Visualization and interaction with 3D spatio-temporal data for decision-making

Abstract

Embodiments of the present disclosure focus on computer-implemented methods for interactive visualization and simulation of spatio-temporal data to aid decision-making. The method involves ingesting various georeferenced data sources, such as textual data, 2D maps, 3D elevation data, and time-series data, to create a unified dataset. This dataset is aligned into a coherent spatial and temporal coordinate system and translated into a common visual language, generating a 3D digital model. Users can interact with this model through inputs like object manipulation, parameter adjustments, and time navigation commands. The system dynamically simulates cause-and-effect scenarios by integrating user inputs with analytical and simulation engines, using both detailed models and physics-based approximations. The updated 3D model visually represents potential outcomes in environmental, industrial, and urban planning contexts, facilitating informed decision-making.

Description

202500551VISUALIZATION AND INTERACTION WITH 3D SPATIO-TEMPORAL DATA FOR DECISION-MAKINGSTATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENTThis invention was made with Government support under Subaward No.: A101561; awarded by the Federal Award Agency: NSF; Subcontract: Woods Hole Oceanographic Institution. The Government has certain rights to this invention.BACKGROUND

[0001] The present disclosure relates to digital simulation and visualization technologies, specifically to systems and methods for interacting with and analyzing large-scale 3D spatio-temporal data for decision-making in environmental and industrial applications.

[0002] In the realm of decision-making for scenarios involving spatial and temporal considerations, stakeholders such as urban planners, local administrations, and industry leaders face significant challenges. These challenges arise from the need to consider the environmental, economic, and social impacts of their decisions. The complexity of these scenarios is compounded by the vast amounts of data involved, which are often presented in formats that are not easily accessible, interpretable or actionable. Traditional methods of data presentation, such as text files and static maps, require extensive manual processing and expert interpretation, making understanding the implications of choices difficult for decisionmakers.

[0003] Existing tools, while capable of integrating georeferenced data layers, often fall short in handling large datasets and providing dynamic representations from heterogenous data sources. These limitations hinder the ability to simulate cause-and-effect scenarios effectively, leaving decision-makers without a comprehensive means to explore various possibilities. The need for a more sophisticated approach that can synthesize diverse data sources into a coherent framework is evident. Such a solution would empower users to make informed decisions by providing a clear representation of complex data, enabling them to predict and evaluate the outcomes of various scenarios with greater confidence.202500551SUMMARY

[0004] Embodiments of the present disclosure are directed to computer-implemented methods for interactive visualization and simulation of spatio-temporal data for decisionmaking. According to an aspect, a computer-implemented method includes ingesting a plurality of georeferenced data sources including textual data, two-dimensional map data, three-dimensional elevation data, and time-series data, creating a unified data set by aligning data from the plurality of georeferenced data sources into a unified spatial and temporal coordinate system, and translating the unified data set into a common visual language by converting numerical and textual inputs into corresponding visual cues to generate a three-dimensional digital model. The method also includes receiving interactive user inputs including object manipulation, parameter adjustments, and time navigation commands to modify elements of the three-dimensional digital model, dynamically simulating cause-and-effect scenarios by integrating the user inputs with analytical and simulation engines that employ both detailed simulation models and physics-based approximations, and rendering an updated three-dimensional digital model to visually represent potential environmental, industrial, and urban planning outcomes, thereby enabling informed decision-making.

[0005] Embodiments also include computer systems and computer program products for interactive visualization and simulation of spatio-temporal data for decision-making.

[0006] Additional technical features and benefits are realized through the techniques of the present disclosure. Embodiments and aspects of the disclosure are described in detail herein and are considered a part of the claimed subject matter. For a better understanding, refer to the detailed description and to the drawings.BRIEF DESCRIPTION OF THE DRAWINGS

[0007] FIG. 1 depicts a schematic block diagram illustrating a system for providing a large-scale 3D spatio-temporal data for decision-making in accordance with an embodiment.

[0008] FIG. 2 depicts a schematic block diagram of a user interface for interacting with the system for providing a large-scale 3D spatio-temporal data for decision-making in accordance with an embodiment.202500551

[0009] FIG. 3 depicts a flow chart diagram of a method for interactive visualization and simulation of spatio-temporal data for decision-making making in accordance with an embodiment.

[0010] FIG. 4 depicts a schematic block diagram of a processing system in accordance with an embodiment.

[0011] In the accompanying figures and following detailed description of the disclosed embodiments, the various elements illustrated in the figures are provided with three-digit reference numbers. In some instances, the leftmost digits of each reference number corresponds to the figure in which its element is first illustrated.DETAILED DESCRIPTION

[0012] In the field of decision-making involving spatio-temporal scenarios, professionals such as urban planners, local governments, and industry leaders encounter considerable obstacles. These challenges stem from the necessity to account for the environmental, economic, and social repercussions of their decisions. The intricacy of these scenarios is further complicated by the sheer volume of heterogeneous data, which is frequently presented in formats that are not easily accessible, understandable or actionable. Traditional data presentation methods, like text files and static maps as well as the output from environmental models, demand extensive manual processing and expert interpretation, making it difficult for decision-makers to grasp the implications of their choices. Current tools, although capable of integrating georeferenced data layers, often struggle with large heterogenous datasets and fail to provide dynamic visualizations and interactions. These shortcomings impede the effective simulation of cause-and-effect scenarios, leaving decisionmakers without a comprehensive way to explore different possibilities. There is a clear need for a more advanced approach that can integrate diverse data sources into a unified framework. Such a solution would empower users to make informed decisions by offering a clear representation of complex data, enabling them to anticipate and assess the outcomes of various scenarios with greater assurance.

[0013] The present system and method address these challenges by providing a solution for visualizing and interacting with large-scale 3D spatio-temporal data. This approach leverages a gaming engine to translate complex data into a common visual language, allowing users to intuitively interact with and analyze the data. By integrating202500551diverse data sources, including 2D, 3D, and 4D data, the system creates a unified spatial representation that supports dynamic simulations and predictive analyses. Users can manipulate elements within the 3D model, explore cause-and-effect scenarios, and visualize potential outcomes, thereby facilitating informed decision-making across various applications, such as environmental planning, industrial impact assessments, and urban development.

[0014] In exemplary embodiments, the disclosed system can be deployed on a cloudbased server, which is accessed by local client devices. This configuration allows the system to leverage the substantial computing resources available in the cloud, thereby offloading intensive data processing and simulation tasks from the client devices. The cloud server handles the aggregation, harmonization, and visualization of large-scale 3D spatio-temporal data, as well as the execution of complex simulations and analytical processes. Local clients, which may have relatively small computing capabilities, connect to the cloud server via a network communication interface. This setup enables users to interact with the system through a lightweight client application that primarily focuses on user interface tasks, such as rendering visualizations and capturing user inputs. The cloud server processes these inputs, performs the necessary computations, and returns the results to the client devices for display. The benefits of deploying the system in a cloud-based configuration include scalability, flexibility, and cost-effectiveness. Scalability is achieved as the cloud infrastructure can dynamically allocate resources to accommodate varying workloads, ensuring consistent performance even as the number of users or the complexity of simulations increases.Flexibility is provided by allowing users to access the system from any location with internet connectivity, facilitating remote collaboration and decision-making. Cost-effectiveness is realized by reducing the need for high-performance hardware on the client side, as the cloud server handles the bulk of computational tasks. This configuration also ensures that updates and maintenance can be centrally managed, minimizing downtime and simplifying system administration. Overall, the cloud-based deployment enhances the accessibility and efficiency of the system, making it suitable for a wide range of applications and user environments.

[0015] Referring now to FIG. 1, a block diagram of a system 100 for providing a large-scale 3D spatio-temporal data for decision-making in accordance with an embodiment is shown. The system 100 is structured to support the visualization and interaction with202500551extensive 3D spatio-temporal data, aiding in decision-making processes. The system 100 includes several components, with each component contributing to the overall operation.

[0016] In exemplary embodiments, the data integration module 102 aggregates and harmonizes large datasets from various sources. The data integration module 102 aligns data, whether 2D, 3D, or 4D (including time as a dimension), within a unified coordinate system. The data integration module 102 supports the ingestion of georeferenced data to create a coherent spatial representation and collaborates with the data storage system 110 to efficiently retrieve and store data, accommodating datasets of different sizes. The integration process involves converting textual and numeric data into a format suitable for visualization and simulation, which is subsequently utilized by the visualization engine 104 and the simulation engine 106.

[0017] For example, consider a scenario where data is collected from multiple sources, including satellite imagery, sensor networks, and historical records. These sources provide data in different formats, such as 2D maps, 3D topographical data, and time-series data. The data integration module 102 processes this diverse data by aligning it within a unified coordinate system. This involves converting each data type into a compatible format, ensuring that all data points correspond to the same spatial and temporal references. For instance, 2D maps of land use can be overlaid with 3D elevation data to create a comprehensive topographical model. Additionally, time-series data, such as temperature changes over time, can be integrated as a fourth dimension, allowing for dynamic temporal analysis. The data integration module 102 supports the ingestion of georeferenced data, ensuring that each data point is accurately placed within the spatial framework. By collaborating with the data storage system 110, the data integration module 102 efficiently retrieves and stores data, accommodating datasets of varying sizes and complexities. Once the data is harmonized, the integration process converts textual and numeric data into a format suitable for visualization and simulation. This unified dataset is then utilized by the visualization engine 104 to create an interactive 3D model, and by the simulation engine 106 to run predictive analyses. The result is a coherent spatial representation that enables users to explore and analyze complex scenarios with ease, facilitating informed decision-making.

[0018] In exemplary embodiments, the visualization engine 104 renders the integrated data into a visual format that users can easily interpret. In one embodiment, the visualization engine 104 utilizes a gaming engine and customized scripts to translate complex data into a202500551common visual language, such as converting depth values into 3D reliefs or temperature data into color gradients. The visualization engine 104 supports multiple data layers, allowing users to observe intersections and interactions between datasets. The visualization engine 104 collaborates with the user interface 108 to provide an intuitive and interactive experience, enabling users to manipulate visualized data and explore various scenarios.

[0019] In exemplary embodiments, the simulation engine 106 models and predicts outcomes based on the integrated data. The simulation engine 106 enables users to simulate scenarios, such as environmental changes or industrial impacts, by altering specific variables and observing the results. In various embodiments, the simulation engine 106 can utilize both precise simulation models and simpler assumptions based on basic physics, providing flexibility in detail and accuracy. The simulation engine 106 interacts with the analytics engine 116 to incorporate analytical results into simulations, enhancing the system's predictive capabilities.

[0020] For instance, users can simulate environmental changes or industrial impacts by adding or removing elements within the simulation environment. Consider a scenario where a user is evaluating the impact of constructing a new wind farm in a coastal area. The user can input data such as the number and location of wind turbines within the 3D model. The simulation engine 106 processes these inputs to predict changes in local wind patterns, energy generation potential, and potential impacts on marine life. Alternatively, a user might want to assess the effects of removing a land bridge on an ecosystem. By removing the land bridge element from the simulation, the user can observe changes in water flow, sediment transport, and the migration patterns of aquatic species. The simulation engine 106 can utilize precise hydrodynamic models or simpler assumptions based on basic physics to provide insights into these changes. The simulation engine 106 interacts with the analytics engine 116 to incorporate analytical results into the simulations, enhancing the system's predictive capabilities. For example, the analytics engine might provide data on historical weather patterns or species population trends, which the simulation engine can use to refine its predictions. By allowing users to manipulate elements within the simulation, the system provides a flexible and interactive platform for exploring cause-and-effect scenarios. This capability enables decision-makers to make informed choices by visualizing potential outcomes and understanding the implications of their actions.202500551

[0021] In exemplary embodiments, the user interface 108 serves as the main interaction point between the user and the system. The user interface 108 provides tools and controls for navigating visualized data, initiating simulations, and analyzing results. Designed to be user-friendly, the user interface 108 caters to both experts and non-experts by offering a gamified approach to data interaction. The user interface 108 communicates with the visualization engine 104 to update displays based on user inputs and with the interaction module 114 to facilitate real-time data manipulation.

[0022] In exemplary embodiments, users can engage with the system through several types of inputs via the user interface 108. For example, a run simulation button provides a straightforward control that allows users to initiate simulations based on the current configuration of the 3D environment, enabling them to observe the outcomes of their configured scenarios in real-time. Object manipulation tools allow users to click on various items within the 3D environment to add, remove, or change their location. For instance, a user might click on a wind turbine to move it to a different location or remove a land bridge to simulate its absence.

[0023] In exemplary embodiments, the user interface 108 also allows for layer selection and adjustment, enabling users to select and adjust different data layers, such as toggling the visibility of certain environmental factors like temperature or salinity, to focus on specific aspects of the simulation. Time navigation controls permit users to navigate forward and backward in time to observe changes in the environment over different periods, aiding in understanding the temporal dynamics of the simulated scenarios. Parameter adjustment sliders enable users to adjust specific parameters, such as wind speed or pollution levels, to see how these changes affect the simulation outcomes.

[0024] In exemplary embodiments, interactive data points allow users to click on data points within the visualization to access detailed information or modify the data inputs, such as changing the population density in a specific area. Additionally, the interface provides scenario save and load options, allowing users to save current scenarios and load previously saved ones, facilitating the revisiting and comparison of different simulation outcomes. By offering these interactive tools, the user interface 108 ensures a user-friendly experience that caters to both experts and non-experts, facilitating intuitive interaction with complex data and simulations. The interface communicates with the visualization engine 104 to update202500551displays based on user inputs and with the interaction module 114 to enable real-time data manipulation.

[0025] In exemplary embodiments, the data storage system 110 manages the vast amounts of data processed by the system. The data storage system 110 provides infrastructure for storing raw data, processed data, and simulation results, ensuring data integrity and accessibility. The data storage system 110 collaborates with the data integration module 102 to ensure seamless data flow throughout the system.

[0026] In exemplary embodiments, the semantic framework 112 adds meaning to the data by abstracting and synthesizing information into semantic categories. The semantic framework 112 presents complex data in a more understandable format, such as displaying the coverage of fauna and flora classes. The semantic framework 112 interacts with the visualization engine 104 to enhance data representation and with the analytics engine 116 to incorporate semantic insights into analysis.

[0027] For instance, consider a dataset containing raw environmental data collected from a coastal region. This dataset includes various parameters such as temperature, salinity, and species population count. The semantic framework 112 processes this raw data to categorize it into meaningful semantic layers, such as "Marine Biodiversity," "Water Quality," and "Climate Impact. " For example, the framework might analyze species population data and classify regions with high biodiversity as "Biodiversity Hotspots." Similarly, it could assess temperature and salinity levels to identify areas of "Optimal Marine Habitat" or "Potential Stress Zones" for marine life. These semantic categories provide a more intuitive understanding of the data, allowing users to quickly grasp the ecological significance of different regions. The semantic framework 112 interacts with the visualization engine 104 to enhance data representation by overlaying these semantic categories onto the 3D model. Users can visually explore the "Biodiversity Hotspots" or "Potential Stress Zones" within the model, gaining insights into the ecological dynamics of the region. Furthermore, the semantic framework 112 collaborates with the analytics engine 116 to incorporate semantic insights into analysis. For instance, when simulating the impact of a new industrial development, the analytics engine can use the semantic categories to predict potential effects on "Marine Biodiversity" and "Water Quality." This integration of semantic insights into the analytical process enhances the system's ability to provide meaningful predictions and support informed decision-making.202500551

[0028] In exemplary embodiments, the interaction module 114 facilitates dynamic and interactive data engagement. The interaction module 114 supports a range of interaction strategies, such as navigating forward and backward in time to observe changes or adjusting individual data elements to explore hypothetical scenarios. The interaction module 114 communicates with the user interface 108 to capture user inputs and with the simulation engine 106 to update simulations based on these interactions.

[0029] In exemplary embodiments, the analytics engine 116 offers advanced analytical capabilities, allowing users to derive insights from the data. The analytics engine 116 processes integrated data to generate analytical results, which inform simulations and decision-making. The engine works with the simulation engine 106 to incorporate analytical findings into simulation models, enhancing prediction accuracy and relevance.

[0030] In exemplary embodiments, the analytics engine 116 employs a variety of advanced technologies to process integrated data and derive meaningful insights. One such technology is machine learning algorithms, which can analyze patterns and trends within the data to make predictions or classify information. For instance, machine learning can be used to predict environmental changes based on historical data or to classify regions based on biodiversity levels. Another technology that the analytics engine 116 may utilize is statistical analysis tools, which help in identifying correlations and causations within the data. These tools can be used to perform regression analysis, hypothesis testing, and other statistical methods to understand the relationships between different variables in the dataset. In some embodiments, the analytics engine 116 might also incorporate data mining techniques to extract useful information from large datasets. Data mining can uncover hidden patterns and associations that are not immediately apparent, providing deeper insights into the data.Additionally, the engine can use geospatial analysis technologies to process and analyze spatial data. This involves techniques such as spatial interpolation, spatial clustering, and network analysis, which are used for understanding spatial relationships and patterns in the data. Finally, the analytics engine 116 may employ predictive modeling to simulate future scenarios based on current data trends. This involves creating models that can forecast potential outcomes, helping decision-makers to anticipate and plan for future events. By integrating these technologies, the analytics engine 116 enhances the system’s ability to provide accurate and relevant analytical results, which inform simulations and support informed decision-making. The analytics engine 116 works closely with the simulation202500551engine 106 to incorporate these analytical findings into simulation models, thereby improving prediction accuracy and relevance.

[0031] In exemplary embodiments, the network communication interface 118 facilitates communication between the system and external networks. The network communication interface 118 allows for data exchange with other systems and devices, supporting both local and remote connectivity. The interface incorporates external data sources and supports cloud-based functionalities provided by the cloud integration system 120.

[0032] In exemplary embodiments, the cloud integration system 120 leverages cloud computing resources to extend the system's capabilities and reach. The cloud integration system 120 allows for scalable data processing and storage, supporting the handling of large datasets and complex simulations. The cloud system can supplement or replace some local processing functionalities, providing flexibility and efficiency in resource utilization. The cloud integration system 120 interacts with the network communication interface 118 to ensure seamless connectivity and data exchange between the local system and the cloud.

[0033] Referring now to FIG. 2, a diagram of a user interface 108 for interacting with the system 100 for providing a large-scale 3D spatio-temporal data for decision- making in accordance with an embodiment is shown. In exemplary embodiments, the user interface 108 serves as the main interaction point between the user and the system 100, offering a variety of tools and controls to facilitate data interaction and simulation. Users can engage with the system through several types of inputs, including a run simulation button 202, an object manipulation tool 204, a layer selection and adjustment panel 206, time navigation controls 208, parameter adjustment sliders 210, interactive data points 212, scenario save, load and replay options 214, a visualization display area 216, an analytics dashboard 218, and a help and tutorial section 220.

[0034] In exemplary embodiments, the run simulation button 202 serves as a straightforward control that allows users to initiate simulations based on the current configuration of the 3D environment. The run simulation button 202 could appear as a prominent, easily identifiable icon labeled "Run Simulation," often located at the top or bottom of the interface for quick access. Users can interact with this component by simply202500551clicking the button, which triggers the system to process the current data inputs and display the simulation results in chosen time intervals and speeds.

[0035] In exemplary embodiments, the object manipulation tools 204 enable users to add, remove, or change the location of items within the 3D environment. The object manipulation tools 204 might be represented by icons resembling a hand or a cursor, often accompanied by a toolbox or menu that lists available objects. Depending on the context of the simulation, a variety of objects could be available for manipulation. For instance, in a simulation focused on environmental planning, users might add objects such as wind turbines, solar panels, or trees to assess their impact on energy generation or carbon sequestration. In a coastal ecosystem simulation, users could introduce elements like coral reefs, fish populations, or marine vessels to study their effects on biodiversity and water quality. For urban planning scenarios, available objects might include buildings, roads, or green spaces, allowing users to explore the implications of different infrastructure layouts on traffic flow and urban heat islands. These objects can be selected from a toolbox or menu within the interface, and users can interact with them by clicking and dragging to reposition them within the 3D space, or by selecting existing objects to remove or modify them. Users can interact with these tools by selecting an object from the menu and then clicking and dragging it within the 3D space to reposition it, or by selecting an existing object to remove or modify it.

[0036] In exemplary embodiments, the layer selection and adjustment panel 206 allows users to select and adjust different data layers, such as toggling the visibility of environmental factors like temperature or salinity. The layer selection and adjustment panel 206 could appear as a sidebar or dropdown menu with checkboxes or toggle switches for each layer. Users can interact with this component by checking or unchecking boxes to show or hide specific layers, or by using sliders to adjust the opacity or intensity of the data layers.

[0037] In exemplary embodiments, the time navigation controls 208 permit users to navigate forward and backward in time to observe changes in the environment over different periods. The time navigation controls 208 might be represented by a timeline slider or a set of forward and backward arrow buttons. Users can interact with these controls by dragging the slider along the timeline or clicking the arrows to move through time increments, allowing them to visualize and interact with temporal dynamics within the simulation.202500551

[0038] For example, once users have navigated to a desired time period, they can use the object manipulation tools 204 or components to make changes to the simulation at that specific point in time. For example, a user might navigate to a future date to simulate the impact of adding a new wind turbine to an existing wind farm. By selecting the wind turbine from the toolbox and placing it within the 3D environment, the user can observe how this addition affects energy generation and local wind patterns over time. Similarly, users can remove or reposition objects, such as relocating a building in an urban planning scenario, to assess the potential outcomes of such changes. This capability allows users to explore cause-and-effect scenarios and understand the implications of their actions across different time periods, providing valuable actionable insights for decision-making.

[0039] In exemplary embodiments, the parameter adjustment sliders 210 enable users to adjust specific parameters, such as wind speed or pollution levels, to see how these changes affect simulation outcomes. The parameter adjustment sliders 210 could be displayed as horizontal bars with draggable knobs, each labeled with the parameter name and current value. Users can interact with these sliders by clicking and dragging the knobs to increase or decrease the parameter values, instantly updating the simulation to reflect these changes. The type of sliders displayed may be determined based on the context of the simulation. For instance, in an environmental simulation, sliders might be available for adjusting parameters like temperature, humidity, or carbon dioxide levels. In an urban planning scenario, sliders could be used to modify traffic density, building height, or green space coverage. This context-sensitive approach ensures that users have access to the most relevant parameters for their specific simulation, allowing for more precise and meaningful adjustments to the model.

[0040] In exemplary embodiments, the interactive data points 212 are clickable data points within the visualization that provide detailed information or allow modification of data inputs. The interactive data points 212 might be represented by small icons or markers on the 3D model, often highlighted or color-coded for visibility. Users can interact with these data points by clicking on them to open a pop-up window or sidebar displaying detailed information, or by entering new data values to modify the inputs.

[0041] In exemplary embodiments, the scenario save and load options 214 allow users to save current scenarios and load previously saved ones for comparison and analysis. The scenario save and load options 214 could be represented by icons resembling a floppy202500551disk for saving and a folder for loading, typically located in a menu bar. Users can interact with these options by clicking the save icon to store the current scenario configuration or the load icon to retrieve and apply a previously saved scenario.

[0042] In exemplary embodiments, the visualization display area 216 is the main section where the 3D model and data visualizations are rendered for user interaction. The visualization display area 216 occupies the central portion of the screen, providing a clear and detailed view of the simulation environment. Users can interact with the display area by using mouse or touch gestures to zoom, pan, and rotate the 3D model, allowing for an immersive exploration of the data.

[0043] In exemplary embodiments, the analytics dashboard 218 displays analytical results and insights derived from the data, supporting decision-making. The analytics dashboard 218 might appear as a panel or overlay with charts, graphs, and summary statistics. Users can interact with the dashboard by clicking on different elements to drill down into specific data points or by selecting filters to customize the displayed information.

[0044] In exemplary embodiments, the help and tutorial section 220 provides guidance and tutorials for users to effectively interact with the system. The help and tutorial section 220 could be accessed via a question mark icon or a dedicated menu option, leading to a series of instructional videos, FAQs, and step-by-step guides. Users can interact with this section by browsing the available resources, watching tutorials, and following instructions to enhance their understanding and usage of the system.

[0045] Referring now to FIG. 3, a flow chart of a computer-implemented method 300 for interactive visualization and simulation of spatio-temporal data for decision-making according to one or more embodiments is shown. In exemplary embodiments, the method 300 is performed by the system 100 depicted in FIG. 1.

[0046] The method 300 begins at block 302 with ingesting a plurality of georeferenced data sources, including textual data, two-dimensional map data, three-dimensional elevation data, and time-series data. This step involves collecting diverse data inputs from various sources to form the basis for further processing and analysis. For example, data might be gathered from satellite imagery, government databases, and loT sensors, providing a comprehensive dataset that includes land use maps, elevation profiles, historical weather patterns and simulation of specific parameters of the environment.202500551

[0047] Next, as shown at block 304, the method 300 involves harmonizing the ingested data by aligning the disparate data types into a unified spatial and temporal coordinate system. This alignment ensures that all data points correspond to the same spatial and temporal references, facilitating coherent integration and analysis. In exemplary embodiments, machine learning algorithms may be employed to automate this alignment process, improving accuracy and efficiency. For instance, a machine learning model could be trained to recognize and correct discrepancies between different coordinate systems, ensuring that a 2D map aligns perfectly with 3D elevation data and time-series information.

[0048] At block 306, the method 300 proceeds with translating the harmonized data into a common visual language by converting numerical and textual inputs into corresponding visual cues via scripts hosted in a gaming engine. This translation generates a three-dimensional digital model that users can intuitively interact with, providing a clear and comprehensive representation of the data. For example, elevation data might be visualized as a 3D terrain model, while temperature data is represented through color gradients, allowing users to easily interpret complex datasets.

[0049] Following this, at block 308, the method 300 involves receiving interactive user inputs, including object manipulation, parameter adjustments, and time navigation commands, to modify elements of the three-dimensional digital model. Users can interact with the model by adding or removing objects, adjusting parameters like wind speed or pollution levels, and navigating through different time periods to explore various scenarios. For instance, a user might add a new building to an urban landscape and adjust its height to see how it affects local wind patterns and sunlight exposure over time.

[0050] As shown at block 310, the method 300 includes dynamically simulating cause-and-effect scenarios by integrating the user inputs with analytical and simulation engines. These engines employ both detailed simulation models and physics-based approximations to predict potential outcomes, allowing users to visualize the implications of their actions. For example, by simulating the removal of a land bridge, users can observe changes in water flow and sediment transport, providing insights into potential future environmental impacts.

[0051] Finally, the method 300 concludes at block 312 by rendering the updated three-dimensional digital model to visually represent potential environmental, industrial, and202500551urban planning outcomes. This rendering enables informed decision-making by providing users with a comprehensive view of the simulated scenarios, complete with options for exporting results in various formats, such as video, reports, or interactive web applications. For instance, a user could export a simulation of a proposed wind farm development as a video to present to stakeholders, illustrating the project's potential impact on local ecosystems and energy production.

[0052] In one embodiment, the system for ingesting georeferenced data sources includes a network of satellite imagery that provides high-resolution spatial data, which can be used to monitor environmental changes in real-time. This embodiment leverages advanced satellite technology to capture detailed images of the Earth’s surface, allowing for precise mapping and analysis of geographical features. In another embodiment, the system incorporates LiDAR data, which uses laser scanning to produce highly accurate three-dimensional representations of the terrain. This data is particularly useful for applications requiring detailed topographical information, such as urban planning or environmental conservation. Additionally, an embodiment may utilize loT sensor networks, which consist of distributed sensors that collect real-time data on various environmental parameters, such as temperature, humidity, and air quality. These sensors can be deployed in diverse locations, providing continuous data streams that enhance the system’s ability to simulate dynamic environmental conditions. Furthermore, the system can be configured to support real-time data ingestion from live data feeds or streaming data sources, enabling users to access the most current information available. This adaptability ensures that the system can be tailored to meet the specific needs of different applications, whether for monitoring pollution levels, assessing the impact of infrastructure projects, or managing natural resources.

[0053] In one embodiment, the alignment of data from multiple georeferenced sources is achieved using an algorithm configured to reconcile discrepancies between different coordinate systems. This algorithm can be implemented in a cloud-based environment, allowing for scalable processing of large datasets. In another embodiment, the alignment process is performed locally on a user's device, utilizing a lightweight machine learning model optimized for speed and efficiency, which is particularly useful in scenarios with limited internet connectivity. Additionally, the system can be configured to support various types of coordinate systems, such as geographic, projected, or custom user-defined systems, providing flexibility in handling diverse data inputs. The alignment process may202500551also include a user interface that allows manual adjustments, enabling users to fine-tune the alignment results based on specific project requirements. Furthermore, the system can incorporate a feedback loop where user corrections are used to iteratively improve the machine learning model's accuracy over time. This adaptability ensures that the system can cater to a wide range of applications, from urban planning to environmental monitoring, while maintaining high precision in data alignment.

[0054] In one embodiment, the system utilizes advanced rendering techniques such as ray tracing to produce highly realistic visualizations of the three-dimensional digital model. This approach can be particularly beneficial in scenarios where precise visual fidelity is important, such as in architectural planning or detailed environmental simulations. In another embodiment, volumetric rendering is employed to visualize complex data sets, such as atmospheric or oceanographic data, where the representation of volume and density is significant. This technique allows users to perceive and interact with data in a more intuitive manner, enhancing the decision-making process. Additionally, the system can offer customizable visual themes or styles, enabling users to tailor the visual output to their specific preferences or requirements. For instance, a user might choose a color scheme that highlights temperature variations or a style that emphasizes topographical features.Furthermore, the system can be configured to support different levels of detail, allowing users to switch between high-resolution and simplified models depending on the computational resources available or the specific needs of the analysis. This flexibility ensures that the system can be adapted to a wide range of applications, from high-performance computing environments to more constrained mobile platforms.

[0055] In one embodiment, the system for receiving interactive user inputs includes gesture-based controls that utilize motion sensors to detect hand movements, allowing users to manipulate the three-dimensional digital model through intuitive gestures such as swiping, pinching, or rotating. This embodiment can be particularly useful in environments where hands-free operation is preferred, such as in industrial settings or when users are wearing protective gear or in educational setups. In another embodiment, voice commands are integrated into the system, enabling users to interact with the model by issuing verbal instructions, which can be processed using natural language processing algorithms to interpret and execute commands. This approach is advantageous in scenarios where users need to maintain visual focus on the model without manual interaction. Additionally, the202500551system may support collaborative features, allowing multiple users of a community to interact with the model simultaneously from different locations. This can be achieved through a networked platform where users can join a shared session, each contributing inputs that are synchronized in real-time, facilitating collaborative decision-making processes. These embodiments demonstrate the adaptability of the system to different operational contexts and user preferences, enhancing the system's utility across diverse applications.

[0056] In one embodiment, the system for dynamically simulating cause-and-effect scenarios includes a library of predefined scenarios tailored for environmental impact assessments, such as evaluating the effects of deforestation on local wildlife populations. This library can be expanded to include industrial scenarios, such as the impact of factory emissions on air quality, or urban planning scenarios, like the effects of new infrastructure on traffic patterns. In another embodiment, the system allows users to define custom simulation parameters, such as adjusting the rate of pollutant dispersal in a water body or modifying the growth rate of urban areas, providing flexibility to address specific user needs. The system can also incorporate various simulation engines, ranging from detailed computational fluid dynamics models for precise environmental simulations to simpler physics-based models for rapid scenario testing. Additionally, the system may support different user interfaces, such as a graphical user interface for desktop applications or a mobile app interface for field use, enabling users to interact with the simulations in diverse environments. Furthermore, the system can be configured to operate in a distributed computing environment, leveraging cloud-based resources to handle large-scale simulations and data processing, ensuring scalability and performance across different use cases.

[0057] In one embodiment, the system integrates augmented reality (AR) capabilities by overlaying digital information onto the physical environment, allowing users to visualize simulation results in real- world settings through AR glasses or mobile devices. This embodiment enhances user interaction by providing a tangible connection between the digital model and the physical world. In another embodiment, virtual reality (VR) capabilities are employed, offering an immersive experience where users can navigate through the three-dimensional digital model using VR headsets. This setup allows for a more engaging exploration of potential outcomes, particularly useful in training or educational contexts. Additionally, the system can be configured to export simulation results in various formats, such as high-resolution videos for presentations, detailed reports for documentation, or202500551interactive web applications for online collaboration. These export options can be tailored to meet the needs of different stakeholders, from technical experts requiring in-depth analysis to non-expert stakeholders needing a simplified overview. Furthermore, the system can support different levels of detail in the visualization, ranging from simplified models for quick assessments to highly detailed simulations for comprehensive analysis, ensuring adaptability to various user requirements and computational resources.

[0058] FIG. 4 illustrates an example of a processing system 400 that can be used to implement the computer-based components described herein. The processing system 400 includes an exemplary computing device (“computer”) 402 configured for performing various aspects of the operations described herein in accordance with aspects of the invention. In addition to computer 402, exemplary processing system 400 includes network 414, which connects computer 402 to additional systems (not depicted) and can include one or more wide area networks (WANs) and / or local area networks (LANs) such as the Internet, intranet(s), and / or wireless communication network(s). Computer 402 and the additional system are in communication via network 414, e.g., to communicate data between them. In exemplary embodiments, the system 100 may be embodied in a processing system 400.

[0059] Exemplary computer 402 includes processor cores 404, main memory (“memory”) 410, and input / output component(s) 412, which are in communication via bus 403. Processor cores 404 includes cache memory (“cache”) 406 and controls 408, which include branch prediction structures and associated search, hit, detect and update logic, which will be described in more detail below. Cache 406 can include multiple cache levels (not depicted) that are on or off-chip from processor 404. Memory 410 can include various data stored therein, e.g., instructions, software, routines, etc., which, e.g., can be transferred to / from cache 406 by controls 408 for execution by processor 404. Input / output component(s) 412 can include one or more components that facilitate local and / or remote input / output operations to / from computer 402, such as a display, keyboard, modem, network adapter, etc. (not depicted).

[0060] A cloud computing system 420 is in wired or wireless electronic communication with the processing system 400. The cloud computing system 420 can supplement, support or replace some or all of the functionality (in any combination) of the processing system 400. Additionally, some or all of the functionality of the processing system 400 can be implemented as a node of the cloud computing system 420.202500551

[0061] For the sake of brevity, conventional techniques related to making and using the disclosed embodiments may or may not be described in detail herein. In particular, various aspects of computing systems and specific computer programs to implement the various technical features described herein are well known. Accordingly, in the interest of brevity, many conventional implementation details are only mentioned briefly or are omitted entirely without providing the well-known system and / or process details.

[0062] The various components / modules / models of the systems illustrated herein are depicted separately for ease of illustration and explanation. In embodiments of the invention, the functions performed by the various components / modules / models can be distributed differently than shown without departing from the scope of the various embodiments of the invention describe herein unless it is specifically stated otherwise.

[0063] Aspects of the invention can be embodied as a system, a method, and / or a computer program product at any possible technical detail level of integration. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention.

[0064] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the present invention. As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises” and / or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and / or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, element components, and / or groups thereof.

[0065] While the present invention has been described with reference to an exemplary embodiment or embodiments, it will be understood by those skilled in the art that various changes may be made and equivalents may be substituted for elements thereof without departing from the scope of the present invention. In addition, many modifications may be made to adapt a particular situation or material to the teachings of the present invention without departing from the essential scope thereof. Therefore, it is intended that the present invention not be limited to the particular embodiment disclosed as the best mode202500551contemplated for carrying out this present invention, but that the present invention will include all embodiments falling within the scope of the claims.

Claims

1. 202500551CLAIMSWhat is claimed is:

1. A computer-implemented method for interactive visualization and simulation of spatio-temporal data for decision-making, the method comprising:ingesting a plurality of georeferenced data sources including textual data, two-dimensional map data, three-dimensional elevation data, and time-series data;creating a unified data set by aligning data from the plurality of georeferenced data sources into a unified spatial and temporal coordinate system;translating the unified data set into a common visual language by converting numerical and textual inputs into corresponding visual cues to generate a three-dimensional digital model;receiving interactive user inputs including object manipulation, parameter adjustments, and time navigation commands to modify elements of the three-dimensional digital model;dynamically simulating cause-and-effect scenarios by integrating the user inputs with analytical and simulation engines that employ both detailed simulation models and physicsbased approximations; andrendering an updated three-dimensional digital model to visually represent potential environmental, industrial, and urban planning outcomes, thereby enabling informed decisionmaking.

2. The method of claim 1, wherein the ingesting of georeferenced data sources includes data from satellite imagery, LiDAR data, and loT sensor networks, simulations allowing for real-time data ingestion from live data feeds or streaming data sources.

3. The method of claim 2, wherein aligning data from the plurality of georeferenced data sources is performed using machine learning algorithms to automate alignment and improve accuracy, supporting different coordinate systems and automatic conversion between them.2025005514. The method of claim 3, wherein the translating of the unified data set into a common visual language utilizes advanced rendering techniques, such as ray tracing or volumetric rendering, and provides customizable visual themes or styles.

5. The method of claim 4, wherein the receiving of interactive user inputs includes gesture-based controls or voice commands and enables collaborative features allowing multiple users to interact with the model simultaneously.

6. The method of claim 5, wherein the dynamically simulating of cause-and-effect scenarios includes a library of predefined scenarios or templates for common use cases and allows users to define custom simulation parameters or constraints.

7. The method of claim 6, wherein the rendering of the updated three-dimensional digital model integrates augmented reality (AR) or virtual reality (VR) capabilities for immersive visualization and provides options for exporting simulation results in various formats, such as video, reports, or interactive web applications.

8. A system for interactive visualization and simulation of spatio-temporal data for decision-making, the system comprising:a processor;a memory coupled to the processor; andone or more computer readable storage media coupled to the processor, the one or more computer readable storage media collectively containing instructions that are executed by the processor via the memory to cause the processor to perform operations comprising: ingesting a plurality of georeferenced data sources including textual data, two-dimensional map data, three-dimensional elevation data, and time-series data;creating a unified data set by aligning data from the plurality of georeferenced data sources into a unified spatial and temporal coordinate system;translating the unified data set into a common visual language by converting numerical and textual inputs into corresponding visual cues to generate a three-dimensional digital model;202500551receiving interactive user inputs including object manipulation, parameter adjustments, and time navigation commands to modify elements of the three-dimensional digital model;dynamically simulating cause-and-effect scenarios by integrating the user inputs with analytical and simulation engines that employ both detailed simulation models and physicsbased approximations; andrendering an updated three-dimensional digital model to visually represent potential environmental, industrial, and urban planning outcomes, thereby enabling informed decisionmaking.

9. The system of claim 8, wherein the ingesting of georeferenced data sources includes data from satellite imagery, LiDAR data, and loT sensor networks, simulations allowing for real-time data ingestion from live data feeds or streaming data sources.

10. The system of claim 9, wherein aligning data from the plurality of georeferenced data sources is performed using machine learning algorithms to automate alignment and improve accuracy, supporting different coordinate systems and automatic conversion between them.

11. The system of claim 10, wherein the translating of the unified data set into a common visual language utilizes advanced rendering techniques, such as ray tracing or volumetric rendering, and provides customizable visual themes or styles.

12. The system of claim 11, wherein the receiving of interactive user inputs includes gesture-based controls or voice commands and enables collaborative features allowing multiple users to interact with the model simultaneously.

13. The system of claim 12, wherein the dynamically simulating of cause-and-effect scenarios includes a library of predefined scenarios or templates for common use cases and allows users to define custom simulation parameters or constraints.

14. The system of claim 13, wherein the rendering of the updated three-dimensional digital model integrates augmented reality (AR) or virtual reality (VR) capabilities for immersive visualization and provides options for exporting simulation results in various formats, such as video, reports, or interactive web applications.20250055115. A computer program product, the computer program product comprising a non-transitory tangible storage device having program code embodied therewith, the program code executable by a processing system to perform operations comprising:ingesting a plurality of georeferenced data sources including textual data, two-dimensional map data, three-dimensional elevation data, and time-series data;creating a unified data set by aligning data from the plurality of georeferenced data sources into a unified spatial and temporal coordinate system;translating the unified data set into a common visual language by converting numerical and textual inputs into corresponding visual cues to generate a three-dimensional digital model;receiving interactive user inputs including object manipulation, parameter adjustments, and time navigation commands to modify elements of the three-dimensional digital model;dynamically simulating cause-and-effect scenarios by integrating the user inputs with analytical and simulation engines that employ both detailed simulation models and physicsbased approximations; andrendering an updated three-dimensional digital model to visually represent potential environmental, industrial, and urban planning outcomes, thereby enabling informed decisionmaking.

16. The computer program product of claim 15, wherein the ingesting of georeferenced data sources includes data from satellite imagery, LiDAR data, and loT sensor networks, simulations allowing for real-time data ingestion from live data feeds or streaming data sources.

17. The computer program product of claim 16, wherein aligning data from the plurality of georeferenced data sources is performed using machine learning algorithms to automate alignment and improve accuracy, supporting different coordinate systems and automatic conversion between them.20250055118. The computer program product of claim 17, wherein the translating of the unified data set into a common visual language utilizes advanced rendering techniques, such as ray tracing or volumetric rendering, and provides customizable visual themes or styles.

19. The computer program product of claim 18, wherein the receiving of interactive user inputs includes gesture -based controls or voice commands and enables collaborative features allowing multiple users to interact with the model simultaneously.

20. The computer program product of claim 19, wherein the dynamically simulating of cause-and-effect scenarios includes a library of predefined scenarios or templates for common use cases and allows users to define custom simulation parameters or constraints.

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