Pub Date : 2021-06-19DOI: 10.5311/josis.2021.22.711
C. Jabbour, Anis Hoayek, P. Maurel, Zaher Khraibani, L. Ghalayini
: The spatial data infrastructures (SDIs) which constitute a direct link between spatial data users and the large Earth observation industry, have a leading role in establishing market opportunities in the space sector. The spatial information supplied through various forms of SDI platforms exhibits large increases in demand volatility. The users’ demand is unpredictable and the market is vulnerable to high evolution shifts. We study the effect of extreme demands for a particular type of spatial information, the satellite images. Drawing on two French SDIs, GEOSUD and PEPS, we examine the shifts occurring on their platforms and assess the probability of witnessing a spike/drop in the short term of different satellite imagery schemes: the high resolution images through GEOSUD; the Landsat (U.S.), Sentinel (Europe) and SPOT (France) images through PEPS. We analyze the market stability through the two SDIs and evaluate the probability of future records by using the Records theory. The results show that the high resolution images demand through GEOSUD, for which the classical i.i.d. model fits the most, is stable. Moreover, the Yang-Nevzorov model fits to the Landsat data, due to more records concentrated beyond the first observations. The Landsat demand is the less stable out of the other three satellite images series, and the probability of having a record in the coming years is the highest. While the use of Records theory drops mathematical constraints, it offers an alternative solution to the non-applicability of the machine learning techniques and long-term memory models.
{"title":"Examining satellite images market stability using the Records theory: Evidence from French spatial data infrastructures","authors":"C. Jabbour, Anis Hoayek, P. Maurel, Zaher Khraibani, L. Ghalayini","doi":"10.5311/josis.2021.22.711","DOIUrl":"https://doi.org/10.5311/josis.2021.22.711","url":null,"abstract":": The spatial data infrastructures (SDIs) which constitute a direct link between spatial data users and the large Earth observation industry, have a leading role in establishing market opportunities in the space sector. The spatial information supplied through various forms of SDI platforms exhibits large increases in demand volatility. The users’ demand is unpredictable and the market is vulnerable to high evolution shifts. We study the effect of extreme demands for a particular type of spatial information, the satellite images. Drawing on two French SDIs, GEOSUD and PEPS, we examine the shifts occurring on their platforms and assess the probability of witnessing a spike/drop in the short term of different satellite imagery schemes: the high resolution images through GEOSUD; the Landsat (U.S.), Sentinel (Europe) and SPOT (France) images through PEPS. We analyze the market stability through the two SDIs and evaluate the probability of future records by using the Records theory. The results show that the high resolution images demand through GEOSUD, for which the classical i.i.d. model fits the most, is stable. Moreover, the Yang-Nevzorov model fits to the Landsat data, due to more records concentrated beyond the first observations. The Landsat demand is the less stable out of the other three satellite images series, and the probability of having a record in the coming years is the highest. While the use of Records theory drops mathematical constraints, it offers an alternative solution to the non-applicability of the machine learning techniques and long-term memory models.","PeriodicalId":45389,"journal":{"name":"Journal of Spatial Information Science","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2021-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45397559","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 : 2021-06-19DOI: 10.5311/josis.2021.22.677
J. Guth, S. Keller, S. Hinz, S. Winter
OpenStreetMap (OSM), with its global coverage and Open Database License, has recently gained popularity. Its quality is adequate for many applications, but since it is crowd-sourced, errors remain an issue. Errors in associated tags of the road network, for example, are impacting routing applications. Particularly road classification errors often lead to false assumptions about capacity, maximum speed, or road quality, possibly resulting in detours for routing applications. This study aims at finding potential classification errors automatically, which can then be checked and corrected by a human expert. We develop a novel approach to detect road classification errors in OSM by searching for disconnected parts and gaps in different levels of a hierarchical road network. Different parameters are identified that indicate gaps in road networks. These parameters are then combined in a rating system to obtain an error probability to suggest possible misclassifications to a human user. The methodology is applied to an exemplar case for the state of New South Wales in Australia. The results demonstrate that (1) more classification errors are found at gaps than at disconnected parts, and (2) the gap search enables the user to find classification errors quickly using the developed rating system that indicates an error probability. In future work, the methodology can be extended to include available tags in OSM for the rating system. The source code of the implementation is available via GitHub.
{"title":"Towards detecting, characterizing, and rating of road class errors in crowd-sourced road network databases","authors":"J. Guth, S. Keller, S. Hinz, S. Winter","doi":"10.5311/josis.2021.22.677","DOIUrl":"https://doi.org/10.5311/josis.2021.22.677","url":null,"abstract":"OpenStreetMap (OSM), with its global coverage and Open Database License, has recently gained popularity. Its quality is adequate for many applications, but since it is crowd-sourced, errors remain an issue. Errors in associated tags of the road network, for example, are impacting routing applications. Particularly road classification errors often lead to false assumptions about capacity, maximum speed, or road quality, possibly resulting in detours for routing applications. This study aims at finding potential classification errors automatically, which can then be checked and corrected by a human expert. We develop a novel approach to detect road classification errors in OSM by searching for disconnected parts and gaps in different levels of a hierarchical road network. Different parameters are identified that indicate gaps in road networks. These parameters are then combined in a rating system to obtain an error probability to suggest possible misclassifications to a human user. The methodology is applied to an exemplar case for the state of New South Wales in Australia. The results demonstrate that (1) more classification errors are found at gaps than at disconnected parts, and (2) the gap search enables the user to find classification errors quickly using the developed rating system that indicates an error probability. In future work, the methodology can be extended to include available tags in OSM for the rating system. The source code of the implementation is available via GitHub.","PeriodicalId":45389,"journal":{"name":"Journal of Spatial Information Science","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2021-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42580735","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}
E. Nyamsuren, Haiqi Xu, S. Scheider, Eric Top, N. Steenbergen
This study investigates the GeoAnQu corpus of geo-analytical questions. Unlike other question corpora, the questions in this corpus imply analytical goals and are thus supposed to be answered with GIS workflows, not with the retrieval of geographic facts. We investigate how geo-analytical questions are structured syntactically and semantically, and how the structure may be interpreted by human analysts to compose workflows. Our question analysis model is based on the notions of a measure, support, and extent, which are inspired by Sinton’s three dimensions of spatial analysis. We use XPath queries to automatically extract syntactic patterns from constituency parse trees corresponding to these notions. Results show that geo-analytical questions are of considerable complexity, yet often have predictable syntactic patterns that can be reliably mapped to measures, supports, and extents. Furthermore, we identify analytical goals attributable to these notions. To our knowledge, this is the first reported systematic analysis of this kind. The findings open new opportunities in Natural Language Interpretation and query generation for the automated answering of geo-analytical questions. Additionally, our study shows that questions asked in a scientific context can be on different levels of concreteness. Therefore, we also discuss best practices for formulating questions clearly and concretely.
{"title":"Deconstruction of geo-analytical questions in terms of measures, supports, and spatio-temporal extents","authors":"E. Nyamsuren, Haiqi Xu, S. Scheider, Eric Top, N. Steenbergen","doi":"10.5311/JOSIS.0.0.741","DOIUrl":"https://doi.org/10.5311/JOSIS.0.0.741","url":null,"abstract":"This study investigates the GeoAnQu corpus of geo-analytical questions. Unlike other question corpora, the questions in this corpus imply analytical goals and are thus supposed to be answered with GIS workflows, not with the retrieval of geographic facts. We investigate how geo-analytical questions are structured syntactically and semantically, and how the structure may be interpreted by human analysts to compose workflows. Our question analysis model is based on the notions of a measure, support, and extent, which are inspired by Sinton’s three dimensions of spatial analysis. We use XPath queries to automatically extract syntactic patterns from constituency parse trees corresponding to these notions. Results show that geo-analytical questions are of considerable complexity, yet often have predictable syntactic patterns that can be reliably mapped to measures, supports, and extents. Furthermore, we identify analytical goals attributable to these notions. To our knowledge, this is the first reported systematic analysis of this kind. The findings open new opportunities in Natural Language Interpretation and query generation for the automated answering of geo-analytical questions. Additionally, our study shows that questions asked in a scientific context can be on different levels of concreteness. Therefore, we also discuss best practices for formulating questions clearly and concretely.","PeriodicalId":45389,"journal":{"name":"Journal of Spatial Information Science","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2021-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42112390","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":"Fuzzy Similarity and Fuzzy Inclusion Measures in the Process of Identification of Polyline Spatial Features","authors":"R. Ďuračiová, Alexandra Rášová, T. Lieskovský","doi":"10.5311/JOSIS.0.0.352","DOIUrl":"https://doi.org/10.5311/JOSIS.0.0.352","url":null,"abstract":"","PeriodicalId":45389,"journal":{"name":"Journal of Spatial Information Science","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2017-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48583680","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":"Aggregating Value Functions: A Parameter-free, Uncertainty-aware Method to Elicit and Aggregate Value Functions from Multiple Experts in Multi Criteria Evaluation","authors":"Beni Rohrbach, R. Weibel, P. Laube","doi":"10.5311/JOSIS.0.0.368","DOIUrl":"https://doi.org/10.5311/JOSIS.0.0.368","url":null,"abstract":"","PeriodicalId":45389,"journal":{"name":"Journal of Spatial Information Science","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2017-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44737638","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 : 2017-06-11DOI: 10.5311/JOSIS.2017.14.359
L. Wallace
{"title":"Fundamentals of Satellite Remote Sensing: An Environmental Approach 2e","authors":"L. Wallace","doi":"10.5311/JOSIS.2017.14.359","DOIUrl":"https://doi.org/10.5311/JOSIS.2017.14.359","url":null,"abstract":"","PeriodicalId":45389,"journal":{"name":"Journal of Spatial Information Science","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2017-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43265690","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 : 2014-12-15DOI: 10.5311/JOSIS.2014.9.204
Andreas Hall
The abbreviation “GIS” has been tricky since Michael F. Goodchild proposed in the early 1990s that the meaning of the “S” should change from “systems” to “science” [5, 6]. Until then, no one had suggested that “GIS” would stand for anything else than “geographic information systems” (although “studies” and “services” were also later suggested [6]). “Geographic information systems” was a term coined in the 1960s, and by the late 1980s had evolved into widely adopted software tools [6]. The reason for Goodchild to challenge the meaning of the abbreviation “GIS” was that, at the time, certain researchers began increasingly to view GIS as more than just a tool or system. A shift of focus from systems to science was a way to address the lack of theory and to raise the status of the researchers involved in the field. Initially, Goodchild argued for the use of the term “spatial information science” (in a keynote address at the 4th International Symposium on Spatial Data Handling), but later used “geographic information science” (in a keynote address at the Second European GIS Conference in 1991). When Goodchild shortly thereafter was asked to combine the two keynotes together into a paper for the International Journal of Geographical Information Systems (IJGIS) he wrote that he settled for “geographic” rather than “spatial” as he was intrigued by the ambiguity it implied about the decoding of “GIS” and as it seemed to him that “there might be general truths to be discovered about geographic space that were not equally true of other spaces” [6]. Goodchild started the ball rolling with his 1992 paper. Five years later, in 1996, the International Geographical Union changed the name and structure of their commission on Geographical Information Systems to two working groups: Geographical Information Science and Geographical Modelling [4]. In 1997, IJGIS changed “Systems” to “Science,” and Cartography and Geographic Information Systems followed suit in 1999. The First International Conference on Geographic Information Science was held in 2000, and in 2014, it was held for the eighth time. Nowadays, the domain addressed by geographic information science is well-defined and persistent [6], although the debate regarding whether it is a science or not still resurfaces every now and then [10]. While the scope of geographic information science as a discipline thus is no longer ambiguous, the denotation of the abbreviation “GIS” still is, and this poses a problem. ∗The author would like to acknowledge Vilja Pitkänen at the Aalto University Language Center for her feedback on issues regarding the use of the English language and the author’s co-workers for their feedback on the text in general, especially Paula Ahonen-Rainio, Kirsi Virrantaus, and Andrei Octavian.
自从Michael F. Goodchild在20世纪90年代初提出“S”的含义应该从“系统”改为“科学”以来，“GIS”的缩写一直很棘手[5,6]。在此之前，没有人建议“GIS”代表“地理信息系统”以外的任何东西(尽管“研究”和“服务”后来也被建议使用)。“地理信息系统”是20世纪60年代创造的一个术语，到20世纪80年代末，它已经演变成被广泛采用的软件工具b[6]。Goodchild质疑缩写“GIS”含义的原因是，当时，某些研究人员开始越来越多地将GIS视为不仅仅是一个工具或系统。将重点从系统转向科学是解决理论缺乏和提高该领域研究人员地位的一种方法。最初，Goodchild主张使用术语“空间信息科学”(在第四届空间数据处理国际研讨会的主题演讲中)，但后来使用了“地理信息科学”(在1991年第二届欧洲地理信息系统会议的主题演讲中)。不久之后，当Goodchild被要求将这两个主题合并成一篇发表在《国际地理信息系统杂志》(IJGIS)上的论文时，他写道，他选择了“地理”而不是“空间”，因为他对“地理信息系统”解码所隐含的模糊性很感兴趣，而且在他看来，“可能有关于地理空间的普遍真理被发现，而这些真理在其他空间中并不同样成立”[6]。古德柴尔德从他1992年的论文开始着手。五年后的1996年，国际地理联盟将其地理信息系统委员会的名称和结构改为两个工作组:地理信息科学和地理建模工作组。1997年，IJGIS将“系统”改为“科学”，1999年，制图和地理信息系统紧随其后。第一届国际地理信息科学会议于2000年召开，2014年已举办第八届。如今，地理信息科学所涉及的领域是定义明确且持久的，尽管关于它是否是一门科学的争论仍然时不时地重新出现。虽然地理信息科学作为一门学科的范围不再模糊不清，但缩写“GIS”的外延仍然模糊不清，这就产生了一个问题。*作者要感谢阿尔托大学语言中心的Vilja Pitkänen对英语使用问题的反馈，以及作者的同事对文本的总体反馈，尤其是Paula Ahonen-Rainio、Kirsi Virrantaus和Andrei Octavian。
{"title":"GI science, not GIScience","authors":"Andreas Hall","doi":"10.5311/JOSIS.2014.9.204","DOIUrl":"https://doi.org/10.5311/JOSIS.2014.9.204","url":null,"abstract":"The abbreviation “GIS” has been tricky since Michael F. Goodchild proposed in the early 1990s that the meaning of the “S” should change from “systems” to “science” [5, 6]. Until then, no one had suggested that “GIS” would stand for anything else than “geographic information systems” (although “studies” and “services” were also later suggested [6]). “Geographic information systems” was a term coined in the 1960s, and by the late 1980s had evolved into widely adopted software tools [6]. The reason for Goodchild to challenge the meaning of the abbreviation “GIS” was that, at the time, certain researchers began increasingly to view GIS as more than just a tool or system. A shift of focus from systems to science was a way to address the lack of theory and to raise the status of the researchers involved in the field. Initially, Goodchild argued for the use of the term “spatial information science” (in a keynote address at the 4th International Symposium on Spatial Data Handling), but later used “geographic information science” (in a keynote address at the Second European GIS Conference in 1991). When Goodchild shortly thereafter was asked to combine the two keynotes together into a paper for the International Journal of Geographical Information Systems (IJGIS) he wrote that he settled for “geographic” rather than “spatial” as he was intrigued by the ambiguity it implied about the decoding of “GIS” and as it seemed to him that “there might be general truths to be discovered about geographic space that were not equally true of other spaces” [6]. Goodchild started the ball rolling with his 1992 paper. Five years later, in 1996, the International Geographical Union changed the name and structure of their commission on Geographical Information Systems to two working groups: Geographical Information Science and Geographical Modelling [4]. In 1997, IJGIS changed “Systems” to “Science,” and Cartography and Geographic Information Systems followed suit in 1999. The First International Conference on Geographic Information Science was held in 2000, and in 2014, it was held for the eighth time. Nowadays, the domain addressed by geographic information science is well-defined and persistent [6], although the debate regarding whether it is a science or not still resurfaces every now and then [10]. While the scope of geographic information science as a discipline thus is no longer ambiguous, the denotation of the abbreviation “GIS” still is, and this poses a problem. ∗The author would like to acknowledge Vilja Pitkänen at the Aalto University Language Center for her feedback on issues regarding the use of the English language and the author’s co-workers for their feedback on the text in general, especially Paula Ahonen-Rainio, Kirsi Virrantaus, and Andrei Octavian.","PeriodicalId":45389,"journal":{"name":"Journal of Spatial Information Science","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2014-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70769607","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 : 2013-01-01DOI: 10.5311/JOSIS.2013.6.105
Maia Williams, W. Kuhn, M. Painho
{"title":"Interactive maps: What we know and what we need to know","authors":"Maia Williams, W. Kuhn, M. Painho","doi":"10.5311/JOSIS.2013.6.105","DOIUrl":"https://doi.org/10.5311/JOSIS.2013.6.105","url":null,"abstract":"","PeriodicalId":45389,"journal":{"name":"Journal of Spatial Information Science","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70769554","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 : 2012-12-19DOI: 10.5311/JOSIS.2011.5.84.EPRA-2-2.LISP
R. Moratz, J. O. Wallgrün
{"title":"EPRA2 specification for SparQ","authors":"R. Moratz, J. O. Wallgrün","doi":"10.5311/JOSIS.2011.5.84.EPRA-2-2.LISP","DOIUrl":"https://doi.org/10.5311/JOSIS.2011.5.84.EPRA-2-2.LISP","url":null,"abstract":"","PeriodicalId":45389,"journal":{"name":"Journal of Spatial Information Science","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2012-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70769484","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}
Franz-Benjamin Mocnik, A. Mobasheri, Luisa Griesbaum, Melanie Eckle, C. Jacobs, Carolin Klonner
{"title":"A Taxonomy of Data Quality Measures","authors":"Franz-Benjamin Mocnik, A. Mobasheri, Luisa Griesbaum, Melanie Eckle, C. Jacobs, Carolin Klonner","doi":"10.5311/JOSIS.0.0.360","DOIUrl":"https://doi.org/10.5311/JOSIS.0.0.360","url":null,"abstract":"","PeriodicalId":45389,"journal":{"name":"Journal of Spatial Information Science","volume":null,"pages":null},"PeriodicalIF":1.4,"publicationDate":"2009-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"70769443","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}
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