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

54
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...
54
GIS Software, Hardware, and Sources of GIS Data01:23

GIS Software, Hardware, and Sources of GIS Data

112
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...
112
Manipulation and Analysis01:21

Manipulation and Analysis

48
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...
48
Statistical Methods for Analyzing Epidemiological Data01:25

Statistical Methods for Analyzing Epidemiological Data

451
Epidemiological data primarily involves information on specific populations' occurrence, distribution, and determinants of health and diseases. This data is crucial for understanding disease patterns and impacts, aiding public health decision-making and disease prevention strategies. The analysis of epidemiological data employs various statistical methods to interpret health-related data effectively. Here are some commonly used methods:
451
Observational Studies01:11

Observational Studies

8.8K
Observational studies are a type of analytical study where researchers observe events without any interventions. In other words, the researcher does not influence the response variable or the experiment's outcome.
There are three types of observational studies – Prospective, retrospective, and cross-sectional.
Prospective Study
Prospective studies, also known as longitudinal or cohort studies, are carried out by collecting future data from groups sharing similar characteristics. One...
8.8K
Archival Research01:40

Archival Research

16.0K
Some researchers gain access to large amounts of data without interacting with a single research participant. Instead, they use existing records to answer various research questions. This type of research approach is known as archival research. Archival research relies on looking at past records or data sets to look for interesting patterns or relationships. For example, a researcher might access the academic records of all individuals who enrolled in college within the past ten years and...
16.0K

You might also read

Related Articles

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

Sort by
Same author

The Role of Control-Based Expectancies and Resilience in Dimensions of Burnout and Work Engagement: A Study of Caregivers in Long-Term Care Settings.

Journal of healthcare management / American College of Healthcare Executives·2026
Same author

Resilience and Perceived Social Support as Predictors of Physical and Psychological Quality of Life: A Multicentric Study in Dementia Caregivers.

The Journal of psychology·2026
Same author

Clinical trial participation in kidney, bladder, and prostate malignancies in the United States: Sociodemographic distribution and impact on survival.

Urologic oncology·2026
Same author

Classification of wildfires in relation to land cover types and associated variables by applying cluster analysis: a case study in the Iberian Peninsula.

Environmental monitoring and assessment·2025
Same author

Predicting cancer detection rates from multiparametric prostate MRI Beyond the PI-RADS classification system.

Canadian Urological Association journal = Journal de l'Association des urologues du Canada·2024
Same author

A novel abbreviated version of the Luria neuropsychological diagnosis battery: reliability and convergent validity in Spanish older adults.

Journal of clinical and experimental neuropsychology·2024

Related Experiment Video

Updated: Aug 8, 2025

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

13.6K

Reconstructing secondary data based on air quality, meteorological and traffic data considering spatiotemporal

Ditsuhi Iskandaryan1, Francisco Ramos1, Sergio Trilles1

  • 1Institute of New Imaging Technologies (INIT), Universitat Jaume I, Av. Vicente Sos Baynat s/n, Castelló de la Plana 12071, Spain.

Data in Brief
|March 2, 2023
PubMed
Summary

This study reconstructs a spatiotemporal dataset for air quality prediction, integrating air quality, meteorological, and traffic data. The dataset enables advanced machine learning models for more accurate environmental forecasting.

Keywords:
Geospatial analysisNitrogen dioxide predictionSecondary dataSpatiotemporal prediction

More Related Videos

Visualizing Field Data Collection Procedures of Exposure and Biomarker Assessments for the Household Air Pollution Intervention Network Trial in India
09:33

Visualizing Field Data Collection Procedures of Exposure and Biomarker Assessments for the Household Air Pollution Intervention Network Trial in India

Published on: December 23, 2022

2.3K
Real-time Breath Analysis by Using Secondary Nanoelectrospray Ionization Coupled to High Resolution Mass Spectrometry
08:23

Real-time Breath Analysis by Using Secondary Nanoelectrospray Ionization Coupled to High Resolution Mass Spectrometry

Published on: March 9, 2018

9.0K

Related Experiment Videos

Last Updated: Aug 8, 2025

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

13.6K
Visualizing Field Data Collection Procedures of Exposure and Biomarker Assessments for the Household Air Pollution Intervention Network Trial in India
09:33

Visualizing Field Data Collection Procedures of Exposure and Biomarker Assessments for the Household Air Pollution Intervention Network Trial in India

Published on: December 23, 2022

2.3K
Real-time Breath Analysis by Using Secondary Nanoelectrospray Ionization Coupled to High Resolution Mass Spectrometry
08:23

Real-time Breath Analysis by Using Secondary Nanoelectrospray Ionization Coupled to High Resolution Mass Spectrometry

Published on: March 9, 2018

9.0K

Area of Science:

  • Environmental Science
  • Data Science
  • Machine Learning

Background:

  • Air quality monitoring generates complex spatiotemporal data.
  • Integrating diverse data sources (air quality, meteorological, traffic) is crucial for accurate prediction.
  • Existing datasets may not fully capture the spatiotemporal dynamics of air pollution.

Purpose of the Study:

  • To introduce a reconstructed dataset for air quality prediction.
  • To develop procedures for implementing spatiotemporal air quality analysis.
  • To facilitate the application of advanced machine learning models to air quality data.

Main Methods:

  • Reconstruction of a spatiotemporal dataset from Madrid City Council's Open Data portal.
  • Incorporation of time series data from various monitoring stations into a spatiotemporal dimension.
  • Application of grid-based (Convolutional Long Short-Term Memory, Bidirectional Convolutional Long Short-Term Memory) and graph-based (Attention Temporal Graph Convolutional Network) machine learning algorithms.

Main Results:

  • A comprehensive dataset suitable for spatiotemporal analysis was created.
  • The dataset was successfully used as input for advanced machine learning models.
  • Demonstrated the feasibility of using reconstructed data for sophisticated air quality prediction.

Conclusions:

  • The reconstructed dataset enhances capabilities for air quality prediction.
  • Spatiotemporal data integration is vital for improving predictive accuracy.
  • Advanced machine learning models show promise in analyzing complex environmental data.