CN109974730A - A Point of Interest Landmark Path Guidance Method Based on Spontaneous Geographic Information - Google Patents

Abstract

The invention discloses a path guidance method for a point of interest landmark of spontaneous geographic information, and relates to the technical field of surveying and mapping geographic information; the path guidance method includes: step 1, obtaining check-in data based on a microblog social platform VGI-POI; step 2, Preprocessing to obtain VGI-POI check-in data; step 3, validity calculation of VGI-POI check-in data; step 4, VGI-POI landmark path to guide the selection of the optimal path; step 5, VGI-POI landmark based on the optimal path Path guidance; the invention can provide path guidance for people to travel, generate path guidance description information that conforms to people's spatial cognition habits, and provide basis and reference for scientifically selecting travel paths, and overcomes traditional navigation methods, POI and traditional POI to a certain extent. The limitations of the landmark path guidance method help to reduce the wayfinding time, reduce the psychological burden and improve the navigation efficiency, and have the characteristics of low cost, and the path guidance conforms to people's spatial cognition habits, which is convenient for practical use.

Description

A Point of Interest Landmark Path Guidance Method Based on Spontaneous Geographic Information

technical field

The invention belongs to the technical field of surveying and mapping geographic information, and relates to a method for guiding a path of a point of interest landmark of spontaneous geographic information.

Background technique

Volunteered Geographic Information (VGI), as a new type of popular geographic information data source, aims to provide geographic data voluntarily by individuals.

Point of Interest (POI) is the core content of digital maps, which refers to all entities that can be abstracted into points, especially some urban infrastructure construction related to people's lives, which can be a building, a shop, a bus station It can play a role in assisted positioning, location query search, route query search, navigation and other services.

Landmarks in a narrow sense refer to iconic geographic objects existing in geographic space, and landmarks in a narrow sense are included in POIs. POI landmarks mainly refer to the spatial elements that are highly recognized, easy to understand and remember in the geospatial environment. POIs with certain influence in the city can be regarded as landmarks.

Route guidance can also be called navigation. During the process of route navigation, the current route status of the user is tracked in real time, and the user is guided to the next guidance point, thereby continuously guiding the user to reach the destination conveniently and quickly.

The existing path guidance methods mainly include the following methods:

One method is the traditional navigation method. Most of this method uses the "turn-distance" path guidance method, and the recommended paths are the paths with the shortest distance or the shortest time, which can basically be classified as the shortest path algorithm.

Another method is the path-guided method of POI. This method usually selects multiple POIs as guide points, and uses multiple POIs to form a guide path in the path description. The collection of POI data is mostly collected by surveying and mapping.

Another method is the path guidance method of traditional POI landmarks. This method selects easy-to-remember and famous spatial objects from POIs as landmarks, and uses multiple POI landmarks to form a guiding path in the path description. The extraction of POI landmarks mainly relies on the field records and recalls and questionnaires of multiple occupational experimenters.

For traditional navigation methods, since people usually do not choose the shortest standard route when choosing a route, they prefer to choose a route that conforms to people's spatial cognition habits, so the existing traditional navigation system cannot meet the requirements. the needs of the people.

For the route guidance method of POI, the effectiveness, efficiency and satisfaction of different POIs for route guidance are different. In order to reduce the distraction of people's energy during route-finding, it is necessary to confirm the role of POI based on user preference in route guidance, usually It is necessary to establish a comprehensive mathematical model to evaluate its rationality, which is relatively complicated and difficult to judge in a short period of time, and the traditional POI data surveying and mapping method requires a lot of professionals and material and financial resources.

For the traditional POI landmark path guidance method, it is not suitable for a large number of POI landmark extraction and timely update of geospatial due to the time-consuming and human capital being too high.

SUMMARY OF THE INVENTION

In order to overcome the above-mentioned deficiencies of the prior art, the present invention provides a method for guiding a landmark path of a point of interest with spontaneous geographic information, namely the VGI-POI landmark path guiding method, which solves the problem that the traditional navigation method does not conform to people's spatial cognition habits, and POI and The traditional POI landmark path guidance method has the technical problem of high cost, and realizes the technical effect of reasonable path guidance, easy practical use, improved navigation efficiency and reduced psychological burden.

The technical scheme adopted in the present invention is:

Step 1, based on the acquisition of the check-in data of the Weibo social platform VGI-POI.

Step 2: Obtain the preprocessing of VGI-POI check-in data.

The VGI-POI check-in data obtained from the Weibo social platform is preprocessed, including the preprocessing of geographical cold spots and geographically heterogeneous points.

To deal with geographic cold spots, set the check-in threshold to filter:

LP={VP _i |N _i ≤N _t ,i=1,2,...,n}

Among them, LP represents the geographic cold spot, VP _i represents the VGI-POI check-in data, N _i is the number of check-ins corresponding to VP _i , and N _t is the set threshold for the number of check-ins.

To deal with geographically heterogeneous points, the method of merging heterogeneous points is adopted. First, calculate the spatial similarity and attribute similarity of heterogeneous points, and then judge whether they belong to the same geographical point for merging according to the set threshold:

MP=MD ≥ _{D t} and MH ≥ _{H t}

Among them, MP represents the geographically heterogeneous points that can be merged, MD represents the spatial similarity, _{D t} represents the spatial similarity threshold, MH represents the attribute similarity, and _{H t} represents the attribute similarity threshold.

The spatial similarity MD is measured by a two- _dimensional plane straight-line distance, and the shorter the distance, the higher the spatial similarity.

Attribute similarity M _H adopts the difference scoring method between the same word and different words to compare the similarity between the two heterogeneous point name strings. Counting, the difference score of different characters is to count the number of different characters in two name strings. The greater the difference between the two scores, the higher the attribute similarity. The specific calculation formula is as follows:

M _H = RT

Among them, _MH represents the attribute similarity, R represents the score of the same word with the same location, and T represents the difference score of different words.

Step 3: Calculate the validity of VGI-POI check-in data.

For the preprocessed VGI-POI check-in data, the effectiveness is comprehensively calculated from the two indicators of attention and spatial distribution characteristics. The specific calculation formula is as follows:

E=α·A+β·S

Among them, E represents the effective value, A represents the attention degree, S represents the spatial distribution feature, α, β are the weight coefficients, and α+β=1, α, β∈[0,1]. When E is greater than or equal to the validity threshold, the VGI-POI check-in data is considered valid.

The degree of attention A is measured by the number of check-ins, and the more the number of check-ins, the higher the degree of attention.

The spatial distribution feature S is measured by the clustering density generated by the spatial distribution density clustering algorithm. The larger the clustering density, the higher the urban centrality of VGI-POI.

Step 4, the VGI-POI landmark path guides the selection of the optimal path.

The VGI-POI landmark is the VGI-POI selected after the validity calculation. The following optimal route selection model is established by integrating the three factors of route distance, the number of VGI-POI landmarks in the route, and the number of travelers familiar with VGI-POI landmarks:

Among them, I represents the path index, and the path with the smallest I value represents the optimal path; N ₁ represents the number of VGI-POI landmarks in the path, and N ₂ represents the number of familiar VGI-POI landmarks; e is a natural constant; γ ₁ , γ ₂ , γ ₃ is the weight coefficient, and γ ₁ +γ ₂ +γ ₃ =1,γ ₁ ,γ ₂ ,γ ₃ ∈[0,1]; D _s represents the two-dimensional plane path distance, which is normalized using the following formula ( max and min represent the maximum and minimum values respectively):

(D _s -min(D _s ))/(max(D _s )-min(D _s ))

Step 5, route guidance of the VGI-POI landmark based on the optimal route.

Follow these steps to implement:

①Set the start point and end point of the path;

②Select possible paths, and calculate the distance D _s of the paths;

③ Establish a buffer zone with the path centerline and a certain distance, and calculate the number N ₁ of VGI-POI landmarks in the buffer zone;

④ Specify the VGI-POI landmarks familiar to travelers from the N1 number of VGI _- POI landmarks, and count the number _N2 ;

⑤ Calculate the path index I according to D _s , N ₁ , and N ₂ , and select the path with the smallest I value as the optimal path, that is, the guide path provided for travelers;

⑥ Generate route guidance description information containing VGI-POI landmarks for the optimal route.

Compared with the prior art, the beneficial effects of the present invention are: the VGI-POI data comes from the geographic data that the general public spontaneously shares according to their own interests, and a large amount of data is updated in a timely manner and with low cost; the check-in data can reflect the VGI-POI closely related to people's lives. The distinctiveness of landmarks and the characteristics of life habits, using VGI-POI landmarks for path guidance will reduce the psychological burden of people's wayfinding; the present invention overcomes the limitations of traditional navigation methods, POI and traditional POI landmark path guidance methods to a certain extent , which helps to reduce the wayfinding time and improve the navigation efficiency, and has the characteristics that the path guidance conforms to people's spatial cognition habits and is convenient for practical use.

Description of drawings

FIG. 1 is a schematic structural diagram of a method for guiding a path of a point of interest landmark of spontaneous geographic information according to the present invention.

Detailed ways

The geographical location-based social network platform is a representative platform of VGI. Through the mobile microblog client, users can share the check-in location in the social network anytime and anywhere, which is called VGI-POI check-in data.

This specific embodiment adopts the following technical solutions: its path guidance method is:

Step 1, based on the acquisition of the check-in data of the Weibo social platform VGI-POI.

The steps to obtain VGI-POI check-in data are as follows:

① The login client requests a token from the software provider.

② After the software provider receives the request, it verifies the customer's application and grants a permission.

③ After the client obtains the permission, it jumps to the authorization page of the software service provider to request authorization. At this time, the permission and the callback link of the client are sent to the provider.

④After the permission is passed, the user obtains the requested resource by calling the API provided by Weibo.

⑤ Parse the acquired data into the record format of VGI-POI sequence, place name, address, longitude, latitude, city code, VGI-POI category code and check-in times.

Step 2: Obtain the preprocessing of VGI-POI check-in data.

The VGI-POI check-in data obtained from the Weibo social platform is preprocessed, including the preprocessing of geographical cold spots and geographically heterogeneous points.

Geographic cold spots represent spots with fewer check-ins. Due to the low degree of attention, they are not enough to attract the public's attention and have no navigation function. To deal with geographic cold spots, set the check-in threshold to filter:

LP={VP _i |N _i ≤N _t ,i=1,2,...,n}

Among them, LP represents the geographic cold spot, VP _i represents the VGI-POI check-in data, N _i is the number of check-ins corresponding to VP _i , and N _t is the set threshold for the number of check-ins.

Geographical heterogeneity is due to the fact that the public has different cognitions to the same geographic object, and there are multiple aliases, which belong to the same feature, but the data have obvious differences in structure. To deal with geographically heterogeneous points, the method of merging heterogeneous points is adopted. First, calculate the spatial similarity and attribute similarity of heterogeneous points, and then judge whether they belong to the same geographical point for merging according to the set threshold:

MP=MD ≥ _{D t} and MH ≥ _{H t}

Among them, MP represents the geographically heterogeneous points that can be merged, MD represents the spatial similarity, _{D t} represents the spatial similarity threshold, MH represents the attribute similarity, and _{H t} represents the attribute similarity threshold.

The spatial similarity MD is measured by a two- _dimensional plane straight-line distance, and the shorter the distance, the higher the spatial similarity.

Attribute similarity M _H adopts the difference scoring method between the same word and different words to compare the similarity between the two heterogeneous point name strings. Counting, the difference score of different characters is to count the number of different characters in two name strings. The greater the difference between the two scores, the higher the attribute similarity. The specific calculation formula is as follows:

M _H = RT

Among them, _MH represents the attribute similarity, R represents the score of the same word with the same location, and T represents the difference score of different words.

Step 3: Calculate the validity of VGI-POI check-in data.

For the preprocessed VGI-POI check-in data, the effectiveness is comprehensively calculated from the two indicators of attention and spatial distribution characteristics. The specific calculation formula is as follows:

E=α·A+β·S

Among them, E represents the effective value, A represents the attention degree, S represents the spatial distribution feature, α, β are the weight coefficients, and α+β=1, α, β∈[0,1]. When E is greater than or equal to the validity threshold, the VGI-POI check-in data is considered valid.

The degree of attention A is measured by the number of check-ins, and the more the number of check-ins, the higher the degree of attention. Attention A needs to be normalized, and the steps are:

①Select the critical value according to the number of division levels m, and divide the number of check-in into several levels O ₁ , O ₂ ,...,O _j ,...,O _m ;

②According to the number of check-in times, assign values Q ₁ , Q ₂ ,…,Q _j ,…,Q _m to the grades from high to low;

③ Standardize the dispersion of each grade value, so that the result is mapped to the interval [0, 1], and the attention A of the same grade takes the same q _j value (max and min represent the maximum and minimum values, respectively):

q _j =(Q _j -min(Q _j ))/(max(Q _j )-min(Q _j )),j=1,2,...,m

The spatial distribution feature S is measured by the clustering density generated by the spatial distribution density clustering algorithm. The larger the clustering density, the higher the urban centrality of VGI-POI. The spatial distribution feature S needs to be normalized, and the steps are:

①Select the critical value according to the number of division levels m, and divide the clustering density into several levels O ₁ , O ₂ ,...,O _j ,...,O _m ;

②According to the size of the clustering density, assign the values Q ₁ , Q ₂ ,...,Q _j ,...,Q _m to the levels from high to low;

③ Standardize the dispersion of each level value, so that the result is mapped to the interval [0, 1], and the spatial distribution feature S of the same level takes the same q _j value (max and min represent the maximum and minimum values, respectively):

q _j =(Q _j -min(Q _j ))/(max(Q _j )-min(Q _j )),j=1,2,...,m

Step 4, the VGI-POI landmark path guides the selection of the optimal path.

The VGI-POI landmark is the VGI-POI selected after the validity calculation.

When the traveler is completely unfamiliar with the route, the traveler will have psychological pressure, and often feel that the route is longer than the actual distance, or the driving (walking) speed will be reduced. Therefore, according to the shortest distance, the traditional shortest path algorithm is not the most efficient. optimal path selection. At the same time, when the number of VGI-POI landmarks that can be provided in the route is more, the more travelers are familiar with the VGI-POI landmarks, it will increase the traveler's self-confidence and be more conducive to the traveler's route decision-making behavior.

The following optimal route selection model is established by integrating the three factors of route distance, the number of VGI-POI landmarks in the route, and the number of travelers familiar with VGI-POI landmarks:

Among them, I represents the path index, and the path with the smallest I value represents the optimal path; N ₁ represents the number of VGI-POI landmarks in the path, and N ₂ represents the number of familiar VGI-POI landmarks; e is a natural constant; γ ₁ , γ ₂ , γ ₃ is the weight coefficient, and γ ₁ +γ ₂ +γ ₃ =1,γ ₁ ,γ ₂ ,γ ₃ ∈[0,1]; D _s represents the two-dimensional plane path distance, which is normalized using the following formula ( max and min represent the maximum and minimum values respectively):

(D _s -min(D _s ))/(max(D _s )-min(D _s ))

Step 5: VGI-POI landmark path guidance based on the optimal path.

Follow these steps to implement:

①Set the start point and end point of the path;

②Select possible paths, and calculate the distance D _s of the paths;

③ Establish a buffer zone with the path center line and a certain distance, and calculate the number N ₁ of VGI-POI landmarks in the buffer zone;

④ Specify the VGI-POI landmarks familiar to travelers from the N1 number of VGI _- POI landmarks, and count the number _N2 ;

⑤ Calculate the path index I according to D _s , N ₁ , and N ₂ , and select the path with the smallest I value as the optimal path, that is, the guide path provided for travelers;

⑥ Generate route guidance description information containing VGI-POI landmarks for the optimal route.

The foregoing has shown and described the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above-mentioned embodiments, and any modifications, equivalent replacements and improvements made within the spirit and scope of the present invention should be included within the protection scope of the present invention. .

Claims (5)

1. a point of interest landmark path guidance method of spontaneous geographic information, is characterized in that: its path guidance method is: Step 1, based on the acquisition of the sign-in data of the Weibo social platform VGI-POI; Step 2: Obtain the preprocessing of VGI-POI check-in data; Step 3, the validity calculation of VGI-POI check-in data; Step 4, the VGI-POI landmark path guides the selection of the optimal path; Step 5: VGI-POI landmark path guidance based on the optimal path. 2. A kind of spontaneous geographic information point of interest landmark path guidance method according to claim 1, is characterized in that: the preprocessing of step 2 comprises the preprocessing of geographic cold spot and geographic heterogeneous point, and geographic cold spot adopts setting Check-in thresholds are used for screening. Geographically heterogeneous points are determined by setting spatial similarity thresholds and attribute similarity thresholds to determine whether to merge. Spatial similarity adopts two-dimensional plane straight-line distance, and attribute similarity adopts the difference scoring method between the same word and different words. . 3. a kind of spontaneous geographic information point-of-interest landmark path guidance method according to claim 1, is characterized in that: the effectiveness of step 3 adopts two indexes of attention degree and spatial distribution feature to be comprehensively calculated, and attention degree adopts the number of check-in times to calculate. To measure, the spatial distribution characteristics are measured by the clustering density generated by the spatial distribution density clustering algorithm. 4. the point of interest landmark path guidance method of a kind of spontaneous geographic information according to claim 1, it is characterized in that: the optimal path of step 4 adopts comprehensive path distance, VGI-POI landmark quantity in the path and traveler to VGI-POI. The optimal path selection model established by the number of POI landmarks is familiar with three factors to make judgment and selection. 5 . The method of claim 1 , wherein the path guidance in step 5 uses VGI-POI landmarks to form a guidance path in the path description. 6 .