This is the first of a four-part series of papers which proposes a general framework for error analysis in measurement-based geographical information systems (MBGIS). The purpose of the series is to investigate the fundamental issues involved in measurement error (ME) analysis in MBGIS, and to...

Investigating long-range correlation by the Hurst exponent, H, is crucial in the study of time series. Recently, empirical-mode-decomposition-based arbitrary-order Hilbert spectral analysis (EMD-HSA) has been proposed to numerically obtain without proof a scaling relationship, generated from the...

The multifractal properties of daily rainfall time series at the stations in Pearl River basin of China over periods of up to 45 years are examined using the universal multifractal approach based on the multiplicative cascade model and the multifractal detrended fluctuation analysis (MF-DFA)....

Geographically weighted regression (GWR) is a way of exploring spatial nonstationarity by calibrating a multiple regression model which allows different relationships to exist at different points in space. Nevertheless, formal testing procedures for spatial nonstationarity have not been...

The present paper examines two neglected issues: fluctuation of flows and probability of occurrence, in entropy-based spatial interaction analysis. Variance of the estimated flow is derived to study its fluctuation. It is shown that unless a lower level of accuracy is acceptable, entropy...

The entropy-maximixing model has been applied with varying degrees of success in the analysis and planning of origin - destination types of spatial interaction. Although theoretical underpinnings and solution methods have been developed over the years, there are still outstanding problems that...

In recent years, there has been a growing interest in the use of local measures such as Anselin's LISAs and Ord and Getis <i>G </i>statistics to identify local patterns of spatial association. The statistical significance test based on local statistics is one of the most important aspects in performing...

Building a feedforward computational neural network model (CNN) involves two distinct tasks: determination of the network topology and weight estimation. The specification of a problem adequate network topology is a key issue and the primary focus of this contribution. Up to now, this issue has...

Building a feedforward computational neural network model (CNN) involves two distinct tasks: determination of the network topology and weight estimation. The specification of a problem adequate network topology is a key issue and the primary focus of this contribution. Up to now, this issue has...