Leisure is defined as activity engaged in during free time [1
]; according to the Athens Charter, in addition to dwelling, work, transportation, and leisure representing one of the four basic functions of a city [3
]. After the reform and opening up program was enacted in 1978, the acceleration of urbanization throughout China has triggered rapid growth of the national economy [6
]. Unfortunately, the proportion of urban nighttime leisure space (UNLS) did not keep pace with the urban growth rate, which led to a decline in the quality of life [9
]. Characterizing the spatial distribution of UNLS can facilitate a better understanding of urban form [13
], particularly as detecting spatial regularities assists in solving certain social problems in the real world [14
]. UNLSs, referring to the spaces where urban residents engage in leisure activities between 6:00 P.M. and 6:00 A.M., have wide-ranging impacts on urban economic growth [16
], employment [17
], urban competitiveness [18
], urban vitality [19
], and social justice [20
]; consequently, UNLS is considered one of the key countermeasures to activate the urban economy; and accordingly falls under the scope of this paper [22
]. A UNLS map can indicate the socioeconomic activities that are carried out in different urban nighttime areas. However, due to the lack of an effective data source, the UNLS distribution is sometimes difficult to acquire at the regional scale. In addition, social census data always suffer from coarse temporal and spatial resolutions because data on basic districts or areas of arbitrary shape are inaccessible in many countries [23
]. Therefore, conducting UNLS mapping at different spatial resolutions using geographical big data as an alternative to traditional social census data is an important and challenging task for the academic community [24
UNLS is an important host of urban nighttime economic activities, and therefore plays an increasingly important role in the urban economy [26
]. In addition, urban leisure space is a kind of living entity because of the continuous integration of service providers and scenic spots, thus forming a dynamic space of consumerism. However, this dynamism makes it somewhat difficult for urban policy-makers and planners to manage and enhance the attraction of urban leisure spaces, and it is also difficult for residents and tourists to fully understand and obtain accurate information about the leisure facilities provided [27
]. At present, studies on NTLS [28
] are mostly focused on economics [31
], sociology [32
], public safety [33
], resident health and happiness [36
], and urban management [37
]. According to the different types of data, the existing research can be divided into the following three categories. (1) Based on survey statistics, Song et al. [10
] conducted a questionnaire survey on the daily leisure activities and places of Shanghai residents. The results show that although, the daily leisure time and disposable income of Shanghai urban residents are relatively sufficient, there are few leisure activities available. Compared with the level of leisure in western developed countries, the leisure level of urban residents in Shanghai is still in its primary stage. At present, there is a small number of outdoor leisure places, and their layout is not optimal, in addition, open public green spaces are the most popular type of leisure space. Ngesan et al. [38
] used Shah Alam as a case study to develop an open-ended questionnaire by collecting previous research data on nighttime activities, and used the questionnaire as a tool to assess whether Shah Alam would be a suitable night market. Hsieh et al. [39
] employed statistical information from the Taiwan Tourism Bureau to understand tourists’ motivations and their preferred leisure activities when they shop at tourist night markets. The result shows that novelty-seeking, exercising, and experiencing local culture and customs are major factors that motivate tourists to shop at tourist night markets: Eating out overwhelmingly dominated the leisure activities (88.5%), followed by everyday shopping (56%), and novelty-seeking (32%). Mohd et al. [40
] used a behavior mapping survey and a questionnaire survey to study the nighttime urban public parks between Shah Alam and Putrajava. The results show that urban public parks could provide more benefits and function to the leisure and recreation lifestyles of urban communities. John et al. [41
] utilized survey data to examine the leisure and recreation activities of people living in urban residential estates in the Lower Hunter, New South Wales. Their results demonstrate that substantial numbers of respondents are dissatisfied with the provision of recreation resources. There was an expressed need for larger areas of open space and parkland and better facilities for a wider range of age groups. (2) Based on network geographic data, Demi et al. [23
] proposed a method to determine the potential of leisure activities from urban spatial network data by using the semantic theme model. Taking the city of Zwolle as an example, network text data and geographical location tags were used to estimate the different types of leisure activities. Revealing that the services and functions of leisure spaces can be identified by combining various text and tag sources from the web. Jing et al. [5
] employed the gradient analysis method which integrates a geographic information system (GIS) technology (kernel density analysis), curve fitting analysis, and correlation analysis to theoretically determine the spatial gradient changes among urban leisure places and their significant correlation with the population distribution. (3) Based on NTL data, taking Guangzhou as an example, Guo et al. [42
] analyzed the factors that influence the development of nightlife tourism by using NTL engineering data and divided the nightlife scene of Guangzhou into three axes, one core, four centers, and one area. The results show that the government does not fully realize the importance of nighttime tourism. One of the major drawbacks is the limited hours of operation, most tourist attractions close before 6:00 P.M., which limits the development of nighttime tourism.
Although these methods have greatly contributed to UNLS research, previous studies suffer from three recurring issues.
Traditionally, the data sources used for UNLS research involve household surveys or census data collected by the government or research organizations. However, the varying qualities of these surveys and censuses and the substantial monetary and time costs required to conduct these studies hamper efforts to evaluate the UNLS distribution.
With respect to NTLS, most previous methods focused on recessive economic factors without considering the spatial distribution, morphological characteristics, and other influencing factors.
Using NTL data alone to map leisure spaces may result in inaccurate determinations due to the excessively high radiance in specific types of areas such as commercial zones and transportation hubs.
To address these issues, we employ NTL and point-of-interest (POI) data instead of census and questionnaire data for UNLS analysis. NTL data record the NTL intensity at the Earth’s surface, thereby reflecting the magnitude of human activities [43
]. In addition, NTL images have certain advantages in monitoring the UNLS distribution because of their large spatial coverage, high temporal resolution, and wide availability [8
]. In addition, the NTL data is widely used to study urban expansion and relevant sociodemographic activities across a variety of spatial scales, such as population density [45
], urban extent mapping [47
], energy consumption [49
], environmental changes [51
], and socioeconomic activities [24
]. Currently, most studies mainly use the following two types of NTL image sources: The Defense Meteorological Satellite Program Operational Line-scan System (DMSP-OLS) and the Suomi National Polar-Orbiting Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS). However, due to the blooming effect, the nighttime leisure spaces observed in the DMSP-OLS dataset are generally larger than the actual area of human activity [54
]. In addition, both DMSP-OLS and NPP-VIIRS data are insufficient to reflect human activities at an adequately fine resolution (in terms of the temporal and spatial resolutions).
POI data, which comprise geographic coordinates and reflect socioeconomic features create new opportunities for UNLS mapping at a high resolution [55
]. Certain POIs, such as service, leisure, and entertainment areas, are more closely associated with human activities and may indicate a richer UNLS than other POIs. Hence, as NTL images do not possess these features, POI data can be viewed as an analog complement to NTL data [56
The combined advantage of these two data sources, boast considerable potential to achieve better insights into UNLS distribution patterns. The physical features extracted from NTL images and the functional features retrieved from POI data can be combined to characterize UNLSs with great accuracy. To the best of our knowledge, no previous reports have been published on the joint use of NTL and POI data to produce UNLS maps. To bridge this gap, we develop a framework that utilizes NTL images and POI data to conduct UNLS mapping. In the remainder of this paper, we describe the details of the framework and its application to Beijing, China. We also examine the strengths and limitations of the proposed framework and formulate suggestions for future work in this field.
In this study, we used NTL images and publicly available social data to identify UNLSs, and we examined the spatial distribution of UNLSs in Beijing. The global UNLS distribution successfully extracted. We deduced the location and morphological and functional attributes of each UNLS from composite data. Available social data can reflect the human activities occurring within a space, especially a UNLS; accordingly, by using POI data, we labeled all detected UNLSs with their functions, as shown in Figure 7
. Entertainment and life services clearly occupied a dominant position among the UNLSs. Next, we will discuss the UNLS distribution, and the sensitivity, uncertainty, and drawbacks of the proposed method.
(1) Spatial distribution of the UNLSs in Beijing
From the NTL images and POI data, the distribution, geometric characteristics, and functional attributes of UNLSs were deduced. These characteristics and attributes may be related to the economic functions of the corresponding regions. Business and entertainment play leading roles in the central part of the city. Compared with those in the city center, the nightlife activities in the suburbs are not only small in both quantity and area, but also occur adjacent to government agencies. However, the growth of nighttime economic activities and the expansion of UNLSs in Tongzhou and other places indicate that Tongzhou is likely to become a core of UNLS development in the future.
(2) Superiority of the proposed method
First, our method can not only detect the locations of UNLSs, but can also determine their spatial scope and boundaries; as a result, we were able to quantitatively analyze the morphological characteristics and related indicators of UNLSs. Second, the detected UNLS boundaries are not limited by those of the administrative unit within which the UNLS in located; consequently, the natural form of a UNLS can be reflected. Moreover, the proposed method can determine the nesting level relationship among different UNLSs and is suitable for identifying UNLSs at different scales. Third, we further performed tests using POI data or only UTL data to extract UNLSs. The results with only POI data reveal that because of the large number of POIs, many locations that are not open at night are also considered, and thus, the UNLS area extracted using POI data is too large to tell people exactly where to go at night. However, the area extracted by NTL data alone can be described only as an area with frequent nighttime activities. In other words, not all the areas with frequent nighttime activities are leisure areas; instead, these areas may be important transportation hubs, such as airports. However, when NTL images and POI data are combined, we can identify UNLSs more accurately.
(3) Limitations of the proposed method
Although our approach can generate an UNLS distribution map for a given urban area, there are limitations, including the following. (1) The parameters selected, and linear model selected have uncertainties. The four parameters adopted in our method are based mainly on trial-and-error experiments and empirical knowledge, hence, these parameters may be arbitrary and subjective to a certain degree. In addition, a linear model may not be the best choice for combining two kinds of data. We will further study the data fusion model in subsequent research to obtain an improved model. Furthermore, the qualities of NTL images and POI data are not effectively verified, which may affect the experimental results to a certain extent. (2) Quantifying a UNLS results in a large area. To map in more detail, more quantitative indicators need to be integrated. (3) The socioeconomic factors of UNLSs need to be further analyzed to better reveal how social and spatial relationships dominate nighttime leisure activities.
Urban management requires maps of the spatial distribution of UNLSs, but the lack of available data sources poses a challenge to UNLS mapping. In addition, the physical form and function of a UNLS constitute the basis of further investigation. NTL images have the advantages of a large spatial coverage, high temporal resolution, and wide availability, etc. and these advantages enable the dynamic monitoring of UNLSs. POI data, directly reflect human activities, contain rich information on the functions of a space and can significantly complement traditional remote sensing data in the context of UNLS mapping. The physical characteristics derived from composite data and the functional attributes obtained from publicly available data are helpful for describing the utilization of UNLSs in detail. We developed a method to identify UNLSs in a large area and evaluated its effectiveness in Beijing. We successfully identified 138 UNLSs in Beijing, demarcated their boundaries, calculated their areas, and determined the overall UNLS distribution in Beijing. Most of the UNLSs in Beijing are distributed in the center of the city, which may indicate that the suburbanization of Beijing is still in its early stage. As long as there are NTL images and POI data, this method can be used to quickly obtain the UNLS spatial distribution map of any given large area. This is particularly important for developing countries.
In the future, we need to further improve the model, for example, by adopting machine learning to obtain more reasonable parameters to enhance the model accuracy.