The extraction of partition lines from long and narrow patches is a key part of thematic data generalization and a challenging endeavor. Existing methods for extracting partition lines have limitations, such as jitter, nonhomogeneous topologies, and inconsistent geometries. Therefore, an approach to extracting partition lines that addresses these issues is proposed. Firstly, the jitter is categorized as types by analyzing the jitter causes, and the corresponding algorithm is herein presented. By employing the connection nodal points of the adjacent polygon boundary as a constraint, the compensation algorithm for partition lines is established. The partition line is then pruned according to its topological infrastructure to make it natural and smooth. An evaluation was conducted in which partition lines of typical long and narrow water and road patches were extracted from survey data under the geographical conditions of China’s Guizhou province. The results verified that the proposed approach is feasible and reasonable.
Analysis of clusters and movement patterns during emergency scenarios has the potential to provide vital information to key decision stakeholders. This paper presents a method using Voronoi Diagrams for defining both clusters and movement patterns in terms of voronoi cell properties: size, elongation, orientation and neighbourhood. Initial experimentation and testing against baseline methods using an evacuation trajectory dataset show promising results.
Parking Guidance and Information (PGI) systems have a potential to reduce the congestion in crowded areas by providing real-time indications of occupancy of parking spaces. To date, such systems are mostly implemented for indoor environments using costly sensor-based techniques. Consequently, with the increasing demand for PGI systems in outdoor environments, inexpensive image-based detection methods have become a focus of research and development recently. Motivated by the remarkable performance of Convolutional Neural Networks (CNNs) in various image category recognition tasks, this study presents a robust parking occupancy detection framework by using a deep CNN and a binary Support Vector Machine (SVM) classifier to detect the occupancy of outdoor parking spaces from images. The classifier was trained and tested by the features learned by the deep CNN from public datasets (PKLot) having different illuminance and weather conditions. Subsequently, we evaluate the transfer learning performance (the ability to generalise results to a new dataset) of the developed method on a parking dataset created for this research. We report detection accuracies of 99.7% and 96.7% for the public dataset and our dataset respectively, which indicates the great potential of this method to provide a low-cost and reliable solution to the PGI systems in outdoor environments.
Very small targets (VSTs) are common elements of national geographical condition data, and the integration of these targets directly affects the quality of results synthesized from these data. Most conventional methods use amalgamation or aggregation to merge VSTs with their proximal patches, but these approaches tend to neglect the competitiveness of each proximal patch. To address this gap, we propose a method of partitioning VSTs that accounts for crossing point constraints. We first analyze how surface area, semantic proximity, length of shared edges, and regional importance affect the splitting ability of a proximal patch. Then, we use the analytic hierarchy process to construct a hierarchical model of these factors, in which the weights of each factor are calculated. Finally, a comprehensive assessment of the splitting ability of each proximal element is performed, and the skeletons of VSTs are amended accordingly, thus realizing the partitioning of VSTs. The viability and effectiveness of the method proposed in this work is validated in experiments using real data.
A place graph is an abstract representation of human place knowledge, which models spatial references. A place graph can be used for various tasks that rely on reasoning and querying of the stored knowledge. In related work, place graphs were constructed from parsing natural language place descriptions using language processing techniques. In this research, we present an innovative approach to derive place graphs from information stored in spatial databases, with a demonstration using OpenStreetMap data. The approach provides a complementary way to generating place graphs from natural language descriptions.
In this work, we have proposed an optimized algorithm for public transit transfers based on the principle of least transfers. The first augmentation introduced by this algorithm concerns the configuration of proximal distances from the start and end points: the proximal distance from the starting point has been set to a user-defined maximum permissible walking distance, and the proximal distance from the end point was allowed to vary dynamically. Second, the one-to-one start-to-end point correspondence of conventional search models was improved by replacing it with a one-to-many correspondence. Third, two threshold values were introduced to constrain the search process, and optimal values were obtained for these thresholds via statistical analyses on large quantities of experimental data. Finally, an experiment was performed using the public transit data of Changzhi City in the Shanxi Province to validate the effectiveness and viability of the augmented algorithm. The results of this experiment demonstrate that our augmented algorithm effectively improves the practicality of routes calculated by least transfer algorithms, and reduces search times. Urban public transport systems are very large, complex and open systems that integrate a city’s transportation system with the socio-economic needs of urban societies. The development of public transportation is a developmental trend in many countries around the world, and research on urban public transportation systems is a topic of keen interest for many cities in China and elsewhere [1]. The continued development and improvement of urban public transportation services has made public transit increasingly convenient, flexible and accessible, but the increasing complexity of transit networks has also led to difficulties in the selection of routes and transfers. Current mainstream algorithms for solving the public transit transfer problem include the least transfer algorithm, Dijkstra algorithm, Floyd algorithm, and A* algorithm. The least transfer algorithm in particular, is a good fit for easy acceptance by public transit users. Many researchers have worked to improve this algorithm, resulting in a considerable number of research achievements. The algorithms proposed by References [2 –7] are augmented least transfer algorithms that include proximal station judgments and walking factors in their analyses, and these algorithms generally produce routes and transfers that are a better fit for the actual selection scenarios of public transit users. Some scholars have also proposed effective measures for resolving the efficiency problem of least transfer algorithms. For example, Liao et al. [2] used the advantages of databases in rapid queries, indexing support and set operations to improve the efficiency of least transfer algorithms. Fu [3] combined the indexing and rapid query capabilities of databases and spatial database engines with high-efficiency processing mechanisms such as internal storage-based queries and set operations, which effectively enhanced the efficiency of least transfer algorithms.
