Annals of GIS最新文献

{"title":"RS-DeepSuperLearner: fusion of CNN ensemble for remote sensing scene classification","authors":"H. Alhichri","doi":"10.1080/19475683.2023.2165544","DOIUrl":"https://doi.org/10.1080/19475683.2023.2165544","url":null,"abstract":"ABSTRACT Scene classification is an important problem in remote sensing (RS) and has attracted a lot of research in the past decade. Nowadays, most proposed methods are based on deep convolutional neural network (CNN) models, and many pretrained CNN models have been investigated. Ensemble techniques are well studied in the machine learning community; however, few works have used them in RS scene classification. In this work, we propose an ensemble approach, called RS-DeepSuperLearner, that fuses the outputs of five advanced CNN models, namely, VGG16, Inception-V3, DenseNet121, InceptionResNet-V2, and EfficientNet-B3. First, we improve the architecture of the five CNN models by attaching an auxiliary branch at specific layer locations. In other words, the models now have two output layers producing predictions each and the final prediction is the average of the two. The RS-DeepSuperLearner method starts by fine-tuning the five CNN models using the training data. Then, it employs a deep neural network (DNN) SuperLearner to learn the best way for fusing the outputs of the five CNN models by training it on the predicted probability outputs and the cross-validation accuracies (per class) of the individual models. The proposed methodology was assessed on six publicly available RS datasets: UC Merced, KSA, RSSCN7, Optimal31, AID, and NWPU-RSC45. The experimental results demonstrate its superior capabilities when compared to state-of-the-art methods in the literature.","PeriodicalId":46270,"journal":{"name":"Annals of GIS","volume":"9 1","pages":"121 - 142"},"PeriodicalIF":5.0,"publicationDate":"2023-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83144041","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}

{"title":"Production of orthophoto map using mobile photogrammetry and comparative assessment of cost and accuracy with satellite imagery for corridor mapping: a case study in Manesar, Haryana, India","authors":"Manu Dev, Shetru M Veerabhadrappa, A. Kainthola, Manas K Jha","doi":"10.1080/19475683.2022.2141853","DOIUrl":"https://doi.org/10.1080/19475683.2022.2141853","url":null,"abstract":"ABSTRACT The study aims to find a low-cost alternate technology to get imagery, using mobile platform, and produce digital orthophoto for corridor mapping, with a higher degree of accuracy and which can reduce the lag time of acquisition of data. The present study uses digital single-lens reflex cameras, mounted on a mobile vehicle, and acquisition of data in the video format rather than still photographs, as traditionally used in mobile mapping systems. The videos are used to create a set of images and orthophotos. A widespread ground control points were recorded in the study area, using the global navigation satellite system receiver, which measured the control points in real-time kinematic mode. Generation of digital orthophoto has been completed using the captured mobile imagery and ground control point. Furthermore, procurement of satellite imagery and aerial triangulation using ground control points have been done. While comparing the planimetric accuracy of orthophoto against satellite imagery using the ground control points, the achieved root mean square error value of produced orthophoto is 0.171 m in X axis and 0.205 m in Y axis. However, for Cartosat -1 satellite imagery, the RMSE value for X is 1.22 m and for Y is 1.98 m. This research proposes the alternate low-cost mobile mapping method to capture the imagery for orthophoto production. The cost of orthophoto production from mobile image was found 77% cheaper than the orthophoto cost from fresh/latest satellite imagery procurement, while the overall production was 70% cost-effective than the orthophoto maps made from archived imagery.","PeriodicalId":46270,"journal":{"name":"Annals of GIS","volume":"22 1","pages":"163 - 176"},"PeriodicalIF":5.0,"publicationDate":"2023-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73526877","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}

{"title":"Correlating Twitter Use with Disaster Resilience at Two Spatial Scales: A Case Study of Hurricane Sandy","authors":"Kejin Wang, N. Lam, V. Mihunov","doi":"10.1080/19475683.2023.2165545","DOIUrl":"https://doi.org/10.1080/19475683.2023.2165545","url":null,"abstract":"ABSTRACT Disaster resilience describes the ability of a community to bounce back from disaster impacts by resilience building activities. Social media provides an innovative way to observe human attitudes and responses, especially during disasters. However, most previous social media and disasters studies were conducted at a coarse spatial scale such as by county. This study analyzes Twitter activities during Hurricane Sandy in 2012, at the county and the zip code area levels in the five affected states. The study examines two questions: (1) will the relationships between disparities in social media use and disparities in disaster resilience found at the county level in previous studies still hold at the zip code area level? And (2) what new information or patterns can be revealed with the zip code area level analysis? Results show that correlations between Twitter use indices and social-environmental variables representing community resilience found at the county level in previous studies still hold, but they are weaker at the zip code area level. The study also shows that zip code areas that have major transportation hubs and commercial activities or low night-time population are major factors affecting Twitter use indices and hence the correlations. Future research should consider adding data on land use types and population dynamics to help improve social media use for disaster resilience analysis. Furthermore, employing a multiscale analysis approach can reduce uncertainties involved in analysis and obtain a more thorough understanding of the relationships between Twitter use and geographical and socioeconomic characteristics of the affected communities.","PeriodicalId":46270,"journal":{"name":"Annals of GIS","volume":"24 1","pages":"1 - 20"},"PeriodicalIF":5.0,"publicationDate":"2023-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89600025","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}

{"title":"Individual level spatial-temporal modelling of exposure potential of livestock in the Cove Wash watershed, Arizona.","authors":"Zhuoming Liu, Yan Lin, Joseph Hoover, Daniel Beene, Perry H Charley, Neilroy Singer","doi":"10.1080/19475683.2022.2075935","DOIUrl":"10.1080/19475683.2022.2075935","url":null,"abstract":"<p><p>Personal exposure studies suffer from uncertainty issues, largely stemming from individual behavior uncertainties. Built on spatial-temporal exposure analysis and methods, this study proposed a novel approach to spatial-temporal modeling that incorporated behavior classifications taking into account uncertainties, to estimate individual livestock exposure potential. The new approach was applied in a community-based research project with a Tribal community in the southwest United States. The community project examined the geospatial and temporal grazing patterns of domesticated livestock in a watershed containing 52 abandoned uranium mines (AUMs). Thus, the study aimed to 1) classify Global Positioning System (GPS) data from livestock into three behavior subgroups - grazing, traveling or resting; 2) calculate the daily cumulative exposure potential for livestock; 3) assess the performance of the computational method with and without behavior classifications. Using Lotek Litetrack GPS collars, we collected data at a 20-minute-interval for 2 flocks of sheep and goats during the spring and summer of 2019. Analysis and modeling of GPS data demonstrated no significant difference in individual cumulative exposure potential within each flock when animal behaviors with probability/uncertainties were considered. However, when daily cumulative exposure potential was calculated without consideration of animal behavior or probability/uncertainties, significant differences among animals within a herd were observed, which does not match animal grazing behaviors reported by livestock owners. These results suggest that the proposed method of including behavior subgroups with probability/uncertainties more closely resembled the observed grazing behaviors reported by livestock owners. Results from the research may be used for future intervention and policy-making on remediation efforts in communities where grazing livestock may encounter environmental contaminants. This research also demonstrates a novel robust geographic information system (GIS)-based framework to estimate cumulative exposure potential to environmental contaminants and provides critical information to address community questions on livestock exposure to AUMs.</p>","PeriodicalId":46270,"journal":{"name":"Annals of GIS","volume":"29 1","pages":"87-107"},"PeriodicalIF":5.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10117392/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9387232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Understanding Public Perspectives on Fracking in the United States using Social Media Big Data.","authors":"Xi Gong, Yujian Lu, Daniel Beene, Ziqi Li, Tao Hu, Melinda Morgan, Yan Lin","doi":"10.1080/19475683.2022.2121856","DOIUrl":"10.1080/19475683.2022.2121856","url":null,"abstract":"<p><p>People's attitudes toward hydraulic fracturing (i.e., \"fracking\") to extract fossil fuels can be shaped by factors associated with socio-demographics, economic development, social equity and politics, environmental impacts, and fracking-related information obtainment. Existing research typically conducts surveys and interviews to study public attitudes toward fracking among a small group of individuals in a specific geographic area, where limited samples may introduce bias. Here, we compiled geo-referenced social media big data from Twitter during 2018-2019 for the entire United States to present a more holistic picture of people's attitudes toward fracking. We used a multiscale geographically weighted regression (MGWR) to investigate county-level relationships between the aforementioned factors and percentages of negative tweets concerning fracking. Results clearly depict spatial heterogeneity and varying scales of those associations. Counties with higher median household income, larger African American populations, and/or lower educational level are less likely to oppose fracking, and these associations show global stationarity in all contiguous U.S. counties. Eastern and Central U.S. counties with higher unemployment rate, counties east of the Great Plains with less fracking sites nearby, and Western and Gulf Coast region counties with higher health insurance enrollments are more likely to oppose fracking activities. These three variables show clear East-West geographical divides in influencing public perspective on fracking. In counties across the southern Great Plains, negative attitudes toward fracking are less often vocalized on Twitter as the share of Republican voters increases. These findings have implications for both predicting public perspectives and needed policy adjustments. The methodology can also be conveniently applied to investigate public perspectives on other controversial topics.</p>","PeriodicalId":46270,"journal":{"name":"Annals of GIS","volume":"29 1","pages":"21-35"},"PeriodicalIF":5.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10038223/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9197752","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatio-temporal evolution analysis of spatial form in Nanfeng based on spatial syntax","authors":"Yuling. Wang, Hui Lin, Jiehong Chen, Qinghua He, Zhuo Liu","doi":"10.1080/19475683.2022.2148122","DOIUrl":"https://doi.org/10.1080/19475683.2022.2148122","url":null,"abstract":"ABSTRACT Urban space is the carrier of human social life, and its form plays an important role in urban development. We here employ space syntax model to analyse the urban morphology of Nanfeng in the city of Fuzhou, China during five time periods: the late Qing Dynasty, 1960, 2004, 2014, and 2021. Results show that the spatial form of Nanfeng has gone through three stages: concentrated development, axial development, and decentralized development. The spatial development of Nanfeng is concentrated in the south and dispersed in the north. The direction of the urban spatial form is consistent with the development direction of the integrated core centre, and the transfer of the urban center is synchronized with the evolution of the spatial form. The ancient city is centred on a ‘cross’ street structure with a relatively dense and complex internal spatial texture, and the overall space of the new city is sparse and has a grid-like form. Here, we reveal the development patterns of urban spatial forms under urbanization, and provide guidance for the protection of ancient cities and the sustainable development of new cities as well as for urban spatial optimization.","PeriodicalId":46270,"journal":{"name":"Annals of GIS","volume":"19 1","pages":"109 - 120"},"PeriodicalIF":5.0,"publicationDate":"2022-11-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82520268","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}

{"title":"Challenges and opportunities of the spatiotemporal responses to the global pandemic of COVID-19","authors":"Chaowei Yang, S. Bao, W. Guan, K. Howell, T. Hu, H. Lan, Yun Li, Qian Liu, Jennifer Smith, Anusha Srirenganathan Malarvizhi, Theo Trefonides, Kevin Wang, Zifu Wang","doi":"10.1080/19475683.2022.2141396","DOIUrl":"https://doi.org/10.1080/19475683.2022.2141396","url":null,"abstract":"As a once-in-100-years pandemic, COVID-19 is changing and reshaping the world. COVID-19 poses grand challenges to human society and drives us to invent new analytical tools to examine the spatiotemporal patterns of the complex system for theories, methodologies, and applications of interdisciplinary research (Yang et al. 2020). The U.S. (US) National Science Foundation (NSF) funded the Spatiotemporal Innovation Center (STC) to conduct a spatiotemporal rapid response to address this global health crisis. Engaging various communities, a diverse team was formed to provide a comprehensive non-medical rapid response to the global COVID-19 pandemic for answering many physically and socially challenging questions. The international team formed by experts and participants from almost every US state and worldwide every time zone including the GeoComputation Center for Social Sciences at Wuhan University, Tsinghua University, the China Data Institute at Michigan, the University of Queensland in Australia, RMDS Lab at Los Angles, and many other institutions to achieve the objectives of (1) providing data support for the spatiotemporal study of COVID-19 at local, regional and global levels with information collected and integrated from different sources; (2) facilitating quantitative research on spatial spreading and impacts of COVID-19 with advanced methodology and technology; (3) promoting collaborative research on the spatiotemporal study of COVID-19 on the Spatial Data Lab and Dataverse platforms; and (4) building research capacity for future collaborative projects. In addition to research and development conducted, a series of webinars and a mini virtual workshop were organized to introduce findings and solicit community feedback. This Special Issue is organized to capture such new developments and findings with a focus on the spatiotemporal analysis of the impact of COVID-19. Research presented in this issue includes studies on theories, methodologies, data and applications, which together help understand the short-term and long-term impacts of COVID-19 on health, demographics, socioeconomics, environment, politics and other fields over space and time. The first four papers studied the space-time patterns of the pandemic’s impacts in different regions of the world (India/Subramanian et al. this issue, China/Pei et al. this issue, United States/Batta et al. this issue, and 12 secondary cities in 10 developing countries across Africa, Asia and South America/Laituri et al. this issue), examining not only the virus infected cases (Pei et al. this issue) but also the excess death of other diseases (Batta et al. this issue), as well as the pandemic’s social, economic and environmental impacts (Laituri et al. this issue). The last four papers explored social media or human mobility data (Shen et al. this issue) in search for their spatiotemporal relationships with COVID-19 transmission (Zhang et al. this issue), non-infectious diseases (Mu et al. this issue),","PeriodicalId":46270,"journal":{"name":"Annals of GIS","volume":"173 1","pages":"425 - 434"},"PeriodicalIF":5.0,"publicationDate":"2022-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74915469","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}

{"title":"Revealing population flow patterns in the Sichuan-Chongqing region, China, during the COVID-19 epidemic in 2020","authors":"Jingwei Shen, Zhongyu Huang, Wei Zhou, Dongzhe Zhao","doi":"10.1080/19475683.2022.2090435","DOIUrl":"https://doi.org/10.1080/19475683.2022.2090435","url":null,"abstract":"ABSTRACT COVID-19 has had a serious impact on the lives and health of people and severely affected the population flow in 2020. Baidu migration data offer great opportunities to study spatiotemporal interactions among cities. Revealing population flow patterns has important scientific significance for the precise prevention and control of the COVID-19 epidemic. The aim of this article is to reveal the spatiotemporal patterns of population flow and associated influential factors in 22 cities in the Sichuan-Chongqing region (SCR), which is regarded as the fourth pole of China’s economy. Four typical time periods are selected to study the spatiotemporal patterns of population flow. The regional population flow intensities in all cities and between different cities in the SCR are illustrated. Stepwise regression is used to analyse the factors affecting regional population flow intensity in four selected periods. The results show that (1) the COVID-19 epidemic greatly affected population flow in the SCR, (2) more travel occurred between cities on holidays than on weekdays in the SCR when the epidemic was not serious, and (3) the regional population flow intensity was strongly correlated with the population education level and transportation facilities when the epidemic was not serious.","PeriodicalId":46270,"journal":{"name":"Annals of GIS","volume":"1 1","pages":"533 - 545"},"PeriodicalIF":5.0,"publicationDate":"2022-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76394838","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}

{"title":"Urban growth dynamics modeling through urban DNA in Tehran metropolitan region","authors":"M. Modiri, Y. Gholami, S. Hosseini","doi":"10.1080/19475683.2022.2071337","DOIUrl":"https://doi.org/10.1080/19475683.2022.2071337","url":null,"abstract":"ABSTRACT The spatial models dealing with urban growth dynamics have been widely studied, while rare works have considered under-developed countries. Several problems have been detected in creating, calibrating and applying urban growth models and changing land use. The present work aims the modelling land-use changes through the CA-GA model in which a frame is provided for analysis and producing a map of growth patterns in urban areas in different spatial scales to study and analyse the increasing urban growth in Tehran. To consider the land-use changes in Tehran, ETM+TM images of 1985, 1992, 2000, and 2020 were selected to be analysed by the CA-GA algorithm to model the growth of the urban areas. The total kappa of results in Tehran is about 0.93, indicating the required precision and confidence of applied combinative genetic-Cellular automata modelling methods to model urban development.","PeriodicalId":46270,"journal":{"name":"Annals of GIS","volume":"41 1","pages":"55 - 74"},"PeriodicalIF":5.0,"publicationDate":"2022-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82668949","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}

{"title":"GIS and urban data science","authors":"Yijing Li, Qunshan Zhao, Chen Zhong","doi":"10.1080/19475683.2022.2070969","DOIUrl":"https://doi.org/10.1080/19475683.2022.2070969","url":null,"abstract":"ABSTRACT With the emergence of new forms of geospatial/urban big data and advanced spatial analytics and machine learning methods, new patterns and phenomena can be explored and discovered in our cities and societies. In this special issue, we presented an overview of nine studies to understand how to use urban data science and GIS in healthcare services, hospitality and safety, transportation and mobility, economy, urban planning, higher education, and natural disasters, spreading across developed countries in North America and Europe, as well as Global South areas in Asia and the Middle East. The embrace of diverse geo-computational methods in this special issue brings forward an outlook to future GIS and Urban Data Science towards more advanced computational capability, global vision and urban-focused research.","PeriodicalId":46270,"journal":{"name":"Annals of GIS","volume":"22 1","pages":"89 - 92"},"PeriodicalIF":5.0,"publicationDate":"2022-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72693328","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}