1 Introduction

A Digital Twin (DT) is a virtual representation of a physical object, system, or process. It is a digital counterpart that simulates and mimics the real-world counterpart in a digital environment. The concept of DTs has received considerable attention across various industries due to its transformational potential in improving efficiency, enhancing decision making, and optimizing real-world systems (Grieves 2023). Existing DTs are used in various industries and applications, including manufacturing for virtual product design, process optimization, and predictive maintenance (Shao et al. 2019; Zheng and Sivabalan 2020; Mylonas et al. 2021; Ali et al. 2021; Liu et al. 2021). In healthcare, they enable patient-specific treatment plans and medical device testing (Sun, He, and Li 2023; Turab and Jamil 2023). Smart cities employ DTs twins for urban planning, traffic management, and energy optimization (Deren, Wenbo, and Zhenfeng 2021; The Editorial Board of Urban Planning Forum 2023; Wang et al. 2023; Ye et al. 2023). The aerospace and defense sector uses them for aircraft maintenance and pilot training simulations (Glaessgen and Stargel n.d.). The Internet of Things (IoT) and sensors benefit from DTs for monitoring devices and optimizing supply chains (VanDerHorn and Mahadevan 2021). A traditional DT consists of several layers as shown in Fig. 1. The data acquisition layer collects data from physical objects or systems equipped with sensors and IoT devices to gather real-time data. The data storage and management layer stores both raw and processed data in a data repository or database. Within this layer, data processing tasks such as aggregation, filtering, and feature extraction are performed to prepare the data for modeling and analysis. The modeling and simulation layer contains computational models that replicate the behavior and characteristics of the physical system. These models are used to create the virtual representation of the physical asset or process. The simulation engine executes these models to simulate the behavior of the physical system in real-time. It takes input data, runs simulations, and generates predictions and insights. The application layer provides user interfaces and dashboards to visualize digital twin data and insights. Users can monitor the physical system’s status, perform analyses, and make informed decisions. All of these layers communicate through the digital twin core, enabling real-time updates and interactions. Recently, several DT models and applications have been proposed to replicate and simulate physical objects. Despite the growing interest in DTs, there are three primary drawbacks to their usage:

  • The cost and complexity of building and maintaining digital twins can be prohibitive, particularly for large-scale applications. Moreover, scaling digital twins to cover extensive systems can be technically challenging, and interoperability issues persist in multi-vendor environments.

  • Integrating data from diverse sources into a coherent model can be cumbersome, and ensuring the model’s accuracy through validation and calibration is crucial but time-consuming.

  • Data privacy and security is required, as these systems often deal with sensitive information. Especially, ethical concerns related to privacy, consent, data ownership, and potential biases in decision-making also arise.

Fig. 1
figure 1

Traditional Digital Twin Layers consist of a a Data acquisition layer, b a Data storage and management layer, c a Modeling and simulation layer, d a Simulation engine layer, and (e) an Application layer

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In the context of these efforts, our work introduces a novel interactive visual analytic system, as illustrated in Fig. 2, aimed at simulating the distribution of classes and the utilization of campus building capacity at Texas A&M University, a large public research university with an enrollment exceeding 70,000 students. The core functionality of our visual analytic system involves several key steps. First, we extract and securely store course enrollment data within a robust database framework. This data serves as the foundational information for our subsequent analyses. Second, we engage in a comprehensive data processing phase where each course item is meticulously mapped in a spatial-temporal format. This transformation of data into a spatial-temporal representation is essential for gaining valuable insights into the dynamic distribution of classes across the campus and the utilization of available resources. Finally, we develop a visual analytic system. This system serves as a powerful tool to unlock the full potential and usefulness of DTs concepts in the context of campus management. It enables users to interactively explore, visualize, and analyze data related to class distribution and building capacity.

Fig. 2
figure 2

The framework of campus digital twin. We collect course enrollment and spatial information from our university data portal. Then, the data is preprocessed, aggregated, transformed and stored in our designed databases. Finally, we develop a Visual Analytic (VA) tool for more insights and analysis

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To validate the effectiveness and practical utility of our visual analytic system, we conducted two comprehensive case studies in two distinct domains. These domains include fire accident management and urban planning. These case studies highlight the system’s adaptability and versatility, demonstrating its ability to support domain experts in critical tasks such as analysis and decision-making. Our work not only addresses the limitations of DTs technologies but also showcases the development of a cutting-edge visual analytic system with real-world applications. In summary, our contributions include:

  • The research highlights the challenges associated with DT implementation, including issues related to cost, complexity, interoperability, and data integration. This indicates a comprehensive understanding of the hurdles faced in leveraging DTs for improved built environment management.

  • The research demonstrates the utilization of enrollment data as a key input, converting it into a spatial-temporal format. This implies a data-driven approach, emphasizing the importance of accurate and relevant data in the system’s functionality.

  • The research presents a novel solution in the form of an interactive visual analytics system designed to address the challenges mentioned earlier. This suggests an innovative approach to overcoming the obstacles associated with DT implementation.

  • The research utilizes a potential analytical approach for future case studies involving the simulation of class distribution and campus building capacity at a large public university. This showcases the system’s applicability to real-world scenarios within the educational environment.

2 Related work

In recent years, the concept of DTs has emerged as a transformative paradigm in various fields. The development of interactive visualization techniques has played a significant role in making these digital replicas not only feasible but also accessible to common users. This section explores the evolution of digital twins and their existing applications within three sectors: aviation, manufacturing, and health care, with a particular emphasis on the critical role of visualization in harnessing their full potential.

2.1 Aviation

In aviation, DTs primarily serve as tools for predictive maintenance, structural analysis, decision support, and optimization (Smarslok, Culler, and Mahadevan 2012; Yang, Zhang, and Liu 2013) demonstrate an aircraft DT using automated image tracking to analyze crack deformation and growth, reducing costs and development time. Majumdar, FaisalHaider, and Reifsnider (2013) focus on DTs modeling the impact of multi-physical environments on structural composites. Bielefeldt, Hochhalter, and Hartl (2015) utilize DTs to identify fatigue cracks by integrating shape memory alloy particles into aircraft structures, providing an innovative solution for structural health. In a similar vein, Wang et al. (2015) combines a Finite Element Alternating Method (FEAM) and Moving Least Square law (MLS) to predict fatigue damage, further enhancing aircraft safety. Further, Li et al. (2017) and Bazilevs et al. (2015) delve into dynamic Bayesian networks and computational control for managing aircraft conditions. These DTs are AI systems that, according to Gabor et al. (2016), should not be considered autonomous but rather rely on human expertise, supervision, and intervention. Ongoing supervision ensures simulations, intervention planning, and system enhancements. This human-machine collaboration optimizes the utilization of DTs in various applications.

2.2 Manufacturing

DTs have emerged as valuable tools for enhancing production processes and the overall product lifecycle. Rosen et al. (2015) emphasize DTs’ potential in creating a modular computerized system that efficiently supervises each step of manufacturing. Qi and Tao (2018) discuss the benefits of a Data-Driven Smart Manufacturing (BDD-SM) approach that employs DTs and leverages sensors and IoT to generate substantial data volumes. Additionally, Tao and Zhang (2017) and Tao et al. (2018) highlight the importance of integrating intelligent “services” within DTs, underscoring data integration from various sources. The Digital Twin model is also considered to have five components: physical space, virtual space, sensors, integration technologies, and analytics (Grieves 2014). Advanced studies in manufacturing (Qi and Tao 2018) highlight the need for maintaining continuous interaction and alignment of Digital Twins to ensure synchronization with their physical counterparts. Uhlemann et al. (2017a, 2017b) emphasizes the importance of real time multi-modal data collection and simulation-based data processing. Finally, Lee et al. (2013) describes their vision of a Cyber-Physical System overseeing manufacturing processes efficiently, while Ameri and Sabbagh (2016) introduce a web application for creating a digital factory through a DT representation.

2.3 Healthcare

In healthcare, DTs were initially introduced for predictive maintenance and performance enhancement of medical devices. Another notable application of DT technology focuses on optimizing hospital operations. General Electric (GE) Healthcare’s “Capacity Command Center” (Polyniak and Matthews 2016), deployed at Johns Hopkins Hospital in Baltimore, employs simulations and analytics to enhance decision-making processes. Similarly, Siemens Healthineers has introduced a DT to improve the performance of Mater Private Hospitals (MPH) in Dublin (Scharff 2018). The hospital faced challenges due to increasing patient demand, clinical complexity, aging infrastructure, space constraints, extended wait times, interruptions, delays, and rapid advancements in medical technology, necessitating additional equipment. The concept of “precision medicine” has emerged, focusing on tailored healthcare solutions based on individual genetic, biomarker, phenotypic, physical, or psychosocial characteristics (Akhoon 2021). The healthcare field is also witnessing significant progress in medical AI applications, simulations, and virtual reality. Concurrently, the adoption of Picture Archiving and Communication Systems (PACS) (Choplin, Boehme, and Maynard 1992) is on the rise, providing cost-effective storage and convenient access for medical examinations, especially images, across various modalities and source machine types. A distinct fourth generationDT for the heart, developed by Siemens Healthineers, is currently employed by cardiologists at Heidelberg University Hospital (HUH) in Germany for research and experimentation (Von Gratz 2019).

Similar to DTs for campus, smart cities have harnessed DTs as a pivotal tool in the pursuit of urban development. One prominent application of DTs in smart cities lies in urban planning and design. Smart City DTs provide a comprehensive digital representation of the city’s physical assets, enabling planners to simulate various urban scenarios and assess the implications of proposed changes (Deng, Zhang, & Shen 2021). By visualizing these scenarios before implementation, city planners and architects can optimize urban designs and infrastructure effectively (Lin et al. 2022; Ye et al. 2022; Gan et al. 2023). DTs are also instrumental in traffic management and optimization. Smart cities utilize DTs to monitor real-time traffic conditions by collecting data from an array of sensors and IoT devices deployed throughout the city (Gao et al. 2021). This data is analyzed to enhance traffic signal timings, reroute traffic, and alleviate congestion, ultimately improving mobility for city residents. Energy optimization is a crucial aspect of smart cities, and DTs play a pivotal role in enhancing urban sustainability. By integrating data from smart grids, energy consumption patterns, and renewable energy sources, DTs empower cities to efficiently manage and optimize their energy resources, leading to lower energy costs, reduced carbon emissions, and improved environmental sustainability. Moreover, DTs are employed in urban resilience and disaster management. Cities can use DTs to simulate various disaster scenarios, such as floods, earthquakes, or extreme weather events, to evaluate their impact and devise effective response strategies (Fan et al. 2021; Ye et al. 2021; Yu and He 2022). This proactive approach ensures cities are better prepared to handle emergencies and protect their residents. In summary, the integration of Digital Twins in smart cities encompasses various domains, from urban planning and traffic management to energy optimization and disaster resilience, offering a comprehensive toolkit for creating more efficient, sustainable, and livable urban environments.

3 Design requirements and methods

Previous studies have collectively identified key insights to visualize campus for digital twins (Azfar et al. 2022; Lu et al. 2019). In this work, we define essential requirements to ensure a successful and effective deployment of campus digital twins. These requirements include:

3.1 R1: robust data integration framework

Create a data integration framework that ensures the seamless collection of data from a wide range of sources. The developed framework must employ data connectors, application program interfaces (APIs), and protocols to gather data from sensors, IoT devices, and existing campus systems. Use standardized data formats for compatibility. This framework allows the DT to access real-time data and maintain a comprehensive view of campus activities and assets.

3.2 R2: spatial-temporal data representation

Represent data in a spatial-temporal format to capture the dynamic nature of campus resources and events. By utilizing geographic information systems (GIS) and time-series data models to map and visualize data in a spatial-temporal context. This representation enables the DT to track changes over time, analyze trends, and provide insights into resource utilization and campus dynamics.

3.3 R3: user-friendly interface

Create user interfaces and dashboards that are intuitive and user-friendly, ensuring easy interaction with the DT. We have designed user interfaces with clear navigation, interactive elements, and visualizations that enable stakeholders to explore data effortlessly. User-friendly interfaces empower campus administrators, faculty, and students to make informed decisions and optimize resource use.

3.4 R4: data visualization and analytics

Provide robust tools for data visualization and analytics to help stakeholders understand campus operations, resource utilization, and trends. Integrate data visualization libraries and analytics platforms that offer insights through charts, graphs, and reports. Stakeholders can gain deeper insights into campus performance, enabling data-driven decision-making and resource optimization.

3.5 R5: case studies and validation

Validate the effectiveness and practical utility of the DT through comprehensive case studies in various domains. Conduct in-depth case studies, such as campus management, urban planning, and emergency response scenarios, to assess the DT’s performance and benefits. Case studies demonstrate the DT’s real-world applications, adaptability, and utility in addressing specific campus challenges and needs.

These design requirements ensure that the DT for campus management not only collects and processes data effectively but also provides user-friendly interfaces, adapts to changing campus dynamics, and supports data-driven decision-making through visualization and analytics. Additionally, the validation through case studies underscores the DT’s real-world value and practicality in various domains.

3.5.1 Data collection and data preprocessing

The class enrollment at Texas A&M University is a dynamic and vital aspect of the institution’s academic ecosystem. As one of the largest and most comprehensive public research universities in the United States, Texas A&M accommodates a diverse and substantial student body. Each semester, students at Texas A&M engage in the process of class enrollment. The university offers a comprehensive curriculum that spans various disciplines, ranging from science and engineering to liberal arts and humanities. Texas A&M’s online registration system streamlines the process for students. Yet, while the university’s registration system is clearly designed and easy to understand, and the university website features interactive mapping tools that show each building, there is currently no tool to help students translate their schedule into an easily navigable map. Given the size (over 5,500 acres) and complexity (more than 200 buildings) of Texas A&M’s campus, navigating it can be daunting to students and visitors, especially those coming on our campus or any campus for the first time. The travel distances required between some classroom buildings mean that students may not have enough time between class periods to make it to their next class. Further, university administrators are quite limited in their ability to assess classroom utilization, currently relying on an elaborate system of spreadsheets.

Figure 2 illustrates the overview of our framework. We first extract class enrollment data from the university’s registrar portal. The class enrollment data contains spreadsheets in a .csv format, which generally contain class information including time ranges, meeting days, building codes, status (open or full), and class title. The class enrollment data generally contain class information including time ranges, meeting days, building codes, status (open or full), and class title. More specifically, the provided enrollment data is aggregated and anonymized by building codes and number of students to protect student privacy. Further, our proposed aggregation and transformation methods can be applied to present trends and insights without revealing individual student information.

Creating a spatial-temporal representation, as required in R2, is an important step in our endeavor to enhance class distribution and campus building capacity. To achieve this, we start by leveraging the extensive spatial data available through the university’s Geographic Information System (GIS) data portal. The GIS data encompasses an array of valuable information about our campus infrastructure. The GIS data provides detailed insights into the physical layout of the university. It includes information such as building codes, which serve as unique identifiers for each campus structure, ensuring precise spatial reference. Additionally, we extract essential attributes associated with each building. These attributes encompass critical details like geographic coordinates (latitude and longitude), allowing us to pinpoint the exact location of each structure on our campus.

Further, the GIS data includes information related to building height and elevation. These parameters offer valuable context when understanding the spatial relationships between different structures on campus, particularly in scenarios where vertical positioning plays a role. Lastly, the data provides the geometric characteristics of each building, helping us understand the physical footprint and shape of each structure. This spatial information forms the foundation for our spatial-temporal representation, enabling us to create a dynamic and informative model of our campus’s physical layout and resources. With this comprehensive dataset in hand, we can proceed with transforming this static information into a spatial-temporal format that captures the evolving activities and resource utilization across our campus.

3.5.2 Data integration and transformation

To support R1, the integration of class enrollment data and building spatial information involves merging two distinct datasets, each with its unique attributes and characteristics. On one side, we have class enrollment data, a repository of information that provides insights into students’ course registrations, schedules, and classroom assignments. It forms the academic heartbeat of our campus, reflecting the dynamic ebb and flow of classes. On the other side, we tap into the wealth of spatial data sourced from our university’s GIS data portal. This dataset offers a comprehensive view of our campus’s physical infrastructure, featuring attributes such as building codes, geographic coordinates, height, and geometry. It serves as the canvas upon which academic activities unfold.

3.6 Data integration

The integration process harmonizes these datasets, connecting academic activities with specific physical locations on our campus. This synergy creates a spatial-temporal representation that not only links the ”what” of class enrollment with the ”where” of campus resources but also captures the ”when” of academic activities. The result is a dynamic model that tracks the movement of students and classes in real-time, providing valuable insights into the utilization of campus spaces.

3.7 Data transformation

The transformation of this integrated data into a spatial-temporal format involves translating static information into a dynamic representation of campus activities and building capacity. This transformation is essential for understanding how resources are allocated, when buildings are most active, and how class distribution evolves over time. Visualizing this data in a spatial-temporal format empowers us to make data-driven decisions about campus management, space optimization, and resource allocation.

3.7.1 Class scheduling and mobility

Our visual analytics system provides users with the capability to simulate walking directions within the campus. To enhance this experience, users can create, modify, retrieve, and delete their class schedules, using the CRUD operations (Create, Read, Update, Delete). This functionality allows users to manage their schedules by adding, viewing, modifying, and removing class entries.

To further streamline the class scheduling process, we’ve implemented a fuzzy search feature. This feature utilizes advanced algorithms, such as Levenshtein distance and string similarity metrics (Behara, Bhaksar and Chung 2020; Zhang, Hu and Bian 2017). These algorithms work by calculating the similarity between a user’s query and the class names stored in the database. For instance, the Levenshtein distance algorithm assesses the number of single-character edits (insertions, deletions, substitutions) required to transform one string into another. This fuzzy search capability aids users in quickly and accurately finding the classes they intend to add to their schedules, enhancing the overall user experience, as follows.

3.8 User input handling

To ensure consistency between user input and database entries, the algorithms preprocess the user’s input in the same way as the database when a user enters a class name or description. For example, convert the input to lowercase and remove any special characters or white spaces.

3.9 Fuzzy matching and sorting

The selected fuzzy search algorithm the preprocessed user input against the class names in the database. A similarity score is calculated for each class name in the database concerning the user’s query. Then, the matched class names are sorted by their similarity scores in descending order.

3.9.1 Walking directions

Our system employs Mapbox’s Directions API for obtaining precise walking directions within the campus. This API streamlines the process of gathering detailed route information by making HTTP GET requests. Users input their desired starting and ending points, defined by the coordinates of specific buildings on campus. These coordinates are essential parameters in constructing the GET request, which includes the latitude and longitude of the chosen locations. The GET request is then sent to the Mapbox API server.

Mapbox’s Directions API further processes the request, considering factors such as the preferred mode of transportation (in our case, walking), optional waypoints, and route customization preferences (e.g., wheelchair accessibility, which can also be used for students operating personal mobility devices such as scooters or skateboards). The API harnesses its mapping and routing algorithms to calculate the optimal walking path, offering a comprehensive response that includes turn-by-turn directions, distance, estimated travel time, and a sequence of geographical coordinates outlining the route. Upon receiving this response, our visual analytics system interprets the data to provide users with clear, accessible walking directions, empowering them to navigate the campus efficiently. This technical integration of the Mapbox Directions API ensures that users can confidently access and follow precise directions to move around the campus with ease.

4 Visualization system

In line with the requirements outlined in R3 and R4, our approach involves the creation of a comprehensive visual analytics system, as illustrated in Fig. 3. This web-based visual analytic system is thoughtfully designed and developed to harness the capabilities offered by the integration of class enrollment data and digital twins, while strictly adhering to the established design requirements. The central objective of this visual analytics system is to provide a platform that empowers users to interact with and explore the wealth of data originating from class enrollments and the digital twin representation of the campus. By implementing this system, we aim to address the challenges associated with effectively managing and optimizing campus resources and activities. To achieve this, the system incorporates a user-friendly interface and advanced functionalities that facilitate data visualization, analysis, and informed decision-making. Additionally, the DT adheres to the requirements set forth in R5, which mandate a comprehensive validation process through diverse case studies in various domains. Through this design and development process, our visual analytics system endeavors to bridge the gap between the promise of digital twins and their practical application, offering a versatile tool for real-world scenarios in campus management and related fields.

Fig. 3
figure 3

Visual Analytic of Campus Digital Twins consist of various components including (a) Course schedule view (b) Total classes view (c) Days distribution view (d) Building timelines view (e) Map view and (f) Building detail view

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4.1 Course schedule view

Figure 3a the course schedule view allows users a streamlined interface to search for, organize, and visualize their course schedules. Users can search for courses by name, aided by fuzzy search and string matching algorithms that provide similarity scores for efficient discovery. The schedule is displayed in a clear tabular format, showing class times, class names, and building locations. Users can effortlessly manage their schedules by adding, updating, or removing courses. When adding a single class, the system automatically displays the building's geo-location on the map view, simplifying navigation. Importantly, the system enforces scheduling rules, preventing users from adding overlapping classes or ones that start before a prior class ends, ensuring realistic schedules.

For added convenience, when multiple courses are added, the system generates walking directions between class locations, optimizing campus mobility. This feature not only guides users to their next class but also streamlines their daily movements on campus. In essence, the course schedule view empowers users with precise control over their schedules, making it an invaluable tool for students, faculty, and staff to navigate campus life with efficiency and ease.

4.2 Total classes view

Figure 3b the building code system simplifies the presentation of diverse campus structures, making it easier for users to quickly identify where their classes are situated. By visually mapping out the meeting times in each building, users can easily comprehend the spatial distribution of their classes. This feature is particularly beneficial for those with complex schedules or multiple courses in different buildings, as it offers a comprehensive overview of how their time is allocated throughout the campus. It facilitates optimized planning and reduces the time and effort needed to move between classes, ultimately enhancing the efficiency of daily campus life.

Furthermore, we complement this spatial visualization with a bar chart that succinctly displays the total number of meetings taking place in each building. This chart provides users with a quick, informative snapshot of the level of activity in various parts of the campus. It is a useful tool for identifying high-traffic areas and can assist students and faculty in selecting study locations that match their preferences—whether they seek a quiet environment or a bustling hub of campus life. By visualizing the distribution of meeting times and offering insights into building usage, our course schedule view not only simplifies scheduling but also contributes to a more efficient, informed, and enjoyable campus experience for all users.

4.3 Day distribution view

Figure 3c the day distribution view is designed to provide users with a visual representation of their class distribution across the days of the week. This view offers a heatmap-style visualization, using shades of blue to indicate the density of classes scheduled on each day, from Monday through Sunday. The heatmap employs a color gradient, with darker shades of blue representing a high density of classes on a particular day of the week. This intuitive visual representation allows users to quickly identify which days are busier in terms of class attendance and which are relatively lighter. For example, if a specific day exhibits a deep blue shade, it indicates that numerous classes are scheduled for that day, suggesting a busy and packed schedule. On the other hand, lighter shades signify days with fewer or no classes, offering users a clear overview of when they may have more flexibility or time for other activities.

4.4 Building timelines view

Figure 3d is designed to offer users a more granular and detailed understanding of the usage patterns within specific campus buildings. This view provides a comprehensive timeline, displayed as a 24-hour heatmap, with specific ranges that allow users to focus on particular time periods of interest. The heatmap's color-coding helps users quickly identify when specific buildings experience peak activity and high capacity. The color ranges within the heatmap signify different levels of activity, with darker shades typically indicating times of high utilization. This enables users to gain a more profound understanding of when a particular building is busiest during the day.

When a time range is filtered (brush filtering), all other associated views within the visual analytic system are updated in real-time. This ensures that users get a synchronized and cohesive perspective on how their selected time range impacts class schedules, building usage, and campus activity. It enables users to make well-informed decisions about their daily routines and activities by considering factors like class availability and building capacity during their chosen time slots.

4.5 Map view

Figure 3e the map view offers a spatial distribution of each building's density across the campus. This distribution is visually represented by a spectrum of shades, ranging from darker to lighter shades of blue. These varying shades help users quickly gauge the relative density of activity within different buildings. In practice, darker blue areas indicate higher building density, signifying that the corresponding buildings are more frequently used or have a greater concentration of classes and activities. By visualizing this spatial distribution, the "map view" empowers users to make informed decisions about where to go on campus, optimize their daily movements, and avoid overcrowded areas during peak times. It enhances user’s understanding of the physical campus layout and the dynamic utilization of various buildings, facilitating a more efficient and enjoyable campus experience.

4.6 Building detail view

Figure 3f the building detail view provides users in-depth insights into individual campus buildings. Within this view, users can access information on class status, determining whether classes within the building are full or open. Additionally, room occupancy is visualized in a pie chart, providing a clear and intuitive representation of which rooms are highly occupied and which have available space. This feature aids students and faculty in making informed decisions about class scheduling and room selection, optimizing space utilization, and ensuring efficient room allocation. It enhances the overall campus experience by helping users navigate and utilize specific building resources effectively.

5 Supported analyses for campus management

The integration of campus DT and our proposed visualization system can be beneficial for analyses that optimize campus management (Fig. 4). It enables a deeper understanding of the physical and operational aspects of a campus environment. Several types of analysis can be conducted, as follows:

Fig. 4
figure 4

Visualize class schedule and walking trajectories to simulate the Create, Read, Update, Delete (CRUD) functionality and walking direction generated from Mapbox’ Directions

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5.1 Space utilization

Smart campus digital twins offer the capability to conduct comprehensive space utilization analyses. By monitoring and evaluating how different areas of the campus are utilized, institutions can optimize space for efficiency and functionality as well as predict future usage and space needs. This analysis helps identify underutilized spaces and informs decision-making on repurposing or redesigning areas to better meet the evolving needs of the campus community. For example, we can explore questions related to faculty-to-student ratios for different disciplines, how different types of students (e.g., female, first-generation, or students with disabilities) use both classroom and study spaces, or how different types of class schedules (weekday, evening, or weekend classes) might influence building capacity and utilization.

5.2 Energy efficiency analysis

Campus digital twins enable detailed analyses of energy consumption patterns within buildings and across the campus. This information facilitates the identification of opportunities for energy savings and the implementation of strategies to reduce environmental impact. Further, DTs can support facility maintenance and help prioritize renovation or deferral decisions. Institutions can make informed decisions to enhance energy efficiency and sustainability based on the insights provided by these analyses.

5.3 Security and safety analysis

Simulation of emergency scenarios and continuous monitoring of surveillance data are essential aspects of security and safety analyses enabled by campus digital twins. Campuses house a range of facilities that house hazardous materials, laboratories that host potentially dangerous equipment and experiments (e.g., nuclear, biohazards, chemicals, diseases), along with students who are learning to handle them. Further, mental health and stress are issues of considerable concern on college campuses (Flett, Khan and Su 2019). Simulation of emergency scenarios can help plan evacuation routes, reduce evacuation times, identify chokepoints, adapt to ongoing threats, and allocate resources more effectively. They can help institutions identify potential security vulnerabilities and enhance their overall safety measures. By visualizing and assessing various security scenarios, campus administrators can make data-driven decisions to create a safer environment for students, faculty, and staff.

5.4 Traffic and transportation planning

Both urban and suburban campuses face numerous challenges with mobility and accessibility for both humans and freight. Campus digital twins can provide valuable insights into pedestrian and vehicular traffic patterns, supporting the optimization of transportation routes and parking facilities for students, employees, visitors, and vendors for both daily activities and special events open to the public. DTs can help institutions assess different mobility options (e.g., bike shares, scooters, skateboards, motorcycles and mopeds), identify congestion points, model post-event traffic (e.g., football games, performing arts events, etc.), or simulate parking scenarios. Institutions can use this analysis to plan infrastructure improvements and enhance overall mobility and accessibility. By understanding and adapting to traffic data, campuses can create a more efficient and user-friendly transportation system.

5.5 Infrastructure planning and development

Simulation and visualization capabilities provided by campus digital twins aid in infrastructure planning and development. Institutions can simulate the impact of new construction projects or renovations, helping to value-engineer projects based on campus priorities, or to phase construction to limit its impact on current activities. Such analyses allow for informed decision-making. By optimizing the layout of buildings and infrastructure, campuses can improve functionality, efficiency, aesthetics, and the overall experience for students, faculty, and visitors.

5.6 Student experience enhancement

Campus digital twins contribute to an improved student experience by providing insights into student behaviors and preferences. Institutions can analyze data on the usage of common areas and study spaces, optimizing their layout based on usage patterns. Our proposed visualization tool can help visitors or non-local students to plan campus visits, providing more predictability and an easier visit. Such analyses help create a campus environment that aligns with the needs and preferences of students, enhancing their overall experience and satisfaction with the educational institution.

6 Conclusion, limitation, and future directions

In conclusion, DTs represent a promising paradigm shift with the potential to revolutionize various industries by enhancing operational efficiency, facilitating informed decision-making, and optimizing real-world systems. However, several challenges continue to impede their widespread adoption, including issues related to cost, complexity, interoperability, data integration, validation, and data security. To address these challenges, our work has introduced an innovative visual analytics system, exemplified by its application in simulating class distribution and campus building capacity. This system leverages robust data integration and spatial-temporal data representation to create a dynamic and interactive platform for stakeholders to explore and analyze class distribution and resource utilization on a campus. The significance of this work lies in its validation through comprehensive case studies, spanning diverse domains like campus management, urban planning, and emergency response. These case studies illustrate the adaptability and real-world applicability of the visual analytics system, effectively bridging the gap between the theoretical promises of DTs and their practical implementation. By offering a solution that not only overcomes the challenges associated with DTs but also showcases the development of a cutting-edge visual analytics system with real-world applications, this work paves the way for the more widespread and effective use of DTs in various domains. Ultimately, the approach holds the potential to transform how industries and organizations operate and make decisions in an increasingly data-driven world.

6.1 Limitations

Utilizing data-driven approaches like building a digital twin to assess campus capacity and its related variables is a promising concept, but it comes with a set of inherent limitations. First and foremost, gathering and integrating data from various campus aspects, including enrollment, mobility, utilities, and security, is a daunting task. Data collection can be hindered by inconsistencies, incompleteness, and data privacy concerns, necessitating rigorous efforts to ensure data accuracy and security. Analyzing the intricate relationships between different facets of campus life, such as how enrollment impacts mobility and services, presents a considerable challenge. This complexity demands a multidisciplinary approach, involving experts from various fields, to accurately model and interpret these interdependencies.

Moreover, campuses are dynamic environments subject to constant change, from fluctuations in enrollment to infrastructure modifications and evolving security threats. Keeping the digital twin and data-driven analyses up-to-date to reflect these changes is a continuous and resource-intensive task. Lastly, determining the most effective investments for expanding campus capacity while maintaining cost-efficiency is a multifaceted decision-making process that requires not only data analysis but also consideration of budget constraints and the trade-offs between different enhancement options. In conclusion, while data-driven approaches offer great potential for campus capacity assessment, addressing the challenges of data integration, complexity, ongoing maintenance, and decision-making will be pivotal in realizing the full benefits of these methodologies.

To increase scalability, the proposed system requires a robust and automated data updating mechanism to ensure that the system consistently reflects real-world changes in class distribution and resource utilization. For example, implement a server backend that automatically extracts course enrollment data from the existing university data portal every semester, and regularly update enrollment data by taking into account changes in student numbers, class schedules, and any other relevant factors that may impact accuracy of the simulation. For technological adaptability, the system needs to be designed with modular components and architecture that allows for easy integration of new technologies and updates. This requires developers to stay informed about advancements in data visualization, analytics, and simulation technologies to incorporate relevant improvements and ensure the system remains technologically adaptable over time. Furthermore, the system’s architecture and operations need to be optimized to minimize costs associated with data storage, processing, and maintenance by considering opensource technologies and collaborative development approaches to leverage community support and reduce development and maintenance expenses.

6.2 Future directions

The future directions for campus capacity analysis and optimization lie in the continuous evolution of data-driven approaches and technology integration. To address the limitations mentioned earlier, campuses should invest in robust data management systems, ensuring the seamless collection, integration, and privacy protection of diverse data sources. Leveraging advanced analytics, AI, and machine learning algorithms will enable more comprehensive and real-time insights into campus dynamics. Furthermore, as campuses increasingly become smart and connected, the integration of IoT devices and sensors can provide richer and more granular data, aiding in the development of dynamic and adaptive campus management systems.

Another significant future focus should be on sustainability and resilience. Campuses are becoming more conscious of their environmental impact and vulnerability to natural disasters. Thus, future endeavors should include assessing the environmental footprint of campus activities, optimizing resource usage, and implementing resilience strategies. Additionally, the use of DTs can extend beyond capacity planning to facilitate effective emergency response and recovery, integrating data on emergency management and security into the models. In this context, fostering collaboration between academia, industry, and government agencies can drive innovation in campus capacity analysis and contribute to the development of safer, more sustainable, and adaptable educational environments.