ISPRS Int. J. Geo-Inf., Volume 8, Issue 4 (April 2019) – 40 articles

In a point set in dimension superior to 1, the statistical distribution of the number of pairs of points as a function of distance between the points of the pair is not uniform. This distribution is not considered in a large number of classic methods based on spatially weighted means used in spatial analysis, such as spatial autocorrelation indices, kernel interpolation methods, or spatial modeling methods (autoregressive, or geographically weighted). It has a direct impact on the calculations and the results of indices and estimations, and by not taking into account this distribution of the distances, spatial analysis calculations can be biased. In this article, we introduce a “spatial standardization”, which corrects and adjusts the calculations with respect to the distribution of point pairs distances. As an example, we apply this correction to the calculation of spatial autocorrelation indices (Moran and Geary indices) and to trend surface calculation (by spatial kernel interpolation) on the results of the 2017 French presidential election. Full article

Transport decision-making problems are typically spatially based and involve a set of feasible alternatives with multiple evaluation criteria. Besides, transport decisions affect citizens’ quality of life, as well as specific interests of general stakeholders (e.g., transport companies), thus needing a participatory approach to decision-making. Geographic Information Systems (GIS) have the ability to visualize spatial data and represent the impact of location based transport alternatives, thus helping experts to conduct robust assessments. Moreover, with the recent diffusion of Volunteered Geographic Information (VGI) and development of Public Participatory GIS (PPGIS) platforms, the process can be enhanced thanks to the collection of a large amount of updated spatial data and the achievement of an active community participation. In this study, we provide an overview based on a structured literature review of the use of VGI and PPGIS in transport studies, exploring the fields of application, role played by GIS, level of public involvement and decision stage at which they are applied. From the overview’s results, we propose a general framework for the evaluation of transport alternatives using GIS from a multiple stakeholder point of view; the main conclusion is the usefulness of the integration between Public Participation, GIS and quantitative evaluation methods, in particular Multi Criteria Decision Analysis, in order to foster technically sound and shared decisions. Full article

Structural measures for retaining and distributing water—i.e., reservoirs, flood retention and power plants—play a key role to protect and feed a growing world population in a rapidly changing climate. In this work, we introduce an automated method to detect potential reservoir or retention area locations in digital terrain models. In this context, a potential reservoir is a larger terrain form that can be turned into an actual reservoir by constructing a dam. Based on contour lines derived from terrain models, potential reservoirs are found within a predefined range of dam lengths, and the locally optimal ones are then extracted. Our method is to be applied in the very early stages of project planning and for area-wide potential analysis. Tests in a 100 km² study area bring promising results, but also show a certain sensitivity regarding terrain model quality and resolution. In total, 250–300 candidate polygons with a total volume of more than 6 million m³ were found. In order to facilitate further processing, these are stored as a GIS vector dataset. Full article

Topographic features impact biomass and other agriculturally relevant observables. However, conventional tools for processing digital elevation model (DEM) data in geographic information systems have severe limitations. Typically, 3-by-3 window sizes are used for evaluating the slope, aspect and curvature. As a consequence, high resolution DEMs have to be resampled to match the size of typical topographic features, resulting in low accuracy and limiting the predictive ability of any model using such features. In this paper, we examined the usefulness of DEM-derived topographic features within Random Forest models that predict biomass. Our model utilized the derived topographic features and achieved 95.31% accuracy in predicting Normalized Difference Vegetation Index (NDVI) compared to a 51.89% accuracy obtained for window size 3-by-3 in the traditional resampling model. The efficacy of partial dependency plots (PDP) in terms of interpretability was also assessed. Full article

This article aims at testing the possibilities of applying hierarchical spatial autoregressive models to create land value maps in urbanized areas. The use of HSAR (Hierarchical Spatial Autoregressive) models for spatial differentiation of prices in the property market supports the multilevel diagnosis of the structure of this phenomenon, taking into account the effect of spatial interactions. The article applies a two-level hierarchical spatial autoregressive model, which will permit the evaluation of interactions and control spatial heterogeneity at two levels of spatial aggregation (general and detailed). The results of the research include both the evaluation of the impact of location on prices (taking into account non-spatial factors) and the creation of the average land price map, taking into consideration the spatial structure of the city. In empirical studies, the HSAR model was compared with classic LM (Linear Model), HLM (Hierarchical Linear Model), and SAR (Spatial Autoregressive) models to perform comparative analyses of the results. Full article

Wildfire, a natural part of many ecosystems, has also resulted in significant disasters impacting ecology and human life in Australia. This study proposes a prototype of fire propagation prediction as an extension of preceding research; this system is called “Cloud computing based bushfire prediction”, the computational performance of which is expected to be about twice that of the traditional client-server (CS) model. As the first step in the modelling approach, this prototype focuses on the prediction of fire propagation. The direction of fire is limited in regular grid approaches, such as cellular automata, due to the shape of the uniformed grid, while irregular grids are freed from this constraint. In this prototype, fire propagation is computed from a centroid regardless of grid shape to remove the above constraint. Additionally, the prototype employs existing fire indices, including the Grassland Fire Danger Index (GFDI), Forest Fire Danger Index (FFDI) and Button Grass Moorland Fire Index (BGML). A number of parameters, such as Digital Elevation Model (DEM) and forecast weather data, are prepared for use in the calculation of the indices above. The fire study area is located around Lake Mackenzie in the central north of Tasmania where a fire burnt approximately 247.11 ${\mathrm{km}}^2$ in January 2016. The prototype produces nine different prediction results with three polygon configurations, including Delaunay Triangulation, Square and Voronoi, using three different resolutions: fine, medium and coarse. The Delaunay Triangulation, which has the greatest number of adjacent grids among three shapes of polygon, shows the shortest elapsed time for spread of fire compared to other shapes. The medium grid performs the best trade-off between cost and time among the three grain sizes of prediction polygons, and the coarse size shows the best cost-effectiveness. A staging approach where coarse size prediction is released initially, followed by a medium size one, can be a pragmatic solution for the purpose of providing timely evacuation guidance. Full article

So-called prismatic 3D building models, following the level-of-detail (LOD) 1 of the OGC City Geography Markup Language (CityGML) standard, are usually generated automatically by combining building footprints with height values. Typically, high-resolution digital elevation models (DEMs) or dense LiDAR point clouds are used to generate these building models. However, high-resolution LiDAR data are usually not available with extensive coverage, whereas globally available DEM data are often not detailed and accurate enough to provide sufficient input to the modeling of individual buildings. Therefore, this paper investigates the possibility of generating LOD1 building models from both volunteered geographic information (VGI) in the form of OpenStreetMap data and remote sensing-derived geodata improved by multi-sensor and multi-modal DEM fusion techniques or produced by synthetic aperture radar (SAR)-optical stereogrammetry. The results of this study show several things: First, it can be seen that the height information resulting from data fusion is of higher quality than the original data sources. Secondly, the study confirms that simple, prismatic building models can be reconstructed by combining OpenStreetMap building footprints and easily accessible, remote sensing-derived geodata, indicating the potential of application on extensive areas. The building models were created under the assumption of flat terrain at a constant height, which is valid in the selected study area. Full article

Smart tourism is the new frontier field of the tourism research. To solve current problems of smart tourism and tourism geographic information system (GIS), individualized tour guide route plan algorithm based on tourist sight spatial interest field is set up in the study. Feature interest tourist sight extracting matrix is formed and basic modeling data is obtained from mass tourism data. Tourism groups are determined by age index. Different age group tourists have various interests; thus interest field mapping model is set up based on individual needs and interests. Random selecting algorithm for selecting interest tourist sights by smart machine is designed. The algorithm covers all tourist sights and relative data information to ensure each tourist sight could be selected equally. In the study, selected tourist sights are set as important nodes while iteration intervals and sub-iteration intervals are defined. According to the principle of proximity and completely random, motive iteration clusters and sub-clusters are formed by all tourist sight parent nodes. Tourist sight data information and geospatial information are set as quantitative indexes to calculate motive iteration values and motive iteration decision trees of each cluster are formed, and then all motive iteration values are stored in descending order in a vector. For each cluster, there is an optimal motive iteration tree and a local optimal solution. For all clusters, there is a global optimal solution. Simulation experiments are performed and results data as well as motive iteration trees are analyzed and evaluated. The evaluation results indicate that the algorithm is effective for mass tourism data mining. The final optimal tour routes planned by the smart machine are closely related to tourists’ needs, interests, and habits, which are fully integrated with geospatial services. The algorithm is an effective demonstration of the application on mass tourism data mining. Full article

Recent technical developments made it possible to supply large-scale satellite image coverage. This poses the challenge of efficient discovery of imagery. One very important task in applications like urban planning and reconstruction is to automatically extract building footprints. The integration of different information, which is presently achievable due to the availability of high-resolution remote sensing data sources, makes it possible to improve the quality of the extracted building outlines. Recently, deep neural networks were extended from image-level to pixel-level labelling, allowing to densely predict semantic labels. Based on these advances, we propose an end-to-end U-shaped neural network, which efficiently merges depth and spectral information within two parallel networks combined at the late stage for binary building mask generation. Moreover, as satellites usually provide high-resolution panchromatic images, but only low-resolution multi-spectral images, we tackle this issue by using a residual neural network block. It fuses those images with different spatial resolution at the early stage, before passing the fused information to the Unet stream, responsible for processing spectral information. In a parallel stream, a stereo digital surface model (DSM) is also processed by the Unet. Additionally, we demonstrate that our method generalizes for use in cities which are not included in the training data. Full article

With the recent rapid development of cities, the dynamics of urban road-traffic commuting are becoming more and more complex. In this research, we study urban road-traffic commuting dynamics based on clustering analysis and a new proposed urban commuting electrostatics model. As a case study, we investigate the characteristics of urban road-traffic commuting dynamics during the morning rush hour in Beijing, China, using over 1.3 million Global Positioning System (GPS) data records of vehicle trajectories. The hotspot clusters are identified using clustering analysis, after which the urban commuting electric field is simulated based on an urban commuting electrostatics model. The results show that the areas with high electric field intensity tend to have slow traffic, and also that the vehicles in most areas tend to head in the same direction as the electric field. The results above verify the validity of the model, in that the electric field intensity can reflect the traffic pressure of an area, and that the direction of the electric field can reflect the traffic direction in that area. This new proposed urban commuting electrostatics model helps greatly in understanding urban road-traffic commuting dynamics and has broad applicability for the optimization of urban and traffic system planning. Full article

The study investigates land use/cover classification and change detection of urban areas from very high resolution (VHR) remote sensing images using deep learning-based methods. Firstly, we introduce a fully Atrous convolutional neural network (FACNN) to learn the land cover classification. In the FACNN an encoder, consisting of full Atrous convolution layers, is proposed for extracting scale robust features from VHR images. Then, a pixel-based change map is produced based on the classification map of current images and an outdated land cover geographical information system (GIS) map. Both polygon-based and object-based change detection accuracy is investigated, where a polygon is the unit of the GIS map and an object consists of those adjacent changed pixels on the pixel-based change map. The test data covers a rapidly developing city of Wuhan (8000 km²), China, consisting of 0.5 m ground resolution aerial images acquired in 2014, and 1 m ground resolution Beijing-2 satellite images in 2017, and their land cover GIS maps. Testing results showed that our FACNN greatly exceeded several recent convolutional neural networks in land cover classification. Second, the object-based change detection could achieve much better results than a pixel-based method, and provide accurate change maps to facilitate manual urban land cover updating. Full article

Single photon sensitive airborne Light Detection And Ranging (LiDAR) enables a higher area performance at the price of an increased outlier rate and a lower ranging accuracy compared to conventional Multi-Photon LiDAR. Single Photon LiDAR, in particular, uses green laser light potentially capable of penetrating clear shallow water. The technology is designed for large-area topographic mapping, which also includes the water surface. While the penetration capabilities of green lasers generally lead to underestimation of the water level heights, we specifically focus on the questions of whether Single Photon LiDAR (i) is less affected in this respect due to the high receiver sensitivity, and (ii) consequently delivers sufficient water surface echoes for precise high-resolution water surface reconstruction. After a review of the underlying sensor technology and the interaction of green laser light with water, we address the topic by comparing the surface responses of actual Single Photon LiDAR and Multi-Photon Topo-Bathymetric LiDAR datasets for selected horizontal water surfaces. The anticipated superiority of Single Photon LiDAR could not be verified in this study. While the mean deviations from a reference water level are less than 5 cm for surface models with a cell size of 10 m, systematic water level underestimation of 5–20 cm was observed for high-resolution Single Photon LiDAR based water surface models with cell sizes of 1–5 m. Theoretical photon counts obtained from simulations based on the laser-radar equation support the experimental data evaluation results and furthermore confirm the feasibility of Single Photon LiDAR based high-resolution water surface mapping when adopting specifically tailored flight mission parameters. Full article

The paper deals with the integration of data provided from traditional transport surveys (small data) with big data, provided from Information and Communication Technology (ICT), in building Transport System Models (TSMs). Big data are used to observe historical mobility patterns and transport facilities and services, but they are not able to assess ex-ante effects of planned interventions and policies. To overcome these limitations, TSMs can be specified, calibrated and validated with small data, but they are expensive to obtain. The paper proposes a procedure to increase the benefits of TSMs’ building in forecasting capabilities, on one side; and limiting the costs connected to traditional surveys thanks to the availability of big data, on the other side. Small data (e.g., census data) are enriched with Floating Car Data (FCD). At the current stage, the procedure focuses on two specific elements of TSMs: zoning and graph building. These processes are both executed considering the estimated values of an intensity function of FCDs, consistently with traditional methods based on small data. The data-fusion of small and big data, operated with a Geographic Information System (GIS) tool, in a real extra-urban context is presented in order to validate the proposed procedure. Full article

Public transit services should favor space equity, and the concern of this study is how the allocation of public transportation resources corresponds to the needs of transit users. Identifying mismatches between urban transit resources and regular transit users benefits the transportation resource allocation policy. This study introduces a location maximum likelihood estimation method and a cell space collector mechanism to explore distribution differences of regular transit riders and transit stations based on data mining. In Beijing, 5.37 million regular transit users were identified, and their first-morning transit stations were found to be within 2 km from their last transit stations used the day before. As their locations were estimated, differences in ratios of the regular transit riders to residents were found among areas. Most regular transit users were located in the suburban areas of 5–20 km from the center of Beijing, and the spatial distribution of transit stations declined from the center to the peripheral urban areas. This mismatch between public transit services and regular transit riders sheds light on urban transportation policies. Full article

Social media has been applied to all natural disaster risk-reduction phases, including pre-warning, response, and recovery. However, using it to accurately acquire and reveal public sentiment during a disaster still presents a significant challenge. To explore public sentiment in depth during a disaster, this study analyzed Sina-Weibo (Weibo) texts in terms of space, time, and content related to the 2018 Shouguang flood, which caused casualties and economic losses, arousing widespread public concern in China. The temporal changes within six-hour intervals and spatial distribution on sub-district and city levels of flood-related Weibo were analyzed. Based on the Latent Dirichlet Allocation (LDA) model and the Random Forest (RF) algorithm, a topic extraction and classification model was built to hierarchically identify six flood-relevant topics and nine types of public sentiment responses in Weibo texts. The majority of Weibo texts about the Shouguang flood were related to “public sentiment”, among which “questioning the government and media” was the most commonly expressed. The Weibo text numbers varied over time for different topics and sentiments that corresponded to the different developmental stages of the flood. On a sub-district level, the spatial distribution of flood-relevant Weibo was mainly concentrated in high population areas in the south-central and eastern parts of Shouguang, near the river and the downtown area. At the city level, the Weibo texts were mainly distributed in Beijing and cities in the Shandong Province, centering in Weifang City. The results indicated that the classification model developed in this study was accurate and viable for analyzing social media texts during a disaster. The findings can be used to help researchers, public servants, and officials to better understand public sentiments towards disaster events, to accelerate disaster responses, and to support post-disaster management. Full article

Formalized knowledge representation is the foundation of Big Data computing, mining and visualization. Current knowledge representations regard information as items linked to relevant objects or concepts by tree or graph structures. However, geographic knowledge differs from general knowledge, which is more focused on temporal, spatial, and changing knowledge. Thus, discrete knowledge items are difficult to represent geographic states, evolutions, and mechanisms, e.g., the processes of a storm “{9:30-60 mm-precipitation}-{12:00-80 mm-precipitation}-…”. The underlying problem is the constructors of the logic foundation (ALC description language) of current geographic knowledge representations, which cannot provide these descriptions. To address this issue, this study designed a formalized geographic knowledge representation called GeoKG and supplemented the constructors of the ALC description language. Then, an evolution case of administrative divisions of Nanjing was represented with the GeoKG. In order to evaluate the capabilities of our formalized model, two knowledge graphs were constructed by using the GeoKG and the YAGO by using the administrative division case. Then, a set of geographic questions were defined and translated into queries. The query results have shown that GeoKG results are more accurate and complete than the YAGO’s with the enhancing state information. Additionally, the user evaluation verified these improvements, which indicates it is a promising powerful model for geographic knowledge representation. Full article

Image-based line segment extraction plays an important role in a wide range of applications. Traditional line segment extraction algorithms focus on the accuracy and efficiency, without considering the integrity. Serious line segmentation fracture problems caused by image quality will result in poor subsequent applications. To solve this problem, a multi-constrained line segment extraction method, based on multi-scale image space, is presented. Firstly, using Gaussian down-sampling with a classical line segment detection method, a multi-scale image space is constructed to extract line segments in each image scale and all line segments are projected onto the original image. Then, a new line segment optimization and purification strategy is proposed with the horizontal and vertical distances and angle geometric constraint relationships between line segments to merge fracture line segments and delete redundant line segments. Finally, line segments with adjacent positions are optimized using the grayscale constraint relationship, based on normalized cross-correlation similarity criterion for realizing the second optimization of fracture line segments. Compared with mainstream line segment detector and edge drawing lines methods, experimental results (i.e., indoor, outdoor, and aerial images) indicate the validity and superiority of our proposed methods which can extract longer and more complete line segments. Full article

With the rapid development of the economy, urgent needs for 3-D Geographical Information System (GIS) have sprung up in many application fields. The precise expression of three-dimensional topological relations is the foundation of spatial analysis, topological query, and spatial reasoning in three-dimensional space. In this paper, we subdivide the topological part “boundary” into face, edge, and vertex and propose a 25-intersection model (25IM) to represent topological relations between two simple spatial objects (point, line, region, and body) in 3-D space. An object in the 25IM has five topological parts: vertex, edge, face, interior, and exterior. The classification of topological relations is simplified by merging contain/inside and cover/coveredby. The 25IM describes ten groups of topological relations: body/body, body/region, body/line, body/point, region/region, region/line, region/point, line/line, line/point, and point/point. The 25IM is demonstrated to be more expressive than the 9IM and the DE-9IM, especially in distinguishing the detail situations when one object meets/covers another object (e.g., two bodies meet/cover at vertex, edge, or face). Full article

Marine oil spills seriously impact the marine environment and transportation. When oil spill accidents occur, oil spill distribution information, in particular, the relative thickness of the oil film, is vital for emergency decision-making and cleaning. Hyperspectral remote sensing technology is an effective means to extract oil spill information. In this study, the concept of deep learning is introduced to the classification of oil film thickness based on hyperspectral remote sensing technology. According to the spatial and spectral characteristics, the stacked autoencoder network model based on the support vector machine is improved, enhancing the algorithm’s classification accuracy in validating data sets. A method for classifying oil film thickness using the convolutional neural network is designed and implemented to solve the problem of space homogeneity and heterogeneity. Through numerous experiments and analyses, the potential of the two proposed deep learning methods for accurately classifying hyperspectral oil spill data is verified. Full article

Digital elevation model (DEM) resolution is closely related to the degree of expression of real terrain, the extraction of terrain parameters, and the uncertainty of statistical models. Therefore, based on DEMs with various resolutions, this paper explores the representation and distinguishing ability of different roughness algorithms to measure terrain parameters. Fuyang, a district of Hangzhou City with various landform types, was selected as the research area. Slope, root mean squared height, vector deviation, and two-dimensional continuous wavelet transform were selected as four typical roughness algorithms. The resolutions used were 5, 10, 25, and 50 m DEM on the scale for plains, hills, and mountainous areas. The statistical criteria of effect size and entropy were used as indicators to evaluate and analyze the different roughness algorithms. The results show that in terms of these measures: (1) The expression ability of the SLOPE and root mean squared height (RMSH) algorithms is better than that of the vector deviation method, while the two-dimensional continuous wavelet method based on frequency analysis emphasizes the terrain information within a certain range. (2) The terrain distinguishing ability of the SLOPE and RMSH is not sensitive to the changes in resolution, with the other two algorithms varying with the changes in resolution. Full article

Delineating the cropping area of cocoa agroforests is a major challenge in quantifying the contribution of land use expansion to tropical deforestation. Discriminating cocoa agroforests from tropical transition forests using multispectral optical images is difficult due to the similarity of the spectral characteristics of their canopies. Moreover, the frequent cloud cover in the tropics greatly impedes optical sensors. This study evaluated the potential of multiseason Sentinel-1 C-band synthetic aperture radar (SAR) imagery to discriminate cocoa agroforests from transition forests in a heterogeneous landscape in central Cameroon. We used an ensemble classifier, Random Forest (RF), to average the SAR image texture features of a grey level co-occurrence matrix (GLCM) across seasons. We then compared the classification performance with results from RapidEye optical data. Moreover, we assessed the performance of GLCM texture feature extraction at four different grey levels of quantization: 32 bits, 8 bits, 6 bits, and 4 bits. The classification’s overall accuracy (OA) from texture-based maps outperformed that from an optical image. The highest OA (88.8%) was recorded at the 6 bits grey level. This quantization level, in comparison to the initial 32 bits in the SAR images, reduced the class prediction error by 2.9%. The texture-based classification achieved an acceptable accuracy and revealed that cocoa agroforests have considerably fragmented the remnant transition forest patches. The Shannon entropy (H) or uncertainty provided a reliable validation of the class predictions and enabled inferences about discriminating inherently heterogeneous vegetation categories. Full article

This article proposes a method for registration of two different point clouds with different point densities and noise recorded by airborne sensors in rural areas. In particular, multi-sensor point clouds with different point densities are considered. The proposed method is marker-less and uses segmented ground areas for registration.Therefore, the proposed approach offers the possibility to fuse point clouds of different sensors in rural areas within an accuracy of fine registration. In general, such registration is solved with extensive use of control points. The source point cloud is used to calculate a DEM of the ground which is further used to calculate point to raster distances of all points of the target point cloud. Furthermore, each cell of the raster DEM gets a height variance, further addressed as reconstruction accuracy, by calculating the grid. An outlier removal based on a dynamic threshold of distances is used to gain more robustness against noise and small geometry variations. The transformation parameters are calculated with an iterative least-squares optimization of the distances weighted with respect to the reconstruction accuracies of the grid. Evaluations consider two flight campaigns of the Mangfall area inBavaria, Germany, taken with different airborne LiDAR sensors with different point density. The accuracy of the proposed approach is evaluated on the whole flight strip of approximately eight square kilometers as well as on selected scenes in a closer look. For all scenes, it obtained an accuracy of rotation parameters below one tenth degrees and accuracy of translation parameters below the point spacing and chosen cell size of the raster. Furthermore, the possibility of registration of airborne LiDAR and photogrammetric point clouds from UAV taken images is shown with a similar result. The evaluation also shows the robustness of the approach in scenes where a classical iterative closest point (ICP) fails. Full article

Probabilistic time geography uses a fixed distance threshold for the definition of the encounter events of moving objects. However, because of the distance-decay effect, different distances within the fixed threshold ensure that the encounter events do not always have the same possibility, and, therefore, the quantitative probabilistic time geography analysis needs to consider the actual distance-decay coefficient (DDC). Thus, this paper introduces the DDC and proposes a new encounter probability measure model that takes into account the distance-decay effect. Given two positions of a pair of moving objects, the traditional encounter probability model is that if the distance between the two positions does not exceed a given threshold, the encounter event may occur, and its probability is equal to the product of the probabilities of the two moving objects in their respective positions. Furthermore, the probability of the encounter at two given positions is multiplied by the DDC in the proposed model, in order to express the influence of the distance-decay effect on the encounter probability. Finally, the validity of the proposed model is verified by an experiment, which uses the tracking data of wild zebras to calculate the encounter probability, and compares it with the former method. Full article

The importance of the distribution of accommodation businesses over a certain area has grown remarkably, especially if such distribution is mapped using tools and techniques that utilize the territory as a variable in the analysis. The purpose of this paper is to demonstrate, by means of a geographical information system (GIS) and spatial statistics, that it is possible to better define the groupings of rural accommodation available in Extremadura, Spain, especially if these are conceptualized by dint of their lodging capacity. To do so, two specific techniques have been used: hotspot analysis and outlier analysis, which yield results that prove the existence of homogeneous and heterogeneous groups of accommodation businesses, based not only on their spatial proximity but also on their lodging capacity. On the basis of this analysis, the regional administration can devise tourist policies and strategic plans in order to improve the management and efficiency of each business. Despite the applicability of the present results, this study also addresses the difficulties in using these techniques—Where establishing the spatial relationships and the boundary distance are key concepts. In the case study here, the ideal configuration utilizes a fixed distance of six miles. Full article

In this study, different in-situ and close-range sensing surveying techniques were compared based on the spatial differences of the resultant datasets. In this context, the DJI Phantom 3 Advanced and Trimble UX5 Unmanned Aerial Vehicle (UAV) platforms, Zoller + Fröhlich 5010C phase comparison for continuous wave-based Terrestrial Laser Scanning (TLS) system and Network Real Time Kinematic (NRTK) Global Navigation Satellite System (GNSS) receiver were used to obtain the horizontal and vertical information about the study area. All data were collected in a gently (mean slope angle 4%) inclined, flat vegetation-free, bare-earth valley bottom near Istanbul, Turkey (the size is approximately 0.7 ha). UAV data acquisitions were performed at 25-, 50-, 120-m (with DJI Phantom 3 Advanced) and 350-m (with Trimble UX5) flight altitudes (above ground level, AGL). The imagery was processed with the state-of-the-art SfM (Structure-from-Motion) photogrammetry software. The ortho-mosaics and digital elevation models were generated from UAV-based photogrammetric and TLS-based data. GNSS- and TLS-based data were used as reference to calculate the accuracy of the UAV-based geodata. The UAV-results were assessed in 1D (points), 2D (areas) and 3D (volumes) based on the horizontal (X- and Y-directions) and vertical (Z-direction) differences. Various error measures, including the RMSE (Root Mean Square Error), ME (Mean Error) or MAE (Mean Average Error), and simple descriptive statistics were used to calculate the residuals. The comparison of the results is simplified by applying a normalization procedure commonly used in multi-criteria-decision-making analysis or visualizing offset. According to the results, low-altitude (25 and 50 m AGL) flights feature higher accuracy in the horizontal dimension (e.g., mean errors of 0.085 and 0.064 m, respectively) but lower accuracy in the Z-dimension (e.g., false positive volumes of 2402 and 1160 m³, respectively) compared to the higher-altitude flights (i.e., 120 and 350 m AGL). The accuracy difference with regard to the observed terrain heights are particularly striking, depending on the compared error measure, up to a factor of 40 (i.e., false positive values for 120 vs. 50 m AGL). This error is attributed to the “doming-effect”—a broad-scale systematic deformation of the reconstructed terrain surface, which is commonly known in SfM photogrammetry and results from inaccuracies in modeling the radial distortion of the camera lens. Within the scope of the study, the “doming-effect” was modeled as a functional surface by using the spatial differences and the results were indicated that the “doming-effect” increases inversely proportional to the flight altitude. Full article

Accurate digital soil mapping (DSM) of soil organic carbon (SOC) is still a challenging subject because of its spatial variability and dependency. This study is aimed at comparing six typical methods in three types of DSM techniques for SOC mapping in an area surrounding Changchun in Northeast China. The methods include ordinary kriging (OK) and geographically weighted regression (GWR) from geostatistics, support vector machines for regression (SVR) and artificial neural networks (ANN) from machine learning, and geographically weighted regression kriging (GWRK) and artificial neural networks kriging (ANNK) from hybrid approaches. The hybrid approaches, in particular, integrated the GWR from geostatistics and ANN from machine learning with the estimation of residuals by ordinary kriging, respectively. Environmental variables, including soil properties, climatic, topographic, and remote sensing data, were used for modeling. The mapping results of SOC content from different models were validated by independent testing data based on values of the mean error, root mean squared error and coefficient of determination. The prediction maps depicted spatial variation and patterns of SOC content of the study area. The results showed the accuracy ranking of the compared methods in decreasing order was ANNK, SVR, ANN, GWRK, OK, and GWR. Two-step hybrid approaches performed better than the corresponding individual models, and non-linear models performed better than the linear models. When considering the uncertainty and efficiency, ML and two-step approach are more suitable than geostatistics in regional landscapes with the high heterogeneity. The study concludes that ANNK is a promising approach for mapping SOC content at a local scale. Full article

The design of a natural gas pipeline route is a very important stage in Natural Gas Transmission Pipeline projects. It is a very complicated process requiring many different criteria for various areas to be evaluated simultaneously. These criteria include geographical, social, economic, and environmental aspects with their obstacles. In the classical approach, the optimum route design is usually determined manually with gathering the spatial references for suitable places and obstructions from the ground. This traditional method is not effective because it does not consider all the factors that affect the route of the pipeline. Today, the powerful tools incorporated in Geographical Information Systems (GIS) can be used to automatically determine the optimum route. An automatic pipeline route finder algorithm can calculate the best convenient route avoiding geographic and topological obstructs and selecting suitable places depending on their weights. In this study, an automatic natural gas pipeline design study was carried out in the east western region of Turkey. At the end of the study, an automatic natural gas pipeline route was determined using GIS and a least cost path algorithm, and an alternative study was conducted using a traditional method. In addition, a cartographic line simplification process with a point removal algorithm was used to eliminate the high vertex points and a simplified route was determined. The results were compared with the results of a finished Muş natural gas project constructed by The Turkish Petroleum Pipeline Corporation (BOTAŞ) and the negative and positive effects were evaluated. It was concluded that the use of GIS capabilities and the lowest cost path distance algorithm resulted in a 20% reduction of the cost through the simplification. Full article

Highway infrastructure plays an important role in assuring the proper function of the nation’s transportation. Highway alignment is an essential part of the highway planning and design phase, which has significant effects on the surroundings. Identifying optimal highway routes while using traditional methods requires significant time, cost, and effort, since it requires a comprehensive assessment of multiple factors, such as cost and environmental impacts. This study proposes an approach for managing highway alignment in the context of a larger landscape that integrates building information modelling (BIM) and geographic information system (GIS) capabilities. To support this integration, semantic web technologies are used to integrate data on a semantic level. Moreover, the approach also uses genetic algorithms (GAs) for optimizing highway alignments. A fully automated model is developed that enables data interoperability between BIM and GIS systems and also allows for data exchange between the integration model and the optimization algorithm. The model enables the full exploitation of features of the project and its surroundings for highway alignment planning. The proposed model is also applied to a real highway project to validate its effectiveness. The visualization model of the highway project and its surroundings provides a realistic three-dimensional image that produces a comprehensive virtual environment, where the project could be effectively planned and designed. That can help to reduce design errors and miscommunication, which, in turn, reduces project risks. Moreover, geological and geographical analyses help to identify geohazards and environmentally sensitive regions. The proposed model facilitates highway alignment planning by providing a cross-disciplinary approach to close the gap between the infrastructural and geotechnical planning processes. Full article

Multi-criterial analysis under the current use of digital geographic data is a quite common method used to evaluate the influence of the geographic environment on a planned or ongoing activity. The advantage of this method is a possibility of complex evaluation of all influences as well as a possibility to observe how the individual influences manifest in the final result. Its critical moment is establishing the structure of individual factors that influence the given activity, setting their weights and, subsequently, a choice of a suitable user function. The article provides guidelines how to set the individual decision-making criteria including setting their weights, and the application of the resulting user function in GIS environment with regards to the problem solved. Furthermore, the influence of change in weights of criteria on the complete result of the analysis is discussed. This article documents detailed studies that show how the results of solutions can differ in the same analytical task based on change in the weights of individual criteria. These studies are documented on a model example of a chosen suitable place for the deployment of decontamination center. Finally, the article describes possibilities of further development of the model solution, with the aim to make it a verified tool that may be implemented in the systems of command in Fire Rescue Service units and Chemical Troops units of the Czech Army. Full article

The interest in mobility data analysis has grown dramatically with the wide availability of devices that track the position of moving objects. Mobility analysis can be applied, for example, to analyze traffic flows. To support mobility analysis, trajectory data warehousing techniques can be used. Trajectory data warehouses typically include, as measures, segments of trajectories, linked to spatial and non-spatial contextual dimensions. This paper goes beyond this concept, by including, as measures, the trajectories of moving objects at any point in time. In this way, online analytical processing (OLAP) queries, typically including aggregation, can be combined with moving object queries, to express queries like “List the total number of trucks running at less than 2 km from each other more than 50% of its route in the province of Antwerp” in a concise and elegant way. Existing proposals for trajectory data warehouses do not support queries like this, since they are based on either the segmentation of the trajectories, or a pre-aggregation of measures. The solution presented here is implemented using MobilityDB, a moving object database that extends the PostgresSQL database with temporal data types, allowing seamless integration with relational spatial and non-spatial data. This integration leads to the concept of mobility data warehouses. This paper discusses modeling and querying mobility data warehouses, providing a comprehensive collection of queries implemented using PostgresSQL and PostGIS as database backend, extended with the libraries provided by MobilityDB. Full article