摘要 :
The integration of multisource heterogeneous spatial data is one of the major challenges for many spatial data users. To facilitate multisource spatial data integration, many initiatives including federated databases, feature mani...
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The integration of multisource heterogeneous spatial data is one of the major challenges for many spatial data users. To facilitate multisource spatial data integration, many initiatives including federated databases, feature manipulation engines (FMEs), ontology-driven data integration and spatial mediators have been proposed. The major aim of these initiatives is to harmonize data sets and establish interoperability between different data sources.
On the contrary, spatial data integration and interoperability is not a pure technical exercise, and there are other nontechnical issues including institutional, policy, legal and social issues involved. Spatial Data Infrastructure (SDI) framework aims to better address the technical and nontechnical issues and facilitate data integration. The SDIs aim to provide a holistic platform for users to interact with spatial data through technical and nontechnical tools.
This article aims to discuss the complexity of the challenges associated with data integration and propose a tool that facilitates data harmonization through the assessment of multisource spatial data sets against many measures. The measures represent harmonization criteria and are defined based on the requirement of the respective jurisdiction. Information on technical and nontechnical characteristics of spatial data sets is extracted to form metadata and actual data. Then the tool evaluates the characteristics against measures and identifies the items of inconsistency. The tool also proposes available manipulation tools or guidelines to overcome inconsistencies among data sets. The tool can assist practitioners and organizations to avoid the time-consuming and costly process of validating data sets for effective data integration.
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We outline our work on using bintrees as an efficient representation for qualitative information about spatial objects. Our approach represents each spatial object as a bintree satisfying the exact same qualitative relationships t...
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We outline our work on using bintrees as an efficient representation for qualitative information about spatial objects. Our approach represents each spatial object as a bintree satisfying the exact same qualitative relationships to other bintree representations as the corresponding spatial objects. We prove that such correct bintrees always exist and that they can be constructed as a sum of local representations, allowing a practically efficient construction. Our representation is both efficient, with respect to storage space and query time, and can represent many well-known qualitative relations, such as the relations in the Region Connection Calculus and Allen's Interval Algebra.
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The Spatial Econometrics Association appeared on the scene only Jive years ago during a time of unprecedented expansion of research activities in the field. This paper tries to summarize the developments that occurred in this firs...
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The Spatial Econometrics Association appeared on the scene only Jive years ago during a time of unprecedented expansion of research activities in the field. This paper tries to summarize the developments that occurred in this first lustrum of life of the Association. The review considers more than 230 papers that appeared in the last five years in various scientific journals. The emerging picture is that of a field that is still experiencing its phase of rapid growth with a large number of theoretical developments together with a progressive enlargement of the fields of application outside the traditional ones.
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The copyright of data is a key point that needs to be solved in spatial data infrastructure for data sharing. In this paper, we propose a decentralized digital rights management model of spatial data, which can provide a novel way...
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The copyright of data is a key point that needs to be solved in spatial data infrastructure for data sharing. In this paper, we propose a decentralized digital rights management model of spatial data, which can provide a novel way of solving the existing copyright management problem or other problems in spatial data infrastructure for data sharing. An Ethereum smart contract is used in this model to realize spatial data digital rights management function. The InterPlanetary File System is utilized as external data storage for storing spatial data in the decentralized file system to avoid data destruction that is caused by a single point of failure. There is no central server in the model architecture, which has a completely decentralized nature and it makes spatial data rights management not dependent on third-party trust institutions. We designed three spatial data copyright management algorithms, developed a prototype system to implement and test the model, used the smart contract security verification tool to check code vulnerabilities, and, finally, discussed the usability, scalability, efficiency, performance, and security of the proposed model. The result indicates that the proposed model not only has diversified functions of copyright management compared with previous studies on the blockchain-based digital rights management, but it can also solve the existing problems in traditional spatial data infrastructure for data sharing due to its characteristics of complete decentralization, mass orientation, immediacy, and high security.
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Centrality of landscape, in territorial planning, has been influencing for years, the testing of innovative analytical techniques aimed to gather peculiarities of urban and suburban context. The advent of Spatial Information Syste...
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Centrality of landscape, in territorial planning, has been influencing for years, the testing of innovative analytical techniques aimed to gather peculiarities of urban and suburban context. The advent of Spatial Information System created the possibility to produce more detailed studies analyzing a lot of information dealing with territorial phenomena of crucial importance in spatial planning. The development of analytical systems based on multidimensional analysis may represent the right way to synthesize different phenomena that interact locally, in order to obtain the intrinsic sensitivity of a specific landscape as a result. In the case of Cremona Urban Variant, the production of thematic maps has allowed the construction of six synthetic indicators, dealing with specific aspects of Cremona landscape. The indicators are: i) insularisation of non -built spaces, ii) morphological/structural values. Hi) perceptual landscape aspects, iv) permanence of urban system, v) degree of imperativeness of environmental constraints, vi) integrity of land use.
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The process of knowledge discovery in databases aims at the discovery of associations within data sets. Data Mining is a central step of this process. It corresponds to the application of algorithms for identifying patterns in dat...
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The process of knowledge discovery in databases aims at the discovery of associations within data sets. Data Mining is a central step of this process. It corresponds to the application of algorithms for identifying patterns in data. Mining geo-referenced data sets constitutes a special case that demands a particular approach in the knowledge discovery process. Those data sets include allusion to geographic objects, which location and extension define implicit relationships of spatial neighbourhood. The Data Mining algorithms have to take this spatial neighbourhood into account when looking for associations among data. This paper presents an approach for knowledge discovery in geo-referenced data sets in which the use of qualitative spatial reasoning strategies makes possible the discovery of patterns that are easily understood by the users. The graphical representation of the results of the knowledge discovery process also allowed a fast understanding of the results achieved.
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Spatial economic studies traditionally exploit areal data at the regional or sub-regional level. More recently, scholars have started to exploit spatial data of a different nature and, at the same time, extend the fields of applic...
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Spatial economic studies traditionally exploit areal data at the regional or sub-regional level. More recently, scholars have started to exploit spatial data of a different nature and, at the same time, extend the fields of application in economics. Specifically, this special issue contributes to the spatial economic literature by providing empirical evidence on a wide range of phenomena (socio-economic deprivation, land price volatility, electoral competition, real estate market, firm survival and tourism economics) and exploiting data at the municipality, firm, house and even individual level. At the same time, it tackles some of the methodological issues faced by the above-mentioned analyses.
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Large volumes of geospatial data are being published on the Semantic Web (SW), yielding a need for advanced analysis of such data. However, existing SW technologies only support advanced analytical concepts such as multidimensiona...
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Large volumes of geospatial data are being published on the Semantic Web (SW), yielding a need for advanced analysis of such data. However, existing SW technologies only support advanced analytical concepts such as multidimensional (MD) data warehouses and Online Analytical Processing (OLAP) over non-spatial SW data. To remedy this need, this paper presents the QB4SOLAP vocabulary, which supports spatially enhanced MD data cubes over RDF data. The paper also defines a number of Spatial OLAP (SOLAP) operators over QB4SOLAP cubes and provides algorithms for generating spatially extended SPARQL queries from the SOLAP operators. The proposals are validated by applying them to a realistic use case.
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The MultiDimER model is a conceptual model used for representing a multidimensional view of data for Data Warehouse (DW) and On-Line Analytical Processing (OLAP) applications. This model includes a spatial extension allowing spati...
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The MultiDimER model is a conceptual model used for representing a multidimensional view of data for Data Warehouse (DW) and On-Line Analytical Processing (OLAP) applications. This model includes a spatial extension allowing spatiality in levels, hierarchies, fact relationships, and measures. In this way decision-making users can represent in an abstract manner their analysis needs without considering complex implementation issues and spatial OLAP tools developers can have a common vision for representing spatial data in a multidimensional model. In this paper we propose the transformation of a conceptual schema based on the MultiDimER constructs to an object-relational schema. We based our mapping on the SQL:2003 and SQL/MM standards giving examples of commercial implementation using Oracle 10g with its spatial extension. Further we use spatial integrity constraints to ensure the semantic equivalence of the conceptual and logical schemas. We also show some examples of Oracle spatial functions, including aggregation functions required for the manipulation of spatial data. The described mappings to the object-relational model along with the examples using a commercial system show the feasibility of implementing spatial DWs in current commercial DBMSs. Further, using integrated architectures, where spatial and thematic data is defined within the same DBMS, facilitates the system management simplifying data definition and manipulation.
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Spatial flow outlier (SFO) detection aims to discover spatial flows whose non-spatial attribute values are significantly different from their neighborhoods. Different from spatial flow clusters, which are the main concern in the c...
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Spatial flow outlier (SFO) detection aims to discover spatial flows whose non-spatial attribute values are significantly different from their neighborhoods. Different from spatial flow clusters, which are the main concern in the current literature, SFOs represent unusual local instabilities and are valuable for revealing anomalous spatial interactions between regions. Detecting SFOs is challenging because the underlying distribution of the flow data is unknown a priori, and inappropriate distribution assumptions may lead to misleading decisions on SFOs. Surprisingly, spatial autocorrelation, which is a common property of geographic data, has not been considered in the null hypothesis for testing spatial outliers. To solve this significant methodological issue, we propose a spatial-autocorrelation-aware detection method. This method detects SFOs by testing the local difference of attribute values in flow neighborhoods against the null hypothesis that neighboring flows are similar. To construct this null hypothesis, we develop a distribution-free model by reconstructing the observed spatial autocorrelation. Synthetic experiments and a case study using the journey-to-work flow data in Chicago demonstrate that the choice and modeling of the null hypothesis has a significant influence on the statistical inference of SFOs. By taking the inherent spatial autocorrelation into account, our method can more objectively assess the significance of SFOs than two baseline methods based on the normality and randomization hypotheses.
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