ProbKS: Keyword Search on Probabilistic Spatial Data

2014 ◽  
pp. 388-402
Author(s):  
Feng Gao ◽  
Rohit Jain ◽  
Sunil Prabhakar ◽  
Luo Si
Keyword(s):  
Spatial Data ◽  
Keyword Search

scholarly journals Efficient NN Spatial Keyword Search Using Spatial Inverted (SI) Index

2018 ◽  
Vol 7 (2.19) ◽  
pp. 17
Author(s):  
B.A. Vishnupriya ◽  
N. Senthamarai ◽  
S. Bharathi
Keyword(s):  
Spatial Data ◽  
Spatial Databases ◽  
Keyword Search ◽  
Spatial Information ◽  
Spatial Database ◽  
Spatial Relations ◽  
Base Station ◽  
Inverted File ◽  
File Structure ◽  
N Gram

"Spatial information mining", or learning revelation in spatial database, alludes to the illustration out of concealed information, spatial relations, or different examples that are not unequivocally put away in spatial databases. To get to the spatial database alongside the catchphrase another kind of inquiry called spatial watchword question is utilized. A spatial watchword inquiry get client area and client given catchphrases as contentions and gives web protests that are spatially and literarily material to these information. The current answers for such inquiries depend on IR2-tree that has a couple of inadequacies as space utilization and event of false hit is extremely huge when the question of the last outcome is far from the inquiry point .To beat this issue a novel file structure called Spatial Inverted file is proposed. Presently a-days use of portable is expanding enormously .In the versatile system an intermediary is set between base station and Location Based Server (LBS).This intermediary utilizes the Spatial modified file procedure to answer the SK inquiry by utilizing spatial data from the base station and printed data from the client question. The outcome from the SI record is given to two file structure in the intermediary called EVR Tree and Grid list. The Estimated Valid Region (EVR) for the present area of the client and required spatial articles are produced and come back to the client. On the off chance that the EVR is absent in the two file structure of intermediary it offer question to LBS. In the event that the client given inquiry is miss written or miss spelled it can be oversee by SI record utilizing n gram/2L Approximation file.

Machine Learning of Motor Vehicle Accident Categories from Narrative Data

1996 ◽  
Vol 35 (04/05) ◽  
pp. 309-316 ◽  
Author(s):  
M. R. Lehto ◽  
G. S. Sorock
Keyword(s):  
Machine Learning ◽  
Bayesian Model ◽  
Keyword Search ◽  
Motor Vehicle ◽  
Motor Vehicle Accident ◽  
Computer Search ◽  
Vehicle Accident ◽  
Learning Technique ◽  
Expert Ratings ◽  
Keyword Searches

Abstract:Bayesian inferencing as a machine learning technique was evaluated for identifying pre-crash activity and crash type from accident narratives describing 3,686 motor vehicle crashes. It was hypothesized that a Bayesian model could learn from a computer search for 63 keywords related to accident categories. Learning was described in terms of the ability to accurately classify previously unclassifiable narratives not containing the original keywords. When narratives contained keywords, the results obtained using both the Bayesian model and keyword search corresponded closely to expert ratings (P(detection)≥0.9, and P(false positive)≤0.05). For narratives not containing keywords, when the threshold used by the Bayesian model was varied between p>0.5 and p>0.9, the overall probability of detecting a category assigned by the expert varied between 67% and 12%. False positives correspondingly varied between 32% and 3%. These latter results demonstrated that the Bayesian system learned from the results of the keyword searches.

ANALYSIS OF FLOOD PRONE AREAS IN NAGAN RAYA REGENCY ACEH PROVINCE

2020 ◽  
Vol 5 (1) ◽  
pp. 414
Author(s):  
Amsar Yunan
Keyword(s):  
Land Use ◽  
Spatial Data ◽  
Flood Hazard ◽  
Parameter Analysis ◽  
Thematic Maps ◽  
Aceh Province ◽  
Object Area ◽  
Use Factors ◽  
Hazard Levels ◽  
Flood Prone Areas

Maps or remote sensing can be interpreted as the process of reading using various sensors where data collected remotely can be analyzed to obtain information about the object, area or phenomenon. In this study, the author develops a flood disaster mapping information system applying overlays with scoring between the parameters. The determinant factors to provide flood hazard levels includes rainfall factors in the dasarian unit, land-use factors and land-use arbitrary factors. Of all these parameters, a scoring process will be carried out by assigning weights and values according to their respective classifications, then an overlay process will be performed using ArcGIS software. The author conducted this study in Nagan Raya Regency since this area experiences flooding annually.  Framing a thematic map of flood-prone areas in Nagan Raya Regency was designed using the flood hazard method. Spatial data that has been presented in the form of thematic maps as parameters are land use maps, landform maps, and dasarian rainfall maps (per 10 daily). The design of thematic maps that are prone to flooding is done by overlapping (overlay process). In contrast, the determination of the classification is done by adding scores to each parameter, with low, medium and high hazard levels. Parameter analysis shows the level of flood vulnerability in Nagan Raya Regency of each district, namely Beutong: high 0.21%, medium 13.68%, low 86.12%. Seunagan District: high 51.17%, medium 48.83%, low 0%. Seunagan Timur District: high 10.07%, medium 46.18%, low 43.75%. Kuala Subdistrict: high 29.66%, medium 68.99%, low 1.35%. Darul Makmur District: high 8.57%, medium 63.37%, low 28.06%. From the overall results of the study, it can be concluded that the danger of flooding in Nagan Raya Regency with a level of vulnerability: high 9.92%, moderate 42.65% and low 47.43%.

An empirical analysis of town-forming enterprises' corporate social responsibility: Evidence from the Volga Federal District

2020 ◽  
Vol 18 (10) ◽  
pp. 1894-1909
Author(s):  
I.R. Badykova
Keyword(s):  
Regression Analysis ◽  
Corporate Social Responsibility ◽  
Social Responsibility ◽  
Empirical Analysis ◽  
Spatial Data ◽  
Univariate Analysis ◽  
Federal District ◽  
Current Situation ◽  
Economic Situation ◽  
Corporate Social

Subject. This article explores the determinants of social responsibility of backbone enterprises. Objectives. The article aims to investigate the relationships between the socio-economic situation of the monotown where the backbone company operates, and corporate social responsibility (CSR). Methods. For the study, I used a regression analysis and univariate analysis of spatial data. The rating estimates calculated using an original methodology are used as a CSR proxy (dependent variable). Results. Presenting information about the current situation of backbone enterprises and monotowns in Russia, the article reveals the existence of relationships between the backbone enterprise's affiliation to a monotown with a certain socio-economic situation and the level of corporate social responsibility. Conclusions. The situation of the backbone companies is likely to deteriorate. Increasing the level of social responsibility during a crisis seems unlikely.

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