Dynamic Land-Use Map Based on Twitter Data

Estimating Urban Shared-Bike Trips with Location-Based Social Networking Data

Dockless shared-bikes have become a new transportation mode in major urban cities in China. Excessive number of shared-bikes can occupy a significant amount of roadway surface and cause trouble for pedestrians and auto vehicle drivers. Understanding the trip pattern of shared-bikes is essential in estimating the reasonable size of shared-bike fleet. This paper proposed a methodology to estimate the shared-bike trip using location-based social network data and conducted a case study in Nanjing, China. The ordinary least square, geographically weighted regression (GWR) and semiparametric geographically weighted regression (SGWR) methods are used to establish the relationship among shared-bike trip, distance to the subway station and check ins in different categories of the point of interest (POI). This method could be applied to determine the reasonable number of shared-bikes to be launched in new places and economically benefit in shared-bike management.

Measuring the Spatial Allocation Rationality of Service Facilities of Residential Areas Based on Internet Map and Location-Based Service Data

The spatial allocation rationality of the service facilities of residential areas, which is affected by the scope of the population and the capacity of service facilities, is meaningful for harmonious urban development. The growth of the internet, especially Internet map and location-based service (LBS) data, provides micro-scale knowledge about residential areas. The purpose is to characterize the spatial allocation rationality of the service facilities of residential areas from Internet map and LBS data. An Internet map provides exact geographical data (e.g., points of interests (POI)) and stronger route planning analysis capability through an application programming interface (API) (e.g., route planning API). Meanwhile, LBS data collected from mobile equipment afford detailed population distribution values. Firstly, we defined the category system of service facilities and calculated the available service facilities capacity of residential areas (ASFC-RA) through a scrappy algorithm integrated with the modified cumulative opportunity measure model. Secondly, we used Thiessen polygon spatial subdivision to gain the population distribution capacity of residential areas (PDC-RA) from Tencent LBS data at the representative moment. Thirdly, we measured the spatial allocation rationality of service facilities of residential areas (SARSF-RA) by combining ASFC-RA and PDC-RA. In this case, a trial strip census, consisting of serval urban residential areas from Wuxi City, Jiangsu Province, is selected as research area. Residential areas have been grouped within several ranges according to their SARSF-RA values. Different residential areas belong to different groups, even if they are spatially contiguous. Spatial locations and other investigation information coordinate with these differences. Those results show that the method that we proposed can express the micro-spatial allocation rationality of different residential areas dramatically, which provide a new data lens for various researchers and applications, such as urban residential areas planning and service facilities allocation.

Integrating Aerial and Street View Images for Urban Land Use Classification

Urban land use is key to rational urban planning and management. Traditional land use classification methods rely heavily on domain experts, which is both expensive and inefficient. In this paper, deep neural network-based approaches are presented to label urban land use at pixel level using high-resolution aerial images and ground-level street view images. We use a deep neural network to extract semantic features from sparsely distributed street view images and interpolate them in the spatial domain to match the spatial resolution of the aerial images, which are then fused together through a deep neural network for classifying land use categories. Our methods are tested on a large publicly available aerial and street view images dataset of New York City, and the results show that using aerial images alone can achieve relatively high classification accuracy, the ground-level street view images contain useful information for urban land use classification, and fusing street image features with aerial images can improve classification accuracy. Moreover, we present experimental studies to show that street view images add more values when the resolutions of the aerial images are lower, and we also present case studies to illustrate how street view images provide useful auxiliary information to aerial images to boost performances.