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 Experiment Videos

Distance-weighted geographic interpolation.

Upton Gjg

    Environment & Planning A
    |May 1, 1985
    PubMed
    Summary
    This summary is machine-generated.

    Related Concept Videos

    You might also read

    Related Articles

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

    Sort by
    Same journal

    An empirically grounded conceptual framework of the determinants of economic resilience: Insights from seven major Canadian regions.

    Environment & planning A·2026
    Same journal

    Steering FinTech: Techno-industrial policy for the data-driven economy in China's Greater Bay Area.

    Environment & planning A·2026
    Same journal

    'This big shadow that we need to turn into light' - How labour intermediaries moralise commodified domestic care work.

    Environment & planning A·2026
    Same journal

    Towards a radical highway geography: Berlin and the remaking of city logistics in global capitalism.

    Environment & planning A·2025
    Same journal

    Municipal structural adjustment: For an institutional analysis of global development finance.

    Environment & planning A·2025
    Same journal

    Searching for housing in the digital age: Neighborhood representation on internet rental housing platforms across space, platform, and metropolitan segregation.

    Environment & planning A·2025
    See all related articles

    New methods provide simpler and equally accurate population density estimates for US states compared to previous approaches. These calculations do not incorporate geographical data.

    Area of Science:

    • Demography
    • Geographic Information Systems
    • Statistical Modeling

    Background:

    • Accurate population density estimation is crucial for resource allocation and policy-making.
    • Previous methods, such as those by Kennedy and Tobler, provided valuable population density estimates but were computationally intensive.
    • The need for more accessible and efficient methods for calculating population density persists.

    Purpose of the Study:

    • To develop and evaluate a new, simplified method for estimating population density at the state level in the United States.
    • To compare the accuracy and computational efficiency of the new method against existing approaches.
    • To highlight the limitations of current methods regarding the utilization of geographical information.

    Main Methods:

    Keywords:
    AmericasComparative StudiesCritiqueData AnalysisDemographic AnalysisDeveloped CountriesDeveloping CountriesDistanceEstimation TechnicsGeographic FactorsNorth AmericaNorthern AmericaPopulationPopulation DensityResearch MethodologySpatial DistributionUnited States

    Related Experiment Videos

  • The study involved recalculating population density estimates for individual US states.
  • A novel, computationally simpler estimation technique was employed.
  • Results were compared quantitatively with previously published estimates from Kennedy and Tobler.

    • Main Results:

    • The newly developed estimation method is significantly simpler to compute than prior techniques.
    • The accuracy of the new population density estimates is comparable to those achieved by Kennedy and Tobler.
    • A key finding is that neither the new method nor the previous ones leverage geographical information in their calculations.

    Conclusions:

    • The proposed method offers a more accessible and efficient alternative for state-level population density estimation.
    • Despite comparable accuracy, the lack of geographical data utilization remains a limitation for all discussed methods.
    • Future research should explore integrating spatial data to further enhance population density models.