Communicating uncertainty in spatial databases

As applications of spatial databases become more sophisticated and diverse, an issue of growing concern is the inability to communicate to users the uncertainty of products derived from their systems. This is important not only because users should be able to compare the quality of database outputs against the quality requirements of their tasks (in order to determine product suitability), but also to protect the integrity of past, present, and future decisions that may be made through the use of such information. While there has already been considerable research undertaken to develop models of spatial data error and uncertainty, there is an additional requirement for the results of these models to be effectively conveyed to users. This paper reviews advances now being made in communicating the uncertainty of spatial databases which may soon assist users in overcoming this critical barrier.     

An effective approach for gap-filling continental scale remotely sensed time-series

The archives of imagery and modeled data products derived from remote sensing programs with high temporal resolution provide powerful resources for characterizing inter- and intra-annual environmental dynamics. The impressive depth of available time-series from such missions (e.g., MODIS and AVHRR) affords new opportunities for improving data usability by leveraging spatial and temporal information inherent to longitudinal geospatial datasets. In this research we develop an approach for filling gaps in imagery time-series that result primarily from cloud cover, which is particularly problematic in forested equatorial regions. Our approach consists of two, complementary gap-filling algorithms and a variety of run-time options that allow users to balance competing demands of model accuracy and processing time. We applied the gap-filling methodology to MODIS Enhanced Vegetation Index (EVI) and daytime and nighttime Land Surface Temperature (LST) datasets for the African continent for 2000–2012, with a 1 km spatial resolution, and an 8-day temporal resolution. We validated the method by introducing and filling artificial gaps, and then comparing the original data with model predictions. Our approach achieved R² values above 0.87 even for pixels within 500 km wide introduced gaps. Furthermore, the structure of our approach allows estimation of the error associated with each gap-filled pixel based on the distance to the non-gap pixels used to model its fill value, thus providing a mechanism for including uncertainty associated with the gap-filling process in downstream applications of the resulting datasets.     

空间数据处理模型误差和不确定性分析

在GIS应用中,涉及到大量的模型应用,这些模型包括了利用GIS进行空间信息处理的大部分阶段中所用到的模型。模型处理以及分析结果往往是进行下一步应用的基础,因此模型处理结果的误差和不确定性制约了实际的GIS应用。影响空间数据处理模型的误差和不确定性的因素主要包括:定位和特征信息,制图,空间分析,空间数据库以及空间数据处理模型等所具有的误差和不确定性。主要分析了空间数据处理模型误差和不确定性的表达、来源以及分析方法。     

空间数据处理模型误差和不确定性分析

在GIS应用中,涉及到大量的模型应用,这些模型包括了利用GIS进行空间信息处理的大部分阶段中所用到的模型.模型处理以及分析结果往往是进行下一步应用的基础,因此模型处理结果的误差和不确定性制约了实际的GIS应用.影响空间数据处理模型的误差和不确定性的因素主要包括:定位和特征信息,制图,空间分析,空间数据库以及空间数据处理模型等所具有的误差和不确定性.主要分析了空间数据处理模型误差和不确定性的表达、来源以及分析方法.     

时空数据质量与模型

时空数据不确定性与数据质量是地理信息科学的基础理论之一,也是GIS应用发展的重要问题。本文作为ISPRS2008大会专题“时空数据质量与模型”导读,从空间实体的位置不确定性、数字高程模型的位置不确定性、遥感数据的不确定性、属性数据的不确定性与质量控制、空间分析与操作的不确定性传播和空间数据采集的不确定性与处理等方面,分析了时空数据不确定性与数据质量的最近发展。     

Building Information Modelling for High‐rise Land Administration

Current land administration systems mainly use 2D plans to define and secure ownership rights associated with properties in high‐rise buildings. These 2D plans may not effectively communicate and manage the spatial complexity associated with multi‐layered and stacked properties in such buildings; additionally, multiple pages of plans (representing sections of the building) are required to represent all ownership boundaries. In response, land administration organizations have been investigating a 3D digital approach to managing information about ownership rights in high‐rise building structures. In this article, Building Information Modeling (BIM) is proposed as a feasible approach for managing land and property information in high‐rise buildings. BIM provides a collaborative, digital and intelligent 3D data environment for managing building information throughout the lifecycle of buildings. However, there is currently no capacity in BIM for recording and representing information about ownership and boundaries of properties, which is core land administration information. Therefore, this article proposes an extension to the BIM standard, which is implemented in a prototype BIM model of a complex building to showcase the potential capability of using BIM for high‐rise land administration and for modeling 3D ownership rights.     

遥感信息不确定性研究

近年来遥感技术及遥感信息产业化发展迅猛 ,但遥感信息的不确定性制约着遥感信息的产品化和实用化的进一步发展。虽然 ,这一问题得到了国内外众多学者的关注 ,并提出和采用相关理论和方法进行分析 ,取得了相当的进展 ,但这些方法在分析遥感信息不确定性时忽略了一个重要的研究点 :遥感信息的不确定性传递机理。本文主要目标就是建立一套遥感信息不确定性的处理方法     

A Review and Evaluation of Uncertainty Classification and the Error‐Band Geometry Model

Advances in computer technologies have improved the quality of maps, making map comparison and analysis easier, but uncertainty and error still exist in GIS when overlaying geographic data with multiple or unknown confidence levels. The goals of this research are to review current geospatial uncertainty literature, present the Error‐Band Geometry Model (EBGM) for classifying the size and shape of spatial confidence intervals for vector GIS data, and to analyze the interpretability of the model by looking at how people use metadata to classify the uncertainty of geographic objects. The results from this research are positive and provide important insight into how people interpret maps and geographic data. They suggest that uncertainty is more easily interpreted for well defined point data and GPS data. When data is poorly defined, people are unable to determine an approach to model uncertainty and generate error‐bands. There is potential for using the EBGM to aid in the development of a GIS tool that can help individuals parameterize and model spatial confidence intervals, but more research is needed to refine the process by which people use the decision tree. A series of guiding questions or an “uncertainty wizard” tool that helps one select an uncertainty modeling approach might improve the way people apply this model to real‐world applications.     

A Conceptual Framework for Uncertainty Investigation in Map‐based Land Cover Change Modelling

Uncertainty research represents a research stream of high interest within the community of geographical information science. Its elements, terminology and typology are still under strong discussion and adopted methods for analysis are currently under intensive development. This paper presents a conceptual framework for systematic investigation of uncertainty which occurs in applications of land cover change modelling in Geographical Information Systems (GIS) based on historical map data. Historical, in this context, means the map is old enough to allow identification of changes in landscape elements of interest, such as vegetation. To date such analyses are rarely conducted or not satisfactorily carried out, despite the fact that historical map data represent a potentially rich information source. The general validity and practicability of the framework for related applications is demonstrated with reference to one example in which forest cover change in Switzerland is investigated. The conceptual model consists of three domains in which main potential sources of uncertainty are systematically exposed. Existing links between data quality research and uncertainty are investigated to access the complex nature of uncertainty and to characterise the most suitable concepts for analysis. In accordance with these concepts appropriate methods and procedures are suggested to assess uncertainty in each domain. One domain is the production‐oriented amount of uncertainty which is inherent in the historical map. Vagueness and ambiguity represent suitable concepts for analysis. Transformation‐oriented uncertainty as the second domain occurs owing to editing and processing of digital data. Thereby, the suitable concept of uncertainty is error. The third domain is the application‐oriented uncertainty which occurs in comparing semantically different data. This domain relates to multi‐temporal discord which assumes the assessment of ‘equi‐temporal’ ambiguity and is thus connected to the production‐oriented domain. The framework provides an estimation of the overall amount of uncertainty. This can be linked to subsequent assessment of ‘fitness for use’. Thus the model provides a practicable and systematic approach to access the complex nature of uncertainty in the scope of land cover change modelling.     

A Framework for Modeling Uncertainty in Spatial Databases

Geographic Information Systems and spatial databases are inherently suited for fuzziness, because of the uncertainty inherent in the assimilation, storage, and representation of spatial data. These objects may also have naturally occurring imprecise boundaries. It is difficult to store and represent these objects while continuing to demonstrate the uncertainty inherent in the objects. This paper describes a fuzzy object–oriented framework to model spatial objects with either precise or uncertain boundaries that will also provide for fuzzy querying of these objects. A prototype system, FOOSBALL, which implements this framework is also discussed.     

The Certainty of Uncertainty: GIS and the Limits of Geographic Knowledge

Considerable effort has been devoted over the years to fighting uncertainty in geographic information in its different manifestations. Thus far, research on handling inaccuracy, fuzziness, error and related issues has focused for the most part on problems with spatial data and their direct products, typically representations of spatial objects or fields. This paper seeks to broaden the discussion of uncertainty in the geospatial domain by shifting the focus from information to knowledge. It turns out that there is a surprising number of things that we cannot know (or questions we cannot answer) that are not the result of imperfect information. Forms of not knowing are pervasive in domains as diverse as mathematics, logic, physics, and linguistics, and are apparently irreducible. This being the case it may help to explore how these realms of ignorance may affect our efforts. The paper distinguishes three different modes or forms of geospatial knowledge production, and argues that each of them has built–in imperfections, for reasons of logical principle and not just empirical fact. While much can and needs to be done to manage and resolve uncertainties where possible, I argue for accepting that uncertainty is an intrinsic property of complex knowledge and not just a flaw that needs to be excised.     

Sonifying data uncertainty with sound dimensions

ABSTRACT

The communication of data uncertainty is a crucial problem in data science, information visualization, and geographic information science (GIScience). Effective ways to communicate the uncertainty of data enables data consumers to interpret the data as intended by the producer, reducing the possibilities of misinterpretation. In this article, we report on an empirical investigation of how sound can be used to convey information about data uncertainty in an intuitive way. To answer the research question How intuitive are sound dimensions to communicate uncertainty? we carry out a cognitive experiment, where participants were asked to interpret the certainty/uncertainty level in two sounds A and B (N = 33). We produce sound stimuli by varying sound dimensions, including loudness, duration, location, pitch, register, attack, decay, rate of change, noise, timbre, clarity, order, and harmony. In the stimuli, both synthetic and natural sounds are used to allow comparison. The experiment results identify three sound dimensions (loudness, order, and clarity) as significantly more intuitive to communicate uncertainty, providing guidelines for sonification and information visualization practitioners.     

GIS and people with visual impairments or blindness: Exploring the potential for education,orientation, and navigation

GIS, with their predominantly visual communication of spatial information, may appear to have little to offer people with visual impairments or blindness. However, because GIS store and manage the spatial relations between objects, alternative, non-visual ways to communicate this information can be utilized. As such, modified GIS could provide people with visual impairments access to detailed spatial information that would aid spatial learning, orientation, and spatial choice and decision making. In this paper, we explore the ways that GIS have been, and might be, adapted for use by people with visual impairments or blindness. We review current developments, report upon a small experimental study that compares the ability of GIS-based and various adaptive technologies to communicate spatial information using non-visual media, and provide an agenda for future research. We argue that adapted GIS hold much promise for implicitly improving the quality of life for visually impaired people by increasing mobility and independence.     

Revealing Uncertainties in Land Change Modeling Using Probabilities

Land change models are frequently used to analyze current land change processes and possible future developments. However, the outcome of such models is accompanied by uncertainties that have to be taken into account in order to address their reliability for science and decision‐making. While a range of approaches exist that quantify the disagreement of land change maps, the quantification of uncertainty remains a major challenge. The aim of this article is therefore to reveal uncertainties in land change modeling by developing two measures: quantity uncertainty and allocation uncertainty. We choose a Bayesian Belief Network modeling approach for deforestation in Brazil to develop and apply the two measures to the resulting probability surface. Quantity uncertainty describes the uncertainty about the correct number of cells in a land change map assigned to different land change categories and allocation uncertainty expresses the uncertainty about the correct spatial placement of a cell in the land change map. Thus, uncertainty can be quantified even in those cases where no reference data exist. Informing about uncertainty in probabilistic outcomes may be an important asset when land change projections are being used in science and decision‐making and moreover, they may also be further evaluated for other spatial applications.     

Fuzzy Set Approaches to Spatial Data Mining of Association Rules

This paper presents an approach to the discovery of association rules for fuzzy spatial data. Association rules provide information of value in assessing significant correlations that can be found in large databases. Here we are interested in correlations of spatially related data such as soil types, directional or geometric relationships, etc. We have combined and extended techniques developed in both spatial and fuzzy data mining in order to deal with the uncertainty found in typical spatial data.     

Inference and analysis across spatial supports in the big data era: Uncertain point observations and geographic contexts

The ways in which geographic information are produced have expanded rapidly over recent decades. These advances have provided new opportunities for geographical information science and spatial analysis—allowing the tools and theories to be expanded to new domain areas and providing the impetus for theory and methodological development. In this light, old problems of inference and analysis are rediscovered and need to be reinterpreted, and new ones are made apparent. This article describes a new typology of geographical analysis problems that relates to uncertainties in the relationship between individual‐level data, represented as point features, and the geographic context(s) that they are associated with. We describe how uncertainty in context linkage (uncertain geographic context problem) is also related to, but distinct from, uncertainty in point‐event locations (uncertain point observation problem) and how these issues can impact spatial analysis. A case study analysis of a geosocial dataset demonstrates how alternative conclusions can result from failure to account for these sources of uncertainty. Sources of point observation uncertainties common in many forms of user‐generated and big spatial data are outlined and methods for dealing with them are reviewed and discussed.     

遥感数据模糊不确定性来源及其处理方法的探讨

近年来遥感信息的应用越加广泛,数据不确定性表现也较明显。本文主要是对遥感数据的质量也就要作进一步的分析:遥感信息本身固有的不确定性,遥感数据的分析与处理过程中所引入的不确定性。在总结不确定性存在原因的基础之上,对不确定性的处理方法作进一步的归纳。     

Distribution of Errors in a Classified Map of Satellite Data

Abstract

The output from any spatial data processing method may contain some uncertainty. With the increasing use of satellite data products as a source of data for Geographical Information Systems (GIS), there have been some major concerns about the accuracy of the satellite‐based information. Due to the nature of spatial data and remotely sensed data acquisition technology, and conventional classification, any single classified image can contain a number of mis‐classified pixels. Conventional accuracy evaluation procedures can report only the number of pixels that are mis‐classified based on some sampling observation. This study investigates the spatial distribution and the amount of these pixels associated with each cover type in a product of satellite data. The study uses Thematic Mapper (TM) and SPOT multispectral data sets obtained for a study area selected in North East New South Wales, Australia. The Fuzzy c‐Means algorithm is used to identify the classified pixels that contained some uncertainty. The approach is based on evaluating the strength of class membership of pixels. This study is important as it can give an indication of the amount of error resulting from the mis‐classification of pixels of specific cover types as well as the spatial distribution of such pixels. The results show that the spatial distribution of erroneously classified pixels are not random and varies depending on the nature of cover types. The proportions of such pixels are higher in spectrally less clearly defined cover types such as grasslands.     

遥感信息处理不确定性的可视化表达

如何全面、准确地度量和可视化表达遥感信息处理中不确定性的程度和空间分布方式,是遥感信息不确定性研究的关键问题之一.传统的度量方法(例如误差矩阵)是将以训练样本集为基础的度量作为总分类精度的度量,而我们需要估计模型对于"样本外数据"的性能.本文首先利用信息论和粗糙集理论等度量遥感分类影像属性信息的不确定性,提出基于像元、目标和影像的遥感信息不确定性度量指标;然后分别描述了基于不同度量指标的可视化表达方式,并对我国黄河三角洲地区的Landsat TM影像进行了分类信息不确定性度量和可视化表达实验.