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Tinghua Ai ’s research interests include map generalization, spatial cognition and spatial data mining. He is currently the editorial board member of Computers, Environment and Urban Systems. He published more than 100 papers on the application of computational geometry in map generalization, cartographic visualization and spatial data analysis. He used to lead a team developing a generalization system which is now widely applied in map production lines in China; the system also played an important role in the engineering project of Chinese 1:50000 map updating.
In the multi-scale representation of maps, a selection operation is usually applied to reduce the number of map elements and improve legibility while maintaining the original distribution characteristics. During the past few decades, many methods for vector building selection have been developed; however, pixel-based methods are relatively lacking. In this paper, a multiple-strategy method for raster building selection is proposed. In this method, to preserve the distribution range, a new homogeneous linear spectral clustering (HLSC) superpixel segmentation method is developed for the relatively homogeneous spatial division of building groups. Then, to preserve the relative distribution density, multi-level spatial division is performed according to the local number of buildings. Finally, to preserve the local geometric, attributive and geographical characteristics, four selection strategies, namely, the minimum centroid distance, minimum boundary distance, maximum area and considering geographical element strategies, are designed to generate selection results. To evaluate the proposed method, dispersed buildings in a suburban area are utilized to perform selection tasks. The experimental results indicate that the proposed method can effectively select dispersed irregular buildings at different levels of detail while maintaining the original distribution range and relative distribution density. In addition, the use of multiple selection strategies considering various geometric, attributive and geographical characteristics provides multiple options for cartography.
Yilang Shen; Tinghua Ai; Rong Zhao. Raster-based method for building selection in the multi-scale representation of two-dimensional maps. Geocarto International 2021, 1 -18.
AMA StyleYilang Shen, Tinghua Ai, Rong Zhao. Raster-based method for building selection in the multi-scale representation of two-dimensional maps. Geocarto International. 2021; ():1-18.
Chicago/Turabian StyleYilang Shen; Tinghua Ai; Rong Zhao. 2021. "Raster-based method for building selection in the multi-scale representation of two-dimensional maps." Geocarto International , no. : 1-18.
Metaphor are commonly used rhetorical devices in linguistics. Among the various types, spatial metaphors are relatively common because of their intuitive and sensible nature. There are also many studies that use spatial metaphors to express non-location data in the field of visualization. For instance, some virtual terrains can be built based on computer technologies and visualization methods. In virtual terrains, the original abstract data can obtain specific positions, shapes, colors, etc. and people’s visual and image thinking can play a role. In addition, the theories and methods used in the space field could be applied to help people observe and analyze abstract data. However, current research has limited the use of these space theories and methods. For instance, many existing map theories and methods are not well combined. In addition, it is difficult to fully display data in virtual terrains, such as showing the structure and relationship at the same time. Facing the above problems, this study takes hierarchical data as the research object and expresses both the data structure and relationship from a spatial perspective. First, the conversion from high-dimensional non-location data to two-dimensional discrete points is achieved by a dimensionality reduction algorithm to reflect the data relationship. Based on this, kernel density estimation interpolation and fractal noise algorithms are used to construct terrain features in the virtual terrains. Under the control of the kernel density search radius and noise proportion, a multi-scale terrain model is built with the help of level of detail (LOD) technology to express the hierarchical structure and support the multi-scale analysis of data. Finally, experiments with actual data are carried out to verify the proposed method.
Rui Xin; Tinghua Ai; Ruoxin Zhu; Bo Ai; Min Yang; Liqiu Meng. A Multi-Scale Virtual Terrain for Hierarchically Structured Non-Location Data. ISPRS International Journal of Geo-Information 2021, 10, 379 .
AMA StyleRui Xin, Tinghua Ai, Ruoxin Zhu, Bo Ai, Min Yang, Liqiu Meng. A Multi-Scale Virtual Terrain for Hierarchically Structured Non-Location Data. ISPRS International Journal of Geo-Information. 2021; 10 (6):379.
Chicago/Turabian StyleRui Xin; Tinghua Ai; Ruoxin Zhu; Bo Ai; Min Yang; Liqiu Meng. 2021. "A Multi-Scale Virtual Terrain for Hierarchically Structured Non-Location Data." ISPRS International Journal of Geo-Information 10, no. 6: 379.
Remote sensing mapping plays an important role in understanding regional development and geographical environment characteristics. Traditional remote sensing mapping at different levels usually fails to consider the shape, quantity, distribution, and position features of map objects. Therefore, a multilevel representation of urban buildings is realized based on the proposed framework for multilevel mapping from remote sensing images. In this process, the Mask R-CNN method is first applied to extract buildings from remote sensing images. Then, the orthogonal shape features of the extracted buildings are reconstructed based on corner detection, and urban roads are generated by extracting the internal structural characteristics of urban buildings for further multilevel representation. Finally, three innovative raster-based generalization algorithms, including simplification, aggregation, and typification based on Hough line detection technology, are developed for a multilevel representation of urban buildings. The experimental results reveal that the proposed methods can effectively realize multilevel mapping of urban buildings from remote sensing images while meeting basic cartographic requirements.
Yilang Shen; Tinghua Ai; Hao Chen; Jingzhong Li. Multilevel Mapping From Remote Sensing Images: A Case Study of Urban Buildings. IEEE Transactions on Geoscience and Remote Sensing 2021, PP, 1 -16.
AMA StyleYilang Shen, Tinghua Ai, Hao Chen, Jingzhong Li. Multilevel Mapping From Remote Sensing Images: A Case Study of Urban Buildings. IEEE Transactions on Geoscience and Remote Sensing. 2021; PP (99):1-16.
Chicago/Turabian StyleYilang Shen; Tinghua Ai; Hao Chen; Jingzhong Li. 2021. "Multilevel Mapping From Remote Sensing Images: A Case Study of Urban Buildings." IEEE Transactions on Geoscience and Remote Sensing PP, no. 99: 1-16.
Previous research has tended to use a global threshold of proximity to determine neighbors, neglecting spatial heterogeneity. Flexible thresholds implemented by adaptive search radii methods account for either the spatial structures or the non-spatial similarities of objects, but few consider both. By combining the spatial and non-spatial information of objects, we propose a novel approach that can automatically determine the neighbors that are strongly related to the object of interest. We introduce the sparse reconstruction technique from the signal processing domain, which aims to remove trivial relationships in a dataset. We extend the sparse reconstruction model by assuring three principles in spatial data, including retention of the correlation of data in the non-spatial attribute domain, preservation of local dependencies in the spatial domain, and removal of trivial relationships. Extensive experiments, based on road network missing value imputation and building clustering, show that our approach can make better use of both spatial and non-spatial information than a simple addition of them.
Wenhao Yu; Yifan Zhang; Zhanlong Chen; Tinghua Ai. Sparse reconstruction with spatial structures to automatically determine neighbors. International Journal of Geographical Information Science 2021, 1 -23.
AMA StyleWenhao Yu, Yifan Zhang, Zhanlong Chen, Tinghua Ai. Sparse reconstruction with spatial structures to automatically determine neighbors. International Journal of Geographical Information Science. 2021; ():1-23.
Chicago/Turabian StyleWenhao Yu; Yifan Zhang; Zhanlong Chen; Tinghua Ai. 2021. "Sparse reconstruction with spatial structures to automatically determine neighbors." International Journal of Geographical Information Science , no. : 1-23.
Research has developed numerous algorithms to simplify building data. Each algorithm has strengths and weaknesses in addressing shape characteristics, but no single algorithm can appropriately simplify all buildings. This study proposes a hybrid approach that identifies the best simplified representation of a building among four existing algorithms. The proposed approach applies the four algorithms to generate simplification candidates. With a backpropagation neural network, an evaluator is built through supervised learning based on measurements describing the changes in position, size, orientation, and shape between the original building and the candidates of its simplified representations. The evaluator determines the most appropriate candidate. Experiments on buildings from residential and commercial areas in Shenzhen city show that the hybrid approach can combine the advantages of different algorithms. The percentages of unreasonable simplified buildings in the results obtained using the hybrid algorithm are 3.8% in the residential area and 0 in the commercial area, respectively, which are significantly lower than those in the results of standalone applications of the four algorithms. Furthermore, comparison with the simplification algorithm in the popular software, ArcGIS, confirms that our approach shows better results in terms of corner squaring and maintaining the regional characteristics of buildings.
Min Yang; Tuo Yuan; Xiongfeng Yan; Tinghua Ai; Chenjun Jiang. A hybrid approach to building simplification with an evaluator from a backpropagation neural network. International Journal of Geographical Information Science 2021, 1 -30.
AMA StyleMin Yang, Tuo Yuan, Xiongfeng Yan, Tinghua Ai, Chenjun Jiang. A hybrid approach to building simplification with an evaluator from a backpropagation neural network. International Journal of Geographical Information Science. 2021; ():1-30.
Chicago/Turabian StyleMin Yang; Tuo Yuan; Xiongfeng Yan; Tinghua Ai; Chenjun Jiang. 2021. "A hybrid approach to building simplification with an evaluator from a backpropagation neural network." International Journal of Geographical Information Science , no. : 1-30.
Identifying the spatial configurations of buildings and grouping them reasonably is an important task in cartography. This study developed a grouping approach using graph deep learning by integrating multiple cognitive features and manual cartographic experiences. Taking building center points as nodes, adjacent buildings were organized as a graph in which cognitive variables including size, orientation, and shape were defined for each node. Then, a learning model combining the graph convolution and neural network was designed to analyse the adjacent buildings modelled by the graph. The center points of groups were used as labels to train the positions of graph nodes and finally, a k-means algorithm was employed to obtain the grouping results based on the predicted node positions. Experiments confirmed that our approach can extract the inherent features describing the grouping relationship between buildings and performed better than two existing approaches referring to the ARI index (from 0.647 to 0.749).
Xiongfeng Yan; Tinghua Ai; Min Yang; Xiaohua Tong; Qian Liu. A graph deep learning approach for urban building grouping. Geocarto International 2020, 1 -24.
AMA StyleXiongfeng Yan, Tinghua Ai, Min Yang, Xiaohua Tong, Qian Liu. A graph deep learning approach for urban building grouping. Geocarto International. 2020; ():1-24.
Chicago/Turabian StyleXiongfeng Yan; Tinghua Ai; Min Yang; Xiaohua Tong; Qian Liu. 2020. "A graph deep learning approach for urban building grouping." Geocarto International , no. : 1-24.
Owing to map scale reduction and other cartographic generalization operations, spatial conflicts may occur between buildings and other features in automatic cartographic generalization. Displacement is an effective map generalization operation to resolve these spatial conflicts to guarantee map clarity and legibility. In this paper, a novel building displacement method based on multipopulation genetic algorithm (BDMPGA) is proposed to resolve spatial conflicts. This approach introduces multiple populations with different control parameters for simultaneous search optimization and adopts an immigration operation to connect different populations to realize coevolution. The optimal individuals of each population are selected and preserved in the elite population through manual selection operation to prevent the optimal individuals from being destroyed and lost in the evolutionary process. Meanwhile, the least preserving generation of the optimal individuals is used as the termination basis. To validate the proposed method, urban building data with a scale of 1:10,000 from Shenzhen, China are used. The experimental results indicate that the method proposed in this paper can effectively resolve spatial conflicts to obtain better results.
Wende Li; Tinghua Ai; Yilang Shen; Wei Yang; Weilin Wang. A Novel Method for Building Displacement Based on Multipopulation Genetic Algorithm. Applied Sciences 2020, 10, 8441 .
AMA StyleWende Li, Tinghua Ai, Yilang Shen, Wei Yang, Weilin Wang. A Novel Method for Building Displacement Based on Multipopulation Genetic Algorithm. Applied Sciences. 2020; 10 (23):8441.
Chicago/Turabian StyleWende Li; Tinghua Ai; Yilang Shen; Wei Yang; Weilin Wang. 2020. "A Novel Method for Building Displacement Based on Multipopulation Genetic Algorithm." Applied Sciences 10, no. 23: 8441.
Yingzhe Lei; Tinghua Ai; Xiang Zhang; Jingzhong Li. A parallel annotation placement method for dense point of interest labels using hexagonal grid. Cartography and Geographic Information Science 2020, 48, 95 -104.
AMA StyleYingzhe Lei, Tinghua Ai, Xiang Zhang, Jingzhong Li. A parallel annotation placement method for dense point of interest labels using hexagonal grid. Cartography and Geographic Information Science. 2020; 48 (2):95-104.
Chicago/Turabian StyleYingzhe Lei; Tinghua Ai; Xiang Zhang; Jingzhong Li. 2020. "A parallel annotation placement method for dense point of interest labels using hexagonal grid." Cartography and Geographic Information Science 48, no. 2: 95-104.
A tile map in image format is one of the most important tools that people can use to acquire multiscale geographic information on the Internet. Traditional methods of typification in map generalization are used to handle traditional vector-based buildings and linear drainages such as rivers and ditches. In this paper, a new raster-tile-based method called artificial pond typification (APT) is developed for the typification of artificial water areas while maintaining the original distribution characteristics and reducing the number of water areas. First, combining the second-order neighborhoods of superpixels and median filtering, the water areas are grouped at different levels of detail. Then, three different types of superpixel generation algorithms, including simple linear iterative clustering (SLIC), linear spectral clustering (LSC) and superpixel extraction via energy-driven sampling (SEEDS), are applied to generate new typified positions. Finally, different strategies, such as maximum area and global feature strategies, are designed to reconstruct the shapes of water areas. To test the proposed APT method, the map tiles from the Baidu map in China are used as the raw experimental data. The experimental results show that the proposed APT method can be effectively used for the typification of artificial ponds at different levels of detail while gradually removing the details of boundaries as the scale changes. In addition, different typification strategies provide various cartographic alternatives in different cases.
Yilang Shen; Tinghua Ai; Jingzhong Li; Lu Wang; Wende Li. A tile-map-based method for the typification of artificial polygonal water areas considering the legibility. Computers & Geosciences 2020, 143, 104552 .
AMA StyleYilang Shen, Tinghua Ai, Jingzhong Li, Lu Wang, Wende Li. A tile-map-based method for the typification of artificial polygonal water areas considering the legibility. Computers & Geosciences. 2020; 143 ():104552.
Chicago/Turabian StyleYilang Shen; Tinghua Ai; Jingzhong Li; Lu Wang; Wende Li. 2020. "A tile-map-based method for the typification of artificial polygonal water areas considering the legibility." Computers & Geosciences 143, no. : 104552.
In traditional change detection methods, remote sensing images are the primary raster data for change detection, and the changes produced from cartography generalization in multi-scale maps are not considered. The aim of this research was to use a new kind of raster data, named map tile data, to detect the change information of a multi-scale water system. From the perspective of spatial cognition, a hierarchical system is proposed to detect water area changes in multi-scale tile maps. The detection level of multi-scale water changes is divided into three layers: the body layer, the piece layer, and the slice layer. We also classify the water area changes and establish a set of indicators and rules used for the change detection of water areas in multi-scale raster maps. In addition, we determine the key technology in the process of water extraction from tile maps. For evaluation purposes, the proposed method is applied in several test areas using a map of Tiandi. After evaluating the accuracy of change detection, our experimental results confirm the efficiency and high accuracy of the proposed methodology.
Yilang Shen; Tinghua Ai. A Raster-Based Methodology to Detect Cross-Scale Changes in Water Body Representations Caused by Map Generalization. Sensors 2020, 20, 3823 .
AMA StyleYilang Shen, Tinghua Ai. A Raster-Based Methodology to Detect Cross-Scale Changes in Water Body Representations Caused by Map Generalization. Sensors. 2020; 20 (14):3823.
Chicago/Turabian StyleYilang Shen; Tinghua Ai. 2020. "A Raster-Based Methodology to Detect Cross-Scale Changes in Water Body Representations Caused by Map Generalization." Sensors 20, no. 14: 3823.
Recognition of building group patterns is of great significance for understanding and modeling the urban space. However, many current methods cannot fully utilize spatial information and have trouble efficiently dealing with topographic data with high complexity. The design of intelligent computational models that can act directly on topographic data to extract spatial features is critical. To this end, we propose a novel deep neural network based on graph convolutions to automatically identify building group patterns with arbitrary forms. The method first models buildings by a general graph, and then the neural network simultaneously learns the structural information as well as vertex attributes to classify building objects. We apply this method to real building data, and the experimental results show that the proposed method can effectively capture spatial information to make more accurate predictions than traditional methods.
Rong Zhao; Tinghua Ai; Wenhao Yu; Yakun He; Yilang Shen. Recognition of building group patterns using graph convolutional network. Cartography and Geographic Information Science 2020, 47, 400 -417.
AMA StyleRong Zhao, Tinghua Ai, Wenhao Yu, Yakun He, Yilang Shen. Recognition of building group patterns using graph convolutional network. Cartography and Geographic Information Science. 2020; 47 (5):400-417.
Chicago/Turabian StyleRong Zhao; Tinghua Ai; Wenhao Yu; Yakun He; Yilang Shen. 2020. "Recognition of building group patterns using graph convolutional network." Cartography and Geographic Information Science 47, no. 5: 400-417.
As a key focus of cartography and terrain analysis, the simplification of a digital elevation model (DEM) is used to preserve the pattern features of the terrain surface while suppressing its details over multiple scales. Statistical filtering and structural analysis methods are commonly used for this process. The structural analysis method performs well in identifying terrain structural edges, while it tends to discard the smooth morphology of a terrain surface. In addition, the filter that aims to reduce noise on a surface may over-smooth the terrain structural edges. Therefore, to preserve both the terrain structural edges and smooth morphology, we propose to combine the techniques of statistical filtering and structural analysis. Specifically, all the critical elevation points and structural edges are first detected from the DEM surface by using the structural analysis method. Then, the iterative guided normal filter is used to smooth the generalized DEM with the guidance of the structure of the original surface. After this process, the terrain structure is retained in the smooth surface of the DEM. The experimental results with a real-world dataset show that our method can inherit the merits of both structural analysis and statistical filter in preserving terrain features for multi-scale DEM representations.
Wenhao Yu; Yifan Zhang; Tinghua Ai; Zhanlong Chen. An integrated method for DEM simplification with terrain structural features and smooth morphology preserved. International Journal of Geographical Information Science 2020, 35, 273 -295.
AMA StyleWenhao Yu, Yifan Zhang, Tinghua Ai, Zhanlong Chen. An integrated method for DEM simplification with terrain structural features and smooth morphology preserved. International Journal of Geographical Information Science. 2020; 35 (2):273-295.
Chicago/Turabian StyleWenhao Yu; Yifan Zhang; Tinghua Ai; Zhanlong Chen. 2020. "An integrated method for DEM simplification with terrain structural features and smooth morphology preserved." International Journal of Geographical Information Science 35, no. 2: 273-295.
The shape of a geospatial object is an important characteristic and a significant factor in spatial cognition. Existing shape representation methods for vector-structured objects in the map space are mainly based on geometric and statistical measures. Considering that shape is complicated and cognitively related, this study develops a learning strategy to combine multiple features extracted from its boundary and obtain a reasonable shape representation. Taking building data as example, this study first models the shape of a building using a graph structure and extracts multiple features for each vertex based on the local and regional structures. A graph convolutional autoencoder (GCAE) model comprising graph convolution and autoencoder architecture is proposed to analyze the modeled graph and realize shape coding through unsupervised learning. Experiments show that the GCAE model can produce a cognitively compliant shape coding, with the ability to distinguish different shapes. It outperforms existing methods in terms of similarity measurements. Furthermore, the shape coding is experimentally proven to be effective in representing the local and global characteristics of building shape in application scenarios such as shape retrieval and matching.
Xiongfeng Yan; Tinghua Ai; Min Yang; Xiaohua Tong. Graph convolutional autoencoder model for the shape coding and cognition of buildings in maps. International Journal of Geographical Information Science 2020, 35, 490 -512.
AMA StyleXiongfeng Yan, Tinghua Ai, Min Yang, Xiaohua Tong. Graph convolutional autoencoder model for the shape coding and cognition of buildings in maps. International Journal of Geographical Information Science. 2020; 35 (3):490-512.
Chicago/Turabian StyleXiongfeng Yan; Tinghua Ai; Min Yang; Xiaohua Tong. 2020. "Graph convolutional autoencoder model for the shape coding and cognition of buildings in maps." International Journal of Geographical Information Science 35, no. 3: 490-512.
In the era of big data, a large volume of location-related semantic data needs to be visualized. The geographic information service needs to consider multi-level requests that can be adaptable to different users. Location-related semantic data can be represented in a thematic map. However, the traditional thematic map visualization can only visualize hierarchical information at one level using a statistical chart. This study presents a multi-level thematic mapping method that can visualize attribute information using LOD (Level of Detail) technology. We combine a Treemap with a cartogram to represent attribute information in thematic maps and build a LOD-Treemap model to realize multi-level visualization of attribute information. In the case study, we use our method to visualize and analyze the Chinese Farmers’ Income Structure.
Mengjie Zhou; Wenqing Hu; Tinghua Ai. Multi-level thematic map visualization using the Treemap hierarchical representation model. Journal of Geovisualization and Spatial Analysis 2020, 4, 1 -11.
AMA StyleMengjie Zhou, Wenqing Hu, Tinghua Ai. Multi-level thematic map visualization using the Treemap hierarchical representation model. Journal of Geovisualization and Spatial Analysis. 2020; 4 (1):1-11.
Chicago/Turabian StyleMengjie Zhou; Wenqing Hu; Tinghua Ai. 2020. "Multi-level thematic map visualization using the Treemap hierarchical representation model." Journal of Geovisualization and Spatial Analysis 4, no. 1: 1-11.
This paper proposes a model to quantify the multiscale representation of a polyline based on iterative head/tail breaks. A polyline is first transformed into a corresponding Fourier descriptor consisting of normalized Fourier-series-expansion coefficients. Then, the most significant finite components of the Fourier descriptor are selected and ranked to constitute the polyline constrained Fourier descriptor. Using Shannon’s information theory, information content of the constrained Fourier-descriptor components is defined. Next, head/tail breaks are introduced to iteratively divide the constrained Fourier descriptor into head and tail components according to the heavy-tailed distribution of information contents. Thus, simplified polylines are reconstructed using ordered heads generated from head/tail breaks. Finally, the radical law is introduced and applied to model multiscale polyline representation by quantifying the scale of each simplified polyline. Three experiments are designed and conducted to evaluate the proposed model. The results demonstrate that the proposed model is valid and efficient for quantifying multiscale polyline representation.
Pengcheng Liu; Tianyuan Xiao; Jia Xiao; Tinghua Ai. A multi-scale representation model of polyline based on head/tail breaks. International Journal of Geographical Information Science 2020, 34, 2275 -2295.
AMA StylePengcheng Liu, Tianyuan Xiao, Jia Xiao, Tinghua Ai. A multi-scale representation model of polyline based on head/tail breaks. International Journal of Geographical Information Science. 2020; 34 (11):2275-2295.
Chicago/Turabian StylePengcheng Liu; Tianyuan Xiao; Jia Xiao; Tinghua Ai. 2020. "A multi-scale representation model of polyline based on head/tail breaks." International Journal of Geographical Information Science 34, no. 11: 2275-2295.
With the rapid development of the internet and information technology, visualization techniques for mobile and interactive web maps have developed different requirements. Small screens make it difficult to simultaneously present information details and the surrounding context. Aiming at this problem, this paper proposes a novel variable-scale method that can allow users to properly specify the size, shape, and number of the focus area(s). Our method first establishes a hierarchical data structure for representing geographic data and then the client-side can request and represent the information according to only the operational command input by users. Experimental results show that this method can realize the variable-scale representation of real geographic data on a single screen. It can effectively solve the contradiction between a small-screen display and a large quantity of information.
Rong Zhao; Tinghua Ai; Chen Wen. A Method for Generating Variable-Scale Maps for Small Displays. ISPRS International Journal of Geo-Information 2020, 9, 250 .
AMA StyleRong Zhao, Tinghua Ai, Chen Wen. A Method for Generating Variable-Scale Maps for Small Displays. ISPRS International Journal of Geo-Information. 2020; 9 (4):250.
Chicago/Turabian StyleRong Zhao; Tinghua Ai; Chen Wen. 2020. "A Method for Generating Variable-Scale Maps for Small Displays." ISPRS International Journal of Geo-Information 9, no. 4: 250.
As a result of the increasing popularity of indoor activities, many facilities and services are provided inside buildings; hence, there is a need to visualize points-of-interest (POIs) that can describe these indoor service facilities on indoor maps. Over the last few years, indoor mapping has been a rapidly developing area, with the emergence of many forms of indoor representation. In the design of indoor map applications, cartographical methodologies such as generalization and symbolization can make important contributions. In this study, a self-adaptive method is applied for the design of a multi-scale and personalized indoor map. Based on methods of map generalization and multi-scale representation, we adopt a scale-adaptive strategy to visualize the building structure and POI data on indoor maps. At smaller map scales, the general floor distribution and functional partitioning of each floor are represented, while the POI data are visualized by simple symbols. At larger map scales, the detailed room distribution is displayed, and the service information of the POIs is described by detailed symbols. Different strategies are used for the generalization of the background building structure and the foreground POI data to ensure that both can satisfy real-time performance requirements. In addition, for better personalization, different POI data, symbols or color schemes are shown to users in different age groups, with different genders or with different purposes for using the map. Because this indoor map is adaptive to both the scale and the user, each map scale can provide different map users with decision support from different perspectives.
Yi Xiao; Tinghua Ai; Min Yang; Xiang Zhang. A Multi-Scale Representation of Point-of-Interest (POI) Features in Indoor Map Visualization. ISPRS International Journal of Geo-Information 2020, 9, 239 .
AMA StyleYi Xiao, Tinghua Ai, Min Yang, Xiang Zhang. A Multi-Scale Representation of Point-of-Interest (POI) Features in Indoor Map Visualization. ISPRS International Journal of Geo-Information. 2020; 9 (4):239.
Chicago/Turabian StyleYi Xiao; Tinghua Ai; Min Yang; Xiang Zhang. 2020. "A Multi-Scale Representation of Point-of-Interest (POI) Features in Indoor Map Visualization." ISPRS International Journal of Geo-Information 9, no. 4: 239.
Colocation mining is useful for understanding the interactions or dependencies that occur among geographic phenomena. Most colocation mining methods are based on planar space. However, in urban spaces, many human-related activities are constrained by a road network. Planar colocation mining methods are not suitable for studying the concerning geographic phenomena in an urban space. In this paper, we propose a visualization method to discover colocation patterns constrained by a road network. The method consists of two major components: network kernel density estimation and network colocation rule map construction. In the colocation rule map construction component, spatial interactions among spatial network geographic phenomena are modeled based on the idea of color mixing. We use simulated datasets with different spatial patterns, different sample sizes, and different maximum distances between road network events to test our method. The results show that our method is effective for mining colocation patterns in different situations. We also change the resolution of the network colocation rule maps, and the results show that the resolution has little influence on the results. In the case study, we apply our method to explore the spatial association between crimes and city facilities in the Loop and the Near North Side districts of Chicago.
Mengjie Zhou; Tinghua Ai; Guohua Zhou; Wenqing Hu. A Visualization Method for Mining Colocation Patterns Constrained by a Road Network. IEEE Access 2020, 8, 51933 -51944.
AMA StyleMengjie Zhou, Tinghua Ai, Guohua Zhou, Wenqing Hu. A Visualization Method for Mining Colocation Patterns Constrained by a Road Network. IEEE Access. 2020; 8 (99):51933-51944.
Chicago/Turabian StyleMengjie Zhou; Tinghua Ai; Guohua Zhou; Wenqing Hu. 2020. "A Visualization Method for Mining Colocation Patterns Constrained by a Road Network." IEEE Access 8, no. 99: 51933-51944.
During map generalization, the collapse of geometry, which is also called geometric dimension reduction, is a basic generalization operation. When the map scale decreases, rivers with long, shallow polygonal shapes, usually require their dual-line representation to be collapsed to a single line. This study presents a new algorithm called superpixel river collapse (SURC) to convert dual-line rivers to single-line rivers based on raster data. In this method, dual-line rivers are first segmented at different levels of detail using a superpixel method called simple linear iterative clustering. Then, by connecting the edge midpoints and centre of mass of each superpixel, single-line rivers are preliminarily generated from dual-line rivers. Finally, an interpolation algorithm called polynomial approximation with an exponential kernel is applied to maintain the uniform distribution of the feature points of single-line rivers at different levels of detail (LOD). The presented method can progressively collapse the river during scale transformation to support the LOD representation in a highly sensitive way. The results show that compared with three typical thinning algorithms, the SURC method can generate smooth single-line rivers from dual-line rivers considering different river widths while effectively avoiding burrs and fractured intersections.
Yilang Shen; Tinghua Ai; Jingzhong Li; Lina Huang; Wende Li. A progressive method for the collapse of river representation considering geographical characteristics. International Journal of Digital Earth 2020, 13, 1366 -1390.
AMA StyleYilang Shen, Tinghua Ai, Jingzhong Li, Lina Huang, Wende Li. A progressive method for the collapse of river representation considering geographical characteristics. International Journal of Digital Earth. 2020; 13 (12):1366-1390.
Chicago/Turabian StyleYilang Shen; Tinghua Ai; Jingzhong Li; Lina Huang; Wende Li. 2020. "A progressive method for the collapse of river representation considering geographical characteristics." International Journal of Digital Earth 13, no. 12: 1366-1390.
The automatic extraction of valley lines (VLs) from digital elevation models (DEMs) has had a long history in the GIS and hydrology fields. The quality of the extracted results relies on the geometrical shape, spatial tessellation, and placement of the grids in the DEM structure. The traditional DEM structure consists of square grids with an eight‐neighborhood relationship, where there is an inconsistent distance measurement between orthogonal neighborhoods and diagonal neighborhoods. The directional difference results in the extracted VLs by the D8 algorithm not guaranteeing isotropy characteristics. Alternatively, hexagonal grids have been proved to be advantageous over square grids due to their consistent connectivity, isotropy of local neighborhoods, higher symmetry, increased compactness, and more. Considering the merits above, this study develops an approach to VL extraction from DEMs based on hexagonal grids. First, the pre‐process phase contains the depression filling, flow direction calculation, and flow accumulation calculation based on the six‐neighborhood relationship. Then, the flow arcs are connected, followed by estimating the flow direction. Finally, the connected paths are organized into a tree structure. To explore the effectiveness of hexagonal grids, comparative experiments are implemented against traditional DEMs with square grids using three sample regions. By analyzing the results between these two grid structures via visual and quantitative comparison, we conclude that the hexagonal grid structure has an outstanding ability in maintaining the location accuracy and bending characteristics of extracted valley networks. That is to say, the DEM‐derived VLs based on hexagonal grids have better spatial agreement with mapped river systems and lower shape diversion under the same resolution representation. Therefore, the DEMs with hexagonal grids can extract finer valley networks with the same data volume relative to traditional DEM.
Lu Wang; Tinghua Ai; Yilang Shen; Jingzhong Li. The isotropic organization of DEM structure and extraction of valley lines using hexagonal grid. Transactions in GIS 2020, 24, 483 -507.
AMA StyleLu Wang, Tinghua Ai, Yilang Shen, Jingzhong Li. The isotropic organization of DEM structure and extraction of valley lines using hexagonal grid. Transactions in GIS. 2020; 24 (2):483-507.
Chicago/Turabian StyleLu Wang; Tinghua Ai; Yilang Shen; Jingzhong Li. 2020. "The isotropic organization of DEM structure and extraction of valley lines using hexagonal grid." Transactions in GIS 24, no. 2: 483-507.