The energy consumption and emissions from vehicles adversely affect human health and urban sustainability. Analysis of GPS big data collected from vehicles can provide useful insights about the quantity and distribution of such energy consumption and emissions. Previous studies, which estimated fuel consumption/emissions from traffic based on GPS sampled data, have not sufficiently considered vehicle activities and may have led to erroneous estimations. By adopting the analytical construct of the space-time path in time geography, this study proposes methods that more accurately estimate and visualize vehicle energy consumption/emissions based on analysis of vehicles’ mobile activities (MA) and stationary activities (SA). First, we build space-time paths of individual vehicles, extract moving parameters, and identify MA and SA from each space-time path segment (STPS). Then we present an N-Dimensional framework for estimating and visualizing fuel consumption/emissions. For each STPS, fuel consumption, hot emissions, and cold start emissions are estimated based on activity type, i.e., MA, SA with engine-on and SA with engine-off. In the case study, fuel consumption and emissions of a single vehicle and a road network are estimated and visualized with GPS data. The estimation accuracy of the proposed approach is 88.6%. We also analyze the types of activities that produced fuel consumption on each road segment to explore the patterns and mechanisms of fuel consumption in the study area. The results not only show the effectiveness of the proposed approaches in estimating fuel consumption/emissions but also indicate their advantages for uncovering the relationships between fuel consumption and vehicles’ activities in road networks.

The design of urban clusters has played an important role in urban planning, but realizing the construction of these urban plans is quite a long process. Hence, how the progress is evaluated is significant for urban managers in the process of urban construction. Traditional methods for detecting urban clusters are inaccurate since the raw data is generally collected from small sample questionnaires of resident trips rather than large-scale studies. Spatiotemporal big data provides a new lens for understanding urban clusters in a natural and fine-grained way. In this article, we propose a novel method for Detecting and Evaluating Urban Clusters (DEUC) with taxi trajectories and Sina Weibo check-in data. Firstly, DEUC applies an agglomerative hierarchical clustering method to detect urban clusters based on the similarities in the daily travel space of urban residents. Secondly, DEUC infers resident demands for land-use functions using a naïve Bayes’ theorem, and three indicators are adopted to assess the rationality of land-use functions in the detected clusters—namely, cross-regional travel index, commuting direction index, and fulfilled demand index. Thirdly, DEUC evaluates the progress of urban cluster construction by calculating a proposed conformance indicator. In the case study, we applied our method to detect and analyze urban clusters in Wuhan, China in the years 2009, 2014, and 2015. The results suggest the effectiveness of the proposed method, which can provide a scientific basis for urban construction.

Given the popularization of GPS technologies, the massive amount of spatiotemporal GPS traces collected by vehicles are becoming a new kind of big data source for urban geographic information extraction. The growing volume of the dataset, however, creates processing and management difficulties, while the low quality generates uncertainties when investigating human activities. Based on the conception of the error distribution law and position accuracy of the GPS data, we propose in this paper a data cleaning method for this kind of spatial big data using movement consistency. First, a trajectory is partitioned into a set of sub-trajectories using the movement characteristic points. In this process, GPS points indicate that the motion status of the vehicle has transformed from one state into another, and are regarded as the movement characteristic points. Then, GPS data are cleaned based on the similarities of GPS points and the movement consistency model of the sub-trajectory. The movement consistency model is built using the random sample consensus algorithm based on the high spatial consistency of high-quality GPS data. The proposed method is evaluated based on extensive experiments, using GPS trajectories generated by a sample of vehicles over a 7-day period in Wuhan city, China. The results show the effectiveness and efficiency of the proposed method.

With the rapid rise of social media, the photo-taking behavior of tourists and their uploaded photos provide a new perspective to explore landscape visual characters. In this study, we provide methodological advancements for assessing landscape visual quality based on content analysis of user-generated photographs. The purpose is to demonstrate an empirical method for evaluating visual indicators reflected in photographs through a case study application. This research takes the core cultural landscape area of Wuhan University as the research scope. The photographs shared on a famous Chinese social media platform Sina Weibo during the Cherry Blossom Festival, together with tourists’ trajectory data, were used as data sources. Based on a fixed-point photography experiment, the spatial relationship between the scenic spot and the observation point was illustrated. Utilizing a semi-automatic photo content analysis founded on computer vision technology, landscape visual attributes of each attraction were studied thoroughly regarding complexity, visual scale, and color. The results indicate that the Old Dormitory is the most popular scenic spot with diverse viewing angles, strikingly vivid colors, and rich color combinations. Complexity and color play key roles in landscape visual quality, while the depth of view has a subtle impact, which suggests the depth-to-height ratio of less than 1 is the best distance for viewers to take photographs. In all, the mapping relationship between landscape visual attributes and viewers’ perception was revealed in the present work.

In the context of sub-health caused by the sedentary lifestyle, sports for health, as an effective way to promote sustainable urban development, has attracted the attention of the government and residents. The proposal of policies represented by the National Fitness Program has facilitated the rapid development of community fitness spaces, which constitute an indispensable part of the Chinese outdoor fitness system. Despite this, many of these spaces still remain unused in reality due to unscientific planning, design, and management. To address this issue, we conducted a study in Liaocheng, Shandong Province, focusing on communities with fitness spaces. Through PSPL, questionnaire surveys and data analysis using software such as Urban Quadrant and SPSS 26, five factors impacting the utilization rates and satisfaction levels of these spaces were identified. And based on MLR, the number of rest facilities was verified as the most important factor and should be considered carefully. By providing insight into the utilization of community fitness spaces, our study can serve as a valuable reference for future planning or renovation of these spaces.