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ISPRS Int. J. Geo-Inf., Volume 7, Issue 4 (April 2018)

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Cover Story (view full-size image) This study explores the inconsistent findings regarding the associations between environmental [...] Read more.
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Open AccessArticle Use of DEMs Derived from TLS and HRSI Data for Landslide Feature Recognition
ISPRS Int. J. Geo-Inf. 2018, 7(4), 160; https://doi.org/10.3390/ijgi7040160
Received: 5 February 2018 / Revised: 10 April 2018 / Accepted: 19 April 2018 / Published: 23 April 2018
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Abstract
This paper addresses the problems arising from the use of data acquired with two different remote sensing techniques—high-resolution satellite imagery (HRSI) and terrestrial laser scanning (TLS)—for the extraction of digital elevation models (DEMs) used in the geomorphological analysis and recognition of landslides, taking
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This paper addresses the problems arising from the use of data acquired with two different remote sensing techniques—high-resolution satellite imagery (HRSI) and terrestrial laser scanning (TLS)—for the extraction of digital elevation models (DEMs) used in the geomorphological analysis and recognition of landslides, taking into account the uncertainties associated with DEM production. In order to obtain a georeferenced and edited point cloud, the two data sets require quite different processes, which are more complex for satellite images than for TLS data. The differences between the two processes are highlighted. The point clouds are interpolated on a DEM with a 1 m grid size using kriging. Starting from these DEMs, a number of contour, slope, and aspect maps are extracted, together with their associated uncertainty maps. Comparative analysis of selected landslide features drawn from the two data sources allows recognition and classification of hierarchical and multiscale landslide components. Taking into account the uncertainty related to the map enables areas to be located for which one data source was able to give more reliable results than another. Our case study is located in Southern Italy, in an area known for active landslides. Full article
(This article belongs to the Special Issue Leading Progress in Digital Terrain Analysis and Modeling)
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Open AccessArticle DASSCAN: A Density and Adjacency Expansion-Based Spatial Structural Community Detection Algorithm for Networks
ISPRS Int. J. Geo-Inf. 2018, 7(4), 159; https://doi.org/10.3390/ijgi7040159
Received: 12 March 2018 / Revised: 15 April 2018 / Accepted: 19 April 2018 / Published: 21 April 2018
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Abstract
Existing spatial community detection algorithms are usually modularity based. Motivated by different applications, these algorithms build appropriate spatial null models to describe spatial effects on the connection of nodes. Then, by choosing certain modularity maximizing strategies, they try to find interesting community structures
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Existing spatial community detection algorithms are usually modularity based. Motivated by different applications, these algorithms build appropriate spatial null models to describe spatial effects on the connection of nodes. Then, by choosing certain modularity maximizing strategies, they try to find interesting community structures hidden behind the null models. In this paper, a novel structural similarity-based spatial network community is defined, which is based on the shared neighbors of nodes. In addition, there are two other special node roles defined: the spatial hub and outlier. Then, a density and adjacency expansion-based spatial structural community detection algorithm for networks (DASSCAN) is proposed for mining these communities, hubs and outliers. DASSCAN uses structural similarity to measure the relationship between nodes, and then, structurally similar and spatially adjacent nodes are merged into communities using a density-based clustering method and spatial adjacency expansion strategy. Comparative experiments on two kinds of Chinese train line networks clarified the accuracy and efficiency of DASSCAN in finding the spatial structural communities, spatial hubs and outliers. The communities found can be used to uncover more interesting spatial structural patterns, and the hubs and outliers are more accurate and have more valuable meanings. Full article
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Open AccessArticle Using the TensorFlow Deep Neural Network to Classify Mainland China Visitor Behaviours in Hong Kong from Check-in Data
ISPRS Int. J. Geo-Inf. 2018, 7(4), 158; https://doi.org/10.3390/ijgi7040158
Received: 26 February 2018 / Revised: 29 March 2018 / Accepted: 19 April 2018 / Published: 21 April 2018
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Abstract
Over the past decade, big data, including Global Positioning System (GPS) data, mobile phone tracking data and social media check-in data, have been widely used to analyse human movements and behaviours. Tourism management researchers have noted the potential of applying these data to
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Over the past decade, big data, including Global Positioning System (GPS) data, mobile phone tracking data and social media check-in data, have been widely used to analyse human movements and behaviours. Tourism management researchers have noted the potential of applying these data to study tourist behaviours, and many studies have shown that social media check-in data can provide new opportunities for extracting tourism activities and tourist behaviours. However, traditional methods may not be suitable for extracting comprehensive tourist behaviours due to the complexity and diversity of human behaviours. Studies have shown that deep neural networks have outpaced the abilities of human beings in many fields and that deep neural networks can be explained in a psychological manner. Thus, deep neural network methods can potentially be used to understand human behaviours. In this paper, a deep learning neural network constructed in TensorFlow is applied to classify Mainland China visitor behaviours in Hong Kong, and the characteristics of these visitors are analysed to verify the classification results. For the social science classification problem investigated in this study, the deep neural network classifier in TensorFlow provides better accuracy and more lucid visualisation than do traditional neural network methods, even for erratic classification rules. Furthermore, the results of this study reveal that TensorFlow has considerable potential for application in the human geography field. Full article
(This article belongs to the Special Issue Web and Mobile GIS)
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Open AccessArticle Land Cover Mapping from Remotely Sensed and Auxiliary Data for Harmonized Official Statistics
ISPRS Int. J. Geo-Inf. 2018, 7(4), 157; https://doi.org/10.3390/ijgi7040157
Received: 16 February 2018 / Revised: 11 April 2018 / Accepted: 14 April 2018 / Published: 21 April 2018
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Abstract
This paper describes a general framework alternative to the traditional surveys that are commonly performed to estimate, for statistical purposes, the areal extent of predefined land cover classes across Europe. The framework has been funded by Eurostat and relies on annual land cover
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This paper describes a general framework alternative to the traditional surveys that are commonly performed to estimate, for statistical purposes, the areal extent of predefined land cover classes across Europe. The framework has been funded by Eurostat and relies on annual land cover mapping and updating from remotely sensed and national GIS-based data followed by area estimation. Map production follows a series of steps, namely data collection, change detection, supervised image classification, rule-based image classification, and map updating/generalization. Land cover area estimation is based on mapping but compensated for mapping error as estimated through thematic accuracy assessment. This general structure was applied to continental Portugal, successively updating a map of 2010 for the following years until 2015. The estimated land cover change was smaller than expected but the proposed framework was proved as a potential for statistics production at the national and European levels. Contextual and structural methodological challenges and bottlenecks are discussed, especially regarding mapping, accuracy assessment, and area estimation. Full article
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Open AccessFeature PaperArticle New Geospatial Approaches for Efficiently Mapping Forest Biomass Logistics at High Resolution over Large Areas
ISPRS Int. J. Geo-Inf. 2018, 7(4), 156; https://doi.org/10.3390/ijgi7040156
Received: 13 February 2018 / Revised: 13 April 2018 / Accepted: 15 April 2018 / Published: 20 April 2018
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Abstract
Adequate biomass feedstock supply is an important factor in evaluating the financial feasibility of alternative site locations for bioenergy facilities and for maintaining profitability once a facility is built. We used newly developed spatial analysis and logistics software to model the variables influencing
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Adequate biomass feedstock supply is an important factor in evaluating the financial feasibility of alternative site locations for bioenergy facilities and for maintaining profitability once a facility is built. We used newly developed spatial analysis and logistics software to model the variables influencing feedstock supply and to estimate and map two components of the supply chain for a bioenergy facility: (1) the total biomass stocks available within an economically efficient transportation distance; (2) the cost of logistics to move the required stocks from the forest to the facility. Both biomass stocks and flows have important spatiotemporal dynamics that affect procurement costs and project viability. Though seemingly straightforward, these two components can be difficult to quantify and map accurately in a useful and spatially explicit manner. For an 8 million hectare study area, we used raster-based methods and tools to quantify and visualize these supply metrics at 10 m2 spatial resolution. The methodology and software leverage a novel raster-based least-cost path modeling algorithm that quantifies off-road and on-road transportation and other logistics costs. The results of the case study highlight the efficiency, flexibility, fine resolution, and spatial complexity of model outputs developed for facility siting and procurement planning. Full article
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Open AccessArticle An Autonomous Ultra-Wide Band-Based Attitude and Position Determination Technique for Indoor Mobile Laser Scanning
ISPRS Int. J. Geo-Inf. 2018, 7(4), 155; https://doi.org/10.3390/ijgi7040155
Received: 2 March 2018 / Revised: 11 April 2018 / Accepted: 14 April 2018 / Published: 20 April 2018
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Abstract
Mobile laser scanning (MLS) has been widely used in three-dimensional (3D) city modelling data collection, such as Google cars for Google Map/Earth. Building Information Modelling (BIM) has recently emerged and become prominent. 3D models of buildings are essential for BIM. Static laser scanning
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Mobile laser scanning (MLS) has been widely used in three-dimensional (3D) city modelling data collection, such as Google cars for Google Map/Earth. Building Information Modelling (BIM) has recently emerged and become prominent. 3D models of buildings are essential for BIM. Static laser scanning is usually used to generate 3D models for BIM, but this method is inefficient if a building is very large, or it has many turns and narrow corridors. This paper proposes using MLS for BIM 3D data collection. The positions and attitudes of the mobile laser scanner are important for the correct georeferencing of the 3D models. This paper proposes using three high-precision ultra-wide band (UWB) tags to determine the positions and attitudes of the mobile laser scanner. The accuracy of UWB-based MLS 3D models is assessed by comparing the coordinates of target points, as measured by static laser scanning and a total station survey. Full article
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Open AccessArticle Land Use/Land Cover Dynamics and Modeling of Urban Land Expansion by the Integration of Cellular Automata and Markov Chain
ISPRS Int. J. Geo-Inf. 2018, 7(4), 154; https://doi.org/10.3390/ijgi7040154
Received: 20 March 2018 / Revised: 10 April 2018 / Accepted: 15 April 2018 / Published: 19 April 2018
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Abstract
This study explored the past and present land-use/land-cover (LULC) changes and urban expansion pattern for the cities of the Kathmandu valley and their surroundings using Landsat satellite images from 1988 to 2016. For a better analysis, LULC change information was grouped into seven
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This study explored the past and present land-use/land-cover (LULC) changes and urban expansion pattern for the cities of the Kathmandu valley and their surroundings using Landsat satellite images from 1988 to 2016. For a better analysis, LULC change information was grouped into seven time-periods (1988–1992, 1992–1996, 1996–2000, 2000–2004, 2004–2008, 2008–2013, and 2013–2016). The classification was conducted using the support vector machines (SVM) technique. A hybrid simulation model that combined the Markov-Chain and Cellular Automata (MC-CA) was used to predict the future urban sprawl existing by 2024 and 2032. Research analysis explored the significant expansion in urban cover which was manifested at the cost of cultivated land. The urban area totaled 40.53 km2 in 1988, which increased to 144.35 km2 in 2016 with an average annual growth rate of 9.15%, an overall increase of 346.85%. Cultivated land was the most affected land-use from this expansion. A total of 91% to 98% of the expanded urban area was sourced from cultivated land alone. Future urban sprawl is likely to continue, which will be outweighed by the loss of cultivated land as in the previous decades. The urban area will be expanded to 200 km2 and 238 km2 and cultivated land will decline to 587 km2 and 555 km2 by 2024 and 2032. Currently, urban expansion is occurring towards the west and south directions; however, future urban growth is expected to rise in the southern and eastern part of the study area, dismantling the equilibrium of environmental and anthropogenic avenues. Since the study area is a cultural landscape and UNESCO heritage site, balance must be found not only in developing a city, but also in preserving the natural environment and maintaining cultural artifacts. Full article
(This article belongs to the Special Issue Urban Environment Mapping Using GIS)
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Open AccessArticle The Implementation of Spatial Planning Objects in a 3D Cadastral Model
ISPRS Int. J. Geo-Inf. 2018, 7(4), 153; https://doi.org/10.3390/ijgi7040153
Received: 8 March 2018 / Revised: 28 March 2018 / Accepted: 15 April 2018 / Published: 18 April 2018
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Abstract
The paper concerns spatial planning in Poland and its connection with the cadastre. The Polish spatial planning system defines the set of colours, lines, hatches, etc. destined for the preparations of spatial plans, though this has so far not been followed by a
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The paper concerns spatial planning in Poland and its connection with the cadastre. The Polish spatial planning system defines the set of colours, lines, hatches, etc. destined for the preparations of spatial plans, though this has so far not been followed by a spatial planning model or application schema. The aim of this paper is to create a preliminary concept of the unified modelling language (UML) schema of database integrating 3D cadastre and 3D spatial planning. The authors initially define five unified modelling language classes representing spatial planning objects (four representing spatial objects and one a dictionary list). As spatial planning and cadastres are very strongly connected, these classes are implemented into a cadastral model that had been earlier enriched with 3D classes. The final results of this research are UML diagrams based on the Polish cadastral model as defined earlier in legal regulations. They comprise original cadastral model classes, 3D cadastral objects added in earlier research work, classes representing spatial planning objects and the relationships among them. Such a solution better connects cadastre and spatial planning on a structural level and introduces 3D elements into spatial planning which has basically been done in two dimensions. Full article
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Open AccessArticle Validity of VR Technology on the Smartphone for the Study of Wind Park Soundscapes
ISPRS Int. J. Geo-Inf. 2018, 7(4), 152; https://doi.org/10.3390/ijgi7040152
Received: 1 March 2018 / Revised: 7 April 2018 / Accepted: 15 April 2018 / Published: 18 April 2018
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Abstract
The virtual reality of the landscape environment supplies a high level of realism of the real environment, and may improve the public awareness and acceptance of wind park projects. The soundscape around wind parks could have a strong influence on the acceptance and
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The virtual reality of the landscape environment supplies a high level of realism of the real environment, and may improve the public awareness and acceptance of wind park projects. The soundscape around wind parks could have a strong influence on the acceptance and annoyance of wind parks. To explore this VR technology on realism and subjective responses toward different soundscapes of ambient wind parks, three different types of virtual reality on the smartphone tests were performed: aural only, visual only, and aural–visual combined. In total, 21 aural and visual combinations were presented to 40 participants. The aural and visual information used were of near wind park settings and rural spaces. Perceived annoyance levels and realism of the wind park environment were measured. Results indicated that most simulations were rated with relatively strong realism. Perceived realism was strongly correlated with light, color, and vegetation of the simulation. Most wind park landscapes were enthusiastically accepted by the participants. The addition of aural information was found to have a strong impact on whether the participant was annoyed. Furthermore, evaluation of the soundscape on a multidimensional scale revealed the key components influencing the individual’s annoyance by wind parks were the factors of “calmness/relaxation” and “naturality/pleasantness”. “Diversity” of the soundscape might correlate with perceived realism. Finally, the dynamic aural–visual stimuli using virtual reality technology could improve the environmental assessment of the wind park landscapes, and thus, provide a more comprehensible scientific decision than conventional tools. In addition, this study could improve the participatory planning process for more acceptable wind park landscapes. Full article
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Open AccessArticle Fusing Georeferenced and Stereoscopic Image Data for 3D Building Façade Reconstruction
ISPRS Int. J. Geo-Inf. 2018, 7(4), 151; https://doi.org/10.3390/ijgi7040151
Received: 8 February 2018 / Revised: 30 March 2018 / Accepted: 5 April 2018 / Published: 17 April 2018
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Abstract
3D building façade reconstruction has become a very popular topic in various applications related to restoration and preservation of architectural structures as well as urban planning. This paper deals with the reconstruction of realistic 3D models of buildings façades, in the urban environment
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3D building façade reconstruction has become a very popular topic in various applications related to restoration and preservation of architectural structures as well as urban planning. This paper deals with the reconstruction of realistic 3D models of buildings façades, in the urban environment for cultural heritage. We present an approach that enables the relation of stereoscopic images with tacheometry data. The proposed multimodal fusing scheme results in an accurate 3D realistic façade reconstruction and provides a fast and low cost solution. In the first stage of the proposed approach a 2D skeleton of the building is extracted from the viewed scene using Active Contour and Hough line extraction. The next stage of our method utilizes depth information, extracted from a stereoscopic layout, to infer the structural details of inner façade structures, such as windows or doors. In the final stage, the structural information extracted from the image data is integrated with georeferenced point datasets. The final output of our method is a georeferenced 3D model of the structure’s façade, which can be further refined with the use of image-driven texture information. Full article
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Open AccessArticle Spatial-Temporal Event Detection from Geo-Tagged Tweets
ISPRS Int. J. Geo-Inf. 2018, 7(4), 150; https://doi.org/10.3390/ijgi7040150
Received: 21 February 2018 / Revised: 7 April 2018 / Accepted: 9 April 2018 / Published: 15 April 2018
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Abstract
As one of the most popular social networking services in the world, Twitter allows users to post messages along with their current geographic locations. Such georeferenced or geo-tagged Twitter datasets can benefit location-based services, targeted advertising and geosocial studies. Our study focused on
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As one of the most popular social networking services in the world, Twitter allows users to post messages along with their current geographic locations. Such georeferenced or geo-tagged Twitter datasets can benefit location-based services, targeted advertising and geosocial studies. Our study focused on the detection of small-scale spatial-temporal events and their textual content. First, we used Spatial-Temporal Density-Based Spatial Clustering of Applications with Noise (ST-DBSCAN) to spatially-temporally cluster the tweets. Then, the word frequencies were summarized for each cluster and the potential topics were modeled by the Latent Dirichlet Allocation (LDA) algorithm. Using two years of Twitter data from four college cities in the U.S., we were able to determine the spatial-temporal patterns of two known events, two unknown events and one recurring event, which then were further explored and modeled to identify the semantic content about the events. This paper presents our process and recommendations for both finding event-related tweets as well as understanding the spatial-temporal behaviors and semantic natures of the detected events. Full article
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Open AccessArticle Verification of a GNSS Time Series Discontinuity Detection Approach in Support of the Estimation of Vertical Crustal Movements
ISPRS Int. J. Geo-Inf. 2018, 7(4), 149; https://doi.org/10.3390/ijgi7040149
Received: 8 February 2018 / Revised: 8 April 2018 / Accepted: 9 April 2018 / Published: 13 April 2018
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Abstract
Vertical crustal movements can be calculated on the basis of Global Navigation Satellite Systems (GNSS) permanent stations positioning results (the absolute motion) as well as on vectors between the stations (the relative motion). The time series, which are created in both cases, include,
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Vertical crustal movements can be calculated on the basis of Global Navigation Satellite Systems (GNSS) permanent stations positioning results (the absolute motion) as well as on vectors between the stations (the relative motion). The time series, which are created in both cases, include, apart from the information about height, measurement noise, and they are burdened with the influence of factors that are sometimes difficult to identify. These factors make momentary or long-term changes in height. The times of sudden changes in height (jumps) can be difficult to identify and estimate. In order to calculate the velocity of vertical movements, each of the jumps should be identified. It means that both the epoch of each jump and its value must be estimated. The authors of this article developed an algorithm that supports the process of creating the models of vertical crustal movements from GNSS data. The algorithm determines the epoch of a jump and estimates the velocity of vertical movements. The aim of the article is to verify the algorithm on the basis of height changes in adjacent stations of polish national CORS network ASG-EUPOS and to set proper algorithm parameters. The results received on the basis of the algorithm were evaluated and verified using four possible methods: visual evaluation, testing the algorithm using adjacent input parameter values, information in .log files and analysis of the loop misclosure. The results indicate that the algorithm functions properly and is useful in the creation of vertical crustal movement models from GNSS data. Full article
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Open AccessFeature PaperArticle Map Archive Mining: Visual-Analytical Approaches to Explore Large Historical Map Collections
ISPRS Int. J. Geo-Inf. 2018, 7(4), 148; https://doi.org/10.3390/ijgi7040148
Received: 1 March 2018 / Revised: 5 April 2018 / Accepted: 5 April 2018 / Published: 13 April 2018
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Abstract
Historical maps are unique sources of retrospective geographical information. Recently, several map archives containing map series covering large spatial and temporal extents have been systematically scanned and made available to the public. The geographical information contained in such data archives makes it possible
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Historical maps are unique sources of retrospective geographical information. Recently, several map archives containing map series covering large spatial and temporal extents have been systematically scanned and made available to the public. The geographical information contained in such data archives makes it possible to extend geospatial analysis retrospectively beyond the era of digital cartography. However, given the large data volumes of such archives (e.g., more than 200,000 map sheets in the United States Geological Survey topographic map archive) and the low graphical quality of older, manually-produced map sheets, the process to extract geographical information from these map archives needs to be automated to the highest degree possible. To understand the potential challenges (e.g., salient map characteristics and data quality variations) in automating large-scale information extraction tasks for map archives, it is useful to efficiently assess spatio-temporal coverage, approximate map content, and spatial accuracy of georeferenced map sheets at different map scales. Such preliminary analytical steps are often neglected or ignored in the map processing literature but represent critical phases that lay the foundation for any subsequent computational processes including recognition. Exemplified for the United States Geological Survey topographic map and the Sanborn fire insurance map archives, we demonstrate how such preliminary analyses can be systematically conducted using traditional analytical and cartographic techniques, as well as visual-analytical data mining tools originating from machine learning and data science. Full article
(This article belongs to the Special Issue Historic Settlement and Landscape Analysis)
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Open AccessEditorial Foreword to the Special Issue on Machine Learning for Geospatial Data Analysis
ISPRS Int. J. Geo-Inf. 2018, 7(4), 147; https://doi.org/10.3390/ijgi7040147
Received: 9 April 2018 / Revised: 9 April 2018 / Accepted: 10 April 2018 / Published: 13 April 2018
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Abstract
Advances in machine learning research are pushing the limits of geographical information sciences (GIScience) by offering accurate procedures to analyze small-to-big GeoData. This Special Issue groups together six original contributions in the field of GeoData-driven GIScience that focus mainly on three different areas:
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Advances in machine learning research are pushing the limits of geographical information sciences (GIScience) by offering accurate procedures to analyze small-to-big GeoData. This Special Issue groups together six original contributions in the field of GeoData-driven GIScience that focus mainly on three different areas: extraction of semantic information from satellite imagery, image recommendation, and map generalization. Different technical approaches are chosen for each sub-topic, from deep learning to latent topic models. Full article
(This article belongs to the Special Issue Machine Learning for Geospatial Data Analysis)
Open AccessArticle A Method of Mining Association Rules for Geographical Points of Interest
ISPRS Int. J. Geo-Inf. 2018, 7(4), 146; https://doi.org/10.3390/ijgi7040146
Received: 1 March 2018 / Revised: 4 April 2018 / Accepted: 6 April 2018 / Published: 10 April 2018
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Abstract
Association rule (AR) mining represents a challenge in the field of data mining. Mining ARs using traditional algorithms generates a large number of candidate rules, and even if we use binding measures such as support, reliability, and lift, there are still several rules
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Association rule (AR) mining represents a challenge in the field of data mining. Mining ARs using traditional algorithms generates a large number of candidate rules, and even if we use binding measures such as support, reliability, and lift, there are still several rules to keep, and domain experts are needed to extract the rules of interest from the remaining rules. The focus of this paper is on whether we can directly provide rule rankings and calculate the proportional relationship between the items in the rules. To address these two questions, this paper proposes a modified FP-Growth algorithm called FP-GCID (novel FP-Growth algorithm based on Cluster IDs) to generate ARs; in addition, a new method called Mean-Product of Probabilities (MPP) is proposed to rank rules and compute the proportion of items for one rule. The experiment is divided into three phases: the DBSCAN (Density-Based Scanning Algorithm with Noise) algorithm is used to cluster the geographic interest points and map the obtained clusters into corresponding transaction data; FP-GCID is used to generate ARs, which contain cluster information; and MPP is used to choose the best rule based on the rankings. Finally, a visualization of the rules is used to validate whether the two previously stated requirements were fulfilled. Full article
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Open AccessArticle Improving ASTER GDEM Accuracy Using Land Use-Based Linear Regression Methods: A Case Study of Lianyungang, East China
ISPRS Int. J. Geo-Inf. 2018, 7(4), 145; https://doi.org/10.3390/ijgi7040145
Received: 5 March 2018 / Revised: 27 March 2018 / Accepted: 4 April 2018 / Published: 7 April 2018
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Abstract
The Advanced Spaceborne Thermal-Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM) is important to a wide range of geographical and environmental studies. Its accuracy, to some extent associated with land-use types reflecting topography, vegetation coverage, and human activities, impacts the results
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The Advanced Spaceborne Thermal-Emission and Reflection Radiometer Global Digital Elevation Model (ASTER GDEM) is important to a wide range of geographical and environmental studies. Its accuracy, to some extent associated with land-use types reflecting topography, vegetation coverage, and human activities, impacts the results and conclusions of these studies. In order to improve the accuracy of ASTER GDEM prior to its application, we investigated ASTER GDEM errors based on individual land-use types and proposed two linear regression calibration methods, one considering only land use-specific errors and the other considering the impact of both land-use and topography. Our calibration methods were tested on the coastal prefectural city of Lianyungang in eastern China. Results indicate that (1) ASTER GDEM is highly accurate for rice, wheat, grass and mining lands but less accurate for scenic, garden, wood and bare lands; (2) despite improvements in ASTER GDEM2 accuracy, multiple linear regression calibration requires more data (topography) and a relatively complex calibration process; (3) simple linear regression calibration proves a practicable and simplified means to systematically investigate and improve the impact of land-use on ASTER GDEM accuracy. Our method is applicable to areas with detailed land-use data based on highly accurate field-based point-elevation measurements. Full article
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Open AccessArticle Evaluating the Open Source Data Containers for Handling Big Geospatial Raster Data
ISPRS Int. J. Geo-Inf. 2018, 7(4), 144; https://doi.org/10.3390/ijgi7040144
Received: 26 February 2018 / Revised: 5 April 2018 / Accepted: 5 April 2018 / Published: 7 April 2018
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Abstract
Big geospatial raster data pose a grand challenge to data management technologies for effective big data query and processing. To address these challenges, various big data container solutions have been developed or enhanced to facilitate data storage, retrieval, and analysis. Data containers were
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Big geospatial raster data pose a grand challenge to data management technologies for effective big data query and processing. To address these challenges, various big data container solutions have been developed or enhanced to facilitate data storage, retrieval, and analysis. Data containers were also developed or enhanced to handle geospatial data. For example, Rasdaman was developed to handle raster data and GeoSpark/SpatialHadoop were enhanced from Spark/Hadoop to handle vector data. However, there are few studies to systematically compare and evaluate the features and performances of these popular data containers. This paper provides a comprehensive evaluation of six popular data containers (i.e., Rasdaman, SciDB, Spark, ClimateSpark, Hive, and MongoDB) for handling multi-dimensional, array-based geospatial raster datasets. Their architectures, technologies, capabilities, and performance are compared and evaluated from two perspectives: (a) system design and architecture (distributed architecture, logical data model, physical data model, and data operations); and (b) practical use experience and performance (data preprocessing, data uploading, query speed, and resource consumption). Four major conclusions are offered: (1) no data containers, except ClimateSpark, have good support for the HDF data format used in this paper, requiring time- and resource-consuming data preprocessing to load data; (2) SciDB, Rasdaman, and MongoDB handle small/mediate volumes of data query well, whereas Spark and ClimateSpark can handle large volumes of data with stable resource consumption; (3) SciDB and Rasdaman provide mature array-based data operation and analytical functions, while the others lack these functions for users; and (4) SciDB, Spark, and Hive have better support of user defined functions (UDFs) to extend the system capability. Full article
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Open AccessArticle Environmental Influences on Leisure-Time Physical Inactivity in the U.S.: An Exploration of Spatial Non-Stationarity
ISPRS Int. J. Geo-Inf. 2018, 7(4), 143; https://doi.org/10.3390/ijgi7040143
Received: 13 February 2018 / Revised: 26 March 2018 / Accepted: 2 April 2018 / Published: 5 April 2018
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Abstract
Considerable research has been conducted to advance our understanding of how environmental factors influence people’s health behaviors (e.g., leisure-time physical inactivity) at the neighborhood level. However, different environmental factors may operate differently at different geographic locations. This study explores the inconsistent findings regarding
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Considerable research has been conducted to advance our understanding of how environmental factors influence people’s health behaviors (e.g., leisure-time physical inactivity) at the neighborhood level. However, different environmental factors may operate differently at different geographic locations. This study explores the inconsistent findings regarding the associations between environmental exposures and physical inactivity. To address spatial autocorrelation and explore the impact of spatial non-stationarity on research results which may lead to biased estimators, this study uses spatial regression models to examine the associations between leisure-time physical inactivity and different social and physical environmental factors for all counties in the conterminous U.S. By comparing the results with the conventional ordinary least squares regression and spatial lag model, the geographically weighted regression model adequately addresses the problem of spatial autocorrelation (Moran’s I of the residual = 0.0293) and highlights the spatial non-stationarity of the associations. The existence of spatial non-stationarity that leads to biased estimators, which were often ignored in past research, may be another reason for the inconsistent findings in previous studies besides the modifiable areal unit problem and the uncertain geographic context problem. Also, the observed associations between environmental variables and leisure-time physical inactivity are helpful for developing location-based policies and interventions to encourage people to undertake more physical activity. Full article
(This article belongs to the Special Issue Geoprocessing in Public and Environmental Health)
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Open AccessArticle Assessment of Accuracy in the Identification of Rock Formations from Aerial and Terrestrial Laser-Scanning Data
ISPRS Int. J. Geo-Inf. 2018, 7(4), 142; https://doi.org/10.3390/ijgi7040142
Received: 3 February 2018 / Revised: 26 March 2018 / Accepted: 30 March 2018 / Published: 4 April 2018
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Abstract
Rock formations are among the most spectacular landscape features both for experts and the public. However, information about these objects is often stored inaccurately in existing spatial databases, their corresponding elevations are missing, or the entire rock object is completely absent. Cartographic depiction
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Rock formations are among the most spectacular landscape features both for experts and the public. However, information about these objects is often stored inaccurately in existing spatial databases, their corresponding elevations are missing, or the entire rock object is completely absent. Cartographic depiction is also reduced to a point of areal symbology of a largely generalized character. This paper discusses options in identifying and analyzing rock formations from two digital terrain models (DTMs), DMR 5G and DMR 5G+, and irregularly spaced points of airborne laser-scanning (ALS) data with different point densities. A semi-automatic method allowing rock formations to be identified from DTMs is introduced at the beginning of the paper. A method to evaluate elevation models (volume differences) is subsequently applied and a 3D model of a selected rock object is created from terrestrial laser-scanning data. Finally, positional and volumetric comparisons of that 3D object are performed in 2D, 2.5D, and 3D. The results of the pilot study confirmed that the digital terrain models studied are a reliable source in identifying and updating rock formations using the semi-automatic method introduced. The results show that DMR 5G model quality decreases with increasing fragmentation and relative rock formation height, while the proportion of gross errors increases. The complementary DMR 5G+ is better in terms of location and altitude. Full article
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Open AccessArticle A Wireless Sensor Network Framework for Real-Time Monitoring of Height and Volume Variations on Sandy Beaches and Dunes
ISPRS Int. J. Geo-Inf. 2018, 7(4), 141; https://doi.org/10.3390/ijgi7040141
Received: 29 January 2018 / Revised: 29 March 2018 / Accepted: 30 March 2018 / Published: 4 April 2018
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Abstract
In this paper, the authors describe the realization and testing of a Wireless Sensor Network (WSN) framework aiming at measuring, remotely and in real time, the level variations of the sand layer of sandy beaches or dunes. The proposed framework is based on
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In this paper, the authors describe the realization and testing of a Wireless Sensor Network (WSN) framework aiming at measuring, remotely and in real time, the level variations of the sand layer of sandy beaches or dunes. The proposed framework is based on an innovative low cost sensing structure, able to measure the level variations with a 5-cm degree of precision and to locally transfer the acquired data through the ZigBee protocol. The described sensor is integrated in a wider ZigBee wireless sensor network architecture composed of an array of sensors that, arranged according to a grid layout, can acquire the same data at different points, allowing the definition of a dynamic map of the area under study. The WSN is connected to a local Global System for Mobile Communications (GSM) gateway that is in charge of data processing and transmission to a cloud infrastructure through a General Packet Radio Service (GPRS) connection. Data are then stored in a MySQL database and made available any time and anywhere through the Internet. The proposed architecture has been tested in a laboratory, to analyze data acquisition, processing timing and power consumption and then in situ to prove the effectiveness of the system. The described infrastructure is expected to be integrated in a wider IoT architecture including different typologies of sensors, in order to create a multi-purpose tool for the study of coastal erosive processes. Full article
(This article belongs to the Special Issue Geospatial Applications of the Internet of Things (IoT))
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Open AccessFeature PaperArticle Mapping Forest Characteristics at Fine Resolution across Large Landscapes of the Southeastern United States Using NAIP Imagery and FIA Field Plot Data
ISPRS Int. J. Geo-Inf. 2018, 7(4), 140; https://doi.org/10.3390/ijgi7040140
Received: 15 February 2018 / Revised: 28 March 2018 / Accepted: 29 March 2018 / Published: 3 April 2018
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Abstract
Accurate information is important for effective management of natural resources. In the field of forestry, field measurements of forest characteristics such as species composition, basal area, and stand density are used to inform and evaluate management activities. Quantifying these metrics accurately across large
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Accurate information is important for effective management of natural resources. In the field of forestry, field measurements of forest characteristics such as species composition, basal area, and stand density are used to inform and evaluate management activities. Quantifying these metrics accurately across large landscapes in a meaningful way is extremely important to facilitate informed decision-making. In this study, we present a remote sensing based methodology to estimate species composition, basal area and stand tree density for pine and hardwood tree species at the spatial resolution of a Forest Inventory Analysis (FIA) program plot (78 m by 70 m). Our methodology uses textural metrics derived at this spatial scale to relate plot summaries of forest characteristics to remotely sensed National Agricultural Imagery Program (NAIP) aerial imagery across broad extents. Our findings quantify strong relationships between NAIP imagery and FIA field data. On average, models of basal area and trees per acre accounted for 43% of the variation in the FIA data, while models identifying species composition had less than 15.2% error in predicted class probabilities. Moreover, these relationships can be used to spatially characterize the condition of forests at fine spatial resolutions across broad extents. Full article
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Open AccessArticle Collecting Typhoon Disaster Information from Twitter Based on Query Expansion
ISPRS Int. J. Geo-Inf. 2018, 7(4), 139; https://doi.org/10.3390/ijgi7040139
Received: 2 February 2018 / Revised: 27 March 2018 / Accepted: 29 March 2018 / Published: 2 April 2018
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Abstract
Social media is a popular source of volunteered geographic information owing to its massive real-time data; however, the use of social media data in the context of geospatial analysis is challenging because complex semantic filters are required for the aggregation of geographic messages
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Social media is a popular source of volunteered geographic information owing to its massive real-time data; however, the use of social media data in the context of geospatial analysis is challenging because complex semantic filters are required for the aggregation of geographic messages from the data streams. This article proposes a new query expansion method for social media streams which updates the query keywords periodically by the words extracted from the preceding search results. The proposed method has optimized the trade-off between precision and coverage of geographical messages by factoring in the influences of the keyword number and refresh cycle in the query process, and some improvements on the classic Term Frequency-Inverse Document Frequency (TF-IDF) method for short texts were achieved. Furthermore, a number of filters based upon relevance to the target topic were established and tested. This method was tested on a dataset from Twitter within the geographic extent of Macau in August 2017 during two consecutive typhoon hits. The result supports its effectiveness with a controllable precision and considerable increment of relevant information. Moreover, the query keywords can adjust themselves to the local language environment by discovering new keywords. To conclude, this query expansion method is able to provide a reliable method for social media-based information retrieval. Full article
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Open AccessArticle An Efficient Visualization Method for Polygonal Data with Dynamic Simplification
ISPRS Int. J. Geo-Inf. 2018, 7(4), 138; https://doi.org/10.3390/ijgi7040138
Received: 12 February 2018 / Revised: 14 March 2018 / Accepted: 21 March 2018 / Published: 2 April 2018
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Abstract
Polygonal data often require rendering with symbolization and simplification in geovisualization. A common issue in existing methods is that simplification, symbolization and rendering are addressed separately, causing computational and data redundancies that reduce efficiency, especially when handling large complex polygonal data. Here, we
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Polygonal data often require rendering with symbolization and simplification in geovisualization. A common issue in existing methods is that simplification, symbolization and rendering are addressed separately, causing computational and data redundancies that reduce efficiency, especially when handling large complex polygonal data. Here, we present an efficient polygonal data visualization method by organizing the simplification, tessellation and rendering operations into a single mesh generalization process. First, based on the sweep line method, we propose a topology embedded trapezoidal mesh data structure to organize the tessellated polygons. Second, we introduce horizontal and vertical generalization operations to simplify the trapezoidal meshes. Finally, we define a heuristic testing algorithm to efficiently preserve the topological consistency. The method is tested using three OpenStreetMap datasets and compared with the Douglas Peucker algorithm and the Binary Line Generalization tree-based method. The results show that the proposed method improves the rendering efficiency by a factor of six. Efficiency-sensitive mapping applications such as emergency mapping could benefit from this method, which would significantly improve their visualization performances. Full article
(This article belongs to the Special Issue Cognitive Aspects of Human-Computer Interaction for GIS)
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Open AccessArticle Long-Term Changes of the Wildland–Urban Interface in the Polish Carpathians
ISPRS Int. J. Geo-Inf. 2018, 7(4), 137; https://doi.org/10.3390/ijgi7040137
Received: 18 February 2018 / Revised: 16 March 2018 / Accepted: 26 March 2018 / Published: 1 April 2018
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Abstract
The Wildland–Urban Interface (WUI) is the area where houses and wildland vegetation meet or intermingle, which causes many environmental problems. The current WUI is widespread in many regions, but it is unclear how the WUI evolved, especially in regions where both houses and
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The Wildland–Urban Interface (WUI) is the area where houses and wildland vegetation meet or intermingle, which causes many environmental problems. The current WUI is widespread in many regions, but it is unclear how the WUI evolved, especially in regions where both houses and forest cover have increased. Here we compared WUI change in the Polish Carpathians for 1860 and 2013 in two study areas with different land use history. Our western study area experienced gradual forest increase and housing growth over time, while the eastern study area was subject to a shock due to post-war resettlements, which triggered rapid reforestation. We found that in both study areas WUI extent increased from 1860 to 2013 (41.3 to 54.6%, and 12.2 to 33.3%, in the west and east, respectively). However the causes of WUI growth were very different. In the western study area new houses were the main cause for new WUI, while in the eastern study area forest cover increase was more important. Our results highlight that regions with similar current WUI cover have evolved very differently, and that the WUI has grown rapidly and is widespread in the Polish Carpathians. Full article
(This article belongs to the Special Issue Historic Settlement and Landscape Analysis)
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Open AccessArticle Saddle Position-Based Method for Extraction of Depressions in Fengcong Areas by Using Digital Elevation Models
ISPRS Int. J. Geo-Inf. 2018, 7(4), 136; https://doi.org/10.3390/ijgi7040136
Received: 7 March 2018 / Revised: 26 March 2018 / Accepted: 26 March 2018 / Published: 1 April 2018
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Abstract
A karst depression is an important sign of the development stage of karst landforms. The morphological characteristics of depressions can help reflect the development and evolution process of such landforms. The accurate identification and extraction of depressions in Fengcong areas are the basis
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A karst depression is an important sign of the development stage of karst landforms. The morphological characteristics of depressions can help reflect the development and evolution process of such landforms. The accurate identification and extraction of depressions in Fengcong areas are the basis of this research on karst depressions. Previous studies on Fengcong depressions were primarily based on manual surveys, remote sensing image interpretation, and manual map plotting or GIS-based techniques. The extracted landform units of Fengcong depressions in these studies were not accurate and even inauthentic in certain cases. Thus, this work proposes a method for extracting Fengcong depressions in karst areas which is based on terrain saddle points and uses digital elevation models (DEMs). First, the surface morphology of the Fengcong karst area is analyzed. Second, saddles are detected from the intersection points, and spatial trend surfaces are generated by interpolating the elevations of these saddle points. The interface between pinnacles and depressions can be determined by the trend surface. We applied the method in a case Fengcong area of the Lijiang River in Guilin, China. Results showed that the proposed method successfully divided the positive terrain form of pinnacles and the negative terrain form of the depressions in the Fengcong karst area. A total of 188 surface depressions were extracted, whose average area was 0.14 km2 and polygonal depression density was 2.5 km2. Results also showed that most of the depressions were stable in terms of the morphological features of area and depth. A total of 94% of the depth measured less than 60 m, and the area was less than 0.5 km2. This proposed method can accurately determine the boundary of depressions and provide an important reference for quantitative research on the Fengcong depression terrain in karst landforms. Full article
(This article belongs to the Special Issue Leading Progress in Digital Terrain Analysis and Modeling)
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Open AccessArticle A Novel Approach for Identifying Urban Built-Up Area Boundaries Using High-Resolution Remote-Sensing Data Based on the Scale Effect
ISPRS Int. J. Geo-Inf. 2018, 7(4), 135; https://doi.org/10.3390/ijgi7040135
Received: 7 March 2018 / Revised: 21 March 2018 / Accepted: 26 March 2018 / Published: 1 April 2018
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Abstract
Identifying urban built-up area boundaries is critical to urban data statistics, size measurement, and spatial control. However, previous methods of extracting urban built-up area boundaries based on low-resolution remote-sensing data are frequently constrained by data accuracy. In this paper, a new method for
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Identifying urban built-up area boundaries is critical to urban data statistics, size measurement, and spatial control. However, previous methods of extracting urban built-up area boundaries based on low-resolution remote-sensing data are frequently constrained by data accuracy. In this paper, a new method for extracting urban built-up area boundaries using high-resolution remote sensing images based on scale effects is proposed. Firstly, we generate a number of different levels of edge-multiplied hexagonal vector grids. Secondly, the impervious surface densities are calculated based on the hexagonal vector grids with the longest edge. Then, the hexagonal grids with higher impervious surface densities are extracted as the built-up area of the first level. Thirdly, we gradually reduce the spatial scale of the hexagonal vector grid and repeat the extraction process based on the extracted built-up area in the previous step. Eventually, we obtain the urban built-up area boundary at the smallest scale. Plausibility checks indicate that the suggested method not only guarantees the spatial continuity of the resultant urban built-up area boundary, but also highlights the prevailing orientation of urban expansion. The extracted Beijing built-up area boundary can serve as a reference in decision-making for space planning and land-use control. Full article
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Open AccessArticle Mapping Heritage: Geospatial Online Databases of Historic Roads. The Case of the N-340 Roadway Corridor on the Spanish Mediterranean
ISPRS Int. J. Geo-Inf. 2018, 7(4), 134; https://doi.org/10.3390/ijgi7040134
Received: 27 January 2018 / Revised: 1 March 2018 / Accepted: 14 March 2018 / Published: 1 April 2018
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Abstract
The study has developed an online geospatial database for assessing the complexity of roadway heritage, overcoming the limitations of traditional heritage catalogues and databases: the itemization of heritage assets and the rigidity of the database structure. Reflecting the current openness in the field
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The study has developed an online geospatial database for assessing the complexity of roadway heritage, overcoming the limitations of traditional heritage catalogues and databases: the itemization of heritage assets and the rigidity of the database structure. Reflecting the current openness in the field of heritage studies, the research proposes an interdisciplinary approach that reframes heritage databases, both conceptually and technologically. Territorial scale is key for heritage interpretation, the complex characteristics of each type of heritage, and social appropriation. The system is based on an open-source content-management system and framework called ProcessWire, allowing flexibility in the definition of data fields and serving as an internal working tool for research collaboration. Accessibility, flexibility, and ease of use do not preclude rigor: the database works in conjunction with a GIS (Geographic Information System) support system and is complemented by a bibliographical archive. A hierarchical multiscalar heritage characterization has been implemented in order to include the different territorial scales and to facilitate the creation of itineraries. Having attained the main goals of conceptual heritage coherence, accessibility, and rigor, the database should strive for broader capacity to integrate GIS information and stimulate public participation, a step toward controlled crowdsourcing and collaborative heritage characterization. Full article
(This article belongs to the Special Issue Storytelling with Geographic Data)
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Open AccessArticle An Efficient Shortest Path Routing Algorithm for Directed Indoor Environments
ISPRS Int. J. Geo-Inf. 2018, 7(4), 133; https://doi.org/10.3390/ijgi7040133
Received: 24 January 2018 / Revised: 19 March 2018 / Accepted: 22 March 2018 / Published: 26 March 2018
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Abstract
Routing systems for outdoor space have become the focus of many research works. Such routing systems are based on spatial road networks where moving objects (such as cars) are affected by the directed roads and the movement of traffic, which may include traffic
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Routing systems for outdoor space have become the focus of many research works. Such routing systems are based on spatial road networks where moving objects (such as cars) are affected by the directed roads and the movement of traffic, which may include traffic jams. Indoor routing, on the other hand, must take into account the features of indoor space such as walls and rooms. In this paper, we take indoor routing in a new direction whereby we consider the features that a building has in common with outdoor spaces. Inside some buildings, there may be directed floors where moving objects must move in a certain direction through directed corridors in order to reach a certain location. For example, on train platforms or in museums, movement in the corridors may be directed. In these directed floor spaces, a routing system enabling a visitor to take the shortest path to a certain location is essential. Therefore, this work proposes a new approach for buildings with directed indoor spaces, where each room can be affected by the density of the moving objects. The proposed system obtains the shortest path between objects or rooms taking into consideration the directed indoor space and the capacity of the objects to move within each room/cell. Full article
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Open AccessArticle Enhancing Location-Related Hydrogeological Knowledge
ISPRS Int. J. Geo-Inf. 2018, 7(4), 132; https://doi.org/10.3390/ijgi7040132
Received: 29 January 2018 / Revised: 19 March 2018 / Accepted: 22 March 2018 / Published: 24 March 2018
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Abstract
We analyzed the corpus of three geoscientific journals to investigate if there are enough locational references in research articles to apply a geographical search method, such as the example of New Zealand. Based on all available abstracts and all freely available papers of
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We analyzed the corpus of three geoscientific journals to investigate if there are enough locational references in research articles to apply a geographical search method, such as the example of New Zealand. Based on all available abstracts and all freely available papers of the “New Zealand Journal of Geology and Geophysics”, the “New Zealand Journal of Marine and Freshwater Research”, and the “Journal of Hydrology, New Zealand”, we searched title, abstracts, and full texts for place name occurrences that match records from the official Land Information New Zealand (LINZ) gazetteer. We generated ISO standard compliant metadata records for each article including the spatial references and made them available in a public catalogue service. This catalogue can be queried for articles based on authors, titles, keywords, topics, and spatial reference. We visualize the results in a map to show which area the research articles are about, and how much and how densely geographic space is described through these geoscientific research articles by mapping mentioned place names by their geographic locations. We outlined the methodology and technical framework for the geo-referencing of the journal articles and the platform design for this knowledge inventory. The results indicate that the use of well-crafted abstracts for journal articles with carefully chosen place names of relevance for the article provides a guideline for geographically referencing unstructured information like journal articles and reports in order to make such resources discoverable through geographical queries. Lastly, this approach can actively support integrated holistic assessment of water resources and support decision making. Full article
(This article belongs to the Special Issue Place-Based Research in GIScience and Geoinformatics)
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Open AccessArticle The Role of Social Factors in the Accessibility of Urban Areas for People with Motor Disabilities
ISPRS Int. J. Geo-Inf. 2018, 7(4), 131; https://doi.org/10.3390/ijgi7040131
Received: 2 February 2018 / Revised: 17 March 2018 / Accepted: 21 March 2018 / Published: 24 March 2018
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Abstract
The United Nations Convention on the Rights of People with Disabilities recognizes the right of people with disabilities to attain full social participation without discrimination on the basis of disability. Furthermore, mobility is one of the most important life habits for achieving such
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The United Nations Convention on the Rights of People with Disabilities recognizes the right of people with disabilities to attain full social participation without discrimination on the basis of disability. Furthermore, mobility is one of the most important life habits for achieving such participation. Providing people with disabilities with information regarding accessible paths and accessible urban places therefore plays a vital role in achieving these goals. The accessibility of urban places and pedestrian networks depends, however, on the interaction between human capabilities and environmental factors, and may be subdivided into physical or social factors. An optimal analysis of accessibility requires both kinds of factors, social as well as physical. Although there has been considerable work concerning the physical aspects of the environment, social aspects have been largely neglected. In this paper, we highlight the importance of the social dimension of environments and consider a more integrated approach for accessibility assessment. We highlight the ways by which social factors such as policies can be incorporated into accessibility assessment of pedestrian networks for people with motor disabilities. Furthermore, we propose a framework to assess the accessibility of pedestrian network segments that incorporates the confidence level of people with motor disabilities. This framework is then used as a tool to investigate the influence of different policies on accessibility conditions of pedestrian networks. The methodology is implemented in the Saint-Roch neighborhood in Quebec City and the effectiveness of three policy actions is examined by way of illustration. Full article
(This article belongs to the Special Issue Urban Environment Mapping Using GIS)
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