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Utilization of an Expert System for the Analysis of Semantic Characteristics for Improved Conflation in Geographical Systems
Published in Don Potter, Manton Matthews, Moonis Ali, Industrial and Engineering Applications of Artificial Intelligence and Expert Systems, 2020
Harold Foley, Fred Petry, Maria Cobb, Kevin Shaw
As GIS become increasingly more popular, efficient methods for performing conflation will have to be developed. Conflation can be defined as the process of merging multiple geographic data sets for the purpose of developing a more accurate geographic data set (map). The first successful attempt at automating the conflation process occurred in the mid-1980’s. The United States Geological Survey, United States Bureau of Census (USGS-USBC) collaborated in an effort to consolidate their respective digitized map files of various US regions [Saa88]. This effort serves as the foundation for much of the conflation research occurring today. Conflation, also referred to as map compilation, entails two major processes: (1) feature matching and (2) deconfliction.
Data collection, processing, and database management
Published in Zongzhi Li, Transportation Asset Management, 2018
Network conflation: Conflation is the process that matches GIS network layers and transfers the geographic attributes of objects from one layer to another. The goal of conflation is to merge the best-quality elements of both data sets to create a composite dataset that is better than either of them. The target layer should be preselected to better represent the geographic attributes required for decision making. Network conflation involves two major steps: (i) match links in the geometric layer with those in the attribute layer; and (ii) transfer link attribute data from the attribute layer to the corresponding links in the geometric layer. Figure 3.12 illustrates network conflation.
Detecting topographic database changes and updating 1:25.000 scale maps population class by changes
Published in Journal of Spatial Science, 2022
Fatih Kalle, Huseyin Zahit Selvi, Ilkay Bugdayci
There is a need for applications to combine existing data with new or revised data in order to complete missing information in databases and to increase the spatial accuracy of the existing data. Although databases contain spatial and textual data of real-world objects, they can differ from each other in terms of different collection tools, methods, purposes and formats (Butenuth et al. 2007). Studies to obtain a better data set by integrating different data groups are defined as map conflation (Lynch and Saalfeld 1985, Longley et al. 2001). With map conflation processes to be created for integration, the aim is to ensure spatial and semantic data integrity, elimination of data inconsistencies and to make new data updates faster. Conflation processes of vector, raster and elevation data can be done within or between each data type either by manual, semi-automatic or fully automatic methods depending on geometric, semantic and topological criteria (Ruiz et al. 2011). The first stage of integration is the data matching process.
Prod-users of geospatial information: some legal perspectives
Published in Journal of Spatial Science, 2019
With many contributors adding to the volunteered geospatial database, there might be difficulties in tracing provenance and lineage of the copyright. The ‘conflated’ data may ‘lose’ its copyright status by providing contributors with only a generic claim to the copyright by the user agency (Porto de Albuquerque et al. 2016). The term ‘conflation’ was proposed based on the common use of this word in the geospatial domain to indicate ‘the process of combining geographic information from overlapping sources so as to retain accurate data, minimize redundancy, and reconcile data conflicts’ (Porto de Albuquerque et al. 2016, p. 5). This loss is because it might be nearly impossible to trace the ownership of small bits of information to their proper owners. Over time the owners themselves may become untraceable as they may have moved on. It has been suggested that the use of Creative Commons licences and copyright regimes could assist in alleviating the problem of tracing and permissions and to avoid what Scassa (2013) has described as the ‘Wiki’ effect – where data are combined from thousands of disparate sources to form one or more coherent works (Creative Commons 2017). The perverse outcome may be that the conflated data may become more inaccurate, corrupted with further errors, and no one may be individually liable as copyright holder or there may be too many owners to be held accountable.