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Issue 5
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ISPRS Int. J. Geo-Inf., Volume 8, Issue 5 (May 2019) – 43 articles

Cover Story (view full-size image): This paper employs an agent-based modeling approach to capture the spatial decisions of private land developers in shaping new urban forms. By drawing on microeconomic theory, the model simulates urban growth in the Jakarta Metropolitan Area, Indonesia, under different scenarios that reflect the decision behaviors of developers. Three types of private land developers were identified based on their level of available capital and the interactions between developers and land characteristics defined through four land development stages; search, assess, acquire, and develop. The findings show that new urban areas are generated through different processes. Our study highlights the need for urban policy to regulate urban expansion. View this paper.
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26 pages, 655 KiB  
Article
Shared Data Sources in the Geographical Domain—A Classification Schema and Corresponding Visualization Techniques
by Franz-Benjamin Mocnik, Christina Ludwig, A. Yair Grinberger, Clemens Jacobs, Carolin Klonner and Martin Raifer
ISPRS Int. J. Geo-Inf. 2019, 8(5), 242; https://doi.org/10.3390/ijgi8050242 - 27 May 2019
Cited by 10 | Viewed by 5387
Abstract
People share data in different ways. Many of them contribute on a voluntary basis, while others are unaware of their contribution. They have differing intentions, collaborate in different ways, and they contribute data about differing aspects. Shared Data Sources have been explored individually [...] Read more.
People share data in different ways. Many of them contribute on a voluntary basis, while others are unaware of their contribution. They have differing intentions, collaborate in different ways, and they contribute data about differing aspects. Shared Data Sources have been explored individually in the literature, in particular OpenStreetMap and Twitter, and some types of Shared Data Sources have widely been studied, such as Volunteered Geographic Information (VGI), Ambient Geographic Information (AGI), and Public Participation Geographic Information Systems (PPGIS). A thorough and systematic discussion of Shared Data Sources in their entirety is, however, still missing. For the purpose of establishing such a discussion, we introduce in this article a schema consisting of a number of dimensions for characterizing socially produced, maintained, and used ‘Shared Data Sources,’ as well as corresponding visualization techniques. Both the schema and the visualization techniques allow for a common characterization in order to set individual data sources into context and to identify clusters of Shared Data Sources with common characteristics. Among others, this makes possible choosing suitable Shared Data Sources for a given task and gaining an understanding of how to interpret them by drawing parallels between several Shared Data Sources. Full article
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<p>Conceptualization of Shared Data Sources (SDSs) by contributors, data and information, consumers, the organizational structure, and organizers. A contributor might also be a consumer or an organizer. Flow of data is depicted by solid arrows while the influence of the organizers on the entire process is depicted by a dashed arrow.</p> Full article ">Figure 2
<p>Trilinear graph of Geographical Shared Data Sources (GSDSs), the ‘Triangle of Shared Data Sources’. The graph (<b>b</b>) depicts a rescaled version of (<b>a</b>), in which the prototypes are represented at the corners of the triangle.</p> Full article ">Figure 3
<p>Parallel coordinates graph of Geographical Shared Data Sources (GSDSs). The legend for the categories can be found in <a href="#ijgi-08-00242-f002" class="html-fig">Figure 2</a>. The data has been slightly randomized.</p> Full article ">Figure 4
<p>Spider chart of Geographical Shared Data Sources (GSDSs), showing the different dimensions in detail. The data has been slightly randomized. The legend for the categories can be found in <a href="#ijgi-08-00242-f002" class="html-fig">Figure 2</a>.</p> Full article ">Figure 5
<p>Individual spider charts of Geographical Shared Data Sources (GSDSs). The individual depiction of each GSDS allows for a grouping by categories.</p> Full article ">Figure 6
<p>Correlation matrix of the dimensions for Geographical Shared Data Sources (SDSs), based on Kendall’s rank correlation coefficient.</p> Full article ">Figure 7
<p>Scatter plots for selected pairs of dimensions for Geographical Shared Data Sources (GSDSs). The legend for the categories can be found in <a href="#ijgi-08-00242-f002" class="html-fig">Figure 2</a>. The data has been normalized and slightly randomized.</p> Full article ">Figure 8
<p>Hierarchical clustering of Geographical Shared Data Sources (GSDSs) by characterization through the dimensions. The legend for the categories can be found in <a href="#ijgi-08-00242-f002" class="html-fig">Figure 2</a>. The data has been normalized.</p> Full article ">Figure 9
<p>Hierarchical clustering of Geographical Shared Data Sources (GSDSs) by similarity to prototypes, using four different distance measures. For Kendall’s rank correlation coefficient, high values indicate high similarity, for the other three measures, low similarity. The colour scale was set individually for each plot using the respective minimum and maximum value. The legend for the categories can be found in <a href="#ijgi-08-00242-f002" class="html-fig">Figure 2</a>.</p> Full article ">Figure 10
<p>Hierarchical clustering of Geographical Shared Data Sources (GSDSs) by similarity to prototypes, only considering dimensions related to the organizer, using the cosine similarity. The legend for the categories can be found in <a href="#ijgi-08-00242-f002" class="html-fig">Figure 2</a>.</p> Full article ">
23 pages, 14902 KiB  
Article
Spatiotemporal Pattern Analysis of China’s Cities Based on High-Resolution Imagery from 2000 to 2015
by Hanchao Zhang, Xiaogang Ning, Zhenfeng Shao and Hao Wang
ISPRS Int. J. Geo-Inf. 2019, 8(5), 241; https://doi.org/10.3390/ijgi8050241 - 22 May 2019
Cited by 27 | Viewed by 3785
Abstract
The urbanization level in China has increased rapidly since beginning of the 21st century, and the monitoring and analysis of urban expansion has become a popular topic in geoscience applications. However, problems, such as inconsistent concepts and extraction standards, low precision, and poor [...] Read more.
The urbanization level in China has increased rapidly since beginning of the 21st century, and the monitoring and analysis of urban expansion has become a popular topic in geoscience applications. However, problems, such as inconsistent concepts and extraction standards, low precision, and poor comparability, existing in urban monitoring may lead to wrong conclusions. This study selects 337 cities at the prefecture level and above in China as research subjects and uses high-resolution images and geographic information data in a semi-automatic extraction method to identify urban areas in 2000, 2005, 2010, and 2015. City size distribution patterns, urban expansion regional characteristics, and expansion types are analyzed. Results show that Chinese cities maintained a high-speed growth trend from 2000 to 2015, with the total area increasing by 115.79%. The overall scale of a city continues to expand, and the system becomes increasingly complex. The urban system is more balanced than the ideal Zipf distribution, but it also exhibited different characteristics in 2005. Urban areas are mostly concentrated in the eastern and central regions, and the difference between the east and the west is considerable. However, cities in the western region continuously expand. Beijing, Shanghai, Tianjin, and Guangzhou are the four largest cities in China. Approximately 73.30% of the cities are expanding in an extended manner; the urban form tends to be scattered, and land use efficiency is low. The new urban areas mainly come from cultivated land and ecological land. Full article
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<p>Distribution of 337 cities and four regions across the country.</p> Full article ">Figure 2
<p>Flowchart of the analysis of China’s urban expansion characteristics.</p> Full article ">Figure 3
<p>Examples of urban feature interpretation symbol: (<b>a</b>) an example of high-rise buildings in urban areas; (<b>b</b>) an example of low-rise buildings in urban area; (<b>c</b>) an example of roads in urban area; (<b>d</b>) an example of green space near the edge of urban area; (<b>e</b>) an example of urban water, namely an artificial lake, in urban area; (<b>f</b>) an example of structure, namely a stadium, in urban area; (<b>g</b>) an example of urban village; (<b>h</b>) an example of enclave, namely an industrial and mining area, of urban area.</p> Full article ">Figure 4
<p>Examples for urban expansion types.</p> Full article ">Figure 5
<p>The comparison of extraction results of urban areas. Product C is the result of proposed method.</p> Full article ">Figure 6
<p>Comparison examples for urban boundaries of A, B and C in 2010—Beijing, Chongqing.</p> Full article ">Figure 7
<p>Cities’ rank-size plots of China for 2000, 2005, 2010, 2015. The abscissa (x) is lg(R), where R stands for the city ranks of top200. The abscissa (x) is lg(R), where R stands for the city rank of top200. The vertical coordinates (y) is lg(S), where S stands for city size, namely urban area.</p> Full article ">Figure 7 Cont.
<p>Cities’ rank-size plots of China for 2000, 2005, 2010, 2015. The abscissa (x) is lg(R), where R stands for the city ranks of top200. The abscissa (x) is lg(R), where R stands for the city rank of top200. The vertical coordinates (y) is lg(S), where S stands for city size, namely urban area.</p> Full article ">Figure 8
<p>Change of Zipf Index and structural capacity in China’s cities system from 2000–2015.</p> Full article ">Figure 9
<p>Spatiotemporal distribution map of urban expansion of China’s cities in 2000–2015. Urban area and expansion area unit: km<sup>2</sup>, Expansion speed unit: km<sup>2</sup> per year.</p> Full article ">Figure 10
<p>Expansion types of capital cities in China from 2000 to 2015.</p> Full article ">Figure 11
<p>Proportion of land use occupied by urban sprawl areas of China’s cities 2000–2015.</p> Full article ">
23 pages, 4691 KiB  
Article
A Comparison Between Major Artificial Intelligence Models for Crop Yield Prediction: Case Study of the Midwestern United States, 2006–2015
by Nari Kim, Kyung-Ja Ha, No-Wook Park, Jaeil Cho, Sungwook Hong and Yang-Won Lee
ISPRS Int. J. Geo-Inf. 2019, 8(5), 240; https://doi.org/10.3390/ijgi8050240 - 21 May 2019
Cited by 107 | Viewed by 9272
Abstract
This paper compares different artificial intelligence (AI) models in order to develop the best crop yield prediction model for the Midwestern United States (US). Through experiments to examine the effects of phenology using three different periods, we selected the July–August (JA) database as [...] Read more.
This paper compares different artificial intelligence (AI) models in order to develop the best crop yield prediction model for the Midwestern United States (US). Through experiments to examine the effects of phenology using three different periods, we selected the July–August (JA) database as the best months to predict corn and soybean yields. Six different AI models for crop yield prediction are tested in this research. Then, a comprehensive and objective comparison is conducted between the AI models. Particularly for the deep neural network (DNN) model, we performed an optimization process to ensure the best configurations for the layer structure, cost function, optimizer, activation function, and drop-out ratio. In terms of mean absolute error (MAE), our DNN model with the JA database was approximately 21–33% and 17–22% more accurate for corn and soybean yields, respectively, than the other five AI models. This indicates that corn and soybean yields for a given year can be forecasted in advance, at the beginning of September, approximately a month or more ahead of harvesting time. A combination of the optimized DNN model and spatial statistical methods should be investigated in future work, to mitigate partly clustered errors in some regions. Full article
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<p>Study area.</p> Full article ">Figure 2
<p>Process for constructing the matchup database for (<b>a</b>) corn and (<b>b</b>) soybean, based on the Cropland Data Layer (CDL).</p> Full article ">Figure 3
<p>Parameter optimization process for a deep neural network (DNN) model in this study.</p> Full article ">Figure 4
<p>The results of the optimized deep neural network (DNN) model: root mean square error (RMSE) and mean absolute percentage error (MAPE) according to the (<b>a</b>) hidden layer structure, (<b>b</b>) loss function, (<b>c</b>) optimizer, (<b>d</b>) activation function, and (<b>e</b>) drop-out ratio.</p> Full article ">Figure 5
<p>Actual versus predicted corn yields from 10 rounds of experiments for July–August (JA) (major productive period), 2006–2015: (<b>a</b>) multivariate adaptive regression splines (MARS), (<b>b</b>) support vector machine (SVM), (<b>c</b>) random forest (RF), (<b>d</b>) extremely randomized trees (ERT), (<b>e</b>) artificial neural network (ANN), and (<b>f</b>) deep neural network (DNN).</p> Full article ">Figure 6
<p>Actual versus predicted soybean yields from 10 rounds of experiments for July–August (JA) (major productive period), 2006–2015: (<b>a</b>) multivariate adaptive regression splines (MARS), (<b>b</b>) support vector machine (SVM), (<b>c</b>) random forest (RF), (<b>d</b>) extremely randomized trees (ERT), (<b>e</b>) artificial neural network (ANN), and (<b>f</b>) deep neural network (DNN).</p> Full article ">Figure 7
<p>Maps of the actual and predicted corn yields, prediction errors, and Local Geary index (Gi*) of the prediction errors, 2006–2010.</p> Full article ">Figure 8
<p>Maps of the actual and predicted corn yields, prediction errors, and Local Geary index (Gi*) of the prediction errors, 2011–2015.</p> Full article ">Figure 9
<p>Maps of the actual and predicted soybean yields, prediction errors, and Local Geary index (Gi*) of the prediction errors, 2006–2010.</p> Full article ">Figure 10
<p>Maps of the actual and predicted soybean yields, prediction errors, and Local Geary index (Gi*) of the prediction errors, 2011–2015.</p> Full article ">
15 pages, 8484 KiB  
Article
Heuristic Bike Optimization Algorithm to Improve Usage Efficiency of the Station-Free Bike Sharing System in Shenzhen, China
by Zhihui Gu, Yong Zhu, Yan Zhang, Wanyu Zhou and Yu Chen
ISPRS Int. J. Geo-Inf. 2019, 8(5), 239; https://doi.org/10.3390/ijgi8050239 - 21 May 2019
Cited by 13 | Viewed by 4573
Abstract
Station-free bike sharing systems (BSSs) are a new type of public bike system that has been widely deployed in China since 2017. However, rapid growth has vastly outpaced the immediate demand and overwhelmed many cities around the world. This paper proposes a heuristic [...] Read more.
Station-free bike sharing systems (BSSs) are a new type of public bike system that has been widely deployed in China since 2017. However, rapid growth has vastly outpaced the immediate demand and overwhelmed many cities around the world. This paper proposes a heuristic bike optimization algorithm (HBOA) to determine the optimal supply and distribution of bikes considering the effect of bicycle cycling. In this approach, the different bike trips with separate bikes can be connected in space and time and converted into a continuous trip chain for a single bike. To improve this cycling efficiency, it is important to properly design the bicycle distribution. Taking Shenzhen as an example, we implement the algorithm with OD matrix data from Mobike and Ofo, the two large bike sharing companies which account for 80% of the shared bike market in Shenzhen, over two days. The HBOA results are as follows. 1) Only one-fifth of the bike supply is needed to meet the current usage demand if the bikes are used efficiently, which means a large number of shared bikes in Shenzhen remain in an idle state for long periods. 2) Although the cycling demand is high in many areas, it does not mean that large numbers of bikes are needed because the continuous inflow caused by the cycling effect of bikes will meet most of the demand by itself. 3) The areas with the highest demands for optimal bikes are residential, followed by industrial, public transportation, official and commercial areas, on both working and non-working days. This algorithm can be an objective basis for city related departments to manage station-free BSSs and be applied to design the layout of bikes in small-scale spatial units to help station-free BSSs operate efficiently and minimize the need to relocate the bikes without reducing the level of user satisfaction. Full article
(This article belongs to the Special Issue Spatial Big Data, BIM and advanced GIS for Smart Transformation: City, Infrastructure and Construction)
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<p>Bike movement and stocks in different scenarios: (1) only one bike at site A and (2) two bikes at each site.</p> Full article ">Figure 2
<p>The calculation process of the HBOA.</p> Full article ">Figure 3
<p>The transportation and building information in Shenzhen.</p> Full article ">Figure 4
<p>Difference between the actual number of available bikes and the number of optimized bikes in two days.</p> Full article ">Figure 5
<p>Cycling-in and cycling-out bikes and stock changes around the Houhai metro station every 10 min.</p> Full article ">Figure 6
<p>Spatial requirements of cycling and the spatial distribution of optimized bikes.</p> Full article ">Figure 7
<p>Built environments in six central areas with high requirement or supply spaces for shared bikes.</p> Full article ">Figure 8
<p>The nearby spatial and temporal characteristics of the first trip for all optimized bikes.</p> Full article ">Figure 9
<p>The spatial distribution of optimized bikes near public transportation facilities.</p> Full article ">Figure 10
<p>Spatial distribution of optimized bikes near different buildings.</p> Full article ">
20 pages, 7090 KiB  
Article
Extracting Main Center Pattern from Road Networks Using Density-Based Clustering with Fuzzy Neighborhood
by Xiaojie Cui, Jiayao Wang, Fang Wu, Jinghan Li, Xianyong Gong, Yao Zhao and Ruoxin Zhu
ISPRS Int. J. Geo-Inf. 2019, 8(5), 238; https://doi.org/10.3390/ijgi8050238 - 21 May 2019
Cited by 6 | Viewed by 3408
Abstract
The spatial pattern is a kind of typical structural knowledge that reflects the distribution characteristics of object groups. As an important semantic pattern of road networks, the city center is significant to urban analysis, cartographic generalization and spatial data matching. Previous studies mainly [...] Read more.
The spatial pattern is a kind of typical structural knowledge that reflects the distribution characteristics of object groups. As an important semantic pattern of road networks, the city center is significant to urban analysis, cartographic generalization and spatial data matching. Previous studies mainly focus on the topological centrality calculation of road network graphs, and pay less attention to the delineation of main centers. Therefore, this study proposes an automatic recognition method of main center pattern in road networks. We firstly extract the main clusters from road nodes by improving the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) with fuzzy set theory. Moreover, the center area is generated with road meshes according to the area ratio with the covering discs of the main clusters. This proposed algorithm is applied to the road networks of a monocentric city and polycentric city respectively. The results show that our method is effective for identifying the main center pattern in the road networks. Furthermore, the contrast experiments demonstrate our method’s higher accuracy. Full article
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<p>Schematic diagram of the main center pattern recognition.</p> Full article ">Figure 2
<p>(<b>a</b>) Principle of the Density-Based Spatial Clustering of Applications with Noise (DBSCAN); (<b>b</b>) density calculation for the core points; (<b>c</b>) two clusters share a common point.</p> Full article ">Figure 3
<p>Membership functions: (<b>a</b>) binary type; (<b>b</b>) exponential type.</p> Full article ">Figure 4
<p>The calculation example of <span class="html-italic">Eps</span>.</p> Full article ">Figure 5
<p>Simulated data and contrast results.</p> Full article ">Figure 5 Cont.
<p>Simulated data and contrast results.</p> Full article ">Figure 6
<p>(<b>a</b>) Covering discs (CD) boundaries of the clusters; (<b>b</b>) main center areas.</p> Full article ">Figure 7
<p>The study area and road networks in Xi’an.</p> Full article ">Figure 8
<p>(<b>a</b>) Main cluster of the road nodes in Xi’an; (<b>b</b>) main center pattern in Xi’an.</p> Full article ">Figure 8 Cont.
<p>(<b>a</b>) Main cluster of the road nodes in Xi’an; (<b>b</b>) main center pattern in Xi’an.</p> Full article ">Figure 9
<p>Analysis of the recognized center pattern in Xi’an.</p> Full article ">Figure 10
<p>The study area and road networks in Shenzhen.</p> Full article ">Figure 11
<p>(<b>a</b>) Main clusters of the road nodes in Shenzhen; (<b>b</b>) main center patterns in Shenzhen.</p> Full article ">Figure 11 Cont.
<p>(<b>a</b>) Main clusters of the road nodes in Shenzhen; (<b>b</b>) main center patterns in Shenzhen.</p> Full article ">Figure 12
<p>Analysis of the recognized center patterns in Shenzhen.</p> Full article ">Figure 13
<p>(<b>a</b>) Results of kernel density estimation of road nodes in Xi’an; (<b>b</b>) center areas by the contrast method in Xi’an.</p> Full article ">Figure 14
<p>(<b>a</b>) Results of kernel density estimation of road nodes in Shenzhen; (<b>b</b>) center areas by the contrast method in Shenzhen.</p> Full article ">Figure 15
<p>(<b>a</b>) The overlay diagram of the recognized center by contrast method and the reference center; (<b>b</b>) the overlay diagram of the recognized center by proposed method and the reference center.</p> Full article ">
18 pages, 2065 KiB  
Article
A Hybrid of Differential Evolution and Genetic Algorithm for the Multiple Geographical Feature Label Placement Problem
by Fuyu Lu, Jiqiu Deng, Shiyu Li and Hao Deng
ISPRS Int. J. Geo-Inf. 2019, 8(5), 237; https://doi.org/10.3390/ijgi8050237 - 21 May 2019
Cited by 11 | Viewed by 5338
Abstract
Label placement is a difficult problem in automated map production. Many methods have been proposed to automatically place labels for various types of maps. While the methods are designed to automatically and effectively generate labels for the point, line and area features, less [...] Read more.
Label placement is a difficult problem in automated map production. Many methods have been proposed to automatically place labels for various types of maps. While the methods are designed to automatically and effectively generate labels for the point, line and area features, less attention has been paid to the problem of jointly labeling all the different types of geographical features. In this paper, we refer to the labeling of all the graphic features as the multiple geographical feature label placement (MGFLP) problem. In the MGFLP problem, the overlapping and occlusion among labels and corresponding features produces poorly arranged labels, and results in a low-quality map. To solve the problem, a hybrid algorithm combining discrete differential evolution and the genetic algorithm (DDEGA) is proposed to search for an optimized placement that resolves the MGFLP problem. The quality of the proposed solution was evaluated using a weighted metric regarding a number of cartographical rules. Experiments were carried out to validate the performance of the proposed method in a set of cartographic tasks. The resulting label placement demonstrates the feasibility and the effectiveness of our method. Full article
(This article belongs to the Special Issue Smart Cartography for Big Data Solutions)
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<p>(<b>a</b>) A candidate-position model of a point feature; (<b>b</b>) a candidate-position model of a line feature; and (<b>c</b>) a candidate-position model of an area feature.</p> Full article ">Figure 2
<p>Flowchart for the DDEGA.</p> Full article ">Figure 3
<p>The crossover and mutation operator of GA: (<b>a</b>) crossover; and (<b>b</b>) mutation.</p> Full article ">Figure 4
<p>The progression of label placement with the DDEGA algorithm: (<b>a</b>) the initial random labeling of a generated map; (<b>b</b>) the map with label placement after 50 iterations of the algorithm; and (<b>c</b>) the final labeling of the map.</p> Full article ">Figure 4 Cont.
<p>The progression of label placement with the DDEGA algorithm: (<b>a</b>) the initial random labeling of a generated map; (<b>b</b>) the map with label placement after 50 iterations of the algorithm; and (<b>c</b>) the final labeling of the map.</p> Full article ">Figure 5
<p>The map of Washington State with labels generated by four approaches: (<b>a</b>) GA; (<b>b</b>) DDE; (<b>c</b>) DDEGA; and (<b>d</b>) Maplex.</p> Full article ">Figure 5 Cont.
<p>The map of Washington State with labels generated by four approaches: (<b>a</b>) GA; (<b>b</b>) DDE; (<b>c</b>) DDEGA; and (<b>d</b>) Maplex.</p> Full article ">Figure 6
<p>Map of near Buena Vista Park in San Francisco with labels generated by four approaches: (<b>a</b>) GA; (<b>b</b>) DDE; (<b>c</b>) DDEGA; and (<b>d</b>) Maplex.</p> Full article ">Figure 6 Cont.
<p>Map of near Buena Vista Park in San Francisco with labels generated by four approaches: (<b>a</b>) GA; (<b>b</b>) DDE; (<b>c</b>) DDEGA; and (<b>d</b>) Maplex.</p> Full article ">Figure 7
<p>(<b>a</b>) Convergence of each algorithm for the first case study; and (<b>b</b>) convergence of each algorithm for the second case study.</p> Full article ">
14 pages, 3360 KiB  
Article
Multi-Mode Two-Step Floating Catchment Area (2SFCA) Method to Measure the Potential Spatial Accessibility of Healthcare Services
by Jianhua Ni, Ming Liang, Yan Lin, Yanlan Wu and Chen Wang
ISPRS Int. J. Geo-Inf. 2019, 8(5), 236; https://doi.org/10.3390/ijgi8050236 - 21 May 2019
Cited by 32 | Viewed by 6278
Abstract
While great progress in the development of a methodological approach to measure the accessibility of healthcare services has been made, the exclusion of the complex multi-mode travel behavior of urban residents and a rough calculation of travel costs from the origin to the [...] Read more.
While great progress in the development of a methodological approach to measure the accessibility of healthcare services has been made, the exclusion of the complex multi-mode travel behavior of urban residents and a rough calculation of travel costs from the origin to the destination limit its potential for making a detailed assessment, especially in urban areas. In this paper, we aim to describe and implement an enhanced method that enables the integration of multiple transportation modes into a two-step floating catchment area (2SFCA) method to estimate accessibility. We used a travel-mode choice survey, based on distance sections, to determine the complex multi-mode travel behavior of urban residents. Taking Nanjing as a study area, we proposed complete door-to-door approaches to determine every aspect of basic transportation modes. Additionally, we processed open data to implement an accurate computing of the origin-destination (OD) time cost. We applied the enhanced method to estimate the accessibility of residents to hospitals and compared it with three single-mode 2SFCA methods. The results showed that the proposed method effectively identified more accessibility details and provided more realistic accessibility values. Full article
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<p>Door-to-door approaches of the four basic transportation models.</p> Full article ">Figure 2
<p>Healthcare service facilities in the study area.</p> Full article ">Figure 3
<p>Population in the study area.</p> Full article ">Figure 4
<p>The calculating procedures of the OD travel cost by Baidu map application program interfaces (APIs).</p> Full article ">Figure 5
<p>Spatial distribution of accessibility using 4 methods.</p> Full article ">Figure 6
<p>Accessibility comparison of the DGa2SFCA method and the MGa2SFCA method.</p> Full article ">Figure 7
<p>Accessibility comparison of the PGa2SFCA method and the MGa2SFCA method.</p> Full article ">Figure 8
<p>Accessibility comparison of the SGa2SFCA method and the MGa2SFCA method.</p> Full article ">
22 pages, 12149 KiB  
Article
Analyzing Newspaper Maps for Earthquake News through Cartographic Approach
by Pınar Sarın and Necla Uluğtekin
ISPRS Int. J. Geo-Inf. 2019, 8(5), 235; https://doi.org/10.3390/ijgi8050235 - 21 May 2019
Cited by 3 | Viewed by 4149
Abstract
This study focuses on newspaper maps, which have an important role in conveying spatial information to newspaper readers. Maps and map-like items in the main Turkish newspapers within a certain period were evaluated in regard to the scope of the study. A database [...] Read more.
This study focuses on newspaper maps, which have an important role in conveying spatial information to newspaper readers. Maps and map-like items in the main Turkish newspapers within a certain period were evaluated in regard to the scope of the study. A database was constructed to organize the collected data and conduct the analysis. In addition to cartographic and thematic analyses, the database allows “georeferencing” to be conducted as well. However, the current study focused on the cartographic and thematic properties of these maps. Their deficiencies were identified from a cartographic perspective and with that, the parts of newspapers that maps are mostly included in were investigated, and we aimed to identify the topics and events that increase map usage in newspapers. For this purpose, maps of earthquake-related news were evaluated as a case study to show some spatial and thematic determinations. Thus, the contribution of newspapers to spatial thinking abilities and geographic knowledge of the readers was evaluated by cartographers. The study proves the importance of cartography in spreading knowledge through maps in newspapers. This opens up new possibilities for future studies to develop a different cartographic perspective on map usage and improve the geographic knowledge of newspaper readers. Full article
(This article belongs to the Special Issue Human-Centered Geovisual Analytics and Visuospatial Display Design)
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<p>Syllabus of principle of the database.</p> Full article ">Figure 2
<p>An example of maps (1999_08_18).</p> Full article ">Figure 3
<p>An example of map-like (1992_03_02).</p> Full article ">Figure 4
<p>Number of map and map-like items on newspapers.</p> Full article ">Figure 5
<p>Number of sub-titles of map-like items.</p> Full article ">Figure 6
<p>Number of thematic maps and satellite images of newspapers.</p> Full article ">Figure 7
<p>Number of scale parameters on newspapers.</p> Full article ">Figure 8
<p>Number of used map and map-like items in sections of newspapers.</p> Full article ">Figure 9
<p>Main topics of reports using map and map-like items.</p> Full article ">Figure 10
<p>Distribution of maps in earthquake-related news.</p> Full article ">Figure 11
<p>Affected industrial complex in the region (1999_08_24 in B).</p> Full article ">Figure 12
<p>Usage of thematic map types in earthquake-related news.</p> Full article ">Figure 13
<p>Usage of scale parameters by newspapers.</p> Full article ">Figure 14
<p>Example of uninformative map (1999_11_19 in B).</p> Full article ">Figure 15
<p>Example of uninformative map (1995_05_30 in A).</p> Full article ">Figure 16
<p>In addition, right legend uses (1999_11_16 in B).</p> Full article ">Figure 17
<p>Example of uninformative map (1999_09_02 in B).</p> Full article ">Figure 18
<p>Illegible text design in maps (1991_11_12 in A).</p> Full article ">Figure 19
<p>Misinformative example (1996_08_10 in B).</p> Full article ">Figure 20
<p>Speculative example given title of ‘Scary Anticipation’ (1997_08_19 in B).</p> Full article ">
21 pages, 12379 KiB  
Article
Anisotropic Diffusion for Improved Crime Prediction in Urban China
by Yicheng Tang, Xinyan Zhu, Wei Guo, Ling Wu and Yaxin Fan
ISPRS Int. J. Geo-Inf. 2019, 8(5), 234; https://doi.org/10.3390/ijgi8050234 - 20 May 2019
Cited by 7 | Viewed by 4470
Abstract
As a major social issue during urban development, crime is closely related to socioeconomic, geographic, and environmental factors. Traditional crime prediction models reveal the spatiotemporal dynamics of crime risks, but usually ignore the environmental context of the geographic areas where crimes occur. Therefore, [...] Read more.
As a major social issue during urban development, crime is closely related to socioeconomic, geographic, and environmental factors. Traditional crime prediction models reveal the spatiotemporal dynamics of crime risks, but usually ignore the environmental context of the geographic areas where crimes occur. Therefore, it is difficult to enhance the spatial accuracy of crime prediction. We propose the use of anisotropic diffusion to include environmental factors of the evaluated geographic area in the traditional crime prediction model, thereby aiming to predict crime occurrence at a finer scale regarding spatiotemporal aspects and environmental similarity. Under different evaluation criteria, the average prediction accuracy of the proposed method is 28.8%, improving prediction accuracy by 77.5%, as compared to the traditional methods. The proposed method can provide strong policing support in terms of conducting targeted hotspot policing and fostering sustainable community development. Full article
(This article belongs to the Special Issue Urban Crime Mapping and Analysis Using GIS)
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<p>Satellite image of study area.</p> Full article ">Figure 2
<p>Building boundary (spatial constraints) and burglary distribution considered in this study.</p> Full article ">Figure 3
<p>Household density.</p> Full article ">Figure 4
<p>Environmental similarity of buildings.</p> Full article ">Figure 5
<p>Changes of environmental similarity according to parameter <span class="html-italic">h.</span></p> Full article ">Figure 6
<p>Anisotropic and isotropic diffusion of crime risk in study area.</p> Full article ">Figure 7
<p>Cumulative crime risk over time.</p> Full article ">Figure 8
<p>Flowchart of crime prediction using diffusion and environmental factors.</p> Full article ">Figure 9
<p>Spatial KDE (first column), IsotDM (second column) and AnisDM (third column).</p> Full article ">Figure 10
<p>Prediction accuracy of IsotDM and AnisDM.</p> Full article ">Figure 11
<p>Effectiveness of AnisDM for crime prediction over time.</p> Full article ">Figure 12
<p>Monthly hit rate of IsotDM and AnisDM.</p> Full article ">Figure 13
<p>Monthly prediction accuracy index (PAI) of IsotDM and AnisDM.</p> Full article ">Figure 14
<p>Sensitivity of AnisDM to similarity smoothness parameter <math display="inline"><semantics> <mrow> <mi>h</mi> </mrow> </semantics></math>.</p> Full article ">Figure 15
<p>Sensitivity of AnisDM to parameter <math display="inline"><semantics> <mi>η</mi> </semantics></math> that weighs location and environmental factors</p> Full article ">
18 pages, 9463 KiB  
Article
Obstacle-Aware Indoor Pathfinding Using Point Clouds
by Lucía Díaz-Vilariño, Pawel Boguslawski, Kourosh Khoshelham and Henrique Lorenzo
ISPRS Int. J. Geo-Inf. 2019, 8(5), 233; https://doi.org/10.3390/ijgi8050233 - 19 May 2019
Cited by 17 | Viewed by 5479
Abstract
With the rise of urban population, updated spatial information of indoor environments is needed in a growing number of applications. Navigational assistance for disabled or aged people, guidance for robots, augmented reality for gaming, and tourism or training emergency assistance units are just [...] Read more.
With the rise of urban population, updated spatial information of indoor environments is needed in a growing number of applications. Navigational assistance for disabled or aged people, guidance for robots, augmented reality for gaming, and tourism or training emergency assistance units are just a few examples of the emerging applications requiring real three-dimensional (3D) spatial data of indoor scenes. This work proposes the use of point clouds for obstacle-aware indoor pathfinding. Point clouds are firstly used for reconstructing semantically rich 3D models of building structural elements in order to extract initial navigational information. Potential obstacles to navigation are classified in the point cloud and directly used to correct the path according to the mobility skills of different users. The methodology is tested in several real case studies for wheelchair and ordinary users. Experiments show that, after several iterations, paths are readapted to avoid obstacles. Full article
(This article belongs to the Special Issue Multidimensional and Multiscale GIS)
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<p>Workflow of the building envelope reconstruction.</p> Full article ">Figure 2
<p>Workflow of the opening detection process.</p> Full article ">Figure 3
<p>(<b>a</b>) Point cloud; (<b>b</b>) point cloud projected into a rectangular matrix; (<b>c</b>) binarized image; (<b>d</b>) edge image (adapted from [<a href="#B35-ijgi-08-00233" class="html-bibr">35</a>]).</p> Full article ">Figure 4
<p>Schema of the obstacle detection strategy (adapted from [<a href="#B35-ijgi-08-00233" class="html-bibr">35</a>]).</p> Full article ">Figure 5
<p>Pathfinding procedure: (<b>a</b>,<b>c</b>) navigable networks (solid lines) with obstacle areas (dashed polygons); (<b>b</b>,<b>d</b>) paths (thick solid lines) calculated in consecutive iterations.</p> Full article ">Figure 6
<p>Three-dimensional view of the point clouds used as case study 1 and case study 2: (<b>a</b>) videoconference room; scale bar of 4.5 m; (<b>b</b>) office; scale bar of 3.0 m. The ceiling is removed in all views to facilitate visualization.</p> Full article ">Figure 7
<p>Three-dimensional view of the point clouds used as (<b>a</b>) case study 3 (scale bar of 6.0 m), and (<b>b</b>) case study 4 (scale bar of 10.0 m). The ceiling is removed in all views to facilitate visualization.</p> Full article ">Figure 8
<p>Obstacle class obtained after segmentation for case study 3 (<b>a</b>) and case study 4 (<b>b</b>).</p> Full article ">Figure 9
<p>Results of window (<b>a</b>) and door (<b>b</b>) detection in case study 1 (videoconference room). From top to bottom: raster image, raster after median filtering, and edge image with detected openings in green.</p> Full article ">Figure 10
<p>The gbXML models for (<b>a</b>) case study 1 and (<b>b</b>) case study 2. The ceiling was given transparency to improve visualization.</p> Full article ">Figure 11
<p>The gbXML models for (<b>a</b>) case study 3 and (<b>b</b>) case study 4. The ceiling was given transparency to improve visualization.</p> Full article ">Figure 12
<p>Buffer size for (<b>a</b>) walking people and (<b>b</b>) people on wheelchairs.</p> Full article ">Figure 13
<p>A schema of the indoor scenes in which doors are represented in green, main pieces of furniture in blue, and indoor positions selected as origin nodes in red: (<b>a</b>) case studies 1, 2, and 3; (<b>b</b>) case study 4.</p> Full article ">Figure 14
<p>The resulting indoor paths are represented in green for the tests carried out inside the videoconference room (“Node A to closest door” and “Node B to closest door”) (units in meters).</p> Full article ">Figure 15
<p>The resulting paths for “Node C to office door” and “Node D to office door” are represented (units in meters).</p> Full article ">Figure 16
<p>“Node D to closest exterior door” and “Node D to Node E” tests are represented. In these cases, no obstacles were found in the first indoor path; thus, no iterations were needed (units in meters).</p> Full article ">Figure 17
<p>“Node F to Node G” and “Node H to Node I” tests are represented. While, in the first case, no obstacles were found, in the last case, the destination could not be reached due to the presence of people in the corridor detected as obstacles (units in meters).</p> Full article ">Figure 18
<p>Zoomed view of the office room during the “Node C to office door” test for the wheelchair buffer (<b>a</b>) and during the “Node H to Node I” for walking people (<b>b</b>). In both cases, the destination could not be reached due to the presence of static and dynamic obstacles, respectively.</p> Full article ">
18 pages, 2812 KiB  
Article
Corporate Editors in the Evolving Landscape of OpenStreetMap
by Jennings Anderson, Dipto Sarkar and Leysia Palen
ISPRS Int. J. Geo-Inf. 2019, 8(5), 232; https://doi.org/10.3390/ijgi8050232 - 18 May 2019
Cited by 62 | Viewed by 32632
Abstract
OpenStreetMap (OSM), the largest Volunteered Geographic Information project in the world, is characterized both by its map as well as the active community of the millions of mappers who produce it. The discourse about participation in the OSM community largely focuses on the [...] Read more.
OpenStreetMap (OSM), the largest Volunteered Geographic Information project in the world, is characterized both by its map as well as the active community of the millions of mappers who produce it. The discourse about participation in the OSM community largely focuses on the motivations for why members contribute map data and the resulting data quality. Recently, large corporations including Apple, Microsoft, and Facebook have been hiring editors to contribute to the OSM database. In this article, we explore the influence these corporate editors are having on the map by first considering the history of corporate involvement in the community and then analyzing historical quarterly-snapshot OSM-QA-Tiles to show where and what these corporate editors are mapping. Cumulatively, millions of corporate edits have a global footprint, but corporations vary in geographic reach, edit types, and quantity. While corporations currently have a major impact on road networks, non-corporate mappers edit more buildings and points-of-interest: representing the majority of all edits, on average. Since corporate editing represents the latest stage in the evolution of corporate involvement, we raise questions about how the OSM community—and researchers—might proceed as corporate editing grows and evolves as a mechanism for expanding the map for multiple uses. Full article
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Full article ">Figure 1
<p>OpenStreetMap Contributors: Over 1 million users have made at least 1 change to the map. Far fewer contributors have contributed more than 10, 100, or 1000 times. Results calculated from an OSM changeset database, created from the OSM changeset files by the open source tool: <a href="http://github.com/toebee/changesetmd" target="_blank">github.com/toebee/changesetmd</a>.</p> Full article ">Figure 2
<p>(<b>a</b>) The top 1% of users are responsible for 87% of all the changes to the map; (<b>b</b>) OSM adheres to the 1% rule: a very small percentage of the editing community contributes the majority of the data. Results calculated from the OSM changeset database described in <a href="#ijgi-08-00232-f001" class="html-fig">Figure 1</a>.</p> Full article ">Figure 3
<p>Where corporate editors are editing. The main map shows an aggregated view for all 10 companies. The sub figures show where each company is editing. In this map, we have combined the Mapbox and Development Seed teams because they merged in late 2017.</p> Full article ">Figure 3 Cont.
<p>Where corporate editors are editing. The main map shows an aggregated view for all 10 companies. The sub figures show where each company is editing. In this map, we have combined the Mapbox and Development Seed teams because they merged in late 2017.</p> Full article ">Figure 4
<p>Each figure shows the types of edit these companies performing, relative to the total editing activity where they are active. These are annual averages over all of the zoom level 12 map tiles where a company is active. “Features” refers to editing any feature (all types of edits). The final figure (j) represents the activity of non-corporate editors in areas where (any) corporate-editors are active.</p> Full article ">Figure 5
<p>Characteristics of Corporate editors: (<b>a</b>) The rate of growth of all OSM editors compared to corporate editors. The solid lines represent number of contributors denoted by the day of their first edit. The dotted lines represent the number of users denoted by the day of their last edit. The shaded area between the solid lines and the dotted lines could be thought of as the relative size of the “active” community. These two lines converge at the end because those are the most recent edits in our data. The steep slope in the corporate-editors dotted line shows that these editors have been active recently (not one-time contributors); (<b>b</b>) Edits per day by the Facebook team in 2018. Consistent activity throughout the year showing 52 weeks of relatively consistent work five days of the week, with no editing on weekends. This pattern of consistent weekday editing is present across all of the data teams we have examined.</p> Full article ">
23 pages, 17141 KiB  
Article
Combining Water Fraction and DEM-Based Methods to Create a Coastal Flood Map: A Case Study of Hurricane Harvey
by Xiaoxuan Li, Anthony R. Cummings, Ali Rashed Alruzuq, Corene J. Matyas and Amobichukwu Chukwudi Amanambu
ISPRS Int. J. Geo-Inf. 2019, 8(5), 231; https://doi.org/10.3390/ijgi8050231 - 18 May 2019
Cited by 5 | Viewed by 4257
Abstract
Tropical cyclones are incredibly destructive and deadly, inflicting immense losses to coastal properties and infrastructure. Hurricane-induced coastal floods are often the biggest threat to life and the coastal environment. A quick and accurate estimation of coastal flood extent is urgently required for disaster [...] Read more.
Tropical cyclones are incredibly destructive and deadly, inflicting immense losses to coastal properties and infrastructure. Hurricane-induced coastal floods are often the biggest threat to life and the coastal environment. A quick and accurate estimation of coastal flood extent is urgently required for disaster rescue and emergency response. In this study, a combined Digital Elevation Model (DEM) based water fraction (DWF) method was implemented to simulate coastal floods during Hurricane Harvey on the South Texas coast. Water fraction values were calculated to create a 15 km flood map from multiple channels of the Advanced Technology Microwave Sound dataset. Based on hydrological inundation mechanism and topographic information, the coarse-resolution flood map derived from water fraction values was then downscaled to a high spatial resolution of 10 m. To evaluate the DWF result, Storm Surge Hindcast product and flood-reported high-water-mark observations were used. The results indicated a high overlapping area between the DWF map and buffered flood-reported high-water-marks (HWMs), with a percentage of more than 85%. Furthermore, the correlation coefficient between the DWF map and CERA SSH product was 0.91, which demonstrates a strong linear relationship between these two maps. The DWF model has a promising capacity to create high-resolution flood maps over large areas that can aid in emergency response. The result generated here can also be useful for flood risk management, especially through risk communication. Full article
(This article belongs to the Special Issue Advances in Satellite-Based Mapping and Monitoring of Natural Disasters)
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<p>Water fraction calculation procedure. <math display="inline"><semantics> <mrow> <mo>Δ</mo> <mi>T</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>N</mi> <mrow> <mo>Δ</mo> <mi>T</mi> </mrow> </msub> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msub> <mi>T</mi> <mrow> <mi>a</mi> <mi>v</mi> <mi>g</mi> </mrow> </msub> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msub> <mi>T</mi> <mi>w</mi> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msub> <mi>T</mi> <mi>l</mi> </msub> </mrow> </semantics></math> represents <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msub> <mi>T</mi> <mrow> <msub> <mrow> <mrow> <mo>(</mo> <mrow> <mn>4</mn> <mo>−</mo> <mn>3</mn> </mrow> <mo>)</mo> </mrow> </mrow> <mrow> <mi>m</mi> <mi>i</mi> <mi>x</mi> </mrow> </msub> </mrow> </msub> </mrow> </semantics></math>, number of neighborhood pixels, <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msub> <mi>T</mi> <mrow> <msub> <mrow> <mrow> <mo>(</mo> <mrow> <mn>4</mn> <mo>−</mo> <mn>3</mn> </mrow> <mo>)</mo> </mrow> </mrow> <mrow> <mi>a</mi> <mi>v</mi> <mi>g</mi> <mo>_</mo> <mi>m</mi> <mi>i</mi> <mi>x</mi> </mrow> </msub> </mrow> </msub> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msub> <mi>T</mi> <mrow> <msub> <mrow> <mrow> <mo>(</mo> <mrow> <mn>4</mn> <mo>−</mo> <mn>3</mn> </mrow> <mo>)</mo> </mrow> </mrow> <mi>w</mi> </msub> </mrow> </msub> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mo>Δ</mo> <msub> <mi>T</mi> <mrow> <msub> <mrow> <mrow> <mo>(</mo> <mrow> <mn>4</mn> <mo>−</mo> <mn>3</mn> </mrow> <mo>)</mo> </mrow> </mrow> <mi>l</mi> </msub> </mrow> </msub> </mrow> </semantics></math>, respectively.</p> Full article ">Figure 2
<p>Hurricane Harvey’s movement within the study area. SSHS represents Saffir–Simpson hurricane wind scales introduced by Taylor et al. [<a href="#B59-ijgi-08-00231" class="html-bibr">59</a>]. The study area (black polygon) consists of a series of hydrological units, at sub-watershed level, used to estimate flood extents. The curved cyan line represents the best tracking path of the hurricane and the proportional dot symbols with labels indicate the specific hurricane intensity across the time it was present on the Texas coast.</p> Full article ">Figure 3
<p>(<b>a</b>–<b>e</b>) shows temperature (BT) values of channel 3–4 and 16 of land samples and water samples collected from 18–19, 23, &amp; 30 August 2017 and 3 September 2017 ATMS data. Ch3W (Ch3L), Ch4W (Ch4L) and Ch16W (Ch16L) represent water (land) samples’ brightness temperature values of channel 3–4 and channel 16 in ATMS data, respectively. Land samples have similar BT values of channel 3–4 and 16 while water samples’ BT values are more fluctuant. Overall, all land samples have higher BT values than water samples.</p> Full article ">Figure 4
<p>(<b>a</b>–<b>e</b>) show temperature (BT) differences between channel 4 and 3 of land samples and water samples collected from 18–19, 23, &amp; 30 August 2018 and 3 September 2018 ATMS data. Ch (4-3) W and Ch (4-3)L represent water and land samples’ brightness temperature differences between channel 4 and 3 in ATMS data. It is obvious that the water samples have larger BT differences compared with land samples.</p> Full article ">Figure 5
<p>WF map before (<b>a</b>) and after (<b>b</b>) Hurricane Harvey.</p> Full article ">Figure 5 Cont.
<p>WF map before (<b>a</b>) and after (<b>b</b>) Hurricane Harvey.</p> Full article ">Figure 6
<p>Water Fraction (WF) difference before and after Hurricane Harvey.</p> Full article ">Figure 7
<p>DEM-based water fraction (DWF) map developed from WF map using SRTM DEM data.</p> Full article ">Figure 8
<p>15 × 15 km comparison grid (black), DWF map (red), CREA SSH product (blue) and overlapping areas (yellow).</p> Full article ">Figure 9
<p>Linear model of DWF map and CERA SSH product.</p> Full article ">Figure 10
<p>DWF map, CERA SSH product, and high-water-marks (HWMs).</p> Full article ">
25 pages, 1849 KiB  
Article
Recommendation of Heterogeneous Cultural Heritage Objects for the Promotion of Tourism
by Landy Rajaonarivo, André Fonteles, Christian Sallaberry, Marie-Noëlle Bessagnet, Philippe Roose, Patrick Etcheverry, Christophe Marquesuzaà, Annig Le Parc Lacayrelle, Cécile Cayèré and Quentin Coudert
ISPRS Int. J. Geo-Inf. 2019, 8(5), 230; https://doi.org/10.3390/ijgi8050230 - 17 May 2019
Cited by 8 | Viewed by 4214
Abstract
The cultural heritage of a region, be it a highly visited one or not, is a formidable asset for the promotion of its tourism. In many places around the world, an important part of this cultural heritage has been catalogued by initiatives backed [...] Read more.
The cultural heritage of a region, be it a highly visited one or not, is a formidable asset for the promotion of its tourism. In many places around the world, an important part of this cultural heritage has been catalogued by initiatives backed by governments and organisations. However, as of today, most of this data has been mostly unknown, or of difficult access, to the general public. In this paper, we present research that aims to leverage this data to promote tourism. Our first field of application focuses on the French Pyrenees. In order to achieve our goal, we worked on two fronts: (i) the ability to export this data from their original databases and data models to well-known open data platforms; and (ii) the proposition of an open-source algorithm and framework capable of recommending a sequence of cultural heritage points of interests (POIs) to be visited by tourists. This itinerary recommendation approach is original in many aspects: it not only considers the user preferences and popularity of POIs, but it also integrates different contextual information about the user as well as the relevance of specific sequences of POIs (strong links between POIs). The ability to export the cultural heritage data as open data and to recommend sequences of POIs are being integrated in a first prototype. Full article
(This article belongs to the Special Issue Open Science in the Geospatial Domain)
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<p>Overview of the architecture of our proposed solution.</p> Full article ">Figure 2
<p>Unified cultural heritage point of interest (POI) data model.</p> Full article ">Figure 3
<p>XML export file sample.</p> Full article ">Figure 4
<p>Data merge process.</p> Full article ">Figure 5
<p>Cultural heritage data dissemination through open data platforms.</p> Full article ">Figure 6
<p>An example of a cultural heritage object (POI) exported to Wikipedia.</p> Full article ">Figure 7
<p>The Tourist Application.</p> Full article ">Figure 8
<p>User model.</p> Full article ">Figure 9
<p>Context model.</p> Full article ">Figure 10
<p>Itinerary model.</p> Full article ">Figure 11
<p>Input and output of the scoring function.</p> Full article ">Figure 12
<p>Travel time graph.</p> Full article ">Figure 13
<p>Social graph.</p> Full article ">Figure 14
<p>H1 itinerary.</p> Full article ">Figure 15
<p>H2 itinerary.</p> Full article ">
19 pages, 10591 KiB  
Article
Generating Different Urban Land Configurations Based on Heterogeneous Decisions of Private Land Developers: An Agent-Based Approach in a Developing Country Context
by Agung Wahyudi, Yan Liu and Jonathan Corcoran
ISPRS Int. J. Geo-Inf. 2019, 8(5), 229; https://doi.org/10.3390/ijgi8050229 - 16 May 2019
Cited by 16 | Viewed by 4357
Abstract
In the provision of urban residential areas, private land developers play critical roles in nearly all stages of the land development process. Despite their important role little is known about how the spatial decisions of individual developers collectively influence urban growth. This paper [...] Read more.
In the provision of urban residential areas, private land developers play critical roles in nearly all stages of the land development process. Despite their important role little is known about how the spatial decisions of individual developers collectively influence urban growth. This paper employs an agent-based modelling approach to capture the spatial decisions of private land developers in shaping new urban forms. By drawing on microeconomic theory, the model simulates urban growth in the Jakarta Metropolitan Area, Indonesia, under different scenarios that reflect the decision behaviours of different types of developers. Results reveal that larger developers favour sites that are more proximate to the city centre whilst smaller developers prefer sites that are located further away from the city, that drive a more sprawled urban form. Our findings show that new urban areas are generated by different developers through different processes. The profit maximisation behaviour by developers with large capital reserves is more predictable than those with small capital funds. The imbalance in capital holdings by different types of developers interacts with one another to exert adverse impacts on the urban development process. Our study provides supporting evidence highlighting the need for urban policy to regulate urban expansion and achieve more sustainable urban development outcomes in a developing world context. Full article
(This article belongs to the Special Issue Urban Growth Modelling in an Era of Live Data Sets for Dynamic Space/Time Analysis and Simulation)
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<p>The Jakarta Metropolitan Area in Indonesia.</p> Full article ">Figure 2
<p>Conceptual framework of agent-based modelling (ABM) with multiple types of developers in the urban development process.</p> Full article ">Figure 3
<p>The rate of land value change as a distance decay function from a new urban area.</p> Full article ">Figure 4
<p>New urban areas at t = 216 months (or 18 years) generated from the ABM after 100 simulations under different scenarios. The first and second radial lines represent 25 km and 50 km distance to Jakarta’s CBD, respectively.</p> Full article ">Figure 5
<p>Landscape matrices under different scenarios.</p> Full article ">Figure 6
<p>The increase in average land values at t = 216 months (or 18 years) in new urban areas, generated from 100 simulations of the ABM under each scenario. The first and second radial lines represent 25 km and 50 km distance to Jakarta’s CBD, respectively.</p> Full article ">Figure 7
<p>New urban areas; number of developers and profit increase from 100 simulations under five scenarios.</p> Full article ">
14 pages, 7294 KiB  
Article
A Robust Early Warning System for Preventing Flash Floods in Mountainous Area in Vietnam
by Thanh Van Hoang, Tien Yin Chou, Ngoc Thach Nguyen, Yao Min Fang, Mei Ling Yeh, Quoc Huy Nguyen and Xuan Linh Nguyen
ISPRS Int. J. Geo-Inf. 2019, 8(5), 228; https://doi.org/10.3390/ijgi8050228 - 10 May 2019
Cited by 8 | Viewed by 6471
Abstract
The early-warning model for flash floods is based on a hydrological and geomorphological concept connected to the river basin, with the principle that flash floods will only occur where there is a high potential risk and when rainfall exceeds the threshold. In the [...] Read more.
The early-warning model for flash floods is based on a hydrological and geomorphological concept connected to the river basin, with the principle that flash floods will only occur where there is a high potential risk and when rainfall exceeds the threshold. In the model used to build flash-floods risk maps, the parameters of the basin are analyzed and evaluated and the weight is determined using Thomas Saaty’s analytic hierarchy process (AHP). The flash-floods early-warning software is built using open source programming tools. With the spatial module and online processing, a predicted precipitation of one to six days in advance for iMETOS (AgriMedia—Vietnam) automatic meteorological stations is interpolated and then processed with the potential risk maps (iMETOS is a weather-environment monitoring system comprising a wide range of equipment and an online platform and can be used in various fields such as agriculture, tourism and services). The results determine the locations of flash floods at several risk levels corresponding to the predicted rainfall values at the meteorological stations. The system was constructed and applied to flash floods disaster early warning for Thuan Chau in Son La province when the rainfall exceeded the 150 mm/d threshold. The system initially supported positive decision-making to prevent and minimize damage caused by flash floods. Full article
(This article belongs to the Special Issue Natural Hazards and Geospatial Information)
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<p>(<b>a</b>) Position of Thuan Chau district and (<b>b</b>) digital elevation model (DEM).</p> Full article ">Figure 2
<p>(<b>a</b>) Model of information processing and integration, (<b>b</b>) Workflow of the processing server for early flash flood warning.</p> Full article ">Figure 3
<p>Evaluation of flood risk information layers in the Thuan Chau district.</p> Full article ">Figure 4
<p>(<b>a</b>) Flash flood risk map and (<b>b</b>) flash flood risk information (FFPI) for each pixel.</p> Full article ">Figure 5
<p>Comparison of forecast results with historical floods.</p> Full article ">Figure 6
<p>Receiver operating characteristic (ROC) curve constructed from the historical flash flood screening system compared with the Thuan Chau flood risk map. AUC: area under curve.</p> Full article ">Figure 7
<p>Results of flash-flood forecast processing by rainfall in Thuan Chau district: (<b>a</b>) Forecast rainfall map, (<b>b</b>) flash-flood risk map and (<b>c</b>) early flash-flood forecast map by the forecast rainfall, with a flash flood generation threshold of 150 mm/d [<a href="#B13-ijgi-08-00228" class="html-bibr">13</a>].</p> Full article ">Figure 8
<p>Generalized model and key features of flash flood warning software.</p> Full article ">Figure 9
<p>Main interface of the software (<b>a</b>) and the risk-warning website (<b>b</b>).</p> Full article ">Figure 9 Cont.
<p>Main interface of the software (<b>a</b>) and the risk-warning website (<b>b</b>).</p> Full article ">
18 pages, 4736 KiB  
Article
Prototype of the 3D Cadastral System Based on a NoSQL Database and a JavaScript Visualization Application
by Nenad Višnjevac, Rajica Mihajlović, Mladen Šoškić, Željko Cvijetinović and Branislav Bajat
ISPRS Int. J. Geo-Inf. 2019, 8(5), 227; https://doi.org/10.3390/ijgi8050227 - 10 May 2019
Cited by 29 | Viewed by 5641
Abstract
3D cadastral systems are more complex than traditional cadastral systems and they require more complex technical solutions and innovative use of developing technologies. Regarding data integrity and data consistency, 3D cadastral data should be maintained by a Database Management System (DBMS). Furthermore, there [...] Read more.
3D cadastral systems are more complex than traditional cadastral systems and they require more complex technical solutions and innovative use of developing technologies. Regarding data integrity and data consistency, 3D cadastral data should be maintained by a Database Management System (DBMS). Furthermore, there are still challenges regarding visualization of 3D cadastral data. A prototype of the 3D cadastral system based on a NoSQL database and a JavaScript application for 3D visualization is designed and tested in order to investigate the possibilities of using new technical solutions. It is assumed that this approach, with further development, could be a good basis for the development of a modern 3D cadastral system. MongoDB database is used for storing data and Cesium JavaScript library is used for 3D visualization. The system uses an LADM (Land Administration Domain Model) based data model. Additionally, script languages, libraries, application programming interfaces (APIs), software and data formats are used for the system development. The case study is based on the real cadastral data. The underground object and building units located below and above the ground level are used to test the proposed data model and the system’s functionality. The proposed system needs further development in order to provide full support to a modern 3D cadastral system. However, it allows maintenance of 3D cadastral data and basic 3D visualization with the interactive approach. Full article
(This article belongs to the Special Issue Applications of GIScience for Land Administration)
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<p>Architecture of the 3D cadastral system based on the MongoDB database and Cesium library.</p> Full article ">Figure 2
<p>The main classes of the 3D real estate cadastre data model.</p> Full article ">Figure 3
<p>The spatial component of the data model.</p> Full article ">Figure 4
<p>Geometry visualized by Cesium library.</p> Full article ">Figure 5
<p>Example of the floor plan used for creating 3D cadastral data.</p> Full article ">Figure 6
<p>Study building.</p> Full article ">Figure 7
<p>Study underground shelter.</p> Full article ">Figure 8
<p>User interface of the 3D cadastre application (available online: <a href="http://osgl.grf.bg.ac.rs/3dcad/" target="_blank">http://osgl.grf.bg.ac.rs/3dcad/</a>).</p> Full article ">Figure 9
<p>The underground shelter viewed above and below the surface of the terrain.</p> Full article ">Figure 10
<p>Visualization by using the floor mode.</p> Full article ">Figure 11
<p>The results of different queries.</p> Full article ">
12 pages, 3502 KiB  
Article
GIS Mapping of Driving Behavior Based on Naturalistic Driving Data
by José Balsa-Barreiro, Pedro M. Valero-Mora, José L. Berné-Valero and Fco-Alberto Varela-García
ISPRS Int. J. Geo-Inf. 2019, 8(5), 226; https://doi.org/10.3390/ijgi8050226 - 9 May 2019
Cited by 29 | Viewed by 8840
Abstract
Naturalistic driving can generate huge datasets with great potential for research. However, to analyze the collected data in naturalistic driving trials is quite complex and difficult, especially if we consider that these studies are commonly conducted by research groups with somewhat limited resources. [...] Read more.
Naturalistic driving can generate huge datasets with great potential for research. However, to analyze the collected data in naturalistic driving trials is quite complex and difficult, especially if we consider that these studies are commonly conducted by research groups with somewhat limited resources. It is quite common that these studies implement strategies for thinning and/or reducing the data volumes that have been initially collected. Thus, and unfortunately, the great potential of these datasets is significantly constrained to specific situations, events, and contexts. For this, to implement appropriate strategies for the visualization of these data is becoming increasingly necessary, at any scale. Mapping naturalistic driving data with Geographic Information Systems (GIS) allows for a deeper understanding of our driving behavior, achieving a smarter and broader perspective of the whole datasets. GIS mapping allows for many of the existing drawbacks of the traditional methodologies for the analysis of naturalistic driving data to be overcome. In this article, we analyze which are the main assets related to GIS mapping of such data. These assets are dominated by the powerful interface graphics and the great operational capacity of GIS software. Full article
(This article belongs to the Special Issue Smart Cartography for Big Data Solutions)
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<p>Route between the city of Valencia (start point) and Puzol (end point). The data plotted in the following figures correspond to the road sections shown in rectangles A and B.</p> Full article ">Figure 2
<p>Point array distributed along the road path: (<b>a</b>) Actual, and (<b>b</b>) theoretical distribution.</p> Full article ">Figure 3
<p>Methodology for the representation of kinematic driving parameters using attributes based on (<b>a</b>) geometries and (<b>b</b>) colors.</p> Full article ">Figure 4
<p>Speed of one driver in study area A. Different mapping results using geometric and color attributes.</p> Full article ">Figure 5
<p>Kinematic driving parameters related to the performance of one subject in study area B. Different mapping results using geometric and color attributes.</p> Full article ">
18 pages, 9315 KiB  
Article
A Convenient Tool for District Heating Route Optimization Based on Parallel Ant Colony System Algorithm and 3D WebGIS
by Yang Zhang, Guoyong Zhang, Huihui Zhao, Yuming Cao, Qinhuo Liu, Zhanfeng Shen and Aimin Li
ISPRS Int. J. Geo-Inf. 2019, 8(5), 225; https://doi.org/10.3390/ijgi8050225 - 9 May 2019
Cited by 5 | Viewed by 3669
Abstract
In a district heating engineering project, the design of the heating route is an indispensable but laborious process. This paper proposes a planning indicator to measure the suitability of a candidate heating route, and provides an intelligent method and a convenient tool for [...] Read more.
In a district heating engineering project, the design of the heating route is an indispensable but laborious process. This paper proposes a planning indicator to measure the suitability of a candidate heating route, and provides an intelligent method and a convenient tool for the preliminary design of the district heating route. The Fengrun heating engineering project was chosen as a case study. The remote sensing imagery and OpenStreetMap were used as the data sources. First, the remote sensing imagery was classified into five classes and converted into binary images. Second, the district heating route planning indicator was defined based on the cost function. The cost function and the updating strategy of the ant colony system algorithm were modified according to the heating route selection requirement. Additionally, the parallel computing technology was adopted to improve the efficiency. With the help of the open source Cesium engine and the three-dimensional (3D) WebGIS technology, an interactive route design platform that combined our algorithm was finally provided. The optimum routes by the platform were compared to the corresponding sequential algorithm, the route selected manually, as well as the commercial ArcGIS platform. The proposed algorithm can get 28 candidate routes with better indicator values than the manually selected route. Compared to the corresponding sequential algorithm, our algorithm improved the efficiency by 4.789 times. The proposed 3D WebGIS tool is more applicable and user-friendly for the heating route design. Full article
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<p>The location of the heating engineering project.</p> Full article ">Figure 2
<p>Classification data of the study area. (<b>a</b>) The downloaded data from OSM; (<b>b</b>) The reprocessed classification maps.</p> Full article ">Figure 3
<p>The distribution of roads and nodes.</p> Full article ">Figure 4
<p>Classification maps of the study area. (<b>a</b>) Binary image of roads; (<b>b</b>) Binary image of buildings; (<b>c</b>) Binary image of water; (<b>d</b>) Binary image of vegetation; (<b>e</b>) Binary image of bare land; (<b>f</b>) Distribution of nodes.</p> Full article ">Figure 5
<p>Flowchart of the parallel ant colony system (ACS) algorithm.</p> Full article ">Figure 6
<p>The interactive design interface by assigning the mandatory passed nodes (red points).</p> Full article ">Figure 7
<p>Scheme comparison interface with alternative routes of the three sub zones (green ones for the first zone, yellow ones for the second zone, and blue ones for the third zone).</p> Full article ">Figure 8
<p>District heating (DH) routes by manual design and the algorithm (cost function value sorted from the smallest to the biggest). (<b>a</b>) The manually designed route; (<b>b</b>) The route with minimum cost function value; (<b>c</b>) The 28th route; (<b>d</b>) The 29th route.</p> Full article ">Figure 9
<p>The cost function value variation from thefirst iteration to the 100th iteration for the 10 repetitions.</p> Full article ">Figure 10
<p>The best route solved by ArcGIS.</p> Full article ">Figure 11
<p>Best route solved by the interactive design platform (red line).</p> Full article ">
18 pages, 10321 KiB  
Article
Top-Bounded Spaces Formed by the Built Environment for Navigation Systems
by Jinjin Yan, Abdoulaye A. Diakité, Sisi Zlatanova and Mitko Aleksandrov
ISPRS Int. J. Geo-Inf. 2019, 8(5), 224; https://doi.org/10.3390/ijgi8050224 - 9 May 2019
Cited by 14 | Viewed by 5139
Abstract
Navigation systems help agents find the right (optimal) path from the origin to the desired destination. Current navigation systems mainly offer the shortest (distance or time) path as the default optimal path. However, under certain circumstances, having a least-top-exposed path can be more [...] Read more.
Navigation systems help agents find the right (optimal) path from the origin to the desired destination. Current navigation systems mainly offer the shortest (distance or time) path as the default optimal path. However, under certain circumstances, having a least-top-exposed path can be more interesting. For instance, on a rainy day, a path with as many places as possible covered by roofs/shelters is more attractive and pragmatic, since roofs/shelters can offer protection from rain. In this paper, we name environments that covered by roofs/shelters but not completely enclosed like indoors as “top-bounded environments/spaces” (e.g., porches), which are generally formed by built structures. This kind of space is completely missing in current navigation models and systems. Thus, we investigate how to use it for space-based navigation. After proposing a definition, a space model, and attributes of top-bounded spaces, we introduce a projection-based approach to generate them. Then, taking a pedestrian as an example agent, we select generated spaces considering whether the agent can visit/use the identified spaces. Finally, examples and a use case study demonstrate that our research can help to include top-bounded spaces in navigation systems/models. More navigation path types (e.g., least-top-exposed) can be offered for different agents (such as pedestrians, drones or robots). Full article
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<p>Examples of top-bounded environments (spaces) formed by built structures. The images in (<b>b</b>,<b>f</b>) come from ArchiExpo (<a href="http://trends.archiexpo.com/mmcite-1-as/project-63740-227548.html" target="_blank">http://trends.archiexpo.com/mmcite-1-as/project-63740-227548.html</a>) and MIT24H (<a href="http://mit24h.com/1yDw077_8377cv0/" target="_blank">http://mit24h.com/1yDw077_8377cv0/</a>), respectively.</p> Full article ">Figure 2
<p>UML diagram of top-bounded space model.</p> Full article ">Figure 3
<p>Two examples of top-bounded spaces. The photographs of the bus stand and the gazebo come from Kuchingtransit (<a href="http://kuchingtransit.blogspot.com/2013/01/l" target="_blank">http://kuchingtransit.blogspot.com/2013/01/l</a>) and Forest (<a href="http://www.forestgarden.co.uk/shop_family.asp?category=Features%20and%20Structures&amp;subcategory=Gazebos" target="_blank">http://www.forestgarden.co.uk/shop_family.asp?category=Features%20and%20Structures&amp;subcategory=Gazebos</a>), respectively.</p> Full article ">Figure 4
<p>Four different cases of top-bounded space creation based on projecting. For projections of upper and lower polygons, (<b>a</b>) they are equal; (<b>b</b>) the former is a subset of the latter; (<b>c</b>) the latter is a subset of the former; (<b>d</b>) they have overlaps but not exactly the same.</p> Full article ">Figure 5
<p>Example of trimming spaces: (<b>a</b>) the three building components; (<b>b</b>–<b>d</b>) top-bounded spaces created based on projections; (<b>e</b>) the space trimmed by the other two polygons; and (<b>f</b>) the final three created top-bounded spaces.</p> Full article ">Figure 6
<p>The dimensions of required space for a pedestrian. <math display="inline"><semantics> <mrow> <msup> <mi>l</mi> <mo>′</mo> </msup> <mo>,</mo> <msup> <mi>w</mi> <mo>′</mo> </msup> <mo>,</mo> <msup> <mi>h</mi> <mo>′</mo> </msup> </mrow> </semantics></math> describe the size of required space for an agent whose size is <math display="inline"><semantics> <mrow> <mi>l</mi> <mo>,</mo> <mi>w</mi> <mo>,</mo> <mi>h</mi> </mrow> </semantics></math>. <math display="inline"><semantics> <msub> <mi>l</mi> <mi>b</mi> </msub> </semantics></math> is extra space, and <math display="inline"><semantics> <msub> <mi>l</mi> <mi>c</mi> </msub> </semantics></math> is the comfort distance. For the width, the parameters are <math display="inline"><semantics> <msub> <mi>w</mi> <mi>b</mi> </msub> </semantics></math> and <math display="inline"><semantics> <msub> <mi>w</mi> <mi>c</mi> </msub> </semantics></math>, but the figures are side views, thus none of them are illustrated. Along the length direction, (<b>a</b>) 0 side case; (<b>b</b>) 1 side case; (<b>c</b>) 2 sides case.</p> Full article ">Figure 7
<p>Space selection based on the size. (<b>a</b>) the dimension of required space for a pedestrian; Light blue parts are physical boundaries, while olive green areas are virtual in (<b>b</b>).</p> Full article ">Figure 8
<p>Examples of top-bounded spaces from an accessibility perspective. The top-bounded spaces are occupied by: (<b>a</b>) plants; and (<b>b</b>) a car.</p> Full article ">Figure 9
<p>Dimensions and space requirements based on body measurements (unit: mm). This figure is made based on the figures in Architects’ Data [<a href="#B47-ijgi-08-00224" class="html-bibr">47</a>].</p> Full article ">Figure 10
<p>A 3D building model and creation of top-bounded spaces: (<b>a</b>) the original 3D model; (<b>b</b>–<b>d</b>) the thirteen top-bounded spaces generated by the proposed approach; (<b>e</b>) the unqualified top-bounded spaces, which are marked by red circles; and (<b>f</b>) the qualified top-bounded spaces.</p> Full article ">
13 pages, 2578 KiB  
Article
The Spatial Equity of Nursing Homes in Changchun: A Multi-Trip Modes Analysis
by Shuju Hu, Wei Song, Chenggu Li and Jia Lu
ISPRS Int. J. Geo-Inf. 2019, 8(5), 223; https://doi.org/10.3390/ijgi8050223 - 9 May 2019
Cited by 12 | Viewed by 4884
Abstract
Based on network analysis, different trip modes were integrated into an improved potential model, and the geography of the spatial equity of nursing homes in Changchun is explored in 5-min, 10-min and 15-min scenarios, respectively. Results show that: (1) trip modes have significant [...] Read more.
Based on network analysis, different trip modes were integrated into an improved potential model, and the geography of the spatial equity of nursing homes in Changchun is explored in 5-min, 10-min and 15-min scenarios, respectively. Results show that: (1) trip modes have significant influence on spatial equity and that the geography of spatial equity varied with trip modes; (2) the spatial equity value in Changchun is overall kept to a very low level. Most areas in urban fringes and urban core areas belong to underserved areas, and the capacity of nursing home, travel cost and the number of seniors, are the main influencing factors; (3) the geography of spatial equity in different scenarios show a very similar ring structure; namely, the spatial equity value within the urban core and at the most urban periphery is lower than that in intermediate areas. The hot spot analysis showed that the southwest urban fringes and east of the urban core are hot spot areas, while the urban core itself has cold spot areas. Full article
(This article belongs to the Special Issue Human-Centric Data Science for Urban Studies)
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<p>The influence of distance and trip modes on accessibility.</p> Full article ">Figure 2
<p>The network analysis data set of nursing homes in Changchun. (<b>a</b>) the road network and locations of nursing homes; (<b>b</b>) the spatial distribution of elderly population and the capacity of nursing homes.</p> Full article ">Figure 3
<p>The technical details and analysis steps.</p> Full article ">Figure 4
<p>The geography of <span class="html-italic">SE<sub>i</sub></span> under different trip modes. (<b>a</b>) 5-min scenario; (<b>b</b>) 10-min scenario; (<b>c</b>) 15-min scenario.</p> Full article ">Figure 5
<p>The geography of <span class="html-italic">SE<sub>i</sub></span> in different integrated scenarios. (<b>a</b>) 5-min integrated scenario; (<b>b</b>) 10-min integrated scenario; (<b>c</b>) 15-min integrated scenario.</p> Full article ">Figure 6
<p>Spatial patterns of <span class="html-italic">SE<sub>i</sub></span> in different scenarios. (<b>a</b>) 5-min scenario; (<b>b</b>) 10-min scenario; (<b>c</b>) 15-min scenario.</p> Full article ">
18 pages, 4879 KiB  
Article
Reliability Analysis of LandScan Gridded Population Data. The Case Study of Poland
by Beata Calka and Elzbieta Bielecka
ISPRS Int. J. Geo-Inf. 2019, 8(5), 222; https://doi.org/10.3390/ijgi8050222 - 8 May 2019
Cited by 47 | Viewed by 5879
Abstract
The issue of population dataset reliability is of particular importance when it comes to broadening the understanding of spatial structure, pattern and configuration of humans’ geographical location. The aim of the paper was to estimate the reliability of LandScan based on the official [...] Read more.
The issue of population dataset reliability is of particular importance when it comes to broadening the understanding of spatial structure, pattern and configuration of humans’ geographical location. The aim of the paper was to estimate the reliability of LandScan based on the official Polish Population Grid. The adopted methodology was based on the change detection approach, spatial pattern and continuity analysis, as well as statistical analysis at the grid-cell level. Our results show that the LandScan data can estimate the Polish population very well. The number of grid cells with equal people counts in both datasets amounts to 10.5%. The most and highly reliable data cover 72% of the country territory, while less reliable ones cover only 4.3%. The LandScan algorithm tends to underestimate people counts, with a total value of 79,735 people (0.21%). The highest underestimation was noticed in densely populated areas as well as in the transition areas between urban and rural, while overestimation was observed in moderately populated regions, along main roads and in city centres. The underestimation results mainly from the spatial pattern and size of Polish rural settlements, namely a big number of shadowed single households dispersed over agricultural areas and in the vicinity of forests. An excessive assessment of the number of people may be a consequence of the well-known blooming effect. Full article
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<p>Poland. First level administration units (voivodships) and selected big cities.</p> Full article ">Figure 2
<p>Population distribution by LandScan (<b>a</b>) and Polish Population Grid (PPG) (<b>b</b>).</p> Full article ">Figure 3
<p>Scatterplot of PPG and LS.</p> Full article ">Figure 4
<p>Disparity indices: (<b>a</b>) ADI—absolute disparity index; (<b>b</b>) histogram of ADI values, the number of grid cells (vertical axis) is presented in logarithmic scale; (<b>c</b>) DRI—deviation rate index; (<b>d</b>) histogram of DRI values.</p> Full article ">Figure 5
<p>Spatial distribution of LS data reliability: (<b>a</b>) LS reliability classes; (<b>b</b>) the most reliable data; (<b>c</b>) highly reliable data; (<b>d</b>) reasonably reliable; (<b>e</b>) poorly reliable LS data.</p> Full article ">Figure 6
<p>Krakow: (<b>a</b>) overestimation of LandScan data presented by DGI index and ADI values for the most overestimated cells; (<b>b</b>) Building types delivered from National Topographic database.</p> Full article ">Figure 7
<p>Poland, districts classification according to share of LS reliability classes.</p> Full article ">
21 pages, 5183 KiB  
Article
Automated Multi-Sensor 3D Reconstruction for the Web
by Arttu Julin, Kaisa Jaalama, Juho-Pekka Virtanen, Mikko Maksimainen, Matti Kurkela, Juha Hyyppä and Hannu Hyyppä
ISPRS Int. J. Geo-Inf. 2019, 8(5), 221; https://doi.org/10.3390/ijgi8050221 - 8 May 2019
Cited by 21 | Viewed by 7053
Abstract
The Internet has become a major dissemination and sharing platform for 3D content. The utilization of 3D measurement methods can drastically increase the production efficiency of 3D content in an increasing number of use cases where 3D documentation of real-life objects or environments [...] Read more.
The Internet has become a major dissemination and sharing platform for 3D content. The utilization of 3D measurement methods can drastically increase the production efficiency of 3D content in an increasing number of use cases where 3D documentation of real-life objects or environments is required. We demonstrated a developed, highly automated and integrated content creation process of providing reality-based photorealistic 3D models for the web. Close-range photogrammetry, terrestrial laser scanning (TLS) and their combination are compared using available state-of-the-art tools in a real-life project setting with real-life limitations. Integrating photogrammetry and TLS is a good compromise for both geometric and texture quality. Compared to approaches using only photogrammetry or TLS, it is slower and more resource-heavy but combines complementary advantages of each method, such as direct scale determination from TLS or superior image quality typically used in photogrammetry. The integration is not only beneficial, but clearly productionally possible using available state-of-the-art tools that have become increasingly available also for non-expert users. Despite the high degree of automation, some manual editing steps are still required in practice to achieve satisfactory results in terms of adequate visual quality. This is mainly due to the current limitations of WebGL technology. Full article
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<p>Test site: the Puhos shopping mall in Helsinki. Project area marked with a red circle. Image courtesy of the City of Helsinki.</p> Full article ">Figure 2
<p>The prepared TLS-based reference point cloud based on 43 scans and consisting total of 260,046,266 points.</p> Full article ">Figure 3
<p>The 3D reconstruction process in RealityCapture.</p> Full article ">Figure 4
<p>The resulting automatically created web-compatible 3D models: (<b>a</b>) photogrammetry; (<b>b</b>) TLS; and (<b>c</b>) hybrid.</p> Full article ">Figure 5
<p>Visual comparison of details in (<b>a</b>) photogrammetry; (<b>b</b>) TLS; and (<b>c</b>) hybrid approaches. The photogrammetry model suffers from blurred details, whereas the texture data of the TLS model suffers from clear overexposure. For visualization purpose, the models are visualized here as colored vertices without the textures.</p> Full article ">Figure 6
<p>Quality issues on the textured 3D models. The photogrammetry-based model (<b>a</b>) suffers from holes in the data in shiny and non-textured surfaces such as taped windows. In the TLS-based model (<b>b</b>) the lack of data underneath the scanning stations causes circular patterns in the texture. In addition, the illumination differences in the scene cause abrupt differences between the textured areas. Many of these problems are fixed in the hybrid model (<b>c</b>).</p> Full article ">Figure 7
<p>Ground floor surface deviations of all modeling approaches vs. the reference: (<b>a</b>) the photogrammetry approach; (<b>b</b>) the terrestrial laser scanning approach; and (<b>c</b>) the hybrid approach. The color scale for the M3C2 distance values is ±2.5 cm.</p> Full article ">Figure 8
<p>Distance values of the compared modeling approaches vs. the reference: photogrammetry (green), TLS (red) and hybrid (blue).</p> Full article ">Figure 9
<p>A histogram analysis including all 8-bit pixel values of all texture atlases for the three modeling approaches: photogrammetry (green), TLS (red) and hybrid (blue). The significant peak in the hybrid model (pixel value 95) is caused by a grey-colored empty space between the texture islands on the texture atlases. This has no perceivable impact on the visual quality of the model.</p> Full article ">Figure 10
<p>Results of the expert evaluation on visual quality for the three modeling approaches: photogrammetry (green), TLS (red) and hybrid (blue).</p> Full article ">Figure 11
<p>Visual comparison of the raw images of TLS (<b>a</b>) and photogrammetry (<b>b</b>). The raw TLS image (<b>a</b>) suffers clearly from overexposure. The quality of the image data is directly transferred into texture information in the content creation process.</p> Full article ">
18 pages, 3864 KiB  
Article
Spatial Interaction Modeling of OD Flow Data: Comparing Geographically Weighted Negative Binomial Regression (GWNBR) and OLS (GWOLSR)
by Lianfa Zhang, Jianquan Cheng and Cheng Jin
ISPRS Int. J. Geo-Inf. 2019, 8(5), 220; https://doi.org/10.3390/ijgi8050220 - 8 May 2019
Cited by 21 | Viewed by 6218
Abstract
Due to the emergence of new big data technology, mobility data such as flows between origin and destination areas have increasingly become more available, cheaper, and faster. These improvements to data infrastructure have boosted spatial and temporal modeling of OD (origin-destination) flows, which [...] Read more.
Due to the emergence of new big data technology, mobility data such as flows between origin and destination areas have increasingly become more available, cheaper, and faster. These improvements to data infrastructure have boosted spatial and temporal modeling of OD (origin-destination) flows, which require the consideration of spatial dependence and heterogeneity. Both ordinary least square (OLS) and negative binomial (NB) regression methods have been used extensively to calibrate OD flow models by processing flow data as different types of dependent variables. This paper aims to compare both global and local spatial interaction modeling of OD flows between traditional and geographically weighted OLS (GWOLSR) and NB (GWNBR) modeling methods. From this study with empirical data it is concluded that GWNBR outperforms GWOLSR in reducing spatial autocorrelation and in detecting spatial non-stationarity. Although, it is noted that both local modeling methods show improvement when compared against the equivalent global models. Full article
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<p>Distribution of 334 toll-gates and expressway network across the study area.</p> Full article ">Figure 2
<p>The Euclidean distance between flow(i,j) and flow(<math display="inline"><semantics> <mrow> <mrow> <msup> <mi mathvariant="normal">i</mi> <mo>′</mo> </msup> <msup> <mi mathvariant="normal">j</mi> <mo>′</mo> </msup> </mrow> </mrow> </semantics></math>).</p> Full article ">Figure 3
<p>The contiguity-based spatial weight of flows.</p> Full article ">Figure 4
<p>Traffic flows on expressway network. (<b>a</b>) Vehicle flows between toll-gates; (<b>b</b>) vehicle flows aggregated to county level.</p> Full article ">Figure 5
<p>Traffic flows at county level. (<b>a</b>) Histograms; (<b>b</b>) log-transformed values.</p> Full article ">Figure 6
<p>Distribution of variables. (<b>a</b>) Spatial distribution of GDP; (<b>b</b>) histogram of the distance.</p> Full article ">Figure 7
<p>Histograms of t-statistics for three parameter estimations with the geographically weighted ordinary least square (GWOLS) model. (<b>a</b>) GDP at origin site (OGDP); (<b>b</b>) GDP at destination site (DGDP); (<b>c</b>) distance.</p> Full article ">Figure 8
<p>Distributions of three parameter estimations with GWOLS model. (<b>a</b>) OGDP; (<b>b</b>) DGDP; (<b>c</b>) distance.</p> Full article ">Figure 9
<p>Histograms of t-statistics for three parameter estimations with the geographically weighted negative binomial regression (GWNBR) model. (<b>a</b>) OGDP; (<b>b</b>) DGDP; (<b>c</b>) distance.</p> Full article ">Figure 10
<p>Distributions of three parameter estimations with the GWNBR model. (<b>a</b>) OGDP; (<b>b</b>) DGDP; (<b>c</b>) distance.</p> Full article ">
16 pages, 8046 KiB  
Article
Integrated UAV-Based Real-Time Mapping for Security Applications
by Daniel Hein, Thomas Kraft, Jörg Brauchle and Ralf Berger
ISPRS Int. J. Geo-Inf. 2019, 8(5), 219; https://doi.org/10.3390/ijgi8050219 - 8 May 2019
Cited by 21 | Viewed by 4720
Abstract
Security applications such as management of natural disasters and man-made incidents crucially depend on the rapid availability of a situation picture of the affected area. UAV-based remote sensing systems may constitute an essential tool for capturing aerial imagery in such scenarios. While several [...] Read more.
Security applications such as management of natural disasters and man-made incidents crucially depend on the rapid availability of a situation picture of the affected area. UAV-based remote sensing systems may constitute an essential tool for capturing aerial imagery in such scenarios. While several commercial UAV solutions already provide acquisition of high quality photos or real-time video transmission via radio link, generating instant high-resolution aerial maps is still an open challenge. For this purpose, the article presents a real-time processing tool chain, enabling generation of interactive aerial maps during flight. Key element of this tool chain is the combination of the Terrain Aware Image Clipping (TAC) algorithm and 12-bit JPEG compression. As a result, the data size of a common scenery can be reduced to approximately 0.4% of the original size, while preserving full geometric and radiometric resolution. Particular attention was paid to minimize computational costs to reduce hardware requirements. The full workflow was demonstrated using the DLR Modular Airborne Camera System (MACS) operated on a conventional aircraft. In combination with a commercial radio link, the latency between image acquisition and visualization in the ground station was about 2 s. In addition, the integration of a miniaturized version of the camera system into a small fixed-wing UAV is presented. It is shown that the described workflow is efficient enough to instantly generate image maps even on small UAV hardware. Using a radio link, these maps can be broadcasted to on-site operation centers and are immediately available to the end-users. Full article
(This article belongs to the Special Issue Innovative Sensing - From Sensors to Methods and Applications)
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<p>Airborne segment of real-time processing chain. The particular processing stages are explained in <a href="#sec2-ijgi-08-00219" class="html-sec">Section 2</a>.</p> Full article ">Figure 2
<p>Ground segment of real-time processing chain. The quad2quad rendering stage is explained in <a href="#sec2dot7-ijgi-08-00219" class="html-sec">Section 2.7</a>.</p> Full article ">Figure 3
<p>Aerial imaging of a non-planar terrain and view cone overlap visualization (profile view): (<b>a</b>) full frame coverage and view cone overlap; and (<b>b</b>) minimized view cone overlap of clipped aerial images by application of TAC algorithm.</p> Full article ">Figure 4
<p>Effect of radiometric correction at aerial image mosaicing. (<b>a</b>) Mosaic without radiometric correction; (<b>b</b>) Mosaic with model-based radiometric correction.</p> Full article ">Figure 5
<p>Compression performance of <span class="html-italic">libjpeg</span> library, version 9b, depending on the JPEG quality setting. Values show average single-thread compression time and relative compression size of a 16 MPix (4864 × 3232 pixels) 12-bit aerial image. Comparison of processing times for an Intel i7-6800K desktop CPU and an embedded System with an Intel Atom E3950 CPU.</p> Full article ">Figure 6
<p>JPEG compression result on sample aerial image with an image resolution of 4864 × 3232 pixels, 12-bit radiometric depth (which corresponds to a raw image data size of 22.5 MB), and a <span class="html-italic">ground sampling distance</span> (GSD) of 5 cm per pixel: (<b>a</b>,<b>c</b>) full image: and (<b>b</b>,<b>d</b>) detail view of the yellow rect section in the full image.</p> Full article ">Figure 7
<p>Case study: Image data size of a sample scene comparing raw imagery, application of JPEG compression, application of TAC clipping and combined application of JPEG compression and TAC clipping. Sample scene comprising 63 aerial images captured by the demonstration UAV camera system. Length of captured scene: 1194 m; altitude: 322 m AGL; speed: 19 m/s (68 km/h); trigger rate: 1 Hz; and flight time: 63 s. Values below scene samples correspond to the data size of the rendered mosaic, the values in parentheses show the relative size compared to raw imagery size. JPEG compression with <span class="html-italic">libjpeg</span> library version 9b; quality setting: 1; and radiometric depth: 12-bit. Size of raw captured imagery is about 1.4 GB (<b>c</b>), which corresponds to a raw data rate of about 180 Mbit/s. By application of both JPEG compression and TAC clipping (<b>f</b>), the total size of the captured scene was reduced to 4.9 MB, which corresponds to a data rate of about 0.62 Mbit/s.</p> Full article ">Figure 8
<p>Real-time data transmission chain. The control loop at airborne segment adjusts compression ratio depending on data link capacity (i.e., its current available bandwidth) and/or transmitter’s buffer fill level.</p> Full article ">Figure 9
<p>Projective mapping: Single aerial image (<b>a</b>) and its perspective projection (<b>b</b>) by application of the corresponding transformation matrix.</p> Full article ">Figure 10
<p>Processing times for real-time processing chain (airborne segment), depending on image size. Results from processing an aerial image dataset comprising 1206 single images, captured by three different camera sensors, each with a 16 MPix Bayer pattern sensor. JPEG compression by <span class="html-italic">libjpeg</span>-library version 9b; radiometric depth: 12-bit; and quality setting: 5. Processing machine: standard desktop computer with an Intel i7-6800K CPU. The different image sizes were obtained by vertical symmetric cropping of original (full) images.</p> Full article ">Figure 11
<p>(<b>a</b>) Real-time capable aerial camera system for UAV; and (<b>b</b>) VTOL carrier.</p> Full article ">Figure 12
<p>Aircraft-based demonstration of real-time aerial mapping: (<b>a</b>) aircraft with DLR <span class="html-italic">Modular Airborne Camera System</span> (MACS) and WiFi-based 10 Mbit/s data downlink; and (<b>b</b>) generation of seamless aerial maps from the transmitted image data in real-time. The latency between image acquisition and visualization is about 2 s.</p> Full article ">
20 pages, 4748 KiB  
Article
NS-DBSCAN: A Density-Based Clustering Algorithm in Network Space
by Tianfu Wang, Chang Ren, Yun Luo and Jing Tian
ISPRS Int. J. Geo-Inf. 2019, 8(5), 218; https://doi.org/10.3390/ijgi8050218 - 8 May 2019
Cited by 33 | Viewed by 7753
Abstract
Spatial clustering analysis is an important spatial data mining technique. It divides objects into clusters according to their similarities in both location and attribute aspects. It plays an essential role in density distribution identification, hot-spot detection, and trend discovery. Spatial clustering algorithms in [...] Read more.
Spatial clustering analysis is an important spatial data mining technique. It divides objects into clusters according to their similarities in both location and attribute aspects. It plays an essential role in density distribution identification, hot-spot detection, and trend discovery. Spatial clustering algorithms in the Euclidean space are relatively mature, while those in the network space are less well researched. This study aimed to present a well-known clustering algorithm, named density-based spatial clustering of applications with noise (DBSCAN), to network space and proposed a new clustering algorithm named network space DBSCAN (NS-DBSCAN). Basically, the NS-DBSCAN algorithm used a strategy similar to the DBSCAN algorithm. Furthermore, it provided a new technique for visualizing the density distribution and indicating the intrinsic clustering structure. Tested by the points of interest (POI) in Hanyang district, Wuhan, China, the NS-DBSCAN algorithm was able to accurately detect the high-density regions. The NS-DBSCAN algorithm was compared with the classical hierarchical clustering algorithm and the recently proposed density-based clustering algorithm with network-constraint Delaunay triangulation (NC_DT) in terms of their effectiveness. The hierarchical clustering algorithm was effective only when the cluster number was well specified, otherwise it might separate a natural cluster into several parts. The NC_DT method excessively gathered most objects into a huge cluster. Quantitative evaluation using four indicators, including the silhouette, the R-squared index, the Davis–Bouldin index, and the clustering scheme quality index, indicated that the NS-DBSCAN algorithm was superior to the hierarchical clustering and NC_DT algorithms. Full article
(This article belongs to the Special Issue Spatial Databases: Design, Management, and Knowledge Discovery)
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Figure 1
<p>A simulated dataset including road network and event points. The 1-neighborhood of central point P<sub>5</sub> is marked as a thick gray line, covering four event points (P<sub>1</sub>, P<sub>7</sub>, P<sub>8</sub>, and P<sub>9</sub>).</p> Full article ">Figure 2
<p>Network space density-based spatial clustering of applications with noise (NS-DBSCAN) algorithm reached the peak of local density from <span class="html-italic">cp1</span> to <span class="html-italic">cp3</span> (<span class="html-italic">cp3</span> is the local density peak).</p> Full article ">Figure 3
<p>An undirected planar graph N = (V∪P, E, W) was generated for the simulated dataset. P is the event vertex, representing the event points. V denotes ordinary vertices, representing the location where the road segments intersect. An ordinary vertex will not be created in the segments’ intersection if an event vertex already exists, such as P<sub>1</sub>. E is the edge, representing the road segments between the two vertices. W is the weight of edges, which is defined as the length of edges in the study.</p> Full article ">Figure 4
<p>A basic expansion is a motion from a source (start) vertex to a target (end) vertex, the path between which is the expansion path. Current distance to central vertex (CDCV) (<span class="html-italic">p</span>) represents <span class="html-italic">p</span>’s current distance to central vertex, and W (<span class="html-italic">p</span>, <span class="html-italic">q</span>) denotes the weight (length) of expansion path between vertices <span class="html-italic">p</span> and <span class="html-italic">q</span>. The CDCV of end vertex is updated to the sum of CDCV of start vertex and the weight of expansion path between them.</p> Full article ">Figure 5
<p>A density ordering graph of simulated dataset. It is a bar chart where the horizontal axis is the identifier of event points and the ordinate is the density of each event point.</p> Full article ">Figure 6
<p>The core points in cluster gradually brought the border points into the cluster: (<b>a</b>) P<sub>1</sub> brought P<sub>5</sub>, P<sub>2</sub>, P<sub>4</sub>, P<sub>8</sub>, and P<sub>3</sub> to cluster 1; (<b>b</b>) P<sub>5</sub> brought P<sub>7</sub> and P<sub>9</sub> to cluster 1; (<b>c</b>) P<sub>8</sub> brought P<sub>6</sub> to cluster 1; The points of the simulated dataset eventually formed two clusters, and two points became noises, as shown in (<b>d</b>).</p> Full article ">Figure 7
<p>Points of interest (POIs) of Hanyang district included six aggregated regions: I, Wangjiawan; II, Wuhan Technician College; III, Longyang Village; IV, Huangjinkou Urban Industrial Park and Wantong Industrial Park; V, Shengyuan Yuanhua Industrial Park; and VI, Huangjinkou Auto Parts Market.</p> Full article ">Figure 8
<p>Steps of preprocessing: (<b>a</b>) original dataset; (<b>b</b>) extraction of skeletons; (<b>c</b>) movement of POI to the nearest road segment; and (<b>d</b>) splitting of road segments at event vertices.</p> Full article ">Figure 9
<p>Clusters of NS-DBSCAN algorithm (<span class="html-italic">eps</span> = 200, <span class="html-italic">MinPts</span> = 20) accurately delineated the six highly populated regions of aggregated POI.</p> Full article ">Figure 10
<p>Clusters of hierarchical clustering algorithm (farthest-pair distance, cluster number = 36) basically delineated the regions of aggregated POI.</p> Full article ">Figure 11
<p>Clusters of NC_DT algorithm does not work effectively for the dataset.</p> Full article ">Figure 12
<p>Density ordering graphs of (<b>a</b>) <span class="html-italic">eps</span> = 100; (<b>b</b>) <span class="html-italic">eps</span> = 200; (<b>c</b>) <span class="html-italic">eps</span> = 300; and (<b>d</b>) <span class="html-italic">eps</span> = 400.</p> Full article ">Figure 13
<p>A basic expansion in a dead-end road segment.</p> Full article ">
14 pages, 1374 KiB  
Article
Why Shape Matters—On the Inherent Qualities of Geometric Shapes for Cartographic Representations
by Silvia Klettner
ISPRS Int. J. Geo-Inf. 2019, 8(5), 217; https://doi.org/10.3390/ijgi8050217 - 8 May 2019
Cited by 11 | Viewed by 7577
Abstract
All human communication involves the use of signs. By following a mutually shared set of signs and rules, meaning can be conveyed from one entity to another. Cartographic semiology provides such a theoretical framework, suggesting how to apply visual variables with respect to [...] Read more.
All human communication involves the use of signs. By following a mutually shared set of signs and rules, meaning can be conveyed from one entity to another. Cartographic semiology provides such a theoretical framework, suggesting how to apply visual variables with respect to thematic content. However, semiotics does not address how the choice and composition of such visual variables may lead to different connotations, interpretations, or judgments. The research herein aimed to identify perceived similarities between geometric shape symbols as well as strategies and processes underlying these similarity judgments. Based on a user study with 38 participants, the (dis)similarities of a set of 12 basic geometric shapes (e.g., circle, triangle, square) were examined. Findings from cluster analysis revealed a three-cluster configuration, while multidimensional scaling further quantified the proximities between the geometric shapes in a two-dimensional space. Qualitative and quantitative content analyses identified four strategies underlying the participants’ similarity judgments, namely visual, affective, associative, and behavioral strategies. With the findings combined, this research provides a differentiated perspective on shape proximities, cognitive relations, and the processes involved. Full article
(This article belongs to the Special Issue Human-Centered Geovisual Analytics and Visuospatial Display Design)
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<p>Stimulus material used in the present study, comprised of 12 geometric shapes.</p> Full article ">Figure 2
<p>Illustration of two visual protocols from two participants, after completing free-sorting task 1 and free-labeling task 3.</p> Full article ">Figure 3
<p>Co-occurrences of geometric shapes from free-sorting task 1, illustrated as (<b>a</b>) co-occurrence matrix: the values indicate the frequency counts of co-associations and those of single-item groups (see diagonal values) - higher values indicate higher similarity; and as (<b>b</b>) dendrogram based on agglomerative hierarchical cluster analysis.</p> Full article ">Figure 4
<p>Two-dimensional configuration of 12 geometric shapes using MDS.</p> Full article ">Figure 5
<p>Shape orientation scheme and frequencies of shape rotations according to the set of 12 geometric shapes (N = 38).</p> Full article ">
24 pages, 4092 KiB  
Article
Constructing Geographic Dictionary from Streaming Geotagged Tweets
by Jeongwoo Lim, Naoko Nitta, Kazuaki Nakamura and Noboru Babaguchi
ISPRS Int. J. Geo-Inf. 2019, 8(5), 216; https://doi.org/10.3390/ijgi8050216 - 8 May 2019
Cited by 4 | Viewed by 3596
Abstract
Geographic information, such as place names with their latitude and longitude (lat/long), is useful to understand what belongs where. Traditionally, Gazetteers, which are constructed manually by experts, are used as dictionaries containing such geographic information. Recently, since people often post about their current [...] Read more.
Geographic information, such as place names with their latitude and longitude (lat/long), is useful to understand what belongs where. Traditionally, Gazetteers, which are constructed manually by experts, are used as dictionaries containing such geographic information. Recently, since people often post about their current experiences in a short text format to microblogs, their geotagged (tagged with lat/long information) posts are aggregated to automatically construct geographic dictionaries containing more diverse types of information, such as local products and events. Generally, the geotagged posts are collected within a certain time interval. Then, the spatial locality of every word used in the collected geotagged posts is examined to obtain the local words, representing places, events, etc., which are observed at specific locations by the users. However, focusing on a specific time interval limits the diversity and accuracy of the extracted local words. Further, bot accounts in microblogs can largely affect the spatial locality of the words used in their posts. In order to handle such problems, we propose an online method for continuously update the geographic dictionary by adaptively determining suitable time intervals for examining the spatial locality of each word. The proposed method further filters out the geotagged posts from bot accounts based on the content similarity among their posts to improve the quality of extracted local words. The constructed geographic dictionary is compared with different geographic dictionaries constructed by experts, crowdsourcing, and automatically by focusing on a specific time interval to evaluate its quality. Full article
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<p>Overview of proposed method.</p> Full article ">Figure 2
<p>Usage history of <math display="inline"><semantics> <msub> <mi>u</mi> <mi>z</mi> </msub> </semantics></math> is accumulated for time windows of different duration so that its spatial locality is properly examined.</p> Full article ">Figure 3
<p>How the area-based frequency histogram of the word <math display="inline"><semantics> <msub> <mi>u</mi> <mi>z</mi> </msub> </semantics></math> is used to determine if <math display="inline"><semantics> <msub> <mi>u</mi> <mi>z</mi> </msub> </semantics></math> is a local or a general word. Intuitively, by using the same thresholds <span class="html-italic">R</span> and <span class="html-italic">r</span> for <math display="inline"><semantics> <mrow> <mi>S</mi> <msub> <mi>L</mi> <mi>z</mi> </msub> </mrow> </semantics></math>, the thresholds representing the maximum and minimum number of areas <math display="inline"><semantics> <msubsup> <mi>λ</mi> <mrow> <mi>m</mi> <mi>a</mi> <mi>x</mi> </mrow> <msubsup> <mi>f</mi> <mi>z</mi> <mrow> <mi>m</mi> <mi>o</mi> <mi>d</mi> <mi>e</mi> </mrow> </msubsup> </msubsup> </semantics></math> and <math display="inline"><semantics> <msubsup> <mi>λ</mi> <mrow> <mi>m</mi> <mi>i</mi> <mi>n</mi> </mrow> <msubsup> <mi>f</mi> <mi>z</mi> <mrow> <mi>m</mi> <mi>o</mi> <mi>d</mi> <mi>e</mi> </mrow> </msubsup> </msubsup> </semantics></math> for determining local/general words are changed according to the peak <math display="inline"><semantics> <msubsup> <mi>f</mi> <mi>z</mi> <mrow> <mi>m</mi> <mi>o</mi> <mi>d</mi> <mi>e</mi> </mrow> </msubsup> </semantics></math> of the frequency histogram.</p> Full article ">Figure 4
<p>Area-based frequency histogram <math display="inline"><semantics> <msub> <mi>F</mi> <mi>z</mi> </msub> </semantics></math> of the word <math display="inline"><semantics> <msub> <mi>u</mi> <mi>z</mi> </msub> </semantics></math> is updated by removing the similar tweets from the set of tweets <math display="inline"><semantics> <msub> <mi>S</mi> <mi>z</mi> </msub> </semantics></math> in its usage history based on the bot score <math display="inline"><semantics> <mrow> <mi>B</mi> <msub> <mi>S</mi> <msub> <mi>w</mi> <mrow> <mi>z</mi> <mo>,</mo> <mi>m</mi> </mrow> </msub> </msub> </mrow> </semantics></math>.</p> Full article ">Figure 5
<p>When the local word <math display="inline"><semantics> <msub> <mi>l</mi> <mi>k</mi> </msub> </semantics></math> in the geographic dictionary is determined as a local word again, its past area-based frequency histogram <math display="inline"><semantics> <msubsup> <mi>F</mi> <mrow> <mi>k</mi> </mrow> <mrow> <mi>o</mi> <mi>l</mi> <mi>d</mi> </mrow> </msubsup> </semantics></math> recorded in the geographic dictionary is updated according to the similarity to <math display="inline"><semantics> <msubsup> <mi>F</mi> <mrow> <mi>k</mi> </mrow> <mrow> <mi>n</mi> <mi>e</mi> <mi>w</mi> </mrow> </msubsup> </semantics></math>, which is its area-based frequency histogram in the current usage history.</p> Full article ">Figure 6
<p>Maximum similarity for tweets from bot accounts and real users. Bot accounts tend to post similar tweets among themselves as shown in <a href="#ijgi-08-00216-t001" class="html-table">Table 1</a> (with the maximum similarity over 0.4), while real users tend to post unique tweets (with the maximum similarity under 0.4).</p> Full article ">Figure 7
<p>Ratio of correctly removed tweets from bot accounts and falsely removed tweets from real users. Setting <math display="inline"><semantics> <mrow> <mi>S</mi> <mi>i</mi> <msubsup> <mi>m</mi> <mrow> <mi>t</mi> <mi>h</mi> </mrow> <mrow> <mi>c</mi> <mi>o</mi> <mi>n</mi> </mrow> </msubsup> <mo>=</mo> <mn>0.4</mn> </mrow> </semantics></math> gave the best results, removing 99% of the tweets from bot accounts without falsely removing many tweets (less than 10%) from real users. Setting <math display="inline"><semantics> <mrow> <mi>S</mi> <mi>i</mi> <msubsup> <mi>m</mi> <mrow> <mi>t</mi> <mi>h</mi> </mrow> <mrow> <mi>c</mi> <mi>o</mi> <mi>n</mi> </mrow> </msubsup> </mrow> </semantics></math> higher would miss more tweets from bot accounts, while setting <math display="inline"><semantics> <mrow> <mi>S</mi> <mi>i</mi> <msubsup> <mi>m</mi> <mrow> <mi>t</mi> <mi>h</mi> </mrow> <mrow> <mi>c</mi> <mi>o</mi> <mi>n</mi> </mrow> </msubsup> </mrow> </semantics></math> lower would falsely remove tweets from real users.</p> Full article ">Figure 8
<p>How the United Stated was divided into <math display="inline"><semantics> <mrow> <mi>J</mi> <mo>=</mo> <mn>64</mn> </mrow> </semantics></math> areas.</p> Full article ">Figure 9
<p>Histogram of the number of areas in which place names and stop words were used when <math display="inline"><semantics> <mrow> <msubsup> <mi>f</mi> <mi>z</mi> <mrow> <mi>m</mi> <mi>o</mi> <mi>d</mi> <mi>e</mi> </mrow> </msubsup> <mo>=</mo> <mi>θ</mi> <mrow> <mo>(</mo> <mo>=</mo> <mn>3</mn> <mo>)</mo> </mrow> </mrow> </semantics></math>. Approximately 80% of the place names and less than 1% of stop words were used in fewer than two areas when <math display="inline"><semantics> <mrow> <msubsup> <mi>f</mi> <mi>z</mi> <mrow> <mi>m</mi> <mi>o</mi> <mi>d</mi> <mi>e</mi> </mrow> </msubsup> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math>. On the other hand, approximately 70% of the stop words and 2% of the place names were used in more than 24 areas when <math display="inline"><semantics> <mrow> <msubsup> <mi>f</mi> <mi>z</mi> <mrow> <mi>m</mi> <mi>o</mi> <mi>d</mi> <mi>e</mi> </mrow> </msubsup> <mo>=</mo> <mn>3</mn> </mrow> </semantics></math>. The dashed red lines show these thresholds corresponding to <span class="html-italic">R</span> and <span class="html-italic">r</span>.</p> Full article ">Figure 10
<p>Relations between the area-based maximum frequency <math display="inline"><semantics> <mrow> <msubsup> <mi>f</mi> <mrow> <mi>z</mi> </mrow> <mrow> <mi>m</mi> <mi>o</mi> <mi>d</mi> <mi>e</mi> </mrow> </msubsup> <mrow> <mo>(</mo> <msubsup> <mi>f</mi> <mi>z</mi> <mrow> <mi>m</mi> <mi>o</mi> <mi>d</mi> <mi>e</mi> </mrow> </msubsup> <mo>≥</mo> <mi>θ</mi> <mrow> <mo>(</mo> <mo>=</mo> <mn>3</mn> <mo>)</mo> </mrow> <mo>)</mo> </mrow> </mrow> </semantics></math> and the number of areas <math display="inline"><semantics> <mrow> <mrow> <mo>|</mo> </mrow> <msub> <mi>A</mi> <mi>z</mi> </msub> <mrow> <mo>|</mo> </mrow> </mrow> </semantics></math> for place names and stop words.</p> Full article ">Figure 11
<p>Average similarity between <math display="inline"><semantics> <msubsup> <mi>F</mi> <mrow> <mi>k</mi> </mrow> <mrow> <mi>o</mi> <mi>l</mi> <mi>d</mi> </mrow> </msubsup> </semantics></math> and <math display="inline"><semantics> <msubsup> <mi>F</mi> <mrow> <mi>k</mi> </mrow> <mrow> <mi>n</mi> <mi>e</mi> <mi>w</mi> </mrow> </msubsup> </semantics></math> for place names in GeoNames.</p> Full article ">Figure 12
<p>Number of extracted local words. A part of them are also in GeoNames.</p> Full article ">Figure 13
<p>Number of users for the extracted local words. Those used by 5, 10, and 20 users accounted for 50%, 70%, and 80% of the extracted local words.</p> Full article ">Figure 14
<p>Location errors of the local words extracted by the proposed method and Cheng’s method, which are also in GeoNames. The numbers of the lines represent the ratio of the words with the corresponding errors.</p> Full article ">Figure 15
<p>Tweet location estimation results from day 1 to day 10.</p> Full article ">Figure 16
<p>Tweet location estimation results from day 11 to day 30.</p> Full article ">Figure 17
<p>Visualization of collected geographic information.</p> Full article ">Figure 18
<p>Examples of stationary local words only in our geographic dictionary.</p> Full article ">Figure 19
<p>Examples of the temporary local words only in our geographic dictionary on different days.</p> Full article ">Figure 20
<p>The locations for the temporary local word <span class="html-italic">beyoncé formation world tour</span> were correctly updated in our geographic dictionary.</p> Full article ">
28 pages, 10372 KiB  
Article
Online Map Services: Contemporary Cartography or a New Cartographic Culture?
by Andriani Skopeliti and Leda Stamou
ISPRS Int. J. Geo-Inf. 2019, 8(5), 215; https://doi.org/10.3390/ijgi8050215 - 7 May 2019
Cited by 18 | Viewed by 7398
Abstract
In this paper, online map services are reviewed from a cartographic point of view. The most popular online map services are selected based on worldwide website traffic data, provided by specialized sites, such as Similarweb, in terms of global coverage and popularity among [...] Read more.
In this paper, online map services are reviewed from a cartographic point of view. The most popular online map services are selected based on worldwide website traffic data, provided by specialized sites, such as Similarweb, in terms of global coverage and popularity among users. Online map services are commented based on cartographic principles, conventions and traditional practices addressing topics, such as: Cartographic projection, orientation, scale, marginalia, content (thematic layers), symbology, generalization, annotation, color use and overall map design. Color schemes utilized in web maps are discussed in more detail, since based on studies concerning the selection of the preferable map by experts and laymen, color is undisputedly the most frequently mentioned factor. It can be stated that online map services generally adopt well-known cartographic practices, which are not always applied as expected. Moreover, suggestions for the improvement of online map services are made regarding cartographic projection, legend, content, symbolization, color, etc. Full article
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>OpenStreetMap (OSM) maps and map key (<b>a</b>,<b>b</b>).</p> Full article ">Figure 2
<p>Generalization issues: (<b>a</b>) excessive detail in relation to the scale level in the road network; (<b>b</b>) very small buildings are portrayed; (<b>c</b>) different degree of generalization in the river network and the land use; (<b>d</b>) overlay problems between layers e.g. road network, urban area, park and lack of detail in layers e.g. the road network; (<b>e</b>) overlay problems between layers e.g. road network, land and sea; (<b>f</b>) problems in establishing information hierarchy.</p> Full article ">Figure 2 Cont.
<p>Generalization issues: (<b>a</b>) excessive detail in relation to the scale level in the road network; (<b>b</b>) very small buildings are portrayed; (<b>c</b>) different degree of generalization in the river network and the land use; (<b>d</b>) overlay problems between layers e.g. road network, urban area, park and lack of detail in layers e.g. the road network; (<b>e</b>) overlay problems between layers e.g. road network, land and sea; (<b>f</b>) problems in establishing information hierarchy.</p> Full article ">Figure 3
<p>The Google Map color scheme.</p> Full article ">Figure 4
<p>The Google map color analysis.</p> Full article ">Figure 5
<p>The OpenStreetMap color scheme.</p> Full article ">Figure 6
<p>The OpenStreetMap color analysis.</p> Full article ">Figure 7
<p>The OpenStreetMap road network color scheme.</p> Full article ">Figure 8
<p>The OpenStreetMap road network symbols color scheme (left) and suggestions: (<b>a</b>) for analogous color scheme; (<b>b</b>,<b>c</b>) alternatives for monochromatic color schemes.</p> Full article ">Figure 9
<p>OpenStreetMap road network colors suggestions plotted in the color wheel: (<b>a</b>) for analogous color scheme; (<b>b</b>,<b>c</b>) alternatives for monochromatic color schemes.</p> Full article ">Figure 10
<p>OSM greens’ color scheme (left) and suggested color schemes: (<b>a</b>) greens with Saturation 20, Brightness 84; (<b>b</b>) greens with Saturation 24, Brightness 84.</p> Full article ">Figure 11
<p>The HERE Maps color scheme.</p> Full article ">Figure 12
<p>The HERE Maps color analysis.</p> Full article ">Figure 13
<p>The HERE Maps road network color scheme (top) and replacement suggestions: (<b>a</b>) monochromatic color scheme Hue 10, Brightness 100, Saturation from 19 to 75; (<b>b</b>) monochromatic color scheme Hue 10, Brightness 100, Saturation from 9 to 39; (<b>c</b>) monochromatic color scheme Hue 339, Brightness 86, Saturation from19 to 75; (<b>d</b>) monochromatic color scheme Hue 339, Brightness 86, Saturation from 12 to 47.</p> Full article ">Figure 14
<p>The Wikimapia color scheme.</p> Full article ">Figure 15
<p>The Wikimapia color analysis.</p> Full article ">Figure 16
<p>Wikimapia current yellow (left: HBS 59-52-98) and replacement suggestions: (<b>a</b>) HBS 48-21-98; (<b>b</b>) HBS 59-21-98.</p> Full article ">Figure 17
<p>Color schemes overview.</p> Full article ">Figure A1
<p>Google maps (<a href="https://www.google.com/maps/@37.989609,23.7156318,13z" target="_blank">https://www.google.com/maps/@37.989609,23.7156318,13z</a>).</p> Full article ">Figure A2
<p>OSM maps (<a href="https://www.openstreetmap.org/#map=13/37.9861/23.7160" target="_blank">https://www.openstreetmap.org/#map=13/37.9861/23.7160</a>).</p> Full article ">Figure A3
<p>HERE maps (<a href="https://wego.here.com/?map=37.98775,23.74855,13,normal" target="_blank">https://wego.here.com/?map=37.98775,23.74855,13,normal</a>).</p> Full article ">Figure A4
<p>Wikimapia maps (<a href="http://wikimapia.org/#lang=el&amp;lat=37.988993&amp;lon=23.715363&amp;z=13&amp;m=w" target="_blank">http://wikimapia.org/#lang=el&amp;lat=37.988993&amp;lon=23.715363&amp;z=13&amp;m=w</a>).</p> Full article ">
19 pages, 2605 KiB  
Article
Bicycle Level of Service for Route Choice—A GIS Evaluation of Four Existing Indicators with Empirical Data
by Ray Pritchard, Yngve Frøyen and Bernhard Snizek
ISPRS Int. J. Geo-Inf. 2019, 8(5), 214; https://doi.org/10.3390/ijgi8050214 - 7 May 2019
Cited by 39 | Viewed by 7158
Abstract
Bicycle Level of Service (BLOS) indicators are used to provide objective ratings of the bicycle suitability (or quality) of links or intersections in transport networks. This article uses empirical bicycle route choice data from 467 university students in Trondheim, Norway to test the [...] Read more.
Bicycle Level of Service (BLOS) indicators are used to provide objective ratings of the bicycle suitability (or quality) of links or intersections in transport networks. This article uses empirical bicycle route choice data from 467 university students in Trondheim, Norway to test the applicability of BLOS rating schemes for the estimation of whole-journey route choice. The methods evaluated share a common trait of being applicable for mixed traffic urban environments: Bicycle Compatibility Index (BCI), Bicycle Stress Level (BSL), Sixth Edition Highway Capacity Manual (HCM6), and Level of Traffic Stress (LTS). Routes are generated based on BLOS-weighted networks and the suitability of these routes is determined by finding the percentage overlap with empirical route choices. The results show that BCI provides the best match with empirical route data in all five origin–destination pairs, followed by HCM6. BSL and LTS which are not empirically founded have a lower match rate, although the differences between the four methods are relatively small. By iterating the detour rate that cyclists are assumed to be willing to make, it is found that the best match with modelled BLOS routes is achieved between 15 and 21% additional length. This falls within the range suggested by existing empirical research on willingness to deviate from the shortest path, however, it is uncertain whether the method will deliver the comparable findings in other cycling environments. Full article
(This article belongs to the Special Issue Human-Centric Data Science for Urban Studies)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Google Maps based mapping API used to collect participant responses on bicycle route choice. The end point, Trondheim City Square, is indicated to users with a flag.</p> Full article ">Figure 2
<p>(<b>a</b>) Bicycle Compatibility Index for Trondheim. (<b>b</b>) Heat map of the student route preferences from the student villages Moholt (n<sub>north</sub> = 140, n<sub>south</sub> = 100) and Karinelund (n = 57).</p> Full article ">Figure 3
<p>Best routes generated (across all iterations of detour rate) along five OD pairs using four BLOS methods.</p> Full article ">Figure 4
<p>Percentage route overlap between empirical and generated routes for four BLOS models. The line of best fit is indicated by the dashed line.</p> Full article ">
34 pages, 16010 KiB  
Article
Voxel-based 3D Point Cloud Semantic Segmentation: Unsupervised Geometric and Relationship Featuring vs Deep Learning Methods
by Florent Poux and Roland Billen
ISPRS Int. J. Geo-Inf. 2019, 8(5), 213; https://doi.org/10.3390/ijgi8050213 - 7 May 2019
Cited by 115 | Viewed by 30430
Abstract
Automation in point cloud data processing is central in knowledge discovery within decision-making systems. The definition of relevant features is often key for segmentation and classification, with automated workflows presenting the main challenges. In this paper, we propose a voxel-based feature engineering that [...] Read more.
Automation in point cloud data processing is central in knowledge discovery within decision-making systems. The definition of relevant features is often key for segmentation and classification, with automated workflows presenting the main challenges. In this paper, we propose a voxel-based feature engineering that better characterize point clusters and provide strong support to supervised or unsupervised classification. We provide different feature generalization levels to permit interoperable frameworks. First, we recommend a shape-based feature set (SF1) that only leverages the raw X, Y, Z attributes of any point cloud. Afterwards, we derive relationship and topology between voxel entities to obtain a three-dimensional (3D) structural connectivity feature set (SF2). Finally, we provide a knowledge-based decision tree to permit infrastructure-related classification. We study SF1/SF2 synergy on a new semantic segmentation framework for the constitution of a higher semantic representation of point clouds in relevant clusters. Finally, we benchmark the approach against novel and best-performing deep-learning methods while using the full S3DIS dataset. We highlight good performances, easy-integration, and high F1-score (> 85%) for planar-dominant classes that are comparable to state-of-the-art deep learning. Full article
(This article belongs to the Special Issue Data Mining and Feature Extraction from Satellite Images and Point Cloud Data)
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Graphical abstract

Graphical abstract
Full article ">Figure 1
<p>Voxel-based three-dimensional (3D) semantic segmentation. From left to right: Raw point cloud, feature engineering, Connected Elements extraction, Classified point cloud.</p> Full article ">Figure 2
<p>Visual patterns on points from left to right: Not grouped; Proximity criterion; Similarity criterion; Common cluster region; Linear criterion; Parallel criterion: Symmetry criterion.</p> Full article ">Figure 3
<p>Methodological workflow for the constitution of Connected Elements and knowledge-based classification. A point cloud goes through seven serialized steps (diamonds) to obtain a fully classified dataset (red square).</p> Full article ">Figure 4
<p>Point Cloud and its extracted voxel structure, where each octree level represents the grid voxels, each subdivided in subsequent eight voxel children.</p> Full article ">Figure 5
<p>Feature transfer between octree levels. We note that each non-empty node describes a voxel which can then permit a point-level access for example to compute feature sets (here, a planar voxel and a corresponding SF1 sample, and a transition voxel and its corresponding SF1 sample).</p> Full article ">Figure 6
<p>Box plot of primary elements feature variation.</p> Full article ">Figure 7
<p>Direct voxel-to-voxel topology in a 26-connectivity graph. Considered voxel <math display="inline"><semantics> <mrow> <msub> <mi mathvariant="script">V</mi> <mi>i</mi> </msub> </mrow> </semantics></math> is red, direct connections are either vertex.touch (grey), edge.touch (yellow), or face.touch (orange).</p> Full article ">Figure 8
<p>Relationship tagging in the voxel-space. (<b>a</b>) represent a mixed relationship <math display="inline"><semantics> <mrow> <mi mathvariant="script">M</mi> <mi mathvariant="script">r</mi> </mrow> </semantics></math>, (<b>b</b>) a pure vertical relationship <math display="inline"><semantics> <mrow> <mi mathvariant="script">V</mi> <mi mathvariant="script">r</mi> </mrow> </semantics></math>, and (<b>c</b>) a pure horizontal relationship <math display="inline"><semantics> <mrow> <mi mathvariant="script">H</mi> <mi mathvariant="script">r</mi> </mrow> </semantics></math>.</p> Full article ">Figure 9
<p>S3DIS points within categorized voxels. (<b>a</b>) Full transition voxels, (<b>b</b>) vertical group of points, (<b>c</b>) horizontal group of points, and (<b>d</b>) mixed group of points.</p> Full article ">Figure 10
<p>Graph representation within a voxel sample of the point cloud.</p> Full article ">Figure 11
<p>Elements detection and categorization. A point cloud is search for Primary Elements (PE), the rest is searched for Secondary elements (SE). The remaining from this step is searched for transition elements (TE), leaving remaining elements (RE). TE permits extracting graphs through SF2 analysis with PE, SE, and RE.</p> Full article ">Figure 12
<p>Edges elements to be decomposed in TE and RE.</p> Full article ">Figure 13
<p>Different graphs generated on voxel categories. (<b>a</b>) Connected Elements (CEL) graph, (<b>b</b>) PE graph, (<b>c</b>) SE graph, (<b>d</b>) TE graph, and (<b>e</b>) RE graph.</p> Full article ">Figure 14
<p>(<b>a</b>) Raw point cloud; (<b>b</b>) {PE, SE, TE, RE} groups of voxels; (<b>c</b>) Connected Elements; and, (<b>d</b>) Classified point cloud.</p> Full article ">Figure 15
<p>Normalized score and processing time in function of the defined octree level.</p> Full article ">Figure 16
<p>Problematics cases which often include point cloud artefacts such as heavy noise, missing parts, irregular shape geometries, mislabelled data.</p> Full article ">Figure 17
<p>Normalized Confusion matrix of our semantic segmentation approach over the full S3DIS dataset.</p> Full article ">Figure 18
<p>Results of the semantic segmentation on a room sample. (<b>a</b>) RGB point cloud, (<b>b</b>) Connected Elements, (<b>c</b>) Ground Truth, and (<b>d</b>) Results of the semantic segmentation.</p> Full article ">Figure 19
<p>Relative temporal performances of our automatic semantic segmentation workflow.</p> Full article ">
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