Integrating Spatial Data and Decision Models in an E-Planning Tool

Antunes, Pedro; Sapateiro, Claudio; Zurita, Gustavo; Baloian, Nelson

doi:10.1007/978-3-642-15714-1_8

Cited by 29 publications

(12 citation statements)

References 27 publications

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“…Decision Support Systems (DSS) have been defined as interactive computer-based systems that help decision makers in the use of data and models in solving unstructured problems. A simplified model for the decision making (DM) process includes the following stages: (1) identifying the problem, (2) identifying and modeling the objective(s) of the decision, (3) collecting, generating and/or combining data to generate alternative scenarios, (4) evaluating options according to the objectives, (5) choosing an option and conducting a sensitivity analysis. If the decision makers estimate that there is enough information, the process ends with a final decision, otherwise the flow goes back to steps 2 or 3, [2].…”

Section: Introductionmentioning

confidence: 99%

Supporting Collaborative Preparation of Emergency Plans

et al. 2018

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Effective preparedness for reacting in case of a severe emergency requires that many experts with various backgrounds evaluate the possible scenarios and come up with a single, unified plan which considers all opinions. This is a typical collaborative decision-making scenario, characterized by a process cycle involving modelling the process, defining the objectives of the decision outcome, gathering data, generating options and evaluating them according to the defined objectives. This is a decision-making scenario which requires the participation of various experts, who must evaluate and compare many scenarios. Each expert will have a partial knowledge about where people may be at the time of the emergency and how they will react. In emergency scenarios the geographical information often plays a significant importance, since plans need to consider the geography of the terrain from which the population should be evacuated, the safe areas where the population should be taken to, the ways connecting evacuations, and how the rescue teams can reach the places where the emergency occurred. This work presents a tool that can help a group of experts with various types of expertise, generate, visualize and compare the outcomes of various hypotheses. The paper also presents a real case simulation in the event of a tsunami following an earthquake at a site in northern Chile and the possibilities of evacuating people to safer zones.

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Section: Introductionmentioning

confidence: 99%

Supporting Collaborative Preparation of Emergency Plans

et al. 2018

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“…Antunes et al [60] uses time and place differences to define collaborative awareness as perception of temporal and spatial structures in group of peers.…”

Section: Time and Spacementioning

confidence: 99%

Collaboration in sustainability vision

Mann

Smith

2011

2011 International Conference on Collaboration Technologies and Systems (CTS)

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“…Contemporary cities need to better use available technical and organizational solutions to improve the current urban transport situation (Hajduk, 2017). Furthermore, citizens' participation should become the base for all strategic decision-making (Antunes, Sapateiro, Zurita, & Baloian, 2010;Horelli, 2013).…”

Section: Background Literaturementioning

confidence: 99%

The Smartness Profile of Selected European Cities in Urban Management – A Comparison Analysis

Hajduk

2019

Journal of Business Economics and Management

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The smart cities concept plays an important role in urban management worldwide as well as should be implemented with the participation of residents and with consideration for their needs. This study examines an analysis of the smartness diversity of European cities on the basis of the International Organization for Standardization 37120 norm dealing with sustainable development of communities. Cities with a higher level of certification exhibit more effective management and their residents show greater commitment to civic life. The assessment of the level of smartness was based on statistics collected by the World Council on City Data using Multidimensional Comparative Analysis. Hellwig’s (1968) synthetic indicators were used to create a ranking gauging the level of smartness of European cities which were classified into four groups. The investigation shows large disparities in the smartness of selected European cities.

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Integrating Spatial Data and Decision Models in an E-Planning Tool

Cited by 29 publications

References 27 publications

Supporting Collaborative Preparation of Emergency Plans

Supporting Collaborative Preparation of Emergency Plans

Collaboration in sustainability vision

The Smartness Profile of Selected European Cities in Urban Management – A Comparison Analysis

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