scholarly journals SMARTKADASTER: OBSERVING BEYOND TRADITIONAL CADASTRE CAPABILITIES FOR MALAYSIA

2015 ◽  
Vol XL-2/W4 ◽  
pp. 53-55
Author(s):  
M. N. Bin Isa ◽  
T. C. Hua ◽  
N. Z. Binti Abdul Halim
Keyword(s):  
Decision Making ◽  
Spatial Data ◽  
Value Added ◽  
Vital Role ◽  
Successful Implementation ◽  
Geospatial Information ◽  
Planning Decision ◽  
Cadastral Surveying ◽  
Land Information System ◽  
Analysis Platform

The digital age for cadastral surveying started in stages, more than 20 years ago in Malaysia and JUPEM played a vital role in its successful implementation nationwide. One of the key products of cadastral survey is cadastral maps, which provide useful information for any land information system. However, as technology evolved and simplicity is familiarised, better services are anticipated and have affected how cadastral survey information are perceived. A paradigm shift is necessary where enriched cadastral information is required for multiple usage and allow real cadastral information based services to users. On that note, JUPEM is intrigued to develop a system where National Digital Cadastral Database is value added with other geospatial information for a smart and multipurpose environment and clearly be interpreted as a decision making tool with the aids of 3D realistic spatial data, namely SmartKADASTER. The SmartKADASTER is an ongoing project developed by JUPEM with the aim to establish a realistic and SMART cadastral-based spatial analysis platform for an effective planning, decision making, enabling efficiencies and enhancing communication and management to support SMART services towards SMART City enablement in Malaysia. It is developed in phases with the Federal Territory of Putrajaya and Kuala Lumpur as the initial project implementation area. This paper provides awareness and insights of the on-going development of the project and how it could benefit potential users and stakeholders.

Application of PCA with georeferenced data in the tourism industry: A case study in the province of Córdoba, Argentina

2021 ◽  
pp. 135481662098768
Author(s):  
Laura I Luna
Keyword(s):  
Decision Making ◽  
Principal Components ◽  
Spatial Data ◽  
Local Development ◽  
Value Added ◽  
Tourism Industry ◽  
Methodological Innovation ◽  
Spatial Principal Components ◽  
Components Analysis

The spatial analysis of tourism industries provides information about their structure, which is necessary for decision-making. In this work, tourism industries in the departments of Córdoba province, Argentina, for the 2001–2014 period were mapped. Multivariate methods with and without spatial restrictions (spatial principal components (sPCs) analysis, MULTISPATI-PCA, and principal components analysis (PCA), respectively) were applied and their performance was compared. MULTISPATI-PCA yielded a higher degree of spatial structuring of the components that summarize tourism activities than PCA. The methodological innovation lies in the generation of statistics for multidimensional spatial data. The departments were classified according to the participation of tourism activities in the value added of tourism using the sPCs obtained as input of the cluster fuzzy k-means analysis. This information provides elements necessary for appropriately defining local development strategies and, therefore, is useful to improve decision-making.

scholarly journals Brain-inspired model for decision-making in the selection of beneficial information patterns among signals received by an unpredictable information-development environment

2021 ◽  
Author(s):  
Alireza Asgari ◽  
yvan beauregard
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Health And Safety ◽  
Vital Role ◽  
Successful Implementation ◽  
Development Environment ◽  
Brain Functions ◽  
Operational Costs ◽  
Industrial Operations ◽  
The Brain

With its diversification in products and services, today’s marketplace makes competition wildly dynamic and unpredictable for industries. In such an environment, daily operational decision-making has a vital role in producing value for products and services while avoiding the risk of loss and hazard to human health and safety. However, it makes a large portion of operational costs for industries. The main reason is that decision-making belongs to the operational tasks dominated by humans. The less involvement of humans, as a less controllable entity, in industrial operation could also favorable for improving workplace health and safety. To this end, artificial intelligence is proposed as an alternative to doing human decision-making tasks. Still, some of the functional characteristics of the brain that allow humans to make decisions in unpredictable environments like the current industry, especially knowledge generalization, are challenging for artificial intelligence. To find an applicable solution, we study the principles that underlie the human brain functions in decision-making. The relative base functions are realized to develop a model in a simulated unpredictable environment for a decision-making system that could decide which information is beneficial to choose. The method executed to build our model's neuronal interactions is unique that aims to mimic some simple functions of the brain in decision-making. It has the potential to develop for systems acting in the higher abstraction levels and complexities in real-world environments. This system and our study will help to integrate more artificial intelligence in industrial operations and settings. The more successful implementation of artificial intelligence will be the steeper decreasing operational costs and risks.

scholarly journals Evaluasi Kesiapan Implementasi Infrastruktur Data Spasial untuk Manajemen Penanggulangan Bencana

2018 ◽  
Vol 2017 (1) ◽  
Author(s):  
Herlina ◽  
Sumarno ◽  
Indrianawati
Keyword(s):  
Decision Making ◽  
Disaster Management ◽  
Spatial Data ◽  
Data Access ◽  
Spatial Data Infrastructure ◽  
Data Availability ◽  
Geospatial Information ◽  
Data Infrastructure ◽  
Ease Of Access ◽  
Interview Questionnaire

ABSTRAK Akses data spasial yang cepat dan akurat mempunyai peranan yang penting dalam pengambilan keputusan untuk manajemen penanggulangan bencana. Infrastruktur Data Spasial (IDS) merupakan suatu cara untuk memudahkan pengguna untuk mengakses data spasial secara konsisten, mudah, dan aman. Dengan kata lain, IDS dapat meningkatkan ketersediaan data, kemudahan dalam akses, dan implementasi data spasial dalam pengambilan keputusan. Dalam hal manajemen penanggulangan bencana, BPBD dan stakeholder kebencanaan Kabupaten Bandung belum mengimplementasikan IDS kebencanaan. Tujuan penelitian ini adalah menentukan model IDS kebencanaan dan mengevaluasi kesiapan implementasi dalam manajemen penanggulangan bencana di Kabupaten Bandung. Metode yang digunakan dalam penelitian adalah penentuan model IDS kebencanaan yang mengacu pada model IDS yang dirumuskan oleh Rajabifard kemudian didetailkan dengan indikator penilaian IDS yang dikeluarkan Badan Informasi Geospasial tahun 2016. Pengambilan data dilakukan pada 18 stakeholder kebencanaan Kabupaten Bandung dengan wawancara, kuesioner, dan penilaian melalui website. Hasil evaluasi dari kesiapan implementasi IDS kebencanaan Kabupaten Bandung adalah 45,8%. Kata kunci: Infrastruktur Data Spasial, Manajemen Penanggulangan Bencana, Kabupaten Bandung ABSTRACT Fast and accurate spatial data access has an important role in decision making for disaster management. Spatial Data Infrastructure (SDI) is a way to facilitate the users to access spatial data consistently, easily, and safety. In the case, SDI can improve data availability, ease of access and implementation of spatial data for decision making. In disaster management, BPBD and disaster stakeholders in Bandung District have not implemented SDI of disaster. The objective of this study is to determine the SDI model of disaster and evaluate the readiness of implementation in disaster management in Bandung District. The method used in this study is determining SDI model of disaster, referred to IDS model which is formulated by Rajabifard, and then the SDI model of disaster is detailed by SDI assessment indicator issued by Geospatial Information Agency (2016). The data collection has been taken on 18 disaster stakeholders in Bandung District with interview, questionnaire, and assessment through the website. The evaluation result of the readiness of implementation the SDI of disaster in Bandung District is 45.8%. Keywords: Spatial Data Infrastructure, Disaster Management, Bandung District

scholarly journals A Literature Review of Factors Influencing Implementation of Management Information Systems in Organizations

2017 ◽  
Vol 12 (8) ◽  
pp. 72
Author(s):  
Shuddha Chowdhury ◽  
K. M. Salahuddin
Keyword(s):  
Decision Making ◽  
Information Systems ◽  
Management Information Systems ◽  
Vital Role ◽  
Decision Making Process ◽  
Successful Implementation ◽  
Management Information ◽  
Successful Execution ◽  
Main Factors ◽  
Process Managers

A proper implementation of Management Information Systems (MIS) can improve an organization's performance, productivity, and work efficiently. Three factors are vital in the successful implementation of MIS. These are organization factors, technology factors and management factors. There are several other factors but these three are the most important ones according to observation. All other factors can be incorporated into these three factors. These three main factors work in an integrated and coordinated way. There are several other important sub-factors in each of these three areas. These are also discussed in this paper. Management Information Systems (MIS) play a vital role in decision-making process. Managers can improve their decision-making process with the successful execution of Information Systems. Our main goal in this paper is to determine the factors and make discussions on them. How they affect in the successful implementation of MIS is also discussed here.

scholarly journals City - Scale Spatial Data Infrastructure for Solar Photovoltaic Energy Generation Assessment

2018 ◽  
Vol 7 (3.34) ◽  
pp. 4
Author(s):  
R Gowri Shankar Rao ◽  
N K. Rayaguru ◽  
N G.Renganathan ◽  
Sunil Kumar Thakur
Keyword(s):  
Spatial Data ◽  
Smart Cities ◽  
Vital Role ◽  
Spatial Data Infrastructure ◽  
Urban Region ◽  
Successful Implementation ◽  
Solar Pv ◽  
Data Infrastructure ◽  
Solar Systems ◽  
Political Governance

Spatial data plays an vital  role in decision-making for development of smart cities.  As it is very evident that  the development of   smart and sustainable city  mainly depends on its physical infrastructure such as intelligent transportation, smart energy, smart metering etc., This paper  provides an analysis  which aims  using the  spatial data infrastructure  tools  for estimation of  the  region  based  solar PV potential  generation  for a specific urban region . This analysis would afford a great insight in deciding a city scaled potential energy production and planning,  an estimate of the geographical PV potentials for solar power generation is adopted.  The total PV potential is evaluated for  a specific defined area and compared with the local electricity demand. The outcomes comprise of an initial valuation of the town's solar potential that can be used to upkeep organization decisions regarding reserves in solar systems.  Successful implementation of SDI  finally depends on the political governance and  their framing policies.

scholarly journals EFFORTS ON GENDER BALANCE CAPACITY BUILDING IN GIT

2019 ◽  
Vol XLII-5/W3 ◽  
pp. 149-152
Author(s):  
P. Tripathi ◽  
R. B. Thapa
Keyword(s):  
Climate Change ◽  
Decision Making ◽  
Capacity Building ◽  
Vital Role ◽  
Gender Mainstreaming ◽  
Fiscal Year ◽  
Geospatial Information ◽  
Participation Of Women ◽  
And Gender ◽  
Nepali Women

Abstract. The Hindu Kush Himalaya (HKH) region is among the most discrete and diverse region facing various ecological, environmental and socio-economic threats in terms of increasing demands for natural resources and its consequences in the form of overexploitation, disaster, droughts, extreme weather, and climate change etc. Geospatial information technology (GIT) with Earth observation (EO) data are effectively supporting the implementation of development agendas in HKH by providing extensive solutions to above-pressing issues by not only addressing them but also providing services in daily life. These technologies have effectively bolstered in time via innovation, creating jobs and confidence in people that supports filling the data and knowledge gaps in the region. However, the involvement and participation of women in GIT is mere in the region despite their vital role in environmental management and decision making. Realizing the issue, we acknowledged and implemented the twin challenges i.e. capacity building and gender equality for building the pathways to sustainable development via innovative steps and processes to bridge the gender imbalance in GIT workforce in HKH. For the purpose, we organized various capacity building trainings and workshops with a broad focus towards GIT applications in forest, agriculture, water management, drought and climate change along with the hands-on exercises. In addition, specific women focused training programs i.e. Empowering Nepali Women through Technology Training and Women in GIT were organized during 2017 and 2018 respectively. These efforts delivered optimistic results in terms of building confidence, decision making and more women participation showing an increment of ~5% participation by women in 2017–2018 fiscal year with respect to 2016–2017 fiscal year. In HKH nations with less social parity, the information delivered by this gender mainstreaming effort will have life-changing implications to achieve workforce parity.

Relative Efficiency of Decision Making Units Producing Both Desirable and Undesirable Outputs: A Case of Textile Processing Units in Pakistan.

2011 ◽  
Vol 50 (4II) ◽  
pp. 685-698
Author(s):  
Samina Khalil
Keyword(s):  
Decision Making ◽  
Data Envelopment Analysis ◽  
Relative Efficiency ◽  
Value Added ◽  
Data Envelopment ◽  
Undesirable Outputs ◽  
Textile Processing ◽  
Decision Making Unit ◽  
Desirable And Undesirable Outputs ◽  
Decision Making Units

This paper aims at measuring the relative efficiency of the most polluting industry in terms of water pollution in Pakistan. The textile processing is country‘s leading sub sector in textile manufacturing with regard to value added production, export, employment, and foreign exchange earnings. The data envelopment analysis technique is employed to estimate the relative efficiency of decision making units that uses several inputs to produce desirable and undesirable outputs. The efficiency scores of all manufacturing units exhibit the environmental consciousness of few producers is which may be due to state regulations to control pollution but overall the situation is far from satisfactory. Effective measures and instruments are still needed to check the rising pollution levels in water resources discharged by textile processing industry of the country. JEL classification: L67, Q53 Keywords: Data Envelopment Analysis (DEA), Decision Making Unit (DMU), Relative Efficiency, Undesirable Output

Geospatial information informs bonobo conservation efforts

2018 ◽  
Author(s):  
Janet Nackoney ◽  
Jena Hickey ◽  
David Williams ◽  
Charly Facheux ◽  
Takeshi Furuichi ◽  
...  
Keyword(s):  
Spatial Data ◽  
Pan Paniscus ◽  
Democratic Republic Of Congo ◽  
Conservation Strategies ◽  
Suitable Habitat ◽  
Community Based ◽  
Geospatial Information ◽  
République Démocratique Du Congo ◽  
Nous Montrons ◽  
Dynamic Conservation

The endangered bonobo (Pan paniscus), endemic to the Democratic Republic of Congo (DRC), is threatened by hunting and habitat loss. Two recent wars and ongoing conflicts in the DRC greatly challenge conservation efforts. This chapter demonstrates how spatial data and maps are used for monitoring threats and prioritizing locations to safeguard bonobo habitat, including identifying areas of highest conservation value to bonobos and collaboratively mapping community-based natural resource management (CBNRM) zones for reducing deforestation in key corridor areas. We also highlight the development of a range-wide model that analysed a variety of biotic and abiotic variables in conjunction with bonobo nest data to map suitable habitat. Approximately 28 per cent of the range was predicted suitable; of that, about 27.5 per cent was located in official protected areas. These examples highlight the importance of employing spatial data and models to support the development of dynamic conservation strategies that will help strengthen bonobo protection. Le bonobo en voie de disparition (Pan paniscus), endémique à la République Démocratique du Congo (DRC), est menacé par la chasse et la perte de l’habitat. Deux guerres récentes et les conflits en cours dans le DRC menacent les efforts de conservation. Ici, nous montrons comment les données spatiales et les cartes sont utilisées pour surveiller les menaces et prioriser les espaces pour protéger l’habitat bonobo, inclut identifier les zones de plus haute valeur de conservation aux bonobos. En plus, la déforestation est réduite par une cartographie collaborative communale de gestion de ressources dans les zones de couloirs essentiels. Nous soulignons le développement d’un modèle de toute la gamme qui a analysé un variété de variables biotiques et abiotiques en conjonction avec les données de nid bonobo pour tracer la carte d’un habitat adéquat. Environ 28 per cent de la gamme est prédit adéquat; de cela, environ 27.5 per cent est dans une zone officiellement protégée. Ces exemples soulignent l’importance d’utiliser les données spatiales et les modèles pour soutenir le développement de stratégies de conservations dynamiques qui aideront à renforcer la protection des bonobos.

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