scholarly journals Three Spatial Verification Techniques: Cluster Analysis, Variogram, and Optical Flow

2009 ◽  
Vol 24 (6) ◽  
pp. 1457-1471 ◽  
Author(s):  
Caren Marzban ◽  
Scott Sandgathe ◽  
Hilary Lyons ◽  
Nicholas Lederer
Keyword(s):  
Cluster Analysis ◽  
Optical Flow ◽  
Covariance Structure ◽  
Object Oriented ◽  
False Alarms ◽  
Verification Method ◽  
Field Displacement ◽  
The Difference ◽  
Verification Techniques ◽  
Spatial Verification

Abstract Three spatial verification techniques are applied to three datasets. The datasets consist of a mixture of real and artificial forecasts, and corresponding observations, designed to aid in better understanding the effects of global (i.e., across the entire field) displacement and intensity errors. The three verification techniques, each based on well-known statistical methods, have little in common and, so, present different facets of forecast quality. It is shown that a verification method based on cluster analysis can identify “objects” in a forecast and an observation field, thereby allowing for object-oriented verification in the sense that it considers displacement, missed forecasts, and false alarms. A second method compares the observed and forecast fields, not in terms of the objects within them, but in terms of the covariance structure of the fields, as summarized by their variogram. The last method addresses the agreement between the two fields by inferring the function that maps one to the other. The map—generally called optical flow—provides a (visual) summary of the “difference” between the two fields. A further summary measure of that map is found to yield useful information on the distortion error in the forecasts.

scholarly journals Cluster Analysis for Object-Oriented Verification of Fields: A Variation

2008 ◽  
Vol 136 (3) ◽  
pp. 1013-1025 ◽  
Author(s):  
Caren Marzban ◽  
Scott Sandgathe
Keyword(s):  
Cluster Analysis ◽  
Spatial Data ◽  
Mesoscale Model ◽  
Spatial Scales ◽  
Weather Prediction ◽  
Model Performance ◽  
Object Oriented ◽  
Previous Method ◽  
The Other ◽  
False Alarms

Abstract In a recent paper, a statistical method referred to as cluster analysis was employed to identify clusters in forecast and observed fields. Further criteria were also proposed for matching the identified clusters in one field with those in the other. As such, the proposed methodology was designed to perform an automated form of what has been called object-oriented verification. Herein, a variation of that methodology is proposed that effectively avoids (or simplifies) the criteria for matching the objects. The basic idea is to perform cluster analysis on the combined set of observations and forecasts, rather than on the individual fields separately. This method will be referred to as combinative cluster analysis (CCA). CCA naturally lends itself to the computation of false alarms, hits, and misses, and therefore, to the critical success index (CSI). A desirable feature of the previous method—the ability to assess performance on different spatial scales—is maintained. The method is demonstrated on reflectivity data and corresponding forecasts for three dates using three mesoscale numerical weather prediction model formulations—the NCEP/NWS Nonhydrostatic Mesoscale Model (NMM) at 4-km resolution (nmm4), the University of Oklahoma’s Center for Analysis and Prediction of Storms (CAPS) Weather Research and Forecasting Model (WRF) at 2-km resolution (arw2), and the NCAR WRF at 4-km resolution (arw4). In the small demonstration sample herein, model forecast quality is efficiently differentiated when performance is assessed in terms of the CSI. In this sample, arw2 appears to outperform the other two model formulations across all scales when the cluster analysis is performed in the space of spatial coordinates and reflectivity. However, when the analysis is performed only on spatial data (i.e., when only the spatial placement of the reflectivity is assessed), the difference is not significant. This result has been verified both visually and using a standard gridpoint verification, and seems to provide a reasonable assessment of model performance. This demonstration of CCA indicates promise in quickly evaluating mesoscale model performance while avoiding the subjectivity and labor intensiveness of human evaluation or the pitfalls of non-object-oriented automated verification.

Role of mall attractiveness in shaping fashion shopping orientation of apparel shoppers: a segmentation approach

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ajay Kumar ◽  
Anil Kumar Kashyap
Keyword(s):  
Cluster Analysis ◽  
Design Methodology ◽  
Economic Value ◽  
Variety Seeking ◽  
Content Type ◽  
Shopping Orientation ◽  
Intercept Survey ◽  
Segmentation Approach ◽  
The Difference

Purpose The purpose of this study is to identify distinct segments of apparel shoppers based on their fashion shopping orientation. The difference among the segments based on mall attractive dimension is also examined. Design/methodology/approach The data were collected through mall intercept survey from the mall shoppers. Samples of 375 respondents are used for data analysis purpose. Exploratory factor analysis is used to extract the factors of fashion shopping orientation and mall attractive dimensions while K-means cluster analysis is applied to identify the segments. Findings This study resulted in three factors of fashion orientation of apparel shoppers, i.e. fashion involvement, variety seeking and economic value, and four factors of mall attractive dimensions: convenience, entertainment, atmosphere and architecture design. Based on these factors, this study came out with three distinct segments of fashion shoppers: pragmatic shoppers, variety seeking shoppers and highly fashioned shoppers. These three segments are attracted towards the mall dimension differently. Originality/value This paper presents the three distinct profiles of fashion shoppers based on their fashion shopping orientation and mall attractive dimensions. The findings of this study may help retailers and mall developers to target mall visitors appropriately.

Global registration by optical radiation of lightning discharges on aboard a low-orbit spacecraft

2021 ◽  
Author(s):  
V.S. Chudnovsky ◽  
L.S. Chudnovsky ◽  
Yu.P. Vagin ◽  
A.N. Pleshanov ◽  
K.E. Tyupikov
Keyword(s):  
Wavelength Range ◽  
Optical Radiation ◽  
False Alarms ◽  
Information Flows ◽  
Range Finding ◽  
Global Registration ◽  
Lightning Detection ◽  
Lightning Discharges ◽  
The Difference ◽  
Finding Method

Registration of the coordinates of lightning by their optical radiation has already been implemented on geostationary spacecraft in the wavelength range of 777.4 nm. However, the algorithms for processing the registered signals, as well as the volumes of information flows, have not yet been sufficiently studied. The choice of the sensor for the global registration of optical radiation of lightning on board a low-orbit spacecraft is substantiated. The prospects of using photodiodes in the difference-ranging method for determining coordinates are shown.The characteristics of lightning detection using matrices and LEDs have been studied. The prospects of using photodiodes in the differential-range-finding method for determining coordinates are shown. It is shown that the registration of optical lightning radiation on board the spacecraft by photodiodes provides the characteristics of detection and false alarms of a higher quality compared with the use of CCD matrices.

scholarly journals Object-oriented change detection based on weighted polarimetric scattering differences on POLSAR images

2015 ◽  
Vol XL-7/W4 ◽  
pp. 149-154 ◽  
Author(s):  
X. Shi ◽  
L. Lu ◽  
S. Yang ◽  
G. Huang ◽  
Z. Zhao
Keyword(s):  
Change Detection ◽  
Covariance Matrix ◽  
Polarization State ◽  
Object Oriented ◽  
Detection Accuracy ◽  
Processing Unit ◽  
Processing Technologies ◽  
The Difference ◽  
Sar Imagery ◽  
Reasonable Choice

For wide application of change detection with SAR imagery, current processing technologies and methods are mostly based on pixels. It is difficult for pixel-based technologies to utilize spatial characteristics of images and topological relations of objects. Object-oriented technology takes objects as processing unit, which takes advantage of the shape and texture information of image. It can greatly improve the efficiency and reliability of change detection. Recently, with the development of polarimetric synthetic aperture radar (PolSAR), more backscattering features on different polarization state can be available for usage of object-oriented change detection study. In this paper, the object-oriented strategy will be employed. Considering the fact that the different target or target's state behaves different backscattering characteristics dependent on polarization state, an object-oriented change detection method that based on weighted polarimetric scattering difference of PolSAR images is proposed. The method operates on the objects generated by generalized statistical region merging (GSRM) segmentation processing. The merit of GSRM method is that image segmentation is executed on polarimetric coherence matrix, which takes full advantages of polarimetric backscattering features. And then, the measurement of polarimetric scattering difference is constructed by combining the correlation of covariance matrix and the difference of scattering power. Through analysing the effects of the covariance matrix correlation and the scattering echo power difference on the polarimetric scattering difference, the weighted method is used to balance the influences caused by the two parts, so that more reasonable weights can be chosen to decrease the false alarm rate. The effectiveness of the algorithm that proposed in this letter is tested by detection of the growth of crops with two different temporal radarsat-2 fully PolSAR data. First, objects are produced by GSRM algorithm based on the coherent matrix in the pre-processing. Then, the corresponding patches are extracted in two temporal images to measure the differences of objects. To detect changes of patches, a difference map is created by means of weighted polarization scattering difference. Finally, the result of change detection can be obtained by threshold determining. The experiments show that this approach is feasible and effective, and a reasonable choice of weights can improve the detection accuracy significantly.

Object-Relational Modeling

2009 ◽  
pp. 162-170
Author(s):  
Jaroslav Zendulka
Keyword(s):  
Relational Database ◽  
Relational Databases ◽  
Conceptual Modeling ◽  
Object Oriented ◽  
Database Systems ◽  
Object Relational ◽  
The Difference ◽  
Object Oriented Approach ◽  
Entity Relationship ◽  
Oriented Approach

Modeling techniques play an important role in the development of database applications. Well-known entity-relationship modeling and its extensions have become a widely-accepted approach for relational database conceptual design. An object-oriented approach has brought a new view of conceptual modeling. A class as a fundamental concept of the object-oriented approach encapsulates both data and behavior, whereas traditional relational databases are able to store only data. In the early 1990s, the difference between the relational and object-oriented (OO) technologies, which were, and are still used together to build complex software systems, was labeled the object-relational impedance mismatch (Ambler, 2003). The object-oriented approach and the need of new application areas to store complex data have greatly influenced database technology since that time. Besides appearance of object-oriented database systems, which fully implement objectoriented paradigm in a database environment (Catell et al., 2003), traditional relational database management systems become object-relational (Stonebraker & Brown, 1999). The most recent versions of the SQL standard, SQL: 1999 (Melton & Simon (2001) and SQL: 2003 (Eisenberg et al., 2004), introduced object-relational features to the standard and leading database producers have already released packages which incorporate them.

scholarly journals Evaluation of Rainfall Forecasts by Three Mesoscale Models during the Mei-Yu Season of 2008 in Taiwan. Part III: Application of an Object-Oriented Verification Method

Atmosphere ◽  
2020 ◽  
Vol 11 (7) ◽  
pp. 705
Author(s):  
Chung-Chieh Wang ◽  
Sahana Paul ◽  
Dong-In Lee
Keyword(s):  
Object Oriented ◽  
Tropical Rainfall Measuring Mission ◽  
Weather Research And Forecasting ◽  
Total Rainfall ◽  
Mesoscale Models ◽  
Subjective Analysis ◽  
Verification Method ◽  
Rain Gauges ◽  
Object Based ◽  
Central Weather Bureau

In this study, the performances of Mei-yu (May–June) quantitative precipitation forecasts (QPFs) in Taiwan by three mesoscale models: the Cloud-Resolving Storm Simulator (CReSS), the Central Weather Bureau (CWB) Weather Research and Forecasting (WRF), and the CWB Non-hydrostatic Forecast System (NFS) are explored and compared using an newly-developed object-oriented verification method, with particular focus on the various properties or attributes of rainfall objects identified. Against a merged dataset from ~400 rain gauges in Taiwan and the Tropical Rainfall Measuring Mission (TRMM) data in the 2008 season, the object-based analysis is carried out to complement the subjective analysis in a parallel study. The Mei-yu QPF skill is seen to vary with different aspects of rainfall objects among the three models. The CReSS model has a total rainfall production closest to the observation but a large number of smaller objects, resulting in more frequent and concentrated rainfall. In contrast, both WRF and NFS tend to under-forecast the number of objects and total rainfall, but with a higher proportion of bigger objects. Location errors inferred from object centroid locations appear in all three models, as CReSS, NFS, and WRF exhibit a tendency to simulate objects slightly south, east, and northwest with respect to the observation. Most rainfall objects are aligned close to an E–W direction in CReSS, in best agreement with the observation, but many towards the NE–SW direction in both WRF and NFS. For each model, the objects are matched with the observed ones, and the results of the matched pairs are also discussed. Overall, though preliminarily, the CReSS model, with a finer grid size, emerges as best performing model for Mei-yu QPFs.

scholarly journals Use of the Semantic Differential in Bilingualism Research and its Relevance to Translation

2002 ◽  
Vol 26 (3) ◽  
pp. 229-243
Author(s):  
David Romney ◽  
John Bynner
Keyword(s):  
Cluster Analysis ◽  
Semantic Differential ◽  
Personality Factors ◽  
Affective Meaning ◽  
Dominant Language ◽  
Separate Type ◽  
Semantic Difference ◽  
The Difference

Abstract To investigate bilingual subjects' perceptions of the connotative differences between concepts in English and French, a form of the semantic differential was employed in which the scales were derived from Cattell's 16 personality factors. Altogether 16 concepts were rated and these were made up of four sets, each set containing a pair of synonyms in English and a pair of synonyms (their translation-equivalents) in French. Even though the sets themselves were easily distinguishable in terms of their affective meaning, no significant differences in affective meaning emerged between the concepts in any of the sets either within or across languages. There were, however, significant differences between individuals in the ways they perceived the concepts. Some of these differences seemed to be due to the effects of dominant language, A cluster analysis of the individuals in terms of the semantic difference between concepts and their translation-equivalents (over and above the difference between synonyms) gave little support to the postulated distinction between the two types of bilingual, compound and coordinate, although there was some evidence that the compound bilingual exists as a separate type.

Temporal adverbials and the present perfect/past tense alternation

2013 ◽  
Vol 34 (2) ◽  
pp. 202-240 ◽  
Author(s):  
Valentin Werner
Keyword(s):  
Cluster Analysis ◽  
Hierarchical Cluster Analysis ◽  
Hierarchical Cluster ◽  
Phylogenetic Networks ◽  
Past Tense ◽  
Present Perfect ◽  
British English ◽  
Cultural Proximity ◽  
Corpus Data ◽  
The Difference

Specification by certain temporal adverbials has been shown to be one of the typical triggers of the present perfect in British English. Often, however, L2 varieties display different patterns of temporal co-occurrence, especially using the simple past tense. This study is based on corpus data from twelve components of the International Corpus of English and analyzes the distribution between present perfect and past tense for a number of co-occurring temporal adverbials. In addition, it establishes three measures of similarity across the varieties (hierarchical cluster analysis, phylogenetic networks and a distribution-based measure). On the basis of 6 353 adverbials in total, this paper suggests (1) that there is a L1–L2 divide, (2) that the difference between “traditional” and “transplanted” L1 varieties is less pronounced, (3) that L2 varieties allow more variation, which indicates that in these varieties, the present perfect is partly used as a tense (sensu Quirk et al. 1985), and (4) that some temporal adverbials are less categorically attached to either present perfect or past tense than others. Finally, some conclusions with regard to the importance of geographical and socio-cultural proximity of certain varieties can be drawn.

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