Knowledge Extraction from Geographical Databases for Land Use Data Production

2019 ◽  
pp. 1688-1710
Author(s):  
Hana Alouaoui ◽  
Sami Yassine Turki ◽  
Sami Faiz
Keyword(s):  
Land Use ◽  
Association Rules ◽  
Spatial Data ◽  
Knowledge Extraction ◽  
Spatial Data Mining ◽  
Discovery Process ◽  
Real World Data ◽  
Territorial Planning ◽  
Time Periods ◽  
Mining Tools

Our study focuses on the task of land use evolution in urban environment which is fundamental in revealing the territorial planning. It refers crucially to the use of spatial data mining tools due to their high potential in handling with spatial data characteristics. The results of our knowledge discovery process are spatial and spatiotemporal association rules referring to the land use and its evolution. Three proposals based on different knowledge extraction techniques are detailed. The first approach aims to extract spatiotemporal association rules by introducing time into the attributes. The second approach forecasts the extracted rules at different dates. The third approach is devoted to the mining of spatiotemporal association rules. This proposal looks for rules that relate properties of reference objects with properties of other spatial relevant objects. The extracted patterns are relationships involving the spatial objects during time periods. To prove the applicability of each approach, experimentations are conducted on real world data. The obtained results are promising.

Knowledge Extraction from Geographical Databases for Land Use Data Production

2017 ◽  
pp. 321-343 ◽  
Author(s):  
Hana Alouaoui ◽  
Sami Yassine Turki ◽  
Sami Faiz
Keyword(s):  
Land Use ◽  
Association Rules ◽  
Spatial Data ◽  
Knowledge Extraction ◽  
Spatial Data Mining ◽  
Discovery Process ◽  
Real World Data ◽  
Territorial Planning ◽  
Time Periods ◽  
Mining Tools

Our study focuses on the task of land use evolution in urban environment which is fundamental in revealing the territorial planning. It refers crucially to the use of spatial data mining tools due to their high potential in handling with spatial data characteristics. The results of our knowledge discovery process are spatial and spatiotemporal association rules referring to the land use and its evolution. Three proposals based on different knowledge extraction techniques are detailed. The first approach aims to extract spatiotemporal association rules by introducing time into the attributes. The second approach forecasts the extracted rules at different dates. The third approach is devoted to the mining of spatiotemporal association rules. This proposal looks for rules that relate properties of reference objects with properties of other spatial relevant objects. The extracted patterns are relationships involving the spatial objects during time periods. To prove the applicability of each approach, experimentations are conducted on real world data. The obtained results are promising.

scholarly journals Detection of the Association Rules of the Occurrence of Brucellosis in Humans Using Spatial Data Mining

2020 ◽  
Vol 11 (1) ◽  
pp. 20-30
Author(s):  
Mohammad Abbasi ◽  
Farid Karimipour ◽  
Sima Gholipour
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Spatial Data ◽  
Spatial Data Mining

scholarly journals Land Cover Mapping Based on Multisource Spatial Data Mining Approach for Climate Simulation: A Case Study in the Farming-Pastoral Ecotone of North China

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Feng Wu ◽  
Jinyan Zhan ◽  
Haiming Yan ◽  
Chenchen Shi ◽  
Juan Huang
Keyword(s):  
Data Mining ◽  
Land Use ◽  
Land Cover ◽  
Spatial Data ◽  
Vegetation Index ◽  
Dynamical Downscaling ◽  
Driving Forces ◽  
Spatial Data Mining ◽  
Climate Simulation ◽  
Land Cover Data

The land use and land cover change (LUCC) is one of the prime driving forces of climate change. Most attention has been paid to the influence of accuracy of the land cover data in numerous climate simulation projects. The accuracy of the temporal land use data from Chinese Academy of Sciences (CAS) is higher than 90%, but the high-precision land cover data is absent. We overlaid land cover maps from different sources, and the grids with consistent classification were selected as the sample grids. By comparing the results obtained with different decision tree classifiers with the WEKA toolkit for data mining, it was found that the C4.5 algorithm was more suitable for converting land use data of CAS classification to land cover data of IGBP classification. We reset the decision rules with Net Primary Productivity (NPP) and Normalized Difference Vegetation Index (NDVI) as the indicators. The accuracy of the reclassified land cover data was proven to reach 83.14% through comparing with the Terrestrial Ecosystem Monitoring Sites and high resolution images. Therefore, it is feasible to produce the temporal land cover data with this method, which can be used as the parameters of dynamical downscaling in the regional climate simulation.

Improvement of the Prediction of Drugs Demand Using Spatial Data Mining Tools

2015 ◽  
Vol 40 (1) ◽  
Author(s):  
M. Isabel Ramos ◽  
Juan José Cubillas ◽  
Francisco R. Feito
Keyword(s):  
Data Mining ◽  
Spatial Data ◽  
Spatial Data Mining ◽  
Mining Tools

scholarly journals Fuzzy Set Approaches to Spatial Data Mining of Association Rules

2003 ◽  
Vol 7 (1) ◽  
pp. 123-138 ◽  
Author(s):  
R Ladner ◽  
F E Petry ◽  
M A Cobb
Keyword(s):  
Data Mining ◽  
Association Rules ◽  
Fuzzy Set ◽  
Spatial Data ◽  
Spatial Data Mining

scholarly journals Adaptation to Land Degradation in Southeast Vietnam

Land ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 302 ◽  
Author(s):  
Anne Gobin ◽  
Le Thi Thu Hien ◽  
Le Trinh Hai ◽  
Pham Ha Linh ◽  
Nguyen Ngoc Thang ◽  
...  
Keyword(s):  
Land Use ◽  
Land Degradation ◽  
Spatial Data ◽  
Agricultural Land ◽  
Landsat Imagery ◽  
Spatial Data Mining ◽  
Ecological Resilience ◽  
Dragon Fruit ◽  
Local Impacts ◽  
South Axis

A framework was developed to elucidate (1) the drivers of land degradation, (2) pressures, (3) local impacts and vulnerabilities and (4) adaptation strategies. The combination of participatory approaches, statistical data analysis, time series Landsat imagery and spatial data mining was tested in southeast Vietnam where the impacts of land degradation on the environment and economy are considerable. The major drivers of land degradation are climate, notably drought, and population density. The pressures include natural resource management and land use/cover change. A Landsat archive analysis showed an increase in agricultural land use from 31% to 50%, mostly at the expense of forests, from 1990 to 2019. Farmers adapted by investing in the irrigation of rice and dragon fruit, and by selecting their rainfed crops in line with the changing environment. The most vulnerable were the rural poor and farmers without access to land and water resources. The best protection against land degradation was prosperity, which is enhanced by the region’s location along Vietnam’s major national route, connecting major cities along a north–south axis. Our analysis shows that southeast Vietnam emerged as a region with an important human ecological resilience strengthened by increased prosperity. The current adaptation options and limitations warrant further research.

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