid size is used to divide the study area into multiple grid cells;

[0210] 3) Use Baidu API's path planning to calculate the time from the grid unit to the center of the area T and travel time to the nearest public service facilities G ;

[0211] 4) According to formulas (3) and (4), calculate the night light kinetic energy index of the grid unit;

[0212] 5) Set corresponding thresholds based on different variables, including: population density lower than 50% of the average of neighboring counties, GDP lower than 85% of the average of neighboring counties, and public budget investment ratio lower than that of neighboring counties;

[0213] 6) Based on the set thresholds, compare population density, GDP and the proportion of public budget investment, and determine the changing trends of total population, per capita GDP and the proportion of public budget investment.

[0214] Step 4: Comprehensively determine and identify internal edge areas:

[0215] 1) Using Baidu API’s path planning, calculate the average time from the county where the grid cell is located and the neighboring counties to the regional center and the average travel time to the nearest public service facility ;

[0216] 2) According to formula (2), calculate the time from the grid unit to the center of the area T Compared with the average level of the county where it is located and the neighboring counties The difference between

[0217] 3) According to formula (5), calculate the night light kinetic energy index of the grid unit With the average The difference between

[0218] 4) According to formula (6), calculate the travel time from the grid unit to the nearest public service facility G Compared with the average level of the county where it is located and the neighboring counties The difference between

[0219] 5) According to formula (1), the grid unit is divided into the part where the average level difference between the grid unit and the county where the grid unit is located and its neighboring counties is less than zero, and the part where this difference is higher than The grid is defined as the inner edge area;

[0220] 6) Set the area threshold to 5km 2 , using the Eliminate Area Components tool in ArcGIS 10.7 to remove areas smaller than 5 km 2 area;

[0221] 7) Adjacent raster cells or those with a distance of less than 5 km were merged into continuous regions using the Merge tool in ArcGIS 10.7, and the smooth polygon tool was used to smooth the boundaries. The inner edge areas with long travel time to the regional center, the inner edge areas with low economic potential, the inner edge areas with difficulty in obtaining general public services, and the inner edge areas with declining development were obtained, as shown in the figure below. Figure 3 , Figure 4 , Figure 5 and Figure 6 As shown;

[0222] 8) Using the field calculator tool in ArcGIS 10.7, the non-interior edge areas in the study area were assigned a value of 0, the interior edge areas were assigned a value of 1, and different coding rules were set to classify the types of interior edge areas, as shown in Table 1;

[0223] 9) Using the overlay analysis method, the internal edge area layers of the four characteristic types are merged to obtain the internal edge area area that integrates the four characteristics, such as Figure 7 shown.

[0224] Table 1 Types and codes of internal marginal areas

[0225]

[0226] The present invention combines the advantages of spatial planning theory with integrated analysis of multiple data sources, and explores a method for identifying internal marginal areas based on the regional collaborative development theory and model of the European Spatial Planning Observation Network.

[0227] The present invention identifies internal marginal areas from multiple aspects such as society, economy, regional center accessibility, population density, etc. based on multi-source data, overcoming the problems of inconsistent data collection standards, panel data or outliers leading to unstable results in previous studies.

[0228] Compared with existing methods, the present invention reflects the comprehensive development status of the region based on multiple dimensional perspectives, thereby improving the evaluation accuracy of coordinated development and avoiding the defects of traditional methods that are too single and lack a dynamic perspective. At the same time, the present invention ensures the comprehensiveness, scientificity, fairness and dynamism of the evaluation system, and provides a more reliable and objective regional development evaluation through multi-angle and multi-level analysis.

[0229] Different from the traditional single-dimensional static analysis, the present invention uses a comprehensive analysis from a multi-dimensional perspective. The results of the invention can reflect the real changing trends of the region in different time and space dimensions, reveal potential problems in urban development, such as spatial unevenness and functional zoning adjustments, and identify details and dynamic changes that may be overlooked by traditional methods, thereby providing a basis for more accurate decision-making and planning.

[0230] An electronic device provided by an embodiment of the present invention includes a processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor. When the device is running, the processor communicates with the memory via the bus, and the processor executes the machine-readable instructions to perform the steps of any internal edge area identification method as described above.

[0231] Specifically, the above-mentioned memory and processor can be general-purpose memory and processor, which are not specifically limited here. When the processor runs the computer program stored in the memory, the above-mentioned internal edge area recognition method can be executed.

[0232] Those skilled in the art will appreciate that the structure of the electronic device does not limit the electronic device and may include more or fewer components than shown in the figure, or combine or split certain components, or arrange the components differently.

[0233] In some embodiments, the electronic device may also include a touch screen that can be used to display a graphical user interface (e.g., a startup interface of an application) and receive user operations on the graphical user interface (e.g., startup operations on an application). The specific touch screen may include a display panel and a touch panel. The display panel may be configured in the form of LCD (Liquid Crystal Display), OLED (Organic Light-Emitting Diode), etc. The touch panel may collect the user's contact or non-contact operations on or near it, and generate pre-set operation instructions, for example, the user uses any suitable object such as a finger, stylus, or accessories on or near the touch panel. In addition, the touch panel may include two parts: a touch detection device and a touch controller. The touch detection device detects the user's touch position and posture, detects the signal brought by the touch operation, and transmits the signal to the touch controller; the touch controller receives the touch information from the touch detection device, converts it into information that the processor can process, and then sends it to the processor, and can receive and execute commands from the processor. In addition, the touch panel can be implemented by various types such as resistive, capacitive, infrared and surface acoustic wave, and any technology developed in the future can also be used to implement the touch panel. Further, the touch panel can cover the display panel, and the user can operate on or near the touch panel covered on the display panel according to the graphical user interface displayed on the display panel. After the touch panel detects the operation on or near it, it is transmitted to the processor to determine the user input, and then the processor provides corresponding visual output on the display panel in response to the user input. In addition, the touch panel and the display panel can be implemented as two independent components or integrated.

[0234] Corresponding to the above-mentioned method for starting the application, an embodiment of the present invention further provides a storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of any of the above-mentioned methods for identifying internal edge areas are executed.

[0235] The application startup device provided in the embodiment of the present application can be specific hardware on the device or software or firmware installed on the device. The device provided in the embodiment of the present application, its implementation principle and the technical effect produced are the same as those in the aforementioned method embodiment. For the sake of brief description, the parts not mentioned in the device embodiment can refer to the corresponding contents in the aforementioned method embodiment. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the systems, devices and units described above can all refer to the corresponding processes in the aforementioned method embodiment, and will not be repeated here.

[0236] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0237] In the embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of modules is only a logical function division. There may be other division methods in actual implementation. For example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, indirect coupling or communication connection of devices or modules, which can be electrical, mechanical or other forms.

[0238] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical modules, that is, they may be located in one place or distributed on multiple network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0239] In addition, each functional module in the embodiments provided in the present application may be integrated into one processing module, or each module may exist physically separately, or two or more modules may be integrated into one module.

[0240] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0241] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0242] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0243] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for identifying an internal edge region, characterized in that: The steps include: The three theoretical models of the inner marginal areas are clarified and transformed into the four-dimensional characteristic indicators of the inner marginal areas. The three theoretical models of the inner marginal areas include the theoretical model of the enclave with low economic potential, the theoretical model of the area with low public service penetration, and the theoretical model of the area with decline due to lack of connection. The four dimensions of the inner marginal areas include the dimension of travel time to the regional center, the dimension of economic development potential, the dimension of accessibility of public service facilities, and the dimension of regional development status. Obtain data information of the target area and integrate multi-source data that reflects the real development level of the city, and pre-process the multi-source data; The pre-processed multi-source data are generally processed, and the four-dimensional characteristic indicators of the internal edge area are respectively identified by using the four-dimensional internal edge area identification methods to identify the internal edge area, and the preliminary internal edge area is obtained; Combining the internal edge area identification method of four dimensions with the spatial overlay analysis method, the internal edge areas of the target area are comprehensively identified based on the preliminary internal edge areas; The four-dimensional internal edge region identification method includes: 1) Method for identifying internal edge areas in the travel time dimension to the regional center: County-level government points were selected as regional research centers; Using the path planning of Baidu API, we calculated the time from the grid unit to the regional center and the average level of the county where it is located and the neighboring counties, and finally calculated the difference: （2）， in, T is the travel time from each grid cell to the nearest regional center, is the average level of the county where the grid unit is located and its neighboring counties, is the difference between the grid unit and the average level of the county where the grid unit is located and its neighboring counties; Use the four-point division Areas with a value < 0 will be above the second quartile The grid of is defined as the inner edge area, that is, the area with a longer time to reach the center of the area compared with the neighboring areas is defined as the inner edge area; Delete, merge and smooth the boundaries of grid cells adjacent to the internal edge areas; 2) Methods for identifying internal marginal areas in terms of economic development potential: Calculate the night light class kinetic energy index of the grid cell and the difference between the grid cell and the average value: （3）， （4）， （5）， in, It is the kinetic energy of regional night lighting. is the total brightness of the night light; is the increase multiple of the total brightness of night lights, is the difference between the grid unit and the average level of the county where the grid unit is located and its neighboring counties. is the average level of the county where the grid unit is located and its neighboring counties; Use the four-point division >0 The area will be above the second quartile The grids of which are areas with low economic potential are defined as inner marginal areas; Delete, merge and smooth the boundaries of grid cells adjacent to the internal edge areas; 3) Methods for identifying internal marginal areas in terms of accessibility of public service facilities: Calculate the travel time from each grid cell to the nearest public service facility through Baidu API’s path planning; Compare the travel time to the nearest public service for each grid cell to the average travel time for its county and neighboring counties, and calculate the difference: （6）， in, G is the travel time from the grid unit to the nearest public service facility, is the average level of the county where the grid unit is located and its neighboring counties, is the difference between the grid unit and the average level of the county where the grid unit is located and its neighboring counties; Use the four-point division Areas with a value < 0 will be above the second quartile The grid of areas with difficulty in accessing public services or medical services or educational services is defined as the inner marginal areas; Delete, merge and smooth the boundaries of grid cells adjacent to the internal edge areas; 4) Method for identifying internal marginal areas in the dimension of regional development status: According to different variables, corresponding thresholds are set: population density is 50% lower than the average of neighboring counties, GDP is 85% lower than the average of neighboring counties, and the proportion of public budget investment is lower than that of neighboring counties; According to the set thresholds, compare population density, GDP and the proportion of public budget investment, and determine the changing trends of total population, per capita GDP and the proportion of public budget investment; Variables that are below the set threshold and show an overall downward trend are identified as internal marginal areas; The internal edge area identification method combining four dimensions and the spatial overlay analysis method comprehensively identifies the internal edge areas of the target area based on the preliminary internal edge areas, including: Assign the non-inner edge areas within the target area a value of 0, and the inner edge areas a value of 1; By setting different coding rules, the types of internal marginal areas are divided; The preliminary internal edge areas obtained by the internal edge area identification method in four dimensions are subjected to spatial overlay analysis to obtain the internal edge areas of the target area.

2. The method for identifying an inner edge region according to claim 1, characterized in that: The four dimensional characteristic indicators of the internal edge area include: Long time to regional center: It is a characteristic indicator of the travel time dimension to the regional center, used to determine whether the travel time to the regional center of the target area is higher than that of its neighboring areas; Low economic development potential: It is a characteristic indicator of the economic development potential dimension, which is used to calculate the economic development potential of the target area and compare it with the surrounding areas to identify the internal marginal areas; Difficulty in obtaining public services: This is a characteristic indicator of the accessibility dimension of public service facilities, which is used to calculate the travel time to the nearest public service facility and determine the difficulty of obtaining public services; Development recession: It is a characteristic indicator of the regional development status dimension, which is used to identify regions where per capita GDP, regional gross product, public budget revenue and expenditure have declined.

3. The method for identifying an inner edge region according to claim 1, characterized in that: The multi-source data includes administrative boundary data, government point data, night light data, public service data, socio-economic statistics data, DEM data, temperature data, precipitation data, city boundary data and mobile phone signaling data.

4. The method for identifying an inner edge region according to claim 1, characterized in that: The preprocessing of multi-source data includes: Use linear interpolation method to supplement the missing data; Convert socioeconomic data into panel data; Clean and reclassify the POI data, including hospitals, pharmacies, banks, primary schools, middle schools, supermarkets, convenience stores, cinemas, railway stations, and bus stations; The multi-source data are transformed into a unified coordinate system and cropped.

5. The method for identifying an inner edge region according to claim 1, characterized in that: The general processing of the preprocessed multi-source data includes: Use the quartile method to divide internal marginal areas and potential internal marginal areas: (1)， in, is the exact value of the quartile, , the first quartile , second quartile and the third quartile Representing 25%, 50%, and 75% of all statistics respectively; is the lower limit of the quartile interval; n is the total number of observations, is the cumulative absolute frequency of the previous interval; is the absolute frequency of the quartile interval; is the width of the interquartile interval; Area identification is performed on the grid scale, and after time standardization, small strips representing inconvenient transportation are formed; Set 5km 2 The area threshold is used to remove areas smaller than 5 km 2 The small strips of area make the spatial pattern clearer; Merge adjacent grid cells or those less than 5 km apart into continuous areas, smooth the boundaries of the merged areas, and remove grid cells with an area less than 5 km. 2 Small strip-shaped areas.

6. An internal edge region recognition device, characterized in that: include: A characteristic index determination module is used to clarify three theoretical models of the inner marginal area and transform them into four dimensional characteristic indicators of the inner marginal area. The three theoretical models of the inner marginal area include a theoretical model of an enclave with low economic potential, a theoretical model of an area with low public service penetration, and a theoretical model of an area with decline due to lack of connection. The four dimensions of the inner marginal area include a travel time dimension to the regional center, an economic development potential dimension, a public service facility accessibility dimension, and a regional development status dimension. The multi-source data acquisition module is used to obtain data information of the target area and integrate the multi-source data reflecting the real development level of the city, and pre-process the multi-source data; The four-dimensional recognition module is used to perform general processing on the pre-processed multi-source data, and to identify the internal edge areas using the four-dimensional internal edge area recognition methods for the four-dimensional characteristic indicators of the internal edge areas, so as to obtain preliminary internal edge areas; The target area comprehensive identification module is used to combine the internal edge area identification method of four dimensions with the spatial overlay analysis method to comprehensively identify the internal edge areas of the target area based on the preliminary internal edge areas; The four-dimensional internal edge region identification method includes: 1) Method for identifying internal edge areas in the travel time dimension to the regional center: County-level government points were selected as regional research centers; Using the path planning of Baidu API, we calculated the time from the grid unit to the regional center and the average level of the county where it is located and the neighboring counties, and finally calculated the difference: （2）， in, T is the travel time from each grid cell to the nearest regional center, is the average level of the county where the grid unit is located and its neighboring counties, is the difference between the grid unit and the average level of the county where the grid unit is located and its neighboring counties; Use the four-point division Areas with a value < 0 will be above the second quartile The grid of is defined as the inner edge area, that is, the area with a longer time to reach the center of the area compared with the neighboring areas is defined as the inner edge area; Delete, merge and smooth the boundaries of grid cells adjacent to the internal edge areas; 2) Methods for identifying internal marginal areas in terms of economic development potential: Calculate the night light class kinetic energy index of the grid cell and the difference between the grid cell and the average value: （3）， （4）， （5）， in, It is the kinetic energy of regional night lighting. is the total brightness of the night light; is the increase multiple of the total brightness of night lights, is the difference between the grid unit and the average level of the county where the grid unit is located and its neighboring counties. is the average level of the county where the grid unit is located and its neighboring counties; Use the four-point division >0 The area will be above the second quartile The grids of which are areas with low economic potential are defined as inner marginal areas; Delete, merge and smooth the boundaries of grid cells adjacent to the internal edge areas; 3) Methods for identifying internal marginal areas in terms of accessibility of public service facilities: Calculate the travel time from each grid cell to the nearest public service facility through Baidu API’s path planning; Compare the travel time to the nearest public service for each grid cell to the average travel time for its county and neighboring counties, and calculate the difference: （6）， in, G is the travel time from the grid unit to the nearest public service facility, is the average level of the county where the grid unit is located and its neighboring counties, is the difference between the grid unit and the average level of the county where the grid unit is located and its neighboring counties; Use the four-point division Areas with a value < 0 will be above the second quartile The grid of areas with difficulty in accessing public services or medical services or educational services is defined as the inner marginal areas; Delete, merge and smooth the boundaries of grid cells adjacent to the internal edge areas; 4) Method for identifying internal marginal areas in the dimension of regional development status: According to different variables, corresponding thresholds are set: population density is 50% lower than the average of neighboring counties, GDP is 85% lower than the average of neighboring counties, and the proportion of public budget investment is lower than that of neighboring counties; According to the set thresholds, compare population density, GDP and the proportion of public budget investment, and determine the changing trends of total population, per capita GDP and the proportion of public budget investment; Variables that are below the set threshold and show an overall downward trend are identified as internal marginal areas; The internal edge area identification method combining four dimensions and the spatial overlay analysis method comprehensively identifies the internal edge areas of the target area based on the preliminary internal edge areas, including: Assign the non-inner edge areas within the target area a value of 0, and the inner edge areas a value of 1; By setting different coding rules, the types of internal marginal areas are divided; The preliminary internal edge areas obtained by the internal edge area identification method in four dimensions are subjected to spatial overlay analysis to obtain the internal edge areas of the target area.

7. An electronic device, characterized in that: It includes a processor, a memory and a bus, the memory stores machine-readable instructions executable by the processor, when the electronic device is running, the processor and the memory communicate through the bus, and the processor executes the machine-readable instructions to perform the steps of the internal edge area identification method as described in any one of claims 1-5.

8. A storage medium, characterized in that: The storage medium stores a computer program, which, when executed by a processor, executes the steps of the method for identifying an internal edge area as described in any one of claims 1 to 5.

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