Rural environmental concern: Effects of position, partisanship and place

The social bases of environmental concern in rural America resemble those for the nation as a whole, but also reflect the influence of place. Some general place characteristics, such as rates of population growth or resource-industry employment, predict responses across a number of environmental issues. Other unique or distinctive aspects of local society and environment matter as well. We extend earlier work on both kinds of place effects, first by analyzing survey data from northeast Oregon. Results emphasize that “environmental concern” has several dimensions. Second, we contextualize the Oregon results using surveys from other regions. Analysis of an integrated dataset (up to 12,000 interviews in 38 U.S. counties) shows effects from respondent characteristics and political views, and from county rates of population growth and resource-based employment. There also are significant place-to-place variations that are not explained by variables in the models. To understand some of these we return to the local scale. In northeast Oregon, residents describe how perceptions of fire danger from unmanaged forest lands shape their response to the word conservation. Their local interpretation contrasts with more general and urban connotations of this term, underlining the importance of place for understanding rural environmental concern

Modeling associations between public understanding, engagement and forest conditions in theInland Northwest, USA

Opinions about public lands and the actions of private non-industrial forest owners in the western United States play important roles in forested landscape management as both public and private forests face increasing risks from large wildfires, pests and disease. This work presents the responses from two surveys, a random-sample telephone survey of more than 1500 residents and a mail survey targeting owners of parcels with 10 or more acres of forest. These surveys were conducted in three counties (Wallowa, Union, and Baker) in northeast Oregon, USA. We analyze these survey data using structural equation models in order to assess how individual characteristics and understanding of forest management issues affect perceptions about forest conditions and risks associated with declining forest health on public lands. We test whether forest understanding is informed by background, beliefs, and experiences, and whether as an intervening variable it is associated with views about forest conditions on publicly managed forests. Individual background characteristics such as age, gender and county of residence have significant direct or indirect effects on our measurement of understanding. Controlling for background factors, we found that forest owners with higher self-assessed understanding, and more education about forest management, tend to hold more pessimistic views about forest conditions. Based on our results we argue that self-assessed understanding, interest in learning, and willingness to engage in extension activities together have leverage to affect perceptions about the risks posed by declining forest conditions on public lands, influence land owner actions, and affect support for public policies. These results also have broader implications for management of forested landscapes on public and private lands amidst changing demographics in rural communities across the Inland Northwest where migration may significantly alter the composition of forest owner goals, understanding, and support for various management actions

Modelling Associations between Public Understanding, Engagement and Forest Conditions in the Inland Northwest, USA.

Abstract Opinions about public lands and the actions of private non-industrial forest owners in the western United States play important roles in forested landscape management as both public and private forests face increasing risks from large wildfires, pests and disease. This work presents the responses from two surveys, a random-sample telephone survey of more than 1500 residents and a mail survey targeting owners of parcels with 10 or more acres of forest. These surveys were conducted in three counties (Wallowa, Union, and Baker) in northeast Oregon, USA. We analyze these survey data using structural equation models in order to assess how individual characteristics and understanding of forest management issues affect perceptions about forest conditions and risks associated with declining forest health on public lands. We test whether forest understanding is informed by background, beliefs, and experiences, and whether as an intervening variable it is associated with views about forest conditions on publicly managed forests. Individual background characteristics such as age, gender and county of residence have significant direct or indirect effects on our measurement of understanding. Controlling for background factors, we found that forest owners with higher self-assessed understanding, and more education about forest management, tend to hold more pessimistic views about forest conditions. Based on our results we argue that self-assessed understanding, interest in learning, and willingness to engage in extension activities together have leverage to affect perceptions about the risks posed by declining forest conditions on public lands, influence land owner actions, and affect support for public policies. These results also have broader implications for management of forested landscapes on public and private lands amidst changing demographics in rural communities across the Inland Northwest where migration may significantly alter the composition of forest owner goals, understanding, and support for various management actions

A Weakly Supervised Approach for Estimating Spatial Density Functions from High-Resolution Satellite Imagery

We propose a neural network component, the regional aggregation layer, that makes it possible to train a pixel-level density estimator using only coarse-grained density aggregates, which reflect the number of objects in an image region. Our approach is simple to use and does not require domain-specific assumptions about the nature of the density function. We evaluate our approach on several synthetic datasets. In addition, we use this approach to learn to estimate high-resolution population and housing density from satellite imagery. In all cases, we find that our approach results in better density estimates than a commonly used baseline. We also show how our housing density estimator can be used to classify buildings as residential or non-residential.Comment: 10 pages, 8 figures. ACM SIGSPATIAL 2018, Seattle, US

Forest management and wildfire risk in inland northwest

This brief reports the results of a mail survey of forest landowners in northeastern Oregon conducted in the fall of 2012 by the Communities and Forests in Oregon (CAFOR) Project at the University of Colorado and the University of New Hampshire in cooperation with Oregon State University College of Forestry Extension. The mail survey--a follow-up to a telephone survey conducted for the counties of Baker, Union, and Wallowa in the fall of 2011 -was administered to understand who constituted forest landowners in these three coun¬ties and their perceptions about forest management on both public and private land, as well as risks to forests in the area and the actions they have taken to reduce those risks. The respondents indicated that they perceive wildfire as the greatest threat to their lands, and they consider cooperation with neighbors as very or extremely important for land management. Forest landowners believe public lands are managed poorly and see a greater risk of wildfire occurring on neighboring public land than on their own land. Their opinions on land management are not strongly related to background factors or ideology (for example, gender, age, political party, wealth) but may be heavily influenced by personal experience with wildfire

Wildfire, climate, and perceptions in northeast Oregon

Wildfire poses a rising threat in the western USA, fueled by synergies between historical fire suppression, changing land use, insects and disease, and shifts toward a drier, warmer climate. The rugged landscapes of northeast Oregon, with their historically forest- and resource-based economies, have been one of the areas affected. A 2011 survey found area residents highly concerned about fire and insect threats, but not about climate change. In 2014 we conducted a second survey that, to explore this apparent disconnect, included questions about past and future summertime (fire season) temperatures. Although regional temperatures have warmed in recent decades at twice the global rate, accompanied by increasing dryness and fire risks, the warming itself is recognized by only 40 % of our respondents. Awareness of recent warming proves unrelated to individual characteristics that might indicate experience on the land: old-timer versus newcomer status, year-round versus seasonal residence, and ownership of forested land. Perceptions of past warming and expectations of future warming are more common among younger respondents and less common among Tea Party supporters. The best-educated partisans stand farthest apart. Perceptions about local temperatures that are important for adaptation planning thus follow ideological patterns similar to beliefs about global climate change

Does it matter if people think climate change is human caused?

There is a growing consensus that climate is changing, but beliefs about the causal factors vary widely among the general public. Current research shows that such causal beliefs are strongly influenced by cultural, political, and identity-driven views. We examined the influence that local perceptions have on the acceptance of basic facts about climate change. We also examined the connection to wildfire by local people. Two recent telephone surveys found that 37% (in 2011) and 46% (in 2014) of eastern Oregon (USA) respondents accept the scientific consensus that human activities are now changing the climate. Although most do not agree with that consensus, large majorities (85–86%) do agree that climate is changing, whether by natural or human causes. Acceptance of anthropogenic climate change generally divides along political party lines, but acceptance of climate change more generally, and concerns about wildfire, transcend political divisions. Support for active forest management to reduce wildfire risks is strong in this region, and restoration treatments could be critical to the resilience of both communities and ecosystems. Although these immediate steps involve adaptations to a changing climate, they can be motivated without necessarily invoking human-caused climate change, a divisive concept among local landowners

Forest Views: Shifting Attitudes Toward the Environment in Northeast Oregon

This brief reports on a telephone survey conducted in fall 2014 as part of the ongoing Communities and Forests in Oregon (CAFOR) project. CAFOR focuses on seven counties in the Blue Mountains of northeast Oregon (Baker, Crook, Grant, Umatilla, Union, Wallowa, and Wheeler), where the landscape and local livelihoods are changing in interconnected ways. In an effort to inform policy development around natural resource management, the study seeks to understand how public perceptions of climate change and forest management intersect. Authors Angela Boag, Joel Hartter, Lawrence Hamilton, Forrest Stevens, Mark Ducey, Michael Palace, Nils Christoffersen, and Paul Oester report that 65 percent of those surveyed believe that forests are less healthy than they were twenty years ago. Approximately half of residents support increased user fees to improve forest health on federal land, and a majority believes that climate change is happening, although opinion is split between those who believe it is human-caused and those who believe it is caused by natural forces. The authors conclude that innovative economic and policy solutions are needed across the Inland West to help people and forests regain a strong and productive relationship that both supports livelihoods and sustains working landscapes

A simulated ‘sandbox’ for exploring the modifiable areal unit problem in aggregation and disaggregation

We present a spatial testbed of simulated boundary data based on a set of very high-resolution census-based areal units surrounding Guadalajara, Mexico. From these input areal units, we simulated 10 levels of spatial resolutions, ranging from levels with 5,515–52,388 units and 100 simulated zonal configurations for each level – totalling 1,000 simulated sets of areal units. These data facilitate interrogating various realizations of the data and the effects of the spatial coarseness and zonal configurations, the Modifiable Areal Unit Problem (MAUP), on applications such as model training, model prediction, disaggregation, and aggregation processes. Further, these data can facilitate the production of spatially explicit, non-parametric estimates of confidence intervals via bootstrapping. We provide a pre-processed version of these 1,000 simulated sets of areal units, meta- and summary data to assist in their use, and a code notebook with the means to alter and/or reproduce these data

Modelling changing population distributions: an example of the Kenyan Coast, 1979–2009

Large-scale gridded population datasets are usually produced for the year of input census data using a top-down approach and projected backward and forward in time using national growth rates. Such temporal projections do not include any subnational variation in population distribution trends and ignore changes in geographical covariates such as urban land cover changes. Improved predictions of population distribution changes over time require the use of a limited number of covariates that are time-invariant or temporally explicit. Here we make use of recently released multi-temporal high-resolution global settlement layers, historical census data and latest developments in population distribution modelling methods to reconstruct population distribution changes over 30 years across the Kenyan Coast. We explore the methodological challenges associated with the production of gridded population distribution time-series in data-scarce countries and show that trade-offs have to be found between spatial and temporal resolutions when selecting the best modelling approach. Strategies used to fill data gaps may vary according to the local context and the objective of the study. This work will hopefully serve as a benchmark for future developments of population distribution time-series that are increasingly required for population-at-risk estimations and spatial modelling in various fields