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ISPRS Int. J. Geo-Inf., Volume 8, Issue 11 (November 2019)

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Open AccessArticle
TerraBrasilis: A Spatial Data Analytics Infrastructure for Large-Scale Thematic Mapping
ISPRS Int. J. Geo-Inf. 2019, 8(11), 513; https://doi.org/10.3390/ijgi8110513 (registering DOI) - 12 Nov 2019
Abstract
The physical phenomena derived from an analysis of remotely sensed imagery provide a clearer understanding of the spectral variations of a large number of land use and cover (LUC) classes. The creation of LUC maps have corroborated this view by enabling the scientific [...] Read more.
The physical phenomena derived from an analysis of remotely sensed imagery provide a clearer understanding of the spectral variations of a large number of land use and cover (LUC) classes. The creation of LUC maps have corroborated this view by enabling the scientific community to estimate the parameter heterogeneity of the Earth’s surface. Along with descriptions of features and statistics for aggregating spatio-temporal information, the government programs have disseminated thematic maps to further the implementation of effective public policies and foster sustainable development. In Brazil, PRODES and DETER have shown that they are committed to monitoring the mapping areas of large-scale deforestation systematically and by means of data quality assurance. However, these programs are so complex that they require the designing, implementation and deployment of a spatial data infrastructure based on extensive data analytics features so that users who lack a necessary understanding of standard spatial interfaces can still carry out research on them. With this in mind, the Brazilian National Institute for Space Research (INPE) has designed TerraBrasilis, a spatial data analytics infrastructure that provides interfaces that are not only found within traditional geographic information systems but also in data analytics environments with complex algorithms. To ensure it achieved its best performance, we leveraged a micro-service architecture with virtualized computer resources to enable high availability, lower size, simplicity to produce an increment, reliable to change and fault tolerance in unstable computer network scenarios. In addition, we tuned and optimized our databases both to adjust to the input format of complex algorithms and speed up the loading of the web application so that it was faster than other systems. Full article
(This article belongs to the Special Issue Free and Open Source Tools for Geospatial Analysis and Mapping)
Open AccessArticle
An Adaptive Construction Method of Hierarchical Spatio-Temporal Index for Vector Data under Peer-to-Peer Networks
ISPRS Int. J. Geo-Inf. 2019, 8(11), 512; https://doi.org/10.3390/ijgi8110512 (registering DOI) - 12 Nov 2019
Abstract
Spatio-temporal indexing is a key technique in spatio-temporal data storage and management. Indexing methods based on spatial filling curves are popular in research on the spatio-temporal indexing of vector data in the Not Relational (NoSQL) database. However, the existing methods mostly focus on [...] Read more.
Spatio-temporal indexing is a key technique in spatio-temporal data storage and management. Indexing methods based on spatial filling curves are popular in research on the spatio-temporal indexing of vector data in the Not Relational (NoSQL) database. However, the existing methods mostly focus on spatial indexing, which makes it difficult to balance the efficiencies of time and space queries. In addition, for non-point elements (line and polygon elements), it remains difficult to determine the optimal index level. To address these issues, this paper proposes an adaptive construction method of hierarchical spatio-temporal index for vector data. Firstly, a joint spatio-temporal information coding based on the combination of the partition and sort key strategies is presented. Secondly, the multilevel expression structure of spatio-temporal elements consisting of point and non-point elements in the joint coding is given. Finally, an adaptive multi-level index tree is proposed to realize the spatio-temporal index (Multi-level Sphere 3, MLS3) based on the spatio-temporal characteristics of geographical entities. Comparison with the XZ3 index algorithm proposed by GeoMesa proved that the MLS3 indexing method not only reasonably expresses the spatio-temporal features of non-point elements and determines their optimal index level, but also avoids storage hotspots while achieving spatio-temporal retrieval with high efficiency. Full article
Open AccessArticle
Using Landsat OLI and Random Forest to Assess Grassland Degradation with Aboveground Net Primary Production and Electrical Conductivity Data
ISPRS Int. J. Geo-Inf. 2019, 8(11), 511; https://doi.org/10.3390/ijgi8110511 (registering DOI) - 12 Nov 2019
Abstract
Grassland coverage, aboveground net primary production (ANPP), and species composition are used as indicators of grassland degradation. However, soil salinization deficiency, which is also a factor of grassland degradation, is rarely used in grassland degradation assessment in semiarid regions. We assessed grassland degradation [...] Read more.
Grassland coverage, aboveground net primary production (ANPP), and species composition are used as indicators of grassland degradation. However, soil salinization deficiency, which is also a factor of grassland degradation, is rarely used in grassland degradation assessment in semiarid regions. We assessed grassland degradation by its quality, quantity, and spatial pattern over semiarid west Jilin, China. Considering soil salinization in west Jilin, electrical conductivity (EC) is used as an index with ANPP to assess grassland degradation. First, the spatial distribution of the grassland was measured with information mined from multi-temporal remote sensing images using an object-based image analysis combined with classification and decision tree methods. Second, with 166 field samples, we utilized the random forest (RF) algorithm as the variable selection and regression method for predicting EC and ANPP. Finally, we created a new grassland degradation model (GDM) based on ANPP and EC. The results showed the R2 (0.91) and RMSE (0.057 mS/cm) of the EC model were generally highest and lowest when the ntree was 400; the ANPP model was optimal (R2 = 0.85 and RMSE = 15.81 gC/m2) when the ntree was 600. Grassland area of west Jilin was 609.67 × 103 ha in 2017, there were 373.79 × 103 ha of degraded grassland, with 210.47 × 103 ha being intensively degraded. This paper surpasses past limitations of excessive reliance on vegetation index to construct a grassland degradation model which considers the characteristics of the study area and soil salinity. The results confirm the positive influence of the ecological conservation projects sponsored by the government. The research outcome could offer supporting data for decision making to help alleviate grassland degradation and promote the rehabilitation of grassland vegetation. Full article
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Open AccessArticle
Ranking of Illegal Buildings Close to Rivers: A Proposal, Its Implementation and Preliminary Validation
ISPRS Int. J. Geo-Inf. 2019, 8(11), 510; https://doi.org/10.3390/ijgi8110510 (registering DOI) - 11 Nov 2019
Abstract
Illegal buildings (IBs) are a dramatic problem in developing countries due to the population explosion, but, at the same time, they represent an unsolved issue in several states usually called advanced (as, for instance, Italy). To protect the environment, and hence, people, land [...] Read more.
Illegal buildings (IBs) are a dramatic problem in developing countries due to the population explosion, but, at the same time, they represent an unsolved issue in several states usually called advanced (as, for instance, Italy). To protect the environment, and hence, people, land authorities must respond to the challenge of IBs by demolishing them. However, in countries where the phenomenon is extended, it is indispensable to provide those figures with an IT tool that returns to them an order of demolition. Through remote sensing methods, suspicious buildings can be identified with a good approximation, but they are all ex aequo. The research summarized in this paper formalizes a two-steps method to deal with a specific category of IBs, namely, those that are close to rivers. These buildings are of special interest to land authorities because people living or simply working inside them are exposed to the flood hazard that each year claims many victims all over the world. The first step of the method computes the census of the IBs located close to rivers, while the second step computes the ranking of these buildings. The ranking may be used as the IBs demolition order. In the paper, it is also proposed the structure of a Spatial DataBase (briefly, SDB) that is suitable to store the input data necessary to solve the problem, as well as the final ranking. Spatial SQL queries against the SDB implement the proposed two-steps method. A real case study was carried out to make a preliminary validation of the method. Full article
(This article belongs to the Special Issue Geo-Information Science in Planning and Development of Smart Cities)
Open AccessArticle
Adaptive Choropleth Mapper: An Open-Source Web-Based Tool for Synchronous Exploration of Multiple Variables at Multiple Spatial Extents
ISPRS Int. J. Geo-Inf. 2019, 8(11), 509; https://doi.org/10.3390/ijgi8110509 (registering DOI) - 11 Nov 2019
Abstract
Choropleth mapping is an essential visualization technique for exploratory spatial data analysis. Visualizing multiple choropleth maps is a technique that spatial analysts use to reveal spatiotemporal patterns of one variable or to compare the geographical distributions of multiple variables. Critical features for effective [...] Read more.
Choropleth mapping is an essential visualization technique for exploratory spatial data analysis. Visualizing multiple choropleth maps is a technique that spatial analysts use to reveal spatiotemporal patterns of one variable or to compare the geographical distributions of multiple variables. Critical features for effective exploration of multiple choropleth maps are (1) automated computation of the same class intervals for shading different choropleth maps, (2) dynamic visualization of local variation in a variable, and (3) linking for synchronous exploration of multiple choropleth maps. Since the 1990s, these features have been developed and are now included in many commercial geographic information system (GIS) software packages. However, many choropleth mapping tools include only one or two of the three features described above. On the other hand, freely available mapping tools that support side-by-side multiple choropleth map visualizations are usually desktop software only. As a result, most existing tools supporting multiple choropleth-map visualizations cannot be easily integrated with Web-based and open-source data visualization libraries, which have become mainstream in visual analytics and geovisualization. To fill this gap, we introduce an open-source Web-based choropleth mapping tool called the Adaptive Choropleth Mapper (ACM), which combines the three critical features for flexible choropleth mapping. Full article
(This article belongs to the Special Issue Open Science in the Geospatial Domain)
Open AccessArticle
A Multilevel Eigenvector Spatial Filtering Model of House Prices: A Case Study of House Sales in Fairfax County, Virginia
ISPRS Int. J. Geo-Inf. 2019, 8(11), 508; https://doi.org/10.3390/ijgi8110508 (registering DOI) - 10 Nov 2019
Abstract
House prices tend to be spatially correlated due to similar physical features shared by neighboring houses and commonalities attributable to their neighborhood environment. A multilevel model is one of the methodologies that has been frequently adopted to address spatial effects in modeling house [...] Read more.
House prices tend to be spatially correlated due to similar physical features shared by neighboring houses and commonalities attributable to their neighborhood environment. A multilevel model is one of the methodologies that has been frequently adopted to address spatial effects in modeling house prices. Empirical studies show its capability in accounting for neighborhood specific spatial autocorrelation (SA) and analyzing potential factors related to house prices at both individual and neighborhood levels. However, a standard multilevel model specification only considers within-neighborhood SA, which refers to similar house prices within a given neighborhood, but neglects between-neighborhood SA, which refers to similar house prices for adjacent neighborhoods that can commonly exist in residential areas. This oversight may lead to unreliable inference results for covariates, and subsequently less accurate house price predictions. This study proposes to extend a multilevel model using Moran eigenvector spatial filtering (MESF) methodology. This proposed model can take into account simultaneously between-neighborhood SA with a set of Moran eigenvectors as well as potential within-neighborhood SA with a random effects term. An empirical analysis of 2016 and 2017 house prices in Fairfax County, Virginia, illustrates the capability of a multilevel MESF model specification in accounting for between-neighborhood SA present in data. A comparison of its model performance and house price prediction outcomes with conventional methodologies also indicates that the multilevel MESF model outperforms standard multilevel and hedonic models. With its simple and flexible feature, a multilevel MESF model can furnish an appealing and useful approach for understanding the underlying spatial distribution of house prices. Full article
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Open AccessCase Report
Testing Different Interpolation Methods Based on Single Beam Echosounder River Surveying. Case Study: Siret River
ISPRS Int. J. Geo-Inf. 2019, 8(11), 507; https://doi.org/10.3390/ijgi8110507 (registering DOI) - 10 Nov 2019
Abstract
Bathymetric measurements play an important role in assessing the sedimentation rate, deposition of pollutants, erosion rate, or monitoring of morphological changes in a river, lake, or accumulation basin. In order to create a coherent and continuous digital elevation model (DEM) of a river [...] Read more.
Bathymetric measurements play an important role in assessing the sedimentation rate, deposition of pollutants, erosion rate, or monitoring of morphological changes in a river, lake, or accumulation basin. In order to create a coherent and continuous digital elevation model (DEM) of a river bed, various data interpolation methods are used, especially when single-beam bathymetric measurements do not cover the entire area and when there are areas which are not measured. Interpolation methods are based on numerical models applied to natural landscapes (e.g., meandering river) by taking into account various morphometric and morphologies and a wide range of scales. Obviously, each interpolation method, used in standard or customised form, yields different results. This study aims at testing four interpolation methods in order to determine the most appropriate method which will give an accurate description of the riverbed, based on single-beam bathymetric measurements. The four interpolation methods selected in the present research are: inverse distance weighting (IDW), radial basis function (RBF) with completely regularized spline (CRS) which uses deterministic interpolation, simple kriging (KRG) which is a geo-statistical method, and Topo to Raster (TopoR), a particular method specifically designed for creating continuous surfaces from various elevation points, contour, or polygon data, suitable for creating surfaces for hydrologic analysis. Digital elevation models (DEM’s) were statistically analyzed and precision and errors were evaluated. The single-beam bathymetric measurements were made on the Siret River, between 0 and 35 km. To check and validate the methods, the experiment was repeated for five randomly selected cross-sections in a 1500 m section of the river. The results were then compared with the data extracted from each elevation model generated with each of the four interpolation methods. Our results show that: 1) TopoR is the most accurate technique, and 2) the two deterministic methods give large errors in bank areas, for the entire river channel and for the particular cross-sections. Full article
Open AccessArticle
Analyzing the Spatiotemporal Patterns in Green Spaces for Urban Studies Using Location-Based Social Media Data
ISPRS Int. J. Geo-Inf. 2019, 8(11), 506; https://doi.org/10.3390/ijgi8110506 (registering DOI) - 10 Nov 2019
Abstract
Green parks are vital public spaces and play a major role in urban living and well-being. Research on the attractiveness of green parks often relies on traditional techniques, such as questionnaires and in-situ surveys, but these methods are typically insignificant in scale, time-consuming, [...] Read more.
Green parks are vital public spaces and play a major role in urban living and well-being. Research on the attractiveness of green parks often relies on traditional techniques, such as questionnaires and in-situ surveys, but these methods are typically insignificant in scale, time-consuming, and expensive, with less transferable results and only site-specific outcomes. This article presents an investigative study that uses location-based social network (LBSN) data to collect spatial and temporal patterns of park visits in Shanghai metropolitan city. During the period from July 2016 to June 2017 in Shanghai, China, we analyzed the spatiotemporal behavior of park visitors for 157 green parks and conducted empirical research on the impacts of green spaces on the public’s behavior in Shanghai. Our main findings show (i) the check-in distribution of users in different green spaces; (ii) the seasonal effects on the public’s behavior toward green spaces; (iii) changes in the number of users based on the hour of the day, the intervals of the day (morning, afternoon, evening), and the day of the week; (iv) interesting user behavior variations that depend on temperature effects; and (v) gender-based differences in the number of green park visitors. These results can be used for the purpose of urban city planning for green spaces by accounting for the preferences of visitors. Full article
(This article belongs to the Special Issue GIS-Based Analysis for Quality of Life and Environmental Monitoring)
Open AccessArticle
Spatiotemporal Distribution of Nonseismic Landslides during the Last 22 Years in Shaanxi Province, China
ISPRS Int. J. Geo-Inf. 2019, 8(11), 505; https://doi.org/10.3390/ijgi8110505 (registering DOI) - 09 Nov 2019
Abstract
The spatiotemporal distribution of landslides provides valuable insight for the understanding of disastrous processes and landslide risk assessment. In this work, we compiled a catalog of landslides from 1996 to 2017 based on existing records, yearbooks, archives, and fieldwork in Shaanxi Province, China. [...] Read more.
The spatiotemporal distribution of landslides provides valuable insight for the understanding of disastrous processes and landslide risk assessment. In this work, we compiled a catalog of landslides from 1996 to 2017 based on existing records, yearbooks, archives, and fieldwork in Shaanxi Province, China. The statistical analyses demonstrated that the cumulative frequency distribution of the annual landslide number was empirically described by a power-law regression. Most landslides occurred from July to October. The relationship between landslide time interval and their cumulative frequency could be fitted using an exponential regression. The cumulative frequency of the landslide number could be approximated using the power-law function. Moreover, many landslides caused fatalities, and the number of fatalities was related to the number of landslides each month. Moreover, the cumulative frequency was significantly correlated with the number of fatalities and exhibited a power-law relationship. Furthermore, obvious differences were observed in the type and density of landslides between the Loess Plateau and the Qinba Mountains. Most landslides were close to stream channels and faults, and were concentrated in cropland at elevations from 600–900 m and on slope gradients from 30–40°. In addition, the landslide frequency increased as the annual rainfall levels increased over a large spatial scale, and the monthly distribution of landslides presented a significant association with the precipitation level. This study provides a powerful method for understanding the spatiotemporal distribution of landslides via a rare landslide catalog, which is important for engineering design and planning and risk management. Full article
(This article belongs to the Special Issue Geospatial Approaches to Landslide Mapping and Monitoring)
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Open AccessArticle
Method for 3D City Building Continuous Transformation Based on an Improved LOD Topological Data Structure
ISPRS Int. J. Geo-Inf. 2019, 8(11), 504; https://doi.org/10.3390/ijgi8110504 - 08 Nov 2019
Abstract
A 3D city model is an intuitive tool that is used to describe cities. Currently, level-of-detail (LOD) technology is used to meet different visual demands for 3D city models by weighting the rendering efficiency against the details of the model. However, when the [...] Read more.
A 3D city model is an intuitive tool that is used to describe cities. Currently, level-of-detail (LOD) technology is used to meet different visual demands for 3D city models by weighting the rendering efficiency against the details of the model. However, when the visual demands change, the “popping” phenomenon appears when making transformations between different LOD models. We optimized this popping phenomenon by improving the data structure that focuses on 3D city building models and combined it with the facet shift algorithm based on minimal features. Unlike generating finite LOD models in advance, the proposed continuous LOD topology data structure is able to store the changes between different LOD models. By reasonably using the change information, continuous LOD transformation becomes possible. The experimental results showed that the continuous LOD transformation based on the proposed data structure worked well, and the improved data structure also performed well in memory occupation. Full article
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Open AccessFeature PaperArticle
Utilizing BIM and GIS for Representation and Visualization of 3D Cadastre
ISPRS Int. J. Geo-Inf. 2019, 8(11), 503; https://doi.org/10.3390/ijgi8110503 - 07 Nov 2019
Abstract
The current three-dimensionally (3D) delimited property units are in most countries registered using two-dimensional (2D) documentation and textual descriptions. This approach has limitations if used for representing the actual extent of complicated 3D property units, in particular in city centers. 3D digital models [...] Read more.
The current three-dimensionally (3D) delimited property units are in most countries registered using two-dimensional (2D) documentation and textual descriptions. This approach has limitations if used for representing the actual extent of complicated 3D property units, in particular in city centers. 3D digital models such as building information model (BIM) and 3D geographic information system (GIS) could be utilized for accurate identification of property units, better representation of cadastral boundaries, and detailed visualization of complex buildings. To facilitate this, several requirements need to be identified considering organizational, legal, and technical aspects. In this study, we formulate these requirements and then develop a framework for integration of 3D cadastre and 3D digital models. The aim of this paper is that cadastral information stored based on the land administration domain model (LADM) are integrated with BIM on building level for accurate representation of legal boundaries and with GIS on city level for visualization of 3D cadastre in urban environments. The framework is implemented and evaluated against the requirements in a practical case study in Sweden. The conclusion is that the integration of the cadastral information and BIM/GIS is possible on both conceptual level and data level which will facilitate that organizations dealing with cadastral information (cadastral units), BIM models (architecture, engineering, and construction companies), and GIS (surveying units on e.g., municipality level) can exchange information; this facilitates better representation and visualization of 3D cadastral boundaries. Full article
(This article belongs to the Special Issue Integration of BIM and GIS for Built Environment Applications)
Open AccessArticle
The Effect of NDVI Time Series Density Derived from Spatiotemporal Fusion of Multisource Remote Sensing Data on Crop Classification Accuracy
ISPRS Int. J. Geo-Inf. 2019, 8(11), 502; https://doi.org/10.3390/ijgi8110502 - 07 Nov 2019
Abstract
Remote sensing data with high spatial and temporal resolutions can help to improve the accuracy of the estimation of crop planting acreage, and contribute to the formulation and management of agricultural policies. Therefore, it is important to determine whether multisource sensors can obtain [...] Read more.
Remote sensing data with high spatial and temporal resolutions can help to improve the accuracy of the estimation of crop planting acreage, and contribute to the formulation and management of agricultural policies. Therefore, it is important to determine whether multisource sensors can obtain high spatial and temporal resolution remote sensing data for the target sensor with the help of the spatiotemporal fusion method. In this study, we employed three different sensor datasets to obtain one normalized difference vegetation index (NDVI) time series dataset with a 5.8-m spatial resolution using a spatial and temporal adaptive reflectance fusion model (STARFM). We studied the effectiveness of using multisource remote sensing data to extract crop classifications and analyzed whether the increase in the NDVI time series density could significantly improve the accuracy of the crop classification. The results indicated that multisource sensor data could be used for crop classification after spatiotemporal fusion and that the data source was not limited by the sensor platform. With the increase in the number of NDVI phases, the classification accuracy of the support vector machine (SVM) and the random forest (RF) classifier gradually improved. If the added NDVI phases were not in the optimal time period for wheat recognition, the classification accuracy was not greatly improved. Under the same conditions, the classification accuracy of the RF classifier was higher than that of the SVM. In addition, this study can serve as a good reference for the selection of the optimal time range for base image pairs in the spatiotemporal fusion method for high accuracy mapping of crops, and help avoid excessive data collection and processing. Full article
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Open AccessArticle
Construction and Verification of a High-Precision Base Map for an Autonomous Vehicle Monitoring System
ISPRS Int. J. Geo-Inf. 2019, 8(11), 501; https://doi.org/10.3390/ijgi8110501 - 06 Nov 2019
Abstract
For autonomous driving, a control system that supports precise road maps is required to monitor the operation status of autonomous vehicles in the research stage. Such a system is also required for research related to automobile engineering, sensors, and artificial intelligence. The design [...] Read more.
For autonomous driving, a control system that supports precise road maps is required to monitor the operation status of autonomous vehicles in the research stage. Such a system is also required for research related to automobile engineering, sensors, and artificial intelligence. The design of Google Maps and other map services is limited to the provision of map support at 20 levels of high-resolution precision. An ideal map should include information on roads, autonomous vehicles, and Internet of Things (IOT) facilities that support autonomous driving. The aim of this study was to design a map suitable for the control of autonomous vehicles in Gyeonggi Province in Korea. This work was part of the project “Building a Testbed for Pilot Operations of Autonomous Vehicles”. The map design scheme was redesigned for an autonomous vehicle control system based on the “Easy Map” developed by the National Geography Center, which provides free design schema. In addition, a vector-based precision map, including roads, sidewalks, and road markings, was produced to provide content suitable for 20 levels. A hybrid map that combines the vector layer of the road and an unmanned aerial vehicle (UAV) orthographic map was designed to facilitate vehicle identification. A control system that can display vehicle and sensor information based on the designed map was developed, and an environment to monitor the operation of autonomous vehicles was established. Finally, the high-precision map was verified through an accuracy test and driving data from autonomous vehicles. Full article
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Open AccessArticle
Eliciting Knowledge on Technical and Legal Aspects of Participatory Toponym Handling
ISPRS Int. J. Geo-Inf. 2019, 8(11), 500; https://doi.org/10.3390/ijgi8110500 - 05 Nov 2019
Abstract
There has been increased collaboration between citizens and scientists to achieve common goals in scientific or geographic data collection, analysis, and reporting. Geospatial technology is leveraging the power of citizens in such efforts. Governments have been exploring participatory approaches. This situation should be [...] Read more.
There has been increased collaboration between citizens and scientists to achieve common goals in scientific or geographic data collection, analysis, and reporting. Geospatial technology is leveraging the power of citizens in such efforts. Governments have been exploring participatory approaches. This situation should be balanced by sharing knowledge and collaborative learning between stakeholders involved in the participatory activity. Training and education are enhanced by providing guidelines, sharing best practices, and developing toolkits. For toponym handling, a generic framework and capacity building are needed to increase public awareness and enable citizen toponymists. This paper addresses issues around citizen involvement in increasing toponymic knowledge through citizen science and geospatial capacity building. First, we examined the current practice of toponym handling and developed a generic framework. We then used stakeholder feedback and other resources to modify the framework for Indonesian use. Second, we conducted collaborative learning to share information and bridge the knowledge gaps among multiple stakeholders. Third, we applied insights and lessons learned from these activities to develop ideas, suggestions, and action plans to implement participatory toponym handling in Indonesia. Full article
(This article belongs to the Special Issue Citizen Science and Geospatial Capacity Building)
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Open AccessArticle
The Feasibility of a BIM-Driven Approach to Support Building Subdivision Workflows—Case Study of Victoria, Australia
ISPRS Int. J. Geo-Inf. 2019, 8(11), 499; https://doi.org/10.3390/ijgi8110499 - 04 Nov 2019
Abstract
Cities are facing dramatic challenges due to population growth and the massive development of high-rises and complex structures, both above and below the ground surface. Decision-makers require access to an efficient land and property information system, which is digital, three-dimensional (3D), spatially accurate, [...] Read more.
Cities are facing dramatic challenges due to population growth and the massive development of high-rises and complex structures, both above and below the ground surface. Decision-makers require access to an efficient land and property information system, which is digital, three-dimensional (3D), spatially accurate, and dynamic containing interests in land (rights, restrictions and responsibilities—RRRs) to manage the legal and physical complexities of urban environments. However, at present, building subdivision workflows only support the two-dimensional (2D) building subdivision plans in PDF or image formats. These workflows result in a number of issues, such as the plan preparation being complex, the examination process being labor intensive and requiring technical expertise, information not being easily reusable by all subdivision stakeholders, queries, analyses, and decision-making being inefficient, and the RRRs interpretation being difficult. The aim of this research is to explore the potential of using Building Information Modelling (BIM) and its open standards to support the building subdivision workflows. The research that is presented in this paper proposes a BIM-driven building subdivision workflow, evaluated through a case study in the state of Victoria, Australia. The results of the study confirmed that the proposed workflow could provide a feasible integrated mechanism for stakeholders to share, document, visualize, analyze, interpret, and reuse 3D digital cadastral data over the lifespan of a building subdivision project. Full article
Open AccessLetter
How Data-Poor Countries Remain Data Poor: Underestimation of Human Settlements in Burkina Faso as Observed from Nighttime Light Data
ISPRS Int. J. Geo-Inf. 2019, 8(11), 498; https://doi.org/10.3390/ijgi8110498 - 04 Nov 2019
Abstract
The traditional ways of measuring global sustainable development and economic development schemes and their progress suffer from a number of serious shortcomings. Remote sensing and specifically nighttime light has become a popular supplement to official statistics by providing an objective measure of human [...] Read more.
The traditional ways of measuring global sustainable development and economic development schemes and their progress suffer from a number of serious shortcomings. Remote sensing and specifically nighttime light has become a popular supplement to official statistics by providing an objective measure of human settlement that can be used as a proxy for population and economic development measures. With the increased availability and use of the Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) and data in social science, it has played an important role in data collection, including measuring human development and economic growth. Numerous studies are using nighttime light data to analyze dynamic regions such as expansions of urban areas and rapid industrialization often highlight the problem of saturation in urban centers with high light intensity. However, the quality of nighttime light data and its appropriateness for analyzing areas and regions with low and fluctuating levels of light have rarely been questioned or studied. This study examines the accuracy of DMSP-OLS and VIIRS-DNB by analyzing 147 communities in Burkina Faso to provide insights about problems related to the study of areas with a low intensity of nighttime light during the studied period from 1992 to 2012. It found that up to 57% of the communities studied were undetectable throughout the period, and only 9% of communities studied had a 100% detection rate. Unsurprisingly, the result provides evidence that detection rates in both datasets are particularly low (3%) for settlements with 0–9999 inhabitants, as well as for larger settlements with population of 10,000–24,999 (28%). Cross-checking with VIIRS-DNB for the year 2012 shows similar results. These findings suggest that careful consideration must be given to the use of nighttime light data in research and global comparisons to monitor the progress of the United Nation’s Sustainable Development Goals, especially when including developing countries with areas containing low electrification rates and low population density. Full article
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Open AccessArticle
Evaluation of the Accuracy of the Field Quadrat Survey of Alpine Grassland Fractional Vegetation Cover Based on the Satellite Remote Sensing Pixel Scale
ISPRS Int. J. Geo-Inf. 2019, 8(11), 497; https://doi.org/10.3390/ijgi8110497 - 03 Nov 2019
Abstract
The fractional vegetation cover (FVC) data measured on the ground is the main source for the calibration and verification of FVC remote sensing inversion, and its accuracy directly affects the accuracy of remote sensing inversion results. However, the existing research on the evaluation [...] Read more.
The fractional vegetation cover (FVC) data measured on the ground is the main source for the calibration and verification of FVC remote sensing inversion, and its accuracy directly affects the accuracy of remote sensing inversion results. However, the existing research on the evaluation of the accuracy of the field quadrat survey of FVC based on the satellite remote sensing pixel scale is inadequate, especially in the alpine grassland of the Qinghai-Tibet Plateau. In this paper, five different alpine grasslands were examined, the accuracy of the FVC obtained by the photography method was analyzed, and the influence of the number of samples on the field survey results was studied. First, the results show that the threshold method could accurately extract the vegetation information in the photos and obtain the FVC with high accuracy and little subjective interference. Second, the number of samples measured on the ground was logarithmically related to the accuracy of the FVC of the sample plot (p < 0.001). When the number of samples was larger, the accuracy of the FVC of the sample plot was higher and closer to the real value, and the stability of data also increased with the increase of the number of samples. Third, the average FVC of the measured quadrats on the ground was able to represent the FVC of the sample plot, but on the basis that there were enough measured quadrats. Finally, the results revealed that the degree of fragmentation reflecting the state of ground vegetation affects the acquisition accuracy of FVC. When the degree of fragmentation of the sample plot is higher, the number of samples needed to achieve the accuracy index is higher. Our results suggest that when obtaining the FVC on the satellite remote sensing pixel scale, the number of samples measured on the ground is an important factor affecting the accuracy, which cannot be ignored. Full article
(This article belongs to the Special Issue Geo-Informatics in Resource Management)
Open AccessArticle
An Ontology-driven Cyberinfrastructure for Intelligent Spatiotemporal Question Answering and Open Knowledge Discovery
ISPRS Int. J. Geo-Inf. 2019, 8(11), 496; https://doi.org/10.3390/ijgi8110496 - 03 Nov 2019
Abstract
The proliferation of geospatial data from diverse sources, such as Earth observation satellites, social media, and unmanned aerial vehicles (UAVs), has created a pressing demand for cross-platform data integration, interoperation, and intelligent data analysis. To address this big data challenge, this paper reports [...] Read more.
The proliferation of geospatial data from diverse sources, such as Earth observation satellites, social media, and unmanned aerial vehicles (UAVs), has created a pressing demand for cross-platform data integration, interoperation, and intelligent data analysis. To address this big data challenge, this paper reports our research in developing a rule-based, semantic-enabled service chain model to support intelligent question answering for leveraging the abundant data and processing resources available online. Four key techniques were developed to achieve this goal: (1) A spatial and temporal reasoner resolves the spatial and temporal information in a given scientific question and enables place-name disambiguation based on support from a gazetteer; (2) a spatial operation ontology categorizes important spatial analysis operations, data types, and data themes, which will be used in automated chain generation; (3) a language-independent chaining rule defines the template for input, spatial operation, and output as well as rules for embedding multiple spatial operations for solving a complex problem; and (4) a recursive algorithm facilitates the generation of executive workflow metadata according to the chaining rules. We implement this service chain model in a cyberinfrastructure for online and reproducible spatial analysis and question answering. Moving the problem-solving environment from a desktop-based environment onto a geospatial cyberinfrastructure (GeoCI) offers better support to collaborative spatial decision-making and ensures science replicability. We expect this work to contribute significantly to the advancement of a reproducible spatial data science and to building the next-generation open knowledge network. Full article
Open AccessArticle
A Method of Population Spatialization Considering Parametric Spatial Stationarity: Case Study of the Southwestern Area of China
ISPRS Int. J. Geo-Inf. 2019, 8(11), 495; https://doi.org/10.3390/ijgi8110495 - 02 Nov 2019
Abstract
Population is a crucial basis for the study of sociology, geography, environmental studies, and other disciplines; accurate estimates of population are of great significance for many countries. Many studies have developed population spatialization methods. However, little attention has been paid to the differential [...] Read more.
Population is a crucial basis for the study of sociology, geography, environmental studies, and other disciplines; accurate estimates of population are of great significance for many countries. Many studies have developed population spatialization methods. However, little attention has been paid to the differential treatment of the spatial stationarity and non-stationarity of variables. Based on a semi-parametric, geographically weighted regression model (s-GWR), this paper attempts to construct a novel, precise population spatialization method considering parametric stationarity to enhance spatialization accuracy; the southwestern area of China is used as the study area for comparison and validation. In this study, the night-time light and land use data were integrated as weighting factors to establish the population model; based on the analysis of variables characteristics, the method uses an s-GWR model to deal with the spatial stationarity of variables and reduce regional errors. Finally, the spatial distribution of the population (SSDP) of the study area in 2010 was obtained. When assessed against the traditional regression models, the model that considers parametric stationarity is more accurate than the models without it. Furthermore, the comparison with three commonly-used population grids reveals that the SSDP has a percentage error close to zero at the county level, while at the township level, the mean relative error of SSDP is 33.63%, and that is >15% better than other population grids. Thus, this study suggests that the proposed method can produce a more accurate population distribution. Full article
Open AccessArticle
Advanced Cyberinfrastructure to Enable Search of Big Climate Datasets in THREDDS
ISPRS Int. J. Geo-Inf. 2019, 8(11), 494; https://doi.org/10.3390/ijgi8110494 - 02 Nov 2019
Abstract
Understanding the past, present, and changing behavior of the climate requires close collaboration of a large number of researchers from many scientific domains. At present, the necessary interdisciplinary collaboration is greatly limited by the difficulties in discovering, sharing, and integrating climatic data due [...] Read more.
Understanding the past, present, and changing behavior of the climate requires close collaboration of a large number of researchers from many scientific domains. At present, the necessary interdisciplinary collaboration is greatly limited by the difficulties in discovering, sharing, and integrating climatic data due to the tremendously increasing data size. This paper discusses the methods and techniques for solving the inter-related problems encountered when transmitting, processing, and serving metadata for heterogeneous Earth System Observation and Modeling (ESOM) data. A cyberinfrastructure-based solution is proposed to enable effective cataloging and two-step search on big climatic datasets by leveraging state-of-the-art web service technologies and crawling the existing data centers. To validate its feasibility, the big dataset served by UCAR THREDDS Data Server (TDS), which provides Petabyte-level ESOM data and updates hundreds of terabytes of data every day, is used as the case study dataset. A complete workflow is designed to analyze the metadata structure in TDS and create an index for data parameters. A simplified registration model which defines constant information, delimits secondary information, and exploits spatial and temporal coherence in metadata is constructed. The model derives a sampling strategy for a high-performance concurrent web crawler bot which is used to mirror the essential metadata of the big data archive without overwhelming network and computing resources. The metadata model, crawler, and standard-compliant catalog service form an incremental search cyberinfrastructure, allowing scientists to search the big climatic datasets in near real-time. The proposed approach has been tested on UCAR TDS and the results prove that it achieves its design goal by at least boosting the crawling speed by 10 times and reducing the redundant metadata from 1.85 gigabytes to 2.2 megabytes, which is a significant breakthrough for making the current most non-searchable climate data servers searchable. Full article
(This article belongs to the Special Issue Big Data Computing for Geospatial Applications)
Open AccessArticle
Analysis of Tourism Hotspot Behaviour Based on Geolocated Travel Blog Data: The Case of Qyer
ISPRS Int. J. Geo-Inf. 2019, 8(11), 493; https://doi.org/10.3390/ijgi8110493 - 01 Nov 2019
Abstract
We contribute a system design and a generalized formal methodology to segment tourists based on their geolocated blogging behaviour according to their interests in identified tourist hotspots. Thus, it is possible to identify and target groups that are possibly interested in alternative destinations [...] Read more.
We contribute a system design and a generalized formal methodology to segment tourists based on their geolocated blogging behaviour according to their interests in identified tourist hotspots. Thus, it is possible to identify and target groups that are possibly interested in alternative destinations to relieve overtourism. A pilot application in a case study of Chinese travel in Switzerland by analysing Qyer travel blog data demonstrates the potential of our method. Accordingly, we contribute four conclusions supported by empirical data. First, our method can enable discovery of plausible geographical distributions of tourist hotspots, which validates the plausibility of the data and its collection. Second, our method discovered statistically significant stochastic dependencies that meaningfully differentiate the observed user base, which demonstrates its value for segmentation. Furthermore, the case study contributes two practical insights for tourism management. Third, Chinese independent travellers, which are the main target group of Qyer, are mainly interested in the discovered travel hotspots, similar to tourists on packaged tours, but also show interest in alternative places. Fourth, the proposed user segmentation revealed two clusters based on users’ social media activity level. For tourism research, users within the second cluster are of interest, which are defined by two segmentation attributes: they blogged about more than just one location, and they have followers. These tourists are significantly more likely to be interested in alternative destinations out of the hotspot axis. Knowing this can help define a target group for marketing activities to promote alternative destinations. Full article
(This article belongs to the Special Issue Convergence of GIS and Social Media)
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Open AccessArticle
Exploiting the Potential of VGI Metadata to Develop A Data-Driven Framework for Predicting User’s Proficiency in OpenStreetMap Context
ISPRS Int. J. Geo-Inf. 2019, 8(11), 492; https://doi.org/10.3390/ijgi8110492 - 31 Oct 2019
Abstract
Volunteered geographic information (VGI) encourages citizens to contribute geographic data voluntarily that helps to enhance geospatial databases. VGI’s significant limitations are trustworthiness and reliability concerning data quality due to the anonymity of data contributors. We propose a data-driven model to address these issues [...] Read more.
Volunteered geographic information (VGI) encourages citizens to contribute geographic data voluntarily that helps to enhance geospatial databases. VGI’s significant limitations are trustworthiness and reliability concerning data quality due to the anonymity of data contributors. We propose a data-driven model to address these issues on OpenStreetMap (OSM), a particular case of VGI in recent times. This research examines the hypothesis of evaluating the proficiency of the contributor to assess the credibility of the data contributed. The proposed framework consists of two phases, namely, an exploratory data analysis phase and a learning phase. The former explores OSM data history to perform feature selection, resulting in “OSM Metadata” summarized using principal component analysis. The latter combines unsupervised and supervised learning through K-means for user-clustering and multi-class logistic regression for user classification. We identified five major classes representing user-proficiency levels based on contribution behavior in this study. We tested the framework with India OSM data history, where 17% of users are key contributors, and 27% are unexperienced local users. The results for classifying new users are satisfactory with 95.5% accuracy. Our conclusions recognize the potential of OSM metadata to illustrate the user’s contribution behavior without the knowledge of the user’s profile information. Full article
(This article belongs to the Special Issue Geospatial Metadata)
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Open AccessReview
Traffic Regulator Detection and Identification from Crowdsourced Data—A Systematic Literature Review
ISPRS Int. J. Geo-Inf. 2019, 8(11), 491; https://doi.org/10.3390/ijgi8110491 - 31 Oct 2019
Abstract
Mapping with surveying equipment is a time-consuming and cost-intensive procedure that makes the frequent map updating unaffordable. In the last few years, much research has focused on eliminating such problems by counting on crowdsourced data, such as GPS traces. An important source of [...] Read more.
Mapping with surveying equipment is a time-consuming and cost-intensive procedure that makes the frequent map updating unaffordable. In the last few years, much research has focused on eliminating such problems by counting on crowdsourced data, such as GPS traces. An important source of information in maps, especially under the consideration of forthcoming self-driving vehicles, is the traffic regulators. This information is largely lacking in maps like OpenstreetMap (OSM) and this article is motivated by this fact. The topic of this systematic literature review (SLR) is the detection and recognition of traffic regulators such as traffic lights (signals), stop-, yield-, priority-signs, right of way priority rules and turning restrictions at intersections, by leveraging non imagery crowdsourced data. More particularly, the aim of this study is (1) to identify the range of detected and recognised regulatory types by crowdsensing means, (2) to indicate the different classification techniques that can be used for these two tasks, (3) to assess the performance of different methods, as well as (4) to identify important aspects of the applicability of these methods. The two largest databases of peer-reviewed literature were used to locate relevant research studies and after different screening steps eleven articles were selected for review. Two major findings were concluded—(a) most regulator types can be identified with over 80% accuracy, even using heuristic-driven approaches and (b) under the current progress on the field, no study can be reproduced for comparative purposes nor can solely rely on open data sources due to lack of publicly available datasets and ground truth maps. Future research directions are highlighted as possible extensions of the reviewed studies. Full article
(This article belongs to the Special Issue State-of-the-Art in Spatial Information Science)
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Open AccessArticle
A Model for Animal Home Range Estimation Based on the Active Learning Method
ISPRS Int. J. Geo-Inf. 2019, 8(11), 490; https://doi.org/10.3390/ijgi8110490 - 30 Oct 2019
Abstract
Home range estimation is the basis of ecology and animal behavior research. Some popular estimators have been presented; however, they have not fully considered the impacts of terrain and obstacles. To address this defect, a novel estimator named the density-based fuzzy home range [...] Read more.
Home range estimation is the basis of ecology and animal behavior research. Some popular estimators have been presented; however, they have not fully considered the impacts of terrain and obstacles. To address this defect, a novel estimator named the density-based fuzzy home range estimator (DFHRE) is proposed in this study, based on the active learning method (ALM). The Euclidean distance is replaced by the cost distance-induced geodesic distance transformation to account for the effects of terrain and obstacles. Three datasets are used to verify the proposed method, and comparisons with the kernel density-based estimator (KDE) and the local convex hulls (LoCoH) estimators and the cross validation test indicate that the proposed estimator outperforms the KDE and the LoCoH estimators. Full article
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Open AccessArticle
Combination of Aerial, Satellite, and UAV Photogrammetry for Mapping the Diachronic Coastline Evolution: The Case of Lefkada Island
ISPRS Int. J. Geo-Inf. 2019, 8(11), 489; https://doi.org/10.3390/ijgi8110489 - 30 Oct 2019
Abstract
Coastline evolution is a proxy of coastal erosion, defined as the wasting of land along the shoreline due to a combination of natural and/or human causes. For countries with a sea border, where a significant proportion of the population lives in coastal areas, [...] Read more.
Coastline evolution is a proxy of coastal erosion, defined as the wasting of land along the shoreline due to a combination of natural and/or human causes. For countries with a sea border, where a significant proportion of the population lives in coastal areas, shoreline retreat has become a very serious global problem. Remote sensing data and photogrammetry have been used in coastal erosion mapping for many decades. In the current study, multi-date analogue aerial photos, digital aerial photos, and declassified satellite imagery provided by the U.S. Geological Survey (USGS), Pleiades satellite data, and unmanned aerial vehicle images were combined for accurate mapping of the southwestern Lefkada (Ionian Sea, Greece) coastline over the last 73 years. Different photogrammetric techniques were used for the orthorectifation of the remote sensing data, and geographical information systems were used in order to calculate the rates of shoreline change. The results indicated that the southwest shoreline of Lefkada Island is under dynamic equilibrium. This equilibrium is strongly controlled by geological parameters, such as subsidence of the studied shoreline during co-seismic deformation and mass wasting. The maximum accretion rate was calculated at 0.55 m per year, while the respective erosion rate reached −1.53 m per year. Full article
(This article belongs to the Special Issue Applications of Photogrammetry for Environmental Research)
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Open AccessArticle
Investigating Contextual Effects on Burglary Risks: A Contextual Effects Model Built Based on Bayesian Spatial Modeling Strategy
ISPRS Int. J. Geo-Inf. 2019, 8(11), 488; https://doi.org/10.3390/ijgi8110488 - 30 Oct 2019
Abstract
A contextual effects model, built based on Bayesian spatial modeling strategy, was used to investigate contextual effects on neighborhood burglary risks in Wuhan, China. The contextual effects denote the impact of the upper-level area on the lower-level units of analysis. These effects are [...] Read more.
A contextual effects model, built based on Bayesian spatial modeling strategy, was used to investigate contextual effects on neighborhood burglary risks in Wuhan, China. The contextual effects denote the impact of the upper-level area on the lower-level units of analysis. These effects are often neglected in Bayesian spatial crime analysis. The contextual effects model accounts for the effects of independent variables, overdispersion, spatial autocorrelation, and contextual effects. Both the contextual effects model and the conventional Bayesian spatial model were fitted to our data. Results showed the two models had almost the same deviance information criterion (DIC). Furthermore, they identified the same set of significant independent variables and gave very similar estimates for burglary risks. Nonetheless, the contextual effects model was preferred in the sense that it provides insights into contextual effects on crime risks. Based on the contextual effects model and the map decomposition technique, we identified, worked out, and mapped the relative contribution of the neighborhood characteristics and contextual effects on the overall burglary risks. The research contributes to the increasing literature on modeling crime data by Bayesian spatial approaches. Full article
(This article belongs to the Special Issue Urban Crime Mapping and Analysis Using GIS)
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Open AccessArticle
An Automatic Annotation Method for Discovering Semantic Information of Geographical Locations from Location-Based Social Networks
ISPRS Int. J. Geo-Inf. 2019, 8(11), 487; https://doi.org/10.3390/ijgi8110487 - 29 Oct 2019
Abstract
Location-Based Social Networks (LBSNs) contain rich information that can be used to identify and annotate points of interest (POIs). Discovering these POIs and annotating them with this information is not only helpful for understanding the social behavior of users, but it also provides [...] Read more.
Location-Based Social Networks (LBSNs) contain rich information that can be used to identify and annotate points of interest (POIs). Discovering these POIs and annotating them with this information is not only helpful for understanding the social behavior of users, but it also provides benefits for location recommendations. However, current methods still have some limitations, such as a long annotating time and a low annotating accuracy. In this study, we develop a hybrid method to annotate POIs with meaningful information from LBSNs. The method integrates three patterns: temporal, spatial, and text patterns. Firstly, we present an approach for preprocessing data based on temporal patterns. Secondly, we describe a way to discover POIs through spatial patterns. Thirdly, we build a keyword dictionary for discovering the categories of POIs to be annotated via mining the text patterns. Finally, we integrate these three patterns to label each POI. Taking New York and London as the target areas, we accomplish automatic POI annotation by using Precision, Recall, and F-values to evaluate the effectiveness. The results show that our F-value is 78%, which is superior to that of the baseline method (Falcone’s method) at 73% and this suggests that our method is effective in extracting POIs and assigning them categories. Full article
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Open AccessArticle
Exploring the Characteristics of an Intra-Urban Bus Service Network: A Case Study of Shenzhen, China
ISPRS Int. J. Geo-Inf. 2019, 8(11), 486; https://doi.org/10.3390/ijgi8110486 - 29 Oct 2019
Abstract
The urban bus service system is one of the most important components of a public transport system. Thus, exploring the spatial configuration of the urban bus service system promotes an understanding of the quality of bus services. Such an understanding is of great [...] Read more.
The urban bus service system is one of the most important components of a public transport system. Thus, exploring the spatial configuration of the urban bus service system promotes an understanding of the quality of bus services. Such an understanding is of great importance to urban transport planning and policy making. In this study, we investigated the spatial characteristics of an urban bus service system by using the complex network approach. First, a three-step workflow was developed to collect a bus operating dataset from a public website. Then, we utilized the P-space method to represent the bus service network by connecting all bus stop pairs along each bus line. With the constructed bus network, a set of network analysis indicators were calculated to quantify the role of nodes in the network. A case study of Shenzhen, China was implemented to understand the statistical properties and spatial characteristics of the urban bus network configuration. The empirical findings can provide insights into the statistical laws and distinct convenient areas in a bus service network, and consequently aid in optimizing the allocation of bus stops and routes. Full article
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Open AccessArticle
A Study on Storm and Flood Insurance Management Mapping: Case Study of Incheon Metropolitan City
ISPRS Int. J. Geo-Inf. 2019, 8(11), 485; https://doi.org/10.3390/ijgi8110485 - 29 Oct 2019
Abstract
In this research, we have used spatial information analysis techniques and procedures to process storm, flood, and snow damage risks, and apply premium rates to produce a Storm and Flood Insurance Management Map. To calculate risk, we used ArcGIS’s main features to overlay, [...] Read more.
In this research, we have used spatial information analysis techniques and procedures to process storm, flood, and snow damage risks, and apply premium rates to produce a Storm and Flood Insurance Management Map. To calculate risk, we used ArcGIS’s main features to overlay, integrate, and classify data. Moreover, we designed an ArcGIS Model Builder program to process very large amounts of risk data quickly and accurately. Excel’s pivot feature was used to calculate areas and premium rates according to flood depth. In the case of Incheon metropolitan city, the average risk was 2.85 on the 4-level scale, which lies between "alert" and "danger" and corresponds to the 1st of the 4 premium rate grades. In particular, there were high risks and high premium rates in areas around ports, ocean beaches, and beaches connected to rivers. We expect that this insurance management map created using spatial information analysis techniques will provide useful data for scientific natural disaster response and prevention planning, rational insurance rate calculation and application, and promotion of policies, which identify and prevent areas at risk for frequent storm and flood damage. Full article
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Open AccessArticle
An Empirical Evaluation of Data Interoperability—A Case of the Disaster Management Sector in Uganda
ISPRS Int. J. Geo-Inf. 2019, 8(11), 484; https://doi.org/10.3390/ijgi8110484 - 26 Oct 2019
Abstract
One of the grand challenges of disaster management is for stakeholders to be able to discover, access, integrate and analyze task-appropriate data together with their associated algorithms and work-flows. Even with a growing number of initiatives to publish data in the disaster management [...] Read more.
One of the grand challenges of disaster management is for stakeholders to be able to discover, access, integrate and analyze task-appropriate data together with their associated algorithms and work-flows. Even with a growing number of initiatives to publish data in the disaster management sector using open principles, integration and reuse are still difficult due to existing interoperability barriers within datasets. Several frameworks for assessing data interoperability exist but do not generate best practice solutions to existing barriers based on the assessment they use. In this study, we assess interoperability for datasets in the disaster management sector in Uganda and identify generic solutions to interoperability challenges in the context of disaster management. Semi-structured interviews and focus group discussions were used to collect qualitative data from sector stakeholders in Uganda. Data interoperability was measured to provide an understanding of interoperability in the sector. Interoperability maturity is measured using qualitative methods, while data compatibility metrics are computed from identifiers in the RDF-triple model. Results indicate high syntactic and technical interoperability maturity for data in the sector. On the contrary, there exists considerable semantic and legal interoperability barriers that hinder data integration and reuse in the sector. A mapping of the interoperability challenges in the disaster management sector to solutions reveals a potential to reuse established patterns for managing data interoperability. These include; the federated pattern, linked data patterns, broadcast pattern, rights and policy harmonization patterns, dissemination and awareness pattern, ontology design patterns among others. Thus a systematic approach to combining patterns is critical to managing data interoperability barriers among actors in the disaster management ecosystem. Full article
(This article belongs to the Special Issue Open Science in the Geospatial Domain)
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