International Journal of Geographical Information Science最新文献_第8页

Gradient-based optimization for multi-scale geographically weighted regression 基于梯度的多尺度地理加权回归优化

IF 5.7 1区地球科学

International Journal of Geographical Information Science Pub Date : 2023-08-24 DOI: 10.1080/13658816.2023.2246154

Xiao-liang Zhou, R. Assunção, H. Shao, Cheng-Chia Huang, Mark V. Janikas, H. Asefaw

引用次数: 0

A kriging interpolation model for geographical flows 地理流动的克里格插值模型

IF 5.7 1区地球科学

International Journal of Geographical Information Science Pub Date : 2023-08-23 DOI: 10.1080/13658816.2023.2248502

Yaqun Fang, T. Pei, Ci Song, Jie Chen, Xi Wang, Xiao Chen, Yaxi Liu

{"title":"A kriging interpolation model for geographical flows","authors":"Yaqun Fang, T. Pei, Ci Song, Jie Chen, Xi Wang, Xiao Chen, Yaxi Liu","doi":"10.1080/13658816.2023.2248502","DOIUrl":"https://doi.org/10.1080/13658816.2023.2248502","url":null,"abstract":"Abstract The kriging model can accommodate various spatial supports and has been extensively applied in hydrology, meteorology, soil science, and other domains. With the expansion of applications, it is essential to extend the kriging model for new spatial support of high-dimensional data. Geographical flows can depict the movements of geographical objects and imply the underlying mobility patterns in geographical phenomena. However, due to the bias, sparsity, and uneven quality of flow data in the real world, research about flows remains hindered by the lack of complete flow data and effective flow interpolation methods. In this study, we design a kriging interpolation model for flows based on several flow-related concepts and the autocorrelation of flows. We also analyze the second-order stationarity and anisotropy in the flow spatial random field. To illustrate the effectiveness and applicability of our method, we conduct two case studies. The former case study compares several experiments of flow density interpolation using Beijing mobile signaling data and illustrates the conditions of applicable areas. The latter case study extends our model to other flow attributes, such as travel time uncertainty, using Beijing taxi origin-destination flow data. The results of these cases demonstrate the effectiveness and high accuracy of our model.","PeriodicalId":14162,"journal":{"name":"International Journal of Geographical Information Science","volume":"37 1","pages":"2150 - 2174"},"PeriodicalIF":5.7,"publicationDate":"2023-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43034131","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}

引用次数: 0

Spatial prediction of groundwater level change based on the Third Law of Geography 基于地理第三定律的地下水位变化空间预测

IF 5.7 1区地球科学

International Journal of Geographical Information Science Pub Date : 2023-08-17 DOI: 10.1080/13658816.2023.2248215

Fang-He Zhao, Jingyi Huang, A-Xing Zhu

{"title":"Spatial prediction of groundwater level change based on the Third Law of Geography","authors":"Fang-He Zhao, Jingyi Huang, A-Xing Zhu","doi":"10.1080/13658816.2023.2248215","DOIUrl":"https://doi.org/10.1080/13658816.2023.2248215","url":null,"abstract":"Abstract Spatial prediction methods are an important means of predicting the spatial variation of groundwater level change. Existing methods extract spatial or statistical relationships from samples to represent the study area for inference and require a representative sample set that is usually in large quantity and is distributed across geographic or covariate space. However, samples for groundwater are usually sparsely and unevenly distributed. In this paper, an approach based on the Third Law of Geography is proposed to make predictions by comparing the similarity between each individual sample and unmeasured site. The approach requires no specific number or distribution of samples and provides individual uncertainty measures at each location. Experiments in three different watersheds across the U.S. show that the proposed methods outperform machine learning methods when available samples do not well represent the area. The provided uncertainty measures are indicative of prediction accuracy by location. The results of this study also show that the spatial prediction based on the Third Law of Geography can also be successfully applied to dynamic variables such as groundwater level change.","PeriodicalId":14162,"journal":{"name":"International Journal of Geographical Information Science","volume":"37 1","pages":"2129 - 2149"},"PeriodicalIF":5.7,"publicationDate":"2023-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46083035","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}

引用次数: 0

A mathematical programming approach for downscaling multi-layered multi-constraint land-use models 一种用于缩减多层多约束土地利用模型的数学规划方法

IF 5.7 1区地球科学

International Journal of Geographical Information Science Pub Date : 2023-07-27 DOI: 10.1080/13658816.2023.2241144

R. Ramos, M. Scarabello, Wanderson Costa, Pedro Ribeiro de Andrade Neto, A. Soterroni, F. Ramos

{"title":"A mathematical programming approach for downscaling multi-layered multi-constraint land-use models","authors":"R. Ramos, M. Scarabello, Wanderson Costa, Pedro Ribeiro de Andrade Neto, A. Soterroni, F. Ramos","doi":"10.1080/13658816.2023.2241144","DOIUrl":"https://doi.org/10.1080/13658816.2023.2241144","url":null,"abstract":"Abstract Land-use and land-cover change (LULCC) models are important tools for environmental policy planning. LULCC models are frequently constrained to the generation of projections at a specific resolution. However, subsequent studies or models may require finer resolutions. In this work, a downscaling method for LULCC models is proposed that uses a mathematical programming approach to disaggregate the multiple layers of the land-use change projections while respecting a series of constraints. The method is calibrated and validated with MapBiomas data for the years 2000 and 2018 converted for the GLOBIOM-Brazil model, successfully predicting land-use at a finer resolution. Also, as proof of concept, the calibrated model is also applied for GLOBIOM-Brazil projections for 2050. This paper advances the state-of-the-art by proposing and testing a downscaling method using a mathematical programming approach with spatial effects, that operates on multi-layered land-use projections with a range of constraints while allowing flexibility on the number and type of the specific layers and constraints.","PeriodicalId":14162,"journal":{"name":"International Journal of Geographical Information Science","volume":"37 1","pages":"2020 - 2042"},"PeriodicalIF":5.7,"publicationDate":"2023-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46140283","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}

引用次数: 0

Toward urban traffic scenarios and more: a spatio-temporal analysis empowered low-rank tensor completion method for data imputation 针对城市交通场景和更多:一种时空分析授权的低秩张量补全方法用于数据输入

IF 5.7 1区地球科学

International Journal of Geographical Information Science Pub Date : 2023-07-19 DOI: 10.1080/13658816.2023.2234434

Zilong Zhao, Luliang Tang, Mengyuan Fang, Xue Yang, Chaokui Li, Qingquan Li

{"title":"Toward urban traffic scenarios and more: a spatio-temporal analysis empowered low-rank tensor completion method for data imputation","authors":"Zilong Zhao, Luliang Tang, Mengyuan Fang, Xue Yang, Chaokui Li, Qingquan Li","doi":"10.1080/13658816.2023.2234434","DOIUrl":"https://doi.org/10.1080/13658816.2023.2234434","url":null,"abstract":"Abstract Existing traffic monitoring approaches cannot completely cover all road segments in real-time, leading to massive amounts of missing traffic data, which limits the implementation of intelligent transportation systems. Most existing methods lack deep mining of the unique spatiotemporal characteristics of traffic flows, resulting in difficulty in application to urban traffic with complex topologies and variable states. In this paper, we propose a novel Spatio-Temporal constrained Low-Rank Tensor Completion (ST-LRTC) method, which adopts a manifold embedding approach to depict the local geometric structure of spatiotemporal domains. Specifically, under the low-rank assumption, the method introduces temporal constraints based on the continuity and periodicity of traffic flow and a spatial constraint matrix reflecting the traffic flow transmission mechanism. We embed low-dimensional spatiotemporal constraint matrices into the low-rank tensor completion solving process to fully utilize the global features and local spatiotemporal characteristics of the traffic tensor. Experiments were performed using traffic data from Xi’an, China, and the results indicated that ST-LRTC outperformed state-of-the-art methods under various missing rates and patterns. Thorough experiments have demonstrated that the incorporation of spatiotemporal analysis can enhance the adaptability of the tensor completion model to complex urban scenarios, which guarantees better monitoring, diagnosis, and optimization of urban traffic states.","PeriodicalId":14162,"journal":{"name":"International Journal of Geographical Information Science","volume":"37 1","pages":"1936 - 1969"},"PeriodicalIF":5.7,"publicationDate":"2023-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48696307","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}

引用次数: 2

Incorporating multimodal context information into traffic speed forecasting through graph deep learning 通过图深度学习将多模式上下文信息纳入交通速度预测

IF 5.7 1区地球科学

International Journal of Geographical Information Science Pub Date : 2023-07-18 DOI: 10.1080/13658816.2023.2234959

Yatao Zhang, Tianhong Zhao, Song Gao, M. Raubal

{"title":"Incorporating multimodal context information into traffic speed forecasting through graph deep learning","authors":"Yatao Zhang, Tianhong Zhao, Song Gao, M. Raubal","doi":"10.1080/13658816.2023.2234959","DOIUrl":"https://doi.org/10.1080/13658816.2023.2234959","url":null,"abstract":"Abstract Accurate traffic speed forecasting is a prerequisite for anticipating future traffic status and increasing the resilience of intelligent transportation systems. However, most studies ignore the involvement of context information ubiquitously distributed over the urban environment to boost speed prediction. The diversity and complexity of context information also hinder incorporating it into traffic forecasting. Therefore, this study proposes a multimodal context-based graph convolutional neural network (MCGCN) model to fuse context data into traffic speed prediction, including spatial and temporal contexts. The proposed model comprises three modules, ie (a) hierarchical spatial embedding to learn spatial representations by organizing spatial contexts from different dimensions, (b) multivariate temporal modeling to learn temporal representations by capturing dependencies of multivariate temporal contexts and (c) attention-based multimodal fusion to integrate traffic speed with the spatial and temporal context representations for multi-step speed prediction. We conduct extensive experiments in Singapore. Compared to the baseline model (spatial-temporal graph convolutional network, STGCN), our results demonstrate the importance of multimodal contexts with the mean-absolute-error improvement of 0.29 km/h, 0.45 km/h and 0.89 km/h in 30-min, 60-min and 120-min speed prediction, respectively. We also explore how different contexts affect traffic speed forecasting, providing references for stakeholders to understand the relationship between context information and transportation systems.","PeriodicalId":14162,"journal":{"name":"International Journal of Geographical Information Science","volume":"37 1","pages":"1909 - 1935"},"PeriodicalIF":5.7,"publicationDate":"2023-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42891843","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}

引用次数: 2

Fuzzy Bayesian inference for mapping vague and place-based regions: a case study of sectarian territory 模糊贝叶斯推理用于绘制模糊区域和基于地点的区域：以教派领土为例

IF 5.7 1区地球科学

International Journal of Geographical Information Science Pub Date : 2023-07-06 DOI: 10.1080/13658816.2023.2229894

J. Huck, J. Whyatt, G. Davies, John Dixon, Brendan Sturgeon, Bree Hocking, C. Tredoux, N. Jarman, Dominic Bryan

{"title":"Fuzzy Bayesian inference for mapping vague and place-based regions: a case study of sectarian territory","authors":"J. Huck, J. Whyatt, G. Davies, John Dixon, Brendan Sturgeon, Bree Hocking, C. Tredoux, N. Jarman, Dominic Bryan","doi":"10.1080/13658816.2023.2229894","DOIUrl":"https://doi.org/10.1080/13658816.2023.2229894","url":null,"abstract":"Abstract The problem of mapping regions with socially-derived boundaries has been a topic of discussion in the GIS literature for many years. Fuzzy approaches have frequently been suggested as solutions, but none have been adopted. This is likely due to difficulties associated with determining suitable membership functions, which are often as arbitrary as the crisp boundaries that they seek to replace. This paper presents a novel approach to fuzzy geographical modelling that replaces the membership function with a possibility distribution that is estimated using Bayesian inference. In this method, data from multiple sources are combined to estimate the degree to which a given location is a member of a given set and the level of uncertainty associated with that estimate. The Fuzzy Bayesian Inference approach is demonstrated through a case study in which census data are combined with perceptual and behavioural evidence to model the territory of two segregated groups (Catholics and Protestants) in Belfast, Northern Ireland, UK. This novel method provides a robust empirical basis for the use of fuzzy models in GIS, and therefore has applications for mapping a range of socially-derived and otherwise vague boundaries.","PeriodicalId":14162,"journal":{"name":"International Journal of Geographical Information Science","volume":"37 1","pages":"1765 - 1786"},"PeriodicalIF":5.7,"publicationDate":"2023-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42303638","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}

引用次数: 1

Simplification of contour lines, based on axial splines, with high-quality results 基于轴向花键简化轮廓线，获得高质量的结果

IF 5.7 1区地球科学

International Journal of Geographical Information Science Pub Date : 2023-05-24 DOI: 10.1080/13658816.2023.2193969

T. Bayer, I. Kolingerová, Marek Celonk, J. Lysák

引用次数: 0

Uncertainty analysis of geodata derived from digital map processing 数字地图处理中地理数据的不确定性分析

IF 5.7 1区地球科学

International Journal of Geographical Information Science Pub Date : 2023-05-15 DOI: 10.1080/13658816.2023.2206890

Cyrill Delfgou, Nikolaos Bakogiannis, P. Laube

{"title":"Uncertainty analysis of geodata derived from digital map processing","authors":"Cyrill Delfgou, Nikolaos Bakogiannis, P. Laube","doi":"10.1080/13658816.2023.2206890","DOIUrl":"https://doi.org/10.1080/13658816.2023.2206890","url":null,"abstract":"Abstract Digital map processing promises computational methods for the extraction of geographic features from scanned historical maps. Such workflows are error prone, with potential spatial uncertainty arising from the initial map production, the processing of the feature extraction, and the eventual application and use of the extracted features. This paper investigates several types of uncertainty emerging the extraction of hydrological features from historical topographic maps for the monitoring of change in ecological indicators describing river ecosystems, such as shoreline length, river sinuosity or number of river nodes and islands. Computational procedures have been developed to simulate various typical, expected sources of error. In a series of experiments investigating three different typical river types, the errors were systematically varied and increased using Monte Carlo simulation whilst studying the errors’ impacts on the derived ecological indicators. The results suggest that production-oriented uncertainties emerging the initial map generalization and simplification process have bigger impacts than processing-oriented uncertainties, such as errors from manual digitizing. The results further indicate that the derivation of ecological indicators from braided rivers is more error prone than from straight or meandering rivers, and that topological indicators such as river sinuosity are more robust than indicators derived from the features’ geometry.","PeriodicalId":14162,"journal":{"name":"International Journal of Geographical Information Science","volume":"37 1","pages":"1667 - 1691"},"PeriodicalIF":5.7,"publicationDate":"2023-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48762390","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}

引用次数: 0

An embedding-based text classification approach for understanding micro-geographic housing dynamics 一种基于嵌入的文本分类方法用于理解微观地理住房动态

IF 5.7 1区地球科学

International Journal of Geographical Information Science Pub Date : 2023-05-08 DOI: 10.1080/13658816.2023.2209803

I. Nilsson, E. Delmelle

引用次数: 2