Cities are human settlements where people engage in different activities and interact with the man-made space and natural environment. Global urban area occupies less than 2% of the Earth’s land surface, but consists of more than 50 percent of the world’s population [1
]. In China, the urbanization rate has increased from 26.4 percent in the year 1992 to 57.4 percent in 2016 as 400 million people have mitigated to cities [2
]. From a global view, it is estimated that total urban population will rise to five billion in the year 2030. This transition has enormous economic, social and environmental consequences [3
]. Targeting the aim of sustainable cities, remote sensing has been widely used to monitor the spatial structure, economy and environment of cities [5
Many studies have been conducted on urban morphology to portray urban spatial structure [11
]. Several significant theories have been developed, such as the concentric zone theory, the sector theory, the multiple nuclei theory and the polycentric theory. These advanced theories capture the urbanization process and benefit associated land management and urban planning. One stand of urban morphology study is to investigate the function provided by urban space. Urban functional zone is a mixture of urban functions and characterized by the role of urban space in the whole city, like urban center, sub-center, suburbs, ecological area, etc. [16
]. The identification of urban functional zones provides useful insights for urban planners to capture the urban growth and make sustainable development policy.
Urban functional zone analysis traditionally relies on land use and land cover (LULC), which can be acquired by labor- and cost-intensive land survey. Remote sensing is another fast and efficient approach to capture land cover and land use data to facilitate related studies. For example, Aubrecht and León Torres [15
] classified mixed or residential areas from nighttime light (NTL) images. Yang and Lo [18
] used time series Landsat TM images to extract land use/cover change data of the Atlanta, Georgia, metropolitan area in the United States. The landscape gradient from the urban center to the rural area has been observed to illustrate urban growth [19
]. Using several landscape metrics, Lin et al. [17
] extracted land use from Pleiades images to investigate the urban functional landscape pattern in Xiamen, China. Yu and Ng [24
] classified land use from Landsat TM images and performed gradient analysis to analyze spatial and temporal urban sprawl dynamics in this city. These studies focus on the spatial features in the city, but ignore the effect of human activity. However, in the highly urbanized cities in Asia, such as Singapore, Hong Kong, Beijing and Shenzhen, most land parcels are covered by man-made infrastructures that are a mix between residential, businesses and work function. Such complex urban environments raise a great challenge in understanding urban structure using only remote sensing imagery.
A city is a complex system that includes human beings and the natural environment. Human activity has a significant impact on urban morphology because of the interaction of urban space and human beings. Human beings are un-ignorable components of the city. So are the humanistic aspects involved [4
]. However, human activities have not been well integrated with remote sensing, due to the lack of massive human activities data. Ubiquitous location awareness technologies such as the Global Navigation Satellite System (GNSS), mobile phone positioning and Wi-Fi positioning allow humans to act as sensors to perceive the surrounding environment [30
]. Massive human sensing data are available, such as vehicle GPS data [32
], mobile phone records [35
] and social media data [41
]. These large-volume human sensing data record the time and the position of people; therefore, they provide much useful information about human activities in the city [35
Human sensing data provide us with unprecedented opportunities to reveal human activity distribution and the implied urban function. They enable us to image the city in alternative approaches. Ratti et al. [35
] mapped the cell phone usage at different times of the day. Their results provided a graphic representation of city-wide human activities and the evolution through space and time. Considering the relationship between human activities and land use, Pei et al. [37
] developed a clustering approach to classify land use with time series aggregated mobile phone data. Using NTL images as the proxy of human activity, Chen et al. [36
] identified the urban center or sub-center and the surface slope to indicate the urban land use intensity gradient by considering human activities implicitly. Recently, Cai et al. [44
] fused NTL images and social media check-in data to identify the polycentric structure in megacities, including Beijing, Chongqing and Shanghai, China. These pioneering studies support the potential of human sensing data in urban studies. However, human sensing data have still not been integrated with remote sensing imagery to portray urban functional zones [45
In this study, we present a novel data fusion framework integrating remote sensing imagery and the highly penetrating mobile phone positioning data to analyze urban functional zones comprehensively. Landscape metrics were calculated based on land cover from SPOT 5 images. Human activities data were extracted from mobile phone positioning data. By coupling them with urban cells, urban functional zones were identified using hierarchical clustering. Gradient analysis [17
] in three typical transects was applied to portray the landscape and human activity pattern from urban center to urban border. An experiment in Shenzhen, China, was conducted to validate the proposed framework. The results were explored to address the following questions: (1) What are the general patterns of landscapes in different urban function zones (taking Shenzhen as a case)? (2) What are the general patterns of human activity in different urban function zones (taking Shenzhen as a case)? (3) What is the composite effect of remote sensing imagery and human sensing data in portraying urban functional zones? The answers to these questions will demonstrate the spatial dynamics of both landscape and human activity in the city. They deepen the understanding of the city growth and help urban planners make sustainable development policies.
The experiment in Shenzhen, China, demonstrates that urban functions differentiate from urban center to urban border, because of the spatially-varying composition of landscape and human activities in this modern city. There are six urban functional zones in Shenzhen, including urban center, sub-center, suburbs, urban buffer, transit region and ecological area. The spatial distribution of these urban functional zones conforms to the classic urban spatial structure theories to a certain degree. For example, there is one urban center in the south, three sub-centers in west and north Shenzhen and one suburb core in northeast Shenzhen (Figure 8
b), all of which have relatively high density of the built-up area and human activities. This urban spatial structure validates that the development of Shenzhen is aiming at the multi-clusters structure in the Shenzhen Comprehensive Plan 2010–2020 [69
], which also complies with the polycentric theory [70
]. The intensity and composition of human activities in these zones further indicate that the suburbs are inferior to sub-centers, which lag behind the aim of the Shenzhen Comprehensive Plan 2010–2020.
It can also be seen that the urban center is aggregated with the most working, social and in-home activities. Following the direction of three typical transects, the intensity of human activity declines greatly from the urban center to the suburbs. The ratio of in-home is lower in the urban center, but higher in the sub-center or suburbs. The ratio of working has a reverse trend. These variations are in line with the concentric zone theory. However, landscapes in these three zones are similar (Table 3
), which does not conform to the classic concentric zone theory.
Compared with traditional urban studies relying on LULC, the proposed framework enables the portraying of urban functional zones by integrating landscape metrics from remote sensing imagery and human activity information from the new mobile phone positioning data, providing comprehensive urban knowledge for city planners, e.g., the description of the urban spatial structure, the accurate assessment of urban development status, etc. Actually, a number of studies has been conducted to analyze urban landscapes or urban functions [17
], without human activities information, but more and more human sensing data give us an alternative approach to mapping the spatial structure of the city [35
]. Fusing human sensing data with remote sensing imagery in urban studies has seldom been reported or analyzed in the literature. This study fills the gap with a novel framework, which differs from the previous studies in three main aspects.
(1) Daily human activities have been extracted for urban studies. Massive human sensing data like mobile phone positioning data and social media data contain much information about human activities. However, they do not explicitly report human activities; therefore, they are used as proxies of human activity in several studies [37
]. Considering the rhythm and the regularity of human activities, three main human activities are extracted from mobile phone positioning data. This useful information provides us alternative images about the city (Figure 7
(2) Both landscape metrics and human activity metrics have been used to describe urban space. Previously, most related studies used only landscape metrics inferred from remote sensing imagery to analyze urban space [17
]. The humanistic aspect of urban space has been ignored. We integrate landscape metrics from SPOT-5 images and human activities from mobile phone positioning data to describe the urban cell. Using these useful metrics, we adopt hierarchal clustering to identify urban functional zones. The experiment in Shenzhen testifies that the fusion of remote sensing imagery and human sensing data can well characterize the complex pattern of the urban functional zones in Shenzhen.
(3) The pattern of landscape and human activities along the urban-suburbs gradient has been investigated. Although landscape gradient analysis has achieved success in many studies, for example Luck and Wu’s research in Phoenix, Arizona, USA [19
], Yu and Ng’s study in Guangzhou, China [24
], Lin et al.’s work in Xiamen, China [17
], etc., it is not effective in Shenzhen, a highly urbanized city with more than 40% of the area covered by built-up land, as Section 4.3
demonstrates. By fusing landscape metrics and human activity, this study uncovers comprehensive patterns of landscape and human activities across the city. It reveals that there is a significant gradient in human activity from the urban center to suburbs and the ecological area.
Portraying urban functional zones provides a useful description of urban space usage. It helps urban planners to understand the urban development status and the urban spatial structure; therefore, it benefits both urban planning and sustainable urban development. This article presents a novel data fusion framework coupling remote sensing imagery and human sensing data to identify urban functional zones in a megacity. LULC were classified from SPOT 5 images with the bi-level set segmentation and the SVM classifier to calculate landscape metrics. Daily human activities were extracted from massive mobile phone positioning data. By integrating landscape metrics and human activity metrics, six urban functional zones including urban center, sub-center, suburbs, transit region, urban buffer and ecological area were identified using a hierarchical clustering method. The gradient of landscapes and human activities was revealed in three typical transects rooted in the urban center.
The experiment was conducted in Shenzhen, China. The results indicate that there are different compositions of landscapes and human activities in different urban functional zones. Following the gradient from the urban center to suburbs and ecological area, the intensity and the composition of human activities vary significantly. Landscape metrics are similar in urbanized areas (including urban centers, sub-centers and suburbs), where the intensity and the composition of human activities are differentiated. On the other hand, landscape metrics are distinguished at the less urbanized areas (including an urban buffer, transit region and ecological area). This proves that existing urban development of Shenzhen has gone far beyond the explanation capacity of the classical theories. It also demonstrates that the fusion of remote sensing imagery and human sensing data can well characterize the complex pattern of the urban functional zones in Shenzhen.
In the future, we plan to collect more mobile phone positioning data. Although previous studies have verified the regularity of human activity, long-term mobile phone positioning data are expected to examine the reliability of human activity labeling. We also plan to conduct an experiment in another city to validate the robustness of the proposed framework. On the other hand, the presented data fusion framework will be extended to be integrated with other human sensing data, like open street map (OSM), geo-tagged photos, social media data, vehicle trajectories, etc. Expanded resources will further deepen the understanding of complex urban spatial structure.