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Geographically weighted machine learning model for untangling spatial heterogeneity of type 2 diabetes mellitus (T2D) prevalence in the USA
16 references·1 citing·42 second hop
Geographically weighted machine learning model for untangling spatial heterogeneity of type 2 diabetes mellitus (T2D) prevalence in the USA (2021)Geographically weighted machine learning …Built environmental characteristics and diabetes: a systematic review and meta-analysis (2018)Built environmental character…Diabetes prevalence is associated with different community factors in the diabetes belt versus the rest of the United States (2016)Diabetes prevalence is associ…Spatial Analysis and Correlates of County-Level Diabetes Prevalence, 2009–2010 (2015)Spatial Analysis and Correlat…The GWmodel R package: further topics for exploring spatial heterogeneity using geographically weighted models (2014)The GWmodel R package: furthe…GWmodel: an R Package for Exploring Spatial Heterogeneity using Geographically Weighted Models (2013)GWmodel: an R Package for Exp…Disparities in Diabetes: The Nexus of Race, Poverty, and Place (2013)Disparities in Diabetes: The …GeoDa: An Introduction to Spatial Data Analysis (2009)GeoDa: An Introduction to Spa…Environmental and Socio-Economic Factors as Contributors to Racial Disparities in Diabetes Prevalence (2009)Environmental and Socio-Econo…The Built Environment and Obesity (2007)The Built Environment and Obe…Multicollinearity and correlation among local regression coefficients in geographically weighted regression (2005)Multicollinearity and correla…Geographically Weighted Regression: The Analysis of Spatially Varying Relationships (2004)Geographically Weighted Regre…Geographically weighted summary statistics — a framework for localised exploratory data analysis (2002)Geographically weighted summa…Geographically Weighted Regression: The Analysis of Spatially Varying Relationships (2002)Geographically Weighted Regre…Age adjustment using the 2000 projected U.S. population. (2001)Age adjustment using the 2000…Age Adjustment Using the 2000 Projected U.S. Population (2001)Age Adjustment Using the 2000…The United States Department of Agriculture (1912)The United States Department …A deep learning approach for estimation of price determinants (2022)A deep learning approach for …Limited-dependent and qualitative variables in econometrics (1983)Geographically Weighted Regression: A Method for Exploring Spatial Nonstationarity (1996)Geographically weighted regression with parameter-specific distance metrics (2016)The Importance of Scale in Spatially Varying Coefficient Modeling (2018)A Route Map for Successful Applications of Geographically Weighted Regression (2022)Local Indicators of Spatial Association—LISA (1995)Local Spatial Autocorrelation Statistics: Distributional Issues and an Application (1995)Spatial Autocorrelation (2009)Geographically weighted regression and multicollinearity: dispelling the myth (2016)mgwr: A Python Implementation of Multiscale Geographically Weighted Regression for Investigating Process Spatial Heterogeneity and Scale (2019)Socioeconomic Disparities In Health: Pathways And Policies (2002)Social Determinants of Health (2005)Neighborhoods, Obesity, and Diabetes — A Randomized Social Experiment (2011)Improving the health of African Americans in the USA: an overdue opportunity for social justice (2016)Neighborhood Environments and Diabetes Risk and Control (2018)Chronic Disease Prevalence in the US: Sociodemographic and Geographic Variations by Zip Code Tabulation Area (2024)Travel demand and the 3Ds: Density, diversity, and design (1997)Neighborhood-Based Differences in Physical Activity: An Environment Scale Evaluation (2003)Inequality in the Built Environment Underlies Key Health Disparities in Physical Activity and Obesity (2006)Built Environments and Obesity in Disadvantaged Populations (2009)Measuring the Built Environment for Physical Activity (2009)Role of Built Environments in Physical Activity, Obesity, and Cardiovascular Disease (2012)The Methods and Materials of Demography. 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References

  • 1912The United States Department of Agriculture5,113 cited
  • 2002Geographically Weighted Regression: The Analysis of Spatially Varying Relationships3,946 cited
  • 2007The Built Environment and Obesity1,078 cited
  • 2004Geographically Weighted Regression: The Analysis of Spatially Varying Relationships1,067 cited
  • 2005Multicollinearity and correlation among local regression coefficients in geographically weighted regression807 cited
  • 2009GeoDa: An Introduction to Spatial Data Analysis722 cited
  • 2001Age Adjustment Using the 2000 Projected U.S. Population578 cited
  • 2001Age adjustment using the 2000 projected U.S. population.442 cited
  • 2013Disparities in Diabetes: The Nexus of Race, Poverty, and Place418 cited
  • 2014The GWmodel R package: further topics for exploring spatial heterogeneity using geographically weighted models294 cited
  • 2002Geographically weighted summary statistics — a framework for localised exploratory data analysis283 cited
  • 2018Built environmental characteristics and diabetes: a systematic review and meta-analysis238 cited
  • 2009Environmental and Socio-Economic Factors as Contributors to Racial Disparities in Diabetes Prevalence142 cited
  • 2013GWmodel: an R Package for Exploring Spatial Heterogeneity using Geographically Weighted Models111 cited

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  • 2015Spatial Analysis and Correlates of County-Level Diabetes Prevalence, 2009–2010100 cited
  • 2016Diabetes prevalence is associated with different community factors in the diabetes belt versus the rest of the United States68 cited
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    • 2022A deep learning approach for estimation of price determinants11 cited