Classification of Changing Regions Based on Temporal Context in Local Spatial Association

2006 ◽  
pp. 17-28
Author(s):  
Jae-Seong Ahn ◽  
Yang-Won Lee ◽  
Key-Ho Park
Keyword(s):  
Spatial Association ◽  
Temporal Context

Classification of changing regions using a temporal signature of local spatial association

2009 ◽  
Vol 36 (5) ◽  
pp. 854-864 ◽  
Author(s):  
Jae-Seong Ahn ◽  
Hwahwan Kim ◽  
Yang-Won Lee
Keyword(s):  
Spatial Association

Kimberlite and kimberlitic intrusives of southeastern Australia

1980 ◽  
Vol 43 (330) ◽  
pp. 727-731 ◽  
Author(s):  
John Ferguson
Keyword(s):  
Partial Melting ◽  
Late Jurassic ◽  
Incompatible Element ◽  
Spatial Association ◽  
South Eastern ◽  
Southeastern Australia ◽  
Eastern Australia ◽  
Kimberlite Magma ◽  
Element Contents

SummaryFifteen widely separated occurrences of kimberlite and kimberlitic rocks are now known in south-eastern Australia. Those that have been satisfactorily dated isotopically give ages ranging from Permian to Late Jurassic. One occurrence exhibits an intimate spatial association with carbonatite. The classification of these rocks as ‘kimberlitic’ is partly based on their mode of emplacement, and particularly on the presence of crust/mantle inclusions. Compared with African kimberlitic magmas, the southeastern Australian examples have lower incompatible-element contents. These differences are interpreted as representing slightly greater degrees of partial melting of a four-phase Iherzolite assemblage at shallower depths (∼ 65 km) than typical African kimberlite magma.

Classification of High Resolution Sar Imagery Using Local Indicators of Spatial Association

2013 ◽  
Vol 333-335 ◽  
pp. 1080-1084
Author(s):  
Zhang Fei ◽  
Ye Xi
Keyword(s):  
Support Vector Machines ◽  
High Resolution ◽  
Classification Accuracy ◽  
Spatial Association ◽  
Support Vector ◽  
Svm Classifier ◽  
Vector Machines ◽  
Good Classification ◽  
Sar Imagery

In this paper, we will propose a novel classification method of high-resolution SAR using local autocorrelation and Support Vector Machines (SVM) classifier. The commonly applied spatial autocorrelation indexes, called Moran's Index; Geary's Index, Getis's Index, will be used to depict the feature of the land-cover. Then, the SVM based on these indexes will be applied as the high-resolution SAR classifier. A Cosmo-SkyMed scene in ChengDu city, China is used for our experiment. It is shown that the method proposed can lead to good classification accuracy.

scholarly journals Mapping the Changes in Urban Greenness Based on Localized Spatial Association Analysis under Temporal Context Using MODIS Data

2018 ◽  
Vol 7 (10) ◽  
pp. 407 ◽  
Author(s):  
Zongyao Sha ◽  
Yahya Ali ◽  
Yuwei Wang ◽  
Jiangping Chen ◽  
Xicheng Tan ◽  
...  
Keyword(s):  
Urban Planning ◽  
Association Analysis ◽  
Vegetation Index ◽  
Spatial Association ◽  
Urban Ecosystem ◽  
Spatiotemporal Patterns ◽  
Temporal Context ◽  
Promising Tool ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
The Impact

Vegetation plays an irreplaceable role for urban ecosystem services. Urban greenness represents all vegetation cover in and around cities. Understanding spatiotemporal patterns of the changes in urban greenness (CUG) provides fundamental clues for urban planning. The impact on CUG can be roughly categorized as being climate-induced and human-induced. Methods for mapping human-induced CUG (H-CUG) are rare. In this paper, a new framework, known as Localized Spatial Association Analysis under Temporal Context (LSAA-TC), was proposed to explore H-CUG. Localized spatial association analysis (LSAA) was performed first to extract local spatial outliers (LSOs), or locations that differ significantly in urban greenness from those located in the neighborhood. LSOs were then analyzed under the temporal context to map their intertemporal variations known as spatiotemporal outliers. We applied LSAA-TC to mapping H-CUG in the Wuhan Metropolitan Area, China during 2000–2015 using the vegetation index from Moderate-resolution Imaging Spectroradiometer (MODIS) 13Q1 as the proxy for urban greenness. The computed H-CUG demonstrated apparent spatiotemporal patterns. The result is consistent with the fact that the traditional downtown area presents the lowest H-CUG, while it is found that the peripheral area in the circular belt within 14–20 km from the urban center demonstrates the most significant H-CUG. We conclude that LSAA-TC can be a widely applicable framework to understand H-CUG patterns and is a promising tool for informative urban planning.

The Way to Analysis of Spatial Association

2013 ◽  
Vol 380-384 ◽  
pp. 2379-2382
Author(s):  
Jing Guo ◽  
De Sheng Liu ◽  
Qin Guo
Keyword(s):  
Association Analysis ◽  
Spatial Data ◽  
Spatial Association ◽  
Link Layer ◽  
The Way

Following the continual development and maturation of the technology of association analysis, it can be used to mine the relation between spatial data. The paper introduced definition and classification of association analysis technology. It analyses the spatial Association from physics layer, link layer and act layer. Based on above, it provides the process of association analysis about spatial data.

scholarly journals Arithmetic operation signs elicit spatial associations: A confirmatory Bayesian analysis

2021 ◽  
Author(s):  
Keelyn Brennan ◽  
Michaela Rutledge ◽  
Thomas J. Faulkenberry
Keyword(s):  
Bayesian Analysis ◽  
Numerical Cognition ◽  
Arithmetic Operation ◽  
Spatial Association ◽  
Spatial Location ◽  
Mathematical Operation ◽  
Spatial Associations ◽  
The Right ◽  
Study Participants

Previous research in numerical cognition has indicated that people form mental associations between numbers and space. The purpose of the present study was to replicate and confirm an operation sign spatial association originally reported by Pinhas, Shaki, and Fischer (2014). In our study, participants were asked to make a speeded classification of the arithmetic operation (addition or subtraction) that was represented by two mathematical operation signs (+ or -). A Bayesian analysis confirmed that spatially congruent responses (i.e., addition mapped to the right hand and subtraction to the left) were faster than spatially incongruent responses. These results confirm those originally reported by Pinhas, Shaki, and Fischer (2014) and indicate that people may form an association between arithmetic signs and spatial location. Further, we demonstrate how to perform a confirmatory Bayesian analysis in the free software package JASP, including the use of sensitivity analysis and informed priors. In addition to situating operational- spatial associations as a potentially fruitful line of inquiry in numerical cognition, the present work provides researchers with a didactic tutorial on how to conduct their own confirmatory Bayesian modeling work.

Note on the spectral classification of carbon stars in the photographic infrared region

1966 ◽  
Vol 24 ◽  
pp. 21-23
Author(s):  
Y. Fujita
Keyword(s):  
Infrared Region ◽  
Spectral Classification ◽  
Carbon Stars

We have investigated the spectrograms (dispersion: 8Å/mm) in the photographic infrared region fromλ7500 toλ9000 of some carbon stars obtained by the coudé spectrograph of the 74-inch reflector attached to the Okayama Astrophysical Observatory. The names of the stars investigated are listed in Table 1.

Diagnostic Value of Electron Microscopy in Soft Tissue Tumors

1970 ◽  
Vol 28 ◽  
pp. 220-221
Author(s):  
Gerald Fine ◽  
Azorides R. Morales
Keyword(s):  
Cardiac Myxoma ◽  
Osteogenic Sarcoma ◽  
Diagnostic Value ◽  
Soft Tissue Tumors ◽  
Amelanotic Melanoma ◽  
Growth Patterns ◽  
Light Microscopic Level ◽  
Mesenchymal Tumors ◽  
Good Agreement

For years the separation of carcinoma and sarcoma and the subclassification of sarcomas has been based on the appearance of the tumor cells and their microscopic growth pattern and information derived from certain histochemical and special stains. Although this method of study has produced good agreement among pathologists in the separation of carcinoma from sarcoma, it has given less uniform results in the subclassification of sarcomas. There remain examples of neoplasms of different histogenesis, the classification of which is questionable because of similar cytologic and growth patterns at the light microscopic level; i.e. amelanotic melanoma versus carcinoma and occasionally sarcoma, sarcomas with an epithelial pattern of growth simulating carcinoma, histologically similar mesenchymal tumors of different histogenesis (histiocytoma versus rhabdomyosarcoma, lytic osteogenic sarcoma versus rhabdomyosarcoma), and myxomatous mesenchymal tumors of diverse histogenesis (myxoid rhabdo and liposarcomas, cardiac myxoma, myxoid neurofibroma, etc.)

Ultrastructure of mesotheliomas

1984 ◽  
Vol 42 ◽  
pp. 98-101
Author(s):  
Irving Dardick
Keyword(s):  
Electron Microscopy ◽  
Latent Period ◽  
Spindle Cell ◽  
Spindle Cell Sarcoma ◽  
Metastatic Adenocarcinoma ◽  
Cell Sarcoma ◽  
Cytological Features ◽  
Industrial Use ◽  
Degree Of Differentiation

With the extensive industrial use of asbestos in this century and the long latent period (20-50 years) between exposure and tumor presentation, the incidence of malignant mesothelioma is now increasing. Thus, surgical pathologists are more frequently faced with the dilemma of differentiating mesothelioma from metastatic adenocarcinoma and spindle-cell sarcoma involving serosal surfaces. Electron microscopy is amodality useful in clarifying this problem.In utilizing ultrastructural features in the diagnosis of mesothelioma, it is essential to appreciate that the classification of this tumor reflects a variety of morphologic forms of differing biologic behavior (Table 1). Furthermore, with the variable histology and degree of differentiation in mesotheliomas it might be expected that the ultrastructure of such tumors also reflects a range of cytological features. Such is the case.

Interpreting HVEM of muscle-cell impulse networks

1992 ◽  
Vol 50 (1) ◽  
pp. 126-127
Author(s):  
Paul DeCosta ◽  
Kyugon Cho ◽  
Stephen Shemlon ◽  
Heesung Jun ◽  
Stanley M. Dunn
Keyword(s):  
Structural Analysis ◽  
Muscle Cell ◽  
Electron Micrographs ◽  
Relative Size ◽  
Automatic Classification ◽  
Nerve Fibers ◽  
Analysis Algorithm ◽  
Structural Networks

Introduction: The analysis and interpretation of electron micrographs of cells and tissues, often requires the accurate extraction of structural networks, which either provide immediate 2D or 3D information, or from which the desired information can be inferred. The images of these structures contain lines and/or curves whose orientation, lengths, and intersections characterize the overall network.Some examples exist of studies that have been done in the analysis of networks of natural structures. In, Sebok and Roemer determine the complexity of nerve structures in an EM formed slide. Here the number of nodes that exist in the image describes how dense nerve fibers are in a particular region of the skin. Hildith proposes a network structural analysis algorithm for the automatic classification of chromosome spreads (type, relative size and orientation).

Sign in / Sign up

Export Citation Format

Share Document