scholarly journals Drought Hazard Modelling of Klaten Regency Central Java Using AHP and TOPSIS Method

2021 ◽  
Vol 936 (1) ◽  
pp. 012043
Author(s):  
Meiga Nugrahani ◽  
Purnama Budi Santosa
Keyword(s):  
Decision Making ◽  
High Risk ◽  
Geographical Information Systems ◽  
Public Works ◽  
Disaster Mitigation ◽  
Geographical Information ◽  
Topsis Method ◽  
Drought Vulnerability ◽  
Central Java ◽  
The Impact

Abstract According to information of areas at high risk of drought provided by Central Java disaster risk assessment in 2016 - 2020, Klaten Regency is in the top ten at high risk of drought in Central Java. Drought is an annual disaster in this region, which usually occurs during the dry season. The impact of the drought has caused some areas to experience a lack of clean water. For the purpose of disaster mitigation in anticipating and minimizing drought disasters losses, it is necessary to analyze the level of drought with a decision-making system by comparing two methods, namely the AHP with TOPSIS. Both methods are decision-making methods that are composed of various criteria to obtain an alternative sequence of choices. Both the AHP and TOPSIS methods produces weight values and a positive ideal solution value, respectively. These are used as input data in the mapping of drought vulnerability analysis with Geographical Information Systems (GIS). The results of the analysis are visualized with a map that shows the level of drought vulnerability. AHP and TOPSIS method decision making generates the order of the drought classes in predicting the distribution of areas experiencing drought. To validate the model, the authors compare the results of the analysis of drought vulnerability of the two methods with drought data from BPBD (Local Agency for Disaster Prevention) and DPUPR (Public Works and Public Housing Department). The results show that AHP provides better results than TOPSIS based on results validation with BPBD and DPUPR data. By comparing the two models with BPBD data, the results show that the percentage of AHP suitability is higher than TOPSIS at 47,619% and 19,048% respectively.

scholarly journals SPATIOTEMPORAL VISUALIZATION OF TSUNAMI WAVES USING KML ON GOOGLE EARTH

2017 ◽  
Vol XLII-2/W7 ◽  
pp. 1291-1299 ◽  
Author(s):  
H. Mohammadi ◽  
M. R. Delavar ◽  
M. A. Sharifi ◽  
M. D. Pirooz
Keyword(s):  
Decision Making ◽  
Disaster Management ◽  
Google Earth ◽  
Disaster Mitigation ◽  
Case Scenario ◽  
Worst Case ◽  
Tsunami Waves ◽  
Spatiotemporal Visualization ◽  
Virtual Globes ◽  
The Impact

Disaster risk is a function of hazard and vulnerability. Risk is defined as the expected losses, including lives, personal injuries, property damages, and economic disruptions, due to a particular hazard for a given area and time period. Risk assessment is one of the key elements of a natural disaster management strategy as it allows for better disaster mitigation and preparation. It provides input for informed decision making, and increases risk awareness among decision makers and other stakeholders. Virtual globes such as Google Earth can be used as a visualization tool. Proper spatiotemporal graphical representations of the concerned risk significantly reduces the amount of effort to visualize the impact of the risk and improves the efficiency of the decision-making process to mitigate the impact of the risk. The spatiotemporal visualization of tsunami waves for disaster management process is an attractive topic in geosciences to assist investigation of areas at tsunami risk. In this paper, a method for coupling virtual globes with tsunami wave arrival time models is presented. In this process we have shown 2D+Time of tsunami waves for propagation and inundation of tsunami waves, both coastal line deformation, and the flooded areas. In addition, the worst case scenario of tsunami on Chabahar port derived from tsunami modelling is also presented using KML on google earth.

Geographical Analysis of Entertainment Services in the City of Daratu Using Geographical Information Systems (GIS)

2021 ◽  
Vol 8 (3) ◽  
pp. 618-644
Author(s):  
Hoshmand Jawhar Abbas ◽  
Sanger Ahmed Hussein ◽  
Fatimah Qader Mustafa
Keyword(s):  
Geographical Information Systems ◽  
Urban Expansion ◽  
Well Being ◽  
Geographical Information ◽  
Population Increase ◽  
Recreational Facilities ◽  
Financial Gain ◽  
Recreational Services ◽  
The Impact ◽  
The City

 The impact of the recreational services that exist within the group of services that are practiced within the geographical framework of the city, is not limited to the lives of its residents and their activities, but also on the residents of the surrounding areas. Recreational services contribute to providing diversified investment opportunities for leisure time, so that they are appropriate and beneficial to the health, comfort and well-being of the population at the lowest possible cost, without the goal of their establishment being financial gain, as they lead to the creation of mental, psychological and physical balance on the level of one individual and on the basis of society in a way. In general, recreational services are an integral part of urban activities in most cities of the world. Rather, the concept of modernity and urbanization in contemporary urban centers is measured to some extent by the availability of recreational facilities for their inhabitants, and the study also showed the low level of efficiency of recreational services in terms of their spatial distribution and numbers. As it is concentrated in some neighborhoods of the city, while it is less or absent in other neighborhoods, as well as not taking into account the planning standards in its distribution and during its construction in line with the population increase, urban expansion and the residents' needs for these services. The success in providing these different types of recreational facilities depends on how they are distributed geographically. The balanced distribution of these activities determines the success of the adopted plans in achieving the required goals and policies.

Intelligence from Space

2011 ◽  
pp. 194-226
Author(s):  
Paul Hendriks
Keyword(s):  
Decision Making ◽  
Information Systems ◽  
Spatial Analysis ◽  
Geographical Information Systems ◽  
Geographical Information ◽  
Specific Class ◽  
Step Procedure ◽  
Decision Making Processes ◽  
Projected Position ◽  
Spatial Element

The spatial element, which is omnipresent in data and information relevant to organizations, is much underused in the decision-making processes within organizations. This applies also to decision-making within the domain of Competitive Intelligence. The chapter explores how the CI function may benefit from developing a spatial perspective on its domain and how building, exploring and using this perspective may be supported by a specific class of information systems designed to handle the spatial element in data: Geographical Information Systems (GIS). The chapter argues that the key element for linking GIS to CI involves the identification of situations in which spatial analysis may support organizational decision-making within the CI domain. It presents a three-step procedure for identifying how CI may recognize spatial decision problems that are useful to boost the operation of the CI function. The first step concerns identifying relevant spatial variables, for instance by analyzing economic, demographic or political trends as to their spatial implications. The second step involves using GIS for positioning the organization with respect to the identified variables (present and projected position). The third step amounts to drawing strategic conclusions from Step 2 by assessing how the competition in relationship with the own organization would be positioned along the identified spatial analysis lines.

scholarly journals Supporting surgeons in patient-centred complex decision-making: a qualitative analysis of the impact of a perioperative physician clinic

BMJ Open ◽  
2019 ◽  
Vol 9 (12) ◽  
pp. e033277
Author(s):  
Clarabelle T Pham ◽  
Catherine L Gibb ◽  
Robert A Fitridge ◽  
Jon Karnon ◽  
Elizabeth Hoon
Keyword(s):  
Decision Making ◽  
High Risk ◽  
Elective Surgery ◽  
Structured Interviews ◽  
Complex Patients ◽  
Complex Decision Making ◽  
Complex Decision ◽  
Additional Value ◽  
The Impact ◽  
High Risk Clinic

ObjectivePatients with comorbidities can be referred to a physician-led high-risk clinic for medical optimisation prior to elective surgery at the discretion of the surgical consultant, but the factors that influence this referral are not well understood. The aims of this study were to understand the factors that influence a surgeon’s decision to refer a patient to the clinic, and how the clinic impacts on the management of complex patients.DesignQualitative study using theoretical thematic analysis to analyse transcribed semi-structured interviews.SettingInterviews were held in either the surgical consultant’s private office or a quiet office/room in the hospital ward.ParticipantsSeven surgical consultants who were eligible to refer patients to the clinic.ResultsWhen discussing the factors that influence a referral to the clinic, all participants initially described the optimisation of comorbidities and would then discuss with examples the challenges with managing complex patients and communicating the risks involved with having surgery. When discussing the role of the clinic, two related subthemes were dominant and focused on the management of risk in complex patients. The participants valued the involvement of the clinic in the decision-making and communication of risks to the patient.ConclusionsThe integration of the high-risk clinic in this study appears to offer additional value in supporting the decision-making process for the surgical team and patient beyond the clinical outcomes. The factors that influence a surgeon’s decision to refer a patient to the clinic appear to be driven by the aim to manage the uncertainty and risk to the patient regarding surgery and it was seen as a strategy for managing difficult and complex cases.

Visualization of Urban Prediction Using Augmented Maps

2012 ◽  
Author(s):  
Sandy Martedi ◽  
Sébastien Callier ◽  
Hideo Saito ◽  
Pega Sanoamuang ◽  
Milica Muminović
Keyword(s):  
Geographical Information Systems ◽  
Green Space ◽  
Geographical Information ◽  
Train Station ◽  
Monitor Display ◽  
Before And After ◽  
Web Camera ◽  
On Line ◽  
The Difference ◽  
The Impact

In this paper, we explore a visualization method using augmented maps for urban prediction. Our implementation allows users to determine the location for prediction in a paper map. As an application example, we examine an area before and after new train station is built. We use the difference between two maps for simulating the changes or predicting the impact if a new train station is built on a location in a paper map. In off-line phase, we gather knowledge data from several reference locations by comparing two aerial maps (before and after the train station is built). We then analyze the difference of green spaces between those two maps using color extraction. We observe that the green space around the new train station mostly decreases due to the area development. This information is then stored for prediction reference. In on-line phase, we use a monocular setup that consists of one camera and a monitor display. A paper map is captured using a web camera and tracked using its geometrical features. These features can be provided using the available data from Geographical Information Systems (GIS) or automatically extracted from the texture. The map is then matched with the reference map in database. When the map is matched, we can overlay the simulation on how the green space will change due to the existence of new train stations on a new location inputted by the user.

Ambient air pollution and health

2016 ◽  
Author(s):  
John Reid ◽  
Giovanni Leonardi ◽  
Alex G. Stewart
Keyword(s):  
Air Pollution ◽  
Geographical Information Systems ◽  
Ambient Air ◽  
Geographical Information ◽  
Ambient Air Pollution ◽  
Background Information ◽  
Traffic Density ◽  
Pollution Indices ◽  
Guideline Values ◽  
The Impact

This chapter describes the impact of air pollution, including particulate matter and nitrogen oxides, on human health, through a case scenario where an increase in respiratory disease has been associated with changes in traffic density and increases in pollution indices. Background information on pollutants and air quality measurements is given based on WHO criteria. The importance of multidisciplinary input to such investigations is emphasized, involving specialists in toxicology and environmental science, as well as health protection staff. The role of geographical information systems in mapping air pollution levels to identify localities where guideline values are exceeded is emphasized, together with health impact assessments to monitor public health outcomes. The complex local, social, and political aspects that may arise are considered, and so endure engagement and communication with different interest groups. The final part clearly outlines currently unanswered questions and how they should be studied and managed in the future.

An integrated approach to water distribution network monitoring and performance prediction using geographical information systems

2003 ◽  
Vol 3 (1-2) ◽  
pp. 307-312 ◽  
Author(s):  
M. Poulton
Keyword(s):  
Decision Making ◽  
Geographical Information Systems ◽  
Distribution Systems ◽  
Water Distribution ◽  
Prediction Models ◽  
Process Integration ◽  
Water Distribution Network ◽  
Integrated Approach ◽  
Geographical Information ◽  
Measurement Technology

The deterioration of water distribution systems and the subsequent need for major rehabilitation has provided researchers with a challenge: to seek new techniques to facilitate decision-making and assist network planners. Consequently, a range of methods have been developed, concerning several key performance indicators. Those with the greatest potential tend to promote a pro-active approach to pipeline rehabilitation, by predicting future performance of individual or groups of pipes. Such an approach is essential if cost-effective solutions are to be found. Prediction models are based on indicators that are perceived to be the most appropriate for measuring and comparing an aspect of performance. The level of “appropriateness” is influenced not only by the availability of data and the ease at which it can be recorded, but also its accuracy and unambiguity. Consequently, indicators such as burst rates are widely used to assess structural performance. Ideally though, a more direct measure of the condition of a pipe could be used, such as its remaining wall thickness. This has previously been largely disregarded, due to the lack of appropriate (non-destructive) measurement technology. However, technological advances are being made to enable the inline monitoring of pipes and determination of deficiencies (particularly corrosion pits in cast iron pipes). This paper illustrates how online condition monitoring of distribution pipelines can be combined with existing prediction models, to facilitate the decision-making process. Integration is achieved through the use of a Geographical Information System (GIS), which greatly enhances representation of spatial and temporal information.

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