Spatial data cubes based on shared dimensions and neighbourhood relationship concepts

2021 ◽  
Vol 37 (3) ◽  
pp. 308
Author(s):  
Tarik De Melo e Silva Rocha ◽  
Rodrigo Rocha Silva ◽  
Tiago Garcia De Senna Carneiro ◽  
Joubert De Castro Lima
Keyword(s):  
Spatial Data ◽  
Data Cubes

scholarly journals A Data Cube Metamodel for Geographic Analysis Involving Heterogeneous Dimensions

2021 ◽  
Vol 10 (2) ◽  
pp. 87
Author(s):  
Jean-Paul Kasprzyk ◽  
Guénaël Devillet
Keyword(s):  
Spatial Data ◽  
Unified Modeling Language ◽  
Social Economy ◽  
Original Data ◽  
Heterogeneous Data ◽  
Data Cube ◽  
Multidimensional Data ◽  
Multiple Sources ◽  
Data Cubes ◽  
Geographic Analysis

Due to their multiple sources and structures, big spatial data require adapted tools to be efficiently collected, summarized and analyzed. For this purpose, data are archived in data warehouses and explored by spatial online analytical processing (SOLAP) through dynamic maps, charts and tables. Data are thus converted in data cubes characterized by a multidimensional structure on which exploration is based. However, multiple sources often lead to several data cubes defined by heterogeneous dimensions. In particular, dimensions definition can change depending on analyzed scale, territory and time. In order to consider these three issues specific to geographic analysis, this research proposes an original data cube metamodel defined in unified modeling language (UML). Based on concepts like common dimension levels and metadimensions, the metamodel can instantiate constellations of heterogeneous data cubes allowing SOLAP to perform multiscale, multi-territory and time analysis. Afterwards, the metamodel is implemented in a relational data warehouse and validated by an operational tool designed for a social economy case study. This tool, called “Racines”, gathers and compares multidimensional data about social economy business in Belgium and France through interactive cross-border maps, charts and reports. Thanks to the metamodel, users remain independent from IT specialists regarding data exploration and integration.

Spatial Data Cubes based on Shared Dimensions and Neighborhood Relationship Concepts

2020 ◽  
Vol 1 (1) ◽  
pp. 1
Author(s):  
Joubert De Castro Lima ◽  
Tiago Garcia De Senna Carneiro ◽  
Tarik De Melo e Silva Rocha ◽  
Rodrigo Rocha Silva
Keyword(s):  
Spatial Data ◽  
Data Cubes ◽  
Neighborhood Relationship

Conceptual model for spatial data cubes: A UML profile and its automatic implementation

2015 ◽  
Vol 38 ◽  
pp. 113-132 ◽  
Author(s):  
Kamal Boulil ◽  
Sandro Bimonte ◽  
Francois Pinet
Keyword(s):  
Conceptual Model ◽  
Spatial Data ◽  
Uml Profile ◽  
Data Cubes

Merging Spatial Data Cubes using the GIS Overlay Operator

2010 ◽  
Vol 19 (3) ◽  
pp. 261-290 ◽  
Author(s):  
Sandro Bimonte ◽  
Michel Schneider ◽  
Hadj Mahboubi ◽  
Francois Pinet ◽  
Jean-Pierre Chanet
Keyword(s):  
Spatial Data ◽  
Data Cubes

Semi-Automatic Design of Spatial Data Cubes from Simulation Model Results

2013 ◽  
Vol 9 (1) ◽  
pp. 70-95 ◽  
Author(s):  
Hadj Mahboubi ◽  
Sandro Bimonte ◽  
Guillaume Deffuant ◽  
Jean-Pierre Chanet ◽  
François Pinet
Keyword(s):  
Simulation Model ◽  
Spatial Data ◽  
Simulation Models ◽  
Automatic Design ◽  
Automatic Method ◽  
Data Cubes

In this paper the authors present a semi-automatic method for designing and generating spatial data cubes in order to visualize and analyze the results of simulation models. In the authors approach users choose their fact of analysis, then the system derives automatically a set of possible measures, dimensions of analysis, and generates the corresponding spatial data cubes. The analysis and visualization of the spatial data cubes are carried out using appropriate SOLAP tools. They also present the SimOLAP system, developed to validate their approach.

A Two-Tier Spatial Index for Non-flat Spatial Data Broadcasting on Air

2014 ◽  
Vol E97.B (12) ◽  
pp. 2809-2818 ◽  
Author(s):  
SeokJin IM ◽  
HeeJoung HWANG
Keyword(s):  
Spatial Data ◽  
Spatial Index ◽  
Data Broadcasting

ANALYSIS OF FLOOD PRONE AREAS IN NAGAN RAYA REGENCY ACEH PROVINCE

2020 ◽  
Vol 5 (1) ◽  
pp. 414
Author(s):  
Amsar Yunan
Keyword(s):  
Land Use ◽  
Spatial Data ◽  
Flood Hazard ◽  
Parameter Analysis ◽  
Thematic Maps ◽  
Aceh Province ◽  
Object Area ◽  
Use Factors ◽  
Hazard Levels ◽  
Flood Prone Areas

Maps or remote sensing can be interpreted as the process of reading using various sensors where data collected remotely can be analyzed to obtain information about the object, area or phenomenon. In this study, the author develops a flood disaster mapping information system applying overlays with scoring between the parameters. The determinant factors to provide flood hazard levels includes rainfall factors in the dasarian unit, land-use factors and land-use arbitrary factors. Of all these parameters, a scoring process will be carried out by assigning weights and values according to their respective classifications, then an overlay process will be performed using ArcGIS software. The author conducted this study in Nagan Raya Regency since this area experiences flooding annually.  Framing a thematic map of flood-prone areas in Nagan Raya Regency was designed using the flood hazard method. Spatial data that has been presented in the form of thematic maps as parameters are land use maps, landform maps, and dasarian rainfall maps (per 10 daily). The design of thematic maps that are prone to flooding is done by overlapping (overlay process). In contrast, the determination of the classification is done by adding scores to each parameter, with low, medium and high hazard levels. Parameter analysis shows the level of flood vulnerability in Nagan Raya Regency of each district, namely Beutong: high 0.21%, medium 13.68%, low 86.12%. Seunagan District: high 51.17%, medium 48.83%, low 0%. Seunagan Timur District: high 10.07%, medium 46.18%, low 43.75%. Kuala Subdistrict: high 29.66%, medium 68.99%, low 1.35%. Darul Makmur District: high 8.57%, medium 63.37%, low 28.06%. From the overall results of the study, it can be concluded that the danger of flooding in Nagan Raya Regency with a level of vulnerability: high 9.92%, moderate 42.65% and low 47.43%.

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