Portraying Urban Functional Zones by Coupling Remote Sensing Imagery and Human Sensing Data

Wei Tu, Zhongwen Hu, Lefei Li, Jinzhou Cao, Jincheng Jiang, Qiuping Li, Qingquan Li
2018 Remote Sensing  
Portraying urban functional zones provides useful insights into understanding complex urban systems and establishing rational urban planning. Although several studies have confirmed the efficacy of remote sensing imagery in urban studies, coupling remote sensing and new human sensing data like mobile phone positioning data to identify urban functional zones has still not been investigated. In this study, a new framework integrating remote sensing imagery and mobile phone positioning data was
more » ... eloped to analyze urban functional zones with landscape and human activity metrics. Landscapes metrics were calculated based on land cover from remote sensing images. Human activities were extracted from massive mobile phone positioning data. By integrating them, urban functional zones (urban center, sub-center, suburbs, urban buffer, transit region and ecological area) were identified by a hierarchical clustering. Finally, gradient analysis in three typical transects was conducted to investigate the pattern of landscapes and human activities. Taking Shenzhen, China, as an example, the conducted experiment shows that the pattern of landscapes and human activities in the urban functional zones in Shenzhen does not totally conform to the classical urban theories. It demonstrates that the fusion of remote sensing imagery and human sensing data can characterize the complex urban spatial structure in Shenzhen well. Urban functional zones have the potential to act as bridges between the urban structure, human activity and urban planning policy, providing scientific support for rational urban planning and sustainable urban development policymaking. to five billion in the year 2030. This transition has enormous economic, social and environmental consequences [3, 4] . Targeting the aim of sustainable cities, remote sensing has been widely used to monitor the spatial structure, economy and environment of cities [5] [6] [7] [8] [9] [10] . Many studies have been conducted on urban morphology to portray urban spatial structure [11] [12] [13] [14] [15] . 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, 17] . 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] [20] [21] [22] [23] [24] [25] . 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, [26] [27] [28] [29] . 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, 31] . Massive human sensing data are available, such as vehicle GPS data [32] [33] [34] , mobile phone records [35] [36] [37] [38] [39] [40] and social media data [41] [42] [43] [44] . 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, 39] . 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] .
doi:10.3390/rs10010141 fatcat:kgxgbcjvwfahfh2v2akt4depgy