A group prevention and group governance management platform system and spatial statistical modeling method

Abstract

The application provides a group prevention and group governance management platform system and a spatial statistical modeling method. The system comprises a data acquisition module, a data processing module, a data analysis module and a data display module. The data acquisition module is used for acquiring basic data in a community. The data processing module is used for processing the acquired basic data. The data analysis module is used for analyzing the processed data to obtain an analysis result. The data display module is used for displaying the analysis result. The application establishes a spatial statistical model by fusing event and spatial geographic information data, intuitively understands the overall distribution of events in a certain region, and predicts future trends and verifies rectification effects according to historical occurrence trends and changes in the region through time-series spatial statistics.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of computer, in particular to a group prevention and group governance management platform system and a spatial statistical modeling method. BACKGROUND

[0002] With the development of society, grassroots social governance work is getting more and more attention. In order to better realize grassroots social governance, it is necessary to establish a complete group prevention and group governance management platform system to effectively manage and solve various problems in the community. At the same time, in order to better understand the situation in the community, it is necessary to statistically model the spatial data in the community to better understand the situation in the community and provide better support for grassroots social governance work.

[0003] With the development of technology and society, social organizational structure, social contradictions and events are developing in a multi-source trend, and events in the process of social operation are also developing in a multi-source, multi-element, multi-point distribution and specific location high incidence trend. The application of grassroots social governance group prevention and group governance management platform system in smart town and social grassroots governance is becoming more and more frequent. It can obtain various events existing and occurring in grassroots society from multiple sources, collect, process and store the events through big data technology, analyze and present the statistical model based on geographic information data and event data, which can help managers better understand the event distribution situation, high incidence points and time series change trend in the whole region, so as to implement targeted improvement measures.

[0004] At present, there are some problems in the grassroots social governance group prevention and group governance management platform system and the spatial statistical modeling method, such as incomplete data collection, insufficient data processing, and unscientific statistical modeling method. The data statistical method of the existing business system is relatively single, which can only statistically and present the event related attributes through data charts, and cannot effectively statistically and present the high incidence area and event distribution situation.

[0005] Therefore, it is necessary to propose a new grassroots social governance group prevention and group governance management platform system and spatial statistical modeling method to solve the problems existing in the existing system and method. SUMMARY

[0006] The purpose of the present application is to provide a group prevention and group governance management platform system and a spatial statistical modeling method, which establishes a spatial statistical model by fusing event and spatial geographic information data, intuitively understands the overall distribution of events in a certain area, and statistically models the time series to enable managers to predict future trends and verify the effect of rectification according to the historical occurrence trend and change in the area.

[0007] To achieve the above objectives, as a first aspect of this application, this application provides a community-based prevention and control management platform system, which includes: a data acquisition module for collecting basic data within the community; a data processing module for processing the collected basic data; a data analysis module for analyzing the processed data and obtaining analysis results; and a data display module for displaying the analysis results.

[0008] The community-based prevention and control management platform system described above includes the following processes for processing the collected basic data: data cleaning, data integration, and data analysis.

[0009] The community-based prevention and control management platform system described above includes spatial analysis, temporal analysis, and correlation analysis for analyzing the processed data.

[0010] The community-based prevention and control management platform system described above collects basic data within the community, including system event data and geographic information data.

[0011] The community-based prevention and control management platform system described above includes processing the collected basic data, which involves fusing the collected system event data and geographic information data.

[0012] The community-based prevention and control management platform system described above also includes GIS applications, which incorporate GIS-based spatial statistical models.

[0013] A GIS-based spatial statistical model is used to generate statistical charts based on the analysis results.

[0014] As a second aspect of this application, this application also provides a spatial statistical modeling method applied to the aforementioned community-based prevention and control management platform system. The method includes the following steps: collecting basic data within the community; processing the collected basic data; performing spatial analysis on the processed data to obtain analysis results; and displaying the analysis results.

[0015] The spatial statistical modeling method described above includes spatial clustering, spatial interpolation, and spatial regression for the processed data.

[0016] The spatial statistical modeling method described above includes the following steps for spatial clustering: acquiring data to be clustered; extracting feature data from the data to be clustered; selecting a clustering algorithm; setting parameters according to the selected clustering algorithm; performing clustering calculations on the data to be clustered according to the selected clustering algorithm and the set parameters to obtain clustering results; evaluating the clustering results; and visualizing the clustering results.

[0017] The spatial statistical modeling methods described above include methods for displaying the analysis results such as event heatmaps, event location distribution maps, and time-series variation maps.

[0018] The beneficial effects achieved by this application are as follows:

[0019] (1) This application supplements event data with relevant map data resources based on GIS technology and creates a spatial statistical model based on the data. The statistical model based on GIS technology is an effective supplement to traditional event statistics, enabling managers to understand the development of events in their jurisdiction more intuitively through maps.

[0020] (2) This application employs a comprehensive data acquisition module, a sophisticated data processing module, a scientific data analysis module, and an intuitive data display module, which can better support grassroots social governance work. Simultaneously, it utilizes scientific spatial statistical modeling methods to better understand the situation within the community, providing better support for grassroots social governance work. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of this application 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 only some embodiments recorded in this application. For those skilled in the art, other drawings can be obtained based on these drawings.

[0022] Figure 1 This is a flowchart of a spatial statistical modeling method according to an embodiment of this application.

[0023] Figure 2 This is a schematic diagram of the structure of a community-based prevention and control management platform system according to an embodiment of this application.

[0024] Figure 3 This is a flowchart of a spatial clustering method according to an embodiment of this application.

[0025] Attached diagram labels: 10-Data acquisition module; 20-Data processing module; 30-Data analysis module; 40-Data display module; 100-Community prevention and control management platform system. Detailed Implementation

[0026] The technical solutions of the embodiments of this application 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 application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0027] Example 1

[0028] like Figure 2 As shown, this application provides a community-based prevention and control management platform system 100, which includes:

[0029] The system includes a data acquisition module 10, a data processing module 20, a data analysis module 30, and a data display module 40.

[0030] The data acquisition module 10 is used to collect basic data within the community. This basic data includes population data, housing data, vehicle data, and event data.

[0031] The data processing module 20 is used to process the collected basic data. This processing includes data cleaning, data integration, and data analysis. Data cleaning involves deduplication, noise reduction, and data padding to ensure accuracy and completeness. Data integration combines data from different sources for subsequent analysis. Data analysis involves analyzing the processed data, including spatial analysis, temporal analysis, and correlation analysis. Spatial analysis involves analyzing the spatial distribution of data, such as heatmaps and point density maps; temporal analysis involves analyzing the temporal distribution of data, such as time series plots and time heatmaps; and correlation analysis involves analyzing the correlation between data, such as correlation coefficient analysis and regression analysis.

[0032] The data analysis module 30 is used to analyze the processed data and obtain analysis results. These analyses include spatial analysis, temporal analysis, and correlation analysis. Spatial analysis refers to the spatial distribution analysis of the data, such as heatmaps and point density maps; temporal analysis refers to the temporal distribution analysis of the data, such as time series plots and time heatmaps; and correlation analysis refers to the correlation analysis of the data, such as correlation coefficient analysis and regression analysis.

[0033] The data visualization module 40 is used to display the analysis results. The results can be presented in formats such as charts or graphs to help users better understand the situation within the community. Display formats may include, but are not limited to, bar charts, line charts, pie charts, and maps.

[0034] As a specific embodiment of the present invention, the data acquisition module can collect data within the community in various ways, such as through questionnaires filled out by community residents, door-to-door surveys by community staff, and data acquisition through community monitoring equipment. The collected data includes, but is not limited to, population data, housing data, vehicle data, and event data. Specifically, population data includes information such as age, gender, occupation, and education level; housing data includes information such as house area, house type, and house location; vehicle data includes information such as vehicle type and vehicle owner; and event data includes various community events, such as traffic accidents, theft cases, and fires.

[0035] As a specific embodiment of the present invention, the community-based prevention and control management platform also includes a GIS application, which includes a GIS-based spatial statistical model. As an information and communication hub and a service hub, the community-based prevention and control management platform, based on the analysis results of the GIS-based spatial statistical model, can provide managers with rich, multi-dimensional, spatial statistical charts, allowing them to intuitively and clearly identify and locate problems through visualization.

[0036] As a specific embodiment of the present invention, the data processing module 20 fuses the collected system event data and geographic information data.

[0037] Specifically, the fusion method includes: pre-planning the merging and storage of system event data and geographic information data within a certain radiation range.

[0038] In a specific embodiment of the present invention, the data processing module 20 calculates the correlation value between system event data and geographic information data corresponding to two radiation ranges. If the correlation value is greater than a preset threshold, the system event data and geographic information data corresponding to the two radiation ranges are merged a second time. This application merges and stores data within radiation ranges where the correlation value is greater than the preset threshold, which is beneficial for merging and analyzing data within radiation ranges with large correlation values. This achieves data integration, improves data analysis efficiency, reduces data analysis difficulty, and to a certain extent improves the accuracy of data analysis. For example, it can analyze whether events within radiation ranges with large correlation values ​​have the same characteristics, enabling unified management and the implementation of certain protective measures for the merged area.

[0039] Specifically, the formula for calculating the correlation value between the corresponding system event data and geographic information data within the two radiation ranges is as follows:

[0040]

[0041] Where X represents the correlation value between system event data and geographic information data corresponding to the two radiation ranges; α represents the correlation factor between the two radiation ranges; if the two radiation ranges belong to the same community, then α = 1; otherwise, α = 0; e1 represents the influence weight of system event data; e2 represents the influence weight of event occurrence time; N1 represents the number of event types (event types such as traffic violations, traffic accidents, theft, and fires) occurring in the first radiation range; N2 represents the number of event types in the second radiation range; N12 represents the number of event types that are the same in the first and second radiation ranges; Tmin represents the minimum time difference between the occurrence of the first type of event of the same kind in the first and second radiation ranges (e.g., the minimum time interval between a traffic accident occurring in the first and second radiation ranges); TY represents the maximum allowable limit of the time difference between the occurrence of the first type of event of the same kind in the first and second radiation ranges; e3 represents the influence weight of the distance between the first and second radiation ranges; Kz represents the maximum allowable limit of the distance between the first and second radiation ranges; Kmax represents the actual maximum distance between the first and second radiation ranges.

[0042] As an example, within a 2-hour sampling period, there are 3 traffic violation incidents within the first radiation range, each lasting a different duration. If the occurrence time of the first incident is the same as the occurrence time of the second incident, it means that the occurrence times of the first incident and the second incident coincide. In this case, the number of incidents with coincident times in the i-th type of event is two. If there is a third incident with the same occurrence time as the first incident or the second incident, then the number of incidents with coincident times in the i-th type of event is three, and so on.

[0043] Example 2

[0044] like Figure 1 As shown, this application provides a spatial statistical modeling method. Spatial statistical modeling is based on the manipulation of spatial map data, also known as "map modeling." The result is a "map model," which is a graphical or symbolic representation of the spatial analysis process and its results. This model helps analysts plan the analysis process and progressively specify the modeling data required to complete the analysis. The method includes the following steps:

[0045] Step S1: Collect basic data within the community.

[0046] The basic data includes system event data and geographic information data. Geographic information data includes population data, housing data, and vehicle data, while system event data consists of event data.

[0047] Data collection is the foundation of data statistics. Based on the target needs of the statistics and analysis, the required data is collected, including event-related attributes and geographic information characteristics, such as: event type, event participants, event occurrence and handling time, radiation range, occurrence location, location characteristics, etc.

[0048] Step S2: Process the collected basic data.

[0049] Specifically, data processing includes data cleaning, data integration, and data analysis. The processed data is then stored. Data processing and storage involves synchronizing the raw data, cleaning the data, converting the data format, organizing it into a unified format, and storing the data with established relationships.

[0050] Specifically, the collected system event data and geographic information data will be integrated.

[0051] Specifically, the fusion method includes merging and storing system event data and geographic information data within the geographic information data coverage area.

[0052] As a specific embodiment of the present invention, the fusion method includes: primary fusion and secondary fusion.

[0053] One fusion method is to merge and store system event data and geographic information data within a pre-planned radiation range.

[0054] The secondary fusion method involves calculating the correlation value between system event data and geographic information data corresponding to two radiation ranges. If this correlation value is greater than a preset threshold, the system event data and geographic information data corresponding to the two radiation ranges are then merged a second time. This application merges and stores data within radiation ranges where the correlation value is greater than the preset threshold. This facilitates the combined analysis of data within radiation ranges with high correlation values, achieving data integration, improving data analysis efficiency and reducing data analysis difficulty, and to a certain extent improving the accuracy of data analysis. For example, it can analyze whether events within radiation ranges with high correlation values ​​have the same characteristics, enabling unified management and the implementation of certain protective measures for the merged area.

[0055] Specifically, the formula for calculating the correlation value between the corresponding system event data and geographic information data within the two radiation ranges is as follows:

[0056]

[0057] Where X represents the correlation value between system event data and geographic information data corresponding to the two radiation ranges; α represents the correlation factor between the two radiation ranges; if the two radiation ranges belong to the same community, then α = 1; otherwise, α = 0; e1 represents the influence weight of system event data; e2 represents the influence weight of event occurrence time; N1 represents the number of event types (event types such as traffic violations, traffic accidents, theft, and fires) occurring in the first radiation range; N2 represents the number of event types in the second radiation range; N12 represents the number of event types that are the same in the first and second radiation ranges; Tmin represents the minimum time difference between the occurrence of the first type of event of the same kind in the first and second radiation ranges (e.g., the minimum time interval between a traffic accident occurring in the first and second radiation ranges); TY represents the maximum allowable limit of the time difference between the occurrence of the first type of event of the same kind in the first and second radiation ranges; e3 represents the influence weight of the distance between the first and second radiation ranges; Kz represents the maximum allowable limit of the distance between the first and second radiation ranges; Kmax represents the actual maximum distance between the first and second radiation ranges.

[0058] Step S3: Perform spatial analysis on the processed data to obtain the analysis results.

[0059] Specifically, spatial analysis is performed on the processed data to establish a spatial statistical model.

[0060] As a specific embodiment of the present invention, spatial analysis includes spatial clustering, spatial interpolation, and spatial regression. Specifically, spatial clustering refers to performing cluster analysis on data collected within a community to discover spatial distribution patterns of the data. Spatial clustering mainly includes six parts: spatiotemporal data collection and preparation, feature extraction, selection and design of spatial clustering algorithms, parameter setting, clustering calculation, evaluation of the effectiveness of spatial clustering, and visualization of spatial clustering results.

[0061] like Figure 3 As shown, the spatial clustering method specifically includes the following steps:

[0062] Y1, Data Preparation: Obtaining the data to be clustered, i.e., preparing the data to be clustered. Spatial clustering requires collecting and organizing spatiotemporal data, including information such as the location, attributes, and relationships of spatial objects. Common spatial data include map data, remote sensing imagery, and GPS tracks.

[0063] Y2, Feature Extraction: Extracting feature data from the data to be clustered. Specifically, based on the problem requirements and data characteristics, appropriate features are selected to represent spatial objects. Feature data includes distance, angle, area, and perimeter, etc. The purpose of feature extraction is to transform spatial objects into numerical data for clustering calculations.

[0064] Y3. Clustering Algorithm Selection: Choose a suitable clustering algorithm based on the problem requirements and data characteristics. Common clustering algorithms include K-Means, DBSCAN, and hierarchical clustering. Different clustering algorithms are suitable for different data types and clustering needs.

[0065] Y4, Parameter Settings: Set the corresponding parameters according to the requirements of the clustering algorithm. For example, the K-Means algorithm requires setting the number of clusters, while the DBSCAN algorithm requires setting distance and density thresholds, etc. The purpose of parameter settings is to adjust the behavior of the clustering algorithm to obtain better clustering results.

[0066] Y5, Clustering Calculation: Based on the selected clustering algorithm and parameters, clustering calculations are performed on spatial data to obtain clustering results. The purpose of clustering calculation is to assign similar spatial objects to the same group for subsequent analysis and application.

[0067] Y6, Result Evaluation: Evaluate the clustering results, such as clustering quality, clustering stability, and clustering efficiency. The purpose of evaluating the clustering results is to determine the merits of the clustering algorithm and parameters so that adjustments and improvements can be made.

[0068] Y7, Results Visualization: Visualize the clustering results to facilitate better understanding and analysis. Common visualization methods include scatter plots, heatmaps, and contour plots. The purpose of visualization is to present the clustering results to users for further analysis and application.

[0069] As a specific embodiment of the present invention, spatial statistical modeling: taking traffic accident statistics as an example, the data range is first determined based on the type of the target event, the spatial information of the incident location, the participating entities, the time of the incident, the incident environment and other event-related attributes. The statistical objects are then calculated using corresponding algorithms through statistical methods and presented through GIS technology.

[0070] Step S4: Display the analysis results.

[0071] The analysis results can be presented in the form of charts or graphs to help users better understand the situation within the community. Presentation methods include, but are not limited to, event heatmaps, event location distribution maps, and time-series change graphs.

[0072] This application integrates GIS geographic information data and event data, combining event data and event-related geographic information data, performing data cleaning and processing, and improving data integrity.

[0073] This application sorts and cleans the attributes of business data and geographic information data from diverse sources and types, builds models based on the identified attribute data, and finally provides decision-makers with a comprehensive and accurate map-based visualization display in an intuitive, clear, and comprehensive manner.

[0074] It is understandable that the spatial statistical modeling method of this invention, through mature and proven statistical models, can obtain more intuitive and concrete data results for decision-making than conventional simple statistical charts. Traditional data statistical analysis can only statistically analyze historical data and display historical situations in charts, but it cannot provide managers with information on specific ranges and locations. The statistical methods and presentation formats provided by this system can lower the professional threshold for decision-makers, reducing the difficulty and workload of decision-making. It also improves the application level of business systems, facilitating better display, analysis, evaluation, prediction, and decision analysis.

[0075] This invention utilizes GIS technology to supplement event data with relevant map data resources and creates a spatial statistical model based on the map data. By integrating event and spatial geographic information data to establish a statistical model, it helps urban area managers intuitively understand the overall distribution of events in a region through map-based visualization, including location distribution and frequency of occurrence. With this information, managers can conduct targeted monitoring and rectification of key areas. Simultaneously, through temporal spatial statistics, managers can analyze historical trends and changes within the region to predict future trends and verify the effectiveness of rectification measures.

[0076] As a specific embodiment of the present invention, the frequency of traffic accidents occurring in a certain area is taken as an example. The method for statistically analyzing the frequency of traffic accidents occurring in a certain area includes the following steps:

[0077] T1 filters and selects event data based on the boundaries of the region and the inclusion relationship of the accident location.

[0078] T2 creates a circular area (Mask) based on the location of each event and the scope of its influence.

[0079] The mask is a circular region with a radius equal to the radius of the area where a point can influence the final heatmap image. The center point has a weight of 1, decreasing towards the edges, with a weight of 0 at the edges. The mask's gradient can take various forms, such as linear changes or quadratic curves. All discrete point masks are superimposed to produce a grayscale image. The overlapping portions of adjacent masks are weighted, and the final grayscale image's pixel value is the sum of the weights of all associated masks. Higher discrete point density results in higher pixel values ​​in the grayscale image, meaning a brighter image. Depending on the specific project, a fixed coefficient may be multiplied by the weights. The generated grayscale image is then mapped onto a color image. This results in a heatmap image with varying colors; generally, redder colors indicate more concentrated events, while greener colors indicate fewer events.

[0080] T3, Display Application.

[0081] Specifically, display applications include calling published data services for loading and display, and business applications.

[0082] The beneficial effects achieved by this application are as follows:

[0083] (1) This application supplements event data with relevant map data resources based on GIS technology and creates a spatial statistical model based on the data. The statistical model based on GIS technology is an effective supplement to traditional event statistics, enabling managers to understand the development of events in their jurisdiction more intuitively through maps.

[0084] (2) This application employs a comprehensive data acquisition module, a sophisticated data processing module, a scientific data analysis module, and an intuitive data display module, which can better support grassroots social governance work. Simultaneously, it utilizes scientific spatial statistical modeling methods to better understand the situation within the community, providing better support for grassroots social governance work.

[0085] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the stated features. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0086] In the description of this application, the word "for example" is used to mean "used as an example, illustration, or description." Any embodiment described as "for example" in this application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use the invention. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that the invention can be made without using these specific details. In other instances, well-known structures and processes will not be described in detail to avoid obscuring the description of the invention with unnecessary detail. Therefore, the invention is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed in this application.

[0087] The above description is merely an embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principle of the present invention should be included within the scope of the claims of the present invention.

Claims

1. A community-based prevention and control management platform system, characterized in that, The system includes: The data acquisition module is used to collect basic data within the community; the basic data collected within the community includes system event data and geographic information data. The data processing module is used to process the collected basic data; The processing of the collected basic data includes: fusing the collected system event data and geographic information data; the fusion methods include: primary fusion and secondary fusion; The one-time fusion method is to merge and store system event data and geographic information data within a pre-planned radiation range of geographic information data; The secondary fusion method is as follows: calculate the correlation value of system event data and geographic information data corresponding to two radiation ranges. If the correlation value is greater than a preset threshold, then the system event data and geographic information data corresponding to the two radiation ranges are merged in a secondary manner. The formula for calculating the correlation value between the system event data and geographic information data corresponding to the two radiation ranges is as follows: ； in, This represents the correlation value between system event data and geographic information data corresponding to two radiation ranges; This represents the correlation factor between two radiation zones; if the two radiation zones belong to the same community, then... ;otherwise, ; This indicates the weight of the impact of system event data; Indicates the weight of the impact of the time of event occurrence; Indicates the number of event types that occurred within the first radiation range; Indicates the number of event types within the second radiation range; This indicates the number of events of the same type in the first and second radiation ranges; This represents the minimum time difference between the occurrence of the first type of event, which belongs to the same category as the first radiation range and the second radiation range. This represents the maximum permissible time difference between the occurrence of the first type of event belonging to the same category as the first radiation range and the second radiation range; The influence weight of the distance between the first and second radiation ranges; This indicates the maximum permissible distance between the first and second radiation zones. This indicates the actual maximum distance between the first and second radiation ranges. The data analysis module is used to analyze the processed data and obtain analysis results; The data display module is used to present the analysis results.

2. The community-based prevention and control management platform system according to claim 1, characterized in that, Processing the collected basic data includes: data cleaning, data integration, and data analysis.

3. The community-based prevention and control management platform system according to claim 2, characterized in that, The analysis of the processed data includes spatial analysis, temporal analysis, and correlation analysis.

4. The community-based prevention and control management platform system according to claim 1, characterized in that, The system also includes GIS applications, which include GIS-based spatial statistical models. A GIS-based spatial statistical model is used to generate statistical charts based on the analysis results.

5. A spatial statistical modeling method, applied to the system described in any one of claims 1-4, characterized in that, The method includes the following steps: Collect basic data within the community; Process the collected basic data; Spatial analysis is performed on the processed data to obtain the analysis results; The analysis results will be presented.

6. The spatial statistical modeling method according to claim 5, characterized in that, Spatial analysis of the processed data includes spatial clustering, spatial interpolation, and spatial regression.

7. The spatial statistical modeling method according to claim 6, characterized in that, Spatial clustering methods include the following steps: Obtain the data to be clustered; Extracting feature data from the data to be clustered; Choose a clustering algorithm; Set the parameters according to the selected clustering algorithm; Based on the selected clustering algorithm and the set parameters, clustering calculations are performed on the data to be clustered to obtain the clustering results; Evaluate the clustering results; Visualize the clustering results.

8. The spatial statistical modeling method according to claim 5, characterized in that, Methods for presenting analysis results include: event heatmaps, event location distribution maps, and time-series variation maps.

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