ALBERT

Description: IJGI, Vol. 7, Pages 300: A Comparative Study of Three Non-Geostatistical Methods for Optimising Digital Elevation Model Interpolation ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7080300 Authors: Serajis Salekin Jack H. Burgess Justin Morgenroth Euan G. Mason Dean F. Meason It is common to generate digital elevation models (DEMs) from aerial laser scanning (ALS) data. However, cost and lack of knowledge may preclude its use. In contrast, global navigation satellite systems (GNSS) are seldom used to collect and generate DEMs. These receivers have the potential to be considered as data sources for DEM interpolation, as they can be inexpensive, easy to use, and mobile. The data interpolation method and spatial resolution from this method needs to be optimised to create accurate DEMs. Moreover, the density of GNSS data is likely to affect DEM accuracy. This study investigates three different deterministic approaches, in combination with spatial resolution and data thinning, to determine their combined effects on DEM accuracy. Digital elevation models were interpolated, with resolutions ranging from 0.5 m to 10 m using natural neighbour (NaN), topo to raster (ANUDEM), and inverse distance weighted (IDW) methods. The GNSS data were thinned by 25% (0.389 points m−2), 50% (0.259 points m−2), and 75% (0.129 points m−2) and resulting DEMs were contrast against a DEM interpolated from unthinned data (0.519 points m−2). Digital elevation model accuracy was measured by root mean square error (RMSE) and mean absolute error (MAE). It was found that the highest resolution, 0.5 m, produced the lowest errors in resulting DEMs (RMSE = 0.428 m, MAE = 0.274 m). The ANUDEM method yielded the greatest DEM accuracy from a quantitative perspective (RMSE = 0.305 m and MAE = 0.197 m); however, NaN produced a more visually appealing surface. In all the assessments, IDW showed the lowest accuracy. Thinning the input data by 25% and even 50% had relatively little impact on DEM quality; however, accuracy decreased markedly at 75% thinning (0.129 points m−2). This study showed that, in a time where ALS is commonly used to generate DEMs, GNSS-surveyed data can be used to create accurate DEMs. This study confirmed the need for optimization to choose the appropriate interpolation method and spatial resolution in order to produce a reliable DEM.

Description: IJGI, Vol. 7, Pages 311: Processing BIM and GIS Models in Practice: Experiences and Recommendations from a GeoBIM Project in The Netherlands ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7080311 Authors: Ken Arroyo Ohori Abdoulaye Diakité Thomas Krijnen Hugo Ledoux Jantien Stoter It is widely acknowledged that the integration of BIM and GIS data is a crucial step forward for future 3D city modelling, but most of the research conducted so far has covered only the high-level and semantic aspects of GIS-BIM integration. This paper presents the results of the GeoBIM project, which tackled three integration problems focussing instead on aspects involving geometry processing: (i) the automated processing of complex architectural IFC models; (ii) the integration of existing GIS subsoil data in BIM; and (iii) the georeferencing of BIM models for their use in GIS software. All the problems have been studied using real world models and existing datasets made and used by practitioners in The Netherlands. For each problem, this paper exposes in detail the issues faced, proposed solutions, and recommendations for a more successful integration.

Description: IJGI, Vol. 7, Pages 218: A Spatiotemporal Multi-View-Based Learning Method for Short-Term Traffic Forecasting ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7060218 Authors: Shifen Cheng Feng Lu Peng Peng Sheng Wu Short-term traffic forecasting plays an important part in intelligent transportation systems. Spatiotemporal k-nearest neighbor models (ST-KNNs) have been widely adopted for short-term traffic forecasting in which spatiotemporal matrices are constructed to describe traffic conditions. The performance of the models is closely related to the spatial dependencies, the temporal dependencies, and the interaction of spatiotemporal dependencies. However, these models use distance functions and correlation coefficients to identify spatial neighbors and measure the temporal interaction by only considering the temporal closeness of traffic, which result in existing ST-KNNs that cannot fully reflect the essential features of road traffic. This study proposes an improved spatiotemporal k-nearest neighbor model for short-term traffic forecasting by utilizing a multi-view learning algorithm named MVL-STKNN that fully considers the spatiotemporal dependencies of traffic data. First, the spatial neighbors for each road segment are automatically determined using cross-correlation under different temporal dependencies. Three spatiotemporal views are built on the constructed spatiotemporal closeness, periodic, and trend matrices to represent spatially heterogeneous traffic states. Second, a spatiotemporal weighting matrix is introduced into the ST-KNN model to recognize similar traffic patterns in the three spatiotemporal views. Finally, the results of traffic pattern recognition under these three spatiotemporal views are aggregated by using a neural network algorithm to describe the interaction of spatiotemporal dependencies. Extensive experiments were conducted using real vehicular-speed datasets collected on city roads and expressways. In comparison with baseline methods, the results show that the MVL-STKNN model greatly improves short-term traffic forecasting by lowering the mean absolute percentage error between 28.24% and 46.86% for the city road dataset and, between 53.80% and 90.29%, for the expressway dataset. The results suggest that multi-view learning merits further attention for traffic-related data mining under such a dynamic and data-intensive environment, which owes to its comprehensive consideration of spatial correlation and heterogeneity as well as temporal fluctuation and regularity in road traffic.

Description: IJGI, Vol. 7, Pages 217: Deep Belief Networks Based Toponym Recognition for Chinese Text ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7060217 Authors: Shu Wang Xueying Zhang Peng Ye Mi Du In Geographical Information Systems, geo-coding is used for the task of mapping from implicitly geo-referenced data to explicitly geo-referenced coordinates. At present, an enormous amount of implicitly geo-referenced information is hidden in unstructured text, e.g., Wikipedia, social data and news. Toponym recognition is the foundation of mining this useful geo-referenced information by identifying words as toponyms in text. In this paper, we propose an adapted toponym recognition approach based on deep belief network (DBN) by exploring two key issues: word representation and model interpretation. A Skip-Gram model is used in the word representation process to represent words with contextual information that are ignored by current word representation models. We then determine the core hyper-parameters of the DBN model by illustrating the relationship between the performance and the hyper-parameters, e.g., vector dimensionality, DBN structures and probability thresholds. The experiments evaluate the performance of the Skip-Gram model implemented by the Word2Vec open-source tool, determine stable hyper-parameters and compare our approach with a conditional random field (CRF) based approach. The experimental results show that the DBN model outperforms the CRF model with smaller corpus. When the corpus size is large enough, their statistical metrics become approaching. However, their recognition results express differences and complementarity on different kinds of toponyms. More importantly, combining their results can directly improve the performance of toponym recognition relative to their individual performances. It seems that the scale of the corpus has an obvious effect on the performance of toponym recognition. Generally, there is no adequate tagged corpus on specific toponym recognition tasks, especially in the era of Big Data. In conclusion, we believe that the DBN-based approach is a promising and powerful method to extract geo-referenced information from text in the future.

Description: IJGI, Vol. 7, Pages 222: A Citizen Science Approach for Collecting Toponyms ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7060222 Authors: Aji Putra Perdana Frank O. Ostermann The emerging trends and technologies of surveying and mapping potentially enable local experts to contribute and share their local geographical knowledge of place names (toponyms). We can see the increasing numbers of toponyms in digital platforms, such as OpenStreetMap, Facebook Place Editor, Swarm Foursquare, and Google Local Guide. On the other hand, government agencies keep working to produce concise and complete gazetteers. Crowdsourced geographic information and citizen science approaches offer a new paradigm of toponym collection. This paper addresses issues in the advancing toponym practice. First, we systematically examined the current state of toponym collection and handling practice by multiple stakeholders, and we identified a recurring set of problems. Secondly, we developed a citizen science approach, based on a crowdsourcing level of participation, to collect toponyms. Thirdly, we examined the implementation in the context of an Indonesian case study. The results show that public participation in toponym collection is an approach with the potential to solve problems in toponym handling, such as limited human resources, accessibility, and completeness of toponym information. The lessons learnt include the knowledge that the success of this approach depends on the willingness of the government to advance their workflow, the degree of collaboration between stakeholders, and the presence of a communicative approach in introducing and sharing toponym guidelines with the community.

Description: IJGI, Vol. 7, Pages 232: A RSSI/PDR-Based Probabilistic Position Selection Algorithm with NLOS Identification for Indoor Localisation ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7060232 Authors: Ke Han Huashuai Xing Zhongliang Deng Yichen Du In recent years, location-based services have been receiving increasing attention because of their great development prospects. Researchers from all over the world have proposed many solutions for indoor positioning over the past several years. However, owing to the dynamic and complex nature of indoor environments, accurately and efficiently localising targets in indoor environments remains a challenging problem. In this paper, we propose a novel indoor positioning algorithm based on the received signal strength indication and pedestrian dead reckoning. In order to enhance the accuracy and reliability of our proposed probabilistic position selection algorithm in mixed line-of-sight (LOS) and non-line-of-sight (NLOS) environments, a low-complexity identification approach is proposed to identify the change in the channel situation between NLOS and LOS. Numerical experiment results indicate that our proposed algorithm has a higher accuracy and is less impacted by NLOS errors than other conventional methods in mixed LOS and NLOS indoor environments.

Description: IJGI, Vol. 7, Pages 229: Automated Orthorectification of VHR Satellite Images by SIFT-Based RPC Refinement ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7060229 Authors: Hakan Kartal Ugur Alganci Elif Sertel Raw remotely sensed images contain geometric distortions and cannot be used directly for map-based applications, accurate locational information extraction or geospatial data integration. A geometric correction process must be conducted to minimize the errors related to distortions and achieve the desired location accuracy before further analysis. A considerable number of images might be needed when working over large areas or in temporal domains in which manual geometric correction requires more labor and time. To overcome these problems, new algorithms have been developed to make the geometric correction process autonomous. The Scale Invariant Feature Transform (SIFT) algorithm is an image matching algorithm used in remote sensing applications that has received attention in recent years. In this study, the effects of the incidence angle, surface topography and land cover (LC) characteristics on SIFT-based automated orthorectification were investigated at three different study sites with different topographic conditions and LC characteristics using Pleiades very high resolution (VHR) images acquired at different incidence angles. The results showed that the location accuracy of the orthorectified images increased with lower incidence angle images. More importantly, the topographic characteristics had no observable impacts on the location accuracy of SIFT-based automated orthorectification, and the results showed that Ground Control Points (GCPs) are mainly concentrated in the “Forest” and “Semi Natural Area” LC classes. A multi-thread code was designed to reduce the automated processing time, and the results showed that the process performed 7 to 16 times faster using an automated approach. Analyses performed on various spectral modes of multispectral data showed that the arithmetic data derived from pan-sharpened multispectral images can be used in automated SIFT-based RPC orthorectification.

Description: IJGI, Vol. 7, Pages 244: Shaking Maps Based on Cumulative Absolute Velocity and Arias Intensity: The Cases of the Two Strongest Earthquakes of the 2016–2017 Central Italy Seismic Sequence ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7070244 Authors: Antonio Costanzo By referring to the two strongest earthquakes of the 2016–2017 Central Italy seismic sequence, this paper presents a procedure to make shaking maps through empirical relationships between macroseismic intensity and ground-motion parameters. Hundreds of waveforms were processed to obtain instrumental ground-motion features which could be correlated with the potential damage intensities. To take into account peak value, frequency, duration, and energy content, which all contribute to damage, cumulative absolute velocity and Arias intensity were used to quantify the features of the ground motion. Once these parameters had been calculated at the recording sites, they were interpolated through geostatistical techniques on the whole struck area. Finally, empirical relationships were used for mapping intensities, i.e., potential effects on the built environment. The results referred to both earthquake scenarios that were analyzed and were also used for assessing the influence of the spatial coverage of the instrumental network. In fact, after the first events, the Italian seismic network was subjected to the addition and thickening of sensors in the epicentral area, especially. The results obtained by models only dependent on ground-motion parameters or even on the epicentral distance were compared with the official ShakeMaps and the observed intensities for assessing their reliability. Finally, some suggestions are proposed to improve the procedure that could be used for rapidly assessing ground shaking and mapping damage potential producing useful information for non-expert audience.

Description: IJGI, Vol. 7, Pages 242: Seamless Upscaling of the Field-Measured Grassland Aboveground Biomass Based on Gaussian Process Regression and Gap-Filled Landsat 8 OLI Reflectance ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7070242 Authors: Gaofei Yin Ainong Li Chaoyang Wu Jiyan Wang Qiaoyun Xie Zhengjian Zhang Xi Nan Huaan Jin Jinhu Bian Guangbin Lei The spatially explicit aboveground biomass (AGB) generated through upscaling field measurements is critical for carbon cycle simulation and optimized management of grasslands. However, the spatial gaps that exist in the optical remote sensing data, underutilization of the multispectral data cube and unavailability of uncertainty information hinder the generation of seamless and accurate AGB maps. This study proposes a novel framework to address the above challenges. The proposed framework filled the spatial gaps in the remote sensing data via the consistent adjustment of the climatology to actual observations (CACAO) method. Gaussian process regression (GPR) was used to fully exploit the multispectral data cube and generated the pixelwise uncertainty concurrent with the AGB estimation. A case study in a 100 km × 100 km area located in the Zoige Plateau, China was used to evaluate this framework. The results show that the CACAO method can fill almost all of the gaps, accounting for 93.1% of the study area, with satisfactory accuracy. The generated AGB map from the GPR was characterized by a relatively high accuracy (R2 = 0.64, RMSE = 48.13 g/m2) compared to vegetation index-derived ones, and was accompanied by a corresponding uncertainty map that provides a new source of information on the credibility of each pixel. This study demonstrates the potential of the joint use of gap-filling and machine-learning methods to generate spatially explicit AGB.

Description: IJGI, Vol. 7, Pages 251: Optical Satellite Image Geo-Positioning with Weak Convergence Geometry ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7070251 Authors: Yang Wu Yongsheng Zhang Donghong Wang Delin Mo High-resolution optical satellites are widely used in environmental monitoring. With the aim to observe the largest possible coverage, the overlapping areas and intersection angles of respective optical satellite images are usually small. However, the conventional bundle adjustment method leads to erroneous results or even failure under conditions of weak geometric convergence. By transforming the traditional stereo adjustment to a planar adjustment and combining it with linear programming (LP) theory, a new method that can solve the bias compensation parameters of all satellite images is proposed in this paper. With the support of freely available open source digital elevation models (DEMs) and sparse ground control points (GCPs), the method can not only ensure the consistent inner precision of all images, but also the absolute geolocation accuracy of the ground points. Tests of the two data sets covering different landscapes validated the effectiveness and feasibility of the method. The results showed that the geo-positioning performance of the method was better in regions of smaller topographic relief or for satellite images with a larger imaging altitude angle. The best accuracy of image geolocation with weak convergence geometry was as high as to 3.693 m in the horizontal direction and 6.510 m in the vertical direction, which is a level of accuracy equal to that of images with good intersection conditions.

Description: IJGI, Vol. 7, Pages 299: Drift-Aware Monocular Localization Based on a Pre-Constructed Dense 3D Map in Indoor Environments ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7080299 Authors: Guanyuan Feng Lin Ma Xuezhi Tan Danyang Qin Recently, monocular localization has attracted increased attention due to its application to indoor navigation and augmented reality. In this paper, a drift-aware monocular localization system that performs global and local localization is presented based on a pre-constructed dense three-dimensional (3D) map. In global localization, a pixel-distance weighted least squares algorithm is investigated for calculating the absolute scale for the epipolar constraint. To reduce the accumulative errors that are caused by the relative position estimation, a map interaction-based drift detection method is introduced in local localization, and the drift distance is computed by the proposed line model-based maximum likelihood estimation sample consensus (MLESAC) algorithm. The line model contains a fitted line segment and some visual feature points, which are used to seek inliers of the estimated feature points for drift detection. Taking advantage of the drift detection method, the monocular localization system switches between the global and local localization modes, which effectively keeps the position errors within an expected range. The performance of the proposed monocular localization system is evaluated on typical indoor scenes, and experimental results show that compared with the existing localization methods, the accuracy improvement rates of the absolute position estimation and the relative position estimation are at least 30.09% and 65.59%, respectively.

Description: IJGI, Vol. 7, Pages 297: The Negative Effects of Alcohol Establishment Size and Proximity on the Frequency of Violent and Disorder Crime across Block Groups of Victoria, British Columbia ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7080297 Authors: Jessica L. Fitterer Trisalyn A. Nelson Timothy Stockwell Multiple studies have associated the density of alcohol establishments with crime. What is not well understood is the influence of establishment patron capacity on the magnitude of crime in an area, or how the spacing of liquor primary establishments impacts crime levels. Using a Poisson spatial lag model, we estimated how patron capacity of on-premises licenses and the total number of off-premises licenses were associated with the frequency of violent and disorder crime occurring on Friday and Saturday nights in Victoria, British Columbia. To identify how the distance between bars and pubs was associated with the frequency of crime within 200 m of each establishment, we applied bivariate curve fitting and change detection techniques. Our model explained 76% percent of the variance in crime frequencies. Bars and pubs within block groups, and in neighboring block groups, had a significant positive association (p < 0.05) with the frequency of crime compared to other on-premises licenses (e.g., restaurants, theatres, clubs, hotels), and off-premises liquor stores. For every additional 1111 bar or pub patron seats the crime frequency per block group is expected to double over a 17 month period (factor of 1.0009 per patron seat). Crime frequency significantly dropped (p < 0.05) around (200 m) bars and pubs that are spaced greater than 300 m apart. Our results provide the first evidenced-based information for evaluating the size and spacing of on-premises licenses in Canada.

Description: IJGI, Vol. 7, Pages 298: Grid-Based Crime Prediction Using Geographical Features ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7080298 Authors: Ying-Lung Lin Meng-Feng Yen Liang-Chih Yu Machine learning is useful for grid-based crime prediction. Many previous studies have examined factors including time, space, and type of crime, but the geographic characteristics of the grid are rarely discussed, leaving prediction models unable to predict crime displacement. This study incorporates the concept of a criminal environment in grid-based crime prediction modeling, and establishes a range of spatial-temporal features based on 84 types of geographic information by applying the Google Places API to theft data for Taoyuan City, Taiwan. The best model was found to be Deep Neural Networks, which outperforms the popular Random Decision Forest, Support Vector Machine, and K-Near Neighbor algorithms. After tuning, compared to our design’s baseline 11-month moving average, the F1 score improves about 7% on 100-by-100 grids. Experiments demonstrate the importance of the geographic feature design for improving performance and explanatory ability. In addition, testing for crime displacement also shows that our model design outperforms the baseline.

Description: IJGI, Vol. 7, Pages 306: Satellite-Derived Bathymetry for Improving Canadian Hydrographic Service Charts ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7080306 Authors: René Chénier Marc-André Faucher Ryan Ahola Approximately 1000 Canadian Hydrographic Service (CHS) charts cover Canada’s oceans and navigable waters. Many charts use information collected with techniques that predate the more advanced technologies available to Hydrographic Offices (HOs) today. Furthermore, gaps in survey data, particularly in the Canadian Arctic where only 6% of waters are surveyed to modern standards, are also problematic. Through a Canadian Space Agency (CSA) Government Related Initiatives Program (GRIP) project, CHS is exploring remote sensing techniques to assist with the improvement of Canadian navigational charts. Projects exploring optical/Synthetic Aperture Radar (SAR) shoreline extraction and change detection, as well as optical Satellite-Derived Bathymetry (SDB), are currently underway. This paper focuses on SDB extracted from high-resolution optical imagery, highlighting current results as well as the challenges and opportunities CHS will encounter when implementing SDB within its operational chart production process. SDB is of particular interest to CHS due to its ability to supplement depths derived from traditional hydrographic surveys. This is of great importance in shallow and/or remote Canadian waters where achieving wide-area depth coverage through traditional surveys is costly, time-consuming and a safety risk to survey operators. With an accuracy of around 1 m, SDB could be used by CHS to fill gaps in survey data and to provide valuable information in dynamic areas.

Description: IJGI, Vol. 7, Pages 305: Prioritizing Abandoned Mine Lands Rehabilitation: Combining Landscape Connectivity and Pattern Indices with Scenario Analysis Using Land-Use Modeling ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7080305 Authors: Liping Zhang Shiwen Zhang Yajie Huang An Xing Zhiqing Zhuo Zhongxiang Sun Zhen Li Meng Cao Yuanfang Huang Connectivity modeling approaches for abandoned mine lands (AML) patches are limited in post-mining landscape restoration, especially where great land use changes might be expected due to large-scale land reclamation. This study presents a novel approach combining AML patch sizes with a proximity index to characterize patch-scaled connectivity for determining the spatial positions of patches with huge sizes and high connectivity. Then this study propose a scenario-based method coupled with landscape-scale metrics for quantifying landscape-scaled connectivity, which aims at exploring the optimal reclamation scheme with the highest connectivity. Using the Mentougou District in Beijing, China, as a case study, this paper confirmed which patches should be reclaimed first to meet the predetermined reclamation numbers; then this paper tested three different reclamation scenarios (i.e., cultivated land-oriented, forest-oriented, and construction land-oriented scenarios) to describe the impact of the different development strategies on landscape connectivity. The research found that the forest-oriented scenario increased connectivity quantitatively, showing an increase in the integral index of connectivity (IIC) and other landscape-scale metrics. Therefore, this paper suggests that future land-use policies should emphasize converting AML into more forest to blend in with the surrounding land-use categories. The findings presented here can contribute to better understanding the quantitative analysis of the connectivity of AML patches at both the patch scale and the landscape scale, thus providing scientific support for AML management in mine-site rehabilitation.

Description: IJGI, Vol. 7, Pages 293: Shp2graph: Tools to Convert a Spatial Network into an Igraph Graph in R ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7080293 Authors: Binbin Lu Huabo Sun Paul Harris Miaozhong Xu Martin Charlton In this study, we introduce the R package shp2graph, which provides tools to convert a spatial network into an ‘igraph’ graph of the igraphR package. This conversion greatly empowers a spatial network study, as the vast array of graph analytical tools provided in igraph are then readily available to the network analysis, together with the inherent advantages of being within the R statistical computing environment and its vast array of statistical functions. Through three urban road network case studies, the calculation of road network distances with shp2graph and with igraph is demonstrated through four key stages: (i) confirming the connectivity of a spatial network; (ii) integrating points/locations with a network; (iii) converting a network into a graph; and (iv) calculating network distances (and travel times). Throughout, the required R commands are given to provide a useful tutorial on the use of shp2graph.

Description: IJGI, Vol. 7, Pages 292: Optimized Location-Allocation of Earthquake Relief Centers Using PSO and ACO, Complemented by GIS, Clustering, and TOPSIS ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7080292 Authors: Bahram Saeidian Mohammad Saadi Mesgari Biswajeet Pradhan Mostafa Ghodousi After an earthquake, it is required to establish temporary relief centers in order to help the victims. Selection of proper sites for these centers has a significant effect on the processes of urban disaster management. In this paper, the location and allocation of relief centers in district 1 of Tehran are carried out using Geospatial Information System (GIS), the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) decision model, a simple clustering method and the two meta-heuristic algorithms of Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO). First, using TOPSIS, the proposed clustering method and GIS analysis tools, sites satisfying initial conditions with adequate distribution in the area are chosen. Then, the selection of proper centers and the allocation of parcels to them are modelled as a location/allocation problem, which is solved using the meta-heuristic optimization algorithms. Also, in this research, PSO and ACO are compared using different criteria. The implementation results show the general adequacy of TOPSIS, the clustering method, and the optimization algorithms. This is an appropriate approach to solve such complex site selection and allocation problems. In view of the assessment results, the PSO finds better answers, converges faster, and shows higher consistency than the ACO.

Description: IJGI, Vol. 7, Pages 308: Identifying Modes of Driving Railway Trains from GPS Trajectory Data: An Ensemble Classifier-Based Approach ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7080308 Authors: Han Zheng Zanyang Cui Xingchen Zhang Recognizing Modes of Driving Railway Trains (MDRT) can help to solve railway freight transportation problems in driver behavior research, auto-driving system design and capacity utilization optimization. Previous studies have focused on analyses and applications of MDRT, but there is currently no approach to automatically and effectively identify MDRT in the context of big data. In this study, we propose an integrated approach including data preprocessing, feature extraction, classifiers modeling, training and parameter tuning, and model evaluation to infer MDRT using GPS data. The highlights of this study are as follows: First, we propose methods for extracting Driving Segmented Standard Deviation Features (DSSDF) combined with classical features for the purpose of improving identification performances. Second, we find the most suitable classifier for identifying MDRT based on a comparison of performances of K-Nearest Neighbor, Support Vector Machines, AdaBoost, Random Forest, Gradient Boosting Decision Tree, and XGBoost. From the real-data experiment, we conclude that: (i) The ensemble classifier XGBoost produces the best performance with an accuracy of 92.70%; (ii) The group of DSSDF plays an important role in identifying MDRT with an accuracy improvement of 11.2% (using XGBoost). The proposed approach has been applied in capacity utilization optimization and new driver training for the Baoshen Railway.

Description: IJGI, Vol. 7, Pages 318: A Methodology for Planar Representation of Frescoed Oval Domes: Formulation and Testing on Pisa Cathedral ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7080318 Authors: Andrea Piemonte Gabriella Caroti Isabel Martínez-Espejo Zaragoza Filippo Fantini Luca Cipriani This paper presents an original methodology for planar development of a frescoed dome with an oval plan. Input data include a rigorous geometric survey, performed with a laser scanner, and a photogrammetry campaign, which associates a high-quality photographic texture to the 3D model. Therefore, the main topics include the development of geometry and, contextually, of the associated textures. In order to overcome the inability to directly develop the surface, an orthographic azimuthal projection is used. Starting from a prerequisite study of building methodology, the dome is divided into sectors and bands, each linked with the maximum acceptable deformations and the actual geometric discontinuities detectable by the analysis of Gaussian curvature. Upon definition of the development model, a custom automation script has been devised for geometry projection. This effectively generates a (u,v) map, associated to the model, which is used for model texturing and provides the planar development of the fresco.

Description: IJGI, Vol. 7, Pages 320: Exploring Railway Network Dynamics in China from 2008 to 2017 ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7080320 Authors: Yaping Huang Shiwei Lu Xiping Yang Zhiyuan Zhao China’s high speed rail (HSR) network has been rapidly constructed and developed during the past 10 years. However, few studies have reported the spatiotemporal changes of railway network structures and how those structures have been affected by the operation of high speed rail systems in different periods. This paper analyzes the evolving network characteristics of China’s railway network during each of the four main stages of HSR development over a 10-year period. These four stages include Stage 1, when no HSR was in place prior to August 2008; Stage 2, when several HSR lines were put into operation between August 2008, and July 2011; Stage 3, when the network skeleton of most main HSR lines was put into place. This covered the period until January 2013. Finally, Stage 4 covers the deep intensification of several new HSR lines and the rapid development of intercity-HSR railway lines between January 2013, and July 2017. This paper presents a detailed analysis of the timetable-based statistical properties of China’s railway network, as well as the spatiotemporal patterns of the more than 2700 stations that have been affected by the opening of HSR lines and the corresponding policy changes. Generally, we find that the distribution of both degrees and strengths are characterized by scale-free patterns. In addition, the decreasing average path length and increasing network clustering coefficient indicate that the small world characteristic is more significant in the evolution of China’s railway network. Correlations between different network indices are explored, in order to further investigate the dynamics of China’s railway system. Overall, our study offers a new approach for assessing the growth and evolution of a real railway network based on train timetables. Our study can also be referenced by policymakers looking to adjust HSR operations and plan future HSR routes.

Description: IJGI, Vol. 7, Pages 331: Use of Unmanned Aerial Vehicles (UAVs) for Updating Farmland Cadastral Data in Areas Subject to Landslides ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7080331 Authors: Edyta Puniach Agnieszka Bieda Paweł Ćwiąkała Anita Kwartnik-Pruc Piotr Parzych The purpose of this study was to verify the applicability of unmanned aerial vehicles (UAVs) to update cadastral records in areas affected by landslides. Its authors intended to compare the accuracy of coordinates determined using different UAV data processing methods for points which form the framework of a cadastral database, and to find out whether products obtained as a result of such UAV data processing are sufficient to define the extent of changes in the cadastral objects. To achieve this, an experiment was designed to take place at the site of a landslide. The entire photogrammetry mission was planned to cover an area of more than 70 ha. Given the steep grade of the site, the UAV was flown over each line at a different, individually preset altitude, such as to ensure consistent mean shooting distance (height above ground level), and thus, appropriate ground sample distance (GSD; pixel size). The results were analyzed in four variants, differing from each other in terms of the number of control points used and the method of their measurement. This allowed identification of the factors that affect surveying accuracy and the indication of the cadastral data updatable based on an UAV photogrammetric survey.

Description: IJGI, Vol. 7, Pages 332: Using the Spatial Knowledge of Map Users to Personalize City Maps: A Case Study with Tourists in Madrid, Spain ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7080332 Authors: María-Teresa Manrique-Sancho Silvania Avelar Teresa Iturrioz-Aguirre Miguel-Ángel Manso-Callejo The aim of personalized maps is to help individual users to read maps and focus on the most task-relevant information. Several approaches have been suggested to develop personalized maps for cities, but few consider the spatial knowledge of its users. We propose the design of “cognitively-aware” personalized maps, which take into account the previous experience of users in the city and how the urban space is configured in their minds. Our aim is to facilitate users’ mental links between maps and city places, stimulating users to recall features of the urban space and to assimilate new spatial knowledge. To achieve this goal, we propose the personalization of maps through a map design process based on user modeling and on inferring personalization guidelines from hand-drawn sketches of urban spaces. We applied this process in an experiment with tourists in Madrid, Spain. We categorized the participants into three types of tourists—“Guided”, “Explorer”, and “Conditioned”—according to individual and contextual factors that can influence their spatial knowledge of the city. We also extracted design guidelines from tourists’ sketches and developed map prototypes. The empirical results seem to be promising for developing personalized city maps that could be produced on-the-fly in the future.

Description: IJGI, Vol. 7, Pages 333: Generating a High-Precision True Digital Orthophoto Map Based on UAV Images ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7090333 Authors: Yu Liu Xinqi Zheng Gang Ai Yi Zhang Yuqiang Zuo Unmanned aerial vehicle (UAV) low-altitude remote sensing technology has recently been adopted in China. However, mapping accuracy and production processes of true digital orthophoto maps (TDOMs) generated by UAV images require further improvement. In this study, ground control points were distributed and images were collected using a multi-rotor UAV and professional camera, at a flight height of 160 m above the ground and a designed ground sample distance (GSD) of 0.016 m. A structure from motion (SfM), revised digital surface model (DSM) and multi-view image texture compensation workflow were outlined to generate a high-precision TDOM. We then used randomly distributed checkpoints on the TDOM to verify its precision. The horizontal accuracy of the generated TDOM was 0.0365 m, the vertical accuracy was 0.0323 m, and the GSD was 0.0166 m. Tilt and shadowed areas of the TDOM were eliminated so that buildings maintained vertical viewing angles. This workflow produced a TDOM accuracy within 0.05 m, and provided an effective method for identifying rural homesteads, as well as land planning and design.

Description: IJGI, Vol. 7, Pages 342: Accuracy Assessment of Point Clouds from LiDAR and Dense Image Matching Acquired Using the UAV Platform for DTM Creation ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7090342 Authors: Adam Salach Krzysztof Bakuła Magdalena Pilarska Wojciech Ostrowski Konrad Górski Zdzisław Kurczyński In this paper, the results of an experiment about the vertical accuracy of generated digital terrain models were assessed. The created models were based on two techniques: LiDAR and photogrammetry. The data were acquired using an ultralight laser scanner, which was dedicated to Unmanned Aerial Vehicle (UAV) platforms that provide very dense point clouds (180 points per square meter), and an RGB digital camera that collects data at very high resolution (a ground sampling distance of 2 cm). The vertical error of the digital terrain models (DTMs) was evaluated based on the surveying data measured in the field and compared to airborne laser scanning collected with a manned plane. The data were acquired in summer during a corridor flight mission over levees and their surroundings, where various types of land cover were observed. The experiment results showed unequivocally, that the terrain models obtained using LiDAR technology were more accurate. An attempt to assess the accuracy and possibilities of penetration of the point cloud from the image-based approach, whilst referring to various types of land cover, was conducted based on Real Time Kinematic Global Navigation Satellite System (GNSS-RTK) measurements and was compared to archival airborne laser scanning data. The vertical accuracy of DTM was evaluated for uncovered and vegetation areas separately, providing information about the influence of the vegetation height on the results of the bare ground extraction and DTM generation. In uncovered and low vegetation areas (0–20 cm), the vertical accuracies of digital terrain models generated from different data sources were quite similar: for the UAV Laser Scanning (ULS) data, the RMSE was 0.11 m, and for the image-based data collected using the UAV platform, it was 0.14 m, whereas for medium vegetation (higher than 60 cm), the RMSE from these two data sources were 0.11 m and 0.36 m, respectively. A decrease in the accuracy of 0.10 m, for every 20 cm of vegetation height, was observed for photogrammetric data; and such a dependency was not noticed in the case of models created from the ULS data.

Description: IJGI, Vol. 7, Pages 348: Nesting Patterns of Loggerhead Sea Turtles (Caretta caretta): Development of a Multiple Regression Model Tested in North Carolina, USA ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7090348 Authors: Joanne N. Halls Alyssa L. Randall Numerous environmental conditions may influence when a female Loggerhead sea turtle (Caretta caretta) selects a nesting site. Limited research has used Geographic Information Systems (GIS) and statistical analysis to study sea turtle spatial patterns and temporal trends. Therefore, the goals of this research were to identify areas that were most prevalent for nesting and to test social and environmental variables to create a nesting suitability predictive model. Data were analyzed at all barrier island beaches in North Carolina, USA (515 km) and several variables were statistically significant: distance to hardened structures, beach nourishment, house density, distance to inlets, and beach elevation, slope, and width. Interestingly, variables that were not significant were population density, proximity to the Gulf Stream, and beach aspect. Several statistical techniques were tested and Negative Binomial Distribution produced good regional results while Geographically Weighted Regression models successfully predicted the number of nests with an average of 75% of the variance explained. Therefore, the combination of traditional and spatial statistics provided insightful predictive modeling results that may be incorporated into management strategies and may have important implications for the designation of critical Loggerhead nesting habitats.

Description: IJGI, Vol. 7, Pages 357: Space–Time Analysis of Vehicle Theft Patterns in Shanghai, China ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7090357 Authors: Yuanyuan Mao Shenzhi Dai Jiajun Ding Wei Zhu Can Wang Xinyue Ye To identify and compare the space–time patterns of vehicle thefts and the effects of associated environmental factors, this paper conducts a case study of the Pudong New Area (PNA), a major urban district in Shanghai, China’s largest city. Geographic information system (GIS)-based analysis indicated that there was a stable pattern of vehicle theft over time. Hotspots of vehicle theft across different time periods were identified. These data provide clues for how law enforcement can prioritize the deployment of limited patrol and investigative resources. Vehicle thefts, especially those of non-motor vehicles, tend to be concentrated in the central-western portion of the PNA, which experienced a dramatic rate of urbanization and has a high concentration of people and vehicles. Important factors contributing to vehicle thefts include a highly mobile and transitory population, a large population density, and high traffic volume.

Description: IJGI, Vol. 7, Pages 354: On the Risk Assessment of Terrorist Attacks Coupled with Multi-Source Factors ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7090354 Authors: Xun Zhang Min Jin Jingying Fu Mengmeng Hao Chongchong Yu Xiaolan Xie Terrorism has wreaked havoc on today’s society and people. The discovery of the regularity of terrorist attacks is of great significance to the global counterterrorism strategy. In this study, we improve the traditional location recommendation algorithm coupled with multi-source factors and spatial characteristics. We used the data of terrorist attacks in Southeast Asia from 1970 to 2016, and comprehensively considered 17 influencing factors, including socioeconomic and natural resource factors. The improved recommendation algorithm is used to build a spatial risk assessment model of terrorist attacks, and the effectiveness is tested. The model trained in this study is tested with precision, recall, and F-Measure. The results show that, when the threshold is 0.4, the precision is as high as 88%, and the F-Measure is the highest. We assess the spatial risk of the terrorist attacks in Southeast Asia through experiments. It can be seen that the southernmost part of the Indochina peninsula and the Philippines are high-risk areas and that the medium-risk and high-risk areas are mainly distributed in the coastal areas. Therefore, future anti-terrorism measures should pay more attention to these areas.

Description: IJGI, Vol. 7, Pages 353: Novel Method for Virtual Restoration of Cultural Relics with Complex Geometric Structure Based on Multiscale Spatial Geometry ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7090353 Authors: Miaole Hou Su Yang Yungang Hu Yuhua Wu Lili Jiang Sizhong Zhao Putong Wei Because of the age of relics and the lack of historical data, the geometric forms of missing parts can only be judged by the subjective experience of repair personnel, which leads to varying restoration effects when the geometric structure of the complex relic is reconstructed. Therefore, virtual repair effects cannot fully reflect the historical appearance of cultural relics. In order to solve this problem, this paper presents a virtual restoration method based on the multiscale spatial geometric features of cultural relics in the case of complex construction where the geometric shape of the damaged area is unknown, using the Dazu Thousand-Hand Bodhisattva statue in China as an example. In this study, the global geometric features of the three-dimensional (3D) model are analyzed in space to determine the geometric shape of the damaged parts of cultural relics. The local geometric features are represented by skeleton lines based on regression analysis, and a geometric size prediction model of the defective parts is established, which is used to calculate the geometric dimensions of the missing parts. Finally, 3D surface reconstruction technology is used to quantitate virtual restoration of the defective parts. This method not only provides a new idea for the virtual restoration of artifacts with complex geometric structure, but also may play a vital role in the protection of cultural relics.

Description: IJGI, Vol. 7, Pages 360: Share Our Cultural Heritage (SOCH): Worldwide 3D Heritage Reconstruction and Visualization via Web and Mobile GIS ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7090360 Authors: Hari K. Dhonju Wen Xiao Jon P. Mills Vasilis Sarhosis Despite being of paramount importance to humanity, tangible cultural heritage is often at risk from natural and anthropogenic threats worldwide. As a result, heritage discovery and conservation remain a huge challenge for both developed and developing countries, with heritage sites often inadequately cared for, be it due to a lack of resources, nonrecognition of the value by local people or authorities, human conflict, or some other reason. This paper presents an online geo-crowdsourcing system, termed Share Our Cultural Heritage (SOCH), which can be utilized for large-scale heritage documentation and sharing. Supported by web and mobile GIS, cultural heritage data such as textual stories, locations, and images can be acquired via portable devices. These data are georeferenced and presented to the public via web-mapping. Using photogrammetric modelling, acquired images are used to reconstruct heritage structures or artefacts into 3D digital models, which are then visualized on the SOCH web interface to enable public interaction. This end-to-end system incubates an online virtual community to encourage public engagement, raise awareness, and stimulate cultural heritage ownership. It also provides valuable resources for cultural heritage exploitation, management, education, and monitoring over time.

Description: IJGI, Vol. 7, Pages 358: Spatial-Temporal Analysis of Human Dynamics on Urban Land Use Patterns Using Social Media Data by Gender ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7090358 Authors: Chengcheng Lei An Zhang Qingwen Qi Huimin Su Jianghao Wang The relationship between urban human dynamics and land use types has always been an important issue in the study of urban problems in China. This paper used location data from Sina Location Microblog (commonly known as Weibo) users to study the human dynamics of the spatial-temporal characteristics of gender differences in Beijing’s Olympic Village in June 2014. We applied mathematical statistics and Local Moran’s I to analyze the spatial-temporal distribution of Sina Microblog users in 100 m × 100 m grids and land use patterns. The female users outnumbered male users, and the sex ratio ( S R varied under different land use types at different times. Female users outnumbered male users regarding residential land and public green land, but male users outnumbered female users regarding workplace, especially on weekends, as the S R on weekends ( S R was 120.5) was greater than that on weekdays ( S R was 118.8). After a Local Moran’s I analysis, we found that High–High grids are primarily distributed across education and scientific research land and residential land; these grids and their surrounding grids have more female users than male users. Low–Low grids are mainly distributed across sports centers and workplaces on weekdays; these grids and their surrounding grids have fewer female users than male users. The average number of users on Saturday was the highest value and, on weekends, the number of female and male users both increased in commercial land, but male users were more active than female users ( S R was 110).

Description: IJGI, Vol. 7, Pages 361: Automatic Seam-Line Detection in UAV Remote Sensing Image Mosaicking by Use of Graph Cuts ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7090361 Authors: Ming Li Deren Li Bingxuan Guo Lin Li Teng Wu Weilong Zhang Image mosaicking is one of the key technologies in data processing in the field of computer vision and digital photogrammetry. For the existing problems of seam, pixel aliasing, and ghosting in mosaic images, this paper proposes and implements an optimal seam-line search method based on graph cuts for unmanned aerial vehicle (UAV) remote sensing image mosaicking. This paper first uses a mature and accurate image matching method to register the pre-mosaicked UAV images, and then it marks the source of each pixel in the overlapped area of adjacent images and calculates the energy value contributed by the marker by using the target energy function of graph cuts constructed in this paper. Finally, the optimal seam-line can be obtained by solving the minimum value of target energy function based on graph cuts. The experimental results show that our method can realize seamless UAV image mosaicking, and the image mosaic area transitions naturally.

Description: IJGI, Vol. 7, Pages 362: Road Extraction from VHR Remote-Sensing Imagery via Object Segmentation Constrained by Gabor Features ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7090362 Authors: Li Chen Qing Zhu Xiao Xie Han Hu Haowei Zeng Automatic road extraction from remote-sensing imagery plays an important role in many applications. However, accurate and efficient extraction from very high-resolution (VHR) images remains difficult because of, for example, increased data size and superfluous details, the spatial and spectral diversity of road targets, disturbances (e.g., vehicles, shadows of trees, and buildings), the necessity of finding weak road edges while avoiding noise, and the fast-acquisition requirement of road information for crisis response. To solve these difficulties, a two-stage method combining edge information and region characteristics is presented. In the first stage, convolutions are executed by applying Gabor wavelets in the best scale to detect Gabor features with location and orientation information. The features are then merged into one response map for connection analysis. In the second stage, highly complete, connected Gabor features are used as edge constraints to facilitate stable object segmentation and limit region growing. Finally, segmented objects are evaluated by some fundamental shape features to eliminate nonroad objects. The results indicate the validity and superiority of the proposed method to efficiently extract accurate road targets from VHR remote-sensing images.

Description: IJGI, Vol. 7, Pages 370: Raising Semantics-Awareness in Geospatial Metadata Management ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7090370 Authors: Cristiano Fugazza Monica Pepe Alessandro Oggioni Paolo Tagliolato Paola Carrara Geospatial metadata are often encoded in formats that either are not aimed at efficient retrieval of resources or are plainly outdated. Particularly, the quantum leap represented by the Semantic Web did not induce so far a consistent, interlinked baseline in the geospatial domain. Datasets, scientific literature related to them, and ultimately the researchers behind these products are only loosely connected; the corresponding metadata intelligible only to humans, duplicated in different systems, seldom consistently. We address these issues by relating metadata items to resources that represent keywords, institutes, researchers, toponyms, and virtually any RDF data structure made available over the Web via SPARQL endpoints. Essentially, our methodology fosters delegated metadata management as the entities referred to in metadata are independent, decentralized data structures with their own life cycle. Our example implementation of delegated metadata envisages: (i) editing via customizable web-based forms (including injection of semantic information); (ii) encoding of records in any XML metadata schema; and (iii) translation into RDF. Among the semantics-aware features that this practice enables, we present a worked-out example focusing on automatic update of metadata descriptions. Our approach, demonstrated in the context of INSPIRE metadata (the ISO 19115/19119 profile eliciting integration of European geospatial resources) is also applicable to a broad range of metadata standards, including non-geospatial ones.

Description: IJGI, Vol. 7, Pages 369: GIS-Assisted Prediction and Risk Zonation of Wildlife Attacks in the Chitwan National Park in Nepal ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7090369 Authors: Aleš Ruda Jaromír Kolejka Thakur Silwal Population growth forces the human community to expand into the natural habitats of wild animals. Their efforts to use natural sources often collide with wildlife attacks. These animals do not only protect their natural environment, but in the face of losing the potential food sources, they also penetrate in human settlements. The research was situated in the Chitwan National Park (CNP) in Nepal, and the aim of this study was to investigate possible geospatial connections between attacks of all kinds of animals on humans in the CNP and its surroundings between 2003 and 2013. The patterns of attacks were significantly uneven across the months, and 89% of attacks occurred outside the park. In total, 74% attacks occurred in the buffer zone forests and croplands within 1 km from the park. There was a strong positive correlation among the number of victims for all attacking animals with a maximum of one victim per 4 km2, except elephant and wild boar. The density of bear victims was higher where the tiger and rhino victims were lower, e.g., in the Madi valley. The data collected during this period did not show any signs of spatial autocorrelation. The calculated magnitude per unit area using the kernel density, together with purpose-defined land use groups, were used to determine five risk zones of wildlife attacks. In conclusion, it was found that the riskiest areas were locations near the forest that were covered by agricultural land and inhabited by humans. Our research results can support any local spatial decision-making processes for improving the co-existence of natural protection in the park and the safety of human communities living in its vicinity.

Description: IJGI, Vol. 7, Pages 368: Critical Review of Methods to Estimate PM2.5 Concentrations within Specified Research Region ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7090368 Authors: Guangyuan Zhang Xiaoping Rui Yonglei Fan Obtaining PM2.5 data for the entirety of a research region underlies the study of the relationship between PM2.5 and human spatiotemporal activity. A professional sampler with a filter membrane is used to measure accurate values of PM2.5 at single points in space. However, there are numerous PM2.5 sampling and monitoring facilities that rely on data from only representative points, and which cannot measure the data for the whole region of research interest. This provides the motivation for researching the methods of estimation of particulate matter in areas having fewer monitors at a special scale, an approach now attracting considerable academic interest. The aim of this study is to (1) reclassify and particularize the most frequently used approaches for estimating the PM2.5 concentrations covering an entire research region; (2) list improvements to and integrations of traditional methods and their applications; and (3) compare existing approaches to PM2.5 estimation on the basis of accuracy and applicability.

Description: IJGI, Vol. 7, Pages 371: An Efficient Graph-Based Spatio-Temporal Indexing Method for Task-Oriented Multi-Modal Scene Data Organization ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7090371 Authors: Bin Feng Qing Zhu Mingwei Liu Yun Li Junxiao Zhang Xiao Fu Yan Zhou Maosu Li Huagui He Weijun Yang Task-oriented scene data in big data and cloud environments of a smart city that must be time-critically processed are dynamic and associated with increasing complexities and heterogeneities. Existing hybrid tree-based external indexing methods are input/output (I/O)-intensive, query schema-fixed, and difficult when representing the complex relationships of real-time multi-modal scene data; specifically, queries are limited to a certain spatio-temporal range or a small number of selected attributes. This paper proposes a new spatio-temporal indexing method for task-oriented multi-modal scene data organization. First, a hybrid spatio-temporal index architecture is proposed based on the analysis of the characteristics of scene data and the driving forces behind the scene tasks. Second, a graph-based spatio-temporal relation indexing approach, named the spatio-temporal relation graph (STR-graph), is constructed for this architecture. The global graph-based index, internal and external operation mechanisms, and optimization strategy of the STR-graph index are introduced in detail. Finally, index efficiency comparison experiments are conducted, and the results show that the STR-graph performs excellently in index generation and can efficiently address the diverse requirements of different visualization tasks for data scheduling; specifically, the STR-graph is more efficient when addressing complex and uncertain spatio-temporal relation queries.

Description: IJGI, Vol. 7, Pages 373: Sino-InSpace: A Digital Simulation Platform for Virtual Space Environments ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7090373 Authors: Liang Lyu Qing Xu Chaozhen Lan Qunshan Shi Wanjie Lu Yang Zhou Yinghao Zhao The implementation of increased space exploration missions reduces the distance between human beings and outer space. Although it is impossible for everyone to enter the remote outer space, virtual environments could provide computer-based digital spaces that we can observe, participate in, and experience. In this study, Sino-InSpace, a digital simulation platform, was developed to support the construction of virtual space environments. The input data are divided into two types, the environment element and the entity object, that are then supported by the unified time-space datum. The platform adopted the pyramid model and octree index to preprocess the geographic and space environment data, which ensured the efficiency of data loading and browsing. To describe objects perfectly, they were abstracted and modeled based on four aspects including attributes, ephemeris, geometry, and behavior. Then, the platform performed the organization of a visual scenario based on logical modeling and data modeling; in addition, it ensured smooth and flexible visual scenario displays using efficient data and rendering engines. Multilevel modes (application directly, visualization development, and scientific analysis) were designed to support multilevel applications for users from different grades and fields. Each mode provided representative case studies, which also demonstrated the capabilities of the platform for data integration, visualization, process deduction, and auxiliary analysis. Finally, a user study with human participants was conducted from multiple views (usability, user acceptance, presence, and software design). The results indicate that Sino-InSpace performs well in simulation for virtual space environments, while a virtual reality setup is beneficial for promoting the experience.

Description: IJGI, Vol. 7, Pages 372: Digital Image Correlation (DIC) Analysis of the 3 December 2013 Montescaglioso Landslide (Basilicata, Southern Italy): Results from a Multi-Dataset Investigation ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7090372 Authors: Paolo Caporossi Paolo Mazzanti Francesca Bozzano Image correlation remote sensing monitoring techniques are becoming key tools for providing effective qualitative and quantitative information suitable for natural hazard assessments, specifically for landslide investigation and monitoring. In recent years, these techniques have been successfully integrated and shown to be complementary and competitive with more standard remote sensing techniques, such as satellite or terrestrial Synthetic Aperture Radar interferometry. The objective of this article is to apply the proposed in-depth calibration and validation analysis, referred to as the Digital Image Correlation technique, to measure landslide displacement. The availability of a multi-dataset for the 3 December 2013 Montescaglioso landslide, characterized by different types of imagery, such as LANDSAT 8 OLI (Operational Land Imager) and TIRS (Thermal Infrared Sensor), high-resolution airborne optical orthophotos, Digital Terrain Models and COSMO-SkyMed Synthetic Aperture Radar, allows for the retrieval of the actual landslide displacement field at values ranging from a few meters (2–3 m in the north-eastern sector of the landslide) to 20–21 m (local peaks on the central body of the landslide). Furthermore, comprehensive sensitivity analyses and statistics-based processing approaches are used to identify the role of the background noise that affects the whole dataset. This noise has a directly proportional relationship to the different geometric and temporal resolutions of the processed imagery. Moreover, the accuracy of the environmental-instrumental background noise evaluation allowed the actual displacement measurements to be correctly calibrated and validated, thereby leading to a better definition of the threshold values of the maximum Digital Image Correlation sub-pixel accuracy and reliability (ranging from 1/10 to 8/10 pixel) for each processed dataset.

Description: IJGI, Vol. 7, Pages 377: Application of Industrial Risk Management Practices to Control Natural Hazards, Facilitating Risk Communication ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7090377 Authors: Jongook Lee Dong Kun Lee Establishing a comprehensive management framework to manage the risk from natural hazards is challenging because of the extensive affected areas, uncertainty in predictions of natural disasters, and the involvement of various stakeholders. Applying risk management practices proven in the industrial sector can assist systematic hazard identification and quantitative risk assessment for natural hazards, thereby promoting interactive risk communication to the public. The objective of this study is to introduce methods of studying risk commonly used in the process industry, and to suggest how such methods can be applied to manage natural disasters. In particular, the application of Hazard and Operability (HAZOP), Safety Integrated Level (SIL), and Quantitative Risk Analysis (QRA) was investigated, as these methods are used to conduct key studies in industry. We present case studies of the application of HAZOP to identify climate-related natural hazards, and of SIL and QRA studies that were performed to provide quantitative risk indices for landslide risk management. The analyses presented in this study can provide a useful framework for improving the risk management of natural hazards through establishing a more systematic context and facilitating risk communication.

Description: IJGI, Vol. 7, Pages 382: Incremental Road Network Generation Based on Vehicle Trajectories ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7100382 Authors: Zhongyi Ni Lijun Xie Tian Xie Binhua Shi Yao Zheng Nowadays, most vehicles are equipped with positioning devices such as GPS which can generate a tremendous amount of trajectory data and upload them to the server in real time. The trajectory data can reveal the shape and evolution of the road network and therefore has an important value for road planning, vehicle navigation, traffic analysis, and so on. In this paper, a road network generation method is proposed based on the incremental learning of vehicle trajectories. Firstly, the input vehicle trajectory data are cleaned by a preprocess module. Then, the original scattered positions are clustered and mapped to the representation points which stand for the feature points of the real roads. After that, the corresponding representation points are connected based on the original connection information of the trajectories. Finally, all representation points are connected by a Delaunay triangulation network and the real road segments are found by a shortest path searching approach between the connected representation point pairs. Experiments show that this method can build the road network from scratch and refine it with the input data continuously. Both the accuracy and timeliness of the extracted road network can continuously be improved with the growth of real-time trajectory data.

Description: IJGI, Vol. 7, Pages 383: Care, Indifference and Anxiety—Attitudes toward Location Data in Everyday Life ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7100383 Authors: Michal Rzeszewski Piotr Luczys Modern mobile devices are replete with advanced sensors that expand the array of possible methods of locating users. This can be used as a tool to gather and use spatial information, but it also brings with it the specter of “geosurveillance” in which the “location” becomes a product in itself. In the realm of software developers, space/place has been reduced and discretized to a set of coordinates, devoid of human experiences and meanings. To function in such digitally augmented realities, people need to adopt specific attitudes, often marked with anxiety. We explored attitudes toward location data collection practices using qualitative questionnaire surveys (n = 280) from Poznan and Edinburgh. The prevailing attitude that we identified is neutral with a strong undertone of resignation—surrendering personal location is viewed as a form of digital currency. A smaller number of people had stronger, emotional views, either very positive or very negative, based on uncritical technological enthusiasm or fear of privacy violation. Such a wide spectrum of attitudes is not only produced by interaction with technology but can also be a result of different values associated with space and place itself. Those attitudes can bring additional bias into spatial datasets that is not related to demographics.

Description: IJGI, Vol. 7, Pages 319: BIM-GIS Integration as Dedicated and Independent Course for Geoinformatics Students: Merits, Challenges, and Ways Forward ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7080319 Authors: Ihab Hijazi Andreas Donaubauer Thomas H. Kolbe Information mined from building information models as well as associated geographical data and Geographic Information System (GIS) analyses can increase the success of construction processes and asset management, including buildings, roads, and public facilities. The integration of information from both domains requires high expertise in both spheres. The existing B.Sc and M.Sc. programs linked to the built environment at the Technical University of Munich offer courses for the Building Information Model (BIM) and GIS that are distributed among study programs in Civil Engineering, Architecture, and Geomatics. Students graduating as professionals in one of these domains rarely know how to solve pre-defined technical problems associated with the integration of information from BIM and GIS. Students in such programs seldom practice skills needed for the integration of information from BIM and GIS at a level that is needed in working life. Conversely, the technologies in both domains create artificial boundaries that do not exist in reality—for example, water and electricity would not be of use if the utilities terminated in front of buildings. To bring a change and bridge the gap between BIM and GIS, a change in the teaching methods of BIM/GIS needs to be considered. The Technical University of Munich (TUM) has developed a master’s course (M.Sc. course) for students in Geoinformatics which focuses on competencies required to achieve BIM/GIS integration. This paper describes the course development process and provides a unique perspective on the curriculum and subjects. It also presents the course objective, course development, the selection and development of learning materials, and the assessment of the intended learning outcome of the course. The developed course is validated through a questionnaire, and feedback is provided by participants of the BIM/GIS integration workshop representing a panel of experts in the domain.

Description: IJGI, Vol. 7, Pages 321: Extracting Indoor Space Information in Complex Building Environments ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7080321 Authors: Yueyong Pang Chi Zhang Liangchen Zhou Bingxian Lin Guonian Lv Indoor space information extraction is an important aspect of reconstruction for building information modeling and a necessary process for geographic information system from outdoor to indoor. Entity model extracting methods provide advantages in terms of accuracy for building indoor spaces, as compared with network and grid model methods, and the extraction results can be converted into a network or grid model. However, existing entity model extracting methods based on a search loop do not consider the complex indoor environment of a building, such as isolated columns and walls or cross-floor spaces. In this study, such complex indoor environments are analyzed in detail, and a new approach for extracting buildings’ indoor space information is proposed. This approach is based on indoor space boundary calculation, the Boolean difference for single-floor space extraction, relationship reconstruction, and cross-floor space extraction. The experimental results showed that the proposed method can accurately extract indoor space information from the complex indoor environment of a building with geometric, semantic, and relationship information. This study is theoretically important for better understanding the complexity of indoor space extraction and practically important for improving the modeling accuracy of buildings.

Description: IJGI, Vol. 7, Pages 323: Analyzing the Tagging Quality of the Spanish OpenStreetMap ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7080323 Authors: Jesús M. Almendros-Jiménez Antonio Becerra-Terón In this paper, a framework for the assessment of the quality of OpenStreetMap is presented, comprising a batch of methods to analyze the quality of entity tagging. The approach uses Taginfo as a reference base and analyses quality measures such as completeness, compliance, consistence, granularity, richness and trust . The framework has been used to analyze the quality of OpenStreetMap in Spain, comparing the main cities of Spain. Also a comparison between Spain and some major European cities has been carried out. Additionally, a Web tool has been also developed in order to facilitate the same kind of analysis in any area of the world.

Description: IJGI, Vol. 7, Pages 322: Comparison of Communication Viewsheds Derived from High-Resolution Digital Surface Models Using Line-of-Sight, 2D Fresnel Zone, and 3D Fresnel Zone Analysis ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7080322 Authors: Jieun Baek Yosoon Choi We compared three methods for deriving communication viewsheds, which indicate the coverage areas for transmitter points from high-resolution digital surface models. Communication viewsheds were analyzed with a novel 3D Fresnel zone method, as well as line-of-sight (LOS) analysis and 2D Fresnel zone analysis, using high-resolution digital surface models (DSM) from a topographical survey. A LOS analysis calculates a visibility index by comparing the profile elevations of landforms between the transmitter and the receiver, using LOS elevations. A 2D Fresnel zone analysis calculates a 2D Fresnel index by comparing the profile elevations of landforms with the transverse plane elevations of the Fresnel zone. A 3D Fresnel zone analysis quantitatively analyzes communication stability by calculating a 3D Fresnel index, obtained by comparing the elevations of every terrain cell in a Fresnel zone with the total altitude of the Fresnel zone. The latter produced the most accurate results. Indexes derived by applying different transmitter offset heights, signal frequencies, and DSM resolutions for each of the three methods were then quantitatively analyzed. As both the offset height of the transmitter and the signal frequency decreased, the differences between the results derived from each method increased significantly. Moreover, larger DSM cells generated less accurate results.

Description: IJGI, Vol. 7, Pages 324: An INS/Floor-Plan Indoor Localization System Using the Firefly Particle Filter ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7080324 Authors: Jian Chen Gang Ou Ao Peng Lingxiang Zheng Jianghong Shi Location-based services for smartphones are becoming more and more popular. The core of location-based services is how to estimate a user’s location. An INS/floor-plan indoor localization system, using the Firefly Particle Filter (FPF), is proposed to estimate a user’s location. INS includes an attitude angle module, a step length module and a step counting module. In the step length module, we propose a hybrid step length model. The proposed step length algorithm reasonably calculates a user’s step length. Because of sensor deviation, non-orthogonality and the user’s jitter, the main bottleneck for INS is that the error grows over time. To reduce the cumulative error, we design cascade filters including the Kalman Filter (KF) and FPF. To a certain extent, KF reduces velocity error and heading drift. On the other hand, the firefly algorithm is used to solve the particle impoverishment problem. Considering that a user may not cross an obstacle, the proposed particle filter is proposed to improve positioning performance. Results show that the average positioning error in walking experiments is 2.14 m.

Description: IJGI, Vol. 7, Pages 325: Journey-to-Crime Distances of Residential Burglars in China Disentangled: Origin and Destination Effects ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7080325 Authors: Luzi Xiao Lin Liu Guangwen Song Stijn Ruiter Suhong Zhou Research on journey-to-crime distance has revealed the importance of both the characteristics of the offender as well as those of target communities. However, the effect of the home community has so far been ignored. Besides, almost all journey-to-crime studies were done in Western societies, and little is known about how the distinct features of communities in major Chinese cities shape residential burglars’ travel patterns. To fill this gap, we apply a cross-classified multilevel regression model on data of 3763 burglary trips in ZG City, one of the bustling metropolises in China. This allows us to gain insight into how residential burglars’ journey-to-crime distances are shaped by their individual-level characteristics as well as those of their home and target communities. Results show that the characteristics of the home community have larger effects than those of target communities, while individual-level features are most influential. Older burglars travel over longer distances to commit their burglaries than the younger ones. Offenders who commit their burglaries in groups tend to travel further than solo offenders. Burglars who live in communities with a higher average rent, a denser road network and a higher percentage of local residents commit their burglaries at shorter distances. Communities with a denser road network attract burglars from a longer distance, whereas those with a higher percentage of local residents attract them from shorter by.

Description: IJGI, Vol. 7, Pages 328: Method for the Analysis and Visualization of Similar Flow Hotspot Patterns between Different Regional Groups ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7080328 Authors: Haiping Zhang Xingxing Zhou Xin Gu Lei Zhou Genlin Ji Guoan Tang Interaction among different regions can be illustrated in the form of a stream. For example, the interaction between the flows of people and information among different regions can reflect city network structures, as well as city functions and interconnections. The popularization of big data has facilitated the acquisition of flow data for various types of individuals. The application of the regional interaction model, which is based on the summary level of individual flow data mining, is currently a hot research topic. Thus far, however, previous research on spatial interaction methods has mainly focused on point-to-point and area-to-area interaction patterns, and investigations on the patterns of interaction hotspots between two regional groups with predefined neighborhood relationships, that being with two regions, remain scarce. In this study, a method for the identification of similar interaction hotspot patterns between two regional groups is proposed, and geo-information Tupu methods are applied to visualize interaction patterns. China’s air traffic flow data are used as an example to illustrate the performance of the proposed method to identify and analyze interaction hotspot patterns between regional groups with adjoining relationships across China. Research results indicate that the proposed method efficiently identifies the patterns of interaction flow hotspots between regional groups. Moreover, it can be applied to analyze any flow space in the excavation of the patterns of regional group interaction hotspots.

Description: IJGI, Vol. 7, Pages 327: A Parallel N-Dimensional Space-Filling Curve Library and Its Application in Massive Point Cloud Management ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7080327 Authors: Xuefeng Guan Peter van Oosterom Bo Cheng Because of their locality preservation properties, Space-Filling Curves (SFC) have been widely used in massive point dataset management. However, the completeness, universality, and scalability of current SFC implementations are still not well resolved. To address this problem, a generic n-dimensional (nD) SFC library is proposed and validated in massive multiscale nD points management. The library supports two well-known types of SFCs (Morton and Hilbert) with an object-oriented design, and provides common interfaces for encoding, decoding, and nD box query. Parallel implementation permits effective exploitation of underlying multicore resources. During massive point cloud management, all xyz points are attached an additional random level of detail (LOD) value l. A unique 4D SFC key is generated from each xyzl with this library, and then only the keys are stored as flat records in an Oracle Index Organized Table (IOT). The key-only schema benefits both data compression and multiscale clustering. Experiments show that the proposed nD SFC library provides complete functions and robust scalability for massive points management. When loading 23 billion Light Detection and Ranging (LiDAR) points into an Oracle database, the parallel mode takes about 10 h and the loading speed is estimated four times faster than sequential loading. Furthermore, 4D queries using the Hilbert keys take about 1~5 s and scale well with the dataset size.

Description: IJGI, Vol. 7, Pages 336: Susceptibility to Translational Slide-Type Landslides: Applicability of the Main Scarp Upper Edge as a Dependent Variable Representation Using by Reduced Chi-Square Analysis ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7090336 Authors: Marco Capitani Adriano Ribolini Monica Bini The applicability of main scarp upper edge (MSUE) as dependent variable representation was performed in a translational slide susceptibility zonation of the Milia and Roglio basins, Italy. Two landslide inventories were built thanks to detailed geomorphological mapping and aerial photograph analysis. The landslides were used to create the models before 1975, while those after 1975 were employed to validate the predictive power of the model. Possible landslide-related factors were chosen from a geomorphological survey. The inventory landslide maps and the landslide-related factor maps were processed by conditional analysis, producing landslide susceptibility maps with five susceptibility classes. A comparison between the distribution of landslides after 1975 and those derived from models provided the predictive power of each model, which in turn was used to define the best predictive model. Reduced chi-square analysis allowed to define the efficiency of MSUE as dependent variable representation. MSUE can be applied as dependent variable representation to landslide susceptibility zonation with appreciable results. In the Roglio basin, slope angle, distance from streams, and from tectonic lineaments proved to be the main controlling factors of translational slides, whereas in the Milia basin, lithology and slope angle gave more satisfactory results as landslide-predisposing factors.

Description: IJGI, Vol. 7, Pages 337: Using Eye Tracking to Explore Differences in Map-Based Spatial Ability between Geographers and Non-Geographers ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7090337 Authors: Weihua Dong Liangyu Zheng Bing Liu Liqiu Meng In this article, we use eye-tracking methods to analyze the differences in spatial ability between geographers and non-geographers regarding topographic maps, as reflected in the following three aspects: map-based spatial localization, map-based spatial orientation, and map-based spatial visualization. We recruited 32 students from Beijing Normal University (BNU) and divided them into groups of geographers and non-geographers based on their major. In terms of their spatial localization ability, geographers had shorter response times, higher fixation frequencies, and fewer saccades than non-geographers, and the differences were significant. For their spatial orientation ability, compared to non-geographers, geographers had significantly lower response times, lower fixation counts and fewer saccades as well as significantly higher fixation frequencies. In terms of their spatial visualization ability, geographers’ response times were significantly shorter than those of non-geographers, but there was no significant difference between the two groups in terms of fixation count, fixation frequency or saccade count. We also found that compared to geographers, non-geographers usually spent more time completing these tasks. The results of this study are helpful in improving the map-based spatial ability of users of topographic maps.

Description: IJGI, Vol. 7, Pages 341: Quantifying Surface Urban Heat Island Formation in the World Heritage Tropical Mountain City of Sri Lanka ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7090341 Authors: Manjula Ranagalage DMSLB Dissanayake Yuji Murayama Xinmin Zhang Ronald C. Estoque ENC Perera Takehiro Morimoto Presently, the urban heat island (UHI) phenomenon, and its adverse impacts, are becoming major research foci in various interrelated fields due to rapid changes in urban ecological environments. Various cities have been investigated in previous studies, and most of the findings have facilitated the introduction of proper mitigation measures to overcome the negative impact of UHI. At present, most of the mountain cities of the world have undergone rapid urban development, and this has resulted in the increasing surface UHI (SUHI) phenomenon. Hence, this study focuses on quantifying SUHI in Kandy City, the world heritage tropical mountain city of Sri Lanka, using Landsat data (1996 and 2017) based on the mean land surface temperature (LST), the difference between the fraction of impervious surfaces (IS), and the fraction of green space (GS). Additionally, we examined the relationship of LST to the green space/impervious surface fraction ratio (GS/IS fraction ratio) and the magnitude of the GS/IS fraction ratio. The SUHI intensity (SUHII) was calculated based on the temperature difference between main land use/cover categories and the temperature difference between urban-rural zones. We demarcated the rural zone based on the fraction of IS recorded, <10%, along with the urban-rural gradient zone. The result shows a SUHII increase from 3.9 °C in 1996 to 6.2 °C in 2017 along the urban-rural gradient between the urban and rural zones (10 < IS). These results relate to the rapid urban expansion of the study areas from 1996 to 2017. Most of the natural surfaces have changed to impervious surfaces, causing an increase of SUHI in Kandy City. The mean LST has a positive relationship with the fraction of IS and a negative relationship with the fraction of GS. Additionally, the GS/IS fraction ratio shows a rapid decline. Thus, the findings of this study can be considered as a proxy indicator for introducing proper landscape and urban planning for the World Heritage tropical mountain city of Kandy in Sri Lanka.

Description: IJGI, Vol. 7, Pages 334: Applications of Internet of Things ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7090334 Authors: Chi-Hua Chen Kuen-Rong Lo This editorial introduces the special issue entitled “Applications of Internet of Things”, of ISPRS International Journal of Geo-Information. Topics covered in this issue include three main parts: (I) intelligent transportation systems (ITS), (II) location-based services (LBS), and (III) sensing techniques and applications. Three papers on ITS are as follows: (1) “Vehicle positioning and speed estimation based on cellular network signals for urban roads,” by Lai and Kuo; (2) “A method for traffic congestion clustering judgment based on grey relational analysis,” by Zhang et al.; and (3) “Smartphone-based pedestrian’s avoidance behavior recognition towards opportunistic road anomaly detection,” by Ishikawa and Fujinami. Three papers on LBS are as follows: (1) “A high-efficiency method of mobile positioning based on commercial vehicle operation data,” by Chen et al.; (2) “Efficient location privacy-preserving k-anonymity method based on the credible chain,” by Wang et al.; and (3) “Proximity-based asynchronous messaging platform for location-based Internet of things service,” by gon Jo et al. Two papers on sensing techniques and applications are as follows: (1) “Detection of electronic anklet wearers’ groupings throughout telematics monitoring,” by Machado et al.; and (2) “Camera coverage estimation based on multistage grid subdivision,” by Wang et al.

Description: IJGI, Vol. 7, Pages 346: Place and City: Toward Urban Intelligence ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7090346 Authors: Albert Acedo Marco Painho Sven Casteleyn Stéphane Roche Place, as a concept, is subject to a lively, ongoing discussion involving different disciplines. However, most of these discussions approach the issue without a geographic perspective, which is the natural habitat of a place. This study contributes to this discourse through the exploratory examination of urban intelligence utilizing the geographical relationship between sense of place and social capital at the collective and individual level. Using spatial data collected through a web map-based survey, we perform an exhaustive examination of the spatial relationship between sense of place and social capital. We found a significant association between sense of place and social capital from a spatial point of view. Sense of place and social capital spatial dimensions obtain a non-disjoint relationship for approximately half of the participants and a spatial clustering when they are aggregated. This research offers a new exploratory perspective for place studies in the context of cities, and simultaneously attempts to depict a platial–social network based on sense of place and social capital, which cities currently lack.

Description: IJGI, Vol. 7, Pages 345: Shortest Paths from a Group Perspective—A Note on Selfish Routing Games with Cognitive Agents ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7090345 Authors: Johannes Scholz Richard L. Church This paper presents an analysis of the effects of cognitive agents employing selfish routing behavior in traffic networks with linear latency functions. Selfish routing occurs when each agent traveling on a network acts in a purely selfish manner, therefore the Braess Paradox is likely to occur. The Braess Paradox describes a situation where an additional edge with positive capacity is added to a given network, which leads to higher total system delay. By applying the concept of cognitive agents, each agent is able to make a range of non-selfish and selfish decisions. In addition, each agent has to cope with uncertainty in terms of travel time information associated with the traffic system, a factor in real-world traffic networks. This paper evaluates the influence of travel time uncertainty, and possible non-selfish decisions of the agents on overall network delay. The results indicate that both non-selfish behavior and uncertainty have an influence on overall travel delay. In addition, understanding the influence of cognitive agents on delay can help to better plan and influence traffic flows resulting in “closer to optimal” flows involving overall lower delays.

Description: IJGI, Vol. 7, Pages 356: Studies on Three-Dimensional (3D) Modeling of UAV Oblique Imagery with the Aid of Loop-Shooting ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7090356 Authors: Jia Li Yongxiang Yao Ping Duan Yun Chen Shuang Li Chi Zhang Oblique imagery obtained from an Unmanned Aerial Vehicle (UAV) has been widely applied to large-scale three-dimensional (3D) reconstruction; however, the problems of partially missing model details caused by such factors as occlusion, distortion, and airflow, are still not well resolved. In this paper, a loop-shooting-aided technology is used to solve the problem of details loss in the 3D model. The use of loop-shooting technology can effectively compensate for losses caused by occlusion, distortion, or airflow during UAV flight and enhance the 3D model details in large scene- modeling applications. Applying this technology involves two key steps. First, based on the 3D modeling construction process, the missing details of the modeling scene are found. Second, using loop-shooting image sets as the data source, incremental iterative fitting based on aerotriangulation theory is used to compensate for the missing details in the 3D model. The experimental data used in this paper were collected from Yunnan Normal University, Chenggong District, Kunming City, Yunnan Province, China. The experiments demonstrate that loop-shooting significantly improves the aerotriangulation accuracy and effectively compensates for defects during 3D large-scale model reconstruction. In standard-scale distance tests, the average relative accuracy of our modeling algorithm reached 99.87% and achieved good results. Therefore, this technique not only optimizes the model accuracy and ensures model integrity, but also simplifies the process of refining the 3D model. This study can be useful as a reference and as scientific guidance in large-scale stereo measurements, cultural heritage protection, and smart city construction.

Description: IJGI, Vol. 7, Pages 355: Achieving Complete and Near-Lossless Conversion from IFC to CityGML ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7090355 Authors: Rudi Stouffs Helga Tauscher Filip Biljecki The Singapore Government has embarked on a project to establish a three-dimensional city model and collaborative data platform for Singapore. The research herein contributes to this endeavour by developing a methodology and algorithms to automate the conversion of Building Information Models (BIM), in the Industry Foundation Classes (IFC) data format, into CityGML building models, capturing both geometric and semantic information as available in the BIM models, and including exterior as well as interior structures. We adopt a Triple Graph Grammar (TGG) to formally relate IFC and CityGML, both semantically and geometrically, and to transform a building information model, expressed as an IFC object graph, into a city model expressed as a CityGML object graph. The work pipeline includes extending the CityGML data model with an Application Domain Extension (ADE), which allows capturing information from IFC that is relevant in the geospatial context but at the same time not supported by CityGML in its standard form. In this paper, we elaborate on the triple graph grammar approach and the motivation and roadmap for the development of the ADE. While a fully complete and lossless conversion may never be achieved, this paper suggests that both a TGG and an ADE are natural choices for supporting the conversion between IFC and CityGML.

Description: IJGI, Vol. 7, Pages 352: Exploring the Factors Driving Changes in Farmland within the Tumen/Tuman River Basin ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7090352 Authors: Cholhyok Kang Yili Zhang Basanta Paudel Linshan Liu Zhaofeng Wang Ryongsu Li Understanding farmland changes and their mechanisms is important for food security and sustainable development. This study assesses the farmland changes and their drivers within the Tumen River of China and the Tuman River within the Democratic People’s Republic of Korea (DPR Korea) from 1991 to 2016 (1991–2000, 2000–2010, and 2010–2016). Farmland surfaces in Tumen/Tuman River Basin (TRB) for each of the years were mapped from satellite imagery using an object-based image segmentation and a support vector machine (SVM) approach. A logistic regression was applied to discern the mechanisms underlying farmland changes. Results indicate that cultivated surfaces changes within the two regions were characterized by large differences during the three time periods. The decreases of cultivated surface of −15.55 km2 (i.e., 0.55% of total cultivated surface area in 2000) and −23.61 km2 (i.e., 0.83% of total cultivated surface area in 2016) occurred in China between 1991 and 2000 and between 2010 and 2016, respectively; while an increase of 30.98 km2 (i.e., 1.09% of total cultivated surface area in 2010) was seen between 2000 and 2010. Cultivated surfaces increased within DPR Korea side over the three time periods; a marked increase, in particular, was seen between 1991 and 2000 by 443.93 km2 (i.e., 23.43% of total cultivated surface area in 2000), while farmland increased by 140.87 km2 (i.e., 6.92% of total cultivated surface area in 2010) and 180.86 km2 (i.e., 1.78% of total cultivated surface area in 2016), respectively, between 2000 and 2010 and between 2010 and 2016. We also found that expansions and contractions in farmland within both regions of the TRB were mainly influenced by topographic, soil, climatic, and distance factors, which had different importance degrees. Among these significant forces, the temperatures in the two regions were paramount positive factors on farmland changes during 1991–2016 and slope in China and precipitation in DPR Korea were the paramount negative factors affecting farmland changes, respectively. Additionally, except for between 2000 and 2010 in DPR Korea TRB region, most of the factors significantly influencing the farmland changes revealed the same positive or negative effects in different periods, because of mountainous topography. This study allows enhancing understanding of the mechanisms underlying farmland changes in the TRB.

Description: IJGI, Vol. 7, Pages 351: Spatially Explicit Age Segregation Index and Self-Rated Health of Older Adults in US Cities ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7090351 Authors: Guangran Deng Liang Mao There have been mixed findings on whether residential (spatial) age segregation causes better or worse health in older adults. These inconsistencies can possibly be attributed to two limitations in the previous studies. First, many studies have used statistical age composition to indicate the residential age segregation in a community, but this statistic does not consider the spatial arrangement of the residents. Second, many national scale studies have focused on averaged (or global) associations between age segregation and senior health and have assumed that these associations represent the situation in every part of a country. Little attention has been paid to local patterns of such association in different places. To address these previously identified limitations, we calculated a spatially explicit age segregation index for each United States (US) city to replace the conventional age composition index. We derived data regarding 92,560 respondents aged 65 and above in 185 US urban areas from the Behavioral Risk Factor Surveillance System (BRFSS). We then examined global and local associations between spatial age segregation and the self-rated health of older adults across US cities. Our multilevel global analysis suggested that older adults living in age-segregated metropolitan areas experienced more mentally unhealthy days. On the other hand, the local regression analysis identified local clusters of positive associations between the age segregation and the elderly’s overall health status in western and southern metropolitan areas, but no significant associations in midwestern and northeastern cities. In short, we advocated for the use of a spatially explicit approach to deepen the understanding of the association between age segregation and senior health. The new age segregation metric and new analytic approach can offer new insights into the ongoing debate regarding aging in place.

Description: IJGI, Vol. 7, Pages 363: Cross-Domain Building Models—A Step towards Interoperability ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7090363 Authors: Laura Knoth Johannes Scholz Josef Strobl Manfred Mittlböck Bernhard Vockner Caroline Atzl Abbas Rajabifard Behnam Atazadeh Buildings have a multifunctional character, which makes it hard to define just one model for all their diverse functions. As these diverse functions are addressed by actors of different perspectives and domain backgrounds, the possibility to exchange available building information would be desirable. Two main models for the creation of building information are Industry Foundation Classes/Building Information Modelling (IFC/BIM) and City Geography Markup Language (CityGML). As the importance of information interchange has been recognized, several authors have tried to develop intermediate models for the information exchange between IFC/BIM and CityGML, e.g., the Unified Building Model (UBM), the BIM Oriented Indoor data Model (BO-IDM), the Indoor Emergency Spatial Model (IESM) and the BIM-GIS integration model for Flood Damage Assessment (FDA model). Nevertheless, all these models have been created with a certain use in mind. Our focus in this article is to identify common elements amongst these proposed models and to combine them into one “core model” that is as simple as possible, while simultaneously containing all important elements. Furthermore, this base model extracted from proposed intermediate models can then be expanded to serve specific use requirements, while still being exchangeable. To show this cross-domain character of the core model, we validated the resulting model with two cases of use (production environment/maintenance and 3D digital cadaster).

Description: IJGI, Vol. 7, Pages 367: Single-Tree Detection in High-Resolution Remote-Sensing Images Based on a Cascade Neural Network ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7090367 Authors: Dong Tianyang Zhang Jian Gao Sibin Shen Ying Fan Jing Traditional single-tree detection methods usually need to set different thresholds and parameters manually according to different forest conditions. As a solution to the complicated detection process for non-professionals, this paper presents a single-tree detection method for high-resolution remote-sensing images based on a cascade neural network. In this method, we firstly calibrated the tree and non-tree samples in high-resolution remote-sensing images to train a classifier with the backpropagation (BP) neural network. Then, we analyzed the differences in the first-order statistic features, such as energy, entropy, mean, skewness, and kurtosis of the tree and non-tree samples. Finally, we used these features to correct the BP neural network model and build a cascade neural network classifier to detect a single tree. To verify the validity and practicability of the proposed method, six forestlands including two areas of oil palm in Thailand, and four areas of small seedlings, red maples, or longan trees in China were selected as test areas. The results from different methods, such as the region-growing method, template-matching method, BP neural network, and proposed cascade-neural-network method were compared considering these test areas. The experimental results show that the single-tree detection method based on the cascade neural network exhibited the highest root mean square of the matching rate (RMS_Rmat = 90%) and matching score (RMS_M = 68) in all the considered test areas.

Description: IJGI, Vol. 7, Pages 366: Introduction to the Special Issue: “State-of-the-Art Virtual/Augmented Reality and 3D Modeling Techniques for Virtual Urban Geographic Experiments” ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7090366 Authors: Jianming Liang Jianhua Gong Yu Liu n/a

Description: IJGI, Vol. 7, Pages 365: Spatial Assessment of the Potential Impact of Infrastructure Development on Biodiversity Conservation in Lowland Nepal ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7090365 Authors: Roshan Sharma Bhagawat Rimal Nigel Stork Himlal Baral Maheshwar Dhakal Biodiversity is declining at an unprecedented rate with infrastructure development being one of the leading causes. New infrastructure, such as roads, provides new access and results in increased land clearing and wildlife hunting. A number of large infrastructure projects, including new roads and rail, are being planned in Nepal. We show the application of readily available remotely sensed data and geospatial tools to assess the potential impact of these future developments on habitat quality under three protection-level scenarios. Our findings reveal that there is currently large spatial heterogeneity in habitat quality across the landscape as a result of current anthropogenic threats, and that three areas in particular could have up to 40% reduction in habitat quality as a result of the planned infrastructure. Further research is required to determine more precisely the impact on key species. Strengthening protected areas and buffer zones will contribute to mitigating degradation to some degree, however, large areas of biologically significant areas outside protected areas will be affected without new controls. Our geographic information systems (GIS) based methodology could be used to conduct studies in data poor developing countries, where rapid infrastructure development across ecological sites are ongoing, in order to make society, policy makers, and development planners aware.

Description: IJGI, Vol. 7, Pages 364: Revisiting the Role of Place in Geographic Information Science ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7090364 Authors: Helena Merschdorf Thomas Blaschke Although place-based investigations into human phenomena have been widely conducted in the social sciences over the last decades, this notion has only recently transgressed into Geographic Information Science (GIScience). Such a place-based GIS comprises research from computational place modeling on one end of the spectrum, to purely theoretical discussions on the other end. Central to all research that is concerned with place-based GIS is the notion of placing the individual at the center of the investigation, in order to assess human-environment relationships. This requires the formalization of place, which poses a number of challenges. The first challenge is unambiguously defining place, to subsequently be able to translate it into binary code, which computers and geographic information systems can handle. This formalization poses the next challenge, due to the inherent vagueness and subjectivity of human data. The last challenge is ensuring the transferability of results, requiring large samples of subjective data. In this paper, we re-examine the meaning of place in GIScience from a 2018 perspective, determine what is special about place, and how place is handled both in GIScience and in neighboring disciplines. We, therefore, adopt the view that space is a purely geographic notion, reflecting the dimensions of height, depth, and width in which all things occur and move, while place reflects the subjective human perception of segments of space based on context and experience. Our main research questions are whether place is or should be a significant (sub)topic in GIScience, whether it can be adequately addressed and handled with established GIScience methods, and, if not, which other disciplines must be considered to sufficiently account for place-based analyses. Our aim is to conflate findings from a vast and dynamic field in an attempt to position place-based GIS within the broader framework of GIScience.

Description: IJGI, Vol. 7, Pages 374: Influences of the Shadow Inventory on a Landslide Susceptibility Model ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7090374 Authors: Cheng-Chien Liu Wei Luo Hsiao-Wei Chung Hsiao-Yuan Yin Ke-Wei Yan A landslide inventory serves as the basis for assessing landslide susceptibility, hazard, and risk. It is generally prepared from optical imagery acquired from spaceborne or airborne platforms, in which shadows are inevitably found in mountainous areas. The influences of shadow inventory on a landslide susceptibility model (LSM), however, have not been investigated systematically. This paper employs both the shadow and landslide inventories prepared from eleven Formosat-2 annual images from the I-Lan area in Taiwan acquired from 2005 to 2016, using a semiautomatic expert system. A standard LSM based on the geometric mean of multivariables was used to evaluate the possible errors incurred by neglecting the shadow inventory. The results show that the LSM performance was significantly improved by 49.21% for the top 1% of the most highly susceptible area and that the performance decreased gradually by 15.25% for the top 10% most highly susceptible areas and 9.71% for the top 20% most highly susceptible areas. Excluding the shadow inventory from the calculation of landslide susceptibility index reveals the real contribution of each factor. They are crucial in optimizing the coefficients of a nondeterministic geometric mean LSM, as well as in deriving the threshold of a landslide hazard early warning system.

Description: IJGI, Vol. 7, Pages 378: Beyond Spatial Proximity—Classifying Parks and Their Visitors in London Based on Spatiotemporal and Sentiment Analysis of Twitter Data ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7090378 Authors: Anna Kovacs-Györi Alina Ristea Ronald Kolcsar Bernd Resch Alessandro Crivellari Thomas Blaschke Parks are essential public places and play a central role in urban livability. However, traditional methods of investigating their attractiveness, such as questionnaires and in situ observations, are usually time- and resource-consuming, while providing less transferable and only site-specific results. This paper presents an improved methodology of using social media (Twitter) data to extract spatial and temporal patterns of park visits for urban planning purposes, along with the sentiment of the tweets, focusing on frequent Twitter users. We analyzed the spatiotemporal park visiting behavior of more than 4000 users for almost 1700 parks, examining 78,000 tweets in London, UK. The novelty of the research is in the combination of spatial and temporal aspects of Twitter data analysis, applying sentiment and emotion extraction for park visits throughout the whole city. This transferable methodology thereby overcomes many of the limitations of traditional research methods. This study concluded that people tweeted mostly in parks 3–4 km away from their center of activity and they were more positive than elsewhere while doing so. In our analysis, we identified four types of parks based on their visitors’ spatial behavioral characteristics, the sentiment of the tweets, and the temporal distribution of the users, serving as input for further urban planning-related investigations.

Description: IJGI, Vol. 7, Pages 380: Determining Optimal Video Length for the Estimation of Building Height through Radial Displacement Measurement from Space ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7090380 Authors: Andrew Plowright Riccardo Tortini Nicholas C. Coops We presented a methodology for estimating building heights in downtown Vancouver, British Columbia, Canada, using a high definition video (HDV) recorded from the International Space Station. We developed an iterative routine based on multiresolution image segmentation to track the radial displacement of building roofs over the course of the HDV, and to predict the building heights using an ordinary least-squares regression model. The linear relationship between the length of the tracking vector and the height of the buildings was excellent (r2 ≤ 0.89, RMSE ≤ 8.85 m, p < 0.01). Notably, the accuracy of the height estimates was not improved considerably beyond 10 s of outline tracking, revealing an optimal video length for estimating the height or elevation of terrestrial features. HDVs are demonstrated to be a viable and effective data source for target tracking and building height prediction when high resolution imagery, spectral information, and/or topographic data from other sources are not available.

Description: IJGI, Vol. 7, Pages 379: Feature Extraction and Selection of Sentinel-1 Dual-Pol Data for Global-Scale Local Climate Zone Classification ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7090379 Authors: Jingliang Hu Pedram Ghamisi Xiao Xiang Zhu The concept of the local climate zone (LCZ) has been recently proposed as a generic land-cover/land-use classification scheme. It divides urban regions into 17 categories based on compositions of man-made structures and natural landscapes. Although it was originally designed for temperature study, the morphological structure concealed in LCZs also reflects economic status and population distribution. To this end, global LCZ classification is of great value for worldwide studies on economy and population. Conventional classification approaches are usually successful for an individual city using optical remote sensing data. This paper, however, attempts for the first time to produce global LCZ classification maps using polarimetric synthetic aperture radar (PolSAR) data. Specifically, we first produce polarimetric features, local statistical features, texture features, and morphological features and compare them, with respect to their classification performance. Here, an ensemble classifier is investigated, which is trained and tested on already separated transcontinental cities. Considering the challenging global scope this work handles, we conclude the classification accuracy is not yet satisfactory. However, Sentinel-1 dual-Pol SAR data could contribute the classification for several LCZ classes. According to our feature studies, the combination of local statistical features and morphological features yields the best classification results with 61.8% overall accuracy (OA), which is 3% higher than the OA produced by the second best features combination. The 3% is considerably large for a global scale. Based on our feature importance analysis, features related to VH polarized data contributed the most to the eventual classification result.

Description: IJGI, Vol. 7, Pages 153: The Implementation of Spatial Planning Objects in a 3D Cadastral Model ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7040153 Authors: Jarosław Bydłosz Agnieszka Bieda Piotr Parzych The paper concerns spatial planning in Poland and its connection with the cadastre. The Polish spatial planning system defines the set of colours, lines, hatches, etc. destined for the preparations of spatial plans, though this has so far not been followed by a spatial planning model or application schema. The aim of this paper is to create a preliminary concept of the unified modelling language (UML) schema of database integrating 3D cadastre and 3D spatial planning. The authors initially define five unified modelling language classes representing spatial planning objects (four representing spatial objects and one a dictionary list). As spatial planning and cadastres are very strongly connected, these classes are implemented into a cadastral model that had been earlier enriched with 3D classes. The final results of this research are UML diagrams based on the Polish cadastral model as defined earlier in legal regulations. They comprise original cadastral model classes, 3D cadastral objects added in earlier research work, classes representing spatial planning objects and the relationships among them. Such a solution better connects cadastre and spatial planning on a structural level and introduces 3D elements into spatial planning which has basically been done in two dimensions.

Description: IJGI, Vol. 7, Pages 158: Using the TensorFlow Deep Neural Network to Classify Mainland China Visitor Behaviours in Hong Kong from Check-in Data ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7040158 Authors: Shanshan Han Fu Ren Chao Wu Ying Chen Qingyun Du Xinyue Ye Over the past decade, big data, including Global Positioning System (GPS) data, mobile phone tracking data and social media check-in data, have been widely used to analyse human movements and behaviours. Tourism management researchers have noted the potential of applying these data to study tourist behaviours, and many studies have shown that social media check-in data can provide new opportunities for extracting tourism activities and tourist behaviours. However, traditional methods may not be suitable for extracting comprehensive tourist behaviours due to the complexity and diversity of human behaviours. Studies have shown that deep neural networks have outpaced the abilities of human beings in many fields and that deep neural networks can be explained in a psychological manner. Thus, deep neural network methods can potentially be used to understand human behaviours. In this paper, a deep learning neural network constructed in TensorFlow is applied to classify Mainland China visitor behaviours in Hong Kong, and the characteristics of these visitors are analysed to verify the classification results. For the social science classification problem investigated in this study, the deep neural network classifier in TensorFlow provides better accuracy and more lucid visualisation than do traditional neural network methods, even for erratic classification rules. Furthermore, the results of this study reveal that TensorFlow has considerable potential for application in the human geography field.

Description: IJGI, Vol. 7, Pages 157: Land Cover Mapping from Remotely Sensed and Auxiliary Data for Harmonized Official Statistics ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7040157 Authors: Hugo Costa Diana Almeida Francisco Vala Filipe Marcelino Mário Caetano This paper describes a general framework alternative to the traditional surveys that are commonly performed to estimate, for statistical purposes, the areal extent of predefined land cover classes across Europe. The framework has been funded by Eurostat and relies on annual land cover mapping and updating from remotely sensed and national GIS-based data followed by area estimation. Map production follows a series of steps, namely data collection, change detection, supervised image classification, rule-based image classification, and map updating/generalization. Land cover area estimation is based on mapping but compensated for mapping error as estimated through thematic accuracy assessment. This general structure was applied to continental Portugal, successively updating a map of 2010 for the following years until 2015. The estimated land cover change was smaller than expected but the proposed framework was proved as a potential for statistics production at the national and European levels. Contextual and structural methodological challenges and bottlenecks are discussed, especially regarding mapping, accuracy assessment, and area estimation.

Description: IJGI, Vol. 7, Pages 160: Use of DEMs Derived from TLS and HRSI Data for Landslide Feature Recognition ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7040160 Authors: Maurizio Barbarella Alessandro Di Benedetto Margherita Fiani Domenico Guida Andrea Lugli This paper addresses the problems arising from the use of data acquired with two different remote sensing techniques—high-resolution satellite imagery (HRSI) and terrestrial laser scanning (TLS)—for the extraction of digital elevation models (DEMs) used in the geomorphological analysis and recognition of landslides, taking into account the uncertainties associated with DEM production. In order to obtain a georeferenced and edited point cloud, the two data sets require quite different processes, which are more complex for satellite images than for TLS data. The differences between the two processes are highlighted. The point clouds are interpolated on a DEM with a 1 m grid size using kriging. Starting from these DEMs, a number of contour, slope, and aspect maps are extracted, together with their associated uncertainty maps. Comparative analysis of selected landslide features drawn from the two data sources allows recognition and classification of hierarchical and multiscale landslide components. Taking into account the uncertainty related to the map enables areas to be located for which one data source was able to give more reliable results than another. Our case study is located in Southern Italy, in an area known for active landslides.

Description: IJGI, Vol. 7, Pages 161: LiDAR—A Technology to Assist with Smart Cities and Climate Change Resilience: A Case Study in an Urban Metropolis ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7050161 Authors: Ryan Garnett Matthew D. Adams In this paper, we demonstrate three unique use cases of LiDAR data and processing, which can be implemented in an urban metropolis to determine the challenges that are associated with climate change. LiDAR data for the City of Toronto were collected in April 2015 with a density of 10 points/m2. We utilized both a digital terrain model and a bare earth digital elevation model in this work. The first case study estimated storm water, in which we compared flow accumulation values and catchment areas generated with a 20-m DEM and a 1-m LiDAR DEM. The finer resolution DEM demonstrated that the urban street features play a significant role in flow accumulation by directing flows. Urban catchment areas were found to occur on spatial scales that were smaller than the 20-m DEM cell size. For the second case study, the solar potential in the City of Toronto was calculated based on the slope and aspect of each land parcel. According to area, 56% of the city was found to have high solar potential, with 33% and 11% having medium and low solar potential. For the third case study, we calculated the building heights for 16,715 high-rise buildings in Toronto, which were combined with ambulance and fire emergency response times required to reach the base of the building. All buildings that had more than 17 stories were within a 5-min response time for both fire and ambulance services. Only 79% and 88% of these buildings were within a 3-min response time for ambulance and fire emergencies, respectively. LiDAR data provides a highly detailed record of the built urban environment and can provide support in the planning and assessment of climate change resilience activities.

Description: IJGI, Vol. 7, Pages 164: A Trajectory Regression Clustering Technique Combining a Novel Fuzzy C-Means Clustering Algorithm with the Least Squares Method ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7050164 Authors: Xiangbing Zhou Fang Miao Hongjiang Ma Hua Zhang Huaming Gong Rapidly growing GPS (Global Positioning System) trajectories hide much valuable information, such as city road planning, urban travel demand, and population migration. In order to mine the hidden information and to capture better clustering results, a trajectory regression clustering method (an unsupervised trajectory clustering method) is proposed to reduce local information loss of the trajectory and to avoid getting stuck in the local optimum. Using this method, we first define our new concept of trajectory clustering and construct a novel partitioning (angle-based partitioning) method of line segments; second, the Lagrange-based method and Hausdorff-based K-means++ are integrated in fuzzy C-means (FCM) clustering, which are used to maintain the stability and the robustness of the clustering process; finally, least squares regression model is employed to achieve regression clustering of the trajectory. In our experiment, the performance and effectiveness of our method is validated against real-world taxi GPS data. When comparing our clustering algorithm with the partition-based clustering algorithms (K-means, K-median, and FCM), our experimental results demonstrate that the presented method is more effective and generates a more reasonable trajectory.

Description: IJGI, Vol. 7, Pages 167: Spatial Heterogeneity, Scale, Data Character and Sustainable Transport in the Big Data Era ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7050167 Authors: Bin Jiang In light of the emergence of big data, I have advocated and argued for a paradigm shift from[...]

Description: IJGI, Vol. 7, Pages 174: Population Synthesis Handling Three Geographical Resolutions ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7050174 Authors: Ana Tsui Moreno Rolf Moeckel In this paper, we develop a synthetic population as the first step in implementing an integrated land use/transport model. The model is agent-based, where every household, person, dwelling, and job is treated as an individual object. Therefore, detailed socioeconomic and demographic attributes are required to support the model. The Iterative Proportional Updating (IPU) procedure is selected for the optimization phase. The original IPU algorithm has been improved to handle three geographical resolutions simultaneously with very little computational time. For the allocation phase, we use Monte Carlo sampling. We applied our approach to the greater Munich metropolitan area. Based on the available data in the control totals and microdata, we selected 47 attributes at the municipality level, 13 attributes at the county level, and 14 additional attributes at the borough level for the city of Munich. Attributes are aggregated at the household, dwelling, and person level. The algorithm is able to synthesize 4.5 million persons in 2.1 million households in less than 1.5 h. Directions regarding how to handle multiple geographical resolutions and how to balance the amount and order of attributes to avoid overfitting are presented.

Description: IJGI, Vol. 7, Pages 182: Semi-Supervised Ground-to-Aerial Adaptation with Heterogeneous Features Learning for Scene Classification ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7050182 Authors: Zhipeng Deng Hao Sun Shilin Zhou Currently, huge quantities of remote sensing images (RSIs) are becoming available. Nevertheless, the scarcity of labeled samples hinders the semantic understanding of RSIs. Fortunately, many ground-level image datasets with detailed semantic annotations have been collected in the vision community. In this paper, we attempt to exploit the abundant labeled ground-level images to build discriminative models for overhead-view RSI classification. However, images from the ground-level and overhead view are represented by heterogeneous features with different distributions; how to effectively combine multiple features and reduce the mismatch of distributions are two key problems in this scene-model transfer task. Specifically, a semi-supervised manifold-regularized multiple-kernel-learning (SMRMKL) algorithm is proposed for solving these problems. We employ multiple kernels over several features to learn an optimal combined model automatically. Multi-kernel Maximum Mean Discrepancy (MK-MMD) is utilized to measure the data mismatch. To make use of unlabeled target samples, a manifold regularized semi-supervised learning process is incorporated into our framework. Extensive experimental results on both cross-view and aerial-to-satellite scene datasets demonstrate that: (1) SMRMKL has an appealing extension ability to effectively fuse different types of visual features; and (2) manifold regularization can improve the adaptation performance by utilizing unlabeled target samples.

Description: IJGI, Vol. 7, Pages 185: Allocation of Tutors and Study Centers in Distance Learning Using Geospatial Technologies ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7050185 Authors: Shahid Khan Kamran Mir Ali Tahir Arshad Awan Zaib Nisa Syeda Gillani Allama Iqbal Open University (AIOU) is Pakistan’s largest distance learning institute, providing education to 1.4 million students. This is a fairly large setup across a country where students are highly geographically distributed. Currently, the system works using a manual approach, which is not efficient. Allocation of tutors and study centers to students plays a key role in creating a better learning environment for distance learning. Assigning tutors and study centers to distance learning students is a challenging task when there is a huge geographic spread. Using geospatial technologies in open and distance learning can fix allocation problems. This research analyzes real data from the twin cities Islamabad and Rawalpindi. The results show that geospatial technologies can be used for efficient and proper resource utilization and allocation, which in turn can save time and money. The overall idea fits into an improved distance learning framework and related analytics.

Description: IJGI, Vol. 7, Pages 189: The Influence of Geographic Factors on Information Dissemination in Mobile Social Networks in China: Evidence from WeChat ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7050189 Authors: Liang Liu Bin Chen Chuan Ai Lingnan He Yiping Wang Xiaogang Qiu Xin Lu Mobile social networking services provide efficient and easy communication, enabling users to create, retrieve and disseminate messages on the go while making their messages widely available. Despite growing evidence suggesting that geographic location and distance restrict online communication and interaction patterns, the role of geographic factors on the information dissemination in mobile social networks is often overlooked. We conducted a large-scale analysis on how the geographic factors influence the information dissemination in mobile social networks, by using two different datasets which recorded billions of users’ viewing and forwarding activities corresponding as well as the temporal and geographic information. The effects of two geographic factors, namely location and distance, on the probability and velocity of information dissemination were explored by measuring the geographic distribution of the four key indicators, namely viewing probability, forwarding probability, response time, and decision-making time. The results verify the distance decay effect of the information dissemination probability, and demonstrate that the velocity of information dissemination is not dependent on geographic distance. Furthermore, both the probability and velocity of information dissemination show heterogeneity and diversity of geographic location. Our research makes up for the gap in the relationship between geographic factors and information dissemination in mobile social networks. Our findings can provide suggestions for mobile social services, public opinion regulation and precision marketing.

Description: IJGI, Vol. 7, Pages 202: Site Selection of Retail Shops Based on Spatial Accessibility and Hybrid BP Neural Network ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7060202 Authors: Luyao Wang Hong Fan Yankun Wang The increase of consumer income has resulted in the rapid development of the retail industry in China, which provides high market potential for retail companies worldwide. However, site selection for retail shops has been a confusing business issue in practical business decisions. In this study, a two-step hybrid model in site selection for small retail shops was proposed. The two steps were spatial accessibility evaluation and market potential estimation. The spatial accessibility of target regions was evaluated based on the improved gravity model to determine regions that lack retail shops. Then, a PCA (principal component analysis)–BP (backpropagation network) model was established to estimate the market potential in the target regions. The two-step model could determine sites with the most market potential and low competition. We conducted the experiment in Guiyang, China and considered 18 socioeconomic factors to make the site selection convincing. Through the experiment, 42 locations were determined with high business value; the locations were recommended to the new retail shops. The accuracy of the PCA–BP model was then proven satisfactory by comparing it with other regression methods. The proposed model could guide retail chains in enhancing business location planning and formulating regional development policies.

Description: IJGI, Vol. 7, Pages 205: The Implications of Field Worker Characteristics and Landscape Heterogeneity for Classification Correctness and the Completeness of Topographical Mapping ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7060205 Authors: Kiira Mõisja Evelyn Uuemaa Tõnu Oja The quality of spatial data may vary spatially. If mapping (interpretation of orthophotos) is done during fieldwork, this variation in quality may occur as a result of differences in the complexity of the landscape, differences in the characteristics of individual field workers, and differences in their perception of the landscape. In this study, we explored the interaction between the characteristics of these workers, including their gender and years of experience (as a proxy for their mapping skills), and landscape heterogeneity. There was no significant difference between male and female workers. Although field workers with more years of experience generally had higher mapping quality, the relationship was not statistically significant. We found differences in the rates of misclassification, omission, and commission errors between workers in different landscape types. We conclude that the error rates due to misclassification, omission, and commission were the lowest in more diverse landscapes (high number of different land use types) with a relatively high amount of buildings, whereas the error rates were the highest in mainly forested landscapes with larger and more complex shaped patches.

Description: IJGI, Vol. 7, Pages 201: Examining the Stream Threshold Approaches Used in Hydrologic Analysis ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7060201 Authors: İbrahim Murat Ozulu Türkay Gökgöz The stream threshold is a user-defined and important parameter that directly affects the drainage network and basin boundaries that would be obtained as a result of hydrological analysis. There have been several approaches developed for the stream threshold. According to the approaches treated in this study, the stream threshold is determined based on (1) the flow accumulation statistics: (a) 1% of the maximum flow accumulation value and (b) the mean flow accumulation value; (2) the flow accumulation values at the cells including the beginning points of the line features representing the existing streams; and (3) the adjacency and direction relationships between the cells including the beginning points of the lines in the drainage network to be derived. In this paper, a new approach as an integrant of the last approach was introduced (not only the adjacency and direction relationships between the cells including the beginning points, but also the adjacency and direction relationships amongst all of the cells, as well as including the lines considered). An experiment was conducted in accordance with the purpose of comparing and evaluating these approaches. According to the experimental results, it was found that the last two approaches differed from the first three approaches in terms of providing clues to the user regarding the quality and the quantity of the drainage network to be derived.

Description: IJGI, Vol. 7, Pages 204: An Automated Processing Method for Agglomeration Areas ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7060204 Authors: Chengming Li Yong Yin Xiaoli Liu Pengda Wu Agglomeration operations are a core component of the automated generalization of aggregated area groups. However, because geographical elements that possess agglomeration features are relatively scarce, the current literature has not given sufficient attention to agglomeration operations. Furthermore, most reports on the subject are limited to the general conceptual level. Consequently, current agglomeration methods are highly reliant on subjective determinations and cannot support intelligent computer processing. This paper proposes an automated processing method for agglomeration areas. Firstly, the proposed method automatically identifies agglomeration areas based on the width of the striped bridging area, distribution pattern index (DPI), shape similarity index (SSI), and overlap index (OI). Next, the progressive agglomeration operation is carried out, including the computation of the external boundary outlines and the extraction of agglomeration lines. The effectiveness and rationality of the proposed method has been validated by using actual census data of Chinese geographical conditions in the Jiangsu Province.

Description: IJGI, Vol. 7, Pages 259: Inferencing Human Spatiotemporal Mobility in Greater Maputo via Mobile Phone Big Data Mining ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7070259 Authors: Mohamed Batran Mariano Gregorio Mejia Hiroshi Kanasugi Yoshihide Sekimoto Ryosuke Shibasaki The mobility patterns and trip behavior of people are usually extracted from data collected by traditional survey methods. However, these methods are generally costly and difficult to implement, especially in developing cities with limited resources. The massive amounts of call detail record (CDR) data passively generated by ubiquitous mobile phone usage provide researchers with the opportunity to innovate alternative methods that are inexpensive and easier and faster to implement than traditional methods. This paper proposes a method based on proven techniques to extract the origin–destination (OD) trips from the raw CDR data of mobile phone users and process the data to capture the mobility of those users. The proposed method was applied to 3.4 million mobile phone users over a 12-day period in Mozambique, and the data processed to capture the mobility of people living in the Greater Maputo metropolitan area in different time frames (weekdays and weekends). Subsequently, trip generation maps, attraction maps, and the OD matrix of the study area, which are all practically usable for urban and transportation planning, were generated. Furthermore, spatiotemporal interpolation was applied to all OD trips to reconstruct the population distribution in the study area on an average weekday and weekend. Comparison of the results obtained with actual survey results from the Japan International Cooperation Agency (JICA) indicate that the proposed method achieves acceptable accuracy. The proposed method and study demonstrate the efficacy of mining big data sources, particularly mobile phone CDR data, to infer the spatiotemporal human mobility of people in a city and understand their flow pattern, which is valuable information for city planning.

Description: IJGI, Vol. 7, Pages 260: Measuring Selection Diversity of Emergency Medical Service for Metro Stations: A Case Study in Beijing ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7070260 Authors: Zhe Zhang Limin Jia Yong Qin It is important for station managers and emergency medical service (EMS) managers to understand the available ambulance stations for each metro station. EMS managers can make use of this information to reallocate EMS resources to improve the performance of EMS system. Station managers can pay attention to the safety management of metro stations with fewer medical rescue support. This paper aims to develop a selection diversity index to address two questions: “How many available ambulance stations are available or can serve the rescue of metro stations in emergency?” and “Which ambulance stations are most vulnerable?”. To implement this measure in practice, definition of available ambulance stations for rescue of metro stations is described based on the response time threshold. The selection diversity is proposed to measure the preparability of medical rescue service for metro stations. To show proof of concept, a real-world case is presented to demonstrate the feasibility of the selection diversity index.

Description: IJGI, Vol. 7, Pages 261: The Influence of the Shape and Size of the Cell on Developing Military Passability Maps ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7070261 Authors: Krzysztof Pokonieczny Albina Mościcka The necessity to divide the analysed area into basic elements, regardless of the administrative division (cells or pixels, also called primary fields), and use them to prepare thematic maps emerged as early as by the end of the 19th century. The automation of map development processes brought a new approach to the function of cells, which made them a carrier that facilitates information processing, and presenting the results of analyses in the form of studies that very often function only in spatial information systems or on the Internet. Cells are currently used to conduct a series of advanced spatial analyses in practically all areas of application. The aim of the presented research was to analyse the influence of the shape and size of cells on the terrain classification results for the purposes of developing military passability maps. The research used the automatic terrain classification method, based on calculating the index of passability, calculated for cells of square, triangular, and hexagonal shapes and of different sizes, ranging from 100 m to 10,000 m. Indices of passability were determined basing on parameters derived from land cover elements that exist in the area of each of the adopted cells. Because of the fact that passability maps are mainly developed for military purposes, the study used a standardised vector spatial database—VMap Level 2. The studies have demonstrated that, if the surface areas of cells are identical, their shapes do not have a significant influence on the resulting passability map. The authors have also determined the sizes of cells that should be adopted for developing passability maps on various levels of accuracy, and, as a consequence, for being used on various levels of command of military troops.

Description: IJGI, Vol. 7, Pages 262: Historical Collaborative Geocoding ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7070262 Authors: Rémi Cura Bertrand Dumenieu Nathalie Abadie Benoit Costes Julien Perret Maurizio Gribaudi The latest developments in the field of digital humanities have increasingly enabled the construction of large data sets which can be easily accessed and used. These data sets often contain indirect spatial information, such as historical addresses. Historical geocoding is the process of transforming indirect spatial information into direct locations which can be placed on a map, thus allowing for spatial analysis and cross-referencing. There are many geocoders that work efficiently for current addresses. However, these do not tackle temporal information, and usually follow a strict hierarchy (country, city, street, house number, etc.) which is difficult—if not impossible—to use with historical data. Historical data is filled with uncertainty (pertaining to temporal, textual, and positional accuracy, as well as to the reliability of historical sources) which can neither be ignored nor entirely resolved. Our open source, open data, and extensible solution for geocoding is based on extracting a large number of simple gazetteers composed of geohistorical objects, from historical maps. Geocoding a historical address becomes the process of finding one or several geohistorical objects in the gazetteers which best match the historical address searched by the user. The matching criteria are customisable, weighted, and include several dimensions (fuzzy string, fuzzy temporal, level of detail, positional accuracy). Since our goal is to facilitate historical work, we also put forward web-based user interfaces which help geocode (one address or batch mode) and display results over current or historical maps. Geocoded results can then be checked and edited collaboratively (no source is modified). The system was tested on the city of Paris, France, for the 19th and 20th centuries. It showed high response rates and worked quickly enough to be used interactively.

Description: IJGI, Vol. 7, Pages 266: Association Rules-Based Multivariate Analysis and Visualization of Spatiotemporal Climate Data ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7070266 Authors: Feng Wang Wenwen Li Sizhe Wang Chris R. Johnson Understanding atmospheric phenomena involves analysis of large-scale spatiotemporal multivariate data. The complexity and heterogeneity of such data pose a significant challenge in discovering and understanding the association between multiple climate variables. To tackle this challenge, we present an interactive heuristic visualization system that supports climate scientists and the public in their exploration and analysis of atmospheric phenomena of interest. Three techniques are introduced: (1) web-based spatiotemporal climate data visualization; (2) multiview and multivariate scientific data analysis; and (3) data mining-enabled visual analytics. The Arctic System Reanalysis (ASR) data are used to demonstrate and validate the effectiveness and usefulness of our method through a case study of “The Great Arctic Cyclone of 2012”. The results show that different variables have strong associations near the polar cyclone area. This work also provides techniques for identifying multivariate correlation and for better understanding the driving factors of climate phenomena.

Description: IJGI, Vol. 7, Pages 270: Shared Execution Approach to ε-Distance Join Queries in Dynamic Road Networks ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7070270 Authors: Hyung-Ju Cho Given a threshold distance ε and two object sets R and S in a road network, an ε-distance join query finds object pairs from R × S that are within the threshold distance ε (e.g., find passenger and taxicab pairs within a five-minute driving distance). Although this is a well-studied problem in the Euclidean space, little attention has been paid to dynamic road networks where the weights of road segments (e.g., travel times) are frequently updated and the distance between two objects is the length of the shortest path connecting them. In this work, we address the problem of ε-distance join queries in dynamic road networks by proposing an optimized ε-distance join algorithm called EDISON, the key concept of which is to cluster adjacent objects of the same type into a group, and then to optimize shared execution for the group to avoid redundant network traversal. The proposed method is intuitive and easy to implement, thereby allowing its simple integration with existing range query algorithms in road networks. We conduct an extensive experimental study using real-world roadmaps to show the efficiency and scalability of our shared execution approach.

Description: IJGI, Vol. 7, Pages 271: LandQv2: A MapReduce-Based System for Processing Arable Land Quality Big Data ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7070271 Authors: Xiaochuang Yao Mohamed F. Mokbel Sijing Ye Guoqing Li Louai Alarabi Ahmed Eldawy Zuliang Zhao Long Zhao Dehai Zhu Arable land quality (ALQ) data are a foundational resource for national food security. With the rapid development of spatial information technologies, the annual acquisition and update of ALQ data covering the country have become more accurate and faster. ALQ data are mainly vector-based spatial big data in the ESRI (Environmental Systems Research Institute) shapefile format. Although the shapefile is the most common GIS vector data format, unfortunately, the usage of ALQ data is very constrained due to its massive size and the limited capabilities of traditional applications. To tackle the above issues, this paper introduces LandQv2, which is a MapReduce-based parallel processing system for ALQ big data. The core content of LandQv2 is composed of four key technologies including data preprocessing, the distributed R-tree index, the spatial range query, and the map tile pyramid model-based visualization. According to the functions in LandQv2, firstly, ALQ big data are transformed by a MapReduce-based parallel algorithm from the ESRI Shapefile format to the GeoCSV file format in HDFS (Hadoop Distributed File System), and then, the spatial coding-based partition and R-tree index are executed for the spatial range query operation. In addition, the visualization of ALQ big data with a GIS (Geographic Information System) web API (Application Programming Interface) uses the MapReduce program to generate a single image or pyramid tiles for big data display. Finally, a set of experiments running on a live system deployed on a cluster of machines shows the efficiency and scalability of the proposed system. All of these functions supported by LandQv2 are integrated into SpatialHadoop, and it is also able to efficiently support any other distributed spatial big data systems.

Description: IJGI, Vol. 7, Pages 272: Data Extraction Algorithm for Energy Performance Certificates (EPC) to Estimate the Maximum Economic Damage of Buildings for Economic Impact Assessment of Floods in Flanders, Belgium ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7070272 Authors: Samuel Van Ackere Jochem Beullens Alain De Wulf Philippe De Maeyer Floods cause major disruptions to energy supply and transportation facilities and lead to significant impacts on the society, economy, and environment. As a result, there is a compelling need for resilience and adaptation against extreme flood events under a changing climate. An accurate focal priority analysis of how societies can adapt to these changing events can provide insight into practical solutions. Besides the social, ecological, and cultural impact assessments of floods, an accurate economic impact analysis is required to define priority zones and priority measures. Unfortunately, studies show that economic impact assessments can be highly inaccurate because of the margin of error in economic value estimation of residential and industrial buildings, as they account for a large part of the total economic damage value. Therefore, tools that can accurately estimate the maximum economic damage value (or replacement value) of residential and industrial buildings are imperative. This paper outlines a methodology to estimate the maximum economic value of buildings by using a data extraction algorithm for Energy Performance Certificates (EPC), through which the replacement value can be calculated for all of the buildings in Flanders, and in addition, across Europe.

Description: IJGI, Vol. 7, Pages 268: Landslide Susceptibility Assessment at Mila Basin (Algeria): A Comparative Assessment of Prediction Capability of Advanced Machine Learning Methods ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7070268 Authors: Abdelaziz Merghadi Boumezbeur Abderrahmane Dieu Tien Bui Landslide risk prevention requires the delineation of landslide-prone areas as accurately as possible. Therefore, selecting a method or a technique that is capable of providing the highest landslide prediction capability is highly important. The main objective of this study is to assess and compare the prediction capability of advanced machine learning methods for landslide susceptibility mapping in the Mila Basin (Algeria). First, a geospatial database was constructed from various sources. The database contains 1156 landslide polygons and 16 conditioning factors (altitude, slope, aspect, topographic wetness index (TWI), landforms, rainfall, lithology, stratigraphy, soil type, soil texture, landuse, depth to bedrock, bulk density, distance to faults, distance to hydrographic network, and distance to road networks). Subsequently, the database was randomly resampled into training sets and validation sets using 5 times repeated 10 k-folds cross-validations. Using the training and validation sets, five landslide susceptibility models were constructed, assessed, and compared using Random Forest (RF), Gradient Boosting Machine (GBM), Logistic Regression (LR), Artificial Neural Network (NNET), and Support Vector Machine (SVM). The prediction capability of the five landslide models was assessed and compared using the receiver operating characteristic (ROC) curve, the area under the ROC curves (AUC), overall accuracy (Acc), and kappa index. Additionally, Wilcoxon signed-rank tests were performed to confirm statistical significance in the differences among the five machine learning models employed in this study. The result showed that the GBM model has the highest prediction capability (AUC = 0.8967), followed by the RF model (AUC = 0.8957), the NNET model (AUC = 0.8882), the SVM model (AUC = 0.8818), and the LR model (AUC = 0.8575). Therefore, we concluded that GBM and RF are the most suitable for this study area and should be used to produce landslide susceptibility maps. These maps as a technical framework are used to develop countermeasures and regulatory policies to minimize landslide damages in the Mila Basin. This research demonstrated the benefit of selecting the best-advanced machine learning method for landslide susceptibility assessment.

Description: IJGI, Vol. 7, Pages 269: Geospatial Analysis and the Internet of Things ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7070269 Authors: Andreas Kamilaris Frank O. Ostermann As the Internet of Things (IoT) penetrates our everyday lives, being used to address a wide variety of real-life challenges and problems, the location of things becomes an important parameter. The exact location of measuring the physical world through IoT is highly relevant to understand local environmental conditions, or to develop powerful, personalized and context-aware location-based services and applications. This survey paper maps and analyzes the IoT based on its location dimension, categorizing IoT applications and projects according to the geospatial analytical methods performed. The survey investigates the opportunities of location-aware IoT, and examines the potential of geospatial analysis in this research area.

Description: IJGI, Vol. 7, Pages 276: Design and Implementation of a 4D Web Application for Analytical Visualization of Smart City Applications ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7070276 Authors: Syed Monjur Murshed Ayah Mohammad Al-Hyari Jochen Wendel Louise Ansart Contemporary development of computer hardware and software, WebGIS and geo-web services as well as the availability of semantic 3D city models, facilitate flexible and dynamic implementation of web applications. The aim of this paper is to introduce 4D CANVAS, a web-based application for dynamic visualization of 3D geospatial data for improved decision making in smart city applications. It is based on the Cesium Virtual Globe, an open-source JavaScript library developed with HTML5 and WebGL. At first, different data formats such as JSON, GeoJSON, Cesium Markup Language (CZML) and 3D Tiles are evaluated for their suitability in 4D visualization applications. Then, an interactive Graphical User Interface (GUI) is built observing the principle of cartographic standards to view, manage, understand and explore different simulation outputs at multiple spatial (3D surface of buildings) and temporal (hourly, daily, monthly) resolutions. In this regard, multiple tools such as aggregation, data classification, etc. are developed utilizing JavaScript libraries. As a proof of concept, two energy simulations and their outputs of different spatial and temporal resolutions are demonstrated in five Asian and European cities. Finally, the 4D CANVAS is deployed both in desktop and multi-touch screens. The proposed application allows easy integration of any other geospatial simulation results, thereby helps the users from different sectors to explore them interactively in 4D.

Description: IJGI, Vol. 7, Pages 275: Temporal Variations and Associated Remotely Sensed Environmental Variables of Dengue Fever in Chitwan District, Nepal ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7070275 Authors: Bipin Kumar Acharya Chunxiang Cao Min Xu Laxman Khanal Shahid Naeem Shreejana Pandit Dengue fever is one of the leading public health problems of tropical and subtropical countries across the world. Transmission dynamics of dengue fever is largely affected by meteorological and environmental factors, and its temporal pattern generally peaks in hot-wet periods of the year. Despite this continuously growing problem, the temporal dynamics of dengue fever and associated potential environmental risk factors are not documented in Nepal. The aim of this study was to fill this research gap by utilizing epidemiological and earth observation data in Chitwan district, one of the frequent dengue outbreak areas of Nepal. We used laboratory confirmed monthly dengue cases as a dependent variable and a set of remotely sensed meteorological and environmental variables as explanatory factors to describe their temporal relationship. Descriptive statistics, cross correlation analysis, and the Poisson generalized additive model were used for this purpose. Results revealed that dengue fever is significantly associated with satellite estimated precipitation, normalized difference vegetation index (NDVI), and enhanced vegetation index (EVI) synchronously and with different lag periods. However, the associations were weak and insignificant with immediate daytime land surface temperature (dLST) and nighttime land surface temperature (nLST), but were significant after 4–5 months. Conclusively, the selected Poisson generalized additive model based on the precipitation, dLST, and NDVI explained the largest variation in monthly distribution of dengue fever with minimum Akaike’s Information Criterion (AIC) and maximum R-squared. The best fit model further significantly improved after including delayed effects in the model. The predicted cases were reasonably accurate based on the comparison of 10-fold cross validation and observed cases. The lagged association found in this study could be useful for the development of remote sensing-based early warning forecasts of dengue fever.

Description: IJGI, Vol. 7, Pages 281: Using Eye Tracking to Evaluate the Usability of Flow Maps ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7070281 Authors: Weihua Dong Shengkai Wang Yizhou Chen Liqiu Meng Flow maps allow users to perceive not only the location where interactions take place, but also the direction and volume of events. Previous studies have proposed numerous methods to produce flow maps. However, how to evaluate the usability of flow maps has not been well documented. In this study, we combined eye-tracking and questionnaire methods to evaluate the usability of flow maps through comparisons between (a) straight lines and curves and (b) line thicknesses and color gradients. The results show that curved flows are more effective than straight flows. Maps with curved flows have more correct answers, fixations, and percentages of fixations in areas of interest. Furthermore, we find that the curved flows require longer finish times but exhibit smaller times to first fixation than straight flows. In addition, we find that using color gradients to indicate the flow volume is significantly more effective than the application of different line thicknesses, which is mainly reflected by the presence of more correct answers in the color-gradient group. These empirical studies could help improve the usability of flow maps employed to visualize geo-data.

Description: IJGI, Vol. 7, Pages 278: The Impact of the Parameterisation of Physiographic Features of Urbanised Catchment Areas on the Spatial Distribution of Components of the Water Balance Using the WetSpass Model ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7070278 Authors: Renata Graf Kamil Jawgiel An analysis was conducted of the activity of individual homogeneous Hydrological Response Units (HRUs) and their impact on the components of the spatially distributed water balance based on the example of two urbanised catchments of the city of Poznań (Western Poland). Water balance was developed using the WetSpass model and GIS spatial data, based on hydrometeorological data from the reference period of 1961–2000 including projected land usage changes and precipitation changes expected by 2025 in the city. The catchments were parameterised with reference to land usage, soil permeability, terrain declivities and the level of groundwater waters in summer and winter. The dependence between HRUs and their impact on components of the water balance was determined. Water balance forecasts have shown two-way changes in the components of approximately 12% of the catchments. A significant increase of surface runoff (an increase of 20–30 mm/HRU) at the expense of effective infiltration reduction (by 15–20 mm/HRU) was determined for arable land intended for development. An increase of infiltration and evapotranspiration at the expense of the surface runoff reduction is forecast for areas designed for urban afforestation. The tendency of increase of atmospheric precipitation within the city until 2025 was indicated by changes in the water balance components. Changes in the landscape resulting from urban expansion may lead to detrimental hydrological effects: accumulation of surface runoffs and occurrence of local flash flooding, as confirmed by the simulations carried out using the WetSpass model. The results may contribute to a more accurate understanding of the impact of urban landscape modification patterns on the water balance at the regional and local scale.

Description: IJGI, Vol. 7, Pages 279: 3D WebGIS: From Visualization to Analysis. An Efficient Browser-Based 3D Line-of-Sight Analysis ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7070279 Authors: Michael Auer Alexander Zipf 3D WebGIS systems have been mentioned in the literature almost since the beginning of the graphical web era in the late 1990s. The potential use of 3D WebGIS is linked to a wide range of scientific and application domains, such as planning, controlling, tracking or simulation in crisis management, military mission planning, urban information systems, energy facilities or cultural heritage management, just to name a few. Nevertheless, many applications or research prototypes entitled as 3D WebGIS or similar are mainly about 3D visualization of GIS data or the visualization of analysis results, rather than about performing the 3D analysis itself online. This research paper aims to step forward into the direction of web-based 3D geospatial analysis. It describes how to overcome speed and memory restrictions in web-based data management by adapting optimization strategies, developed earlier for web-based 3D visualization. These are applied in a holistic way in the context of a fully 3D line-of-sight computation over several layers with split (tiled) and unsplit (static) data sources. Different optimization approaches are combined and evaluated to enable an efficient client side analysis and a real 3D WebGIS functionality using new web technologies such as HTML5 and WebGL.

Description: IJGI, Vol. 7, Pages 280: Using Geospatial Analysis and Hydrologic Modeling to Estimate Climate Change Impacts on Nitrogen Export: Case Study for a Forest and Pasture Dominated Watershed in North Carolina ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7070280 Authors: Md Jahangir Alam Mehmet B. Ercan Faria Tuz Zahura Jonathan L. Goodall Many watersheds are currently experiencing streamflow and water quality related problems that are caused by excess nitrogen. Given that weather is a major driver of nitrogen transport through watersheds, the objective of this study was to predict climate change impacts on streamflow and nitrogen export. A forest and pasture dominated watershed in North Carolina Piedmont region was used as the study area. A physically-based Soil and Water Assessment Tool (SWAT) model parameterized using geospatial data layers and spatially downscaled temperature and precipitation estimates from eight different General Circulation Models (GCMs) were used for this study. While temperature change predictions are fairly consistent across the GCMs for the study watershed, there is significant variability in precipitation change predictions across the GCMs, and this leads to uncertainty in the future conditions within the watershed. However, when the downscaled GCM projections were taken as a model ensemble, the results suggest that both high and low emission scenarios would result in an average increase in streamflow of 14.1% and 12.5%, respectively, and a decrease in the inorganic nitrogen export by 12.1% and 8.5%, respectively, by the end of the century. The results also show clear seasonal patterns with streamflow and nitrogen loading both increasing in fall and winter months by 97.8% and 50.8%, respectively, and decreasing by 20.2% and 35.5%, respectively, in spring and summer months by the end of the century.

Description: IJGI, Vol. 7, Pages 256: Relevance Assessment of Crowdsourced Data (CSD) Using Semantics and Geographic Information Retrieval (GIR) Techniques ISPRS International Journal of Geo-Information doi: 10.3390/ijgi7070256 Authors: Saman Koswatte Kevin McDougall Xiaoye Liu Crowdsourced data (CSD) generated by citizens is becoming more popular as its potential utilization in many applications increases due to its currency and availability. However, the quality of CSD, including its relevance, is often questioned as the data is not generated by professionals nor follows standard data-collection procedures. The quality of CSD can be assessed according to a range of characteristics including its relevance. In this paper, information relevance has been explored through using geographic information retrieval (GIR) techniques to identify the most highly relevant information from a set of crowdsourced data. This research tested a relevance assessment approach for CSD by adapting relevance assessment techniques available in the GIR domain. Thematic and geographic relevance were assessed by analyzing the frequency of selected terms which appeared in CSD reports using natural language processing techniques. The study analyzed crowdsourced reports from the 2011 Australian flood’s Crowdmap to examine a proof of concept on relevance assessment using a subset of this dataset based on a defined set of queries. The results determined that the thematic and geographic specificities of the queries were 0.44 and 0.67, respectively, which indicated the queries used were more geographically specific than thematically specific. The Spearman’s rho value of 0.62 indicated that the final ranked relevance lists showed reasonable agreement with a manually classified list and confirmed the potential of the approach for CSD relevance assessment. In particular, this research has contributed to the field of CSD relevance assessment through an integrated thematic and geographic relevance ranking process by using a user-query specificity approach to improve the final ranking.