How people travel to receive health services is essential for understanding healthcare shortages. The rational service areas (RSAs) are defined to represent local healthcare markets and used as the basic units to evaluate whether people have access to health resources. Therefore, finding an appropriate way to develop RSAs is important for understanding the utilization of health resources and supporting accurate resource allocation to the health professional shortage areas (HPSAs). Existing RSAs are usually developed based on the local knowledge of public health needs and are created through time‐intensive manual work by health service officials. In this research, a travel data‐driven and spatially constrained community detection method based on human mobility flow is proposed to automate the process of establishing the statewide RSAs and further identifying HPSAs based on healthcare criteria in a geographic information system (GIS) software. The proposed method considers the difference between rural and urban populations by assigning different parameters and delineates RSAs with the goal of reducing health resource inequalities faced by rural areas. Using the data in the State of Wisconsin, our experiment shows that the proposed RSA delineation method outperforms other baselines including the traditional Dartmouth method in the aspects of RSA compactness, region size balances, and health shortage scores. Furthermore, the whole process of delineating RSAs and identifying HPSAs is automated using Python toolboxes in ArcGIS to support future analyses and practices in a timely and repeatable manner.
{"title":"Automatic delineation of rational service areas and health professional shortage areas in GIS based on human movements and health resources","authors":"Yunlei Liang, Song Gao","doi":"10.1111/tgis.13207","DOIUrl":"https://doi.org/10.1111/tgis.13207","url":null,"abstract":"How people travel to receive health services is essential for understanding healthcare shortages. The rational service areas (RSAs) are defined to represent local healthcare markets and used as the basic units to evaluate whether people have access to health resources. Therefore, finding an appropriate way to develop RSAs is important for understanding the utilization of health resources and supporting accurate resource allocation to the health professional shortage areas (HPSAs). Existing RSAs are usually developed based on the local knowledge of public health needs and are created through time‐intensive manual work by health service officials. In this research, a travel data‐driven and spatially constrained community detection method based on human mobility flow is proposed to automate the process of establishing the statewide RSAs and further identifying HPSAs based on healthcare criteria in a geographic information system (GIS) software. The proposed method considers the difference between rural and urban populations by assigning different parameters and delineates RSAs with the goal of reducing health resource inequalities faced by rural areas. Using the data in the State of Wisconsin, our experiment shows that the proposed RSA delineation method outperforms other baselines including the traditional Dartmouth method in the aspects of RSA compactness, region size balances, and health shortage scores. Furthermore, the whole process of delineating RSAs and identifying HPSAs is automated using Python toolboxes in ArcGIS to support future analyses and practices in a timely and repeatable manner.","PeriodicalId":47842,"journal":{"name":"Transactions in GIS","volume":"109 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141547295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The introduction of the carbon peak and carbon‐neutral targets by many countries' central governments has put low‐carbon‐oriented spatial planning at the forefront of discussions. However, few studies have focused on the balance of carbon emission reduction and economic goals in spatial planning, and the governance influence on land use change simulation. This study addresses this gap by conducting an empirical analysis in the rapidly urbanizing area of Hangzhou, China, taking into consideration low‐carbon constraints and economic development demands. Using the stochastic impacts by regression on population, affluence, and technology (STRIPAT) model and linear programming–Markov, we simulate the governance decision‐making process to calculate the optimal land‐use structures under both low‐carbon and baseline scenario, then simulated land use patterns by using artificial‐neural‐network‐based cellular automata (ANN‐CA). The results showed 12.35% and 2.5% growth in urban and forest land, and 9.69% and 6.4% decline in farm and rural land under the low‐carbon scenario. 92.31% of urban land change occur in the downtown districts and suburbs; while 59.77% of farm land change and 95.53% of forest land change occur in the exurban districts. The low‐carbon performance of land use was reflected in carbon storage release, carbon emission capability change, and low‐carbon capability. The most common conversion of land use categories under the low‐carbon scenario was between farm and forest land, and between rural and urban land, which resulted in less carbon storage release and carbon emissions compared with the baseline scenario. Furthermore, under the low‐carbon scenario, the compactness of construction land increased by 2 × 10−5, while its fragmentation decreased by 0.0027. This study sheds light on the impact of low‐carbon‐oriented land use planning on urban land expansion, providing empirical evidence for city governments in rapid urbanization areas to improve land use efficiency.
{"title":"The forecast and low‐carbon performance of land use in rapid urbanization area under the low‐carbon oriented spatial planning: Evidence from Hangzhou, China","authors":"Weicheng Gu, Weifeng Qi, Mingyu Zhang","doi":"10.1111/tgis.13199","DOIUrl":"https://doi.org/10.1111/tgis.13199","url":null,"abstract":"The introduction of the carbon peak and carbon‐neutral targets by many countries' central governments has put low‐carbon‐oriented spatial planning at the forefront of discussions. However, few studies have focused on the balance of carbon emission reduction and economic goals in spatial planning, and the governance influence on land use change simulation. This study addresses this gap by conducting an empirical analysis in the rapidly urbanizing area of Hangzhou, China, taking into consideration low‐carbon constraints and economic development demands. Using the stochastic impacts by regression on population, affluence, and technology (STRIPAT) model and linear programming–Markov, we simulate the governance decision‐making process to calculate the optimal land‐use structures under both low‐carbon and baseline scenario, then simulated land use patterns by using artificial‐neural‐network‐based cellular automata (ANN‐CA). The results showed 12.35% and 2.5% growth in urban and forest land, and 9.69% and 6.4% decline in farm and rural land under the low‐carbon scenario. 92.31% of urban land change occur in the downtown districts and suburbs; while 59.77% of farm land change and 95.53% of forest land change occur in the exurban districts. The low‐carbon performance of land use was reflected in carbon storage release, carbon emission capability change, and low‐carbon capability. The most common conversion of land use categories under the low‐carbon scenario was between farm and forest land, and between rural and urban land, which resulted in less carbon storage release and carbon emissions compared with the baseline scenario. Furthermore, under the low‐carbon scenario, the compactness of construction land increased by 2 × 10<jats:sup>−5</jats:sup>, while its fragmentation decreased by 0.0027. This study sheds light on the impact of low‐carbon‐oriented land use planning on urban land expansion, providing empirical evidence for city governments in rapid urbanization areas to improve land use efficiency.","PeriodicalId":47842,"journal":{"name":"Transactions in GIS","volume":"2 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141527075","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Xuguo Shi, Dianqiang Chen, Jianing Wang, Pan Wang, Yunlong Wu, Shaocheng Zhang, Yi Zhang, Chen Yang, Lunche Wang
Landslides are widely distributed mountainous geological hazards that threaten economic development and people's daily lives. Interferometric synthetic aperture radar (InSAR) with comprehensive coverage and high‐precision ground displacement monitoring abilities are frequently utilized for regional‐scale active slope detection. Moreover, InSAR measurements that characterize ground dynamics are integrated with conventional topographic, hydrological, and geological landslide conditioning factors (LCFs) for landslide susceptibility mapping (LSM). Weining County in southwest China, with complex geological conditions, steep terrain, and frequent tectonic activities, is prone to catastrophic landslide failures. In this study, we refined the landslide inventory of Weining County using one ascending and one descending Sentinel‐1 dataset acquired during 2015–2021 through a small baseline subset InSAR (SBAS InSAR) analysis. We then combine the LOS measurements from both datasets using multidimensional SBAS to obtain time series two‐dimensional (2D) displacements to characterize the kinematics of active slopes. Hot spot and cluster analysis (HCA) was carried out on 2D displacement rate maps to highlight clustered deformed areas and suppress noisy signals that occurred on single pixels. Two hundred fifty‐eight landslides (including 71 active identified in this study) are used to construct 76,412 positive samples for LSM. In our study, the HCA maps, instead of the 2D displacement maps, are integrated with conventional LCFs to form an LCF_HCA set to feed support vector machine (SVM), Random Forest (RF), extreme Gradient Boosting (XGBoost) and Light Gradient‐Boosting Machine (LightGBM) models. A conventional LCF (LCF_CON) set and an integrated 2D displacement maps (LCF_2D) set have also been adapted for comparison. The performance of the tree‐based ensemble methods distinctly outperforms the SVM model. In the meantime, models' performances using the LCF_HCA set are superior to that of the other 2 LCF sets from all evaluation metrics. The ranks of HCA maps increased compared with 2D displacement maps from feature importance analysis, which might lead to the better performance of models using the LCF_HCA set. With the continuous accumulation of SAR images, ground dynamic characteristics from InSAR can offer us opportunities to understand landslide kinematics and enhance LSM.
{"title":"Refined landslide inventory and susceptibility of Weining County, China, inferred from machine learning and Sentinel‐1 InSAR analysis","authors":"Xuguo Shi, Dianqiang Chen, Jianing Wang, Pan Wang, Yunlong Wu, Shaocheng Zhang, Yi Zhang, Chen Yang, Lunche Wang","doi":"10.1111/tgis.13202","DOIUrl":"https://doi.org/10.1111/tgis.13202","url":null,"abstract":"Landslides are widely distributed mountainous geological hazards that threaten economic development and people's daily lives. Interferometric synthetic aperture radar (InSAR) with comprehensive coverage and high‐precision ground displacement monitoring abilities are frequently utilized for regional‐scale active slope detection. Moreover, InSAR measurements that characterize ground dynamics are integrated with conventional topographic, hydrological, and geological landslide conditioning factors (LCFs) for landslide susceptibility mapping (LSM). Weining County in southwest China, with complex geological conditions, steep terrain, and frequent tectonic activities, is prone to catastrophic landslide failures. In this study, we refined the landslide inventory of Weining County using one ascending and one descending Sentinel‐1 dataset acquired during 2015–2021 through a small baseline subset InSAR (SBAS InSAR) analysis. We then combine the LOS measurements from both datasets using multidimensional SBAS to obtain time series two‐dimensional (2D) displacements to characterize the kinematics of active slopes. Hot spot and cluster analysis (HCA) was carried out on 2D displacement rate maps to highlight clustered deformed areas and suppress noisy signals that occurred on single pixels. Two hundred fifty‐eight landslides (including 71 active identified in this study) are used to construct 76,412 positive samples for LSM. In our study, the HCA maps, instead of the 2D displacement maps, are integrated with conventional LCFs to form an LCF_HCA set to feed support vector machine (SVM), Random Forest (RF), extreme Gradient Boosting (XGBoost) and Light Gradient‐Boosting Machine (LightGBM) models. A conventional LCF (LCF_CON) set and an integrated 2D displacement maps (LCF_2D) set have also been adapted for comparison. The performance of the tree‐based ensemble methods distinctly outperforms the SVM model. In the meantime, models' performances using the LCF_HCA set are superior to that of the other 2 LCF sets from all evaluation metrics. The ranks of HCA maps increased compared with 2D displacement maps from feature importance analysis, which might lead to the better performance of models using the LCF_HCA set. With the continuous accumulation of SAR images, ground dynamic characteristics from InSAR can offer us opportunities to understand landslide kinematics and enhance LSM.","PeriodicalId":47842,"journal":{"name":"Transactions in GIS","volume":"39 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141547363","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Understanding interactions through movement provides critical insights into urban dynamic, social networks, and wildlife behaviors. With widespread tracking of humans, vehicles, and animals, there is an abundance of large and high‐resolution movement data sets. However, there is a gap in efficient GIS tools for analyzing and contextualizing movement patterns using large movement datasets. In particular, tracing space–time interactions among a group of moving individuals is a computationally demanding task, which would uncover insights into collective behaviors across systems. This article develops a Spark‐based geo‐computational framework through the integration of Esri's ArcGIS GeoAnalytics Engine and Python to optimize the computation of time geography for scaling up movement interaction analysis. The computational framework is then tested using a case study on migratory turkey vultures with over 2 million GPS tracking points across 20 years. The outcomes indicate a drastic reduction in interaction detection time from 14 days to 6 hours, demonstrating a remarkable increase in computational efficiency. This work contributes to advancing GIS computational capabilities in movement analysis, highlighting the potential of GeoAnalytics Engine in processing large spatiotemporal datasets.
{"title":"Scaling up time–geographic computation for movement interaction analysis","authors":"Yifei Liu, Sarah Battersby, Somayeh Dodge","doi":"10.1111/tgis.13205","DOIUrl":"https://doi.org/10.1111/tgis.13205","url":null,"abstract":"Understanding interactions through movement provides critical insights into urban dynamic, social networks, and wildlife behaviors. With widespread tracking of humans, vehicles, and animals, there is an abundance of large and high‐resolution movement data sets. However, there is a gap in efficient GIS tools for analyzing and contextualizing movement patterns using large movement datasets. In particular, tracing space–time interactions among a group of moving individuals is a computationally demanding task, which would uncover insights into collective behaviors across systems. This article develops a Spark‐based geo‐computational framework through the integration of Esri's ArcGIS GeoAnalytics Engine and Python to optimize the computation of time geography for scaling up movement interaction analysis. The computational framework is then tested using a case study on migratory turkey vultures with over 2 million GPS tracking points across 20 years. The outcomes indicate a drastic reduction in interaction detection time from 14 days to 6 hours, demonstrating a remarkable increase in computational efficiency. This work contributes to advancing GIS computational capabilities in movement analysis, highlighting the potential of GeoAnalytics Engine in processing large spatiotemporal datasets.","PeriodicalId":47842,"journal":{"name":"Transactions in GIS","volume":"67 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141547364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Spatial co‐location pattern (CP) mining can discover sets of geographical features frequently appearing in adjacent locations, which is valuable for comprehending the co‐occurrence relationship between features. However, due to the quantitative differences and heterogeneous distribution of features, the probabilities that features appear in each other's neighborhood are unequal, resulting in an asymmetric spatial pattern. Current studies have paid little attention to the asymmetric characteristics of CPs. Therefore, this study explores the CPs and their asymmetric relationships. Firstly, we adopt the weighted participation index to evaluate the frequency of global candidate CPs. Secondly, we employ an asymmetry index we developed and the local co‐location quotient to quantify the asymmetry intensity of CPs. The results indicate that the frequent CPs mainly comprise facilities related to the residents' daily lives. Investigating the asymmetric relationships and spatial associations among features in the CPs is significant for identifying resource shortages and rationally planning urban resources.
{"title":"Discovering spatial co‐location patterns of urban facilities and their asymmetric characteristics","authors":"Sijia Jin, Disheng Yi, Junlei Yuan, Yuxin Zhao, Jiahiu Qin, Huijun Zhou, Jing Zhang","doi":"10.1111/tgis.13203","DOIUrl":"https://doi.org/10.1111/tgis.13203","url":null,"abstract":"Spatial co‐location pattern (CP) mining can discover sets of geographical features frequently appearing in adjacent locations, which is valuable for comprehending the co‐occurrence relationship between features. However, due to the quantitative differences and heterogeneous distribution of features, the probabilities that features appear in each other's neighborhood are unequal, resulting in an asymmetric spatial pattern. Current studies have paid little attention to the asymmetric characteristics of CPs. Therefore, this study explores the CPs and their asymmetric relationships. Firstly, we adopt the weighted participation index to evaluate the frequency of global candidate CPs. Secondly, we employ an asymmetry index we developed and the local co‐location quotient to quantify the asymmetry intensity of CPs. The results indicate that the frequent CPs mainly comprise facilities related to the residents' daily lives. Investigating the asymmetric relationships and spatial associations among features in the CPs is significant for identifying resource shortages and rationally planning urban resources.","PeriodicalId":47842,"journal":{"name":"Transactions in GIS","volume":"23 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141527077","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Crafting beautiful map colors is challenging for not only experts but also novices, suggesting the need for an aesthetic quality assessment of map colors for the effective design of maps and visualizations. To fill this gap, we present a computational method to assess the aesthetic quality of map colors. First, we couple the idea of computational aesthetics with map aesthetic principles and identify four and two basic types of aesthetic features in terms of order and complexity, respectively. Then, we collect 2000 map samples and derive 149 aesthetic metrics by instantiating the above aesthetic features while considering the spatial weights and figure–ground organization of the map samples. We also recruit participants (N = 438) to rate the aesthetic quality of the map colors. Finally, we train an aesthetic predictor by fitting those aesthetic metrics with user ratings. The experimental results show that the proposed method can assess aesthetic quality of map colors with high accuracy (R2 = 0.73 on the training set and R2 = 0.65 on the validation set). We also explore the dominant aesthetic metrics that positively and negatively influence aesthetic appreciation, as well as those metrics that have no significant influence. This work offers a portable and flexible aesthetic quality assessment approach for map colors and can be further improved by considering complex symbols, spatial structures, and color–semantic and color–emotion associations.
{"title":"Computational assessment of the aesthetic quality of map colors","authors":"Mingguang Wu, Yanjie Sun, Xianqin Xia","doi":"10.1111/tgis.13206","DOIUrl":"https://doi.org/10.1111/tgis.13206","url":null,"abstract":"Crafting beautiful map colors is challenging for not only experts but also novices, suggesting the need for an aesthetic quality assessment of map colors for the effective design of maps and visualizations. To fill this gap, we present a computational method to assess the aesthetic quality of map colors. First, we couple the idea of computational aesthetics with map aesthetic principles and identify four and two basic types of aesthetic features in terms of <jats:italic>order</jats:italic> and <jats:italic>complexity</jats:italic>, respectively. Then, we collect 2000 map samples and derive 149 aesthetic metrics by instantiating the above aesthetic features while considering the spatial weights and figure–ground organization of the map samples. We also recruit participants (<jats:italic>N</jats:italic> = 438) to rate the aesthetic quality of the map colors. Finally, we train an aesthetic predictor by fitting those aesthetic metrics with user ratings. The experimental results show that the proposed method can assess aesthetic quality of map colors with high accuracy (<jats:italic>R</jats:italic><jats:sup>2</jats:sup> = 0.73 on the training set and <jats:italic>R</jats:italic><jats:sup>2</jats:sup> = 0.65 on the validation set). We also explore the dominant aesthetic metrics that positively and negatively influence aesthetic appreciation, as well as those metrics that have no significant influence. This work offers a portable and flexible aesthetic quality assessment approach for map colors and can be further improved by considering complex symbols, spatial structures, and color–semantic and color–emotion associations.","PeriodicalId":47842,"journal":{"name":"Transactions in GIS","volume":"18 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141527076","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Knowledge embedding for geographic knowledge graphs can effectively improve computational efficiency and provide support for knowledge reasoning, knowledge answering and other applications of knowledge graphs. To maintain a more comprehensive understanding of spatial features through knowledge embedding, it is crucial to integrate the representation and computation of various entity types, encompassing points, lines, and polygons. This article proposes a geographic entities uniformly explicit knowledge embedding model (GEUKE). In GEUKE, spatial data of point, line, and polygon‐type geographic entities are expressed in the form of subgraphs, and space embedding is generated using a SubGNN‐based uniform spatial feature encoder. GEUKE improves the energy function in TransE to train spatial feature‐based embedding and structural‐based embedding of geographic entities into a unified vector space. Experimental results show that GEUKE has higher performance than TransE, TransH, TransD, and TransE‐GDR on link prediction and triple classification task. Within the spatial feature embedding process, GEUKE effectively preserves the inherent features of entities, encompassing location, neighborhood, and structural attributes, while simultaneously ensuring a coherent spatial data representation across all three entity types: points, lines, and polygons. By maintaining the spatial features of geographic entities and their interrelations, this capability unleashes the full potential of applications such as knowledge reasoning and geospatial question answering in a manner that is conducive to diverse geospatial scenarios.
{"title":"GEUKE: A geographic entities uniformly explicit knowledge embedding model","authors":"Yongquan Yang, Dehui Kong, Min Cao, Min Chen","doi":"10.1111/tgis.13191","DOIUrl":"https://doi.org/10.1111/tgis.13191","url":null,"abstract":"Knowledge embedding for geographic knowledge graphs can effectively improve computational efficiency and provide support for knowledge reasoning, knowledge answering and other applications of knowledge graphs. To maintain a more comprehensive understanding of spatial features through knowledge embedding, it is crucial to integrate the representation and computation of various entity types, encompassing points, lines, and polygons. This article proposes a geographic entities uniformly explicit knowledge embedding model (GEUKE). In GEUKE, spatial data of point, line, and polygon‐type geographic entities are expressed in the form of subgraphs, and space embedding is generated using a SubGNN‐based uniform spatial feature encoder. GEUKE improves the energy function in TransE to train spatial feature‐based embedding and structural‐based embedding of geographic entities into a unified vector space. Experimental results show that GEUKE has higher performance than TransE, TransH, TransD, and TransE‐GDR on link prediction and triple classification task. Within the spatial feature embedding process, GEUKE effectively preserves the inherent features of entities, encompassing location, neighborhood, and structural attributes, while simultaneously ensuring a coherent spatial data representation across all three entity types: points, lines, and polygons. By maintaining the spatial features of geographic entities and their interrelations, this capability unleashes the full potential of applications such as knowledge reasoning and geospatial question answering in a manner that is conducive to diverse geospatial scenarios.","PeriodicalId":47842,"journal":{"name":"Transactions in GIS","volume":"52 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141189144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The delineation of two‐dimensional ascending and descending manifolds represents the theoretical basis for a large number of applications in which functions are used to describe phenomena related to climate, economy, or engineering, to mention only a few. Whereas the applications are related to the pits, passes, peaks, courses, ridges, basins, and hills, of mathematical interest are the corresponding critical points, separatrices as well as two‐dimensional ascending and descending manifolds. The present article demonstrates how the boundaries of the latter, which represent the pre‐images of basins and hills, can be characterized in a graph‐theoretic way. An algorithm for their extraction, which is based on a newly proved theorem, is presented together with its implementation in C#. Finally, the modus operandi of the algorithm is illustrated by two examples, thereby demonstrating how it works even in the case of surfaces with topologically complicated structures.
{"title":"Delineation of basins and hills by Morse theory and critical nets","authors":"Gert W. Wolf","doi":"10.1111/tgis.13161","DOIUrl":"https://doi.org/10.1111/tgis.13161","url":null,"abstract":"The delineation of two‐dimensional ascending and descending manifolds represents the theoretical basis for a large number of applications in which functions are used to describe phenomena related to climate, economy, or engineering, to mention only a few. Whereas the applications are related to the pits, passes, peaks, courses, ridges, basins, and hills, of mathematical interest are the corresponding critical points, separatrices as well as two‐dimensional ascending and descending manifolds. The present article demonstrates how the boundaries of the latter, which represent the pre‐images of basins and hills, can be characterized in a graph‐theoretic way. An algorithm for their extraction, which is based on a newly proved theorem, is presented together with its implementation in C#. Finally, the <jats:italic>modus operandi</jats:italic> of the algorithm is illustrated by two examples, thereby demonstrating how it works even in the case of surfaces with topologically complicated structures.","PeriodicalId":47842,"journal":{"name":"Transactions in GIS","volume":"26 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141172801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Methods for evaluating cognitively inspired geospatial interfaces have been important for revealing and helping solve their cognitive and usability issues. We argue that this is now true of interfaces in GIScience that deliver narrative visualizations, including 3D virtual narrative environments. These spaces allow for controlled conditions and realistic natural settings, where spatio‐temporal data can be collected and used to ascertain how well an interface design fulfilled a given narrative function. This study investigates the function of a cognitively inspired geospatial interface (Future Vision) that aimed to determine how mental images can be situated in geospatial environments and used to convey narratives that improve user cognition and decision‐making. The results of a two‐alternative forced‐choice (2AFC) decision‐making task showed that participants using future thinking guidance (mental images as a split‐second display of correct path choice) had statistically significant improvements in their task completion times, movement speeds and 2AFC decision‐making, compared to the unguided control group. Implications of the results include benefits for cue‐based navigation of real and conceptual spaces in GIScience. Future research can improve the interface design by modifying the interface code to reduce visual loss caused by eye blinks and saccades.
{"title":"Incorporating mental imagery into geospatial environments for narrative visualizations","authors":"Ronny A. Rowe, Antoni B. Moore","doi":"10.1111/tgis.13187","DOIUrl":"https://doi.org/10.1111/tgis.13187","url":null,"abstract":"Methods for evaluating cognitively inspired geospatial interfaces have been important for revealing and helping solve their cognitive and usability issues. We argue that this is now true of interfaces in GIScience that deliver narrative visualizations, including 3D virtual narrative environments. These spaces allow for controlled conditions and realistic natural settings, where spatio‐temporal data can be collected and used to ascertain how well an interface design fulfilled a given narrative function. This study investigates the function of a cognitively inspired geospatial interface (Future Vision) that aimed to determine how mental images can be situated in geospatial environments and used to convey narratives that improve user cognition and decision‐making. The results of a two‐alternative forced‐choice (2AFC) decision‐making task showed that participants using future thinking guidance (mental images as a split‐second display of correct path choice) had statistically significant improvements in their task completion times, movement speeds and 2AFC decision‐making, compared to the unguided control group. Implications of the results include benefits for cue‐based navigation of real and conceptual spaces in GIScience. Future research can improve the interface design by modifying the interface code to reduce visual loss caused by eye blinks and saccades.","PeriodicalId":47842,"journal":{"name":"Transactions in GIS","volume":"41 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2024-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141188910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jianbin Zhou, Jin Ben, Qishuang Liang, Xinhai Huang, Junjie Ding
One of the basic scientific problems concerning geographic information science is how to rapidly organize, query, and compute spatiotemporal big data. The spatiotemporal discrete global grid system (DGGS) provides a homogenized discrete structure for processing multiscale and multitype spatiotemporal data. To date, most research in spatiotemporal DGGS has focused on spatial discretization while neglecting temporal discretization. Here, we propose a general modeling scheme for spatiotemporal DGGS with emphasis on encoding and operating multiscale time grids. We subdivide continuous time into multiscale temporal grids, which are then encoded as integers. Moreover, we designed integer code operations, including hierarchical traversal, neighborhood finding, and temporal relationship calculations. Compared to the multiscale time segment integer coding (MTSIC) approach, the proposed method resulted in 22% higher encoding efficiency, 10.92 times faster decoding, 2.81 times better parent code finding efficiency, 41% improved efficiency, 100% accuracy in finding children codes (compared to less than 100% with MTSIC), and a 62% enhancement in temporal relationship calculation efficiency. The application of querying spatiotemporal trajectory data validates the feasibility and practicality of substituting conventional string‐based time and floating‐point location coordinates with spatiotemporal integer codes to query data. The time encoding and operation methods proposed here indicate high efficiency, superior accuracy, and broad application prospects.
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