CN116150178A - Spatial reachability measuring and calculating method based on DBSCAN clustering algorithm - Google Patents

Spatial reachability measuring and calculating method based on DBSCAN clustering algorithm Download PDF

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CN116150178A
CN116150178A CN202310184765.4A CN202310184765A CN116150178A CN 116150178 A CN116150178 A CN 116150178A CN 202310184765 A CN202310184765 A CN 202310184765A CN 116150178 A CN116150178 A CN 116150178A
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park green
space
park
poi data
residential
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马婵
连恒
张宙
姬霖
蒲永峰
王思雨
王重波
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Second Topographic Survey Team Of Ministry Of Natural Resources (third Surveying And Mapping Engineering Institute Of Shaanxi Province)
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Abstract

The invention provides a space reachability measuring and calculating method based on a DBSCAN clustering algorithm, which relates to the technical field of space reachability and comprises the following steps: acquiring residence POI data and urban park green space data; acquiring a dense region in the residential POI data by using a DBSCAN clustering algorithm, marking and deleting abnormal values of a low-density region to determine a park green land service object gathering range; obtaining the co-occurrence frequency of the residential data of the target park green land and the periphery subjected to DBSCAN clustering treatment in the webpage through webpage searching, measuring the association degree between the target park green land and the periphery, and determining the weight of the park green land; and (5) introducing the weight into an improved two-step mobile search method to calculate the urban park green space accessibility. The invention identifies the residential POI data set through the DBSCAN clustering algorithm, determines the gathering range of the park green land service objects, but does not simply define the buffer area, and the service objects are more gathered, thereby having reference to urban planning.

Description

Spatial reachability measuring and calculating method based on DBSCAN clustering algorithm
Technical Field
The invention relates to the technical field of space reachability, in particular to a space reachability measuring and calculating method based on a DBSCAN clustering algorithm.
Background
The spatial clustering algorithm can extract the clustering clusters from the dense data sets with any shapes according to the standard, so that the data sets in the clustering clusters are ensured to have larger differentiation and clustering difference, noise points can be effectively restrained, and the cluster distribution and spatial characteristics of POI data can be more accurately analyzed. The spatial clustering algorithm mainly comprises the following steps: (1) Constructing a network structure through a quantization space to perform data clustering based on a grid method; (2) old customer based model giving method: carrying out data clustering through optimizing the relation between data and a mathematical model; (3) a density-based method: the segmentation of the data set is performed by clusters. The DBSCAN (Density-based Spatial Clustering of Application withNoise) algorithm is a Density-based method, and the spatial clustering algorithm has different application and development in different fields.
The quality of urban park green space and its service surrounding residents gradually become an important index for measuring urban environment and resident living standard. The space accessibility of the urban park green space can quantify the urban ecology and the human living environment construction level to a certain extent. The evaluation method of urban park green space is generally classified into the following network analysis method, cost weighted distance method, minimum proximity distance method, potential model analysis method, two-step mobile search method, etc. The two-step mobile search method is widely applied after the data considers the distance attenuation and the situation of introducing more accurate traffic network.
The prior art drawbacks mainly include: the address of the POI is not canonical and has redundant information, and there are outliers. The Web script crawler utilizing the html unit at the later stage is affected, errors occur when concurrent queries are submitted, and the space accessibility of the park greenbelt to surrounding servers is usually analyzed for a given fixed-distance buffer area based on the traffic guidance development field, so that subjective appeal expression of the servers is not considered.
Disclosure of Invention
The invention provides a space reachability measuring and calculating method based on a DBSCAN clustering algorithm and aims to solve the defects in the prior art.
In order to achieve the above object, the present invention provides the following technical solutions: a space reachability measurement method based on DBSCAN clustering algorithm comprises the following steps:
acquiring residence POI data and urban park green space data;
acquiring a dense region in the residential POI data by using a DBSCAN clustering algorithm and using the dense region as a cluster, and marking and deleting abnormal values of a low-density region in the residential POI data;
obtaining the co-occurrence times of the residential data of the target park green land and the surrounding DBSCAN clustered data in the webpage through website searching, obtaining the association degree between the park green land and the surrounding residential POI data, and determining the weight of the park green land;
introducing a distance attenuation function under a certain search radius of the two-step mobile search method to obtain an improved two-step mobile search method, and introducing the weight into the improved two-step mobile search method to calculate the park green space accessibility.
Preferably, after the residential POI data is obtained, the residential POI data needs to be preprocessed, which specifically includes the following steps:
performing data cleaning on the acquired POI data to obtain accurately classified residential POI data;
combining universality, consistency, expansibility, extensibility and public property in the classification principle of the urban residential POI data, and performing secondary classification on the classified urban residential POI data;
and (3) normalizing the address information in the secondarily classified residential POI data by using a regular expression method in NLP, and matching the address information into a uniform address format.
Preferably, the acquiring the dense area in the residential POI data by using a DBSCAN clustering algorithm is used as a cluster, and the abnormal value of the low-density area in the residential POI data is marked and deleted, which specifically comprises the following steps:
searching all residential POI data points through a DBSCAN clustering algorithm, defining the point as a core point when the number of the radius range points of the residential POI data points is larger than or equal to the minimum point, classifying the point into a core point set, and forming a temporary cluster corresponding to the density direct-reaching collecting point;
randomly selecting a core point from the temporary cluster, and identifying residential POI data points with reachable adjacent densities to generate a cluster until each point in the temporary cluster is selected;
and (3) calculating the distance between each point in the temporary clustering cluster and the K-th neighbor of the point, drawing a K distance graph, determining the optimal radius of the temporary clustering cluster, and removing the abnormal value of the low-density area in the residential POI data.
Preferably, the association degree between the park green and the surrounding residence POI data is obtained, and the weight of the park green is determined, which comprises the following steps:
adopting a Web script crawler of an html unit to submit and inquire about the park green land name and the residence POI address;
obtaining association degrees of park greenbelts and surrounding residence POIs through web page searching, and determining the searching quantity of the park greenbelts and the surrounding residence POIs;
and regarding the co-occurrence times as the POI weight of the park green land through the co-occurrence times of the park green land and the surrounding residential POI addresses in the webpage.
Preferably, the weight is carried into an improved two-step mobile search method to measure and calculate the park green space accessibility, and the method specifically comprises the following steps:
obtaining real travel time consumption and distance from a house to a park green land by adopting a shortest transit time distance measuring and calculating method;
acquiring a minimum cost matrix according to the weight and the travel time consumption and distance, and acquiring a minimum cost path;
and carrying the weight and the minimum cost path into an improved two-step mobile search method to calculate the park green space accessibility.
Preferably, the method for measuring and calculating the shortest passing time distance is used for obtaining the real travel time and distance from the house to the park green land, and comprises the following steps:
calculating the shortest time from a residential district to a nearest park green by adopting an OD matrix module in ArcGIS network analysis, and carrying out unit statistics;
based on the directionLiteAPI path planning interface, the real travel time consumption and distance are obtained.
Preferably, the path planning interface based on directionliteAPI obtains real travel time consumption and distance, and specifically includes the following steps:
taking coordinate points of the residential POI data and the urban park green space POI data as a starting point and an end point of distance calculation respectively, solving any path from the starting point to the end point, and calculating the linear distance of any path;
selecting an average speed in path planning to limit the linear distance from a starting point to an end point;
and writing a get request, and acquiring the path passing time from each starting point to the end point by using a network map API path planning interface to obtain the real travel time consumption and distance of walking.
Preferably, the weights and the minimum cost paths are carried into an improved two-step mobile search method to calculate the park green space accessibility, and the method specifically comprises the following steps:
setting a space distance threshold d for a random park green spot j, and searching for a population forming each cell k in a space action domain for the threshold d;
weighting the population of each cell k through Gaussian equation calculation, and summing the weighted population to obtain the number of all potential servers in the park green place j;
the weight of park green sites calculated by the number of network co-occurrence times is divided by the number of all potential park servers to obtain the supply-demand ratio R j Obtaining a park green space accessibility result;
and performing Kriging interpolation on the reachability result, and performing visualization processing.
Preferably, the supply-demand ratio R j The calculated expression of (2) is:
Figure BDA0004103341020000051
wherein ,Pk Is the space action area (d is less than or equal to d) of the park green land 0 ) Population of cell k, d kj The travel distance is obtained by introducing a shortest transit time distance measuring and calculating method from the arrival time of the center of the cell k to the center of the park green land j, S j Is the weight of the green land of the park;
wherein ,
Figure BDA0004103341020000052
G(d kj ,d 0 ) Is a gaussian equation that introduces spatial friction, the number of people that need to be served for each park green.
Compared with the prior art, the invention has the following beneficial effects: the invention identifies the residential POI data set through a DBSCAN clustering algorithm and determines the gathering range of the park green land service object. Instead of simply defining a buffer zone, service objects are more aggregated, the method has referential property for urban planning, and the method searches in websites to obtain concurrent query results of park greenbelts and surrounding POIs thereof, and uses the concurrent query results as park greenbelt weight parameters to calculate the space accessibility of park greenbelts. The traditional rough calculation of space accessibility by taking parameters such as building area and the like as weights is replaced.
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Fig. 1 is a schematic flow chart provided by the present invention.
Detailed Description
The following describes the embodiments of the present invention further with reference to the accompanying drawings. The following examples are only for more clearly illustrating the technical aspects of the present invention, and are not intended to limit the scope of the present invention.
Aiming at the defects of the prior art, the invention mainly solves the problems including:
1. after the POI points in the study are preprocessed by data cleaning and the like, a corpus in a JSON format is established by using a regular expression method in NLP neuro-linguistic science, and POI address information is normalized.
2. By using the density-based noisy application DBSCAN clustering method, dense areas of residential POI data are found through calculation and used as clustering clusters, abnormal values of low-density areas are marked and deleted, noise can be effectively suppressed, and outliers of the type can be reduced.
3. And collecting partial dynamic webpages by utilizing the webpage multiline Cheng Wanglao crawler of the HtmlUnit, submitting concurrent queries, and obtaining the co-occurrence times of urban public facilities and surrounding residential POIs on webpages when the human visual angle is taken.
4. In order to simulate the actual condition of accessibility, park entrances and geometric centers are adopted to replace urban park greenbelts.
For understanding and explanation, the space reachability measuring and calculating method based on the DBSCAN clustering algorithm provided by the embodiment of the invention is described in detail below, and comprises the following steps:
step 1: and acquiring the residence POI data and POI data.
Step 2: and acquiring dense areas in the residential POI data by using a DBSCAN clustering algorithm, marking and deleting abnormal values of low-density areas in the residential POI data as clusters, suppressing noise, and determining the gathering range of the park green land service objects.
Step 3: obtaining the co-occurrence times of residential data of the target park green land and the periphery subjected to DBSCAN clustering treatment in the webpage through webpage searching, obtaining the association degree of the park green land and the residential POI data around the park green land, and determining the weight of the park green land;
step 4: introducing a distance attenuation function under a certain search radius of the two-step mobile search method to obtain an improved two-step mobile search method, and introducing weights into the improved two-step mobile search method to calculate the park green space accessibility.
The detailed process is as follows:
1. technical process for data acquisition and preprocessing
And obtaining urban park greenbelts and residence POI data through a network map API, and obtaining data which can be accurately classified through data cleaning.
Because the address of the POI is not standard and has redundant information, the POI is used as a keyword for searching to influence the search result when the subsequent webpage features are extracted. Therefore, address information of the POI is normalized by using a regular expression method (Regular Expression) in the NLP, and in a unified address format, more accurate contact quantity results can be obtained when the webpage co-occurrence processing is performed. The address medium town, street and area are utilized for segmentation, and the address format is unified.
2. DBSCAN clustering algorithm for extracting clusters and inhibiting noise
POIs with repeated addresses or information errors can influence the calculation of the accessibility of a later analysis space, and in order to reduce and eliminate noise points, after repeated POIs are subjected to de-duplication processing, a DBSCAN clustering method is utilized to find out dense areas of the POIs through calculation, and the dense areas are used as clusters to mark and delete abnormal values of low-density areas. The DBSCAN clustering algorithm can extract the POI data set with high density as clusters, and can find the clusters with arbitrary shapes in the noisy spatial database. And using a Scikit-learn machine learning library in the Python running environment, and effectively identifying the dense region of the POI by using a DBSCAN clustering method. The implementation steps are as follows:
(1) Finding core points to form temporary cluster
The algorithm retrieves all POI data points when the number of radius range (Epsilon) points of the POI data points is greater than or equal to the minimum point (min Pts ) And defining the point as a core point, classifying the core point into a set, and finally forming a corresponding temporary cluster by the set point with direct density.
(2) Merging temporary cluster clusters
Based on the core point set, randomly selecting a core point from the set, identifying POI points with reachable adjacent densities to generate a cluster, and searching each point in the core point set.
The DBSCAN clustering is used as a spatial clustering algorithm based on density, and the finally obtained conclusion is that clustering clusters with different space geographies are obtained, and POIs of the DBSCAN clustering can be more accurately used for extracting the clustering clusters and outliers. And removing outliers to obtain POI data with stronger correlation.
3. Obtaining park green land weight
When the website search description reaches the park position route or the marked park information, the park green land name and the address are appeared in the same webpage, and the webpage co-appearance times of the POIs of the park green land and the residence are included. The co-occurrence number is regarded as the weight of the POI of each park green.
And submitting concurrent query [ "park green place name", "POI address" ], searching in a network website, acquiring the association degree of POIs of the park green place and surrounding residences, and displaying the search quantity simultaneously containing two search keywords on a search page by utilizing a Web script crawler of the html unit. And measuring the association degree between the park green land and the peripheral residences by the co-occurrence times of the park green land and the peripheral residences on the webpage, and determining the weight of the park green land.
4. Improved two-step mobile search park green space accessibility implementation
The improved two-step mobile search method takes the distance attenuation problem into consideration when calculating the space accessibility of the park green land, and introduces a distance attenuation function under a certain search radius of the traditional two-step mobile search method, wherein the attenuation rate of the space accessibility is accelerated and then slowed down according to the increase of the service distance.
The main technical process for measuring the space accessibility of the park green land based on the improved two-step mobile search method is as follows:
4.1, obtaining real travel time consumption and distance by a shortest transit time distance measuring and calculating method
The average speed per hour of the bus is set to 17km/h according to the network map provided data, and the average speed per hour of the subway is set to 4.5km/h according to the network provided data. Considering various travel modes for transfer, the average waiting time of buses is 10min, and the subway waiting time is set according to departure frequencies of different stations. Considering transfer time, travel below 10min is basically based on walking. And calculating the shortest time from the house to the nearest park green land by adopting an OD matrix module in ArcGIS network analysis, and then taking a street or loop area as a unit for statistics.
And obtaining real travel time consumption and distance by considering actual road traffic conditions based on a DirectionLiteAPI (lightweight route planning service) path planning interface of the network map. Firstly, taking coordinate points of residence data and urban park green land POI data as a starting point and an ending point of distance calculation respectively, solving any path from the starting point to the ending point, calculating the linear distance of the path, and taking actual conditions into consideration, wherein the residence in a district and the surrounding park green land are basically walking, so that the average walking speed in path planning is selected to limit the linear distance from the starting point to the ending point; and then, writing a get request, and acquiring the path passing time from each starting point to the end point by using a network map API path planning interface to obtain the real travel time and distance of walking.
4.2, weight-considered minimum cost matrix
Based on python programming, the weight of the POI data around the park green land and the real travel time and distance of walking are filtered, and an OD cost matrix with the shortest time effect and larger influence of the shortest weight is used for searching and measuring the minimum cost paths from a plurality of starting points to a plurality of destinations, so that the shortest distance and the shortest time from the residential community to the nearest park green land are obtained.
4.3 improved two-step Mobile search park Green space accessibility
Setting a space distance threshold d for a random park green place j, searching the population of each cell k forming a space action domain for the threshold d, calculating and weighting by a Gaussian equation, and summing the weighted population to obtain the number of all potential servers of the park green place j. Finally, the weight of the park green places obtained through calculation of the network co-occurrence times is divided by the number of all potential park servers to obtain the supply-demand ratio Rj.
See formula (1).
Figure BDA0004103341020000101
In the formula (1), P k Is the space of the green land of the parkWithin the action range (d is less than or equal to d) 0 ) Population of cell k, d kj The travel distance is obtained by introducing a shortest transit time distance measuring and calculating method from the center of the cell k to the center of the park green land j. S is S j The weight of the park green land is calculated by the number of times of network co-occurrence, and replaces the traditional expert experience setting method.
Figure BDA0004103341020000102
In the formula (2), G (d) kj ,d 0 ) Is a gaussian equation that introduces spatial friction, the number of people that need to be served for each park green.
And finally obtaining the improved two-step mobile search park green space accessibility result through calculation. And performing Kriging interpolation on the reachability result, and performing visualization processing.
The above embodiments are merely preferred embodiments of the present invention, the protection scope of the present invention is not limited thereto, and any simple changes or equivalent substitutions of technical solutions that can be obviously obtained by those skilled in the art within the technical scope of the present invention disclosed in the present invention belong to the protection scope of the present invention.

Claims (9)

1. The space reachability measurement and calculation method based on the DBSCAN clustering algorithm is characterized by comprising the following steps of:
acquiring residence POI data and urban park green space data;
acquiring a dense region in the residential POI data by using a DBSCAN clustering algorithm and using the dense region as a cluster, and marking and deleting abnormal values of a low-density region in the residential POI data;
obtaining the co-occurrence times of the residential POI data of the target park green land and the residential POI data of the periphery after DBSCAN clustering treatment in a webpage through website searching, obtaining the association degree between the park green land and the residential POI data of the periphery, and determining the weight of the park green land;
introducing a distance attenuation function under a certain search radius of the two-step mobile search method to obtain an improved two-step mobile search method, and introducing the weight into the improved two-step mobile search method to calculate the park green space accessibility.
2. The space reachability measurement method based on DBSCAN clustering algorithm as claimed in claim 1, wherein after obtaining the residential POI data, the residential POI data is preprocessed, and the method specifically comprises the following steps:
performing data cleaning on the acquired residential POI data to obtain accurately classified residential POI data;
combining universality, consistency, expansibility, extensibility and public property in the classification principle of the urban residential POI data, and performing secondary classification on the classified urban residential POI data;
and (3) normalizing the address information in the secondarily classified residential POI data by using a regular expression method in NLP, and matching the address information into a uniform address format.
3. The space reachability measurement and calculation method based on a DBSCAN clustering algorithm according to claim 1, wherein the method for obtaining dense areas in the residential POI data by using the DBSCAN clustering algorithm and using the dense areas as clusters, marking and deleting abnormal values of low-density areas in the residential POI data specifically comprises the following steps:
searching all residential POI data points through a DBSCAN clustering algorithm, defining the point as a core point when the number of the radius range points of the residential POI data points is larger than or equal to the minimum point, classifying the point into a core point set, and forming a temporary cluster corresponding to the density direct-reaching collecting point;
randomly selecting a core point from the temporary cluster, and identifying residential POI data points with reachable adjacent densities to generate a cluster until each point in the temporary cluster is selected;
and (3) calculating the distance between each point in the temporary clustering cluster and the K-th neighbor of the point, drawing a K distance graph, determining the optimal radius of the temporary clustering cluster, and removing the abnormal value of the low-density area in the residential POI data.
4. The space reachability measurement and calculation method based on the DBSCAN clustering algorithm of claim 1, wherein the correlation degree between the park greenbelt and surrounding residential POI data is obtained, and the weight of the park greenbelt is determined, and the method specifically comprises the following steps:
adopting a Web script crawler of an html unit to submit and inquire about the park green land name and the residence POI address;
obtaining association degrees of park greenbelts and surrounding residence POIs through web page searching, and determining the searching quantity of the park greenbelts and the surrounding residence POIs;
and regarding the co-occurrence times as the POI weight of the park green land through the co-occurrence times of the park green land and the surrounding residential POI addresses in the webpage.
5. The method for measuring and calculating the space accessibility of the park green space based on the DBSCAN clustering algorithm according to claim 1, wherein the method for measuring and calculating the space accessibility of the park green space by bringing the weight into an improved two-step mobile search method comprises the following steps:
obtaining real travel time consumption and distance from a house to a park green land by adopting a shortest transit time distance measuring and calculating method;
acquiring a minimum cost matrix according to the weight and the travel time consumption and distance, and acquiring a minimum cost path;
and carrying the weight and the minimum cost path into an improved two-step mobile search method to calculate the park green space accessibility.
6. The method for measuring and calculating the space accessibility based on the DBSCAN clustering algorithm according to claim 5, wherein the method for measuring and calculating the distance of the shortest transit time is adopted to obtain the real travel time and distance from the house to the park green land, and comprises the following steps:
calculating the shortest time from a residential district to a nearest park green by adopting an OD matrix module in ArcGIS network analysis, and carrying out unit statistics;
based on the directionLiteAPI path planning interface, the real travel time consumption and distance are obtained.
7. The method for measuring and calculating space accessibility based on DBSCAN clustering algorithm as claimed in claim 6, wherein the method for measuring and calculating space accessibility based on DirectionLiteAPI path planning interface obtains real travel time and distance, and comprises the following steps:
taking coordinate points of the residential POI data and the park green POI data as a starting point and an end point of distance calculation respectively, solving any path from the starting point to the end point, and calculating the linear distance of any path;
selecting an average speed in path planning to limit the linear distance from a starting point to an end point;
and writing a get request, and acquiring the path passing time from each starting point to the end point by using a network map API path planning interface to obtain the real travel time consumption and distance of walking.
8. The method for measuring and calculating the space reachability of the park green space based on the DBSCAN clustering algorithm as recited in claim 5, wherein the method for measuring and calculating the space reachability of the park green space by introducing the weight and the minimum cost path into an improved two-step mobile search method comprises the following steps:
setting a space distance threshold d for a random park green spot j, and searching for a population forming each cell k in a space action domain for the threshold d;
weighting the population of each cell k through Gaussian equation calculation, and summing the weighted population to obtain the number of all potential servers in the park green place j;
the weight of park green sites calculated by the number of network co-occurrence times is divided by the number of all potential park servers to obtain the supply-demand ratio R j Obtaining a park green space accessibility result;
and (5) performing Kriging interpolation on the park green space accessibility result, and performing visualization processing.
9. The method for measuring and calculating space accessibility based on DBSCAN clustering algorithm as recited in claim 5, whereinCharacterized in that the supply-demand ratio R j The calculated expression of (2) is:
Figure FDA0004103341010000041
wherein ,Pk Is the space action area (d is less than or equal to d) of the park green land 0 ) Population of cell k, d kj The travel distance is obtained by introducing a shortest transit time distance measuring and calculating method from the arrival time of the center of the cell k to the center of the park green land j, S j Is the weight of the green land of the park;
wherein ,
Figure FDA0004103341010000042
/>
G(d kj ,d 0 ) Is a gaussian equation that introduces spatial friction, the number of people that need to be served for each park green.
CN202310184765.4A 2023-03-01 2023-03-01 Spatial reachability measuring and calculating method based on DBSCAN clustering algorithm Pending CN116150178A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116862063A (en) * 2023-07-12 2023-10-10 山东宜鑫致远信息科技有限公司 Smart city planning optimization method and system based on big data

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116862063A (en) * 2023-07-12 2023-10-10 山东宜鑫致远信息科技有限公司 Smart city planning optimization method and system based on big data
CN116862063B (en) * 2023-07-12 2024-04-19 甘肃飞天云科技信息有限公司 Smart city planning optimization method and system based on big data

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