Evaluating Spatial Data Quality in GIS Database

doi:10.1109/WICOM.2007.1463

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Evaluating Spatial Data Quality in GIS Database

Proceedings Article•DOI•

Evaluating Spatial Data Quality in GIS Database

Ying Su¹, Lei Yang, Zhanming Jin•Institutions (1)

Tsinghua University¹

08 Oct 2007-pp 5967-5970

TL;DR: Four quantitative measures are introduced to assess the quality of spatial data and four assumptions are presented where the measures can be evaluated efficiently by numerical calculation.

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Abstract: The quality of spatial data is often limited by the quality of their sources such as paper maps and satellite images. Spatial operations performed on database of geographical information systems (GIS) such as selection, projection, and Cartesian product, do not always work correctly because their accuracy and completeness depends on the quality of spatial data. The present paper suggests a methodology to evaluate two data quality characteristics - accuracy and completeness - of the spatial database. Four quantitative measures are introduced to assess the quality of spatial data. Their explicit forms are derived for a tuple, and four assumptions are presented where the measures can be evaluated efficiently by numerical calculation.

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Proceedings Article•DOI•

Building Service Oriented Applications for Disaster Management - An Earthquake Assessment Example

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Ying Su, Zhanming Jin¹•Institutions (1)

Tsinghua University¹

21 Nov 2009

TL;DR: An extensible QoS model for DQ evaluation and a proposed statistics algorithm to predict the decision risks are defined and implemented, demonstrating the applicability of the proposed model in supporting disaster management.

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Abstract: GIS could help rescuers for disaster management to assess the fitness for use of spatial data to reduce the risk of decision-making. However, GIS presents limitations and remain largely unused. It needs to provide the required information to rescuers with high quality. In this paper, we develop a prototype system to grant the data quality. The system aims to provide rescuers with information in a rapid and intuitive way in order to reduce rescuer decision-uncertainty due to poor data quality (DQ). We define an extensible QoS model for DQ evaluation and a proposed statistics algorithm to predict the decision risks. Our implementation demonstrates the applicability of the proposed model in supporting disaster management.

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6 citations

Cites methods from "Evaluating Spatial Data Quality in ..."

...As Catalog we use con terra terraCatalog, which is an implementation of the OGC Web Catalog Service (OGC, 2004a) specification and makes it possible to store and retrieve information about spatial data and services....
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Book Chapter•DOI•

Building Service Oriented Sharing Platform for Emergency Management – An Earthquake Damage Assessment Example

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Ying Su¹, Zhanming Jin¹, Jie Peng•Institutions (1)

Tsinghua University¹

01 Jan 2010

TL;DR: The feasibility of using services offered by a Spatial Data Infrastructure as the basis for distributed service oriented sharing is studied and the OGC specifications provide a sound basis for developing service oriented architectures for disaster sharing platform.

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Abstract: In this paper we study the feasibility of using services offered by a Spatial Data Infrastructure as the basis for distributed service oriented sharing. By developing a prototype we demonstrate that a Spatial Data Infrastructure facilitates rapid development of sharing platform that solves image sharing problems. The prototype provides clients with a distributed application that enables the assessment of earthquake damage areas based on house collapsed data in a given area. We present the architecture of the application and describe details about implementations specific issues. We conclude that the OGC specifications provide a sound basis for developing service oriented architectures for disaster sharing platform. The prototype was validated by enhancing image quality (I-Q) generated by remote sensing through the technologies of visualization.

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3 citations

Cites methods from "Evaluating Spatial Data Quality in ..."

...We have developed indicator-based approach for spatial data quality risk and implemented the resulting approach in a geographical information system [8]....
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Proceedings Article•DOI•

Information Quality Assurance Models for Experts Assessing in Disaster Management

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Ying Su, Jie Peng, Zhanming Jin¹•Institutions (1)

Tsinghua University¹

31 Mar 2009

TL;DR: Information Quality Assurance Models for Experts Assessing in Disaster Management and how these models can be modified for different types of disasters are presented.

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Abstract: Disaster management depends on large volumes of comprehensive, accurate, relevant, on-time information that various organizations systematically create and maintain. One cannot assure information quality (IQ) without first being able to measure it meaningfully and establishing a causal connection between the source of change, the problem types, the types of activities affected, and their implications. In this paper we propose a model that can manage heterogeneous data and provide functions to support expert experts in the assessment of the fitness for use of given information. In contrast to other assurance approaches, this approach provides interactive, multigranularity and context-sensitive IQ indicators that help experts to build and justify their opinions. The prototype system was designed and validated by enhancing IQ generated by remote sensing through the technologies of visualization.

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2 citations

Cites methods from "Evaluating Spatial Data Quality in ..."

...We have developed indicator-based approach for spatial data quality communication and implemented the resulting approach in a geographical information system[16]....
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Proceedings Article•DOI•

Assuring Image Quality in Spatial Data Sharing Platform for Disaster Management

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Ying Su, Lei Yang¹•Institutions (1)

Tsinghua University¹

21 Dec 2008

TL;DR: A Web-based spatial data sharing platform for disaster management that provides interactive, multi-granularity and contextsensitive IQ indicators that help experts to build and justify their opinions is described.

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Abstract: This paper aims to describe development of a Web-based spatial data sharing platform for disaster management. Alongside functionality, quality of image(IQ) is basic to successful sharing of distributed geo-services.This paper explores IQ provisioning in the context of geo-service sharing for disaster management.The paper presents an IQ model for disaster management and illustrates how user level IQ requirements can be supported in IQ-aware geo-service architecture. In contrast to context-specific IQ assurance approaches, which usually focus on a few variables determined by local needs, this approach provides interactive, multi-granularity and contextsensitive IQ indicators that help experts to build and justify their opinions. The prototype system was designed and validated by enhancing IQ generated by remote sensing through the technologies of visualization.

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1 citations

Cites background or methods from "Evaluating Spatial Data Quality in ..."

...In our previous work we defined a framework for I-QoS provisioning in geo-service architectures [4]....
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...We have developed indicator-based approach for spatial data quality communication and implemented the resulting approach in a geographical information system[4]....
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...Based on the definitions proposed in [4], we define the following quality metrics for a cube C....
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Geographic information system (gis) to manage environmental noise in the city of medellin, co- lombia

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Luis Tafur, Claudia Durango, Diana Garza, Libardo Londoño, Héctor García - Show less +1 more

01 Jan 2015

TL;DR: The main goal of this environmental noise sources characterization is to find first, the acoustic noise prediction method that best agrees with local noise sources and second, to evaluate the need of acoustic corrections for the noise indicators analysed in the strategic noise maps.

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Abstract: This paper presents the results obtained so far in the research project “GIS to manage environmental noise in the city of Medellin, Colombia”, developed by the Engineering Faculty of the San Buenaventura University. The implementation of the GIS to manage environmental noise includes three main aspects: the design and construction of the geodatabase, the geodata quality metrics and the environmental noise sources characterization. The first aspect takes into account all the information required to elaborate strategic noise maps and detailed studies to assess noise exposure due to environmental noise, by means of noise modelling software. The designing process considers the geodatabase as the central management engine, in which the data to be exported to the noise mapping software is managed and the results are presented. The second aspect defines the following basic elements of geodata quality to the information provided by local authorities: completeness, logical consistency, positional accuracy, temporal accuracy and precision of attributes. The third aspect considers the uncertainty in noise prediction results given by local context environment characteristics, including terrain features and transportation infrastructure. In this sense, an exploratory statistical analysis of acoustic measurements results has been realized for each public transportation system such as Metro, Metrocable and Metroplus. In addition, a measurement protocol has been proposed in order to characterize the noise generation behaviour of the different type of vehicles found in the city, taking into account speed and terrain slope. The main goal of this environmental noise sources characterization is to find first, the acoustic noise prediction method that best agrees with local noise sources and second, to evaluate the need of acoustic corrections for the noise indicators analysed in the strategic noise maps.

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References

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Journal Article•DOI•

Data Warehousing Process Maturity: An Exploratory Study of Factors Influencing User Perceptions

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Arun Sen¹, Atish P. Sinha, K. Ramamurthy•Institutions (1)

Texas A&M University¹

24 Jul 2006-IEEE Transactions on Engineering Management

TL;DR: The results of this exploratory study indicate that several factors-data quality, alignment of architecture, change management, organizational readiness, and data warehouse size-have an impact on DWP maturity, as perceived by IT professionals.

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Abstract: This paper explores the factors influencing perceptions of data warehousing process maturity. Data warehousing, like software development, is a process, which can be expressed in terms of components such as artifacts and workflows. In software engineering, the Capability Maturity Model (CMM) was developed to define different levels of software process maturity. We draw upon the concepts underlying CMM to define different maturity levels for a data warehousing process (DWP). Based on the literature in software development and maturity, we identify a set of features for characterizing the levels of data warehousing process maturity and conduct an exploratory field study to empirically examine if those indeed are factors influencing perceptions of maturity. Our focus in this paper is on managerial perceptions of DWP. The results of this exploratory study indicate that several factors-data quality, alignment of architecture, change management, organizational readiness, and data warehouse size-have an impact on DWP maturity, as perceived by IT professionals. From a practical standpoint, the results provide useful pointers, both managerial and technological, to organizations aspiring to elevate their data warehousing processes to more mature levels. This paper also opens up several areas for future research, including instrument development for assessing DWP maturity

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65 citations

"Evaluating Spatial Data Quality in ..." refers background or methods in this paper

...We estimate the sizes of the various subsets of R and of the set RC using the attribute-level quality metrics derived in Equality(1) and(2)....
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...From Equality (1), we know that each projected identifier attribute of S has accuracy k α , whereas each projected nonidentifier attribute of S has an accuracy of γ (2)....
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Journal Article•DOI•

Framework and Workflows for Spatial Database Generalization

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Byong-Nam Choe, Young-Gul Kim¹•Institutions (1)

KAIST¹

01 Feb 2007-Transactions in Gis

TL;DR: This study suggests a framework for database generalization, and then defines operators that reflect the changes in database schema and content within the generalization process.

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Abstract: Spatial database generalization deals not only with geometrical simplification, but also with changes in database schema and content. The purpose of this research is to suggest methods for database generalization through the abstraction of a detailed spatial database. To accomplish this goal, this study suggests a framework for database generalization, and then defines operators that reflect the changes in database schema and content within the generalization process. A set of operator sequences (workflows) is used to specify and arrange the operators required to abstract a given feature. In order to assess the validity of the suggested method, a prototype system is developed. The results show that the efficiency of generalization can be improved, and data loss or distortion reduced as well.

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6 citations

"Evaluating Spatial Data Quality in ..." refers background or methods in this paper

...Using 1 2R R R= and the definitions in Section II, we have 1 2 1 2 1 2 A A R S S S S α α α= = (7) 1 2 1 2 1 2 1 2 1 2 1 1 1 2 A I I A I I R S S S S S S S S β α β α β β β + + = = + + 1 2 1 2 1 2 1 2 1 2 1 2 1 2 A N I N N A R N I N N S S S S S S S S S S S S S S μ + + = + + ( ) ( )1 2 2 1 1 21 1μ μ μ μ μ μ= − + − + 1 2 1 2μ μ μ μ= + − From equality(6), we have ( ) ( ) ( ) ( ) 1 2 1 2 1 2 1 2 1 1 1 1 CR R χ χ χ χμ μ χ χ + − = − − − − Therefore, we have C R R C R R R R χ μ = − +1 ( ) ( ) ( ) ( ) χ χ χ χμ μ χ χ + −= − − − − 1 2 1 2 1 2 1 2 1 1 1 1 ( ) ( ) ( ) ( ) ( ) 1 2 1 2 1 1 2 1 2 1 2 1 2 1 1 1 1 1 μ μ μ μ χ χ χ χμ μ χ χ − − + − + + − − − − − 1 2 1 2χ χ χ χ= + − (8) From Equality(7), we can see that the accuracy of the output of the Cartesian product operator is less than the accuracy of either of the input relations, and that the accuracy can become very low if the participating tables are not of high quality....
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...We estimate the sizes of the various subsets of R and of the set RC using the attribute-level quality metrics derived in Equality(1) and(2)....
[...]
...From Equality (1), we know that each projected identifier attribute of S has accuracy kα , whereas each projected nonidentifier attribute of S has an accuracy of γ (2)....
[...]
...From Equality (1), we know that each projected identifier attribute of S has accuracy k α , whereas each projected nonidentifier attribute of S has an accuracy of γ (2)....
[...]