本次给大家整理的是《International Journal of Geographical Information Science》杂志2024年第38卷第9期的论文的题目和摘要，一共包括9篇SCI论文！
论文1
A movement-aware measure for trajectory similarity and its application for ride-sharing path extraction in a road network
一种基于运动感知的轨迹相似性度量及其在道路网络中共享乘车路径提取中的应用
【摘要】
Recognizing common travel paths of crowds in a road network is valuable for understanding human mobility patterns and developing intelligent ride-sharing services. To achieve this, it is critical to measure the similarity of their trajectories. Although many measures have been proposed in the past decades, they often ignore movement consistency, exhibit one or more deficiencies in the face of noise and misaligned trajectories, or require extra parameters to tune predictions. In this paper, we propose an improved similarity measure called the directed segment path distance (DSPD), which considers the spatial proximity and movement consistency of trajectories. By integrating the spatial proximity distance and moving direction similarity between trajectories, the DSPD is a competitive parameter-free similarity measure that can effectively distinguish trajectories with different movement characteristics. To verify the effectiveness of the DSPD, we conducted a quantitative comparative study between the DSPD measure and 11 state-of-the-art trajectory similarity measures on six simulated trajectory datasets and applied the DSPD to two typical application scenarios: trajectory clustering for road network generation and retrieving common trajectories for ride-sharing path planning. The results demonstrate the effectiveness, robustness, and superiority of the DSPD and its great potential in trajectory search, clustering, and classification.
【摘要翻译】
在道路网络中识别人群的常见旅行路径对于理解人类移动模式和开发智能共享乘车服务具有重要价值。为了实现这一目标，关键在于测量其轨迹的相似性。尽管在过去几十年中提出了许多度量方法，但它们往往忽视了运动的一致性，在面对噪声和错位轨迹时表现出一种或多种缺陷，或者需要额外的参数来调优预测。本文提出了一种改进的相似性度量，称为定向段路径距离（DSPD），该度量考虑了轨迹的空间接近性和运动一致性。通过整合轨迹之间的空间接近距离和运动方向相似性，DSPD是一种具有竞争力的无参数相似性度量，可以有效区分具有不同运动特征的轨迹。为了验证DSPD的有效性，我们对DSPD度量与11种最先进的轨迹相似性度量在六个模拟轨迹数据集上进行了定量比较研究，并将DSPD应用于两个典型应用场景：道路网络生成的轨迹聚类和共享乘车路径规划中的常见轨迹检索。结果证明了DSPD的有效性、鲁棒性和优越性，以及其在轨迹搜索、聚类和分类中的巨大潜力。
【doi】
https://doi.org/10.1080/13658816.2024.2353695
【作者信息】
Ju Peng，中南大学，地理信息学系，中国湖南长沙
Min Deng，中南大学，地理信息学系；湖南省地理空间信息工程技术研究中心，中国湖南长沙
Jianbo Tang，中南大学，地理信息学系；湖南省地理空间信息工程技术研究中心，中国湖南长沙
Zhiyuan Hu，中南大学，地理信息学系，中国湖南长沙
Heyan Xia，中南大学，地理信息学系，中国湖南长沙
Huimin Liu，中南大学，地理信息学系，中国湖南长沙
Xiaoming Mei，中南大学，地理信息学系，中国湖南长沙
论文2
ST-ADPTC: a method for clustering spatiotemporal raster data based on improved density peak detection
ST-ADPTC：一种基于改进密度峰检测的时空栅格数据聚类方法
【摘要】
Spatiotemporal raster (STR) data employ an array of grids to represent temporally varying and spatially distributed information, commonly utilized for recording environmental variables and socioeconomic indices. To reveal the geographic patterns embedded in STR data, the clustering by fast search and finding of density peaks (CFSFDP) algorithm is considered effective and suitable. However, this algorithm encounters limitations in identifying cluster centers, handling large data volumes, and measuring the coupled spatial-temporal-attribute distance when applied to STR data. To overcome these challenges, we propose an improved method named spatial temporal-adaptive density peak tree clustering (ST-ADPTC). This method leverages adaptive density peak tree segmentation to identify cluster centers and optimizes memory usage through the k-nearest neighbors (kNN) technique. By constructing a neighborhood that incorporates both spatiotemporal and thematic attribute similarities, ST-ADPTC computes the local density of STR data, facilitating the discovery of time-varying clusters. Based on the proposed method, we develop an open-source Python package (Geo_ADPTC). Experiments conducted using benchmarking datasets illustrate improvements in cluster identification and memory reduction. Additionally, a case study of sea surface temperature data demonstrates the feasibility and effectiveness of exploring spatial and temporal distribution patterns using the proposed method.
【摘要翻译】
时空光栅（STR）数据利用一系列网格来表示时变和空间分布的信息，通常用于记录环境变量和社会经济指标。为了揭示嵌入STR数据中的地理模式，基于快速搜索和密度峰值发现的聚类（CFSFDP）算法被认为是有效且适用的。然而，在应用于STR数据时，该算法在识别聚类中心、处理大数据量以及测量耦合的时空属性距离方面遇到了局限性。为了解决这些挑战，我们提出了一种改进的方法，称为空间时间自适应密度峰值树聚类（ST-ADPTC）。该方法利用自适应密度峰值树分割来识别聚类中心，并通过k最近邻（kNN）技术优化内存使用。通过构建一个同时结合时空和主题属性相似性的邻域，ST-ADPTC计算STR数据的局部密度，从而促进时变聚类的发现。基于所提出的方法，我们开发了一个开源Python包（Geo_ADPTC）。使用基准数据集进行的实验表明，聚类识别和内存减少方面有所改善。此外，对海表温度数据的案例研究展示了使用该方法探索空间和时间分布模式的可行性和有效性。
【doi】
https://doi.org/10.1080/13658816.2024.2353703
【作者信息】
Jie Song，虚拟地理环境重点实验室，南京师范大学，教育部，南京，中国；地理环境演变省部共建国家重点实验室培育基地，南京师范大学，南京，中国；地理信息资源开发与应用协同创新江苏中心，南京师范大学，南京，中国；武汉测绘研究院，武汉，中国
Songshan Yue，虚拟地理环境重点实验室，南京师范大学，教育部，南京，中国；地理环境演变省部共建国家重点实验室培育基地，南京师范大学，南京，中国；地理信息资源开发与应用协同创新江苏中心，南京师范大学，南京，中国
Min Chen，虚拟地理环境重点实验室，南京师范大学，教育部，南京，中国；地理环境演变省部共建国家重点实验室培育基地，南京师范大学，南京，中国；江苏省地理信息资源开发与应用协同创新中心，南京师范大学，南京，中国
Zhuo Sun，虚拟地理环境重点实验室，南京师范大学，教育部，南京，中国；地理环境演变省部共建国家重点实验室培育基地，南京师范大学，南京，中国；江苏省地理信息资源开发与应用协同创新中心，南京师范大学，南京，中国
Yongning Wen，虚拟地理环境重点实验室，南京师范大学，教育部，南京，中国；地理环境演变（江苏省）国家重点实验室培育基地，南京师范大学，南京，中国；江苏省地理信息资源开发与应用协同创新中心，南京师范大学，南京，中国
Lingzhi Sun，自然资源部第三地理信息制图研究院，中国成都
论文3
Combining hierarchies for spatial reasoning of hierarchical relations among places in different contexts
结合层次结构进行不同背景下地点层次关系的空间推理
【摘要】
Spatial reasoning of hierarchical relations is an important research topic in geographic information science. Existing literature focuses mainly on hierarchical relations according to ordered containment of point sets based on the 9-Intersection Model (9IM). However, in natural language descriptions, hierarchical relations expressed by prepositions such as in can have spatial configurations conflicting with those hierarchical relations based on 9IM. This paper proposes an algorithm employing combined hierarchies to infer hierarchical relations among places in different contexts. Two graph databases containing combined hierarchies conforming to administrative reach and geometric enclosure are established with places represented by polygons with misaligned boundaries on prototypical and real data. The 13170 pairs of conflicting hierarchical relations with the same start nodes but different end nodes detected in the combined hierarchies on real data validate the proposed algorithm for spatial reasoning of hierarchical relations in different contexts. This research facilitates consistent spatial reasoning of hierarchical relations in different contexts for spatial information retrieval and question answering.
【摘要翻译】
空间层次关系的推理是地理信息科学中的一个重要研究课题。现有文献主要集中于基于九交集模型（9IM）根据点集的有序包含来描述层次关系。然而，在自然语言描述中，通过介词如“在”表达的层次关系，其空间配置可能与基于9IM的层次关系相矛盾。本文提出了一种利用组合层次推断不同上下文中地点间层次关系的算法。建立了两个图形数据库，其中包含符合行政范围和几何封闭的组合层次，地点由具有不对齐边界的多边形表示。在真实数据中检测到的13170对具有相同起始节点但不同终止节点的冲突层次关系，验证了所提算法在不同上下文中进行空间层次关系推理的有效性。本研究促进了不同上下文中层次关系的一致空间推理，有助于空间信息检索和问答系统的实现。
【doi】
https://doi.org/10.1080/13658816.2024.2354826
【作者信息】
Xiaonan Wang，墨尔本大学基础设施工程系，澳大利亚
论文4
Multi-observation points setting problem based on stepwise maximum viewshed approach
基于逐步最大视域方法的多观测点设置问题
【摘要】
Terrain visibility analysis is vital in geospatial research, employing computer geometry and graphics principles for computing and visualizing visibility between observation and target points. The observation point setup problem is crucial for tasks like sentry point selection and signal base station placement. This problem involves choosing the minimum viewpoints on terrain for optimal joint view coverage, presenting a combinatorial optimization challenge. As technology advances, data can be acquired with increasing precision, the number of viewpoints that can be extracted from the same area is gradually increasing as well, obtaining candidate viewpoints and determining optimal combinations become challenging. This paper proposes a novel method, Stepwise Maximum Viewshed (SMV), addressing observation point setup by stepwise filtering the maximum viewshed and can customize the size of the observation point combination. In complex mountainous terrain, the SMV algorithm demonstrates superior joint view coverage compared to Candidate Viewpoints Filtering (CVF) and Simulated Annealing (SA) algorithms. Experimental results reveal up to 5.59% improvement over CVF and a maximum of 12.52% over SA in joint viewshed coverage.
【摘要翻译】
地形可见性分析在地理空间研究中至关重要，利用计算几何和图形学原理计算和可视化观察点与目标点之间的可见性。观察点设置问题对哨所选择和信号基站布置等任务至关重要。该问题涉及选择地形上的最小观察点以实现最佳联合视域覆盖，呈现出组合优化的挑战。随着技术的进步，数据获取的精度不断提高，同时从同一区域提取的观察点数量也逐渐增加，因此获取候选观察点和确定最佳组合变得更加困难。本文提出了一种新方法，即逐步最大视域（SMV），通过逐步过滤最大视域来解决观察点设置问题，并且可以自定义观察点组合的大小。在复杂的山区地形中，SMV算法的联合视域覆盖优于候选观察点过滤（CVF）和模拟退火（SA）算法。实验结果显示，在联合视域覆盖方面，SMV相较于CVF最多提高了5.59%，相较于SA最高提高了12.52%。
【doi】
https://doi.org/10.1080/13658816.2024.2354822
【作者信息】
Peng Wang，南京师范大学地理学院，中国南京；南京师范大学虚拟地理环境教育部重点实验室，中国南京
Junfei Ma，南京师范大学地理学院，中国南京；南京师范大学虚拟地理环境教育部重点实验室，中国南京
Fayuan Li，南京师范大学地理学院，中国南京；南京师范大学虚拟地理环境教育部重点实验室，中国南京
论文5
GeoAR: a calibration method for Geographic-Aware Augmented Reality
GeoAR：一种地理感知增强现实的校准方法
【摘要】
The applicability of augmented reality (AR) as a tool for geospatial data analysis and urban environment interaction relies on developing robust and accurate systems capable of aligning the virtual reference frame with the geographic one. In this article, we introduce our work toward the conceptualization and realization of Geographic-Aware Augmented Reality (GeoAR), including an evaluated framework for the automatic registration of georeferenced AR content. The proposed framework uses a novel calibration method that enables highly accurate placement of augmentations at their assigned geographic coordinate. Moreover, it introduces four calibration approaches suitable for different user needs. The framework was evaluated to assess the robustness of the augmentation’s positional accuracy in three areas with different environmental characteristics, using references up to 50 m away while the user moves around. The results demonstrate that this framework supports novel outdoor AR applications, extending the possibilities in research and urban applications.
【摘要翻译】
增强现实（AR）作为地理空间数据分析和城市环境交互工具的适用性，依赖于开发能够将虚拟参考框架与地理参考框架对齐的强大而准确的系统。本文介绍了我们在地理感知增强现实（GeoAR）的概念化和实现方面的工作，包括一个评估框架，用于自动注册地理参考的AR内容。该框架采用了一种新颖的校准方法，能够在其指定的地理坐标上实现高度准确的增强放置。此外，它引入了四种适合不同用户需求的校准方法。通过在三个具有不同环境特征的区域评估该框架，以检验增强物体位置精度的鲁棒性，使用的参考点距离用户移动位置最多50米。结果表明，该框架支持新颖的户外AR应用，拓展了研究和城市应用的可能性。
【doi】
https://doi.org/10.1080/13658816.2024.2355326
【作者信息】
Marcelo L. Galvão，维也纳科技大学测量与地理信息系，奥地利维也纳
Paolo Fogliaroni，维也纳科技大学测量与地理信息系，奥地利维也纳
Ioannis Giannopoulos，维也纳科技大学测量与地理信息系，奥地利维也纳
Gerhard Navratil，维也纳科技大学测量与地理信息系，奥地利维也纳
Markus Kattenbeck，维也纳科技大学测量与地理信息系，奥地利维也纳
Negar Alinaghi，维也纳科技大学测量与地理信息系，奥地利维也纳
论文6
Optimisation of spatiotemporal context-constrained full-view area coverage deployment in camera sensor networks via quantum annealing
通过量子退火优化受时空上下文约束的全视野区域覆盖部署在摄像头传感器网络中的应用
【摘要】
Full-view coverage of a space area with a camera sensor network (CSN) is key to monitoring tasks like security monitoring. Unlike traditional CSN challenges that focus on mere target detection, the full-view area coverage problem (FVACP) demands recognition of targets irrespective of their locations or orientations. However, prior approaches often neglect real-world spatiotemporal context constraints like buildings and pedestrian dynamics, leading to inefficient CSN deployment. Moreover, FVACP’s complexity, being an NP-hard issue, underscores the need for effective optimisation strategies. Recently, quantum annealing (QA) has emerged as a promising solution, which potentially outperforms classical computing in optimisation tasks. Therefore, this study proposes a QA-based FVACP optimisation framework. It addresses spatial constraints by optimising candidate deployment points and tackles temporal constraints by optimising sensor orientations. These optimisation tasks are converted into quadratic unconstrained binary optimisation problems, which are suitable for QA techniques and benchmarking against classical methods. The effectiveness of the framework is validated through facial recognition-oriented experiments. Results demonstrate not only efficient CSN deployment with larger benefits and fewer cameras but also confirm the superiority of QA over classical computing given that it delivers approximate optimum outcomes across various scenarios. Consequently, CSN monitoring capabilities in real-world applications can be enhanced.
【摘要翻译】
使用摄像头传感器网络（CSN）实现空间区域的全视野覆盖对安全监控等监测任务至关重要。与传统的仅关注目标检测的CSN挑战不同，全视野区域覆盖问题（FVACP）要求识别目标，无论其位置或方向如何。然而，以往的方法常常忽视了建筑物和行人动态等现实世界的时空上下文约束，导致CSN部署效率低下。此外，FVACP的复杂性是一个NP难题，这突显了有效优化策略的必要性。最近，量子退火（QA）作为一种有前景的解决方案，可能在优化任务中优于经典计算。因此，本研究提出了一个基于QA的FVACP优化框架。该框架通过优化候选部署点来解决空间约束，并通过优化传感器方向来应对时间约束。这些优化任务被转换为适合QA技术的二次无约束二进制优化问题，并与经典方法进行基准测试。通过面部识别相关实验验证了该框架的有效性。结果表明，不仅实现了更大收益和更少摄像头的高效CSN部署，还确认了QA相较于经典计算的优越性，因为它在各种场景下提供了近似最优的结果。因此，可以增强CSN在现实应用中的监控能力。
【doi】
https://doi.org/10.1080/13658816.2024.2358045
【作者信息】
Jie Li，中国地质大学（武汉）计算机科学学院
Zhenqiang Li，中国地质大学（武汉）地理与信息工程学院 国家地理信息系统工程研究中心
Long Yao，中国地质大学（武汉）地理与信息工程学院 国家地理信息系统工程研究中心
Ke Wang，国家地理信息系统工程研究中心，中国地质大学（武汉）地理与信息工程学院，武汉，中国
Jialin Li，地理信息系统国家工程研究中心，中国地质大学（武汉）地理与信息工程学院，武汉，中国
Yao Lu，地理信息系统国家工程研究中心，中国地质大学（武汉）地理与信息工程学院，武汉，中国
Chuli Hu，地理信息系统国家工程研究中心，中国地质大学（武汉）地理与信息工程学院，武汉，中国；自然资源信息管理与数字孪生工程软件教育部工程研究中心，中国地质大学（武汉），武汉，中国
论文7
An ensemble spatial prediction method considering geospatial heterogeneity
考虑地理空间异质性的集成空间预测方法
【摘要】
Ensemble learning synthesizes the advantages of different models and has been widely applied in the field of spatial prediction. However, the nonlinear constraints of spatial heterogeneity on the model ensemble process make it difficult to adaptively determine the ensemble weights, greatly limiting the predictive ability of the ensemble learning model. This paper therefore proposes a novel geographical spatial heterogeneous ensemble learning method (GSH-EL). Firstly, the geographically weighted regression model, geographically optimal similarity model, and random forest model are used as three base learners to express local spatial heterogeneity, global feature correlation, and nonlinear relationship of geographic elements, respectively. Then, a spatially weighted ensemble neural network module (SWENN) of GSH-EL is proposed to express spatial heterogeneity by exploring the complex nonlinear relationship between the spatial proximity and ensemble weights. Finally, the outputs of the three base learners are combined with the spatial heterogeneous ensemble weights from SWENN to obtain the spatial prediction results. The proposed method is validated on the PM2.5 air quality and landslide dataset in China, both of which obtain more accurate prediction results than the existing ensemble learning strategies. The results confirm the need to accurately express spatial heterogeneity in the model ensemble process.
【摘要翻译】
集成学习综合了不同模型的优点，广泛应用于空间预测领域。然而，空间异质性的非线性约束对模型集成过程的影响使得自适应确定集成权重变得困难，这极大限制了集成学习模型的预测能力。因此，本文提出了一种新颖的地理空间异质性集成学习方法（GSH-EL）。首先，使用地理加权回归模型、地理最优相似性模型和随机森林模型作为三个基础学习器，分别表达局部空间异质性、全局特征相关性和地理元素的非线性关系。接着，提出了一种空间加权集成神经网络模块（SWENN），通过探索空间邻近性与集成权重之间的复杂非线性关系来表达空间异质性。最后，将三个基础学习器的输出与SWENN得到的空间异质性集成权重相结合，获得空间预测结果。所提出的方法在中国的PM2.5空气质量和滑坡数据集上得到了验证，均比现有的集成学习策略获得了更准确的预测结果。结果确认了在模型集成过程中准确表达空间异质性的必要性。
【doi】
https://doi.org/10.1080/13658816.2024.2358052
【作者信息】
Shifen Cheng，中国科学院地理科学与资源研究所资源与环境信息系统国家重点实验室，北京，中国；中国科学院大学，北京，中国
Lizeng Wang，中国科学院地理科学与资源研究所资源与环境信息系统国家重点实验室，北京，中国；中国科学院大学，北京，中国
Peixiao Wang，中国科学院地理科学与资源研究所资源与环境信息系统国家重点实验室，北京，中国；中国科学院大学，北京，中国
Feng Lu，中国科学院地理科学与资源研究所资源与环境信息系统国家重点实验室，北京，中国；中国科学院大学，北京，中国；数字中国研究院，福州大学，福州，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
论文8
A machine learning based approach for generating point sketch maps from qualitative directional information
基于机器学习的方法，从定性方向信息生成点草图地图
【摘要】
People often use qualitative relations to describe locations or directional information, especially in written communication, such as ‘the restaurant is located at the southeast corner of the square’. However, when a large number of spatial entities are involved, qualitative relations alone are not intuitive enough for people to understand a spatial configuration. In fact, many applications, e.g. pertaining to sharing travel experiences, use sketch maps, i.e. maps focusing on the main features of an area whilst abstracting exact scale measurements, to help demonstrate abstract qualitative relations with more intuitive geometric points. Current approaches for generating point sketch maps from qualitative spatial relations require a high level of expertise, face inherent difficulties with efficiently processing large-scale data in bulk, and are vulnerable to inaccurate or conflicting information contained in qualitative data. To address these limitations, by incorporating machine learning techniques, we propose to translate the problem into an optimization problem of data reconstruction, enabling a novel end-to-end approach for generating point sketch maps from qualitative directional relations in bulk. Experiments on real-world datasets show that the proposed approach has very high accuracy and is robust even with a large portion of inaccurate or incomplete information.
【摘要翻译】
人们常常使用定性关系来描述位置或方向信息，尤其是在书面交流中，例如“餐厅位于广场的东南角”。然而，当涉及大量空间实体时，单独使用定性关系并不足够直观，难以帮助人们理解空间配置。实际上，许多应用（例如与分享旅行体验相关的应用）使用草图地图，即侧重于一个区域主要特征而抽象确切比例尺的地图，以帮助用更直观的几何点展示抽象的定性关系。目前，从定性空间关系生成点草图地图的方法需要较高的专业知识，且在高效处理大规模数据时面临固有困难，容易受到定性数据中不准确或冲突信息的影响。为了解决这些局限性，我们提出将这一问题转化为数据重建的优化问题，通过引入机器学习技术，实现一种新颖的端到端方法，从定性方向关系中批量生成点草图地图。在真实世界数据集上的实验表明，所提出的方法具有很高的准确性，并且在面对大量不准确或不完整信息时仍然表现出稳健性。
【doi】
https://doi.org/10.1080/13658816.2024.2358405
【作者信息】
Zhiguo Long，西南交通大学计算与人工智能学院，中国成都；四川省制造产业链协同与信息支撑技术重点实验室，中国成都
Qingqian Li，西南交通大学计算与人工智能学院，中国成都；四川省制造产业链协同与信息支撑技术重点实验室，中国成都
Hua Meng，西南交通大学数学学院，中国成都
Michael Sioutis，法国蒙彼利埃大学与国家科学研究中心（CNRS）LIRMM UMR 5506，蒙彼利埃
论文9
Detecting synthetic population bias using a spatially-oriented framework and independent validation data
使用空间导向框架和独立验证数据检测合成人口偏差
【摘要】
Models of human mobility can be broadly applied to find solutions addressing diverse topics such as public health policy, transportation management, emergency management, and urban development. However, many mobility models require individual-level data that is limited in availability and accessibility. Synthetic populations are commonly used as the foundation for mobility models because they provide detailed individual-level data representing the different types and characteristics of people in a study area. Thorough evaluation of synthetic populations is required to detect data biases before the prejudices are transferred to subsequent applications. Although synthetic populations are commonly used for modeling mobility, they are conventionally validated by their sociodemographic characteristics, rather than mobility attributes. Mobility microdata provides an opportunity to independently/externally validate the mobility attributes of synthetic populations. This study demonstrates a spatially-oriented data validation framework and independent data validation to assess the mobility attributes of two synthetic populations at different spatial granularities. Validation using independent data (SafeGraph) and the validation framework replicated the spatial distribution of errors detected using source data (LODES) and total absolute error. Spatial clusters of error exposed the locations of underrepresented and overrepresented communities. This information can guide bias mitigation efforts to generate a more representative synthetic population.
【摘要翻译】
人类移动模型可以广泛应用于解决公共卫生政策、交通管理、应急管理和城市发展等多种主题。然而，许多移动模型需要个体级别的数据，而这些数据的可用性和可获得性有限。合成种群通常被用作移动模型的基础，因为它们提供了详细的个体级别数据，代表了研究区域内不同类型和特征的人。为了在后续应用中避免将偏见转移，必须对合成种群进行彻底评估，以检测数据偏差。尽管合成种群通常用于建模移动性，但它们通常是通过社会人口特征来验证的，而不是移动属性。移动微数据提供了独立/外部验证合成种群移动属性的机会。本研究展示了一种面向空间的数据验证框架和独立数据验证，以评估不同空间颗粒度的两个合成种群的移动属性。使用独立数据（SafeGraph）和验证框架的验证复现了使用源数据（LODES）检测到的错误空间分布和总绝对误差。错误的空间集群揭示了被低估和高估社区的位置。这些信息可以指导偏见缓解工作，以生成更具代表性的合成种群。
【doi】
https://doi.org/10.1080/13658816.2024.2358399
【作者信息】
Jessica Embury，美国加利福尼亚州圣迭戈，圣迭戈州立大学地理系
Atsushi Nara，美国加利福尼亚州圣迭戈，圣迭戈州立大学地理系
Sergio Rey，美国加利福尼亚州圣迭戈，圣迭戈州立大学地理系
Ming-Hsiang Tsou，美国加利福尼亚州圣迭戈，圣迭戈州立大学地理系
Sahar Ghanipoor Machiani，美国加利福尼亚州圣迭戈，圣迭戈州立大学土木、建筑与环境工程系
