CN111369089A - Urban resident demand supply bidirectional evaluation method based on big data - Google Patents
Urban resident demand supply bidirectional evaluation method based on big data Download PDFInfo
- Publication number
- CN111369089A CN111369089A CN201811593505.8A CN201811593505A CN111369089A CN 111369089 A CN111369089 A CN 111369089A CN 201811593505 A CN201811593505 A CN 201811593505A CN 111369089 A CN111369089 A CN 111369089A
- Authority
- CN
- China
- Prior art keywords
- public service
- demand
- supply
- cell
- population
- Prior art date
- Legal status (The legal status 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 status listed.)
- Withdrawn
Links
- 230000002457 bidirectional effect Effects 0.000 title claims abstract description 13
- 238000011156 evaluation Methods 0.000 title claims abstract description 13
- 230000011664 signaling Effects 0.000 claims abstract description 4
- 238000000547 structure data Methods 0.000 claims description 6
- 238000000034 method Methods 0.000 claims description 5
- 230000000694 effects Effects 0.000 claims description 2
- 235000013311 vegetables Nutrition 0.000 claims description 2
- 238000010276 construction Methods 0.000 abstract description 3
- 230000009286 beneficial effect Effects 0.000 abstract 1
- 238000010295 mobile communication Methods 0.000 description 2
- 238000012935 Averaging Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000013210 evaluation model Methods 0.000 description 1
- 238000013468 resource allocation Methods 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
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
- 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
- G06Q10/06315—Needs-based resource requirements planning or analysis
-
- 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
- G06Q50/40—Business processes related to the transportation industry
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Tourism & Hospitality (AREA)
- Theoretical Computer Science (AREA)
- Entrepreneurship & Innovation (AREA)
- General Physics & Mathematics (AREA)
- Marketing (AREA)
- General Business, Economics & Management (AREA)
- Physics & Mathematics (AREA)
- Educational Administration (AREA)
- Quality & Reliability (AREA)
- Operations Research (AREA)
- Game Theory and Decision Science (AREA)
- Development Economics (AREA)
- Health & Medical Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention provides a city resident demand supply bidirectional evaluation method based on big data, which effectively utilizes mobile signaling to combine with POI information to quantify a resident life circle from two aspects of supply and demand of public service facilities, so that the actual demand of residents can be known, the supply state of the current resident life circle can be mastered, the difference between the supply and the demand can be analyzed, and the supply and demand relationship of each public service facility in the resident life circle can be evaluated, thereby being beneficial to making up supply short boards in the construction of the resident life circle, reducing repeated construction, creating community convenience and habitability, improving the use efficiency of city public resources and improving the satisfaction degree of residents.
Description
Technical Field
The invention belongs to the technical field of mobile communication big data application, and particularly relates to a method for evaluating urban resident demand supply by utilizing mobile communication big data.
Background
The existing urban public service facility planning and design pay excessive attention to the averaging scheme, such as the per-capita index, the thousand-capita index and the like. Although the overall layout has rationality, the layout is very extensive, group differences are not considered, and problems of low resource allocation efficiency, repeated allocation, insufficient area coordination and the like are easily caused.
Disclosure of Invention
The invention aims to provide a city resident demand supply bidirectional evaluation method based on big data aiming at the problems of low efficiency and unreasonable configuration of the conventional public service facilities.
The technical scheme of the invention is as follows:
a city resident demand supply bidirectional evaluation method based on big data is characterized by comprising the following steps:
(1) the method comprises the steps of determining the types of public service facilities capable of providing supply for resident demands in advance, and collecting POI facility information in a certain area of a city according to the facility types, wherein the POI facility information comprises the number, types, distribution positions and supply capacity information of various public service facilities;
(2) acquiring the reachable range of residents in each cell within a certain time according to the road network information in the region and the common pedestrian walking speed, and determining the life circle of the residents in each cell according to the range;
(3) matching the POI information collected in the step (1) to the life circle of each residential quarter, and calculating the supply capacity of various public service facilities in each life circle;
(4) according to the mobile big data, collecting population information of each cell in the area, wherein the population information comprises population number and population structure data, and the population structure data comprises each age group and gender of the population;
(5) calculating the requirements of each cell on various public service facilities according to the demand degree of the population structure on various public service facilities and by combining the population number;
(6) comparing the supply capacity of each public service facility of each cell with the demand degree of each public service facility of the cell, and evaluating the supply-demand relationship of each public service facility of each cell.
The supply bidirectional evaluation model provided by the invention effectively utilizes mobile signaling data to obtain population related data, combines the actual demands of residents, can grasp the practical demands of residents, makes up supply shortboards, reduces repeated construction, creates community convenience and livability, improves the use efficiency of urban public resources, obtains the highest output with the minimum investment, fully meets the multiple demands of residents, is truly people-oriented, and realizes good life.
Drawings
FIG. 1 is a flow chart of the method of the present invention.
Detailed Description
As shown in fig. 1, the specific implementation of the present invention is as follows:
(1) POI information acquisition: the method comprises the steps of determining the types of public service facilities capable of providing supply for resident demands in advance, and collecting POI information in a certain area of a city according to the types of the POI facilities, wherein the POI information comprises the number, types, distribution positions and supply capacity information of various public service facilities.
The public service facilities have various types, and the screening and determination of the invention according to some convenient service facilities related to the daily life of residents comprises the following steps: convenience stores, vegetable markets, supermarkets, shopping centers, gymnasiums, subway stations, bus stations, primary schools, kindergartens, middle schools, senior citizen' activity centers, community culture centers, parks, leisure squares, pension stations, and community health service centers, totaling 17 types.
All POI information can be collected via the internet, for example: POI information can be obtained from a Baidu map or a map platform such as the extremely sea, and the POI information related to public services, including the information such as the type, the number and the longitude and latitude of public service facilities, is screened out from the POI information.
The supply capacity of each public service facility refers to the capacity of how many people a facility can provide corresponding services in a normal state, and the capacity is quantified according to a statistical result during information acquisition. For example: assuming that the area of a convenience store C is Cs square meters under the standard condition, the daily service can be provided for Cn residents under the state of Cm workers; there are 2 convenience stores C around the current cell, and this cell can serve 2 x Cn people at the convenience store, and has a supply capacity of 2 x Cn at the convenience store.
(2) According to the road network information in the region and the normal walking speed of the pedestrians, the reachable range of each cell resident within a certain time is obtained, and the life circle of each cell resident is determined according to the range.
The normal walking speed of the pedestrian can be taken according to the walking speed of 5km/h of a common adult; and according to the road network information on the map, calculating the farthest distance which can be reached by walking for 15 minutes from each entrance and exit of the cell, thereby determining the living circle coverage of each cell resident.
(3) Matching POI information in the area to the life circle of each residential quarter, and calculating the supply capacity of various public service facilities in each life circle;
and the POI information matching can be used for counting the quantity of various public service facilities distributed in the coverage range of each residential life circle according to the longitude and latitude of each public service facility and the longitude and latitude of the residential life circle boundary of each residential area. And multiplying the supply capacity of each type of the public service facilities by the number of each type of the public service facilities to obtain the total supply capacity of each type of the public service facilities in the life circle.
(4) According to the big mobile data, population information of each cell in the area is collected, wherein the population information comprises population number and population structure data, and the population structure data comprises age groups and sexes of the population. The information is acquired by using a mobile signaling mode, which is a general information acquisition means.
(5) And calculating the requirements of each cell on various public service facilities according to the demand degree of the population structure on various public service facilities and by combining the population number.
People of different age groups and genders have different public service facility requirements. The invention can count the demands of the population of different age groups and sexes on various public service facilities in advance, and quantize the statistical result; multiplying the demand of different age groups and genders on various public service facilities by the number of people with each type of people mouth structure respectively to obtain the demands of different age groups and genders on various POIs; the sum of POI demand for each age group and gender is calculated, the total demand of the cell for various types of public service facilities.
(6) Comparing the supply capacity of each public service facility of each cell with the demand degree of each public service facility of the cell, and evaluating the supply-demand relationship of each public service facility of each cell.
Specifically, the evaluation was performed by the following formula:
r in the formulai，jRepresenting the supply-demand relationship of the jth class of public service facilities of the ith cell, Si，jIndicating the provision capability of a utility of class j of the ith cell, Di，jRepresenting the demand of the jth type public service facility of the ith cell;
when the supply-demand relation of a certain POI facility in a certain cell is equal to 1, the supply of the public service facilities in the current cell can meet the demand; a value equal to 0 means that the current cell cannot meet the demand of the residents on such public service facilities.
Claims (7)
1. A city resident demand supply bidirectional evaluation method based on big data is characterized by comprising the following steps:
(1) the method comprises the steps of determining the types of public service facilities capable of providing supply for resident demands in advance, and collecting POI information in a certain area of a city according to the types of the facilities, wherein the POI information comprises the number, types, distribution positions and supply capacity information of each type of public service facility;
(2) acquiring the reachable range of residents in each cell within a certain time according to the road network information in the region and the common pedestrian walking speed, and determining the life circle of the residents in each cell according to the range;
(3) matching the POI information collected in the step (1) to the life circle of each residential quarter, and calculating the supply capacity of various public service facilities in each life circle;
(4) according to the mobile big data, collecting population information of each cell in the area, wherein the population information comprises population number and population structure data, and the population structure data comprises each age group and gender of the population;
(5) calculating the requirements of each cell on various public service facilities according to the demand degree of the population structure on various public service facilities and by combining the population number;
(6) comparing the supply capacity of each public service facility of each cell with the demand degree of each facility of the cell, and evaluating the supply and demand relation of each public service facility of each cell.
2. The big-data based urban resident demand supply bidirectional evaluation method according to claim 1, wherein: the public service facility type in the step (1) is determined according to some convenient service facilities related to the daily life of residents, and comprises the following steps: convenience stores, vegetable markets, supermarkets, shopping centers, gymnasiums, subway stations, bus stations, primary schools, kindergartens, middle schools, senior citizen activity centers, community culture centers, parks, leisure squares, pension stations, and community health service centers, totaling 17 types; the supply capacity of each facility refers to the capacity of how many people the facility can provide corresponding services in a normal state, and the capacity is quantified in the step (1) of information acquisition.
3. The big-data based urban resident demand supply bidirectional evaluation method according to claim 1, wherein: and (3) determining the life circle of each residential quarter in the step (2), namely calculating the farthest distance which can be reached by walking for 15 minutes from each entrance and exit of the residential quarter according to the walking speed of 5km/h of ordinary adults and road network information, and determining the life circle of each residential quarter.
4. The big-data based urban resident demand supply bidirectional evaluation method according to claim 1, wherein: and (4) matching the POI information in the step (3), namely matching the quantity of various public service facilities in each residential life circle according to the longitude and latitude of each public service facility in the POI information and the longitude and latitude of the boundary of each residential life circle of the residential area.
5. The big-data based urban resident demand supply bidirectional evaluation method according to claim 1, wherein: the population information of each cell is collected in the step (4), and the population data in the cell, the sex and the age distribution of people in the cell are obtained by using a mobile signaling mode.
6. The big-data based urban resident demand supply bidirectional evaluation method according to claim 1, wherein: the degree of the requirement of the population structure on various public service facilities in the step (5) is a quantitative value obtained by counting the requirements of the population of different age groups and genders on various public service facilities in advance and carrying out a statistical result; the demand degree of different personnel structures for various public service facilities is multiplied by the number of people of each type of people structure, and the sum is the demand of the community for various public service facilities.
7. The big-data based urban resident demand supply bidirectional evaluation method according to claim 1, wherein: and (6) evaluating the supply and demand relationship of various public service facilities of each cell by the following formula:
r in the formulai，jRepresenting the supply-demand relationship of the jth class of public service facilities of the ith cell, Si，jIndicating the provision capability of a utility of class j of the ith cell, Di，jRepresenting the demand of the jth type public service facility of the ith cell;
when the supply and demand relation of a certain public service facility in a certain cell is equal to 1, the supply of the public service facility in the current cell can meet the demand; a value equal to 0 indicates that the current cell cannot meet the residential demand on such a public service provision.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811593505.8A CN111369089A (en) | 2018-12-25 | 2018-12-25 | Urban resident demand supply bidirectional evaluation method based on big data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811593505.8A CN111369089A (en) | 2018-12-25 | 2018-12-25 | Urban resident demand supply bidirectional evaluation method based on big data |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111369089A true CN111369089A (en) | 2020-07-03 |
Family
ID=71209830
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811593505.8A Withdrawn CN111369089A (en) | 2018-12-25 | 2018-12-25 | Urban resident demand supply bidirectional evaluation method based on big data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111369089A (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114219379A (en) * | 2022-02-22 | 2022-03-22 | 北京融信数联科技有限公司 | Resource matching evaluation method and system suitable for community service circle |
| CN114386529A (en) * | 2022-01-19 | 2022-04-22 | 北京融信数联科技有限公司 | Community service analysis method and system based on big data and readable storage medium |
| CN114466312A (en) * | 2022-01-27 | 2022-05-10 | 同济大学 | Mobile phone signaling data-based method for evaluating barrier-free facilities at entrance and exit of subway station |
| CN116502837A (en) * | 2023-04-19 | 2023-07-28 | 长沙市规划设计院有限责任公司 | Sports facility configuration method and device based on big data and storage medium |
Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| 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 |
-
2018
- 2018-12-25 CN CN201811593505.8A patent/CN111369089A/en not_active Withdrawn
Patent Citations (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| 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 |
Non-Patent Citations (2)
| Title |
|---|
| 李萌;: "基于居民行为需求特征的"15分钟社区生活圈"规划对策研究" * |
| 赵彦云;张波;周芳: "基于POI的北京市\"15分钟社区生活圈\"空间测度研究" * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114386529A (en) * | 2022-01-19 | 2022-04-22 | 北京融信数联科技有限公司 | Community service analysis method and system based on big data and readable storage medium |
| CN114466312A (en) * | 2022-01-27 | 2022-05-10 | 同济大学 | Mobile phone signaling data-based method for evaluating barrier-free facilities at entrance and exit of subway station |
| CN114219379A (en) * | 2022-02-22 | 2022-03-22 | 北京融信数联科技有限公司 | Resource matching evaluation method and system suitable for community service circle |
| CN114219379B (en) * | 2022-02-22 | 2022-05-24 | 北京融信数联科技有限公司 | Resource matching evaluation method and system suitable for community service circle |
| CN116502837A (en) * | 2023-04-19 | 2023-07-28 | 长沙市规划设计院有限责任公司 | Sports facility configuration method and device based on big data and storage medium |
| CN116502837B (en) * | 2023-04-19 | 2024-02-02 | 长沙市规划设计院有限责任公司 | Sports facility configuration method and device based on big data and storage medium |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Zhao et al. | Urban form, demographic and socio-economic correlates of walking, cycling, and e-biking: Evidence from eight neighborhoods in Beijing | |
| CN111369089A (en) | Urban resident demand supply bidirectional evaluation method based on big data | |
| Cervero | Mixed land-uses and commuting: Evidence from the American Housing Survey | |
| Wibowo et al. | Modeling walking accessibility to public transport terminals: case study of Singapore mass rapid transit | |
| Cervero et al. | Job accessibility as a performance indicator: An analysis of trends and their social policy implications in the San Francisco Bay Area | |
| Wang et al. | Effects of open space accessibility and quality on older adults' visit: Planning towards equal right to the city | |
| Liang et al. | Walking accessibility of urban parks in a compact megacity | |
| CN110852479A (en) | Method for evaluating service radius of public service facility based on population coverage rate | |
| CN109509351B (en) | Method for calculating bus sharing rate in areas around bus stop | |
| CN111754135A (en) | Regional development index calculation method applied to rural planning | |
| Raza et al. | Public transport equity with the concept of time-dependent accessibility using Geostatistics methods, Lorenz curves, and Gini coefficients | |
| Willemse | A flowmap-geographic information systems approach to determine community neighbourhood park proximity in Cape Town | |
| CN109829658B (en) | Parking berth distribution method based on different crowd demands | |
| CN116050879A (en) | City updating method and system based on city physical examination evaluation | |
| Zhang et al. | The characteristics of leisure activities and the built environment influences in large-scale social housing communities in China: The case study of Shanghai and Nanjing | |
| CN112954624B (en) | Hybrid land use form estimation method based on mobile phone signaling big data | |
| Xiao et al. | Does the culture service supply of green spaces match the demand of residents in a new district? A perspective from China. | |
| CN114154880A (en) | Method for evaluating walking accessibility of public service facility on street unit level | |
| CN110930063B (en) | Urban residential district parking supply-demand ratio analysis method based on shared gravitation model | |
| Zhou et al. | Village-town system in suburban areas based on cellphone signaling mining and network hierarchy structure analysis | |
| CN115830856A (en) | Big data based information service system and method | |
| CN109190896A (en) | A kind of rural scenic spot based on satisfaction easily reaching property evaluation method | |
| Ma et al. | Research on pricing method of public bicycle service: a case study in guangzhou | |
| Li | Planning City Green Space Locations For Public Access: A Capacitated Location-Allocation Modeling Approach | |
| Zhao et al. | Research on Integrated Bicycle Network Planning Method in Tourism City |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| WW01 | Invention patent application withdrawn after publication |
Application publication date: 20200703 |
|
| WW01 | Invention patent application withdrawn after publication |