WO2021136406A1 - Gis-based scenic spot service facility layout analysis method - Google Patents

Gis-based scenic spot service facility layout analysis method Download PDF

Info

Publication number
WO2021136406A1
WO2021136406A1 PCT/CN2020/141503 CN2020141503W WO2021136406A1 WO 2021136406 A1 WO2021136406 A1 WO 2021136406A1 CN 2020141503 W CN2020141503 W CN 2020141503W WO 2021136406 A1 WO2021136406 A1 WO 2021136406A1
Authority
WO
WIPO (PCT)
Prior art keywords
real
point
population distribution
data
population
Prior art date
Application number
PCT/CN2020/141503
Other languages
French (fr)
Chinese (zh)
Inventor
许俊萍
高琪
马学梅
Original Assignee
华侨大学
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by 华侨大学 filed Critical 华侨大学
Publication of WO2021136406A1 publication Critical patent/WO2021136406A1/en

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/14—Travel agencies
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
    • G06F16/29—Geographical information databases
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
    • G06F16/90—Details of database functions independent of the retrieved data types
    • G06F16/95—Retrieval from the web
    • G06F16/953—Querying, e.g. by the use of web search engines
    • G06F16/9537—Spatial or temporal dependent retrieval, e.g. spatiotemporal queries
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06313—Resource planning in a project environment

Definitions

  • the invention relates to a GIS-based method for analyzing the layout of service facilities in scenic spots.
  • the present invention proposes a GIS-based analysis method for the layout of service facilities in scenic spots, which helps to promote the balanced and reasonable layout of public service facilities in scenic spots, thereby improving tourism quality and effectiveness of tourism services.
  • the technical scheme adopted by the present invention specifically includes the following steps:
  • the real-time population big data is expressed by spatial sampling points with population distribution characteristics, namely population value values, and the data is imported into the ArcGIS platform to establish a database ;
  • C i is a real population distribution of large data sample points i (x i, y i) point coupling;
  • P i refers to the real population distribution of large data sample points i (x i, y i) point population value value,
  • P max is P i The maximum value, P* is the ratio of Pi to P max ;
  • D ij is the distance from the sampling point i(x i , y i ) of real-time population distribution big data to the POI point j(x j , y j ) of the nearest facility Distance,
  • D max is the maximum value in D ij ,
  • D* is the ratio of D max to D ij;
  • C O is the coupling degree value of any point O(x o , y o ) in space
  • C i is the coupling degree value of the big data sampling point i(x i , y i ) of the real-time population distribution in the known space
  • d is the distance between O(x o , y o ) point and i(x i , y i ) point
  • ⁇ is a constant, usually 1 or 2.
  • the invention has the advantages of high accuracy, strong operability, etc., and accurately reflects the coupling relationship between service facilities and population distribution in the scenic area, thereby providing a basis for the layout of service facilities.
  • Figure 1 is a flow chart of the present invention
  • Figure 2 shows the POI point data of a catering service facility.
  • Figure 3 shows the center point of the grid with the average of the population data, which reflects the average population distribution during the period.
  • Figure 4 is a schematic diagram of using ArcGIS to build a network dataset tool to network the road vector data.
  • Figure 5 shows the input data of real-time population sampling points and service facilities (restaurants) POI distribution data in 2018;
  • Figure 6 is a schematic diagram of the evaluation results of the service facility (restaurant) layout in 2018.
  • Figure 1 is a flow chart of the GIS-based analysis method for the layout of service facilities in scenic spots.
  • the real-time population big data is expressed by spatial sampling points with population distribution characteristics, namely population value values, and the data is imported into the ArcGIS platform to establish a database ;
  • the real-time population distribution big data collected within a period of time is averagely processed, such as randomly selecting 5 days in January, April, July, and October of a certain year, and selecting every 8:00-20:00
  • the real-time population distribution data obtained in one hour use ArcGIS to create a fishing net tool to construct a grid with 20*20m intervals within the scenic area, calculate the average of the population data in each grid, and finally extract the square with the average of the population data
  • the grid center point is used as a real-time population distribution big data sampling point to reflect the characteristics of the average population distribution during the period;
  • ArcGIS Use ArcGIS to build a network dataset tool to network the road vector data to prepare data for the next step of calculating distance; use ArcGIS network analysis to find the nearest facility tool to solve the real-time population distribution big data sampling point i(x i , y i ) and its nearest service facility POI point j(x j , y j ) point, that is, the actual distance D ij between the two points in the road network;
  • C i is a real population distribution of large data sample points i (x i, y i) point coupling;
  • P i refers to the real population distribution of large data sample points i (x i, y i) point population value value,
  • P max is P i The maximum value, P* is the ratio of Pi to P max ;
  • D ij is the distance from the sampling point i(x i , y i ) of real-time population distribution big data to the POI point j(x j , y j ) of the nearest facility Distance,
  • D max is the maximum value in D ij ,
  • D* is the ratio of D max to D ij;
  • C O is the coupling degree value of any point O(x o , y o ) in space
  • C i is the coupling degree value of the big data sampling point i(x i , y i ) of the real-time population distribution in the known space
  • d is the distance between O(x o , y o ) point and i(x i , y i ) point
  • ⁇ is a constant, usually 1 or 2.
  • Figure 2 shows the real-time population sampling points and POI distribution data of service facilities
  • Figure 3 shows the analysis result of the service facility layout obtained by the method proposed by the present invention.
  • the GIS-based analysis method for the layout of service facilities in scenic spots proposed by the present invention has the advantages of high accuracy and strong operability, and accurately reflects the coupling relationship between service facilities and population distribution within the scope of the scenic spot, thereby providing a layout for service facilities. Provide evidence.
  • Example 1 Catering service facilities in a scenic spot
  • the center point of the grid based on the average value is used as the sampling point of real-time population distribution big data to reflect the characteristics of the average population distribution during the period, as shown in Figure 3;
  • Data processing 3 Use ArcGIS to build a network dataset tool to network the road vector data to prepare the data for the next step of calculating the distance, as shown in Figure 4;
  • C i is a real population distribution of large data sample points i (x i, y i) point coupling;
  • P i refers to the real population distribution of large data sample points i (x i, y i) point population value value,
  • P max is P i The maximum value, P* is the ratio of Pi to P max ;
  • D ij is the distance from the sampling point i(x i , y i ) of real-time population distribution big data to the POI point j(x j , y j ) of the nearest facility Distance,
  • D max is the maximum value in D ij ,
  • D* is the ratio of D max to D ij;
  • C O is the coupling degree value of any point O(x o , y o ) in space
  • C i is the coupling degree value of the big data sampling point i(x i , y i ) of the real-time population distribution in the known space
  • d is the distance between O(x o , y o ) point and i(x i , y i ) point
  • ⁇ is a constant, usually 1 or 2.
  • the result is expressed by the natural discontinuity method (Jenks) 2 , and the result is shown in Figure 6.
  • the bright color in the figure indicates the area with good coupling, that is, the area is relatively dense and the catering service facilities are relatively more distributed, or the population in the area is relatively sparse, and the catering service facilities are relatively less distributed; the dark color indicates the area with poor coupling. That is, the population in the area is relatively dense, but the catering service facilities are relatively less distributed, or the population in the area is relatively sparse, but the catering service facilities are relatively more distributed.
  • the darker the color it means that the layout of facilities in the area needs to be adjusted according to the situation.
  • the invention discloses a GIS-based analysis method for the layout of scenic spots and facilities, including the following steps: 1) Import the acquired data into a GIS platform; 2) Select real-time population big data sampling points and the nearest service facilities through GIS and calculate both 3) Calculate the coupling degree between real-time population sampling points and service facilities; 4) Express the coupling degree graphically to output the final analysis result of the layout of scenic facilities.
  • the invention has the advantages of high precision, strong operability, etc., accurately reflects the coupling relationship between service facilities and population distribution in the scenic area, thereby providing a basis for the layout of service facilities, and has industrial practicability.

Landscapes

  • Engineering & Computer Science (AREA)
  • Business, Economics & Management (AREA)
  • Theoretical Computer Science (AREA)
  • Human Resources & Organizations (AREA)
  • Databases & Information Systems (AREA)
  • Tourism & Hospitality (AREA)
  • General Physics & Mathematics (AREA)
  • Physics & Mathematics (AREA)
  • Economics (AREA)
  • Strategic Management (AREA)
  • General Business, Economics & Management (AREA)
  • Marketing (AREA)
  • General Engineering & Computer Science (AREA)
  • Data Mining & Analysis (AREA)
  • Entrepreneurship & Innovation (AREA)
  • Quality & Reliability (AREA)
  • Educational Administration (AREA)
  • Development Economics (AREA)
  • Biodiversity & Conservation Biology (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Remote Sensing (AREA)
  • Game Theory and Decision Science (AREA)
  • Operations Research (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Primary Health Care (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

A GIS-based scenic spot facility layout analysis method, comprising the following steps: importing obtained data into a GIS platform; selecting a real-time population big data sampling point and a most adjacent service facility by means of a GIS, and calculating an actual distance between the two; calculating a coupling degree between the real-time population sampling point and the service facility; graphically expressing the coupling degree so as to output a final scenic spot facility layout analysis result. The method has the advantages of being high in precision, high in operability and the like, a coupling relationship between the service facilities and the population distribution in the scenic spot range is accurately reflected, so that a basis is provided for layout of service facilities.

Description

一种基于GIS的景区服务设施布局分析方法A GIS-based analysis method for the layout of service facilities in scenic spots 技术领域Technical field
本发明涉及一种基于GIS的景区服务设施布局分析方法。The invention relates to a GIS-based method for analyzing the layout of service facilities in scenic spots.
背景技术Background technique
游客是旅游活动的主体，是旅游产品的需求者和最终消费者，而景区内公共服务设施规划布局的合理性将直接影响到景区旅游服务质量。公共服务设施的完善配套已成为社会文明进步的标志，随着游客游览内容和旅游方式的多元化需求不断扩张，尤其是自驾游、休闲游、特色游的兴起，游客对旅游公共服务的需求也越来越强烈。尤其是位于城市内部开放的社区型旅游景区，不但要保障游客的旅游质量，还要顾及居民的正常生活，其相对自然风景区等更为复杂的社会环境使得公共服务设施的配套成为提升景区环境质量的重要途径之一。Tourists are the main body of tourism activities, demanders and final consumers of tourism products, and the rationality of the planning and layout of public service facilities in scenic spots will directly affect the quality of tourism services in scenic spots. The complete supporting facilities of public service facilities have become a sign of social civilization and progress. With the continuous expansion of the diversified needs of tourists' tour content and travel methods, especially the rise of self-driving tours, leisure tours, and characteristic tours, tourists also demand tourism public services. More and more intense. In particular, the community-based tourist attractions that are open within the city must not only guarantee the tourist quality of tourists, but also take into account the normal life of residents. Compared with natural scenic spots, the more complex social environment makes the supporting of public service facilities to enhance the scenic environment. One of the important ways of quality.
目前已有相关规范规定旅游景区内公共服务设施配置问题，国内外也提出了许多服务设施布局的方法。但传统服务设施布局问题的研究仍然存在不少问题，譬如设施设置严格的覆盖标准，在覆盖半径内的所有需求点都被覆盖，而只要不在覆盖半径之内就完全不被覆盖，这种覆盖模型方法往往不太切合实际情况；对设施布局的需求分析不充分，往往忽视了选址的随机性、不确定问题。At present, there are relevant regulations stipulating the allocation of public service facilities in tourist attractions, and many methods for the layout of service facilities have also been proposed at home and abroad. However, there are still many problems in the research on the layout of traditional service facilities. For example, the facilities set strict coverage standards, and all demand points within the coverage radius are covered. As long as they are not within the coverage radius, they will not be covered at all. The model method is often not suitable for the actual situation; the demand analysis of the facility layout is insufficient, and the randomness and uncertainty of the site selection are often ignored.
因此，在定量研究游客集聚特征的前提下，对景区公共服务设施布局进行梳理和优化，提高规划的精度，将有助于促进景区公共服务设施的均衡、合理布局，从而提高旅游质量和旅游服务的有效性。Therefore, under the premise of quantitatively studying the characteristics of tourist agglomeration, sorting out and optimizing the layout of public service facilities in scenic spots and improving the accuracy of planning will help promote the balanced and reasonable layout of public service facilities in scenic spots, thereby improving tourism quality and tourism services. Effectiveness.
发明内容Summary of the invention
本发明提出一种基于GIS的景区服务设施布局分析方法，有助于促进景区公共服务设施的均衡、合理布局，从而提高旅游质量和旅游服务的有效性。The present invention proposes a GIS-based analysis method for the layout of service facilities in scenic spots, which helps to promote the balanced and reasonable layout of public service facilities in scenic spots, thereby improving tourism quality and effectiveness of tourism services.
本发明采用的技术方案，具体包括如下步骤：The technical scheme adopted by the present invention specifically includes the following steps:
1)获取景区内各类服务设施POI点数据，以及实时人口分布大数据，其中实时人口大数据通过带有人口分布特征即人口value值的空间采样点表达，将数据导入ArcGIS平台中，建立数据库；1) Obtain POI point data of various service facilities in the scenic area and real-time population distribution big data. The real-time population big data is expressed by spatial sampling points with population distribution characteristics, namely population value values, and the data is imported into the ArcGIS platform to establish a database ；
2)利用ArcGIS网络分析中查找最近设施点工具，求解实时人口分布大数据采样点i(x i，y i)与其最邻近服务设施POI点j(x j，y j)点，即两点之间道路网的实际距离D ij； 2) Use ArcGIS network analysis to find the nearest facility tool to solve the real-time population distribution big data sampling point i (x i , y i ) and its nearest service facility POI point j (x j , y j ) point, that is, one of two points The actual distance D ij between the road network;
3)计算实时人口分布大数据采样点与服务设施POI点的耦合度，即：3) Calculate the coupling degree between big data sampling points of real-time population distribution and POI points of service facilities, namely:
C i＝P*×D*＝P i/P max×D max/D ij  (1) C i =P*×D*=P i /P max ×D max /D ij (1)
C i为实时人口分布大数据采样点i(x i，y i)点耦合度；P i指实时人口分布大数据采样点i(x i，y i)点人口value值，P max为P i中最大值，P*为P i与P max比值；D ij为实时人口分布大数据采样点i(x i，y i)点到与其最邻近设施POI点j(x j，y j)点的距离，D max为D ij中最大值，D*为D max与D ij的比值； C i is a real population distribution of large data sample points i (x i, y i) point coupling; P i refers to the real population distribution of large data sample points i (x i, y i) point population value value, P max is P i The maximum value, P* is the ratio of Pi to P max ; D ij is the distance from the sampling point i(x i , y i ) of real-time population distribution big data to the POI point j(x j , y j ) of the nearest facility Distance, D max is the maximum value in D ij , D* is the ratio of D max to D ij;
4)在GIS中利用反距离插值算法计算并可视化展示景区服务设施耦合度面，即4) Use the inverse distance interpolation algorithm in GIS to calculate and visually display the coupling degree of scenic service facilities, namely
Figure PCTCN2020141503-appb-000001
Figure PCTCN2020141503-appb-000001
其中,C O为空间上任意一点O(x o，y o)的耦合度数值，C i为已知空间上实时人口分布大数据采样点i(x i，y i)点的耦合度数值,d为O(x o，y o)点与i(x i，y i)点距离，α为一常量，通常取1或2。 Among them, C O is the coupling degree value of any point O(x o , y o ) in space, and C i is the coupling degree value of the big data sampling point i(x i , y i ) of the real-time population distribution in the known space, d is the distance between O(x o , y o ) point and i(x i , y i ) point, and α is a constant, usually 1 or 2.
本发明具有精度高、可操作性强等优点，准确地反映景区范围内服务设施与人口分布耦合关系，从而为服务设施布局提供依据。The invention has the advantages of high accuracy, strong operability, etc., and accurately reflects the coupling relationship between service facilities and population distribution in the scenic area, thereby providing a basis for the layout of service facilities.
附图说明Description of the drawings
图1为本发明的流程图Figure 1 is a flow chart of the present invention
图2为个餐饮服务设施POI点数据。Figure 2 shows the POI point data of a catering service facility.
图3带有人口数据平均值的方格网中心点，其反应该段时间内人口平均分布特征。Figure 3 shows the center point of the grid with the average of the population data, which reflects the average population distribution during the period.
图4为利用ArcGIS中构建网络数据集工具将道路矢量数据网络化示意图。Figure 4 is a schematic diagram of using ArcGIS to build a network dataset tool to network the road vector data.
图5为输入的2018年实时人口采样点及服务设施(餐馆)POI分布数据；Figure 5 shows the input data of real-time population sampling points and service facilities (restaurants) POI distribution data in 2018;
图6为2018年服务设施(餐馆)布局评价结果示意图。Figure 6 is a schematic diagram of the evaluation results of the service facility (restaurant) layout in 2018.
具体实施方式Detailed ways
以下通过具体实施方式对本发明作进一步的描述。The present invention will be further described below through specific embodiments.
如图1是基于GIS的景区服务设施布局分析方法的流程图。Figure 1 is a flow chart of the GIS-based analysis method for the layout of service facilities in scenic spots.
1)获取景区内各类服务设施POI点数据，以及实时人口分布大数据，其中实时人口大数据通过带有人口分布特征即人口value值的空间采样点表达，将数据导入ArcGIS平台中，建立数据库；对一段时间以内分段 采集的人口实时分布大数据进行平均处理，如在某年1月、4月、7月和10月份中分别随机选取5日，选择8:00-20:00每隔一小时获取的人口实时分布大数据，使用ArcGIS创建渔网工具在景区范围内构建20*20m间隔的方格网，计算每个格网内人口数据平均值，最后提取带有人口数据平均值的方格网中心点，作为实时人口分布大数据采样点，使其反应该段时间内人口平均分布特征；1) Obtain POI point data of various service facilities in the scenic area and real-time population distribution big data. The real-time population big data is expressed by spatial sampling points with population distribution characteristics, namely population value values, and the data is imported into the ArcGIS platform to establish a database ; The real-time population distribution big data collected within a period of time is averagely processed, such as randomly selecting 5 days in January, April, July, and October of a certain year, and selecting every 8:00-20:00 The real-time population distribution data obtained in one hour, use ArcGIS to create a fishing net tool to construct a grid with 20*20m intervals within the scenic area, calculate the average of the population data in each grid, and finally extract the square with the average of the population data The grid center point is used as a real-time population distribution big data sampling point to reflect the characteristics of the average population distribution during the period;
2)利用ArcGIS中构建网络数据集工具将道路矢量数据网络化，为下一步计算距离做数据准备；利用ArcGIS网络分析中查找最近设施点工具，求解实时人口分布大数据采样点i(x i，y i)与其最邻近服务设施POI点j(x j，y j)点，即两点之间道路网的实际距离D ij； 2) Use ArcGIS to build a network dataset tool to network the road vector data to prepare data for the next step of calculating distance; use ArcGIS network analysis to find the nearest facility tool to solve the real-time population distribution big data sampling point i(x i , y i ) and its nearest service facility POI point j(x j , y j ) point, that is, the actual distance D ij between the two points in the road network;
3)计算实时人口分布大数据采样点与服务设施POI点的耦合度，即：3) Calculate the coupling degree between big data sampling points of real-time population distribution and POI points of service facilities, namely:
C i＝P*×D*＝P i/P max×D max/D ij  (1) C i =P*×D*=P i /P max ×D max /D ij (1)
C i为实时人口分布大数据采样点i(x i，y i)点耦合度；P i指实时人口分布大数据采样点i(x i，y i)点人口value值，P max为P i中最大值，P*为P i与P max比值；D ij为实时人口分布大数据采样点i(x i，y i)点到与其最邻近设施POI点j(x j，y j)点的距离，D max为D ij中最大值，D*为D max与D ij的比值； C i is a real population distribution of large data sample points i (x i, y i) point coupling; P i refers to the real population distribution of large data sample points i (x i, y i) point population value value, P max is P i The maximum value, P* is the ratio of Pi to P max ; D ij is the distance from the sampling point i(x i , y i ) of real-time population distribution big data to the POI point j(x j , y j ) of the nearest facility Distance, D max is the maximum value in D ij , D* is the ratio of D max to D ij;
5)在GIS中利用反距离插值算法计算并可视化展示景区服务设施耦合度面，即5) Use the inverse distance interpolation algorithm in GIS to calculate and visually display the coupling degree of scenic service facilities, namely
Figure PCTCN2020141503-appb-000002
Figure PCTCN2020141503-appb-000002
其中,C O为空间上任意一点O(x o，y o)的耦合度数值，C i为已知空间上实时人口分布大数据采样点i(x i，y i)点的耦合度数值,d为O(x o，y o)点与i(x i，y i)点距离，α为一常量，通常取1或2。 Among them, C O is the coupling degree value of any point O(x o , y o ) in space, and C i is the coupling degree value of the big data sampling point i(x i , y i ) of the real-time population distribution in the known space, d is the distance between O(x o , y o ) point and i(x i , y i ) point, and α is a constant, usually 1 or 2.
如图2为实时人口采样点及服务设施POI分布数据，图3为利用本发明提出方法得到的服务设施布局的分析结果。Figure 2 shows the real-time population sampling points and POI distribution data of service facilities, and Figure 3 shows the analysis result of the service facility layout obtained by the method proposed by the present invention.
从以上分析结果可知，本发明提出的基于GIS的景区服务设施布局分析方法，具有精度高、可操作性强等优点，准确地反映景区范围内服务设施与人口分布耦合关系，从而为服务设施布局提供依据。From the above analysis results, it can be seen that the GIS-based analysis method for the layout of service facilities in scenic spots proposed by the present invention has the advantages of high accuracy and strong operability, and accurately reflects the coupling relationship between service facilities and population distribution within the scope of the scenic spot, thereby providing a layout for service facilities. Provide evidence.
实施例1：某景点餐饮服务设施Example 1: Catering service facilities in a scenic spot
1)数据获取：获取某景区内餐饮服务设施POI点数据；2019年1月、4月、7月和10月份实时人口分布大数据；鼓浪屿道路中心线数据。1) Data acquisition: Obtain POI point data of catering service facilities in a scenic spot; real-time big data on population distribution in January, April, July and October 2019; data on the center line of Gulangyu Road.
2)数据处理1：共取得175个餐饮服务设施POI点数据，如图2所示；2) Data processing 1: A total of 175 POI point data of catering service facilities are obtained, as shown in Figure 2;
3)数据处理2：实时人口大数据通过带有人口分布特征即人口value值的空间采样点表达 1，将数据导入ArcGIS平台中，建立数据库；对分段采集的人口实时分布大数据进行平均处理，将2019年1月、4月、7月和10月份中分别随机选取5日，选择8:00-20:00每隔一小时获取的人口实时分布大数据，使用ArcGIS创建渔网工具在景区范围内构建20*20m间隔的方格网，计算每个格网内人口数据平均值，最后提取带有人口数
Figure PCTCN2020141503-appb-000003
3) Data Processing 2: real-time data with a large population Population distribution of spatial sampling points value i.e. a value of the expression of the population, the data into ArcGIS platform, the establishment of a database; segment of real-time distributed collection of the population of large data are averaged , Randomly select the 5th of January, April, July and October of 2019, choose the big data of population real-time distribution obtained every hour from 8:00-20:00, and use ArcGIS to create a fishing net tool in the scenic area Construct a grid with 20*20m intervals inside, calculate the average of the population data in each grid, and finally extract the population with
Figure PCTCN2020141503-appb-000003
据平均值的方格网中心点，作为实时人口分布大数据采样点，使其反应该段时间内人口平均分布特征，如图3所示；The center point of the grid based on the average value is used as the sampling point of real-time population distribution big data to reflect the characteristics of the average population distribution during the period, as shown in Figure 3;
4)数据处理3：利用ArcGIS中构建网络数据集工具将道路矢量数据网络化，为下一步计算距离做数据准备，如图4所示；4) Data processing 3: Use ArcGIS to build a network dataset tool to network the road vector data to prepare the data for the next step of calculating the distance, as shown in Figure 4;
5)利用ArcGIS网络分析中查找最近设施点工具，求解实时人口分布大数据采样点i(x i，y i)与其最邻近服务设施POI点j(x j，y j)点，即两点之间道路网的实际距离D ij,，如图5所示； 5) Use ArcGIS network analysis to find the nearest facility tool to solve the real-time population distribution big data sampling point i (x i , y i ) and its nearest service facility POI point j (x j , y j ) point, that is, one of two points The actual distance D ij, between the road network, as shown in Figure 5;
6)计算实时人口分布大数据采样点与服务设施POI点的耦合度，即：6) Calculate the coupling degree between big data sampling points of real-time population distribution and POI points of service facilities, namely:
C i＝P*×D*＝P i/P max×D max/D ij  (1) C i =P*×D*=P i /P max ×D max /D ij (1)
C i为实时人口分布大数据采样点i(x i，y i)点耦合度；P i指实时人口分布大数据采样点i(x i，y i)点人口value值，P max为P i中最大值，P*为P i与P max比值；D ij为实时人口分布大数据采样点i(x i，y i)点到与其最邻近设施POI点j(x j，y j)点的距离，D max为D ij中最大值，D*为D max与D ij的比值； C i is a real population distribution of large data sample points i (x i, y i) point coupling; P i refers to the real population distribution of large data sample points i (x i, y i) point population value value, P max is P i The maximum value, P* is the ratio of Pi to P max ; D ij is the distance from the sampling point i(x i , y i ) of real-time population distribution big data to the POI point j(x j , y j ) of the nearest facility Distance, D max is the maximum value in D ij , D* is the ratio of D max to D ij;
经计算，在本次研究时间段内，P max为17，D max为680.57米；将两数值带入公式1，计算每一个实时人口分布大数据采样点的耦合度。 After calculation, in this research period, P max is 17 and D max is 680.57 meters; the two values are put into formula 1, and the coupling degree of each big data sampling point of real-time population distribution is calculated.
7)在ArcGIS中利用反距离插值算法计算并可视化展示景区服务设施耦合度面，即：7) Use the inverse distance interpolation algorithm in ArcGIS to calculate and visualize the coupling degree of scenic service facilities, namely:
Figure PCTCN2020141503-appb-000004
Figure PCTCN2020141503-appb-000004
其中,C O为空间上任意一点O(x o，y o)的耦合度数值，C i为已知空间上实时人口分布大数据采样点i(x i，y i)点的耦合度数值,d为O(x o，y o)点与i(x i，y i)点距离，α为一常量，通常取1或2。 Among them, C O is the coupling degree value of any point O(x o , y o ) in space, and C i is the coupling degree value of the big data sampling point i(x i , y i ) of the real-time population distribution in the known space, d is the distance between O(x o , y o ) point and i(x i , y i ) point, and α is a constant, usually 1 or 2.
计算完成后，将结果通过自然间断法(Jenks) 2表示，结果如图6所示。图中亮色表示耦合度较好地区，即该地区人群相对密集，且餐饮服务设施相对分布较多，或该地区人群相对稀疏，且餐饮服务设施相对分布较少；暗色表示耦合度较差地区，即该地区人群相对密集，但餐饮服务设施相对分布较少，或该地区人群相对稀疏，但餐饮服务设施相对分布较多。颜色越暗，表示该地区设施布置需要根据情况做出调整。 After the calculation is completed, the result is expressed by the natural discontinuity method (Jenks) 2 , and the result is shown in Figure 6. The bright color in the figure indicates the area with good coupling, that is, the area is relatively dense and the catering service facilities are relatively more distributed, or the population in the area is relatively sparse, and the catering service facilities are relatively less distributed; the dark color indicates the area with poor coupling. That is, the population in the area is relatively dense, but the catering service facilities are relatively less distributed, or the population in the area is relatively sparse, but the catering service facilities are relatively more distributed. The darker the color, it means that the layout of facilities in the area needs to be adjusted according to the situation.
上述仅为本发明的具体实施方式，但本发明的设计构思并不局限于此，凡利用此构思对本发明进行非实质性的改动，均应属于侵犯本发明保护范围的行为。The above are only specific embodiments of the present invention, but the design concept of the present invention is not limited to this. Any insubstantial modification of the present invention using this concept should be an act that violates the protection scope of the present invention.
工业实用性Industrial applicability
本发明公开了一种基于GIS的景区设施布局分析方法，包括以下步骤：1)将获取的数据导入GIS平台中；2)通过GIS选择实时人口大数据采样点与最邻近服务设施并计算两者间实际距离；3)计算实时人口采样点与服务设施之间耦合度；4)将耦合度图示化表达，从而输出最终的景区设施布局分析结果。本发明具有精度高、可操作性强等优点，准确地反映景区范围内服务设施与人口分布耦合关系，从而为服务设施布局提供依据,具有工业实用性。The invention discloses a GIS-based analysis method for the layout of scenic spots and facilities, including the following steps: 1) Import the acquired data into a GIS platform; 2) Select real-time population big data sampling points and the nearest service facilities through GIS and calculate both 3) Calculate the coupling degree between real-time population sampling points and service facilities; 4) Express the coupling degree graphically to output the final analysis result of the layout of scenic facilities. The invention has the advantages of high precision, strong operability, etc., accurately reflects the coupling relationship between service facilities and population distribution in the scenic area, thereby providing a basis for the layout of service facilities, and has industrial practicability.

Claims (1)

  1. 一种基于GIS的景区服务设施布局分析方法，其特征在于，包括以下步骤：A GIS-based method for analyzing the layout of service facilities in scenic spots is characterized by including the following steps:
    1)获取景区内各类服务设施POI点数据，以及实时人口分布大数据，其中实时人口大数据通过带有人口分布特征即人口value值的空间采样点表达，将数据导入ArcGIS平台中，建立数据库；1) Obtain POI point data of various service facilities in the scenic area and real-time population distribution big data. The real-time population big data is expressed by spatial sampling points with population distribution characteristics, namely population value values, and the data is imported into the ArcGIS platform to establish a database ；
    2)利用ArcGIS中构建网络数据集工具将道路矢量数据网络化，为下一步计算距离做数据准备；利用ArcGIS网络分析中查找最近设施点工具，求解实时人口分布大数据采样点i(x i，y i)与其最邻近服务设施POI点j(x j，y j)，即两点之间道路网的实际距离D ij； 2) Use ArcGIS to build a network dataset tool to network the road vector data to prepare data for the next step of calculating distance; use ArcGIS network analysis to find the nearest facility tool to solve the real-time population distribution big data sampling point i(x i , y i ) and its nearest service facility POI point j(x j , y j ), that is, the actual distance D ij between the two points in the road network;
    3)计算实时人口分布大数据采样点与服务设施POI点的耦合度，即：3) Calculate the coupling degree between big data sampling points of real-time population distribution and POI points of service facilities, namely:
    C i＝P*×D*＝P i/P max×D max/D ij  (1) C i =P*×D*=P i /P max ×D max /D ij (1)
    C i为实时人口分布大数据采样点i(x i，y i)点耦合度；P i指实时人口分布大数据采样点i(x i，y i)点人口value值，P max为Pi中最大值，P*为P i与P max比值；D ij为实时人口分布大数据采样点i(x i，y i)点到与其最邻近设施POI点j(x j，y j)点的距离，Dmax为D ij中最大值，D*为D max与D ij的比值； C i is a real population distribution of large data sample points i (x i, y i) point coupling; P i refers to the real population distribution of large data sample points i (x i, y i) point population value value, P max is Pi in The maximum value, P* is the ratio of P i to P max ; D ij is the distance from the sampling point i(x i , y i ) of the real-time population distribution big data to the POI point j(x j , y j) of the nearest facility , Dmax is the maximum value in D ij , and D* is the ratio of D max to D ij;
    6)在GIS中利用反距离插值算法计算并可视化展示景区服务设施耦合度面，即6) Use the inverse distance interpolation algorithm in GIS to calculate and visually display the coupling degree of scenic service facilities, namely
    Figure PCTCN2020141503-appb-100001
    Figure PCTCN2020141503-appb-100001
    其中,C O为空间上任意一点O(x o，y o)的耦合度数值，C i为已知空间上实时人口分布大数据采样点i(x i，y i)点的耦合度数值,d为O(x o，y o)点与i(x i，y i)点距离，α为一常量，通常取1或2。 Among them, C O is the coupling degree value of any point O(x o , y o ) in space, C i is the coupling degree value of the big data sampling point i(x i , y i ) of the real-time population distribution in the known space, d is the distance between O(x o , y o ) point and i(x i , y i ) point, and α is a constant, usually 1 or 2.
PCT/CN2020/141503 2019-12-31 2020-12-30 Gis-based scenic spot service facility layout analysis method WO2021136406A1 (en)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
CN201911424832.5 2019-12-31
CN201911424832.5A CN111161102B (en) 2019-12-31 2019-12-31 Scenic spot service facility layout analysis method based on GIS

Publications (1)

Publication Number Publication Date
WO2021136406A1 true WO2021136406A1 (en) 2021-07-08

Family

ID=70560706

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/CN2020/141503 WO2021136406A1 (en) 2019-12-31 2020-12-30 Gis-based scenic spot service facility layout analysis method

Country Status (2)

Country Link
CN (1) CN111161102B (en)
WO (1) WO2021136406A1 (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116957305A (en) * 2023-09-20 2023-10-27 安徽互联智库数据技术有限公司 Regional service resource intelligent adaptation method and system based on big data

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111161102B (en) * 2019-12-31 2022-04-05 华侨大学 Scenic spot service facility layout analysis method based on GIS

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20150039523A1 (en) * 2013-07-30 2015-02-05 Universitat De Girona Methods and devices for urban planning
CN106447573A (en) * 2016-09-08 2017-02-22 河南理工大学 Spatial accessibility analysis method and device based on public facility differences
CN107145724A (en) * 2017-04-25 2017-09-08 武汉大学 A kind of spacial analytical method of the green trace integration service ability assay in city
CN107609748A (en) * 2017-08-18 2018-01-19 深圳航天智慧城市系统技术研究院有限公司 A kind of method and system that Development Assessment is carried out to urban facilities
CN111161102A (en) * 2019-12-31 2020-05-15 华侨大学 Scenic spot service facility layout analysis method based on GIS

Family Cites Families (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20160125338A1 (en) * 2014-10-31 2016-05-05 Mona Serageldin Planning System Using Spatial-Based Visualization Aids
US20170293878A1 (en) * 2016-04-12 2017-10-12 Softvu Llc System and process for matching seniors and staffers with senior living communities
CN110532337B (en) * 2019-08-28 2024-02-27 北京市测绘设计研究院 Smart community-oriented public facility service capability improving method

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20150039523A1 (en) * 2013-07-30 2015-02-05 Universitat De Girona Methods and devices for urban planning
CN106447573A (en) * 2016-09-08 2017-02-22 河南理工大学 Spatial accessibility analysis method and device based on public facility differences
CN107145724A (en) * 2017-04-25 2017-09-08 武汉大学 A kind of spacial analytical method of the green trace integration service ability assay in city
CN107609748A (en) * 2017-08-18 2018-01-19 深圳航天智慧城市系统技术研究院有限公司 A kind of method and system that Development Assessment is carried out to urban facilities
CN111161102A (en) * 2019-12-31 2020-05-15 华侨大学 Scenic spot service facility layout analysis method based on GIS

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116957305A (en) * 2023-09-20 2023-10-27 安徽互联智库数据技术有限公司 Regional service resource intelligent adaptation method and system based on big data
CN116957305B (en) * 2023-09-20 2024-02-27 合肥工业大学 Regional service resource intelligent adaptation method and system based on big data

Also Published As

Publication number Publication date
CN111161102B (en) 2022-04-05
CN111161102A (en) 2020-05-15

Similar Documents

Publication Publication Date Title
Yu et al. View-based greenery: A three-dimensional assessment of city buildings’ green visibility using Floor Green View Index
Sun et al. Spatial pattern of urban green spaces in a long-term compact urbanization process—A case study in China
CN113487181B (en) Urban area ecological security pattern evaluation method
CN111222661B (en) Urban planning implementation effect analysis and evaluation method
CN108764193A (en) Merge the city function limited region dividing method of POI and remote sensing image
WO2021136406A1 (en) Gis-based scenic spot service facility layout analysis method
Rosik et al. Regional dispersion of potential accessibility quotient at the intra-European and intranational level. Core-periphery pattern, discontinuity belts and distance decay tornado effect
Ren et al. The accuracy of LCZ maps generated by the world urban database and access portal tools (WUDAPT) method: A case study of Hong Kong
Zhang et al. Unravelling urban governance challenges: Objective assessment and expert insights on livability in Longgang District, Shenzhen
CN103337035A (en) Method for determining site selection of urban center system based on quantitative evaluation
CN116151610B (en) Method for simulating risk exposure space of disaster-bearing body on underlying surface of heterogeneous city
Liu et al. Spatial heterogeneity of the driving forces of cropland change in China
Chen et al. Evaluating implied urban nature vitality in San Francisco: An interdisciplinary approach combining census data, street view images, and social media analysis
CN114219521A (en) Sports center site selection evaluation method based on multi-source data
CN115146990B (en) Urban activity quantitative evaluation method integrating multisource geographic big data
Costache et al. The touristic accessibility in the Hunedoara county in terms of road network
Mazhi et al. Site selection of retail shop based on spatial analysis and machine learning
CN113887993A (en) Method, system, equipment and medium for evaluating coupling harmony of sports facilities and population
Bhadra et al. An objective assessment of walkability in Khulna City: A GIS based approach
CN114282934A (en) Urban low-income crowd distribution prediction method and system based on mobile phone signaling data and storage medium
Kovács-Gyori et al. Reflecting Individual Preferences and Spatiality in Livability Measurements: A Livability Assessment Platform for the City of Salzburg
Chen et al. Measuring livability at the neighborhood scale–development of indicators and methods for the comparison between neighborhoods and best practice within the chosen city
Su et al. Data exchange analysis for property valuation on sustainability perspective
Marco et al. Spatial data modeling for mapping of slum region using multi-attribute utility theory method
Lin et al. An effect evaluation of the predictive open communities based on simulation techniques–Taking the traffic congestion in wuhan as an example

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 20908896

Country of ref document: EP

Kind code of ref document: A1

NENP Non-entry into the national phase

Ref country code: DE

122 Ep: pct application non-entry in european phase

Ref document number: 20908896

Country of ref document: EP

Kind code of ref document: A1