Urban areas account for 0.5% of the planet’s surface but accommodate more than half the global population [1
]. Over recent decades, most cities have experienced dramatic increases in population and rapid expansion of their urban areas [2
]. Such unprecedented development of modern cities has led to the emergence of some important urban problems, e.g., urban heat islands [3
], transportation congestion [4
], and urban villages [5
]. Effective monitoring of urban areas is urgently needed for their sustainable development. Specifically, identifying detailed land use patterns is important for decision-makers, because it is fundamental to understanding the relationship between physical and socioeconomic structures within urban areas [6
Remote sensing is an approach used widely for urban mapping, because it can reveal the physical structure of the surface. Remotely sensed images have been acquired by many different sensors and applied to urban land cover mapping. For example, images obtained by the Landsat Operational Land Imager and Thematic Mapper have been used for land cover classification of fast-developing cities in China [8
]. However, medium-spatial-resolution remote sensing imagery (RSI) is primarily useful for mapping land cover, and not well suited for distinguishing land uses. This is because the reflectances of several land use classes are very similar, which means they can often be categorized as a single class in a land cover map. For example, the land cover class of impervious surface could comprise the land use classes of business, residential, and factory.
High-spatial-resolution RSI has shown promise as a data source for obtaining urban land use maps, because it can provide both spectral and textural properties of urban areas [10
]. Various spectral-spatial classification methods have been proposed to extract urban land use maps. One popular method is to add into the original remote sensing spectral bands textural information derived from the original RSI, e.g., a grayscale co-occurrence matrix [13
], morphological features [15
], and a pixel shape index [16
]. For example, Li et al. used a Bayesian network to categorize building objects into different land use types based on their morphological, geometric, and contextual attributes [17
]. Another method involves object-based classification, in which a land cover map is first interpreted from high-spatial-resolution RSI. Then, spatial metrics, image texture, and nearest-neighbor relationships of land cover objects are adopted as parameters to identify urban functional zones [18
]. Using the object-based classification method, Voltersen et al. classified every block in Berlin (Germany) into specific land use types [12
]. Moreover, scene classification, which often includes scene segmentation, scene feature extraction, and semantic classification, is another promising method for urban land use mapping [20
]. Zhao et al. used the scene classification method to construct a bag-of-visual-words representation according to multiscale spatial features characterized by a convolutional neural network, which they applied to classification of the University of California Merced Land Use Dataset [22
Although these spectral-spatial approaches applied to high-spatial-resolution RSI have the potential to distinguish different urban land use types, some limitations remain. The main reason is that an urban land use category is commonly composed of various land cover types, and urban land use classes are heterogeneous in their spectral and textural characteristics. It is difficult to distinguish them accurately using classification algorithms based on these physical characteristics alone [23
]. However, land use types have distinct social functions. Therefore, integrating auxiliary social information and expert knowledge could be effective in improving the performance of urban land use identification [23
]. In particular, the emergence of geospatial big data has brought new possibilities regarding the identification of land use because of its natural ability to capture the socioeconomic characteristics of an area.
Of the various sources of auxiliary social information that are available for the extraction of urban land use information, mobile phone data, points of interest (POIs), and human trajectories have been used for functional urban area mapping [25
]. Specifically, Pei at el. used mobile phone data collected from base transceiver stations in Singapore and fuzzy c-means to obtain a land use map of Singapore [25
]. Yuan et al. proposed a parcel-based method for land use identification, where POIs and taxi trajectories are integrated to attribute parcel types using Dirichlet multinomial regression [26
]. Fan et al. proposed a method to discern the life patterns of pixels using a nonnegative tensor factorization approach to decompose geolocation data from 1.5 million volunteer trajectories in Tokyo [27
Given that urban land use should be recognized using physical or social attributes, identifying the land use of a city based on either RSI or geospatial big data alone has unavoidable limitations. Several land use classes are difficult to distinguish using remote sensing or social sensing data in isolation. For example, the remotely sensed spectral and spatial features of business and commercial land uses are similar, while their respective social characteristics are distinctive. Furthermore, human mobility associated with bare-land and farmland is similar, while their land covers are different. Thus, both remote sensing and social sensing data have drawbacks regarding several land use classes. From another perspective, however, RSI has a natural advantage concerning several physical land use classes, e.g., green land, bare land, and water, while socially sensed data have advantages regarding social land use classes, e.g., business, residential, and commercial areas. Therefore, fusing remotely sensed and socially sensed data could be advantageous when applied to urban land use mapping by exploiting the complementary features of the two methods.
Few methods have been proposed for mapping urban land use by fusing remotely sensed and socially sensed data. Hu proposed a land use mapping method that derived social and physical features from POI and Landsat Operational Land Imager data, and adopted similarity measures and thresholds to obtain the land use type of every land parcel in Beijing [29
]. Liu et al. combined spectral, textural, and spatial features calculated from high-resolution RSI and social features derived from mobile phone positioning data (MPPD) to identify land use types of different scenes. Compared with other socially sensed data, such as POI and mobile phone data, MPPD are most suitable for land use mapping, because this type of data has the advantage of expressing human mobility [30
]. Although high-spatial-resolution RSI has shown potential for use in land use mapping when combined with social features, medium-spatial-resolution RSI is easier to acquire with little cost and appropriate for large-area mapping. However, the spatial and textural features derived from medium-spatial-resolution RSI are inadequate to reflect the specific features of buildings, roads, and farmland. Therefore, an additional fusion strategy is needed to combine medium-spatial-resolution RSI and MPPD to obtain large-area land use maps.
This paper proposes an urban land use mapping method that combines medium-spatial-resolution RSI and mobile phone positioning data (MPPD) to extract urban land use information. The proposed method first classifies the remote sensing data and the MPPD separately. The urban land use map is then derived by fusing the two classification results using a decision fusion method, and activity density is analyzed for some representative classes in different regions. The remainder of this paper is organized as follows. Section 2
elaborates on the details of the study area and the data used. Section 3
describes the method. The results obtained by applying the method to the study area are presented in Section 4
. An analysis of the significance and future of the work are discussed in Section 5
. Finally, our conclusions are stated in Section 6
5.1. Advantages of the Fused Land Cover Map and Future Work
The experimental results indicate that the class-specific accuracy of residential, business, and open areas increases after the MPPD-based land use and WFV-based land cover maps are fused. Comparison between the confusion matrices of the fused and MPPD-based land use maps indicates that four zones, where the true label is residential or business and the misclassified label is open or other, are the main areas contributing to the improved results. Several factors contribute to this improvement. In general, the WFV data by themselves are inadequate for mapping detailed land use, and the MPPD-based mapping is considerably improved by fusing it with the WFV results. As shown in Figure 7
, the core area of the study area is occupied primarily by built-up areas, thus it is difficult to discern detailed land use that could adequately reflect the city’s structure. Different from the WFV remote sensing data, MPPD have a natural advantage in revealing unnatural urban functions. However, as shown in Table 5
and Table 6
, MPPD are deficient in distinguishing several classes, e.g., open and business. Nevertheless, integrating the WFV data and MPPD decreases the levels of omission and commission errors of the above classes.
In addition to identifying traditional residential areas, the proposed method could also identify several low-density and suburban developments (shown in Figure 10
). As it did for residential areas, the fusion method performs well in identifying both traditional urban working areas and suburban factory areas. Compared with the central urban area, human activity is weaker in suburban areas and low-density developments. The human mobility patterns in these areas could not be represented by the residential class, but they can be characterized by the open class in the MPPD-based land use map. Meanwhile, the built-up class is the land cover label assigned to these areas. With the help of the fusion strategy illustrated in Table 5
, the labels of these areas are corrected. Additionally, several areas of suburban factories or warehouses are labeled as other in the MPPD-based land use map, because human activity is even weaker in these areas (shown in Figure 10
). After integrating the MPPD-based land use map with the land cover map, the proposed method reclassifies these areas into the correct categories. The class-specific accuracy of open areas is increased significantly because of this reclassification.
Despite the improvement, several mixtures of business and residential areas remain in the fused land use map. The main reasons are that human mobility patterns in these areas blend the characteristics of business and residential, thus they could not be separated by the MPPD or land-cover information. The mixture could be divided into horizontal and vertical mixtures. For the horizontal mixture, in most scenarios, the land surface pattern could not be covered by a square pixel. Especially for MPPD, the spatial resolution is higher than that of the common widths and lengths of buildings. In addition to the horizontal mixture, in several multifunction apartments and working/residential factories, different patterns of activity of mobile phone users may occur in the same location at different elevations. The unique phenomenon of MPPD, that is different from the mixture of remote sensing data, is its vertical mixture for business and residential classes.
The work index is developed to distinguish business and entertainment in the proposed method. With the help of the work index, several shopping centers, such as Xidan Mall and Joy City, are recognized correctly. Nevertheless, for several universities and research institutes, the work index is insufficient to distinguish business from entertainment, because the population density during the weekends is almost the same as on workdays.
The WFV-based land cover map and the MPPD-based land use map are preliminary results used for the fused land use map. Therefore, the classification system of the preliminary results could affect the fused land use map. Based on the fusion strategy shown in Table 5
, it is evident that the most important element of the land cover map is the built-up class. This is because it is the fundamental class for most land use types. Reasonably high PA of the built-up class is a prerequisite for the fused land use map. As shown in Figure 7
, residential and business areas occupy most parts of the core city, while open areas are mostly around the periphery.
In addition to the classification system, the fusion strategy is another import factor in producing a land use map of the study area. As the physical and social characteristics are distinctive and related features for land use mapping, adopting decision fusion is a suitable choice [39
]. Although the overall agreement of the fused land use map is 5.7% higher than that of the MPPD-based map, the decision fusion strategy is primitive and exploratory in relation to the combination of WFV data and MPPD. Developing an automatic and robust decision fusion method for land use mapping will be the focus of future work.
Although the MPPD data are suitable for land use mapping, they might be available only in China and quite limited in other countries in the world. Several alternative sources of social sensing data, including POIs [30
], check-in data with location information [40
], mobile phone data [25
], and trajectory data [42
], can then be applied to replace the MPPD data. A similar approach that fuses remote sensing and various social sensing data can be used to obtain a land use map. When fusion is performed, a key step is to aggregate the social data into a certain spatial unit, and different aggregation methods might be used for these various social sensing data [25
]. For the POIs data, spatial distribution density of POIs by category is the prevalent way to express social features with a certain spatial unit [30
]. For the check-in data, the amount of check-in data within a spatial unit should be aggregated according to the category and period [40
]. For the mobile phone data, the volume density in spatial units is often aggregated via spatial interpolation according to the location and volume of base transceiver stations [25
]. For the trajectory data, not only the volume of inflow and outflow of periods, but also the connections between places are factors that should be taken into consideration to aggregate the data [42
]. These extracted social features can then be fused with the physical features derived from remote sensing data to produce the final land use map.
5.2. Spatial Pattern of User Activity
Land use is an important factor in reflecting the urban structure of the patterns of human activity. Analyzing human activity in some key areas during the daytime and nighttime is a useful way to depict where and how dense people work and live. Additionally, it is important in helping to make better urban plans on the basis of population mobility across different zones. In order to reveal the patterns of human activity in the main city, spatial statistics tools were adopted to analyze the user activity (user density) of different zones for the different classes. The five ring roads (2nd, 3rd, 4th, 5th, and 6th) were used to segment the main city into five zones: the inner-2nd-ring, 2nd-3rd-ring, 3rd-4th-ring, 4th-5th-ring, and 5th-6th-ring. As residential and business areas occupy most of the main city and are of critical importance for urban planning, we only analyzed the user activity of these two classes. The user activity of daytime was calculated by averaging the user density between 08:00 and 18:00 local time during workdays. The user activity of rest time was estimated by averaging the user density between 20:00 and 23:00 local time during workdays.
The spatial patterns of user activity for the business class during daytime and nighttime are shown in Figure 11
a,b, respectively. It can be seen that during daytime, the main business area is in the 2nd-3rd-ring and the 3rd-4th-ring. However, the most active business area is located in the outer fringe, i.e., the Zhongguancun Software Park, where several notable IT companies (e.g., Baidu and Oracle) are located. During nighttime, the user activity of the entire city is similar, and it remained at a low level.
The flows of users of the five zones were calculated, and are summarized in Table 9
. With regard to the total flow, the zones of the 4th-5th-ring, 3rd-4th-ring, and 2nd-3rd-ring had the highest influx of people from nighttime to daytime. With regard to the average flow of users, the 2nd-3rd-ring had the highest inflow, followed by the 3rd-4th-ring and the inner-2nd-ring.
Similar to the business area, the spatial distribution of user activity for the residential class are presented in Figure 12
and Table 10
, respectively. User activity during daytime and nighttime shows that user activity in residential areas within the 5th-ring remained stable. However, in the north and west of the city, user density had an obvious increase from daytime to nighttime.
The flows of user density show that the biggest total inflow from daytime to nighttime occurs in the 5th-6th-ring zone, although the average inflow of 5th-6th-ring zone is almost equal to that of the 4th-5th-ring. In addition, the totals and averages of the other three zones are negative, indicating that user activity in these areas decreased.