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Related Concept Videos

Cluster Sampling Method01:20

Cluster Sampling Method

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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
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After budding out from the ER membrane, some COPII vesicles lose their coat and fuse with one another to form larger vesicles and interconnected tubules called vesicular tubular clusters or VTCs. These clusters constitute a compartment at the ER-Golgi interface known as ERGIC (Endoplasmic Reticulum Golgi Intermediate Compartment). The ERGIC is a mobile membrane-bound cargo transport system that sorts proteins secreted from ER and delivers them to the Golgi.
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A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
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How Data are Classified: Numerical Data00:59

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Data that are countable or measurable in specific units are called numerical or quantitative data. Quantitative data are always numbers. Quantitative data are the result of counting or measuring the attributes of a population. Amount of money, pulse rate, weight, number of people living in a town, and number of students who opt for statistics are examples of quantitative data.
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Data Reporting and Recording01:24

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Reporting and recording are crucial in data documentation. The timely, thorough, and accurate documentation of facts is essential when recording patient data. Failure to record findings during an assessment or interpretation of a problem will result in loss of information and make the patient document unreliable. The reader is left with general impressions if the information is not specific. A recording is documenting data of the individual's health information in a traceable, secure, and...
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Data Validation01:15

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Method validation is a crucial process in analytical chemistry designed to confirm that a given method consistently produces reliable and high-quality results. This process is essential when a method is applied to different sample matrices or when procedural modifications are made, ensuring that the results meet acceptable standards across various applications.
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Related Experiment Video

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Evaluating the Effect of Roadside Parking on a Dual-Direction Urban Street
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Detecting and Evaluating Urban Clusters with Spatiotemporal Big Data.

Luliang Tang1, Jie Gao2, Chang Ren3

  • 1State Key Laboratory of Information Engineering in Surveying, Mapping, and Remote Sensing, Wuhan University, Wuhan 430079, China. tll@whu.edu.cn.

Sensors (Basel, Switzerland)
|January 26, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a new method to detect and evaluate urban clusters using spatiotemporal big data. The approach accurately assesses urban construction progress, aiding urban managers.

Keywords:
clusteringconformancerationalityspatiotemporal big datatravel activitiesurban clusters

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Area of Science:

  • Urban Planning and Big Data Analytics
  • Geographic Information Systems (GIS) and Spatial Analysis
  • Computational Social Science

Background:

  • Traditional urban cluster detection relies on limited survey data, leading to inaccuracies.
  • Evaluating urban construction progress is crucial for effective urban management.
  • Spatiotemporal big data offers a more granular and natural approach to understanding urban dynamics.

Purpose of the Study:

  • To propose a novel method, Detecting and Evaluating Urban Clusters (DEUC), for identifying and assessing urban clusters.
  • To leverage taxi trajectories and Sina Weibo check-in data for fine-grained urban analysis.
  • To provide a scientific basis for evaluating urban construction progress.

Main Methods:

  • Utilizing agglomerative hierarchical clustering on daily travel patterns to detect urban clusters.
  • Inferring land-use function demands via Naïve Bayes' theorem.
  • Assessing land-use rationality with cross-regional travel, commuting direction, and fulfilled demand indices.
  • Evaluating construction progress using a proposed conformance indicator.

Main Results:

  • The DEUC method effectively detects and analyzes urban clusters.
  • The case study in Wuhan (2009, 2014, 2015) demonstrates the method's applicability.
  • The proposed indicators provide a quantitative measure of land-use rationality and construction progress.

Conclusions:

  • The DEUC method offers a significant improvement over traditional approaches for urban cluster analysis.
  • Spatiotemporal big data analysis is a powerful tool for urban planning and management.
  • The findings support evidence-based decision-making in urban development projects.