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Related Concept Videos

Levels of Use of a GIS01:29

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Geographic Information Systems (GIS) operate across three levels of application, each representing an increasing degree of complexity: data management, analysis, and prediction. These levels reflect the expanding functionality and versatility of GIS technology in handling spatial data for diverse purposes.Data ManagementAt its foundational level, GIS serves as a tool for data management, enabling the input, storage, retrieval, and organization of spatial data. This level is often employed in...
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Selected Data About Geographic Locations01:25

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Geographic Information Systems (GIS) rely on two core types of data: spatial data and attribute data.Spatial DataSpatial data defines the physical location of features within a coordinate system, typically expressed in terms of latitude and longitude. It provides precise positioning for elements like roads, rivers, or buildings.Attribute DataAttribute data complements spatial data by adding descriptive information about these features. For example, a road's spatial data includes its start and...
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GIS manipulation and analysis functions are vital for decision-making and planning. These activities range from data retrieval tasks, such as selecting information based on specific criteria, to advanced analytical techniques that address complex spatial problems.One critical GIS analysis method is overlaying, which combines multiple data layers to examine impacts. For example, overlaying a river-dammed lake boundary with road networks can identify affected infrastructure. Another common...
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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Geographic Information Systems (GIS) are tools for storing, analyzing, and displaying spatial data alongside related attributes. Unlike traditional information systems that address general queries, GIS incorporates spatial components, enabling users to answer "where" and "how far." For example, GIS can process housing data linked to geographic locations like zip codes, allowing insights into population density or housing distribution through thematic maps.GIS integrates technologies such as...
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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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Geographic information systems adoption model: A partial least square-structural equation modeling analysis approach.

Nouf Abdulaziz Alzahrani1, Siti Norul Huda Sheikh Abdullah1, Noridayu Adnan1

  • 1Centre for Cyber Security, Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, 43600, Bangi, Malaysia.

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Summary

This study developed a model for Geographic Information System (GIS) adoption in Saudi Public Sector Organizations (PSOs). Key factors like system quality and perceived usefulness significantly enhance GIS adoption and PSO performance.

Keywords:
Geographic information systemPLS-SEMSocio-demographic factorsSystem quality

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Area of Science:

  • Information Systems
  • Geographic Information Systems (GIS)
  • Public Administration

Background:

  • Geographic Information Systems (GIS) offer powerful data management capabilities but have limited implementation in public sector disaster response.
  • Previous GIS adoption models faced challenges including pandemic pressure, competitiveness, change management, and security concerns, especially in developing nations.
  • Existing Information Systems (IS) adoption frameworks need adaptation to incorporate geographical elements effectively within Public Sector Organizations (PSOs).

Purpose of the Study:

  • To develop and validate a model for successful GIS adoption within Public Sector Organizations (PSOs).
  • To analyze the applicability of established factors, including the Technology Acceptance Model (TAM) and DeLone and McLean Information Success Model, for enhancing PSO performance.
  • To propose a conceptual framework examining the impact of various factors on GIS adoption and its subsequent effect on PSO performance in Saudi Arabia.

Main Methods:

  • Quantitative research methodology employing a questionnaire distributed to 350 respondents from Saudi PSOs.
  • Data analysis using Partial Least Square Structural Equation Modeling (PLS-SEM) on 272 valid responses to validate the proposed GIS adoption model.
  • Utilized established theoretical frameworks like TAM and DeLone and McLean Information Success Model to guide factor selection and analysis.

Main Results:

  • System quality, service quality, change management, competitiveness pressure, perceived ease of use, perceived usefulness, and security factors significantly and positively influenced GIS adoption.
  • GIS adoption was found to substantially enhance the overall performance of Public Sector Organizations (PSOs).
  • The validated model demonstrates a clear pathway through which specific factors drive GIS adoption and improve organizational outcomes.

Conclusions:

  • The proposed model offers valuable insights into optimizing GIS adoption strategies for PSOs.
  • Successful GIS adoption, driven by identified factors, can lead to significant improvements in PSO performance.
  • The findings provide actionable recommendations for policymakers and PSO leaders to enhance operational efficiency through GIS implementation.