Spatial Data Quality Research Articles

Combining Satellite Data and Community-Based Observations for Forest Monitoring

Within the Reducing Emissions from Deforestation and Degradation (REDD+) framework, the involvement of local communities in national forest monitoring activities has the potential to enhance monitoring efficiency at lower costs while simultaneously promoting transparency and better forest management. We assessed the consistency of forest monitoring data (mostly activity data related to forest change) collected by local experts in the UNESCO Kafa Biosphere Reserve, Ethiopia. Professional ground measurements and high resolution satellite images were used as validation data to assess over 700 forest change observations collected by the local experts. Furthermore, we examined the complementary use of local datasets and remote sensing by assessing spatial, temporal and thematic data quality factors. Based on this complementarity, we propose a framework to integrate local expert monitoring data with satellite-based monitoring data into a National Forest Monitoring System (NFMS) in support of REDD+ Measuring, Reporting and Verifying (MRV) and near real-time forest change monitoring.

Assessing the impact of demographic characteristics on spatial error in volunteered geographic information features

The proliferation of volunteered geographic information (VGI), such as OpenStreetMap (OSM) enabled by technological advancements, has led to large volumes of user-generated geographical content. While this data is becoming widely used, the understanding of the quality characteristics of such data is still largely unexplored. An open research question is the relationship between demographic indicators and VGI quality. While earlier studies have suggested a potential relationship between VGI quality and population density or socio-economic characteristics of an area, such relationships have not been rigorously explored, and mainly remained qualitative in nature. This paper addresses this gap by quantifying the relationship between demographic properties of a given area and the quality of VGI contributions. We study specifically the demographic characteristics of the mapped area and its relation to two dimensions of spatial data quality, namely positional accuracy and completeness of the corresponding VGI contributions with respect to OSM using the Denver (Colorado, US) area as a case study. We use non-spatial and spatial analysis techniques to identify potential associations among demographics data and the distribution of positional and completeness errors found within VGI data. Generally, the results of our study show a lack of statistically significant support for the assumption that demographic properties affect the positional accuracy or completeness of VGI. While this research is focused on a specific area, our results showcase the complex nature of the relationship between VGI quality and demographics, and highlights the need for a better understanding of it. By doing so, we add to the debate of how demographics impact on the quality of VGI data and lays the foundation to further work.

Evaluating the fitness for use of spatial data sets to promote quality in ecological assessment and monitoring

This article proposes and illustrates a practical methodological framework to evaluate the fitness for use of spatial data sets for environmental and ecological applications, focusing on user requirements for specified application contexts. The methodology is based on the use of metadata to analyze similarity between the data characteristics and the user’s needs or expectations for several quality indicators. Additionally, the concept of ‘critical factors’ is introduced in this framework, allowing users to define which quality indicators have greater importance given their own requirements or expectations and the specified application contexts. The proposed methodology further allows integrating and interconnecting the spatial data quality (SDQ) evaluation methodology with metadata geoportals in WebGIS platforms, facilitating its operation by users from non-spatial disciplines and with often limited expertise on this subject. Examples of the evaluation of fitness for use for specific application contexts within the project BIO_SOS (‘Biodiversity Multi-SOurce Monitoring System: From Space To Species’ FP7 project) are presented. By providing a prompt and straightforward evaluation tool, the proposed methodology can encourage the implementation of SDQ evaluation routines in ecological assessment and monitoring programs, promoting a more adequate use of geospatial data and ultimately contributing to well-supported policy and management decisions.

Classifying pedestrian movement behaviour from GPS trajectories using visualization and clustering

The quantity and quality of spatial data are increasing rapidly. This is particularly evident in the case of movement data. Devices capable of accurately recording the position of moving entities have become ubiquitous and created an abundance of movement data. Valuable knowledge concerning processes occurring in the physical world can be extracted from these large movement data sets. Geovisual analytics offers powerful techniques to achieve this. This article describes a new geovisual analytics tool specifically designed for movement data. The tool features the classic space-time cube augmented with a novel clustering approach to identify common behaviour. These techniques were used to analyse pedestrian movement in a city environment which revealed the effectiveness of the tool for identifying spatiotemporal patterns.

Spatial Data Quality Control for the Coral Triangle Atlas

The Coral Triangle is a global priority for conservation and since the creation of the Coral Triangle Initiative in 2007 it has been a major focus for a multi-lateral conservation partnership uniting the region's six governments. The Coral Triangle (CT) Atlas was developed to provide scientists and managers with the best available data on marine resources in the Coral Triangle. Endorsed as an official supporting tool to the Coral Triangle Initiative, the CT Atlas strives to provide the most accurate information possible to track the success of the conservation efforts of the Initiative. Focusing on marine protected areas and key marine habitats, the CT Atlas tested a process to assess the quality, reliability, and accuracy of different data layers. This article describes the mechanism used to evaluate these layers and to provide accurate data. Results of the preliminary quality control process showed errors in reputable datasets, outdated and missing data, metadata gaps, and a lack of user instructions to interpret layers. It highlighted the need to challenge existing datasets and demonstrated that regional efforts could improve the data available to evaluate the effectiveness of conservation measures. The Coral Triangle Atlas is continuously being updated to be as accurate as possible for reliable analysis.

Uncertainty and Sensitivity Assessments of GPS and GIS Integrated Applications for Transportation

Uncertainty and sensitivity analysis methods are introduced, concerning the quality of spatial data as well as that of output information from Global Positioning System (GPS) and Geographic Information System (GIS) integrated applications for transportation. In the methods, an error model and an error propagation method form a basis for formulating characterization and propagation of uncertainties. They are developed in two distinct approaches: analytical and simulation. Thus, an initial evaluation is performed to compare and examine uncertainty estimations from the analytical and simulation approaches. The evaluation results show that estimated ranges of output information from the analytical and simulation approaches are compatible, but the simulation approach rather than the analytical approach is preferred for uncertainty and sensitivity analyses, due to its flexibility and capability to realize positional errors in both input data. Therefore, in a case study, uncertainty and sensitivity analyses based upon the simulation approach is conducted on a winter maintenance application. The sensitivity analysis is used to determine optimum input data qualities, and the uncertainty analysis is then applied to estimate overall qualities of output information from the application. The analysis results show that output information from the non-distance-based computation model is not sensitive to positional uncertainties in input data. However, for the distance-based computational model, output information has a different magnitude of uncertainties, depending on position uncertainties in input data.

Introducing mapping standards in the quality assessment of buildings extracted from very high resolution satellite imagery

Many municipal activities require updated large-scale maps that include both topographic and thematic information. For this purpose, the efficient use of very high spatial resolution (VHR) satellite imagery suggests the development of approaches that enable a timely discrimination, counting and delineation of urban elements according to legal technical specifications and quality standards. Therefore, the nature of this data source and expanding range of applications calls for objective methods and quantitative metrics to assess the quality of the extracted information which go beyond traditional thematic accuracy alone. The present work concerns the development and testing of a new approach for using technical mapping standards in the quality assessment of buildings automatically extracted from VHR satellite imagery. Feature extraction software was employed to map buildings present in a pansharpened QuickBird image of Lisbon. Quality assessment was exhaustive and involved comparisons of extracted features against a reference data set, introducing cartographic constraints from scales 1:1000, 1:5000, and 1:10,000. The spatial data quality elements subject to evaluation were: thematic (attribute) accuracy, completeness, and geometric quality assessed based on planimetric deviation from the reference map. Tests were developed and metrics analyzed considering thresholds and standards for the large mapping scales most frequently used by municipalities. Results show that values for completeness varied with mapping scales and were only slightly superior for scale 1:10,000. Concerning the geometric quality, a large percentage of extracted features met the strict topographic standards of planimetric deviation for scale 1:10,000, while no buildings were compliant with the specification for scale 1:1000.

Standard deviation of line objects in geographic information science

Standard deviation of points is regarded as an effective precision indicator and has been used widely for over 100 years. However, to date, no standard deviation for line objects exists, despite lines being the most fundamental geometric objects in geographic information science. This paper proposes a new theory: the measurement of random line precision using standard deviation. The new theory involves: (1) standard deviation presented graphically, in a band-shape: termed the standard deviation band; (2) the rigorous derivation of analytical equations for the standard deviation band; (3) the probability that a line falls within the standard deviation band. The main contributions of this research include: (1) the derivation of an analytical equation of the standard deviation band; (2) a method to estimate the probability of a line falling within a standard deviation band. These contributions form a foundation for the proposition of further control measures for spatial data quality.

Spatial data for fungal specimens: retrospective georeferencing and practical recommendations for mycologists

The number of studies based on herbarium data for analyzing biogeographical patterns and environmental questions is increasing, as herbaria are making their collections available online. However, the quality of a specimen's spatial data still varies dramatically among records. Most historical specimen records either lack geographic information or have only vague textual descriptions about the locality, while contemporary records may exhibit unwarranted variation in spatial data quality, requiring increased awareness among mycologists about the importance of high quality primary spatial data for specimens. Georeferencing is the process of assigning geographic coordinates to a record linking it to a geographic location on Earth, and it can be processed retrospectively for records without geographical coordinates based on locality descriptions or directly collected in the field using GPS handheld units. Here we provide an overview of methods for georeferencing historical data retrospectively, discuss practical recommendations for collecting high quality spatial data for fungal specimens, and suggest decimal degrees as a standard form for citing geographic coordinates.

A Random Set Approach for Modeling Integrated Uncertainties of Traffic Islands Derived from Airborne Laser Scanning Points

Traffic islands play a major role in transport studies by affecting traffic behavior safety, air pollution, and transport decision support. Point data obtained by laser scanning enable the determination of their locations. Planimetric errors, vertical errors, and limited point spacing however affect their spatial data quality (SDQ). In this study the authors defined uncertainty as the lack of accuracy and analyzed its importance by modeling each traffic island as a random set. The covering functions of the point data and their immediate locations were determined by point segmentation, followed by interpolation. In this way, traffic islands were delineated from the background with a transition zone. The study showed that point spacing has the largest contribution to the positional accuracy of a traffic island. The area of transition zone has a linear relation with the planimetric errors, whereas the influence of the vertical errors on the accuracy decreases with increasing point spacing. Experiments were conducted to investigate the influences of the parameters in an SDQ analysis. The study demonstrated how different sources of uncertainty can be integrated. Results showed the advantages of using random sets for SQD modeling. The authors concluded that modeling of traffic islands by random sets provides meaningful information to integrated uncertainties.

Exploring the Possibility of Semi-Automated Quality Evaluation of Spatial Datasets in Spatial Data Infrastructure

Over the past decades, World Wide Web technology has developed greatly. One of the most important outcomes of this technology is to share data in a worldwide domain. A considerable amount of available data have spatial components and are hence called spatial data. The level of quality that spatial datasets conform plays an important role in their reliability for use in projects. This research aims to overview spatial data quality elements and select appropriate elements suitable for means of semi-automated quality evaluation. In this paper, the ISO 19100 series of standards for geographic information is used as basis for quality evaluation. The possibilities of use of different spatial data quality elements for semi-automated quality evaluation are explored and discussed. Finally, based on argumentation and reference to other research studies, a list of spatial data quality elements and sub-elements suitable for semi-automated quality evaluation of spatial datasets are presented.

Mapping provisioning ecosystem services at the local scale using data of varying spatial and temporal resolution

Spatial data on land use and land cover (LULC) are broadly available on different scales and are used widely for mapping ecosystem services as LULC and their changes impact on the provision of multiple ecosystem services. Here four spatial data sets were compared for their practicability as input data for the LULC based assessment method in the Bornhöved Lakes study area. The results for this 60km² study area are that more detailed land use information (ATKIS and a combined ATKIS/InVeKoS/Landsat data set) is preferred to CORINE land cover data due to the possibility of including spatial details (e.g. number of LULC classes and crop information) in the assessment of provisioning ecosystem services. The CORINE data set overestimated the supply of the two analyzed provisioning services crops and fodder in comparison to the combined data set which revealed information on the specific crops, making quantification with statistical information on yields easier. Spatial input data quality has an effect on the resulting provisioning service maps and quantifications of ecosystem services in the study area due to the identification/omission of ecosystem services, their extent and change. Consequently they also influence decision-making and the development of the ecosystem services concept in the future.

STUDY ON THE QUALITY CONTROL METHODS OF CLUSTER-BASED REMOTE SENSING IMAGE PROCESSING

Abstract. Along with the advances in technology, modern surveying technology developed rapidly, a series of high-tech equipments have been invited and developed. A lot of cluster-based Remote Sensing image processing systems, at home or abroad, have been produced and used in production, such as PixelFactory, PixelGrid, CIPS etc. Been popularized and used, processing technology and procedure have changed to be different from the old pattern, and traditional ways of quality control no longer meet the need of new technology. Above all, it is of vital importance for us to find new ways of procedure quality control for the geographical spatial data quality certification under new technology system. Based on the mainstream image fast processing systems – PixelFactory and PixelGrid, the nodes and methods of quality control during image processing will be studied in this paper, including image preprocessing, AT, DSM, DEM, ortho-rectification, mosaic etc. After that, the feasibility of quality control ways proposed will be verified by actual production project. All the advices can be used as references for large-scale application of new technology in information surveying system.

DATA QUALITY IN THE INTEGRATION AND ANALYSIS OF DATA FROM MULTIPLE SOURCES: SOME RESEARCH CHALLENGES

Abstract. This paper describes preliminary work to investigate what it means to manage data quality in a simple data integration and analysis prototype for research purposes, where input datasets are from a range of different sources. Consideration is given to how standard elements of spatial data quality (as in ISO 19115:2003) apply in the context of the prototype, which is based on a relatively straight forward "house hunting" scenario. Based on initial findings the paper aims to position further work, identifying a series of research questions around needs for improved data quality management and communication in analytical processes involving geographic information. While not providing solutions or raising novel issues it is hoped the paper may serve to add support for more applied and user focused data quality research in the area of analytics.

Visualizing perceived spatial data quality of 3D objects within virtual globes

Virtual globes (VGs) allow Internet users to view geographic data of heterogeneous quality created by other users. This article presents a new approach for collecting and visualizing information about the perceived quality of 3D data in VGs. It aims at improving users' awareness of the quality of 3D objects. Instead of relying on the existing metadata or on formal accuracy assessments that are often impossible in practice, we propose a crowd-sourced quality recommender system based on the five-star visualization method successful in other types of Web applications. Four alternative five-star visualizations were implemented in a Google Earth-based prototype and tested through a formal user evaluation. These tests helped identifying the most effective method for a 3D environment. Results indicate that while most websites use a visualization approach that shows a ‘number of stars’, this method was the least preferred by participants. Instead, participants ranked the ‘number within a star’ method highest as it allowed reducing the visual clutter in urban settings, suggesting that 3D environments such as VGs require different design approaches than 2D or non-geographic applications. Results also confirmed that expert and non-expert users in geographic data share similar preferences for the most and least preferred visualization methods.

Geospatial Applications in Disease Surveillance

Given the current attention on national health care reform and the electronic exchange of health information, this article highlights the major developments in disease surveillance and identifies a number of public health opportunities for geospatial thought leaders to undertake. Issues surrounding spatial data quality and resolution, the legal and ethical issues of volunteered geographical health information, and the technical demands of formulating a synthetic and integrative disease surveillance system represent the types of research questions that only geographers can address successfully.

Which spatial data quality can be meta-propagated?

As a mid-way between the usability criteria proposed by the upcoming ISO 19157 and error propagation methods, the concept of meta-propagation allows derivation of an estimate of the propagated uncertainties using only metadata information about the quality of the datasets and processes used in a scientific workflow. The principle of meta-propagation has been illustrated using the thematic accuracy with the quantitative attribute accuracy sub-element. This paper explores other data quality elements that can be meta-propagated. Using a similar approach as for thematic accuracy, we address further the metadata quality for the processes and their links with data quality. The paper focuses on generic principles and a few specific situations where appropriate quality measures can be fully described. Basic meta-propagation, based on separability of the assessments (i.e. one input to one output), is presented but the paper also discusses the potential of using quality measures for non-separable approaches. Spatiality of the measures, i.e. taking the benefit of a spatial data quality value being a map, is also investigated. Practical considerations between the burden of deriving needed quality metadata and the will to derive useful uncertainty assessments for large scientific workflows are also expressed.

InSpace3D: A Middleware for Built Environment Data Access and Analytics

Standardisation, archiving, and digital access of spatial data pertaining to built-up environments is an area acquiring increasing attention amongst several interest groups: policy makers, designers and planners, civil engineers, infrastructure management and public service personnel, building users. Initiatives such as the Building Information Model (BIM), Industry Founda- tion Classes (IFC), and CityGML are creating the information-theoretic backbone that guides the crucial aspects of quality, exchange, and interoperability of spatial data at the environmental and urban scale. However, due to the inherent scale, com- plexity, and detailed geometric character of building information data, extracting useful semantic and qualitative knowledge for accomplishing high-level analytical tasks is still an extremely complex and error prone process involving data intensive computing. We propose a uniform spatial data access middleware that can provide a combination of high-level, multi-modal, semantic, and quantitative-qualitative spatial data access and analytical capability. We present the core computational capabil- ities for the proposed middleware and present an overview of the high-level spatial model and its compliance with the industry standard IFC. A key theoretical contribution is a framework for investigating the computational complexity of deriving spatial artefacts within the context of building informatics. Additionally, we empirically investigate the feasibility and practicality of the derivation of spatial artefacts by conducting experiments on seven industry-scale IFC models. The experiment results show that, despite having non-linear polynomial increase with respect to time, deriving spatial artefacts is practical with large designs.

Prostovoljno množično zajemanje prostorskih podatkov

Crowdsourcing can be a starting-point for the process of spatial data-gathering in the field of land management, cadastral systems and other databases holding spatial data. Such voluntary supply of spatial data on the part of users might significantly improve the acquisition and especially the updating of spatial databases. The crowdsourcing process also has perceivable influence on the quality of formal spatial data. Therefore, this research domain is, together with information technology, a highly promising developing concept with respect to the effectiveness of such activities. Voluntary massive data acquisition, besides professional doubts, also brings several legal, organizational, economic and social concerns that should be resolved in parallel with rapid technological development. Thus far, international cooperation in the domain of voluntary massive spatial data supply does not yet exist to an appropriate extent. The COST ICT action IC1203 European Network Exploring Research into Geospatial Information Crowdsourcing: software and methodologies for harnessing geographic information from the crowd (ENERGIC) enables the connections and exchange of scientific methodology and technological sources in the field of massive data acquisition from informal sources.

Spatial data quality and beyond

Issues of accuracy, uncertainty, and spatial data quality have been on the top of most GIScience research agendas around the world from the late 1980s. Ever since then, growing research efforts have been directed toward uncertainty characterization in spatial information, analysis, and applications, aiming for better understanding of spatial uncertainty and thus improved methods and techniques for assessing and managing data quality. Impressive progress has been made in various issues concerning data quality. In addition, growing research on extensions to the conventional norms of data quality, such as the quality aspects of geospatial information services, has been observed. Chinese researchers have contributed to this great cause by keeping abreast with the developments abroad and striving for their own innovative work. This paper reviews the past research on data quality-related issues and provides a perspective on future developments. These will be seen not only in continued research on theoretical and technical issues concerning data quality, but also in developments of tools for quality assessment and decision-making under uncertainty through geospatial information processing and applications.