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ISPRS Int. J. Geo-Inf., Volume 7, Issue 2 (February 2018)

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Cover Story (view full-size image) We present a Spatial Information System (SIS) developed in the research project, “Traditional [...] Read more.
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Editorial

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Open AccessEditorial Introduction to the Special Issue: “Research and Development Progress in 3D Cadastral Systems”
ISPRS Int. J. Geo-Inf. 2018, 7(2), 59; doi:10.3390/ijgi7020059
Received: 5 February 2018 / Revised: 5 February 2018 / Accepted: 6 February 2018 / Published: 9 February 2018
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Abstract
The content of this Special Issue has its origin in the “5th International FIG Workshop on 3D Cadastres”, organized in Athens, Greece, 18–20 October 2016 [1][...] Full article
(This article belongs to the Special Issue Research and Development Progress in 3D Cadastral Systems)
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Research

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Open AccessArticle Studying the Association between Green Space Characteristics and Land Surface Temperature for Sustainable Urban Environments: An Analysis of Beijing and Islamabad
ISPRS Int. J. Geo-Inf. 2018, 7(2), 38; doi:10.3390/ijgi7020038
Received: 13 November 2017 / Revised: 19 January 2018 / Accepted: 21 January 2018 / Published: 24 January 2018
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Abstract
Increasing trends of urbanization lead to vegetation degradation in big cities and affect the urban thermal environment. This study investigated (1) the cooling effect of urban green space spatial patterns on Land Surface Temperature (LST); (2) how the surrounding environment influences the green
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Increasing trends of urbanization lead to vegetation degradation in big cities and affect the urban thermal environment. This study investigated (1) the cooling effect of urban green space spatial patterns on Land Surface Temperature (LST); (2) how the surrounding environment influences the green space cool islands (GCI), and vice versa. The study was conducted in two Asian capitals: Beijing, China and Islamabad, Pakistan by utilizing Gaofen-1 (GF-1) and Landsat-8 satellite imagery. Pearson’s correlation and normalized mutual information (NMI) were applied to investigate the relationship between green space characteristics and LST. Landscape metrics of green spaces including Percentage of Landscape (PLAND), Patch Density (PD), Edge Density (ED), and Landscape Shape Index (LSI) were selected to calculate the spatial patterns of green spaces, whereas GCI indicators were defined by Green Space Range (GR), Temperature Difference (TD), and Temperature Gradient (TG). The results indicate that both vegetation composition and configuration influence LST distributions; however, vegetation composition appeared to have a slightly greater effect. The cooling effect can be produced more effectively by increasing green space percentage, planting trees in large patches with equal distribution, and avoiding complex-shaped green spaces. The GCI principle indicates that LST can be decreased by increasing the green space area, increasing the water body fraction, or by decreasing the fraction of impervious surfaces. GCI can also be strengthened by decreasing the fraction of impervious surfaces and increasing the fraction of water body or vegetation in the surrounding environment. The cooling effect of vegetation and water could be explained based on their thermal properties. Beijing has already enacted the green-wedge initiative to increase the vegetation canopy. While designing the future urban layout of Islamabad, the construction of artificial lakes within the urban green spaces would also be beneficial, as is the case with Beijing. Full article
(This article belongs to the Special Issue Urban Environment Mapping Using GIS)
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Open AccessArticle Extraction of Pluvial Flood Relevant Volunteered Geographic Information (VGI) by Deep Learning from User Generated Texts and Photos
ISPRS Int. J. Geo-Inf. 2018, 7(2), 39; doi:10.3390/ijgi7020039
Received: 7 November 2017 / Revised: 17 January 2018 / Accepted: 21 January 2018 / Published: 25 January 2018
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Abstract
In recent years, pluvial floods caused by extreme rainfall events have occurred frequently. Especially in urban areas, they lead to serious damages and endanger the citizens’ safety. Therefore, real-time information about such events is desirable. With the increasing popularity of social media platforms,
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In recent years, pluvial floods caused by extreme rainfall events have occurred frequently. Especially in urban areas, they lead to serious damages and endanger the citizens’ safety. Therefore, real-time information about such events is desirable. With the increasing popularity of social media platforms, such as Twitter or Instagram, information provided by voluntary users becomes a valuable source for emergency response. Many applications have been built for disaster detection and flood mapping using crowdsourcing. Most of the applications so far have merely used keyword filtering or classical language processing methods to identify disaster relevant documents based on user generated texts. As the reliability of social media information is often under criticism, the precision of information retrieval plays a significant role for further analyses. Thus, in this paper, high quality eyewitnesses of rainfall and flooding events are retrieved from social media by applying deep learning approaches on user generated texts and photos. Subsequently, events are detected through spatiotemporal clustering and visualized together with these high quality eyewitnesses in a web map application. Analyses and case studies are conducted during flooding events in Paris, London and Berlin. Full article
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Open AccessArticle A Space-Time Periodic Task Model for Recommendation of Remote Sensing Images
ISPRS Int. J. Geo-Inf. 2018, 7(2), 40; doi:10.3390/ijgi7020040
Received: 31 October 2017 / Revised: 14 January 2018 / Accepted: 21 January 2018 / Published: 29 January 2018
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Abstract
With the rapid development of remote sensing technology, the quantity and variety of remote sensing images are growing so quickly that proactive and personalized access to data has become an inevitable trend. One of the active approaches is remote sensing image recommendation, which
[...] Read more.
With the rapid development of remote sensing technology, the quantity and variety of remote sensing images are growing so quickly that proactive and personalized access to data has become an inevitable trend. One of the active approaches is remote sensing image recommendation, which can offer related image products to users according to their preference. Although multiple studies on remote sensing retrieval and recommendation have been performed, most of these studies model the user profiles only from the perspective of spatial area or image features. In this paper, we propose a spatiotemporal recommendation method for remote sensing data based on the probabilistic latent topic model, which is named the Space-Time Periodic Task model (STPT). User retrieval behaviors of remote sensing images are represented as mixtures of latent tasks, which act as links between users and images. Each task is associated with the joint probability distribution of space, time and image characteristics. Meanwhile, the von Mises distribution is introduced to fit the distribution of tasks over time. Then, we adopt Gibbs sampling to learn the random variables and parameters and present the inference algorithm for our model. Experiments show that the proposed STPT model can improve the capability and efficiency of remote sensing image data services. Full article
(This article belongs to the Special Issue Machine Learning for Geospatial Data Analysis)
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Open AccessArticle A New Approach to Line Simplification Based on Image Processing: A Case Study of Water Area Boundaries
ISPRS Int. J. Geo-Inf. 2018, 7(2), 41; doi:10.3390/ijgi7020041
Received: 29 October 2017 / Revised: 16 January 2018 / Accepted: 28 January 2018 / Published: 30 January 2018
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Abstract
Line simplification is an important component of map generalization. In recent years, algorithms for line simplification have been widely researched, and most of them are based on vector data. However, with the increasing development of computer vision, analysing and processing information from unstructured
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Line simplification is an important component of map generalization. In recent years, algorithms for line simplification have been widely researched, and most of them are based on vector data. However, with the increasing development of computer vision, analysing and processing information from unstructured image data is both meaningful and challenging. Therefore, in this paper, we present a new line simplification approach based on image processing (BIP), which is specifically designed for raster data. First, the key corner points on a multi-scale image feature are detected and treated as candidate points. Then, to capture the essence of the shape within a given boundary using the fewest possible segments, the minimum-perimeter polygon (MPP) is calculated and the points of the MPP are defined as the approximate feature points. Finally, the points after simplification are selected from the candidate points by comparing the distances between the candidate points and the approximate feature points. An empirical example was used to test the applicability of the proposed method. The results showed that (1) when the key corner points are detected based on a multi-scale image feature, the local features of the line can be extracted and retained and the positional accuracy of the proposed method can be maintained well; and (2) by defining the visibility constraint of geographical features, this method is especially suitable for simplifying water areas as it is aligned with people’s visual habits. Full article
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Open AccessArticle Incrementally Detecting Change Types of Spatial Area Object: A Hierarchical Matching Method Considering Change Process
ISPRS Int. J. Geo-Inf. 2018, 7(2), 42; doi:10.3390/ijgi7020042
Received: 2 November 2017 / Revised: 10 January 2018 / Accepted: 28 January 2018 / Published: 30 January 2018
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Abstract
Detecting and extracting the change types of spatial area objects can track area objects’ spatiotemporal change pattern and provide the change backtracking mechanism for incrementally updating spatial datasets. To respond to the problems of high complexity of detection methods, high redundancy rate of
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Detecting and extracting the change types of spatial area objects can track area objects’ spatiotemporal change pattern and provide the change backtracking mechanism for incrementally updating spatial datasets. To respond to the problems of high complexity of detection methods, high redundancy rate of detection factors, and the low automation degree during incrementally update process, we take into account the change process of area objects in an integrated way and propose a hierarchical matching method to detect the nine types of changes of area objects, while minimizing the complexity of the algorithm and the redundancy rate of detection factors. We illustrate in details the identification, extraction, and database entry of change types, and how we achieve a close connection and organic coupling of incremental information extraction and object type-of-change detection so as to characterize the whole change process. The experimental results show that this method can successfully detect incremental information about area objects in practical applications, with the overall accuracy reaching above 90%, which is much higher than the existing weighted matching method, making it quite feasible and applicable. It helps establish the corresponding relation between new-version and old-version objects, and facilitate the linked update processing and quality control of spatial data. Full article
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Open AccessArticle Estimating the Spatial Distribution of Crime Events around a Football Stadium from Georeferenced Tweets
ISPRS Int. J. Geo-Inf. 2018, 7(2), 43; doi:10.3390/ijgi7020043
Received: 30 November 2017 / Revised: 19 January 2018 / Accepted: 28 January 2018 / Published: 31 January 2018
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Abstract
Crowd-based events, such as football matches, are considered generators of crime. Criminological research on the influence of football matches has consistently uncovered differences in spatial crime patterns, particularly in the areas around stadia. At the same time, social media data mining research on
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Crowd-based events, such as football matches, are considered generators of crime. Criminological research on the influence of football matches has consistently uncovered differences in spatial crime patterns, particularly in the areas around stadia. At the same time, social media data mining research on football matches shows a high volume of data created during football events. This study seeks to build on these two research streams by exploring the spatial relationship between crime events and nearby Twitter activity around a football stadium, and estimating the possible influence of tweets for explaining the presence or absence of crime in the area around a football stadium on match days. Aggregated hourly crime data and geotagged tweets for the same area around the stadium are analysed using exploratory and inferential methods. Spatial clustering, spatial statistics, text mining as well as a hurdle negative binomial logistic regression for spatiotemporal explanations are utilized in our analysis. Findings indicate a statistically significant spatial relationship between three crime types (criminal damage, theft and handling, and violence against the person) and tweet patterns, and that such a relationship can be used to explain future incidents of crime. Full article
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Open AccessArticle The Ordered Capacitated Multi-Objective Location-Allocation Problem for Fire Stations Using Spatial Optimization
ISPRS Int. J. Geo-Inf. 2018, 7(2), 44; doi:10.3390/ijgi7020044
Received: 16 December 2017 / Revised: 26 January 2018 / Accepted: 28 January 2018 / Published: 31 January 2018
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Abstract
Determining the positions of facilities, and allocating demands to them, is a vitally important problem. Location-allocation problems are optimization NP-hard procedures. This article evaluates the ordered capacitated multi-objective location-allocation problem for fire stations, using simulated annealing and a genetic algorithm, with goals such
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Determining the positions of facilities, and allocating demands to them, is a vitally important problem. Location-allocation problems are optimization NP-hard procedures. This article evaluates the ordered capacitated multi-objective location-allocation problem for fire stations, using simulated annealing and a genetic algorithm, with goals such as minimizing the distance and time as well as maximizing the coverage. After tuning the parameters of the algorithms using sensitivity analysis, they were used separately to process data for Region 11, Tehran. The results showed that the genetic algorithm was more efficient than simulated annealing, and therefore, the genetic algorithm was used in later steps. Next, we increased the number of stations. Results showed that the model can successfully provide seven optimal locations and allocate high demands (280,000) to stations in a discrete space in a GIS, assuming that the stations’ capacities are known. Following this, we used a weighting program so that in each repetition, we could allot weights to each target randomly. Finally, by repeating the model over 10 independent executions, a set of solutions with the least sum and the highest number of non-dominated solutions was selected from among many non-dominated solutions as the best set of optimal solutions. Full article
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Open AccessArticle 3D Visualization of Trees Based on a Sphere-Board Model
ISPRS Int. J. Geo-Inf. 2018, 7(2), 45; doi:10.3390/ijgi7020045
Received: 5 September 2017 / Revised: 12 January 2018 / Accepted: 28 January 2018 / Published: 31 January 2018
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Abstract
Because of the smooth interaction of tree systems, the billboard and crossed-plane techniques of image-based rendering (IBR) have been used for tree visualization for many years. However, both the billboard-based tree model (BBTM) and the crossed-plane tree model (CPTM) have several notable limitations;
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Because of the smooth interaction of tree systems, the billboard and crossed-plane techniques of image-based rendering (IBR) have been used for tree visualization for many years. However, both the billboard-based tree model (BBTM) and the crossed-plane tree model (CPTM) have several notable limitations; for example, they give an impression of slicing when viewed from the top side, and they produce an unimpressive stereoscopic effect and insufficient lighted effects. In this study, a sphere-board-based tree model (SBTM) is proposed to eliminate these defects and to improve the final visual effects. Compared with the BBTM or CPTM, the proposed SBTM uses one or more sphere-like 3D geometric surfaces covered with a virtual texture, which can present more details about the foliage than can 2D planes, to represent the 3D outline of a tree crown. However, the profile edge presented by a continuous surface is overly smooth and regular, and when used to delineate the outline of a tree crown, it makes the tree appear very unrealistic. To overcome this shortcoming and achieve a more natural final visual effect of the tree model, an additional process is applied to the edge of the surface profile. In addition, the SBTM can better support lighted effects because of its cubic geometrical features. Interactive visualization effects for a single tree and a grove are presented in a case study of Sabina chinensis. The results show that the SBTM can achieve a better compromise between realism and performance than can the BBTM or CPTM. Full article
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Open AccessArticle Survey on Urban Warfare Augmented Reality
ISPRS Int. J. Geo-Inf. 2018, 7(2), 46; doi:10.3390/ijgi7020046
Received: 31 October 2017 / Revised: 24 January 2018 / Accepted: 28 January 2018 / Published: 31 January 2018
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Abstract
Urban warfare has become one of the main forms of modern combat in the twenty-first century. The main reason why urban warfare results in hundreds of casualties is that the situational information of the combatant is insufficient. Accessing information via an Augmented Reality
[...] Read more.
Urban warfare has become one of the main forms of modern combat in the twenty-first century. The main reason why urban warfare results in hundreds of casualties is that the situational information of the combatant is insufficient. Accessing information via an Augmented Reality system can elevate combatants’ situational awareness to effectively improve the efficiency of decision-making and reduce the injuries. This paper begins with the concept of Urban Warfare Augmented Reality (UWAR) and illuminates the objectives of developing UWAR, i.e., transparent battlefield, intuitional perception and natural interaction. Real-time outdoor registration, information presentation and natural interaction are presented as key technologies of a practical UWAR system. Then, the history and current research state of these technologies are summarized and their future developments are highlighted from three perspectives, i.e., (1) Better integration with Geographic Information System and Virtual Geographic Environment; (2) More intelligent software; (3) More powerful hardware. Full article
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Open AccessArticle Strategic Actions for Increasing the Submission of Digital Cadastral Data by the Surveying Industry Based on Lessons Learned from Victoria, Australia
ISPRS Int. J. Geo-Inf. 2018, 7(2), 47; doi:10.3390/ijgi7020047
Received: 12 December 2017 / Revised: 29 January 2018 / Accepted: 1 February 2018 / Published: 4 February 2018
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ePlan, as a digital cadastral data initiative, is a collaborative program between the land authorities and the surveying industry which aims to replace paper and PDF cadastral plans and surveys with digital data. ePlan is currently operational in Australia, New Zealand and Singapore.
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ePlan, as a digital cadastral data initiative, is a collaborative program between the land authorities and the surveying industry which aims to replace paper and PDF cadastral plans and surveys with digital data. ePlan is currently operational in Australia, New Zealand and Singapore. ePlan was introduced in the State of Victoria in 2011 and has been operational in this jurisdiction for 2D plans since 2013. On average, one ePlan application is currently submitted to a digital plan lodgment portal every two weeks. The low uptake of ePlan is caused by several technical and non-technical challenges. This paper provides an overview of cadastral information transitioning from paper to digital in Victoria. The research methodology to identify the challenges in Victoria for the adoption of ePlan is then described. This is followed by a discussion on the identified challenges. The paper then proposes a generic framework of strategic actions to increase the uptake of digital cadastral data based on the lessons learned from Victoria. The initiatives suggested by this framework to address the ePlan challenges in Victoria and increase its uptake are also introduced. The paper concludes with a direction for future research. Full article
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Open AccessArticle A Case Study of the Forced Invariance Approach for Soil Salinity Estimation in Vegetation-Covered Terrain Using Airborne Hyperspectral Imagery
ISPRS Int. J. Geo-Inf. 2018, 7(2), 48; doi:10.3390/ijgi7020048
Received: 20 October 2017 / Revised: 29 January 2018 / Accepted: 1 February 2018 / Published: 4 February 2018
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Soil spectroscopy is a promising technique for soil analysis, and has been successfully utilized in the laboratory. When it comes to space, the presence of vegetation significantly affects the performance of imaging spectroscopy or hyperspectral imaging on the retrieval of topsoil properties. The
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Soil spectroscopy is a promising technique for soil analysis, and has been successfully utilized in the laboratory. When it comes to space, the presence of vegetation significantly affects the performance of imaging spectroscopy or hyperspectral imaging on the retrieval of topsoil properties. The Forced Invariance Approach has been proven able to effectively suppress the vegetation contribution to the mixed image pixel. It takes advantage of scene statistics and requires no specific a priori knowledge of the referenced spectra. However, the approach is still mainly limited to lithological mapping. In this case study, the objective was to test the performance of the Forced Invariance Approach to improve the estimation accuracy of soil salinity for an agricultural area located in the semi-arid region of Northwest China using airborne hyperspectral data. The ground truth data was obtained from an eco-hydrological wireless sensing network. The relationship between Normalized Difference Vegetation Index (NDVI) and soil salinity is discussed. The results demonstrate that the Forced Invariance Approach is able to improve the retrieval accuracy of soil salinity at a depth of 10 cm, as indicated by a higher value for the coefficient of determination (R2). Consequently, the vegetation suppression method has the potential to improve quantitative estimation of soil properties with multivariate statistical methods. Full article
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Open AccessArticle A Spatial Information System (SIS) for the Architectural and Cultural Heritage of Sardinia (Italy)
ISPRS Int. J. Geo-Inf. 2018, 7(2), 49; doi:10.3390/ijgi7020049
Received: 18 December 2017 / Revised: 29 January 2018 / Accepted: 1 February 2018 / Published: 4 February 2018
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Abstract
The paper presents a spatial information system (SIS) developed in the research project, “Tecniche murarie tradizionali: conoscenza per la conservazione ed il miglioramento prestazionale” (Traditional building techniques: from knowledge to conservation and performance improvement), with the aim of archiving and managing the data
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The paper presents a spatial information system (SIS) developed in the research project, “Tecniche murarie tradizionali: conoscenza per la conservazione ed il miglioramento prestazionale” (Traditional building techniques: from knowledge to conservation and performance improvement), with the aim of archiving and managing the data derived from the project. The research project has the purpose of studying the building techniques of the 13th–18th centuries in the Sardinia region (Italy) for their knowledge, conservation, and promotion. The research is founded on a multidisciplinary approach involving several specialists integrating their expertise and providing their input to the knowledge of the dimensional, technical constructive, mensiochronological, materials, physical-mechanical, and energy performance features. This multidisciplinary approach is used to define the peculiarities and behavior of the examined structures, including their performance levels, and then direct the interventions toward innovative, mindful, and ethically correct solutions. The management of the huge amount of data produced during the research required the building of a SIS composed of a geodatabase connected to a GIS and a WebGIS through a Web Map Service (WMS). The entire infrastructure is developed and implemented using open source software components, and will make the research data available to the scientific and professional communities, both for further development and for technical uses. As of today, we surveyed and archived more than 500 buildings belonging to the Sardinia region architectural heritage, and classified them into four main macro categories: defensive architectures, religious architectures, residential architectures, and industrial architectures. Full article
(This article belongs to the Special Issue Web and Mobile GIS)
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Open AccessArticle The Application of the Analytic Hierarchy Process and a New Correlation Algorithm to Urban Construction and Supervision Using Multi-Source Government Data in Tianjin
ISPRS Int. J. Geo-Inf. 2018, 7(2), 50; doi:10.3390/ijgi7020050
Received: 1 December 2017 / Revised: 15 January 2018 / Accepted: 1 February 2018 / Published: 5 February 2018
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Abstract
As the era of big data approaches, big data has attracted increasing amounts of attention from researchers. Various types of studies have been conducted and these studies have focused particularly on the management, organization, and correlation of data and calculations using data. Most
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As the era of big data approaches, big data has attracted increasing amounts of attention from researchers. Various types of studies have been conducted and these studies have focused particularly on the management, organization, and correlation of data and calculations using data. Most studies involving big data address applications in scientific, commercial, and ecological fields. However, the application of big data to government management is also needed. This paper examines the application of multi-source government data to urban construction and supervision in Tianjin, China. The analytic hierarchy process and a new approach called the correlation degree algorithm are introduced to calculate the degree of correlation between different approval items in one construction project and between different construction projects. The results show that more than 75% of the construction projects and their approval items are highly correlated. The results of this study suggest that most of the examined construction projects are well supervised, have relatively high probabilities of satisfying the relevant legal requirements, and observe their initial planning schemes. Full article
(This article belongs to the Special Issue Geospatial Big Data and Urban Studies)
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Open AccessArticle Analysis of Hydrological Sensitivity for Flood Risk Assessment
ISPRS Int. J. Geo-Inf. 2018, 7(2), 51; doi:10.3390/ijgi7020051
Received: 30 November 2017 / Revised: 30 January 2018 / Accepted: 1 February 2018 / Published: 5 February 2018
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Abstract
In order for the Indian government to maximize Integrated Water Resource Management (IWRM), the Brahmaputra River has played an important role in the undertaking of the Pilot Basin Study (PBS) due to the Brahmaputra River’s annual regional flooding. The selected Kulsi River—a part
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In order for the Indian government to maximize Integrated Water Resource Management (IWRM), the Brahmaputra River has played an important role in the undertaking of the Pilot Basin Study (PBS) due to the Brahmaputra River’s annual regional flooding. The selected Kulsi River—a part of Brahmaputra sub-basin—experienced severe floods in 2007 and 2008. In this study, the Rainfall-Runoff-Inundation (RRI) hydrological model was used to simulate the recent historical flood in order to understand and improve the integrated flood risk management plan. The ultimate objective was to evaluate the sensitivity of hydrologic simulation using different Digital Elevation Model (DEM) resources, coupled with DEM smoothing techniques, with a particular focus on the comparison of river discharge and flood inundation extent. As a result, the sensitivity analysis showed that, among the input parameters, the RRI model is highly sensitive to Manning’s roughness coefficient values for flood plains, followed by the source of the DEM, and then soil depth. After optimizing its parameters, the simulated inundation extent showed that the smoothing filter was more influential than its simulated discharge at the outlet. Finally, the calibrated and validated RRI model simulations agreed well with the observed discharge and the Moderate Imaging Spectroradiometer (MODIS)-detected flood extents. Full article
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Open AccessArticle Expansion Analysis of Yangtze River Delta Urban Agglomeration Using DMSP/OLS Nighttime Light Imagery for 1993 to 2012
ISPRS Int. J. Geo-Inf. 2018, 7(2), 52; doi:10.3390/ijgi7020052
Received: 24 December 2017 / Revised: 25 January 2018 / Accepted: 1 February 2018 / Published: 5 February 2018
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Investigating the characteristics of urban expansion is helpful in managing the relationship between urbanization and the ecological and environmental issues related to sustainable development. The Defense Meteorological Satellite Program/Operational Line-scan System (DMSP/OLS) collects visible and near-infrared light from the Earth’s surface at night
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Investigating the characteristics of urban expansion is helpful in managing the relationship between urbanization and the ecological and environmental issues related to sustainable development. The Defense Meteorological Satellite Program/Operational Line-scan System (DMSP/OLS) collects visible and near-infrared light from the Earth’s surface at night without moonlight. It generates effective time series data for mapping the dynamics of urban expansion. As a major urban agglomeration in the world, the Yangtze River Delta Urban Agglomeration (YRDUA) is an important intersection zone of both the “Belt and Road Initiative” and the “Yangtze River Economic Belt” in China. Therefore, this paper analyses urban expansion characteristics of the YRDUA for 1993–2012 from urban extents extracted from the DMSP/OLS for 1993, 1997, 2002, 2007, and 2012. First, calibration procedures are applied to DMSP/OLS data, including intercalibration, intra-annual composition, and inter-annual series correction procedures. Spatial extents are then extracted from the corrected DMSP/OLS data, and a threshold is determined via the spatial comparison method. Finally, three models are used to explore urban expansion characteristics of the YRDUA from expansion rates, expansion spatial patterns, and expansion evaluations. The results show that the urban expansion of the YRDUA occurred at an increasing rate from 1993–2007 and then declined after 2007 with the onset of the global financial crisis. The Suxichang and Ningbo metropolitan circles were seriously affected by the financial crisis, while the Hefei metropolitan circle was not. The urban expansion of the YRDUA moved from the northeast to the southwest over the 20-year period. Urban expansion involved internal infilling over the first 15 years and then evolved into external sprawl and suburbanization after 2007. Full article
(This article belongs to the Special Issue Urban Environment Mapping Using GIS)
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Open AccessArticle A Heterogeneous Distributed Virtual Geographic Environment—Potential Application in Spatiotemporal Behavior Experiments
ISPRS Int. J. Geo-Inf. 2018, 7(2), 54; doi:10.3390/ijgi7020054
Received: 11 December 2017 / Revised: 1 February 2018 / Accepted: 5 February 2018 / Published: 7 February 2018
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Abstract
Due to their strong immersion and real-time interactivity, helmet-mounted virtual reality (VR) devices are becoming increasingly popular. Based on these devices, an immersive virtual geographic environment (VGE) provides a promising method for research into crowd behavior in an emergency. However, the current cheaper
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Due to their strong immersion and real-time interactivity, helmet-mounted virtual reality (VR) devices are becoming increasingly popular. Based on these devices, an immersive virtual geographic environment (VGE) provides a promising method for research into crowd behavior in an emergency. However, the current cheaper helmet-mounted VR devices are not popular enough, and will continue to coexist with personal computer (PC)-based systems for a long time. Therefore, a heterogeneous distributed virtual geographic environment (HDVGE) could be a feasible solution to the heterogeneous problems caused by various types of clients, and support the implementation of spatiotemporal crowd behavior experiments with large numbers of concurrent participants. In this study, we developed an HDVGE framework, and put forward a set of design principles to define the similarities between the real world and the VGE. We discussed the HDVGE architecture, and proposed an abstract interaction layer, a protocol-based interaction algorithm, and an adjusted dead reckoning algorithm to solve the heterogeneous distributed problems. We then implemented an HDVGE prototype system focusing on subway fire evacuation experiments. Two types of clients are considered in the system: PC, and all-in-one VR. Finally, we evaluated the performances of the prototype system and the key algorithms. The results showed that in a low-latency local area network (LAN) environment, the prototype system can smoothly support 90 concurrent users consisting of PC and all-in-one VR clients. HDVGE provides a feasible solution for studying not only spatiotemporal crowd behaviors in normal conditions, but also evacuation behaviors in emergency conditions such as fires and earthquakes. HDVGE could also serve as a new means of obtaining observational data about individual and group behavior in support of human geography research. Full article
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Open AccessArticle Characterizing 3D City Modeling Projects: Towards a Harmonized Interoperable System
ISPRS Int. J. Geo-Inf. 2018, 7(2), 55; doi:10.3390/ijgi7020055
Received: 8 December 2017 / Revised: 19 January 2018 / Accepted: 5 February 2018 / Published: 7 February 2018
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Abstract
3D city models have become common geospatial data assets for cities that can be utilized in numerous fields, in tasks related to planning, visualization, and decision-making among others. We present a study of 3D city modeling focusing on the six largest cities in
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3D city models have become common geospatial data assets for cities that can be utilized in numerous fields, in tasks related to planning, visualization, and decision-making among others. We present a study of 3D city modeling focusing on the six largest cities in Finland. The study portrays a contradiction between the realized 3D city modeling projects and the expectations towards them: models do not appear to reach the broad applicability envisioned. In order to deal with contradiction and to support the development of future 3D city models, characteristics of different operational cultures in 3D city modeling are presented, and a concept for harmonizing the 3D city modeling is suggested. Full article
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Open AccessArticle Examining the Association of Economic Development with Intercity Multimodal Transport Demand in China: A Focus on Spatial Autoregressive Analysis
ISPRS Int. J. Geo-Inf. 2018, 7(2), 56; doi:10.3390/ijgi7020056
Received: 30 December 2017 / Revised: 29 January 2018 / Accepted: 5 February 2018 / Published: 7 February 2018
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Abstract
Transportation is generally perceived as a catalyst for economic development. This has been highlighted in previous studies. However, less attention has been paid to examine the relationship between economy and transport demand by exploring spatially cross-sectional data, especially for countries with significant regional
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Transportation is generally perceived as a catalyst for economic development. This has been highlighted in previous studies. However, less attention has been paid to examine the relationship between economy and transport demand by exploring spatially cross-sectional data, especially for countries with significant regional economic imbalance, like China. In this article, we assess the economic influence of intercity multimodal transport demand at the prefecture level in China. Spatial autoregressive regression models are used to examine the impact of transport demand on economy by deep analysis of transport modes (land, air, and water) and regions (eastern, central, and western). Through contrasting results from spatial lag model and spatial error model with those from the ordinary least square, this study finds that the estimation results can become more accurate by controlling for spatial autocorrelation, especially at the national level. Through rigorous analysis it is identified that except for water passenger traffic, all other intercity transport demand significantly contribute to a city’s economic development level in gross domestic product. In particular, air transport demands distribute more evenly and are estimated with the highest beta coefficients at both national and regional levels. In addition, the beta coefficients for land, air and water transportation are estimated with different magnitudes and significances at the national and regional levels. This study contributes to the ongoing discussion on the relationship between intercity multimodal transport demand and economic development level. Findings from this paper provide planning makers with valid and efficient strategies to better develop the economy by leveraging the special “⊣” cluster pattern of economic development and the benefits of air transportation. Full article
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Open AccessArticle Changes in Gross Primary Production (GPP) over the Past Two Decades Due to Land Use Conversion in a Tourism City
ISPRS Int. J. Geo-Inf. 2018, 7(2), 57; doi:10.3390/ijgi7020057
Received: 8 November 2017 / Revised: 2 February 2018 / Accepted: 5 February 2018 / Published: 7 February 2018
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Abstract
Understanding the changes in gross primary production (GPP), which is the total carbon fixation by terrestrial ecosystems through vegetation photosynthesis, due to land use conversion in a tourism city is important for carbon cycle studies. Satellite data from Landsat 5, Landsat 7 and
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Understanding the changes in gross primary production (GPP), which is the total carbon fixation by terrestrial ecosystems through vegetation photosynthesis, due to land use conversion in a tourism city is important for carbon cycle studies. Satellite data from Landsat 5, Landsat 7 and Landsat 8 and meteorological data are used to calculate annual GPP for 1995, 2003 and 2014, respectively, using the vegetation production model (VPM) in the tourism city Denpasar, Bali, Indonesia. Five land use types generated from topographic maps in three different years over the past two decades are used to quantify the impacts of land use changes on GPP estimation values. Analysis was performed for two periods to determine changes in land use and GPP value as well as their speed. The results demonstrated that urban land development, namely, the increase of settlement areas due to tourism activity, had overall negative effects on terrestrial GPP. The total GPP of the whole area decreased by 7793.96 tC year−1 (12.65%) during the study period. The decline is due to the conversion of agriculture and grassland area into settlements, which caused the city to lose half of its ability to uptake carbon through vegetation. However, although forest area is declining, forest maintenance and restoration by making them protection areas has been helpful in preventing a drastic decline in GPP value over the past two decades. This study provides information that is useful for carbon resource management, tourism, policy making and scholars concerned about carbon reduction in a tourism city. Full article
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Open AccessArticle Interpreting the Fuzzy Semantics of Natural-Language Spatial Relation Terms with the Fuzzy Random Forest Algorithm
ISPRS Int. J. Geo-Inf. 2018, 7(2), 58; doi:10.3390/ijgi7020058
Received: 18 January 2018 / Revised: 30 January 2018 / Accepted: 1 February 2018 / Published: 7 February 2018
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Abstract
Naïve Geography, intelligent geographical information systems (GIS), and spatial data mining especially from social media all rely on natural-language spatial relations (NLSR) terms to incorporate commonsense spatial knowledge into conventional GIS and to enhance the semantic interoperability of spatial information in social media
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Naïve Geography, intelligent geographical information systems (GIS), and spatial data mining especially from social media all rely on natural-language spatial relations (NLSR) terms to incorporate commonsense spatial knowledge into conventional GIS and to enhance the semantic interoperability of spatial information in social media data. Yet, the inherent fuzziness of NLSR terms makes them challenging to interpret. This study proposes to interpret the fuzzy semantics of NLSR terms using the fuzzy random forest (FRF) algorithm. Based on a large number of fuzzy samples acquired by transforming a set of crisp samples with the random forest algorithm, two FRF models with different membership assembling strategies are trained to obtain the fuzzy interpretation of three line-region geometric representations using 69 NLSR terms. Experimental results demonstrate that the two FRF models achieve good accuracy in interpreting line-region geometric representations using fuzzy NLSR terms. In addition, fuzzy classification of FRF can interpret the fuzzy semantics of NLSR terms more fully than their crisp counterparts. Full article
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Open AccessArticle Holo3DGIS: Leveraging Microsoft HoloLens in 3D Geographic Information
ISPRS Int. J. Geo-Inf. 2018, 7(2), 60; doi:10.3390/ijgi7020060
Received: 4 January 2018 / Revised: 6 February 2018 / Accepted: 8 February 2018 / Published: 9 February 2018
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Abstract
Three-dimensional geographic information systems (3D GIS) attempt to understand and express the real world from the perspective of 3D space. Currently, 3D GIS perspective carriers are mainly 2D and not 3D, which influences how 3D information is expressed and further affects the user
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Three-dimensional geographic information systems (3D GIS) attempt to understand and express the real world from the perspective of 3D space. Currently, 3D GIS perspective carriers are mainly 2D and not 3D, which influences how 3D information is expressed and further affects the user cognition and understanding of 3D information. Using mixed reality as a carrier of 3D GIS is promising and may overcome problems when using 2D perspective carriers in 3D GIS. The objective of this paper is to propose an architecture and method to leverage the Microsoft HoloLens in 3D geographic information (Holo3DGIS). The architecture is designed according to three processes for developing holographic 3D GIS; the three processes are the creation of a 3D asset, the development of a Holo3DGIS application, and the compiler deployment of the Holo3DGIS application. Basic geographic data of Philadelphia were used to test the proposed methods and Holo3DGIS. The experimental results showed that the Holo3DGIS can leverage 3D geographic information with the Microsoft HoloLens. By changing the traditional 3D geographic information carrier from a 2D computer screen perspective to mixed reality glasses using the HoloLens 3D holographic perspective, it changed the traditional vision, body sense, and interaction modes, which enables GIS users to experience real 3D GIS. Full article
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Open AccessArticle Spatial Analysis of Digital Imagery of Weeds in a Maize Crop
ISPRS Int. J. Geo-Inf. 2018, 7(2), 61; doi:10.3390/ijgi7020061
Received: 16 December 2017 / Revised: 6 February 2018 / Accepted: 8 February 2018 / Published: 10 February 2018
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Abstract
Modern photographic imaging of agricultural crops can pin-point individual weeds, the patterns of which can be analyzed statistically to reveal how they are affected by variation in soil, by competition from other species and by agricultural operations. This contrasts with previous research on
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Modern photographic imaging of agricultural crops can pin-point individual weeds, the patterns of which can be analyzed statistically to reveal how they are affected by variation in soil, by competition from other species and by agricultural operations. This contrasts with previous research on the patchiness of weeds that has generally used grid sampling and ignored processes operating at a fine scale. Nevertheless, an understanding of the interaction of biology, environment and management at all scales will be required to underpin robust precise control of weeds. We studied the spatial distributions of six common weed species in a maize field in central Spain. We obtained digital imagery of a rectangular plot 41.0 m by 10.5 m (= 430.5 m2) and from it recorded the exact coordinates of every seedling: more than 82,000 individuals in all. We analyzed the resulting body of data using three techniques: an aggregation analysis of the punctual distributions, a geostatistical analysis of quadrat counts and wavelet analysis of quadrat counts. We found that all species were aggregated with average distances across patches ranging from 3 cm–18 cm. Species with small seeds tended to occur in larger patches than those with large seeds. Several species had aggregation patterns that repeated periodically at right angles to the direction of the crop rows. Wheel tracks favored some species (e.g., thornapple), whereas other species (e.g., johnsongrass) were denser elsewhere. Interactions between species at finer scales (<1 m) were negligible, although a negative correlation between thornapple and cocklebur was evident. We infer that the spatial distributions of weeds at the fine scales are products both of their biology and local environment caused by cultivation, with interactions between species playing a minor role. Spatial analysis of such high-resolution imagery can reveal patterns that are not immediately evident from sampling at coarser scales and aid our understanding of how and why weeds aggregate in patches. Full article
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Open AccessArticle A Smart Web-Based Geospatial Data Discovery System with Oceanographic Data as an Example
ISPRS Int. J. Geo-Inf. 2018, 7(2), 62; doi:10.3390/ijgi7020062
Received: 15 January 2018 / Revised: 22 January 2018 / Accepted: 1 February 2018 / Published: 11 February 2018
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Abstract
Discovering and accessing geospatial data presents a significant challenge for the Earth sciences community as massive amounts of data are being produced on a daily basis. In this article, we report a smart web-based geospatial data discovery system that mines and utilizes data
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Discovering and accessing geospatial data presents a significant challenge for the Earth sciences community as massive amounts of data are being produced on a daily basis. In this article, we report a smart web-based geospatial data discovery system that mines and utilizes data relevancy from metadata user behavior. Specifically, (1) the system enables semantic query expansion and suggestion to assist users in finding more relevant data; (2) machine-learned ranking is utilized to provide the optimal search ranking based on a number of identified ranking features that can reflect users’ search preferences; (3) a hybrid recommendation module is designed to allow users to discover related data considering metadata attributes and user behavior; (4) an integrated graphic user interface design is developed to quickly and intuitively guide data consumers to the appropriate data resources. As a proof of concept, we focus on a well-defined domain-oceanography and use oceanographic data discovery as an example. Experiments and a search example show that the proposed system can improve the scientific community’s data search experience by providing query expansion, suggestion, better search ranking, and data recommendation via a user-friendly interface. Full article
(This article belongs to the Special Issue Web and Mobile GIS)
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Open AccessArticle A Knowledge-Informed and Pareto-Based Artificial Bee Colony Optimization Algorithm for Multi-Objective Land-Use Allocation
ISPRS Int. J. Geo-Inf. 2018, 7(2), 63; doi:10.3390/ijgi7020063
Received: 18 December 2017 / Revised: 4 February 2018 / Accepted: 6 February 2018 / Published: 12 February 2018
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Abstract
Land-use allocation is of great significance in urban development. This type of allocation is usually considered to be a complex multi-objective spatial optimization problem, whose optimized result is a set of Pareto-optimal solutions (Pareto front) reflecting different tradeoffs in several objectives. However, obtaining
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Land-use allocation is of great significance in urban development. This type of allocation is usually considered to be a complex multi-objective spatial optimization problem, whose optimized result is a set of Pareto-optimal solutions (Pareto front) reflecting different tradeoffs in several objectives. However, obtaining a Pareto front is a challenging task, and the Pareto front obtained by state-of-the-art algorithms is still not sufficient. To achieve better Pareto solutions, taking the grid-representative land-use allocation problem with two objectives as an example, an artificial bee colony optimization algorithm for multi-objective land-use allocation (ABC-MOLA) is proposed. In this algorithm, the traditional ABC’s search direction guiding scheme and solution maintaining process are modified. In addition, a knowledge-informed neighborhood search strategy, which utilizes the auxiliary knowledge of natural geography and spatial structures to facilitate the neighborhood spatial search around each solution, is developed to further improve the Pareto front’s quality. A series of comparison experiments (a simulated experiment with small data volume and a real-world data experiment for a large area) shows that all the Pareto fronts obtained by ABC-MOLA totally dominate the Pareto fronts by other algorithms, which demonstrates ABC-MOLA’s effectiveness in achieving Pareto fronts of high quality. Full article
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Open AccessArticle Accounting for and Predicting the Influence of Spatial Autocorrelation in Water Quality Modeling
ISPRS Int. J. Geo-Inf. 2018, 7(2), 64; doi:10.3390/ijgi7020064
Received: 28 November 2017 / Revised: 7 February 2018 / Accepted: 17 February 2018 / Published: 19 February 2018
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Abstract
Several studies in the hydrology field have reported differences in outcomes between models in which spatial autocorrelation (SAC) is accounted for and those in which SAC is not. However, the capacity to predict the magnitude of such differences is still ambiguous. In this
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Several studies in the hydrology field have reported differences in outcomes between models in which spatial autocorrelation (SAC) is accounted for and those in which SAC is not. However, the capacity to predict the magnitude of such differences is still ambiguous. In this study, we hypothesized that SAC, inherently possessed by a response variable, influences spatial modeling outcomes. We selected ten watersheds in the USA and analyzed if water quality variables with higher Moran’s I values undergo greater increases in the coefficient of determination (R²) and greater decreases in residual SAC (rSAC). We compared non-spatial ordinary least squares to two spatial regression approaches, namely, spatial lag and error models. The predictors were the principal components of topographic, land cover, and soil group variables. The results revealed that water quality variables with higher inherent SAC showed more substantial increases in R² and decreases in rSAC after performing spatial regressions. In this study, we found a generally linear relationship between the spatial model outcomes (R² and rSAC) and the degree of SAC in each water quality variable. We suggest that the inherent level of SAC in response variables can predict improvements in models before spatial regression is performed. Full article
Open AccessArticle An Automatic User Grouping Model for a Group Recommender System in Location-Based Social Networks
ISPRS Int. J. Geo-Inf. 2018, 7(2), 67; doi:10.3390/ijgi7020067
Received: 29 December 2017 / Revised: 10 February 2018 / Accepted: 18 February 2018 / Published: 21 February 2018
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Abstract
Spatial group recommendation refers to suggesting places to a given set of users. In a group recommender system, members of a group should have similar preferences in order to increase the level of satisfaction. Location-based social networks (LBSNs) provide rich content, such as
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Spatial group recommendation refers to suggesting places to a given set of users. In a group recommender system, members of a group should have similar preferences in order to increase the level of satisfaction. Location-based social networks (LBSNs) provide rich content, such as user interactions and location/event descriptions, which can be leveraged for group recommendations. In this paper, an automatic user grouping model is introduced that obtains information about users and their preferences through an LBSN. The preferences of the users, proximity of the places the users have visited in terms of spatial range, users’ free days, and the social relationships among users are extracted automatically from location histories and users’ profiles in the LBSN. These factors are combined to determine the similarities among users. The users are partitioned into groups based on these similarities. Group size is the key to coordinating group members and enhancing their satisfaction. Therefore, a modified k-medoids method is developed to cluster users into groups with specific sizes. To evaluate the efficiency of the proposed method, its mean intra-cluster distance and its distribution of cluster sizes are compared to those of general clustering algorithms. The results reveal that the proposed method compares favourably with general clustering approaches, such as k-medoids and spectral clustering, in separating users into groups of a specific size with a lower mean intra-cluster distance. Full article
(This article belongs to the Special Issue Geoinformatics in Citizen Science)
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Open AccessArticle Integrated Participatory and Collaborative Risk Mapping for Enhancing Disaster Resilience
ISPRS Int. J. Geo-Inf. 2018, 7(2), 68; doi:10.3390/ijgi7020068
Received: 29 November 2017 / Revised: 22 January 2018 / Accepted: 17 February 2018 / Published: 21 February 2018
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Abstract
Critical knowledge gaps seriously hinder efforts for building disaster resilience at all levels, especially in disaster-prone least developed countries. Information deficiency is most serious at local levels, especially in terms of spatial information on risk, resources, and capacities of communities. To tackle this
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Critical knowledge gaps seriously hinder efforts for building disaster resilience at all levels, especially in disaster-prone least developed countries. Information deficiency is most serious at local levels, especially in terms of spatial information on risk, resources, and capacities of communities. To tackle this challenge, we develop a general methodological approach that integrates community-based participatory mapping processes, one that has been widely used by governments and non-government organizations in the fields of natural resources management, disaster risk reduction and rural development, with emerging collaborative digital mapping techniques. We demonstrate the value and potential of this integrated participatory and collaborative mapping approach by conducting a pilot study in the flood-prone lower Karnali river basin in Western Nepal. The process engaged a wide range of stakeholders and non-stakeholder citizens to co-produce locally relevant geographic information on resources, capacities, and flood risks of selected communities. The new digital community maps are richer in content, more accurate, and easier to update and share than those produced by conventional Vulnerability and Capacity Assessments (VCAs), a variant of Participatory Rural Appraisal (PRA), that is widely used by various government and non-government organizations. We discuss how this integrated mapping approach may provide an effective link between coordinating and implementing local disaster risk reduction and resilience building interventions to designing and informing regional development plans, as well as its limitations in terms of technological barrier, map ownership, and empowerment potential. Full article
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Open AccessArticle Roughness Spectra Derived from Multi-Scale LiDAR Point Clouds of a Gravel Surface: A Comparison and Sensitivity Analysis
ISPRS Int. J. Geo-Inf. 2018, 7(2), 69; doi:10.3390/ijgi7020069
Received: 29 November 2017 / Revised: 7 February 2018 / Accepted: 18 February 2018 / Published: 22 February 2018
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Abstract
The roughness spectrum (i.e., the power spectral density) is a derivative of digital terrain models (DTMs) that is used as a surface roughness descriptor in many geomorphological and physical models. Although light detection and ranging (LiDAR) has become one of the main data
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The roughness spectrum (i.e., the power spectral density) is a derivative of digital terrain models (DTMs) that is used as a surface roughness descriptor in many geomorphological and physical models. Although light detection and ranging (LiDAR) has become one of the main data sources for DTM calculation, it is still unknown how roughness spectra are affected when calculated from different LiDAR point clouds, or when they are processed differently. In this paper, we used three different LiDAR point clouds of a 1 m × 10 m gravel plot to derive and analyze the roughness spectra from the interpolated DTMs. The LiDAR point clouds were acquired using terrestrial laser scanning (TLS), and laser scanning from both an unmanned aerial vehicle (ULS) and an airplane (ALS). The corresponding roughness spectra are derived first as ensemble averaged periodograms and then the spectral differences are analyzed with a dB threshold that is based on the 95% confidence intervals of the periodograms. The aim is to determine scales (spatial wavelengths) over which the analyzed spectra can be used interchangeably. The results show that one TLS scan can measure the roughness spectra for wavelengths larger than 1 cm (i.e., two times its footprint size) and up to 10 m, with spectral differences less than 0.65 dB. For the same dB threshold, the ULS and TLS spectra can be used interchangeably for wavelengths larger than about 1.2 dm (i.e., five times the ULS footprint size). However, the interpolation parameters should be optimized to make the ULS spectrum more accurate at wavelengths smaller than 1 m. The plot size was, however, too small to draw particular conclusions about ALS spectra. These results show that novel ULS data has a high potential to replace TLS for roughness spectrum calculation in many applications. Full article
(This article belongs to the Special Issue Leading Progress in Digital Terrain Analysis and Modeling)
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Open AccessArticle Evaluating and Optimizing Urban Green Spaces for Compact Urban Areas: Cukurova District in Adana, Turkey
ISPRS Int. J. Geo-Inf. 2018, 7(2), 70; doi:10.3390/ijgi7020070
Received: 13 December 2017 / Revised: 8 February 2018 / Accepted: 17 February 2018 / Published: 22 February 2018
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Abstract
In recent decades, the ever-decreasing number of green spaces have become insufficient to meet public demands in terms of accessibility, spatial distribution and the size of urban green areas. This is mainly due to increasing attention on the issue of accessibility to urban
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In recent decades, the ever-decreasing number of green spaces have become insufficient to meet public demands in terms of accessibility, spatial distribution and the size of urban green areas. This is mainly due to increasing attention on the issue of accessibility to urban green spaces. This paper aims to quantify accessibility according to existing qualitative and quantitative characteristics of urban green spaces (UGS) in Çukurova district in Adana, Turkey. Firstly, qualitative and quantitative characteristics of UGS are divided into five main categories: area size, amenities of the UGS, transportation, focal points and population density. A set of 59 criteria are used by referring to the literature and expert views. Secondly, the Weighted Criteria Method was used to determine the significance of levels within these criteria and the existing situation of each park was identified and scored via field work. Thirdly, accounts of the distance of UGS service areas distance from people or users were optimized according to the total scores of existing UGS sites. Finally, the service areas of UGS were mapped by using Network Analysis tools. Results highlight some practical implications of optimizing accessibility for urban planning, for instance, specific land uses might be chosen for highly accessible UGS particularly those characterized by their high area size and equipment variety, low population density, and proximity to units. Full article
(This article belongs to the Special Issue Urban Environment Mapping Using GIS)
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Open AccessReview Visually-Enabled Active Deep Learning for (Geo) Text and Image Classification: A Review
ISPRS Int. J. Geo-Inf. 2018, 7(2), 65; doi:10.3390/ijgi7020065
Received: 29 December 2017 / Revised: 12 February 2018 / Accepted: 17 February 2018 / Published: 20 February 2018
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Abstract
This paper investigates recent research on active learning for (geo) text and image classification, with an emphasis on methods that combine visual analytics and/or deep learning. Deep learning has attracted substantial attention across many domains of science and practice, because it can find
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This paper investigates recent research on active learning for (geo) text and image classification, with an emphasis on methods that combine visual analytics and/or deep learning. Deep learning has attracted substantial attention across many domains of science and practice, because it can find intricate patterns in big data; but successful application of the methods requires a big set of labeled data. Active learning, which has the potential to address the data labeling challenge, has already had success in geospatial applications such as trajectory classification from movement data and (geo) text and image classification. This review is intended to be particularly relevant for extension of these methods to GISience, to support work in domains such as geographic information retrieval from text and image repositories, interpretation of spatial language, and related geo-semantics challenges. Specifically, to provide a structure for leveraging recent advances, we group the relevant work into five categories: active learning, visual analytics, active learning with visual analytics, active deep learning, plus GIScience and Remote Sensing (RS) using active learning and active deep learning. Each category is exemplified by recent influential work. Based on this framing and our systematic review of key research, we then discuss some of the main challenges of integrating active learning with visual analytics and deep learning, and point out research opportunities from technical and application perspectives—for application-based opportunities, with emphasis on those that address big data with geospatial components. Full article
Open AccessReview A Critical Review of the Integration of Geographic Information System and Building Information Modelling at the Data Level
ISPRS Int. J. Geo-Inf. 2018, 7(2), 66; doi:10.3390/ijgi7020066
Received: 5 February 2018 / Revised: 5 February 2018 / Accepted: 18 February 2018 / Published: 20 February 2018
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Abstract
The benefits brought by the integration of Building Information Modelling (BIM) and Geographic Information Systems (GIS) are being proved by more and more research. The integration of the two systems is difficult for many reasons. Among them, data incompatibility is the most significant,
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The benefits brought by the integration of Building Information Modelling (BIM) and Geographic Information Systems (GIS) are being proved by more and more research. The integration of the two systems is difficult for many reasons. Among them, data incompatibility is the most significant, as BIM and GIS data are created, managed, analyzed, stored, and visualized in different ways in terms of coordinate systems, scope of interest, and data structures. The objective of this paper is to review the relevant research papers to (1) identify the most relevant data models used in BIM/GIS integration and understand their advantages and disadvantages; (2) consider the possibility of other data models that are available for data level integration; and (3) provide direction on the future of BIM/GIS data integration. Full article
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Open AccessConference Report An Effective Privacy Architecture to Preserve User Trajectories in Reward-Based LBS Applications
ISPRS Int. J. Geo-Inf. 2018, 7(2), 53; doi:10.3390/ijgi7020053
Received: 11 December 2017 / Revised: 25 January 2018 / Accepted: 5 February 2018 / Published: 7 February 2018
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Abstract
How can training performance data (e.g., running or walking routes) be collected, measured, and published in a mobile program while preserving user privacy? This question is becoming important in the context of the growing use of reward-based location-based service (LBS) applications, which aim
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How can training performance data (e.g., running or walking routes) be collected, measured, and published in a mobile program while preserving user privacy? This question is becoming important in the context of the growing use of reward-based location-based service (LBS) applications, which aim to promote employee training activities and to share such data with insurance companies in order to reduce the healthcare insurance costs of an organization. One of the main concerns of such applications is the privacy of user trajectories, because the applications normally collect user locations over time with identities. The leak of the identified trajectories often results in personal privacy breaches. For instance, a trajectory would expose user interest in places and behaviors in time by inference and linking attacks. This information can be used for spam advertisements or individual-based assaults. To the best of our knowledge, no existing studies can be directly applied to solve the problem while keeping data utility. In this paper, we identify the personal privacy problem in a reward-based LBS application and propose privacy architecture with a bounded perturbation technique to protect user’s trajectory from the privacy breaches. Bounded perturbation uses global location set (GLS) to anonymize the trajectory data. In addition, the bounded perturbation will not generate any visiting points that are not possible to visit in real time. The experimental results on real-world datasets demonstrate that the proposed bounded perturbation can effectively anonymize location information while preserving data utility compared to the existing methods. Full article
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