Volunteered Geographic Information (VGI) is currently a "hot topic" in the GIS community. The OpenStreetMap (OSM) project is one of the most popular and well supported examples of VGL Traditional measures of spatial data quality are often not applicable to OSM as in many cases it is not possible to access ground-truth spatial data for all regions mapped by OSM. We investigate to develop measures of quality for OSM which operate in an unsupervised manner without reference to a "trusted" source of ground-truth data. We provide results of analysis of OSM data from several European countries. The results highlight specific quality issues in OSM. Results of comparing OSM with ground-truth data for Ireland are also presented.
{"title":"Towards quality metrics for OpenStreetMap","authors":"P. Mooney, P. Corcoran, A. Winstanley","doi":"10.1145/1869790.1869875","DOIUrl":"https://doi.org/10.1145/1869790.1869875","url":null,"abstract":"Volunteered Geographic Information (VGI) is currently a \"hot topic\" in the GIS community. The OpenStreetMap (OSM) project is one of the most popular and well supported examples of VGL Traditional measures of spatial data quality are often not applicable to OSM as in many cases it is not possible to access ground-truth spatial data for all regions mapped by OSM. We investigate to develop measures of quality for OSM which operate in an unsupervised manner without reference to a \"trusted\" source of ground-truth data. We provide results of analysis of OSM data from several European countries. The results highlight specific quality issues in OSM. Results of comparing OSM with ground-truth data for Ireland are also presented.","PeriodicalId":359068,"journal":{"name":"ACM SIGSPATIAL International Workshop on Advances in Geographic Information Systems","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132622701","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}
Raster maps contain valuable road information, which is especially important for the areas where road vector data are otherwise not readily accessible. However, converting the road information in raster maps to road vector data usually requires significant user effort to achieve high accuracy. In this demo, we present Strabo, which is a system that extracts road vector data from heterogeneous raster maps. We demonstrate Strabo's fully automatic technique for extracting road vector data from raster maps with good image quality and the semi-automatic technique for handling raster maps with poor image quality. We show that Strabo requires minimal user input for extracting road vector data from raster maps with varying map complexity (i.e., overlapping features in maps) and image quality.
{"title":"Strabo: a system for extracting road vector data from raster maps","authors":"Yao-Yi Chiang, Craig A. Knoblock","doi":"10.1145/1869790.1869889","DOIUrl":"https://doi.org/10.1145/1869790.1869889","url":null,"abstract":"Raster maps contain valuable road information, which is especially important for the areas where road vector data are otherwise not readily accessible. However, converting the road information in raster maps to road vector data usually requires significant user effort to achieve high accuracy. In this demo, we present Strabo, which is a system that extracts road vector data from heterogeneous raster maps. We demonstrate Strabo's fully automatic technique for extracting road vector data from raster maps with good image quality and the semi-automatic technique for handling raster maps with poor image quality. We show that Strabo requires minimal user input for extracting road vector data from raster maps with varying map complexity (i.e., overlapping features in maps) and image quality.","PeriodicalId":359068,"journal":{"name":"ACM SIGSPATIAL International Workshop on Advances in Geographic Information Systems","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133773635","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}
We present an optimization approach to simplify sets of building footprints represented as polygons. We simplify each polygonal ring by selecting a subsequence of its original edges; the vertices of the simplified ring are defined by intersections of consecutive (and possibly extended) edges in the selected sequence. Our aim is to minimize the number of all output edges subject to a user-defined error tolerance. Since we earlier showed that the problem is NP-hard when requiring non-intersecting simple polygons as output, we cannot hope for an efficient, exact algorithm. Therefore, we present an efficient algorithm for a relaxed problem and an integer program (IP) that allows us to solve the original problem with existing software. Our IP is large, since it has O(m6) constraints, where m is the number of input edges. In order to keep the running time small, we first consider a subset of only O(m) constraints. The choice of the constraints ensures some basic properties of the solution. Constraints that were neglected are added during optimization whenever they become violated by a new solution encountered. Using this approach we simplified a set of 144 buildings with a total of 2056 edges in 4.1 seconds on a standard desktop PC; the simplified building set contained 762 edges. During optimization, the number of constraints increased by a mere 13%. We also show how to apply cartographic quality measures in our method and discuss their effects on examples.
{"title":"Optimal and topologically safe simplification of building footprints","authors":"J. Haunert, A. Wolff","doi":"10.1145/1869790.1869819","DOIUrl":"https://doi.org/10.1145/1869790.1869819","url":null,"abstract":"We present an optimization approach to simplify sets of building footprints represented as polygons. We simplify each polygonal ring by selecting a subsequence of its original edges; the vertices of the simplified ring are defined by intersections of consecutive (and possibly extended) edges in the selected sequence. Our aim is to minimize the number of all output edges subject to a user-defined error tolerance. Since we earlier showed that the problem is NP-hard when requiring non-intersecting simple polygons as output, we cannot hope for an efficient, exact algorithm. Therefore, we present an efficient algorithm for a relaxed problem and an integer program (IP) that allows us to solve the original problem with existing software. Our IP is large, since it has O(m6) constraints, where m is the number of input edges. In order to keep the running time small, we first consider a subset of only O(m) constraints. The choice of the constraints ensures some basic properties of the solution. Constraints that were neglected are added during optimization whenever they become violated by a new solution encountered. Using this approach we simplified a set of 144 buildings with a total of 2056 edges in 4.1 seconds on a standard desktop PC; the simplified building set contained 762 edges. During optimization, the number of constraints increased by a mere 13%. We also show how to apply cartographic quality measures in our method and discuss their effects on examples.","PeriodicalId":359068,"journal":{"name":"ACM SIGSPATIAL International Workshop on Advances in Geographic Information Systems","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133718026","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}
Luis Manuel Vilches Blázquez, B. Villazón-Terrazas, Víctor Saquicela, A. D. León, Óscar Corcho, Asunción Gómez-Pérez
In this paper we present the process that has been followed for the development of an application that makes use of several heterogeneous Spanish public datasets that are related to three themes of INSPIRE Directive, specifically Administrative Units, Hydrography, and Statistical Units. Our application aims at analysing existing relations between the Spanish coastal area and different statistical variables such as population, unemployment, dwelling, industry, and building trade. Besides providing methodological guidelines for the generation, publishing and exploitation of Linked Data from such datasets, we provide an important innovation with respect to other similar processes followed in other initiatives by dealing with the geometrical information of features.
{"title":"GeoLinked data and INSPIRE through an application case","authors":"Luis Manuel Vilches Blázquez, B. Villazón-Terrazas, Víctor Saquicela, A. D. León, Óscar Corcho, Asunción Gómez-Pérez","doi":"10.1145/1869790.1869858","DOIUrl":"https://doi.org/10.1145/1869790.1869858","url":null,"abstract":"In this paper we present the process that has been followed for the development of an application that makes use of several heterogeneous Spanish public datasets that are related to three themes of INSPIRE Directive, specifically Administrative Units, Hydrography, and Statistical Units. Our application aims at analysing existing relations between the Spanish coastal area and different statistical variables such as population, unemployment, dwelling, industry, and building trade. Besides providing methodological guidelines for the generation, publishing and exploitation of Linked Data from such datasets, we provide an important innovation with respect to other similar processes followed in other initiatives by dealing with the geometrical information of features.","PeriodicalId":359068,"journal":{"name":"ACM SIGSPATIAL International Workshop on Advances in Geographic Information Systems","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114924959","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}
In this paper, we study the research issues in realizing location recommendation services for large-scale location-based social networks, by exploiting the social and geographical characteristics of users and locations/places. Through our analysis on a dataset collected from Foursquare, a popular location-based social networking system, we observe that there exists strong social and geospatial ties among users and their favorite locations/places in the system. Accordingly, we develop a friend-based collaborative filtering (FCF) approach for location recommendation based on collaborative ratings of places made by social friends. Moreover, we propose a variant of FCF technique, namely Geo-Measured FCF (GM-FCF), based on heuristics derived from observed geospatial characteristics in the Foursquare dataset. Finally, the evaluation results show that the proposed family of FCF techniques holds comparable recommendation effectiveness against the state-of-the-art recommendation algorithms, while incurring significantly lower computational overhead. Meanwhile, the GM-FCF provides additional flexibility in tradeoff between recommendation effectiveness and computational overhead.
{"title":"Location recommendation for location-based social networks","authors":"Mao Ye, Peifeng Yin, Wang-Chien Lee","doi":"10.1145/1869790.1869861","DOIUrl":"https://doi.org/10.1145/1869790.1869861","url":null,"abstract":"In this paper, we study the research issues in realizing location recommendation services for large-scale location-based social networks, by exploiting the social and geographical characteristics of users and locations/places. Through our analysis on a dataset collected from Foursquare, a popular location-based social networking system, we observe that there exists strong social and geospatial ties among users and their favorite locations/places in the system. Accordingly, we develop a friend-based collaborative filtering (FCF) approach for location recommendation based on collaborative ratings of places made by social friends. Moreover, we propose a variant of FCF technique, namely Geo-Measured FCF (GM-FCF), based on heuristics derived from observed geospatial characteristics in the Foursquare dataset. Finally, the evaluation results show that the proposed family of FCF techniques holds comparable recommendation effectiveness against the state-of-the-art recommendation algorithms, while incurring significantly lower computational overhead. Meanwhile, the GM-FCF provides additional flexibility in tradeoff between recommendation effectiveness and computational overhead.","PeriodicalId":359068,"journal":{"name":"ACM SIGSPATIAL International Workshop on Advances in Geographic Information Systems","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130198088","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}
With the wide usage of location tracking systems, continuously tracking relationships among moving objects over their location changes is possible and important to many real applications. This paper proposes a novel continuous location-based query, called the continuous top-k unsafe moving objects query or CTUO. This query continuously monitors the k most unsafe moving objects, where the unsafety of an object (protectee) is defined by the difference between its safety requirement and the protection provided by protection forces (protectors) around it. Compared with the traditional top-k queries where the score of an object represents its own characteristics, CTUO describes the relationships between protectees and protectors, which introduces computational challenges since naively all objects should be inspected to answer such a query. To avoid this, two efficient algorithms, GridPrune and GridPrune-Pro, are proposed based on the basic pruning technology from the Threshold Algorithm. Experiments show that the proposed algorithms outperform the naive solution with nearly two orders of magnitude on I/O savings.
{"title":"On continuous monitoring top-k unsafe moving objects","authors":"Jian Wen, V. Tsotras","doi":"10.1145/1869790.1869849","DOIUrl":"https://doi.org/10.1145/1869790.1869849","url":null,"abstract":"With the wide usage of location tracking systems, continuously tracking relationships among moving objects over their location changes is possible and important to many real applications. This paper proposes a novel continuous location-based query, called the continuous top-k unsafe moving objects query or CTUO. This query continuously monitors the k most unsafe moving objects, where the unsafety of an object (protectee) is defined by the difference between its safety requirement and the protection provided by protection forces (protectors) around it. Compared with the traditional top-k queries where the score of an object represents its own characteristics, CTUO describes the relationships between protectees and protectors, which introduces computational challenges since naively all objects should be inspected to answer such a query. To avoid this, two efficient algorithms, GridPrune and GridPrune-Pro, are proposed based on the basic pruning technology from the Threshold Algorithm. Experiments show that the proposed algorithms outperform the naive solution with nearly two orders of magnitude on I/O savings.","PeriodicalId":359068,"journal":{"name":"ACM SIGSPATIAL International Workshop on Advances in Geographic Information Systems","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129258310","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}
M. Adelfio, Michael D. Lieberman, H. Samet, K. Firozvi
Ontuition, a system for mapping ontologies, is presented. Transforming data to a usable format for Ontuition involves recognizing and resolving data values corresponding to concepts in multiple ontological domains. In particular, for datasets with a geographic component an attempt is made to identify and extract enough spatio-textual data that specific lat/long values to dataset entries can be assigned. Next, a gazetteer is used to transform the textually-specified locations into lat/long values that can be displayed on a map. Non-spatial ontological concepts are also discovered. This methodology is applied to the National Library of Medicine's very popular clinical trials website (http://clinicaltrials.gov/) whose users are generally interested in locating trials near where they live. The trials are specified using XML files. The location data is extracted and coupled with a disease ontology to enable general queries on the data with the result being of use to a very large group of people. The goal is to do this automatically for such ontology datasets with a locational component.
{"title":"Ontuition: intuitive data exploration via ontology navigation","authors":"M. Adelfio, Michael D. Lieberman, H. Samet, K. Firozvi","doi":"10.1145/1869790.1869887","DOIUrl":"https://doi.org/10.1145/1869790.1869887","url":null,"abstract":"Ontuition, a system for mapping ontologies, is presented. Transforming data to a usable format for Ontuition involves recognizing and resolving data values corresponding to concepts in multiple ontological domains. In particular, for datasets with a geographic component an attempt is made to identify and extract enough spatio-textual data that specific lat/long values to dataset entries can be assigned. Next, a gazetteer is used to transform the textually-specified locations into lat/long values that can be displayed on a map. Non-spatial ontological concepts are also discovered. This methodology is applied to the National Library of Medicine's very popular clinical trials website (http://clinicaltrials.gov/) whose users are generally interested in locating trials near where they live. The trials are specified using XML files. The location data is extracted and coupled with a disease ontology to enable general queries on the data with the result being of use to a very large group of people. The goal is to do this automatically for such ontology datasets with a locational component.","PeriodicalId":359068,"journal":{"name":"ACM SIGSPATIAL International Workshop on Advances in Geographic Information Systems","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134027537","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}
Zhixian Yan, Lazar Spremic, D. Chakraborty, C. Parent, S. Spaccapietra, K. Aberer
With the prevalence of GPS-embedded mobile devices, enormous amounts of mobility data are being collected in the form of trajectory - a stream of (x,y,t) points. Such trajectories are of heterogeneous entities - vehicles, people, animals, parcels etc. Most applications primarily analyze raw trajectory data and extract geometric patterns. Real-life applications however, need a far more comprehensive, semantic representation of trajectories. This paper demonstrates the automatic construction and visualization capabilities of SeMiTri - a system we built that exploits 3rd party information sources containing geographic information, to semantically enrich trajectories. The construction stack encapsulates several spatio-temporal data integration and mining techniques to automatically compute and annotate all meaningful parts of heterogeneous trajectories. The visualization interface exhibits different levels of data abstraction, from low-level raw trajectories (i.e. the initial GPS trace) to high-level semantic trajectories (i.e. the sequence of interesting places where moving objects have passed and/or stayed).
{"title":"Automatic construction and multi-level visualization of semantic trajectories","authors":"Zhixian Yan, Lazar Spremic, D. Chakraborty, C. Parent, S. Spaccapietra, K. Aberer","doi":"10.1145/1869790.1869879","DOIUrl":"https://doi.org/10.1145/1869790.1869879","url":null,"abstract":"With the prevalence of GPS-embedded mobile devices, enormous amounts of mobility data are being collected in the form of trajectory - a stream of (x,y,t) points. Such trajectories are of heterogeneous entities - vehicles, people, animals, parcels etc. Most applications primarily analyze raw trajectory data and extract geometric patterns. Real-life applications however, need a far more comprehensive, semantic representation of trajectories. This paper demonstrates the automatic construction and visualization capabilities of SeMiTri - a system we built that exploits 3rd party information sources containing geographic information, to semantically enrich trajectories. The construction stack encapsulates several spatio-temporal data integration and mining techniques to automatically compute and annotate all meaningful parts of heterogeneous trajectories. The visualization interface exhibits different levels of data abstraction, from low-level raw trajectories (i.e. the initial GPS trace) to high-level semantic trajectories (i.e. the sequence of interesting places where moving objects have passed and/or stayed).","PeriodicalId":359068,"journal":{"name":"ACM SIGSPATIAL International Workshop on Advances in Geographic Information Systems","volume":"287 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131891925","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}
The protection of semantic positions, for instance that an individual is inside a hospital, is a challenging privacy issue. For example, it can be shown that popular protection mechanisms, like location cloaking, can be easily defeated when certain mobility patterns are known, e.g., certain places are more or less popular than other places. To prevent this kind of attack, novel semantic location cloaking heuristics are being investigated. These methods are built on the knowledge of population distribution and sensitive locations. In this demonstration, we present SAWL (Semantics-aware Location cloaking), a tool supporting the comparison of semantic location cloaking methods over real and synthetic spatial scenarios.
{"title":"Analyzing semantic locations cloaking techniques in a probabilistic grid-based map","authors":"M. Damiani, C. Silvestri, E. Bertino","doi":"10.1145/1869790.1869878","DOIUrl":"https://doi.org/10.1145/1869790.1869878","url":null,"abstract":"The protection of semantic positions, for instance that an individual is inside a hospital, is a challenging privacy issue. For example, it can be shown that popular protection mechanisms, like location cloaking, can be easily defeated when certain mobility patterns are known, e.g., certain places are more or less popular than other places. To prevent this kind of attack, novel semantic location cloaking heuristics are being investigated. These methods are built on the knowledge of population distribution and sensitive locations. In this demonstration, we present SAWL (Semantics-aware Location cloaking), a tool supporting the comparison of semantic location cloaking methods over real and synthetic spatial scenarios.","PeriodicalId":359068,"journal":{"name":"ACM SIGSPATIAL International Workshop on Advances in Geographic Information Systems","volume":"23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114880628","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}
Geospatial information changes continually, and geospatial datasets become outdated and unsuitable for decision support due to inadequate data quality. They are also costly to maintain. There is a growing demand for accurate and consistent geospatial information in critical sectors such as emergency response. Within the land fabric geospatial vector data context, the ownership of data is distributed vertically among many government agencies by thematic types and horizontally by different administrative domains. A geospatial transaction, that updates changes, say for example, the release of a portion of forest-land for residential development, could inherently span across multiple agencies, infringing the jurisdictional responsibilities of individual agencies who own the participating data layers. Unauthorized editing and even publishing of updated data takes place in different organizations, unaware of editing taking place in other agencies. While updating of geospatial datasets for improved accuracy and distribution takes place, the legal aspects are generally ignored. In the context of current jurisdictional structure, to avoid duplicate geospatial editing as well as to support legally binding transaction, a geodatabase must transcend organizational boundaries and become truly federated. In addition, it needs to support a mechanism for versioned geospatial editing and more importantly support commits that could span many days. In this paper, we present a versioned editing model for a geospatial cloud database environment. The paper also discusses a new data workflow paradigm that utilizes the salient features of cloud for updating geospatial data addressing the issues highlighted. This is followed by a succinct discussion of the controlled investigation of the paradigm for the case of Australia's NSW state.
{"title":"Geospatial editing over a federated cloud geodatabase for the state of NSW","authors":"K. K. Janakiraman, M. Orgun, Abhaya C. Nayak","doi":"10.1145/1869790.1869813","DOIUrl":"https://doi.org/10.1145/1869790.1869813","url":null,"abstract":"Geospatial information changes continually, and geospatial datasets become outdated and unsuitable for decision support due to inadequate data quality. They are also costly to maintain. There is a growing demand for accurate and consistent geospatial information in critical sectors such as emergency response.\u0000 Within the land fabric geospatial vector data context, the ownership of data is distributed vertically among many government agencies by thematic types and horizontally by different administrative domains. A geospatial transaction, that updates changes, say for example, the release of a portion of forest-land for residential development, could inherently span across multiple agencies, infringing the jurisdictional responsibilities of individual agencies who own the participating data layers. Unauthorized editing and even publishing of updated data takes place in different organizations, unaware of editing taking place in other agencies. While updating of geospatial datasets for improved accuracy and distribution takes place, the legal aspects are generally ignored.\u0000 In the context of current jurisdictional structure, to avoid duplicate geospatial editing as well as to support legally binding transaction, a geodatabase must transcend organizational boundaries and become truly federated. In addition, it needs to support a mechanism for versioned geospatial editing and more importantly support commits that could span many days. In this paper, we present a versioned editing model for a geospatial cloud database environment. The paper also discusses a new data workflow paradigm that utilizes the salient features of cloud for updating geospatial data addressing the issues highlighted. This is followed by a succinct discussion of the controlled investigation of the paradigm for the case of Australia's NSW state.","PeriodicalId":359068,"journal":{"name":"ACM SIGSPATIAL International Workshop on Advances in Geographic Information Systems","volume":"251 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123272000","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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