Jove
Visualize
Contact Us
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 Concept Videos

Selected Data About Geographic Locations01:25

Selected Data About Geographic Locations

314
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...
314
Levels of Use of a GIS01:29

Levels of Use of a GIS

443
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...
443
Introduction to GIS01:28

Introduction to GIS

698
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...
698
Design Example: Identifying the Locations of Monuments in the Field Using Global Positioning System Device01:30

Design Example: Identifying the Locations of Monuments in the Field Using Global Positioning System Device

443
Surveyors use Global Positioning System (GPS) technology to measure the precise location and elevation of points on Earth. In a recent survey, GPS receivers were used to determine the coordinates and elevations of two park monuments. The process involved careful mission planning, data collection, and correction to ensure accuracy. The survey began with mission planning to identify optimal satellite visibility and minimize Position Dilution of Precision (PDOP). A geodetic control point...
443
GIS Software, Hardware, and Sources of GIS Data01:23

GIS Software, Hardware, and Sources of GIS Data

955
A Geographic Information System (GIS) combines specialized software and hardware to effectively manage, analyze, and present spatial and related data. GIS software includes critical functionalities such as a user interface for easy navigation, database management tools for handling spatial and attribute data, and data retrieval features for efficient access. Analytical tools transform raw data into insights, while display functions produce maps and reports in various formats for effective...
955
Manipulation and Analysis01:21

Manipulation and Analysis

322
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...
322

You might also read

Related Articles

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

Sort by
Same author

Editorial: The Computational Analysis of Cultural Conflicts.

Frontiers in big data·2022
Same author

Geographical Network Analysis.

Tijdschrift voor economische en sociale geografie = Journal of economic and social geography = Revue de geographie economique et humaine = Zeitschrift fur okonomische und soziale Geographie = Revista de geografia economica y social·2021
Same author

Why it is important to consider negative ties when studying polarized debates: A signed network analysis of a Dutch cultural controversy on Twitter.

PloS one·2021
Same author

An introduction to DUIA: The database on urban inequality and amenities.

PloS one·2021
Same author

The Twitter parliamentarian database: Analyzing Twitter politics across 26 countries.

PloS one·2020
Same author

Exclusion as urban policy: The Dutch 'Act on Extraordinary Measures for Urban Problems'.

Urban studies (Edinburgh, Scotland)·2018

Related Experiment Video

Updated: Mar 19, 2026

Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

14.3K

How to Study the City on Instagram.

John D Boy1, Justus Uitermark1,2

  • 1Sociology Department, University of Amsterdam, Amsterdam, The Netherlands.

Plos One
|June 24, 2016
PubMed
Summary

Instagram data offers new insights into urban studies, revealing segregation and social divisions through geotagged posts. This study demonstrates methods for analyzing these patterns in cities like Amsterdam and Copenhagen.

Area of Science:

  • Urban Studies
  • Sociology
  • Geographic Information Science

Background:

  • Traditional urban studies methods face challenges in capturing dynamic social patterns.
  • The rise of social media platforms like Instagram presents novel data opportunities.

Purpose of the Study:

  • To introduce Instagram as a viable data source for urban studies.
  • To propose methods for operationalizing urban concepts using Instagram data.
  • To explore sociospatial patterns and divisions in urban environments.

Main Methods:

  • Utilizing geotagged Instagram posts from Amsterdam and Copenhagen.
  • Applying network analysis to identify user groups.
  • Employing metrics of unevenness and diversity to analyze sociospatial divisions.

More Related Videos

Façade-Level Monitoring of CO2 Variability under Urban Heat Island Conditions using Low-Cost Sensor Data Loggers
07:12

Façade-Level Monitoring of CO2 Variability under Urban Heat Island Conditions using Low-Cost Sensor Data Loggers

Published on: December 12, 2025

255
Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

9.7K

Related Experiment Videos

Last Updated: Mar 19, 2026

Trajectory Data Analyses for Pedestrian Space-time Activity Study
16:14

Trajectory Data Analyses for Pedestrian Space-time Activity Study

Published on: February 25, 2013

14.3K
Façade-Level Monitoring of CO2 Variability under Urban Heat Island Conditions using Low-Cost Sensor Data Loggers
07:12

Façade-Level Monitoring of CO2 Variability under Urban Heat Island Conditions using Low-Cost Sensor Data Loggers

Published on: December 12, 2025

255
Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

9.7K

Main Results:

  • Demonstrated a proof of concept for visualizing urban social patterns.
  • Identified distinct user groups and sociospatial divisions within the studied cities.
  • Highlighted the potential of Instagram data to complement existing urban research methods.

Conclusions:

  • Instagram data can illuminate segregation, subcultures, and status hierarchies.
  • Proposed methods offer a novel approach to urban sociospatial analysis.
  • Acknowledged data and methodological limitations while emphasizing complementarity with established research.