WO2017166370A1 - 一种基于区域城际流强度测算模型的划定大都市圈的方法 - Google Patents

一种基于区域城际流强度测算模型的划定大都市圈的方法 Download PDF

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WO2017166370A1
WO2017166370A1 PCT/CN2016/081020 CN2016081020W WO2017166370A1 WO 2017166370 A1 WO2017166370 A1 WO 2017166370A1 CN 2016081020 W CN2016081020 W CN 2016081020W WO 2017166370 A1 WO2017166370 A1 WO 2017166370A1
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city
flow
inter
cities
regional
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杨俊宴
王玉琢
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东南大学
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Priority to US16/086,316 priority Critical patent/US10621604B2/en
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Publication of WO2017166370A1 publication Critical patent/WO2017166370A1/zh

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    • G06F30/10—Geometric CAD
    • G06F30/13—Architectural design, e.g. computer-aided architectural design [CAAD] related to design of buildings, bridges, landscapes, production plants or roads
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  • the invention belongs to the field of regional analysis in urban planning, and particularly relates to a quantitative analysis and calculation of inter-city relations in urban planning regional analysis and a method for delineating a specific region in a large scale.
  • the present invention provides a quantitative, comprehensive, multi-element measurement of the city linkage strength and thereby delineates the multi-circle range of the metropolitan circle, and determines the circle of the metropolitan circle.
  • the method of forming a city is a quantitative, comprehensive, multi-element measurement of the city linkage strength and thereby delineates the multi-circle range of the metropolitan circle, and determines the circle of the metropolitan circle.
  • the present invention provides a method for demarcating a metropolitan area based on a regional inter-city flow intensity measurement model, comprising the following steps:
  • Step 1 Determine the regional scope of the urban agglomeration centered on the metropolis as the spatial extent of the metropolitan area to be defined and clarify the basic unit of the city to be measured within the region, and further determine the associated city of the intercity flow;
  • Step 2 According to the associated cities identified in Step 1, the sub-regional inter-city flow intensity of each group of associated cities is determined, including five sub-items: economic flow, passenger flow, freight flow, technical flow, and information flow;
  • Step 3 Calculate the comprehensive intercity flow intensity of the central city and other related cities by weighting according to the five sub-regional inter-city flow intensity values measured in step 2: economic flow, passenger flow, freight flow, technical flow, and information flow. And its membership;
  • Step 4 Draw the relevant vector CAD drawings of the provinces and cities in the region and record the data into the ArcGIS library. Calculate the five sub-regional inter-city stream intensity values measured in step two and the central city and other associated cities measured in step three.
  • the integrated intercity flow intensity membership data is entered into the ArcGIS library, and the regional intercity flow database is established through the association of spatial data and tabular data;
  • Step 5 The degree of membership of the integrated inter-city flow in the regional inter-city flow database established in step four
  • the data is used for four sets of natural discontinuous clustering analysis. According to the clustering data segments from large to small, the constituent cities belonging to the core layer, the edge layer and the radiating layer of the metropolitan area are respectively obtained, and the narrow scope and generalized range of the metropolitan circle are finally determined. .
  • the specific steps in the first step to determine the area of the urban agglomeration to be measured and the inter-city flow associated city are as follows:
  • Step 1.1 Select Metropolis A as the core of the region and serve as the central city of the metropolitan area;
  • Step 1.2 Delineate the surrounding provinces and cities bordering the metropolis A selected in step 1.1, and the province where metropolitan A is located, as the spatial extent of the inter-city flow to be measured;
  • Step 1.3 Based on the spatial extent of the regional inter-city flow to be measured in step 1.2, select the prefecture-level and above cities in the range as the basic unit of the city to be measured, and determine the city with the inter-city linkage effect of metropolitan A. N, and numbered B 1 , B 2 ... B i ... B N ;
  • Step 1.4 Based on the city B i determined in step 1.3, obtain the associated cities of the regional inter-city flows to be determined, and combine them into A_B 1 , A_B 2 ... A_B i ... A_B N .
  • step 2 the specific steps in the second sub-regional flow intensity including economic flow, passenger flow, freight flow, technical flow and information flow in step 2 are as follows:
  • Step 2.1 Measure the inter-city economic flow of the associated city A_B i determined in step 1.4; obtain the non-agricultural population U A of each city through the urban statistical yearbook of each province and city and relevant city data. GDPG A of each city, And the linear distance between the central city A and other cities B i Calculate the inter-city economic linkages of associated cities And the corresponding inter-city economic flow intensity
  • Step 2.2 Measure the intercity passenger flow of the associated city A_B i determined in step 1.4; obtain the daily highway long-distance passenger service between the central city A and other cities B i And the daily stopover of the railway
  • the daily average departure frequency and unit traffic volume of long-distance bus and railway the daily average traffic coefficient ⁇ of long-distance bus is 0.1, and the daily average traffic coefficient ⁇ of the railway is 0.9.
  • Step 2.3 Measure the intercity freight flow of the associated city A_B i determined in step 1.4; obtain the number of highway mileage within each city: M A , The total amount of road freight turnover in each city, R A , And the linear distance D ABi between the central city A and other cities B i ; calculate the intercity freight contact of each group of related cities And intercity freight flow intensity
  • Step 2.4 Determine the inter-city technology flow of the associated city A_B i determined in step 1.4. Get universities, research institutions from the central city A and city B i each other of the completion of cooperation Lunwenpianshu Calculate the ratio of the frequency of innovation and technology cooperation in each group of related cities to the total amount of innovation and technology cooperation in the region, and obtain the inter-city technical flow intensity of each group of related cities in the region.
  • Step 2.5 Determination of inter-city traffic determined in step 1.4 A_B i associated with the city. Get the frequency of mutual search between Central City A and other cities' B i networks Calculate the ratio of the network information contact of each group of related cities to the total amount of network information in the area, and obtain the inter-city information flow intensity of each group of related cities in the area.
  • step three according to the economical flow, passenger flow, freight flow, technical flow and information flow measured in step two, the inter-city flow intensity values of the five sub-regions are calculated by weighting the comprehensive inter-city of the central city and other related cities.
  • the specific steps of flow intensity and its membership are as follows:
  • Step 3.1 Determine the relative importance of the inter-city flows of the five sub-regions of economic flow, passenger flow, freight flow, technical flow and information flow, and express the weight coefficient of each regional inter-city flow by ⁇ k , and And based on the importance of the five regional inter-city flows, the average weighted method is adopted, that is, the weight coefficient of the five sub-regional inter-city flows is 0.25;
  • Step 3.2 According to the inter-city economic flow intensity of the central city A and other cities B i measured in step two Intercity passenger flow intensity Intercity freight flow intensity Inter-city technology flow intensity Intercity information flow intensity Calculate the comprehensive intercity flow intensity of Central City A and other cities B I by the following average weighting formula
  • Step 3.3 According to the integrated intercity flow intensity of the central city A and other cities B i measured in step 3.2 The comprehensive inter-city stream strength membership degree of central city A and other cities B i through normalization standardization
  • step 4 the relevant vector CAD data of each province and city in the region and the sub-items and comprehensive inter-city stream strength data measured in steps 2 and 3 are recorded into ArcGIS, and the specific steps of establishing a regional inter-city flow database are as follows:
  • Step 4.1 Import the vector point CAD data layer of the vector boundary line of each province and city within the urban agglomeration area defined in step one, and the vector point CAD data layer of the spatial position of the administrative center of the central city and other cities into the ArcGIS database;
  • Step 4.2 In AutoCAD, draw the central city A and other cities B i to contact the network line, and number it as A_B i , and import the CAD line of the vector link between the associated cities into the ArcGIS database;
  • Step 4.3 Calculate the inter-city flow intensity values of the five sub-regions of the central city A and other cities B i measured in step 2: economic flow intensity Passenger flow intensity Freight flow intensity Technical flow intensity Information flow intensity And comprehensive inter-city flow intensity membership Translated into the ArcGIS database by EXCEL file format;
  • Step 4.4 correlate the spatial correlation of the vector layers in step 4.1, step 4.2, and step 4.3 with the table of the measured data to establish a regional inter-city flow database;
  • Step 4.5 Automatically display the line thickness of the “A_B i city contact line” according to the size of the five regional inter-city flow intensity values, and output the five-item regional inter-city flow intensity map by ArcGIS; according to the comprehensive inter-city flow intensity degree
  • the size of the value automatically displays the line thickness of the “A_B i City Contact Line” and outputs a summary of the integrated intercity flow intensity.
  • the comprehensive inter-city flow intensity membership degree in the regional inter-city flow database in step five The data is used for four sets of natural discontinuous clustering analysis. According to the cluster data segments from large to small, the constituent cities belonging to the core layer, the edge layer and the radiation layer of the metropolitan circle are respectively obtained, and the narrow scope of the metropolitan circle is finally delineated.
  • the specific steps of the broad scope are as follows:
  • Step 5.1 Use the natural discontinuous clustering method in the ArcGIS software “cluster analysis” module to calculate the comprehensive intercity flow intensity membership of each “city line” in step 4.4)
  • the data is divided into four groups of natural intermittent clustering, and the four groups of data segments after clustering are sorted from large to small: first group, second group, third group, fourth group;
  • Step 5.2 The other cities associated with the central city A corresponding to the “city line of communication” in the data segments of the first group, the second group, and the third group are the constituent cities of the core layer, the edge layer, and the radiation layer of the metropolitan area; The “city line” in the four data segments should not be included in the metropolitan area;
  • Step 5.3 Based on step 5.2, obtain the narrow scope and broad scope of the metropolitan area with city A as the regional center; among them, the narrow scope of the metropolitan circle is composed of the central city and the core city; the general scope of the metropolitan circle is from the central city. , core city, edge city and radiation layer city.
  • the present invention has the following advantages over the prior art:
  • Figure 1 is a flow chart of the present invention
  • Figure 2 Schematic diagram of the inter-city flow associated city in the case area
  • FIG. 1 Schematic diagram of the inter-city economic flow intensity in the case area
  • Figure 4 Schematic diagram of the intensity of intercity passenger flow in the case area
  • Figure 5 Schematic diagram of the intensity of intercity freight flow in the case area
  • Figure 6 Schematic diagram of the inter-city technical flow intensity of the case area
  • Figure 7 is a schematic diagram of the intensity of the inter-city information flow in the case area
  • Figure 8 is a schematic diagram of the integrated intercity flow intensity in the case area
  • Figure 9 is a schematic diagram of the division of the metropolitan area in the case area.
  • Step 1 Determine the regional scope of the urban agglomeration centered on Shanghai as the spatial extent of the metropolitan area to be defined and It is true that the basic units of the city to be measured in the region are cities above the prefecture level, and the associated cities of inter-city flows are further determined.
  • Step 1.1 Select the metropolis Shanghai (city code: A) as the regional core as the central city of the metropolitan circle.
  • Step 1.2 Delineate Jiangsu province and Zhejiang province, which are adjacent to Shanghai (boundary), plus the central city of Shanghai, and delineate the spatial extent of the inter-city flow to be measured.
  • Step 1.4 Based on step 1.3, further determine the associated cities of the regional inter-city flows to be determined, and combine them into A_B 1 , A_B 2 ... A_B i ... A_B 24 .
  • Step 2 According to the associated cities identified in Step 1, the sub-regional inter-city flows of each group of associated cities are determined, including five sub-items: economic flow, passenger flow, freight flow, technical flow, and information flow.
  • Step 2.1 Determine the inter-city economic flow of the associated city A_B i determined in step 1.4.
  • the number of non-agricultural population in each city is obtained from the urban statistical yearbooks of Shanghai, Jiangsu, and Zhejiang provinces and related city data: U A , GDP of each city (gross domestic product): G A , The linear distance between Shanghai and other cities, Bi , is measured by electronic map.
  • the inter-city economic linkages of the related cities in the region are calculated by the following formula And the corresponding inter-city economic flow intensity
  • Step 2.2 Determine the intercity passenger flow of the associated city A_B i determined in step 1.4. Get daily road long-distance passengers between Shanghai A and other cities B i And the daily stopover of the railway According to the daily average departure frequency and unit traffic volume of long-distance bus and railway, the daily average traffic coefficient ⁇ of long-distance bus is 0.1, and the daily average traffic coefficient ⁇ of the railway is 0.9. Calculate the intercity passenger contact quantity of each group of related cities. The ratio of the total passenger traffic in the region, and the intercity passenger flow intensity of each group of related cities in the region
  • Step 2.3 Determine the intercity freight flow of the associated city A_B i determined in step 1.4. Get the number of highways within each city: M A , The total amount of road freight turnover in each city, R A , The linear distance between Shanghai A and other cities B i is obtained by electronic map measurement. Calculate the intercity freight contact volume of each group of associated cities And intercity freight flow intensity
  • Step 2.4 Determine the inter-city technology flow of the associated city A_B i determined in step 1.4.
  • the geo-search tool of the “author unit” item in the “China Knowledge Network” literature database the number of papers completed by cooperation between universities and research institutions in Shanghai A and other cities B i is counted. Calculate the ratio of the frequency of innovation and technology cooperation in each group of related cities to the total amount of innovation and technology cooperation in the region, and obtain the inter-city technical flow intensity of each group of related cities in the region.
  • Step 2.5 Determine the inter-city information flow of the associated city A_B i determined in step 1.4.
  • Network attention among users represents the amount of network connections between cities. Calculate the ratio of the network information contact of each group of related cities to the total amount of network information in the area by the following formula, and obtain the inter-city information flow intensity of each group of related cities in the area.
  • Step 3 Calculate the comprehensive intercity flow intensity of the central city and other cities by weighting according to the five regional inter-city flow items measured in step 2: economic flow, passenger flow, freight flow, technical flow, and information flow. Membership.
  • Step 3.1 Based on the similarity of the five regional inter-city flows, the average weighting method is adopted, that is, the weight coefficient of the five sub-regional inter-city flows is 0.25.
  • Step 3.2 According to the inter-city economic flow intensity of the central city A and other cities B i measured in step two Intercity passenger flow intensity Intercity freight flow intensity Inter-city technology flow intensity Intercity information flow intensity Calculate the integrated intercity flow intensity of A and other cities B i by the following average weighting formula
  • Step 3.3 Calculate the integrated intercity flow intensity of A and other cities B i according to step 3.2 Through the normalization standardization, it is the comprehensive inter-city stream strength membership degree of A and other cities B i
  • Step 4 Draw the relevant vector CAD data of each province and city in the region and enter the ArcGIS library. Calculate the five sub-regional inter-city stream intensity values measured in step two and the comprehensive inter-city between Shanghai and other cities measured in step three.
  • the flow intensity membership data is entered into the ArcGIS library, and a regional inter-city flow database is established by associating spatial data with tabular data.
  • Step 4.1 In step 1, the CAD data layer of the provincial and municipal vector boundary lines of Shanghai, Jiangsu, and Zhejiang provinces is delineated, and the vector point CAD data layer of the spatial location of the administrative center of Shanghai and other cities is imported into ArcGIS.
  • the software outputs a polygon layer named "City Boundary” and a dot layer called "City Point”.
  • Step 4.2 Take the municipal administrative center of each city identified in step 4.1 as the connection endpoint, draw the contact network line between Shanghai A and other cities B i in AutoCAD, and number each contact network line as A_B i to get the association.
  • the CAD line of the vector line between the cities, and the CAD data layer is imported into the ArcGIS software, and a line layer named "City Line" is output.
  • Step 4.3 Calculate the inter-city flow intensity values of the five sub-regions of Shanghai A and other cities B i measured in Step 2: Economic flow intensity Passenger flow intensity Freight flow intensity Technical flow intensity Information flow intensity And comprehensive inter-city flow intensity membership Enter the ArcGIS database and correlate the measured data with the “A_B i city contact line” in step 4.2).
  • Step 4.4 Enter the data of each layer of ArcGIS in Step 4.1, Step 4.2 and Step 4.3 to associate the spatial association of the vector layer with the table of the measured data to establish a regional inter-city flow database.
  • Step 4.5 According to the five regional inter-city flow intensity values The size of the line automatically displays the line thickness of the "A_B i city contact line", and the ArcGIS outputs five sub-regional inter-city flow intensity diagrams; according to the comprehensive inter-city flow intensity membership degree The size of the value automatically displays the line thickness of the “A_B i city contact line”, and then the integrated intercity flow intensity map is output by ArcGIS.
  • Step 5 The degree of membership of the integrated inter-city flow in the regional inter-city flow database established in step four
  • the data is used for four sets of natural discontinuous clustering analysis. According to the cluster data segments from large to small, the constituent cities belonging to the core layer, the edge layer and the radiation layer of the metropolitan circle are respectively obtained, and the narrow scope of the metropolitan circle is finally delineated. Broad range.
  • Step 5.1 Using the ArcGIS software “cluster analysis” module, the comprehensive inter-city stream strength membership of each “city line” in step 4.4) by natural discontinuous clustering method
  • the data is divided into four groups of natural discontinuous clustering, which makes the difference in membership degree of inter-city flow intensity between groups the largest, and the difference in membership degree of comprehensive inter-city flow intensity within the group is the smallest.
  • the four groups of data segments after clustering are sorted from large to small: first group, second group, third group, and fourth group.
  • Step 5.2 The other cities associated with Shanghai A corresponding to the “city line” in the first group of data segments are the core B cores of the metropolitan area, including: Nantong City, Wuxi City, Suzhou City, Zhejiang City, and Zhejiang City.
  • the other cities associated with the central city A corresponding to the “city line” in the second data segment are the B side of the metropolitan circle, including: Yangzhou City, Nanjing City, Changzhou City, Zhejiang Province Huzhou City, Hangzhou City, Shaoxing City, Ningbo City, and Zhoushan City; the other cities associated with the central city A corresponding to the “city line of communication” in the third group of data segments are the urban spokes of the metropolitan area radiating layer, including : Yancheng City, Taizhou City, Zhenjiang City, Jiangsu province, and Taizhou City, Zhejiang Province; other cities associated with Central City A corresponding to the “Urban Contact Line” in the fourth data segment are not included in the metropolitan area centered on City A.
  • Step 5.3: 5.2 based on obtained in step with a broad range narrow range a large urban metropolitan area A is the area of the center; wherein the narrow confines of a large metropolitan city center City A and core B core layer composition, comprising: Shanghai, Nantong City, Wuxi City, Suzhou City, Jiaxing City; the general scope of the metropolitan area consists of the central city A, the core city B core , the edge layer city B side and the radiation layer city B spoke , including: Shanghai, Nantong, Wuxi City, Suzhou City, Jiaxing City, Yangzhou City, Nanjing City, Changzhou City, Huzhou City, Hangzhou City, Shaoxing City, Ningbo City, Zhoushan City, Yancheng City, Taizhou City, Zhenjiang City, Taizhou City.

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Abstract

一种基于区域城际流强度测算模型划定大都市圈的方法，包括以下步骤：1、确定有待测算的城市群区域范围及城际流关联城市；2、根据确定的关联城市，分别测定各组关联城市的分项区域城际流强度，包括经济流、客运流、货运流、技术流、信息流五个分项；3、根据上述五个分项区域城际流强度值，通过加权计算出中心城市与其他关联城市的综合城际流强度及其隶属度；4、将上述数据都录入ArcGIS库，并通过空间数据与表格数据的关联，建立区域城际流数据库；5、对建立的区域城际流数据库中的数据作四组自然间断式聚类分析，并最终确定大都市圈的狭义与广义范围。该方法能够定量、综合、多要素的测算城市联动强度并由此划定大都市圈多圈层范围。

Description

一种基于区域城际流强度测算模型的划定大都市圈的方法 技术领域
本发明属于城市规划中的区域分析领域，特别涉及一种城市规划区域分析中的城市间关系的定量分析测算以及大尺度的特定区域范围划定的方法。
背景技术
随着社会经济发展和全球化、信息化的推进，城市在区域发展中的地位和作用日益加强，区域内部城市之间的社会经济联系更加密切与复杂，大都市圈的内部联通性对大都市圈的各级圈层的范围划定起到了更大的影响作用。
至今，在城市规划领域，对大都市圈的划定多采用经济地理学模型，如：经济距离法、经济引力法以及经济场强法，简单通过城市个体的经济数据以及城市之间的距离数据计算中心城市对周边其他城市的经济影响，进而根据影响力的大小划定大都市圈范围。此类方法存在视角单一、要素单一的问题，未考虑到影响城市间关联程度的除直接经济影响外的其他社会性因子；另外，目前的划定方法多为采用感性及经验判断中心城市对其他城市经济影响力的区间值，进而判定大都市圈各圈层内的组成城市。总的来说，目前传统的大都市圈圈层划定方法尚存在较多问题，也尚不适用于大尺度区域城市分析工作。
发明内容
发明目的：为了克服现有技术中存在的不足，本发明提供一种定量的、综合的、多要素的测算城市联动强度并由此划定大都市圈多圈层范围，确定大都市圈各圈层组成城市的方法。
技术方案：为实现上述目的，本发明提供一种基于区域城际流强度测算模型划定大都市圈的方法，包括以下步骤：
步骤一：确定以大都市为中心的城市群区域范围作为待划定大都市圈的空间范围并明确该区域范围内有待测算的城市基本单元，进一步确定城际流的关联城市；
步骤二：根据步骤一中确定的关联城市，分别测定各组关联城市的分项区域城际流强度，包括经济流、客运流、货运流、技术流、信息流五个分项；
步骤三：根据步骤二中测算的经济流、客运流、货运流、技术流、信息流五个分项区域城际流强度值，通过加权计算出中心城市与其他关联城市的综合城际流强度及其隶属度；
步骤四：绘制区域内各省市的相关矢量CAD图并将数据录入ArcGIS库，将步骤二测算出的五个分项区域城际流强度值以及步骤三中测算出的中心城市与其他关联城市的综合城际流强度隶属度数据录入ArcGIS库，并通过空间数据与表格数据的关联，建立区域城际流数据库；
步骤五：对步骤四中建立的区域城际流数据库中的综合城际流强度隶属度
Figure PCTCN2016081020-appb-000001
数据作四组自然间断式聚类分析，根据由大到小的聚类数据段分别得到属于大都市圈核心层、边缘层以及辐射层的组成城市，最终确定大都市圈的狭义范围与广义范围。
其中，步骤一中确定有待测算的城市群区域范围及城际流关联城市的具体步骤如下：
步骤1.1：选定大都市A为区域核心并作为大都市圈的中心城市；
步骤1.2：划定与步骤1.1中选定的大都市A相接接壤的周边省、市，以及大都市A所处的省，作为有待测算的区域城际流的空间范围；
步骤1.3：基于步骤1.2中划定有待测算的区域城际流的空间范围，选择该范围中地级及以上城市作为待测算的城市基本单位，确定与大都市A有城际联动作用的城市共N个，并编号为B1、B2…Bi…BN；
步骤1.4：基于步骤1.3中确定的城市Bi，得到待测定的区域城际流的关联城市，组合为A_B1、A_B2…A_Bi…A_BN。
其中，步骤二中测算包括经济流、客运流、货运流、技术流和信息流五个分项区域城际流强度的具体步骤如下：
步骤2.1：测定步骤1.4中确定的关联城市A_Bi的城际经济流；通过各省市的城市统计年鉴及相关城市数据资料获取各城市的非农业人口数UA、
Figure PCTCN2016081020-appb-000002
各城市的GDPGA、
Figure PCTCN2016081020-appb-000003
以及中心城市A与其他各城市Bi之间的空间直线距离
Figure PCTCN2016081020-appb-000004
计算关联城市的城际经济联系量
Figure PCTCN2016081020-appb-000005
以及对应的城际经济流强度
Figure PCTCN2016081020-appb-000006
Figure PCTCN2016081020-appb-000007
步骤2.2：测定步骤1.4中确定的关联城市A_Bi的城际客运流；通过获取中心城市A与其他各城市Bi之间的每日公路长途客运班次
Figure PCTCN2016081020-appb-000008
以及铁路每日经停班次
Figure PCTCN2016081020-appb-000009
根据长途客车与铁路的日均发车频率及单位运量差异确定长途客车的日均运量系数α为0.1，铁路的日均运量系数β为0.9；计算各组关联城市的城际客运联系量占区域内客运联系总量的比值，得到区域内各组关联城市的城际客运流强度
Figure PCTCN2016081020-appb-000010
Figure PCTCN2016081020-appb-000011
步骤2.3：测定步骤1.4中确定的关联城市A_Bi的城际货运流；通过获取各城市内部的公路里程数：MA、
Figure PCTCN2016081020-appb-000012
各城市的公路货运周转总量RA、
Figure PCTCN2016081020-appb-000013
以及中心城市A与其他各城市Bi之间的空间直线距离DABi；计算各组关联城市的城际货运联系量
Figure PCTCN2016081020-appb-000014
与城际货运流强度
Figure PCTCN2016081020-appb-000015
Figure PCTCN2016081020-appb-000016
步骤2.4：测定步骤1.4中确定的关联城市A_Bi的城际技术流。获取来自中心城市A与其他各城市Bi的高校、科研机构合作完成的论文篇数
Figure PCTCN2016081020-appb-000017
计算各组关联城市的创新技术合作频数占区域内创新技术合作总量的比值，得到区域内各组关联城市的城际技术流强度
Figure PCTCN2016081020-appb-000018
Figure PCTCN2016081020-appb-000019
步骤2.5：测定步骤1.4中确定的关联城市A_Bi的城际信息流。获取中心城市A与其他各城市Bi网络互相搜索频次
Figure PCTCN2016081020-appb-000020
计算各组关联城市的网络信息联系量占区域内网络信息联系总量的比值，得到区域内各组关联城市的城际信息流强度
Figure PCTCN2016081020-appb-000021
Figure PCTCN2016081020-appb-000022
其中，步骤三中根据步骤二中测算的经济流、客运流、货运流、技术流和信息流五个分项区域城际流强度值，通过加权计算出中心城市与其他关联城市的综合城际流强度及其隶属度的具体步骤如下：
步骤3.1：确定经济流、客运流、货运流、技术流和信息流五个分项区域城际流的相对重要性，并以δk表示每项区域城际流的权重系数，且
Figure PCTCN2016081020-appb-000023
并基于五项区域城际流相近的重要性，采取平均加权的方式，即五项分项区域城际流的权重系数均为0.25；
步骤3.2：根据步骤二中测算的中心城市A与其他各城市Bi的城际经济流强度
Figure PCTCN2016081020-appb-000024
城际客运流强度
Figure PCTCN2016081020-appb-000025
城际货运流强度
Figure PCTCN2016081020-appb-000026
城际技术流强度
Figure PCTCN2016081020-appb-000027
城际信息流强度
Figure PCTCN2016081020-appb-000028
通过以下平均加权公式，计算中心城市A与其他各城市BI的综合城际流强度
Figure PCTCN2016081020-appb-000029
Figure PCTCN2016081020-appb-000030
步骤3.3：根据步骤3.2中测算的中心城市A与其他各城市Bi的综合城际流强度
Figure PCTCN2016081020-appb-000031
通过归一标准化得到中心城市A与其他各城市Bi的综合城际流强度隶属度
Figure PCTCN2016081020-appb-000032
Figure PCTCN2016081020-appb-000033
其中，步骤四中将区域内各省市的相关矢量CAD数据以及步骤二与步骤三中测算出的分项与综合城际流强度数据录入ArcGIS，建立区域城际流数据库的具体步骤如下：
步骤4.1：将步骤一中划定的城市群区域范围内各省市的矢量边界线CAD数据图层，中心城市及其他各城市的行政中心所处空间位置的矢量点CAD数据图层导入ArcGIS数据库；
步骤4.2：在AutoCAD中绘制中心城市A与其他各市Bi联系网络线，并编号为A_Bi，并将关联城市间的矢量联系线CAD图层导入至ArcGIS数据库；
步骤4.3：将步骤二中测算得到的中心城市A与其他各城市Bi的五个分项区域城际流强度值：经济流强度
Figure PCTCN2016081020-appb-000034
客运流强度
Figure PCTCN2016081020-appb-000035
货运流强度
Figure PCTCN2016081020-appb-000036
技术流强度
Figure PCTCN2016081020-appb-000037
信息流强度
Figure PCTCN2016081020-appb-000038
以及综合城际流强度隶属度
Figure PCTCN2016081020-appb-000039
由EXCEL文件格式转译录入ArcGIS数据库；
步骤4.4：将步骤4.1、步骤4.2及步骤4.3中的各层数据进行矢量图层的空间关联与测算数据的表格关联，建立区域城际流数据库；
步骤4.5：分别按照五项区域城际流强度值的大小自动显示“A_Bi城市联系线”的线型粗细，由ArcGIS输出五张分项区域城际流强度示意图；按照综合城际流强度隶属度
Figure PCTCN2016081020-appb-000040
值的大小自动显示“A_Bi城市联系线”的线型粗细，输出综合城际流强度示意图。
其中，步骤五中对区域城际流数据库中的综合城际流强度隶属度
Figure PCTCN2016081020-appb-000041
数据作四组自然间断式聚类分析，根据由大到小的聚类数据段分别得到属于大都市圈核心层、边缘层以及辐射层的组成城市，最终划定出大都市圈的狭义范围与广义范围的具体步骤如下：
步骤5.1：利用ArcGIS软件“聚类分析”模块中的自然间断聚类法对步骤4.4)中的各“城市联系线”的综合城际流强度隶属度
Figure PCTCN2016081020-appb-000042
数据作四组自然间断式聚类，对聚类后的 四组数据段由大到小进行排序为：第一组、第二组、第三组、第四组；
步骤5.2：第一组、第二组、第三组数据段中“城市联系线”分别对应的与中心城市A关联的其他城市为大都市圈核心层、边缘层、辐射层的组成城市；第四组数据段中“城市联系线”应其他城市不纳入都市圈范围；
步骤5.3：基于步骤5.2得到以城市A为区域中心的大都市圈的狭义范围与广义范围；其中，大都市圈的狭义范围由中心城市及核心层城市组成；大都市圈的广义范围由中心城市、核心层城市、边缘层城市及辐射层城市组成。
有益效果：本发明与现有技术相比具有以下优点：
(1)创新的关系视角：本方法从“流”的视角关注区域范围内城市间的联动关系及相互作用关系，以此测算划定大都市圈圈层范围，弥补了传统方法中过于关注城市个体的本身特征而忽略了城市间的社会经济联系的不足。
(2)客观理性的定量测算：本方法通过获取多样的社会经济数据，通过相关公式计算以测算城际联动关系强度，定量的测算方法更为客观理性，使划定出的大都市圈圈层范围更加科学合理。
(3)多要素的综合全面性：本方法从经济联系、客运交通、货运交通、创新技术以及网络信息五个方面来对区域城际流作分项的测算，由分项测算再归纳综合；多要素的测算内容较为全面地涵盖了城际联动中所关系的社会经济的各个方面，使本方法有好的综合性。
附图说明
图1是本发明的流程图；
图2案例区域城际流关联城市示意图；
图3案例区域城际经济流强度示意图；
图4案例区域城际客运流强度示意图；
图5案例区域城际货运流强度示意图；
图6案例区域城际技术流强度示意图；
图7案例区域城际信息流强度示意图；
图8案例区域综合城际流强度示意图；
图9案例区区域大都市圈划分示意图。
具体实施方式
以下将结合基于区域城际流划定上海大都市圈(以上海为区域中心城市)的方法案例和附图来详细地说明本发明的技术方案，如：
步骤一：确定以上海为中心的城市群区域范围作为待划定大都市圈的空间范围并明 确该区域范围内有待测算的城市基本单元为地级以上城市，进一步确定城际流的关联城市。
步骤1.1：选定大都市上海(城市代号：A)为区域核心作为大都市圈的中心城市。
步骤1.2：划定与上海相接邻(接壤)的江苏省、浙江省，加之中心城市上海，划定出有待测算的区域城际流的空间范围。
步骤1.3：基于步骤1.2中划定的有待测算的区域城际流的空间范围，选择该范围中地级及以上城市作为待测算的城市基本单位，其中江苏省13个地级以上城市(依次编号为B1～B13、浙江省11个地级以上城市(依次编号为B14～B24)，确定与上海A有城际联动作用的城市共24个，城市代号为Bi(i＝1～24)。
步骤1.4：基于步骤1.3进一步确定有待测定的区域城际流的关联城市，组合为A_B1、A_B2…A_Bi…A_B24。
步骤二：根据步骤一中确定的关联城市，分别测定各组关联城市的分项区域城际流，包括经济流、客运流、货运流、技术流、信息流五个分项。
步骤2.1：测定步骤1.4中确定的关联城市A_Bi的城际经济流。由上海市、江苏省、浙江省的城市统计年鉴及相关城市数据资料获取各城市的非农业人口数：UA、
Figure PCTCN2016081020-appb-000043
各城市的GDP(国内生产总值)：GA、
Figure PCTCN2016081020-appb-000044
通过电子地图测量得到上海与其他各城市Bi之间的空间直线距离
Figure PCTCN2016081020-appb-000045
由以下公式计算得到区域内各关联城市的城际经济联系量
Figure PCTCN2016081020-appb-000046
以及对应的城际经济流强度
Figure PCTCN2016081020-appb-000047
Figure PCTCN2016081020-appb-000048
步骤2.2：测定步骤1.4中确定的关联城市A_Bi的城际客运流。获取上海A与其他各城市Bi之间的每日公路长途客运班次
Figure PCTCN2016081020-appb-000049
以及铁路每日经停班次
Figure PCTCN2016081020-appb-000050
根据长途客 车与铁路的日均发车频率及单位运量差异确定长途客车的日均运量系数α为0.1，铁路的日均运量系数β为0.9；计算各组关联城市的城际客运联系量占区域内客运联系总量的比值，得到区域内各组关联城市的城际客运流强度
Figure PCTCN2016081020-appb-000051
Figure PCTCN2016081020-appb-000052
步骤2.3：测定步骤1.4中确定的关联城市A_Bi的城际货运流。获取各城市内部的公路里程数：MA、
Figure PCTCN2016081020-appb-000053
各城市的公路货运周转总量RA、
Figure PCTCN2016081020-appb-000054
通过电子地图测量得到上海A与其他各城市Bi之间的空间直线距离
Figure PCTCN2016081020-appb-000055
计算各组关联城市的城际货运联系量
Figure PCTCN2016081020-appb-000056
与城际货运流强度
Figure PCTCN2016081020-appb-000057
Figure PCTCN2016081020-appb-000058
步骤2.4：测定步骤1.4中确定的关联城市A_Bi的城际技术流。利用“中国知网”文献数据库中“作者单位”项的地理检索工具，统计出来自上海A与其他各城市Bi的高校、科研机构合作完成的论文篇数
Figure PCTCN2016081020-appb-000059
计算各组关联城市的创新技术合作频数占区域内创新技术合作总量的比值，得到区域内各组关联城市的城际技术流强度
Figure PCTCN2016081020-appb-000060
Figure PCTCN2016081020-appb-000061
步骤2.5：测定步骤1.4中确定的关联城市A_Bi的城际信息流。利用“百度指数”中的网络用户搜索关注度数据统计出上海A与其他各城市Bi网络互相搜索频次
Figure PCTCN2016081020-appb-000062
以用户间的网络关注度代表城市间的网络联系量。通过以下公式计算各组关联城市的网络 信息联系量占区域内网络信息联系总量的比值，得到区域内各组关联城市的城际信息流强度
Figure PCTCN2016081020-appb-000063
Figure PCTCN2016081020-appb-000064
步骤三：根据步骤二中测算的经济流、客运流、货运流、技术流、信息流五个区域城际流分项，通过加权计算出中心城市与其他各城市的综合城际流强度及其隶属度。
步骤3.1：基于五项区域城际流相近的重要性，采取平均加权的方式，即五项分项区域城际流的权重系数均为0.25。
步骤3.2：根据步骤二中测算的中心城市A与其他各城市Bi的城际经济流强度
Figure PCTCN2016081020-appb-000065
城际客运流强度
Figure PCTCN2016081020-appb-000066
城际货运流强度
Figure PCTCN2016081020-appb-000067
城际技术流强度
Figure PCTCN2016081020-appb-000068
城际信息流强度
Figure PCTCN2016081020-appb-000069
通过以下平均加权公式，计算是A与其他各城市Bi的综合城际流强度
Figure PCTCN2016081020-appb-000070
Figure PCTCN2016081020-appb-000071
步骤3.3：根据步骤3.2中测算的是A与其他各城市Bi的综合城际流强度
Figure PCTCN2016081020-appb-000072
通过归一标准化得到是A与其他各城市Bi的综合城际流强度隶属度
Figure PCTCN2016081020-appb-000073
Figure PCTCN2016081020-appb-000074
步骤四：绘制区域内各省市的相关矢量CAD数据并录入ArcGIS库，将步骤二测算出的五个分项区域城际流强度值以及步骤三中测算出的上海与其他各城市的综合城际流强度隶属度数据录入ArcGIS库，并通过空间数据与表格数据的关联，建立区域城际流数据库。
步骤4.1：将步骤一中划定上海市、江苏省、浙江省的省、市矢量边界线CAD数据图层，上海及其他各城市的行政中心所处空间位置的矢量点CAD数据图层导入ArcGIS 软件，输出名为“城市边界”的面状图层以及名为“城市点”的点状图层。
步骤4.2：以步骤4.1中确定的各城市的市级行政中心为连线端点，在AutoCAD中绘制上海A与其他各市Bi的联系网络线，并编号各条联系网络线为A_Bi，得到关联城市间的矢量联系线CAD图层，并将该CAD数据图层导入ArcGIS软件，输出名为“城市联系线”的线状图层。
步骤4.3：将步骤二中测算得到的上海A与其他各城市Bi的五个分项区域城际流强度值：经济流强度
Figure PCTCN2016081020-appb-000075
客运流强度
Figure PCTCN2016081020-appb-000076
货运流强度
Figure PCTCN2016081020-appb-000077
技术流强度
Figure PCTCN2016081020-appb-000078
信息流强度
Figure PCTCN2016081020-appb-000079
以及综合城际流强度隶属度
Figure PCTCN2016081020-appb-000080
录入ArcGIS数据库，并将测算出的以上数据与步骤4.2)中的“A_Bi城市联系线”进行一一对应的数据关联。
步骤4.4：将步骤4.1、步骤4.2及步骤4.3中录入ArcGIS的各层数据进行矢量图层的空间关联与测算数据的表格关联，建立区域城际流数据库。
步骤4.5：分别按照五项区域城际流强度值
Figure PCTCN2016081020-appb-000081
的大小自动显示“A_Bi城市联系线”的线型粗细，由ArcGIS输出五张分项区域城际流强度示意图；按照综合城际流强度隶属度
Figure PCTCN2016081020-appb-000082
值的大小自动显示“A_Bi城市联系线”的线型粗细，进而由ArcGIS输出综合城际流强度示意图。
步骤五：对步骤四中建立的区域城际流数据库中的综合城际流强度隶属度
Figure PCTCN2016081020-appb-000083
数据作四组自然间断式聚类分析，根据由大到小的聚类数据段分别得到属于大都市圈核心层、边缘层以及辐射层的组成城市，最终划定出大都市圈的狭义范围与广义范围。
步骤5.1：利用ArcGIS软件“聚类分析”模块，通过自然间断聚类法对步骤4.4)中的各“城市联系线”的综合城际流强度隶属度
Figure PCTCN2016081020-appb-000084
数据作四组自然间断式聚类，使得组间综合城际流强度隶属度差异最大，组内综合城际流强度隶属度差异最小。对聚类后 的四组数据段由大到小进行排序为：第一组、第二组、第三组、第四组。
步骤5.2：第一组数据段中“城市联系线”对应的与上海A关联的其他城市为大都市圈核心层的组成城市B核，包括：江苏省南通市、无锡市、苏州市以及浙江省嘉兴市；第二组数据段中“城市联系线”对应的与中心城市A关联的其他城市为大都市圈边缘层的组成城市B边，包括：江苏省扬州市、南京市、常州市以及浙江省湖州市、杭州市、绍兴市、宁波市、舟山市；第三组数据段中“城市联系线”对应的与中心城市A关联的其他城市为大都市圈辐射层的组成城市B辐，包括：江苏省盐城市、泰州市、镇江市以及浙江省台州市；第四组数据段中“城市联系线”对应的与中心城市A关联的其他城市不划入以城市A为中心的大都市圈范围内，包括：江苏省连云港市、徐州市、宿迁市、淮安市以及浙江省衢州市、金华市、丽水市、温州市。
步骤5.3：基于步骤5.2得到以城市A为区域中心的大都市圈的狭义范围与广义范围；其中，大都市圈的狭义范围由中心城市A及核心层城市B核组成，包括：上海市、南通市、无锡市、苏州市、嘉兴市；大都市圈的广义范围由中心城市A、核心层城市B核、边缘层城市B边及辐射层城市B辐组成，包括：上海市、南通市、无锡市、苏州市、嘉兴市、扬州市、南京市、常州市、湖州市、杭州市、绍兴市、宁波市、舟山市、盐城市、泰州市、镇江市、台州市。
以上所述仅是本发明的优选实施方式，应当指出：对于本技术领域的普通技术人员来说，在不脱离本发明原理的前提下，还可以做出若干改进和润饰，这些改进和润饰也应视为本发明的保护范围。

Claims (6)

  1. 一种基于区域城际流强度测算模型划定大都市圈的方法，其特征在于：包括以下步骤：
    步骤一：确定以大都市为中心的城市群区域范围作为待划定大都市圈的空间范围并明确该区域范围内有待测算的城市基本单元，进一步确定城际流的关联城市；
    步骤二：根据步骤一中确定的关联城市，分别测定各组关联城市的分项区域城际流强度，包括经济流、客运流、货运流、技术流、信息流五个分项；
    步骤三：根据步骤二中测算的经济流、客运流、货运流、技术流、信息流五个分项区域城际流强度值，通过加权计算出中心城市与其他关联城市的综合城际流强度及其隶属度；
    步骤四：绘制区域内各省市的相关矢量CAD图并将数据录入ArcGIS库，将步骤二测算出的五个分项区域城际流强度值以及步骤三中测算出的中心城市与其他关联城市的综合城际流强度隶属度数据录入ArcGIS库，并通过空间数据与表格数据的关联，建立区域城际流数据库；
    步骤五：对步骤四中建立的区域城际流数据库中的综合城际流强度隶属度
    Figure PCTCN2016081020-appb-100001
    数据作四组自然间断式聚类分析，根据由大到小的聚类数据段分别得到属于大都市圈核心层、边缘层以及辐射层的组成城市，最终确定大都市圈的狭义范围与广义范围。
  2. 根据权利要求1所述的一种基于区域城际流强度测算模型划定大都市圈的方法，其特征在于：所述步骤一中确定有待测算的城市群区域范围及城际流关联城市的具体步骤如下：
    步骤1.1：选定大都市A为区域核心并作为大都市圈的中心城市；
    步骤1.2：划定与步骤1.1中选定的大都市A相接接壤的周边省、市，以及大都市A所处的省，作为有待测算的区域城际流的空间范围；
    步骤1.3：基于步骤1.2中划定有待测算的区域城际流的空间范围，选择该范围中地级及以上城市作为待测算的城市基本单位，确定与大都市A有城际联动作用的城市共N个，并编号为B1、B2…Bi…BN；
    步骤1.4：基于步骤1.3中确定的城市Bi，得到待测定的区域城际流的关联城市，组合为A_B1、A_B2…A_Bi…A_BN。
  3. 根据权利要求1所述的一种基于区域城际流强度测算模型划定大都市圈的方法，其特征在于：所述步骤二中测算包括经济流、客运流、货运流、技术流和信息流五个分项区域城际流强度的具体步骤如下：
    步骤2.1：测定步骤1.4中确定的关联城市A_Bi的城际经济流；通过各省市的城市统计年鉴及相关城市数据资料获取各城市的非农业人口数UA、
    Figure PCTCN2016081020-appb-100002
    各城市的GDPGA、以及中心城市A与其他各城市Bi之间的空间直线距离
    Figure PCTCN2016081020-appb-100004
    计算关联城市的城际经济联系量
    Figure PCTCN2016081020-appb-100005
    以及对应的城际经济流强度
    Figure PCTCN2016081020-appb-100006
    Figure PCTCN2016081020-appb-100007
    步骤2.2：测定步骤1.4中确定的关联城市A_Bi的城际客运流；通过获取中心城市A与其他各城市Bi之间的每日公路长途客运班次
    Figure PCTCN2016081020-appb-100008
    以及铁路每日经停班次
    Figure PCTCN2016081020-appb-100009
    根据长途客车与铁路的日均发车频率及单位运量差异确定长途客车的日均运量系数α为0.1，铁路的日均运量系数β为0.9；计算各组关联城市的城际客运联系量占区域内客运联系总量的比值，得到区域内各组关联城市的城际客运流强度
    Figure PCTCN2016081020-appb-100010
    Figure PCTCN2016081020-appb-100011
    步骤2.3：测定步骤1.4中确定的关联城市A_Bi的城际货运流；通过获取各城市内部的公路里程数：MA、
    Figure PCTCN2016081020-appb-100012
    各城市的公路货运周转总量RA、
    Figure PCTCN2016081020-appb-100013
    以及中心城市A与其他 各城市Bi之间的空间直线距离
    Figure PCTCN2016081020-appb-100014
    计算各组关联城市的城际货运联系量
    Figure PCTCN2016081020-appb-100015
    与城际货运流强度
    Figure PCTCN2016081020-appb-100016
    Figure PCTCN2016081020-appb-100017
    步骤2.4：测定步骤1.4中确定的关联城市A_Bi的城际技术流。获取来自中心城市A与其他各城市Bi的高校、科研机构合作完成的论文篇数
    Figure PCTCN2016081020-appb-100018
    计算各组关联城市的创新技术合作频数占区域内创新技术合作总量的比值，得到区域内各组关联城市的城际技术流强度
    Figure PCTCN2016081020-appb-100019
    Figure PCTCN2016081020-appb-100020
    步骤2.5：测定步骤1.4中确定的关联城市A_Bi的城际信息流。获取中心城市A与其他各城市Bi网络互相搜索频次
    Figure PCTCN2016081020-appb-100021
    计算各组关联城市的网络信息联系量占区域内网络信息联系总量的比值，得到区域内各组关联城市的城际信息流强度
    Figure PCTCN2016081020-appb-100022
    Figure PCTCN2016081020-appb-100023
  4. 根据权利要求1至3之一所述的一种基于区域城际流强度测算模型划定大都市圈的方法，其特征在于：所述步骤三中根据步骤二中测算的经济流、客运流、货运流、技术流和信息流五个分项区域城际流强度值，通过加权计算出中心城市与其他关联城市的综合城际流强度及其隶属度的具体步骤如下：
    步骤3.1：确定经济流、客运流、货运流、技术流和信息流五个分项区域城际流的 相对重要性，并以δk表示每项区域城际流的权重系数，且
    Figure PCTCN2016081020-appb-100024
    并基于五项区域城际流相近的重要性，采取平均加权的方式，即五项分项区域城际流的权重系数均为0.25；
    步骤3.2：根据步骤二中测算的中心城市A与其他各城市Bi的城际经济流强度
    Figure PCTCN2016081020-appb-100025
    城际客运流强度
    Figure PCTCN2016081020-appb-100026
    城际货运流强度
    Figure PCTCN2016081020-appb-100027
    城际技术流强度
    Figure PCTCN2016081020-appb-100028
    城际信息流强度
    Figure PCTCN2016081020-appb-100029
    通过以下平均加权公式，计算中心城市A与其他各城市Bi的综合城际流强度
    Figure PCTCN2016081020-appb-100030
    Figure PCTCN2016081020-appb-100031
    步骤3.3：根据步骤3.2中测算的中心城市A与其他各城市Bi的综合城际流强度
    Figure PCTCN2016081020-appb-100032
    通过归一标准化得到中心城市A与其他各城市Bi的综合城际流强度隶属度
    Figure PCTCN2016081020-appb-100033
    Figure PCTCN2016081020-appb-100034
  5. 根据权利要求1所述的一种基于区域城际流强度测算模型划定大都市圈的方法，其特征在于：步骤四中将区域内各省市的相关矢量CAD数据以及步骤二与步骤三中测算出的分项与综合城际流强度数据录入ArcGIS，建立区域城际流数据库的具体步骤如下：
    步骤4.1：将步骤一中划定的城市群区域范围内各省市的矢量边界线CAD数据图层，中心城市及其他各城市的行政中心所处空间位置的矢量点CAD数据图层导入ArcGIS数据库；
    步骤4.2：在AutoCAD中绘制中心城市A与其他各市Bi联系网络线，并编号为A_Bi，并将关联城市间的矢量联系线CAD图层导入至ArcGIS数据库；
    步骤4.3：将步骤二中测算得到的中心城市A与其他各城市Bi的五个分项区域城际 流强度值：经济流强度
    Figure PCTCN2016081020-appb-100035
    客运流强度
    Figure PCTCN2016081020-appb-100036
    货运流强度
    Figure PCTCN2016081020-appb-100037
    技术流强度
    Figure PCTCN2016081020-appb-100038
    信息流强度
    Figure PCTCN2016081020-appb-100039
    以及综合城际流强度隶属度
    Figure PCTCN2016081020-appb-100040
    由EXCEL文件格式转译录入ArcGIS数据库；
    步骤4.4：将步骤4.1、步骤4.2及步骤4.3中的各层数据进行矢量图层的空间关联与测算数据的表格关联，建立区域城际流数据库；
    步骤4.5：分别按照五项区域城际流强度值的大小自动显示“A_Bi城市联系线”的线型粗细，由ArcGIS输出五张分项区域城际流强度示意图；按照综合城际流强度隶属度
    Figure PCTCN2016081020-appb-100041
    值的大小自动显示“A_Bi城市联系线”的线型粗细，输出综合城际流强度示意图。
  6. 根据权利要求1所述的一种基于区域城际流强度测算模型划定大都市圈的方法，其特征在于：所述步骤五中对区域城际流数据库中的综合城际流强度隶属度
    Figure PCTCN2016081020-appb-100042
    数据作四组自然间断式聚类分析，根据由大到小的聚类数据段分别得到属于大都市圈核心层、边缘层以及辐射层的组成城市，最终划定出大都市圈的狭义范围与广义范围的具体步骤如下：
    步骤5.1：利用ArcGIS软件“聚类分析”模块中的自然间断聚类法对步骤4.4)中的各“城市联系线”的综合城际流强度隶属度
    Figure PCTCN2016081020-appb-100043
    数据作四组自然间断式聚类，对聚类后的四组数据段由大到小进行排序为：第一组、第二组、第三组、第四组；
    步骤5.2：第一组、第二组、第三组数据段中“城市联系线”分别对应的与中心城市A关联的其他城市为大都市圈核心层、边缘层、辐射层的组成城市；第四组数据段中“城市联系线”应其他城市不纳入都市圈范围；
    步骤5.3：基于步骤5.2得到以城市A为区域中心的大都市圈的狭义范围与广义范围；其中，大都市圈的狭义范围由中心城市及核心层城市组成；大都市圈的广义范围由中心城市、核心层城市、边缘层城市及辐射层城市组成。
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CN116993245B (zh) * 2023-09-26 2023-12-15 交通运输部科学研究院 区域水路运输量测算方法、装置、电子设备及存储介质
CN117275243B (zh) * 2023-11-22 2024-02-02 上海随申行智慧交通科技有限公司 基于多源交通出行数据的区域流控预测预警方法及应用

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102024206A (zh) * 2010-12-20 2011-04-20 江苏省交通科学研究院股份有限公司 市郊轨道交通客流的预测方法
US20130046586A1 (en) * 2011-08-16 2013-02-21 Walk Score Management LLC System and method for assessing quality of transit networks at specified locations
CN103106630A (zh) * 2011-11-10 2013-05-15 李红彬 生态城市规划
US20130211963A1 (en) * 2009-11-24 2013-08-15 Joseph Williams Facilitation of interaction between providers, buyers, and agents

Family Cites Families (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CA2682517C (en) * 2007-04-02 2016-08-02 Kamran Khan System and method to predict the global spread of infectious agents via commercial air travel
CN101866546A (zh) * 2010-05-18 2010-10-20 南京市城市与交通规划设计研究院有限责任公司 基于差别化策略与规划的城市交通分区方法
CN102289581A (zh) * 2011-08-10 2011-12-21 武汉大学 一种基于空间作用划分的模拟城市扩展的方法
CN102508869B (zh) * 2011-10-11 2013-11-20 西安煤航信息产业有限公司 将cad图形与属性数据导入gis系统的方法
US20140089036A1 (en) * 2012-09-26 2014-03-27 Xerox Corporation Dynamic city zoning for understanding passenger travel demand
US9483846B2 (en) * 2013-05-29 2016-11-01 Microsoft Technology Licensing, Llc Data interpolation and classification method for map data visualization
US20160125628A1 (en) * 2014-11-05 2016-05-05 Geologis Ltd. Geographical information systems

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20130211963A1 (en) * 2009-11-24 2013-08-15 Joseph Williams Facilitation of interaction between providers, buyers, and agents
CN102024206A (zh) * 2010-12-20 2011-04-20 江苏省交通科学研究院股份有限公司 市郊轨道交通客流的预测方法
US20130046586A1 (en) * 2011-08-16 2013-02-21 Walk Score Management LLC System and method for assessing quality of transit networks at specified locations
CN103106630A (zh) * 2011-11-10 2013-05-15 李红彬 生态城市规划

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
LUO, CHENGSHU: "Research on the Formation and Development of Hangzhou Metropolitan Area", MASTER'S THESES OF ZHEJIANG UNIVERSITY, 31 December 2010 (2010-12-31), pages 19 - 29, XP009507513 *

Cited By (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112115536A (zh) * 2020-09-16 2020-12-22 浙大城市学院 一种高校教学建筑平面学习空间连接凝聚性的评估方法及系统
WO2022057955A1 (zh) * 2020-09-16 2022-03-24 浙大城市学院 一种高校教学建筑平面学习空间连接凝聚性的评估方法及系统
CN112115536B (zh) * 2020-09-16 2023-11-03 浙大城市学院 一种高校教学建筑平面学习空间连接凝聚性的评估方法及系统
CN113177657A (zh) * 2021-04-20 2021-07-27 上海大学 轨道交通客流预测方法及装置
CN113177657B (zh) * 2021-04-20 2022-08-30 上海大学 轨道交通客流预测方法及装置
CN117455078A (zh) * 2023-12-20 2024-01-26 中铁第四勘察设计院集团有限公司 基于空间圈层需求的都市圈轨道交通一体化客流预测方法

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