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ISPRS Int. J. Geo-Inf., Volume 7, Issue 6 (June 2018) – 35 articles

Cover Story (view full-size image): In the last years, new approaches aimed to increase the automation level of the positional accuracy assessment processes of spatial data have been developed. However, in such cases, an aspect as significant as the sample size has not been addressed. In our work, we study the influence of the sample size when estimating the planimetric positional accuracy of urban databases by means of an automatic assessment polygon-based methodology. Our research is based on a simulation process which extracts pairs of homologous polygons from the assessed data source and from the reference data source and applies two buffer-based methods. Our results show a significant reduction in the variability of the estimations when the sample size increased from 5 Km to 100 Km. View Paper here.
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10 pages, 761 KiB  
Article
Analysis of Thematic Similarity Using Confusion Matrices
by José L. García-Balboa, María V. Alba-Fernández, Francisco J. Ariza-López and José Rodríguez-Avi
ISPRS Int. J. Geo-Inf. 2018, 7(6), 233; https://doi.org/10.3390/ijgi7060233 - 20 Jun 2018
Cited by 20 | Viewed by 4884
Abstract
The confusion matrix is the standard way to report on the thematic accuracy of geographic data (spatial databases, topographic maps, thematic maps, classified images, remote sensing products, etc.). Two widely adopted indices for the assessment of thematic quality are derived from the confusion [...] Read more.
The confusion matrix is the standard way to report on the thematic accuracy of geographic data (spatial databases, topographic maps, thematic maps, classified images, remote sensing products, etc.). Two widely adopted indices for the assessment of thematic quality are derived from the confusion matrix. They are overall accuracy (OA) and the Kappa coefficient (?), which have received some criticism from some authors. Both can be used to test the similarity of two independent classifications by means of a simple statistical hypothesis test, which is the usual practice. Nevertheless, this is not recommended, because different combinations of cell values in the matrix can obtain the same value of OA or ?, due to the aggregation of data needed to compute these indices. Thus, not rejecting a test for equality between two index values does not necessarily mean that the two matrices are similar. Therefore, we present a new statistical tool to evaluate the similarity between two confusion matrices. It takes into account that the number of sample units correctly and incorrectly classified can be modeled by means of a multinomial distribution. Thus, it uses the individual cell values in the matrices and not aggregated information, such as the OA or ? values. For this purpose, it is considered a test function based on the discrete squared Hellinger distance, which is a measure of similarity between probability distributions. Given that the asymptotic approximation of the null distribution of the test statistic is rather poor for small and moderate sample sizes, we used a bootstrap estimator. To explore how the p-value evolves, we applied the proposed method over several predefined matrices which are perturbed in a specified range. Finally, a complete numerical example of the comparison of two matrices is presented. Full article
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Representation of the <span class="html-italic">p</span>-value for each perturbed matrix. (<b>a</b>–<b>c</b>) are the results for CM<sub>95</sub>, CM<sub>80</sub>, and CM<sub>50</sub>, respectively. Cases in which <span class="html-italic">OA</span> remains the same. The <span class="html-italic">x</span>-axis is the sum of the absolute values of the perturbations. The dotted line represents a <span class="html-italic">p</span>-value of 0.05.</p> Full article ">Figure 2
<p>Representation of the <span class="html-italic">p</span>-value for each perturbed matrix. (<b>a</b>–<b>c</b>) are the results for CM<sub>95</sub>, CM<sub>80</sub>, and CM<sub>50</sub>, respectively. Cases in which the <span class="html-italic">OA</span> improves or worsens. The <span class="html-italic">x</span>-axis is the sum of the values of the perturbations. The dotted line represents a <span class="html-italic">p</span>-value of 0.05.</p> Full article ">
21 pages, 5949 KiB  
Article
A RSSI/PDR-Based Probabilistic Position Selection Algorithm with NLOS Identification for Indoor Localisation
by Ke Han, Huashuai Xing, Zhongliang Deng and Yichen Du
ISPRS Int. J. Geo-Inf. 2018, 7(6), 232; https://doi.org/10.3390/ijgi7060232 - 20 Jun 2018
Cited by 26 | Viewed by 4791
Abstract
In recent years, location-based services have been receiving increasing attention because of their great development prospects. Researchers from all over the world have proposed many solutions for indoor positioning over the past several years. However, owing to the dynamic and complex nature of [...] Read more.
In recent years, location-based services have been receiving increasing attention because of their great development prospects. Researchers from all over the world have proposed many solutions for indoor positioning over the past several years. However, owing to the dynamic and complex nature of indoor environments, accurately and efficiently localising targets in indoor environments remains a challenging problem. In this paper, we propose a novel indoor positioning algorithm based on the received signal strength indication and pedestrian dead reckoning. In order to enhance the accuracy and reliability of our proposed probabilistic position selection algorithm in mixed line-of-sight (LOS) and non-line-of-sight (NLOS) environments, a low-complexity identification approach is proposed to identify the change in the channel situation between NLOS and LOS. Numerical experiment results indicate that our proposed algorithm has a higher accuracy and is less impacted by NLOS errors than other conventional methods in mixed LOS and NLOS indoor environments. Full article
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<p>(<b>a</b>) The architecture of the Bluetooth Low Energy (BLE) indoor positioning system; (<b>b</b>) the BLE beacons and the smartphone used in this paper.</p> Full article ">Figure 2
<p>The process of the initial positioning.</p> Full article ">Figure 3
<p>The normal positioning process.</p> Full article ">Figure 4
<p>The procedure of the probabilistic position selection algorithm (PPSA).</p> Full article ">Figure 5
<p>The schematic drawing of our proposed non-line-of-sight (NLOS) identification method.</p> Full article ">Figure 6
<p>The schematic drawing of particle selection.</p> Full article ">Figure 7
<p>The received signal strength indication (RSSI) values at 1–8 m to BLE beacon collected by different phone models.</p> Full article ">Figure 8
<p>(<b>a</b>) The developed Android app; (<b>b</b>) Test environment with deployed BLE beacons.</p> Full article ">Figure 9
<p>The procedure for evaluation of the proposed PPSA.</p> Full article ">Figure 10
<p>The localisation results of the three localisation algorithms.</p> Full article ">Figure 11
<p>The cumulative distribution function (CDF) of the three algorithms in the test environment (the average NLOS rate ≈ 0.22).</p> Full article ">Figure 12
<p>The CDF of (<b>a</b>) PPSA algorithm; (<b>b</b>) trilateration + PDR; (<b>c</b>) MLE + PDR in the NLOS rate of 0, 0.25, 0.5, 0.75, and 1.</p> Full article ">Figure 13
<p>The NLOS rate versus the mean RMSE.</p> Full article ">Figure 14
<p>The procedure used for the evaluation of the proposed NLOS identification method.</p> Full article ">Figure 15
<p>The evaluation results of the proposed NLOS identification method (the average NLOS rate ≈ 0.6).</p> Full article ">Figure 16
<p>The results of the proposed localisation algorithm when NLOS identification is applied or not.</p> Full article ">
16 pages, 1689 KiB  
Article
Spatial Variability of Local Rural Landscape Change under Rapid Urbanization in Eastern China
by He Xiao, Yunhui Liu, Liangtao Li, Zhenrong Yu and Xiaotong Zhang
ISPRS Int. J. Geo-Inf. 2018, 7(6), 231; https://doi.org/10.3390/ijgi7060231 - 20 Jun 2018
Cited by 15 | Viewed by 3979
Abstract
Understanding the characteristics of rural landscape change during the urbanization process is crucial to developing more elaborate rural landscape management plans for sustainable development. However, there is little information revealing how rural landscapes change at a local scale and limited evidence addressing how [...] Read more.
Understanding the characteristics of rural landscape change during the urbanization process is crucial to developing more elaborate rural landscape management plans for sustainable development. However, there is little information revealing how rural landscapes change at a local scale and limited evidence addressing how to improve the practicability of these management approaches. This paper aims to investigate local rural landscape compositions and patterns and to identify the spatial variability of local rural landscape change under rapid urbanization in eastern China to provide detail approaches to practicable and efficient local landscape management. The land use composition and landscape pattern from 2009 to 2012 were analyzed in three rural areas, namely, Daxing (DX) in Beijing, Quzhou (QZ) in Hebei Province and Changshu (CS) in Jiangsu Province. The results showed that the three rural areas varied in landscape pattern and land use composition change, even in the short term. Local farmland decreased slightly, demonstrating the effectiveness of the national farmland protection policy. Compared to the other two rural areas, CS was more diverse, fragmented and complex, and it had the greatest change rate between 2009 and 2012. In this rural area, semi-natural land dramatically increased, from 9.15% to 39.85%, and settlement construction unexpectedly decreased. QZ was characterized by a highly homogenous landscape dominated by farmland, which accounted for more than 80% of the total area, and it showed a slow decrease in farmland with weak increases in semi-natural land and construction. DX was characterized by a simple and homogenous landscape and had a median change rate of 9.32%, presenting a common land use change trend of a fast expansion in construction but decreases in farmland and semi-natural land. During decreases in highly valuable natural land, semi-natural land was important for nature conservation in rural areas at a local scale, but that process needs further improvement, especially in DX and QZ. Generally, local rural landscapes became more disaggregated and diverse during landscape change. Land use switches among farmland, orchards, nurseries, and other production lands were the major driving force for local change. Considering differential characteristics of landscape change among rural areas, we suggest that efficient landscape management requires the development of strategies that account for the spatial variability of urbanization effects. Subsidies for the management of semi-natural land with high natural value are meaningful for local natural conservation. Full article
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<p>Map of study areas: (<b>a</b>) Study locations in China; (<b>b</b>) In DX, three villages, Yanggezhuang, Gaozhuang and Shaziying, had a total area of 707.45 ha; (<b>c</b>) The QZ study area comprised the two villages of Wangzhuang and Xingyuan, with a total area of 472.36 ha; (<b>d</b>) CS contained the three villages of Zhujiaqiao, Yangzhong and Donggangjing, with a total area of 588.08 ha.</p> Full article ">Figure 2
<p>An example of a result from the local field surveys. The left picture is a remote sensing image displaying a base map in a field survey. The right picture is a digitalized map showing local land use. The small woodland patches along the river and around settlements could be accurately identified, and areas of aquaculture could be distinguished from other waters through local field surveys.</p> Full article ">Figure 3
<p>Land use map of the three study areas in 2012.</p> Full article ">Figure 4
<p>Maps of plots with changed land use these maps present the shape, distribution, and increased land use of changed patches from 2009 to 2012. Each patch showed land use in 2012. (<b>a</b>) The changed land use patches map of Daxing; (<b>b</b>) the changed land use patches map of Quzhou; (<b>c</b>) the changed land use patches map of Changshu</p> Full article ">
18 pages, 3283 KiB  
Article
Uncovering Spatial Inequality in Taxi Services in the Context of a Subsidy War among E-Hailing Apps
by Rongxiang Su, Zhixiang Fang, Hong Xu and Lian Huang
ISPRS Int. J. Geo-Inf. 2018, 7(6), 230; https://doi.org/10.3390/ijgi7060230 - 20 Jun 2018
Cited by 12 | Viewed by 4248
Abstract
Spatial inequalities in urban public transportation are a major concern in many countries but little of this research has focused specifically on taxi services. The taxi situation has grown more complex, as traditional ride-for-hire services face growing competition from e-hailing apps like Uber [...] Read more.
Spatial inequalities in urban public transportation are a major concern in many countries but little of this research has focused specifically on taxi services. The taxi situation has grown more complex, as traditional ride-for-hire services face growing competition from e-hailing apps like Uber in the U.S., or Didi and Kuaidi in China. In 2014, Didi and Kuaidi triggered a nationwide subsidy war, with possible effects on the spatial inequality of taxi services. Taxi trajectory data from Shenzhen collected during the subsidy war shows that this competition reduced spatial inequality in the inner city but aggravated it in the outer city. In this study, a measure of service rate to depict the quantity of taxi services is proposed to calculate a Gini coefficient for evaluating change in the spatial inequality of taxi services. The Theil index and its decomposition were used to distinguish the contribution of Traffic Analysis Zones (TAZs) in the inner and the outer city and compare them to the overall spatial inequality of taxi services in Shenzhen, TAZs in the outer city had greater inequality in taxi services than the inner city. Furthermore, the primary contributor to overall inequality in taxi services was inequality within, rather than between, the inner and outer city. Moreover, the mean values for the changed service rates in the inner city were always larger than the outer city, and the inner city had a more equitable changed service rate than the outer city. These results could serve as a foundation for improving taxi services citywide. Full article
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<p>The administrative districts and traffic analysis zones (TAZs) of Shenzhen city.</p> Full article ">Figure 2
<p>The spatial distribution of ASR (<b>a</b>) and DSR (<b>b</b>) on weekdays in Period 1.</p> Full article ">Figure 3
<p>The changes of Gini coefficient and the changes of the mean of service rate on weekdays during the subsidy war. Specifically, they are the overall changes of the Gini coefficient and mean of ASR (<b>a</b>), the changes of the inner-city Gini coefficient and the inner-city mean of ASR (<b>b</b>), the changes of outer-city Gini coefficient and the outer-city mean of ASR (<b>c</b>), the overall changes of the Gini coefficient and the mean of DSR (<b>d</b>), the changes of inner-city Gini coefficient and the inner-city mean of DSR (<b>e</b>), and the changes of the outer-city Gini coefficient and the outer-city mean of DSR (<b>f</b>).</p> Full article ">Figure 4
<p>The changes of the Gini coefficient and the changes of the mean of service rate on weekends during the subsidy war. Specifically, they are the overall changes of the Gini coefficient and mean of ASR (<b>a</b>), the changes of the inner-city Gini coefficient and the inner-city mean of ASR (<b>b</b>), the changes of the outer-city Gini coefficient and the outer-city mean of ASR (<b>c</b>), the overall changes of the Gini coefficient and the mean of DSR (<b>d</b>), the changes of the inner-city Gini coefficient and the inner-city mean of DSR (<b>e</b>), and the changes of the outer-city Gini coefficient and the outer-city mean of DSR (<b>f</b>).</p> Full article ">Figure 5
<p>The changes of the Gini coefficient and the changes of the mean of the service rate change on weekdays between each pair of neighboring periods. Specifically, they are the overall changes of the Gini coefficient and the mean of increased ASR (<b>a</b>), the changes of the inner-city Gini coefficient and the inner-city mean of increased ASR (<b>b</b>), the changes of the outer-city Gini coefficient and the outer-city mean of increased ASR (<b>c</b>), the overall changes of the Gini coefficient and the mean of decreased ASR (<b>d</b>), the changes of the inner-city Gini coefficient and the inner-city mean of decreased ASR (<b>e</b>), the changes of the outer-city Gini coefficient and the outer-city mean of decreased ASR (<b>f</b>), the overall changes of the Gini coefficient and the mean of increased DSR (<b>g</b>), the changes of the inner-city Gini coefficient and the inner-city mean of increased DSR (<b>h</b>), the changes of the outer-city Gini coefficient and the outer-city mean of increased DSR (<b>i</b>), the overall changes of the Gini coefficient and the mean of decreased DSR (<b>j</b>), the changes of the inner-city Gini coefficient and the inner-city mean of decreased DSR (<b>k</b>), and the changes of the outer-city Gini coefficient and the outer-city mean of decreased DSR (<b>l</b>).</p> Full article ">
18 pages, 5981 KiB  
Article
Automated Orthorectification of VHR Satellite Images by SIFT-Based RPC Refinement
by Hakan Kartal, Ugur Alganci and Elif Sertel
ISPRS Int. J. Geo-Inf. 2018, 7(6), 229; https://doi.org/10.3390/ijgi7060229 - 20 Jun 2018
Cited by 19 | Viewed by 5613
Abstract
Raw remotely sensed images contain geometric distortions and cannot be used directly for map-based applications, accurate locational information extraction or geospatial data integration. A geometric correction process must be conducted to minimize the errors related to distortions and achieve the desired location accuracy [...] Read more.
Raw remotely sensed images contain geometric distortions and cannot be used directly for map-based applications, accurate locational information extraction or geospatial data integration. A geometric correction process must be conducted to minimize the errors related to distortions and achieve the desired location accuracy before further analysis. A considerable number of images might be needed when working over large areas or in temporal domains in which manual geometric correction requires more labor and time. To overcome these problems, new algorithms have been developed to make the geometric correction process autonomous. The Scale Invariant Feature Transform (SIFT) algorithm is an image matching algorithm used in remote sensing applications that has received attention in recent years. In this study, the effects of the incidence angle, surface topography and land cover (LC) characteristics on SIFT-based automated orthorectification were investigated at three different study sites with different topographic conditions and LC characteristics using Pleiades very high resolution (VHR) images acquired at different incidence angles. The results showed that the location accuracy of the orthorectified images increased with lower incidence angle images. More importantly, the topographic characteristics had no observable impacts on the location accuracy of SIFT-based automated orthorectification, and the results showed that Ground Control Points (GCPs) are mainly concentrated in the “Forest” and “Semi Natural Area” LC classes. A multi-thread code was designed to reduce the automated processing time, and the results showed that the process performed 7 to 16 times faster using an automated approach. Analyses performed on various spectral modes of multispectral data showed that the arithmetic data derived from pan-sharpened multispectral images can be used in automated SIFT-based RPC orthorectification. Full article
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<p>Study sites and overview of the images used in this research: (<b>a</b>) Geographic distribution of the study regions (Google Earth©); (<b>b</b>) Overview of the Istanbul region; (<b>c</b>) Overview of the Bursa region; (<b>d</b>) Overview of the Izmir region.</p> Full article ">Figure 2
<p>Topographic characteristics of the study regions.</p> Full article ">Figure 3
<p>Land cover (LC) characteristics of the study regions.</p> Full article ">Figure 4
<p>Diagram of the Scale Invariant Feature Transform (SIFT)-based automated orthorectification.</p> Full article ">Figure 5
<p>The steps of SIFT feature extraction: (<b>a</b>) scale space generation; (<b>b</b>) DOG image generation; (<b>c</b>) detection of local maximum and minimum; (<b>d</b>) gradient calculation; (<b>e</b>) histogram calculation and generation of 128 dimensional vectors.</p> Full article ">Figure 6
<p>Comparison of the results of the orthorectification process for different study areas based on the RMSE (number of the validated Ground Control Points (GCPs) given over each bar).</p> Full article ">Figure 7
<p>Accuracy of the results obtained using the original RPC model.</p> Full article ">Figure 8
<p>Improvement ratio of the GCPs retrieved using SIFT to the results using the original RPC-based orthorectification results as a reference.</p> Full article ">Figure 9
<p>Comparison of the process times for single and multithread approaches.</p> Full article ">Figure A1
<p>Distribution of the SIFT-based GCPs and Independent Check Points (ICPs) for the arithmetic mean of the RGB channels.</p> Full article ">
22 pages, 6655 KiB  
Article
Advanced Sidereal Filtering for Mitigating Multipath Effects in GNSS Short Baseline Positioning
by Minghua Wang, Jiexian Wang, Danan Dong, Wen Chen, Haojun Li and Zhiren Wang
ISPRS Int. J. Geo-Inf. 2018, 7(6), 228; https://doi.org/10.3390/ijgi7060228 - 20 Jun 2018
Cited by 25 | Viewed by 4683
Abstract
Advanced sidereal filtering (ASF) is an observation-domain sidereal filtering that adopts the repeat time of each individual satellite separately rather than the mean repeat time, adopted by the modified sidereal filtering (MSF). To evaluate the performance of ASF, we apply the method to [...] Read more.
Advanced sidereal filtering (ASF) is an observation-domain sidereal filtering that adopts the repeat time of each individual satellite separately rather than the mean repeat time, adopted by the modified sidereal filtering (MSF). To evaluate the performance of ASF, we apply the method to filter the multipath for a short baseline based on a dual-antenna Global Navigation Satellite System (GNSS) receiver. The errors from satellite and receiver clocks, satellite orbit, troposphere, ionosphere, and antenna phase center variations are greatly eliminated by single difference between the two antennas because they are connected to the same receiver clock. The performances of ASF are compared with MSF to evaluate the gain for multipath mitigation. Comparisons indicate that ASF slightly outperforms MSF when the repeat time values of all satellites incorporated in data processing are within the normal range (86,145–86,165 s), but the difference of variance reduction rate between ASF and MSF is statistically significant. When the data of a satellite with repeat time outside the normal range are included, the performances of MSF become much worse, but ASF is almost not affected. This advantage of ASF over MSF is important because the proportion of the days on which at least one satellite’s repeat time exceeds the normal range reaches 71.19% based on the statistics on the data of 2014 and 2015. After applying ASF multipath corrections on the test days, the averages of standard deviations of north, east, and up component are reduced from 3.8 to 2.1 mm, 3.2 to 1.7 mm, and 7.6 to 4.3 mm, respectively. Comparison between applying ASF with the single-day model and with the seven-day model indicates that the former is generally more effective in multipath reduction. Full article
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<p>Surrounding conditions of the GPS receiving antennas (Photos from Dong et al. [<a href="#B19-ijgi-07-00228" class="html-bibr">19</a>]). The position of the antenna shown in the upper panel is (31.035631° N, 121.444421° E), and the one in the bottom panel is located at (31.035640° N, 121.444550° E).</p> Full article ">Figure 2
<p>Recalculated (red line) and original (blue line) post-fit single-differenced observable residuals for satellite PRN 04 on DOY 342, 2014. The green line denotes the elevation angles of PRN 04. The unit of <span class="html-italic">x</span>-axis is hour. The <span class="html-italic">y</span>-axis on the left is residual in meters. The <span class="html-italic">y</span>-axis on the right is elevation angle in degrees.</p> Full article ">Figure 3
<p>Flowchart of results analyses.</p> Full article ">Figure 4
<p>Repeat time (daily advance time) estimates of all GPS satellites for each day from 2014 to 2015. For clarity, the legend only shows the satellites whose repeat time values deviate from the normal range ([235, 255] s) for a time span of tens to hundreds of days.</p> Full article ">Figure 5
<p>Repeat time (daily advance time) values of GPS satellite PRN 16, 01, and 13. Each figure represents a pattern of time series of satellite repeat time. The <span class="html-italic">x</span>-axes are ‘year+DOY’.</p> Full article ">Figure 5 Cont.
<p>Repeat time (daily advance time) values of GPS satellite PRN 16, 01, and 13. Each figure represents a pattern of time series of satellite repeat time. The <span class="html-italic">x</span>-axes are ‘year+DOY’.</p> Full article ">Figure 6
<p>Proportion of the days when the number of satellites with repeat time outside the normal range ([235, 255] s) are 0, 1, 2, and 3, respectively, based on the statistic of 2014 and 2015.</p> Full article ">Figure 7
<p>Average 3D variance reduction rates for ASF and MSF corrections that adopt the multipath correction models constructed from the residual time series filtered with the representative cutoff frequencies. The cutoff frequency of 0.5 Hz represents the absence of a low-pass filter. For each cutoff frequency, the average 3D variance reduction rates for ASF and MSF correction are the averages of daily 3D variance reduction rates from DOY 336 to 354, 2014. Average 3D variance reduction rates for ASF correction are marked on the top of the bars.</p> Full article ">Figure 8
<p>Power spectral density (PSD) of the up (height) component time series of DOY 342, 2014. Black lines, blue lines, and red lines denote the PSDs of up component solutions estimated from observables without correction, with MSF correction, and with ASF correction, respectively. (<b>a</b>) The residual time series for multipath model construction were not low-pass filtered; (<b>b</b>) The residual time series for multipath model construction were low-pass filtered with the cutoff frequency of 0.02 Hz.</p> Full article ">Figure 9
<p>3D variance reduction rates of ASF and MSF correction for each day from DOY 336 to 354, 2014. (<b>a</b>) The 3D variance reduction rates of ASF (excluding PRN 13) and MSF (both including and excluding PRN 13) correction; (<b>b</b>) The 3D variance reduction rates of ASF (both including and excluding PRN 13) correction.</p> Full article ">Figure 10
<p>Solutions of baseline components and constant bias of DOY 342, 2014. (<b>a</b>) Solutions estimated from uncorrected GPS observables; (<b>b</b>) Solutions estimated from ASF corrected GPS observables. The <span class="html-italic">y</span>-axis unit of each plot is meter.</p> Full article ">Figure 11
<p>3D variance reduction rates of ASF corrections adopting the single-day model and seven-day model, respectively, for DOY 347 to 354, 2014.</p> Full article ">Figure 12
<p>Maximal correlation coefficients of the residual time series for PRN 01, 05, 10, and 23. For each satellite, the maximal correlation coefficients between the residual time series from DOY 347 and that from DOY 341, 342, 343, 344, 345, and 346 are calculated, respectively. The maximal correlation coefficients for most other satellites show a similar trend, and this figure shows the results of four representative satellites for brevity.</p> Full article ">
17 pages, 13117 KiB  
Article
Multi-Criteria Land Evaluation of Suitability for the Sport of Foot Orienteering: A Case Study of Croatia and Slovenia
by Dražen Tutić, Matjaž Štanfel and Martina Triplat Horvat
ISPRS Int. J. Geo-Inf. 2018, 7(6), 227; https://doi.org/10.3390/ijgi7060227 - 19 Jun 2018
Cited by 7 | Viewed by 5240
Abstract
This paper describes a new multi-criteria land evaluation method, based on geomorphology and land cover, for the automated detection of suitable terrain for the sport of foot orienteering (footO). Reference data, in the form of areas already mapped and used for footO, was [...] Read more.
This paper describes a new multi-criteria land evaluation method, based on geomorphology and land cover, for the automated detection of suitable terrain for the sport of foot orienteering (footO). Reference data, in the form of areas already mapped and used for footO, was used to define criteria for geomorphology and land cover, and represents an expert knowledge component. The motivation for this research is that orienteering maps are often drawn for unfamiliar terrain that organizers of the event or mapmakers need to determine in advance, usually from base maps or by random reconnaissance. In a presented case study of Croatia and Slovenia, the geomorphology was derived from Digital Elevation Model over Europe (EU-DEM). The slope and aspect define components of the direction of the surface, and we tested the usability of these simple terrain parameters for the task. The CORINE dataset was used for the definition of the land cover. The results of the case study give potentially suitable areas for foot orienteering in Croatia and Slovenia, and in neighboring areas. Evaluation of the results, using reference areas as the control, proved that the proposed methodology gives a reliable indication of terrain suitability for orienteering. The method is simple, straightforward, and can be performed using standard GIS with common raster algorithms. Full article
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<p>Automatically created orienteering map as an indicator of the quality of the orienteering terrain: (<b>a</b>) Rich terrain features suitable for orienteering; (<b>b</b>) inadequate terrain for orienteering (source MapAnt, <a href="http://www.mapant.fi" target="_blank">http://www.mapant.fi</a>).</p> Full article ">Figure 2
<p>Workflow for preparing required maps for multi-criteria evaluation from input maps.</p> Full article ">Figure 3
<p>Workflow for determination of each criterion for multi-criteria evaluation.</p> Full article ">Figure 4
<p>Multi-criteria evaluation model involving only the Boolean AND operation.</p> Full article ">Figure 5
<p>Part of the CORINE land cover dataset.</p> Full article ">Figure 6
<p>Part of the EU-DEM data overlaid with areas of existing orienteering terrain (blue).</p> Full article ">Figure 7
<p>Histograms of the slope median determined for the spatial neighborhood, defined as a circular area with a diameter of 15 pixels, with cut off values determined from existing O-terrains then applied to the whole area.</p> Full article ">Figure 8
<p>Histograms of the slope interquartile range determined for the spatial neighborhood, defined as a circular area with a diameter of 15 pixels, with cut off values determined from existing O-terrains then applied to the whole area.</p> Full article ">Figure 9
<p>Areas filtered by slope criteria. Flat and too steep areas, as well as areas with very low and very high variation of the slope, are excluded (white).</p> Full article ">Figure 10
<p>Histograms of slope median (<b>a</b>) and interquartile range (<b>b</b>) on existing Croatian and Slovenian orienteering terrains.</p> Full article ">Figure 11
<p>Histograms of aspect (rounded to an integer) diversity determined for the spatial neighborhood, defined as a circular area with a diameter of 15 pixels.</p> Full article ">Figure 12
<p>Areas filtered by the aspect diversity criteria. Big slopes with a uniform aspect and some flat areas are excluded (white).</p> Full article ">Figure 13
<p>Histograms of the aspect diversity of existing Croatian and Slovenian orienteering terrains.</p> Full article ">Figure 14
<p>Bar chart of the ratio of land cover classes in existing orienteering terrains.</p> Full article ">Figure 15
<p>Areas filtered by land cover criteria.</p> Full article ">Figure 16
<p>Final areas of potentially suitable terrain for foot orienteering.</p> Full article ">Figure 17
<p>Rules set to indicate the quality of potentially suitable areas.</p> Full article ">Figure 18
<p>Potentially suitable orienteering terrains with an indication of quality.</p> Full article ">Figure 19
<p>Potentially suitable areas for foot orienteering over the whole area (left) and areas with the most existing orienteering terrains used for the case study (the whole dataset can be downloaded from the GitHub, <a href="https://github.com/GEOF-OSGL/Orienteering-Maps/blob/master/Potential_O-terrains_HR_SI_50m_EPSG3035.tif" target="_blank">https://github.com/GEOF-OSGL/Orienteering-Maps/blob/master/Potential_O-terrains_HR_SI_50m_EPSG3035.tif</a>).</p> Full article ">
18 pages, 6374 KiB  
Article
Research on a 3D Geological Disaster Monitoring Platform Based on REST Service
by Xiaopeng Leng, Dunlong Liu, Junsong Luo and Zhanyong Mei
ISPRS Int. J. Geo-Inf. 2018, 7(6), 226; https://doi.org/10.3390/ijgi7060226 - 19 Jun 2018
Cited by 13 | Viewed by 4574
Abstract
Representational state transfer (REST) is a resource-based service architectural style. It abstracts data and services as resources and accesses them through a unique Uniform Resource Identifier (URI). Compared with traditional Simple Object Access Protocol (SOAP) methods, REST is more concise. It takes full [...] Read more.
Representational state transfer (REST) is a resource-based service architectural style. It abstracts data and services as resources and accesses them through a unique Uniform Resource Identifier (URI). Compared with traditional Simple Object Access Protocol (SOAP) methods, REST is more concise. It takes full advantage of HyperText Transfer Protocol (HTTP) and has better scalability and extensibility. Based on REST services, this article integrates geographic information, real-time disaster monitoring data, and warning services in a three-dimensional (3D) digital Earth infrastructure and establishes a three-dimensional geological disaster monitoring GIS platform with good service compatibility and extensibility. The platform visually displays geographical and geological information and real-time monitoring data in a three-dimensional Earth, accesses warning model services to implement disaster warnings, and realizes comprehensive information management, monitoring, and warnings of multiple types of geological disasters. This can provide decision support for disaster prevention and relief and improve the informatization of geological disaster prevention and control. Full article
(This article belongs to the Special Issue Geospatial Methods and Tools for Natural Risk Management and Communications)
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<p>Platform infrastructure.</p> Full article ">Figure 2
<p>Platform design structure.</p> Full article ">Figure 3
<p>Vector data organization XML structure.</p> Full article ">Figure 4
<p>Sliced level relationship for Levels 0, 1, and 2.</p> Full article ">Figure 5
<p>Map slice coordinates.</p> Full article ">Figure 6
<p>Three-dimensional application model.</p> Full article ">Figure 7
<p>Remote monitoring application process.</p> Full article ">Figure 8
<p>Integrated warning model construction process.</p> Full article ">Figure 9
<p>Monitoring and warning process.</p> Full article ">Figure 10
<p>Representational state transfer (REST) application model in the design of this platform.</p> Full article ">Figure 11
<p>Geographic information service flow in this platform.</p> Full article ">Figure 12
<p>Map image data returned by REST request.</p> Full article ">Figure 13
<p>URI request and database operation mapping.</p> Full article ">Figure 14
<p>Alarm model call process.</p> Full article ">Figure 15
<p>Platform operation results, (<b>a</b>) Integrated loading of high-definition image layers and terrain data; Map Area is the main display area for a three-dimensional (3D) map, Search Tools can help users navigate quickly by place names or latitude and longitude, Map Layers can load or remove the map layers, Custom Layers can manage user-defined layers, and Status Bar can display the position parameters such as the latitude and longitude of the mouse, angle of view, azimuth, and inclination angle; (<b>b</b>) Integration and presentation of thematic layers; Thematic Layers can manage the geological map, river network map, contour map, and other thematic layers, all of which can be overlaid on the Image Map. Opacity Scale can adjust the transparency of the layer in order to present a better multi-layer fusion effect; (<b>c</b>) Location of disaster sites and basic information display; Disaster Point Information is displayed in the bottom table, and the Detail Popup window is triggered by clicking the disaster point, which contains the relevant pictures and the introduction of governance; (<b>d</b>) Access and monitoring data display of warning information; Normal Point is a green point that indicates the normal state on the map, Alarm Point will display the corresponding highlighted color according to the warning level, Data Graph area displays the data graphically, and the Export Data button can export the monitoring data to an Excel file.</p> Full article ">
20 pages, 6359 KiB  
Article
Metaphor Representation and Analysis of Non-Spatial Data in Map-Like Visualizations
by Rui Xin, Tinghua Ai and Bo Ai
ISPRS Int. J. Geo-Inf. 2018, 7(6), 225; https://doi.org/10.3390/ijgi7060225 - 19 Jun 2018
Cited by 11 | Viewed by 5942
Abstract
Metaphors are rhetorical devices in linguistics that facilitate the understanding of an unfamiliar concept based on a familiar concept. Map representations are usually referred to as the second language of geo-science studies, and the metaphor method could be applied to maps to visualize [...] Read more.
Metaphors are rhetorical devices in linguistics that facilitate the understanding of an unfamiliar concept based on a familiar concept. Map representations are usually referred to as the second language of geo-science studies, and the metaphor method could be applied to maps to visualize non-spatial data via spatial element symbols. This study performs a cross-domain application of the map representation method through a map-like visualization. The procedure first designs the map layout with the aid of the Gosper curve. Under the guidance of the Gosper curve, the leaf data items without spatial attributes are arranged on the space plane. Through the bottom-up regional integration, one can complete the construction of the map framework. Then, the cartographic method is used to complete map-like renderings that reflect different data features through diverse visualizations. The map representation advantages, such as overview sensing and multi-scale representation, are also reflected in the map-like visualization and used to identify the characteristics of non-spatial data. Additionally, the electronic map provides a series of interactive convenience features for map observation and analysis. Using the help of map-like visualizations, one can perform a series of analyses of non-spatial data in a new form. To verify the proposed method, the authors conducted map-making experiments and data analyses using real data. Full article
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<p>The data sources of the experiment: (<b>a</b>) ArcGIS Desktop 10.1 folder; (<b>b</b>) MyEclipse 2017 folder; (<b>c</b>) Visual Studio 2015 folder.</p> Full article ">Figure 2
<p>The construction of a hexagon base map and Gosper curve: (<b>a</b>) discrete points generated by a certain rule; (<b>b</b>) Thiessen polygons generated by discrete points; (<b>c</b>) Gosper curve corresponding to the hexagon base map.</p> Full article ">Figure 3
<p>Converting a multi-tree into a corresponding Gosper map: (<b>a</b>) multi-tree; (<b>b</b>) map regions of the leaf nodes of the multi-tree; (<b>c</b>) map regions of the second layer nodes of the multi-tree; (<b>d</b>) map region of the top layer node of the multi-tree.</p> Full article ">Figure 4
<p>The Gosper map and Hilbert map generated from the same data: (<b>a</b>) Gosper map and (<b>b</b>) Hilbert map.</p> Full article ">Figure 5
<p>The different map regional layouts corresponding to different node sequences: (<b>a</b>) Map layout corresponding to node sequence “N1N2N3N4N5”; (<b>b</b>) Map layout corresponding to node sequence “N5N1N4N2N3”; (<b>c</b>) Map layout corresponding to node sequence “N5N4N1N3N2”.</p> Full article ">Figure 6
<p>The sorting process of a hierarchical tree from top to bottom: (<b>a</b>) disordered multi-tree; (<b>b</b>) sorted second layer nodes; and (<b>c</b>) sorted leaf nodes.</p> Full article ">Figure 7
<p>The relationship construction among the map scales, LOD levels, and map scenes.</p> Full article ">Figure 8
<p>The polygon map frames of different data sources: (<b>a</b>) for ArcGIS Desktop 10.1; (<b>b</b>) for MyEclipse 2017; (<b>c</b>) for Visual Studio 2015.</p> Full article ">Figure 9
<p>The construction process of the virtual terrain of ArcGIS Desktop 10.1: (<b>a</b>) bottom hexagons corresponding to files; (<b>b</b>) geometric center points of the bottom hexagons; (<b>c</b>) grid field model result of files; (<b>d</b>) mountain shadow layer generated by the grid data; (<b>e</b>) two-dimensional virtual terrain; (<b>f</b>) three-dimensional virtual terrain.</p> Full article ">Figure 10
<p>The virtual terrain of ArcGIS Desktop 10.1 produced without the cluster process.</p> Full article ">Figure 11
<p>The spatial autocorrelation result of the clustered data.</p> Full article ">Figure 12
<p>The two- and three-dimensional virtual terrain map results for different data sources: (<b>a</b>,<b>b</b>) for MyEclipse 2017; (<b>c</b>,<b>d</b>) for Visual Studio 2015.</p> Full article ">Figure 13
<p>The two- and three-dimensional virtual water depth map results of different data sources: (<b>a</b>,<b>b</b>) for ArcGIS Desktop 10.1; (<b>c</b>,<b>d</b>) for MyEclipse 2017; (<b>e</b>,<b>f</b>) for Visual Studio 2015.</p> Full article ">Figure 14
<p>The comparison of the file base map before and after clustering: (<b>a</b>) data that have not been clustered; (<b>b</b>) data that have been clustered.</p> Full article ">Figure 15
<p>The multilevel search and location of large file communities: (<b>a</b>) Map scene of LOD1; (<b>b</b>) Map scene of LOD2; (<b>c</b>) Map scene of LOD3 (<b>d</b>) Map scene of LOD4; (<b>e</b>) Map scene of LOD5.</p> Full article ">Figure 16
<p>Two large file communities that are divided into the smallest unit: (<b>a</b>) for LOD3; (<b>b</b>) for LOD4.</p> Full article ">
25 pages, 7773 KiB  
Article
A Regional Mapping Method for Oilseed Rape Based on HSV Transformation and Spectral Features
by Dong Wang, Shenghui Fang, Zhenzhong Yang, Lin Wang, Wenchao Tang, Yucui Li and Chunyan Tong
ISPRS Int. J. Geo-Inf. 2018, 7(6), 224; https://doi.org/10.3390/ijgi7060224 - 16 Jun 2018
Cited by 28 | Viewed by 5523
Abstract
This study proposed a colorimetric transformation and spectral features-based oilseed rape extraction algorithm (CSRA) to map oilseed rape at the provincial scale as a first step towards country-scale coverage. Using a stepwise analysis strategy, our method gradually separates vegetation from non-vegetation, crop from [...] Read more.
This study proposed a colorimetric transformation and spectral features-based oilseed rape extraction algorithm (CSRA) to map oilseed rape at the provincial scale as a first step towards country-scale coverage. Using a stepwise analysis strategy, our method gradually separates vegetation from non-vegetation, crop from non-crop, and oilseed rape from winter wheat. The wide-field view (WFV) images from Chinese Gaofen satellite no. 1 (GF-1) at six continuous flowering stages in Wuxue City, Hubei Province, China are used to extract the unique characteristics of oilseed rape during the flowering period and predict the parameter of the CSRA method. The oilseed rape maps of Hubei Province from 2014 to 2017 are obtained automatically based on the CSRA method using GF-1 WFV images. As a result, the CSRA-derived provincial oilseed rape maps achieved at least 85% overall accuracy of spatial consistency when comparing with local reference oilseed rape maps and lower than 20% absolute error of provincial planting areas when comparing with agricultural census data. The robustness of the CSRA method is also tested on other satellite images including one panchromatic and multispectral image from GF-2 and two RapidEye images. Moreover, the comparison between the CSRA and other previous methods is discussed using the six GF-1 WFV images of Wuxue City, showing the proposed method has better mapping accuracy than other tested methods. These results highlight the potential of our method for accurate extraction and regional mapping capacity for oilseed rape. Full article
(This article belongs to the Special Issue Spatial Analysis for Terrestrial Ecosystems: Advances in Mapping, Analyses and Management)
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<p>(<b>a</b>) Hubei Province with OR experimental base, running river, agro-meteorological station, and topography; (<b>b</b>) Wuxue City with field survey points of OR and WW; (<b>c</b>) the resized GF-2 PMS fusion image of overlap region between GF-2 PMS image and RapidEye images in Shayang County; and (<b>d</b>) the corresponding reference OR map of (<b>c</b>).</p> Full article ">Figure 2
<p>Local (<b>a</b>) Google Earth image of part of Qianjiang City on 25 March 2015; (<b>b</b>) oilseed rape map corresponding to (<b>a</b>); (<b>c</b>) Google Earth image of part of Wuxue City on 19 March 2016; and (<b>d</b>) oilseed rape map corresponding to (<b>c</b>). The NOR in the legend is the abbreviation for non-oilseed rape.</p> Full article ">Figure 3
<p>The HSV color space about (<b>a</b>) H, S, and V components; (<b>b</b>) the geometric relationship of transformation from RGB to HSV. The reflectance at red, green, and blue bands of remote sensing images are used to make the R, G, and B color composite in this study. The values of R, G, and B are between 0 and 1.</p> Full article ">Figure 4
<p>The multi-temporal (<b>a</b>) NDVI of each land cover type; (<b>b</b>) reflectance at near-infrared band of vegetation types; and (<b>c</b>) <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="normal">H</mi> <mrow> <mi>norm</mi> </mrow> </msub> </mrow> </semantics></math> and V values of crop types for the training samples of Wuxue City. (<b>d</b>) Three <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="normal">H</mi> <mrow> <mi>norm</mi> </mrow> </msub> </mrow> </semantics></math> -V spaces separate OR and WW. 03/12, 03/17, 03/25, 03/28, 04/02, and 04/10 are the abbreviations of 12 March 2015, 17 March 2014, 25 March 2015, 28 March 2016, 2 April 2014, and 10 April 2014, respectively. FL, BuL, WB, and BL in the legends mean forest land, built-up land, water body, and bare land, respectively.</p> Full article ">Figure 5
<p>The workflow of this study.</p> Full article ">Figure 6
<p>Histogram of NDVI on (<b>a</b>) 03/12; (<b>b</b>) 03/17; (<b>c</b>) 03/25; (<b>d</b>) 03/28; (<b>e</b>) 04/02; and (<b>f</b>) 04/10 for vegetation types and non-vegetation types.</p> Full article ">Figure 7
<p>Histogram of <span class="html-italic">NIR</span> on (<b>a</b>) 03/12; (<b>b</b>) 03/17; (<b>c</b>) 03/25; (<b>d</b>) 03/28; (<b>e</b>) 04/02; and (<b>f</b>) 04/10 for crop types and FL.</p> Full article ">Figure 8
<p>RRCI histogram of the <math display="inline"><semantics> <mrow> <msub> <mi>H</mi> <mrow> <mi>n</mi> <mi>o</mi> <mi>r</mi> <mi>m</mi> </mrow> </msub> </mrow> </semantics></math>-V space of (<b>a</b>) part 1 (V ≥ 0.07 and <math display="inline"><semantics> <mrow> <msub> <mi>H</mi> <mrow> <mi>n</mi> <mi>o</mi> <mi>r</mi> <mi>m</mi> </mrow> </msub> </mrow> </semantics></math> ≤ 0.25); (<b>b</b>) part 2 (V ≥ 0.12 and 0.25 &lt; <math display="inline"><semantics> <mrow> <msub> <mi>H</mi> <mrow> <mi>n</mi> <mi>o</mi> <mi>r</mi> <mi>m</mi> </mrow> </msub> </mrow> </semantics></math> ≤ 0.42); and (<b>c</b>) part 3 (0.07 ≤ V &lt; 0.12 and 0.25 &lt; <math display="inline"><semantics> <mrow> <msub> <mi>H</mi> <mrow> <mi>n</mi> <mi>o</mi> <mi>r</mi> <mi>m</mi> </mrow> </msub> </mrow> </semantics></math> ≤ 0.42) for OR and WW.</p> Full article ">Figure 9
<p>The decision tree for extracting oilseed rape based on the CSRA approach.</p> Full article ">Figure 10
<p>CSRA-derived oilseed rape maps on (<b>a</b>) 17 March 2014; (<b>b</b>) 2 April 2014; (<b>c</b>) 10 April 2014; (<b>d</b>) 12 March 2015; (<b>e</b>) 25 March 2015; and (<b>f</b>) 28 March 2016 in Wuxue City.</p> Full article ">Figure 11
<p>The CSRA-derived oilseed rape planting maps of Hubei Province in (<b>a</b>) 2014; (<b>b</b>) 2015; (<b>c</b>) 2016; and (<b>d</b>) 2017.</p> Full article ">Figure 12
<p>(<b>a</b>) Google Earth-derived oilseed rape map, (<b>b</b>) CSRA-derived oilseed rape map, and (<b>c</b>) spatial comparison between (<b>a</b>) and (<b>b</b>) at part of Qianjiang City in 2015. (<b>d</b>) Google Earth-derived oilseed rape map, (<b>e</b>) CSRA-derived oilseed rape map, and (<b>f</b>) spatial comparison between (<b>d</b>) and (<b>e</b>) in part of Wuxue City in 2016.</p> Full article ">Figure 13
<p>Comparison of CSRA-estimated oilseed rape acreages and agricultural census data at the municipal level in (<b>a</b>) 2014; (<b>b</b>) 2015; and (<b>c</b>) 2016.</p> Full article ">Figure 14
<p>CSRA-derived oilseed rape maps using (<b>a</b>) GF-2 PMS MSS image on 18 March 2016; (<b>b</b>) RapidEye image on 18 March 2016; (<b>c</b>) RapidEye image on 4 April 2016; and (<b>d</b>) GF-2 WFV image on 28 March 2016 in part of Shayang County. (<b>e</b>–<b>g</b>), and (<b>h</b>) were the zoom image of the black rectangle in (<b>a</b>–<b>d</b>).</p> Full article ">
19 pages, 5676 KiB  
Article
SmartEscape: A Mobile Smart Individual Fire Evacuation System Based on 3D Spatial Model
by Umit Atila, Yasin Ortakci, Kasim Ozacar, Emrullah Demiral and Ismail Rakip Karas
ISPRS Int. J. Geo-Inf. 2018, 7(6), 223; https://doi.org/10.3390/ijgi7060223 - 16 Jun 2018
Cited by 39 | Viewed by 10588
Abstract
We propose SmartEscape, a real-time, dynamic, intelligent and user-specific evacuation system with a mobile interface for emergency cases such as fire. Unlike past work, we explore dynamically changing conditions and calculate a personal route for an evacuee by considering his/her individual features. SmartEscape, [...] Read more.
We propose SmartEscape, a real-time, dynamic, intelligent and user-specific evacuation system with a mobile interface for emergency cases such as fire. Unlike past work, we explore dynamically changing conditions and calculate a personal route for an evacuee by considering his/her individual features. SmartEscape, which is fast, low-cost, low resource-consuming and mobile supported, collects various environmental sensory data and takes evacuees’ individual features into account, uses an artificial neural network (ANN) to calculate personal usage risk of each link in the building, eliminates the risky ones, and calculates an optimum escape route under existing circumstances. Then, our system guides the evacuee to the exit through the calculated route with vocal and visual instructions on the smartphone. While the position of the evacuee is detected by RFID (Radio-Frequency Identification) technology, the changing environmental conditions are measured by the various sensors in the building. Our ANN (Artificial Neural Network) predicts dynamically changing risk states of all links according to changing environmental conditions. Results show that SmartEscape, with its 98.1% accuracy for predicting risk levels of links for each individual evacuee in a building, is capable of evacuating a great number of people simultaneously, through the shortest and the safest route. Full article
(This article belongs to the Special Issue Productive, Livable and Accessible Cities—The Role of 3D City Models)
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<p>The overview of SmartEscape.</p> Full article ">Figure 2
<p>Settings screen of the mobile application.</p> Full article ">Figure 3
<p>Oracle Network data model (adapted from [<a href="#B15-ijgi-07-00223" class="html-bibr">15</a>]).</p> Full article ">Figure 4
<p>Architecture of an MLP neural network.</p> Full article ">Figure 5
<p>A node of MLP: an artificial neuron.</p> Full article ">Figure 6
<p>ANN structure for fire evacuation model.</p> Full article ">Figure 7
<p>(<b>a</b>) ANN based routing; (<b>b</b>) evaluating links states.</p> Full article ">Figure 7 Cont.
<p>(<b>a</b>) ANN based routing; (<b>b</b>) evaluating links states.</p> Full article ">Figure 8
<p>Risk levels predicted for Evacuee-1.</p> Full article ">Figure 9
<p>Some screenshots taken from Evacuee-1’s smartphone.</p> Full article ">Figure 10
<p>Temperature–time chart.</p> Full article ">Figure 11
<p>Carbon monoxide density–time chart.</p> Full article ">Figure 12
<p>Visibility–time chart.</p> Full article ">
18 pages, 3191 KiB  
Article
A Citizen Science Approach for Collecting Toponyms
by Aji Putra Perdana and Frank O. Ostermann
ISPRS Int. J. Geo-Inf. 2018, 7(6), 222; https://doi.org/10.3390/ijgi7060222 - 16 Jun 2018
Cited by 14 | Viewed by 9192
Abstract
The emerging trends and technologies of surveying and mapping potentially enable local experts to contribute and share their local geographical knowledge of place names (toponyms). We can see the increasing numbers of toponyms in digital platforms, such as OpenStreetMap, Facebook Place Editor, Swarm [...] Read more.
The emerging trends and technologies of surveying and mapping potentially enable local experts to contribute and share their local geographical knowledge of place names (toponyms). We can see the increasing numbers of toponyms in digital platforms, such as OpenStreetMap, Facebook Place Editor, Swarm Foursquare, and Google Local Guide. On the other hand, government agencies keep working to produce concise and complete gazetteers. Crowdsourced geographic information and citizen science approaches offer a new paradigm of toponym collection. This paper addresses issues in the advancing toponym practice. First, we systematically examined the current state of toponym collection and handling practice by multiple stakeholders, and we identified a recurring set of problems. Secondly, we developed a citizen science approach, based on a crowdsourcing level of participation, to collect toponyms. Thirdly, we examined the implementation in the context of an Indonesian case study. The results show that public participation in toponym collection is an approach with the potential to solve problems in toponym handling, such as limited human resources, accessibility, and completeness of toponym information. The lessons learnt include the knowledge that the success of this approach depends on the willingness of the government to advance their workflow, the degree of collaboration between stakeholders, and the presence of a communicative approach in introducing and sharing toponym guidelines with the community. Full article
(This article belongs to the Special Issue Geoinformatics in Citizen Science)
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Graphical abstract
Full article ">Figure 1
<p>Challenges and opportunities to explore the potential use of a toponym collection approach with multiple stakeholders.</p> Full article ">Figure 2
<p>Organizational structure of public authorities for the standardization of toponyms in Indonesia.</p> Full article ">Figure 3
<p>Names form used by the national naming authority (NNA) in Indonesia: (<b>a</b>) an example of the “Name Form” for collecting toponyms in the field; (<b>b</b>) complete name form from fieldwork in Yogyakarta. (Courtesy of Badan Informasi Geospasial).</p> Full article ">Figure 4
<p>Toponyms with alternative names, meaning, and history of names: (<b>a</b>) urban names in the case study of Yogyakarta provided 63 toponyms; (<b>b</b>) natural and man-made features in the case study of Lombok provided 367 toponyms.</p> Full article ">Figure 5
<p>Verification process in the toponymic survey in Lombok: (<b>a</b>) compilation of place names with approval from local authority; (<b>b</b>) respondents (local people) share their local geographical knowledge and put place names on the map. (Courtesy of Badan Informasi Geospasial).</p> Full article ">Figure 6
<p>Selected screenshots of SAKTI (Sistem Akusisi Data Toponim Indonesia/Indonesian Toponymic Data Acquisition System): (<b>a</b>) “Name Form” for collecting toponyms in the field in SAKTI mobile application; (<b>b</b>) SAKTI Web-GIS (<a href="http://sakti.big.go.id/sakti/webgis/" target="_blank">http://sakti.big.go.id/sakti/webgis/</a>).</p> Full article ">
30 pages, 3008 KiB  
Article
A Graph Database Model for Knowledge Extracted from Place Descriptions
by Hao Chen, Maria Vasardani, Stephan Winter and Martin Tomko
ISPRS Int. J. Geo-Inf. 2018, 7(6), 221; https://doi.org/10.3390/ijgi7060221 - 15 Jun 2018
Cited by 31 | Viewed by 8642
Abstract
Everyday place descriptions provide a rich source of knowledge about places and their relative locations. This research proposes a place graph model for modelling this spatial, non-spatial, and contextual knowledge from place descriptions. The model extends a prior place graph, and overcomes a [...] Read more.
Everyday place descriptions provide a rich source of knowledge about places and their relative locations. This research proposes a place graph model for modelling this spatial, non-spatial, and contextual knowledge from place descriptions. The model extends a prior place graph, and overcomes a number of limitations. The model is implemented using a graph database, and a management system has also been developed that allows operations including querying, mapping, and visualizing the stored knowledge in an extended place graph. Then three experimental tasks, namely georeferencing, reasoning, and querying, are selected to demonstrate the superiority of the extended model. Full article
(This article belongs to the Special Issue Place-Based Research in GIScience and Geoinformatics)
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<p>Place graph consisting of two example triplets: &lt;courtyard, <span class="html-italic">on</span>, campus&gt; and &lt;courtyard, <span class="html-italic">beside</span>, clocktower&gt;.</p> Full article ">Figure 2
<p>Parts of a merged place graph constructed from descriptions of the University of Melbourne campus, with node size corresponding to node degree, and edge size corresponding to number of relationships between the linked nodes. Multiple relationships between two nodes are represented by only one edge.</p> Full article ">Figure 3
<p>UML of the original place graph model.</p> Full article ">Figure 4
<p>UML diagram illustrating the extended place graph database model, with seven types of classes (nodes) and nine types of relationships (edges).</p> Full article ">Figure 5
<p>An example of modelling a relative direction relationship using the extended place graph.</p> Full article ">Figure 6
<p>The implemented extended place graph database management system interface, with an example visualization of part of the test extended place graph. A clearer example of an extended place graph as well as explanations is provided in the <a href="#app1-ijgi-07-00221" class="html-app">Appendix A</a>.</p> Full article ">Figure 7
<p>An example of deriving the ALR for place <span class="html-italic">b</span> through intersection of search spaces.</p> Full article ">Figure 8
<p>The spatial context of a merged, original place graph (<b>left</b>), and separated spatial contexts of an extended place graph (<b>right</b>).</p> Full article ">Figure 9
<p>Search space of place <span class="html-italic">B</span> for relationship &lt;<span class="html-italic">B</span>, <span class="html-italic">right of</span>, <span class="html-italic">A</span>&gt; without a reference direction (<b>left</b>) compared to with anchored reference direction information (<b>right</b>).</p> Full article ">Figure 10
<p>The search space of a qualitative distance relationship with contrast set information, represented by the shaded region.</p> Full article ">Figure 11
<p>Determining consistency of directional relationships between a locatum <span class="html-italic">B</span> and a relatum <span class="html-italic">A</span> by search spaces. (<b>a</b>) an existing relationship and its search space; (<b>b</b>) another relationship that is consistent with the existing one, determined by search space; (<b>c</b>) another relationship that is inconsistent with the previous two.</p> Full article ">Figure 12
<p>Consistency reasoning through reference direction translation. (<b>a</b>) an existing relationship; (<b>b</b>) another new relationship that is consistent with the existing one, determined by reference direction translation; (<b>c</b>) another relationship that is inconsistent with the previous two.</p> Full article ">Figure 13
<p>Percentages of places from different ALR refinement situations compared to baseline.</p> Full article ">Figure 14
<p>Refined approximate location region (ALR) sizes as percentages of the original (baseline) ALR size using the SC, RF, CS, and Hybrid methods.</p> Full article ">Figure 15
<p>Distance errors between ground-truth and matched gazetteer locations for the baseline and hybrid methods.</p> Full article ">Figure 16
<p>The locations of the three places mentioned in the descriptions above, with a red arrow indicating the walking direction.</p> Full article ">Figure A1
<p>Creation of description and user nodes.</p> Full article ">Figure A2
<p>Creation of <span class="html-italic">n</span>-plet nodes.</p> Full article ">Figure A3
<p>Creation of place reference, spatial relation, and route nodes.</p> Full article ">Figure A4
<p>Linking different place reference nodes to the same place node through node merging.</p> Full article ">Figure A5
<p>The resulting extended place graph database of the place description example. Gray: description; Pink: user; Green: <span class="html-italic">n</span>-plet; Blue: (mapped) spatial relation; Red: route; Yellow: place reference; Purple: place.</p> Full article ">
19 pages, 6544 KiB  
Technical Note
HidroMap: A New Tool for Irrigation Monitoring and Management Using Free Satellite Imagery
by Laura Piedelobo, Damián Ortega-Terol, Susana Del Pozo, David Hernández-López, Rocío Ballesteros, Miguel A. Moreno, José-Luis Molina and Diego González-Aguilera
ISPRS Int. J. Geo-Inf. 2018, 7(6), 220; https://doi.org/10.3390/ijgi7060220 - 15 Jun 2018
Cited by 22 | Viewed by 8160
Abstract
Proper control and planning of water resource use, especially in those catchments with large surface, climatic variability and intensive irrigation activity, is essential for a sustainable water management. Decision support systems based on useful tools involving main stakeholders and hydrological planning offices of [...] Read more.
Proper control and planning of water resource use, especially in those catchments with large surface, climatic variability and intensive irrigation activity, is essential for a sustainable water management. Decision support systems based on useful tools involving main stakeholders and hydrological planning offices of the river basins play a key role. The free availability of Earth observation products with high temporal resolution, such as the European Sentinel-2B, has allowed us to combine remote sensing with cadastral and agronomic data. This paper introduces HidroMap to the scientific community, an open source tool as a geographic information system (GIS) organized in two different modules, desktop-GIS and web-GIS, with complementary functions and based on PostgreSQL/PostGIS database. Through an effective methodology HidroMap allows monitoring irrigation activity, managing unregulated irrigation, and optimizing available fluvial surveillance resources using satellite imagery. This is possible thanks to the automatic download, processing and storage of satellite products within field data provided by the River Surveillance Agency (RSA) and the Hydrological Planning Office (HPO). The tool was successfully validated in Duero Hydrographic Basin along the 2017 summer irrigation period. In conclusion, HidroMap comprised an important support tool for water management tasks and decision making tackled by Duero Hydrographic Confederation which can be adapted to any additional need and transferred to other river basin organizations. Full article
(This article belongs to the Special Issue Free and Open Source Tools for Geospatial Analysis and Mapping)
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<p>Duero Hydrographic Basin. Arid areas and agricultural demands (Source: Spanish National Hydrological Plan 2015–2021).</p> Full article ">Figure 2
<p>Flowchart of the HidroMap tool functionalities and main engines: PostgreSQL/PostGIS database, desktop-GIS, and web-GIS modules.</p> Full article ">Figure 3
<p>HidroMap desktop-GIS module interface. (<b>A</b>) Processing type; (<b>B</b>) Study area definition; (<b>C</b>) Filtering forest areas and others; and (<b>D</b>) Parameters setting.</p> Full article ">Figure 4
<p>Main flowchart of the proposed methodology for estimating agricultural plots with non-regulated irrigation activity and irrigated surface and plots involved in an area of interest (yellow background: inputs; blue background: flux or process; green background: outputs).</p> Full article ">Figure 5
<p>Crops resulted from spatial intersections of an irrigated normalized difference vegetation index (NDVI) response and layers with information of the existence of irrigation concession, SIGPAC parceling and river inspection sectors. Detection and definition of HidroMap cases and temporally monitoring of the information stored in forms.</p> Full article ">Figure 6
<p>Screenshot of the web-GIS module. (<b>A</b>) web-GIS viewer; (<b>B</b>) Several layers to visualize, i.e. S2 and L8 grids, 2017 crop classification (source: Agrarian Technological Institute of Castilla y León, ITACYL), crop declarations to the Common Agricultural Policy (CAP) and field inspections; (<b>C</b>) Usual NDVI variation graphic for an irrigated summer crop: user could choose to visualize NDVI values from L8, S2 or both satellite platforms; (<b>D</b>) Timeline and images availability depending on the area shown by the viewer; (<b>E</b>) Selection of derived products to visualize: RGB (Red, Green and Blue) false colour and NDVI images from both L8 and S2.</p> Full article ">Figure 7
<p>HidroMap cases in the Southern-lower Duero agrarian area in 2017. Plots detected with irrigation activity for an NDVI<sub>TOA</sub> threshold of 0.70, a minimum area of 0.5 ha and filtering forest surfaces.</p> Full article ">Figure 8
<p>Comparison between the estimation of the irrigated area offered by HidroMap and extension declared for that purpose by the water users of Payuelos for the summer of 2017.</p> Full article ">Figure 9
<p>False color images from Sentinel-2 (combination of bands 11, 8 and 4) and a graph of the variation of NDVI<sub>TOA</sub> values for different dates. This graph represents the growing season profile, including phenological stages, of a sugar beet crop.</p> Full article ">
17 pages, 3865 KiB  
Article
Ghost City Extraction and Rate Estimation in China Based on NPP-VIIRS Night-Time Light Data
by Wei Ge, Hong Yang, Xiaobo Zhu, Mingguo Ma and Yuli Yang
ISPRS Int. J. Geo-Inf. 2018, 7(6), 219; https://doi.org/10.3390/ijgi7060219 - 15 Jun 2018
Cited by 33 | Viewed by 7869
Abstract
The ghost city phenomenon is a serious problem resulting from the rapid urbanization process in China. Estimation of the ghost city rate (GCR) can provide information about vacant dwellings. This paper developed a methodology to quantitatively evaluate GCR values at the national scale [...] Read more.
The ghost city phenomenon is a serious problem resulting from the rapid urbanization process in China. Estimation of the ghost city rate (GCR) can provide information about vacant dwellings. This paper developed a methodology to quantitatively evaluate GCR values at the national scale using multi-resource remote sensing data. The Suomi National Polar-Orbiting Partnership–Visible Infrared Imaging Radiometer (NPP-VIIRS) night-time light data and moderate resolution imaging spectroradiometer (MODIS) land cover data were used in the evaluation of the GCR values in China. The average ghost city rate (AGCR) was 35.1% in China in 2013. Shanghai had the smallest AGCR of 21.7%, while Jilin has the largest AGCR of 47.27%. There is a significant negative correlation between both the provincial AGCR and the per capita disposable income of urban households (R = −0.659, p < 0.01) and the average selling prices of commercial buildings (R = −0.637, p < 0.01). In total, 31 ghost cities are mainly concentrated in the economically underdeveloped inland provinces. Ghost city areas are mainly located on the edge of urban built-up areas, and the spatial pattern of ghost city areas changed in different regions. This approach combines statistical data with the distribution of vacant urban areas, which is an effective method to capture ghost city information. Full article
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<p>The Suomi National Polar-Orbiting Partnership–Visible Infrared Imaging Radiometer (NPP-VIIRS) imagery night-time light from December 2013 and MODIS land cover type yearly data (MCD12Q1, 2013) urban and built-up areas from 2013, and zoomed in Shanghai.</p> Full article ">Figure 2
<p>Workflow of the ghost city extraction and rate estimation.</p> Full article ">Figure 3
<p>Spatial distribution of China’s ghost city rate in 2013. The four regions are magnified and shown in detail.</p> Full article ">Figure 4
<p>Four different spatial distributions of the estimated night-time light (NTL), total built-up (TB) area and ghost city rate (GCR) in four typical cities. The <b>a1</b>, <b>b1</b>, <b>c1</b> and <b>d1</b> are the NPP-VIIRS night-time light radiance; <b>a2</b>, <b>b2</b>, <b>c2</b>, and <b>d2</b> are the total built-up areas and the optimal threshold; <b>a3</b>, <b>b3</b>, <b>c3</b>, and <b>d3</b> are ghost city rate imagery. The red lines in figures are the municipal administrative boundaries.</p> Full article ">Figure 5
<p>The scatter plots between 31 provincial AGCRs with the per capita disposable income of urban households and the average selling price of commercial buildings in China.</p> Full article ">Figure 6
<p>Normal distribution of municipal AGCRs.</p> Full article ">Figure 7
<p>The spatial distribution of China’s municipal AGCRs. The five categories of municipal AGCR are shown in the different colors. The 31 ghost cities are labeled with a gray circle with three categories.</p> Full article ">Figure 8
<p>Different spatial patterns of eight ghost cities, four in eastern China (D, Daqing, AGCR is 46.22%; J, Jilin, AGCR is 40.9%; H, Harbin, AGCR is 52.23%; C, Changchun, AGCR is 55.35%), two in central China (O, Ordos, AGR is 47.16%; X, Xilinhot, AGCR is 46.94%), and two in western China (B, Bortala, AGCR is 41.32%; N, Nyingchi, AGCR is 47.26%); and the black lines in the figures are the municipal administrative boundaries.</p> Full article ">
21 pages, 4791 KiB  
Article
A Spatiotemporal Multi-View-Based Learning Method for Short-Term Traffic Forecasting
by Shifen Cheng, Feng Lu, Peng Peng and Sheng Wu
ISPRS Int. J. Geo-Inf. 2018, 7(6), 218; https://doi.org/10.3390/ijgi7060218 - 14 Jun 2018
Cited by 25 | Viewed by 4263
Abstract
Short-term traffic forecasting plays an important part in intelligent transportation systems. Spatiotemporal k-nearest neighbor models (ST-KNNs) have been widely adopted for short-term traffic forecasting in which spatiotemporal matrices are constructed to describe traffic conditions. The performance of the models is closely related to [...] Read more.
Short-term traffic forecasting plays an important part in intelligent transportation systems. Spatiotemporal k-nearest neighbor models (ST-KNNs) have been widely adopted for short-term traffic forecasting in which spatiotemporal matrices are constructed to describe traffic conditions. The performance of the models is closely related to the spatial dependencies, the temporal dependencies, and the interaction of spatiotemporal dependencies. However, these models use distance functions and correlation coefficients to identify spatial neighbors and measure the temporal interaction by only considering the temporal closeness of traffic, which result in existing ST-KNNs that cannot fully reflect the essential features of road traffic. This study proposes an improved spatiotemporal k-nearest neighbor model for short-term traffic forecasting by utilizing a multi-view learning algorithm named MVL-STKNN that fully considers the spatiotemporal dependencies of traffic data. First, the spatial neighbors for each road segment are automatically determined using cross-correlation under different temporal dependencies. Three spatiotemporal views are built on the constructed spatiotemporal closeness, periodic, and trend matrices to represent spatially heterogeneous traffic states. Second, a spatiotemporal weighting matrix is introduced into the ST-KNN model to recognize similar traffic patterns in the three spatiotemporal views. Finally, the results of traffic pattern recognition under these three spatiotemporal views are aggregated by using a neural network algorithm to describe the interaction of spatiotemporal dependencies. Extensive experiments were conducted using real vehicular-speed datasets collected on city roads and expressways. In comparison with baseline methods, the results show that the MVL-STKNN model greatly improves short-term traffic forecasting by lowering the mean absolute percentage error between 28.24% and 46.86% for the city road dataset and, between 53.80% and 90.29%, for the expressway dataset. The results suggest that multi-view learning merits further attention for traffic-related data mining under such a dynamic and data-intensive environment, which owes to its comprehensive consideration of spatial correlation and heterogeneity as well as temporal fluctuation and regularity in road traffic. Full article
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<p>Schematic of the MVL-STKNN model.</p> Full article ">Figure 2
<p>Location distribution of traffic flow in PeMS dataset.</p> Full article ">Figure 3
<p>Location distribution of traffic flow in the Beijing dataset.</p> Full article ">Figure 4
<p>Impact of ST-KNN model parameters.</p> Full article ">Figure 5
<p>Impact of temporal dependent parameters.</p> Full article ">Figure 6
<p>Adaptive spatial neighbors of each road segment.</p> Full article ">Figure 7
<p>Comparison with baselines using the Beijing data set.</p> Full article ">Figure 8
<p>Comparison with baselines using the PeMS data set.</p> Full article ">Figure 9
<p>Impact of space-time weighting matrix.</p> Full article ">Figure 10
<p>Impact of spatial and temporal dependencies.</p> Full article ">
22 pages, 3957 KiB  
Article
Deep Belief Networks Based Toponym Recognition for Chinese Text
by Shu Wang, Xueying Zhang, Peng Ye and Mi Du
ISPRS Int. J. Geo-Inf. 2018, 7(6), 217; https://doi.org/10.3390/ijgi7060217 - 14 Jun 2018
Cited by 19 | Viewed by 5808
Abstract
In Geographical Information Systems, geo-coding is used for the task of mapping from implicitly geo-referenced data to explicitly geo-referenced coordinates. At present, an enormous amount of implicitly geo-referenced information is hidden in unstructured text, e.g., Wikipedia, social data and news. Toponym recognition is [...] Read more.
In Geographical Information Systems, geo-coding is used for the task of mapping from implicitly geo-referenced data to explicitly geo-referenced coordinates. At present, an enormous amount of implicitly geo-referenced information is hidden in unstructured text, e.g., Wikipedia, social data and news. Toponym recognition is the foundation of mining this useful geo-referenced information by identifying words as toponyms in text. In this paper, we propose an adapted toponym recognition approach based on deep belief network (DBN) by exploring two key issues: word representation and model interpretation. A Skip-Gram model is used in the word representation process to represent words with contextual information that are ignored by current word representation models. We then determine the core hyper-parameters of the DBN model by illustrating the relationship between the performance and the hyper-parameters, e.g., vector dimensionality, DBN structures and probability thresholds. The experiments evaluate the performance of the Skip-Gram model implemented by the Word2Vec open-source tool, determine stable hyper-parameters and compare our approach with a conditional random field (CRF) based approach. The experimental results show that the DBN model outperforms the CRF model with smaller corpus. When the corpus size is large enough, their statistical metrics become approaching. However, their recognition results express differences and complementarity on different kinds of toponyms. More importantly, combining their results can directly improve the performance of toponym recognition relative to their individual performances. It seems that the scale of the corpus has an obvious effect on the performance of toponym recognition. Generally, there is no adequate tagged corpus on specific toponym recognition tasks, especially in the era of Big Data. In conclusion, we believe that the DBN-based approach is a promising and powerful method to extract geo-referenced information from text in the future. Full article
(This article belongs to the Special Issue Place-Based Research in GIScience and Geoinformatics)
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<p>The framework of toponym recognition based on DBN model.</p> Full article ">Figure 2
<p>The path of the object character in the context of the Huffman tree. <math display="inline"><semantics> <mrow> <msub> <mi>c</mi> <mi>a</mi> </msub> </mrow> </semantics></math> to <math display="inline"><semantics> <mrow> <msub> <mi>c</mi> <mi>n</mi> </msub> </mrow> </semantics></math> is the characters in the document ordered by the frequency. <math display="inline"><semantics> <mrow> <msub> <mi>c</mi> <mi>m</mi> </msub> </mrow> </semantics></math> is the root character and <math display="inline"><semantics> <mrow> <msub> <mi>c</mi> <mi>i</mi> </msub> </mrow> </semantics></math> is the target character. The black path with direction is the way to calculate the probability of target character <math display="inline"><semantics> <mrow> <msub> <mi>c</mi> <mi>i</mi> </msub> </mrow> </semantics></math>.</p> Full article ">Figure 3
<p>The selection of the dimensionality interval boundaries.</p> Full article ">Figure 4
<p>The processes of toponym recognition after the DBN structure. <math display="inline"><semantics> <mrow> <mover accent="true"> <mrow> <msub> <mi>C</mi> <mi>i</mi> </msub> </mrow> <mo stretchy="true">⇀</mo> </mover> <mo>’</mo> </mrow> </semantics></math> represents the binary vector of character <math display="inline"><semantics> <mrow> <msub> <mi>c</mi> <mi>i</mi> </msub> </mrow> </semantics></math>. <math display="inline"><semantics> <mrow> <mover accent="true"> <mrow> <msub> <mi>V</mi> <mi>i</mi> </msub> </mrow> <mo stretchy="true">⇀</mo> </mover> </mrow> </semantics></math> is the input data of DBN structure composed by the joint vectors of the previous and next characters around the target character <math display="inline"><semantics> <mrow> <msub> <mi>c</mi> <mi>i</mi> </msub> </mrow> </semantics></math>. <math display="inline"><semantics> <mrow> <msub> <mi>x</mi> <mi>i</mi> </msub> </mrow> </semantics></math> is the probability of the character <math display="inline"><semantics> <mrow> <msub> <mi>c</mi> <mi>i</mi> </msub> </mrow> </semantics></math> that belongs to toponyms, and <math display="inline"><semantics> <mrow> <msub> <mi>y</mi> <mi>i</mi> </msub> </mrow> </semantics></math> = 1 − <math display="inline"><semantics> <mrow> <msub> <mi>x</mi> <mi>i</mi> </msub> </mrow> </semantics></math>.</p> Full article ">Figure 5
<p>The experimental framework.</p> Full article ">Figure 6
<p>An example of an annotated document in the ECCG corpus.</p> Full article ">Figure 7
<p>The relationship between the vector dimension and F1 value.</p> Full article ">Figure 8
<p>The relationship between the number of layers and F1 values.</p> Full article ">Figure 9
<p>The relationship between the number of nodes in each layer and F1 values.</p> Full article ">Figure 10
<p>The relationship between the probability threshold and F1 values.</p> Full article ">Figure 11
<p>The main processes of a CRF-based approach.</p> Full article ">Figure 12
<p>The F1 trends of CRF and DBN on different corpus sizes.</p> Full article ">
20 pages, 10794 KiB  
Article
Handling Points of Interest (POIs) on a Mobile Web Map Service Linked to Indoor Geospatial Objects: A Case Study
by Kwangseob Kim and Kiwon Lee
ISPRS Int. J. Geo-Inf. 2018, 7(6), 216; https://doi.org/10.3390/ijgi7060216 - 14 Jun 2018
Cited by 9 | Viewed by 4697
Abstract
Managing geo-based indoor content is important, because the components used to construct an urban environment are complex. Geospatial data are available worldwide, but services are tailored only to local features. As the accuracy of online maps increases, the buildings in a web-mapping service [...] Read more.
Managing geo-based indoor content is important, because the components used to construct an urban environment are complex. Geospatial data are available worldwide, but services are tailored only to local features. As the accuracy of online maps increases, the buildings in a web-mapping service can be created exactly as they are, in terms of actual features and geometric properties, and can provide some information on indoor elements. Nevertheless, not many practical use cases exist, as the available scope and volume of indoor content are limited. In Korea’s metropolitan areas, an indoor geospatial information management scheme was built to manage internal facility information for public and underground buildings on a three-dimensional (3D) basis and to provide online visualization services for users. Based on this enterprise system for public use of indoor 3D content, we conducted a case study with add-on features to manipulate and manage data by adding two-dimensional (2D) building data that are linked to the 3D models. We also changed the classification system of the points of interest (POIs) for each internal facility. To enhance public usability, a portion of the usable information in this scheme can be offered via an open application programming interface (Open API). To create a 2D POIs obtained from an indoor 3D object that was provided as a relative coordinate with only 3D geometric features, several steps were needed: adding the object to the system, storing the object as an absolute coordinate, and linking the object with an outdoor mapping service. In addition, to provide more useful information about indoor POIs generated from 3D models for users, detailed information should be further managed by directly using the Open APIs designed in this study. Subsequently, a mobile web mapping service system to visualize indoor contents was deployed to deliver practical processing and improvements based on the deployed Open API. The possibility of effective management and application of POIs related to indoor contents was confirmed through the mobile web-mapping demo service that was established using Open API. Full article
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<p>Main features of the indoor map service system. Add-on functions are shown as white boxes, and the gray shaded boxes represent existing functions for three-dimensional (3D) contents.</p> Full article ">Figure 2
<p>Two-dimensional (2D) data model for an indoor content management system: (<b>a</b>) geospatial object visualization and management, and (<b>b</b>) management mode of indoor points of interest (POIs) and open application programming interface (API).</p> Full article ">Figure 3
<p>Implemented cases: (<b>a</b>) 2D data visualization, and (<b>b</b>) attribute information.</p> Full article ">Figure 4
<p>Two-dimensional data translation and rotation function: (<b>a</b>) user interface, (<b>b</b>) data translation, and (<b>c</b>) data rotation.</p> Full article ">Figure 5
<p>Management cases on indoor POI detail information: (<b>a</b>) facilities, (<b>b</b>) shopping center, (<b>c</b>) entrances, and (<b>d</b>) subway.</p> Full article ">Figure 5 Cont.
<p>Management cases on indoor POI detail information: (<b>a</b>) facilities, (<b>b</b>) shopping center, (<b>c</b>) entrances, and (<b>d</b>) subway.</p> Full article ">Figure 6
<p>Conceptual view of request and response processing for an indoor POI and a 2D building.</p> Full article ">Figure 7
<p>Implementation examples of an Open API related to 2D building information.</p> Full article ">Figure 8
<p>Main page of the mobile web mapping service for indoor facilities for foreigners using (<b>a</b>) iPad mini and (<b>b</b>) iPhone.</p> Full article ">Figure 9
<p>Application examples of requests by JavaScript and their responses based on Open API and GeoJSON.</p> Full article ">Figure 10
<p>Results of the 2D building visualization, object selection, and indoor POI radio button for (<b>a</b>) iPad mini and (<b>b</b>) iPhone.</p> Full article ">Figure 11
<p>Visualization results of indoor POI detailed information: (<b>a</b>) entrance, (<b>b</b>) handicap facility, and (<b>c</b>) shopping center.</p> Full article ">Figure 12
<p>Visualization results of detailed information about a nearby building on (<b>a</b>) iPad mini and (<b>b</b>) iPhone.</p> Full article ">
16 pages, 2243 KiB  
Article
Construction and Optimization of Three-Dimensional Disaster Scenes within Mobile Virtual Reality
by Ya Hu, Jun Zhu, Weilian Li, Yunhao Zhang, Qing Zhu, Hua Qi, Huixin Zhang, Zhenyu Cao, Weijun Yang and Pengcheng Zhang
ISPRS Int. J. Geo-Inf. 2018, 7(6), 215; https://doi.org/10.3390/ijgi7060215 - 14 Jun 2018
Cited by 28 | Viewed by 5721
Abstract
Because mobile virtual reality (VR) is both mobile and immersive, three-dimensional (3D) visualizations of disaster scenes based in mobile VR enable users to perceive and recognize disaster environments faster and better than is possible with other methods. To achieve immersion and prevent users [...] Read more.
Because mobile virtual reality (VR) is both mobile and immersive, three-dimensional (3D) visualizations of disaster scenes based in mobile VR enable users to perceive and recognize disaster environments faster and better than is possible with other methods. To achieve immersion and prevent users from feeling dizzy, such visualizations require a high scene-rendering frame rate. However, the existing related visualization work cannot provide a sufficient solution for this purpose. This study focuses on the construction and optimization of a 3D disaster scene in order to satisfy the high frame-rate requirements for the rendering of 3D disaster scenes in mobile VR. First, the design of a plugin-free browser/server (B/S) architecture for 3D disaster scene construction and visualization based in mobile VR is presented. Second, certain key technologies for scene optimization are discussed, including diverse modes of scene data representation, representation optimization of mobile scenes, and adaptive scheduling of mobile scenes. By means of these technologies, smartphones with various performance levels can achieve higher scene-rendering frame rates and improved visual quality. Finally, using a flood disaster as an example, a plugin-free prototype system was developed, and experiments were conducted. The experimental results demonstrate that a 3D disaster scene constructed via the methods addressed in this study has a sufficiently high scene-rendering frame rate to satisfy the requirements for rendering a 3D disaster scene in mobile VR. Full article
(This article belongs to the Special Issue State-of-the-Art Virtual/Augmented Reality and 3D Modeling Techniques for Virtual Urban Geographic Experiments)
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<p>Plugin-free browser/server (B/S) framework for the construction and visualization of three-dimensional (3D) disaster scenes based on mobile virtual reality (VR).</p> Full article ">Figure 2
<p>Screenshot of the prototype system on a smartphone.</p> Full article ">Figure 3
<p>Tests of the influence of the loading different house models and flood routing models on the rendering frame rate: (<b>a</b>) flight tests on the Android smartphone; (<b>b</b>) flight tests on the iOS smartphone.</p> Full article ">Figure 4
<p>Tests of the influence of reducing the maximum tile level that can be loaded on the rendering frame rate: (<b>a</b>) flight tests on the Android smartphone; (<b>b</b>) flight tests on the iOS smartphone.</p> Full article ">Figure 5
<p>Tests of the influence of the loading image tiles of various tile sizes on the rendering frame rate: (<b>a</b>) flight tests on the Android smartphone; (<b>b</b>) flight tests on the iOS smartphone.</p> Full article ">
17 pages, 3301 KiB  
Article
Trend Analysis of Relationship between Primary Productivity, Precipitation and Temperature in Inner Mongolia
by Tianyang Chen, Yichun Xie, Chao Liu, Yongfei Bai, Anbing Zhang, Lishen Mao and Siyu Fan
ISPRS Int. J. Geo-Inf. 2018, 7(6), 214; https://doi.org/10.3390/ijgi7060214 - 5 Jun 2018
Cited by 11 | Viewed by 4905
Abstract
This study mainly examined the relationships among primary productivity, precipitation and temperature by identifying trends of change embedded in time-series data. The paper also explores spatial variations of the relationship over four types of vegetation and across two precipitation zones in Inner Mongolia, [...] Read more.
This study mainly examined the relationships among primary productivity, precipitation and temperature by identifying trends of change embedded in time-series data. The paper also explores spatial variations of the relationship over four types of vegetation and across two precipitation zones in Inner Mongolia, China. Traditional analysis of vegetation response to climate change uses minimum, maximum, average or cumulative measurements; focuses on a whole region instead of fine-scale regional or ecological variations; or adopts generic analysis techniques. We innovatively integrate Empirical Mode Decomposition (EMD) and Redundancy Analysis (RDA) to overcome the weakness of traditional approaches. The EMD filtered trend surfaces reveal clear patterns of Enhanced Vegetation Index (EVI), precipitation, and temperature changes in both time and space. The filtered data decrease noises and cyclic fluctuations in the original data and are more suitable for examining linear relationship than the original data. RDA is further applied to reveal partial effect of precipitation and temperature, and their joint effect on primary productivity. The main findings are as follows: (1) We need to examine relationships between the trends of change of the variables of interest when investigating long-term relationships among them. (2) Long-term trend of change of precipitation or temperature can become a critical factor influencing primary productivity depending on local environments. (3) Synchronization (joint effect) of precipitation and temperature in growing season is critically important to primary productivity in the study area. (4) Partial and joint effects of precipitation and temperature on primary productivity vary over different precipitation zones and different types of vegetation. The method developed in this paper is applicable to ecosystem research in other regions. Full article
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<p>The study area of Inner Mongolia Plateau and the east–west transects.</p> Full article ">Figure 2
<p>Flow chart of the synthesized research design and method.</p> Full article ">Figure 3
<p>Original time series and EMD intrinsic fractions (including residual) of the EVI of Steppe Grassland, 2000–2014.</p> Full article ">Figure 4
<p>EVI residuals displayed in spatial and temporal surface.</p> Full article ">Figure 5
<p>Precipitation residuals displayed in spatial and temporal surface.</p> Full article ">Figure 6
<p>Temperature residuals displayed in spatial and temporal surface.</p> Full article ">Figure 7
<p>Relation between temperature and precipitation after EMD extraction. (<b>a</b>) the entire region; (<b>b</b>) the west transect; (<b>c</b>) the east transect.</p> Full article ">
21 pages, 5721 KiB  
Article
Improving Building Change Detection in VHR Remote Sensing Imagery by Combining Coarse Location and Co-Segmentation
by Jie Chen, Haifei Liu, Jialiang Hou, Minhua Yang and Min Deng
ISPRS Int. J. Geo-Inf. 2018, 7(6), 213; https://doi.org/10.3390/ijgi7060213 - 4 Jun 2018
Cited by 30 | Viewed by 5191
Abstract
Building change detection based on remote sensing imagery is a significant task for urban construction, management, and planning. Feature differences caused by changes are fundamental in building change detection, but the spectral and spatial disturbances of adjacent geo-objects that can extensively affect the [...] Read more.
Building change detection based on remote sensing imagery is a significant task for urban construction, management, and planning. Feature differences caused by changes are fundamental in building change detection, but the spectral and spatial disturbances of adjacent geo-objects that can extensively affect the results are not considered. Moreover, the diversity of building features often renders change detection difficult to implement accurately. In this study, an effective approach is proposed for the detection of individual changed buildings. The detection process mainly consists of two phases: (1) locating the local changed area with the differencing method and (2) detecting changed buildings by using a fuzzy clustering-guided co-segmentation algorithm. This framework is broadly applicable for detecting changed buildings with accurate edges even if their colors and shapes differ to some extent. The results of the comparative experiment show that the strategy proposed in this study can improve building change detection. Full article
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<p>Flowchart of the proposed change detection framework.</p> Full article ">Figure 2
<p>Changed pixels in RGB and Lab color spaces obtained with different <span class="html-italic">T</span> values. Although the increment of <span class="html-italic">T</span> is similarly set to 0.25, the contrasting images exhibit different degrees in the two-color spaces.</p> Full article ">Figure 3
<p>Schematic of block construction.</p> Full article ">Figure 4
<p>Clustering-guided co-segmentation.</p> Full article ">Figure 5
<p>Study data: (<b>a</b>) image captured in 2013; (<b>b</b>) image captured in 2017; (<b>c</b>) ground truth of changed buildings; and (<b>d</b>) pertinent details of the two images.</p> Full article ">Figure 6
<p>Experimental results of the proposed method: (<b>a</b>) changed pixels; (<b>b</b>) changed objects; (<b>c</b>) final detection result; and (<b>d</b>) undetected building.</p> Full article ">Figure 7
<p>Co-segmentation results of some blocks.</p> Full article ">Figure 8
<p>Differencing results with different <span class="html-italic">T<sub>s</sub></span> and <span class="html-italic">T<sub>t</sub></span> values.</p> Full article ">Figure 9
<p>Co-segmentation results with different <span class="html-italic">D</span> values.</p> Full article ">Figure 10
<p>Comparison evaluation: (<b>a</b>,<b>b</b>) changes obtained by combining pixel-based detection and object-based recognition (CPDOR) when <math display="inline"><semantics> <mrow> <msub> <mi>T</mi> <mi>S</mi> </msub> </mrow> </semantics></math> = 0.5 and <math display="inline"><semantics> <mrow> <msub> <mi>T</mi> <mi>S</mi> </msub> </mrow> </semantics></math> = 1; (<b>c</b>) overlay between CPDOR result and ground truth, and (<b>d</b>) overlay between results of this study and ground truth. Green represents correct detection, red represents missing detection, and blue represents false detection.</p> Full article ">
19 pages, 4311 KiB  
Article
4D Time Density of Trajectories: Discovering Spatiotemporal Patterns in Movement Data
by Yebin Zou, Yijin Chen, Jing He, Gehu Pang and Kaixuan Zhang
ISPRS Int. J. Geo-Inf. 2018, 7(6), 212; https://doi.org/10.3390/ijgi7060212 - 4 Jun 2018
Cited by 13 | Viewed by 5994
Abstract
Modern positioning and sensor technology enable the acquisition of movement positions and attributes on an unprecedented scale. Therefore, a large amount of trajectory data can be used to analyze various movement phenomena. In cartography, a common way to visualize and explore trajectory data [...] Read more.
Modern positioning and sensor technology enable the acquisition of movement positions and attributes on an unprecedented scale. Therefore, a large amount of trajectory data can be used to analyze various movement phenomena. In cartography, a common way to visualize and explore trajectory data is to use the 3D cube (e.g., space-time cube), where trajectories are presented as a tilted 3D polyline. As larger movement datasets become available, this type of display can easily become confusing and illegible. In addition, movement datasets are often unprecedentedly massive, high-dimensional, and complex (e.g., implicit spatial and temporal relations and interactions), making it challenging to explore and analyze the spatiotemporal movement patterns in space. In this paper, we propose 4D time density as a visualization method for identifying and analyzing spatiotemporal movement patterns in large trajectory datasets. The movement range of the objects is regarded as a 3D geographical space, into which the fourth dimension, 4D time density, is incorporated. The 4D time density is derived by modeling the movement path and velocity separately. We present a time density algorithm, and demonstrate it on the simulated trajectory and a real dataset representing the movement data of aircrafts in the Hong Kong International and the Macau International Airports. Finally, we consider wider applications and further developments of time density. Full article
(This article belongs to the Special Issue Cognitive Aspects of Human-Computer Interaction for GIS)
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<p>Basic principle of time density estimation. Some points and trajectories (polylines) of the Lorenz model is displayed in the data cube.</p> Full article ">Figure 2
<p>TrajectoryTimeDensity around the four simulated trajectories. Each view shows the time density around a single trajectory.</p> Full article ">Figure 3
<p>Time Density of four simulated trajectories. (<b>a</b>) The TotalTimeDensity of the simulated trajectories; (<b>b</b>) the TotalTimeDensity volume stacked with the simulated trajectory; and (<b>c</b>) AverageTimeDensity volume—the resulting density volume obtained by normalizing the TotalTimeDensity volume by the number of trajectories.</p> Full article ">Figure 4
<p>Visualizing the TotalTimeDensity volume and the AverageTimeDensity volume. (<b>a</b>) direct volume rendering of the trajectory stacking region using the grayscale color scheme; (<b>b</b>) two views of direct volume rendering of the TotalTimeDensity volume using the grayscale color scheme; (<b>c</b>) comparison of volume rendering of (<b>left</b>) TotalTimeDensity volume and (<b>right</b>) AverageTimeDensity volume using rainbow color scheme; (<b>d</b>) volume slicing of TotalTimeDensity volume in different directions and (<b>e</b>) volume slicing of AverageTimeDensity volume in different directions.</p> Full article ">Figure 5
<p>Trajectories of flights on 12 December, 2017, shown in (<b>a</b>) a traditional 2D map and (<b>b</b>) a 3D data cube.</p> Full article ">Figure 6
<p>Time density volume of flight movement trajectory at VHHH and VMMC. TotalTimeDensity volume of (<b>a</b>) arrival flights and (<b>b</b>) departure flights shown with direct volume rendering. TotalTimeDensity volume of flight movement trajectory shown with (<b>c</b>) a horizontal clipping plane and (<b>d</b>) multiple clipping planes in different orientations. Density volume of (<b>e</b>) the number of visits and (<b>f</b>) AverageTimeDensity.</p> Full article ">Figure 7
<p>TotalTimeDensity of flights’ movement data displayed in different directions. TotalTimeDensity observed from (<b>a</b>) the top view; (<b>b</b>) the bottom of the cube and (<b>c</b>,<b>d</b>) the side views. The map transparency in (<b>b</b>) is set to 35%.</p> Full article ">
26 pages, 8162 KiB  
Article
Assessment of Groundwater Nitrate Pollution Potential in Central Valley Aquifer Using Geodetector-Based Frequency Ratio (GFR) and Optimized-DRASTIC Methods
by Anil Shrestha and Wei Luo
ISPRS Int. J. Geo-Inf. 2018, 7(6), 211; https://doi.org/10.3390/ijgi7060211 - 2 Jun 2018
Cited by 20 | Viewed by 5895
Abstract
Groundwater nitrate contamination in the Central Valley (CV) aquifer of California is widespread throughout the valley because of excess nitrogen fertilizer leaching down into the aquifer. The percolation of nitrate depends on several hydrogeological conditions of the valley. Groundwater contamination vulnerability mapping uses [...] Read more.
Groundwater nitrate contamination in the Central Valley (CV) aquifer of California is widespread throughout the valley because of excess nitrogen fertilizer leaching down into the aquifer. The percolation of nitrate depends on several hydrogeological conditions of the valley. Groundwater contamination vulnerability mapping uses hydrogeologic conditions to predict vulnerable areas. This paper presents a new Geodetector-based Frequency Ratio (GFR) method and an optimized-DRASTIC method to generate nitrate vulnerability index values for the CV. The optimized-DRASTIC method combined the individual weights and rating values for Depth to water, Recharge rate, Aquifer media, Soil media, Topography, Impact of vadose zone, and Hydraulic conductivity. The GFR method incorporated the Frequency-Ratio (FR) method to derive rating values and the Geodetector method to derive relative Power of Determinant (PD) values as weights to generate nitrate susceptibility index map. The optimized-DRASTIC method generated very-high to high index values in the eastern part of the CV. The GFR method showed very-high index values in most part of the San Joaquin and Tulare basin. The quantitatively derived rating values and weights in the GFR method improved the vulnerability index and showed better consistency with the observed nitrate contamination pattern than optimized-DRASTIC index, suggesting that GFR is a better method for groundwater contamination vulnerability mapping in the CV aquifer. Full article
(This article belongs to the Special Issue Geographic Information Science and Spatial Analysis in Water Resources)
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<p>Central Valley Aquifer System.</p> Full article ">Figure 2
<p>Cross-section of San Joaquin Valley during predevelopment (<b>top</b>) and post-development (<b>bottom</b>) period. Figure from reference [<a href="#B40-ijgi-07-00211" class="html-bibr">40</a>].</p> Full article ">Figure 3
<p>Groundwater basins and well samples in Central Valley.</p> Full article ">Figure 4
<p>Distribution of depth to water in Central Valley.</p> Full article ">Figure 5
<p>Recharge rate in Central Valley.</p> Full article ">Figure 6
<p>Aquifer media in Central Valley (see <a href="#ijgi-07-00211-t003" class="html-table">Table 3</a> for details on Geocodes).</p> Full article ">Figure 7
<p>Soil Media in Central Valley.</p> Full article ">Figure 8
<p>Percent slope in Central Valley.</p> Full article ">Figure 9
<p>Impact of Vadose Zone in Central Valley.</p> Full article ">Figure 10
<p>Hydraulic Conductivity in Central Valley.</p> Full article ">Figure 11
<p>DRASTIC Susceptibility Index in Central Valley.</p> Full article ">Figure 12
<p>Precipitation distribution in Central Valley. (Data: 1981–2010).</p> Full article ">Figure 13
<p>Fertilizer loading in Central Valley.</p> Full article ">Figure 14
<p>Manure Loading in Central Valley.</p> Full article ">Figure 15
<p>Elevation in Central Valley.</p> Full article ">Figure 16
<p>Percent clay in Central Valley.</p> Full article ">Figure 17
<p>GFR Susceptibility Index.</p> Full article ">Figure 18
<p>Histogram of DRASTIC Index values.</p> Full article ">Figure 19
<p>Histogram of GFR Index Values.</p> Full article ">
19 pages, 3090 KiB  
Article
Analyzing Space-Time Dynamics of Theft Rates Using Exchange Mobility
by Yicheng Tang, Xinyan Zhu, Wei Guo, Lian Duan and Ling Wu
ISPRS Int. J. Geo-Inf. 2018, 7(6), 210; https://doi.org/10.3390/ijgi7060210 - 2 Jun 2018
Cited by 1 | Viewed by 3977
Abstract
A critical issue in the geography of crime is the quantitative analysis of the spatial distribution of crimes which usually changes over time. In this paper, we use the concept of exchange mobility across different time periods to determine the spatial distribution of [...] Read more.
A critical issue in the geography of crime is the quantitative analysis of the spatial distribution of crimes which usually changes over time. In this paper, we use the concept of exchange mobility across different time periods to determine the spatial distribution of the theft rate in the city of Wuhan, China, in 2016. To this end, we use a newly-developed spatial dynamic indicator, the Local Indicator of Mobility Association (LIMA), which can detect differences in the spatial distribution of theft rate rankings over time from a distributional dynamics perspective. Our results provide a scientific reference for the evaluation of the effects of crime prevention efforts and offer a decision-making tool to enhance the application of temporal and spatial analytical methods. Full article
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<p>Wuhan’s geographical location and community boundaries.</p> Full article ">Figure 2
<p>Wuhan’s districts.</p> Full article ">Figure 3
<p>Distribution of theft rate incidents in Wuhan during the study period.</p> Full article ">Figure 4
<p>Changes in global criminal ranking.</p> Full article ">Figure 5
<p>Accumulated mobility of criminal incidents.</p> Full article ">Figure 6
<p>Local Indicator of Mobility Association (LIMA) values in quarters 1–4.</p> Full article ">
22 pages, 26807 KiB  
Article
Feasibility of the Space–Time Cube in Temporal Cultural Landscape Visualization
by Edyta P. Bogucka and Mathias Jahnke
ISPRS Int. J. Geo-Inf. 2018, 7(6), 209; https://doi.org/10.3390/ijgi7060209 - 31 May 2018
Cited by 15 | Viewed by 7297
Abstract
Change acts as an inherent characteristic of the landscape, and expresses dynamic interactions between its tangible and intangible elements. While the documentation and analysis of spatiotemporal patterns have been broadly discussed, major challenges concern the design of task-oriented, user-friendly landscape visualizations. Geographic information [...] Read more.
Change acts as an inherent characteristic of the landscape, and expresses dynamic interactions between its tangible and intangible elements. While the documentation and analysis of spatiotemporal patterns have been broadly discussed, major challenges concern the design of task-oriented, user-friendly landscape visualizations. Geographic information system (GIS) techniques and approaches from visual analytics may bring solutions to those questions. This paper considers the milestone documents for the representation of cultural heritage, and proposes a workflow for assessing the feasibility of the space–time cube concept in landscape representation. The usability of the visualization was examined during the interview with domain experts and potential interdisciplinary users. The evaluation session covered benchmark tasks, feedback, and eye-tracking. The performance of the space–time cube was compared with another spatiotemporal visualization technique and measured in terms of correctness, response time, and satisfaction. The Royal Castle in Warsaw, which was registered in 1980 as a part of Warsaw’s World Heritage Site of United Nations Educational, Scientific and Cultural Organization (UNESCO), served as the case study. The user tests show that the designed space–time cube excels for the completion rate; however, more time is required to provide answers to question tasks focusing on comparisons. Together, the case study and feedback from domain experts and participants demonstrate the benefit of the space–time cube concept in designing landscape visualizations. Full article
(This article belongs to the Special Issue Historic Settlement and Landscape Analysis)
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<p>The map of Warsaw showing the location of the Royal Castle in the context of the UNESCO World Heritage Site (the inner ring) with its buffer zone (the outer ring). On the right, two photographs of the castle are shown (images by Geociekawostki, distributed in Wikimedia Commons under a CC-BY-SA-3.0-PL license).</p> Full article ">Figure 2
<p>The Royal Castle in Warsaw presented on the historical maps from 1762, 1867, an aerial image from 1945, and an orthophotomap from 2015 (map images distributed by the National Library in Poland under a public domain license; an aerial image and an orthophotomap distributed by the Office of Surveying and Cadastre of Warsaw as public information).</p> Full article ">Figure 3
<p>General design approach for spatiotemporal visualizations in (<b>a</b>) a slider-based visualization; and (<b>b</b>) a space–time cube.</p> Full article ">Figure 4
<p>Interface of the slider-based spatiotemporal visualization of the Royal Castle in Warsaw.</p> Full article ">Figure 5
<p>Interface of the space–time cube for the Royal Castle in Warsaw.</p> Full article ">Figure 6
<p>The overview of the experiment setups with two feedback loops on the presented spatiotemporal visualizations.</p> Full article ">Figure 7
<p>Completion rate for benchmark tasks for tested applications: the slider-based visualization and the space–time cube.</p> Full article ">Figure 8
<p>Solving task time for tested applications: the slider-based visualization and the space–time cube.</p> Full article ">Figure 9
<p>Solving task time to successfully complete the benchmark tasks. The users gave correct answers to the questions.</p> Full article ">Figure 10
<p>Solving task time to complete the benchmark tasks. The users gave false answers to the questions.</p> Full article ">Figure 11
<p>Solving task time grouped by questions and users’ familiarity with spatiotemporal visualization techniques.</p> Full article ">Figure 12
<p>Completion rate for benchmark tasks grouped by questions and users’ familiarity with spatiotemporal visualization techniques.</p> Full article ">Figure 13
<p>Searching strategies for Q1: (<b>a</b>) overview and temporal filter in the slider-based visualization; (<b>b</b>) overview and temporal filter in the space–time cube; (<b>c</b>) zoom and relate operations in the space–time cube; (<b>d</b>) overview and relate operations in the space–time cube.</p> Full article ">Figure 13 Cont.
<p>Searching strategies for Q1: (<b>a</b>) overview and temporal filter in the slider-based visualization; (<b>b</b>) overview and temporal filter in the space–time cube; (<b>c</b>) zoom and relate operations in the space–time cube; (<b>d</b>) overview and relate operations in the space–time cube.</p> Full article ">Figure 14
<p>Searching strategies for Q2: (<b>a</b>) overview, temporal filter, and details-on-demand in the slider-based visualization; (<b>b</b>) overview and details-on-demand in the space–time cube; (<b>c</b>) overview, relate, and details-on-demand operations in the space–time cube; (<b>d</b>) zoom and relate operations in the space–time cube.</p> Full article ">Figure 15
<p>Searching strategies for Q3 in the space–time cube: (<b>a</b>) overview and relate; (<b>b</b>) overview, relate, and details-on-demand.</p> Full article ">Figure 16
<p>Searching strategies for Q3 in the slider-based visualization: (<b>a</b>) overview, temporal filter, and details-on-demand; (<b>b</b>) overview and temporal filter; (<b>c</b>) overview, temporal filter, and details-on-demand; (<b>d</b>) overview and temporal filter with focus on edge time values.</p> Full article ">Figure 17
<p>Searching strategies for Q4: (<b>a</b>) overview and details-on-demand in the slider-based visualization; (<b>b</b>) overview and details-on-demand in the slider-based visualization; (<b>c</b>) zoom and details-on-demand operations in the space–time cube; (<b>d</b>) overview and relate operations in the space–time cube.</p> Full article ">
19 pages, 6914 KiB  
Article
A Moment-Based Shape Similarity Measurement for Areal Entities in Geographical Vector Data
by Zhongliang Fu, Liang Fan, Zhiqiang Yu and Kaichun Zhou
ISPRS Int. J. Geo-Inf. 2018, 7(6), 208; https://doi.org/10.3390/ijgi7060208 - 31 May 2018
Cited by 19 | Viewed by 4510
Abstract
Shape similarity measurement model is often used to solve shape-matching problems in geospatial data matching. It is widely used in geospatial data integration, conflation, updating and quality assessment. Many shape similarity measurements apply only to simple polygons. However, areal entities can be represented [...] Read more.
Shape similarity measurement model is often used to solve shape-matching problems in geospatial data matching. It is widely used in geospatial data integration, conflation, updating and quality assessment. Many shape similarity measurements apply only to simple polygons. However, areal entities can be represented either by simple polygons, holed polygons or multipolygons in geospatial data. This paper proposes a new shape similarity measurement model that can be used for all kinds of polygons. In this method, convex hulls of polygons are used to extract boundary features of entities and local moment invariants are calculated to extract overall shape features of entities. Combined with convex hull and local moment invariants, polygons can be represented by convex hull moment invariant curves. Then, a shape descriptor is obtained by applying fast Fourier transform to convex hull moment invariant curves, and shape similarity between areal entities is measured by the shape descriptor. Through similarity measurement experiments of different lakes in multiple representations and matching experiments between two urban area datasets, results showed that the method could distinguish areal entities even if they are represented by different kinds of polygons. Full article
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<p>Qinghai Lake in multiple representations (<b>a</b>) Simple polygon, (<b>b</b>) holed polygon, (<b>c</b>) multipolygon; and (<b>d</b>) Bing maps.</p> Full article ">Figure 1 Cont.
<p>Qinghai Lake in multiple representations (<b>a</b>) Simple polygon, (<b>b</b>) holed polygon, (<b>c</b>) multipolygon; and (<b>d</b>) Bing maps.</p> Full article ">Figure 2
<p>Same lakes at different scales and convex hulls of the lakes: (<b>A</b>) Polygon A is the lake in scale of 1:10,000, (<b>B</b>) polygon B is the lake in scale of 1:50,000, (<b>C</b>) polygon C is convex hull of polygon A; and (<b>D</b>) polygon D is convex hull of polygon B.</p> Full article ">Figure 3
<p>Different areal entities with similar convex hulls: (<b>a</b>) the canals and (<b>b</b>) the canal with two pools.</p> Full article ">Figure 4
<p>Transformation method of center moments and local moments: (<b>a</b>) Original position of the polygon, (<b>b</b>) moving the origin of coordinates to the centroid of the polygon; and (<b>c</b>) the red polyline is the boundary of convex hull, moving the origin of coordinates to the point on the boundary of convex hull.</p> Full article ">Figure 5
<p>Convex hull moment invariant curves of the polygon in <a href="#ijgi-07-00208-f004" class="html-fig">Figure 4</a>: (<b>a</b>) Convex hull moment invariant curves and (<b>b</b>) centroid distance of convex hull vertices.</p> Full article ">Figure 6
<p>The lake at different scales: (<b>a</b>) the lake in scale of 1:10,000 and (<b>b</b>) the lake in scale of 1:50,000.</p> Full article ">Figure 7
<p>Convex hull moment invariant curves of the lake in scale of 1:10,000.</p> Full article ">Figure 8
<p>Convex hull moment invariant curves of the lake in scale of 1:50,000.</p> Full article ">Figure 9
<p>Shape descriptor of lakes in <a href="#ijgi-07-00208-f004" class="html-fig">Figure 4</a>: (<b>a</b>) Shape descriptor of the lake in scale of 1:10,000 and (<b>b</b>) shape descriptor of the lake in scale of 1:50,000.</p> Full article ">Figure 10
<p>Three countries represented by different types of polygons: (<b>a</b>) Switzerland, (<b>b</b>) South Africa, (<b>c</b>) Japan, (<b>d</b>) Switzerland after transformation, (<b>e</b>) South Africa after transformation; and (<b>f</b>) Japan after transformation.</p> Full article ">Figure 11
<p>Three different lakes represented by nine polygons at multiscales.</p> Full article ">Figure 12
<p>Shape similarity results of three different lakes: (<b>a</b>) shape similarity matrix by Zernike moments, (<b>b</b>) shape similarity matrix by Fourier descriptors of convex hull; and (<b>c</b>) shape similarity matrix by our method.</p> Full article ">Figure 13
<p>Urban area datasets in different scales: (<b>a</b>) dataset A in scale of 1:100,000 and (<b>b</b>) dataset B in scale of 1:500,000.</p> Full article ">Figure 14
<p>F1-score of our method under different value of parameters.</p> Full article ">
14 pages, 3366 KiB  
Article
Mapping Spatiotemporal Patterns and Multi-Perspective Analysis of the Surface Urban Heat Islands across 32 Major Cities in China
by Juan Wang, Bin Meng, Dongjie Fu, Tao Pei and Chengdong Xu
ISPRS Int. J. Geo-Inf. 2018, 7(6), 207; https://doi.org/10.3390/ijgi7060207 - 30 May 2018
Cited by 16 | Viewed by 4370
Abstract
As urban thermal environments are being caused by global climatic changes and urbanization is not uniform on diurnal, seasonal, or annual scales, the spatiotemporal patterns of surface urban heat islands (SUHI) similarly vary between cities across regions. This research assessed the spatiotemporal variations [...] Read more.
As urban thermal environments are being caused by global climatic changes and urbanization is not uniform on diurnal, seasonal, or annual scales, the spatiotemporal patterns of surface urban heat islands (SUHI) similarly vary between cities across regions. This research assessed the spatiotemporal variations in SUHI intensities (SUHII), and then revealed their spatiotemporal patterns and relationships that existed within 32 major cities in China using spatialization technologies, such as the self-organizing map (SOM) method and statistical methods. Results showed that the spatial patterns of the SUHII patterns in China were significantly affected by the climatic types, whereas human heat discharge also disturbed the patterns to a certain extent. Specifically, the daytime SUHIIs in China had much higher seasonal variations in North China than in South China. The nighttime SUHIIs were much weaker and more stable than the daytime SUHIIs, and had far more obvious spatial patterns with much higher values in North China than in South China. As for the temporal regimes, the temporal variation in the SUHIIs in one city was more related to the development of the urbanization. To be specific, not all cities were experiencing increasingly worse urban thermal environments with urbanization as reported by previous studies. This research not only proposes a spatiotemporal framework to study the SUHIIs patterns and their relationships, but also provides an in-depth and comprehensive understanding of SUHIIs in China. Full article
(This article belongs to the Special Issue Urban Environment Mapping Using GIS)
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<p>The study area and the distribution of selected cities in this research.</p> Full article ">Figure 2
<p>The urban and rural areas of Beijing City in 2010.</p> Full article ">Figure 3
<p>Flow chart in this research.</p> Full article ">Figure 4
<p>Seasonal averaged surface urban heat islands intensity (SUHII) values and their variances from 2003 to 2013 for different climate zones. Zone 1: severe cold region, Zone 2: cold region; Zone 3: hot summer cold winter region, Zone 4: temperate region, and Zone 5: hot summer warm winter region.</p> Full article ">Figure 5
<p>Self-organizing map (SOM) clustering results based on the SUHII calculated using the following normalization methods: (<b>a</b>) global; (<b>b</b>) column; and (<b>c</b>) row.</p> Full article ">Figure 6
<p>Spatial patterns of annual averaged daytime SUHII in summer during 2003–2013.</p> Full article ">Figure 7
<p>The change rate, or the linear trend of the seasonal averaged daytime SUHIIs, in summer from 2003 to 2013.</p> Full article ">
17 pages, 6597 KiB  
Article
Hydrological Modeling with Respect to Impact of Land-Use and Land-Cover Change on the Runoff Dynamics in Godavari River Basin Using the HEC-HMS Model
by Sunitha Koneti, Sri Lakshmi Sunkara and Parth Sarathi Roy
ISPRS Int. J. Geo-Inf. 2018, 7(6), 206; https://doi.org/10.3390/ijgi7060206 - 30 May 2018
Cited by 83 | Viewed by 11638
Abstract
Hydrological modeling and the hydrological response to land-use/land-cover changes induced by human activities have gained enormous research interest over the last few decades. The study presented here analyzes the spatial and qualitative changes in the rainfall–runoff that have resulted from the land-cover changes [...] Read more.
Hydrological modeling and the hydrological response to land-use/land-cover changes induced by human activities have gained enormous research interest over the last few decades. The study presented here analyzes the spatial and qualitative changes in the rainfall–runoff that have resulted from the land-cover changes between 1985–2014 in the Godavari River Basin using the Hydrologic Engineering Centre-Hydrologic Modeling System(HEC-HMS) model and remote sensing—GIS (geographic information system) techniques. The purpose of this paper is to analyze the dynamics of land-use/land-cover (LULC) changes for the years 1985, 1995, 2005, and 2014 for the Godavari Basin. The findings reveal an increase of 0.64% of built-up land, a decrease of 0.92% in shrubland, and an increase of 0.56% in waterbodies between 1985–2014. The LULC change detection results between the years 1985–2014 indicated a drastic change in the cropland, forest, built-up land, and water bodies among all of the other classes. The urbanization and agricultural activities are the major reasons for the increase of cropland, built-up land, and water bodies, at the expense of decreases in shrubland and forest. The study had an overall classification accuracy of 92% and an overall Kappa coefficient of 0.9. The HEC-HMS model is used to simulate the hydrology of the Godavari Basin. The analyses carried out were mainly focussed on the impact of LULC changes on the streamflow pattern. The surface runoff was simulated for the year 2014 to quantify the changes that have taken place due to changes in LULC. The observed and the simulated peak streamflow was found to be the same i.e., 56,780 m3/s on 9 September 2014. In the validation part, the linear regression method was used to correlate the observed and simulated streamflow data at the prominent gauge station of the Badrachalam outlet for the Godavari River Basin and give a correlation coefficient value of 0.83. It was found that the HEC-HMS model is compatible and works better for the rainfall–runoff modeling, as it takes into account the various parameters that are influencing the process. The hydrological modeling that was carried out using the HEC-HMS model has brought out the significant impact of LULCC on rainfall–runoff at the Pranhita sub-basinscale, indicating the model’s ability to successfully accommodate all of the environmental and landscape variables. The study indicates that deforestation at the cost of urbanization and cropland expansions leads to decreases in the overall evapotranspiration (ET) and infiltration, with an increase in runoff. The results of the study show that the integration of remote sensing, GIS, and the hydrological model (HEC-HMS) can solve hydrological problems in a river basin. Full article
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<p>Location map of the study area of the Godavari River basin, covering 73°26′ E to 83°07′ E longitudes, and 16°16′ N to 23°43′ N latitudes.</p> Full article ">Figure 2
<p>Annual variations in the rainfall of the Godavari Basin.</p> Full article ">Figure 3
<p>Soil map of the Godavari Basin.</p> Full article ">Figure 4
<p>Land use/land cover (LULC) map of the Godavari basin of the years (<b>a</b>) 1985; (<b>b</b>) 1995; (<b>c</b>) 2005 and (<b>d</b>) 2014.</p> Full article ">Figure 4 Cont.
<p>Land use/land cover (LULC) map of the Godavari basin of the years (<b>a</b>) 1985; (<b>b</b>) 1995; (<b>c</b>) 2005 and (<b>d</b>) 2014.</p> Full article ">Figure 5
<p>Reclassified land-use/land-cover (LULC) map of the Godavari Basin for the year 2014 showing the various classes of built-up land, cropland, fallow land, plantation, forest, shrubland, barrenland, and waterbodies.</p> Full article ">Figure 6
<p>Hydrological soil group map of the study area.</p> Full article ">Figure 7
<p>Curve number map of the study area for the year 2014.</p> Full article ">Figure 8
<p>Percent imperviousness map of the study area.</p> Full article ">Figure 9
<p>Simulated and observed discharge graph.</p> Full article ">Figure 10
<p>Simulated runoff for the years (<b>a</b>) 1985; (<b>b</b>) 1995; (<b>c</b>) 2005; and (<b>d</b>) 2014 for the Pranhita watershed.</p> Full article ">Figure 10 Cont.
<p>Simulated runoff for the years (<b>a</b>) 1985; (<b>b</b>) 1995; (<b>c</b>) 2005; and (<b>d</b>) 2014 for the Pranhita watershed.</p> Full article ">
16 pages, 2806 KiB  
Article
The Implications of Field Worker Characteristics and Landscape Heterogeneity for Classification Correctness and the Completeness of Topographical Mapping
by Kiira Mõisja, Evelyn Uuemaa and Tõnu Oja
ISPRS Int. J. Geo-Inf. 2018, 7(6), 205; https://doi.org/10.3390/ijgi7060205 - 29 May 2018
Cited by 3 | Viewed by 3188
Abstract
The quality of spatial data may vary spatially. If mapping (interpretation of orthophotos) is done during fieldwork, this variation in quality may occur as a result of differences in the complexity of the landscape, differences in the characteristics of individual field workers, and [...] Read more.
The quality of spatial data may vary spatially. If mapping (interpretation of orthophotos) is done during fieldwork, this variation in quality may occur as a result of differences in the complexity of the landscape, differences in the characteristics of individual field workers, and differences in their perception of the landscape. In this study, we explored the interaction between the characteristics of these workers, including their gender and years of experience (as a proxy for their mapping skills), and landscape heterogeneity. There was no significant difference between male and female workers. Although field workers with more years of experience generally had higher mapping quality, the relationship was not statistically significant. We found differences in the rates of misclassification, omission, and commission errors between workers in different landscape types. We conclude that the error rates due to misclassification, omission, and commission were the lowest in more diverse landscapes (high number of different land use types) with a relatively high amount of buildings, whereas the error rates were the highest in mainly forested landscapes with larger and more complex shaped patches. Full article
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<p>Overview of the ISO 19157:2013 data quality elements modified [<a href="#B14-ijgi-07-00205" class="html-bibr">14</a>]. The focus of the present study is highlighted.</p> Full article ">Figure 2
<p>Locations of the sites (red dots) where quality control was performed.</p> Full article ">Figure 3
<p>Plot of the mean factor values of landscape factors and built-up areas for the three landscape clusters (types) and examples of maps for those landscape clusters: (<b>1</b>) built-up-diverse landscape; (<b>2</b>) open–simple landscape; and (<b>3</b>) closed-complex landscape.</p> Full article ">Figure 4
<p>Box plots of the error rates by field workers based on (<b>a</b>) gender (M—male; F—female) and (<b>b</b>) years of experience. For each fieldworker, we calculated the median value across the sites they examined.</p> Full article ">Figure 5
<p>Relationship between the field worker’s (<span class="html-italic">n =</span> 21) years of experience and their median misclassification, commission, and omission (MCO) error rate across sites (all error types summed for one site).</p> Full article ">Figure 6
<p>Box plots for the rates of misclassification, commission, and omission errors in the different landscapes defined in <a href="#ijgi-07-00205-t004" class="html-table">Table 4</a>. For a given error type, based on the Kruskal–Wallis multiple comparison of mean ranks for all groups: 1 = statistically significant difference from built-up-diverse, 2 = statistically significant difference from open-simple, 3 = statistically significant difference from closed-complex.</p> Full article ">Figure 7
<p>Box plots of the summed values of MCO error rates (all three categories combined) by field workers in the three landscape types defined in <a href="#ijgi-07-00205-t004" class="html-table">Table 4</a>. Field workers who mapped all three landscape types are shaded grey.</p> Full article ">
18 pages, 8970 KiB  
Article
An Automated Processing Method for Agglomeration Areas
by Chengming Li, Yong Yin, Xiaoli Liu and Pengda Wu
ISPRS Int. J. Geo-Inf. 2018, 7(6), 204; https://doi.org/10.3390/ijgi7060204 - 29 May 2018
Cited by 13 | Viewed by 3527
Abstract
Agglomeration operations are a core component of the automated generalization of aggregated area groups. However, because geographical elements that possess agglomeration features are relatively scarce, the current literature has not given sufficient attention to agglomeration operations. Furthermore, most reports on the subject are [...] Read more.
Agglomeration operations are a core component of the automated generalization of aggregated area groups. However, because geographical elements that possess agglomeration features are relatively scarce, the current literature has not given sufficient attention to agglomeration operations. Furthermore, most reports on the subject are limited to the general conceptual level. Consequently, current agglomeration methods are highly reliant on subjective determinations and cannot support intelligent computer processing. This paper proposes an automated processing method for agglomeration areas. Firstly, the proposed method automatically identifies agglomeration areas based on the width of the striped bridging area, distribution pattern index (DPI), shape similarity index (SSI), and overlap index (OI). Next, the progressive agglomeration operation is carried out, including the computation of the external boundary outlines and the extraction of agglomeration lines. The effectiveness and rationality of the proposed method has been validated by using actual census data of Chinese geographical conditions in the Jiangsu Province. Full article
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<p>Agglomeration of vector polygons: (<b>a</b>) Original graphic; (<b>b</b>) supplementation of the original graphic; (<b>c</b>) skeleton of the supplementation; and (<b>d</b>) final result.</p> Full article ">Figure 2
<p>Minimum area-bounding rectangle (MABR) of an area group: (<b>a</b>) Original area group and (<b>b</b>) corresponding MABR.</p> Full article ">Figure 3
<p>Framework for the proposed method.</p> Full article ">Figure 4
<p>Boundary of an aggregated area group: (<b>a</b>) Original map and (<b>b</b>) MBR boundary.</p> Full article ">Figure 5
<p>Adding Steiner nodes: (<b>a</b>) Original nodes of the area elements and (<b>b</b>) Steiner nodes.</p> Full article ">Figure 6
<p>Distance calculation: (<b>a</b>) Boundary-constrained Delaunay triangulation and (<b>b</b>) height of a triangle between adjacent elements.</p> Full article ">Figure 7
<p>Internal structure characteristics of adjacent elements: (<b>a</b>) Sum of widths &gt; <span class="html-italic">B<sub>Distance</sub></span> and (<b>b</b>) sum of widths &lt; <span class="html-italic">B<sub>Distance</sub></span>.</p> Full article ">Figure 8
<p>Calculating the average width of an area element: (<b>a</b>) Area element and (<b>b</b>) baseline of an area element.</p> Full article ">Figure 9
<p>Adjacency of adjacent area elements: (<b>a</b>) Side-adjacency along longer edges and (<b>b</b>) side-adjacency along shorter edges.</p> Full article ">Figure 10
<p>Calculation for the overlap degree index (<span class="html-italic">OI</span>): (<b>a</b>) 0≤ <span class="html-italic">OI</span> ≤ 1 and (<b>b</b>) <span class="html-italic">OI =1.</span></p> Full article ">Figure 11
<p>Three situations where OI = 0: (<b>a</b>) Parallel and non-overlapping; (<b>b</b>) orthogonality and (<b>c</b>) collineation.</p> Full article ">Figure 12
<p>Flow chart of the progressive agglomeration operation.</p> Full article ">Figure 13
<p>Dilation and erosion transformations: (<b>a</b>) Original map; (<b>b</b>) dilation transformation and (<b>c</b>) erosion transformation.</p> Full article ">Figure 14
<p>Restoration of concave areas: (<b>a</b>) Concave area based on semantic topology and (<b>b</b>) peripheral boundary contour of an agglomeration area.</p> Full article ">Figure 15
<p>Issues arising from the use of the main skeleton line as the agglomeration line: (<b>a</b>) Fluctuations in the junction and (<b>b</b>) inaccurate extraction of terminal nodes.</p> Full article ">Figure 16
<p>Adjustment of the agglomeration lines: (<b>a</b>) Adjustment of the fluctuations in the junction and (<b>b</b>) adjustment of terminal nodes.</p> Full article ">Figure 17
<p>Progressive method for agglomeration. (<b>a</b>) 1st stage: Compute the bridging areas; (<b>b</b>) 1st stage: Generate the agglomeration lines; (<b>c</b>) 2nd stage: Compute the bridging areas; (<b>d</b>) 2nd stage: Generate the agglomeration lines and (<b>e</b>) final result of the agglomeration.</p> Full article ">Figure 18
<p>Identification and processing of the agglomeration areas: (<b>a</b>) Original pond data; (<b>b</b>) candidate agglomeration area and (<b>c</b>) first-stage agglomeration areas.</p> Full article ">Figure 19
<p>The agglomeration process: (<b>a</b>) Computation of the peripheral boundary contour, (<b>b</b>) extraction of the bridging area skeleton, (<b>c</b>) partial enlarged detail, <b>(d)</b> correction of the main skeleton and (<b>e</b>) result of the agglomeration.</p> Full article ">Figure 20
<p>Experimental results: (<b>a</b>) Second-stage agglomeration areas and (<b>b</b>) overall results for the agglomeration of the experimental area.</p> Full article ">
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