scholarly journals Small-Area Estimation for the USDA Forest Service, National Woodland Owner Survey: Creating a Fine-Scale Land Cover and Ownership Layer to Support County-Level Population Estimates

2021 ◽  
Vol 4 ◽  
Author(s):  
Vance Harris ◽  
Jesse Caputo ◽  
Andrew Finley ◽  
Brett J. Butler ◽  
Forrest Bowlick ◽  
...  
Keyword(s):  
United States ◽  
Land Cover ◽  
Small Area ◽  
Small Area Estimation ◽  
Fine Scale ◽  
Ancillary Data ◽  
United States Census Bureau ◽  
Area Estimation ◽  
Model Based ◽  
National Woodland Owner Survey

Small area estimation is a powerful modeling technique in which ancillary data can be utilized to “borrow” additional information, effectively increasing sample sizes in small spatial, temporal, or categorical domains. Though more commonly applied to biophysical variables within the study of forest inventory analyses, small area estimation can also be implemented in the context of understanding social values, behaviors, and trends among types of forest landowners within small domains. Here, we demonstrate a method for deriving a continuous fine-scale land cover and ownership layer for the state of Delaware, United States, and an application of that ancillary layer to facilitate small-area estimation of several variables from the USDA Forest Service’s National Woodland Owner Survey. Utilizing a proprietary parcel layer alongside the National Land Cover Database, we constructed a continuous layer with 10-meter resolution depicting land cover and land ownership classes. We found that the National Woodland Owner Survey state-level estimations of total acreage and total ownerships by ownership class were generally within one standard error of the population values calculated from the raster layer, which supported the direct calculation of several population-level summary variables at the county levels. Subsequently, we compare design-based and model-based methods of predicting commercial harvesting by family forest ownerships in Delaware in which forest ownership acreage, taken from the parcel map, was utilized to inform the model-based approach. Results show general agreement between the two modes, indicating that a small area estimation approach can be utilized successfully in this context and shows promise for other variables, especially if additional variables, e.g., United States Census Bureau data, are also incorporated.

scholarly journals Small Area Estimation of Smoke-free Workplace Polices and Home Rules in U.S. Counties

2021 ◽  
Author(s):  
Benmei Liu ◽  
Isaac Dompreh ◽  
Anne M Hartman
Keyword(s):  
Tobacco Control ◽  
Small Area ◽  
Small Sample Size ◽  
Health Hazard ◽  
Small Area Estimation ◽  
Small Sample ◽  
County Level ◽  
Area Estimation ◽  
Model Based ◽  
Workplace Policies

Abstract Background The workplace and home are sources of exposure to secondhand smoke (SHS), a serious health hazard for nonsmoking adults and children. Smoke-free workplace policies and home rules protect nonsmoking individuals from SHS and help individuals who smoke to quit smoking. However, estimated population coverages of smoke-free workplace policies and home rules are not typically available at small geographic levels such as counties. Model-based small area estimation techniques are needed to produce such estimates. Methods Self-reported smoke-free workplace policies and home rules data came from the 2014-2015 Tobacco Use Supplement to the Current Population Survey. County-level design-based estimates of the two measures were computed and linked to county-level relevant covariates obtained from external sources. Hierarchical Bayesian models were then built and implemented through Markov Chain Monte Carlo methods. Results Model-based estimates of smoke-free workplace policies and home rules were produced for 3,134 (out of 3,143) U.S. counties. In 2014-2015, nearly 80% of U.S. adult workers were covered by smoke-free workplace policies, and more than 85% of U.S. adults were covered by smoke-free home rules. We found large variations within and between states in the coverage of smoke-free workplace policies and home rules. Conclusions The small-area modeling approach efficiently reduced the variability that was attributable to small sample size in the direct estimates for counties with data and predicted estimates for counties without data by borrowing strength from covariates and other counties with similar profiles. The county-level modeled estimates can serve as a useful resource for tobacco control research and intervention. Implications Detailed county- and state-level estimates of smoke-free workplace policies and home rules can help identify coverage disparities and differential impact of smoke-free legislation and related social norms. Moreover, this estimation framework can be useful for modeling different tobacco control variables and applied elsewhere, e.g., to other behavioral, policy, or health related topics.

Establishing relationships between parcel data and land cover for demographic small area estimation

2013 ◽  
Vol 40 (4) ◽  
pp. 305-315 ◽  
Author(s):  
Stefan Leyk ◽  
Barbara P. Buttenfield ◽  
Nicholas N. Nagle ◽  
Alexander K. Stum
Keyword(s):  
Land Cover ◽  
Small Area ◽  
Small Area Estimation ◽  
Area Estimation

scholarly journals Multilevel Small-Area Estimation of Colorectal Cancer Screening in the United States

2018 ◽  
Vol 27 (3) ◽  
pp. 245-253 ◽  
Author(s):  
Zahava Berkowitz ◽  
Xingyou Zhang ◽  
Thomas B. Richards ◽  
Marion Nadel ◽  
Lucy A. Peipins ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
United States ◽  
Cancer Screening ◽  
Colorectal Cancer Screening ◽  
Small Area ◽  
Small Area Estimation ◽  
The United States ◽  
Area Estimation

Small Area Estimation of Mental Distress Among United States Military Veterans in Illinois

2019 ◽  
Vol 56 (2) ◽  
pp. 298-302
Author(s):  
Justin T. McDaniel ◽  
Minjee Lee ◽  
David L. Albright ◽  
Hee Y. Lee ◽  
Jay Maddock
Keyword(s):  
United States ◽  
Small Area ◽  
Mental Distress ◽  
Small Area Estimation ◽  
Military Veterans ◽  
United States Military ◽  
Area Estimation

scholarly journals Small Area Model-Based Estimators Using Big Data Sources

2015 ◽  
Vol 31 (2) ◽  
pp. 263-281 ◽  
Author(s):  
Stefano Marchetti ◽  
Caterina Giusti ◽  
Monica Pratesi ◽  
Nicola Salvati ◽  
Fosca Giannotti ◽  
...  
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Small Area ◽  
Small Area Estimation ◽  
The Body ◽  
Statistical Research ◽  
Social Data ◽  
Area Estimation ◽  
Model Based ◽  
Social Data Mining

Abstract The timely, accurate monitoring of social indicators, such as poverty or inequality, on a finegrained spatial and temporal scale is a crucial tool for understanding social phenomena and policymaking, but poses a great challenge to official statistics. This article argues that an interdisciplinary approach, combining the body of statistical research in small area estimation with the body of research in social data mining based on Big Data, can provide novel means to tackle this problem successfully. Big Data derived from the digital crumbs that humans leave behind in their daily activities are in fact providing ever more accurate proxies of social life. Social data mining from these data, coupled with advanced model-based techniques for fine-grained estimates, have the potential to provide a novel microscope through which to view and understand social complexity. This article suggests three ways to use Big Data together with small area estimation techniques, and shows how Big Data has the potential to mirror aspects of well-being and other socioeconomic phenomena.

scholarly journals United States Forest Service Use of Forest Inventory Data: Examples and Needs for Small Area Estimation

2021 ◽  
Vol 4 ◽  
Author(s):  
Sarah S. Wiener ◽  
Renate Bush ◽  
Amy Nathanson ◽  
Kristen Pelz ◽  
Marin Palmer ◽  
...  
Keyword(s):  
United States ◽  
Forest Inventory ◽  
Small Area ◽  
Service Use ◽  
Small Area Estimation ◽  
Simulation Models ◽  
The United States ◽  
Northern Region ◽  
Data Set ◽  
Area Estimation

Forest Inventory and Analysis (FIA) data provides robust information for the United States Forest Service’s (USFS) mid-to-broad-scale planning and assessments, but ecological challenges (i.e., climate change, wildfire) necessitate increasingly strategic information without significantly increasing field sampling. Small area estimation (SAE) techniques could provide more precision supported by a rapidly growing suite of landscape-scale datasets. We present three Regional case studies demonstrating current FIA uses, how SAE techniques could enhance existing uses, and steps FIA could take to enable SAE applications that are user-friendly, comprehensive, and statistically appropriate. The Northern Region uses FIA data for planning and assessments, but SAE techniques could provide more specificity to guide vegetation management activities. State and transition simulation models (STSM) are run with FIA data in the Southwestern Region to predict effects of treatments and disturbances, but SAE could support model validation and more precision to identify treatable areas. The Southern Region used FIA to identify existing longleaf pine stands and evaluate condition, but SAE techniques within FIA tools would streamline analyses. Each case study demonstrates a desire to have FIA data on non-forested conditions and non-tree variables. Additional tools to measure statistical confidence would help maximize utility. FIA’s SAE techniques could add value to a widely used data set, if FIA can support key supplements to basic data and functionality.

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