Urban night tour development analysis method and system

Abstract

The invention provides an urban night tour development analysis method and system, and belongs to the technical field of data analysis, and the method comprises the steps: calculating a first standard deviation ellipse used for representing tourist popularity space distribution according to the space coordinates and the mention frequency of all night tour scenic spots; calculating a second standard deviation ellipse used for representing the spatial distribution of night lighting facilities according to the spatial coordinates and the lighting brightness values of all the night scenic spots; according to the geometric parameters of the first standard deviation ellipse and the second standard deviation ellipse, determining urban night travel spatial-temporal characteristics used for representing the cooperative relationship between tourist popularity and night light facility spatial distribution; and analyzing the spatial-temporal characteristics of the urban night tour to obtain an analysis result. According to the method, social media data and night light remote sensing data are cooperatively fused, and a spatial feature modeling and collaborative analysis method based on a standard deviation ellipse is constructed, so that the spatial and temporal distribution pattern of urban night travel activities and the coupling evolution law of the urban night travel activities and night light facilities are quantitatively revealed.

Description

Technical Field

[0001] This invention relates to the field of data analysis technology, and in particular to a method and system for analyzing the development of urban nighttime tourism. Background Technology

[0002] With the continuous rise in urban nighttime consumption demand, nighttime tourism has gradually become an important part of the integration of urban culture and tourism. Urban management departments and scenic spot operators are paying increasing attention to the spatial distribution characteristics and evolution trends of nighttime tourism activities in order to optimize the layout of lighting facilities and enhance the tourist experience.

[0003] However, under current technological conditions, the analysis of nighttime tourism spatial characteristics often relies on a single data source, such as nighttime light intensity estimation based on remote sensing imagery or tourist hotspot identification based on social media. These methods typically focus on information expression in one dimension and lack a systematic integration of tourist behavior data and nighttime lighting data, making it difficult to reveal the spatiotemporal coupling relationship between tourist popularity and nighttime lighting facilities and its evolutionary trends. Summary of the Invention

[0004] This invention provides a method and system for analyzing the development of urban nighttime tourism, which addresses the shortcomings of existing technologies and reveals the spatiotemporal coupling relationship and evolution trend between tourist popularity and nighttime lighting facilities.

[0005] This invention provides a method for analyzing the development of urban nighttime tourism, comprising the following steps: Based on social media data of the target city, multiple nighttime tourist attractions and the frequency of mentions of each nighttime tourist attraction were determined, and the light brightness values ​​of each nighttime tourist attraction were determined based on remote sensing data of nighttime lights in the target city. Based on the spatial coordinates of all the nighttime tourist attractions and the frequency of mention, calculate the first standard deviation ellipse to characterize the spatial distribution of tourist popularity. Based on the spatial coordinates of all the nighttime attractions and the light brightness values, calculate the second standard deviation ellipse used to characterize the spatial distribution of nighttime lighting facilities; Based on the geometric parameters of the first standard deviation ellipse and the second standard deviation ellipse, the spatiotemporal characteristics of urban nighttime tourism, used to characterize the synergistic relationship between tourist popularity and the spatial distribution of nighttime lighting facilities, are determined. The spatiotemporal characteristics of nighttime tourism in the city were analyzed, and the analysis results were obtained.

[0006] According to the method for analyzing urban nighttime tourism development provided by the present invention, the step of calculating a first standard deviation ellipse to characterize the spatial distribution of tourist popularity based on the spatial coordinates of all nighttime tourist attractions and the frequency of mention includes: Using the frequency of mention of each of the aforementioned night tour attractions as the first weight, a weighted average is performed on the spatial coordinates of all the aforementioned night tour attractions to obtain the first centroid of the first standard deviation ellipse; Based on the first coordinate deviation of each night tour attraction relative to the first centroid and the first weight, calculate the first orientation angle of the first standard deviation ellipse; Based on the first orientation angle and the first coordinate deviation of each of the night tour attractions, calculate the length of the first major semi-axis and the length of the first minor semi-axis of the first standard deviation ellipse.

[0007] According to the urban nighttime tourism development analysis method provided by the present invention, the step of calculating a second standard deviation ellipse characterizing the spatial distribution of nighttime lighting facilities based on the spatial coordinates of all nighttime tourist attractions and the light intensity values ​​includes: Using the light brightness value of each of the aforementioned night tour attractions as the second weight, a weighted average is performed on the spatial coordinates of all the aforementioned night tour attractions to obtain the second centroid of the second standard deviation ellipse; Based on the second coordinate deviation of each night tour attraction relative to the second centroid and the second weight, calculate the second orientation angle of the second standard deviation ellipse; Based on the second orientation angle and the second coordinate deviation of each of the night tour attractions, calculate the length of the second major semi-axis and the length of the second minor semi-axis of the second standard deviation ellipse.

[0008] According to the present invention, an urban nighttime tourism development analysis method is provided, wherein determining the spatiotemporal characteristics of urban nighttime tourism, which characterize the synergistic relationship between tourist popularity and the spatial distribution of nighttime lighting facilities, based on the geometric parameters of the first and second standard deviation ellipses, includes: Extract the first centroid of the first standard deviation ellipse and the second centroid of the second standard deviation ellipse; Calculate the Euclidean distance between the first centroid and the second centroid; The Euclidean distance is used as a spatial offset distance to quantify the synergistic relationship between tourist popularity and the spatial distribution of nighttime lighting facilities.

[0009] The urban nighttime tourism development analysis method provided by the present invention further includes: Kernel density estimation analysis was performed on the spatial coordinates of all the nighttime tourist attractions to generate a continuous density surface characterizing the spatial clustering degree of the nighttime tourist attractions; Based on the continuous density surface, hotspot areas for nighttime tourism are identified, and the spatial clustering characteristics of all the nighttime tourist attractions are obtained.

[0010] According to the method for analyzing urban nighttime tourism development provided by the present invention, the step of analyzing the spatiotemporal characteristics of urban nighttime tourism to obtain analysis results includes: Keywords are extracted from the social media data to construct a topic semantic network, and topic clustering analysis is performed based on the topic semantic network to identify topics of public interest. Using the aforementioned topics of public concern as driving factors, the spatiotemporal characteristics of urban nighttime tourism were analyzed, and the analysis results were obtained.

[0011] According to a method for analyzing urban nighttime tourism development provided by the present invention, the step of extracting keywords from the social media data to construct a topic semantic network, and performing topic clustering analysis based on the topic semantic network to identify topics of public interest, includes: Keywords were extracted from the social media data using the term frequency-inverse document frequency method. The topic semantic network is constructed using the keywords as network nodes and the co-occurrence relationships between the keywords as network edges. The topic semantic network is clustered into communities, and the communities obtained from the clustering are taken as the topics of public concern.

[0012] This invention also provides an urban nighttime tourism development analysis system, comprising the following modules: The first processing module is used to determine multiple nighttime tourist attractions, the spatial coordinates of each nighttime tourist attraction, and the frequency of mention of each nighttime tourist attraction based on social media data of the target city, and to determine the light brightness value of each nighttime tourist attraction based on remote sensing data of nighttime lights of the target city. The second processing module is used to calculate the first standard deviation ellipse representing the spatial distribution of tourist popularity based on the spatial coordinates of all the night tour attractions and the frequency of mention. The second processing module is also used to calculate a second standard deviation ellipse to characterize the spatial distribution of nighttime lighting facilities based on the spatial coordinates of all the nighttime tourist attractions and the light brightness values. The third processing module is used to determine the spatiotemporal characteristics of urban night tourism based on the geometric parameters of the first standard deviation ellipse and the second standard deviation ellipse, which characterize the synergistic relationship between tourist popularity and the spatial distribution of nighttime lighting facilities. The fourth processing module is used to analyze the spatiotemporal characteristics of the city's nighttime tourism and obtain the analysis results.

[0013] The present invention also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the urban nighttime tourism development analysis method as described above.

[0014] The present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the urban nighttime tourism development analysis method as described above.

[0015] The present invention also provides a computer program product, including a computer program that, when executed by a processor, implements the urban nighttime tourism development analysis method as described above.

[0016] In summary, one or more technical solutions provided in the embodiments of this application have at least the following technical effects or advantages: By identifying multiple nighttime tourist attractions and their mention frequencies based on social media data from the target city, and combining this with nighttime light remote sensing data to determine the light intensity values ​​of each attraction, a multi-source data association is established between the tourist behavior layer and the urban lighting layer, providing a comprehensive data foundation for subsequent spatial feature modeling. Furthermore, by calculating the first standard deviation ellipse representing the spatial distribution of tourist popularity based on the spatial coordinates and mention frequencies of all nighttime tourist attractions, the spatial concentration and directionality of urban nighttime activities can be quantified, reflecting the spatial pattern of tourist popularity. Finally, by calculating the second standard deviation ellipse representing the spatial distribution of nighttime lighting facilities based on the spatial coordinates and light intensity values ​​of all nighttime tourist attractions, a second standard deviation ellipse representing the spatial distribution of nighttime lighting facilities is also calculated. This study aims to characterize the spatial distribution and dominant direction of urban nighttime lighting facilities, reflecting the spatial characteristics of nighttime environmental construction. By determining the spatiotemporal characteristics of urban nighttime tourism based on the geometric parameters of the first and second standard deviation ellipses, which characterize the synergistic relationship between tourist activity and the spatial distribution of nighttime lighting facilities, the study can quantitatively analyze the spatial matching degree between tourist gathering areas and nighttime lighting facility coverage areas, revealing the spatial coupling law of nighttime tourism development. By analyzing the spatiotemporal characteristics of urban nighttime tourism and obtaining the analysis results, the study can identify the evolution trend of nighttime tourism spatial patterns in different periods or regions, clarify the spatiotemporal evolution law of urban nighttime tourism development, and the synergistic relationship between lighting construction and tourist behavior. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0018] Figure 1 This is one of the flowcharts illustrating the urban nighttime tourism development analysis method provided by this invention.

[0019] Figure 2 This is the second flowchart of the urban nighttime tourism development analysis method provided by the present invention.

[0020] Figure 3 This is the third flowchart of the urban nighttime tourism development analysis method provided by this invention.

[0021] Figure 4 This is the fourth flowchart of the urban nighttime tourism development analysis method provided by the present invention.

[0022] Figure 5 This is a schematic diagram illustrating the temporal changes of the center of gravity provided by the present invention.

[0023] Figure 6 This is a schematic diagram illustrating the change in Euclidean distance between the positional centroid and the luminous centroid provided by the present invention.

[0024] Figure 7 This is the fifth flowchart of the urban nighttime tourism development analysis method provided by this invention.

[0025] Figure 8 This is a schematic diagram illustrating the evolution trend of the spatial pattern of nighttime tourism hotspots provided by the present invention, based on kernel density analysis.

[0026] Figure 9 This is the sixth flowchart of the urban nighttime tourism development analysis method provided by this invention.

[0027] Figure 10 This is the seventh flowchart of the urban nighttime tourism development analysis method provided by the present invention.

[0028] Figure 11 This is a schematic diagram of the urban nighttime tourism development analysis system provided by the present invention.

[0029] Figure 12 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0030] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of this invention. All other embodiments obtained by those skilled in the art based on the embodiments of this invention without creative effort are within the scope of protection of this invention.

[0031] It should be noted that in the description of this invention, the terms "comprising," "including," or any other variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element. The terms "upper," "lower," etc., indicating orientation or positional relationships according to the accompanying drawings, are only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the system or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation of the invention. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0032] The terms "first," "second," etc., used in this invention are used to distinguish similar objects, not to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that embodiments of the invention can be implemented in orders other than those illustrated or described herein, and the objects distinguished by "first," "second," etc., are generally of the same class, without limiting the number of objects; for example, a first object can be one or more. Furthermore, "and / or" indicates at least one of the connected objects, and the character " / " generally indicates that the preceding and following objects are in an "or" relationship.

[0033] The following is combined with Figures 1-8 This invention describes the urban nighttime tourism development analysis method, system, electronic device, storage medium, and computer program product provided by this invention.

[0034] Reference Figure 1 , Figure 1 This is one of the flowcharts illustrating the urban nighttime tourism development analysis method provided by this invention, such as... Figure 1 As shown, steps 101 to 105 are included: Step 101: Based on social media data of the target city, identify multiple nighttime tourist attractions and the frequency of mentions for each attraction, and based on remote sensing data of nighttime lights in the target city, determine the light brightness values ​​for each attraction.

[0035] In step 101, social media data refers to text data related to tourism activities in the target city obtained from various social media platforms. It can be obtained by searching social media platforms using keywords such as "city + tourism" or "city + travel".

[0036] Nighttime light remote sensing data refers to objective physical data reflecting the nighttime light intensity of a target city, obtained from remote sensing products such as NPP / VIIRS.

[0037] Nighttime tourist attractions refer to specific scenic spots or locations identified from social media data that are associated with nighttime tourism activities.

[0038] Frequency of mention is a quantitative indicator that refers to the number of times each nighttime attraction is mentioned in filtered social media data, used to represent tourist popularity.

[0039] Light brightness value is also a quantitative indicator, referring to the pixel value in the remote sensing data map of nighttime light corresponding to the location of each nighttime tourist attraction, used to characterize the construction level of nighttime facilities.

[0040] In practice, taking Luoyang City, Henan Province as an example, the text data on social media platforms during March to May of each year (e.g., from 2014 to 2024, excluding data from special years) are collected using keywords such as "Luoyang" and "tourism" as the raw social media data.

[0041] Then, keywords such as "evening," "night," and "light show" were used to initially filter the raw social media data. Next, a large model, such as the ERNIE model, was used to perform topic filtering on the initially filtered text to classify social media data with nighttime tourism semantics.

[0042] Next, named entity recognition tools, such as CNN and BERT models, are used to extract scenic area information from the filtered text, thereby identifying multiple night tour attractions, and counting the number of times each night tour attraction appears as the mention frequency corresponding to each night tour attraction.

[0043] Simultaneously, monthly average nighttime light remote sensing products from NPP / VIIRS covering Luoyang City within the corresponding time period were acquired as nighttime light remote sensing data. Finally, the spatial coordinates of each identified nighttime tourist attraction were spatially matched with the nighttime light remote sensing data, and the brightness values ​​at each attraction's coordinates were extracted as the light brightness values ​​for each nighttime tourist attraction.

[0044] Step 102: Calculate the first standard deviation ellipse to characterize the spatial distribution of tourist popularity based on the spatial coordinates and mention frequency of all night tour attractions.

[0045] The first standard deviation ellipse is a geometric figure whose center, orientation angle, and major and minor axes, among other geometric parameters, collectively describe the spatial distribution characteristics of a set of geographical features. Here, this ellipse is generated by statistically calculating the spatial coordinates of nighttime tourist attractions with mention frequency weights, and the results reflect the overall distribution of tourist popularity within the urban geographic space.

[0046] In practice, for a specific time period obtained from step 101, such as all nighttime tourist attractions in 2014, their spatial coordinates and corresponding mention frequencies are used as input parameters. Then, the standard deviation ellipse calculation process is performed. In this calculation, the mention frequency of each nighttime tourist attraction is used as a weight to determine the degree of influence of that attraction on the geometric parameters of the final generated ellipse. Attractions with higher mention frequencies contribute more to the center position and shape of the ellipse. Through this calculation, a first standard deviation ellipse that characterizes the spatial distribution of tourist popularity for that year is obtained. Repeating this operation for other years yields a series of ellipses that vary by year.

[0047] In one specific implementation, refer to Figure 2 , Figure 2 This is the second flowchart of the urban nighttime tourism development analysis method provided by the present invention. Step 102 specifically includes steps 201 to 203, which are used to generate the geometric parameters of the first standard deviation ellipse.

[0048] First, perform step 201: using the frequency of mention of each night tour attraction as the first weight, perform a weighted average of the spatial coordinates of all night tour attractions to obtain the first centroid of the first standard deviation ellipse.

[0049] The first weight is the frequency of mention of each night tour attraction determined in step 101, which is used to measure the importance of different attractions when calculating the center of gravity.

[0050] The first centroid represents the geographical center point of tourist popularity distribution. Specifically, in the calculation, the spatial coordinates of the i-th nighttime attraction are defined as follows: Its corresponding first weight is denoted as Through the formula and Calculate the weighted average center of all n nighttime tourist attractions, and obtain the coordinates. This is the first center of gravity.

[0051] After obtaining the first centroid, proceed to step 202: calculate the first orientation angle of the first standard deviation ellipse based on the first coordinate deviation and the first weight of each night tour attraction relative to the first centroid.

[0052] The first coordinate deviation refers to the difference between the spatial coordinates of each night tour attraction and the coordinates of its first centroid, denoted as . and The first direction angle is the angle that characterizes the main direction of the spatial distribution of tourist popularity.

[0053] In practice, the first step is to calculate the first coordinate deviation of all nighttime tourist attractions. Then, based on the first coordinate deviation and corresponding first weight of each attraction, a calculation is performed to obtain the first direction angle θ. This angle is typically defined as the clockwise angle between the major axis of the ellipse and true north. .

[0054] After determining the first orientation angle, proceed to step 203: calculate the length of the first major semi-axis and the length of the first minor semi-axis of the first standard deviation ellipse based on the first orientation angle and the first coordinate deviation of each night tour attraction.

[0055] The lengths of the first major and minor axes represent the degree of dispersion of tourist popularity in the primary and secondary directions, respectively.

[0056] In the specific calculation, the obtained first direction angle θ and the first coordinate deviation of all night tour attractions will be used. Substitute the values ​​into the formulas for calculating the major and minor semi-axles, that is, use the formulas... The length of the first major semi-axis is calculated using the formula. The length of the first minor semi-axis is calculated. The lengths of the first major semi-axis and the first minor semi-axis together define the shape and size of the first standard deviation ellipse, reflecting the dispersion and directionality of the spatial distribution; coefficients This ensures that the ellipse covers approximately 68% of the point data.

[0057] Step 103: Calculate the second standard deviation ellipse to characterize the spatial distribution of nighttime lighting facilities based on the spatial coordinates and light brightness values ​​of all nighttime attractions.

[0058] The second standard deviation ellipse is also a geometric figure. Its center, orientation angle, major and minor axes, and other geometric parameters are used to comprehensively describe the distribution of nighttime lighting facilities in geographic space. This ellipse is generated by statistically calculating the spatial coordinates of nighttime tourist attractions with weighted light intensity values, and the results reflect the overall spatial pattern of urban nighttime physical lighting construction.

[0059] In practice, for a specific time period obtained from step 101, such as all nighttime tourist attractions in 2014, their spatial coordinates and corresponding light intensity values ​​are used as input parameters. Then, the standard deviation ellipse calculation process is performed. In this calculation, the light intensity value of each nighttime tourist attraction is used as a weight to determine the degree of influence that attraction has on the geometric parameters of the final generated ellipse. Attractions with higher light intensity values ​​contribute more to the center position and shape of the ellipse. Through this calculation, a second standard deviation ellipse that characterizes the spatial distribution of nighttime lighting facilities for that year is obtained. Repeating this operation for other years yields a series of second standard deviation ellipses that vary by year.

[0060] In one specific implementation, refer to Figure 3 , Figure 3 This is the third flowchart of the urban nighttime tourism development analysis method provided by the present invention. Step 103 specifically includes steps 301 to 303, which are used to generate the geometric parameters of the second standard deviation ellipse.

[0061] First, perform step 301: using the light brightness value of each night tour attraction as the second weight, perform a weighted average of the spatial coordinates of all night tour attractions to obtain the second centroid of the second standard deviation ellipse.

[0062] The second centroid is the geographical center point representing the distribution of light intensity. Specifically, in the calculation, the spatial coordinates of the i-th nighttime attraction are defined as follows: Its corresponding second weight is denoted as Through the formula and Calculate the weighted average center of all n nighttime tourist attractions, and obtain the coordinates. This is the second center of gravity.

[0063] After obtaining the second centroid, proceed to step 302: calculate the second orientation angle of the second standard deviation ellipse based on the second coordinate deviation and second weight of each night tour attraction relative to the second centroid.

[0064] The second coordinate deviation refers to the difference between the spatial coordinates of each night tour attraction and the coordinates of its second centroid, denoted as . and The second directional angle is the angle that characterizes the main direction of the spatial distribution of tourist popularity.

[0065] In practice, the second coordinate deviation of all nighttime tourist attractions is first calculated. Then, based on the second coordinate deviation of all nighttime tourist attractions and their corresponding second weights, the second direction angle β is calculated. This angle is usually defined as the clockwise angle between the major axis of the ellipse and true north. .

[0066] After determining the second orientation angle, proceed to step 303: calculate the length of the second major semi-axis and the length of the second minor semi-axis of the second standard deviation ellipse based on the second orientation angle and the second coordinate deviation of each night tour attraction.

[0067] The lengths of the second major and minor semi-axis represent the degree of dispersion of light intensity in the primary and secondary directions, respectively.

[0068] In the specific calculation, the obtained second direction angle β and the second coordinate deviation of all night tour attractions will be used. Substitute the values ​​into the formulas for calculating the major and minor semi-axles, that is, use the formulas... The length of the second major semi-axis is calculated using the formula. The length of the second minor semi-axis is calculated. Similarly, the lengths of the second major semi-axis and the second minor semi-axis together define the shape and size of the second standard deviation ellipse, reflecting the dispersion and directionality of the spatial distribution; coefficients This ensures that the ellipse covers approximately 68% of the point data.

[0069] The above series of steps, by generating first and second standard deviation ellipses, intuitively and quantitatively reveal the overall spatial pattern of urban nighttime tourism hotspots, rather than focusing on the performance of individual attractions. This transforms discrete locations of nighttime tourism attractions, representing tourist popularity and light intensity, into standardized geometric features that can be analyzed macroscopically. This provides a quantitative geometric basis for subsequent analysis of the temporal migration of tourist popularity and light intensity centers of gravity, the evolution of distribution direction, and the trend of aggregation or dispersion.

[0070] Step 104: Based on the geometric parameters of the first standard deviation ellipse and the second standard deviation ellipse, determine the spatiotemporal characteristics of urban nighttime tourism used to characterize the synergistic relationship between tourist popularity and the spatial distribution of nighttime lighting facilities.

[0071] In step 104, the spatiotemporal characteristics of urban nighttime tourism used to characterize the synergistic relationship between tourist popularity and the spatial distribution of nighttime lighting facilities refer to one or more quantitative indicators used to describe the degree of spatial coupling between the overall distribution of tourist popularity and the overall distribution of nighttime lighting facilities. These indicators are obtained by comparing and analyzing the geometric parameters of two ellipses (such as the position of the centroid, orientation angle, length of the major semi-axis, length of the minor semi-axis, etc.).

[0072] In practice, for the same time period obtained from steps 102 and 103, such as the first and second standard deviation ellipses for 2014, their geometric parameters are compared and analyzed to obtain a quantitative value that characterizes the spatial relationship between the distribution of tourist activity and the distribution of nighttime lighting facilities for that year. Applying this operation to multiple consecutive time periods (e.g., each year from 2014 to 2024) yields a time series composed of this quantitative value. This time series dynamically reflects the evolution of the synergistic relationship between the center of gravity of tourist activity and the center of gravity of nighttime lighting facilities.

[0073] In one specific implementation, refer to Figure 4 , Figure 4 This is the fourth flowchart of the urban nighttime tourism development analysis method provided by the present invention. Step 104 specifically includes steps 401 to 403: The process first executes step 401: extracting the first centroid of the first standard deviation ellipse and the second centroid of the second standard deviation ellipse.

[0074] The first centroid, calculated in step 201, represents the central trend of the spatial distribution of tourist popularity. The second centroid, calculated in subsequent similar calculation steps with weights based on light brightness values, represents the central trend of the spatial distribution of nighttime lighting facilities.

[0075] In practice, the geospatial coordinates of the first centroid are obtained from the calculation results of step 102. The geospatial coordinates of the second centroid are obtained from the calculation results of step 103. These two coordinate points represent the central areas of tourist attention and the central areas of nighttime lighting construction, respectively, during the same period.

[0076] Next, proceed to step 402: calculate the Euclidean distance between the first centroid and the second centroid.

[0077] Euclidean distance is the straight-line distance between two points on a two-dimensional plane, and it is the most direct geometric indicator for measuring differences in spatial location.

[0078] In specific implementation, the two centroid coordinates extracted in step 401 will be used... and Substitute into the Euclidean distance calculation formula The calculated value D, in meters, directly represents the straight-line distance between the center of tourist activity and the center of the nighttime lighting facilities in geographical space.

[0079] Next, step 403 is executed: using Euclidean distance as a spatial offset distance to quantify the synergistic relationship between tourist popularity and the spatial distribution of nighttime lighting facilities.

[0080] Spatial offset distance is a quantitative indicator that directly reflects the degree of matching between subjective tourist hotspots and objectively constructed nighttime lighting areas in terms of macro-spatial layout.

[0081] In practice, the Euclidean distance D calculated in step 402 is directly used as the spatial offset distance for that period.

[0082] For example, refer to Figure 5 , Figure 5 This is a schematic diagram illustrating the temporal change of the center of gravity provided by this invention. In the diagram, the positional center of gravity is the first center of gravity, and the luminescent center of gravity is the second center of gravity. From Figure 5 It can be seen that the spatial center of gravity of tourist popularity and nighttime lighting exhibits significant dynamic changes over time.

[0083] Reference Figure 6 , Figure 6 This is a schematic diagram illustrating the change in Euclidean distance between the center of gravity of the location and the center of gravity of the luminous material provided by this invention. For example... Figure 5 and Figure 6As shown, in 2014, the center of gravity of the luminous display was located northeast of the center of gravity of the location, and the Euclidean distance between the center of gravity of the location and the center of gravity of the luminous display was 6072.05m, indicating a significant disconnect between early nighttime lighting activities and tourist gathering areas. In 2015, the center of gravity of the luminous display was located north of the center of gravity of the location, and the Euclidean distance between the center of gravity of the location and the center of gravity of the luminous display plummeted to 3303.61m, indicating that the luminous facilities rapidly moved closer to tourist hotspots, and the spatial fit improved significantly. From 2017 to 2019, the Euclidean distance between the center of gravity of the location and the center of gravity of the luminous display remained within the range of 2912.5-4035.05m, showing slight fluctuations, and the spatial matching was relatively stable. Entering 2023-2024, the Euclidean distance between the center of gravity of the location and the center of gravity of the luminous display rebounded to approximately 4400m, reflecting a trend where the expansion of the luminous display coverage was faster than the adjustment of actual tourist activity hotspots, causing some spatial misalignment. Overall, the center of gravity of the night lights shifted eastward to northeastward, while the center of gravity of the attractions remained relatively stable near the city's core area.

[0084] In one possible implementation, refer to Figure 7 , Figure 7 This is the fifth flowchart of the urban night tourism development analysis method provided by the present invention. The method also includes steps 501 and 502, which are used to more precisely identify the spatial clustering characteristics of night tourism attractions.

[0085] The process first performs step 501: performing kernel density estimation analysis on the spatial coordinates of all nighttime tourist attractions to generate a continuous density surface characterizing the spatial clustering degree of the nighttime tourist attractions.

[0086] Kernel density estimation analysis is a spatial analysis method that transforms discrete point features into a continuous raster surface. The continuous density surface is the output of this analysis, where the value at any point on the surface represents the density of the point features surrounding that location. In practice, the spatial coordinates of all nighttime tourist attractions determined in step 101 are used as the input point set. Then, the kernel density estimation calculation formula is applied... The calculation is performed. In this formula, n is the total number of nighttime tourist attractions, and h is the specified search radius. Let x be the distance between position x and the i-th night tour attraction, and K be the kernel function.

[0087] By performing this calculation on each grid cell within the target city area, a density estimate f(x) for each cell is obtained, and the estimates of all cells together constitute a complete continuous density surface.

[0088] Next, step 502 is executed: identify hot spots for nighttime tourism based on continuous density surfaces to obtain the spatial clustering characteristics of nighttime tourist attractions.

[0089] Nighttime tourism hotspots refer to areas that exhibit high density values ​​on a continuous density surface. These areas represent high-frequency clusters of nighttime tourism attractions in geographical space.

[0090] In practice, the generated continuous density surface is analyzed. By setting a density threshold or by performing hierarchical rendering of density values, contiguous areas with significantly higher density values ​​than the surrounding areas are identified and delineated. These delineated high-value areas are the hot spots for nighttime tourism.

[0091] For example, refer to Figure 8 , Figure 8 This invention provides a schematic diagram of the kernel density analysis of the evolution trend of the spatial pattern of nighttime tourism hotspots. Analysis of the continuous density surface in Luoyang City from 2014 to 2024 reveals that from 2014 to 2016, nighttime tourism hotspots were characterized by low intensity, dispersion, and a lack of stable core areas. From 2017 to 2019, the intensity of hotspots significantly increased, forming a "dual-core" pattern in the old city and Longmen area. From 2023 to 2024, the intensity of core hotspots further increased, while "emerging hotspots" appeared near subway lines; meanwhile, the popularity of some early suburban attractions declined, showing a clear hotspot replacement effect. This change reflects that tourists' nighttime tourism preferences are gradually concentrating on historical and cultural core areas and distinctive nighttime consumption zones. By comparing the continuous density surfaces generated in different years, the evolution trend of the spatial pattern of nighttime tourism hotspots can also be dynamically displayed.

[0092] Step 105: Analyze the spatiotemporal characteristics of urban nighttime tourism and obtain the analysis results.

[0093] In one specific implementation, refer to Figure 9 , Figure 9 This is the sixth flowchart of the urban nighttime tourism development analysis method provided by the present invention. Step 105 specifically includes steps 601 to 602: Step 601: Extract keywords from social media data to construct a topic semantic network, and perform topic clustering analysis based on the topic semantic network to identify topics of public interest.

[0094] In step 601, keywords refer to words selected from social media texts related to nighttime activities that represent the core content of the text. A topic semantic network is a structured model that uses keywords as basic units and the relationships between keywords as connections to reveal the inherent logic between public discussion focuses. Topic clustering analysis is an analytical method for dividing this network, aiming to aggregate closely related keywords into different communities. The public's focus topics are the core semantics represented by each keyword community obtained from topic clustering analysis, reflecting the public's specific interests and discussion directions during nighttime activities.

[0095] In practice, the process begins by extracting keywords from relevant social media text data related to a specific nighttime tourist attraction or a specific time period to obtain a set of high-value keywords. Next, the patterns of these keywords' interconnected occurrences in the original text are analyzed, and a thematic semantic network is constructed based on this analysis. Then, the constructed network undergoes thematic clustering analysis, dividing the entire network into several sub-communities with tight internal connections and sparse external connections. Finally, the core vocabulary within each sub-community is analyzed and summarized into a specific topic of public concern.

[0096] In one specific implementation, refer to Figure 10 , Figure 10 This is the seventh flowchart of the urban nighttime tourism development analysis method provided by the present invention. Step 601 specifically includes steps 701 to 703: The process first performs step 701: extracting keywords from social media data using the term frequency-inverse document frequency method.

[0097] Term frequency-inverse document frequency (TF-IDF) is a statistical method used to assess the importance of a word to a document in a set of documents or a corpus.

[0098] In practice, the term frequency (TF) is first calculated, and the formula is as follows: ,in This indicates the number of times the word 'w' appears in the idth social media text. This indicates the frequency of the most frequently occurring word in the text.

[0099] Then calculate the inverse document frequency (IDF), the formula of which is: Where N is the total number of social media texts, This represents the total number of texts containing the word 'w'.

[0100] Finally, multiply the two to obtain the TF-IDF value, i.e. This method calculates the TF-IDF value of each word in every social media text related to nighttime travel, and selects words with higher values ​​as keywords representing the core semantics of the text.

[0101] Next, perform step 702: construct a topic semantic network using keywords as network nodes and co-occurrence relationships between keywords as network edges.

[0102] In a network, a node represents an independent entity, which in this case is the keyword extracted in step 701. A network edge connects two nodes, representing a specific relationship between them, which in this case is the co-occurrence relationship between keywords.

[0103] In practice, all extracted keywords are created as nodes in the network. Then, all social media texts are traversed; if two keywords appear together in the same text, an edge is created between the nodes representing those two keywords. Simultaneously, visual attributes of the network elements are set to reflect their weights: the size of a node is determined by the keyword's frequency. The higher the word frequency, the larger the node; the thickness of the edge is determined by the co-occurrence strength of the two keywords (i.e., the number of times they appear together), and the higher the co-occurrence strength, the thicker the edge.

[0104] Next, step 703 is performed: community clustering is performed on the topic semantic network, and the clustered communities are taken as topics of public concern.

[0105] Community clustering is a network analysis technique that aims to divide nodes in a network into subsets such that the connections between nodes within each subset are much stronger than the connections between nodes in different subsets.

[0106] In practice, the Louvain algorithm is used to analyze the topic semantic network constructed in step 702. This algorithm divides the entire network into multiple independent communities by maximizing the network's modularity index. Each community is a set of semantically closely related keywords. Finally, the keywords within each community are interpreted and summarized into a public concern topic. For example, keywords such as "night view," "taking photos," and "Hanfu" surrounding "Yingtianmen" are clustered into a community, which is identified as the public concern topic of "night tour participation."

[0107] Step 602: Using public concerns as driving factors, analyze the spatiotemporal characteristics of urban nighttime tourism and obtain the analysis results.

[0108] In step 602, the driving factors refer to the underlying reasons explaining the evolution of the identified urban nighttime tourism spatial patterns. Analyzing the spatiotemporal characteristics of urban nighttime tourism is a comprehensive explanatory process aimed at establishing a logical connection between tourists' subjective preferences and the evolution of urban nighttime tourism spatial patterns.

[0109] In practice, the analysis unfolds from two aspects. On the one hand, it uses topics of public concern as driving factors to analyze the spatiotemporal characteristics of urban nighttime tourism.

[0110] For example, by comparing the changes in spatial offset distance in different years with the evolution of public attention themes, it was found that tourists’ attention themes have shifted from scattered sightseeing needs to concentrated cultural experience needs. This shift is the reason why the first center of gravity representing tourist popularity remains stable in the urban core area, and does not migrate with the outward expansion of the second center of gravity representing lighting construction. This explains the spatiotemporal characteristics of the spatial offset distance between the two centers of gravity first decreasing and then increasing.

[0111] On the other hand, the spatial clustering characteristics of nighttime tourist attractions identified based on continuous density surfaces are analyzed by using public interest themes as driving factors. For example, the evolution of the identified nighttime tourist hotspot patterns (such as the evolution from a decentralized to a "dual-core" pattern) is correlated with the identified public interest themes. Based on the above example, it is precisely because public interest themes have evolved into immersive experiences centered on "Hanfu," "night views," and "performances" that tourist behavior has become highly concentrated in areas such as the old city and Longmen that offer such experiences, thus explaining the formation and strengthening of the "dual-core" hotspot pattern presented by the kernel density analysis.

[0112] By establishing the connection between public demand (driving factors), macro-spatial patterns (spatial-temporal characteristics), and micro-spatial agglomeration (hotspot areas), this study reveals the intrinsic driving mechanism of urban nighttime tourism development. This solves the technical problem that existing technologies can usually only describe phenomena but cannot explain causes, enabling the analysis results to provide a scientific basis for optimizing nighttime tourism policies and accurately planning nighttime tourism projects.

[0113] Reference Figure 11 , Figure 11 This is a schematic diagram of the urban nighttime tourism development analysis system provided by the present invention. The system includes: The first processing module is used to determine multiple nighttime tourist attractions and the frequency of mention of each nighttime tourist attraction based on social media data of the target city, and to determine the light brightness value of each nighttime tourist attraction based on remote sensing data of nighttime lights of the target city. The second processing module is used to calculate the first standard deviation ellipse representing the spatial distribution of tourist popularity based on the spatial coordinates and mention frequency of all night tour attractions; The second processing module is also used to calculate the second standard deviation ellipse to characterize the spatial distribution of nighttime lighting facilities based on the spatial coordinates and light brightness values ​​of all nighttime tourist attractions. The third processing module is used to determine the spatiotemporal characteristics of urban night tourism based on the geometric parameters of the first and second standard deviation ellipses to characterize the synergistic relationship between tourist popularity and the spatial distribution of nighttime lighting facilities. The fourth processing module is used to analyze the spatiotemporal characteristics of urban nighttime tourism and obtain the analysis results.

[0114] In one possible implementation, the second processing module is further configured to: Using the frequency of mention of each night tour attraction as the first weight, the spatial coordinates of all night tour attractions are weighted and averaged to obtain the first centroid of the first standard deviation ellipse; Based on the first coordinate deviation and first weight of each night tour attraction relative to the first centroid, calculate the first orientation angle of the first standard deviation ellipse; Based on the first orientation angle and the first coordinate deviation of each night tour attraction, calculate the length of the first major semi-axis and the length of the first minor semi-axis of the first standard deviation ellipse.

[0115] In one possible implementation, the second processing module is further configured to: Using the light intensity value of each night tour attraction as the second weight, the spatial coordinates of all night tour attractions are weighted and averaged to obtain the second centroid of the second standard deviation ellipse; Based on the second coordinate deviation and second weight of each night tour attraction relative to the second centroid, calculate the second orientation angle of the second standard deviation ellipse; Based on the second direction angle and the second coordinate deviation of each night tour attraction, calculate the length of the second major semi-axis and the length of the second minor semi-axis of the second standard deviation ellipse.

[0116] In one possible implementation, the third processing module is further configured to: Extract the first centroid of the ellipse with the first standard deviation and the second centroid of the ellipse with the second standard deviation; Calculate the Euclidean distance between the first and second centroids; Euclidean distance is used as a quantifiable spatial offset distance to characterize the synergistic relationship between tourist popularity and the spatial distribution of nighttime lighting facilities.

[0117] In one possible implementation, the third processing module is further configured to: Kernel density estimation analysis was performed on the spatial coordinates of all nighttime tourist attractions to generate a continuous density surface characterizing the spatial clustering degree of nighttime tourist attractions; By identifying nighttime tourism hotspots based on continuous density surfaces, the spatial clustering characteristics of all nighttime tourist attractions can be obtained.

[0118] In one possible implementation, the fourth processing module is further configured to: Keywords were extracted from social media data to construct a topic semantic network, and topic clustering analysis was performed based on the topic semantic network to identify topics of public interest. By taking topics of public concern as driving factors, we analyzed the spatiotemporal characteristics of urban nighttime tourism and obtained the analysis results.

[0119] In one possible implementation, the fourth processing module is further configured to: Keyword extraction from social media data using the term frequency-inverse document frequency method; A topic semantic network is constructed using keywords as network nodes and co-occurrence relationships between keywords as network edges. Community clustering is performed on the topic semantic network, and the communities obtained from the clustering are taken as topics of public concern.

[0120] It should be noted that the urban nighttime tourism development analysis system provided by the present invention can execute the urban nighttime tourism development analysis method of any of the above embodiments during specific operation, which will not be elaborated in this embodiment.

[0121] Figure 12 This is a schematic diagram of the structure of the electronic device provided by the present invention, such as... Figure 12 As shown, the electronic device may include: a processor 1210, a communication interface 1220, a memory 1230, and a communication bus 1240, wherein the processor 1210, the communication interface 1220, and the memory 1230 communicate with each other through the communication bus 1240. The processor 1210 can call logical instructions in the memory 1230 to execute an urban nighttime tourism development analysis method. This method includes: determining multiple nighttime tourism attractions and their mention frequencies based on social media data of the target city; determining the light brightness values ​​of each nighttime tourism attraction based on remote sensing data of nighttime lights in the target city; calculating a first standard deviation ellipse characterizing the spatial distribution of tourist popularity based on the spatial coordinates and mention frequencies of all nighttime tourism attractions; calculating a second standard deviation ellipse characterizing the spatial distribution of nighttime lighting facilities based on the spatial coordinates and light brightness values ​​of all nighttime tourism attractions; determining the spatiotemporal characteristics of urban nighttime tourism characterizing the synergistic relationship between tourist popularity and the spatial distribution of nighttime lighting facilities based on the geometric parameters of the first and second standard deviation ellipses; and analyzing the spatiotemporal characteristics of urban nighttime tourism to obtain analysis results.

[0122] Furthermore, the logical instructions in the aforementioned memory 1230 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0123] On the other hand, the present invention also provides a computer program product, which includes a computer program stored on a non-transitory computer-readable storage medium. The computer program includes program instructions, and when the program instructions are executed by a computer, the computer is able to execute the urban night tourism development analysis method provided in the above embodiments.

[0124] In another aspect, the present invention also provides a non-transitory computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, is implemented to perform the urban nighttime tourism development analysis method provided in the above embodiments.

[0125] The system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0126] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of various embodiments or some parts of embodiments.

[0127] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for analyzing the development of urban nighttime tourism, characterized in that, include: Based on social media data of the target city, multiple nighttime tourist attractions and the frequency of mentions of each nighttime tourist attraction were determined, and the light brightness values ​​of each nighttime tourist attraction were determined based on remote sensing data of nighttime lights in the target city. Based on the spatial coordinates of all the nighttime tourist attractions and the frequency of mention, calculate the first standard deviation ellipse to characterize the spatial distribution of tourist popularity. Based on the spatial coordinates of all the nighttime attractions and the light brightness values, calculate the second standard deviation ellipse used to characterize the spatial distribution of nighttime lighting facilities; Based on the geometric parameters of the first standard deviation ellipse and the second standard deviation ellipse, the spatiotemporal characteristics of urban nighttime tourism, used to characterize the synergistic relationship between tourist popularity and the spatial distribution of nighttime lighting facilities, are determined. The spatiotemporal characteristics of nighttime tourism in the city were analyzed, and the analysis results were obtained.

2. The urban nighttime tourism development analysis method according to claim 1, characterized in that, The step of calculating the first standard deviation ellipse to characterize the spatial distribution of tourist popularity based on the spatial coordinates of all the nighttime tourist attractions and the frequency of mention includes: Using the frequency of mention of each of the aforementioned night tour attractions as the first weight, a weighted average is performed on the spatial coordinates of all the aforementioned night tour attractions to obtain the first centroid of the first standard deviation ellipse; Based on the first coordinate deviation of each night tour attraction relative to the first centroid and the first weight, calculate the first orientation angle of the first standard deviation ellipse; Based on the first orientation angle and the first coordinate deviation of each of the night tour attractions, calculate the length of the first major semi-axis and the length of the first minor semi-axis of the first standard deviation ellipse.

3. The urban nighttime tourism development analysis method according to claim 1, characterized in that, The step of calculating the second standard deviation ellipse characterizing the spatial distribution of nighttime lighting facilities based on the spatial coordinates of all the nighttime tourist attractions and the light brightness values ​​includes: Using the light brightness value of each of the aforementioned night tour attractions as the second weight, a weighted average is performed on the spatial coordinates of all the aforementioned night tour attractions to obtain the second centroid of the second standard deviation ellipse; Based on the second coordinate deviation of each night tour attraction relative to the second centroid and the second weight, calculate the second orientation angle of the second standard deviation ellipse; Based on the second orientation angle and the second coordinate deviation of each of the night tour attractions, calculate the length of the second major semi-axis and the length of the second minor semi-axis of the second standard deviation ellipse.

4. The urban nighttime tourism development analysis method according to claim 1, characterized in that, The step of determining the spatiotemporal characteristics of urban nighttime tourism, based on the geometric parameters of the first and second standard deviation ellipses, to characterize the synergistic relationship between tourist popularity and the spatial distribution of nighttime lighting facilities, includes: Extract the first centroid of the first standard deviation ellipse and the second centroid of the second standard deviation ellipse; Calculate the Euclidean distance between the first centroid and the second centroid; The Euclidean distance is used as a spatial offset distance to quantify the synergistic relationship between tourist popularity and the spatial distribution of nighttime lighting facilities.

5. The urban nighttime tourism development analysis method according to claim 1, characterized in that, Also includes: Kernel density estimation analysis was performed on the spatial coordinates of all the nighttime tourist attractions to generate a continuous density surface characterizing the spatial clustering degree of the nighttime tourist attractions; Based on the continuous density surface, hotspot areas for nighttime tourism are identified, and the spatial clustering characteristics of all the nighttime tourist attractions are obtained.

6. The urban nighttime tourism development analysis method according to claim 1, characterized in that, The analysis of the spatiotemporal characteristics of the city's nighttime tourism yields the following results: Keywords are extracted from the social media data to construct a topic semantic network, and topic clustering analysis is performed based on the topic semantic network to identify topics of public interest. Using the aforementioned topics of public concern as driving factors, the spatiotemporal characteristics of urban nighttime tourism were analyzed, and the analysis results were obtained.

7. The urban nighttime tourism development analysis method according to claim 6, characterized in that, The process of extracting keywords from the social media data to construct a topic semantic network, and performing topic clustering analysis based on the topic semantic network to identify topics of public interest, includes: Keywords were extracted from the social media data using the term frequency-inverse document frequency method. The topic semantic network is constructed using the keywords as network nodes and the co-occurrence relationships between the keywords as network edges. The topic semantic network is clustered into communities, and the communities obtained from the clustering are taken as the topics of public concern.

8. A system for analyzing the development of urban nighttime tourism, characterized in that, include: The first processing module is used to determine multiple nighttime tourist attractions and the frequency of mention of each nighttime tourist attraction based on social media data of the target city, and to determine the light brightness value of each nighttime tourist attraction based on remote sensing data of nighttime lights of the target city. The second processing module is used to calculate the first standard deviation ellipse representing the spatial distribution of tourist popularity based on the spatial coordinates of all the night tour attractions and the frequency of mention. The second processing module is also used to calculate a second standard deviation ellipse to characterize the spatial distribution of nighttime lighting facilities based on the spatial coordinates of all the nighttime tourist attractions and the light brightness values. The third processing module is used to determine the spatiotemporal characteristics of urban night tourism based on the geometric parameters of the first standard deviation ellipse and the second standard deviation ellipse, which characterize the synergistic relationship between tourist popularity and the spatial distribution of nighttime lighting facilities. The fourth processing module is used to analyze the spatiotemporal characteristics of the city's nighttime tourism and obtain the analysis results.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the urban nighttime tourism development analysis method as described in any one of claims 1 to 7.

10. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the urban nighttime tourism development analysis method as described in any one of claims 1 to 7.

11. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the urban nighttime tourism development analysis method as described in any one of claims 1 to 7.

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