摘要：
Drainage network pattern recognition is a significant task with wide applications in geographic information mining, map cartography, water resources management, and urban planning. Accurate identification of spatial patterns in river networks can help us understand geographic phenomena, optimize map cartographic quality, assess water resource potential, and provide a scientific basis for urban development planning. However, river network pattern recognition still faces challenges due to the complexity and diversity of river networks. To address this issue, this study proposes a river network pattern recognition method based on graph convolutional networks (GCNs), aiming to achieve accurate classification of different river network patterns. We utilize binary trees to construct a hierarchical tree structure based on river reaches and progressively determine the tree hierarchy by identifying the upstream and downstream relationships among river reaches. Based on this representation, input features for the graph convolutional model are extracted from both spatial and geometric perspectives. The effectiveness of the proposed method is validated through classification experiments on four types of vector river network data (dendritic, fan-shaped, trellis, and fan-shaped). The experimental results demonstrate that the proposed method can effectively classify vector river networks, providing strong support for research and applications in related fields.
作者机构：
[刘鹏程; 黄欣; 马宏然] Key Laboratory for Geographical Process Analysis and Simulation of Hubei Province, Central China Normal University, Wuhan;430079, China;School of Urban and Environmental Sciences, Central China Normal University, Wuhan;[杨敏] School of Resource and Environment Sciences, Wuhan University, Wuhan;[刘鹏程; 黄欣; 马宏然] 430079, China<&wdkj&>School of Urban and Environmental Sciences, Central China Normal University, Wuhan
作者机构：
[晏雄锋] College of Surveying and Geo-Informatics, Tongji University, Shanghai;200092, China;[袁拓; 杨敏; 孔博] School of Resource and Environmental Sciences, Wuhan University, Wuhan;430079, China;[刘鹏程] School of Urban and Environmental Sciences, Central China Normal University, Wuhan
通讯机构：
[Yang, M.] S;School of Resource and Environmental Sciences, Wuhan University, Wuhan, China
摘要：
For point clusters, the conflict and crowding of map symbols is an inevitable problem during the transition from large to small scales. The cartographic generalization involved in this problem as a spatial decision-making process is usually related to the analysis of spatial context, the choice of abstraction operators, and the judgment of the resulting data quality. The rules summarized by traditional generalization methods usually require manual setting of conditions or thresholds and sometimes encounter special cases that make it difficult to directly match certain rules or integrate different rules together. An alternative method is using a data-driven strategy under AI technology background to simulate cartographer behaviors through typical sample training, such as deep learning. The integration of cartography domain knowledge and deep learning is a better choice to settle generalization decisions. This study uses a combination of domain knowledge and a data-driven approach to introduce graph neural networks into point cluster generalization. First, we construct a virtual graph structure of point clusters using Delaunay triangulation, secondly, we extract spatial features, contextual features, and attributes of each point separately, and then propose a generalization model based on the TAGCN network. Finally, this model is trained with the manually generalized sample to realize the automatic point cluster generalization. The results demonstrate that the proposed model is valid and efficient for point cluster generalization and that this algorithm can better maintain various characteristics of the point cluster in both the local area and the overall map compared to other methods.
期刊：
ISPRS International Journal of Geo-Information,2021年10(10):705- ISSN：2220-9964
通讯作者：
Yi Chao
作者机构：
[Li, Yang; Jiang, Le; Yang, Nai] School of Geography and Information Engineering, China University of Geosciences, Wuhan 430078, China;Author to whom correspondence should be addressed.;[Liu, Pengcheng] College of Urban and Environmental Science, Central China Normal University, Wuhan 430079, China;[Chao, Yi] School of Geography and Information Engineering, China University of Geosciences, Wuhan 430078, China<&wdkj&>Author to whom correspondence should be addressed.
通讯机构：
[Yi Chao] S;School of Geography and Information Engineering, China University of Geosciences, Wuhan 430078, China<&wdkj&>Author to whom correspondence should be addressed.
关键词：
street network complexity;OSMnx;street orientations;China’s topography
摘要：
The relief degree of land surface (RDLS) was often calculated to describe the topographic features of a region. It is a significant factor in designing urban street networks. However, existing studies do not clarify how RDLS affects the distribution of urban street networks. We used a Python package named OSMnx to extract the street networks of different cities in China. The street complexity metrics information (i.e., street grain, connectedness, circuity, and street network orientation entropy) were obtained and analyzed statistically. The results indicate that street network exhibits more complexity in regions with high RDLS. Further analysis of the correlation between RDLS and street network complexity metrics indicates that RDLS presents the highest correlation with street network circuity; that is, when RDLS is higher, the routes of an urban street network is more tortuous, and the additional travel costs for urban residents is higher. This study enriches and expands research on street networks in China, providing a reference value for urban street network planning.
期刊：
ISPRS International Journal of Geo-Information,2020年9(6):410- ISSN：2220-9964
通讯作者：
Xiao, Jia
作者机构：
[Xiao, Jia; Liu, Pengcheng] Cent China Normal Univ, Hubei Prov Key Lab Geog Proc Anal & Simulat, Wuhan 430079, Peoples R China.;[Xiao, Jia; Liu, Pengcheng] Cent China Normal Univ, Coll Urban & Environm Sci, Wuhan 430079, Peoples R China.
通讯机构：
[Xiao, Jia] C;Cent China Normal Univ, Hubei Prov Key Lab Geog Proc Anal & Simulat, Wuhan 430079, Peoples R China.;Cent China Normal Univ, Coll Urban & Environm Sci, Wuhan 430079, Peoples R China.
关键词：
level of detail;graphical unit;geographical feature;digital map
摘要：
This paper proposes a method to evaluate the level of detail (LoD) of geographic features on digital maps and assess their LoD consistency. First, the contour of the geometry of the geographic feature is sketched and the hierarchy of its graphical units is constructed. Using the quartile measurement method of statistical analysis, outliers of graphical units are eliminated and the average value of the graphical units below the bottom quartile is used as the statistical LoD parameter for a given data sample. By comparing the LoDs of homogeneous and heterogeneous features, we analyze the differences between the nominal scale and actual scale to evaluate the LoD consistency of features on a digital map. The validation of this method is demonstrated by experiments conducted on contour lines at a 1:5K scale and artificial building polygon data at scales of 1:2K and 1:5K. The results show that our proposed method can extract the scale of features on maps and evaluate their LoD consistency.
作者机构：
[刘鹏程; 肖天元; 肖佳] School of Urban and Environmental Sciences, Central China Normal University, Wuhan;430079, China;Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province, Central China Normal University, Wuhan;[艾廷华] School of Resource and Environment Sciences, Wuhan University, Wuhan;[刘鹏程; 肖天元; 肖佳] 430079, China<&wdkj&>Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province, Central China Normal University, Wuhan
通讯机构：
[Xiao, J.] S;School of Urban and Environmental Sciences, Central China Normal University, Wuhan, China
期刊：
International Journal of Geographical Information Science,2020年34(11):2275-2295 ISSN：1365-8816
通讯作者：
Xiao, Jia
作者机构：
[Xiao, Tianyuan; Xiao, Jia; Liu, Pengcheng] Cent China Normal Univ, Key Lab Geog Proc Anal & Simulat, Wuhan, Peoples R China.;[Xiao, Tianyuan; Xiao, Jia; Liu, Pengcheng] Cent China Normal Univ, Coll Urban & Environm Sci, Wuhan, Peoples R China.;[Ai, Tinghua] Wuhan Univ, Sch Resource & Environm Sci, Wuhan, Peoples R China.
通讯机构：
[Xiao, Jia] C;Cent China Normal Univ, Key Lab Geog Proc Anal & Simulat, Wuhan, Peoples R China.;Cent China Normal Univ, Coll Urban & Environm Sci, Wuhan, Peoples R China.
摘要：
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.
作者机构：
[刘鹏程] Key Laboratory for Geographical Process Analysis &, Simulation, College of Urban and Environmental Science, Central China Normal University, Wuhan, Hubei, 430079, China;[艾廷华; 李精忠] School of Resource and Environmental Sciences, Wuhan University, Wuhan, Hubei, 430079, China
通讯机构：
Key Laboratory for Geographical Process Analysis & Simulation, College of Urban and Environmental Science, Central China Normal University, Wuhan, Hubei, China
