From CAD to GIS to the Geoweb: A Natural Evolution

2010 ◽  
Author(s):  
Steve Adam
Keyword(s):  
Decision Making ◽  
Spatial Data ◽  
Oil And Gas ◽  
Spatial Information ◽  
Digital Data ◽  
Computer Hardware ◽  
Disruptive Technology ◽  
Data Sets ◽  
Spatial Data Sets ◽  
Pipeline Design

Computer hardware and software have played a significant role in supporting the design and maintenance of pipeline systems. CAD systems allowed designers and drafters to compile drawings and make edits at a pace unmatched by manual pen drawings. Although CAD continues to provide the environment for a lot of pipeline design, Geographic Information Systems (GIS) are also innovating pipeline design through routines such as automated alignment sheet generation. What we have seen over the past two or three decades is an evolution in how we manage the data and information required for decision making in pipeline design and system operation. CAD provided designers and engineers a rapid electronic method for capturing information in a drawing, editing it, and sharing it. As the amount of digital data available to users grows rapidly, CAD has been unable to adequately exploit data’s abundance and managing change in a CAD environment is cumbersome. GIS and spatial data management have proven to be the next evolution in situations where engineering, integrity, environmental, and other spatial data sets dominate the information required for design and operational decision making. It is conceivable that GIS too will crumble under the weight of its own data usage as centralized databases become larger and larger. The Geoweb is likely to emerge as the geospatial world’s evolution. The Geoweb implies the merging of spatial information with the abstract information that currently dominates the Internet. This paper and presentation will discuss this fascinating innovation, it’s force as a disruptive technology, and oil and gas applications.

IMPACT ANALYSIS OF THE INSPIRE DIRECTIVE ON LAND-USE PLANNING PUBLICATION AND DEVELOPMENT

2017 ◽  
Vol 165 (45) ◽  
pp. 76-85
Author(s):  
WALDEMAR IZDEBSKI ◽  
ZBIGNIEW MALINOWSKI
Keyword(s):  
Land Use ◽  
Land Use Planning ◽  
Spatial Data ◽  
Local Governments ◽  
Impact Analysis ◽  
Spatial Information ◽  
Technological Development ◽  
Data Sets ◽  
Gis Technology ◽  
Spatial Data Sets

The INSPIRE Directive went into force in May 2007 and it resulted in changing the way of thinking about spatial data in local government. Transposition of the Directive on Polish legislation is the Law on spatial information infrastructure from 4 March 2010., which indicates the need for computerization of spatial data sets (including land-use planning). This act resulted in an intensification of thinking about the computerization of spatial data, but, according to the authors, the needs and aspirations of the digital land-use planning crystallized already before the INSPIRE Directive and were the result of technological development and increasing the awareness of users. The authors analyze the current state of land-use planning data computerization in local governments. The analysis was conducted on a group of more than 1,700 local governments, which are users of spatial data management (GIS) technology eGmina.

scholarly journals Digital Data Literacy in an Economic World: Geo-Spatial Data Literacy Aspects

2020 ◽  
Vol 9 (6) ◽  
pp. 373 ◽  
Author(s):  
Carsten Juergens
Keyword(s):  
Spatial Data ◽  
Business Processes ◽  
Digital Data ◽  
Data Sets ◽  
Way Of Life ◽  
Specific Kind ◽  
Data Literacy ◽  
Textual Data ◽  
Spatial Data Sets ◽  
Selection Of

Data literacy is an essential skill for today’s digital way of life, to be able to judge the reliability of different data presented, for instance in news and media or in business processes. Geo-Spatial data are a specific kind of data and influence most of our daily decisions. In school one learns how to critically read and reflect texts and literature. So one is literate in the case of textual data. For other data, and especially geo-spatial data, one seems to be less skilled. This contribution is supposed to open one’s eyes to understand the origin of geo-spatial data sets, their specific nature and how to gain geo-spatial data sets with specific focus on economic applications. In addition to that, how the selection of geo-spatial data and the processing of geo-spatial data can influence the decision-making of people in thematic fields such as economy and business is discussed. The overall goal is to make people of disciplines other than those that are geo-related aware of the characteristics and possible ways of manipulation of maps and geo-spatial products as well as of the power of geo-spatial data and map products in their specific thematic field of operation.

scholarly journals An ABC-optimized fuzzy ELECTRE approach for assessing petroleum potential at the petroleum system level

2020 ◽  
Vol 12 (1) ◽  
pp. 580-597
Author(s):  
Mohamad Hamzeh ◽  
Farid Karimipour
Keyword(s):  
Spatial Data ◽  
Spatial Models ◽  
Model Performance ◽  
Essential Elements ◽  
Petroleum System ◽  
Petroleum Exploration ◽  
System Level ◽  
Data Sets ◽  
Petroleum Potential ◽  
Spatial Data Sets

AbstractAn inevitable aspect of modern petroleum exploration is the simultaneous consideration of large, complex, and disparate spatial data sets. In this context, the present article proposes the optimized fuzzy ELECTRE (OFE) approach based on combining the artificial bee colony (ABC) optimization algorithm, fuzzy logic, and an outranking method to assess petroleum potential at the petroleum system level in a spatial framework using experts’ knowledge and the information available in the discovered petroleum accumulations simultaneously. It uses the characteristics of the essential elements of a petroleum system as key criteria. To demonstrate the approach, a case study was conducted on the Red River petroleum system of the Williston Basin. Having completed the assorted preprocessing steps, eight spatial data sets associated with the criteria were integrated using the OFE to produce a map that makes it possible to delineate the areas with the highest petroleum potential and the lowest risk for further exploratory investigations. The success and prediction rate curves were used to measure the performance of the model. Both success and prediction accuracies lie in the range of 80–90%, indicating an excellent model performance. Considering the five-class petroleum potential, the proposed approach outperforms the spatial models used in the previous studies. In addition, comparing the results of the FE and OFE indicated that the optimization of the weights by the ABC algorithm has improved accuracy by approximately 15%, namely, a relatively higher success rate and lower risk in petroleum exploration.

scholarly journals Mining Co-Location Patterns with Rare Events from Spatial Data Sets

2006 ◽  
Vol 10 (3) ◽  
pp. 239-260 ◽  
Author(s):  
Yan Huang ◽  
Jian Pei ◽  
Hui Xiong
Keyword(s):  
Spatial Data ◽  
Rare Events ◽  
Data Sets ◽  
Location Patterns ◽  
Spatial Data Sets

scholarly journals Refinement Algoritm of Hypotheses Testing Research Based on Geochronological Tracking

2020 ◽  
Vol 6 (1) ◽  
pp. 86-93
Author(s):  
R. Ivakin ◽  
Y. Ivakin ◽  
S. Potapichev
Keyword(s):  
Spatial Data ◽  
Optimization Problem ◽  
Retrospective Studies ◽  
Subject Area ◽  
Statistical Testing ◽  
Time Interval ◽  
Data Sets ◽  
Hypotheses Testing ◽  
Practical Application ◽  
Spatial Data Sets

Geochronological tracking is an effective information technology for digital cartographic spatial data sets processing. It is widely known in retrospective patterns research about geographic relocation of figures, or any other units for a given time interval. Software component of geochronological tracking is becoming one the most popular GIS-integrated applications. The article presents the basic provisions for the algorithmization of the geochronological tracking procedure for statistical testing of retrospective studies hypotheses. We can observe the results of solving this optimization problem in a general form and in a number of the most typical variants. The obtained results of solving the optimization problem are interpreted in terms of the retrospective studies subject area. There are shown the ways of further practical application of the optimized algorithm in the tasks of modern logistics, data mining and formalized knowledge.

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