A Multilevel Road Alignment Model for Spatial-Query-by-Sketch

A sketch map represents an individual’s perception of a specific location. However, the information in sketch maps is often distorted and incomplete. Nevertheless, the main roads of a given location often exhibit considerable similarities between the sketch maps and metric maps. In this work, a shape-based approach was outlined to align roads in the sketch maps and metric maps. Specifically, the shapes of main roads were compared and analyzed quantitatively and qualitatively in three levels pertaining to an individual road, composite road, and road scene. An experiment was performed in which for eight out of nine maps sketched by our participants, accurate road maps could be obtained automatically taking as input the sketch and the metric map. The experimental results indicate that accurate matches can be obtained when the proposed road alignment approach Shape-based Spatial-Query-by-Sketch (SSQbS) is applied to incomplete or distorted roads present in sketch maps and even to roads with an inconsistent spatial relationship with the roads in the metric maps. Moreover, highly similar matches can be obtained for sketches involving fewer roads

Time Series Analysis and Forecasting of Air Pollutants Based on Prophet Forecasting Model in Jiangsu Province, China

Due to recent developments in the global economy, transportation, and industrialization, air pollution is one of main environmental issues in the 21st century. The current study aimed to predict both short-term and long-term air pollution in Jiangsu Province, China, based on the Prophet forecasting model (PFM). We collected data from 72 air quality monitoring stations to forecast six air pollutants: PM10, PM2.5, SO2, NO2, CO, and O3. To determine the accuracy of the model and to compare its results with predicted and actual values, we used the correlation coefficient (R), mean squared error (MSE), root mean squared error (RMSE), and mean absolute error (MAE). The results show that PFM predicted PM10 and PM2.5 with R values of 0.40 and 0.52, RMSE values of 16.37 and 12.07 μg/m3, and MAE values of 11.74 and 8.22 μg/m3, respectively. Among other pollutants, PFM also predicted SO2, NO2, CO, and O3 with R values are between 5 μg/m3 to 12 μg/m3; and MAE values between 2 μg/m3 to 11 μg/m3. PFM has extensive power to accurately predict the concentrations of air pollutants and can be used to forecast air pollution in other regions. The results of this research will be helpful for local authorities and policymakers to control air pollution and plan accordingly in upcoming years

Automatic Determination of Clustering Centers for “Clustering by Fast Search and Find of Density Peaks”

Dividing abstract object sets into multiple groups, called clustering, is essential for effective data mining. Clustering can find innate but unknown real-world knowledge that is inaccessible by any other means. Rodriguez and Laio have published a paper about a density-based fast clustering algorithm in Science called CFSFDP. CFSFDP is a highly efficient algorithm that clusters objects by using fast searching of density peaks. But with CFSFDP, the essential second step of finding clustering centers must be done manually. Furthermore, when the amount of data objects increases or a decision graph is complicated, determining clustering centers manually is difficult and time consuming, and clustering accuracy reduces sharply. To solve this problem, this paper proposes an improved clustering algorithm, ACDPC, that is based on data detection, which can automatically determinate clustering centers without manual intervention. First, the algorithm calculates the comprehensive metrics and sorts them based on the CFSFDP method. Second, the distance between the sorted objects is used to judge whether they are the correct clustering centers. Finally, the remaining objects are grouped into clusters. This algorithm can efficiently and automatically determine clustering centers without calculating additional variables. We verified ACDPC using three standard datasets and compared it with other clustering algorithms. The experimental results show that ACDPC is more efficient and robust than alternative methods

Stereo Image Matching for Vehicle-Borne Mobile Mapping System Based on Digital Parallax Model

Considering automatic and effective stereo image matching for vehicle-borne mobile mapping system (VMMS), a new stereo image matching algorithm based on digital parallax model (DPM) is proposed in this paper. The new matching propagation strategy is designed in this algorithm, which includes two processes as DPM construction and parallax prediction. With some known matched points, the DPM of stereo image pairs is firstly constructed, and parameters for confirming conjugate epipolar line is also worked out. Then searching range during dense matching can be confirmed under constraints of DPM and epipolar line, which can improve matching speed and accuracy. Furthermore, to improve matching robustness, the computation model of similarity measurement combined with local structure feature and global color feature is designed. The new algorithm is applied to actual stereo images taken by VMMS to verify its validity. Experimental results show that the proposed approach has higher reliability and accuracy

Cooperative Navigation Algorithm of Extended Kalman Filter Based on Combined Observation for AUVs

The navigation and positioning of multi-autonomous underwater vehicles (AUVs) in the complex and variable marine environment is a significant and much-needed area of attention, especially considering the fact that cooperative navigation technology is the essential method for multiple AUVs to solve positioning problems. When the extended Kalman filter (EKF) is applied for underwater cooperative localization, the outliers in the sensor observations cause unknown errors in the measurement system due to deep-sea environmental factors, which are difficult to calibrate and cause a significant reduction in the co-location accuracy of AUVs, and can even cause problems with a divergence of estimation error. In this paper, we proposed a cooperative navigation method of the EKF algorithm based on the combined observation of multiple AUVs. Firstly, the corresponding cooperative navigation model is established, and the corresponding measurement model is designed. Then, the EKF model based on combined observation is designed and constructed, and the unknown error is eliminated by introducing a previously measured value. Finally, simulation tests and lake experiments are designed to verify the effectiveness of the algorithm. The results indicate that the EKF algorithm based on combined observation can approximately eliminate errors and improve the accuracy of cooperative localization when the unknown measurement error cannot be calibrated by common EKF methods. The effect of state estimation is improved, and the accuracy of co-location can be effectively improved to avoid serious declines in—and divergence of—estimation accuracy

A Hierarchical Spatial Network Index for Arbitrarily Distributed Spatial Objects

The range query is one of the most important query types in spatial data processing. Geographic information systems use it to find spatial objects within a user-specified range, and it supports data mining tasks, such as density-based clustering. In many applications, ranges are not computed in unrestricted Euclidean space, but on a network. While the majority of access methods cannot trivially be extended to network space, existing network index structures partition the network space without considering the data distribution. This potentially results in inefficiency due to a very skewed node distribution. To improve range query processing on networks, this paper proposes a balanced Hierarchical Network index (HN-tree) to query spatial objects on networks. The main idea is to recursively partition the data on the network such that each partition has a similar number of spatial objects. Leveraging the HN-tree, we present an efficient range query algorithm, which is empirically evaluated using three different road networks and several baselines and state-of-the-art network indices. The experimental evaluation shows that the HN-tree substantially outperforms existing methods

Geographic Process Modeling Based on Geographic Ontology

Considerable attention has been paid to geographic process-based studies in geographic information science research. Finding appropriate methods to express geographic processes is challenging, and working to reveal the dynamic evolution and underlying mechanisms behind these processes is worthwhile. This research proposes a process-centric ontology model that describes the geographical environment from three perspectives, namely, geographic scenes, geographic processes and geographic elements. These three aspects are combined to represent the dynamic changes of geographic phenomena. This research proposes a framework and constructs ten sub-ontologies. These sub-ontologies include the Element ontology, Scene ontology, and Process ontology. A soil erosion process is then selected to demonstrate the applicability of this framework. The entire process is divided into three sub-processes (detachment, transport and deposition), and each sub-process is described by identifying when and where the process occurred, the elements and their reactions, and the changes in the geographic scene. Different relationships among elements, scenes and processes are defined to explain how and why soil erosion occurred. This proposed approach can reveal the underlying mechanisms of geographic scenes, can be used to explore the occurrence and causes of geographic processes and support the complex representation of geographic elements

3D spatial morphological analysis of mound tombs based on LiDAR data

Mound tombs, popular in the south Yangzi River area in Shang and Zhou Dynasties, are regional cultural remains in China. With the aims of protecting and scientifically analysing cultural relics, laser scanning technology was adopted to study Zhaihuatou mound tombs located in Nonglin Village in Tianwang town, Jiangsu Province. Multiple tombs are held within one mound in good keep and with the typical construction of centripetalism. Accurate tomb LiDAR (Light Detection And Ranging) data were acquired by applying terrestrial laser scanning technology to the field mound excavation. Subsequently, a spatial morphological analysis of the tombs was conducted on the basis of archaeological rules and GIS spatial data processing methods. Using the theory of centripetalism construction of multi-tomb-one-mounds, we proposed an algorithm to determine the concentrated area of the geometric directions of tombs, and centripetalism theory was scientifically validated in comparing results with the excavation data. Spatial data clustering methods were used to analyse and deduce the spatial distribution characteristics of the tombs. We propose and demonstrate that the burial system is in the form of family-clan aggregation, and is useful for developing research on regional burials. Experimental data show that the proposed method is a novel example of how spatial analysis can foster more precise field archaeological excavations on a large scale, and it is significant to study various types of tombs, relics and ruins

Two-Step Simulation of Underwater Terrain in River Channel

When studying river hydrodynamics and water quality evolution laws on the basis of numerical simulation analysis, it is necessary to carry out topographic interpolation along the bend direction of the river on the basis of the measured river section, as this can provide accurate and reliable topographic data for river numerical modeling. In this paper, a two-step terrain simulation method based on sparse and discrete river sections is proposed by comprehensively considering the river trends and the lack of monitoring sections. On the basis of establishing a reference using the river centerline and coding the spatial relationship of the river, the linear weight method, which uses the distance and gradient change between the known sampling elevation section to realize the preliminary encryption of spatial points with any number of longitudinal sections and any horizontal distance, is carried out first. Considering the structural and anisotropic characteristics of the river, the improved inverse distance weighting (IDW) method is further used to locally interpolate the encrypted points to obtain the continuous surface of the river terrain. In order to prove the effectiveness of this method, a part of the Qinhuai River in Nanjing was taken as the research object. The experiment was carried out by setting different spacing distances for preliminary densification and by using different interpolation methods for further local terrain simulation. Root mean square error (RMSE) and mean absolute error (MAE) are used to evaluate the overall performance of different simulation methods. The experimental results show that the method proposed in this paper overcomes the obvious inaccuracy of directly using an interpolation algorithm to generate the river terrain due to sparse section data. The river terrain generated by the preliminary densification and improved IDW interpolation calculation method is more reasonable, and continuous and unobstructed, reflecting the original river terrain more accurately