Abstract
Abstract. Large amounts of data can be sensed and analyzed to discover patterns of human behavior in cities for the benefit of urban authorities and citizens, especially in the areas of traffic forecasting, urban planning, and social science. In New York, USA, social sensing, remote sensing, and urban land use information support the discovery of patterns of human behavior. This research uses two types of openly accessible data, namely, social sensing data and remote sensing data. Bike and taxi data are examples of social sensing data, whereas sentinel remote sensed imagery is an example of remote sensing data. This research aims to sense and analyze the patterns of human behavior and to classify land use from the combination of remote sensing data and social sensing data. A decision tree is used for land use classification. Bike and taxi density maps are generated to show the locations of people around the city during the two peak times. On the basis of a geographic information system, the maps also reflect the residential and office areas in the city. The overall accuracy of land use classification after the consideration of social sensing data is 85.3%. The accuracy assessment shows that the combination of remote sensing data and social sensing data facilitates accurate urban land use classification.
Highlights
Urban land use information is crucial to urban planning, economic analysis, hazard and pollution analysis, and environmental conservation (Jensen et al, 2011)
To investigate human behavior for the purpose of traffic forecasting, we identify the patterns of human behavior in New York on the basis of the bike and taxi usage data obtained every Wednesday for the whole month of June in 2016
The results offer interesting insights into the patterns of human behavior toward taxi and bike usage by people in New York
Summary
Urban land use information is crucial to urban planning, economic analysis, hazard and pollution analysis, and environmental conservation (Jensen et al, 2011). The demand for urban land use maps for utilization by urban authorities, researchers, and citizens, gas steadily increased. Remote sensed imagery provides abundant and detailed information on the spectral, textural, contextual, and spatial configuration of urban land cover (Herold et al, 2003). Remote sensed imagery is unable to examine the socioeconomic and demographic characteristics of urban land. Liu et al (2015) showed that social sensing data (e.g., social media, mobile phones, digital maps, and GPS trajectories) can reveal the socioeconomic and demographic characteristics of urban land. The combination of remote sensing data and social sensing data is expected to provide insights into urban landscape patterns and thereby facilitate accurate urban land use classification
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
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 call
