Pub Date : 2023-01-29DOI: 10.1080/19475683.2023.2166111
B. Mashhoodi, Pablo Muñoz Unceta
ABSTRACT This study develops a multiscale model for allocation of EV infrastructure to accommodate residents’ demand during nights and that of residents and visitors during days under two scenarios: maximum 40% or 80% increase in load on the electricity grid. Developing a mixed-integer linear optimization model including regional traffic flow, local electricity demand and parking availability in Amsterdam Metropolitan Area (AMA), the scenarios’ optimal solutions offer different spatial strategies. This study shows that multiscale allocation of EV chargers substantially improves the efficiency of use: in both scenarios, more than 53% of EVs can charge at their daily destination. However, in the 40% scenario, the extra electricity load is homogeneously allocated across the towns and villages around the AMA centre. In an 80% scenario, in contrast, the load is concentrated in a few areas (1) accessible for substantial numbers of EVs at the regional scale, (2) with relatively low annual consumption, (3) reasonably high number of registered EVs to use chargers in the nights. The manuscript ends with a discussion of the results and their policy implications and offers further studies.
{"title":"Regional allocation of EV chargers’ grid load","authors":"B. Mashhoodi, Pablo Muñoz Unceta","doi":"10.1080/19475683.2023.2166111","DOIUrl":"https://doi.org/10.1080/19475683.2023.2166111","url":null,"abstract":"ABSTRACT This study develops a multiscale model for allocation of EV infrastructure to accommodate residents’ demand during nights and that of residents and visitors during days under two scenarios: maximum 40% or 80% increase in load on the electricity grid. Developing a mixed-integer linear optimization model including regional traffic flow, local electricity demand and parking availability in Amsterdam Metropolitan Area (AMA), the scenarios’ optimal solutions offer different spatial strategies. This study shows that multiscale allocation of EV chargers substantially improves the efficiency of use: in both scenarios, more than 53% of EVs can charge at their daily destination. However, in the 40% scenario, the extra electricity load is homogeneously allocated across the towns and villages around the AMA centre. In an 80% scenario, in contrast, the load is concentrated in a few areas (1) accessible for substantial numbers of EVs at the regional scale, (2) with relatively low annual consumption, (3) reasonably high number of registered EVs to use chargers in the nights. The manuscript ends with a discussion of the results and their policy implications and offers further studies.","PeriodicalId":46270,"journal":{"name":"Annals of GIS","volume":"120 1","pages":"227 - 241"},"PeriodicalIF":5.0,"publicationDate":"2023-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87669816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-23DOI: 10.1080/19475683.2023.2166113
Ali Rashed Alruzuq, J. Remo, J. Mossa, Kevin D. Ash
ABSTRACT The maintenance of flood mitigation levees in the U.S. and elsewhere, has often found to be insufficient. Recent U.S. levee safety inspections have discovered substantial deficiencies in many federally monitored levees resulting in some of them being assigned an ‘unacceptable’ safety rating. Most levees in the U.S. were constructed using federal money but then were turned over to local government entities to maintain, thus socioeconomic characteristics of these communities may impact their ability to maintain their levee(s). We used dasymetric mapping to assign socioeconomic parameters to levee protected areas along the lower Illinois River to explore differences in these characteristics between communities with an unacceptable levee safety rating to those with an acceptable rating. Principal components analysis was used to determine which socioeconomic parameters explained the majority of variance between levee protected communities and the Mann-Whitney-Wilcoxon U-Test for significance testing. These analyses revealed that differences in total population , race, average per-capita income, and number of residential homes were influential indicators for explaining differences between comunities with levees with acceptable versus unacceptable ratings. These results suggest populations who inhabit levee systems with unacceptable safety ratings are white and relatively wealthier than communities located within levee systems with acceptable ratings. This finding is counter to research that shows that the poor and poorer minorities live in areas with higher flood risk. This is perhaps wealthier floodplain property owners inhabitating unacceptable levee systems may be foregoing necessary levee maintenance, inastead relying on other government programs to pay for flood damages.
{"title":"Socio-hydrology and vulnerability of levee systems along the lower Illinois River","authors":"Ali Rashed Alruzuq, J. Remo, J. Mossa, Kevin D. Ash","doi":"10.1080/19475683.2023.2166113","DOIUrl":"https://doi.org/10.1080/19475683.2023.2166113","url":null,"abstract":"ABSTRACT The maintenance of flood mitigation levees in the U.S. and elsewhere, has often found to be insufficient. Recent U.S. levee safety inspections have discovered substantial deficiencies in many federally monitored levees resulting in some of them being assigned an ‘unacceptable’ safety rating. Most levees in the U.S. were constructed using federal money but then were turned over to local government entities to maintain, thus socioeconomic characteristics of these communities may impact their ability to maintain their levee(s). We used dasymetric mapping to assign socioeconomic parameters to levee protected areas along the lower Illinois River to explore differences in these characteristics between communities with an unacceptable levee safety rating to those with an acceptable rating. Principal components analysis was used to determine which socioeconomic parameters explained the majority of variance between levee protected communities and the Mann-Whitney-Wilcoxon U-Test for significance testing. These analyses revealed that differences in total population , race, average per-capita income, and number of residential homes were influential indicators for explaining differences between comunities with levees with acceptable versus unacceptable ratings. These results suggest populations who inhabit levee systems with unacceptable safety ratings are white and relatively wealthier than communities located within levee systems with acceptable ratings. This finding is counter to research that shows that the poor and poorer minorities live in areas with higher flood risk. This is perhaps wealthier floodplain property owners inhabitating unacceptable levee systems may be foregoing necessary levee maintenance, inastead relying on other government programs to pay for flood damages.","PeriodicalId":46270,"journal":{"name":"Annals of GIS","volume":"22 1","pages":"273 - 291"},"PeriodicalIF":5.0,"publicationDate":"2023-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90949108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-20DOI: 10.1080/19475683.2023.2166585
L. Seegmiller, T. Shirabe
ABSTRACT The least-cost path problem is a widely studied problems in geographic information science. In raster space, the problem is to find a path that accumulates the least amount of cost between two locations based on the assumptions that the path is a one-dimensional object (represented by a string of cells) and that the cost (per unit length) is measured on a quantitative scale. Efficient methods are available for solution of this problem when at least one of these assumptions is upheld. This is not the case when the path has a width and is a two-dimensional object called a corridor (represented by a swath of cells) and the cost (per unit area) is measured on an ordinal scale. In this paper, we propose one additional model that characterizes a least-cost corridor on an ordinal-scaled raster cost surface – or a least ordinal-scaled cost corridor for short – and show that it can be transformed into an instance of a multiobjective optimization problem known as the preferred path problem with a lexicographic preference relation and solved accordingly. The model is tested through computational experiments with artificial landscape data as well as real-world data. Results show that least ordinal-scaled cost corridors are guaranteed to contain smaller areas of higher cost than conventional least-cost corridors at the expense of more elongated and winding forms. The least ordinal-scaled cost corridor problem has computational complexity of O(n 2.5) in the worst case, resulting in a longer computational time than least-cost corridors. However, this difference is smaller in practice.
{"title":"A method for finding a least-cost corridor on an ordinal-scaled raster cost surface","authors":"L. Seegmiller, T. Shirabe","doi":"10.1080/19475683.2023.2166585","DOIUrl":"https://doi.org/10.1080/19475683.2023.2166585","url":null,"abstract":"ABSTRACT The least-cost path problem is a widely studied problems in geographic information science. In raster space, the problem is to find a path that accumulates the least amount of cost between two locations based on the assumptions that the path is a one-dimensional object (represented by a string of cells) and that the cost (per unit length) is measured on a quantitative scale. Efficient methods are available for solution of this problem when at least one of these assumptions is upheld. This is not the case when the path has a width and is a two-dimensional object called a corridor (represented by a swath of cells) and the cost (per unit area) is measured on an ordinal scale. In this paper, we propose one additional model that characterizes a least-cost corridor on an ordinal-scaled raster cost surface – or a least ordinal-scaled cost corridor for short – and show that it can be transformed into an instance of a multiobjective optimization problem known as the preferred path problem with a lexicographic preference relation and solved accordingly. The model is tested through computational experiments with artificial landscape data as well as real-world data. Results show that least ordinal-scaled cost corridors are guaranteed to contain smaller areas of higher cost than conventional least-cost corridors at the expense of more elongated and winding forms. The least ordinal-scaled cost corridor problem has computational complexity of O(n 2.5) in the worst case, resulting in a longer computational time than least-cost corridors. However, this difference is smaller in practice.","PeriodicalId":46270,"journal":{"name":"Annals of GIS","volume":"140 1","pages":"205 - 225"},"PeriodicalIF":5.0,"publicationDate":"2023-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86626344","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-17DOI: 10.1080/19475683.2023.2166989
Rana Waqar Aslam, H. Shu, Andaleeb Yaseen
ABSTRACT Cities are complex and dynamic entities in close proximity of people, implying multi temporal observations to analyse and understand the urban context. At present, open-source data and geospatial intelligence are becoming the important means of exploring, monitoring and predicting urban status of area growth and population increase. In last few decades, unemployment and absence of infrastructures in the rural areas promoted the unplanned and haphazard urbanization across the urban centres in Pakistan. This study focuses on exploring the potential of open-source/freely available datasets for city mapping and monitoring spatially. The study gives a spatial perspective of rapidly growing cities of Pakistan using Google Earth Engine to classify Landsat images over last four decades, and discovers sprawl patterns across cities. The study works out that the built-up area is significantly increasing with population growth over four decades and there is a strong positive correlation between population growth and built-up expansion. Using Open-Source Data (Landsat images and LandScan data), this study has offered a technical solution of Google Earth Engine-supported analysis of statistics and machine learning to spatially monitoring the population change and urban growth of four major Pakistan cities. It is undoubted that our working results will provide the timely and cost-effective information to policymakers, Govt Officials and citizens for more sustainable urbanization.
{"title":"Monitoring the population change and urban growth of four major Pakistan cities through spatial analysis of open source data","authors":"Rana Waqar Aslam, H. Shu, Andaleeb Yaseen","doi":"10.1080/19475683.2023.2166989","DOIUrl":"https://doi.org/10.1080/19475683.2023.2166989","url":null,"abstract":"ABSTRACT Cities are complex and dynamic entities in close proximity of people, implying multi temporal observations to analyse and understand the urban context. At present, open-source data and geospatial intelligence are becoming the important means of exploring, monitoring and predicting urban status of area growth and population increase. In last few decades, unemployment and absence of infrastructures in the rural areas promoted the unplanned and haphazard urbanization across the urban centres in Pakistan. This study focuses on exploring the potential of open-source/freely available datasets for city mapping and monitoring spatially. The study gives a spatial perspective of rapidly growing cities of Pakistan using Google Earth Engine to classify Landsat images over last four decades, and discovers sprawl patterns across cities. The study works out that the built-up area is significantly increasing with population growth over four decades and there is a strong positive correlation between population growth and built-up expansion. Using Open-Source Data (Landsat images and LandScan data), this study has offered a technical solution of Google Earth Engine-supported analysis of statistics and machine learning to spatially monitoring the population change and urban growth of four major Pakistan cities. It is undoubted that our working results will provide the timely and cost-effective information to policymakers, Govt Officials and citizens for more sustainable urbanization.","PeriodicalId":46270,"journal":{"name":"Annals of GIS","volume":"60 1","pages":"355 - 367"},"PeriodicalIF":5.0,"publicationDate":"2023-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84387813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-12DOI: 10.1080/19475683.2023.2166112
Su Zhang, S. Bogus, Shirley V. Baros, P. Neville, H. Barrett, Tyler Eshelman
ABSTRACT Bridge decks need to be routinely inspected to ensure their serviceability, capacity, and safety under current traffic conditions. Traditionally, bridge deck inspection is performed on the ground by having inspectors either visually inspect surface conditions or interpret the acoustic feedback from hammer sounding or chain dragging to determine subsurface conditions. These traditional methods have many limitations, including but not limited to, expensive, labour-intensive, time-consuming, subjective, can exhibit a high degree of variability, requiring specialized staff on a regular basis, and unsafe. Recent advancements in remote sensing, especially small-uncrewed aircraft systems (S-UAS) based airborne imaging techniques and advanced image analysis techniques, have shown promise in improving current bridge deck inspection practices by providing an above-ground inspection method. This research explored the utility of S-UAS-based airborne imaging techniques and image processing techniques to develop a complete aerial data acquisition and analysis system to accurately detect and assess bridge deck wearing surface distresses in a timely and cost-effective manner. As part of the research project, a robust tool was also developed with the aim of being able to detect, extract, and map bridge deck wearing surface distresses with an adequate degree of accuracy while maximizing the ability to assist bridge inspectors with varying expertise. Research results revealed that the developed tool is able to effectively detect and map bridge deck wearing surface distresses at a high accuracy.
{"title":"Bridge deck surface distress evaluation using S-UAS acquired high-spatial resolution aerial imagery","authors":"Su Zhang, S. Bogus, Shirley V. Baros, P. Neville, H. Barrett, Tyler Eshelman","doi":"10.1080/19475683.2023.2166112","DOIUrl":"https://doi.org/10.1080/19475683.2023.2166112","url":null,"abstract":"ABSTRACT Bridge decks need to be routinely inspected to ensure their serviceability, capacity, and safety under current traffic conditions. Traditionally, bridge deck inspection is performed on the ground by having inspectors either visually inspect surface conditions or interpret the acoustic feedback from hammer sounding or chain dragging to determine subsurface conditions. These traditional methods have many limitations, including but not limited to, expensive, labour-intensive, time-consuming, subjective, can exhibit a high degree of variability, requiring specialized staff on a regular basis, and unsafe. Recent advancements in remote sensing, especially small-uncrewed aircraft systems (S-UAS) based airborne imaging techniques and advanced image analysis techniques, have shown promise in improving current bridge deck inspection practices by providing an above-ground inspection method. This research explored the utility of S-UAS-based airborne imaging techniques and image processing techniques to develop a complete aerial data acquisition and analysis system to accurately detect and assess bridge deck wearing surface distresses in a timely and cost-effective manner. As part of the research project, a robust tool was also developed with the aim of being able to detect, extract, and map bridge deck wearing surface distresses with an adequate degree of accuracy while maximizing the ability to assist bridge inspectors with varying expertise. Research results revealed that the developed tool is able to effectively detect and map bridge deck wearing surface distresses at a high accuracy.","PeriodicalId":46270,"journal":{"name":"Annals of GIS","volume":"22 1","pages":"261 - 272"},"PeriodicalIF":5.0,"publicationDate":"2023-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89960078","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-11DOI: 10.1080/19475683.2023.2166584
Ya Han, Yujie Hu, Haojie Zhu, Fahui Wang
ABSTRACT This paper presents a new method for predictive crime hotspot analysis that further improves the kernel density estimation (KDE) method and the spatio-temporal kernel density estimation (STKDE) method by accounting for temporal crime cycles and is therefore termed the ‘cyclically adjusted STKDE (cSTKDE) method’. The case study on robbery incidents in Baton Rouge, Louisiana, shows a temporal cycle with a 6-month period of statistical significance from January 2010 to May 2018. This identified period is incorporated into the temporal kernel function of the new cSTKDE method. For validation, the Forecast Accuracy Index (FAI) and Forecast Precision Index (FPI) are used to evaluate the performance across 52 weeks in 2013. For 11 consecutive weeks since the beginning of 2013, the cSTKDE method outperforms the STKDE by 89% lower average abs(1-FAI) and 17% higher average FPI, and outperforms the KDE by 90% lower average abs(1-FAI) and 8% higher average FPI. Overall, the scenario with the best predictive accuracy by the cSTKDE is recommended over the traditional KDE or STKDE method as most feasible and effective in implementation of hotspot policing in practice.
{"title":"A cyclically adjusted spatio-temporal kernel density estimation method for predictive crime hotspot analysis","authors":"Ya Han, Yujie Hu, Haojie Zhu, Fahui Wang","doi":"10.1080/19475683.2023.2166584","DOIUrl":"https://doi.org/10.1080/19475683.2023.2166584","url":null,"abstract":"ABSTRACT This paper presents a new method for predictive crime hotspot analysis that further improves the kernel density estimation (KDE) method and the spatio-temporal kernel density estimation (STKDE) method by accounting for temporal crime cycles and is therefore termed the ‘cyclically adjusted STKDE (cSTKDE) method’. The case study on robbery incidents in Baton Rouge, Louisiana, shows a temporal cycle with a 6-month period of statistical significance from January 2010 to May 2018. This identified period is incorporated into the temporal kernel function of the new cSTKDE method. For validation, the Forecast Accuracy Index (FAI) and Forecast Precision Index (FPI) are used to evaluate the performance across 52 weeks in 2013. For 11 consecutive weeks since the beginning of 2013, the cSTKDE method outperforms the STKDE by 89% lower average abs(1-FAI) and 17% higher average FPI, and outperforms the KDE by 90% lower average abs(1-FAI) and 8% higher average FPI. Overall, the scenario with the best predictive accuracy by the cSTKDE is recommended over the traditional KDE or STKDE method as most feasible and effective in implementation of hotspot policing in practice.","PeriodicalId":46270,"journal":{"name":"Annals of GIS","volume":"23 1","pages":"177 - 191"},"PeriodicalIF":5.0,"publicationDate":"2023-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81637901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-10DOI: 10.1080/19475683.2023.2165543
Jinqu Zhang, Lang Qian, Shu Wang, Yunqiang Zhu, Zhenji Gao, Hailong Yu, Weirong Li
ABSTRACT For geoscience text, rich domain corpora have become the basis of improving the model performance in word segmentation. However, the lack of domain-specific corpus with annotation labelled has become a major obstacle to professional information mining in geoscience fields. In this paper, we propose a corpus augmentation method based on Levenshtein distance. According to the technique, a geoscience dictionary of 20,137 words was collected and constructed by crawling the keywords from published papers in China National Knowledge Infrastructure (CNKI). The dictionary was further used as the main source of synonyms to enrich the geoscience corpus according to the Levenshtein distance between words. Finally, a Chinese word segmentation model combining the BERT, Bi-gated recurrent neural network (Bi-GRU), and conditional random fields (CRF) was implemented. Geoscience corpus composed of complex long specific vocabularies has been selected to test the proposed word segmentation framework. CNN-LSTM, Bi-LSTM-CRF, and Bi-GRU-CRF models were all selected to evaluate the effects of Levenshtein data augmentation technique. Experiments results prove that the proposed methods achieve a significant performance improvement of more than 10%. It has great potential for natural languages processing tasks like named entity recognition and relation extraction.
{"title":"A Levenshtein distance-based method for word segmentation in corpus augmentation of geoscience texts","authors":"Jinqu Zhang, Lang Qian, Shu Wang, Yunqiang Zhu, Zhenji Gao, Hailong Yu, Weirong Li","doi":"10.1080/19475683.2023.2165543","DOIUrl":"https://doi.org/10.1080/19475683.2023.2165543","url":null,"abstract":"ABSTRACT For geoscience text, rich domain corpora have become the basis of improving the model performance in word segmentation. However, the lack of domain-specific corpus with annotation labelled has become a major obstacle to professional information mining in geoscience fields. In this paper, we propose a corpus augmentation method based on Levenshtein distance. According to the technique, a geoscience dictionary of 20,137 words was collected and constructed by crawling the keywords from published papers in China National Knowledge Infrastructure (CNKI). The dictionary was further used as the main source of synonyms to enrich the geoscience corpus according to the Levenshtein distance between words. Finally, a Chinese word segmentation model combining the BERT, Bi-gated recurrent neural network (Bi-GRU), and conditional random fields (CRF) was implemented. Geoscience corpus composed of complex long specific vocabularies has been selected to test the proposed word segmentation framework. CNN-LSTM, Bi-LSTM-CRF, and Bi-GRU-CRF models were all selected to evaluate the effects of Levenshtein data augmentation technique. Experiments results prove that the proposed methods achieve a significant performance improvement of more than 10%. It has great potential for natural languages processing tasks like named entity recognition and relation extraction.","PeriodicalId":46270,"journal":{"name":"Annals of GIS","volume":"12 1","pages":"293 - 306"},"PeriodicalIF":5.0,"publicationDate":"2023-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78644962","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-02DOI: 10.1080/19475683.2023.2165546
Jiuying Han, Neng Wan, Simon C. Brewer, M. McCrum
ABSTRACT Hospital bypass behaviours (i.e. people go to faraway hospitals instead of close ones) of Emergency General Surgery (EGS) patients may cause treatment delays and worsened health outcomes. This study aimed to find the associations between EGS bypass behaviours and spatial access to EGS services as well as other factors at the individual level and ZIP Code Tabulation Area (ZCTA) level in California. We used a gravity model to calculate spatial access to EGS services at the ZCTA level. A Bayesian hierarchical spatial model was used to access associated variables of EGS bypass while accounting for spatial autocorrelation. Results show that better spatial access to EGS hospitals was associated with lower likelihood of EGS bypass. Other factors such as rural–urban status, health insurance type and race/ethnicity were also related to EGS bypass behaviours. Besides, people with similar EGS bypass behaviours seemed to cluster together due to spatial effect. Our results have important implications for EGS resource allocation, utilization and EGS disparity alleviation.
{"title":"Spatial access to Emergency General Surgery (EGS) services and EGS bypass behaviours in California","authors":"Jiuying Han, Neng Wan, Simon C. Brewer, M. McCrum","doi":"10.1080/19475683.2023.2165546","DOIUrl":"https://doi.org/10.1080/19475683.2023.2165546","url":null,"abstract":"ABSTRACT Hospital bypass behaviours (i.e. people go to faraway hospitals instead of close ones) of Emergency General Surgery (EGS) patients may cause treatment delays and worsened health outcomes. This study aimed to find the associations between EGS bypass behaviours and spatial access to EGS services as well as other factors at the individual level and ZIP Code Tabulation Area (ZCTA) level in California. We used a gravity model to calculate spatial access to EGS services at the ZCTA level. A Bayesian hierarchical spatial model was used to access associated variables of EGS bypass while accounting for spatial autocorrelation. Results show that better spatial access to EGS hospitals was associated with lower likelihood of EGS bypass. Other factors such as rural–urban status, health insurance type and race/ethnicity were also related to EGS bypass behaviours. Besides, people with similar EGS bypass behaviours seemed to cluster together due to spatial effect. Our results have important implications for EGS resource allocation, utilization and EGS disparity alleviation.","PeriodicalId":46270,"journal":{"name":"Annals of GIS","volume":"28 23 1","pages":"75 - 85"},"PeriodicalIF":5.0,"publicationDate":"2023-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81179025","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-02DOI: 10.1080/19475683.2022.2064911
V. Khokhlov, V.Ye. Lukin, Sergey Khokhlov
ABSTRACT This article is dedicated to the problem of realistic colour rendering of space object images using the tools of computer graphics. In the form of a short essay, the authors describe the essence, sources and functionality of modern graphics applications. Particular attention is paid to the application of modern graphics in space science. The specific purpose of the study is the use of computer graphics in the field of remote sensing of the Earth’s surface. This article describes a method for synthesizing images to develop realistic 3D models of colour Earth images in the visible spectral range, observed from geostationary orbits. The method is based on the improved model of atmospheric radiation for arbitrary sighting conditions in an inhomogeneous spherical atmosphere. Physical models of horizontally inhomogeneous distributions of atmospheric density, temperature and albedo of the Earth were improved. All calculations were performed in accordance with the model of molecular scattering of radiation in a spherical atmosphere, taking into account sunlight forward-scattering and reflection from the planet’s surface. This allows us to obtain images of the Earth in its various phases, observed from arbitrary heights. The obtained theoretical colour images of the Earth were compared with black and white images from modern geostationary satellites.
{"title":"Modelling full-colour images of Earth: simulation of radiation brightness field of Earth’s atmosphere and underlying surface","authors":"V. Khokhlov, V.Ye. Lukin, Sergey Khokhlov","doi":"10.1080/19475683.2022.2064911","DOIUrl":"https://doi.org/10.1080/19475683.2022.2064911","url":null,"abstract":"ABSTRACT This article is dedicated to the problem of realistic colour rendering of space object images using the tools of computer graphics. In the form of a short essay, the authors describe the essence, sources and functionality of modern graphics applications. Particular attention is paid to the application of modern graphics in space science. The specific purpose of the study is the use of computer graphics in the field of remote sensing of the Earth’s surface. This article describes a method for synthesizing images to develop realistic 3D models of colour Earth images in the visible spectral range, observed from geostationary orbits. The method is based on the improved model of atmospheric radiation for arbitrary sighting conditions in an inhomogeneous spherical atmosphere. Physical models of horizontally inhomogeneous distributions of atmospheric density, temperature and albedo of the Earth were improved. All calculations were performed in accordance with the model of molecular scattering of radiation in a spherical atmosphere, taking into account sunlight forward-scattering and reflection from the planet’s surface. This allows us to obtain images of the Earth in its various phases, observed from arbitrary heights. The obtained theoretical colour images of the Earth were compared with black and white images from modern geostationary satellites.","PeriodicalId":46270,"journal":{"name":"Annals of GIS","volume":"47 1","pages":"143 - 161"},"PeriodicalIF":5.0,"publicationDate":"2023-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82156803","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-01-02DOI: 10.1080/19475683.2023.2166109
Mei Yang, Yihong Yuan, Benjamin Zhan
ABSTRACT Transport data are important for understanding human mobility and urban interactions within a city. As China’s transportation infrastructure continues to grow, more research is needed to analyse the spatial patterns of travel flows and to understand how these patterns change over time. With the development of online car-hailing and ride sharing services, floating car data have become a new resource to facilitate the analysis of human mobility patterns and the interactions of urban mobility within a city. The detection of urban communities based on urban networks is a helpful way to represent urban interactions. However, understanding community changes using online car-hailing data remains an underexplored topic. To this end, this study applies a community detection method to explore community changes over time based on the newly available floating car data (DiDi Chuxing (‘DiDi’)) in Chengdu, China. We applied undirected graphs to examine the spatial distribution of DiDi usage and the spatial patterns of travel distance. In addition, we explored the spatial-temporal variations of the communities at the taxi zone level using Blondel’s iterative algorithm, a modularity optimization approach. Results suggest that: 1) taxi zones on the south and west sides of Chengdu have more average daily trips compared to those in other areas; 2) residential taxi zones in the northeast area have a long median travel distance, indicating people living in those areas travel longer distances; and 3) the detected community structures change at different times. These findings provide valuable information for urban planning and location-based services in Chengdu.
{"title":"Explore urban interactions based on floating car data – a case study of Chengdu, China","authors":"Mei Yang, Yihong Yuan, Benjamin Zhan","doi":"10.1080/19475683.2023.2166109","DOIUrl":"https://doi.org/10.1080/19475683.2023.2166109","url":null,"abstract":"ABSTRACT Transport data are important for understanding human mobility and urban interactions within a city. As China’s transportation infrastructure continues to grow, more research is needed to analyse the spatial patterns of travel flows and to understand how these patterns change over time. With the development of online car-hailing and ride sharing services, floating car data have become a new resource to facilitate the analysis of human mobility patterns and the interactions of urban mobility within a city. The detection of urban communities based on urban networks is a helpful way to represent urban interactions. However, understanding community changes using online car-hailing data remains an underexplored topic. To this end, this study applies a community detection method to explore community changes over time based on the newly available floating car data (DiDi Chuxing (‘DiDi’)) in Chengdu, China. We applied undirected graphs to examine the spatial distribution of DiDi usage and the spatial patterns of travel distance. In addition, we explored the spatial-temporal variations of the communities at the taxi zone level using Blondel’s iterative algorithm, a modularity optimization approach. Results suggest that: 1) taxi zones on the south and west sides of Chengdu have more average daily trips compared to those in other areas; 2) residential taxi zones in the northeast area have a long median travel distance, indicating people living in those areas travel longer distances; and 3) the detected community structures change at different times. These findings provide valuable information for urban planning and location-based services in Chengdu.","PeriodicalId":46270,"journal":{"name":"Annals of GIS","volume":"57 1","pages":"37 - 53"},"PeriodicalIF":5.0,"publicationDate":"2023-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83421265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：