Abstract
The social function of areas of interest (AOIs) is crucial to the identification of urban functional zoning and land use classification, which has been a hot topic in various fields such as urban planning and smart city fields. Most existing studies on urban functional zoning and land use classification either largely rely on low-frequency remote sensing images, which are constrained to the block level due to their spatial scale limitation, or suffer from low accuracy and high uncertainty when using dynamic data, such as social media and traffic data. This paper proposes an hour-day-spectrum (HDS) approach for generating six types of distribution waveforms of taxi pick-up and drop-off points which serve as interpretation indicators of the social functions of AOIs. To achieve this goal, we first performed fine-grained cleaning of the drop-off points to eliminate the spatial errors caused by taxi drivers. Next, buffer and spatial clustering were integrated to explore the associations between travel behavior and AOIs. Third, the identification of AOI types was made by using the standard HDS method combined with the k-nearest neighbor (KNN) algorithm. Finally, some matching tests were carried out by similarity indexes of a standard HDS and sample HDS, i.e., the Gaussian kernel function and Pearson coefficient, to ensure matching accuracy. The experiment was conducted in the Chongchuan and Gangzha Districts, Nantong, Jiangsu Province, China. By training 50 AOI samples, six types of standard HDS of residential districts, schools, hospitals, and shopping malls were obtained. Then, 108 AOI samples were tested, and the overall accuracy was found to be 90.74%. This approach generates value-added services of the taxi trajectory and provides a continuous update and fine-grained supplementary method for the identification of land use types. In addition, the approach is object-oriented and based on AOIs, and can be combined with image interpretation and other methods to improve the identification effect.
Highlights
Urban functional zoning refers to the division of regions according to the dominant functions of a city, which is an organic whole with relatively independent functions and mutual connections
Urban areas of interest (AOIs) refer to units with a social function that attract the attention of humans
As a basic unit in urban functional zoning and land use types, the identification of the social function types of AOIs plays an important role in land use classification and urban functional partition [1,2]
Summary
Urban functional zoning refers to the division of regions according to the dominant functions of a city, which is an organic whole with relatively independent functions and mutual connections. As a basic unit in urban functional zoning and land use types, the identification of the social function types of AOIs plays an important role in land use classification and urban functional partition [1,2]. The identification and renewal of urban functional zones and land use types are hot research topics with broad applications ranging from transportation, urban planning, and smart cities [3]. It is generally agreed that existing studies on the identification of land use types and urban functional zoning are mainly based on field surveys [4], remote sensing images [5,6,7,8], and social media data, such as Sina Weibo [9], Dazhong Dianping [10], Twitter [11] and points of interest (POIs) data
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
