CN112669340B - GIS big data analysis-based public facility site selection method and system - Google Patents
GIS big data analysis-based public facility site selection method and system Download PDFInfo
- Publication number
- CN112669340B CN112669340B CN202011556430.3A CN202011556430A CN112669340B CN 112669340 B CN112669340 B CN 112669340B CN 202011556430 A CN202011556430 A CN 202011556430A CN 112669340 B CN112669340 B CN 112669340B
- Authority
- CN
- China
- Prior art keywords
- thermodynamic diagram
- planning
- area
- population
- region
- 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
- 238000007405 data analysis Methods 0.000 title claims abstract description 39
- 238000010187 selection method Methods 0.000 title claims abstract description 13
- 238000010586 diagram Methods 0.000 claims abstract description 340
- 238000000034 method Methods 0.000 claims abstract description 35
- 238000003708 edge detection Methods 0.000 claims description 49
- 230000005484 gravity Effects 0.000 claims description 24
- 238000003707 image sharpening Methods 0.000 claims description 18
- 238000004590 computer program Methods 0.000 claims description 10
- 230000002708 enhancing effect Effects 0.000 claims description 9
- 238000005192 partition Methods 0.000 claims description 9
- 238000004364 calculation method Methods 0.000 claims description 3
- 235000020061 kirsch Nutrition 0.000 claims description 3
- 238000007781 pre-processing Methods 0.000 claims description 3
- 230000011664 signaling Effects 0.000 claims description 3
- 230000000694 effects Effects 0.000 description 5
- 230000006870 function Effects 0.000 description 4
- 238000004422 calculation algorithm Methods 0.000 description 3
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000000605 extraction Methods 0.000 description 2
- 230000002349 favourable effect Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000001413 cellular effect Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 230000002068 genetic effect Effects 0.000 description 1
- 238000007726 management method Methods 0.000 description 1
- 230000000191 radiation effect Effects 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
Images
Landscapes
- Image Analysis (AREA)
Abstract
The invention discloses a GIS big data analysis-based public facility site selection method and a GIS big data analysis-based public facility site selection system, wherein a corresponding public facility planning area and a corresponding public facility planning point are quickly calculated from an area with the highest population moving intensity according to the characteristics of high density, large color and deep gray value of a human-mouth thermodynamic diagram through pixel gray processing of the image of the human-mouth thermodynamic diagram; the requirement on the computing capacity of a computer is low, the shortest distance to an area with dense population distribution can be screened out quickly by hierarchical pertinence on the actual population distribution, and site selection points of a plurality of public facilities in different population dense areas can be planned in batch; the method and the device keep the continuity of the planning areas in the image, and each planning area has a communicated area, thereby preventing isolated planning data points from appearing, greatly saving the resource utilization of public setting, and improving the rationality of planning.
Description
Technical Field
The invention relates to the technical field of spatial geographic information data processing technology and geographic information systems, in particular to a GIS big data analysis-based public facility site selection method and a GIS big data analysis-based public facility site selection system.
Background
The public facilities refer to the social infrastructure, public buildings or equipment such as education, medical health, cultural entertainment, traffic, sports, social welfare and security, administrative management and community service, postal telecommunications, commercial financial service and the like which are distributed in a dotted manner in cities, the problems to be considered for site selection are more, the scientific and reasonable distribution of social resources is achieved mainly by referring to population activity distribution, the uniform construction on a map cannot be realized, for example, public seats, garbage cans and public toilets are densely arranged in regions with dense population activities to improve the utilization rate of the social public resources, and are rarely arranged in regions with sparse population activities to avoid waste of the public resources; in the existing public facility site selection method, generally, the site selection of the shortest distance is carried out on public service facilities by using population distribution grid data as a basis and through an intelligent genetic algorithm or a Dijkastra algorithm after map rasterization, but the algorithms have higher requirements on the computing capacity of a computer, and the shortest distance from an area with dense population distribution is difficult to be quickly screened out by layers according to the actual population distribution, and the area with the highest population moving density cannot be simultaneously planned with site selection points of a plurality of public facilities in different population dense areas in large batch, is not close to the actual value, and cannot judge and check the rationality of site selection.
Disclosure of Invention
The invention aims to provide a GIS big data analysis-based public facility site selection method and a GIS big data analysis-based public facility site selection system, which are used for solving one or more technical problems in the prior art and providing at least one beneficial selection or creation condition.
The invention is based on population distribution thermodynamic map (population thermodynamic map), also called contour map (chord map), according to the position (longitude and latitude) data of different areas, color filling with different degrees is carried out, thus reflecting different distribution of each area, each data point presents a circle filled with radial gradient (so-called radial gradient means that the circle center gradually changes along with the increase of radius), and the gradient circle presents the radiation effect of the data from strong weakness to weak weakness, and is an image of red strong blue weak linear gradient.
In order to achieve the above object, according to an aspect of the present invention, there is provided a method for locating a public facility based on GIS big data analysis, the method comprising the steps of:
s100, collecting population thermodynamic diagrams of an area to be planned for the last N days at intervals of one collection interval Gap, wherein the population thermodynamic diagrams form thermodynamic diagram sequences according to the sequence of collection time; the acquisition interval Gap is the duration of the acquisition interval and can be adjusted within [1,24] hours, and the default value of Gap is 3; n is a natural number and takes the value of [1,30] days; for example, if N takes 2 days and Gap is 3, after at least 2 days of collection, a thermodynamic sequence of images of 24N/Gap personal oral thermodynamic diagrams for the last 2 days (i.e., the last 24 × 2-48 hours) is finally obtained;
s200, graying each population thermodynamic diagram in the thermodynamic diagram sequence to obtain a gray level diagram, and binarizing the gray level diagram to obtain a binary diagram;
s300, performing edge detection on each binary image through an edge detection operator, wherein each binary image respectively obtains a plurality of closed areas formed by edge lines;
s400, sequentially carrying out three times of internal partition edge detection on corresponding positions of each closed region in each gray scale map to obtain a plurality of divided closed regions of the thermodynamic diagram;
s500, sequentially calculating the gravity center point of each thermodynamic diagram closed region in each population thermodynamic diagram in the thermodynamic diagram sequence, and taking the gravity center point of each thermodynamic diagram closed region with the area larger than a preset planning area as a node to be planned;
s600, sequentially scanning each population thermodynamic diagram in the thermodynamic diagram sequence, and selecting the population thermodynamic diagram with the most intersection with the closed region of the thermodynamic diagrams in other population thermodynamic diagrams as a reference planning image;
s700, taking the nodes to be planned of all population thermodynamic diagrams in the thermodynamic diagram sequence as a node set to be planned; removing repeated nodes to be planned in the nodes to be planned;
s800, scanning each thermodynamic diagram closed area of each population thermodynamic diagram in the thermodynamic diagram sequence in sequence, taking the number of nodes to be planned in a node set to be planned in each thermodynamic diagram closed area as the number of concentration points, and selecting each thermodynamic diagram closed area with the largest number of concentration points from each thermodynamic diagram closed area with intersection positions in each population thermodynamic diagram closed area to form a concentrated planning area set;
s900, dividing the reference planning image again by the boundary line of each thermodynamic diagram closed region in the centralized planning region set; and taking a node to be planned in the centralized planning region of the reference planning image as an address selection site, taking the area of each closed region of the thermodynamic diagrams in the population thermodynamic diagrams in the centralized planning region set as a building planning area, and outputting the address selection site and the building planning area to a GIS map corresponding to the region to be planned to a client for display or outputting the GIS map to a database for storage.
Further, in S100, the population thermodynamic diagram of the area to be planned is acquired by acquiring any one of a mobile phone signaling population thermodynamic diagram, a WeChat trip population thermodynamic diagram, a Baidu map population thermodynamic diagram, Bai Bi Yi data, and population census data of a public security department of the area to be planned.
Further, in S300, the edge detection operator includes any one of Roberts Cross operator, Prewitt operator, Sobel operator, Kirsch operator, Marr-Hildreth operator, Canny operator, and Laplacian operator.
Further, in S400, the method for sequentially performing three times of internal partition edge detection on the corresponding position of each closed region in the grayscale map to obtain a plurality of newly partitioned closed regions in the thermodynamic map includes the following steps:
s401, taking the gray-scale image and the area image of the corresponding position of the closed area as an image to be adjusted; enhancing an image to be adjusted by an image sharpening method to obtain a primary sharpened image; carrying out edge detection on the primary sharpened image through an edge detection operator to obtain a plurality of primary subregions formed by edge lines;
s402, enhancing the primary sharpened image by an image sharpening method to obtain a secondary sharpened image; carrying out edge detection on the secondary sharpened image through an edge detection operator to obtain a plurality of secondary sub-regions consisting of edge lines in the primary sharpened image;
s403, enhancing the secondary sharpened image by an image sharpening method to obtain a tertiary sharpened image; carrying out edge detection on the three-level sharpened image through an edge detection operator to obtain a plurality of three-level sub-regions consisting of edge lines in the two-level sharpened image;
s404, dividing the gray scale map into a plurality of divided thermodynamic diagram closed areas according to the corresponding positions of the edge lines and the gray scale map by using all the edge lines of the closed area, the primary sub-area, the secondary sub-area and the tertiary sub-area.
Further, in S401, since the traditional image sharpening method cannot well highlight the circle of radial gradient presented by each data point in the population thermodynamic diagram, the improved image sharpening method can remove the circle presented by the data point with lighter gradient, that is, the data point with smaller population density data, so as to better distinguish the linear superposition structure of the population thermodynamic diagram for the edge detection operator to perform edge extraction, and therefore the improved image sharpening method is as follows:
the sharpened image Imsharp is obtained by sequentially and linearly superposing the pixels in the detail sub-image and the pixels of the original gray level image,
wherein IMA ═ Sharp/(1+ e)-0.25*Im) D, IMA is the pixel of the detail partial image converted after the sharpening in the pixel Im of the grey scale image, and the grey scale value of the detail partial image pixel IMA is kept at [0,255 ] for]Taking the sharpening degree Sharp as 255 and the sharpening difference D as 12;
the gray level image of the population thermodynamic diagram is sharpened through an image sharpening method, the edges of all linearly superposed circles in the gray level image are balanced, and the edges of all linearly superposed circles are mutually distinguished to be favorable for edge detection.
Further, in S500, the preset planned area is the actual planned area size of the set public facility, and the value is [10,2000] square meter.
Further, in S500, the method for sequentially calculating the gravity center point of each thermodynamic diagram closed region includes the following sub-steps:
s501, traversing each thermodynamic diagram closed region in sequence, and marking the thermodynamic diagram closed region with the average value of the gray values of all pixels larger than the average gray value of the gray diagram corresponding to each thermodynamic diagram closed region as a candidate planning region; (the principle is that the larger the grayscale value, the darker the color represents the more the population there is in the population thermodynamic diagram);
s502, recording all thermodynamic diagram closed regions directly adjacent to the candidate planning region as adjacent regions, judging whether the average gray value in each candidate planning region is larger than the average gray value of pixel points in all thermodynamic diagram closed regions directly adjacent to the candidate planning region or not when the average gray value in not less than one adjacent region is larger than the average gray value of a gray diagram corresponding to each thermodynamic diagram closed region, marking the candidate planning region as a planning region if the average gray value in the candidate planning region is larger than the average gray value of the pixel points in all thermodynamic diagram closed regions directly adjacent to the candidate planning region, and taking each obtained planning region as a planning region set;
s503, sequentially traversing each planning region in the planning region set, and deleting the planning regions from the planning region set if the average gray value of all pixel points in all adjacent regions of the planning regions is smaller than the average gray value of the gray image corresponding to each planning region; (in order to keep the continuity of each planning region in the planning region set, each planning region is provided with a communicated region as much as possible, isolated planning data points are prevented from appearing, the resource utilization of public setting is greatly saved, and the planning rationality is improved, so the isolated data points are screened and excluded from the population thermodynamic diagram);
and S504, sequentially calculating the gravity center point of each planning region in the planning region set or the center of mass of each planning region image as the gravity center point of each thermodynamic diagram closed region and outputting the gravity center points.
Further, in S600, sequentially scanning each population thermodynamic diagram in the thermodynamic diagram sequence, and selecting the population thermodynamic diagram with the largest intersection with the closed region of the thermodynamic diagrams in other population thermodynamic diagrams as the reference planning image includes the following steps:
setting i and j as natural numbers, setting the initial values of i and j as 1, and setting the value ranges of i and j as [1,24N/Gap ]; 24N/Gap is the number of the human mouth thermodynamic diagrams in the thermodynamic diagram sequence; setting k and m as natural numbers, setting the initial values of k and m as 1, and setting the value ranges of k and m as [1, Sum ]; wherein Sum is the total amount of thermodynamic diagram closed areas in the human mouth thermodynamic diagram;
s601, taking a kth thermodynamic diagram closed area in an ith population thermodynamic diagram in the thermodynamic diagram sequence as a comparison area; setting the initial value of the total intersection number of the ith population thermodynamic diagram to be 0;
s602, if j is less than or equal to 24N/Gap and m is less than or equal to Sum, judging whether the mth thermodynamic diagram closed area and the comparison area in the jth population thermodynamic diagram have an intersection area, namely judging whether the position of the mth thermodynamic diagram closed area on the population thermodynamic diagram and the position of the comparison area on the population thermodynamic diagram have a common intersection area, and if so, increasing the value of the total intersection number of the ith population thermodynamic diagram by 1;
s603, if m is smaller than Sum, increasing the value of m by 1 and going to S602; when m is greater than or equal to Sum, setting the value of m to 1 and going to S604;
s604, if j is smaller than 24N/Gap, increasing the value of j by 1 and turning to S602; when j is greater than or equal to 24N/Gap, setting the value of j to 1 and going to S605;
s605, if k is less than Sum, increasing the value of k by 1 and going to S601; when k is greater than or equal to Sum, setting the value of k to 1 and going to S606;
s606, if i is smaller than 24N/Gap, increasing the value of i by 1 and turning to S601; when i is greater than or equal to 24N/Gap, the step goes to S607;
and S607, selecting the population thermodynamic diagram with the maximum value of the total number of intersections in the thermodynamic diagram sequence as the reference planning image.
Further, in S800, taking the number of nodes to be planned in the planning node set in each thermodynamic diagram closed area as the number of concentration points, and selecting each thermodynamic diagram closed area with the largest number of concentration points in each thermodynamic diagram closed area with an intersection position in each population thermodynamic diagram to form a concentrated planning area set specifically includes the following steps:
s801, sequentially forming one or more closed thermodynamic areas having a common intersection area at the position of the closed thermodynamic area in each of the population thermodynamic diagrams, to form one or more different intersection area sets ZoneSet ═ ZoneSet-1、ZoneSet2、ZoneSetsum2Each element in ZoneSet is a different subset of intersection regions, where subscript Sum2 denotes the total number of sets of intersection regions; setting q as a natural number, setting the initial value of q as 1, and setting the value range of q as [1, Sum2]；
S802, calculating Zones set in turnqThe total number of nodes to be planned in each thermodynamic diagram closed area;
s803, if q is less than Sum2, increasing the value of q by 1 and going to S802, if q is greater than or equal to Sum2, setting the value of q to 1 and going to S804;
s804, selecting thermodynamic diagram closed regions with the maximum total number of nodes to be planned in each intersection region set in the intersection region set Zones to form a centralized planning region set (namely selecting Zones set respectively1、ZoneSet2、ZoneSetsum2The respective thermodynamic diagram closed area with the maximum total number of nodes to be planned constitutes a centralized planning area set).
Further, in S900, the client includes any one of a desktop computer, a tablet computer, an industrial PDA device, and a mobile phone.
The invention also provides a GIS big data analysis-based public facility site selection system, which comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the computer program to run in the units of the following system:
the thermodynamic diagram sequence acquisition unit is used for acquiring population thermodynamic diagrams of the area to be planned for the last N days at each acquisition interval Gap, and the population thermodynamic diagrams form a thermodynamic diagram sequence according to the sequence of acquisition time;
the thermodynamic diagram preprocessing unit is used for carrying out graying on each population thermodynamic diagram in the thermodynamic diagram sequence to obtain a gray level diagram, and carrying out binarization on the gray level diagram to obtain a binarization diagram;
the initial edge detection unit is used for carrying out edge detection on each binary image through an edge detection operator, and each binary image respectively obtains a plurality of closed areas formed by edge lines;
the edge re-dividing unit is used for sequentially carrying out three times of internal partition edge detection on the corresponding positions of each closed region in each gray scale map to obtain a plurality of re-divided thermodynamic diagram closed regions;
the node to be planned calculation unit is used for sequentially calculating the gravity center points of all thermodynamic diagram closed areas in all population thermodynamic diagrams in the thermodynamic diagram sequence, and taking the gravity center points of all thermodynamic diagram closed areas with the areas larger than the preset planned area as nodes to be planned;
the reference image selecting unit is used for scanning each population thermodynamic diagram in the thermodynamic diagram sequence in sequence, and selecting the population thermodynamic diagram with the largest intersection with the closed region of the thermodynamic diagrams in other population thermodynamic diagrams as a reference planning image;
the planning node duplication removing unit is used for taking the nodes to be planned of all population thermodynamic diagrams in the thermodynamic diagram sequence as a node set to be planned; removing repeated nodes to be planned in the nodes to be planned;
the planning region selection unit is used for scanning each thermodynamic diagram closed region of each population thermodynamic diagram in the thermodynamic diagram sequence in sequence, taking the number of nodes to be planned in a node set to be planned in each thermodynamic diagram closed region as the number of concentration points, and selecting each thermodynamic diagram closed region with the largest number of concentration points from each thermodynamic diagram closed region with intersection positions of each population thermodynamic diagram to form a concentrated planning region set;
a result image output unit, which is used for dividing the reference planning image again by the boundary line of each thermodynamic diagram closed area in the centralized planning area set; and taking a node to be planned in the centralized planning region of the reference planning image as an address selection site, taking the area of each closed region of the thermodynamic diagrams in the population thermodynamic diagrams in the centralized planning region set as a building planning area, and outputting the address selection site and the building planning area to a GIS map corresponding to the region to be planned to a client for display or outputting the GIS map to a database for storage.
The invention has the beneficial effects that: the invention provides a GIS big data analysis-based public facility site selection method and a GIS big data analysis-based public facility site selection system, which have lower requirement on the computing capacity of a computer, can quickly screen out an area with the shortest distance to a population distribution dense area and the highest population moving intensity by aiming at the actual population distribution hierarchically, and can plan site selection points of a plurality of public facilities in different population dense areas in a large batch and approach to actual values; the continuity of the planning regions in the image is maintained, and each planning region is provided with a communicated region, so that isolated planning data points are prevented, the resource utilization of public setting is greatly saved, and the planning rationality is improved.
Drawings
The above and other features of the present invention will become more apparent by describing in detail embodiments thereof with reference to the attached drawings in which like reference numerals designate the same or similar elements, it being apparent that the drawings in the following description are merely exemplary of the present invention and other drawings can be obtained by those skilled in the art without inventive effort, wherein:
FIG. 1 is a flow chart of a GIS big data analysis-based public facility site selection method;
fig. 2 is a structural diagram of a GIS big data analysis-based public facility site selection system.
Detailed Description
The conception, the specific structure and the technical effects of the present invention will be clearly and completely described in conjunction with the embodiments and the accompanying drawings to fully understand the objects, the schemes and the effects of the present invention. It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
Referring to fig. 1, a flowchart of a method for locating a public facility based on GIS big data analysis according to the present invention is shown, and a method for locating a public facility based on GIS big data analysis according to an embodiment of the present invention is described below with reference to fig. 1.
The invention provides a GIS big data analysis-based public facility site selection method, which specifically comprises the following steps:
s100, collecting population thermodynamic diagrams of an area to be planned for the last N days at intervals of one collection interval Gap, wherein the population thermodynamic diagrams form thermodynamic diagram sequences according to the sequence of collection time; the acquisition interval Gap is the duration of the acquisition interval and can be adjusted within [1,24] hours, and the default value of Gap is 3; n is a natural number and takes the value of [1,30] days; for example, if N takes 2 days and Gap is 3, after at least 2 days of collection, a thermodynamic sequence of images of 24N/Gap personal oral thermodynamic diagrams for the last 2 days (i.e., the last 24 × 2-48 hours) is finally obtained;
s200, graying each population thermodynamic diagram in the thermodynamic diagram sequence to obtain a gray level diagram, and binarizing the gray level diagram to obtain a binary diagram;
s300, performing edge detection on each binary image through an edge detection operator, wherein each binary image respectively obtains a plurality of closed areas formed by edge lines;
s400, sequentially carrying out three times of internal partition edge detection on corresponding positions of each closed region in each gray scale map to obtain a plurality of divided closed regions of the thermodynamic diagram;
s500, sequentially calculating the gravity center points of the thermodynamic diagram closed areas in each population thermodynamic diagram in the thermodynamic diagram sequence, and taking the gravity center points of the thermodynamic diagram closed areas with the areas larger than the preset planning area as nodes to be planned;
s600, sequentially scanning each population thermodynamic diagram in the thermodynamic diagram sequence, and selecting the population thermodynamic diagram with the most intersection with the closed region of the thermodynamic diagrams in other population thermodynamic diagrams as a reference planning image;
s700, taking the nodes to be planned of all population thermodynamic diagrams in the thermodynamic diagram sequence as a node set to be planned; removing repeated nodes to be planned in the nodes to be planned;
s800, scanning each thermodynamic diagram closed area of each population thermodynamic diagram in the thermodynamic diagram sequence in sequence, taking the number of nodes to be planned in a node set to be planned in each thermodynamic diagram closed area as the number of concentration points, and selecting each thermodynamic diagram closed area with the largest number of concentration points from each thermodynamic diagram closed area with intersection positions in each population thermodynamic diagram closed area to form a concentrated planning area set;
s900, dividing the reference planning image again by the boundary line of each thermodynamic diagram closed region in the centralized planning region set; and taking a node to be planned in the centralized planning region of the reference planning image as an address selection site, taking the area of each closed region of the thermodynamic diagrams in the population thermodynamic diagrams in the centralized planning region set as a building planning area, and outputting the address selection site and the building planning area to a GIS map corresponding to the region to be planned to a client for display or outputting the GIS map to a database for storage.
Further, in S100, the population thermodynamic diagram of the area to be planned is acquired by acquiring any one of a mobile phone signaling population thermodynamic diagram, a WeChat trip population thermodynamic diagram, a Baidu map population thermodynamic diagram, Bai Bi Yi data, and population census data of a public security department of the area to be planned.
Further, in S300, the edge detection operator includes any one of Roberts Cross operator, Prewitt operator, Sobel operator, Kirsch operator, Marr-Hildreth operator, Canny operator, and Laplacian operator.
Further, in S400, the method for sequentially performing three times of internal partition edge detection on the corresponding position of each closed region in the grayscale map to obtain a plurality of newly partitioned closed regions in the thermodynamic map includes the following steps:
s401, taking the gray-scale image and the area image of the corresponding position of the closed area as an image to be adjusted; enhancing an image to be adjusted by an image sharpening method to obtain a primary sharpened image; carrying out edge detection on the primary sharpened image through an edge detection operator to obtain a plurality of primary subregions formed by edge lines;
s402, enhancing the primary sharpened image by an image sharpening method to obtain a secondary sharpened image; carrying out edge detection on the secondary sharpened image through an edge detection operator to obtain a plurality of secondary sub-regions consisting of edge lines in the primary sharpened image;
s403, enhancing the secondary sharpened image by an image sharpening method to obtain a tertiary sharpened image; carrying out edge detection on the three-level sharpened image through an edge detection operator to obtain a plurality of three-level sub-regions consisting of edge lines in the two-level sharpened image;
s404, dividing the gray scale map into a plurality of divided thermodynamic diagram closed areas according to the corresponding positions of the edge lines and the gray scale map by using all the edge lines of the closed area, the primary sub-area, the secondary sub-area and the tertiary sub-area.
Further, in S401, since the traditional image sharpening method cannot well highlight the circle of radial gradient presented by each data point in the population thermodynamic diagram, the improved image sharpening method can remove the circle presented by the data point with lighter gradient, that is, the data point with smaller population density data, so as to better distinguish the linear superposition structure of the population thermodynamic diagram for the edge detection operator to perform edge extraction, and therefore the improved image sharpening method is as follows:
the sharpened image Imsharp is obtained by sequentially and linearly superposing the pixels in the detail sub-image and the pixels of the original gray level image,
wherein IMA ═ Sharp/(1+ e)-0.25*Im) D, IMA is the pixel of the detail partial image converted after the sharpening in the pixel Im of the grey scale image, and the grey scale value of the detail partial image pixel IMA is kept at [0,255 ] for]Taking the sharpening degree Sharp as 255 and the sharpening difference D as 12;
the gray level image of the population thermodynamic diagram is sharpened through an image sharpening method, the edges of all linearly superposed circles in the gray level image are balanced, and the edges of all linearly superposed circles are mutually distinguished to be favorable for edge detection.
Further, in S500, the preset planned area is the actual planned area size of the set public facility, and the value is [10,2000] square meter.
Further, in S500, the method for sequentially calculating the gravity center point of each thermodynamic diagram closed region includes the following sub-steps:
s501, traversing each thermodynamic diagram closed region in sequence, and marking the thermodynamic diagram closed region with the average value of the gray values of all pixels larger than the average gray value of the gray diagram corresponding to each thermodynamic diagram closed region as a candidate planning region; (the principle is that the larger the grayscale value, the darker the color represents the more the population there is in the population thermodynamic diagram);
s502, recording all thermodynamic diagram closed regions directly adjacent to the candidate planning region as adjacent regions, judging whether the average gray value in each candidate planning region is larger than the average gray value of pixel points in all thermodynamic diagram closed regions directly adjacent to the candidate planning region or not when the average gray value in not less than one adjacent region is larger than the average gray value of a gray diagram corresponding to each thermodynamic diagram closed region, marking the candidate planning region as a planning region if the average gray value in the candidate planning region is larger than the average gray value of the pixel points in all thermodynamic diagram closed regions directly adjacent to the candidate planning region, and taking each obtained planning region as a planning region set;
s503, sequentially traversing each planning region in the planning region set, and deleting the planning regions from the planning region set if the average gray value of all pixel points in all adjacent regions of the planning regions is smaller than the average gray value of the gray image corresponding to each planning region; (in order to keep the continuity of each planning region in the planning region set, each planning region is provided with a communicated region as much as possible, isolated planning data points are prevented from appearing, the resource utilization of public setting is greatly saved, and the planning rationality is improved, so the isolated data points are screened and excluded from the population thermodynamic diagram);
and S504, sequentially calculating the gravity center point of each planning region in the planning region set or the center of mass of each planning region image as the gravity center point of each thermodynamic diagram closed region and outputting the gravity center points.
Further, in S600, sequentially scanning each population thermodynamic diagram in the thermodynamic diagram sequence, and selecting the population thermodynamic diagram with the largest intersection with the closed region of the thermodynamic diagrams in other population thermodynamic diagrams as the reference planning image includes the following steps:
setting i and j as natural numbers, setting the initial values of i and j as 1, and setting the value ranges of i and j as [1,24N/Gap ]; 24N/Gap is the number of the human mouth thermodynamic diagrams in the thermodynamic diagram sequence; setting k and m as natural numbers, setting the initial values of k and m as 1, and setting the value ranges of k and m as [1, Sum ]; wherein Sum is the total amount of thermodynamic diagram closed areas in the human mouth thermodynamic diagram;
s601, taking a kth thermodynamic diagram closed area in an ith population thermodynamic diagram in the thermodynamic diagram sequence as a comparison area; setting the initial value of the total intersection number of the ith population thermodynamic diagram to be 0;
s602, if j is less than or equal to 24N/Gap and m is less than or equal to Sum, judging whether the mth thermodynamic diagram closed area and the comparison area in the jth population thermodynamic diagram have an intersection area, namely judging whether the position of the mth thermodynamic diagram closed area on the population thermodynamic diagram and the position of the comparison area on the population thermodynamic diagram have a common intersection area, and if so, increasing the value of the total intersection number of the ith population thermodynamic diagram by 1;
s603, if m is smaller than Sum, increasing the value of m by 1 and going to S602; when m is greater than or equal to Sum, setting the value of m to 1 and going to S604;
s604, if j is smaller than 24N/Gap, increasing the value of j by 1 and turning to S602; when j is greater than or equal to 24N/Gap, setting the value of j to 1 and going to S605;
s605, if k is less than Sum, increasing the value of k by 1 and going to S601; when k is greater than or equal to Sum, setting the value of k to 1 and going to S606;
s606, if i is smaller than 24N/Gap, increasing the value of i by 1 and turning to S601; when i is greater than or equal to 24N/Gap, the step goes to S607;
and S607, selecting the population thermodynamic diagram with the maximum value of the total number of intersections in the thermodynamic diagram sequence as the reference planning image.
Further, in S800, taking the number of nodes to be planned in the planning node set in each thermodynamic diagram closed area as the number of concentration points, and selecting each thermodynamic diagram closed area with the largest number of concentration points in each thermodynamic diagram closed area with an intersection position in each population thermodynamic diagram to form a concentrated planning area set specifically includes the following steps:
s801, sequentially forming one or more closed thermodynamic areas having a common intersection area at the position of the closed thermodynamic area in each of the population thermodynamic diagrams, to form one or more different intersection area sets ZoneSet ═ ZoneSet-1、ZoneSet2、ZoneSetsum2Each element in ZoneSet is a different subset of intersection regions, where subscript Sum2 denotes the total number of sets of intersection regions; setting q as a natural number, setting the initial value of q as 1, and taking a value range of qIs enclosed as [1, Sum2]；
S802, calculating Zones set in turnqThe total number of nodes to be planned in each thermodynamic diagram closed area;
s803, if q is less than Sum2, increasing the value of q by 1 and going to S802, if q is greater than or equal to Sum2, setting the value of q to 1 and going to S804;
s804, selecting thermodynamic diagram closed regions with the maximum total number of nodes to be planned in each intersection region set in the intersection region set Zones to form a centralized planning region set (namely selecting Zones set respectively1、ZoneSet2、ZoneSetsum2The respective thermodynamic diagram closed area with the maximum total number of nodes to be planned constitutes a centralized planning area set).
Further, in S900, the client includes any one of a desktop computer, a tablet computer, an industrial PDA device, and a mobile phone.
An embodiment of the present invention provides a public facility site selection system based on GIS big data analysis, as shown in fig. 2, which is a structure diagram of the public facility site selection system based on GIS big data analysis, and the public facility site selection system based on GIS big data analysis of the embodiment includes: the GIS big data analysis-based public facility addressing system comprises a processor, a memory and a computer program which is stored in the memory and can run on the processor, wherein the processor executes the computer program to realize the steps in the GIS big data analysis-based public facility addressing system embodiment.
The system comprises: a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the computer program to run in the units of the following system:
the thermodynamic diagram sequence acquisition unit is used for acquiring population thermodynamic diagrams of the area to be planned for the last N days at each acquisition interval Gap, and the population thermodynamic diagrams form a thermodynamic diagram sequence according to the sequence of acquisition time;
the thermodynamic diagram preprocessing unit is used for carrying out graying on each population thermodynamic diagram in the thermodynamic diagram sequence to obtain a gray level diagram, and carrying out binarization on the gray level diagram to obtain a binarization diagram;
the initial edge detection unit is used for carrying out edge detection on each binary image through an edge detection operator, and each binary image respectively obtains a plurality of closed areas formed by edge lines;
the edge re-dividing unit is used for sequentially carrying out three times of internal partition edge detection on the corresponding positions of each closed region in each gray scale map to obtain a plurality of re-divided thermodynamic diagram closed regions;
the node to be planned calculation unit is used for sequentially calculating the gravity center points of all thermodynamic diagram closed areas in all population thermodynamic diagrams in the thermodynamic diagram sequence, and taking the gravity center points of all thermodynamic diagram closed areas with the areas larger than the preset planned area as nodes to be planned;
the reference image selecting unit is used for scanning each population thermodynamic diagram in the thermodynamic diagram sequence in sequence, and selecting the population thermodynamic diagram with the largest intersection with the closed region of the thermodynamic diagrams in other population thermodynamic diagrams as a reference planning image;
the planning node duplication removing unit is used for taking the nodes to be planned of all population thermodynamic diagrams in the thermodynamic diagram sequence as a node set to be planned; removing repeated nodes to be planned in the nodes to be planned;
the planning region selection unit is used for scanning each thermodynamic diagram closed region of each population thermodynamic diagram in the thermodynamic diagram sequence in sequence, taking the number of nodes to be planned in a node set to be planned in each thermodynamic diagram closed region as the number of concentration points, and selecting each thermodynamic diagram closed region with the largest number of concentration points from each thermodynamic diagram closed region with intersection positions of each population thermodynamic diagram to form a concentrated planning region set;
a result image output unit, which is used for dividing the reference planning image again by the boundary line of each thermodynamic diagram closed area in the centralized planning area set; and taking a node to be planned in the centralized planning region of the reference planning image as an address selection site, taking the area of each closed region of the thermodynamic diagrams in the population thermodynamic diagrams in the centralized planning region set as a building planning area, and outputting the address selection site and the building planning area to a GIS map corresponding to the region to be planned to a client for display or outputting the GIS map to a database for storage.
The GIS big data analysis-based public facility site selection system can be operated in computing equipment such as desktop computers, notebooks, palm computers and cloud servers. The GIS big data analysis-based public facility site selection system can be operated by a system comprising but not limited to a processor and a memory. Those skilled in the art will appreciate that the example is merely an example of a GIS big data analysis based utility addressing system, and does not constitute a limitation of a GIS big data analysis based utility addressing system, and may include more or less components than the other, or some components in combination, or different components, for example, the GIS big data analysis based utility addressing system may further include input and output devices, network access devices, buses, etc.
The Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, or the like. The general processor can be a microprocessor or the processor can be any conventional processor and the like, the processor is a control center of the GIS big data analysis-based public facility site selection system operation system, and various interfaces and lines are utilized to connect all parts of the whole GIS big data analysis-based public facility site selection system operable system.
The memory may be used to store the computer programs and/or modules, and the processor may implement the various functions of the GIS big data analysis-based utility addressing system by running or executing the computer programs and/or modules stored in the memory and calling the data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the cellular phone, and the like. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.
Although the present invention has been described in considerable detail and with reference to certain illustrated embodiments, it is not intended to be limited to any such details or embodiments or any particular embodiment, so as to effectively encompass the intended scope of the invention. Furthermore, the foregoing describes the invention in terms of embodiments foreseen by the inventor for which an enabling description was available, notwithstanding that insubstantial modifications of the invention, not presently foreseen, may nonetheless represent equivalent modifications thereto.
Claims (9)
1. A GIS big data analysis-based public facility site selection method is characterized by comprising the following steps:
s100, collecting population thermodynamic diagrams of an area to be planned for the last N days at intervals of one collection interval Gap, wherein the population thermodynamic diagrams form thermodynamic diagram sequences according to the sequence of collection time;
s200, graying each population thermodynamic diagram in the thermodynamic diagram sequence to obtain a gray level diagram, and binarizing the gray level diagram to obtain a binary diagram;
s300, performing edge detection on each binary image through an edge detection operator, wherein each binary image respectively obtains a plurality of closed areas formed by edge lines;
s400, sequentially carrying out three times of internal partition edge detection on corresponding positions of each closed region in each gray scale map to obtain a plurality of divided closed regions of the thermodynamic diagram;
s500, sequentially calculating the gravity center point of each thermodynamic diagram closed region in each population thermodynamic diagram in the thermodynamic diagram sequence, and taking the gravity center point of each thermodynamic diagram closed region with the area larger than a preset planning area as a node to be planned;
s600, sequentially scanning each population thermodynamic diagram in the thermodynamic diagram sequence, and selecting the population thermodynamic diagram with the most intersection with the closed region of the thermodynamic diagrams in other population thermodynamic diagrams as a reference planning image;
s700, taking the nodes to be planned of all population thermodynamic diagrams in the thermodynamic diagram sequence as a node set to be planned; removing repeated nodes to be planned in the nodes to be planned;
s800, scanning each thermodynamic diagram closed area of each population thermodynamic diagram in the thermodynamic diagram sequence in sequence, taking the number of nodes to be planned in a planning node set in each thermodynamic diagram closed area as the number of concentration points, and selecting each thermodynamic diagram closed area with the largest number of concentration points from each thermodynamic diagram closed area with intersection positions in each population thermodynamic diagram closed area to form a concentrated planning area set;
s900, dividing the reference planning image again by the boundary line of each thermodynamic diagram closed region in the centralized planning region set; and taking a node to be planned in the centralized planning region of the reference planning image as an address selection site, taking the area of each closed region of the thermodynamic diagrams in the population thermodynamic diagrams in the centralized planning region set as a building planning area, and outputting the address selection site and the building planning area to a GIS map corresponding to the region to be planned to a client for display or outputting the GIS map to a database for storage.
2. The GIS big data analysis-based public facility site selection method as claimed in claim 1, wherein in S100, the population thermodynamic diagram of the area to be planned is collected by any one of a mobile phone signaling population thermodynamic diagram, a WeChat trip population thermodynamic diagram, a Baidu map population thermodynamic diagram, Baidu Huiyan data and population census data of a public security department.
3. The GIS big data analysis-based public facility site selection method according to claim 1, wherein in S300, the edge detection operator comprises any one of a Roberts Cross operator, a Prewitt operator, a Sobel operator, a Kirsch operator, a Marr-Hildreth operator, a Canny operator, and a Laplacian operator.
4. The GIS big data analysis-based public facility addressing method according to claim 1, wherein in S400, the method for obtaining the plurality of subdivided thermodynamic diagram closed regions by sequentially performing three times of internal partition edge detection on the corresponding positions of each closed region in the gray-scale map comprises the following steps:
s401, taking the gray-scale image and the area image of the corresponding position of the closed area as an image to be adjusted; enhancing an image to be adjusted by an image sharpening method to obtain a primary sharpened image; carrying out edge detection on the primary sharpened image through an edge detection operator to obtain a plurality of primary subregions formed by edge lines;
s402, enhancing the primary sharpened image by an image sharpening method to obtain a secondary sharpened image; carrying out edge detection on the secondary sharpened image through an edge detection operator to obtain a plurality of secondary sub-regions consisting of edge lines in the primary sharpened image;
s403, enhancing the secondary sharpened image by an image sharpening method to obtain a tertiary sharpened image; carrying out edge detection on the three-level sharpened image through an edge detection operator to obtain a plurality of three-level sub-regions consisting of edge lines in the two-level sharpened image;
s404, dividing the gray scale map into a plurality of divided thermodynamic diagram closed areas according to the corresponding positions of the edge lines and the gray scale map by using all the edge lines of the closed area, the primary sub-area, the secondary sub-area and the tertiary sub-area.
5. The GIS big data analysis-based public facility locating method according to claim 4, wherein in S401, the image sharpening method is as follows:
sharpened image Imsharp = Im + IMA, wherein,
IMA is the pixel value of the detail sub-image converted after being clarified in the pixel value Im of the gray image, Sharp is the sharpening degree, Sharp takes a value of 255, D is the sharpening difference value, and D takes a value of 12.
6. The GIS big data analysis-based public facility site selection method according to claim 4, wherein in S500, the method for sequentially calculating the gravity center points of the thermodynamic diagram closed areas comprises the following sub-steps:
s501, traversing each thermodynamic diagram closed region in sequence, and marking the thermodynamic diagram closed region with the average value of the gray values of all pixels in each thermodynamic diagram closed region larger than the average gray value of the gray diagram corresponding to each thermodynamic diagram closed region as a candidate planning region;
s502, recording all thermodynamic diagram closed regions directly adjacent to the candidate planning region as adjacent regions, judging whether the average gray value in each candidate planning region is larger than the average gray value of pixel points in all thermodynamic diagram closed regions directly adjacent to the candidate planning region or not when the average gray value in not less than one adjacent region is larger than the average gray value of a gray diagram corresponding to each thermodynamic diagram closed region, marking the candidate planning region as a planning region if the average gray value in the candidate planning region is larger than the average gray value of the pixel points in all thermodynamic diagram closed regions directly adjacent to the candidate planning region, and taking each obtained planning region as a planning region set;
s503, sequentially traversing each planning region in the planning region set, and deleting the planning regions from the planning region set if the average gray value of all pixel points in all adjacent planning regions of the planning regions is smaller than the average gray value of the gray map corresponding to each planning region;
and S504, sequentially calculating the gravity center point of each planning region in the planning region set or the center of mass of each planning region image as the gravity center point of each thermodynamic diagram closed region and outputting the gravity center points.
7. The GIS big data analysis-based public facility addressing method according to claim 6, wherein in S600, each population thermodynamic diagram in the thermodynamic diagram sequence is scanned in turn, and the population thermodynamic diagram with the most intersection with the thermodynamic diagram closed area in other population thermodynamic diagrams is selected as the reference planning image, and the method comprises the following steps:
setting i and j as natural numbers, setting the initial values of i and j as 1, and setting the value ranges of i and j as [1,24N/Gap ]; 24N/Gap is the number of the human mouth thermodynamic diagrams in the thermodynamic diagram sequence; setting k and m as natural numbers, setting the initial values of k and m as 1, and setting the value ranges of k and m as [1, Sum ]; wherein Sum is the total amount of thermodynamic diagram closed areas in the human mouth thermodynamic diagram;
s601, taking a kth thermodynamic diagram closed area in an ith population thermodynamic diagram in the thermodynamic diagram sequence as a comparison area; setting the initial value of the total intersection number of the ith population thermodynamic diagram to be 0;
s602, if j is less than or equal to 24N/Gap and m is less than or equal to Sum, judging whether the mth thermodynamic diagram closed area and the comparison area in the jth population thermodynamic diagram have an intersection area, namely judging whether the position of the mth thermodynamic diagram closed area on the population thermodynamic diagram and the position of the comparison area on the population thermodynamic diagram have a common intersection area, and if so, increasing the value of the total intersection number of the ith population thermodynamic diagram by 1;
s603, if m is smaller than Sum, increasing the value of m by 1 and going to S602; when m is greater than or equal to Sum, setting the value of m to 1 and going to S604;
s604, if j is smaller than 24N/Gap, increasing the value of j by 1 and turning to S602; when j is greater than or equal to 24N/Gap, setting the value of j to 1 and going to S605;
s605, if k is less than Sum, increasing the value of k by 1 and going to S601; when k is greater than or equal to Sum, setting the value of k to 1 and going to S606;
s606, if i is smaller than 24N/Gap, increasing the value of i by 1 and turning to S601; when i is greater than or equal to 24N/Gap, the step goes to S607;
and S607, selecting the population thermodynamic diagram with the maximum value of the total number of intersections in the thermodynamic diagram sequence as the reference planning image.
8. The method for public facility addressing based on GIS big data analysis according to claim 7, wherein in S800, the number of nodes to be planned in the planning node set in each thermodynamic diagram closed area is taken as the number of concentration points, and the method for selecting each thermodynamic diagram closed area with the largest number of concentration points from the thermodynamic diagram closed areas with intersection positions in each population thermodynamic diagram to form the concentrated planning area set specifically comprises the following steps:
s801, sequentially forming one or more different intersection region sets Zones = { Zones set by the thermodynamic diagram closed regions with common intersection regions at the positions of the thermodynamic diagram closed regions on each population thermodynamic diagram1、ZoneSet2、ZoneSetsum2Each element in ZoneSet is a different subset of intersection regions, where subscript Sum2 denotes the total number of sets of intersection regions; setting q as a natural number, setting the initial value of q as 1, and setting the value range of q as [1, Sum2 ]]；
S802, calculating Zones set in turnqThe total number of nodes to be planned in each thermodynamic diagram closed area;
s803, if q is less than Sum2, increasing the value of q by 1 and going to S802, if q is greater than or equal to Sum2, setting the value of q to 1 and going to S804;
s804, selecting the thermodynamic diagram closed region with the maximum total number of the nodes to be planned in each intersection region set in the intersection region set Zoneset to form a centralized planning region set.
9. A GIS big data analysis-based public facility site selection system, which is characterized by comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the computer program to run in the units of the following system:
the thermodynamic diagram sequence acquisition unit is used for acquiring population thermodynamic diagrams of the area to be planned for the last N days at each acquisition interval Gap, and the population thermodynamic diagrams form a thermodynamic diagram sequence according to the sequence of acquisition time;
the thermodynamic diagram preprocessing unit is used for carrying out graying on each population thermodynamic diagram in the thermodynamic diagram sequence to obtain a gray level diagram, and carrying out binarization on the gray level diagram to obtain a binarization diagram;
the initial edge detection unit is used for carrying out edge detection on each binary image through an edge detection operator, and each binary image respectively obtains a plurality of closed areas formed by edge lines;
the edge re-dividing unit is used for sequentially carrying out three times of internal partition edge detection on the corresponding positions of each closed region in each gray scale map to obtain a plurality of re-divided thermodynamic diagram closed regions;
the node to be planned calculation unit is used for sequentially calculating the gravity center points of all thermodynamic diagram closed areas in all population thermodynamic diagrams in the thermodynamic diagram sequence, and taking the gravity center points of all thermodynamic diagram closed areas with the areas larger than the preset planned area as nodes to be planned;
the reference image selecting unit is used for scanning each population thermodynamic diagram in the thermodynamic diagram sequence in sequence, and selecting the population thermodynamic diagram with the largest intersection with the closed region of the thermodynamic diagrams in other population thermodynamic diagrams as a reference planning image;
the planning node duplication removing unit is used for taking the nodes to be planned of all population thermodynamic diagrams in the thermodynamic diagram sequence as a node set to be planned; removing repeated nodes to be planned in the nodes to be planned;
the planning region selection unit is used for scanning each thermodynamic diagram closed region of each population thermodynamic diagram in the thermodynamic diagram sequence in sequence, taking the number of nodes to be planned in a node set to be planned in each thermodynamic diagram closed region as the number of concentration points, and selecting each thermodynamic diagram closed region with the largest number of concentration points from each thermodynamic diagram closed region with intersection positions of each population thermodynamic diagram to form a concentrated planning region set;
a result image output unit, which is used for dividing the reference planning image again by the boundary line of each thermodynamic diagram closed area in the centralized planning area set; and taking a node to be planned in the centralized planning region of the reference planning image as an address selection site, taking the area of each closed region of the thermodynamic diagrams in the population thermodynamic diagrams in the centralized planning region set as a building planning area, and outputting the address selection site and the building planning area to a GIS map corresponding to the region to be planned to a client for display or outputting the GIS map to a database for storage.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011556430.3A CN112669340B (en) | 2020-12-23 | 2020-12-23 | GIS big data analysis-based public facility site selection method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011556430.3A CN112669340B (en) | 2020-12-23 | 2020-12-23 | GIS big data analysis-based public facility site selection method and system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112669340A CN112669340A (en) | 2021-04-16 |
| CN112669340B true CN112669340B (en) | 2021-08-20 |
Family
ID=75408746
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011556430.3A Active CN112669340B (en) | 2020-12-23 | 2020-12-23 | GIS big data analysis-based public facility site selection method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112669340B (en) |
Families Citing this family (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113313315B (en) * | 2021-06-11 | 2024-05-28 | 北京中农大农业规划设计院有限公司 | GIS-based agricultural product origin wholesale market planning method and system |
| CN113626426B (en) * | 2021-07-06 | 2022-06-14 | 佛山市禅城区政务服务数据管理局 | Method and system for collecting and transmitting ecological grid data |
| CN115880323B (en) * | 2023-02-17 | 2023-06-02 | 长沙中联重科环境产业有限公司 | Greening environment-friendly method and equipment for regional density population positioned by thermal imaging |
| CN117114210B (en) * | 2023-10-24 | 2024-02-27 | 广州市城市规划勘测设计研究院 | Barrier-free public facility layout optimization method, device, equipment and storage medium |
Family Cites Families (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105260796A (en) * | 2015-10-20 | 2016-01-20 | 深圳市佐学科技有限公司 | Large scale industrial meta-heuristic multi-addressing system |
| CN107316098B (en) * | 2017-05-19 | 2021-03-30 | 安徽智博新材料科技有限公司 | Automobile leasing point addressing method based on user behavior analysis |
| CN108319650B (en) * | 2017-12-28 | 2020-07-24 | 浙江大学 | Interactive visual house site selection system |
| CN108519094B (en) * | 2018-02-11 | 2022-03-25 | 华为技术有限公司 | Local path planning method and cloud processing terminal |
| US10853865B2 (en) * | 2018-07-09 | 2020-12-01 | Mastercard International Incorporated | Systems and methods for dynamically determining activity levels in a selected geographical region |
| CN109214669B (en) * | 2018-08-24 | 2024-03-29 | 佛山市城市规划设计研究院 | Urban park green space monitoring system |
| CN110517487B (en) * | 2019-08-16 | 2020-11-06 | 重庆特斯联智慧科技股份有限公司 | Urban area traffic resource regulation and control method and system based on thermodynamic diagram change identification |
| CN111125490B (en) * | 2019-11-15 | 2020-11-20 | 广州市城市规划勘测设计研究院 | Population activity number extraction method, device and medium based on Baidu thermodynamic diagram |
| CN111144693B (en) * | 2019-11-27 | 2023-08-22 | 中建科技有限公司 | Decision method, device and computer readable storage medium for urban public toilet site selection |
| CN111062970A (en) * | 2019-12-10 | 2020-04-24 | 广州电力工程监理有限公司 | Track generation method and system based on thermodynamic diagram |
| CN111432417B (en) * | 2020-03-27 | 2021-07-16 | 哈尔滨工业大学 | Sports center site selection method based on mobile phone signaling data |
-
2020
- 2020-12-23 CN CN202011556430.3A patent/CN112669340B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN112669340A (en) | 2021-04-16 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112669340B (en) | GIS big data analysis-based public facility site selection method and system | |
| Hecht et al. | Automatic identification of building types based on topographic databases–a comparison of different data sources | |
| Lloyd | Local models for spatial analysis | |
| Wilson et al. | Development of a geospatial model to quantify, describe and map urban growth | |
| Turker et al. | Building‐based damage detection due to earthquake using the watershed segmentation of the post‐event aerial images | |
| CN110598541B (en) | Method and equipment for extracting road edge information | |
| CN109493119B (en) | POI data-based urban business center identification method and system | |
| Kim et al. | Pycnophylactic interpolation revisited: integration with the dasymetric-mapping method | |
| Nduwayezu et al. | Modeling urban growth in Kigali city Rwanda | |
| Richter et al. | Object class segmentation of massive 3D point clouds of urban areas using point cloud topology | |
| Bayram et al. | A novel algorithm for coastline fitting through a case study over the Bosphorus | |
| Rubio et al. | Adaptive non-parametric identification of dense areas using cell phone records for urban analysis | |
| Bhuyan et al. | Mapping and characterising buildings for flood exposure analysis using open-source data and artificial intelligence | |
| Šimbera | Neighborhood features in geospatial machine learning: the case of population disaggregation | |
| CN114662774A (en) | City block vitality prediction method, storage medium and terminal | |
| Drobnjaković et al. | Re-thinking rurality: Towards a new research approach and rural-urban spatial gradient establishment in Serbia | |
| CN113379269A (en) | Urban business function zoning method, device and medium for multi-factor spatial clustering | |
| CN111310340A (en) | Urban area interaction abnormal relation identification method and equipment based on human movement | |
| Miller et al. | An object extraction approach for impervious surface classification with very-high-resolution imagery | |
| US9483846B2 (en) | Data interpolation and classification method for map data visualization | |
| Martínez-Navarro et al. | The earliest Ethiopian wolf: implications for the species evolution and its future survival | |
| CN114359352A (en) | Image processing method, apparatus, device, storage medium, and computer program product | |
| Yin et al. | Disaggregation of an urban population with M_IDW interpolation and building information | |
| Svoray | Integrating automatically processed SPOT HRV Pan imagery in a DEM-based procedure for channel network extraction | |
| Hagen et al. | Discretization of urban areas using POI-based tesselation |
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 | ||
| TA01 | Transfer of patent application right | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20210721 Address after: 528000 floor 26, block B, Vanke financial center, No. 57, Jihua fifth road, Chancheng District, Foshan City, Guangdong Province (residence declaration) Applicant after: Foshan planning and Testing Institute Co.,Ltd. Address before: 528000 No.62, Lingnan Avenue North, Chancheng District, Foshan City, Guangdong Province Applicant before: FOSHAN URBAN PLANNING AND DESIGN INSTITUTE |
|
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| TR01 | Transfer of patent right | ||
| TR01 | Transfer of patent right |
Effective date of registration: 20240416 Address after: 528000, 26th Floor, Building B, Vanke Financial Center, No. 57 Jihua Fifth Road, Zumiao Street, Chancheng District, Foshan City, Guangdong Province (Residence Declaration) Patentee after: Foshan Urban Planning and Design Research Institute Co.,Ltd. Country or region after: China Address before: 528000 floor 26, block B, Vanke financial center, No. 57, Jihua fifth road, Chancheng District, Foshan City, Guangdong Province (residence declaration) Patentee before: Foshan planning and Testing Institute Co.,Ltd. Country or region before: China |