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ISPRS Int. J. Geo-Inf., Volume 7, Issue 1 (January 2018)

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Cover Story (view full-size image) Are we in Boswash yet? The extent of urban areas is commonly defined through administrative [...] Read more.
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Open AccessArticle Traffic Command Gesture Recognition for Virtual Urban Scenes Based on a Spatiotemporal Convolution Neural Network
ISPRS Int. J. Geo-Inf. 2018, 7(1), 37; https://doi.org/10.3390/ijgi7010037
Received: 11 November 2017 / Revised: 21 December 2017 / Accepted: 16 January 2018 / Published: 22 January 2018
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Abstract
Intelligent recognition of traffic police command gestures increases authenticity and interactivity in virtual urban scenes. To actualize real-time traffic gesture recognition, a novel spatiotemporal convolution neural network (ST-CNN) model is presented. We utilized Kinect 2.0 to construct a traffic police command gesture skeleton
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Intelligent recognition of traffic police command gestures increases authenticity and interactivity in virtual urban scenes. To actualize real-time traffic gesture recognition, a novel spatiotemporal convolution neural network (ST-CNN) model is presented. We utilized Kinect 2.0 to construct a traffic police command gesture skeleton (TPCGS) dataset collected from 10 volunteers. Subsequently, convolution operations on the locational change of each skeletal point were performed to extract temporal features, analyze the relative positions of skeletal points, and extract spatial features. After temporal and spatial features based on the three-dimensional positional information of traffic police skeleton points were extracted, the ST-CNN model classified positional information into eight types of Chinese traffic police gestures. The test accuracy of the ST-CNN model was 96.67%. In addition, a virtual urban traffic scene in which real-time command tests were carried out was set up, and a real-time test accuracy rate of 93.0% was achieved. The proposed ST-CNN model ensured a high level of accuracy and robustness. The ST-CNN model recognized traffic command gestures, and such recognition was found to control vehicles in virtual traffic environments, which enriches the interactive mode of the virtual city scene. Traffic command gesture recognition contributes to smart city construction. Full article
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Open AccessArticle Framework for Virtual Cognitive Experiment in Virtual Geographic Environments
ISPRS Int. J. Geo-Inf. 2018, 7(1), 36; https://doi.org/10.3390/ijgi7010036
Received: 31 October 2017 / Revised: 29 December 2017 / Accepted: 17 January 2018 / Published: 22 January 2018
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Abstract
Virtual Geographic Environment Cognition is the attempt to understand the human cognition of surface features, geographic processes, and human behaviour, as well as their relationships in the real world. From the perspective of human cognition behaviour analysis and simulation, previous work in Virtual
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Virtual Geographic Environment Cognition is the attempt to understand the human cognition of surface features, geographic processes, and human behaviour, as well as their relationships in the real world. From the perspective of human cognition behaviour analysis and simulation, previous work in Virtual Geographic Environments (VGEs) has focused mostly on representing and simulating the real world to create an ‘interpretive’ virtual world and improve an individual’s active cognition. In terms of reactive cognition, building a user ‘evaluative’ environment in a complex virtual experiment is a necessary yet challenging task. This paper discusses the outlook of VGEs and proposes a framework for virtual cognitive experiments. The framework not only employs immersive virtual environment technology to create a realistic virtual world but also involves a responsive mechanism to record the user’s cognitive activities during the experiment. Based on the framework, this paper presents two potential implementation methods: first, training a deep learning model with several hundred thousand street view images scored by online volunteers, with further analysis of which visual factors produce a sense of safety for the individual, and second, creating an immersive virtual environment and Electroencephalogram (EEG)-based experimental paradigm to both record and analyse the brain activity of a user and explore what type of virtual environment is more suitable and comfortable. Finally, we present some preliminary findings based on the first method. Full article
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Open AccessArticle Short-Range Prediction of the Zone of Moving Vehicles in Arterial Networks
ISPRS Int. J. Geo-Inf. 2018, 7(1), 35; https://doi.org/10.3390/ijgi7010035
Received: 29 October 2017 / Revised: 15 January 2018 / Accepted: 18 January 2018 / Published: 22 January 2018
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Abstract
In many moving object databases, future locations of vehicles in arterial networks are predicted. While most of studies apply the frequent behavior of historical trajectories or vehicles’ recent kinematics as the basis of predictions, consideration of the dynamics of the intersections is mostly
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In many moving object databases, future locations of vehicles in arterial networks are predicted. While most of studies apply the frequent behavior of historical trajectories or vehicles’ recent kinematics as the basis of predictions, consideration of the dynamics of the intersections is mostly neglected. Signalized intersections make vehicles experience different delays, which vary from zero to some minutes based on the traffic state at intersections. In the absence of traffic signal information (red and green times of traffic signal phases, the queue lengths, approaching traffic volume, turning volumes to each intersection leg, etc.), the experienced delays in traffic signals are random variables. In this paper, we model the probability distribution function (PDF) and cumulative distribution function (CDF) of the delay for any point in the arterial networks based on a spatiotemporal model of the queue at the intersection. The probability of the presence of a vehicle in a zone is determined based on the modeled probability function of the delay. A comparison between the results of the proposed method and a well-known kinematic-based method indicates a significant improvement in the precisions of the predictions. Full article
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Open AccessArticle Mapping Lithologic Components of Ophiolitic Mélanges Based on ASTER Spectral Analysis: A Case Study from the Bangong-Nujiang Suture Zone (Tibet, China)
ISPRS Int. J. Geo-Inf. 2018, 7(1), 34; https://doi.org/10.3390/ijgi7010034
Received: 6 December 2017 / Revised: 7 January 2018 / Accepted: 10 January 2018 / Published: 22 January 2018
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Abstract
ASTER (Advanced Spaceborne Thermal Emission and Reflection) satellite imagery is useful in assisting lithologic mapping and, however, its effectiveness is yet to be evaluated for lithologic complex such as tectonic mélange. The Mugagangri Group (MG), the signature unit of the Bangong-Nujiang suture zone
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ASTER (Advanced Spaceborne Thermal Emission and Reflection) satellite imagery is useful in assisting lithologic mapping and, however, its effectiveness is yet to be evaluated for lithologic complex such as tectonic mélange. The Mugagangri Group (MG), the signature unit of the Bangong-Nujiang suture zone (BNSZ), Tibet and consisting of ophiolitic mélanges, was previously mapped as a single unit due to its poorly-described internal structures and an informative map with refined lithologic subdivision is needed for future petrologic and tectonic studies. In this paper, based on a combination of field work and ASTER data analysis, the MG is mapped as five subunits according to our newly-proposed lithologic subdivision scheme. In particular, we apply a data-processing sequence to first analyze the TIR band ratios to reveal approximate distribution of carbonates and silicate-dominated lithologies and then the VNIR/SWIR band ratios and false color images to differentiate the lithologic units and delineate their boundaries. The generalized procedures of ASTER data processing and lithologic mapping are applicable for future studies in not only the BNSZ but also other Tibetan ranges. Moreover, the mapping result is consistent with that the MG represents an accretionary complex accreted to the south Qiangtang margin as a result of northward-subduction of the Bangong-Nujiang oceanic crust. Full article
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Open AccessArticle Real-Time Location-Based Rendering of Urban Underground Pipelines
ISPRS Int. J. Geo-Inf. 2018, 7(1), 32; https://doi.org/10.3390/ijgi7010032
Received: 29 October 2017 / Revised: 10 January 2018 / Accepted: 18 January 2018 / Published: 21 January 2018
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Abstract
The concealment and complex spatial relationships of urban underground pipelines present challenges in managing them. Recently, augmented reality (AR) has been a hot topic around the world, because it can enhance our perception of reality by overlaying information about the environment and its
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The concealment and complex spatial relationships of urban underground pipelines present challenges in managing them. Recently, augmented reality (AR) has been a hot topic around the world, because it can enhance our perception of reality by overlaying information about the environment and its objects onto the real world. Using AR, underground pipelines can be displayed accurately, intuitively, and in real time. We analyzed the characteristics of AR and their application in underground pipeline management. We mainly focused on the AR pipeline rendering procedure based on the BeiDou Navigation Satellite System (BDS) and simultaneous localization and mapping (SLAM) technology. First, in aiming to improve the spatial accuracy of pipeline rendering, we used differential corrections received from the Ground-Based Augmentation System to compute the precise coordinates of users in real time, which helped us accurately retrieve and draw pipelines near the users, and by scene recognition the accuracy can be further improved. Second, in terms of pipeline rendering, we used Visual-Inertial Odometry (VIO) to track the rendered objects and made some improvements to visual effects, which can provide steady dynamic tracking of pipelines even in relatively markerless environments and outdoors. Finally, we used the occlusion method based on real-time 3D reconstruction to realistically express the immersion effect of underground pipelines. We compared our methods to the existing methods and concluded that the method proposed in this research improves the spatial accuracy of pipeline rendering and the portability of the equipment. Moreover, the updating of our rendering procedure corresponded with the moving of the user’s location, thus we achieved a dynamic rendering of pipelines in the real environment. Full article
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Open AccessArticle Uncertainty in Upscaling In Situ Soil Moisture Observations to Multiscale Pixel Estimations with Kriging at the Field Level
ISPRS Int. J. Geo-Inf. 2018, 7(1), 33; https://doi.org/10.3390/ijgi7010033
Received: 3 December 2017 / Revised: 13 January 2018 / Accepted: 18 January 2018 / Published: 20 January 2018
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Abstract
Upscaling in situ soil moisture observations (ISMO) to multiscale pixel estimations with kriging is a key step in the comprehensive usage of ISMO and remote sensing (RS) soil moisture data. Scale effects occur and introduce uncertainties during upscaling processes because of spatial heterogeneity
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Upscaling in situ soil moisture observations (ISMO) to multiscale pixel estimations with kriging is a key step in the comprehensive usage of ISMO and remote sensing (RS) soil moisture data. Scale effects occur and introduce uncertainties during upscaling processes because of spatial heterogeneity and the kriging method. A nested hierarchical scale series was established at the field level, and upscaled estimations at each scale were obtained by block kriging (BK) to illustrate multiscale ISMO upscaling processes. Those uncertainties were described with the results of comparison analysis against RS data, statistical analysis, and spatial trend surface analysis on multiscale estimations and were explained from the spatial heterogeneity perspective with a semivariogram analysis on ISMO. The results show that uncertainties exist and vary in multiscale upscaling processes, and the range of the empirical semivariogram could indicate scale effects. When the target scale is shorter than the range, BK maintains similar scale effects and global trends during upscaling processes, and the direct pixel estimation by BK is relatively close to the average of nested pixel estimations. This has great implications for understanding the kriging method in similar works. Full article
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Open AccessArticle A Hybrid Approach Combining the Multi-Temporal Scale Spatio-Temporal Network with the Continuous Triangular Model for Exploring Dynamic Interactions in Movement Data: A Case Study of Football
ISPRS Int. J. Geo-Inf. 2018, 7(1), 31; https://doi.org/10.3390/ijgi7010031
Received: 13 November 2017 / Revised: 17 December 2017 / Accepted: 18 January 2018 / Published: 20 January 2018
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Abstract
Benefiting from recent advantages in location-aware technologies, movement data are becoming ubiquitous. Hence, numerous research topics with respect to movement data have been undertaken. Yet, the research of dynamic interactions in movement data is still in its infancy. In this paper, we propose
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Benefiting from recent advantages in location-aware technologies, movement data are becoming ubiquitous. Hence, numerous research topics with respect to movement data have been undertaken. Yet, the research of dynamic interactions in movement data is still in its infancy. In this paper, we propose a hybrid approach combining the multi-temporal scale spatio-temporal network (MTSSTN) and the continuous triangular model (CTM) for exploring dynamic interactions in movement data. The approach mainly includes four steps: first, the relative trajectory calculus (RTC) is used to derive three types of interaction patterns; second, for each interaction pattern, a corresponding MTSSTN is generated; third, for each MTSSTN, the interaction intensity measures and three centrality measures (i.e., degree, betweenness and closeness) are calculated; finally, the results are visualized at multiple temporal scales using the CTM and analyzed based on the generated CTM diagrams. Based on the proposed approach, three distinctive aims can be achieved for each interaction pattern at multiple temporal scales: (1) exploring the interaction intensities between any two individuals; (2) exploring the interaction intensities among multiple individuals, and (3) exploring the importance of each individual and identifying the most important individuals. The movement data obtained from a real football match are used as a case study to validate the effectiveness of the proposed approach. The results demonstrate that the proposed approach is useful in exploring dynamic interactions in football movement data and discovering insightful information. Full article
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Open AccessArticle Comparison of Split Window Algorithms for Retrieving Measurements of Sea Surface Temperature from MODIS Data in Near-Land Coastal Waters
ISPRS Int. J. Geo-Inf. 2018, 7(1), 30; https://doi.org/10.3390/ijgi7010030
Received: 10 November 2017 / Revised: 9 January 2018 / Accepted: 12 January 2018 / Published: 18 January 2018
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Abstract
Split window (SW) methods, which have been successfully used to retrieve measurements of land surface temperature (LST) and sea surface temperature (SST) from MODIS images, were exploited to evaluate the SST data of three sections of Italian coastal waters. For this purpose, sea
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Split window (SW) methods, which have been successfully used to retrieve measurements of land surface temperature (LST) and sea surface temperature (SST) from MODIS images, were exploited to evaluate the SST data of three sections of Italian coastal waters. For this purpose, sea surface emissivity (SSE) values were estimated by adding the effects of salinity and total suspended particulate matter (SPM) concentrations, sea surface wind speed, and zenith observation angle. The total column atmospheric water vapor contents were retrieved from MODIS data. SST data retrieved from MODIS images using these algorithms were compared with SSTskin measurements evaluated from in situ data. The comparison showed that the algorithms for retrieving LST measurements minimized the error in SST data in near-land coastal waters with respect to the algorithms for retrieving SST measurements: a method for retrieving LST measurements highlighted the smallest root-mean-square deviation (RMSD) value (0.48 K) and values of maximum bias and standard deviation (σ) equal to −3.45 K and 0.41 K; the current operation algorithm for retrieving LST data highlighted the smallest values of maximum bias and σ (−1.37 K and 0.35 K) and an RMSD value of 0.66 K; and the current operation algorithm for retrieving global measurements of SST showed values of RMSD, maximum bias, and σ equal to 0.68 K, −1.90 K, and 0.40 K, respectively. Full article
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Open AccessArticle Assessment of Tangible Direct Flood Damage Using a Spatial Analysis Approach under the Effects of Climate Change: Case Study in an Urban Watershed in Hanoi, Vietnam
ISPRS Int. J. Geo-Inf. 2018, 7(1), 29; https://doi.org/10.3390/ijgi7010029
Received: 23 November 2017 / Revised: 4 January 2018 / Accepted: 10 January 2018 / Published: 16 January 2018
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Abstract
Due to climate change, the frequency and intensity of Hydro-Meteorological disasters, such as floods, are increasing. Therefore, the main purpose of this work is to assess tangible future flood damage in the urban watershed of the To Lich River in Hanoi, Vietnam. An
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Due to climate change, the frequency and intensity of Hydro-Meteorological disasters, such as floods, are increasing. Therefore, the main purpose of this work is to assess tangible future flood damage in the urban watershed of the To Lich River in Hanoi, Vietnam. An approach based on spatial analysis, which requires the integration of several types of data related to flood characteristics that include depth, in particular, land-use classes, property values, and damage rates, is applied for the analysis. To simulate the future scenarios of flooding, the effects of climate change and land-use changes are estimated for 2030. Additionally, two scenarios based on the implementation of flood control measures are analyzed to demonstrate the effect of adaptation strategies. The findings show that climate change combined with the expansion of built-up areas increases the vulnerability of urban areas to flooding and economic damage. The results also reveal that the impacts of climate change will increase the total damage from floods by 26%. However, appropriate flood mitigation will be helpful in reducing the impacts of losses from floods by approximately 8% with the restoration of lakes and by approximately 29% with the implementation of water-sensitive urban design (WSUD). This study will be useful in helping to identify and map flood-prone areas at local and regional scales, which can lead to the detection and prioritization of exposed areas for appropriate countermeasures in a timely manner. In addition, the quantification of flood damage can be an important indicator to enhance the awareness of local decision-makers on improving the efficiency of regional flood risk reduction strategies. Full article
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Open AccessArticle A Knowledge Base for Automatic Feature Recognition from Point Clouds in an Urban Scene
ISPRS Int. J. Geo-Inf. 2018, 7(1), 28; https://doi.org/10.3390/ijgi7010028
Received: 4 October 2017 / Revised: 29 December 2017 / Accepted: 11 January 2018 / Published: 16 January 2018
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Abstract
LiDAR technology can provide very detailed and highly accurate geospatial information on an urban scene for the creation of Virtual Geographic Environments (VGEs) for different applications. However, automatic 3D modeling and feature recognition from LiDAR point clouds are very complex tasks. This becomes
[...] Read more.
LiDAR technology can provide very detailed and highly accurate geospatial information on an urban scene for the creation of Virtual Geographic Environments (VGEs) for different applications. However, automatic 3D modeling and feature recognition from LiDAR point clouds are very complex tasks. This becomes even more complex when the data is incomplete (occlusion problem) or uncertain. In this paper, we propose to build a knowledge base comprising of ontology and semantic rules aiming at automatic feature recognition from point clouds in support of 3D modeling. First, several modules for ontology are defined from different perspectives to describe an urban scene. For instance, the spatial relations module allows the formalized representation of possible topological relations extracted from point clouds. Then, a knowledge base is proposed that contains different concepts, their properties and their relations, together with constraints and semantic rules. Then, instances and their specific relations form an urban scene and are added to the knowledge base as facts. Based on the knowledge and semantic rules, a reasoning process is carried out to extract semantic features of the objects and their components in the urban scene. Finally, several experiments are presented to show the validity of our approach to recognize different semantic features of buildings from LiDAR point clouds. Full article
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Open AccessArticle Developing an Agent-Based Simulation System for Post-Earthquake Operations in Uncertainty Conditions: A Proposed Method for Collaboration among Agents
ISPRS Int. J. Geo-Inf. 2018, 7(1), 27; https://doi.org/10.3390/ijgi7010027
Received: 19 October 2017 / Revised: 28 December 2017 / Accepted: 11 January 2018 / Published: 15 January 2018
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Abstract
Agent-based modeling is a promising approach for developing simulation tools for natural hazards in different areas, such as during urban search and rescue (USAR) operations. The present study aimed to develop a dynamic agent-based simulation model in post-earthquake USAR operations using geospatial information
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Agent-based modeling is a promising approach for developing simulation tools for natural hazards in different areas, such as during urban search and rescue (USAR) operations. The present study aimed to develop a dynamic agent-based simulation model in post-earthquake USAR operations using geospatial information system and multi agent systems (GIS and MASs, respectively). We also propose an approach for dynamic task allocation and establishing collaboration among agents based on contract net protocol (CNP) and interval-based Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methods, which consider uncertainty in natural hazards information during agents’ decision-making. The decision-making weights were calculated by analytic hierarchy process (AHP). In order to implement the system, earthquake environment was simulated and the damage of the buildings and a number of injuries were calculated in Tehran’s District 3: 23%, 37%, 24% and 16% of buildings were in slight, moderate, extensive and completely vulnerable classes, respectively. The number of injured persons was calculated to be 17,238. Numerical results in 27 scenarios showed that the proposed method is more accurate than the CNP method in the terms of USAR operational time (at least 13% decrease) and the number of human fatalities (at least 9% decrease). In interval uncertainty analysis of our proposed simulated system, the lower and upper bounds of uncertain responses are evaluated. The overall results showed that considering uncertainty in task allocation can be a highly advantageous in the disaster environment. Such systems can be used to manage and prepare for natural hazards. Full article
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Open AccessArticle Approach to Accelerating Dissolved Vector Buffer Generation in Distributed In-Memory Cluster Architecture
ISPRS Int. J. Geo-Inf. 2018, 7(1), 26; https://doi.org/10.3390/ijgi7010026
Received: 6 November 2017 / Revised: 9 January 2018 / Accepted: 11 January 2018 / Published: 15 January 2018
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Abstract
The buffer generation algorithm is a fundamental function in GIS, identifying areas of a given distance surrounding geographic features. Past research largely focused on buffer generation algorithms generated in a stand-alone environment. Moreover, dissolved buffer generation is data- and computing-intensive. In this scenario,
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The buffer generation algorithm is a fundamental function in GIS, identifying areas of a given distance surrounding geographic features. Past research largely focused on buffer generation algorithms generated in a stand-alone environment. Moreover, dissolved buffer generation is data- and computing-intensive. In this scenario, the improvement in the stand-alone environment is limited when considering large-scale mass vector data. Nevertheless, recent parallel dissolved vector buffer algorithms suffer from scalability problems, leaving room for further optimization. At present, the prevailing in-memory cluster-computing framework—Spark—provides promising efficiency for computing-intensive analysis; however, it has seldom been researched for buffer analysis. On this basis, we propose a cluster-computing-oriented parallel dissolved vector buffer generating algorithm, called the HPBM, that contains a Hilbert-space-filling-curve-based data partition method, a data skew and cross-boundary objects processing strategy, and a depth-given tree-like merging method. Experiments are conducted in both stand-alone and cluster environments using real-world vector data that include points and roads. Compared with some existing parallel buffer algorithms, as well as various popular GIS software, the HPBM achieves a performance gain of more than 50%. Full article
(This article belongs to the Special Issue Geospatial Big Data and Urban Studies)
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Open AccessArticle Detecting Anomalous Trajectories and Behavior Patterns Using Hierarchical Clustering from Taxi GPS Data
ISPRS Int. J. Geo-Inf. 2018, 7(1), 25; https://doi.org/10.3390/ijgi7010025
Received: 3 November 2017 / Revised: 7 January 2018 / Accepted: 11 January 2018 / Published: 12 January 2018
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Abstract
Anomalous taxi trajectories are those chosen by a small number of drivers that are different from the regular choices of other drivers. These anomalous driving trajectories provide us an opportunity to extract driver or passenger behaviors and monitor adverse urban traffic events. Because
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Anomalous taxi trajectories are those chosen by a small number of drivers that are different from the regular choices of other drivers. These anomalous driving trajectories provide us an opportunity to extract driver or passenger behaviors and monitor adverse urban traffic events. Because various trajectory clustering methods have previously proven to be an effective means to analyze similarities and anomalies within taxi GPS trajectory data, we focus on the problem of detecting anomalous taxi trajectories, and we develop our trajectory clustering method based on the edit distance and hierarchical clustering. To achieve this objective, first, we obtain all the taxi trajectories crossing the same source–destination pairs from taxi trajectories and take these trajectories as clustering objects. Second, an edit distance algorithm is modified to measure the similarity of the trajectories. Then, we distinguish regular trajectories and anomalous trajectories by applying adaptive hierarchical clustering based on an optimal number of clusters. Moreover, we further analyze these anomalous trajectories and discover four anomalous behavior patterns to speculate on the cause of an anomaly based on statistical indicators of time and length. The experimental results show that the proposed method can effectively detect anomalous trajectories and can be used to infer clearly fraudulent driving routes and the occurrence of adverse traffic events. Full article
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Open AccessEditorial Acknowledgement to Reviewers of IJGI in 2017
ISPRS Int. J. Geo-Inf. 2018, 7(1), 24; https://doi.org/10.3390/ijgi7010024
Received: 12 January 2018 / Accepted: 12 January 2018 / Published: 12 January 2018
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Abstract
Peer review is an essential part in the publication process, ensuring that IJGI maintains high quality standards for its published papers. In 2017, a total of 403 papers were published in the journal.[...] Full article
Open AccessArticle Spatial Analysis of Clustering of Foreclosures in the Poorest-Quality Housing Urban Areas: Evidence from Catalan Cities
ISPRS Int. J. Geo-Inf. 2018, 7(1), 23; https://doi.org/10.3390/ijgi7010023
Received: 6 December 2017 / Revised: 10 January 2018 / Accepted: 11 January 2018 / Published: 12 January 2018
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Abstract
This paper uses data on housing stock owned by financial entities as a result of foreclosures to analyze (1) the spatial logic of Spain’s mortgage crisis in urban areas, and (2) the characteristics of the types of housing most affected by this phenomenon.
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This paper uses data on housing stock owned by financial entities as a result of foreclosures to analyze (1) the spatial logic of Spain’s mortgage crisis in urban areas, and (2) the characteristics of the types of housing most affected by this phenomenon. Nearest-Neighbor Index and Ripley’s K function analyses were applied in two Catalan cities (Tarragona and Terrassa). The results obtained show that foreclosures tend to be concentrated in the most deprived neighborhoods. The general pattern of clustering also tends to be most intense for smaller and cheaper housing. Our findings show that home foreclosures have been concentrated in only a few neighborhoods and precisely in those containing the poorest-quality housing stock. They also provide new evidence of the characteristics and spatial patterns of the housing stock accumulated by banks in Catalonia as a result of the recent wave of evictions associated with foreclosures. Full article
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