scholarly journals Improved Density Based Spatial Clustering of Applications of Noise Clustering Algorithm for Knowledge Discovery in Spatial Data

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Arvind Sharma ◽  
R. K. Gupta ◽  
Akhilesh Tiwari
Keyword(s):  
Data Mining ◽  
Information System ◽  
Geographic Information System ◽  
Spatial Data ◽  
Spatial Clustering ◽  
Spatial Data Mining ◽  
Geographic Information ◽  
Data Sets ◽  
Clustering Methods ◽  
Data Objects

There are many techniques available in the field of data mining and its subfield spatial data mining is to understand relationships between data objects. Data objects related with spatial features are called spatial databases. These relationships can be used for prediction and trend detection between spatial and nonspatial objects for social and scientific reasons. A huge data set may be collected from different sources as satellite images, X-rays, medical images, traffic cameras, and GIS system. To handle this large amount of data and set relationship between them in a certain manner with certain results is our primary purpose of this paper. This paper gives a complete process to understand how spatial data is different from other kinds of data sets and how it is refined to apply to get useful results and set trends to predict geographic information system and spatial data mining process. In this paper a new improved algorithm for clustering is designed because role of clustering is very indispensable in spatial data mining process. Clustering methods are useful in various fields of human life such as GIS (Geographic Information System), GPS (Global Positioning System), weather forecasting, air traffic controller, water treatment, area selection, cost estimation, planning of rural and urban areas, remote sensing, and VLSI designing. This paper presents study of various clustering methods and algorithms and an improved algorithm of DBSCAN as IDBSCAN (Improved Density Based Spatial Clustering of Application of Noise). The algorithm is designed by addition of some important attributes which are responsible for generation of better clusters from existing data sets in comparison of other methods.

Untangling origin-destination flows in geographic information systems

2017 ◽  
Vol 18 (1) ◽  
pp. 153-172 ◽  
Author(s):  
Anita Graser ◽  
Johanna Schmidt ◽  
Florian Roth ◽  
Norbert Brändle
Keyword(s):  
Information System ◽  
Information Systems ◽  
Geographic Information System ◽  
Geographic Information Systems ◽  
Spatial Data ◽  
Geographic Information ◽  
Data Sets ◽  
Flow Maps ◽  
Local Strength ◽  
Edge Bundling

Origin–destination flow maps are a popular option to visualize connections between different spatial locations, where specific routes between the origin and destination are unknown or irrelevant. Visualizing origin–destination flows is challenging mainly due to visual clutter which appears quickly as data sets grow. Clutter reduction techniques are intensively explored in the information visualization and cartography domains. However, current automatic techniques for origin–destination flow visualization, such as edge bundling, are not available in geographic information systems which are widely used to visualize spatial data, such as origin–destination flows. In this article, we explore the applicability of edge bundling to spatial data sets and necessary adaptations under the constraints inherent to platform-independent geographic information system scripting environments. We propose (1) a new clustering technique for origin–destination flows that provides within-cluster consistency to speed up computations, (2) an edge bundling approach based on force-directed edge bundling employing matrix computations, (3) a new technique to determine the local strength of a bundle leveraging spatial indexes, and (4) a geographic information system–based technique to spatially offset bundles describing different flow directions. Finally, we evaluate our method by applying it to origin–destination flow data sets with a wide variety of different data characteristics.

Techniques for Building Multijurisdictional Geographic Information System Platforms for Transportation Analysis

2002 ◽  
Vol 1804 (1) ◽  
pp. 187-195
Author(s):  
Kai Han ◽  
Jeannette Montufar ◽  
Scott Minty ◽  
Alan Clayton
Keyword(s):  
Information System ◽  
Geographic Information System ◽  
Data Sharing ◽  
Spatial Data ◽  
Geographic Information ◽  
Data Sources ◽  
Data Sets ◽  
Data Interoperability ◽  
Gis Data ◽  
Transportation Applications

Transportation analysis involving multiple jurisdictions requires data sharing and spatial data interoperability among geographic information system (GIS) data sets. Data sharing and spatial data interoperability issues related to multijurisdictional transportation analysis are discussed. Specific techniques based on practical data-sharing, problem-solving experience are developed. To further enhance the data-sharing process, a conceptual framework is established to guide technique implementations. Regional GIS transportation (GIS-T) platforms integrated from various data sources by applying the framework and the associated techniques are also presented. To better support different transportation applications, an open GIS-T platform is proposed, consisting of a series of customized base maps, each tailored to suit individual applications and, as a whole, linked together by inherently established interoperability.

Development of the geoinformation system for hunting farms.

2017 ◽  
Author(s):  
М.Р.  Вагизов ◽  
Д.А. Дубов
Keyword(s):  
Information System ◽  
Geographic Information System ◽  
Organizational Structure ◽  
Spatial Data ◽  
Web Application ◽  
Geographic Information ◽  
Educational Process ◽  
Information Support ◽  
Quality Level ◽  
Strategy Of Development

Рассматривается необходимость разработки специализированной геоинформационной системы для отрасли охотничьего хозяйства. Указаны основные принципы проектирования и разработки приложения. Определены необходимые требования к проектируемой ГИС и задачи, которые способна решать система. Проведено описание организационной структуры геоинформационной системы и функции, подлежащие визуализации. Составлено схемотехнические решение и визуализация графического интерфейса взаимодействия пользователя с системой. В соответствии со Стратегией развития охотничьего хозяйства РФ до 2030 года, требуется повышать программно-информационное обеспечение охотпользователей. Обоснованием разработки является отсутствие единой системы сбора, хранения и систематизации данных о состоянии охотхозяйств, численности популяций и проведении необходимых мероприятий. Разработка специальной ГИС способна обеспечить поддержку в системе принятия решений человеком в интересах управления территориями охотхозяйств. Разработка продуманного интерактивного интерфейса позволит вывести ГИС на наиболее качественный уровень взаимодействия. Общеизвестно, что пространственные данные являются основой информационного обеспечения геоинформационных систем, в том числе локальных ГИС. Для непрерывного обновления геоданных требуется своевременная актуализация информации на сервере, в которой данная система развернута. Поэтому в качестве технологической основы выбрано проектирование именно веб-приложения, работающего через предустановленный браузер пользователя. Реализация функций данной ГИС, продуманный человеко-машинный интерфейс, включенный в ее состав, позволит использовать данную систему не только охотпользователям, но и заинтересованным специалистам в смежных отраслях: лесное хозяйство, картография, геоинформатика, зоогегография, охотоведение. Разработанная система может использоваться в учебном процессе при подготовке студентов по различным направлениям в высших образовательных учреждениях. Discusses the necessity of developing a specialized geographic information system for hunting groung. Carried out a description of the organizational structure of geoinformation systems and functions to be rendering. Determined necessary requirements for the design of the GIS and the tasks which the system are able to solve. The organizational structure of the geographic information system and the functions to be visualized are described. Compiled technical scheme and visualization GUI. In accordance with the strategy of development of the hunting industry of the Russian Federation until 2030, it is required to increase the software and information support for hunting users. The basis for development is the lack of a unified system of collection, storage and systematization of data on the state of hunting, population and carry out the necessary measures. The development of a special GIS is able to provide support in the system of decision-making by a person in the interests of managing the territories of hunting farms. The development of a thoughtful interactive interface will bring the GIS to the most high-quality level of interaction. It is well known that the spatial data are the basis for information support of geographic information systems, including local GIS. For continuous updates of the geodata requires a timely update information on the server in which the system is deployed. Therefore, as the technological base of the selected design it is the web application running through a preset user's browser. The implementation of the functions of this GIS, intelligent man-machine interface, included in its composition, will allow you to use this system not only hunters, but also to interested professionals in related industries, forestry, cartography, geoinformatics, zoogeography, and hunting. The developed system can be used in educational process for training students in various areas in higher educational institutions.

scholarly journals Geographic Information System Produksi Energi dan Pertambangan Kabupaten Musi Banyuasin

2018 ◽  
Vol 3 (3) ◽  
pp. 360-369 ◽  
Author(s):  
Usman Ependi
Keyword(s):  
Decision Making ◽  
Information System ◽  
Geographic Information System ◽  
Spatial Data ◽  
Open Data ◽  
Geographic Information ◽  
Extreme Programming ◽  
Programming Approach ◽  
User Needs ◽  
Government Control

Energy and mining reporting have to conduct for the exploration company in order to make control while exploration. Government control can perform by making profiling of energy and mining data that exist in the area as consideration in taking policy or decision. Stages of energy and mining reporting are very important to do especially in areas that have energy and mining resources such as Musi Banyuasin regency. Profiling can performed by mapping the location of energy and mining results using a geographic information system (GIS) to organize data between explorers and governments. Based on these conditions GIS was developed using a technique that prioritizes user needs with extreme programming development techniques. The result of GIS development shows that the processing of data becomes information based on spatial and non-spatial data with the final result of energy and mining report. The report presented can be used as a report to the relevant parties as an effort to open data of energy and mining as material in decision-making or policy. Geographic information system generated systematically developed using extreme programming approach with five stages of exploration, planning, iteration, production and maintenance so that it can run funtionaly according to its function

The Integral of Spatial Data Mining in the Era of Big Data

2017 ◽  
pp. 90-126
Author(s):  
Gebeyehu Belay Gebremeskel ◽  
Chai Yi ◽  
Zhongshi He
Keyword(s):  
Data Mining ◽  
Data Warehouse ◽  
Spatial Data ◽  
High Volume ◽  
Spatial Data Mining ◽  
Research Field ◽  
Data Sets ◽  
Data Types ◽  
Basic Principles ◽  
Gis Data

Data Mining (DM) is a rapidly expanding field in many disciplines, and it is greatly inspiring to analyze massive data types, which includes geospatial, image and other forms of data sets. Such the fast growths of data characterized as high volume, velocity, variety, variability, value and others that collected and generated from various sources that are too complex and big to capturing, storing, and analyzing and challenging to traditional tools. The SDM is, therefore, the process of searching and discovering valuable information and knowledge in large volumes of spatial data, which draws basic principles from concepts in databases, machine learning, statistics, pattern recognition and 'soft' computing. Using DM techniques enables a more efficient use of the data warehouse. It is thus becoming an emerging research field in Geosciences because of the increasing amount of data, which lead to new promising applications. The integral SDM in which we focused in this chapter is the inference to geospatial and GIS data.

A Survey of Spatial Data Mining Methods Databases and Statistics Point of Views

2011 ◽  
pp. 229-242 ◽  
Author(s):  
Karine Zeitouni
Keyword(s):  
Data Mining ◽  
Information Systems ◽  
Spatial Analysis ◽  
Geographic Information Systems ◽  
Spatial Statistics ◽  
Spatial Data ◽  
Spatial Databases ◽  
Spatial Data Mining ◽  
Geographic Information ◽  
Mining Methods

This chapter reviews the data mining methods that are combined with Geographic Information Systems (GIS) for carrying out spatial analysis of geographic data. We will first look at data mining functions as applied to such data and then highlight their specificity compared with their application to classical data. We will go on to describe the research that is currently going on in this area, pointing out that there are two approaches: the first comes from learning on spatial databases, while the second is based on spatial statistics. We will conclude by discussing the main differences between these two approaches and the elements they have in common.

Sign in / Sign up

Export Citation Format

Share Document