Jove
Visualize
Contact Us

Related Concept Videos

Introduction to GIS01:28

Introduction to GIS

68
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...
68
Thematic Layering in GIS01:30

Thematic Layering in GIS

38
In the past, planning projects such as schools or public facilities required extensive manual effort to gather and compile data. Information such as property boundaries, soil characteristics, road networks, zoning regulations, and flood zones had to be sourced individually from courthouses, utility providers, and registry offices. Assembling these datasets into a coherent format often took several months, delaying project timelines.The introduction of Geographic Information Systems (GIS)...
38
Stereotype Content Model02:16

Stereotype Content Model

14.7K
The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
14.7K
Selected Data About Geographic Locations01:25

Selected Data About Geographic Locations

27
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...
27
Manipulation and Analysis01:21

Manipulation and Analysis

26
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...
26
Levels of Use of a GIS01:29

Levels of Use of a GIS

52
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...
52

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Transparent Rule Enablement Based on Commonization Approach in Heterogeneous IoT Edge Networks.

Sensors (Basel, Switzerland)·2023
Same author

A Survey on Air-Gap Attacks: Fundamentals, Transport Means, Attack Scenarios and Challenges.

Sensors (Basel, Switzerland)·2023
Same author

Toward Mapping an NGSI-LD Context Model on RDF Graph Approaches: A Comparison Study.

Sensors (Basel, Switzerland)·2022
Same author

Harnessing the Power of Smart and Connected Health to Tackle COVID-19: IoT, AI, Robotics, and Blockchain for a Better World.

IEEE internet of things journal·2022
Same author

IoT-Enabled Smart Cities: Evolution and Outlook.

Sensors (Basel, Switzerland)·2021
Same author

Software-Defined Vehicular Cloud Networks: Architecture, Applications and Virtual Machine Migration.

Sensors (Basel, Switzerland)·2020
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Jul 9, 2025

Eye Tracking During Visually Situated Language Comprehension: Flexibility and Limitations in Uncovering Visual Context Effects
07:36

Eye Tracking During Visually Situated Language Comprehension: Flexibility and Limitations in Uncovering Visual Context Effects

Published on: November 30, 2018

15.7K

Towards Semantic Smart Cities: A Study on the Conceptualization and Implementation of Semantic Context Inference

Jieun Lee1, JaeSeung Song1

  • 1Depatment of Convergence Engineering for Intelligent Drone, Sejong University, Gwangjin-gu, Seoul 05006, Republic of Korea.

Sensors (Basel, Switzerland)
|December 9, 2023
PubMed
Summary

This study introduces a smart city system using semantic reasoning and 5W1H principles to proactively predict and address urban issues like diseases and terrorism, enhancing city management and safety.

Keywords:
context extractionsemantic reasoningsemanticssmart city

More Related Videos

Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community
08:53

Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community

Published on: May 31, 2019

5.2K
A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants
06:28

A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants

Published on: August 26, 2018

6.0K

Related Experiment Videos

Last Updated: Jul 9, 2025

Eye Tracking During Visually Situated Language Comprehension: Flexibility and Limitations in Uncovering Visual Context Effects
07:36

Eye Tracking During Visually Situated Language Comprehension: Flexibility and Limitations in Uncovering Visual Context Effects

Published on: November 30, 2018

15.7K
Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community
08:53

Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community

Published on: May 31, 2019

5.2K
A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants
06:28

A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants

Published on: August 26, 2018

6.0K

Area of Science:

  • Urban Informatics
  • Artificial Intelligence
  • Public Safety

Background:

  • Smart cities leverage integrated urban data for services but struggle with autonomous identification of emergent threats like diseases and terrorism.
  • Existing smart city frameworks lack the capability to proactively recognize and respond to unprecedented urban challenges.
  • Unforeseen urban issues pose significant financial and human risks, necessitating advanced predictive capabilities.

Purpose of the Study:

  • To develop a system capable of predicting potential urban issues and supporting proactive resolution.
  • To enhance the autonomous identification and mitigation of emergent urban challenges within smart city environments.

Main Methods:

  • Utilized semantic reasoning and the 5W1H (Who, What, When, Where, Why, How) principles as inference rules.
  • Constructed a semantic graph by integrating diverse urban data sources (municipal data, IoT platforms) using domain-specific templates.
  • Implemented autonomous context inference and accumulation based on 5W1H reasoning to identify situational patterns.

Main Results:

  • The system successfully infers potential urban problems by detecting recurring service disruptions and their interconnections.
  • Identified specific times and locations prone to urban issues through accumulated contextual data.
  • Demonstrated the system's capability to connect disparate events, revealing underlying urban challenges.

Conclusions:

  • Proposed a comprehensive conceptual model for a proactive urban issue prediction system.
  • Provided implementation cases and use cases highlighting practical applications.
  • Enhanced awareness for city administrators and citizens regarding potential problem areas, enabling preemptive mitigation strategies.