Sustainable Industrial Site Redevelopment Planning Support

2017 ◽  
Vol 6 (2) ◽  
pp. 39-53 ◽  
Author(s):  
Tong Wang ◽  
Qi Han ◽  
Bauke de Vries
Keyword(s):  
Spatial Data ◽  
Web Application ◽  
Machine Learning Techniques ◽  
Case Based Reasoning ◽  
Data Sets ◽  
Industrial Site ◽  
Industrial Sites ◽  
The North ◽  
Learning Techniques ◽  
Spatial Data Sets

Abandoned industrial sites could be redeveloped in a sustainable way with the help of previous experience. This paper presents a case-based reasoning (CBR) approach to support sustainable industrial site redevelopment. For a target site that needs to be redeveloped, qualitative important key concerns are identified and quantitative attributes, which are important for sustainability, are calculated. The key concerns are generated from zoning documents and the attributes are calculated from spatial data sets. Machine learning techniques are used to find the most influential attributes to determine transition forms. Similar cases from the constructed case base are retrieved based on the algorithm the authors have proposed. The North Brabant region in the Netherlands is used as a case study. A web application is presented to illustrate the approach. The e-planning method provides a straightforward way to retrieve transition forms from similarly redeveloped cases for new regional planning tasks with a focus on sustainability.

Digital Resources: HGIS de las Indias

2020 ◽  
Author(s):  
Werner Stangl
Keyword(s):  
Spatial Data ◽  
Web Application ◽  
Spanish America ◽  
Data Sets ◽  
Tabular Data ◽  
Territorial Organization ◽  
Data Files ◽  
Mapping Resource ◽  
Spatial Data Sets ◽  
Independence Movements

HGIS de las Indias is an open-access Spanish-language database and web platform on the temporal and spatial developments in the territorial organization and settlements of all Spanish America (from Nutka to the Malvinas) during the reign of the Bourbon dynasty until the eve of the independence movements (1701–1808). It consists of several components: a platform for visualization of the database in an interactive web application, an engine for the creation of base maps, and a repository for the raw data files that can be used in specialized software. Also, HGIS de las Indias has a feature that allows registered users to create spatial data sets from tabular data. Beyond its practical use as finding aid, data provider, and mapping resource, it aims at fulfilling an even more fundamental function of infrastructure. The unique resource identifiers (URIs) for places and territorial concepts in HGIS de las Indias can be used as identifiers across projects and text annotations. Also, there exist easy workflows to prepare research data with a spatial component in tabular form and connect it with the database. HGIS de las Indias may thus serve as a link between otherwise unconnected data sets and is itself integrated in more fundamental infrastructures like Pelagios or the World Historical Gazetteer that constitute a bridge to the wider world of the semantic web.

Image processing in case-based reasoning

2005 ◽  
Vol 20 (3) ◽  
pp. 311-314 ◽  
Author(s):  
PETRA PERNER ◽  
ALEC HOLT ◽  
MICHAEL RICHTER
Keyword(s):  
Remote Sensing ◽  
Image Processing ◽  
Computer Tomography ◽  
Spatial Data ◽  
Case Based Reasoning ◽  
Data Sets ◽  
Spatial Data Sets ◽  
Case Based

This commentary summarizes case-based reasoning (CBR) research applied to image processing. It includes references to the systems, workshops, and landmark publications. Image processing includes a variety of image formats, from computer tomography images to remote sensing and spatial data sets.

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
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