Spatial Data Uncertainty

2018 ◽  
pp. 313-340 ◽  
Author(s):  
Linna Li ◽  
Hyowon Ban ◽  
Suzanne P. Wechsler ◽  
Bo Xu
Keyword(s):  
Spatial Data ◽  
Data Uncertainty

A total entropy model of spatial data uncertainty

2006 ◽  
Vol 32 (4) ◽  
pp. 316-323 ◽  
Author(s):  
Y. F. Shi ◽  
F. X. Jin ◽  
M. Y. Li
Keyword(s):  
Spatial Data ◽  
Data Uncertainty ◽  
Total Entropy ◽  
Entropy Model

scholarly journals Impact of spatial data uncertainty in debris flow susceptibility analysis

2021 ◽  
Author(s):  
Laurie Jayne Kurilla ◽  
Giandomenico Fubelli
Keyword(s):  
Debris Flow ◽  
Maximum Entropy ◽  
Spatial Data ◽  
Data Uncertainty ◽  
Susceptibility Map ◽  
Contributing Factors ◽  
European Continent ◽  
Value Differences ◽  
The Impact ◽  
Debris Flow Susceptibility

Abstract. In a study of debris flow susceptibility on the European continent, an analysis of the impact between known location and a location accuracy offset for 99 debris flows, demonstrates the impact of uncertainty in defining appropriate predisposing factors, and consequent analysis for areas of susceptibility. The dominant predisposing environmental factors, as determined through Maximum Entropy modeling, are presented, and analyzed with respect to the values found at debris flow event points versus a buffered distance of locational uncertainty around each point. Five Maximum Entropy susceptibility models are developed utilizing the original debris flow inventory of points, randomly generated points, and two models utilizing a subset of points with an uncertainty of 5 km, 1 km, and a model utilizing only points with a known location of “exact”. The AUCs are 0.891, 0.893, 0.896, 0.921, and 0.93, respectively. The “exact” model, with the highest AUC, is ignored in final analyses due to the small number of points, and localized distribution, and hence susceptibility results likely non-representational of the continent. Each model is analyzed with respect to the AUC, highest contributing factors, factor classes, susceptibility impact, and comparisons of the susceptibility distributions and susceptibility value differences. Based on model comparisons, geographic extent and context of this study, the models utilizing points with a location uncertainty of less than or equal to 5 km best represent debris flow susceptibility of the continent of Europe. A novel representation of the uncertainty is expressed, and included in a final susceptibility map, as an overlay of standard deviation and mean of susceptibility values for the two best models, providing additional insight for subsequent action.

scholarly journals SPATIAL DATA UNCERTAINTY IN A WEBGIS TOOL SUPPORTING SEDIMENTS MANAGEMENT IN WALLONIA

2015 ◽  
Vol XL-3/W3 ◽  
pp. 625-628
Author(s):  
N. R. Stéphenne ◽  
B. Beaumont ◽  
M. Veschkens ◽  
S. Palm ◽  
C. Charlemagne
Keyword(s):  
Spatial Data ◽  
Web Application ◽  
Data Uncertainty ◽  
Special Focus ◽  
Central Administration ◽  
Communication Tool ◽  
Map Scale ◽  
Search Quality ◽  
Potential Pollution

This paper describes a WebGIS prototype developed for the Walloon administration to improve the communication and the management of sediments dredging actions carried out in rivers and lakes. In Wallonia, levelling dredged sediments on banks requires an official authorization from the administration. This request refers to geospatial datasets such as the official land use map, the cadastral map or the distance to potential pollution sources. Centralising geodatabases within a web interface facilitate the management of these authorizations for the managers and the central administration. The proposed system integrates various data from disparate sources. Some issues in map scale, spatial search quality and cartographic visualisation are discussed in this paper with the solutions provided. The prototype web application is currently discussed with some potential users in order to understand in which way this tool facilitate the communication, the management and the quality of the authorisation process. The structure of the paper states the why, what, who and how of this communication tool with a special focus on errors and uncertainties.

Spatial Data Uncertainty

2017 ◽  
Vol 2017 (Q4) ◽  
Author(s):  
Linna Li
Keyword(s):  
Spatial Data ◽  
Data Uncertainty

scholarly journals Deeper Spatial Statistical Insights into Small Geographic Area Data Uncertainty

2020 ◽  
Vol 18 (1) ◽  
pp. 231
Author(s):  
Daniel A. Griffith ◽  
Yongwan Chun ◽  
Monghyeon Lee
Keyword(s):  
Big Data ◽  
Spatial Autocorrelation ◽  
Spatial Data ◽  
Statistical Significance ◽  
Geographic Area ◽  
Data Uncertainty ◽  
Small Areas ◽  
Age Cohorts ◽  
Cancer Data ◽  
Post Stratification

Small areas refer to small geographic areas, a more literal meaning of the phrase, as well as small domains (e.g., small sub-populations), a more figurative meaning of the phrase. With post-stratification, even with big data, either case can encounter the problem of small local sample sizes, which tend to inflate local uncertainty and undermine otherwise sound statistical analyses. This condition is the opposite of that afflicting statistical significance in the context of big data. These two definitions can also occur jointly, such as during the standardization of data: small geographic units may contain small populations, which in turn have small counts in various age cohorts. Accordingly, big spatial data can become not-so-big spatial data after post-stratification by geography and, for example, by age cohorts. This situation can be ameliorated to some degree by the large volume of and high velocity of big spatial data. However, the variety of any big spatial data may well exacerbate this situation, compromising veracity in terms of bias, noise, and abnormalities in these data. The purpose of this paper is to establish deeper insights into big spatial data with regard to their uncertainty through one of the hallmarks of georeferenced data, namely spatial autocorrelation, coupled with small geographic areas. Impacts of interest concern the nature, degree, and mixture of spatial autocorrelation. The cancer data employed (from Florida for 2001–2010) represent a data category that is beginning to enter the realm of big spatial data; its volume, velocity, and variety are increasing through the widespread use of digital medical records.

Towards an Ontologically-driven GIS to Characterize Spatial Data Uncertainty

2006 ◽  
pp. 465-476 ◽  
Author(s):  
Ashton Shortridge ◽  
Joseph Messina ◽  
Sarah Hession ◽  
Yasuyo Makido
Keyword(s):  
Spatial Data ◽  
Data Uncertainty

scholarly journals Visualisation of Spatial Data Uncertainty. A Case Study of a Database of Topographic Objects

2019 ◽  
Vol 9 (1) ◽  
pp. 16 ◽  
Author(s):  
Marek Ślusarski ◽  
Magdalena Jurkiewicz
Keyword(s):  
Spatial Data ◽  
Local Governments ◽  
Spatial Databases ◽  
Spatial Location ◽  
User Preference ◽  
Data Uncertainty ◽  
Topographic Map ◽  
Positional Uncertainty ◽  
Starting Point ◽  
Map Symbol

The Database of Topographic Objects (DTO) is the official database of Poland for collecting and providing spatial data with the detail level of a topographic map. Polish national DTOs manage information about the spatial location and attribute values of geographic objects. Data in the DTO are the starting point for geographic information systems (GISs) for various central and local governments as well as private institutions. Every set of spatial data based on measurement-derived data is susceptible to uncertainty. Therefore, the widespread awareness of data uncertainty is of vital importance to all GIS users. Cartographic visualisation techniques are an effective approach to informing spatial dataset users about the uncertainty of the data. The objective of the research was to define a set of methods for visualising the DTO data uncertainty using expert know-how and experience. This set contains visualisation techniques for presenting three types of uncertainty: positional, attribute, and temporal. The positional uncertainty for point objects was presented using visual variables, object fill with hue colour and lightness, and glyphs placed at map symbol positions. The positional uncertainty for linear objects was presented using linear object contours made of dotted lines and glyphs at vertices. Fill grain density and contour crispness were employed to represent the positional uncertainty for surface objects. The attribute value uncertainty and the temporal uncertainty were represented using fill grain density and fill colour value. The proposed set of the DTO uncertainty visualisation methods provides a finite array of visualisation techniques that can be tested and juxtaposed. The visualisation methods were comprehensively evaluated in a survey among experts who use spatial databases. Results of user preference analysis have demonstrated that the set of the DTO data uncertainty visualisation techniques may be applied to the full extent. The future implementation of the proposed visualisation methods in GIS databases will help data users interpret values correctly.

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