Approaches to Uncertainty in Spatial Data

ثبت نشده
چکیده

Geographical information systems (GIS) are designed to handle large amounts of information about the natural and built environments. Any such large collection of observational information is prone to uncertainty in a number of forms. If that uncertainty is ignored there may be anything from slightly incorrect predictions or advice, to analyses that are completely logical, but fatally flawed. In either case, future trust in the work of the system or the operator can be undermined. It is therefore of crucial importance to all users of GIS that awareness of uncertainty should be as widespread as possible. Fundamental to that understanding is the nature of the uncertainty – the subject of this chapter.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

Application of truncated gaussian simulation to ore-waste boundary modeling of Golgohar iron deposit

Truncated Gaussian Simulation (TGS) is a well-known method to generate realizations of the ore domains located in a spatial sequence. In geostatistical framework geological domains are normally utilized for stationary assumption. The ability to measure the uncertainty in the exact locations of the boundaries among different geological units is a common challenge for practitioners. As a simple a...

متن کامل

Assessment of uncertainty for coal quality-tonnage curves through minimum spatial cross-correlation simulation

Coal quality-tonnage curves are helpful tools in optimum mine planning and can be estimated using geostatistical simulation methods. In the presence of spatially cross-correlated variables, traditional co-simulation methods are impractical and time consuming. This paper investigates a factor simulation approach based on minimization of spatial cross-correlations with the objective of modeling s...

متن کامل

 The Quantification of Uncertainties in Production Prediction Using Integrated Statistical and Neural Network Approaches: An Iranian Gas Field Case Study

Uncertainty in production prediction has been subject to numerous investigations. Geological and reservoir engineering data comprise a huge number of data entries to the simulation models. Thus, uncertainty of these data can largely affect the reliability of the simulation model. Due to these reasons, it is worthy to present the desired quantity with a probability distribution instead of a sing...

متن کامل

Proposing a Robust Model of Interval Data Envelopment Analysis to Performance Measurement under Double Uncertainty Situations

It is very necessary to consider the uncertainty in the data and how to deal with it when performance measurement using data envelopment analysis. Because a little deviation in the data can lead to a significant change in the performance results. However, in the real world and in many cases, the data is uncertain. Interval data envelopment analysis is one of the most widely used approaches to d...

متن کامل

Spatial Regression in the Presence of Misaligned data

In this paper, four approaches are presented to the problem of fitting a linear regression model in the presence of spatially misaligned data. These approaches are plug-in method‎, ‎simulation‎, ‎regression calibration and maximum likelihood‎. In the first two approaches‎, ‎with modeling the correlation between the explanatory variable, prediction of explanatory variable is determined at sites...

متن کامل

Modeling spatial distribution of Tehran air pollutants using geostatistical methods incorporate uncertainty maps

The estimation of pollution fields, especially in densely populated areas, is an important application in the field of environmental science due to the significant effects of air pollution on public health. In this paper, we investigate the spatial distribution of three air pollutants in Tehran’s atmosphere: carbon monoxide (CO), nitrogen dioxide (NO2), and atmospheric particulate matters less ...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2013