Mit dem Aufkommen des Nachtlaufs als beliebte Form körperlicher Aktivität hat die Rolle der künstlichen Straßenbeleuchtung bei der Verbesserung des Komforts und der Sicherheit nächtlicher Außenbereiche erheblich an Aufmerksamkeit gewonnen. Dieses Kapitel vertieft sich in die komplizierte Beziehung zwischen verschiedenen Arten künstlicher Beleuchtung und dem Komfort nächtlicher Laufumgebungen. Durch eine sorgfältige Methodik, die die Sammlung von Bewertungstexten, Untersuchungen vor Ort und umfassende Umfragen umfasst, identifiziert die Studie zentrale Beleuchtungsfaktoren, die die nächtlichen Outdoor-Erlebnisse von Läufern beeinflussen. Die Forschung unterstreicht die Bedeutung von Helligkeit, Farbe, Abdeckung und der Vielfalt der Beleuchtungsarten für die Schaffung einer komfortablen und sicheren nächtlichen Laufatmosphäre. Durch die Analyse der Auswirkungen dieser Faktoren liefert das Kapitel wertvolle Erkenntnisse darüber, wie Stadtplaner und Lichtplaner die Straßenbeleuchtung optimieren können, um nächtliche körperliche Aktivität zu fördern und die allgemeine Qualität des urbanen Nachtlebens zu verbessern. Die Ergebnisse unterstreichen die Notwendigkeit eines differenzierten Verständnisses der Lichtgestaltung, um den spezifischen Bedürfnissen von Nachtläufern gerecht zu werden und letztlich zur Schaffung einladenderer und sichererer städtischer Umgebungen nach Einbruch der Dunkelheit beizutragen.
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Abstract
Night running is becoming an important physical activity during nighttime hours. However, the formal running spaces available in cities are often insufficient to meet the daily fitness needs of urban populations. Consequently, street spaces are frequently incorporated into the choices of night runners. Artificial lighting is an indispensable factor in the street running environment, however, the research on how artificial lighting in urban streets affects the comfort of night running spaces remains unclear. This study selected three classic night running routes in Suzhou City for evaluation using a fitness app for running route recommendations and conducted on-site photography using a panoramic camera. Subsequently, individuals experienced in night running were recruited to conduct a questionnaire survey on the artificial lighting factors depicted in the photographs. The results indicate that the brightness of environmental lighting in streets has the greatest impact on the comfort of night runners, followed by lighting coverage and color, while the type of environmental lighting has the least impact on the comfort of night runners. The final results can provide a theoretical basis for optimizing the lighting conditions of urban night running spaces, thereby contributing to the creation of a more comfortable urban night running environment.
1 Introduction
With the increasing emphasis on health among the general population, night running has become a crucial form of physical activity in urban nightlife. Particularly, people have recognized running as an integral component of an active and healthy lifestyle, which yields positive impacts [1]. Research also indicates the positive effects of such activities on health improvement [2, 3]. In cities, formal running spaces are often inadequate to meet the daily fitness needs of urban dwellers, thus street spaces, which offer mixed-use urban environmental functions, are frequently incorporated into the options for night running environments. Linear spaces such as streets in urban areas provide an informal platform for residents’ daily fitness activities, characterized by wide coverage, strong accessibility, and high openness [4].
In street night running spaces, artificial lighting is an indispensable factor. Outdoor artificial illumination can influence individuals’ psychological perception and physical behavior, thereby exerting significant effects on their nocturnal outdoor experiences. In recent years, the impact of artificial lighting on outdoor experiences has garnered increasing attention. Different intensities and proportions of light can lead to varied effects on individuals’ spatial perception, coexistence experiences, and environmental awareness [5]. Empirical evidence demonstrates that lighting enhances safety measures, fosters a sense of security for individuals and properties, and encourages the use of public facilities after dark [6]. Improved outdoor environmental lighting in public spaces such as parks and streets can enhance safety perceptions in darkness, reduce fear [6‐8], and mitigate crime risks associated with darkness [9], thereby promoting activities like walking and jogging outdoors [6]. Additionally, scholars have investigated how park lighting influences spatial legibility, safety perceptions, and landscape preferences [10].
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However, there is currently limited research on the impact of different types of artificial lighting at night on the comfort of urban street jogging spaces. The purpose of this study is to investigate the influence ranking of artificial lighting elements in urban street jogging spaces on the comfort of night runners. The article comprises three main sections: (1) Collecting evaluation texts of running routes in Suzhou City through artificial means and selecting typical routes suitable for night jogging. (2) Conducting on-site investigations, collecting real-life images, and recruiting volunteers to fill out questionnaires. (3) Analyzing the survey results and exploring the underlying mechanisms. This paper aims to provide a reference for the renewal planning of urban nighttime lighting by examining the impact ranking of different types of artificial lighting on the comfort of night jogging spaces, thereby creating a more comfortable and pleasant urban night jogging environment.
2 Methods
2.1 Research Methodology
Firstly, this study is based on the running route recommendation texts collected artificially from the Keep application, with “night running” as the keyword filter, to ensure the selection of recommended routes specifically for night running contexts. Subsequently, three representative night running routes were selected from the filtered results, and on-site photography sampling was conducted for these routes. Then, based on a comprehensive analysis of existing literature, the types of artificial lighting that may affect the comfort of night running spaces were determined, and a research questionnaire was developed following the process referenced from Q sort [11]. Finally, statistical analysis was performed on the collected data (see Fig. 1).
Fig. 1.
Workflow of the article (Maps drawn by the authors; Photos taken by the authors)
2.2 Research Area and Route Selection
As of 2022, Suzhou had a total permanent population of 12.91 million. The proportion of residents regularly participating in physical exercise reached 42.8%, with 95% meeting or exceeding the criteria outlined in the “National Physical Fitness Measurement Standards” [12], indicating a favorable exercise atmosphere. The study area is located in the Suzhou Industrial Park, where there is a rich variety of street running spaces. For this experiment, three recommended running routes located in the Suzhou Industrial Park on the Keep app were selected for on-site sampling and photography (see Fig. 2).
Fig. 2.
Study area and selected routes. Drawn by the author.
2.3 Data Source
With the widespread use of the internet, vast amounts of usage data from smartphone users can often be aggregated to form datasets. In recent years, many fitness apps have introduced route recommendation features, allowing users to choose and recommend their preferred running and fitness paths. These abundant recommended routes cover the entire city and include informal linear running spaces.
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Selection of Running Routes.
The running routes were selected based on recommended routes from the Keep app. As one of China's most influential fitness apps, Keep has accumulated a vast dataset of running data. In the Keep app, whenever a user completes a run on any route, they can recommend and publicly evaluate that route. These evaluations reflect users’ personal feelings and preferences about the completed routes. Due to Keep's privacy policy, this study collected all evaluation texts of recommended running routes in Suzhou from June 2017 to December 2021 through manual collection. A total of 215 evaluation messages were collected, with 130 being valid. The data collected in this experiment do not involve any privacy concerns.
The collected data were searched using the keyword “night running,” and three running routes were selected as the research objects. To minimize the influence of differences in regional population density on the number of completed routes, the three selected routes are close in distance and have different surrounding environments, ensuring a diverse range of photographed scenes. To enhance the credibility of the experimental scenario, the selected routes had all been completed over 1,000 times.
Acquisition and Processing of Streetscape Images.
Hartig et al.’s research findings confirm the reliability of using photographs to depict visual landscapes [13]. Currently, the acquisition of real-life images is often done through street view image software such as Baidu and Google Maps. However, these images are mostly captured during the daytime, and the camera's mounting height is typically at the level of a car roof, which differs from the height of the human eye [14]. Moreover, the shooting points are located in the center of the roadway, which makes it difficult to authentically recreate the real scenes of linear running spaces. Therefore, this study adopts on-site photography for image acquisition. The selected camera is the InstaX3 panoramic camera, set to HDR photo mode, with a shooting height of 1.6 m [14]. The central point of the street is chosen as the shooting point, with a sampling point every 10 m. Each photo has a resolution of 18 million pixels, and a total of 678 photos were collected. Batch processing of the collected panoramic images is conducted using the Insta360 Studio 2023 platform to convert them into wide-angle images. The aspect ratio of each image is 2.35:1. Six representative and typical photos are selected for each route as scenes for the questionnaire sorting.
Determination of Influencing Factors.
Artificial lighting has been demonstrated to enhance activities in outdoor spaces during nighttime [15]. This study identified several factors influencing outdoor activities by reviewing relevant literature in the field, which were subsequently utilized as indicators for this experiment (Table 1).
The number of types of urban environmental lighting, mainly including street lights, building lights, traffic signals, vehicle lights, and other indicator lights
Using the Q sort methodology, developed by Stephenson, participants ranked the impact of artificial light factors on their comfort during night running, based on photographs of various sites [11]. Each photo was ranked once, considering lighting intensity, color, coverage, and type, with the option to revisit and adjust previous rankings. The routes in the photographs were selected based on positive feedback from night runners, implying a positive impact of the artificial light environment. The survey concluded with an open-ended question about the artificial lighting conditions experienced during night runs.
2.5 Data Processing
The scoring of single-item options in the questionnaire follows the option's average comprehensive score, calculated using the formula:
$$ f\left( x \right) = \sum_{x} n\left( x \right)w\left( x \right)/p\left( x \right) $$
(1)
n(x) represents the frequency, w(x) represents the weight, and p(x) represents the number of respondents for this question.
The weight of each option is determined by its position in the ranking. For example, if there are 4 options for sorting, the weight of the option ranked first is 4, the second option is 3, and so on. Then, the arithmetic mean method is used to calculate the scores of the four factors for each of the six points on each street, yielding the score for each factor.
In the Keep app, each time a user completes a run on a recommended route, the completion data is automatically accumulated and added to the public running route completion count. Different recommended running routes have different completion frequencies. We consider a higher completion frequency to indicate a higher popularity of the route and a better suitability of the lighting factors for night running. Therefore, the completion frequency is regarded as the weight for the final statistical score. The weighted value of each street is determined using standardized weighting, with the formula:
$$ f(i) = c\left( i \right)/C $$
(2)
c(i) represents the completion frequency of a specific street, while C represents the total completion frequency of all streets.
Finally, the scores corresponding to each street's factors are multiplied by their respective weights and added together to obtain the final score for each lighting factor:
Using SPSS26.0 software to test the reliability of ambient light brightness, ambient light colour, ambient light coverage, and the number of ambient light species respectively, the Cronbach coefficients of the four groups were 0.837 (ambient light brightness), 0.695 (ambient light colour), 0.781 (ambient light coverage), and 0.823 (number of ambient light species). Overall results the questionnaire ranking of the four groups of artificial light factors had good internal reliability.
3.2 Ambient Differences in Artificial Light Types on Four Streets
This study investigated ambient light conditions on three streets, assessing six different points on each street. Parameters measured included ambient light brightness, ambient light colour, ambient light coverage, and number of lighting types. In order to explore the environmental differences between each of the four artificial light types, the top three highest and lowest scores were selected from the 18 point locations for each element, and the photographs taken of them were analyzed in comparison.
In the assessment of environmental lighting across 18 locations, significant variations were observed in brightness, color, coverage, and types. The highest brightness score was 3.35 at location 3, with the lowest at 2.80 at location 18, indicating more abundant lighting at higher-scoring locations. In terms of lighting color, the highest score was 2.99 at location 1 and the lowest 2.52 at location 18, though differences in the proportion of cold light were not significant. For lighting coverage, location 11 scored the highest (2.50) and location 1 the lowest (2.10), without a clear pattern. Regarding the variety of lighting, location 18 scored the highest (2.15) and location 6 the lowest (1.68), showing richer lighting types at higher-scoring spots. These results suggest that the different aspects of environmental lighting at various locations could significantly impact the comfort of night runners (see Fig. 3).
Fig. 3.
Plots of field samples. Created by the author.
4 Discussion and Conclusion
This study aims to explore the impact of various artificial lighting factors (ambient light intensity, light color, coverage, and variety of lighting types) on the comfort of night runners. The results indicate that ambient light intensity is the most significant factor affecting the comfort of night runners, followed by light color and coverage, while the variety of lighting types has a relatively minor impact. Appropriate light intensity not only enhances the sense of safety for night runners but may also reduce the risk of injury and improve the overall exercise experience [6‐8]. The diversity and suitable color temperature of light can enhance alertness and mood [22, 23], while the uniformity and continuity of light coverage are crucial for providing a consistent and uninterrupted night running experience. In contrast, the marginal benefit of additional types of lighting in enhancing comfort appears to be limited, as the focus of night runners is more on the act of running itself rather than on identifying types of lighting. This study provides valuable insights for urban planners, park managers, and outdoor activity organizers in designing and optimizing night running environments, emphasizing the importance of adequately adjusting lighting factors. Although this study has limitations in sample range and survey methods, it does not contextualize the research within the broader field of mapping visual experiences. Future research could further deepen our understanding by expanding the sample size and employing more diversified data collection methods, offering practical guidance for improving night running environments and enhancing the experience of night runners.
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