Artykuły w czasopismach: „Geographical data mining”

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Estivill-Castro, Vladimir, i Ickjai Lee. "Clustering with obstacles for Geographical Data Mining". ISPRS Journal of Photogrammetry and Remote Sensing 59, nr 1-2 (sierpień 2004): 21–34. http://dx.doi.org/10.1016/j.isprsjprs.2003.12.003.

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M. Khedr, Ahmed, Zaher AL Aghbari i Ibrahim Kamel. "Privacy Preserving Decomposable Mining Association Rules on Distributed Data". International Journal of Engineering & Technology 7, nr 3.13 (27.07.2018): 157. http://dx.doi.org/10.14419/ijet.v7i3.13.16343.

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In distributed computing, data sharing is inevitable, however, moving local databases from one site to another should be avoided because of the computational overhead and privacy consideration. Most of the data mining algorithms are designed assuming that data repository is stored locally. This paper presents a scheme and algorithms for mining association rules in geographically distributed data. The proposed scheme preserves data privacy of the different geographical site by passing secure messages between them. The algorithms minimize the communication cost by exchanging statistical summaries of the local databases. We provide a privacy and security analysis that shows the privacy preserving aspects of the proposed algorithms. Moreover, the paper presents extensive simulation experiments to evaluate the efficiency of the proposed scheme.

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Lee, Sang-Moon, i Jeong-Min Seo. "A Spatial Data Mining and Geographical Customer Relationship Management System". Journal of the Korea Society of Computer and Information 15, nr 6 (30.06.2010): 121–28. http://dx.doi.org/10.9708/jksci.2010.15.6.121.

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Lu, Y. L., C. W. Liu, J. W. Li i J. W. Jiang. "CONSTRUCTION METHOD OF “CELL-CUBE” SPATIO-TEMPORAL DATA MODEL FOR BIG DATA". ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-3/W10 (7.02.2020): 25–30. http://dx.doi.org/10.5194/isprs-archives-xlii-3-w10-25-2020.

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Abstract. In recent years, with high accuracy, high frequency, considerable coverage of remote sensing images, map tiles, video surveillance, web crawlers, social networking platforms and other types of spatiotemporal data have exploded in geometric progression. Human society has come into the era of big data in time and space. In view of the characteristics of multi-attribute, multi-dimensional, multisource and heterogeneous spatiotemporal big data, how to make use of the emerging information technology means, combined with the geographic information data analysis means, the rapid mining and utilization of spatiotemporal big data has become a key problem to be solved. Built on the background of spatiotemporal big data and the process of geospatial cognition, this paper proposes a "cell-cube" spatiotemporal object data model. This paper constructs a model system of geo-spatiotemporal big data from the aspects of data organization, data storage and data partition, and abstracts the geo-space into an infinite number of geo-cells, and the adjacent geo-cells gather around the core cells to form geographical clusters, and the geographical clusters with similar attributes are clustered into geographical blocks. At the level of data organization, the spatial and temporal characteristics of structured data and unstructured data are considered as organizational dimensions, and a multi-factor extended cube data model is proposed. In the aspect of data storage, the organization model is further abstracted into the cell-cube structure of distributed data warehouse, and then the spatiotemporal data is stored uniformly. At the level of data segmentation, the mathematical table and space calculation method of multi-feature extended cube are proposed, and the geographical cell data division model based on connection is established. It solves the organization and management problem of spatiotemporal big data, provides a more complete data organization framework and solution for the application of geo-spatiotemporal big data, and promotes the development of deep mining of spatiotemporal extensive data in GIS. And to achieve space-time big data in the geographical space microscopic and the macroscopic unification cognition.

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Banerjee, Soumita. "The Role of Global Educational Database in Educational Data Mining". European Journal of Engineering and Technology Research 1, nr 6 (27.07.2018): 16–26. http://dx.doi.org/10.24018/ejeng.2016.1.6.194.

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Educational Data Mining is one of the major on-going research platforms now. Students’ records need to be maintained and analyzed in a manner so that they can be utilized to predict students’ behavior and learning methods. Although students’ academic records need to be processed and analyzed through data mining tools, the primary challenge is to gather individual academic student details. This paper proposes a global database of students irrespective of geographical boundaries. Academic performance of every student from every country will be updated in this platform. Students’ performance on major examinations will be available in the database. Supporting documents and performance details will be readily available and accessible to the evaluators from any geographic location. This will be helpful to standardize the evaluation process and analyze the performance of a student, irrespective of geographic boundaries. The following paper will discuss the available EDM tools and how data can be analyzed to extract information.

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Leon, Florin, Gabriela Maria Atanasiu i Dan Gâlea. "Using Data Mining Techniques for the Management of Seismic Vulnerability". Key Engineering Materials 326-328 (grudzień 2006): 501–4. http://dx.doi.org/10.4028/www.scientific.net/kem.326-328.501.

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Natural hazards, and especially earthquakes, are often recurring phenomena. Therefore, there is a permanent need for solutions to reduce earthquake losses by developing technologies, procedures, knowledge, and tools for seismic design and rehabilitation of buildings and infrastructure. A key point to an effective decision making process that aims at mitigating their effects is building a model of the underlying facts. A Geographical Information System (GIS) is a framework able to assemble, keep, process and display specific information, identified by geographical location, which can combine layers of information to give the user a better understanding about that location. By using a Geographical Information System containing geospatial data, one can develop useful scenarios to reduce natural disaster risk and vulnerability of structures. In this paper, we describe a way of applying data mining techniques from the artificial intelligence field to earthquake analysis in order to make a better investigation of the available data. These methods are capable of finding “hidden” correlations among different subsets of data, which cannot be revealed by means of simple statistics.

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Chakri, Sana, Said Raghay i Salah El Hadaj. "Semantic Trajectory Knowledge Discovery: A Promising Way to Extract Meaningful Patterns from Spatiotemporal Data". International Journal of Software Engineering and Knowledge Engineering 27, nr 03 (kwiecień 2017): 399–421. http://dx.doi.org/10.1142/s0218194017500140.

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Spatiotemporal data mining studies the field of discovering interesting patterns from large spatiotemporal databases. Although these databases generate a huge volume of data daily from satellite images and mobile sensors like GPS, among these data we find first spatiotemporal and geographical data; secondly, the trajectories browsed by moving objects in some time intervals. Combination of these types of data leads to producing semantic trajectory data. Enriching trajectories with semantic geographical information leads to ease queries, analysis, and mining, in order to give more meaning to behaviors potentially extracted from trajectories. Therefore, applying mining techniques on semantic trajectories continue to prove to be a success story in discovering useful and nontrivial behavioral patterns of moving objects. The purpose of this paper is to make an overview of spatiotemporal knowledge discovery (STKD) and techniques recently used to extract knowledge from spatiotemporal data based on analysis of recent literature. Then leading towards a deeper analysis about semantic trajectory knowledge discovery as a specified field from STKD that integrates trajectory sample points with geographical data before applying mining techniques in order to extract behavioral knowledge from semantic trajectories which can be more useful and significant for the application users.

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Liu, Zhewei, Xiaolin Zhou, Wenzhong Shi i Anshu Zhang. "Towards Detecting Social Events by Mining Geographical Patterns with VGI Data". ISPRS International Journal of Geo-Information 7, nr 12 (17.12.2018): 481. http://dx.doi.org/10.3390/ijgi7120481.

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Detecting events using social media data is important for timely emergency response and urban monitoring. Current studies primarily use semantic-based methods, in which “bursts” of certain semantic signals are detected to identify emerging events. Nevertheless, our consideration is that a social event will not only affect semantic signals but also cause irregular human mobility patterns. By introducing depictive features, such irregular patterns can be used for event detection. Consequently, in this paper, we develop a novel, comprehensive workflow for event detection by mining the geographical patterns of VGI. This workflow first uses data geographical topic modeling to detect the hashtag communities with VGI semantic data. Both global and local indicators are then constructed by introducing spatial autocorrelation measurements. We then adopt an outlier test and generate indicator maps to spatiotemporally identify the potential social events. This workflow was implemented using a real-world dataset (104,000 geo-tagged photos) and the evaluation was conducted both qualitatively and quantitatively. A set of experiments showed that the discovered semantic communities were internally consistent and externally differentiable, and the plausibility of the detected events was demonstrated by referring to the available ground truth. This study examined the feasibility of detecting events by investigating the geographical patterns of social media data and can be applied to urban knowledge retrieval.

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Gadekar, Dr Prof Amit R. "Location based Anomalies Detection on Geographical Map using Data Mining Techniques". International Journal for Research in Applied Science and Engineering Technology 8, nr 4 (30.04.2020): 138–41. http://dx.doi.org/10.22214/ijraset.2020.4021.

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M. Almuttairi, Rafah, Mahdi S. Almhanna, Mohammed Q. Mohammed i Saif Q Muhamed. "Promote Replica Management based on Data Mining Techniques". International Journal of Engineering & Technology 7, nr 4.19 (27.11.2018): 838. http://dx.doi.org/10.14419/ijet.v7i4.19.28006.

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The data grid technique evolved largely in sharing the data in multiple geographical stations across different sites to improve the data access and increases the speed of transmission data. The performance and the availability of the resources is taken into account, when a total of sites holding a copy of files, there is a considerable benefit in selecting the best set of replica sites to be cooperated for increasing data transfer job. In this paper, new selecrtion strategy is proposed to reduce the total transfer time of required files. Pincer-Search algorithm is used to explore the common characteristics of sites to select uncongested replica sites.

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VÁZQUEZ-RODRÍGUEZ, R., C. PÉREZ-RISQUET i J. C. TORRES-CANTERO. "A Novel Visual Data Mining Module for the Geographical Information System gvSIG". Anuário do Instituto de Geociências - UFRJ 36_1, nr 1 (16.07.2013): 98–111. http://dx.doi.org/10.11137/2013_1_98_111.

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Zhu, Ling, Dejun Gao, Tao Jia i Jingyi Zhang. "Using Eco-Geographical Zoning Data and Crowdsourcing to Improve the Detection of Spurious Land Cover Changes". Remote Sensing 13, nr 16 (16.08.2021): 3244. http://dx.doi.org/10.3390/rs13163244.

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To address problems in remote sensing image change detection, this study proposes a method for identifying spurious changes based on an eco-geographical zoning knowledge base and crowdsourced data mining. After preliminary change detection using the super pixel cosegmentation method, eco-geographical zoning is introduced, and the rules of spurious change are collected based on the knowledge of expert interpreters, and from statistics on existing land cover products according to each eco-geographical zone. Uncertain changed patches with a high possibility of spurious change according to the eco-geographical zoning rule were published in the form of a map service on an online platform, and then crowd tagging information on spurious changed patches was collected. The Hyperlink-Induced Topic Search (HITS) algorithm was used to calculate the spurious change degree of changed patches. We selected the northern part of Laos as the experimental area and the Chinese GF-1 Wide Field View (WFV) images for change detection to verify the effectiveness of the method. The results show that the accuracy of change detection improves by 23% after removing the spurious changes. Spurious changes caused by clouds, river water turbidity, spectral differences in cultivated land before and after harvest, and changes in shrubs, grassland, and forest density, can be removed using an eco-geographical zoning knowledge base and crowdsourced data mining methods.

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Wang, Hongyi, i Meichang Zhang. "Cost Control of Mining Personnel Based on Wireless Communication Network from the Perspective of Operations Research". Computational Intelligence and Neuroscience 2022 (10.01.2022): 1–11. http://dx.doi.org/10.1155/2022/9932603.

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The sublevel caving method without sill pillar is used to improve the cost of mining. The analysis is performed according to unique geographical environment and the current mining technology of the mine. The wireless communication network is used to budget and control the work cost of mining. Simulation operation about unit explosive dosage, fan-shaped deep hole interval, hole bottom distance, and collapse step distance is performed. Experiments have shown that budget and control of the cost of mining workers with wireless communication technology can manage mining data and guide the design of mining data.

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Liu, Peng Zhen. "Study on Data Correlation Method of Spatial-Temporal Data". Advanced Materials Research 726-731 (sierpień 2013): 4610–14. http://dx.doi.org/10.4028/www.scientific.net/amr.726-731.4610.

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Combination the time characteristics with the spatial features of geographical spatial entity, it is the foundation of dynamic reflection in the world of objective reality. The correlation method of spatial-temporal data is studied with three data types, including point, line, and polygon, by analyzing the changes types and characteristics of multi-temporal spatial data, mining the data correlation information in different periods using the overlay method, and establishing corresponding relationship among objects in different periods with unique identifier. And the experiment is taken based on the land use data of two periods, the correlation is established to the data of two periods, and the simple object tracking query is implemented.

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Zhao, Wenshuang, Nan Jiang, Xinkai Yu, Yunhai Chen i Xinke Zhao. "A Correlation Analysis Method for Geographical Object Flows from a Geoeconomic Perspective". Sustainability 14, nr 15 (25.07.2022): 9085. http://dx.doi.org/10.3390/su14159085.

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Geographic object flow is the reason behind the relationship of geographic units. There are interactions in the process of dynamic change of a geographic object flow, and its regularity, which can reflect the relationship or pattern of geographic units in a region. In this paper, an association rule mining method for the geographic object flow linkage process is studied from a geoeconomics perspective. Additionally, an association rule mining algorithm with hierarchical constraints is proposed. Data segmentation is performed according to the time series characteristics of geographic object flow data. The basic attributes for the association rule mining are determined based on the basic parameters of geographic object flows, and a database for the association rule mining is formed according to the characteristics of the hierarchical structure of the geographic object flows. Based on the obtained data, the association rule mining algorithm with hierarchical constraints obtained using a parent–child matrix is improved by adding the Apriori algorithm. With the Indo-Pacific region as an example, the trade flow association rules for 25 countries in the region from 2010 to 2021 are selected. In addition, a mathematical statistical analysis of the strongly associated mined trade flows and geoeconomic factors is conducted in terms of both a basic feature analysis of trade flow associations and a country-oriented trade flow association analysis by considering domain knowledge. The effectiveness of the method has been evaluated from various perspectives such as correlation analysis, mathematical statistics, and comparison with the findings of existing studies and proved the validity of the method.

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D’Andrea, Antonino, Claudio Cappadona, Gianluca La Rosa i Orazio Pellegrino. "A FUNCTIONAL ROAD CLASSIFICATION WITH DATA MINING TECHNIQUES". TRANSPORT 29, nr 4 (16.12.2014): 419–30. http://dx.doi.org/10.3846/16484142.2014.984329.

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The current international road standards, in order to give organization and safety, promote the classification of roads according to their technical and functional characteristics beyond their administrative membership, but the procedures are yet strongly based on the expertise’s judgment. In fact, although this activity has a great importance for the consequences that produces in terms of responsibility and allocation of economic resources, it is solely based on the quantification of some variables without specifying methods or analytical procedures. In this paper, after an instrumental survey of the road environment, we applied data mining techniques that consider the ‘vagueness’ of the analysed scenario. The type of algorithms used, therefore, permits to quantify a degree of membership (among 0 and 1) of a road to the groupings provided and to prepare any corrective action in order to direct the final result towards a specific class with greater precision. In addition, this method is very flexible and willing to contain new variables or observations at different times with great easiness. Moreover, the geographical location of the individual observations, as it was done also in this research, can be transferred to a GIS system, with a positive impact on maintenance programs.

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Yang, Tengfei, Jibo Xie, Guoqing Li, Naixia Mou, Zhenyu Li, Chuanzhao Tian i Jing Zhao. "Social Media Big Data Mining and Spatio-Temporal Analysis on Public Emotions for Disaster Mitigation". ISPRS International Journal of Geo-Information 8, nr 1 (15.01.2019): 29. http://dx.doi.org/10.3390/ijgi8010029.

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Social media contains a lot of geographic information and has been one of the more important data sources for hazard mitigation. Compared with the traditional means of disaster-related geographic information collection methods, social media has the characteristics of real-time information provision and low cost. Due to the development of big data mining technologies, it is now easier to extract useful disaster-related geographic information from social media big data. Additionally, many researchers have used related technology to study social media for disaster mitigation. However, few researchers have considered the extraction of public emotions (especially fine-grained emotions) as an attribute of disaster-related geographic information to aid in disaster mitigation. Combined with the powerful spatio-temporal analysis capabilities of geographical information systems (GISs), the public emotional information contained in social media could help us to understand disasters in more detail than can be obtained from traditional methods. However, the social media data is quite complex and fragmented, both in terms of format and semantics, especially for Chinese social media. Therefore, a more efficient algorithm is needed. In this paper, we consider the earthquake that happened in Ya’an, China in 2013 as a case study and introduce the deep learning method to extract fine-grained public emotional information from Chinese social media big data to assist in disaster analysis. By combining this with other geographic information data (such population density distribution data, POI (point of interest) data, etc.), we can further assist in the assessment of affected populations, explore emotional movement law, and optimize disaster mitigation strategies.

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Sui, Jia Yin, Ying Nan Wang i Zhe Ren. "The Distribution Network Reconfiguration Based on Spatial Data Mining". Applied Mechanics and Materials 427-429 (wrzesień 2013): 2748–51. http://dx.doi.org/10.4028/www.scientific.net/amm.427-429.2748.

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Distribution network reconfiguration is one of the essential functions of the DMS system; it can be attributed to a number of constraints of large-scale nonlinear combinatorial optimization problem in mathematics. The characteristic of load change of time and space will affect the results of load forecasting. Distribution network reconfiguration relies on the load forecasting results. This paper proposes the more realistic distribution reconfiguration scheme based on the GIS system with space information through spatial data mining. For the real-time and efficiency of effective assurance data, the interface design in GIS system and distribution automation system are also proposed. The geographical information and real-time information are connected seamlessly, so that this two system information is highly unified. The solution can provide the data basis for distribution reconfiguration scheme accurately, and improve power supply reliability of distribution network. It shows that through the example: GIS based on the spatial data mining can provide load in quantity, time, space prediction for the deeper research of distribution network reconfiguration.

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Kalogeras, I. S., G. Marketos i Y. Theodoridis. "A TOOL FOR COLLECTING, QUERYING AND MINING MACROSEISMIC DATA". Bulletin of the Geological Society of Greece 36, nr 3 (1.01.2004): 1406. http://dx.doi.org/10.12681/bgsg.16509.

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SEISMO-SURFER is a tool for collecting, querying and mining seismic data being developed in Java programming language using Oracle database system. The objective is to combine recent research trends and results in the fields of spatial and spatio-temporal databases, data warehouses and data mining, as well as well established visualization techniques for geographical information. The database of the tool is automatically updated from remote sources while existing possibilities allow the querying on different earthquakes parameters, the analysis of the data for extraction of useful information and the graphical representation of the results via maps, charts etc. In the present work, we extend SEISMO-SURFER to include macroseismic data collected by the Geodynamic Institute and filled in a relative database. More specifically, the seismic parameters of the strong earthquakes, stored into the SEISMO-SURFER database, are linked to the macroseismic intensities observed at different sites. Administrative information for each site, local surface geology, tectonic lines, damage photographs and detailed descriptions from newspapers are also included. University of Piraeus and Geodynamic Institute are working together to continuously update and develop SEISMO-SURFER, concerning the data included, the variety of parameters stored and the mining algorithms supported for exploiting knowledge.

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Irfan Rifai, Andri, Susanty Handayani i Ronal Al Rasyid. "Data mining applied for national road maintenance decision support system". MATEC Web of Conferences 195 (2018): 04007. http://dx.doi.org/10.1051/matecconf/201819504007.

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National roads are one of the main networks of a country’s transportation system. To maintain the performance level of national roads requires a well-structured pavement management system (PMS). The decision support system (DSS) is inseparable in the modern PMS, which required the development of a new approach for the DSS in support of national road network maintenance. The proposed model integrates data mining (DM) and geographical information system (GIS) to construct a simple DSS. DM is used to developed road maintenance optimization models, and then integrated with DSS with the help of GIS as an interface application. Historical data on the national road network in West Java, Indonesia is used as a case study. Examples show that the proposed model can determine a decision support solution efficiently. In addition, a userfriendly computer interface is developed so that PMS stakeholders can plan pavement maintenance simply and effectively.

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Mallupattu, Praveen Kumar, i Jayarama Reddy Sreenivasula Reddy. "Analysis of Land Use/Land Cover Changes Using Remote Sensing Data and GIS at an Urban Area, Tirupati, India". Scientific World Journal 2013 (2013): 1–6. http://dx.doi.org/10.1155/2013/268623.

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Land use/land cover (LU/LC) changes were determined in an urban area, Tirupati, from 1976 to 2003 by using Geographical Information Systems (GISs) and remote sensing technology. These studies were employed by using the Survey of India topographic map 57 O/6 and the remote sensing data of LISS III and PAN of IRS ID of 2003. The study area was classified into eight categories on the basis of field study, geographical conditions, and remote sensing data. The comparison of LU/LC in 1976 and 2003 derived from toposheet and satellite imagery interpretation indicates that there is a significant increase in built-up area, open forest, plantation, and other lands. It is also noted that substantial amount of agriculture land, water spread area, and dense forest area vanished during the period of study which may be due to rapid urbanization of the study area. No mining activities were found in the study area in 1976, but a small addition of mining land was found in 2003.

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Schoier, Gabriella, i Giuseppe Borruso. "Spatial Data Mining for Highlighting Hotspots in Personal Navigation Routes". International Journal of Data Warehousing and Mining 8, nr 3 (lipiec 2012): 45–61. http://dx.doi.org/10.4018/jdwm.2012070103.

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Rapid developments in the availability and access to spatially referenced information in a variety of areas have induced the need for better analytical techniques to understand the various phenomena. In particular, the authors’ analysis is an insight into a wealth of geographical data collected by individuals as activity dairy data. The attention is drawn on point datasets corresponding to GPS traces driven along a same route in different days. In this paper, the authors explore the presence of clusters along the route, trying to understand the origins and motivations behind that to better understand the road network structure in terms of ’dense’ spaces along the network. Therefore, the attention is focused on methods to highlight such clusters and see their impact on the network structure. Spatial clustering algorithms are examined (DBSCAN) and a comparison with other non-parametric density based algorithm (Kernel Density Estimation) is performed. Different tests are performed over the urban area of Trieste (Italy), considering both multiple users and different origin/destination journeys.

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ZHENG, X. L. "RESEARCH AND APPLICATION OF GIS AND DATA MINING TECHNOLOGY IN MONITORING AND ASSESSMENT OF NATURAL GEOGRAPHY ENVIRONMENT X". Latin American Applied Research - An international journal 48, nr 4 (31.10.2018): 299–304. http://dx.doi.org/10.52292/j.laar.2018.244.

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At present, with the acceleration of the economic development process, the maintenance of the ecological environment has received extensive attention. In order to simplify the workflow of natural geographical environment monitoring and evaluation, this paper combines GIS technology and data mining technology, and builds a decision tree model with monitoring and evaluation as the core. Dongting Lake is taken as the research object to verify the validity of the model. The research results show that the algorithm designed in this paper can classify the land types of natural geographical environment and improve the accuracy of environmental monitoring and evaluation.

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Mushtaq, Muhammad Faheem, Mian Muhammad Sadiq Fareed, Mubarak Almutairi, Saleem Ullah, Gulnaz Ahmed i Kashif Munir. "Analyses of Public Attention and Sentiments towards Different COVID-19 Vaccines Using Data Mining Techniques". Vaccines 10, nr 5 (22.04.2022): 661. http://dx.doi.org/10.3390/vaccines10050661.

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COVID-19 is a widely spread disease, and in order to overcome its spread, vaccination is necessary. Different vaccines are available in the market and people have different sentiments about different vaccines. This study aims to identify variations and explore temporal trends in the sentiments of tweets related to different COVID-19 vaccines (Covaxin, Moderna, Pfizer, and Sinopharm). We used the Valence Aware Dictionary and Sentiment Reasoner (VADER) tool to analyze the public sentiments related to each vaccine separately and identify whether the sentiments are positive (compound ≥ 0.05), negative (compound ≤ −0.05), or neutral (−0.05 < compound < 0.05). Then, we analyzed tweets related to each vaccine further to find the time trends and geographical distribution of sentiments in different regions. According to our data, overall sentiments about each vaccine are neutral. Covaxin is associated with 28% positive sentiments and Moderna with 37% positive sentiments. In the temporal analysis, we found that tweets related to each vaccine increased in different time frames. Pfizer- and Sinopharm-related tweets increased in August 2021, whereas tweets related to Covaxin increased in July 2021. Geographically, the highest sentiment score (0.9682) is for Covaxin from India, while Moderna has the highest sentiment score (0.9638) from the USA. Overall, this study shows that public sentiments about COVID-19 vaccines have changed over time and geographically. The sentiment analysis can give insights into time trends that can help policymakers to develop their policies according to the requirements and enhance vaccination programs.

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Dallagassa, Marcelo Rosano, Franciele Iachecen, Deborah Ribeiro Carvalho i Sergio Ossamu Ioshii. "Design of a geospatial model applied to Health management". Revista Brasileira de Enfermagem 72, nr 2 (kwiecień 2019): 420–26. http://dx.doi.org/10.1590/0034-7167-2018-0589.

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ABSTRACT Objective: To identify geographically the beneficiaries categorized as prone to Type 2 Diabetes Mellitus, using the recognition of patterns in a database of a health plan operator, through data mining. Method: The following steps were developed: the initial step, the information survey. Development, construction of the process of extraction, transformation, and loading of the database. Deployment, presentation of the geographical information through a georeferencing tool. Results: As a result, the mapping of Paraná according to its health care network and the concentration of Type 2 Diabetes Mellitus is presented, enabling the identification of cause-and-effect relationships. Conclusion: It is concluded that the analysis of georeferenced information, linked to health information obtained through the data mining technique, can be an excellent tool for the health management of a health plan operator, contributing to the decision-making process in Health.

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Pandey, Sachin Kumar. "An Analysis of Association Rule Mining Algorithm Techniques Geographical Point of Interest in Big Data". International Journal of Computer Sciences and Engineering 6, nr 6 (30.06.2018): 776–81. http://dx.doi.org/10.26438/ijcse/v6i6.776781.

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Antunes, Ana, João Botelho, José João Mendes, Ana Sintra Delgado, Vanessa Machado i Luís Proença. "Geographical Distribution of Periodontitis Risk and Prevalence in Portugal Using Multivariable Data Mining and Modeling". International Journal of Environmental Research and Public Health 19, nr 20 (20.10.2022): 13634. http://dx.doi.org/10.3390/ijerph192013634.

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We aimed to estimate the geographical distribution of periodontitis prevalence and risk based on sociodemographic and economic data. This study used sociodemographic, economic, and health services data obtained from a regional survey and governmental open data sources. Information was gathered for all 308 Portuguese municipalities and compiled in a large set of 52 variables. We employed principal component analysis (PCA), factor analysis (FA) and clustering techniques to model the Portuguese nationwide geographical distribution of the disease. Estimation of periodontitis risk for each municipality was achieved by calculation of a normalized score, obtained as an adjusted linear combination of six independent factors that were extracted through PCA/FA. The municipalities were also classified according to a quartile-based risk grade in each cluster. Additionally, linear regression was used to estimate the periodontitis prevalence within the peri-urban municipality clusters, accounting for 30.5% of the Portuguese population. A total of nine municipality clusters were obtained with the following characteristics: mainly rural/low populated, including small villages (one), partly rural, including small cities (two), mainly urban/peri-urban, including medium-sized to large cities (4), and urban/large cities (2). Within the clusters, a higher periodontitis risk was identified for municipalities with lower income, older populations. The estimated periodontitis prevalence for the 18 municipalities included in the four peri-urban clusters ranged from 41.2% to 69.0%. Periodontitis prevalence estimates range from 41.2% to 69.0% for the municipalities characterized as peri-urban and mainly urban, most of them located in the Lisbon Metropolitan Area, the tenth largest in Europe.

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Akay, A., A. Dragomir, A. Yardimci, D. Canatan, A. Yesilipek i B. W. Pogue. "A Data-Mining Approach for Investigating Social and Economic Geographical Dynamics of $\beta$-Thalassemia's Spread". IEEE Transactions on Information Technology in Biomedicine 13, nr 5 (wrzesień 2009): 774–80. http://dx.doi.org/10.1109/titb.2009.2020062.

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Zeng, Xuhui, Shu Wang, Yunqiang Zhu, Mengfei Xu i Zhiqiang Zou. "A Knowledge Graph Convolutional Networks Method for Countryside Ecological Patterns Recommendation by Mining Geographical Features". ISPRS International Journal of Geo-Information 11, nr 12 (15.12.2022): 625. http://dx.doi.org/10.3390/ijgi11120625.

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The recommendation system is one of the hotspots in the field of artificial intelligence that can be applied to recommend suitable ecological patterns for the countryside. Countryside ecological patterns mean advanced patterns that can be recommended to those developing areas which have similar geographical features, which provides huge benefits for countryside development. However, current recommendation methods have low recommendation accuracy due to some limitations, such as data-sparse and ‘cold start’, since they do not consider the complex geographical features. To address the above issues, we propose a geographical Knowledge Graph Convolutional Networks method for Countryside Ecological Patterns Recommendation (KGCN4CEPR). Specifically, a geographical knowledge graph of countryside ecological patterns is established first, which makes up for the sparsity of countryside ecological pattern data. Then, a convolutional network for mining the geographical similarity of ecological patterns is designed among adjacent countryside, which effectively solves the ‘cold start’ problem in the existing recommended methods. The experimental results show that our KGCN4CEPR method is suitable for recommending countryside ecological patterns. Moreover, the proposed KGCN4CEPR method achieves the best recommendation accuracy (60%), which is 9% higher than the MKR method and 6% higher than the RippleNet method.

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Yang, Sai Ming, i Wen Guang Lu. "Decision Support System of Ecological Security in Mining Area". Advanced Materials Research 361-363 (październik 2011): 1810–13. http://dx.doi.org/10.4028/www.scientific.net/amr.361-363.1810.

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Ecological security is the basis of the sustainable development. Based on the concept of ecological security, The framework and subsystems’ functions of decision support system for mining area’s ecological security are put forward. The support system, which gets data by methods of remote sensing(RS),geographical information system(GIS),global positioning system(GPS), survey data, statistics data and experiment data, being mainly composed of database management system, knowledge management system and models management system, can make regulating and controlling decision for mining area’s ecological security.

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Wang, Fei Chao. "A Novel Approach to Mine Knowledge from Social Images". Advanced Materials Research 430-432 (styczeń 2012): 1068–71. http://dx.doi.org/10.4028/www.scientific.net/amr.430-432.1068.

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With the popularity of various social media website, currently, lots of social images attached with different kinds of metadata have been uploaded to social media websites. Mining useful knowledge from social images has been an emerging important research topic in web search and data mining. In this paper, we propose a novel approach to find geographical difference of a given concept from social image community. We put a given concept to social image community, and then downloaded social images with metadata, particularly, the place where the photo was taken should be provided in advance. Firstly, concept is submitted to social image community, and then social images with different kinds of metadata are downloaded. Secondly, social images are clustered according to metadata of images. Finally, the information of concept’s geographical difference is found. Experiments conducted on social image community proof the effectiveness of our approach. Keywords: Social Images, Data Mining, Social Image Community, Image Clustering.

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Alguliev, Rasim M., Ramiz M. Aliguliyev i Saadat A. Nazirova. "Classification of Textual E-Mail Spam Using Data Mining Techniques". Applied Computational Intelligence and Soft Computing 2011 (2011): 1–8. http://dx.doi.org/10.1155/2011/416308.

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A new method for clustering of spam messages collected in bases of antispam system is offered. The genetic algorithm is developed for solving clustering problems. The objective function is a maximization of similarity between messages in clusters, which is defined byk-nearest neighbor algorithm. Application of genetic algorithm for solving constrained problems faces the problem of constant support of chromosomes which reduces convergence process. Therefore, for acceleration of convergence of genetic algorithm, a penalty function that prevents occurrence of infeasible chromosomes at ranging of values of function of fitness is used. After classification, knowledge extraction is applied in order to get information about classes. Multidocument summarization method is used to get the information portrait of each cluster of spam messages. Classifying and parametrizing spam templates, it will be also possible to define the thematic dependence from geographical dependence (e.g., what subjects prevail in spam messages sent from certain countries). Thus, the offered system will be capable to reveal purposeful information attacks if those occur. Analyzing origins of the spam messages from collection, it is possible to define and solve the organized social networks of spammers.

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Darwance, R. Sari, M. S. Anwar i D. Saputra. "The Effect of oil palm plantations on the reputation of white pepper as the geographical indications of environmental aspect". IOP Conference Series: Earth and Environmental Science 1108, nr 1 (1.11.2022): 012054. http://dx.doi.org/10.1088/1755-1315/1108/1/012054.

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Abstract Besides tin mining, oil palm plantations are also being carried out. On the other hand, this region is well known as a producer of good quality white pepper which in 2010 its reputation was legally registered as a geographical indication. This protection is given due to the environmental factor in this region which cause the quality of white pepper in this region is different from pepper in other region. However, the massive opening of oil palm plantations causes Pepper plantation area in Bangka and Belitung continues to decrease. This study uses empirical juridical methods; linking the effect of tin mining and oil palm plantations with the reputation of white pepper as a geographical indication. It uses primary and secondary data in which the primary is obtained from the results of interviews while the secondary data is obtained from literature study data on a number of documents. From the results of the conducted study, it was found that the oil palm plantations which reduce the area of pepper plantations and also change the quality make impact to the reputation. The impact can eliminate the reputation of white pepper as a juridical geographical indication and at the same time automatically end the legal protection of white pepper as a geographical indication.

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Forghani, M., i F. Karimipour. "EXTRACTING HUMAN BEHAVIORAL PATTERNS BY MINING GEO-SOCIAL NETWORKS". ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-2/W3 (22.10.2014): 115–20. http://dx.doi.org/10.5194/isprsarchives-xl-2-w3-115-2014.

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Accessibility of positioning technologies such as GPS offer the opportunity to store one’s travel experience and publish it on the web. Using this feature in web-based social networks and considering location information shared by users as a bridge connecting the users’ network to location information layer leads to the formation of Geo-Social Networks. The availability of large amounts of geographical and social data on these networks provides rich sources of information that can be utilized for studying human behavior through data analysis in a spatial-temporal-social context. This paper attempts to investigate the behavior of around 1150 users of Foursquare network by making use of their check-ins. The authors analyzed the metadata associated with the whereabouts of the users, with an emphasis on the type of places, to uncover patterns across different temporal and geographical scales for venue category usage. The authors found five groups of meaningful patterns that can explore region characteristics and recognize a number of major crowd behaviors that recur over time and space.

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Todor, Scrobota, Todor, Lucan i Vaida. "Environmental Factors Associated with Malocclusion in Children Population from Mining Areas, Western Romania". International Journal of Environmental Research and Public Health 16, nr 18 (12.09.2019): 3383. http://dx.doi.org/10.3390/ijerph16183383.

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Our study is based on the hypothesis that the prevalence of malocclusions in children is higher in the mining areas from North-Western (NW) Romania than in other geographic areas. We also considered that the distribution of the different types of malocclusions can be correlated with environmental factors. Therefore, the main purpose of the current study was to assess the prevalence of malocclusions in children from the mining areas in NW Romania. Another purpose was to establish the influence of certain environmental factors such as gender, geographical area of origin, and ethnicity on the distribution of malocclusions in order to provide an epidemiological reference for the planning of preventive and treatment programs adapted to the particularity of the mining areas. This cross-sectional study was performed in 2015–2016. The study batch consisted of 960 children from the mining areas, aged 7–14 years, in the period of mixed dentition and early permanent dentition. The clinical examination was conducted by a single examiner, an orthodontic specialist (TBI), in order to avoid inter-operator bias. Occlusion was registered according to Bjoerk. Occlusal clinical signs were followed for the determination of malocclusions. Most children had malocclusions (93.5%). The percentage of anomalies was significantly higher in subjects from Rosia Montana, in girls, and in the Romanians. Data showed that Angle Class I was the most prevalent malocclusion (60.21%), followed by crowding (47.5%), midline shift (43.33%), and deep bite (28.65%). The independent association between ethnicity and total malocclusions shows that the Romanian subjects presented a 3.31 higher chance of developing malocclusions than the Romani ones. The presence of malocclusions was independently influenced by all the studied environmental factors, namely gender, geographical area, and ethnicity. Our results could be relevant for oral health policy-making, i.e., planning preventive and treatment measures of malocclusions, adapted to the peculiarity of the studied mining areas.

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Mierzejowska, Aleksandra, i Monika Żogała. "The characteristics of geographical information systems in terms of their current use". Journal of Water and Land Development 39, nr 1 (1.12.2018): 101–8. http://dx.doi.org/10.2478/jwld-2018-0064.

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AbstractFrom the beginning of human existence, man collected and analysed information about the space that surrounded him. Nowadays, due to the huge amount of data, it would be impossible without geographic information systems. According to the definition, the main function of GIS (Geographic Information Systems) is to collect, process, integrate, analyse and present data directly collected in the database or obtained through analysis. The basis of each information system’s functioning is the collection of relevant data about real-world objects, in terms of their completeness, quality and reliability. Due to the very rapid development of information technology, GIS systems have found a wide application, eg. in spatial planning, real estate management, administration, infrastructure management and many other areas of life. The authors within the article have made a detailed review of the current using of GIS, with particular emphasis on the mining industry. In the latter, particular attention was focused on the use of this type of tools to monitor and analyse the effects of mining activities. At the same time, the authors indicated new possibilities related to the application of geographic information systems in this branch.

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Yan, Liang, Hao Wang, Zhaokun Wang, Tingting Wu, Wandi Fu i Xu Zhang. "Differentially Private Timestamps Publishing in Trajectory". Electronics 12, nr 2 (10.01.2023): 361. http://dx.doi.org/10.3390/electronics12020361.

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In recent years, location-based social media has become popular, and a large number of spatiotemporal trajectory data have been generated. Although these data have significant mining value, they also pose a great threat to the privacy of users. At present, many studies have realized the privacy-preserving mechanism of location data in social media in terms of data utility and privacy preservation, but rarely have any of them considered the correlation between timestamps and geographical location. To solve this problem, in this paper, we first propose a k-anonymity-based mechanism to hide the user’s specific time segment during a single day, and then propose an optimized truncated Laplacian mechanism to add noise to each data grid (the frequency of time data) of the anonymized time distribution. The time data after secondary processing are fuzzy and uncertain, which not only protects the privacy of the user’s geographical location from the time dimension but also retains a certain value of data mining. Experiments on real datasets show that the TDP privacy-preserving model has good utility.

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Zou, Lei. "Visualizing and Mining Social Media Data for Smart Emergency Management". Abstracts of the ICA 1 (15.07.2019): 1–2. http://dx.doi.org/10.5194/ica-abs-1-442-2019.

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Abstract. The ability of a community to prepare for, absorb, recover from, and more successfully adapt to disastrous events is defined as disaster resilience. Disaster resilience can be better understood by investigating human behaviors during the four phases of emergency management – preparedness, response, recovery, and mitigation. However, a major challenge is that data describing communities’ behaviors in different phases of emergency management are difficult to access through traditional databases. Social media such as Twitter is increasingly being used as an effective platform to observe human behaviors in disastrous events. These responses and behaviors could be better understood by analyzing real-time social media data through categorizing them into different phases of the emergency management.This research studies the Twitter use during 2012 Hurricane Sandy and 2017 Hurricane Harvey, which struck the U.S. northeast and south coasts, respectively. The objectives are fourfold: (1) to develop a Twitter data mining and visualization framework and a set of indexes for emergency management and resilience analysis; (2) to visualize the spatial-temporal patterns of disaster-related Twitter activities during the two hurricane events; (3) to examine and compare the social-geographical disparities of disaster-related Twitter activities during Sandy and Harvey; and (4) to build applications using social media data for smart management, including surveying human behaviors and emergency rescue.

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Das, Abinash. "Application of Spatial Data Mining in the Process of Discovering Interesting and Previously Unknown, but Potentially Useful, Patterns from Large Spatial Datasets". TechnoareteTransactions on Intelligent Data Mining and Knowledge Discovery 1, nr 1 (18.11.2022): 6–10. http://dx.doi.org/10.36647/ttidmkd/01.01.a002.

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Spatial data mining has been typically used in the Geographical Information system from physical datasets and locations to real-world events. One of the procedures generally used in capital data mining is vector data representation. Vector data is the most commonly used data across the world. Information in this format consists of tips, angles, and quadrilaterals. It is the simplest method of analysing the data where the vector data consists of tips correlate pairs to indicate a physical location in the world. These points can be joined in a particular way to form closed areas marked as quadrilaterals. Vector data is extremely useful for storing and representing data that has discrete boundaries such as international borders, streets, buildings, and many more. Modern technologies such as Google use geological information and open street maps to represent the data in vector data stricture wise. Keyword : Spatial data mining, Vector data, Information system, Security, Data

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dos Santos, Bruno Dias, Carolina Moutinho Duque de Pinho, Gilberto Eidi Teramoto Oliveira, Thales Sehn Korting, Maria Isabel Sobral Escada i Silvana Amaral. "Identifying Precarious Settlements and Urban Fabric Typologies Based on GEOBIA and Data Mining in Brazilian Amazon Cities". Remote Sensing 14, nr 3 (2.02.2022): 704. http://dx.doi.org/10.3390/rs14030704.

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Although 70% of the Amazon population lives in urban areas, studies on the urban Amazon are scarce. Much of the urban Amazon population lives in precarious settlements. The distinctiveness and diversity of Amazonian precarious settlements are vast and must be identified to be considered in the development of appropriate public policies. Aiming at investigating precarious settlements in Amazon, this study is guided by the following questions: For the Brazilian Amazon region, is it possible to identify areas of precarious settlements by combining geoprocessing and remote sensing techniques? Are there different typologies of precarious settlements distinguishable by their spatial arrangements? Thus, we developed a methodology for identifying precarious settlements and subsequently classifying them into urban fabric typologies (UFT), choosing the cities of Altamira, Cametá, and Marabá as study sites. Our classification model utilized geographic objects-based image analysis (GEOBIA) and data mining of spectral data from WPM sensor images from the CBERS-4A satellite, jointly with texture metrics, context metrics, biophysical index, voluntary geographical information, and neighborhood relationships. With the C5.0 decision tree algorithm we carried out variable selection and classification of these geographic objects. Our estimated models show accuracy above 90% when applied to the study sites. Additionally, we described Amazonian UFT in six types to be identified. We concluded that Amazonian precarious settlements are morphologically diverse, with an urban fabric different from those commonly found in Brazilian metropolitan areas. Identifying and characterizing distinct precarious areas is vital for the planning and development of sustainable and effective public policies for the urban Amazon.

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Liu, H. C., G. J. He, X. M. Zhang, W. Jiang i S. G. Ling. "Spatiotemporal Mining of Time-Series Remote Sensing Images Based on Sequential Pattern Mining". ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-4/W2 (10.07.2015): 111–18. http://dx.doi.org/10.5194/isprsannals-ii-4-w2-111-2015.

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With the continuous development of satellite techniques, it is now possible to acquire a regular series of images concerning a given geographical zone with both high accuracy and low cost. Research on how best to effectively process huge volumes of observational data obtained on different dates for a specific geographical zone, and to exploit the valuable information regarding land cover contained in these images has received increasing interest from the remote sensing community. In contrast to traditional land cover change measures using pair-wise comparisons that emphasize the compositional or configurational changes between dates, this research focuses on the analysis of the temporal sequence of land cover dynamics, which refers to the succession of land cover types for a given area over more than two observational periods. Using a time series of classified Landsat images, ranging from 2006 to 2011, a sequential pattern mining method was extended to this spatiotemporal context to extract sets of connected pixels sharing similar temporal evolutions. The resultant sequential patterns could be selected (or not) based on the range of support values. These selected patterns were used to explore the spatial compositions and temporal evolutions of land cover change within the study region. Experimental results showed that continuous patterns that represent consistent land cover over time appeared as quite homogeneous zones, which agreed with our domain knowledge. Discontinuous patterns that represent land cover change trajectories were dominated by the transition from vegetation to bare land, especially during 2009–2010. This approach quantified land cover changes in terms of the percentage area affected and mapped the spatial distribution of these changes. Sequential pattern mining has been used for string mining or itemset mining in transactions analysis. The expected novel significance of this study is the generalization of the application of the sequential pattern mining method for capturing the spatial variability of landscape patterns, and their trajectories of change, to reveal information regarding process regularities with satellite imagery.

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Chaker, Rawad, i Rémi Bachelet. "Internationalizing Professional Development: Using Educational Data Mining to Analyze Learners’ Performance and Dropouts in a French MOOC". International Review of Research in Open and Distributed Learning 21, nr 4 (2.07.2020): 199–221. http://dx.doi.org/10.19173/irrodl.v21i4.4787.

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This paper uses data mining from a French project management MOOC to study learners’ performance (i.e., grades and persistence) based on a series of variables: age, educational background, socio-professional status, geographical area, gender, self- versus mandatory-enrollment, and learning intentions. Unlike most studies in this area, we focus on learners from the French-speaking world: France and French-speaking European countries, the Caribbean, North Africa, and Central and West Africa. Results show that the largest gaps in MOOC achievements occur between 1) learners from partner institutions versus self-enrolled learners 2) learners from European countries versus low- and middle-income countries, and 3) learners who are professionally active versus inactive learners (i.e., with available time). Finally, we used the CHAID data-mining method to analyze the main characteristics and discriminant factors of MOOC learner performance and dropout.

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Dateu, M., i K. Seidel. "Image information mining : exploration of Earth observation archives". Geographica Helvetica 58, nr 2 (30.06.2003): 154–68. http://dx.doi.org/10.5194/gh-58-154-2003.

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Abstract. The new generation of high resolution imaging satellites acquires huge amounts of data which are stored in large archives. The state-of-the-art Systems for data access allow only queries by geographical location, time of acquisition or type of sensor. This information is often less important than the content of the scene, i.e. structures, objects or scattering properties. Meanwhile, many new applications of remote sensing data are closer to Computer vision and require the knowledge of complicated spatial and structural relationships among image objects. We are creating an intelligent satellite information mining system, a next generation architecture to help users to rapidly collect information, a tool to enhance and to manage the huge amount of historical and newly acquired satellite data-sets by giving experts access to relevant information in an understandable and directly usable form and to provide friendly interfaces for information query and browsing. Research topics are within the frame of Bayesian learning, content-based querying, data modelling and adaptation to user conjecture.

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Purwanto, Ajun, i Dony Andrasmoro. "The Utilization of Remote Sensing and Geographic Information Systems for Monitoring Damage of The Mandor Natural Reserves in West Kalimantan Province". GeoEco 7, nr 2 (29.04.2021): 145. http://dx.doi.org/10.20961/ge.v7i2.44837.

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This study aims to determine the level of damage to the protected area of the Mandor Nature Reserve in 2019 by utilizing Remote Sensing and Geographical Information Systems. The method used in this research is secondary data analysis. The data used are the boundaries, area, condition of the Nature Reserve area, and landscape imagery 8. The data collection technique uses secondary data interpretation, while the tools used are Landsat Image 8. The data analysis technique uses the Geographic Information System with ArcGis 10.2.2 software. The results of interpretation of Landsat 8 images recorded on July 5, 2019, and analysis using Arcgis 10.2.2, it is known that from the total forest area of 3.080 ha, the condition of the Nature Reserve is 2,052, 327 ha are still in good condition or have not been damaged, 234,443 ha are lightly damaged, 66,421 ha moderately damaged and 726,809 Ha heavily damaged. Most of the damage to protected areas in the form of Nature Reserve forests was caused by illegal mining activities, namely unauthorized gold mining, forest fires, and forest encroachment or cultivation.

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Trjapitzin, V. A. "A brief review of species of the genus Parablastothrix with description of a new species from Mexico (Hymenoptera: Encyrtidae)". Zoosystematica Rossica 18, nr 2 (25.12.2009): 291–94. http://dx.doi.org/10.31610/zsr/2009.18.2.291.

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A diagnosis of the genus Parablastothrix Mercet, 1917 and a list of its species with some data on their geographical distribution and hosts, leaf-mining Lepidoptera, are provided. A new species P. ninelpetrovae from Mexico is described and compared with P. nearctica Miller, 1965 from USA.

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ANDOH, AKIRA, TOSHIO KOBAYASHI, HIROYUKI KUZUOKA, YASUO SUZUKI, TOSHIYUKI MATSUI, SHIRO NAKAMURA, TAKAYUKI MATSUMOTO, YOSHIHIDE FUJIYAMA i TADAO BAMBA. "Data mining analysis of terminal restriction fragment length polymorphism shows geographical differences in the human gut microbiota". Biomedical Reports 1, nr 4 (30.05.2013): 559–62. http://dx.doi.org/10.3892/br.2013.127.

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Kamp, Vera, Jean Pierre Knust, Reinhard Moratz, Kevin Stehn i Soeren Stoehrmann. "Data Mining for Integration and Verification of Socio-Geographical Trend Statements in the Context of Conflict Risk". International Journal of Artificial Intelligence & Applications 10, nr 4 (31.07.2019): 1–8. http://dx.doi.org/10.5121/ijaia.2019.10401.

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PAPAKOSTA, Malamati A., Kyriaki KITIKIDOU, Dimitrios E. BAKALOUDIS, Christos G. VLACHOS, Evangelos CHATZINIKOS, Olga ALEXANDROU i Anastasios SAKOULIS. "Geographical variation in morphometry, craniometry, and diet of amammalian species (Stone marten, Martes foina) using data mining". TURKISH JOURNAL OF ZOOLOGY 42 (2018): 99–106. http://dx.doi.org/10.3906/zoo-1611-29.

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Karageorgiou, M. M. D., E. Karymbalis i D. E. Karageorgiou. "THE USE OF THE GEOGRAPHICAL INFORMATION SYSTEMS (G.I.S) IN THE GEOLOGICAL – MINERALOGICAL MAPPING OF THE PARANESTI AREA". Bulletin of the Geological Society of Greece 43, nr 3 (24.01.2017): 1601. http://dx.doi.org/10.12681/bgsg.11334.

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The aim of this study is to emphasize the role of the Geographical Information Systems (GIS) technology for the collection, organization, analysis, modeling and presentation of data required for the geological and ore-deposits mapping. As a case study area the region of Paranesti in Northern Greece was selected as it is of exceptional geological and uranium mining interest. For the geological and ore deposit mapping of the study area data derived from analogue maps at various scales (topographical and geological) along with detailed field geological observations and measurements were organized in a spatial database with a common geographical coordinate system utilizing GIS technology. This procedure revealed the relation between the geographical distribution of uranium deposits and the geological structure of the area as well as the effect of the active tectonics.

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Zhang, Hong Mei, i Yang Gao. "The Dynamic Monitoring of Coal Mining Seeper Subsidence District Based on RS and GIS Technology — A Case Study of Huainan City". Applied Mechanics and Materials 256-259 (grudzień 2012): 2311–14. http://dx.doi.org/10.4028/www.scientific.net/amm.256-259.2311.

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Using the 1987 TM and 2006 ETM+ remote sensing images and other related ancillary geographical data of Huainan city, combined with GIS Technology, the dynamic change of the coal mining subsidence of Huainan city was extracted. In order to unify the two period image data difference because of the different date, the research used modified normalized difference water index (MNDWI) to get the information of seeper subsidence in coal mining district. Using the change detection function of RS, Combined with the spatial analysis and statistics function of GIS, all the subsidence area of coal mining district and the seeper subsidence area were extracted. The research shows that the seeper subsidence extent is different because of the mining period, scale, method, groundwater level and groundwater extraction, surface coverage, roof’s management and other coal seam geological environments, especially dip angle of coal seam. The Xiejiaji and Bagongshan mine’s mining seeper subsidence are the serious due to its bigger dip angle of coal seam, the seeper subsidence percentage are 38.57%, 25.95% respectively, while Jiulonggang- Datong mining seeper subsidence is the least even if it’s the oldest and abandoned mine in Huainan.

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