Error-bounded and Number-bounded Approximate Spatial Query for Interactive Visualization

In the big data era, an enormous amount of spatial and spatiotemporal data are generated every day. However, spatial query result sets that satisfy a query condition are very large, sometimes over hundreds or thousands of terabytes. Interactive visualization of big geospatial data calls for continuous query requests, and large query results prevent visual efficiency. Furthermore, traditional methods based on random sampling or line simplification are not suitable for spatial data visualization with bounded errors and bound vertex numbers. In this paper, we propose a vertex sampling method—the Balanced Douglas Peucker (B-DP) algorithm—to build hierarchical structures, where the order and weights of vertices are preserved in binary trees. Then, we develop query processing algorithms with bounded errors and bounded numbers, where the vertices are retrieved by binary trees’ breadth-first-searching (BFS) with a maximum-error-first (MEF) queue. Finally, we conduct an experimental study with OpenStreetMap (OSM) data to determine the effectiveness of our query method in interactive visualization. The results show that the proposed approach can markedly reduce the query results’ size and maintain high accuracy, and its performance is robust against the data volume

Exploiting Two-Dimensional Geographical and Synthetic Social Influences for Location Recommendation

With the rapid development of location-based social networks (LBSNs), because human behaviors exhibit specific distribution patterns, personalized geo-social recommendation has played a significant role for LBSNs. In addition to user preference and social influence, geographical influence has also been widely researched in location recommendation. Kernel density estimation (KDE) is a key method in modeling geographical influence. However, most current studies based on KDE do not consider the problems of influence range and outliers on users’ check-in behaviors. In this paper, we propose a method to exploit geographical and synthetic social influences (GeSSo) on location recommendation. GeSSo uses a kernel estimation approach with a quartic kernel function to model geographical influences, and two kinds of weighted distance are adopted to calculate bandwidth. Furthermore, we consider the social closeness and connections between friends, and a synthetic friend-based recommendation method is introduced to model social influences. Finally, we adopt a sum framework which combines user’s preferences on a location with geographical and social influences. Extensive experiments are conducted on three real-life datasets. The results show that our method achieves superior performance compared to other advanced geo-social recommendation techniques

An Automatic Road Network Construction Method Using Massive GPS Trajectory Data

Automatically acquiring comprehensive, accurate, and real-time mapping information and translating this information into digital maps are challenging problems. Traditional methods are time consuming and costly because they require expensive field surveying and labor-intensive post-processing. Recently, the ubiquitous use of positioning technology in vehicles and other devices has produced massive amounts of trajectory data, which provide new opportunities for digital map production and updating. This paper presents an automatic method for producing road networks from raw vehicle global positioning system (GPS) trajectory data. First, raw GPS positioning data are processed to remove noise using a newly proposed algorithm employing flexible spatial, temporal, and logical constraint rules. Then, a new road network construction algorithm is used to incrementally merge trajectories into a directed graph representing a digital map. Furthermore, the average road traffic volume and speed are calculated and assigned to corresponding road segments. To evaluate the performance of the method, an experiment was conducted using 5.76 million trajectory data points from 200 taxis. The result was qualitatively compared with OpenStreetMap and quantitatively compared with two existing methods based on the F-score. The findings show that our method can automatically generate a road network representing a digital map

The shortest path approximation algorithm for large scale road network

Node importance has significant influence on the calculation of shortest path of large-scale road network. A shortest path estimation method based on node importance is proposed in this paper that is suitable for large-scale network. This method integrates the criteria importance though intercrieria correlation (CRITIC) method with complex network theory, with a view to evaluate nodes importance. By combining the restriction strategy to realize network division, the effective simplification of large-scale road network and shortest path estimation are realized through the construction of hierarchical network. The results show that this method can be used to distribute the center nodes evenly, and make little difference in the size of the subnetwork. As the constraint parameter increases, the numbers of nodes and edges reduced gradually, and the query accuracy reached 1.026. Compared with single index and unlimited parameters methods, this paper significantly reduces the size of the network and obtains a high accuracy on the approximate calculation of the shortest path. These will provide a new way of thinking for approximate analysis of large-scale complex networks

An incremental construction method of road network considering road complexity

Aiming at the problem of needing expensive field survey and a large number of subsequent indoor processing for traditional road network acquisition and update, a method for automatically generating road network from large-scale raw trajectory data is proposed. The road network is constructed in two steps:trajectory selection and road network incremental construction. The trajectory selection process divides the raw trajectory records and filters them by constructing a spatial, temporal and rule constraint model to eliminate noise and redundancy in the data, and forms a set of canonical trajectories; the road network incrementally construction process calculates the road complexity around the processing point based on the information entropy to automatically adjust the road segmentation parameters, and continuously adds the newly generated road segments to the road network, and simultaneously calculates road traffic information such as average traffic volume and speed, and traverses the points of each trajectory, repeats the above process, and finally gets the complete road network. The road network construction experiment was carried out through about 68.51 million trajectory data collected by 200 taxis in Kunming. The results were compared with the OpenStreetMap data, which proved the effectiveness of the proposed method. When compared with the existing methods, our method can extract higher quality road network with fewer nodes

A Local Polynomial Geographically and Temporally Weight Regression

Geographically and temporally weight regression (GTWR) estimates regression coefficients and fitted value by weighted least squares (WLS), which under the assumption of the same minimum random variance. As without considering the spatio-temporal heteroscedasticity, it may reduce the accuracy of estimation. Local polynomial estimation is a nonparametric estimation method to eliminate heteroscedasticity in statistics. On the basis of the local polynomial estimation, the local polynomial geographically and weight regression temporally (LPGTWR) approach is proposed in this paper. It reconstructs the spatio-temporal coefficients using three-dimensional Taylor Series in order to satisfy the Gauss-Markov assumption of independent identical distribution. Then estimate the regression coefficients and fitting value using weighted least squares. The experiments use both simulated data and real data to compare LPGTWR, GTWR and local linear-fitting-based geographically weight regression (LGWR). Experiments using simulated data showed that LPGTWR can significantly improve the accuracy of estimation not only in goodness-of-fit of the fitted value, but also in reducing bias of the coefficient estimation and the estimation. It is useful by adopting LPGTWR to eliminate heteroscedasticity effect and improve estimation accuracy

Multi-Scale Massive Points Fast Clustering Based on Hierarchical Density Spanning Tree

Spatial clustering is dependent on spatial scales. With the widespread use of web maps, a fast clustering method for multi-scale spatial elements has become a new requirement. Therefore, to cluster and display elements rapidly at different spatial scales, we propose a method called Multi-Scale Massive Points Fast Clustering based on Hierarchical Density Spanning Tree. This study refers to the basic principle of Clustering by Fast Search and Find of Density Peaks aggregation algorithm and introduces the concept of a hierarchical density-based spanning tree, combining the spatial scale with the tree links of elements to propose the corresponding pruning strategy, and finally realizes the fast multi-scale clustering of elements. The first experiment proved the time efficiency of the method in obtaining clustering results by the distance-scale adjustment of parameters. Accurate clustering results were also achieved. The second experiment demonstrated the feasibility of the method at the aggregation point element and showed its visual effect. This provides a further explanation for the application of tree-link structures

Spatial Downscaling of NPP-VIIRS Nighttime Light Data Using Multiscale Geographically Weighted Regression and Multi-Source Variables

Remote sensing images of nighttime lights (NTL) were successfully used at global and regional scales for various applications, including studies on population, politics, economics, and environmental protection. The Suomi National Polar-orbiting Partnership with the Visible Infrared Imaging Radiometer Suite (NPP-VIIRS) NTL data has the advantages of high temporal resolution, long coverage time series, and wide spatial range. The spatial resolution of the monthly and annual composite data of NPP-VIIRS NTL is only 500 m, which hinders studies requiring higher resolution. We propose a multi-source spatial variable and Multiscale Geographically Weighted Regression (MGWR)-based method to achieve the downscaling of NPP-VIIRS NTL data. An MGWR downscaling framework was implemented to obtain NTL data at 120 m resolution based on auxiliary data representing socioeconomic or physical geographic attributes. The downscaled NTL data were validated against LuoJia1-01 imagery based on the coefficient of determination (R2) and the root-mean-square error (RMSE). The results suggested that the spatial resolution of the data was enhanced after downscaling, and the MGWR-based downscaling results demonstrated higher R2 (R2 = 0.9141) and lower RMSE than those of Geographically Weighted Regression and Random Forest-based algorithms. Additionally, MGWR can reveal the different relationships between multiple auxiliary and NTL data. Therefore, this study demonstrates that the spatial resolution of NPP-VIIRS NTL data is improved from 500 m to 120 m upon downscaling, thereby facilitating NTL-based applications

A Mixed Geographically and Temporally Weighted Regression: Exploring Spatial-Temporal Variations from Global and Local Perspectives

To capture both global stationarity and spatiotemporal non-stationarity, a novel mixed geographically and temporally weighted regression (MGTWR) model accounting for global and local effects in both space and time is presented. Since the constant and spatial-temporal varying coefficients could not be estimated in one step, a two-stage least squares estimation is introduced to calibrate the model. Both simulations and real-world datasets are used to test and verify the performance of the proposed MGTWR model. Additionally, an Akaike Information Criterion (AIC) is adopted as a key model fitting diagnostic. The experiments demonstrate that the MGTWR model yields more accurate results than do traditional spatially weighted regression models. For instance, the MGTWR model decreased AIC value by 2.7066, 36.368 and 112.812 with respect to those of the mixed geographically weighted regression (MGWR) model and by 45.5628, −38.774 and 35.656 with respect to those of the geographical and temporal weighted regression (GTWR) model for the three simulation datasets. Moreover, compared to the MGWR and GTWR models, the MGTWR model obtained the lowest AIC value and mean square error (MSE) and the highest coefficient of determination (R2) and adjusted coefficient of determination (R2adj). In addition, our experiments proved the existence of both global stationarity and spatiotemporal non-stationarity, as well as the practical ability of the proposed method