Urban Vitality Measurement Through Big Data and Internet of Things Technologies

Abstract

This paper examines the evolution of urban vitality measurement, emphasizing the transformative impact of big data and Internet of Things (IoT) technologies. Traditionally assessed through direct observations and surveys, urban vitality measurement has shifted with the advent of these technologies, enabling the collection of vast amounts of urban data. This approach offers a more dynamic and comprehensive picture of urban vitality, facilitated by advanced analytical tools such as machine learning and predictive analytics, which can interpret complex datasets to offer real-time insights and better decision-making for urban planning. However, this shift also raises significant methodological and ethical concerns, particularly regarding privacy, reliability, and accuracy. The paper discusses the theoretical underpinnings of urban vitality, current technological advancements, and the challenges and future directions in urban studies. It highlights the need for an interdisciplinary approach to fully harness the potential of emerging technologies in developing livable, sustainable, and responsive cities.

1. Introduction

Urban vitality, traditionally conceptualized as the liveliness or energy that characterizes urban spaces, plays a pivotal role in the socioeconomic fabric of cities [1,2]. While definitions and metrics vary, urban vitality generally encompasses the dynamics of social interactions, economic transactions, environmental factors, and digital engagements within an urban setting. Jacobs was seminal in framing urban vitality through the lens of bustling streets and active public spaces, emphasizing that vibrant urban areas are essential for economic and social well-being [1]. This notion has been widely accepted and expanded upon in the subsequent urban studies in the literature [3,4].

The measurement of urban vitality has historically relied on direct observations and surveys, methods that are both labor-intensive and limited in temporal and spatial scope. For instance, traditional techniques often involve manual pedestrian counts or surveys that gauge subjective experiences of space [5,6]. These methods, while effective in capturing specific instances of urban vitality, are constrained by their episodic nature and the small scale of their implementation.

In recent decades, the advent of big data and Internet of Things (IoT) technologies has revolutionized the approach to measuring urban vitality, offering new opportunities and challenges. Big data, characterized by volume, velocity, and variety, provide a rich layer of complexity and detail to urban analysis, capturing real-time data from a multitude of sources including social media feeds, mobile phone tracking, and sensor networks [7]. This data-rich environment allows for a more nuanced understanding of urban dynamics on a scale previously unattainable [8]. For example, studies utilizing mobile phone data have demonstrated the ability to track population movements and activities across a city, offering insights into the pulse of urban life [9].

IoT devices further enhance this capability by providing continuous streams of data from specific locations and objects within the urban fabric. Interconnected IoT devices can be interacted, and the sensed data are transmitted, transformed, and aggregated on a real-time basis [10]. In the urban setting, sensors deployed across urban areas can monitor everything from traffic flow and air quality to noise levels and energy usage, each serving as a proxy for different aspects of urban vitality [11]. These technologies not only automate the collection of data but also enable the integration of diverse data streams, presenting a comprehensive picture of urban life that is dynamic and responsive to changes over time.

Moreover, the integration of big data and IoT into urban studies has led to the development of sophisticated analytical tools and models that can predict urban trends and behaviors. Machine learning algorithms and predictive analytics are now commonly used to interpret the vast amounts of data collected, providing urban planners and policymakers with actionable insights that were previously beyond reach [12]. These tools allow for the analysis of the complex, interdependent factors influencing urban vitality, from economic cycles and traffic patterns to social mobility and environmental conditions.

However, the shift from traditional data collection methods to big-data-driven approaches raises significant methodological and ethical questions. The reliance on digital data sources introduces concerns about privacy, data ownership, and the representativeness of data samples. For instance, the biases inherent in data collection processes can skew the understandings of urban vitality, potentially leading to misguided policy interventions [13,14]. The digital divide also poses a substantial challenge, as reliance on data from smartphones and online interactions may exclude segments of the population less engaged with these technologies, thus providing an incomplete or skewed picture of urban vitality [15].

In summary, the evolution of methodologies for measuring urban vitality reflects the broader trends in urban studies and technology. Measuring urban vitality and its effective use for urban planning depend on how much we take advantage of the great potential of the big data collected from IoT devices. If big data are analyzed with appropriate methods, they also provide novel approaches to business and public policies. While big data and IoT devices offer profound opportunities for obtaining insights into the dynamics of city life, they also necessitate a critical reassessment of how urban vitality is conceptualized, measured, and interpreted. The rich potential of these technologies must be navigated carefully to ensure they enhance, rather than obscure, our understanding of urban spaces.

This paper first summarizes the theoretical foundations of urban vitality for further discussion on the recent developments in data and devices for its measurement in Section 2. Section 3 discusses how the new data and devices have contributed to the measurement of urban vitality in recent years. Section 4 reviews how the data are collected, integrated, and analyzed for urban vitality. In Section 5, this paper critically discusses the strengths and weaknesses and opportunities and challenges of the recent development of devices and methods. Then, it concludes in Section 6 with the future directions of measuring urban vitality in creating more dynamic and livable cities.

2. Urban Vitality and Its Measurement

2.1. Theoretical Foundations

Urban vitality, traditionally linked to the dynamism and liveliness of urban spaces, involves a complex interplay of social interactions and economic activities. This multidimensional view has been substantially enriched by the contributions of Jane Jacobs, who emphasized the role of bustling streets and active public spaces in fostering urban vitality. Her ideas about the social and economic dimensions of urban environments have been foundational, suggesting that vibrant public spaces are crucial for better quality of life in cities [1]. Building on Jacobs’ work, theorists such as Lynch [3] and Gehl [4] have further explored these concepts. Their studies emphasize that the design of urban environments significantly influences social behaviors and, by extension, urban vitality. Gehl’s work particularly illustrates how pedestrian-friendly and fine-scaled urban forms contribute to more lively urban areas [16].

The economic dimension of urban vitality often refers to the vibrancy of commercial activities and the diversity of economic opportunities available within city centers. This dimension can be quantified through indicators like employment rates, business density, and consumer spending, which collectively provide insights into the economic health of urban areas. Research in this area has utilized various economic metrics to measure vitality, reflecting the intricate relationship between urban economic health and overall vitality [17].

In recent years, the concept of urban vitality has expanded to include the virtual dimensions brought forth by digital technologies. The integration of big data and IoT in urban studies has introduced new methods of measuring and understanding urban vitality through technology-enabled interactions in both physical and virtual spaces. Following the changing trend, the density and location of Wi-Fi access points were used as indicators of the virtual dimension of urban vitality [18]. This perspective aligns with a view on the transformative potential of digital technologies in urban spaces [19], particularly their role in enhancing the functionality and responsiveness of urban infrastructure. Additionally, the virtual dimension of urban vitality has gained recognition, highlighting how digital interactions and the presence of digital infrastructure such as broadband connectivity, Wi-Fi hotspots, and smartphones influence urban life [20]. This dimension is crucial in understanding the contemporary urban experience, where digital and physical spaces are increasingly intertwined. This integration shows how virtual spaces, exemplified by the spread of Wi-Fi access points, not only complement but also enhance our understanding of physical urban vitality by providing a new layer of data on human interactions and activities.

The theoretical frameworks for urban vitality now encompass these advanced technological perspectives, suggesting that a city’s vitality can no longer be understood solely through social and economic indicators. The ongoing digital transformation implies that our understanding of urban spaces must evolve to include the significant impacts of digital data and infrastructure on urban dynamics. The theoretical foundations of urban vitality have broadened from the social and economic focus to include significant digital aspects. The evolution of these theories reflects changes in how urban spaces are planned, experienced, and analyzed, emphasizing the need for integrated approaches that consider the convergence of various dimensions in enhancing urban vitality.

To effectively utilize the concept of urban vitality in urban planning beyond its theoretical discourse, its measurement becomes crucial. Accurate measurement that captures the diverse dimensions in a manner reflective of real-world dynamics provides valuable insights for urban planning. As emphasized in the literature discussed in this section, urban vitality serves not only as a theoretical framework but also as a practical tool to inform evidence-based planning decisions. Therefore, it is essential to review both the historical techniques for measuring urban vitality and the recent technological advancements in this domain, which are discussed in the following sections.

2.2. Historical Measurement Techniques

Urban vitality has traditionally been assessed through direct observational studies, structured surveys, pedestrian counts, and economic indicators, each method bringing its own set of insights and limitations. Early seminal works by scholars such as Jane Jacobs and William Whyte relied heavily on direct observations and interactions within urban spaces. Jacobs illuminated the complexities of urban life through meticulous descriptions of street activities, arguing that the vibrancy of public spaces was critical to the economic and social health of cities [1]. Similarly, Whyte utilized time-lapse photography to study human behavior in New York City’s public spaces, providing valuable insights that influenced urban planning and design [5]. While these observational techniques offered profound qualitative insights, they were labor-intensive and limited in scope, often confined to specific locales or short periods.

Structured surveys and interviews have also been pivotal in understanding urban vitality, allowing researchers to capture subjective experiences and perceptions of urban environments. By gathering data directly from residents, researchers have been able to assess the perceived quality of life and satisfaction with urban amenities [3]. However, the reliability of these methods is frequently challenged by the biases inherent in self-reported data and the practical constraints of survey scope and sample representativeness.

Furthermore, traditional methods like pedestrian counts have been employed to measure the liveliness of urban areas, with a higher frequency of foot traffic often equated with greater vitality [21]. Manual counting at strategic urban hotspots has been a common approach, yet such methods are not only resource-intensive but also provide only a snapshot view of urban dynamics, failing to capture the continuous flow of urban life [22].

Economic indicators have provided another lens through which to view urban vitality, focusing on metrics such as employment rates, retail sales, and property values. These indicators are critical for understanding the economic dimensions of urban vitality but often overlook the social interactions that contribute significantly to the liveliness of urban spaces [23].

3. Technological Advances in Measuring Urban Vitality

The advent of big data and IoT technologies has begun to transform how urban vitality is measured, offering methods to overcome the many limitations of traditional approaches. The integration of big data into urban studies represents a transformative shift in how researchers and city planners understand and manage urban vitality. Big data, characterized by immense volume, high velocity, and extensive variety, offer unprecedented opportunities to explore the complexities of urban life in real time [24]. This new approach significantly extends beyond the capabilities of traditional methods, enabling a nuanced understanding of urban dynamics that was previously unattainable.

With the capacity to gather vast amounts of data in real time, these technologies enable a more dynamic and comprehensive analysis of urban life. Sensor networks, GPS data from mobile devices, and social media analytics now provide continuous streams of data that reflect the complex patterns of urban movement and interaction [25,26,27]. These technological advancements not only enhance the accuracy and efficiency of data collection but also enable researchers and policymakers to integrate urban vitality insights into smart city projects and policies [25,26,27].

Urban vitality, as a multifaceted concept, is often examined through its social, economic, and environmental dimensions. The advent of big data and IoT technologies has significantly transformed the methods of measuring these dimensions, enabling a deeper and more nuanced understanding of urban dynamics. This section explores the technological advances in measuring the social, economic, and environmental dimensions of urban vitality, focusing on data collection methods, metrics, and their implications (

Table 1. Recent urban vitality studies based on big data and IoT technologies.

Dimension	Data	Proxy	Reference
Social	Mobile phone activity	Pedestrian mobility	Manfredini et al., 2013 [28] Louail et al., 2014 [29] Dong et al., 2024 [30] Liu et al., 2023 [31] Kim and Jun, 2022 [32] Yabe et al., 2022 [33]
	Car navigation	Vehicular mobility	Cohn, 2009 [34] Kan et al., 2019 [35] Liu et al., 2012 [36]
	Social media check-in	Social activity	Arribas-Bel et al., 2015 [37] Lu et al., 2023 [38] Wang et al., 2023 [39] Wang et al., 2024 [40]
Economic	Bank card transaction	Consumption	Carpio-Pinedo et al., 2022 [41] Kim and Kim, 2022 [42] Kim, 2020 [43]
	Smart card	Public transportation	Tu et al., 2022 [44] Li et al., 2024 [45] Sulis et al., 2018 [46]
Environmental	Temperature	Weather	Park and Kim, 2023 [47] Park and Baek, 2023 [48]
	Fine particulate	Air pollution	English et al., 2020 [49] Ahn, 2024 [50]

).

3.1. Social Dimension

The social dimension of urban vitality has traditionally been assessed by manually counting the number of pedestrians at specific locations, a method that provides limited temporal and spatial coverage [17]. The emergence of big data has revolutionized this process, offering more scalable and precise approaches. One of the primary sources of big data in urban studies is mobile phone data, which can be used to track movement patterns and communication behaviors across a city [28]. Mobile data provide insights into how people navigate urban spaces, highlighting busy areas and times of day and revealing the rhythms of city life [29,30,31]. These data are collected through the triangulation of cellular towers, often supplemented by Wi-Fi hotspots, beacons, and other IoT devices. Such data collection methods allow for the real-time tracking of individuals within an urban space, significantly expanding the temporal and spatial scope of analysis.

Origin–destination (O-D) mobility data track the start and end points of individual journeys within a city, invaluable for understanding how people move through urban spaces [32]. Analyzing O-D data can reveal patterns in commuter flows, inform public transportation planning, and even help in disaster response planning by predicting how people might evacuate from various parts of a city [33]. This type of data has been instrumental in studies examining the correlation between human mobility patterns and urban vitality, offering detailed mappings of pedestrian flows that inform urban planning.

As many drivers use online-based navigation services via mounted devices or smartphones, vast quantities of real-time traffic data, including speed, congestion, and accidents, are continuously collected [34,35]. These data reflect not only road conditions but also the number of vehicles in motion, offering insights into urban vitality in its mobile state. To enhance the accuracy of the data, service providers employ probe vehicles, often taxis and trucks, that regularly traverse city roads, supplementing data from individual users [51]. While the primary purpose of these traffic data is to calculate the shortest travel times between origin and destination points, the aggregated number of selected destinations can serve as a proxy for measuring visitor volumes. This metric provides a distinct perspective on urban vitality, complementing other measures such as pedestrian activity, while acknowledging its unique focus on vehicular movement.

The temporal and spatial scope and extensibility of big data in the social dimension largely depend on the balance between passive and active data collection techniques. Active data collection, often purpose-specific, involves deliberate efforts such as manual pedestrian counts at strategic urban locations. While this method provides precise and targeted data, its utility is constrained by limited temporal and spatial coverage. Passive data collection, in contrast, relies on devices and systems not originally intended for data collection but which continuously gather valuable information as a by-product of their primary functions [52]. The shift from active to passive methods has significantly enhanced the efficiency and scale of data collection, allowing researchers to monitor urban vitality with greater breadth and detail.

Passive data collection is particularly advantageous for measuring the social dimension of urban vitality. This method enables researchers to map urban activity patterns with unprecedented accuracy, providing rich datasets that are scalable across both space and time. Nevertheless, active data collection remains an essential complement, offering unique insights through deliberate user actions on social media [37]. By analyzing social media posts, photos, and check-ins, researchers can gauge public sentiment and participation in urban life, identifying hotspots of social and cultural activity. Platforms like Google Maps, Yelp, and Weibo allow users to actively log visits to specific locations, contributing valuable contextual data about urban interactions [38,39,40]. By combining these active and passive approaches, researchers can achieve a more nuanced and comprehensive understanding of the social dynamics underpinning urban vitality. This approach not only captures the immediate reactions of city dwellers to events and changes within their environment but also offers a form of participatory urbanism where citizens contribute directly to the data pool that shapes their city [53].

3.2. Economic Dimension

The economic dimension of urban vitality primarily encompasses the economic transactions and consumption activities occurring within urban spaces. Throughout history, economic transactions have been systematically recorded, with accounting emerging as a structured method of bookkeeping. This meticulous documentation has traditionally served state purposes, primarily for taxation and governance. However, the recognition of economic activity as a vital component of urban vitality is a relatively recent development, reflecting shifts in how cities are studied and managed.

Traditionally, the economic dimension was challenging to measure due to the difficulty of capturing detailed information about consumer transactions in urban spaces. Unlike the social dimension, which could be partially observed through manual pedestrian counts, economic activities were often obscured by their private and decentralized nature. The proliferation of digital technologies, however, has revolutionized this process. The widespread adoption of bank and credit cards has transformed the landscape of consumer transactions, providing more precise and accessible data on economic activity [41].

In many developing economies, digital payments have leapfrogged card-based payment systems, with consumers and businesses transitioning directly to mobile payment platforms using QR codes and barcodes. This rapid adoption bypasses intermediary steps like debit or credit cards, reflecting unique technological pathways shaped by local economic histories. In contrast, advanced economies such as Japan and Germany have been slower to adopt these digital payment methods, often due to entrenched preferences for cash transactions and institutional inertia. These variations highlight the diverse trajectories of digital transformation around the globe.

Another transformative advancement in data collection is the use of smart cards in public transportation. While transportation agencies traditionally relied on ticket sales to understand passenger volumes, smart card data provide more granular insights into individual mobility patterns [44]. These data reveal when, where, and how people move through urban spaces, offering a consumer-centric perspective on mobility [45,46]. For example, smart card systems not only record the number of passengers but also capture entry, exit, and transfer points, enabling researchers to track flows and their contributions to urban vitality.

The granularity of economic data collection in urban spaces has significantly improved with these technological advancements. Economic transactions, for instance, can be analyzed either by their total value or by the frequency of transactions [18]. The total value of transactions is often used as a metric of urban vitality because it reflects the aggregate economic activity within a specific area. This measure is particularly useful for understanding the scale and intensity of consumption in central business districts or popular retail hubs.

On the other hand, the frequency of transactions provides complementary insights into urban vitality by highlighting patterns of consumer behavior. A high frequency of smaller transactions might indicate vibrant neighborhoods with thriving local economies, where residents and visitors engage in diverse activities such as dining, shopping, or attending cultural events. This metric is particularly valuable for identifying emerging hotspots of economic activity or evaluating the success of urban regeneration projects.

The spatial and temporal granularity of transaction data further enhances the analysis of the economic dimension of urban vitality by allowing researchers to identify nuanced patterns of economic activity. For instance, bank card data can reveal the temporal concentration of retail activity, such as peak hours during the day or vibrant night-time economies in specific areas [42,43]. This information is invaluable for urban planners seeking to understand how economic activities fluctuate throughout the day and in different parts of the city. Additionally, this granularity can highlight underutilized areas where economic activity is sparse, providing critical insights for targeted urban renewal initiatives. Moreover, these datasets can be integrated with other sources, such as transportation data, to explore the interplay between mobility and economic behavior.

3.3. Environmental Dimension

The environmental dimension of urban vitality encompasses the influence of environmental conditions on human behavior and urban dynamics. Advances in IoT technologies and urban sensors have transformed the way environmental data are collected and analyzed. By providing real-time and location-specific data, these sensors offer invaluable insights into how environmental variables such as air quality, temperature, and humidity impact citizens’ use of urban spaces.

One of the most widespread applications of IoT in urban studies is in the deployment of sensors across the cityscape. These sensors are utilized to monitor a variety of environmental and infrastructural parameters. For instance, air quality sensors distributed throughout a city can provide real-time data on pollution levels, crucial for public health monitoring and urban planning decisions. Similarly, noise sensors can help map noise pollution, a significant urban issue, especially in densely populated areas [54,55]. These sensors not only supply data that inform policy but also engage citizens directly with their living environment, often feeding data back to mobile apps that notify users of local conditions.

In the landscape of urban studies, the role of sensors in enhancing our understanding of urban dynamics has grown significantly. These sensors collect a wide array of environmental and activity data, which, when integrated into urban systems, provide invaluable insights that influence everything from policy making to smart city management.

Some of the most comprehensive applications of urban sensors can be seen through initiatives like Chicago’s Array of Things (AoT) and Seoul’s Data of Things (S-DoT), which have set new benchmarks in urban data collection. The AoT, for instance, uses a network of interconnected sensors to collect detailed real-time data on the city’s environment, infrastructure, and activity. These sensors measure variables including air quality, climate conditions, and pedestrian and vehicle traffic, providing data that support a broad range of research and public utility functions [56]. For instance, AoT sensors contribute to urban safety by providing data that can predict and thus mitigate issues like urban flooding and heavy traffic, enhancing both emergency response strategies and day-to-day urban management. Similarly, S-DoT incorporates sensors that monitor 17 different urban metrics, including temperature, humidity, illumination levels, noise, and ultrafine particles. These sensors are strategically placed not only in downtown areas but also in mountains and riversides, providing a diverse dataset that reflects the various living environments within the city [57].

The integration of these sensors into urban systems enables cities to become more responsive and adaptable to the needs of their residents. For example, the real-time data collected by S-DoT sensors are made publicly available, allowing residents to access up-to-date information about their immediate environment. This kind of data democratization not only increases transparency but also empowers citizens by providing them with the knowledge they need to make informed decisions about their activities and their environment.

Environmental factors also significantly affect social behavior and public space usage. Studies have shown that extreme weather conditions, such as heatwaves and coldwaves, discourage outdoor activities, thereby reducing urban vitality [40,47]. Urban sensors, often installed at ground level, can monitor these conditions more effectively than traditional weather systems such as the automated weather systems operated by meteorological agencies [48]. By providing highly localized data, these sensors enable a more precise assessment of how environmental conditions influence walking conditions and, consequently, social interactions in urban spaces.

Another example of environmental factors impacting urban vitality is fine particulate matter, such as PM_2.5 and PM₁₀ [49,50]. The data collected from these sensors support the development of prewarning systems that recommend citizens stay indoors during periods of high pollution. These systems not only protect public health but also influence patterns of urban activity by reducing outdoor engagement during periods of poor air quality. As a result, air pollution levels directly impact the vitality of urban spaces by altering citizens’ willingness to participate in social and economic activities.

In summary, the environmental dimension of urban vitality highlights the interconnectedness of environmental conditions, human behavior, and urban dynamics. By leveraging urban sensors and IoT technologies, cities can better monitor and respond to environmental challenges, ensuring that public spaces remain vibrant, accessible, and resilient in the face of changing environmental conditions. This evidence-based approach underscores the growing importance of environmental considerations in the study and management of urban vitality.

4. Methodological Considerations

4.1. Data Collection and Integration

The integration of diverse data streams from IoT devices and sensors represents a pivotal advancement in the methodology of urban vitality studies. The potential to harness data from various sources, reflecting the multidimensional nature of human activity in urban environments, offers profound insights into the dynamism and health of cities. To capitalize on this potential, a sophisticated methodology for data collection and integration is necessary, tailored to navigate the complexities inherent in urban data.

The methods of data collection for urban vitality studies can be broadly classified into active and passive collection [52]. Active data collection, often purpose-specific, includes actions like manual pedestrian counts at strategic urban locations. While providing targeted data, it is constrained by its limited temporal and spatial coverage and is increasingly being supplanted by passive data collection methods. Passive data collection leverages devices and systems not originally intended for data collection but that nonetheless gather valuable information.

Data collection in the urban context typically spans a variety of sources, each contributing a unique perspective on urban vitality. The IoT devices and sensors deployed throughout a city can collect real-time data on everything from traffic patterns and air quality to public Wi-Fi usage and pedestrian flows. These devices operate continuously, providing a temporal resolution that allows for the monitoring of changes over time, which is crucial for understanding the patterns and trends within the urban landscape. For instance, traffic cameras and air quality monitors may collect data in intervals of minutes or seconds, offering granular insights into the fluctuating conditions of city life.

The process of integrating these data streams, however, requires careful consideration to ensure that the combined data provide a coherent and comprehensive picture of urban vitality. One of the primary considerations is the harmonization of data formats and standards. Different devices often operate on disparate data collection protocols and formats, which can pose significant challenges for data integration [52,58]. For example, data from traffic sensors might be in a different format or use different metrics than data from weather sensors. To effectively integrate these data, they must be converted into a common format with standardized metrics, ensuring that all data points are compatible and can be analyzed together.

Temporal and spatial alignment is another critical aspect of data integration. As urban data are collected continuously, it is essential to synchronize data points in time and space. This means aligning data from various sources to the same time stamps and geospatial references. Such alignment ensures that the data reflect a true and consistent picture of urban conditions at any given moment [59]. For example, if one sensor measures traffic flow while another measures air quality at the same intersection, aligning these data streams temporally and spatially allows for a detailed analysis of the relationship between traffic density and air pollution levels.

Data aggregation and fusion techniques also play a crucial role in integrating diverse data streams. Data aggregation involves combining data from multiple sources to reduce complexity and enhance interpretability. Data fusion goes a step further by integrating data to create new datasets that provide insights that are not possible from single data sources alone [60]. This can involve complex statistical methods and machine learning models that analyze patterns across different data types to predict urban vitality indicators [61].

Moreover, sophisticated software platforms are employed to facilitate the integration and analysis of large datasets. These platforms utilize advanced analytics tools that can handle the volume, velocity, and variety of urban data. They enable the real-time processing and visualization of data, allowing city planners and researchers to observe urban dynamics as they unfold and respond with timely interventions. Such platforms often feature user-friendly dashboards that present data in an accessible format, making it easier for decision makers to understand and utilize the information effectively [62]. In addition to these software platforms, the role of digital infrastructure, such as data collecting hardware, data storage, and computing power, is integral. Such infrastructure not only enhances the ability to monitor urban dynamics but also provides evidence for the development of data-driven strategies in smart city planning.

4.2. Data Analysis Techniques

Advanced data analysis techniques have revolutionized our ability to interpret the vast datasets generated by sensors and IoT devices. These techniques enable us to not only describe urban phenomena but also predict future trends and outcomes, thereby enhancing urban planning and policy making. In this paper, four examples of these techniques are reviewed.

First, the use of social network analysis (SNA) in urban studies exemplifies the application of relational data analysis. SNA looks at the relationships between entities, such as individuals or places, to understand the structure of the social interactions within these networks [63]. By analyzing social media data, for instance, urban researchers can identify vital nodes within a city—popular areas or hubs of social interaction—that are crucial for urban vitality [64]. This type of analysis helps city planners understand where social interactions are most concentrated and can guide interventions aimed at enhancing these interactions to boost urban vitality.

Second, spatiotemporal analysis is crucial in managing the spatial and temporal dimensions of urban data. This method allows researchers to examine data over time and across different geographical locations, providing insights into how urban processes evolve and interact across a city [43]. For instance, the spatiotemporal analysis of traffic sensor data can help identify the progression of traffic jams throughout the day, and cross-referencing this with weather conditions or event schedules can provide deeper insights into causal factors [65].

Third, predictive analytics utilize historical data to forecast future events, trends, or behaviors. This approach is particularly beneficial in urban settings where anticipating changes can significantly improve the efficiency of urban systems. For instance, predictive models can forecast traffic congestion, helping to optimize traffic light sequences and reduce idle times, thereby easing urban congestion and reducing pollution [66]. Similarly, predictive analytics can be used to anticipate public transport demands, adjusting routes and schedules to meet user needs more effectively [67].

Lastly, machine learning (ML), a subset of artificial intelligence, involves training algorithms to make predictions or decisions based on data. In urban studies, ML techniques are employed to analyze patterns from sensor data, such as identifying peak times or predicting areas at higher risk. ML models are particularly adept at handling complex, nonlinear relationships in data that traditional statistical methods might not effectively address [68].

The integration of these advanced analytical techniques into urban studies allows for a more dynamic and nuanced understanding of urban vitality. Social network analysis and spatiotemporal analysis provide deeper insights into the relational and dimensional aspects of urban life while predictive analytics and machine learning offer powerful tools for forecasting and decision making. Together, these methodologies facilitate a comprehensive approach to studying and enhancing urban vitality, ensuring that cities not only understand their current state but can also react to future challenges and opportunities.

5. Challenges and Limitations

The proliferation of big data and IoT devices in cities has ushered in a new era of city management and planning. However, this development brings with it significant privacy and ethical concerns, particularly given the passive nature of data collection involved in these technologies. Moreover, the vast volume, velocity, and variety of data and technical issues inherent in measuring devices pose concerns regarding reliability and accuracy.

5.1. Privacy and Ethical Considerations

Most big data are generated by individuals, including subscribers to network services as well as users of bank cards, smart cards, and social media. Since such data often include personal, biometric, or sensitive information, they are particularly susceptible to security and privacy issues. To address these concerns, personal information must be removed, altered, or replaced to ensure compliance with privacy standards. In large-scale urban data collection, these processes can be technically automated based on predetermined privacy policies. Techniques such as pseudonymization and anonymization are commonly employed to preserve privacy while maintaining the utility of the data for research and analysis.

In general, the privacy and ethical challenges primarily stem from the difficulty in obtaining explicit consent from individuals whose data are being collected and the potential for misuse of these data. Unlike traditional data collection methods that often require active participation or at least notification of the subjects involved, IoT devices and sensors collect data continuously and often inconspicuously. This type of data collection can include personal information such as location, activities, and even biometric data, which are gathered without the explicit consent of the individuals being monitored [69]. The issue of consent is particularly problematic in cities where public spaces equipped with IoT devices and sensors are frequented by large numbers of people. It is both impractical and technically challenging to obtain consent from every individual who might interact with or pass by an IoT device. This situation is further complicated by the fact that many individuals may not be aware that they are being monitored, nor understand the extent of data collection and its implications [70].

Even when data are collected for legitimate urban management purposes, the storage and processing of these data present additional risks. Data breaches can expose sensitive personal information, leading to potential misuse. Furthermore, the aggregation of data from various sources can enable the tracking of a person’s movements and activities throughout a city, creating detailed personal profiles [58]. Unintended inferences and profiling can discriminate against or unfairly target certain groups or individuals [71]. If data collected for one purpose are subsequently used for another unrelated purpose, it further complicates the ethical landscape.

In the broader scope, the integration of surveillance technologies in the guise of urban management tools can lead to a decrease in trust between citizens and local governments [72]. If citizens feel they are constantly being monitored, it can lead to a sense of surveillance, potentially stifling free movement and expression in urban spaces [73]. This can have a chilling effect on the very vibrancy and spontaneity that urban vitality initiatives aim to promote.

Therefore, robust regulatory frameworks are required to ensure that data are collected, stored, and used in ways that respect individual privacy and are transparent to the public. Regulations should enforce strict data minimization practices, ensuring that only the data necessary for specific urban management objectives area collected, and retaining data only as long as they are needed for the intended purpose. Additionally, there must be clear guidelines on data sharing and usage, with strong penalties for noncompliance to deter potential misuse [74].

Furthermore, it is important to engage the public in discussions about how their data are used, and the benefits and risks associated with IoT technologies can help in building trust. Transparent practices, including public reports on data usage and the benefits derived from big data and IoT projects, can demystify these technologies and help in gaining public support [75].

In summary, while the capabilities of big data and IoT devices present unprecedented opportunities for enhancing urban vitality, they also bring substantial privacy and ethical challenges that must be addressed. Establishing rigorous ethical standards and strong regulatory frameworks, coupled with proactive public engagement and transparency, are essential for navigating these challenges effectively. These measures will not only protect individual privacy but also ensure that urban vitality projects harness the benefits of technology in a manner that is ethical and supported by the public.

5.2. Reliability and Accuracy

Challenges in the passive data collection include managing vast amounts of unstructured data, which require complex processing and analysis methods to extract useful information. The challenges regarding the reliability and accuracy of data stem not only from the inherent limitations of the data sources and collection methods but also from the complexities involved in data processing and analysis.

One primary concern is that much of the big data used in urban studies are not originally collected with the purpose of measuring urban vitality but for other operational purposes. For instance, data from traffic sensors intended for congestion management or data from social media platforms intended for user engagement may be repurposed to analyze urban vitality. This repurposing can lead to significant discrepancies in the relevance and accuracy of the data for studying urban dynamics [76]. The original data may lack the critical attributes of urban vitality, such as granular details about pedestrian interactions or nuanced aspects of public space usage, leading to incomplete or skewed analyses.

While big data collection schemes are often considered superior to survey-based methods in covering populations rather than samples, achieving true population-level coverage remains challenging. In many countries, mobile networks and bank card systems are operated by multiple competing companies rather than a single provider. To address this, data-collecting organizations often adjust datasets by multiplying the inverse of their market share. However, this approach does not guarantee that the resulting data accurately reflect the population, introducing potential biases in urban vitality analyses.

The digital divide further complicates big data collection methods, which often depend on digital devices. For instance, pedestrian footfall data are typically derived from individuals’ mobile phone activity. Although smartphone penetration is high among adults in advanced societies, younger and older generations are not necessarily included in the mobility data derived from such activities. Beyond penetration rates, socially marginalized groups, regions, and classes may also be excluded from datasets, leading to incomplete representations of urban dynamics. This limitation highlights the need for inclusive data collection strategies to capture the full spectrum of urban vitality.

Moreover, the quality of data collected by sensors and IoT devices can be affected by various factors, including the precision of the devices, device maintenance, and the environmental conditions under which they operate [77]. Sensor drift, calibration issues, or damage from environmental exposure can lead to inaccurate data [78]. If not properly checked and corrected, data could misinform rather than enlighten decision-making processes. Automated processes for error detection are necessary, while manual review is also required. However, it is still impractical given the scale of the data.

Another issue is the variability in the data collection methods across different platforms and devices, which can result in inconsistent data quality [79]. For example, different sensors may measure similar phenomena such as air quality or noise levels but use varying methodologies or scales. The deployment locations of sensors can also bias the data collected, as sensors placed in more active or affluent areas may depict a different picture of urban vitality than those in less developed areas. For example, urban sensors measuring fine dust are highly sensitive to their surroundings, including location, height, and environmental context. A sensor placed at a bus stop may overestimate fine dust levels due to emissions from vehicles accelerating and decelerating nearby. However, such placements may provide more contextually relevant data for evaluating the impact of fine dust on individuals waiting at bus stops. Ensuring strategic placement aligned with the study’s objectives is essential for accurate data collection. Without contextual data to provide a fuller picture, furthermore, the conclusions drawn from raw data may be misleading [75].

The maintenance of sensors and IoT devices is another critical factor in ensuring reliable, continuous data streams. Devices installed in outdoor urban environments are particularly susceptible to weather conditions, which can degrade their performance or cause outright damage. In some cases, pedestrian behavior, whether intentional or accidental, can result in sensor malfunction. Without careful consideration, these data sources can lead to errors in interpretation and analysis, potentially misleading policymakers about the true conditions of urban environments.

Data processing and analysis also introduce challenges related to reliability and accuracy. Big datasets, especially those integrating multiple sources, require complex processing, which can introduce errors. Misalignment in data integration, errors in data cleansing, or inappropriate handling of missing data can all compromise the integrity of the resulting dataset [80]. Additionally, the use of sophisticated algorithms and machine learning models, while powerful, can sometimes lead to overfitting or biased predictions if not properly trained and validated with comprehensive and representative data [81].

In addressing these challenges, it is crucial for urban researchers and planners to not only implement robust data management practices but also maintain a critical perspective on the data sources and analytical methods used. Ensuring the reliability and accuracy of data-driven insights into urban vitality requires the continuous evaluation and adaptation of data collection, processing, and analysis techniques. By acknowledging and addressing these limitations, researchers can better harness the potential of big data to enhance urban environments effectively and equitably.

6. Future Directions

As urban environments continue to evolve, the methodologies and technologies for measuring and enhancing urban vitality also progress. The future of urban vitality studies is promising with advancements in emerging technologies, the integration of interdisciplinary approaches, and the adoption of evidence-based urban planning. Together, these elements offer a transformative framework to refine how cities are understood and managed.

6.1. Emerging Technologies

The future of measuring urban vitality is likely to be heavily influenced by advancements in artificial intelligence (AI) and edge computing. Machine learning models are becoming increasingly adept at pattern recognition and predictive analytics, enabling them to forecast urban trends and behaviors with high accuracy [82]. These capabilities could transform everything from traffic management to public safety, making urban environments more responsive to the needs of their inhabitants. The proliferation of edge computing within IoT frameworks allows data processing to be conducted closer to where data are collected, significantly reducing response times and enabling real-time data analytics [83].

Emerging technologies such as augmented reality (AR) and virtual reality (VR) also offer novel ways to engage with urban data. By visualizing complex datasets in an intuitive and interactive manner, these technologies can help city planners, policymakers, and the public better understand and analyze the factors that contribute to urban vitality. AR and VR can transform the presentation of urban data, making them more accessible and understandable to a broader audience, thereby facilitating more informed community engagement in urban planning processes [84,85].

6.2. Interdisciplinary Approaches

The complexity of urban environments necessitates that urban vitality be approached from multiple disciplinary perspectives. Integrating insights from sociology, environmental science, public health, and information technology can provide a more holistic understanding of what constitutes a vibrant urban space [86]. Collaborations between technologists and urban planners are particularly crucial as cities become more digitized. Technologists can provide the tools and methodologies necessary to collect and analyze large datasets, while urban planners can ensure that the insights gained are translated into actionable strategies that enhance urban vitality. Although this technocentric approach to smart city development has faced criticism for sidelining social perspectives [87,88], the design, production, and maintenance of devices remain central to the early phases of smart city projects. These technical disciplines provide the foundational tools and methodologies necessary for collecting and processing data, which are vital to understanding and enhancing urban vitality.

This interdisciplinary approach not only ensures technological advancements but also enriches the understanding of urban dynamics that are effectively integrated into practical urban management and planning. In the analytical dimension, urban physics and social physics offer compelling frameworks for understanding urban dynamics. These approaches operate on the premise that cities, like ecosystems, follow certain physical principles [89,90,91]. Emerging fields such as complexity theory [92] and network science [93,94] have further contributed to urban analytics through the application of advanced mathematical and computational techniques to model and predict urban phenomena. In this context, urban studies grounded in big data and IoT devices have become a pivotal domain within the broader field of urban science.

Moreover, the integration of arts and humanities into urban studies can enrich the narrative around urban vitality, offering deeper insights into the human experience of city life. Cultural studies, for example, can provide context to the data collected on urban interactions, helping to interpret the qualitative aspects of urban vitality that quantitative methods might overlook. This approach can lead to more culturally sensitive urban development strategies that enhance the quality of life for all city dwellers [95].

In summary, the future directions for measuring and enhancing urban vitality lie in harnessing the potential of emerging technologies while fostering interdisciplinary collaborations that encapsulate the full complexity of urban life. By embracing these developments, urban researchers and practitioners can better anticipate and respond to the challenges and opportunities presented by rapidly evolving urban landscapes, ultimately leading to more vibrant, livable, and resilient cities.

6.3. Evidence-Based Urban Planning

In recent years, the urgency of incorporating evidence into policy-making and decision-making processes has grown significantly. In that sense, urban vitality is a crucial part of the evidence for the processes in urban planning because the concept encapsulates the dynamic interplay of collective human behaviors within urban spaces [95]. Understanding urban vitality is crucial not as an end in itself but as a metric for evaluating and improving the quality of life of those in cities. This approach positions urban vitality as a key indicator for urban planners and policymakers to assess the health and functionality of urban spaces.

Urban planning has long oscillated between natural city development and the visionary works of prominent urban planners. While these top-down approaches have resulted in iconic urban spaces, they often fail to reflect the nuanced realities of citizens’ daily lives. To address this gap, bottom-up approaches that encourage citizen participation have gained traction. Nevertheless, these participatory approaches are often skewed by the over-representation of self-interest groups, leaving large proportions of the population unheard. This highlights the need for the systematic monitoring of the quality of life across diverse demographic groups.

Historically, urban policies have often been shaped by the subjective decisions of a few politicians or political parties, with limited data to support or challenge these decisions. However, the advent of big data and the widespread adoption of IoT devices in everyday urban life have created opportunities to include the needs and behaviors of citizens in the planning process more comprehensively than ever before. This shift heralds a transformative era in urban planning, emphasizing evidence-based approaches over subjective, top-down strategies.

Cities are complex networks where individuals, vehicles, buildings, and environments interact in intricate ways. Despite this complexity, human behavior in cities is not random; it follows systemic patterns, shaped by collective routines such as commuting, shopping, and socializing. By leveraging big data and IoT technologies, these collective human behaviors and their interactions with urban environments, both natural and built, can be measured and analyzed. This evidence-based understanding of urban dynamics provides a foundation for more informed urban planning that reflects the lived realities of citizens.

7. Conclusions

This paper extensively explores the multifaceted concept of urban vitality and emphasizes its significance in understanding the complexity of cities. Urban vitality, which encompasses the dynamics of social interactions and economic transactions, has historically been measured through traditional methods such as direct observations and surveys. However, the advent of big data and IoT technologies has revolutionized this approach, offering new depths of analysis and understanding. These technologies not only automate data collection but also enable the integration of diverse data streams. This shift to a technology-driven approach helps in predicting urban trends and behaviors, providing actionable insights, though it also introduces new methodological and ethical challenges.

The paper discusses the theoretical foundations of urban vitality, highlighting the evolution from physical and economic indicators to include digital dimensions, propelled by advancements in technology. The use of big data and IoT devices has introduced sophisticated methods to measure and analyze urban vitality, utilizing tools such as machine learning and predictive analytics. These technological advances have enhanced the accuracy and efficiency of data collection and allowed for the real-time processing and visualization of data, thereby facilitating more informed urban planning and decision making. The interplay of computational and cognitive engineering within urban studies presents a transformative avenue for addressing the complex challenges of modern urban environments. These tools not only provide deeper insights into the dynamics of city life but also enable more proactive and informed decision making in urban planning and policy making.

However, future urban vitality studies must also consider the integration of various disciplinary approaches. The convergence of fields such as computational engineering, cognitive psychology, environmental science, and urban sociology is crucial for developing a holistic understanding of urban systems. This interdisciplinary approach can enrich our insights into the human-centric aspects of city living, ensuring that technological advancements cater to enhancing human well-being and societal development.

The intersection of computational and cognitive engineering with urban studies is rich with opportunities for innovation and improvement. By continuing to leverage these technologies while ensuring ethical standards and interdisciplinary integration, we can aspire to create urban environments efficient and sustainable. This process will require collaboration among researchers, technologists, urban planners, and the public to navigate the complexities of urban life and harness the full potential of these technological advancements.

In closing, a deeper question that underpins urban planning is why urban vitality truly matters. Urban vitality is not an ultimate objective but rather a means to achieve a better quality of life of those in cities. It highlights how urban spaces are utilized in citizens’ everyday lives and the various factors that influence these patterns. These factors encompass both the “hardware” of urban spaces such as the built and natural environments and the “software”, including urban design, neighborhood atmosphere, and cultural amenities. To maximize the impact of evidence-based urban planning, it is critical to not only measure urban vitality but also conduct thorough investigations into the factors that drive it, thereby fostering vibrant, inclusive, and sustainable urban environments. By understanding these dynamics, we can foster vibrant, inclusive, and sustainable urban environments.