Context-awareness in similarity measures and pattern discoveries of trajectories: a context-based dynamic time warping method

Analyzing the spatial behaviors of moving-point objects (MPOs) and discovering their movement patterns have been of great interest to the geographic information science community recently. These interests can be explored through analyzing similarities in the MPO trajectories. Because movements of objects take place in various contexts, their trajectories are also highly influenced by such contexts. Therefore, it is essential to fully understand the contexts and to realize how they can be incorporated into movement analysis. This article first proposes a taxonomy for contexts. Then, a modified version of dynamic time warping called context-based dynamic time warping (CDTW) is introduced, to contextually assess the multidimensional weighted similarities of trajectories. Ultimately, the results of similarity searches are utilized in discovering the relative movement patterns of the MPOs. To evaluate the performance and effectiveness of our proposed CDTW method, we run several experiments on real datasets that were obtained from commercial airplanes in a constrained Euclidean space, taking into account contextual information. Specifically, these experiments were conducted to explore the role of contexts and their interactions in similarity measures of trajectories. The results yielded the robustness of CDTW method in quantifying the commonalities of trajectories and discovering movement patterns with 80% accuracy. Moreover, the results revealed the importance of exploiting contextual information because it can enhance and restrict movements.     

Sensitivity analysis and accuracy assessment of the land transformation model using cellular automata

This study evaluates the effects of cellular automata (CA) with different neighborhood sizes on the predictive performance of the Land Transformation Model (LTM). Landsat images were used to extract urban footprints and the driving forces behind urban growth seen for the metropolitan areas of Tehran and Isfahan in Iran. LTM, which uses a back-propagation neural network, was applied to investigate the relationships between urban growth and the associated drivers, and to create the transition probability map. To simulate urban growth, the following two approaches were implemented: (a) the LTM using a top-down approach for cell allocation grounding on the highest values in the transition probability map and (b) a CA with varying spatial neighborhood sizes. The results show that using the LTM-CA approach increases the accuracy of the simulated land use maps when compared with the use of the LTM top-down approach. In particular, the LTM-CA with a 7 × 7 neighborhood size performed well and improved the accuracy. The level of agreement between simulated and actual urban growth increased from 58% to 61% for Tehran and from 39% to 43% for Isfahan. In conclusion, even though the LTM-CA outperforms the LTM with a top-down approach, more studies have to be carried out within other geographical settings to better evaluate the effect of CA on the allocation phase of the urban growth simulation.     

Climatology of diurnal tide and its long-term variability in the lower middle atmosphere over a tropical station

ECMWF reanalysis (ERA–interim) data of winds for two solar cycles (1991–2012) are harmonically analyzed to delineate the characteristics and variability of diurnal tide over a tropical site (13.5° N, 79.5° E). The diurnal cycle horizontal winds measured by Gadanki (13.5° N, 79.2° E) mesosphere–stratosphere–troposphere (MST) radar between May 2005 and April 2006 have been used to compute 24 h tidal amplitudes and phases and compared with the corresponding results obtained from ERA winds. The climatological diurnal tidal amplitudes and phases have been estimated from surface to ～33 km using ERA interim data. The amplitudes and phases obtained in the present study are found to compare reasonably well with Global Scale Wave Model (GSWM–09). Diurnal tides show larger amplitudes in the lower troposphere below 5 km during summer and in the mid-stratosphere mainly during equinoctial months and early winter. Water vapor and convection in the lower troposphere are observed to play major roles in exciting 24-h tide. Correlations between diurnal amplitude and integrated water vapor and between diurnal amplitude and outgoing longwave radiation (OLR) are 0.59 and ?0.34, respectively. Ozone mixing ratio correlates (ρ?=?0.66) well with diurnal amplitude and shows annual variation in the troposphere whereas semi-annual variation is observed at stratospheric heights with stronger peaks in equinoctial months. A clear annual variation of diurnal amplitude is displayed in the troposphere and interannual variability becomes prominent in the stratosphere which could be partly due to the influence of equatorial stratospheric QBO. The influence of solar activity on diurnal oscillations is found to be insignificant.     

Quantifying the uncertainty in the spatial layout of rock type domains in an iron ore deposit

This work addresses the problem of delineating the spatial layout of ten rock type domains in an iron ore body and of assessing the uncertainty in the domain boundaries. A stochastic approach is proposed to this end, based on truncated Gaussian simulation, which consists in defining successive partitions of the space that comply with the desired spatial continuity and contact relationships between rock type domains. The sequencing of these domains is driven by their position (surficial vs. underlying rocks), granulometry (compact vs. friable rocks), and grades (rich vs. poor) of iron, alumina, manganese and loss on ignition. A total of 100 realizations are produced, conditioned to available drill hole data, and used to quantify the uncertainty in the occurrence of each rock type domain, at both global and local scales.     

Effect of Fines Type and Content of Sand on Correlation Between Shear Wave Velocity and Liquefaction Resistance

The use of shear wave velocity (V _s) measurements as an in situ test for evaluation of liquefaction potential has increased substantially due to its advantages. Relatively large numbers of studies have been performed to establish the correlation between V _s and liquefaction resistance (CRR) of clean sands. Usually, natural sands contain silt and/or clay, and previous studies have shown that both the amount of fines and their nature influence the values of CRR as well as V _s. Therefore, the CRR–V _s correlations may also be affected by fines content and type of sandy soils. However, effect of fines content and especially fines type of sandy soils on the correlation between V _s and CRR is inadequately addressed in the literature. In this study, cyclic triaxial and bender element tests were conducted on samples of sand containing various amounts of different types of fines, and the effects of fines on the values of CRR and V _s are investigated. The results show that G ₀ and CRR reduce even when small amounts of fines are added to sand. Therefore, use of plasticity index (PI) of the fines fraction is better than the PI of the overall soil when trying to assess the effects of fines. Using obtained experimental data as well as the established semiempirical CRR–V _s relationship, the CRR–V _s correlation was developed for all the tested soils, and the effect of fines type on the correlation is also examined. Based on the results obtained in this study, CRR–V _s correlation is affected by both the amount and the plasticity of the fines present in the sand, and this correlation is soil specific.     

Semi Automatic Road Extraction by Fusion of High Resolution Optical and Radar Images

Two new methods for fusion of high-resolution optical and radar satellite images have been proposed to extract roads in high quality in this paper. Two fusion methods, including neural network and knowledge-based fusion are introduced. The first proposed method consists of two stages: (i) separate road detection using each dataset and (ii) fusion of the results obtained using a neural network. In this method, the neural networks are separately applied on high-resolution IKONOS and TerraSAR-X images for road detection, using a variety of texture parameters. The outputs of two neural networks, as well as the spectral features of optical image, are used in a third neural network as inputs. The second method is a knowledge-based fusion using thresholds of narrow roads and vegetation gray levels. First roads are extracted from each source separately. The outputs are then compared and advantages and disadvantages of each data source are investigated . The results obtained from accuracy assessment show the efficiency of the proposed methods. Furthermore, the comparison of the results showed the superiority of the first algorithm.