Spatial association analysis between hydrocarbon fields and sedimentary residual magnetic anomalies using Weights of Evidence: An example from the Triassic Province of Algeria

2016 ◽  
Vol 135 ◽  
pp. 100-110 ◽  
Author(s):  
Karim Allek ◽  
Djamel Boubaya ◽  
Abderrahmane Bouguern ◽  
Mohamed Hamoudi
Keyword(s):  
Association Analysis ◽  
Spatial Association ◽  
Magnetic Anomalies ◽  
Weights Of Evidence ◽  
Hydrocarbon Fields

A statistical analysis of the spatial association of seismicity with drainage patterns and magnetic anomalies in western Quebec

1993 ◽  
Vol 217 (3-4) ◽  
pp. 285-305 ◽  
Author(s):  
A.K. Goodacre ◽  
G.F. Bonham-Carter ◽  
F.P. Agterberg ◽  
D.F. Wright
Keyword(s):  
Statistical Analysis ◽  
Spatial Association ◽  
Magnetic Anomalies ◽  
Drainage Patterns

Spatial Association Analysis

2008 ◽  
pp. 1077-1077
Author(s):  
Shashi Shekhar ◽  
Hui Xiong
Keyword(s):  
Association Analysis ◽  
Spatial Association

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 MINERAL POTENTIAL MAPPING USING GEOGRAPHIC INFORMATION SYSTEMS (GIS) FOR GOLD MINERALIZATION IN WEST JAVA, INDONESIA

2015 ◽  
Vol 7 (1) ◽  
pp. 61
Author(s):  
Seang Sirisokha ◽  
Lucas Donny Setijadji ◽  
I Wayan Warmada
Keyword(s):  
Gold Mineralization ◽  
Spatial Association ◽  
Geographic Information ◽  
Gold Deposits ◽  
Weights Of Evidence ◽  
Rock Unit ◽  
West Java ◽  
Mineral Potential ◽  
Potential Mapping ◽  
Potential Area

Western Java is a part of the Sunda Banda magmatic belt. This belt is well known to be host for several gold deposits in Indonesia, the distribution of 107 Au occurrences in this area was examined in terms of spatial association with various geological phenomena. The goal of this project is to use GIS to conduct weights of evidence (WofE) model for gold mineralization in West Java, Indonesia. A Geographic Information System (GIS) is a computer system for capturing, storing, querying, analyzing, and displaying geospatial data and weight of evidence method is one of the most important datadriven methods for mapping in GIS. The method is a probability based on technique for mapping mineral potential using the spatial distribution of known mineral occurrences. Therefore this method is very useful for gold potential mapping. There are six evidences maps such as NE–SW lineaments NW–SE Lineament, host rocks, heat sources, clay alteration and limonitic alteration, have been combined using a weights of evidence model to predict gold potential in West Java. The best predictive map generated by this method defines 21.62% (9902 km) of study area as favourable zones for gold mineralization further exploration work. It predicts correctly 74 (92.5%) of the 80 model deposits and predicts correctly 26 (96.35%) of the 27 validation deposits, has 6 main 2 prospective target for future exploration are located in Bayah Dome, southern mountain, Honjie Igneous Complex and Bogor zone, Purwakarta. Bayah Dome is highest potential area for gold deposit like Gunung Pongor, Cikidang, Cirotan, Ciawitali, Cikotok destricts and other deposits. The potential area of Au occurrences in research area is associated with NE–SW and NW–SE structure/ lineaments, dominated surrounding the Tertiary intrusive rock unit and hosted in Miocene to Pleistocene lithology rock unit.

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