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Beyond Where: Modeling Spatial Relationships and Making Predictions

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  • Опубликовано: 18 авг 2024
  • This workshop will cover regression analysis concepts for the analysis of geographic data. Using these statistical methods in many areas (e.g., business, public health, natural resources) allows you to examine, model, and explore data relationships to help answer questions such as “why do we see so much disease in particular areas?” Regression analysis also allows you to predict spatial outcomes for other places or time periods. Application and use of ordinary least squares regression (OLS) and geographically weighted regression (GWR) will be demonstrated. You will learn how to build a properly specified OLS model and interpret the results and diagnostics. The latest advancements in regression and prediction in ArcGIS will be covered.

Комментарии • 12

  • @MaxwellDjecoIII
    @MaxwellDjecoIII 4 года назад +2

    I wonder why people are not watching these videos, so good

  • @Batyskafff
    @Batyskafff 5 лет назад +1

    I cannot imagine better slides to explain such complex topics. Lauren, all of your presentations are masterpieces..

  • @connieball5287
    @connieball5287 4 года назад

    Great workshop!

  • @petarkoljensic7823
    @petarkoljensic7823 2 года назад

    Great work, thanks!

  • @paulocovele6091
    @paulocovele6091 3 года назад +1

    Very good lessons. It is helping me too much in my MSC Thesis

  • @nehas3519
    @nehas3519 6 лет назад

    @dr.lauren- appreciate ur videos
    These are very helpful to better understand the tools

  • @zainabkhan2475
    @zainabkhan2475 5 лет назад

    Thank you so much from India

  • @darleyt1
    @darleyt1 5 лет назад

    This is amazing

  • @ewanharris5433
    @ewanharris5433 3 года назад +1

    Wow

  • @yoshuareece8785
    @yoshuareece8785 2 года назад

    So cool

  • @zainabkhan2475
    @zainabkhan2475 5 лет назад

    Please upload video explaining how to work with OLS in ArcGIS Desktop...from India

  • @bright1402
    @bright1402 3 года назад

    33:24 GWR