CN108876027B - GIS-based rural residential point centralized residential area site selection and optimization method - Google Patents
GIS-based rural residential point centralized residential area site selection and optimization method Download PDFInfo
- Publication number
- CN108876027B CN108876027B CN201810587348.3A CN201810587348A CN108876027B CN 108876027 B CN108876027 B CN 108876027B CN 201810587348 A CN201810587348 A CN 201810587348A CN 108876027 B CN108876027 B CN 108876027B
- Authority
- CN
- China
- Prior art keywords
- residential
- area
- data
- points
- rural
- 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.)
- Active
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
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
- G06Q10/043—Optimisation of two dimensional placement, e.g. cutting of clothes or wood
-
- 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—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
- G06Q50/10—Services
- G06Q50/26—Government or public services
Abstract
The invention discloses a GIS-based rural residential point centralized residential area site selection and optimization method, which comprises the steps of firstly obtaining space and attribute data of a target area based on the GIS, constructing an information quantity model for carrying out land suitability evaluation on rural residential point centralized residential areas, superposing land utilization planning allowable construction areas, obtaining candidate addresses, setting area size thresholds for screening, and then optimizing the candidate addresses by using a P-median problem model. The method utilizes a GIS technology to construct an information quantity model, quantitatively evaluates the land suitability of rural residential sites and selects the site, and uses an integer linear programming P-meso position problem model to perform zone bit optimization, so that the requirement of candidate addresses can be flexibly set, and the site selection result is positioned to a plot; the candidate address is based on an actual road network, daily work requirements of farmers after relocation are considered, the distance cost is minimum, and the method has the advantages of high precision and strong operability.
Description
Technical Field
The invention relates to a GIS technology, in particular to a GIS-based rural residential point centralized residential area site selection and optimization method.
Background
The rural residential points are the main gathering forms of farmers, the external forms and the spatial structures of the rural residential points are the results of the interaction between the rural residential points and the surrounding natural environment, social economy and human and land, and the accurate grasp of the internal rules of the rural residential points has important influence on the land improvement work of the rural residential points. In recent years, many domestic scholars discuss the relationship between rural residential point distribution and various influence factors in many aspects from the aspects of natural environment, economic development and social culture based on the locational theory, evaluate the suitability of different types of rural residential points, optimize the layout or regulate and control the areas, and analyze the layout optimization of the rural residential points from the qualitative aspect. Quantitative analysis is less, for example, part of scholars use the region division characteristics of the weighted Voronoi diagram, and determine the influence range of rural residential points by using the influence degree as the weight, so that the relocation and the retention of the residential points are determined, but the Voronoi diagram is based on distance analysis and does not consider the actual requirements of the residential points. The P-median problem area optimization model considers the actual requirements of demand points, is successfully applied to layout optimization of police patrol areas, airports and refuge places, and is introduced to be applied to centralized residential site location optimization of rural residential points.
Disclosure of Invention
The invention provides a GIS-based rural residential site centralized residential district site selection and optimization method, which can effectively solve the problems in the background art.
In order to achieve the purpose, the invention provides the following technical scheme:
the invention relates to a GIS-based rural residential point centralized residential area site selection and optimization method, which comprises the following steps of firstly obtaining space and attribute data of a target area based on the GIS, constructing an information quantity model for carrying out rural residential point centralized residential area land suitability evaluation, superposing a land utilization planning allowable construction area, obtaining a candidate address, setting an area size threshold value for screening, and then optimizing the candidate address by using a P-median problem model, wherein the method comprises the following steps:
step 1, acquiring construction land, elevation, gradient and slope spatial data and population economic attribute data of roads, rivers and rural residential sites in a target area: in order to obtain vector data of a target area, firstly, a remote sensing image, a DEM (digital elevation model), a land utilization planning map and social economic population data of the target area need to be collected, a high-definition remote sensing image is loaded in ArcGIS (geographic information System), land and thing interpretation is carried out through visual interpretation so as to read digital road, river, rural residential points, central village and town construction land data of the target area, the DEM data is subjected to surface analysis in the ArcGIS so as to obtain gradient and slope data of the target area, and the demographic economic data is input into a corresponding vector data table;
and 2, constructing an information quantity suitability evaluation model based on GIS spatial analysis: classifying the influence factors, counting the information quantity of rural residential points, then superposing the information quantities of all the factors, and expressing the suitability degree by using the information quantity of a single grid unit, which is specifically as follows: firstly, determining and grading influence factors, wherein an information quantity model visually expresses the closeness degree between the influence factors and a research object through the magnitude of the information quantity, selects suitability evaluation influence factors such as gradient, slope direction, elevation, distance from a river, distance from a road and distance from a town according to the actual condition of a target area, grades the influence factors in ArcGIS, and uses an area statistical tool to count the number of rural residents in classification, and substitutes a formula to calculate the information quantity, wherein the calculation formula is as follows:
in the formula: wi is the information amount size of a certain factor; densclass is the density of population points within a certain factor; densmap is the density of the population points in the entire target area; npix (Si) is the number of grids of the residential site included in a certain factor; npix (Ni) is the number of all grids of a certain factor layer; snpix (si) is the number of grids of residential sites in the entire target area; SNpix (Ni) is the number of all grids in the entire target region;
adding the information amounts Wi of all the factors to obtain the information amount of a certain grid unit, and superposing all the factor layers according to the following formula by using a grid calculator in ArcGIS:
W＝∑Wi
in the formula: and W is expressed as a predicted value of the information amount of a certain unit of the evaluation area.
Step 3, reclassifying the information quantity of all grids in the target area, judging the effectiveness of the model according to the distribution condition of existing residential points in the suitability subarea, if the model is effective, continuing to execute the model, otherwise, returning, checking the data quality of the influence factors or updating the influence factors, and then re-evaluating;
and 4, aiming at minimizing the total distance from the crop production point to the candidate addresses, setting the number of the candidate addresses by using a P-median problem model based on a road network data set, and optimizing the screened candidate addresses: after the suitability partition of the target area is obtained, vectorizing a grid unit with high suitability, and screening a certain number of candidate addresses according to the area size of a land parcel after a land utilization planning allowable construction area is superposed; the P-median problem model is as follows: assuming that the distance between an original residential site and a production area is ignored, residents need to go back and forth between the production area and the residential area in daily work, and a certain number of candidate addresses are selected to achieve the minimum total distance or time from the production site to the residential site, wherein the P-median problem formula is as follows:
Z＝∑∑aidijxij (1)
in the formula: i is a production point number (i ═ 1, 2.., n); j is a candidate residential point code (j ═ 1, 2.. times, m); p is the number of the resident points to be selected; a isiIs the total demand of production point i; dijIs the distance or time between the production point i to the residential point j; x is the number ofij1 denotes that the service facility at the jth point covers the ithProduction points, otherwise 0;
solving the minimum value of the formula (1) under the following constraint condition; the constraint conditions are as follows:
xjj1 means that the service facility is established at the jth point, otherwise 0;
and 5, determining candidate addresses, dividing service areas, and determining rural residential points needing to be moved in the subareas according to the suitability grade: creating a road network data set according to an actual road network in ArcGIS, connecting rural residential points (demand points) and screened candidate addresses to nearest road network nodes, converting all data into a coverage format, setting the quantity of the demand of the candidate addresses by using a Mindistance module in ArcInfo works, carrying out location optimization based on the road network data, selecting a certain quantity of candidate addresses, and dividing a service area;
and 6, outputting a final farmer concentrated residential area site selection scheme: determining rural residential points needing to be moved in a service area according to the suitability grade, performing superposition analysis on vectorized suitability partition map layers and residential point map layers in ArcGIS, selecting residential points in a poor suitability area, preliminarily determining the residential points as rural residential points needing to be moved, and moving the residential points to corresponding candidate addresses in the service area.
Preferably, the remote sensing image data in the step 1 is derived from google earth high-definition images, the DEM data is derived from geospatial data clouds, the social population economic data is derived from a local government information public network, and the vector data in the step 1 comprises town sets, central villages, rural residential points, roads, rivers, slopes and slopes.
Preferably, in the step 3, the information contents of all grids in the target area are reclassified, and the validity of the model is judged according to the distribution situation of existing residential points in the suitability sub-area.
Preferably, the production points in the step 4 are production area particles, and the residential points are residential area particles.
The invention has the beneficial effects that: the method utilizes a GIS technology to construct an information quantity model, quantitatively evaluates the land suitability of rural residential sites and selects the site, and uses an integer linear programming P-meso position problem model to perform zone bit optimization, so that the requirement of candidate addresses can be flexibly set, and the site selection result is positioned to a plot; the candidate address is based on an actual road network, daily work requirements of farmers after relocation are considered, the distance cost is minimum, and the method has the advantages of high precision and strong operability.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
FIG. 1 is a schematic view of the process flow structure of the present invention;
FIG. 2 is a diagram of original residential points and pre-optimization candidate address points;
FIG. 3 shows the residential points to be relocated and the address points to be optimized.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments, and all other embodiments obtained by a person of ordinary skill in the art without creative efforts based on the embodiments of the present invention belong to the protection scope of the present invention.
Referring to fig. 1-3, the present invention provides a technical solution:
a rural residential point centralized residential area site selection and optimization method based on GIS comprises the following steps of firstly obtaining space and attribute data of a target area based on GIS, constructing an information quantity model to evaluate the land suitability of the rural residential point centralized residential area, overlapping land utilization planning allowable construction areas, obtaining candidate addresses, setting area size threshold values for screening, and then optimizing the candidate addresses by using a P-median problem model, wherein the method comprises the following steps:
step 1, acquiring spatial data and population economic attribute data such as construction land, elevation, gradient and slope of roads, rivers and rural residential points in a target area: in order to obtain vector data of a target area, firstly, data such as a remote sensing image, a DEM (digital elevation model), a land utilization planning map, social economic population and the like of the target area need to be collected, a high-definition remote sensing image is loaded in ArcGIS, land and thing interpretation is carried out through visual interpretation to read data such as roads, rivers, rural residential sites, central villages and town construction lands of the digital target area, surface analysis is carried out on the DEM data in the ArcGIS to obtain gradient and slope data of the target area, and the demographic economic data are input into a corresponding vector data table;
and 2, constructing an information quantity suitability evaluation model based on GIS spatial analysis: classifying the influence factors, counting the information quantity of rural residential points, then superposing the information quantities of all the factors, and expressing the suitability degree by using the information quantity of a single grid unit, which is specifically as follows: firstly, determining and grading influence factors, wherein an information quantity model visually expresses the closeness degree between the influence factors and a research object through the magnitude of the information quantity, selects suitability evaluation influence factors such as gradient, slope direction, elevation, distance from a river, distance from a road, distance from a town and the like by combining the actual situation of a target area, grades the influence factors in ArcGIS, uses an area statistical tool to count the number of rural residents in classification, and substitutes a formula to calculate the information quantity, and the calculation formula is as follows:
in the formula: wi is the information amount size of a certain factor; densclass is the density of population points within a certain factor; densmap is the density of the population points in the entire target area; npix (Si) is the number of grids of the residential site included in a certain factor; npix (Ni) is the number of all grids of a certain factor layer; snpix (si) is the number of grids of residential sites in the entire target area; SNpix (Ni) is the number of all grids in the entire target region;
adding the information amounts Wi of all the factors to obtain the information amount of a certain grid unit, and superposing all the factor layers according to the following formula by using a grid calculator in ArcGIS:
W＝∑Wi
in the formula: and W is expressed as a predicted value of the information amount of a certain unit of the evaluation area.
Step 3, reclassifying the information quantity of all grids in the target area, judging the effectiveness of the model according to the distribution condition of existing residential points in the suitability subarea, if the model is effective, continuing to execute the model, otherwise, returning, checking the data quality of the influence factors or updating the influence factors, and then re-evaluating;
and 4, aiming at minimizing the total distance from the crop production point to the candidate addresses, setting the number of the candidate addresses by using a P-median problem model based on a road network data set, and optimizing the screened candidate addresses: after the suitability partition of the target area is obtained, vectorizing a grid unit with high suitability, stacking a land utilization planning allowable construction area, and screening 81 candidate addresses according to the area size of a land parcel greater than 2000 square meters, please refer to fig. 2; the P-median problem model is as follows: assuming that the distance between an original residential site and a production area is ignored, residents need to go back and forth between the production area and a residential area in daily work, and a certain number of candidate addresses are selected to achieve the minimum total distance or time from the production site to the residential site, wherein a P-median problem formula is as follows:
Z＝∑∑aidijxij (1)
in the formula: i is a production point number (i ═ 1, 2.., n); j is a candidate residential point code (j ═ 1, 2.. times, m); p is the number of the resident points to be selected; a isiIs the total demand of production point i; dijIs the distance or time between the production point i to the residential point j; x is the number ofij1 means that the service facility at the jth point covers the ith production point, otherwise 0;
solving the minimum value of the formula (1) under the following constraint condition; the constraint conditions are as follows:
xjj1 means that the service facility is established at the jth point, otherwise 0;
and 5, determining candidate addresses, dividing service areas, and determining rural residential points needing to be moved in the subareas according to the suitability grade: creating a road network data set according to an actual road network in ArcGIS, connecting 291 rural residential points (demand points) and 81 screened candidate addresses to a nearest road network node, converting all data into a coverage format, setting the quantity of the demand of the candidate addresses by using a Mindistance module in ArcInfo works, performing location optimization based on the road network data, selecting 6 candidate addresses, and dividing a service area;
and 6, outputting a final farmer concentrated residential area site selection scheme: determining rural residential points to be moved in a service area according to the suitability level, performing superposition analysis on vectorized suitability partition map layers and residential point map layers in ArcGIS, selecting residential points in a poor suitability area, preliminarily determining 2392 people as 49 rural residential points to be moved, and moving to 6 corresponding candidate addresses in the service area, please refer to FIG. 3.
In the above embodiment, the remote sensing image data in step 1 is derived from google earth high definition images, the DEM data is derived from geospatial data clouds, the socioeconomic data is derived from local government information public networks, and the vector data in step 1 includes town, central village, rural residential points, roads, rivers, slopes, and slopes.
In the above embodiment, in step 3, the information contents of all grids in the target area are reclassified, and the validity of the model is determined according to the distribution of existing residents in the suitability region.
The validity of the model is tested by carrying out partition statistical test on the existing residential site grids (data binarization) and the regions subjected to suitability classification in the research area, and the results are as follows:
according to the above table, 43.26% of the existing population points are distributed in the suitable area, 38.46% of the existing population points are distributed in the optimum area, and about 81.72% of the existing population points fall within the suitable area and the optimum area, indicating that the suitability evaluation using the information content model is feasible and the evaluation result is successful.
In the above embodiment, the production points in step 4 are production area particles, and the residential points are residential area particles.
The method utilizes a GIS technology to construct an information quantity model, quantitatively evaluates the land suitability of rural residential sites and selects the site, and uses an integer linear programming P-meso position problem model to perform zone bit optimization, so that the requirement of candidate addresses can be flexibly set, and the site selection result is positioned to a plot; the candidate address is based on an actual road network, daily work requirements of farmers after relocation are considered, the distance cost is minimum, and the method has the advantages of high precision and strong operability.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments or portions thereof without departing from the spirit and scope of the invention.
Claims (3)
1. A rural residential point centralized residential area site selection and optimization method based on GIS is characterized in that firstly, target area space and attribute data are obtained based on GIS, an information quantity model is constructed to evaluate the land suitability of the rural residential point centralized residential area, the land utilization planning allowed construction areas are superposed to obtain candidate addresses, area size thresholds are set for screening, and then a P-median problem model is used for optimizing the candidate addresses, and the method comprises the following steps:
step 1, acquiring construction land, elevation, gradient and slope spatial data and population economic attribute data of roads, rivers and rural residential sites in a target area: in order to obtain vector data of a target area, firstly, a remote sensing image, a DEM (digital elevation model), a land utilization planning map and social economic population data of the target area need to be collected, a high-definition remote sensing image is loaded in ArcGIS (geographic information System), land and thing interpretation is carried out through visual interpretation so as to read digital road, river, rural residential points, central village and town construction land data of the target area, the DEM data is subjected to surface analysis in the ArcGIS so as to obtain gradient and slope data of the target area, and the demographic economic data is input into a corresponding vector data table;
and 2, constructing an information quantity suitability evaluation model based on GIS spatial analysis: classifying the influence factors and counting the information quantity of rural residential points, then superposing all the factor information quantities, and expressing the suitability degree by using the information quantity of a single grid unit, which is concretely as follows: firstly, determining and grading influence factors, wherein an information quantity model visually expresses the closeness degree between the influence factors and a research object through the magnitude of the information quantity, selects suitability evaluation influence factors such as gradient, slope direction, elevation, distance from a river, distance from a road and distance from a town according to the actual condition of a target area, grades the influence factors in ArcGIS, and uses an area statistical tool to count the number of rural residents in classification, and substitutes a formula to calculate the information quantity, wherein the calculation formula is as follows:
in the formula: wi is the information amount size of a certain factor; densclass is the density of population points within a certain factor; densmap is the density of the population points in the entire target area; npix (Si) is the number of grids of the residential site included in a certain factor; npix (Ni) is the number of all grids of a certain factor layer; snpix (si) is the number of grids of residential sites in the entire target area; SNpix (Ni) is the number of all grids in the entire target region;
adding the information amounts Wi of all the factors to obtain the information amount of a certain grid unit, and superposing all the factor layers according to the following formula by using a grid calculator in ArcGIS:
W＝∑Wi
in the formula: w represents a predicted value of the information quantity of a certain unit of the evaluation area;
step 3, reclassifying the information quantity of all grids in the target area, judging the effectiveness of the model according to the distribution condition of existing residential points in the suitability subarea, if the model is effective, continuing to execute the model, otherwise, returning, checking the data quality of the influence factors or updating the influence factors, and then re-evaluating;
and 4, aiming at minimizing the total distance from the crop production point to the candidate addresses, setting the number of the candidate addresses by using a P-median problem model based on a road network data set, and optimizing the screened candidate addresses: after the suitability partition of the target area is obtained, vectorizing a grid unit with high suitability, and screening a certain number of candidate addresses according to the area size of a land parcel after a land utilization planning allowable construction area is superposed; the P-median problem model is as follows: assuming that the distance between an original residential site and a production area is ignored, residents need to go back and forth between the production area and the residential area in daily work, and a certain number of candidate addresses are selected to achieve the minimum total distance or time from the production site to the residential site, wherein the P-median problem formula is as follows:
Z＝∑∑aidijxij (1)
in the formula: i is a production point number (i ═ 1, 2.., n); j is a candidate residential point code (j ═ 1, 2.. times, m); p is the number of the resident points to be selected; a isiIs the total demand of production point i; dijIs the distance or time between the production point i to the residential point j; x is the number ofij1 means that the service facility at the jth point covers the ith production point, otherwise 0;
solving the minimum value of the formula (1) under the following constraint condition; the constraint conditions are as follows:
xjj1 means that the service facility is established at the jth point, otherwise 0;
and 5, determining candidate addresses, dividing service areas, and determining rural residential points needing to be moved in the subareas according to the suitability grade: creating a road network data set according to an actual road network in ArcGIS, connecting rural residential points and screened candidate addresses to nearest road network nodes, converting all data into a coverage format, using a Mindistance module in ArcInfo work, setting the required number of candidate addresses, performing location optimization based on road network data, selecting a certain number of candidate addresses, and dividing a service area;
and 6, outputting a final farmer concentrated residential area site selection scheme: determining rural residential points needing to be moved in a service area according to the suitability grade, performing superposition analysis on vectorized suitability partition map layers and residential point map layers in ArcGIS, selecting residential points in a poor suitability area, preliminarily determining the residential points as rural residential points needing to be moved, and moving the residential points to corresponding candidate addresses in the service area.
2. The GIS based rural residential site centralized residential district addressing and optimizing method according to claim 1, wherein the remote sensing image data in step 1 is derived from Google Earth high definition images, DEM data is derived from geospatial data cloud, socioeconomic data is derived from local government information public networks, and the vector data in step 1 includes town, central village, rural residential site, road, river, grade and slope.
3. The GIS based rural residential site centralized residential site locating and optimizing method according to claim 1, wherein the production site in the step 4 is a production site particle, and the residential site is a residential site particle.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810587348.3A CN108876027B (en) | 2018-06-06 | 2018-06-06 | GIS-based rural residential point centralized residential area site selection and optimization method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810587348.3A CN108876027B (en) | 2018-06-06 | 2018-06-06 | GIS-based rural residential point centralized residential area site selection and optimization method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108876027A CN108876027A (en) | 2018-11-23 |
| CN108876027B true CN108876027B (en) | 2022-03-29 |
Family
ID=64337408
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810587348.3A Active CN108876027B (en) | 2018-06-06 | 2018-06-06 | GIS-based rural residential point centralized residential area site selection and optimization method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108876027B (en) |
Families Citing this family (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110502593A (en) * | 2018-12-27 | 2019-11-26 | 厦门理工学院 | Multifactor assessment Self-service Library lays the method, device and equipment of suitability |
| CN111950823A (en) * | 2019-05-15 | 2020-11-17 | 河南理工大学 | Rural residential point regulation and control classification method based on space suitability and internal characteristics |
| CN110555654A (en) * | 2019-08-30 | 2019-12-10 | 广州大学 | logistics distribution center site selection method and device for fresh food chain store in community |
| CN110851548A (en) * | 2019-10-14 | 2020-02-28 | 上海市政工程设计研究总院（集团）有限公司 | Municipal facility site selection layout method based on ArcGIS analysis |
| CN110933601B (en) * | 2019-12-25 | 2021-06-15 | 中国移动通信集团内蒙古有限公司 | Target area determination method, device, equipment and medium |
| CN111260392B (en) * | 2020-01-07 | 2023-11-07 | 广州大学 | Automatic vending machine site selection method and device based on multi-source big data |
| CN111077280B (en) * | 2020-01-14 | 2020-08-25 | 浙江清华长三角研究院 | River network-based source tracing analysis method between rural sewage treatment facility and water quality monitoring station |
| CN112307533A (en) * | 2020-10-30 | 2021-02-02 | 深圳集智数字科技有限公司 | Control and regulation graph generation method and related equipment |
| CN112926029B (en) * | 2021-01-29 | 2022-08-26 | 四川省环境政策研究与规划院 | Residential area identification and division method for rural domestic sewage treatment |
| CN113268900B (en) * | 2021-04-02 | 2022-09-16 | 中国人民解放军战略支援部队信息工程大学 | Task-oriented airborne field site selection method and device |
| CN112950084A (en) * | 2021-04-07 | 2021-06-11 | 中国海洋大学 | Reverse osmosis seawater desalination plant site selection method |
| CN113112068A (en) * | 2021-04-13 | 2021-07-13 | 东南大学 | Method and system for addressing and layout of public facilities in villages and small towns |
| CN114118602B (en) * | 2021-12-02 | 2023-08-08 | 碧空环境科技有限公司 | GIS-based high-altitude spray equipment location method |
| CN114757549B (en) * | 2022-04-24 | 2023-06-27 | 中交第二航务工程勘察设计院有限公司 | Method for deciding functions and scale of water service area of inland main channel |
| CN114926741A (en) * | 2022-06-02 | 2022-08-19 | 广东卓越土地房地产评估咨询有限公司 | Agricultural land data acquisition system and method thereof |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103136603A (en) * | 2013-03-26 | 2013-06-05 | 武汉大学 | Intelligent land utilization layout optimal allocation method |
| US8756322B1 (en) * | 2011-06-16 | 2014-06-17 | Amazon Technologies, Inc | Fulfillment of requests for computing capacity |
| CN103971312A (en) * | 2014-05-27 | 2014-08-06 | 武汉大学 | Rural network node radiation domain-oriented rural residential area renovation zoning method |
| CN105184006A (en) * | 2015-09-22 | 2015-12-23 | 广州市城市规划勘测设计研究院 | Selecting method and device for construction land in low-hill mild slope region |
| CN106055803A (en) * | 2016-06-02 | 2016-10-26 | 中国石油大学(华东) | Method for optimizing site selection of gas detecting alarm instrument of oil refining device by considering conditional risk value |
| CN107832958A (en) * | 2017-11-15 | 2018-03-23 | 云南电网有限责任公司 | A kind of electric taxi charging station planing method based on demand analysis |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8874477B2 (en) * | 2005-10-04 | 2014-10-28 | Steven Mark Hoffberg | Multifactorial optimization system and method |
-
2018
- 2018-06-06 CN CN201810587348.3A patent/CN108876027B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8756322B1 (en) * | 2011-06-16 | 2014-06-17 | Amazon Technologies, Inc | Fulfillment of requests for computing capacity |
| CN103136603A (en) * | 2013-03-26 | 2013-06-05 | 武汉大学 | Intelligent land utilization layout optimal allocation method |
| CN103971312A (en) * | 2014-05-27 | 2014-08-06 | 武汉大学 | Rural network node radiation domain-oriented rural residential area renovation zoning method |
| CN105184006A (en) * | 2015-09-22 | 2015-12-23 | 广州市城市规划勘测设计研究院 | Selecting method and device for construction land in low-hill mild slope region |
| CN106055803A (en) * | 2016-06-02 | 2016-10-26 | 中国石油大学(华东) | Method for optimizing site selection of gas detecting alarm instrument of oil refining device by considering conditional risk value |
| CN107832958A (en) * | 2017-11-15 | 2018-03-23 | 云南电网有限责任公司 | A kind of electric taxi charging station planing method based on demand analysis |
Non-Patent Citations (8)
| Title |
|---|
| A fuzzy set covering-clustering algorithm for facility location problem;Rashed Sahraeian等;《 2011 IEEE International Conference on Industrial Engineering and Engineering Management》;20111229;第32-37页，全文 * |
| GIS在购物中心选址中的应用研究;宋广飞;《中国优秀博硕士学位论文全文数据库(硕士)基础科学辑》;20090515(第5期);第A008-12页，全文 * |
| Multicriteria method for a site selection of a new hospital in Sfax;Ennouri Wissem;《2011 4th International Conference on Logistics》;20110705;第1098-1102页，全文 * |
| Research on the guarantee rate and suitability of pushover analysis method for reinforced concrete frame structure;zhang yong等;《Journal of Hunan University (Natural Science)》;20150325;第42卷(第3期);第9-13页，全文 * |
| 基于GIS的合肥市BRT和Metro交通可达性研究;朱传华等;《地理与地理信息科学》;20141231(第6期);第21-24、30、127页，全文 * |
| 基于GIS的建筑工业化基地选址研究;杨宏安;《中国优秀博硕士学位论文全文数据库(硕士)工程科技Ⅱ辑》;20180215(第2期);第C038-2823页，全文 * |
| 液化天然气码头选址关键因素;张勇等;《水运工程》;20140228(第2期);第96-99页，全文 * |
| 湖北省仙桃市农村公共医疗服务可达性与均等化研究;罗蕾;《中国优秀博硕士学位论文全文数据库(博士)医药卫生科技辑》;20160515(第5期);第E053-4页，全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108876027A (en) | 2018-11-23 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108876027B (en) | GIS-based rural residential point centralized residential area site selection and optimization method | |
| Sakieh et al. | Simulating urban expansion and scenario prediction using a cellular automata urban growth model, SLEUTH, through a case study of Karaj City, Iran | |
| Firozjaei et al. | A geographical direction-based approach for capturing the local variation of urban expansion in the application of CA-Markov model | |
| Kadavi et al. | Landslide-susceptibility mapping in Gangwon-do, South Korea, using logistic regression and decision tree models | |
| Batisani et al. | Urban expansion in Centre County, Pennsylvania: Spatial dynamics and landscape transformations | |
| Wu et al. | Performance evaluation of the SLEUTH model in the Shenyang metropolitan area of northeastern China | |
| Akın et al. | The impact of historical exclusion on the calibration of the SLEUTH urban growth model | |
| Das et al. | Assessment of urban sprawl using landscape metrics and Shannon’s entropy model approach in town level of Barrackpore sub-divisional region, India | |
| Dezhkam et al. | Simulating the urban growth dimensions and scenario prediction through sleuth model: a case study of Rasht County, Guilan, Iran | |
| Lei et al. | Does Urban planning affect urban growth pattern? A case study of Shenzhen, China | |
| Wang et al. | A patch‐based cellular automaton for simulating land‐use changes at fine spatial resolution | |
| Li et al. | Application and verification of fractal approach to landslide susceptibility mapping | |
| Pascale et al. | Landslide susceptibility mapping using artificial neural network in the urban area of Senise and San Costantino Albanese (Basilicata, Southern Italy) | |
| Alvioli et al. | Rockfall susceptibility and network-ranked susceptibility along the Italian railway | |
| Dutta et al. | Exploring the dynamics of urban sprawl using geo-spatial indices: a study of English Bazar Urban Agglomeration, West Bengal | |
| Díaz-Pacheco et al. | The importance of scale in land use models: Experiments in data conversion, data resampling, resolution and neighborhood extent | |
| CN107506433A (en) | Urban development space general layout Scene Simulation system | |
| CN113240257A (en) | Territorial space partitioning method and device based on minimum cumulative resistance model | |
| Weldu et al. | Identification of potential sites for housing development using GIS based multi-criteria evaluation in Dire Dawa City, Ethiopia | |
| Asefi et al. | A multi-criteria decision support framework for municipal solid waste landfill siting: a case study of New South Wales (Australia) | |
| Bui et al. | Landslide susceptibility prediction mapping with advanced ensemble models: Son La province, Vietnam | |
| Lamichhane et al. | Land use land cover (LULC) change projection in Kathmandu valley using the clue-s model | |
| Kumar et al. | Urban modelling and forecasting of landuse using SLEUTH model | |
| Hua-xi et al. | Study on spatial prediction and time forecast of landslide | |
| Mubea et al. | Spatial effects of varying model coefficients in urban growth modeling in Nairobi, Kenya |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |