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Related Concept Videos

Selected Data About Geographic Locations01:25

Selected Data About Geographic Locations

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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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Hybrid zones are narrow regions where two closely related species interact, mate, and produce hybrids. Relative to either parent species, hybrids may possess distinct phenotypic or genetic differences that impact their survival and reproductive success. The genetic variances introduced by hybridization influence species diversity and speciation processes within the hybrid zone.
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Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

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Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...
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Area Computation by the Alternative Coordinate Method01:24

Area Computation by the Alternative Coordinate Method

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The alternative coordinate method, also known as the Shoelace Formula, is a technique for determining the area of a traverse using Cartesian coordinates. This method relies on the sequential arrangement of x and y coordinates for each point of the shape, ensuring accuracy and ease of application.In this approach, each corner's x and y coordinates are listed as fractions, with the x-coordinate as the numerator and the y-coordinate as the denominator. These coordinates are arranged sequentially...
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Design Example: Alignment of a Road Line Using GIS01:17

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The alignment of a road line using Geographic Information Systems (GIS) is a critical process in civil engineering, combining advanced technology with practical decision-making. This methodology begins with the collection of geospatial data, including information on land cover, geomorphology, drainage patterns, slope, and contour details. Such data is typically acquired through satellite imagery and GIS tools, offering a comprehensive understanding of the terrain.Once the data is gathered, it...
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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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Related Experiment Video

Updated: Jun 14, 2025

Evaluation of an Exclusive Spur Dike U-Turn Design with Radar-Collected Data and Simulation
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New Commuting Zone delineation for the U.S. based on 2020 data.

Christopher S Fowler1

  • 1Department of Geography Penn State University, University Park, USA. csfowler@psu.edu.

Scientific Data
|September 6, 2024
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Summary

New commuting zones for the United States were created using 2020 data, updating previous definitions. This research accounts for pandemic-era shifts in work and residence patterns for improved accuracy.

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Area of Science:

  • Regional Economics
  • Economic Geography
  • Urban Studies

Background:

  • Commuting zones, initially developed in the late 1980s, delineate functional economic regions across the U.S.
  • These zones link rural areas to their nearest economic centers, aiding regional analysis.
  • Previous delineations were based on older data and did not account for recent societal shifts.

Purpose of the Study:

  • To create updated commuting zones for the United States using 2020 data and geographies.
  • To incorporate quality measures for comparing current delineations with historical data.
  • To address the impact of changing work and residence patterns, particularly post-COVID-19, on regional definitions.

Main Methods:

  • Utilized hierarchical clustering on a proportional flows matrix of county-to-county commuting data.
  • Replicated methodologies from earlier commuting zone delineations where possible.
  • Incorporated 2020 geographic and commuting flow data for the United States.

Main Results:

  • Successfully generated new commuting zone definitions based on 2020 data.
  • Introduced quality measures to enhance comparability across different time periods.
  • Provided updated functional economic region delineations reflecting contemporary commuting patterns.

Conclusions:

  • The new commuting zones offer a more accurate representation of U.S. economic geography.
  • These updated zones are crucial for analyzing regional economic trends in light of recent societal changes.
  • The methodology ensures consistency while adapting to evolving work and living arrangements.