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

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Open AccessArticle Design and Implementation of a 4D Web Application for Analytical Visualization of Smart City Applications
ISPRS Int. J. Geo-Inf. 2018, 7(7), 276; https://doi.org/10.3390/ijgi7070276
Received: 8 June 2018 / Revised: 28 June 2018 / Accepted: 6 July 2018 / Published: 12 July 2018
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Abstract
Contemporary development of computer hardware and software, WebGIS and geo-web services as well as the availability of semantic 3D city models, facilitate flexible and dynamic implementation of web applications. The aim of this paper is to introduce 4D CANVAS, a web-based application for
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Contemporary development of computer hardware and software, WebGIS and geo-web services as well as the availability of semantic 3D city models, facilitate flexible and dynamic implementation of web applications. The aim of this paper is to introduce 4D CANVAS, a web-based application for dynamic visualization of 3D geospatial data for improved decision making in smart city applications. It is based on the Cesium Virtual Globe, an open-source JavaScript library developed with HTML5 and WebGL. At first, different data formats such as JSON, GeoJSON, Cesium Markup Language (CZML) and 3D Tiles are evaluated for their suitability in 4D visualization applications. Then, an interactive Graphical User Interface (GUI) is built observing the principle of cartographic standards to view, manage, understand and explore different simulation outputs at multiple spatial (3D surface of buildings) and temporal (hourly, daily, monthly) resolutions. In this regard, multiple tools such as aggregation, data classification, etc. are developed utilizing JavaScript libraries. As a proof of concept, two energy simulations and their outputs of different spatial and temporal resolutions are demonstrated in five Asian and European cities. Finally, the 4D CANVAS is deployed both in desktop and multi-touch screens. The proposed application allows easy integration of any other geospatial simulation results, thereby helps the users from different sectors to explore them interactively in 4D. Full article
(This article belongs to the Special Issue Web and Mobile GIS)
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Open AccessArticle Temporal Variations and Associated Remotely Sensed Environmental Variables of Dengue Fever in Chitwan District, Nepal
ISPRS Int. J. Geo-Inf. 2018, 7(7), 275; https://doi.org/10.3390/ijgi7070275
Received: 29 May 2018 / Revised: 3 July 2018 / Accepted: 7 July 2018 / Published: 12 July 2018
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Abstract
Dengue fever is one of the leading public health problems of tropical and subtropical countries across the world. Transmission dynamics of dengue fever is largely affected by meteorological and environmental factors, and its temporal pattern generally peaks in hot-wet periods of the year.
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Dengue fever is one of the leading public health problems of tropical and subtropical countries across the world. Transmission dynamics of dengue fever is largely affected by meteorological and environmental factors, and its temporal pattern generally peaks in hot-wet periods of the year. Despite this continuously growing problem, the temporal dynamics of dengue fever and associated potential environmental risk factors are not documented in Nepal. The aim of this study was to fill this research gap by utilizing epidemiological and earth observation data in Chitwan district, one of the frequent dengue outbreak areas of Nepal. We used laboratory confirmed monthly dengue cases as a dependent variable and a set of remotely sensed meteorological and environmental variables as explanatory factors to describe their temporal relationship. Descriptive statistics, cross correlation analysis, and the Poisson generalized additive model were used for this purpose. Results revealed that dengue fever is significantly associated with satellite estimated precipitation, normalized difference vegetation index (NDVI), and enhanced vegetation index (EVI) synchronously and with different lag periods. However, the associations were weak and insignificant with immediate daytime land surface temperature (dLST) and nighttime land surface temperature (nLST), but were significant after 4–5 months. Conclusively, the selected Poisson generalized additive model based on the precipitation, dLST, and NDVI explained the largest variation in monthly distribution of dengue fever with minimum Akaike’s Information Criterion (AIC) and maximum R-squared. The best fit model further significantly improved after including delayed effects in the model. The predicted cases were reasonably accurate based on the comparison of 10-fold cross validation and observed cases. The lagged association found in this study could be useful for the development of remote sensing-based early warning forecasts of dengue fever. Full article
(This article belongs to the Special Issue Geoprocessing in Public and Environmental Health)
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Open AccessArticle The Classification of Noise-Afflicted Remotely Sensed Data Using Three Machine-Learning Techniques: Effect of Different Levels and Types of Noise on Accuracy
ISPRS Int. J. Geo-Inf. 2018, 7(7), 274; https://doi.org/10.3390/ijgi7070274
Received: 8 May 2018 / Revised: 12 June 2018 / Accepted: 26 June 2018 / Published: 12 July 2018
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Abstract
Remotely sensed data are often adversely affected by many types of noise, which influences the classification result. Supervised machine-learning (ML) classifiers such as random forest (RF), support vector machine (SVM), and back-propagation neural network (BPNN) are broadly reported to improve robustness against noise.
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Remotely sensed data are often adversely affected by many types of noise, which influences the classification result. Supervised machine-learning (ML) classifiers such as random forest (RF), support vector machine (SVM), and back-propagation neural network (BPNN) are broadly reported to improve robustness against noise. However, only a few comparative studies that may help investigate this robustness have been reported. An important contribution, going beyond previous studies, is that we perform the analyses by employing the most well-known and broadly implemented packages of the three classifiers and control their settings to represent users’ actual applications. This facilitates an understanding of the extent to which the noise types and levels in remotely sensed data impact classification accuracy using ML classifiers. By using those implementations, we classified the land cover data from a satellite image that was separately afflicted by seven-level zero-mean Gaussian, salt–pepper, and speckle noise. The modeling data and features were strictly controlled. Finally, we discussed how each noise type affects the accuracy obtained from each classifier and the robustness of the classifiers to noise in the data. This may enhance our understanding of the relationship between noises, the supervised ML classifiers, and remotely sensed data. Full article
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Open AccessArticle Using High-Performance Computing to Address the Challenge of Land Use/Land Cover Change Analysis on Spatial Big Data
ISPRS Int. J. Geo-Inf. 2018, 7(7), 273; https://doi.org/10.3390/ijgi7070273
Received: 30 April 2018 / Revised: 28 June 2018 / Accepted: 6 July 2018 / Published: 11 July 2018
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Abstract
Land use/land cover change (LUCC) analysis is a fundamental issue in regional and global geography that can accurately reflect the diversity of landscapes and detect the differences or changes on the earth’s surface. However, a very heavy computational load is often unavoidable, especially
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Land use/land cover change (LUCC) analysis is a fundamental issue in regional and global geography that can accurately reflect the diversity of landscapes and detect the differences or changes on the earth’s surface. However, a very heavy computational load is often unavoidable, especially when processing multi-temporal land cover data with fine spatial resolution using more complicated procedures, which often takes a long time when performing the LUCC analysis over large areas. This paper employs a graph-based spatial decomposition that represents the computational loads as graph vertices and edges and then uses a balanced graph partitioning to decompose the LUCC analysis on spatial big data. For the decomposing tasks, a stream scheduling method is developed to exploit the parallelism in data moving, clipping, overlay analysis, area calculation and transition matrix building. Finally, a change analysis is performed on the land cover data from 2015 to 2016 in China, with each piece of temporal data containing approximately 260 million complex polygons. It took less than 6 h in a cluster with 15 workstations, which was an indispensable task that may surpass two weeks without any optimization. Full article
(This article belongs to the Special Issue Geospatial Big Data and Urban Studies)
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Open AccessArticle Data Extraction Algorithm for Energy Performance Certificates (EPC) to Estimate the Maximum Economic Damage of Buildings for Economic Impact Assessment of Floods in Flanders, Belgium
ISPRS Int. J. Geo-Inf. 2018, 7(7), 272; https://doi.org/10.3390/ijgi7070272
Received: 17 April 2018 / Revised: 9 May 2018 / Accepted: 18 June 2018 / Published: 10 July 2018
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Abstract
Floods cause major disruptions to energy supply and transportation facilities and lead to significant impacts on the society, economy, and environment. As a result, there is a compelling need for resilience and adaptation against extreme flood events under a changing climate. An accurate
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Floods cause major disruptions to energy supply and transportation facilities and lead to significant impacts on the society, economy, and environment. As a result, there is a compelling need for resilience and adaptation against extreme flood events under a changing climate. An accurate focal priority analysis of how societies can adapt to these changing events can provide insight into practical solutions. Besides the social, ecological, and cultural impact assessments of floods, an accurate economic impact analysis is required to define priority zones and priority measures. Unfortunately, studies show that economic impact assessments can be highly inaccurate because of the margin of error in economic value estimation of residential and industrial buildings, as they account for a large part of the total economic damage value. Therefore, tools that can accurately estimate the maximum economic damage value (or replacement value) of residential and industrial buildings are imperative. This paper outlines a methodology to estimate the maximum economic value of buildings by using a data extraction algorithm for Energy Performance Certificates (EPC), through which the replacement value can be calculated for all of the buildings in Flanders, and in addition, across Europe. Full article
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Open AccessFeature PaperArticle LandQv2: A MapReduce-Based System for Processing Arable Land Quality Big Data
ISPRS Int. J. Geo-Inf. 2018, 7(7), 271; https://doi.org/10.3390/ijgi7070271
Received: 25 May 2018 / Revised: 24 June 2018 / Accepted: 6 July 2018 / Published: 10 July 2018
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Abstract
Arable land quality (ALQ) data are a foundational resource for national food security. With the rapid development of spatial information technologies, the annual acquisition and update of ALQ data covering the country have become more accurate and faster. ALQ data are mainly vector-based
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Arable land quality (ALQ) data are a foundational resource for national food security. With the rapid development of spatial information technologies, the annual acquisition and update of ALQ data covering the country have become more accurate and faster. ALQ data are mainly vector-based spatial big data in the ESRI (Environmental Systems Research Institute) shapefile format. Although the shapefile is the most common GIS vector data format, unfortunately, the usage of ALQ data is very constrained due to its massive size and the limited capabilities of traditional applications. To tackle the above issues, this paper introduces LandQv2, which is a MapReduce-based parallel processing system for ALQ big data. The core content of LandQv2 is composed of four key technologies including data preprocessing, the distributed R-tree index, the spatial range query, and the map tile pyramid model-based visualization. According to the functions in LandQv2, firstly, ALQ big data are transformed by a MapReduce-based parallel algorithm from the ESRI Shapefile format to the GeoCSV file format in HDFS (Hadoop Distributed File System), and then, the spatial coding-based partition and R-tree index are executed for the spatial range query operation. In addition, the visualization of ALQ big data with a GIS (Geographic Information System) web API (Application Programming Interface) uses the MapReduce program to generate a single image or pyramid tiles for big data display. Finally, a set of experiments running on a live system deployed on a cluster of machines shows the efficiency and scalability of the proposed system. All of these functions supported by LandQv2 are integrated into SpatialHadoop, and it is also able to efficiently support any other distributed spatial big data systems. Full article
(This article belongs to the Special Issue Distributed and Parallel Architectures for Spatial Data)
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Open AccessArticle Shared Execution Approach to ε-Distance Join Queries in Dynamic Road Networks
ISPRS Int. J. Geo-Inf. 2018, 7(7), 270; https://doi.org/10.3390/ijgi7070270
Received: 30 April 2018 / Revised: 1 July 2018 / Accepted: 6 July 2018 / Published: 10 July 2018
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Abstract
Given a threshold distance ε and two object sets R and S in a road network, an ε-distance join query finds object pairs from R × S that are within the threshold distance ε (e.g., find passenger and taxicab pairs within a
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Given a threshold distance ε and two object sets R and S in a road network, an ε-distance join query finds object pairs from R × S that are within the threshold distance ε (e.g., find passenger and taxicab pairs within a five-minute driving distance). Although this is a well-studied problem in the Euclidean space, little attention has been paid to dynamic road networks where the weights of road segments (e.g., travel times) are frequently updated and the distance between two objects is the length of the shortest path connecting them. In this work, we address the problem of ε-distance join queries in dynamic road networks by proposing an optimized ε-distance join algorithm called EDISON, the key concept of which is to cluster adjacent objects of the same type into a group, and then to optimize shared execution for the group to avoid redundant network traversal. The proposed method is intuitive and easy to implement, thereby allowing its simple integration with existing range query algorithms in road networks. We conduct an extensive experimental study using real-world roadmaps to show the efficiency and scalability of our shared execution approach. Full article
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Open AccessArticle Geospatial Analysis and the Internet of Things
ISPRS Int. J. Geo-Inf. 2018, 7(7), 269; https://doi.org/10.3390/ijgi7070269
Received: 1 June 2018 / Revised: 25 June 2018 / Accepted: 3 July 2018 / Published: 10 July 2018
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Abstract
As the Internet of Things (IoT) penetrates our everyday lives, being used to address a wide variety of real-life challenges and problems, the location of things becomes an important parameter. The exact location of measuring the physical world through IoT is highly relevant
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As the Internet of Things (IoT) penetrates our everyday lives, being used to address a wide variety of real-life challenges and problems, the location of things becomes an important parameter. The exact location of measuring the physical world through IoT is highly relevant to understand local environmental conditions, or to develop powerful, personalized and context-aware location-based services and applications. This survey paper maps and analyzes the IoT based on its location dimension, categorizing IoT applications and projects according to the geospatial analytical methods performed. The survey investigates the opportunities of location-aware IoT, and examines the potential of geospatial analysis in this research area. Full article
(This article belongs to the Special Issue Geospatial Applications of the Internet of Things (IoT))
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Open AccessArticle Landslide Susceptibility Assessment at Mila Basin (Algeria): A Comparative Assessment of Prediction Capability of Advanced Machine Learning Methods
ISPRS Int. J. Geo-Inf. 2018, 7(7), 268; https://doi.org/10.3390/ijgi7070268
Received: 7 May 2018 / Revised: 3 July 2018 / Accepted: 7 July 2018 / Published: 10 July 2018
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Abstract
Landslide risk prevention requires the delineation of landslide-prone areas as accurately as possible. Therefore, selecting a method or a technique that is capable of providing the highest landslide prediction capability is highly important. The main objective of this study is to assess and
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Landslide risk prevention requires the delineation of landslide-prone areas as accurately as possible. Therefore, selecting a method or a technique that is capable of providing the highest landslide prediction capability is highly important. The main objective of this study is to assess and compare the prediction capability of advanced machine learning methods for landslide susceptibility mapping in the Mila Basin (Algeria). First, a geospatial database was constructed from various sources. The database contains 1156 landslide polygons and 16 conditioning factors (altitude, slope, aspect, topographic wetness index (TWI), landforms, rainfall, lithology, stratigraphy, soil type, soil texture, landuse, depth to bedrock, bulk density, distance to faults, distance to hydrographic network, and distance to road networks). Subsequently, the database was randomly resampled into training sets and validation sets using 5 times repeated 10 k-folds cross-validations. Using the training and validation sets, five landslide susceptibility models were constructed, assessed, and compared using Random Forest (RF), Gradient Boosting Machine (GBM), Logistic Regression (LR), Artificial Neural Network (NNET), and Support Vector Machine (SVM). The prediction capability of the five landslide models was assessed and compared using the receiver operating characteristic (ROC) curve, the area under the ROC curves (AUC), overall accuracy (Acc), and kappa index. Additionally, Wilcoxon signed-rank tests were performed to confirm statistical significance in the differences among the five machine learning models employed in this study. The result showed that the GBM model has the highest prediction capability (AUC = 0.8967), followed by the RF model (AUC = 0.8957), the NNET model (AUC = 0.8882), the SVM model (AUC = 0.8818), and the LR model (AUC = 0.8575). Therefore, we concluded that GBM and RF are the most suitable for this study area and should be used to produce landslide susceptibility maps. These maps as a technical framework are used to develop countermeasures and regulatory policies to minimize landslide damages in the Mila Basin. This research demonstrated the benefit of selecting the best-advanced machine learning method for landslide susceptibility assessment. Full article
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Open AccessArticle GIS Application to Regional Geological Structure Relationship Modelling Considering Semantics
ISPRS Int. J. Geo-Inf. 2018, 7(7), 267; https://doi.org/10.3390/ijgi7070267
Received: 11 April 2018 / Revised: 26 June 2018 / Accepted: 6 July 2018 / Published: 9 July 2018
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Abstract
GIS modelling, which is often employed to establish the abstract structural forms of geological phenomena and their structural relationships, is of great importance for the expression and analysis of geological structures to describe and express such phenomena accurately and intuitively. However, current GIS
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GIS modelling, which is often employed to establish the abstract structural forms of geological phenomena and their structural relationships, is of great importance for the expression and analysis of geological structures to describe and express such phenomena accurately and intuitively. However, current GIS modelling schemes value structural forms over structural relationships, and existing geological semantic expressions in the modelling of geological relationships are incomplete. Therefore, this paper categorizes geological relationships into three levels: geological phenomena, geological objects and geological spatial objects: (1) based on their definitions, this work categorizes geological relationships into internal composition relationships and external combined relationships for a total of two categories, eight classes and 27 small groups; (2) this work also improves the system with a total of 33 classified geological objects by transforming the relationships between geological phenomena into relationships between geological objects; and (3) based on the 27 small groups of geological relationships, through the corresponding geometric and semantic expressions between topological rules and geological rules and between relationship rules and geological rules, this work then expresses internal composition relationships as topological relationships between geological spatial objects and expresses external combined relationships as association relationships between geological spatial objects. A GIS model of geological relationships that integrates their geometries and semantics is then built. Finally, taking the Dagang-Danyang section of the Ningzhen mountains as an example, the results show that the proposed GIS modelling method can better store and express geological phenomena, geological objects and geological spatial objects in a way that integrates geometry and semantics. Full article
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Open AccessArticle Association Rules-Based Multivariate Analysis and Visualization of Spatiotemporal Climate Data
ISPRS Int. J. Geo-Inf. 2018, 7(7), 266; https://doi.org/10.3390/ijgi7070266
Received: 24 May 2018 / Revised: 29 June 2018 / Accepted: 3 July 2018 / Published: 9 July 2018
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Abstract
Understanding atmospheric phenomena involves analysis of large-scale spatiotemporal multivariate data. The complexity and heterogeneity of such data pose a significant challenge in discovering and understanding the association between multiple climate variables. To tackle this challenge, we present an interactive heuristic visualization system that
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Understanding atmospheric phenomena involves analysis of large-scale spatiotemporal multivariate data. The complexity and heterogeneity of such data pose a significant challenge in discovering and understanding the association between multiple climate variables. To tackle this challenge, we present an interactive heuristic visualization system that supports climate scientists and the public in their exploration and analysis of atmospheric phenomena of interest. Three techniques are introduced: (1) web-based spatiotemporal climate data visualization; (2) multiview and multivariate scientific data analysis; and (3) data mining-enabled visual analytics. The Arctic System Reanalysis (ASR) data are used to demonstrate and validate the effectiveness and usefulness of our method through a case study of “The Great Arctic Cyclone of 2012”. The results show that different variables have strong associations near the polar cyclone area. This work also provides techniques for identifying multivariate correlation and for better understanding the driving factors of climate phenomena. Full article
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Open AccessArticle Model of Point Cloud Data Management System in Big Data Paradigm
ISPRS Int. J. Geo-Inf. 2018, 7(7), 265; https://doi.org/10.3390/ijgi7070265
Received: 30 April 2018 / Revised: 26 June 2018 / Accepted: 3 July 2018 / Published: 9 July 2018
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Abstract
Modern geoinformation technologies for collecting and processing data, such as laser scanning or photogrammetry, can generate point clouds with billions of points. They provide abundant information that can be used for different types of analysis. Due to its characteristics, the point cloud is
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Modern geoinformation technologies for collecting and processing data, such as laser scanning or photogrammetry, can generate point clouds with billions of points. They provide abundant information that can be used for different types of analysis. Due to its characteristics, the point cloud is often viewed as a special type of geospatial data. In order to efficiently manage such volumes of data, techniques based on a computer cluster have to be used. The Apache Spark framework has proven to be a solution for efficient processing of large volumes of data. This paper thoroughly examines the representation of point cloud data type using Apache Spark constructs. The common operations over point clouds, range queries and k-nearest neighbors queries (kNN) are implemented using Apache Spark DataFrame Application Programming Interface (API). It enabled the design of point cloud related user defined types (UDT) and user defined functions (UDF). The structure of the point cloud for efficient storing in Big Data key-value stores was analyzed and described. The methods presented in this paper were compared to PostgreSQL RDBMS, and the results were discussed. Full article
(This article belongs to the Special Issue Geospatial Big Data and Urban Studies)
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Open AccessArticle Structured Knowledge Base as Prior Knowledge to Improve Urban Data Analysis
ISPRS Int. J. Geo-Inf. 2018, 7(7), 264; https://doi.org/10.3390/ijgi7070264
Received: 14 May 2018 / Revised: 27 June 2018 / Accepted: 3 July 2018 / Published: 7 July 2018
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Abstract
Urban computing at present often relies on a large number of manually extracted features. This may require a considerable amount of feature engineering, and the procedure may miss certain hidden features and relationships among data items. In this paper, we propose a method
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Urban computing at present often relies on a large number of manually extracted features. This may require a considerable amount of feature engineering, and the procedure may miss certain hidden features and relationships among data items. In this paper, we propose a method to use structured prior knowledge in the form of knowledge graphs to improve the precision and interpretability in applications such as optimal store placement and traffic accident inference. Specifically, we integrate sub-graph feature extraction, sub-knowledge graph gated neural networks, and kernel-based knowledge graph convolutional neural networks as ways of incorporating large urban knowledge graphs into a fully end-to-end learning system. Experiments using data from several large cities showed that our method outperforms the baseline methods. Full article
(This article belongs to the Special Issue Geospatial Big Data and Urban Studies)
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Open AccessArticle Mapping Creative Spaces in Omaha, NE: Resident Perceptions versus Creative Firm Locations
ISPRS Int. J. Geo-Inf. 2018, 7(7), 263; https://doi.org/10.3390/ijgi7070263
Received: 30 May 2018 / Revised: 26 June 2018 / Accepted: 3 July 2018 / Published: 4 July 2018
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Abstract
In an era increasingly shaped by automation and globalization, industries that rely on creativity, innovation, and knowledge-generation are considered key drivers of economic growth in the U.S. and other advanced capitalist economies. This study examines the spatial distribution of creative firms and how
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In an era increasingly shaped by automation and globalization, industries that rely on creativity, innovation, and knowledge-generation are considered key drivers of economic growth in the U.S. and other advanced capitalist economies. This study examines the spatial distribution of creative firms and how they might align with perceptions of creativity in Omaha, Nebraska, a mid-sized U.S. urban area. Utilizing a survey, participant mapping exercise, and geospatial analyses, the primary goal was to identify formal and informal spaces of creative production and consumption, and determine to what extent the location of creative firms (both arts/media- and science/technology-focused) may shape perceptions of creativity across the urban landscape. The results suggest that local area residents primarily view dense, vibrant, mixed-use, and often historic urban neighborhoods as particularly creative, whether or not there exists a dense concentration of creative firms. Similarly, creative firms were more spatially diffuse than the clusters of “creative locations” identified by residents, and were more frequently found in suburban locations. Furthermore, while there was no discernible difference among “creative” and “non-creative” workers, science/technology firms were more likely than arts/media firms to be found in suburban locations, and less likely to be associated with perceptions of creativity in Omaha. Full article
(This article belongs to the Special Issue Urban Environment Mapping Using GIS)
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Open AccessArticle Historical Collaborative Geocoding
ISPRS Int. J. Geo-Inf. 2018, 7(7), 262; https://doi.org/10.3390/ijgi7070262
Received: 6 April 2018 / Revised: 31 May 2018 / Accepted: 26 June 2018 / Published: 4 July 2018
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Abstract
The latest developments in the field of digital humanities have increasingly enabled the construction of large data sets which can be easily accessed and used. These data sets often contain indirect spatial information, such as historical addresses. Historical geocoding is the process of
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The latest developments in the field of digital humanities have increasingly enabled the construction of large data sets which can be easily accessed and used. These data sets often contain indirect spatial information, such as historical addresses. Historical geocoding is the process of transforming indirect spatial information into direct locations which can be placed on a map, thus allowing for spatial analysis and cross-referencing. There are many geocoders that work efficiently for current addresses. However, these do not tackle temporal information, and usually follow a strict hierarchy (country, city, street, house number, etc.) which is difficult—if not impossible—to use with historical data. Historical data is filled with uncertainty (pertaining to temporal, textual, and positional accuracy, as well as to the reliability of historical sources) which can neither be ignored nor entirely resolved. Our open source, open data, and extensible solution for geocoding is based on extracting a large number of simple gazetteers composed of geohistorical objects, from historical maps. Geocoding a historical address becomes the process of finding one or several geohistorical objects in the gazetteers which best match the historical address searched by the user. The matching criteria are customisable, weighted, and include several dimensions (fuzzy string, fuzzy temporal, level of detail, positional accuracy). Since our goal is to facilitate historical work, we also put forward web-based user interfaces which help geocode (one address or batch mode) and display results over current or historical maps. Geocoded results can then be checked and edited collaboratively (no source is modified). The system was tested on the city of Paris, France, for the 19th and 20th centuries. It showed high response rates and worked quickly enough to be used interactively. Full article
(This article belongs to the Special Issue Historic Settlement and Landscape Analysis)
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