An adaptive method for clustering spatio‐temporal events

The clustering of spatio‐temporal events has become one of the most important research branches of spatio‐temporal data mining. However, the discovery of clusters of spatio‐temporal events with different shapes and densities remains a challenging problem because of the subjectivity in the choice of two critical parameters: the spatio‐temporal window for estimating the density around each event, and the density threshold for evaluating the significance of clusters. To make the clustering of spatio‐temporal events objective, in this study these two parameters were adaptively generated from statistical information about the dataset. More precisely, the density threshold was statistically modeled as an adjusted significance level controlled by the cardinality and support domain of the dataset, and the appropriate sizes of spatio‐temporal windows for clustering were determined by the spatio‐temporal classification entropy and stability analysis. Experiments on both simulated and earthquake datasets were conducted, and the results show that the proposed method can identify clusters of different shapes and densities.     

Harnessing spatio‐temporal patterns in data for nominal attribute imputation

Missing data in Volunteered Geographic Information (VGI) are an unavoidable consequence of data collection by non‐experts, guided by only vague and informal mapping guidelines. While various Missing Value Imputation (MVI) techniques have been proposed as data cleansing strategies, they have primarily targeted numerical data attributes in non‐spatial databases. There remains a significant gap in methods for imputing nominal attribute values (e.g., Street Name) in map databases. Here, we present an imputation algorithm called the Membership Imputation Algorithm (MIA), targeting spatial databases and enabling imputation of nominal values in spatially referenced records. By targeting membership classes of spatial objects, MIA harnesses spatio‐temporal characteristics of data and proposes efficient heuristics to impute the class name (i.e., a membership). Experimental results show that the proposed algorithm is able to impute the membership with high levels of accuracy (over 94%) when assigning Street Name(s), across highly diverse regional contexts. MIA is effective in challenging spatial contexts such as street intersections. Our research serves as a first step in highlighting the effectiveness of spatio‐temporal measures as a key driver for nominal imputation techniques.     

Extraction of spatio‐temporal data about historical events from text documents

Often, we are faced with questions regarding past events and the answers are hidden in the historical text archives. The growing developments in geographic information retrieval and temporal information retrieval techniques have given new ways to explore digital text archives for spatio‐temporal data. The question is how to retrieve the answers from the text documents. This work contributes to a better understanding of spatio‐temporal information extraction from text documents. Natural language processing techniques were used to develop an information extraction approach using the GATE language processing software. The developed framework uses gazetteer matching, spatio‐temporal relationship extraction and pattern‐based rules to recognize and annotate elements in historical text documents. The extracted spatio‐temporal data is used as input for GIS studies on the time–geography context of the German–Herero resistance war of 1904 in Namibia. Related issues when analyzing the historical data in current GIS are discussed. Particularly problematic are movement data in small scale with poor temporal density and trajectories that are short or connect very distant locations.     

Integrated compression of vehicle spatio‐temporal trajectories under the road stroke network constraint

With fast growth of all kinds of trajectory datasets, how to effectively manage the trajectory data of moving objects has received a lot of attention. This study proposes a spatio‐temporal data integrated compression method of vehicle trajectories based on stroke paths coding compression under the road stroke network constraint. The road stroke network is first constructed according to the principle of continuous coherence in Gestalt psychology, and then two types of Huffman tree—a road strokes Huffman tree and a stroke paths Huffman tree—are built, based respectively on the importance function of road strokes and vehicle visiting frequency of stroke paths. After the vehicle trajectories are map matched to the spatial paths in the road network, the Huffman codes of the road strokes and stroke paths are used to compress the trajectory spatial paths. An opening window algorithm is used to simplify the trajectory temporal data depicted on a time–distance polyline by setting the maximum allowable speed difference as the threshold. Through analysis of the relative spatio‐temporal relationship between the preceding and latter feature tracking points, the spatio‐temporal data of the feature tracking points are all converted to binary codes together, accordingly achieving integrated compression of trajectory spatio‐temporal data. A series of comparative experiments between the proposed method and representative state‐of‐the‐art methods are carried out on a real massive taxi trajectory dataset from five aspects, and the experimental results indicate that our method has the highest compression ratio. Meanwhile, this method also has favorable performance in other aspects: compression and decompression time overhead, storage space overhead, and historical dataset training time overhead.     

Advancing New Testament interpretation through spatio‐temporal analysis: Demonstrated by case studies

This article explores, via three case studies, how spatio‐temporal analysis can advance New Testament text interpretation. Acts 2, verse 9 to 11 is the text of study. Case study 1 applies network analysis to data representing the Roman road network constrained by parameters valid for ancient times. This analysis provided new information on the background of people attending a festival in Jerusalem. Case study 2 located geographical entities from the text in a cartographic visualization and provided supportive information to compare contemporary textual resources. For the disciplines of textual and conjectural criticism (case study 3), spatio‐temporal analysis opens a new window to study what would be the most probable variant of the original text. The case study puts emendations that have been proposed over centuries in a 3D spatial context and provides in this way a sophisticated tool to relate different alternative variants of a specific text. From the case studies, it can be concluded that spatializing, visualizing, and spatially analyzing geographical concepts from the texts in Acts 2 contributes to the field of New Testament interpretation. Further work will elaborate on the findings.     

Cumulative spatial impact layers: A novel multivariate spatio‐temporal analytical summarization tool

Scientific inquiry often requires analysis of multiple spatio‐temporal datasets, ranging in type and size, using complex multi‐step processes demanding an understanding of GIS theory and software. Cumulative spatial impact layers (CSIL) is a GIS‐based tool that summarizes spatio‐temporal datasets based on overlapping features and attributes. Leveraging a recursive quadtree method, and applying multiple additive frameworks, the CSIL tool allows users to analyze raster and vector datasets by calculating data, record, or attribute density. Providing an efficient and robust method for summarizing disparate, multi‐format, multi‐source geospatial data, CSIL addresses the need for a new integration approach and resulting geospatial product. The built‐in flexibility of the CSIL tool allows users to answer a range of spatially driven questions. Example applications are provided in this article to illustrate the versatility and variety of uses for this CSIL tool and method. Use cases include addressing regulatory decision‐making needs, economic modeling, and resource management. Performance reviews for each use case are also presented, demonstrating how CSIL provides a more efficient and robust approach to assess a range of multivariate spatial data for a variety of uses.     

基于生成对抗网络的建筑物损毁检测EI北大核心CSCD

建筑物作为自然灾害中最受影响的承灾体之一,其损毁信息的准确提取对灾后应急救援具有十分重要的意义。本文借鉴多模态的思想,提出了一种自动检测损毁建筑物的recursive-generative adversarial networks(RS-GAN)方法,将损毁建筑物检测分为灾前建筑物识别和灾后损毁建筑物检测两个任务,且分别在两个GAN分支中完成。RS-GAN加入联合损失函数将两个GAN分支进行连接,充分利用两个任务之间的潜在互利性提升检测效果。RS-GAN利用第1条GAN分支识别建筑物灾前形状与位置,并将识别结果作为第2条GAN分支的输入进行损毁建筑物检测任务,从而使检测结果具有更清晰的轮廓。该方法为端到端模型,在不需要过多的人工干预情形下,实现了损毁建筑物的自动检测。为了验证RS-GAN模型的效果,在圣罗莎和密苏里两个数据集上进行了测试。试验结果表明,RS-GAN方法拥有更好的检测性能,在圣罗莎数据集上的总体精度和平均精度分别达到了0.90和0.86。     

Out‐of‐Core GPU‐based Change Detection in Massive 3D Point Clouds

If sites, cities, and landscapes are captured at different points in time using technology such as LiDAR, large collections of 3D point clouds result. Their efficient storage, processing, analysis, and presentation constitute a challenging task because of limited computation, memory, and time resources. In this work, we present an approach to detect changes in massive 3D point clouds based on an out‐of‐core spatial data structure that is designed to store data acquired at different points in time and to efficiently attribute 3D points with distance information. Based on this data structure, we present and evaluate different processing schemes optimized for performing the calculation on the CPU and GPU. In addition, we present a point‐based rendering technique adapted for attributed 3D point clouds, to enable effective out‐of‐core real‐time visualization of the computation results. Our approach enables conclusions to be drawn about temporal changes in large highly accurate 3D geodata sets of a captured area at reasonable preprocessing and rendering times. We evaluate our approach with two data sets from different points in time for the urban area of a city, describe its characteristics, and report on applications.     

Analyzing relationship between user‐generated content and local visual information with augmented reality‐based location‐based social networks

Location‐based social networks (LBSNs) have become an important source of spatial data for geographers and GIScientists to acquire knowledge of human–place interactions. A number of studies have used geotagged data from LBSNs to investigate how user‐generated content (UGC) can be affected by or correlated with the external environment. However, local visual information at the micro‐level, such as brightness, colorfulness, or particular objects/events in the surrounding environment, is usually not captured and thus becomes a missing component in LBSN analysis. To provide a solution to this issue, we argue in this study that the integration of augmented reality (AR) and LBSNs proves to be a promising avenue. In this first empirical study on AR‐based LBSNs, we propose a methodological framework to extract and analyze data from AR‐based LBSNs and demonstrate the framework via a case study with WallaMe. Our findings bolster existing psychological findings on the color–mood relationship and display intriguing geographic patterns of the influence of local visual information on UGC in social media.     

A high accuracy surface modeling method based on GPU accelerated multi‐grid method

A high accuracy surface modeling method (HASM) has been developed to provide a solution to many surface modeling problems such as DEM construction, surface estimation and spatial prediction. Although HASM is able to model surfaces with a higher accuracy, its low computing speed limits its popularity in constructing large scale surfaces. Hence, the research described in this article aims to improve the computing efficiency of HASM with a graphic processor unit (GPU) accelerated multi‐grid method (HASM‐GMG). HASM‐GMG was tested with two types of surfaces: a Gauss synthetic surface and a real‐world example. Results indicate that HASM‐GMG can gain significant speedups compared with CPU‐based HASM without acceleration on GPU. Moreover, both the accuracy and speed of HASM‐GMG are superior to the classical interpolation methods including Kriging, Spline and IDW.     

Mapping areas of asynchronous‐temporal interaction in animal‐telemetry data

Animal interactions are a crucial aspect of behavioral ecology that affect mating, territorial behavior, resource use, and disease spread. Commonly, animals will interact because of shared resources. Recent methods have used time geography to map landscape areas where interactions were possible. However, such methods do not identify areas of less direct interaction, like through smell or sight. These indirect or asynchronous interactions are also a crucial aspect of animal behavioral ecology and affect group behaviors such as leading/following hierarchies and joint resource use. Asynchronous interactions are difficult to map because they can occur in a synchronous space at asynchronous times, as well as in asynchronous spaces at a synchronous time. Here, we present a method termed the temporally asynchronous‐joint potential path area (ta‐jPPA) that maps areas of potential temporally asynchronous–spatially synchronous interactions. We used simulated data to statistically test ta‐jPPA and empirical data to demonstrate how ta‐jPPA can find patterns in habitat use.     

An enhanced approach for surface flow routing over drainage‐constrained triangulated irregular networks

The accuracy and efficiency of the simulations in distributed hydrological models must depend on the proper estimation of flow directions and paths. Numerous studies have been carried out to delineate the drainage patterns based on gridded digital elevation models (DEMs). The triangulated irregular network (TIN) has been increasingly applied in hydrological applications due to the advantages of high storage efficiency and multi‐scale adaptive performance. Much of the previous literature focuses mainly on filling the depressions on gridded DEMs rather than treating the special cases in TIN structures, which has hampered its applications to hydrological models. This study proposes a triangulation‐based solution for the removal of flat areas and pits to enhance the simulation of flow routing on triangulated facet networks. Based on the drainage‐constrained TIN generated from only a gridded DEM by the compound point extraction (CPE) method, the inconsistent situations including flat triangles, V‐shape flat edges and sink nodes are respectively identified and rectified. The optimization algorithm is an iterative process of TIN reconstruction, in which the flat areas are generalized into their center points and the pits are rectified by embedding break lines. To verify the proposed algorithm and investigate the potential for flow routing, flow paths of steepest descent are derived by the vector‐based tracking algorithm based on the optimized TIN. A case study of TIN optimization and flow path tracking was performed on a real‐world DEM. The outcomes indicate that the proposed approach can effectively solve the problem of inconsistencies without a significant loss in accuracy of the terrain model.     

A reliable traffic prediction approach for bike‐sharing system by exploiting rich information with temporal link prediction strategy

Bike‐sharing systems have been widely used in major cities across the world. As bike borrowing and return at different stations in different periods are not balanced, the bikes in a bike‐sharing system need to be redistributed frequently to rebalance the system. Therefore, traffic flow forecasting of the bike‐sharing system is an important issue, as this is conducive to achieving rebalancing of the bike system. In this article, we present a new traffic flow prediction approach based on the temporal links in dynamic traffic flow networks. A station clustering algorithm is first introduced to cluster stations into groups. A temporal link prediction method based on the dynamic traffic flow network method (STW+M) is then proposed to predict the traffic flow between stations. In our method, the non‐negative tensor decomposition and time‐series analysis model capture the rich information (temporal variabilities, spatial characteristics, and weather information) of the across‐clusters transition. Then, a temporal link prediction strategy is used to forecast potential links and weights in the traffic flow network by investigating both the network structure and the results of tensor computations. In order to assess the methods proposed in this article, we have used the data of bike‐sharing systems in New York and Washington, DC to conduct bike traffic prediction and the experimental results have shown that our method produces the lowest root mean square error (RMSE) and mean square error (MSE). Compared to four prediction methods from the literature, our RMSE and MSE of the two datasets have been lowered by an average of 2.55 (Washington, DC) and 2.41 (New York) and 3.35 (Washington, DC) and 2.96 (New York), respectively. The results show that the proposed approach improves predictions of traffic flow.     

Geometric quality assessment of trajectory‐generated VGI road networks based on the symmetric arc similarity

As large amounts of trajectories from a wide variety of Volunteered Geographic Information (referred to as VGI) contributors pour into the spatial database, the geometric qualities of the VGI road networks generated from these trajectories are different from the ground truth road dataset and so need to be differently assessed. To address this issue, an assessment approach based on symmetric arc similarity is proposed, and the geometric quality of a VGI road network is assessed by its conformity with the corresponding ground truth road network, the results being visualized as hierarchical thematic maps. To compute the conformity, the geometric similarity between the VGI road arc and the corresponding ground truth road arc, which is selected by the adaptive searching distance, is measured based on the symmetric arc similarity method; the geometric quality is assessed based on an assessment matrix. Also, the symmetric arc similarity method is independent of directions and with a feature of shift‐independence, which is applicable to assess the geometric qualities of different VGI road networks and makes the assessment result consistent with the actual situation of the real world. The robustness and scalability of the approach are examined using VGI road networks from different sources.     

Morphology‐based modeling of aggregation effect on the patch area size for GlobeLand30 data

Currently, research on the scale effect mainly concentrates on revealing and describing the phenomenon; little work has been done to model the scaling of analysis indicators along with the scale change of the data. Such models can help explain and estimate the scale effect and are thus of great interest. As patch morphology significantly influences patch evolution over data scale transformation, this study proposes a model that applies patch morphology metrics to explain and estimate the change in the patch and category area ratio in the upscaling of raster categorical data. In the experimental evaluation, area scaling of GlobeLand30 data under majority aggregation is studied intensively. Two metrics—the ratio of the patch area after aggregation to its original area and the ratio of the category area after aggregation to its original area—are used in this evaluation. The experimental results indicate that this model can adequately describe the pattern of the patch area change and precisely estimate the category area change arising from the resolution change. It is also demonstrated that the spatial distribution of patches has much less influence on the change in the category areas compared with the patch morphology. Additionally, the modeling approach in this study may also be adopted to investigate the scaling of other landscape metrics.     

“Laser Discoverers” – Web‐based User‐generated Content in Heritage Detection in Poland

This article presents the results and potential of using volunteered geographic information (VGI) in heritage detection. Research was completed under the project entitled “Laser Discoverers – non‐invasive examination and documentation of archeological and historical objects in the ?wi?tokrzyskie Voivodeship”, carried out as a part of the Ministry of Science and Higher Education program entitled “The Paths of Copernicus”. Within the project, strong emphasis was placed on promotional and awareness‐raising activities, to involve as many voluntary users as possible. Project participants had at their disposal a web application, which provided access to a digital terrain model (DTM) where they identified possible heritage objects. All samples of data were additionally available in eight variants of sunshine, based on the simulation of sunlight from eight directions and at a constant angle. In total, 5,989 elementary areas with dimensions of 100 × 100 m were used for the project. After conducting a field inventory, Internet users together with specialists were able to recognize several thousands of potential archaeological and historic objects. During the project, approximately 10% of those features were verified through non‐invasive (field survey) work, with 75% success.     

Extracting urban functional regions from points of interest and human activities on location‐based social networks

Data about points of interest (POI) have been widely used in studying urban land use types and for sensing human behavior. However, it is difficult to quantify the correct mix or the spatial relations among different POI types indicative of specific urban functions. In this research, we develop a statistical framework to help discover semantically meaningful topics and functional regions based on the co‐occurrence patterns of POI types. The framework applies the latent Dirichlet allocation (LDA) topic modeling technique and incorporates user check‐in activities on location‐based social networks. Using a large corpus of about 100,000 Foursquare venues and user check‐in behavior in the 10 most populated urban areas of the US, we demonstrate the effectiveness of our proposed methodology by identifying distinctive types of latent topics and, further, by extracting urban functional regions using K‐means clustering and Delaunay triangulation spatial constraints clustering. We show that a region can support multiple functions but with different probabilities, while the same type of functional region can span multiple geographically non‐adjacent locations. Since each region can be modeled as a vector consisting of multinomial topic distributions, similar regions with regard to their thematic topic signatures can be identified. Compared with remote sensing images which mainly uncover the physical landscape of urban environments, our popularity‐based POI topic modeling approach can be seen as a complementary social sensing view on urban space based on human activities.     

A heuristic‐based approach to mitigating positional errors in patrol data for species distribution modeling

Species distribution modeling (SDM) at fine spatial resolutions requires species occurrence data of high positional accuracy to achieve good model performance. However, wildlife occurrences recorded by patrols in ranger‐based monitoring programs suffer from positional errors, because recorded locations represent the positions of the ranger and differ from the actual occurrence locations of wildlife (hereinafter referred to as positional errors in patrol data). This study presented an evaluation of the impact of such positional errors in patrol data on SDM and developed a heuristic‐based approach to mitigating the positional errors. The approach derives probable wildlife occurrence locations from ranger positions, utilizing heuristics based on species preferred habitat and the observer's field of view. The evaluations were conducted through a case study of SDM using patrol records of the black‐and‐white snub‐nosed monkey (Rhinopithecus bieti) in Yunnan, China. The performance of the approach was also compared against alternative sampling methods. The results showed that the positional errors in R. bieti patrol data had an adverse effect on SDM performance, and that the proposed approach can effectively mitigate the impact of the positional errors to greatly improve SDM performance.     

基于字袋模型的交通标志识别方法研究

交通标志识别是智能交通中一个重要的研究领域,实现辅助驾驶和自动导航等应用正获得越来越多的关注.本文提出一种自然场景下交通标志的识别方法:提取标志的局部特征以提高获取效率也使其较少受到遮挡的影响,通过字袋模型量化后获得标志的本征特征,基于该本征特征训练支持向量机,最后使用得到的判别分类器进行交通标志的分类.实验结果表明,...     

Enriching the GIScience research agenda: Fusing augmented reality and location‐based social networks

Augmented reality (AR) overlays real‐world views or scenes with virtual, computer‐generated objects that appear to visually coexist in the same space. Location‐based social networks (LBSNs) are platforms for individuals to be connected through the interdependency derived from their physical locations and their location‐tagged social media content. Current research and development in both areas focuses on integrating mobile‐based AR and LBSNs. Several applications (e.g., Sekai Camera and Wallame) have been developed and commercialized successfully. However, little research has been done on the potential impacts and successful evaluation methods of AR‐integrated LBSNs in the GIScience field. To close this gap, the article outlines the impacts and benefits of AR‐integrated LBSNs and highlights the importance of LBSNs in GIScience research. Based on the status quo of AR‐integrated LBSNs, this article discusses—from theoretical and application‐oriented perspectives—how AR‐integrated LBSNs could enrich the GIScience research agenda in three aspects: data conflation, platial GIS, and multimedia storytelling. The article concludes with guidelines on visualization, functionality, and ethics that aim to help users develop and evaluate AR‐integrated LBSNs.