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OpenStreetMap (OSM) as one of the most successful projects of Volunteered Geographical Information (VGI) has attracted millions of contributors to work together and produces massive open geographical data. However, the co-work does not always run smoothly since mapping can involve conflicted understandings of the reality. In this paper, we investigate behaviors of mapping related to territorial disputes to reveal the characteristics of contributions and examine the contradictions between ground truth as the vision of OSM and the theory of critical cartography. We perform our experiments from the perspectives of entities, changesets, and contributors using the full history data of OSM. The experiments show that territorial-dispute-related contributions have substantially different characteristics from various aspects but they cannot be treated as outliers either, considering that most contributors do not focus on disputed boundaries. Interpreting OSM data as a converging state to ground truth or equally opinions can both be inaccurate. We also find that mapping disputes may not be absolutely negative in a VGI project. We perform quantitative, large-scale (global) analysis of dispute-related mapping. The results show that territorial-dispute-related contributions and contributors are different from contributions and contributors in general. Territorial-dispute-related mapping is not an independent phenomenon for OSM. The contributors make much more disputes-unrelated contributions. Dispute-related entities have more (divergent) versions than normal boundaries, attract more participants, and are more semantically complete, especially for names. Dispute-related changesets generally attract more discussions. The spatial distribution of the dispute-related changesets is consistent with real-world territorial disputes and very different from that of all boundary-related changesets and all changesets. Contributors who participate in dispute-related contributions are generally more active. These users tend to have a special interest in boundaries but most of them do not focus on disputed boundaries. [ABSTRACT FROM AUTHOR]

Reading vector data files to in‐memory data models efficiently is a crucial step to handle the ever‐growing volume of geographical data. Although advanced IO solutions like distributed file systems or Message Passing Interface parallel IO work well in some high‐end computing environments, the 20‐year‐old OGR simple features library is still the de facto tool for loading vector files when developing GIS algorithms in most scenarios, which is not very efficient when data become larger. In this article, we analyze the bottleneck of the OGR library and find that excessive small objects are the main source of slowness. We then offer advice to improve efficiency when using OGR. To further verify our findings and provide an alternative to the OGR library in performance‐sensitive scenarios, we develop a library based on continuous memory pools to avoid small objects. Experiments show that our advice is effective and our library can be several times faster than the OGR library for IO‐intensive programs. [ABSTRACT FROM AUTHOR]

In this paper, we present a data-driven framework to support exploratory spatial, temporal, and statistical analysis of intra-urban human mobility. We leveraged a new mobility data source, the dockless bike-sharing service Mobike, to quantify short-trip transportation patterns in Shanghai, China, the world's largest bike-share city. A data-driven framework was established to integrate multiple data sources, including transportation network data (roads, bikes, and public transit), road characteristics, and urban land use, to achieve a detailed, accurate analysis of cycling patterns at both the individual and group levels. The results provide a comprehensive view of mobility patterns in the use of shared-ride bicycles, including: (1) the temporal and spatiotemporal distribution of shared-bike usage and how this varies according to different land use; (2) the statistical distribution of Mobike trips, which are primarily short-distance; and (3) the travel behavior and road factors that influence Mobike users' route choice. The findings offer valuable insights for city planners regarding infrastructure development, for shared-ride bike companies to offer better bike rebalancing strategies to meet user demand, and for the promotion of this new green transportation mode to alleviate traffic congestion and enhance public health. [ABSTRACT FROM AUTHOR]

The first application of GaoFen-3 synthetic aperture radar (SAR) interferometry to monitoring co-seismic deformation of an earthquake is presented in this paper. First, two GaoFen-3 SAR images spanning the 2017 surface-wave magnitude ( M s ) 7.0 Jiuzhaigou earthquake were mosaicked, coregistered and processed into an interferogram with visible fringes. After the removal of severe orbital errors and unwrapping, the co-seismic deformation field is obtained. Finally, the fault parameters and slip distribution are determined based on the deformation field. To evaluate the performance of GaoFen-3 in this case, the interferogram, co-seismic deformation field, derived fault parameters and slip distribution are compared with those derived from Radarsat-2, Sentinel-1A and Advanced Land Observing Satellite-2 (ALOS-2) Phased Array type L-band Synthetic Aperture Radar-2 (PALSAR-2). The comparisons between the deformation field obtained from GaoFen-3 and those obtained from Radarsat-2 and Sentinel-1A show Pearson's correlation coefficient ( r) values > 90%, demonstrating that GaoFen-3 interferometry can measure the co-seismic deformation effectively. The resultant slip model obtained by GaoFen-3 interferometric measurements shows a simple, elliptical pattern of slip, reaching a maximum of approximately 1 m at depths of 10–13 km. The rake angle is almost 0 ∘ , which suggests left-lateral strike-slip motion, consistent with previous studies. [ABSTRACT FROM AUTHOR]

Oblique photogrammetry models are indispensable for implementing digital twins of cities. Geographic information system researchers have proposed plenty of methods to load and visualize these city-scaled scenes. However, when the area viewed changes quickly in real-time rendering, current methods still require excessive GPU calculation and memory occupation. In this study, we propose a data organization method in which we merged all quadtrees and used a binary encoding method to encode nodes in a merged tree so that the parent–child relationship between the tree nodes could be calculated using rapid binary operations. After that, we developed a strategy to cancel the loading of redundant nodes based on the parent–child relationship, which helped to reduce the hard disk loading time and the amount of memory occupied in visualization. Moreover, we introduced a parameter to measure the area of the triangle mesh per pixel to achieve unified data scheduling under different production standards. We implemented our method based on Unreal Engine (UE), and three experiments were designed to illustrate the advantages of our methods in index acceleration, frame time, and memory reduction. The results show that our methods can significantly improve visualization fluency and reduce memory usage. [ABSTRACT FROM AUTHOR]