HK1242066A - Hotspot region determination method and device based on geographic position data - Google Patents

Description

Hot spot area determination method and device based on geographic position data

Technical Field

The present application relates to the field of internet technologies, and in particular, to a method and an apparatus for determining a hotspot region based on geographical location data.

Background

Nowadays, applications based on geographical location data are becoming more and more widespread, in particular maps based on geographical location data.

In actual user needs, a user may want to find a hot spot area in a map, for example, the user wants to go to the hot spot area for shopping, entertainment, dining, and the like; accordingly, the merchant may also want to go to a hot spot area to open a store. Thus, hot spots are typically marked in the map according to the user's needs.

In the prior art, the hot spot area is often determined manually. However, it is inefficient to manually determine the hot spot area, and the cost is also high due to the use of labor.

Disclosure of Invention

The application provides a hotspot area determining method and device based on geographical position data, and aims to solve the problems of low efficiency and high cost of determining a hotspot area in the prior art.

According to the hotspot area determining method based on the geographic position data, the hotspot area determining method comprises the following steps:

carrying out gridding processing on a map area to be processed;

counting the grid value of each grid in a preset time length, wherein the grid value is the number of users reporting geographic position data in the corresponding grid;

screening out a grid value larger than a preset threshold value from the grid values obtained through statistics;

determining a positioning point in the grid corresponding to the screened grid value;

according to the positioning points, a curve containing all the positioning points is calculated based on a preset algorithm;

and determining an area in the curve as a hot spot area.

Optionally, the gridding the map area to be processed specifically includes:

and dividing the map area to be processed into at least one grid according to the preset side length.

Optionally, the locating point is a central point of the mesh.

Optionally, the method further includes:

acquiring the type of the POI points in the curve range;

counting the number of POI points of the same type;

and determining the type with the maximum POI point number as the hotspot type of the hotspot area.

Optionally, the method further includes:

after a positioning point is determined in the grid corresponding to the screened grid value, POI points in the map area are obtained;

the calculating a curve including all the positioning points based on a preset algorithm according to the positioning points specifically includes:

and calculating a curve containing all the positioning points and the POI points based on a preset algorithm according to the POI points and the positioning points.

Optionally, the preset algorithm is a convex hull algorithm;

the curve is a convex hull curve.

Optionally, the convex hull algorithm includes a graham algorithm, a jarvis algorithm, a center method, a horizontal method, or a fast hull method.

According to an embodiment of the present application, an apparatus for determining a hotspot region based on geographical location data includes:

the processing unit is used for carrying out gridding processing on the map area to be processed;

the statistical unit is used for counting the grid value of each grid in a preset time length, wherein the grid value is the number of users reporting the geographical position data in the corresponding grid;

the screening unit screens out the grid values larger than a preset threshold value from the grid values obtained through statistics;

a first determining unit, which determines positioning points in the grids corresponding to the screened grid values;

the calculation unit is used for calculating a curve containing all positioning points based on a preset algorithm according to the positioning points;

and the second determining unit is used for determining the area in the curve as a hot spot area.

Optionally, the processing unit specifically includes:

and dividing the map area to be processed into at least one grid according to the preset side length.

Optionally, the locating point is a central point of the mesh.

Optionally, the apparatus further comprises:

the acquisition subunit acquires the type of the POI point in the curve range;

the statistics subunit is used for counting the number of POI points of the same type;

and the determining subunit determines the type with the maximum POI point number as the hot spot type of the hot spot area.

Optionally, the apparatus further comprises:

an obtaining subunit, configured to obtain POI points in the map region after determining a location point in a grid corresponding to the screened grid value;

correspondingly, the computing unit specifically includes:

and calculating a curve containing all the positioning points and the POI points based on a preset algorithm according to the POI points and the positioning points.

Optionally, the preset algorithm is a convex hull algorithm;

the curve is a convex hull curve.

Optionally, the convex hull algorithm includes a graham algorithm, a jarvis algorithm, a center method, a horizontal method, or a fast hull method.

In the embodiment of the application, gridding processing is carried out on a map area to be processed; counting the grid value of each grid in a preset time length, wherein the grid value is the number of users reporting geographic position data in the corresponding grid; screening out a grid value larger than a preset threshold value from the grid values obtained through statistics; determining a positioning point in the grid corresponding to the screened grid value; according to the positioning points, a curve containing all the positioning points is calculated based on a preset algorithm; and determining an area in the curve as a hot spot area. Therefore, the server can automatically determine the hot spot area of the map area by using the geographical position data really reported by the user, so that the low efficiency and high cost caused by manually defining the hot spot area are avoided, the efficiency of determining the hot spot area is improved, and the cost of determining the hot spot area is reduced.

Drawings

Fig. 1 is a flowchart of a hotspot region determination method based on geographical location data according to an embodiment of the present disclosure;

FIG. 2 is a schematic illustration of a map region provided herein;

FIG. 3 is a schematic diagram of a map region after a gridding process provided by the present application;

FIG. 4 is a schematic illustration of a map region marked with grid values provided herein;

FIG. 5 is a schematic representation of screened grid values provided herein;

FIG. 6 is a schematic diagram of a coordinate system established by the present application;

FIG. 7 is a schematic diagram of a hot spot region determined according to an anchor point provided in the present application;

FIG. 8 is a schematic view of the map area of FIG. 2 with POI points added thereto;

FIG. 9 is a schematic diagram of a hotspot region determined from POI points and location fixes provided herein;

fig. 10 is a hardware structure diagram of a device in which a hotspot region determination device based on geographical location data is provided;

fig. 11 is a schematic block diagram of a hotspot region determination device based on geographical location data according to an embodiment of the present disclosure.

Detailed Description

Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.

The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in this application and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.

It is to be understood that although the terms first, second, third, etc. may be used herein to describe various information, such information should not be limited to these terms. These terms are only used to distinguish one type of information from another. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information, without departing from the scope of the present application. The word "if" as used herein may be interpreted as "at … …" or "when … …" or "in response to a determination", depending on the context.

In the prior art, hot spot areas in a map are usually divided manually, and because the manual division is often influenced by subjective mapping, the finally divided hot spot area boundaries have large errors. For example, it is easy to classify a place that is not a hot spot region into the hot spot region, or not classify a place that belongs to the hot spot region into the hot spot region.

In order to solve the above problem, please refer to fig. 1, which is a flowchart of a method for determining a hotspot area based on geographical location data according to an embodiment of the present application, where the embodiment is described from a server side, and the method includes the following steps:

step 110: and carrying out gridding processing on the map area to be processed.

In this embodiment, the map area to be processed may be selected by a worker, for example, an area circled in the map by the worker;

the server may determine the geographical area according to a geographical area list, which may be preset, and different geographical areas are configured in the geographical area list. For example, the area list is configured with an area a, an area B, and an area C; wherein, the hot spot area of the area A is determined; the server can determine the area B as a map area to be processed according to the area list. When the hot spot area of the area B is also determined, the server may determine the area C as the map area to be processed.

The gridding process may include: the server divides the map area to be processed into at least one grid according to the preset side length.

The preset side length may be an empirical value preset manually.

For example, fig. 2 is a schematic diagram of a map area provided by the present application, the map area having a length of 1300 m and a width of 1050 m. It is assumed that the preset side length is 200 meters, that is, the map area is divided into a plurality of grids with the side length of 200 meters. Fig. 3 is a schematic diagram of a map area after being subjected to a gridding process, for example, each grid in fig. 3 has a side length of 200 meters, and the map area can be divided into grids of the same size through the gridding process. As shown in fig. 3, the gridding process may not be performed for an edge area less than a preset side length in the map area. Of course, the area with less than the predetermined side length may be gridded, which is not limited herein.

In another embodiment, the gridding process may further include: the server divides the map area to be processed into a preset number of grids.

The preset number may be an empirical value that is artificially preset. For example, the map area is divided into 100 meshes.

Step 120: and counting the grid value of each grid in a preset time length, wherein the grid value is the number of users reporting the geographic position data in the corresponding grid.

In this embodiment, the reported geographic location data is, specifically, the geographic location data of the user is uploaded by the mobile terminal by using an application installed on the mobile terminal under the control of the user.

For example, in a mobile payment scenario, the mobile terminal may complete the payment process with the interacting party at the same location under the control of the user. The interacting party may be a user holding another mobile terminal or may be a fixed terminal device. The above process may involve a third party providing payment services in addition to the two or more parties interacting. Such third party payment service providers provide secure interaction services during the interaction process.

The third party payment service provider may erect its own website or provide a server to specialize in payments, such as providing a payment platform. In this way, two or more parties to a payment may complete the payment through the services provided on the payment platform. Such as eBay, Alibaba. The mobile terminal and/or the fixed terminal can access a payment platform provided by a third party payment service provider frame through the Internet, and the payment platform is utilized to complete a specific process involved in payment. Furthermore, for convenience and some security considerations, the mobile terminal and/or the fixed terminal may be equipped with a dedicated client (one of which is in the form of an application), such as an application provided by the third party payment service provider, to efficiently complete the payment.

The geographic position data is, for example, the position of the mobile terminal where the client is located, and coordinate information representing the geographic position recorded by the positioning device that can record the geographic position data via the mobile terminal. Common positioning devices may be GPS, galileo, GLONASS, or beidou, or similar combinations thereof. The coordinate information of this type of positioning is also called mobile positioning. In addition, the reported geographical location data also carries a reported timestamp under normal circumstances, and the timestamp may be the time when the positioning apparatus determines the geographical location data; or, it may be the time when the client reports the geographical location data.

The geographical location data may be obtained by converting, by the network device, based on signal characteristics of the mobile terminal where the client is located, for example, location information obtained by a network operator through base station positioning calculation by using a base station coverage principle through signals of the mobile terminal where the client is located. In the latter positioning calculation, the mobile terminal generally measures downlink pilot signals of different base stations to obtain Time of Arrival (TOA) or Time Difference of Arrival (TDOA) of downlink pilot signals of different base stations, and calculates the position of the mobile terminal by using a trigonometric formula estimation algorithm according to the measurement result and the coordinates of the base stations. The actual position estimation algorithm needs to consider the situation of multi-base-station (3 or more than 3) positioning, and in the prior art, a plurality of algorithms are complex. Generally speaking, the more base stations the mobile station measures, the higher the measurement accuracy, and the more obvious the improvement of the positioning performance.

In addition, the geographical location data can also be a more accurate location obtained by the base station assisted positioning and combined positioning device in the mobile terminal for common positioning.

Generally, the uploaded geographic location data is represented by latitude and longitude, and according to the latitude and longitude, which grid of the map area the uploaded geographic location data is located in can be determined. And the grid value in each grid within the preset time length can be counted according to the time stamp.

The preset time period may be an empirical value preset by a human. For example, 1 day is to count the grid value in each grid within 1 day, i.e. the number of users reporting location information in each grid.

And counting the number of the users to actually reflect the heat of the area corresponding to each grid in the preset time. The larger the grid value is, the more users in the grid are, the more the area corresponding to the grid tends to the hot spot area; conversely, the smaller the grid value is, the fewer users in the grid are, and the more the area corresponding to the grid tends to be the non-hotspot area.

Step 130: and screening the grid values which are larger than a preset threshold value from the grid values obtained through statistics.

In this embodiment, the preset threshold may be an empirical value preset manually.

In the example of fig. 3, after counting the number of users reporting the geographical location data in each grid, the grid value in each grid can be obtained, as shown in fig. 4, which is a schematic diagram of a map area marked with the grid value. In fig. 4, the specific map in fig. 3 has been omitted for clarity of the illustration, leaving only the grid.

Assuming that the preset threshold is 200, the grid corresponding to the grid value greater than 200 is selected. As shown in FIG. 5, the mesh values selected are shown in a schematic diagram, and the mesh numbers corresponding to the mesh values larger than 200 are numbered from p0 to p 9.

Step 140: and determining positioning points in the grids corresponding to the screened grid values.

In this embodiment, the anchor point may be a center point of the mesh.

In other embodiments, the positioning points may also be other points within the mesh, such as vertices of the mesh, points from the center points 1/2, 1/3, and the like, which are not limited in this application.

It is worth mentioning that the anchor point may also be determined by:

in the case that one of the grid values of two adjacent grids is greater than the preset threshold value and the other is less than the preset threshold value, the positioning point can be determined by the ratio of the two grid values.

For example, in the case where one grid value is 100 and the other grid value is 300, the anchor point may be set at 1/3 of the distance between the two grids.

Step 150: and calculating a curve containing all the positioning points based on a preset algorithm according to the positioning points.

In this embodiment, the preset algorithm may be used to calculate a curve including all the positioning points. Generally, when a point set X { X1, X2.,. Xn } is marked on a two-dimensional plane, a curve containing all the points in the point set X can be formed by connecting the outermost points.

In one embodiment, the predetermined algorithm may be a convex hull algorithm, and the curve may be a convex hull curve.

In this embodiment, the convex hull algorithm is used to refer to the intersection S of all convex sets containing a set of points X, for a given set of points X { X1, X2., Xn } in a real vector space, as the convex hull of X. The convex hull of X may be constructed with a linear combination of all points (X1.. Xn) within X.

In brief, in a point set on a two-dimensional plane, a convex hull is a convex polygon formed by connecting outermost points, and the convex polygon may contain all the points in the point set.

The convex hull algorithm may include a graham algorithm, a jarvis algorithm, a centric approach, a horizontal approach, or a fast hull approach.

The graham algorithm is described below as an example. On the basis of the grid shown in fig. 5, a coordinate system is established as shown in fig. 6.

The coordinates of each point in the actual point set { p0, p1, p2, p3, p4, p5, p6, p7, p8, p9} are formed by longitude and latitude, wherein the longitude is an X axis, and the latitude is a Y axis. In this embodiment, for convenience of explanation, the coordinates are simplified to integers, and in fig. 6, the coordinates of p0 are assumed to be (1, -2); p1 has the coordinate of (1, 2); p2 has coordinates of (1, -2); p3 has the coordinate of (1, 2); p4 has coordinates of (1, -2); p5 has the coordinate of (1, 2); p6 has coordinates of (1, -2); p7 has the coordinate of (1, 2); p8 has coordinates of (1, -2); p9 has coordinates of (1, -2).

The operation process of the whole convex hull algorithm is described in detail as follows:

a1: one point among all the points is selected as a base point.

Specifically, the selection of one base point may be any one of the following manners:

the first method comprises the following steps: it may be that a point having the smallest Y coordinate among all points is selected as the base point. And if a plurality of points with the minimum Y coordinate exist, selecting the point with the minimum X coordinate.

And the second method comprises the following steps: the point with the smallest X coordinate among all the points may be selected as the base point. And if a plurality of points with the minimum X coordinate exist, selecting the point with the minimum Y coordinate.

And the third is that: the point with the largest Y coordinate among all points may be selected as the base point. And if a plurality of points with the maximum Y coordinate exist, selecting the point with the maximum X coordinate.

And fourthly: the point with the largest X coordinate among all the points may be selected as the base point. And if a plurality of points with the maximum X coordinate exist, selecting the point with the maximum Y coordinate.

In the present embodiment, the second way is adopted, that is, the point with the smallest X coordinate is selected, and since p0 and p1 exist, the point with the smallest y coordinate is selected, that is, p0 is selected as the base point.

A2: and sorting according to the cosine values of the included angles between the vector formed by other points and the base points and the X axis.

In this embodiment, for any triangle, the square of any side is equal to the sum of the squares of the other two sides minus the double product of the cosines of the two sides and their included angles.

Assuming three sides as a, B and C, triangles as A, B and C, and cosine values as shown in the following formula:

wherein the cosine value is in the range of [ -1,1 ]. In the rectangular coordinate system, the cosine value is a positive value in one four quadrants, a negative value in two three quadrants, and a value of 0 on the X axis or the Y axis.

After the cosine value of the vector included angle formed by each point and the base point is calculated according to the formula, the cosine values are sorted from big to small to obtain the scanning sequence as follows: p0, p1, p2, p4, p3, p5, p6, p7, p8, p 9.

A3: and scanning according to the sorted sequence, and reserving points with the vector product larger than 0 to obtain a convex hull curve.

In this embodiment, the vector product is a binary operation of a vector in a vector space, and a calculation formula is as follows:

sin θ is the sine value of the vector AB and the vector BC, and the result of the vector product may be a positive value, a negative value, or 0.

When the vector product of the vector AB and the vector BC is larger than 0, the vector AB is represented in the clockwise direction of the vector BC; retention point B;

in the case that the vector product of the vector AB and the vector BC is less than 0, it represents that the vector AB is in the counterclockwise direction of the vector BC; deleting the point B, and connecting the AC to form a vector AC;

in the case that the vector product of vector AB and vector BC is equal to 0, it means that vector AB is collinear with vector BC; deleting point B, the connecting AC constitutes vector AC.

Scanning steps (1-14) as shown in fig. 6:

1: first, a vector p0p1 is formed by connecting p0 and p 1.

2: connecting p1 with p2 to form a vector p1p 2;

since the vector product of the vector p0p1 and the vector p1p2 is 0; so p1 is deleted and the connections p0 and p2 constitute the vector p0p 2.

3: connecting p2 with p4 to form a vector p2p 4;

since the vector product of vector p0p2 and vector p2p4 is greater than 0, p2 is retained.

4: connecting p4 with p3 to form a vector p4p 3;

since the vector product of vector p2p4 and vector p4p3 is greater than 0, p4 is retained.

5: connecting p3 with p5 to form a vector p3p 5;

since the vector product of vector p4p3 and vector p3p5 is less than 0, p3 is deleted.

6: connecting p4 and p5 to form a vector p4p 5;

since the vector product of the vector p2p4 and the vector p4p5 is greater than 0, p4 continues to be retained.

7: connecting p5 and p6 to form a vector p5p 6;

since the vector product of vector p4p5 and vector p5p6 is less than 0, p5 is deleted.

8: connecting p4 and p6 to form a vector p4p 6;

since the vector product of the vector p2p4 and the vector p4p6 is greater than 0, p4 continues to be retained.

9: connecting p6 and p7 to form a vector p6p 7;

since the vector product of vector p4p6 and vector p6p7 is greater than 0, p6 is retained.

10: connecting p7 and p8 to form a vector p7p 8;

since the vector product of vector p6p7 and vector p7p8 is less than 0, p7 is deleted.

11: connecting p6 and p8 to form a vector p6p 8;

since the vector product of the vector p4p6 and the vector p6p8 is greater than 0, p6 continues to be retained.

12: connecting p8 and p9 to form a vector p8p 9;

since the vector product of vector p6p8 and vector p8p9 is less than 0, p8 is deleted.

13: connecting p6 and p9 to form a vector p6p 9;

since the vector product of the vector p4p6 and the vector p6p9 is greater than 0, p6 continues to be retained.

14: connecting p9 and p0 to form a vector p9p 0;

since the vector product of vector p6p9 and vector p9p0 is greater than 0, p9 is retained.

In summary, the final reserved points include p0, p2, p4, p6, p 9. And the curve formed by the vector p0p2, the vector p2p4, the vector p4p6, the vector p6p9 and the vector p9p0 is a convex hull curve.

Step 160: and determining an area in the curve as a hot spot area.

Fig. 7 is a schematic diagram of a hot spot region determined according to anchor points, provided by the present application, where the curve (hot spot region boundary) in fig. 7 includes all point sets (p0 to p 9).

According to the embodiment, firstly, a server carries out gridding processing on a map area to be processed, and carries out statistics on grid values in each grid, the grid values larger than a preset threshold value are screened out from the grid values obtained through statistics, positioning points of grids corresponding to the screened grid values are determined, then a curve containing all the positioning points is calculated based on a preset algorithm, and finally the area in the curve is determined to be a hot spot area. Therefore, the server can automatically determine the hot spot area of the map area by using the geographical position data really reported by the user, so that the low efficiency and high cost caused by manually defining the hot spot area are avoided, the efficiency of determining the hot spot area is improved, and the cost of determining the hot spot area is reduced.

In practical applications, different hot spot areas are of different types, for example, the type of food square is usually a dining type, the type of residential district is usually a community type, etc. In the above embodiments, the hotspot areas obtained by the reported geographical location data do not have a type.

In order to solve the above problem, in a specific embodiment of the present application, after the step 160, the method may further include:

acquiring the type of the POI points in the curve range;

counting the number of POI points of the same type;

and determining the type with the maximum POI point number as the hotspot type of the hotspot area.

In this embodiment, the POI Point (Point of Information) is an Information Point provided by a map. The POI point may refer to an actual location. Such as a mall, supermarket, school, residential area, etc.

Generally, POI spots have categories for distinguishing different functions, such as a restaurant category, a community category, a campus category, a hospital category, a shopping category, and the like.

It is worth mentioning that the POI points may be information points provided by a local graph. Or may be information points provided by other maps.

Through the embodiment, the server can count the POI point data of the same type according to the types of the POI points in the hotspot area, and determine the type with the largest number of the POI points as the hotspot type of the hotspot area. Therefore, the hotspot type can be marked on the obtained hotspot area, a user can conveniently and quickly know the type of the hotspot area, and the user experience is improved.

In practical applications, the geographical location data reported by the user may have a certain deviation. For example, the positioning device of the mobile terminal has a deviation in positioning, which results in a deviation in the reported geographic location data. For another example, when the mobile terminal turns off the positioning device, the previous geographic location data may also be obtained, but the uploaded geographic location data is a hotspot (e.g., wifi) to which the mobile terminal is connected or a geographic location data where the base station is located, and the geographic location data may also have a deviation.

In order to solve the above problem, in a specific embodiment of the present application, the method may further include:

after a positioning point is determined in the grid corresponding to the screened grid value (step 140 above), the POI point in the map region is obtained.

Correspondingly, the step 150 specifically includes:

and calculating a curve containing all the positioning points and the POI points based on a preset algorithm according to the POI points and the positioning points.

In this embodiment, the POI points are information points provided by a map. The POI point may refer to an actual location. Such as a mall, supermarket, school, residential area, etc.

Since the POI point itself represents a certain degree of heat, and the geographical location data of the POI point is generally accurate.

It is worth mentioning that the POI points may be information points provided by a local graph. Or may be information points provided by other maps.

In this embodiment, the server calculates a curve including all the positioning points and the POI points based on a preset algorithm according to the positioning points and the obtained POI points. The calculation process of the preset algorithm is shown in the above embodiment, and is not described in detail in this embodiment.

As shown in fig. 8, a schematic diagram of POI points is added to the map area shown in fig. 2.

Accordingly, fig. 9 is a schematic diagram of a hot spot region determined according to the POI spot and the positioning point. The hot spot area shown in fig. 9 has some more hot spots than the hot spot area shown in fig. 7, so that the hot spot area is more accurate,

by the embodiment, the geographical position data reported by the user can be corrected by using the POI, so that the finally obtained hotspot area is more accurate.

Corresponding to the embodiment of the hotspot region determining method based on the geographical location data, the application also provides an embodiment of a hotspot region determining device based on the geographical location data.

The embodiments of the hotspot region determination device based on the geographic position data can be respectively applied to server equipment. The device embodiments may be implemented by software, or by hardware, or by a combination of hardware and software. The software implementation is taken as an example, and is formed by reading corresponding computer program instructions in the nonvolatile memory into the memory for operation through the processor of the device where the software implementation is located as a logical means. From a hardware aspect, as shown in fig. 10, a hardware structure diagram of a device where a hotspot region determination apparatus based on geographical location data is located according to the present application is provided, where, in addition to the processor, the network interface, the memory, and the nonvolatile memory shown in fig. 10, the device where the apparatus is located in the embodiment may generally include other hardware according to an actual function determined by the hotspot region based on the geographical location data, and details thereof are not repeated.

Referring to fig. 11, a schematic block diagram of a hotspot area determination device based on geographic location data according to an embodiment of the present application is described from a server side, where the device includes: a processing unit 610, a statistics unit 620, a filtering unit 630, a first determination unit 640, a calculation unit 650 and a second determination unit 660.

The processing unit 610 performs meshing processing on a map area to be processed;

a counting unit 620, configured to count a grid value of each grid within a preset time period, where the grid value is the number of users reporting geographic location data in a corresponding grid;

a screening unit 630, which screens out a grid value greater than a preset threshold value from the grid values obtained by statistics;

a first determining unit 640, configured to determine a positioning point in a grid corresponding to the screened grid value;

the calculating unit 650 calculates a curve containing all the positioning points based on a preset algorithm according to the positioning points;

the second determining unit 660 determines an area within the curve as a hot spot area.

In an alternative implementation:

the processing unit 610 specifically includes:

and dividing the map area to be processed into at least one grid according to the preset side length.

In an alternative implementation:

the locating point is the central point of the grid.

In an alternative implementation:

the device further comprises:

the acquisition subunit acquires the type of the POI point in the curve range;

the statistics subunit is used for counting the number of POI points of the same type;

and the determining subunit determines the type with the maximum POI point number as the hot spot type of the hot spot area.

In an alternative implementation:

the device further comprises:

an obtaining subunit, configured to obtain POI points in the map region after determining a location point in a grid corresponding to the screened grid value;

accordingly, the calculating unit 650 specifically includes:

and calculating a convex hull curve containing all the positioning points and the POI points based on a preset algorithm according to the POI points and the positioning points.

In an alternative implementation:

the convex hull algorithm comprises a graham algorithm, a jarvis algorithm, a center method, a horizontal method or a fast hull method.

In summary, according to the embodiment of the application, firstly, the server performs meshing processing on a map area to be processed, and counts the grid values in each grid, screens out the grid values larger than a preset threshold value from the counted grid values, determines the positioning points of the grid corresponding to the screened grid values, then calculates a curve including all the positioning points based on a preset algorithm, and finally determines the area in the curve as a hot spot area. Therefore, the server can automatically determine the hot spot area of the map area by using the geographical position data really reported by the user, so that the low efficiency and high cost caused by manually defining the hot spot area are avoided, the efficiency of determining the hot spot area is improved, and the cost of determining the hot spot area is reduced.

The implementation process of the functions and actions of each unit in the above device is specifically described in the implementation process of the corresponding step in the above method, and is not described herein again.

For the device embodiments, since they substantially correspond to the method embodiments, reference may be made to the partial description of the method embodiments for relevant points. The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the scheme of the application. One of ordinary skill in the art can understand and implement it without inventive effort.

Other embodiments of the present application will be apparent to those skilled in the art from consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of the invention following, in general, the principles of the application and including such departures from the present disclosure as come within known or customary practice within the art to which the invention pertains. It is intended that the specification and examples be considered as exemplary only, with a true scope and spirit of the application being indicated by the following claims.

It will be understood that the present application is not limited to the precise arrangements described above and shown in the drawings and that various modifications and changes may be made without departing from the scope thereof. The scope of the application is limited only by the appended claims.

Claims (14)

1. A hotspot region determination method based on geographical location data is characterized by comprising the following steps:

carrying out gridding processing on a map area to be processed;

counting the grid value of each grid in a preset time length, wherein the grid value is the number of users reporting geographic position data in the corresponding grid;

screening out a grid value larger than a preset threshold value from the grid values obtained through statistics;

determining a positioning point in the grid corresponding to the screened grid value;

according to the positioning points, a curve containing all the positioning points is calculated based on a preset algorithm;

and determining an area in the curve as a hot spot area.

2. The method according to claim 1, wherein the gridding the map area to be processed specifically comprises:

and dividing the map area to be processed into at least one grid according to the preset side length.

3. The method of claim 1, wherein the anchor point is a center point of the mesh.

4. The method of claim 1, further comprising:

acquiring the type of the POI points in the curve range;

counting the number of POI points of the same type;

and determining the type with the maximum POI point number as the hotspot type of the hotspot area.

5. The method of claim 1, further comprising:

after a positioning point is determined in the grid corresponding to the screened grid value, POI points in the map area are obtained;

the calculating a curve including all the positioning points based on a preset algorithm according to the positioning points specifically includes:

and calculating a curve containing all the positioning points and the POI points based on a preset algorithm according to the POI points and the positioning points.

6. The method of claim 1, wherein the predetermined algorithm is a convex hull algorithm;

the curve is a convex hull curve.

7. The method of claim 6, wherein the convex hull algorithm comprises a graham algorithm, a jarvis algorithm, a centric method, a horizontal method, or a fast hull method.

8. An apparatus for determining hot spot regions based on geographical location data, the apparatus comprising:

the processing unit is used for carrying out gridding processing on the map area to be processed;

the statistical unit is used for counting the grid value of each grid in a preset time length, wherein the grid value is the number of users reporting the geographical position data in the corresponding grid;

the screening unit screens out the grid values larger than a preset threshold value from the grid values obtained through statistics;

a first determining unit, which determines positioning points in the grids corresponding to the screened grid values;

the calculation unit is used for calculating a curve containing all positioning points based on a preset algorithm according to the positioning points;

and the second determining unit is used for determining the area in the curve as a hot spot area.

9. The apparatus according to claim 8, wherein the processing unit specifically includes:

and dividing the map area to be processed into at least one grid according to the preset side length.

10. The apparatus of claim 8, wherein the location point is a center point of the mesh.

11. The apparatus of claim 8, further comprising:

the acquisition subunit acquires the type of the POI point in the curve range;

the statistics subunit is used for counting the number of POI points of the same type;

and the determining subunit determines the type with the maximum POI point number as the hot spot type of the hot spot area.

12. The apparatus of claim 8, further comprising:

an obtaining subunit, configured to obtain POI points in the map region after determining a location point in a grid corresponding to the screened grid value;

correspondingly, the computing unit specifically includes:

and calculating a curve containing all the positioning points and the POI points based on a preset algorithm according to the POI points and the positioning points.

13. The apparatus according to claim 8, wherein the predetermined algorithm is a convex hull algorithm;

the curve is a convex hull curve.

14. The apparatus of claim 13, wherein the convex hull algorithm comprises a graham algorithm, a jarvis algorithm, a centric method, a horizontal method, or a fast hull method.