Pub Date : 2023-11-03DOI: 10.1080/13658816.2023.2274475
Jing Huang, Teng Fei, Yuhao Kang, Jun Li, Ziyu Liu, Guofeng Wu
AbstractEstimating road traffic noise is essential for examining the quality of sounding environment and mitigating such a non-negligible pollutant in urban areas. However, existing estimated models often have limited applicability to specific traffic conditions, while the required parameters may not be readily available for city-wide collection. This paper proposes a data-driven approach for measuring road-level acoustic information of traffic with street view imagery. Specifically, we utilize portable vehicle-equipped hardware for in-situ noise acquisition and employ a deep learning model ResNet to learn high-level visual features from street view images that are closely associated with road traffic noise. The ResNet captures meaningful patterns from the input data, and the output probability vectors are then fed into a Random-Forest regression algorithm to quantitatively estimate the noise in decibels for different road segments. The MAE and RMSE of the DCNN-RF model are 2.01 and 2.71, respectively. Additionally, we employ a gradient-weighted Class Active Mapping approach to visually interpret our deep learning model and explore the significant elements in streetscapes that contribute to the model's estimations. Our proposed framework facilitates low-cost and fine-scale road traffic noise estimations and sheds light on how auditory information could be inferred from street imagery, which may benefit practices in geography and urban planning.Keywords: Road traffic noisestreet view imagerydeep learningbuild environmenturban planning AcknowledgmentThe authors would like to thank Urli for the valuable advice provided during the initial stages of the experiment and Mr. Mengze Gao for designing and 3D-printing the enclosure for the data acquisition device. Thanks to the financial support from the National Natural Science Foundation of China [42271476]; the Wuhan University 351 Talent Program, 2020; the State Key Laboratory of Resources and Environmental Information System [2023OPEN007] and the Guangdong Science and Technology Strategic Innovation Fund [2020B1212030009].Disclosure statementNo potential conflict of interest was reported by the author(s).Data and codes availability statementThe original in-situ road traffic noise data with geographic coordinates collected by the experimental vehicle using our portable device, as well as the road traffic noise estimation model and some sample street view images used for demonstration are available at https://github.com/kellyhuang313/traffic-noise-estimation. Instructions for executing the code are provided in the README.txt.Notes1 https://www.openstreetmap.org/Additional informationFundingThis work was supported by the National Natural Science Foundation of China [42271476]; the Wuhan University 351 Talent Program, 2020; and the Guangdong Science and Technology Strategic Innovation Fund [2020B1212030009]. This work was also supported by State Key Laboratory of Resources and Environmental Information Sys
{"title":"Estimating urban noise along road network from street view imagery","authors":"Jing Huang, Teng Fei, Yuhao Kang, Jun Li, Ziyu Liu, Guofeng Wu","doi":"10.1080/13658816.2023.2274475","DOIUrl":"https://doi.org/10.1080/13658816.2023.2274475","url":null,"abstract":"AbstractEstimating road traffic noise is essential for examining the quality of sounding environment and mitigating such a non-negligible pollutant in urban areas. However, existing estimated models often have limited applicability to specific traffic conditions, while the required parameters may not be readily available for city-wide collection. This paper proposes a data-driven approach for measuring road-level acoustic information of traffic with street view imagery. Specifically, we utilize portable vehicle-equipped hardware for in-situ noise acquisition and employ a deep learning model ResNet to learn high-level visual features from street view images that are closely associated with road traffic noise. The ResNet captures meaningful patterns from the input data, and the output probability vectors are then fed into a Random-Forest regression algorithm to quantitatively estimate the noise in decibels for different road segments. The MAE and RMSE of the DCNN-RF model are 2.01 and 2.71, respectively. Additionally, we employ a gradient-weighted Class Active Mapping approach to visually interpret our deep learning model and explore the significant elements in streetscapes that contribute to the model's estimations. Our proposed framework facilitates low-cost and fine-scale road traffic noise estimations and sheds light on how auditory information could be inferred from street imagery, which may benefit practices in geography and urban planning.Keywords: Road traffic noisestreet view imagerydeep learningbuild environmenturban planning AcknowledgmentThe authors would like to thank Urli for the valuable advice provided during the initial stages of the experiment and Mr. Mengze Gao for designing and 3D-printing the enclosure for the data acquisition device. Thanks to the financial support from the National Natural Science Foundation of China [42271476]; the Wuhan University 351 Talent Program, 2020; the State Key Laboratory of Resources and Environmental Information System [2023OPEN007] and the Guangdong Science and Technology Strategic Innovation Fund [2020B1212030009].Disclosure statementNo potential conflict of interest was reported by the author(s).Data and codes availability statementThe original in-situ road traffic noise data with geographic coordinates collected by the experimental vehicle using our portable device, as well as the road traffic noise estimation model and some sample street view images used for demonstration are available at https://github.com/kellyhuang313/traffic-noise-estimation. Instructions for executing the code are provided in the README.txt.Notes1 https://www.openstreetmap.org/Additional informationFundingThis work was supported by the National Natural Science Foundation of China [42271476]; the Wuhan University 351 Talent Program, 2020; and the Guangdong Science and Technology Strategic Innovation Fund [2020B1212030009]. This work was also supported by State Key Laboratory of Resources and Environmental Information Sys","PeriodicalId":14162,"journal":{"name":"International Journal of Geographical Information Science","volume":"26 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135819359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-27DOI: 10.1080/13658816.2023.2270285
Alexis Comber, Paul Harris, Chris Brunsdon
This paper proposes a novel spatially varying coefficient (SVC) regression through a Geographical Gaussian Process GAM (GGP-GAM): a Generalized Additive Model (GAM) with Gaussian Process (GP) splines parameterised at observation locations. A GGP-GAM was applied to multiple simulated coefficient datasets exhibiting varying degrees of spatial heterogeneity and out-performed the SVC brand-leader, Multiscale Geographically Weighted Regression (MGWR), under a range of fit metrics. Both were then applied to a Brexit case study and compared, with MGWR marginally out-performing GGP-GAM. The theoretical frameworks and implementation of both approaches are discussed: GWR models calibrate multiple models whereas GAMs provide a full single model; GAMs can automatically penalise local collinearity; GWR-based approaches are computationally more demanding; MGWR is still only for Gaussian responses; MGWR bandwidths are intuitive indicators of spatial heterogeneity. GGP-GAM calibration and tuning are also discussed and areas of future work are identified, including the creation of a user-friendly package to support model creation and coefficient mapping, and to facilitate ease of comparison with alternate SVC models. A final observation that GGP-GAMs have the potential to overcome some of the long-standing reservations about GWR-based regression methods and to elevate the perception of SVCs amongst the broader community.
{"title":"Multiscale spatially varying coefficient modelling using a Geographical Gaussian Process GAM","authors":"Alexis Comber, Paul Harris, Chris Brunsdon","doi":"10.1080/13658816.2023.2270285","DOIUrl":"https://doi.org/10.1080/13658816.2023.2270285","url":null,"abstract":"This paper proposes a novel spatially varying coefficient (SVC) regression through a Geographical Gaussian Process GAM (GGP-GAM): a Generalized Additive Model (GAM) with Gaussian Process (GP) splines parameterised at observation locations. A GGP-GAM was applied to multiple simulated coefficient datasets exhibiting varying degrees of spatial heterogeneity and out-performed the SVC brand-leader, Multiscale Geographically Weighted Regression (MGWR), under a range of fit metrics. Both were then applied to a Brexit case study and compared, with MGWR marginally out-performing GGP-GAM. The theoretical frameworks and implementation of both approaches are discussed: GWR models calibrate multiple models whereas GAMs provide a full single model; GAMs can automatically penalise local collinearity; GWR-based approaches are computationally more demanding; MGWR is still only for Gaussian responses; MGWR bandwidths are intuitive indicators of spatial heterogeneity. GGP-GAM calibration and tuning are also discussed and areas of future work are identified, including the creation of a user-friendly package to support model creation and coefficient mapping, and to facilitate ease of comparison with alternate SVC models. A final observation that GGP-GAMs have the potential to overcome some of the long-standing reservations about GWR-based regression methods and to elevate the perception of SVCs amongst the broader community.","PeriodicalId":14162,"journal":{"name":"International Journal of Geographical Information Science","volume":"156 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136261882","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-13DOI: 10.1080/13658816.2023.2268665
Qiliang Liu, Yongchuan Zhu, Jie Yang, Xiancheng Mao, Min Deng
AbstractIn multivariate spatial interpolation, the accuracy of a variable of interest can be improved using ancillary variables. Although geostatistical methods are widely used for multivariate spatial interpolation, these methods usually require second-order stationary assumption of spatial processes, which is difficult to satisfy in practice. We developed a new multivariate spatial interpolation method based on Yang-Chizhong filtering (CoYangCZ) to overcome this limitation. CoYangCZ does not solve the multivariate spatial interpolation problem from a purely statistical point of view but integrates geometry and statistics-based strategies. First, we used a weighted moving average method based on binomial coefficients (i.e. Yang-Chizhong filtering) to fit the spatial autocorrelation structure of each spatial variable from a geometric perspective. We then quantified the spatial autocorrelation of each spatial variable and the correlations between different spatial variables by analyzing the variances of different spatial variables. Finally, we obtain the best linear unbiased estimators at the unsampled locations. Experiments on air pollution and meteorological datasets show that CoYangCZ has a higher interpolation accuracy than cokriging, regression kriging, gradient plus-inverse distance squared, sequential Gaussian co-simulation, and the kriging convolutional network. CoYangCZ can adapt to second-order non-stationary spatial processes; therefore, it has a wider scope of application than purely statistical methods.Keywords: Multivariate spatial processesspatial interpolationYang Chizhong filteringgeostatistics AcknowledgementsWe gratefully acknowledge the comments from the editor and the reviewers.Author contributionsQiliang Liu, Yongchuan Zhu, and Jie Yang conceived and designed the presented idea. Yongchuan Zhu and Jie Yang implemented the experiments and analysed the results. Qiliang Liu and Yongchuan Zhu wrote the manuscript. Xiancheng Mao and Min Deng reviewed the manuscript, and provided comments.Disclosure statementNo potential conflict of interest was reported by the author(s).Data and codes availability statementThe findings of this study are backed by data and codes that can be found on ‘figshare.com’, with the identifier at the public link: https://doi.org/10.6084/m9.figshare.24230179.Additional informationFundingThis study was funded through support from National Natural Science Foundation of China (NSFC) [No. 42271484 and 41971353] and Natural Science Foundation of Hunan Province [No. 2021JJ20058].Notes on contributorsQiliang LiuQiliang Liu is currently a professor at Central South University, Hunan, China. His research interests focus on multi-scale spatio-temporal data mining and spatiotemporal statistics. He has published more than 30 peer-reviewed journal articles in these areas.Yongchuan ZhuYongchuan Zhu is currently a postgraduate student at Central South University and his research interests focus on spatial statistics.Jie Yan
{"title":"CoYangCZ: a new spatial interpolation method for nonstationary multivariate spatial processes","authors":"Qiliang Liu, Yongchuan Zhu, Jie Yang, Xiancheng Mao, Min Deng","doi":"10.1080/13658816.2023.2268665","DOIUrl":"https://doi.org/10.1080/13658816.2023.2268665","url":null,"abstract":"AbstractIn multivariate spatial interpolation, the accuracy of a variable of interest can be improved using ancillary variables. Although geostatistical methods are widely used for multivariate spatial interpolation, these methods usually require second-order stationary assumption of spatial processes, which is difficult to satisfy in practice. We developed a new multivariate spatial interpolation method based on Yang-Chizhong filtering (CoYangCZ) to overcome this limitation. CoYangCZ does not solve the multivariate spatial interpolation problem from a purely statistical point of view but integrates geometry and statistics-based strategies. First, we used a weighted moving average method based on binomial coefficients (i.e. Yang-Chizhong filtering) to fit the spatial autocorrelation structure of each spatial variable from a geometric perspective. We then quantified the spatial autocorrelation of each spatial variable and the correlations between different spatial variables by analyzing the variances of different spatial variables. Finally, we obtain the best linear unbiased estimators at the unsampled locations. Experiments on air pollution and meteorological datasets show that CoYangCZ has a higher interpolation accuracy than cokriging, regression kriging, gradient plus-inverse distance squared, sequential Gaussian co-simulation, and the kriging convolutional network. CoYangCZ can adapt to second-order non-stationary spatial processes; therefore, it has a wider scope of application than purely statistical methods.Keywords: Multivariate spatial processesspatial interpolationYang Chizhong filteringgeostatistics AcknowledgementsWe gratefully acknowledge the comments from the editor and the reviewers.Author contributionsQiliang Liu, Yongchuan Zhu, and Jie Yang conceived and designed the presented idea. Yongchuan Zhu and Jie Yang implemented the experiments and analysed the results. Qiliang Liu and Yongchuan Zhu wrote the manuscript. Xiancheng Mao and Min Deng reviewed the manuscript, and provided comments.Disclosure statementNo potential conflict of interest was reported by the author(s).Data and codes availability statementThe findings of this study are backed by data and codes that can be found on ‘figshare.com’, with the identifier at the public link: https://doi.org/10.6084/m9.figshare.24230179.Additional informationFundingThis study was funded through support from National Natural Science Foundation of China (NSFC) [No. 42271484 and 41971353] and Natural Science Foundation of Hunan Province [No. 2021JJ20058].Notes on contributorsQiliang LiuQiliang Liu is currently a professor at Central South University, Hunan, China. His research interests focus on multi-scale spatio-temporal data mining and spatiotemporal statistics. He has published more than 30 peer-reviewed journal articles in these areas.Yongchuan ZhuYongchuan Zhu is currently a postgraduate student at Central South University and his research interests focus on spatial statistics.Jie Yan","PeriodicalId":14162,"journal":{"name":"International Journal of Geographical Information Science","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135858084","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-13DOI: 10.1080/13658816.2023.2266493
Hao Guo, Andre Python, Yu Liu
AbstractIn spatial regression models, spatial heterogeneity may be considered with either continuous or discrete specifications. The latter is related to delineation of spatially connected regions with homogeneous relationships between variables (spatial regimes). Although various regionalization algorithms have been proposed and studied in the field of spatial analytics, methods to optimize spatial regimes have been largely unexplored. In this paper, we propose two new algorithms for spatial regime delineation, two-stage K-Models and Regional-K-Models. We also extend the classic Automatic Zoning Procedure to a spatial regression context. The proposed algorithms are applied to a series of synthetic datasets and two real-world datasets. Results indicate that all three algorithms achieve superior or comparable performance to existing approaches, while the two-stage K-Models algorithm largely outperforms existing approaches on model fitting, region reconstruction and coefficient estimation. Our work enriches the spatial analytics toolbox to explore spatial heterogeneous processes.Keywords: Regionalizationspatial heterogeneityspatial regimespatial regression NotesAcknowledgmentsThe authors thank members of Spatial Analysis Group, Spatio-temporal Social Sensing Lab for helpful discussion. The constructive comments from anonymous reviewers are gratefully acknowledged.Data and codes availability statementThe data and codes that support the findings of this study are available at https://github.com/Nithouson/regreg.Disclosure statementThe authors declare that they have no conflict of interest.Notes1 For example, the population in each region is required to be as similar as possible or above a predefined value (see Duque et al. (Citation2012), Folch and Spielman (Citation2014), Wei et al. (Citation2021)).2 Note that the optimization of spatial regimes differs from Openshaw (Citation1978), where spatial units are aggregated into areas, and each area is treated as an observation in a global regression model.3 Note that in EquationEquation 1(1) L(R)=∑j=1p∑1≤i1<i2≤nI[ui1,ui2∈Rj]||xi1−xi2||2,(1) , the number of considered unit pairs in the sum is ∑j=1M(|Rj|2), which is smaller if |Rj|(j=1,…,M) are close to each other. Hence the objective function might favor solutions whose regions have similar numbers of units.4 Throughout the paper, we describe the case of lattice data (spatial data on areal units). Our approach is also applicable to point observation data after building adjacency (with k-nearest neighbors (KNN) or Delaunay triangulation, for example).5 This usually happens when min_obs is close to n/p, where p is the number of regions. Given min_obs≪n/p, this issue does not cause problems, as observed in our experiments.6 If a region with inadequate units has two or more neighboring regions, we select the neighbor which minimizes the total SSR after the merge.7 When min_obs is too large or K is too small (close to p), exceptions may occur that the number of
在空间回归模型中，空间异质性可以考虑连续或离散规格。后者与空间连接区域的描述有关，这些区域具有变量之间的均匀关系(空间制度)。虽然在空间分析领域已经提出和研究了各种区划算法，但优化空间制度的方法在很大程度上尚未得到探索。本文提出了两种新的空间状态描述算法:两阶段k -模型和区域k -模型。我们还将经典的自动分区程序扩展到空间回归环境。提出的算法应用于一系列合成数据集和两个真实数据集。结果表明，这三种算法的性能都优于或与现有方法相当，而两阶段K-Models算法在模型拟合、区域重建和系数估计方面大大优于现有方法。我们的工作丰富了空间分析工具箱，以探索空间异构过程。关键词:区域化;空间异质性;空间制度;感谢匿名审稿人提出的建设性意见。数据和代码可用性声明支持本研究结果的数据和代码可从https://github.com/Nithouson/regreg.Disclosure获取声明作者声明无利益冲突。注1例如，要求每个地区的人口尽可能接近或高于预定义值(参见Duque et al. (Citation2012)， Folch and Spielman (Citation2014)， Wei et al. (Citation2021))请注意，空间制度的优化不同于Openshaw (Citation1978)，在Openshaw中，空间单元被聚合成区域，每个区域被视为全局回归模型中的一个观测值注意，在等式1(1)L(R)=∑j=1p∑1≤i1<i2≤nI[ui1,ui2∈Rj]||xi1−xi2||2，(1)中，求和中考虑的单位对个数为∑j=1M(|Rj|2)，当|Rj|(j=1，…，M)彼此接近时，考虑的单位对个数越小。因此，目标函数可能倾向于具有相似单元数的区域的解在整篇论文中，我们描述了点阵数据(面单位上的空间数据)的情况。我们的方法也适用于建立邻接关系后的点观测数据(例如，与k近邻(KNN)或Delaunay三角剖分)这通常发生在min_obs接近n/p时，其中p是区域的数量。鉴于min_obs≪n/p，正如我们在实验中观察到的那样，这个问题不会造成问题如果单元不足的区域有两个或两个以上的相邻区域，我们选择合并后总SSR最小的相邻区域当min_obs太大或K太小(接近p)时，可能会出现区域数量小于p的例外情况，因此算法无法通过合并'微集群'来产生所需的区域数量。这个问题可以通过调整min_obs和K.8来解决。系数向量的OLS估计为β=(XTX) - 1XTy，其中X为自变量的nrx (m+1)矩阵，y为因变量的n维向量。在这里，通过添加一个常数为1的自变量，将截距包含在β中。通过应用Sherman-Morrison公式(Bartlett Citation1951)来更新(XTX)−1项，可以将时间复杂度从O(m2(nr+m))降低到O(m(nr+m))设βi,j表示系数βi在区域Rj中的值。在每次模拟中，列表(−2，−1,0,1,2)被随机洗牌两次，分别用作(β1,1，…，β1,5)和(β2,1，…，β2,5)Helbich等人(Citation2013)也对GWR系数进行了主成分分析。跳过这一步，因为在我们的实验中不需要降维在K-Models算法的两个阶段可以使用不同的min_obs值。这里min_obs=10用于合并阶段，而分区阶段的min_obs是本文中自变量的数量加1即使考虑平均SSR而不是最低SSR，两阶段k模型和AZP的表现也始终优于GWR-Skater和Skater-reg;Regional-K-Models与skater - regg相当，优于gwr - skater在我们的机器上，GWR估计没有在30分钟内完成在一台CPU为Intel酷睿i5-1135G7 (2.40 GHz)、内存为16GB的计算机上对King County房价数据集进行了实验。 基金资助:国家自然科学基金项目(41830645,42271426,41971331,82273731)、智慧广州时空信息云平台建设项目(GZIT2016-A5-147)和国家重点研发计划项目(2021YFC2701905)。郭浩，北京大学遥感与地理信息系统研究所博士研究生。他于2020年获得北京大学地理信息科学学士学位和数学双学士学位。主要研究方向为空间分析、地理空间人工智能和空间优化。Andre Python，浙江大学数据科学中心ZJU100青年统计学教授。他在瑞士弗里堡大学获得学士学位和硕士学位，在英国圣安德鲁斯大学获得博士学位。他开发并应用空间模型和可解释的机器学习算法，以更好地理解观察到的空间现象模式背后的机制。刘宇，现任北京大学遥感与地理信息系统研究所gisscience博雅教授。他分别于1994年、1997年和2003年获得北京大学学士学位、硕士学位和博士学位。主要研究方向为基于大地理数据的人文社会科学。
{"title":"Extending regionalization algorithms to explore spatial process heterogeneity","authors":"Hao Guo, Andre Python, Yu Liu","doi":"10.1080/13658816.2023.2266493","DOIUrl":"https://doi.org/10.1080/13658816.2023.2266493","url":null,"abstract":"AbstractIn spatial regression models, spatial heterogeneity may be considered with either continuous or discrete specifications. The latter is related to delineation of spatially connected regions with homogeneous relationships between variables (spatial regimes). Although various regionalization algorithms have been proposed and studied in the field of spatial analytics, methods to optimize spatial regimes have been largely unexplored. In this paper, we propose two new algorithms for spatial regime delineation, two-stage K-Models and Regional-K-Models. We also extend the classic Automatic Zoning Procedure to a spatial regression context. The proposed algorithms are applied to a series of synthetic datasets and two real-world datasets. Results indicate that all three algorithms achieve superior or comparable performance to existing approaches, while the two-stage K-Models algorithm largely outperforms existing approaches on model fitting, region reconstruction and coefficient estimation. Our work enriches the spatial analytics toolbox to explore spatial heterogeneous processes.Keywords: Regionalizationspatial heterogeneityspatial regimespatial regression NotesAcknowledgmentsThe authors thank members of Spatial Analysis Group, Spatio-temporal Social Sensing Lab for helpful discussion. The constructive comments from anonymous reviewers are gratefully acknowledged.Data and codes availability statementThe data and codes that support the findings of this study are available at https://github.com/Nithouson/regreg.Disclosure statementThe authors declare that they have no conflict of interest.Notes1 For example, the population in each region is required to be as similar as possible or above a predefined value (see Duque et al. (Citation2012), Folch and Spielman (Citation2014), Wei et al. (Citation2021)).2 Note that the optimization of spatial regimes differs from Openshaw (Citation1978), where spatial units are aggregated into areas, and each area is treated as an observation in a global regression model.3 Note that in EquationEquation 1(1) L(R)=∑j=1p∑1≤i1<i2≤nI[ui1,ui2∈Rj]||xi1−xi2||2,(1) , the number of considered unit pairs in the sum is ∑j=1M(|Rj|2), which is smaller if |Rj|(j=1,…,M) are close to each other. Hence the objective function might favor solutions whose regions have similar numbers of units.4 Throughout the paper, we describe the case of lattice data (spatial data on areal units). Our approach is also applicable to point observation data after building adjacency (with k-nearest neighbors (KNN) or Delaunay triangulation, for example).5 This usually happens when min_obs is close to n/p, where p is the number of regions. Given min_obs≪n/p, this issue does not cause problems, as observed in our experiments.6 If a region with inadequate units has two or more neighboring regions, we select the neighbor which minimizes the total SSR after the merge.7 When min_obs is too large or K is too small (close to p), exceptions may occur that the number of","PeriodicalId":14162,"journal":{"name":"International Journal of Geographical Information Science","volume":"130 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135859064","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-11DOI: 10.1080/13658816.2023.2268668
Wenhao Yu, Guanwen Wang
AbstractAs an important task in spatial data mining, trajectory transportation mode recognition can reflect various individual behaviors and traveling patterns in urban space. As trajectory is essentially a sequence, many scholars use the sequence inference models to mine the information in trajectory data. However, such methods often ignored the spatial correlation between trajectory points and implemented the evaluation based only on representative feature statistics selected in the trajectory data preprocessing stage, thus have difficulties in acquiring high-order traveling pattern features. In this study, we propose a novel ensemble recognition method for representing trajectory data with the graph structure based on sequence and dependency relations. This method integrates the sequence of trajectory points and the correlation between characteristic points of a travel path into a fused graph convolutional network to obtain semantic feature information at multiple levels. We validate our proposed method with experiments on the trajectory benchmark dataset from the Microsoft GeoLife project. The results demonstrated that our proposed graph network outperforms other baseline methods in the transportation mode recognition task of trajectories. This method can help to discover the movement patterns of urban residents, and further provide effective assistance for the management of cities.Keywords: Trajectory datagraph convolution networktransportation mode recognitionfeature extractionfeature fusion AcknowledgmentsThe authors are grateful to the associate editor, Urska Demsar, and the anonymous referees for their valuable comments and suggestions. The project was supported by the National Natural Science Foundation of China (42371446 and 42071442) and by the Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan) (No.CUG170640). This research was also supported by Meituan.Author contributionsWenhao Yu: Conceptualization, methodology, formal analysis, validation, writing—original draft preparation, writing—review and editing, supervision, project administration, funding acquisition; Guanwen Wang: Methodology, validation, formal analysis, investigation, writing—original draft preparation, writing—review and editing, visualization. All authors have read and agreed to the published version of the manuscript.Disclosure statementNo potential conflict of interest was reported by the author(s).Data and codes availability statementThe data and codes that support the findings of this study are available with a DOI at (https://doi.org/10.6084/m9.figshare.21608310).Additional informationNotes on contributorsWenhao YuWenhao Yu received the B.S. and Ph.D. degrees in Geoinformatics from the Wuhan University, Wuhan, China, in 2010 and 2015, respectively. He is a professor at China University of Geosciences, Wuhan, China (CUG). His research interests include spatial data mining, map generalization, and deep learning.Guanwen
{"title":"Graph based embedding learning of trajectory data for transportation mode recognition by fusing sequence and dependency relations","authors":"Wenhao Yu, Guanwen Wang","doi":"10.1080/13658816.2023.2268668","DOIUrl":"https://doi.org/10.1080/13658816.2023.2268668","url":null,"abstract":"AbstractAs an important task in spatial data mining, trajectory transportation mode recognition can reflect various individual behaviors and traveling patterns in urban space. As trajectory is essentially a sequence, many scholars use the sequence inference models to mine the information in trajectory data. However, such methods often ignored the spatial correlation between trajectory points and implemented the evaluation based only on representative feature statistics selected in the trajectory data preprocessing stage, thus have difficulties in acquiring high-order traveling pattern features. In this study, we propose a novel ensemble recognition method for representing trajectory data with the graph structure based on sequence and dependency relations. This method integrates the sequence of trajectory points and the correlation between characteristic points of a travel path into a fused graph convolutional network to obtain semantic feature information at multiple levels. We validate our proposed method with experiments on the trajectory benchmark dataset from the Microsoft GeoLife project. The results demonstrated that our proposed graph network outperforms other baseline methods in the transportation mode recognition task of trajectories. This method can help to discover the movement patterns of urban residents, and further provide effective assistance for the management of cities.Keywords: Trajectory datagraph convolution networktransportation mode recognitionfeature extractionfeature fusion AcknowledgmentsThe authors are grateful to the associate editor, Urska Demsar, and the anonymous referees for their valuable comments and suggestions. The project was supported by the National Natural Science Foundation of China (42371446 and 42071442) and by the Fundamental Research Funds for the Central Universities, China University of Geosciences (Wuhan) (No.CUG170640). This research was also supported by Meituan.Author contributionsWenhao Yu: Conceptualization, methodology, formal analysis, validation, writing—original draft preparation, writing—review and editing, supervision, project administration, funding acquisition; Guanwen Wang: Methodology, validation, formal analysis, investigation, writing—original draft preparation, writing—review and editing, visualization. All authors have read and agreed to the published version of the manuscript.Disclosure statementNo potential conflict of interest was reported by the author(s).Data and codes availability statementThe data and codes that support the findings of this study are available with a DOI at (https://doi.org/10.6084/m9.figshare.21608310).Additional informationNotes on contributorsWenhao YuWenhao Yu received the B.S. and Ph.D. degrees in Geoinformatics from the Wuhan University, Wuhan, China, in 2010 and 2015, respectively. He is a professor at China University of Geosciences, Wuhan, China (CUG). His research interests include spatial data mining, map generalization, and deep learning.Guanwen ","PeriodicalId":14162,"journal":{"name":"International Journal of Geographical Information Science","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136211026","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-10DOI: 10.1080/13658816.2023.2264921
Chanwoo Jin, Sohyun Park, Hui Jeong Ha, Jinhyung Lee, Junghwan Kim, Johan Hutchenreuther, Atsushi Nara
AbstractHuman mobility analytics using artificial intelligence (AI) has gained significant attention with advancements in computational power and the availability of high-resolution spatial data. However, the application of deep learning in social sciences and human geography remains limited, primarily due to concerns with model explainability. In this study, we employ an explainable GeoAI approach called geographically localized interpretable model-agnostic explanation (GLIME) to explore human mobility patterns over large spatial and temporal extents. Specifically, we develop a two-layered long short-term memory (LSTM) model capable of predicting individual-level residential mobility patterns across the United States from 2012 to 2019. We leverage GLIME to provide geographical perspectives and interpret deep neural networks at the state level. The results reveal that GLIME enables spatially explicit interpretations of local impacts attributed to different variables. Our findings underscore the significance of considering path dependency in residential mobility dynamics. While the prediction of complex human spatial decision-making processes still presents challenges, this research demonstrates the utility of deep neural networks and explainable GeoAI to support human dynamics understanding. It sets the stage for further finely tuned investigations in the future, promising deep insights into intricate mobility phenomena.Keywords: Explainable GeoAImodel-agnostic explanationlong short-term memory (LSTM)trajectory predictionresidential mobility AcknowledgementsAny opinions, findings, and conclusions or recommendations expressed in this material are those of the author and do not necessarily reflect the views of the National Science Foundation.Disclosure statementNo potential conflict of interest was reported by the author(s).Data and codes availability statementThe data, codes, and instructions that support the findings of this study are available on figshare at https://doi.org/10.6084/m9.figshare.21543549.v1Notes1 We have 75 variables in total as categorical variables including state and housing type are input as dummy variables into the models.Additional informationFundingThis research was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (Grant No. RS-2022-00165821) and the Faculty of Social Science at Western University. This work was also supported in part by the National Science Foundation under Grant No. 2031407.Notes on contributorsChanwoo JinChanwoo Jin is an assistant professor in the Department of Humanities and Social Sciences at Northwest Missouri State University. He holds a PhD in Geography at the University of California, Santa Barbara/San Diego State University (Joint Doctoral Program). His main research interests include big spatiotemporal data analysis, Geospatial Artificial Intelligence (GeoAI), human mobility and urban dynamics.Sohyun ParkSohyun Park is an assistant professo
{"title":"Predicting households’ residential mobility trajectories with geographically localized interpretable model-agnostic explanation (GLIME)","authors":"Chanwoo Jin, Sohyun Park, Hui Jeong Ha, Jinhyung Lee, Junghwan Kim, Johan Hutchenreuther, Atsushi Nara","doi":"10.1080/13658816.2023.2264921","DOIUrl":"https://doi.org/10.1080/13658816.2023.2264921","url":null,"abstract":"AbstractHuman mobility analytics using artificial intelligence (AI) has gained significant attention with advancements in computational power and the availability of high-resolution spatial data. However, the application of deep learning in social sciences and human geography remains limited, primarily due to concerns with model explainability. In this study, we employ an explainable GeoAI approach called geographically localized interpretable model-agnostic explanation (GLIME) to explore human mobility patterns over large spatial and temporal extents. Specifically, we develop a two-layered long short-term memory (LSTM) model capable of predicting individual-level residential mobility patterns across the United States from 2012 to 2019. We leverage GLIME to provide geographical perspectives and interpret deep neural networks at the state level. The results reveal that GLIME enables spatially explicit interpretations of local impacts attributed to different variables. Our findings underscore the significance of considering path dependency in residential mobility dynamics. While the prediction of complex human spatial decision-making processes still presents challenges, this research demonstrates the utility of deep neural networks and explainable GeoAI to support human dynamics understanding. It sets the stage for further finely tuned investigations in the future, promising deep insights into intricate mobility phenomena.Keywords: Explainable GeoAImodel-agnostic explanationlong short-term memory (LSTM)trajectory predictionresidential mobility AcknowledgementsAny opinions, findings, and conclusions or recommendations expressed in this material are those of the author and do not necessarily reflect the views of the National Science Foundation.Disclosure statementNo potential conflict of interest was reported by the author(s).Data and codes availability statementThe data, codes, and instructions that support the findings of this study are available on figshare at https://doi.org/10.6084/m9.figshare.21543549.v1Notes1 We have 75 variables in total as categorical variables including state and housing type are input as dummy variables into the models.Additional informationFundingThis research was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (Grant No. RS-2022-00165821) and the Faculty of Social Science at Western University. This work was also supported in part by the National Science Foundation under Grant No. 2031407.Notes on contributorsChanwoo JinChanwoo Jin is an assistant professor in the Department of Humanities and Social Sciences at Northwest Missouri State University. He holds a PhD in Geography at the University of California, Santa Barbara/San Diego State University (Joint Doctoral Program). His main research interests include big spatiotemporal data analysis, Geospatial Artificial Intelligence (GeoAI), human mobility and urban dynamics.Sohyun ParkSohyun Park is an assistant professo","PeriodicalId":14162,"journal":{"name":"International Journal of Geographical Information Science","volume":"49 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136357441","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-09DOI: 10.1080/13658816.2023.2266495
Yingjie Hu, Gengchen Mai, Chris Cundy, Kristy Choi, Ni Lao, Wei Liu, Gaurish Lakhanpal, Ryan Zhenqi Zhou, Kenneth Joseph
Social media messages posted by people during natural disasters often contain important location descriptions, such as the locations of victims. Recent research has shown that many of these location descriptions go beyond simple place names, such as city names and street names, and are difficult to extract using typical named entity recognition (NER) tools. While advanced machine learning models could be trained, they require large labeled training datasets that can be time-consuming and labor-intensive to create. In this work, we propose a method that fuses geo-knowledge of location descriptions and a Generative Pre-trained Transformer (GPT) model, such as ChatGPT and GPT-4. The result is a geo-knowledge-guided GPT model that can accurately extract location descriptions from disaster-related social media messages. Also, only 22 training examples encoding geo-knowledge are used in our method. We conduct experiments to compare this method with nine alternative approaches on a dataset of tweets from Hurricane Harvey. Our method demonstrates an over 40% improvement over typically used NER approaches. The experiment results also show that geo-knowledge is indispensable for guiding the behavior of GPT models. The extracted location descriptions can help disaster responders reach victims more quickly and may even save lives.
{"title":"Geo-knowledge-guided GPT models improve the extraction of location descriptions from disaster-related social media messages","authors":"Yingjie Hu, Gengchen Mai, Chris Cundy, Kristy Choi, Ni Lao, Wei Liu, Gaurish Lakhanpal, Ryan Zhenqi Zhou, Kenneth Joseph","doi":"10.1080/13658816.2023.2266495","DOIUrl":"https://doi.org/10.1080/13658816.2023.2266495","url":null,"abstract":"Social media messages posted by people during natural disasters often contain important location descriptions, such as the locations of victims. Recent research has shown that many of these location descriptions go beyond simple place names, such as city names and street names, and are difficult to extract using typical named entity recognition (NER) tools. While advanced machine learning models could be trained, they require large labeled training datasets that can be time-consuming and labor-intensive to create. In this work, we propose a method that fuses geo-knowledge of location descriptions and a Generative Pre-trained Transformer (GPT) model, such as ChatGPT and GPT-4. The result is a geo-knowledge-guided GPT model that can accurately extract location descriptions from disaster-related social media messages. Also, only 22 training examples encoding geo-knowledge are used in our method. We conduct experiments to compare this method with nine alternative approaches on a dataset of tweets from Hurricane Harvey. Our method demonstrates an over 40% improvement over typically used NER approaches. The experiment results also show that geo-knowledge is indispensable for guiding the behavior of GPT models. The extracted location descriptions can help disaster responders reach victims more quickly and may even save lives.","PeriodicalId":14162,"journal":{"name":"International Journal of Geographical Information Science","volume":"266 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135043452","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-09DOI: 10.1080/13658816.2023.2262550
Jinmeng Rao, Song Gao, Sijia Zhu
AbstractThe prevalence of ubiquitous location-aware devices and mobile Internet enables us to collect massive individual-level trajectory dataset from users. Such trajectory big data bring new opportunities to human mobility research but also raise public concerns with regard to location privacy. In this work, we present the Conditional Adversarial Trajectory Synthesis (CATS), a deep-learning-based GeoAI methodological framework for privacy-preserving trajectory data generation and publication. CATS applies K-anonymity to the underlying spatiotemporal distributions of human movements, which provides a distributional-level strong privacy guarantee. By leveraging conditional adversarial training on K-anonymized human mobility matrices, trajectory global context learning using the attention-based mechanism, and recurrent bipartite graph matching of adjacent trajectory points, CATS is able to reconstruct trajectory topology from conditionally sampled locations and generate high-quality individual-level synthetic trajectory data, which can serve as supplements or alternatives to raw data for privacy-preserving trajectory data publication. The experiment results on over 90k GPS trajectories show that our method has a better performance in privacy preservation, spatiotemporal characteristic preservation, and downstream utility compared with baseline methods, which brings new insights into privacy-preserving human mobility research using generative AI techniques and explores data ethics issues in GIScience.Keywords: Geoprivacygenerative adversarial networkhuman mobilityGeoAIsynthetic data generation AcknowledgmentThe authors acknowledge the funding support provided by the American Family Insurance Data Science Institute Funding Initiative at the University of Wisconsin-Madison. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the funder(s).Disclosure statementNo potential conflict of interest was reported by the author(s).Data and codes availability statementThe data and codes that support the findings of this study are available at the following link on figshare: https://doi.org/10.6084/m9.figshare.20760970. It is worth noting that due to the non-disclosure agreement with the data provider, we are not releasing the original individual-level GPS trajectory data but sharing the k-anonymized aggregated human mobility data used in our experiments.Additional informationNotes on contributorsJinmeng RaoJinmeng Rao is a research scientist at Mineral Earth Sciences. He received his PhD degree from the Department of Geography, University of Wisconsin-Madison. His research interests include GeoAI, Privacy-Preserving AI, and Location Privacy.Song GaoSong Gao is an associate professor in GIScience at the Department of Geography, University of Wisconsin-Madison. He holds a PhD in Geography at the University of California, Santa Barbara. His main research intere
{"title":"CATS: Conditional Adversarial Trajectory Synthesis for privacy-preserving trajectory data publication using deep learning approaches","authors":"Jinmeng Rao, Song Gao, Sijia Zhu","doi":"10.1080/13658816.2023.2262550","DOIUrl":"https://doi.org/10.1080/13658816.2023.2262550","url":null,"abstract":"AbstractThe prevalence of ubiquitous location-aware devices and mobile Internet enables us to collect massive individual-level trajectory dataset from users. Such trajectory big data bring new opportunities to human mobility research but also raise public concerns with regard to location privacy. In this work, we present the Conditional Adversarial Trajectory Synthesis (CATS), a deep-learning-based GeoAI methodological framework for privacy-preserving trajectory data generation and publication. CATS applies K-anonymity to the underlying spatiotemporal distributions of human movements, which provides a distributional-level strong privacy guarantee. By leveraging conditional adversarial training on K-anonymized human mobility matrices, trajectory global context learning using the attention-based mechanism, and recurrent bipartite graph matching of adjacent trajectory points, CATS is able to reconstruct trajectory topology from conditionally sampled locations and generate high-quality individual-level synthetic trajectory data, which can serve as supplements or alternatives to raw data for privacy-preserving trajectory data publication. The experiment results on over 90k GPS trajectories show that our method has a better performance in privacy preservation, spatiotemporal characteristic preservation, and downstream utility compared with baseline methods, which brings new insights into privacy-preserving human mobility research using generative AI techniques and explores data ethics issues in GIScience.Keywords: Geoprivacygenerative adversarial networkhuman mobilityGeoAIsynthetic data generation AcknowledgmentThe authors acknowledge the funding support provided by the American Family Insurance Data Science Institute Funding Initiative at the University of Wisconsin-Madison. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the funder(s).Disclosure statementNo potential conflict of interest was reported by the author(s).Data and codes availability statementThe data and codes that support the findings of this study are available at the following link on figshare: https://doi.org/10.6084/m9.figshare.20760970. It is worth noting that due to the non-disclosure agreement with the data provider, we are not releasing the original individual-level GPS trajectory data but sharing the k-anonymized aggregated human mobility data used in our experiments.Additional informationNotes on contributorsJinmeng RaoJinmeng Rao is a research scientist at Mineral Earth Sciences. He received his PhD degree from the Department of Geography, University of Wisconsin-Madison. His research interests include GeoAI, Privacy-Preserving AI, and Location Privacy.Song GaoSong Gao is an associate professor in GIScience at the Department of Geography, University of Wisconsin-Madison. He holds a PhD in Geography at the University of California, Santa Barbara. His main research intere","PeriodicalId":14162,"journal":{"name":"International Journal of Geographical Information Science","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135142073","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-09DOI: 10.1080/13658816.2023.2266497
Hang Zhang, Guanpeng Dong, Jinfeng Wang, Tong-Lin Zhang, Xiaoyu Meng, Dongyang Yang, Yong Liu, Binbin Lu
The Geographical Detector Model (GDM) is a popular statistical toolkit for geographical attribution analysis. Despite the striking resemblance of the q-statistic in GDM to the R-squared in linear regression models, their explicit connection has not yet been established. This study proves that the q-statistic reduces into the R-squared under a linear regression framework. Under linear regression and moderate-to-strong spatial autocorrelation, Monte Carlo simulation results show that the GDM tends to underestimate the importance of variables. In addition, an almost perfect power law relationship is present between the percentage bias and the degree of the spatial autocorrelations, indicating the presence of fast uplifting bias in response to increasing levels of spatial autocorrelations. We propose an integrated approach for variable importance quantification by bringing together the spatial econometrics model and the game theory based-Shapley value method. By applying our proposed methodology to a case study of land desertification in African, it is found human activity tends to affect land desertification both directly and indirectly. However, such effects appear to be underestimated or undistinguished in the classic GDM.
{"title":"Understanding and extending the geographical detector model under a linear regression framework","authors":"Hang Zhang, Guanpeng Dong, Jinfeng Wang, Tong-Lin Zhang, Xiaoyu Meng, Dongyang Yang, Yong Liu, Binbin Lu","doi":"10.1080/13658816.2023.2266497","DOIUrl":"https://doi.org/10.1080/13658816.2023.2266497","url":null,"abstract":"The Geographical Detector Model (GDM) is a popular statistical toolkit for geographical attribution analysis. Despite the striking resemblance of the q-statistic in GDM to the R-squared in linear regression models, their explicit connection has not yet been established. This study proves that the q-statistic reduces into the R-squared under a linear regression framework. Under linear regression and moderate-to-strong spatial autocorrelation, Monte Carlo simulation results show that the GDM tends to underestimate the importance of variables. In addition, an almost perfect power law relationship is present between the percentage bias and the degree of the spatial autocorrelations, indicating the presence of fast uplifting bias in response to increasing levels of spatial autocorrelations. We propose an integrated approach for variable importance quantification by bringing together the spatial econometrics model and the game theory based-Shapley value method. By applying our proposed methodology to a case study of land desertification in African, it is found human activity tends to affect land desertification both directly and indirectly. However, such effects appear to be underestimated or undistinguished in the classic GDM.","PeriodicalId":14162,"journal":{"name":"International Journal of Geographical Information Science","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135141958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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