CN105912598A - Method and system for determining high-frequency regions for roadside stall business in urban streets - Google Patents
Method and system for determining high-frequency regions for roadside stall business in urban streets Download PDFInfo
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- CN105912598A CN105912598A CN201610206554.6A CN201610206554A CN105912598A CN 105912598 A CN105912598 A CN 105912598A CN 201610206554 A CN201610206554 A CN 201610206554A CN 105912598 A CN105912598 A CN 105912598A
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- street
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/28—Databases characterised by their database models, e.g. relational or object models
- G06F16/284—Relational databases
- G06F16/285—Clustering or classification
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/29—Geographical information databases
Abstract
The invention relates to a method and system for determining high-frequency regions for roadside stall business in urban streets. The method comprises following steps: utilizing an optimized DBSCAN algorithm to process acquired data of roadside stall business in urban streets and classifying regions for roadside stall business in urban streets; and determining high-frequency regions for roadside stall business in urban streets based on classification results of regions for roadside stall business, wherein the optimized DBSCAN algorithm is used for selecting a point outside of the neighbor radius for pre-set threshold value of a current data object as a next data object to be processed. The method and system for determining high-frequency regions for roadside stall business in urban streets have following beneficial effects: the optimized DBSCAN algorithm is adopted to select the point outside of the neighborhood radius for pre-set threshold value of the current data object as the next data object to be processed; repeated search of objects in public neighborhood is avoided; calculations of neighborhood search for core objects are reduced; complexity of the algorithm is decreased; and determination efficiency of high-frequency regions for roadside stall business in urban streets can be increased for providing information and making decisions for urban management work.
Description
Technical field
The present invention relates to Spatial Data Mining Technique field, particularly relate to one and determine avenue
The method and system in occupy-street-exploit region occurred frequently.
Background technology
Along with the fast development of economic society, city, various places occupy-street-exploit phenomenon of breaking rules and regulations is prohibited not repeatly
Only, it is difficult to radical cure, this directly affects the lifting of urban civilization degree, affects the periphery masses
Quality of life.In city appearance environment works, occupy-street-exploit has become as city city appearance environment and defends
The big persistent ailment of on students management one.Governments at all levels and functional department take various means and control
Reason, but produce little effect.Spatial Data Mining refer to extract from spatial database in advance unknown,
Potentially useful, implicit wherein, final intelligible space or the general knowledge rule of non-space
Process.With foundation and the operation of various places urban griddization management system, will certainly produce big
Amount city management related data, utilizes Spatial Data Mining algorithm can have these data
Effect is analyzed, and finds out the region occurred frequently of avenue occupy-street-exploit.
DBSCAN algorithm is a kind of Spatial Data Clustering algorithm based on density, by Ester
Martin et al. proposes.Its basic thought is: for a point in spatial database, giving
As long as its data object number comprised is more than certain set-point in determining the neighborhood of radius, continue to
Cluster.Existing determine avenue occupy-street-exploit region occurred frequently by DBSCAN algorithm
Method time complexity is high, efficiency is low.
Summary of the invention
The technical problem to be solved is: existing determine that avenue occupy-street-exploit is high
Send out the method time complexity problem high, inefficient in region.
For solving above-mentioned technical problem, one aspect of the present invention proposes one and determines that avenue accounts for
The method in region occurred frequently is managed in road, and the method includes:
Use the DBSCAN algorithm optimized that the avenue occupy-street-exploit data collected are entered
Row processes, and classifies avenue occupy-street-exploit region；
Determine that avenue accounts for according to the described classification results to avenue occupy-street-exploit region
Region occurred frequently is managed in road；
Wherein, the DBSCAN algorithms selection current data object predetermined threshold value neighborhood of described optimization
Point outside radius is as next pending data object.
Alternatively, use the DBSCAN algorithm optimized that the avenue collected is accounted for described
Before road management data processes, also include:
Gather avenue occupy-street-exploit data, and described avenue occupy-street-exploit data are deposited
Storage is in data set.
Alternatively, described avenue occupy-street-exploit data include scene, time of origin and
Event Description.
Alternatively, use the DBSCAN algorithm optimized that the avenue collected is accounted for described
Before road management data processes, also include:
Determine radius of neighbourhood EPS and density threshold MinPts of the DBSCAN algorithm of optimization.
Alternatively, the DBSCAN algorithm that described employing the optimizes avenue road occupying to collecting
Management data processes, and avenue occupy-street-exploit region is carried out classification and includes:
Using each avenue occupy-street-exploit data as data object, if current data object is
Kernel object, then be classified as a class by the point in the predetermined threshold value radius of neighbourhood of current data object,
And the point outside the predetermined threshold value radius of neighbourhood of current data object is stored in search data acquisition system S
In；
Traversal search data acquisition system S, classifies to the data object in search data acquisition system S.
Alternatively, described predetermined threshold value is less than or equal to 3/4 more than or equal to 2/3.
Another aspect of the present invention proposes and a kind of determines avenue occupy-street-exploit region occurred frequently
System, this system includes:
Territorial classification unit, is used for the DBSCAN algorithm the using optimization city street to collecting
Road occupy-street-exploit data process, and classify avenue occupy-street-exploit region；
Area determination unit occurred frequently, for dividing avenue occupy-street-exploit region according to described
Class result determines avenue occupy-street-exploit region occurred frequently；
Wherein, the DBSCAN algorithms selection current data object predetermined threshold value neighborhood of described optimization
Point outside radius is as next pending data object.
Alternatively, this system also includes:
Occupy-street-exploit data storage cell, is used for gathering avenue occupy-street-exploit data, and will
Described avenue occupy-street-exploit data are stored in data set.
Alternatively, described avenue occupy-street-exploit data include scene, time of origin and
Event Description.
Alternatively, described territorial classification unit is further used for:
Using each avenue occupy-street-exploit data as data object, if current data object is
Kernel object, then be classified as a class by the point in the predetermined threshold value radius of neighbourhood of current data object,
And the point outside the predetermined threshold value radius of neighbourhood of current data object is stored in search data acquisition system S
In；
Traversal search data acquisition system S, classifies to the data object in search data acquisition system S.
The method and system in the determination avenue occupy-street-exploit region occurred frequently that the present invention provides,
Use at the DBSCAN algorithm the optimized avenue occupy-street-exploit data to collecting
Reason, selects the point outside the current data object predetermined threshold value radius of neighbourhood pending as the next one
Data object, it is to avoid public neighborhood object repeat inquiry, reduce kernel object neighborhood is looked into
The calculating ask, reduces the complexity of algorithm, improves and can determine that the occurred frequently of street occupy-street-exploit
The efficiency in region, for city management work offer information and aid decision.
Accompanying drawing explanation
By being more clearly understood from the features and advantages of the present invention with reference to accompanying drawing, accompanying drawing is to show
Meaning property and should not be construed as the present invention is carried out any restriction, in the accompanying drawings:
Fig. 1 shows the determination avenue occupy-street-exploit region occurred frequently of one embodiment of the invention
The schematic flow sheet of method；
Fig. 2 show the DBSCAN algorithm of the optimization of one embodiment of the invention to public number
The schematic diagram limiting inquiry according to object；
Fig. 3 shows the flow chart of the DBSCAN algorithm of the optimization of one embodiment of the invention；
Fig. 4 a illustrate 1 year in the Two dimensional Distribution schematic diagram of somewhere street occupy-street-exploit event；
Fig. 4 b shows that the occupy-street-exploit that the DBSCAN algorithm according to optimization of the present invention is found out is high
Send out the result schematic diagram in region；
Fig. 5 shows the determination avenue occupy-street-exploit region occurred frequently of one embodiment of the invention
The structural representation of system.
Detailed description of the invention
Below in conjunction with accompanying drawing, embodiments of the present invention is described in detail.
Fig. 1 shows the determination avenue occupy-street-exploit region occurred frequently of one embodiment of the invention
The schematic flow sheet of method.As it is shown in figure 1, the determination avenue road occupying warp of this embodiment
The method seeking region occurred frequently, including:
S11: use the DBSCAN algorithm the optimized avenue occupy-street-exploit number to collecting
According to processing, is classified in avenue occupy-street-exploit region；
S12: determine city street according to the described classification results to avenue occupy-street-exploit region
Road occupy-street-exploit region occurred frequently；
Wherein, the DBSCAN algorithms selection current data object predetermined threshold value neighborhood of described optimization
Point outside radius is as next pending data object.
The method in the determination avenue occupy-street-exploit region occurred frequently of the present embodiment, uses and optimizes
DBSCAN algorithm the avenue occupy-street-exploit data collected are processed, select
Point outside the current data object predetermined threshold value radius of neighbourhood is as next pending data pair
Repeat inquiry as, it is to avoid public neighborhood object, reduce the meter to kernel object Region Queries
Calculating, reduce the complexity of algorithm, raising can determine that the region occurred frequently of street occupy-street-exploit
Efficiency, for city management work offer information and aid decision.
In the optional embodiment of one, at the described DBSCAN algorithm pair using and optimizing
Before the avenue occupy-street-exploit data collected process, also include:
Gather avenue occupy-street-exploit data, and described avenue occupy-street-exploit data are deposited
Storage is in data set.
Further, described avenue occupy-street-exploit data include scene, time of origin
And Event Description.
The DBSCAN algorithm the optimized avenue occupy-street-exploit to collecting is used described
Before data process, also include:
Determine radius of neighbourhood EPS and density threshold MinPts of the DBSCAN algorithm of optimization.
Specifically, the DBSCAN algorithm that described employing the optimizes avenue road occupying to collecting
Management data processes, and avenue occupy-street-exploit region is carried out classification and includes:
Using each avenue occupy-street-exploit data as data object, if current data object is
Kernel object, then be classified as a class by the point in the predetermined threshold value radius of neighbourhood of current data object,
And the point outside the predetermined threshold value radius of neighbourhood of current data object is stored in search data acquisition system S
In；
Traversal search data acquisition system S, classifies to the data object in search data acquisition system S.
Preferably, described predetermined threshold value is less than or equal to 3/4 more than or equal to 2/3.
In actual applications, the method in complete determination avenue occupy-street-exploit region occurred frequently can
To comprise the following steps:
Step 1: data acquisition, city grid Regional Admin reports the daily road occupying warp of discovery
Battalion's event, grid-based management system storage dependent event, event attribute includes event category,
Scene (two-dimensional coordinate position), time of origin, event detailed description etc..By grid pipe
Occupy-street-exploit event that reason person finds in daily inspection and the occupy-street-exploit that department of municipal administration receives
The relevant report of event, extracts event category, scene, time of origin, event specifically
The information such as bright, uploads to grid-based management system.
Business datum in grid-based management system is screened, for road occupying warp therein
Battalion's event, according to venue location point, time of origin, stores data base by association attributes
In.Ready for next step data mining exercises.
Step 2: parameter is arranged, according to urban road and block scale, sets DBSCAN
The radius of neighbourhood EPS of algorithm and density threshold MinPts.
The algorithm radius of neighbourhood arrange typically with reference to urban road length, densely populated degree,
The factors such as block scale.Density threshold MinPts is typically added up road occupying warp by relevant departments
The Frequency of battalion's event is arranged.
Step 3: use the DBSCAN algorithm the optimized avenue road occupying warp to collecting
Battalion's Transaction Information processes.
The basic thought of DBSCAN algorithm optimization is: carry out neighborhood at the point concentrating data
Each point need not be carried out inquiry operation during inquiry.As it is shown in figure 1, current data object p
After end of operation, first get rid of current data object before selecting next pending data object pre-
If the point in the threshold value radius of neighbourhood, select the point in neighborhood other regions interior, it is to avoid part data
Object repeat inquiry, thus reduce query time.Distance between two data objects is adopted
Use Euclid's mathematical model, as follows:
Wherein d (x, y) meets three below condition:
D (x, y) >=0,
As x=y, d (x, y)=0,
D (x, y)=d (y, x)；
Wherein, x, y represent two data objects, it will be appreciated that be two points, as x=y,
When i.e. x and y represents at same, and d (x, y)=0, i.e. distance between x and y is 0.
((y x) represents that (x, y) (y x) represents the distance between x and y point to d with d to d for x, y)=d.
Step 4: cluster analysis result is informed manager intuitively by program visualization,
Manager is facilitated to make resolution.The data collected according to step 1, extract occupy-street-exploit event
Two-dimensional coordinate attribute, draw out occupy-street-exploit event two-dimensional distribution, as shown in fig. 4 a.Warp
Data set after being processed by step 3, according to ID attribute labelling (classification of each data object),
To obtain that cluster result is visual feeds back to management personnel, as Fig. 4 b shows.
Fig. 3 shows the flow chart of the DBSCAN algorithm of the optimization of one embodiment of the invention.
As it is shown on figure 3, the DBSCAN algorithm of the optimization of the present embodiment comprises the steps:
(1) the initialization of raw data set
1. in the data structure definition of data object, add a new Field ID, represent poly-
Class classification results, is initialized as zero.
2. one interim search data acquisition system S of definition, for search result storage.
3. density threshold MinPts and radius of neighbourhood EPS are initialized.
(2) travel through raw data set
1. each point of data set is investigated as seed node successively, make i=1, j=1,
Cluster=1.For existing object qjIf, qjID=0, then search for its neighborhood, if neighborhood
Inside comprise and count more than density threshold MinPts, illustrate that it is kernel object, qjAnd neighborhood
The ID of interior all objects is set to Cluster, and by qjIts 2/3 radius of neighbourhood of distance in neighborhood
(predetermined threshold value of the present embodiment is 2/3, and in actual applications, predetermined threshold value may be greater than
The value less than or equal to 3/4 equal to 2/3) being stored in a little in S outward.
2. S is traveled through, each point in S is investigated successively as seed points,
For a piIf, piCurrently it is not belonging to any class i.e. pi.ID=0, its neighborhood is searched for.If piIt is
Kernel object, illustrates that it is qjDirect density accessible point, make pi.ID=Cluster, will simultaneously
In its neighborhood, the ID of all objects is set to Cluster, and by piIts 2/3 neighbour of distance in neighborhood
Being stored in a little in S outside the radius of territory.Finally delete the p in Si。
3. make i=i+1, perform step 2. until S is empty.
(3) make j=j+1, Cluster=Cluster+1, repeat step (2), until data are concentrated
All objects are all traveled through.
Outside the DBSCAN algorithms selection current data object predetermined threshold value radius of neighbourhood optimized
Point as next pending data object, it is to avoid public neighborhood object repeat inquiry, subtract
Few calculating to kernel object Region Queries, reduces the complexity of algorithm, and raising can determine that
The efficiency in the region occurred frequently of street occupy-street-exploit, for city management work offer information and auxiliary
Decision-making.
Fig. 5 shows the determination avenue occupy-street-exploit region occurred frequently of one embodiment of the invention
The structural representation of system.As it is shown in figure 5, the determination avenue road occupying warp of this embodiment
The system seeking region occurred frequently includes territorial classification unit 51 and area determination unit occurred frequently 52, specifically
Ground,
Territorial classification unit 51, is used for the DBSCAN algorithm the using optimization city to collecting
Street occupy-street-exploit data process, and classify avenue occupy-street-exploit region；
Area determination unit 52 occurred frequently, for according to described to avenue occupy-street-exploit region
Classification results determines avenue occupy-street-exploit region occurred frequently；
Wherein, the DBSCAN algorithms selection current data object predetermined threshold value neighborhood of described optimization
Point outside radius is as next pending data object.
In the optional embodiment of one, this system also includes:
Occupy-street-exploit data storage cell, is used for gathering avenue occupy-street-exploit data, and will
Described avenue occupy-street-exploit data are stored in data set.
Further, described avenue occupy-street-exploit data include scene, time of origin
And Event Description.
Specifically, territorial classification unit 51 is further used for:
Using each avenue occupy-street-exploit data as data object, if current data object is
Kernel object, then be classified as a class by the point in the predetermined threshold value radius of neighbourhood of current data object,
And the point outside the predetermined threshold value radius of neighbourhood of current data object is stored in search data acquisition system S
In；
Traversal search data acquisition system S, classifies to the data object in search data acquisition system S.
The system in the determination avenue occupy-street-exploit region occurred frequently described in the present embodiment can be used
In performing said method embodiment, its principle is similar with technique effect, and here is omitted.
The method and system in the determination avenue occupy-street-exploit region occurred frequently that the present invention provides,
Use at the DBSCAN algorithm the optimized avenue occupy-street-exploit data to collecting
Reason, selects the point outside the current data object predetermined threshold value radius of neighbourhood pending as the next one
Data object, it is to avoid public neighborhood object repeat inquiry, reduce kernel object neighborhood is looked into
The calculating ask, reduces the complexity of algorithm, improves and can determine that the occurred frequently of street occupy-street-exploit
The efficiency in region, for city management work offer information and aid decision.
Although being described in conjunction with the accompanying embodiments of the present invention, but those skilled in the art can
To make various modifications and variations without departing from the spirit and scope of the present invention, so
Amendment and within the scope of modification each falls within and is defined by the appended claims.
Claims (10)
1. the method determining avenue occupy-street-exploit region occurred frequently, it is characterised in that
Including:
Use the DBSCAN algorithm optimized that the avenue occupy-street-exploit data collected are entered
Row processes, and classifies avenue occupy-street-exploit region；
Determine that avenue accounts for according to the described classification results to avenue occupy-street-exploit region
Region occurred frequently is managed in road；
Wherein, the DBSCAN algorithms selection current data object predetermined threshold value neighborhood of described optimization
Point outside radius is as next pending data object.
The side determining avenue occupy-street-exploit region occurred frequently the most according to claim 1
Method, it is characterised in that use the DBSCAN algorithm the optimized city street to collecting described
Before road occupy-street-exploit data process, also include:
Gather avenue occupy-street-exploit data, and described avenue occupy-street-exploit data are deposited
Storage is in data set.
The side determining avenue occupy-street-exploit region occurred frequently the most according to claim 1
Method, it is characterised in that when described avenue occupy-street-exploit data include scene, generation
Between and Event Description.
The side determining avenue occupy-street-exploit region occurred frequently the most according to claim 1
Method, it is characterised in that use the DBSCAN algorithm the optimized city street to collecting described
Before road occupy-street-exploit data process, also include:
Determine radius of neighbourhood EPS and density threshold MinPts of the DBSCAN algorithm of optimization.
The side determining avenue occupy-street-exploit region occurred frequently the most according to claim 1
Method, it is characterised in that the DBSCAN algorithm of the described employing optimization avenue to collecting
Occupy-street-exploit data process, and avenue occupy-street-exploit region is carried out classification and includes:
Using each avenue occupy-street-exploit data as data object, if current data object is
Kernel object, then be classified as a class by the point in the predetermined threshold value radius of neighbourhood of current data object,
And the point outside the predetermined threshold value radius of neighbourhood of current data object is stored in search data acquisition system S
In；
Traversal search data acquisition system S, classifies to the data object in search data acquisition system S.
The side determining avenue occupy-street-exploit region occurred frequently the most according to claim 5
Method, it is characterised in that described predetermined threshold value is less than or equal to 3/4 more than or equal to 2/3.
7. the system determining avenue occupy-street-exploit region occurred frequently, it is characterised in that
Including:
Territorial classification unit, is used for the DBSCAN algorithm the using optimization city street to collecting
Road occupy-street-exploit data process, and classify avenue occupy-street-exploit region；
Area determination unit occurred frequently, for dividing avenue occupy-street-exploit region according to described
Class result determines avenue occupy-street-exploit region occurred frequently；
Wherein, the DBSCAN algorithms selection current data object predetermined threshold value neighborhood of described optimization
Point outside radius is as next pending data object.
The most according to claim 7 determine avenue occupy-street-exploit region occurred frequently be
System, it is characterised in that also include:
Occupy-street-exploit data storage cell, is used for gathering avenue occupy-street-exploit data, and will
Described avenue occupy-street-exploit data are stored in data set.
The most according to claim 7 determine avenue occupy-street-exploit region occurred frequently be
System, it is characterised in that when described avenue occupy-street-exploit data include scene, generation
Between and Event Description.
The most according to claim 7 determine avenue occupy-street-exploit region occurred frequently be
System, it is characterised in that described territorial classification unit is further used for:
Using each avenue occupy-street-exploit data as data object, if current data object is
Kernel object, then be classified as a class by the point in the predetermined threshold value radius of neighbourhood of current data object,
And the point outside the predetermined threshold value radius of neighbourhood of current data object is stored in search data acquisition system S
In；
Traversal search data acquisition system S, classifies to the data object in search data acquisition system S.
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| Application Number | Priority Date | Filing Date | Title |
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| CN201610206554.6A CN105912598A (en) | 2016-04-05 | 2016-04-05 | Method and system for determining high-frequency regions for roadside stall business in urban streets |
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| CN201610206554.6A CN105912598A (en) | 2016-04-05 | 2016-04-05 | Method and system for determining high-frequency regions for roadside stall business in urban streets |
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| CN109949231A (en) * | 2019-02-02 | 2019-06-28 | 浙江工业大学 | A kind of method and device for urban managing information acquiring and processing |
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Application publication date: 20160831 |