本次给大家整理的是《International Journal of Geographical Information Science》杂志2024年第38卷第10期的论文的题目和摘要，一共包括9篇SCI论文！
论文1
Next track point prediction using a flexible strategy of subgraph learning on road networks
在道路网络上使用灵活的子图学习策略进行下一个轨迹点预测
【摘要】
Accurately predicting the next track point of vehicle travel is crucial for various Intelligent Transportation System (ITS) applications, such as travel behavior studies, traffic control, and traffic congestion monitoring. Recent works on trajectory prediction follow a paradigm that first represents the raw trajectory and subsequently makes predictions based on that representation. Currently, trajectory representation methods tend to project trajectory points to road networks by map matching and represent trajectories based on the representation of matched roads. However, precisely matching trajectories to roads is a challenge in ITS, as the matching precision is greatly affected by the quality of the trajectory. Meanwhile, since it is difficult to discern whether trajectory matching results are accurate or confounded, how to effectively utilize this type of uncertain geographic context information is also a challenge, which is defined as the Uncertain Geographic Context Problem (UGCoP) in geographic information science. Therefore, we propose a flexible strategy of subgraph learning, referred to as SLM, for predicting the next track point of vehicles. Specifically, a subgraph generation module is first proposed to extract topology contextual information of the roads around historical trajectory points. Secondly, a subgraph learning module is designed to learn rich spatial and temporal features from generated subgraphs. Finally, the extracted spatiotemporal features will be fed into a prediction module to predict the next track points of vehicles on road networks. Our model enables the effective utilization of uncertain geographic context information of trajectories on road networks while avoiding the error brought by map matching. Extensive experiments based on trajectory datasets in two different cities confirm the effectiveness of our approach.
【摘要翻译】
准确预测车辆行驶的下一个轨迹点对于各种智能交通系统（ITS）应用至关重要，例如出行行为研究、交通控制和交通拥堵监测。近年来，轨迹预测的研究遵循一种范式，即首先表示原始轨迹，然后基于该表示进行预测。目前，轨迹表示方法通常通过地图匹配将轨迹点投影到道路网络，并基于匹配道路的表示来表示轨迹。然而，在智能交通系统中，准确地将轨迹与道路匹配是一项挑战，因为匹配精度受到轨迹质量的重大影响。同时，由于很难判断轨迹匹配结果是否准确或存在混淆，因此如何有效利用这种不确定的地理上下文信息也是一个挑战，这在地理信息科学中被定义为不确定地理上下文问题（UGCoP）。因此，我们提出了一种灵活的子图学习策略，称为SLM，用于预测车辆的下一个轨迹点。具体来说，首先提出一个子图生成模块，以提取历史轨迹点周围道路的拓扑上下文信息。其次，设计了一个子图学习模块，从生成的子图中学习丰富的时空特征。最后，将提取的时空特征输入到预测模块，以预测道路网络上车辆的下一个轨迹点。我们的模型能够有效利用道路网络上轨迹的不确定地理上下文信息，同时避免地图匹配带来的误差。基于两个不同城市的轨迹数据集的广泛实验验证了我们方法的有效性。
【doi】
https://doi.org/10.1080/13658816.2024.2358527
【作者信息】
Yifan Zhang，中国地质大学（武汉）地理与信息工程学院，武汉，中国
Wenhao Yu，中国地质大学（武汉）地理与信息工程学院，武汉，中国；国家地理信息系统工程技术研究中心，中国地质大学（武汉），武汉，中国
Di Zhu，美国明尼苏达大学双城校区地理、环境与社会系，明尼苏达州，美国
论文2
Next location prediction using heterogeneous graph-based fusion network with physical and social awareness
使用具有物理和社会意识的异构图融合网络进行下一个位置预测
【摘要】
Location prediction based on social media information is highly valuable in human mobility research and has multiple real-life applications. However, existing research methods often ignore social influences, largely ignoring implicit information regarding interactions between users and geographical locations. Additionally, they generally employ single modeling structures, which restricts the effective integration of complex spatiotemporal characteristics and factors influencing user mobility. In this context, we propose a novel network with physical and social awareness that expresses both physical and social influences of user mobility from a global perspective based on a heterogeneous graph constructed using users and spatial locations as nodes and relationships between them as edges. This graph enables the model to leverage information from connected nodes and edges to infer missing or unobserved data. The model predicts future locations of users by effectively integrating the temporal and spatial features of user trajectory series. The proposed model is validated using three social media datasets. The experimental results demonstrate that the proposed method outperforms the state-of-the-art baseline models. This indicates the importance of considering complex interactions between users and locations, as well as the various influences of physical and social spaces.
【摘要翻译】
基于社交媒体信息的地点预测在人类移动研究中具有重要价值，并且有多种实际应用。然而，现有的研究方法往往忽视社会影响，基本上忽略了用户与地理位置之间互动的隐性信息。此外，它们通常采用单一建模结构，这限制了复杂时空特征和影响用户移动因素的有效整合。在这种背景下，我们提出了一种新颖的网络模型，该模型具有物理和社会意识，从全球视角表达用户移动的物理和社会影响。该模型基于一个异构图构建，其中用户和空间位置作为节点，它们之间的关系作为边。这个图使得模型能够利用连接节点和边的信息来推断缺失或未观察到的数据。模型通过有效整合用户轨迹序列的时间特征和空间特征来预测用户的未来位置。我们使用三个社交媒体数据集对所提出的模型进行了验证。实验结果表明，该方法优于最先进的基线模型。这表明考虑用户与位置之间的复杂互动以及物理和社会空间的各种影响是至关重要的。
【doi】
https://doi.org/10.1080/13658816.2024.2375725
【作者信息】
Sijia He,国家地理信息系统工程技术研究中心，中国地质大学（武汉），武汉，中国
Wenying Du,国家地理信息系统工程技术研究中心，中国地质大学（武汉），武汉，中国
Yan Zhang,香港中文大学，空间与地球信息科学研究所，香港，中国
Lai Chen,国家地理信息系统工程技术研究中心，中国地质大学（武汉），武汉，中国
Zeqiang Chen,国家地理信息系统工程技术研究中心，中国地质大学（武汉），武汉，中国
Nengcheng Chen，国家地理信息系统工程技术研究中心，中国地质大学（武汉），武汉，中国
论文3
Spatiotemporal mobility network of global scientists, 1970–2020
全球科学家的时空流动网络，1970–2020
【摘要】
The mobility of scientists, manifested by movements to new academic institutions, grows with globalization and plays a crucial role in individual careers, institutional productivity, and knowledge dissemination. Current research on scientists’ mobility focuses on aggregated levels such as inter-country mobility, with little attention paid to fine-grained institutional level, leading to a simplified spatial portrayal of the mobility. To fill the gap, we take scientists in geography as examples, and reconstructed their dynamic mobility network among institutions from 1970 to 2020 based on massive literature metadata. Our findings reveal the spatial mobility pattern that is now dominated by North America, Western and Northern Europe, East Asia, and Oceania, with the trend of intensification, multipolarity, and inequality over time. Specifically, the mobility network exhibits clear community structure largely constrained by spatial proximity and national borders. We also uncovered a universal downward mobility pattern embedded in the hierarchical structure. Our quantitative analysis further suggest that mobility is facilitated by multiple realities, including spatial, cultural, and scientific proximity, institutional rankings and national economic levels, cooperation, and visa-free policies, with varying dynamics. These results contribute to spatiotemporal insights into the mechanisms of scientific development in theory, and the basis for talent policymaking in practice.
【摘要翻译】
科学家的流动性，表现为向新学术机构的迁移，随着全球化的发展而增加，并在个人职业、机构生产力和知识传播中发挥着关键作用。当前对科学家流动性的研究主要集中在国家间的汇总水平，而很少关注细粒度的机构层面，这导致流动性的空间表现被简化。为填补这一空白，我们以地理学领域的科学家为例，基于大量文献元数据重建了1970年至2020年间机构之间的动态流动网络。我们的研究发现，当前的空间流动模式主要由北美、西欧和北欧、东亚和大洋洲主导，且随着时间的推移，流动性呈现出增强、多极化和不平等的趋势。具体而言，流动网络表现出明显的社区结构，这在很大程度上受到空间邻近性和国界的限制。我们还揭示了嵌入在层级结构中的普遍向下流动模式。我们的定量分析进一步表明，流动性受到多种现实因素的促进，包括空间、文化和科学邻近性、机构排名和国家经济水平、合作关系以及免签政策等，这些因素的影响动态各异。这些结果为科学发展的机制提供了时空视角的见解，并为人才政策的制定奠定了基础。
【doi】
https://doi.org/10.1080/13658816.2024.2369540
【作者信息】
Tianyu Liu，国家资源与环境信息系统重点实验室，地理科学与资源研究所，中国科学院，北京，中国；中国科学院大学，北京，中国
Tao Pei，国家资源与环境信息系统重点实验室，地理科学与资源研究所，中国科学院，北京，中国；中国科学院大学，北京，中国；江苏省地理信息资源开发与应用协同创新中心，南京，中国
Zidong Fang， 国家资源与环境信息系统重点实验室，地理科学与资源研究所，中国科学院，北京，中国；中国科学院大学，北京，中国
Mingbo Wu，国家资源与环境信息系统重点实验室，地理科学与资源研究所，中国科学院，北京，中国；中国科学院大学，北京，中国
Xiaohan Liu，国家资源与环境信息系统重点实验室，地理科学与资源研究所，中国科学院，北京，中国；中国科学院大学，北京，中国
Xiaorui Yan，国家资源与环境信息系统重点实验室，地理科学与资源研究所，中国科学院，北京，中国；中国科学院大学，北京，中国
Ci Song，国家资源与环境信息系统重点实验室，地理科学与资源研究所，中国科学院，北京，中国；中国科学院大学，北京，中国
Jingyu Jiang, 国家资源与环境信息系统重点实验室，地理科学与资源研究所，中国科学院，北京，中国；南京大学，地理与海洋科学学院，南京，中国
Linfeng Jiang，兰州大学，地球与环境科学学院，兰州，中国
Jie Chen，国家资源与环境信息系统重点实验室，地理科学与资源研究所，中国科学院，北京，中国
论文4
An empirical study of the limitations of minimum bounding boxes for defining the extent of geospatial resources: the use of DGGS and other alternatives for improving the performance of spatial searches
关于最小包围盒在定义地理空间资源范围时局限性的实证研究：使用DGGS及其他替代方案以提高空间搜索的性能
【摘要】
A Minimum Bounding Box (MBB) is a rectangle which bounds a geographic feature or dataset. It is commonly used in spatial information systems as a simplified way of describing the spatial extent of a resource. MBBs are typically indexed for searching and discovering resources relevant to a given geographic area of interest. However, this simplification leads to a loss of precision in the description of the extent and can affect the overall precision of the search results. We propose an alternative technique for describing the spatial extent based on the use of DGGS tiles. To measure the precision improvements offered by our method, we designed and implemented an empirical method for evaluating the average precision, and applied it to three different systems: one based on MBB, another on Convex Hull, and ours based on DGGS. The three methods were evaluated with the same test collection obtained from some of the main European geospatial data catalogues compliant with the INSPIRE directive. The results showed that our method outperformed the other two. Where the catalogue average precision of the MBB search scenarios is between 73% and 97%, the DGGS is between 96% and 99%. Additionally, we propose a realistic method of transitioning from the current technologies to the technology we are proposing, considering the current state of the spatial data infrastructures.
【摘要翻译】
最小外接矩形（MBB）是一个矩形，用于包围地理特征或数据集。它通常用于空间信息系统，作为描述资源空间范围的一种简化方式。MBB通常被索引，以便搜索和发现与特定地理兴趣区域相关的资源。然而，这种简化会导致在描述范围时精度的丧失，并可能影响搜索结果的整体精度。我们提出了一种基于使用DGGS瓦片描述空间范围的替代技术。为了衡量我们的方法所带来的精度改进，我们设计并实施了一种经验方法来评估平均精度，并将其应用于三个不同的系统：一个基于MBB，另一个基于凸包，而我们的系统基于DGGS。使用从一些符合INSPIRE指令的主要欧洲地理空间数据目录中获得的相同测试集合对这三种方法进行了评估。结果显示，我们的方法优于其他两种方法。在MBB搜索场景中，目录的平均精度在73%到97%之间，而DGGS的平均精度在96%到99%之间。此外，我们提出了一种现实的方法来从当前技术过渡到我们所提议的技术，考虑到空间数据基础设施的现状。
【doi】
https://doi.org/10.1080/13658816.2024.2361274
【作者信息】
Sergio Martin-Segura，阿拉贡工程研究所（I3A），萨拉戈萨大学，西班牙
Francisco J. Lopez-Pellicer,阿拉贡工程研究所（I3A），萨拉戈萨大学，西班牙
Rubén Béjar,阿拉贡工程研究所（I3A），萨拉戈萨大学，西班牙
Javier Nogueras-Iso,阿拉贡工程研究所（I3A），萨拉戈萨大学，西班牙
Francisco Javier Zarazaga-Soria，阿拉贡工程研究所（I3A），萨拉戈萨大学，西班牙
论文5
Geographical and linguistic perspectives on developing geoparsers with generic resources
关于使用通用资源开发地理解析器的地理和语言视角
【摘要】
Geoparsers aim to find place names in unstructured texts and locate them geographically. This process produces georeferenced data usable for spatial analyses or visualisations. Much geoparsing research and development has thus far focused on the English language, yet languages are not alike. Geoparsing them may necessitate language-specific processing steps or data for training geoparsing systems. In this article, we applied generic language and GIS resources to geoparsing Finnish texts. We argue that using generic resources can ease the development of geoparsers, and free up resources to other tasks, such as annotating evaluation corpora. A quantitative evaluation on new human-annotated news and tweet corpora indicates robust overall performance. A systematic analysis of the geoparser output reveals errors and their causes at each processing step. Some of the causes are specific to Finnish, and offer insights to geoparsing other morphologically complex languages as well. Our results highlight how the language of the input text affects geoparsing. Additionally, we argue that toponym resolution metrics based on error distance have limitations, and proposed metrics based on spatial intersection with ground-truth polygons.
【摘要翻译】
地理解析器的目标是在非结构化文本中查找地名并将其进行地理定位。这个过程生成了可用于空间分析或可视化的地理参考数据。目前，大多数地理解析研究和开发主要集中在英语语言上，但不同语言并不相同。对它们进行地理解析可能需要特定于语言的处理步骤或数据来训练地理解析系统。在本文中，我们应用了通用语言和地理信息系统（GIS）资源来解析芬兰文本。我们认为，使用通用资源可以简化地理解析器的开发，并将资源释放到其他任务中，例如标注评估语料库。对新的人工标注新闻和推特语料库的定量评估表明整体性能稳健。对地理解析器输出的系统分析揭示了每个处理步骤中的错误及其原因，其中一些原因是特定于芬兰的，并为对其他形态复杂语言的地理解析提供了见解。我们的结果突显了输入文本的语言如何影响地理解析。此外，我们认为，基于错误距离的地名解析指标存在局限性，并提出了基于与真实多边形空间交集的指标。
【doi】
https://doi.org/10.1080/13658816.2024.2369539
【作者信息】
Tatu Leppämäki，数字地理实验室，地球科学与地理系，赫尔辛基大学，芬兰赫尔辛基
Tuuli Toivonen，数字地理实验室，地球科学与地理系，赫尔辛基大学，芬兰赫尔辛基
Tuomo Hiippala， 数字地理实验室，地球科学与地理系，赫尔辛基大学，芬兰赫尔辛基；赫尔辛基大学语言系，芬兰赫尔辛基
论文6
Reasoning cartographic knowledge in deep learning-based map generalization with explainable AI
在基于深度学习的地图概括中推理制图知识与可解释人工智能
【摘要】
Cartographic map generalization involves complex rules, and a full automation has still not been achieved, despite many efforts over the past few decades. Pioneering studies show that some map generalization tasks can be partially automated by deep neural networks (DNNs). However, DNNs are still used as black-box models in previous studies. We argue that integrating explainable AI (XAI) into a DL-based map generalization process can give more insights to develop and refine the DNNs by understanding what cartographic knowledge exactly is learned. Following an XAI framework for an empirical case study, visual analytics and quantitative experiments were applied to explain the importance of input features regarding the prediction of a pre-trained ResU-Net model. This experimental case study finds that the XAI-based visualization results can easily be interpreted by human experts. With the proposed XAI workflow, we further find that the DNN pays more attention to the building boundaries than the interior parts of the buildings. We thus suggest that boundary intersection over union is a better evaluation metric than commonly used intersection over union in qualifying raster-based map generalization results. Overall, this study shows the necessity and feasibility of integrating XAI as part of future DL-based map generalization development frameworks.
【摘要翻译】
制图地图概括涉及复杂的规则，尽管过去几十年来进行了许多努力，但尚未实现完全自动化。开创性的研究表明，某些地图概括任务可以通过深度神经网络（DNN）部分实现自动化。然而，在以往的研究中，DNN仍被视为黑箱模型。我们认为，将可解释人工智能（XAI）整合到基于深度学习的地图概括过程中，可以通过理解到底学习了哪些制图知识，为DNN的开发和优化提供更多见解。根据XAI框架进行的实证案例研究中，采用了可视分析和定量实验来解释输入特征对预训练ResU-Net模型预测的重要性。这项实验性案例研究发现，基于XAI的可视化结果可以被人类专家轻松解读。通过提出的XAI工作流程，我们进一步发现，DNN对建筑边界的关注程度高于建筑内部。我们因此建议，在评估基于光栅的地图概括结果时，边界交并比比常用的交并比更具优势。总体而言，本研究表明，将XAI整合为未来基于深度学习的地图概括开发框架的一部分是必要且可行的。
【doi】
https://doi.org/10.1080/13658816.2024.2369535
【作者信息】
Cheng Fu，瑞士苏黎世大学地理系
Zhiyong Zhou， 瑞士苏黎世大学地理系
Yanan Xin， 瑞士联邦理工学院（ETH Zurich）制图与地理信息研究所
Robert Weibel，瑞士苏黎世大学地理系
论文7
Measuring trust in maps: development and evaluation of the MAPTRUST scale
衡量对地图的信任：MAPTRUST量表的发展和评估
【摘要】
The emergence of deepfake geographies and the growing role that maps play in shaping public opinion on key issues has prompted cartographers to interrogate the concept of map trust. However, this growing area of research is hampered by inconsistent and untested measures of map trust. This study addresses this critical gap by developing and validating a numerical rating scale that exclusively measures map trust. A model of map trust consisting of specific indicators is derived from an exploratory factor analysis. This model is then evaluated using a confirmatory factor analysis. The results indicate that map trust can be explained from a single factor related to veracity and reliability. Two factors pertaining to bias and appearance did not explain enough variance in the model. Findings also suggest that map trust can be measured by having participants evaluate maps according to twelve empirically-derived indicators: accurate, correct, error-free, honest, trustworthy, credible, fair, reliable, reputable, objective, authentic, and balanced. Measurement validity and reliability assessments of this new scale are not only based on theory but are also empirically validated. This scale can be a useful tool for researchers and practitioners alike to measure an individual’s trust in maps.
【摘要翻译】
深伪地理的出现以及地图在塑造公众对关键问题的看法中的日益重要的作用，使得制图师们开始审视“地图信任”这一概念。然而，这一日益增长的研究领域受到不一致和未经测试的地图信任测量方法的阻碍。本研究通过开发和验证一个专门测量地图信任的数字评分量表来解决这一关键空白。该地图信任模型由特定指标构成，源于探索性因子分析。随后，使用验证性因子分析对该模型进行了评估。结果表明，地图信任可以通过与真实性和可靠性相关的单一因子进行解释。与偏见和外观相关的两个因子未能在模型中解释足够的方差。研究结果还表明，可以通过让参与者根据十二个经验得出的指标来评估地图，从而测量地图信任：准确、正确、无错误、诚实、值得信赖、可信、公正、可靠、声誉良好、客观、真实和均衡。该新量表的测量有效性和可靠性评估不仅基于理论，还得到了实证验证。这个量表可以成为研究人员和从业者测量个体对地图信任的有用工具。
【doi】
https://doi.org/10.1080/13658816.2024.2370076
【作者信息】
Timothy J. Prestby美国宾夕法尼亚州州立大学地理系，州立学院；宾夕法尼亚州立大学地理图形实验室，州立学院
论文8
Spatially constrained statistical approach for determining the optimal number of regions in regionalization
空间约束统计方法用于确定区域化的最优区域数量
【摘要】
Determining the optimal number of regions is a challenging issue in regionalization. Although cluster validity indices developed for non-spatial clustering have been used to determine the optimal number of regions, spatial contiguity constraints for regionalization are often neglected. Consequently, different regionalization results can share the same validity index value, which reduces the reliability of identifying the optimal number of regions in regionalization. To overcome this limitation, this study proposes a spatially constrained statistical approach for determining the optimal number of regions using two metrics: (i) a permutation-based variance for measuring the homogeneity within regions and (ii) a proportion index based on spatially constrained k-nearest neighbors to quantify the separation between regions. Furthermore, a distance-based method is employed to balance these two metrics to automatically determine the optimal number of regions. Experimental results on five synthetic datasets, the US presidential election and climate datasets show that the statistical approach developed in this study outperforms three widely used cluster validity indices in determining the optimal number of regions. The proposed statistical approach is straightforward to implement and can effectively reduce subjectivity in regionalization.
【摘要翻译】
确定最佳区域数量是区域划分中的一个挑战性问题。尽管为非空间聚类开发的聚类有效性指数已被用于确定最佳区域数量，但区域划分中的空间相邻约束常常被忽视。因此，不同的区域划分结果可能具有相同的有效性指数值，这降低了识别区域划分中最佳区域数量的可靠性。为克服这一局限性，本研究提出了一种空间约束统计方法，用于确定最佳区域数量，采用两个指标：(i) 基于置换的方差，用于测量区域内的同质性；(ii) 基于空间约束的k近邻的比例指数，用于量化区域之间的分离度。此外，采用基于距离的方法来平衡这两个指标，从而自动确定最佳区域数量。对五个合成数据集、美国总统选举和气候数据集的实验结果表明，本研究开发的统计方法在确定最佳区域数量方面优于三种广泛使用的聚类有效性指数。所提出的统计方法易于实施，并能有效减少区域划分中的主观性。
【doi】
https://doi.org/10.1080/13658816.2024.2372779
【作者信息】
Yuxuan Chen,中南大学，地理信息系，长沙，湖南，中国
Qiliang Liu,中南大学，地理信息系，长沙，湖南，中国
Jie Yang，中南大学，地理信息系，长沙，湖南，中国
Xinghua Cheng，康奈尔大学，自然资源与环境系，斯托尔斯，康涅狄格州，美国
Min Deng，中南大学，地理信息系，长沙，湖南，中国
论文9
MNCD-KE: a novel framework for simultaneous attribute- and interaction-based geographical regionalization
MNCD-KE：一种用于同时基于属性和交互的地理区域化的新框架
【摘要】
Existing regionalization methods tend to be either spatial attribute- or spatial interaction-based, while real-world tasks usually involve both considerations to satisfy multiple objectives simultaneously. In this research, we propose Multilayer Network Community Detection and Kernel Extension (MNCD-KE), a two-step regionalization framework, as a feasible solution for such tasks. First, spatial attributes are embedded into attributes of nodes in a spatial interaction-defined multilayer network, and the kernel and marginal parts of the regions are determined by giving the membership value of the regionalization units to network communities. Second, the final result is obtained through a kernel extension process considering geographical constraints, including spatial contiguity, size balance, morphological regularity, and existing boundary consistency of the regions. Empirical experiments show that the proposed method yields outcomes that, in maintaining comparable performances with most baseline algorithms with either ‘attribute’ or ‘interaction’ objectives as measured by the respective criteria, simultaneously meet the dual objectives with results intuitively comprehensible. Its low computing costs and parameter adjustment flexibility make the proposed framework a convenient approach for real-world multi-objective regionalization tasks. We conclude the research with discussions on the boundary conditions for the framework to work and their relevance to city science theories, along with practical implications.
【摘要翻译】
现有的区域划分方法通常基于空间属性或空间交互，而现实世界的任务通常涉及两者，以同时满足多个目标。在本研究中，我们提出了多层网络社区检测与核扩展（MNCD-KE），这是一种两步区域划分框架，作为解决此类任务的可行方案。首先，空间属性被嵌入到由空间交互定义的多层网络中节点的属性中，通过给予区域划分单元的成员值来确定区域的核心部分和边缘部分。其次，通过考虑地理约束（包括空间相邻性、大小平衡、形态规律性和区域的现有边界一致性）来进行核扩展过程，从而获得最终结果。实证实验表明，所提出的方法在维持与大多数基准算法的性能相当的同时（无论是基于“属性”还是“交互”目标），同时满足双重目标，且结果易于理解。其低计算成本和参数调整灵活性使得该框架成为处理现实世界多目标区域划分任务的便捷方法。我们在研究的最后讨论了该框架运作的边界条件及其与城市科学理论的相关性，以及实际应用的意义。
【doi】
https://doi.org/10.1080/13658816.2024.2375732
【作者信息】
Liyan Xu，北京大学建筑与景观建筑学院，中国北京
Jintong Tang，北京大学地球与空间科学学院遥感与地理信息系统研究所，中国北京
Hezhishi Jiang,北京大学高级跨学科研究院，中国北京
Hongbin Yu,北京大学高级跨学科研究院，中国北京
Qian Huang,华为技术有限公司全球技术服务部，中国北京
Yinsheng Zhou，华为技术有限公司全球技术服务部，中国北京
Yu Liu， 北京大学地球与空间科学学院遥感与地理信息系统研究所，中国北京