scholarly journals Data Dissemination Techniques using DBSCAN and DD-Rtree for Spatial Data Mining

2020 ◽  
Vol 8 (5) ◽  
pp. 4465-4470
Keyword(s):  
Data Processing ◽  
Spatial Data ◽  
Data Dissemination ◽  
Clustering Algorithms ◽  
Research Work ◽  
Three Dimensional ◽  
Spatial Data Mining ◽  
X Ray Crystallography ◽  
Dynamic Level ◽  
Data Volume

In today’s scenario where data volumes are growing on enormous speed over cloud or internet, we want to limit this growing data size. This can be achieved by data processing methods where data processing can be done in parallel. To make the data processing done in parallel, various clustering sampling methodologies are in use such as Slink, DBSCAN, and Optics and so on. The power accomplished by various methodologies which already exist will be focusing to the preservation of three-dimensional surroundings such as grid tree, grid files, quad tree and tree like k-d-tree, etc. This all compartmentalization constructions are generally done in static way which is a fix way. Since this data volume size is very big, this results in a high cost of information sharing and clustering. Hence through this research work we want to analyze various clustering algorithms both on static level and at dynamic level. For doing this we are majorly comparing the dynamic distribution using DBSCAN and DD-Rtree algorithm by proposing a DD-Rtree will help us to preserver the spatial vicinity. In addition, DD-Rtree is not static but more than that it is dynamic, i.e. it will create build the data as we progress with clustering. DD-Rtree methodologies are based on R-Tree concepts which analyses the data at dynamic random way. We tend to compare DD-RTree’s information distribution norm with one of the clustering system recently published, DBSCAN. On the side of the potential of DBSCAN formula, we tend to distinguish the potential of queries managed by these compartmentalization structures. Numerous applications requires such kind of implementation at dynamic level of spatial database system such as satellite images, X-Ray crystallography, metrological department or other such atomic equipment’s spatial datasets. Our research work will help to implements spatial data dynamically using DDR-tree mechanism.

scholarly journals Serial Electron Diffraction Data Processing With diffractem and CrystFEL

2021 ◽  
Vol 8 ◽  
Author(s):  
Robert Bücker ◽  
Pascal Hogan-Lamarre ◽  
R. J. Dwayne Miller
Keyword(s):  
Electron Diffraction ◽  
Data Processing ◽  
Three Dimensional ◽  
Electron Diffraction Data ◽  
New Techniques ◽  
X Ray ◽  
X Ray Crystallography ◽  
Level Of Automation ◽  
Rotation Method ◽  
High Level

Serial electron diffraction (SerialED) is an emerging technique, which applies the snapshot data-collection mode of serial X-ray crystallography to three-dimensional electron diffraction (3D Electron Diffraction), forgoing the conventional rotation method. Similarly to serial X-ray crystallography, this approach leads to almost complete absence of radiation damage effects even for the most sensitive samples, and allows for a high level of automation. However, SerialED also necessitates new techniques of data processing, which combine existing pipelines for rotation electron diffraction and serial X-ray crystallography with some more particular solutions for challenges arising in SerialED specifically. Here, we introduce our analysis pipeline for SerialED data, and its implementation using the CrystFEL and diffractem program packages. Detailed examples are provided in extensive supplementary code.

scholarly journals A Grid-Based Approach for Measuring Similarities of Taxi Trajectories

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3118
Author(s):  
Wei Jiao ◽  
Hongchao Fan ◽  
Terje Midtbø
Keyword(s):  
Spatial Data ◽  
Behavior Pattern ◽  
Spatial Data Mining ◽  
Spatial Data Analysis ◽  
Exact Matching ◽  
The Road ◽  
Data Volume ◽  
Grid Codes ◽  
Position Prediction ◽  
Pass Through

Similarity measurement is one of the key tasks in spatial data analysis. It has a great impact on applications i.e., position prediction, mining and analysis of social behavior pattern. Existing methods mainly focus on the exact matching of polylines which result in the trajectories. However, for the applications like travel/drive behavior analysis, even for objects passing by the same route the trajectories are not the same due to the accuracy of positioning and the fact that objects may move on different lanes of the road. Further, in most cases of spatial data mining, locations and sometimes sequences of locations on trajectories are most important, while how objects move from location to location (the exact geometries of trajectories) is of less interest. For the abovementioned situations, the existing approaches cannot work anymore. In this paper, we propose a grid aware approach to convert trajectories into sequences of codes, so that shape details of trajectories are neglected while emphasizing locations where trajectories pass through. Experiments with Shanghai Float Car Data (FCD) show that the proposed method can calculate trajectories with high similarity if these pass through the same locations. In addition, the proposed methods are very efficient since the data volume is considerably reduced when trajectories are converted into grid-codes.

The environmental product variety and retail rents on central urban shopping areas: A multi-stage spatial data mining method

2020 ◽  
pp. 239980832096660
Author(s):  
Tony Shun-Te Yuo ◽  
Tzuhui Angie Tseng
Keyword(s):  
Spatial Data ◽  
Diversity Index ◽  
Three Dimensional ◽  
Product Variety ◽  
Spatial Data Mining ◽  
The Core ◽  
Point Of Interest ◽  
Number Of Layers ◽  
Multi Stage ◽  
The Relationship

This study examines the relationship between various measures of environmental product variety and retail rents in central urban shopping areas. Using a Geographic Information System (GIS)-based detailed survey database, this research identified 34 layers of environmental product variety in the most representative single-centred shopping areas of the six largest cities in Taiwan. This research extracted layers of product variety and other measures of product variety, such as the number of layers of product variety above each point of interest, the density, the Core/Periphery factor scores, the Shannon entropy index, the Simpson diversity index and the Herfindahl–Hirschman index of each street line buffer area. The proposed method was used to generate three-dimensional maps of the rent gradient and the extracted core and periphery layers of product variety. Thus, a tool was developed for examining the variety features from various angles. The results showed that, in general, the higher the product variety, the higher the rents. Nevertheless, the scores for the core and periphery of the environmental product variety were the dominant determinants; street line buffer areas can only have lower rents if they lacked the correct (i.e. the core layers) environmental product variety, even if they have higher measurements of other variety features.

scholarly journals Analysis of Visualization Technology of 3D Spatial Geographic Information System

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Jiudong Yang ◽  
Fenghua Wu ◽  
Erlong Lai ◽  
Mingyue Liu ◽  
Bo Liu ◽  
...  
Keyword(s):  
Information System ◽  
Geographic Information System ◽  
Data Processing ◽  
Optimization Algorithm ◽  
Spatial Data ◽  
Three Dimensional ◽  
Geographic Information ◽  
3D Gis ◽  
Visualization Technology ◽  
Spatial Data Processing

Traditional urban planning is generally expressed in a two-dimensional geographic information system, but its performance is limited to the plane direction. It cannot give people more natural feelings and visionary experiences. The rapid development of three-dimensional geographic information systems brings people geographic information. The three-dimensional intuitive experience, but the traditional three-dimensional geographic information system has the disadvantages that the spatial properties are incompatible, the image rendering speed is slow, and the visualization effect is poor. In this paper, the traditional domain-oriented processing method is improved in spatial data processing and modeling. An optimized object-oriented optimization algorithm is proposed. The three-dimensional geographic information is optimized based on a dynamic multiresolution model and multilevel detail processing technology. The rendering of the system enhances the visualization. Based on the optimization algorithm of data processing and visualization technology proposed in this paper, the spatial data processing platform GISdata of 3D GIS is designed in this paper. At the same time, the 3D GIS is visualized based on OpenGL visualization software. It is shown that the optimization algorithm proposed in this paper has excellent preexperimental effects.

scholarly journals Deformation information system for facilitating studies of mining-ground deformations, development, and applications

2014 ◽  
Vol 14 (7) ◽  
pp. 1677-1689 ◽  
Author(s):  
J. Blachowski ◽  
W. Milczarek ◽  
P. Stefaniak
Keyword(s):  
Data Mining ◽  
Information System ◽  
Spatial Data ◽  
Ground Deformation ◽  
Data Extraction ◽  
Three Dimensional ◽  
Horizontal Displacement ◽  
Spatial Data Mining ◽  
Calculation Of Parameters ◽  
Ground Deformations

Abstract. The paper presents the concept of the deformation information system (DIS) to support and facilitate studies of mining-ground deformations. The proposed modular structure of the system includes data collection and data visualisation components, as well as spatial data mining, modelling and classification modules. In addition, the system integrates interactive three-dimensional models of the mines and local geology. The system is used to calculate various parameters characterising ground deformation in space and time, i.e. vertical and horizontal displacement fields, deformation parameters (tilt, curvature, and horizontal strain) and input spatial variables for spatial data classifications. The core of the system in the form of an integrated spatial and attributive database has been described. The development stages and the functionality of the particular components have been presented and example analyses utilising the spatial data mining and modelling functions have been shown. These include, among other things, continuous vertical and horizontal displacement field interpolations, calculation of parameters characterising mining-ground deformations, mining-ground category classifications, data extraction procedures and data preparation preprocessing procedures for analyses in external applications. The DIS has been developed for the Walbrzych coal mines area in SW Poland where long-time mining activity ended at the end of the 20th century and surface monitoring is necessary to study the present-day condition of the former mining grounds.

scholarly journals Solving Big GIS Projects on Desktop Computers

2019 ◽  
Vol 18 (32) ◽  
pp. 44-62
Author(s):  
Dalibor Bartoněk
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Spatial Data ◽  
Geographical Information Systems ◽  
Information Technologies ◽  
Digital Data ◽  
Geographical Information ◽  
Digital Information ◽  
Big Data Processing ◽  
Data Volume

We are witnessing great developments in digital information technologies. The situation encroaches on spatial data, which contain both attributive and localization features, and this determines their position unequally within an obligatory coordinate system. These changes have resulted in the rapid growth of digital data, significantly supported by technical advances regarding the devices which produce them. As technology for making spatial data advances, methods and software for big data processing are falling behind. Paradoxically, only about 2% of the total volume of data is actually used. Big data processing often requires high computation performance hardware and software. Only a few users possess the appropriate information infrastructure. The proportion of processed data would improve if big data could be processed by ordinary users. In geographical information systems (GIS), these problems arise when solving projects related to extensive territory or considerable secondary complexity, which require big data processing. This paper focuses on the creation and verification of methods by which it would be possible to process effectively extensive projects in GIS supported by desktop hardware and software. It is a project regarding new quick methods for the functional reduction of the data volume, optimization of processing, edge detection in 3D and automated vectorization.

scholarly journals Deformation information system for facilitating studies of mining ground deformations – development and applications

2013 ◽  
Vol 1 (5) ◽  
pp. 4801-4831
Author(s):  
J. B. Blachowski ◽  
W. Milczarek ◽  
P. Stefaniak
Keyword(s):  
Data Mining ◽  
Information System ◽  
Spatial Data ◽  
Ground Deformation ◽  
Data Extraction ◽  
Three Dimensional ◽  
Horizontal Displacement ◽  
Spatial Data Mining ◽  
Displacement Fields ◽  
Ground Deformations

Abstract. The paper presents the concept of the Deformation Information System (DIS) to support and facilitate studies of mining ground deformations. The proposed modular structure of the system includes data collection and data visualisation components, as well as spatial data mining, modelling and classification modules. In addition, the system integrates interactive three-dimensional models of the mines and local geology. The system is used to calculate various parameters characterising ground deformation in space and time, i.e. vertical and horizontal displacement fields, deformation parameters (tilt, curvature and horizontal strain) and input spatial variables for spatial data classifications. The core of the system in the form of an integrated spatial and attributive database has been described. The development stages and the functionality of the particular components have been presented and example analyses utilising the spatial data mining and modelling functions have been shown. These include, among other things, continuous vertical and horizontal displacement fields interpolations, calculation of parameters characterising mining ground deformations, mining ground category classifications, data extraction procedures and data preparation, pre-processing procedures for analyses in external applications. The DIS has been developed for the Walbrzych Coal Mines area in SW Poland where long-time mining activity has finished at the end of the 20th Century and surface monitoring is necessary to study present day condition of the former mining grounds.

On the MDBSCAN Algorithm in a Spatial Data Mining Context

2013 ◽  
pp. 263-273
Author(s):  
Gabriella Schoier
Keyword(s):  
Spatial Data ◽  
Spatial Clustering ◽  
Clustering Algorithms ◽  
Synthetic Data ◽  
Spatial Data Mining ◽  
Data Sets ◽  
Data Set ◽  
Spatial Variables ◽  
Spatial Objects ◽  
Spatial Data Sets

The rapid developments in the availability and access to spatially referenced information in a variety of areas, has induced the need for better analysis techniques to understand the various phenomena. In particular, spatial clustering algorithms, which group similar spatial objects into classes, can be used for the identification of areas sharing common characteristics. The aim of this chapter is to present a density based algorithm for the discovery of clusters of units in large spatial data sets (MDBSCAN). This algorithm is a modification of the DBSCAN algorithm (see Ester (1996)). The modifications regard the consideration of spatial and non spatial variables and the use of a Lagrange-Chebychev metrics instead of the usual Euclidean one. The applications concern a synthetic data set and a data set of satellite images

On the MDBSCAN Algorithm in a Spatial Data Mining Context

Data Mining ◽  
2013 ◽  
pp. 435-444
Author(s):  
Gabriella Schoier
Keyword(s):  
Spatial Data ◽  
Spatial Clustering ◽  
Clustering Algorithms ◽  
Synthetic Data ◽  
Spatial Data Mining ◽  
Data Sets ◽  
Data Set ◽  
Spatial Variables ◽  
Spatial Objects ◽  
Spatial Data Sets

The rapid developments in the availability and access to spatially referenced information in a variety of areas, has induced the need for better analysis techniques to understand the various phenomena. In particular, spatial clustering algorithms, which group similar spatial objects into classes, can be used for the identification of areas sharing common characteristics. The aim of this chapter is to present a density based algorithm for the discovery of clusters of units in large spatial data sets (MDBSCAN). This algorithm is a modification of the DBSCAN algorithm (see Ester (1996)). The modifications regard the consideration of spatial and non spatial variables and the use of a Lagrange-Chebychev metrics instead of the usual Euclidean one. The applications concern a synthetic data set and a data set of satellite images

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