Search Results (8)

Success in large carnivore conservation often hinges on local residents’ tolerance towards those species. Feelings of powerlessness and frustration with wildlife policies can lead to intolerance of the species. In extreme cases, intolerance may manifest in poaching. Thus, changes in policy may influence the tolerance of wildlife. To examine the connections between policy and tolerance, we examined how policy scenarios influenced anticipated changes in tolerance to wolves Canis lupus. We administered a survey in 2015–2016 in the core wolf range within northern Wisconsin, USA. Using hierarchical cluster analysis, we clustered respondents into groups based on their current tolerance of wolves. We evaluated the behavioral intentions of the clusters and examined the influence of policy scenarios on respondents’ anticipated changes in tolerance. Finally, using an information-theoretic model selection framework, we assessed the effects of tolerance clusters and demographic factors. The respondents were clustered into three clusters relative to their current tolerance towards wolves: positive, ambivalent, and negative. Each cluster exhibited significantly different behavioral intentions and anticipated changes in tolerance for all scenarios. In all scenarios, respondents who already held positive attitudes towards wolves were significantly less likely to report expected changes in tolerance toward wolves following changes in wolf management. However, respondents who held ambivalent or negative attitudes towards wolves were significantly more likely to report expected changes in tolerance towards wolves following changes in wolf management. Regarding a regulated wolf hunting and trapping season, we observed a Simpson’s Paradox, wherein, when examined in aggregate, no clear pattern emerged, but when examined at the cluster level, important and intuitive patterns emerged. Our demographic model results suggest that policy changes resulting in greater state management authority over wolves, especially authority to implement certain forms of legal killing of wolves, could result in significant increases in tolerance for individuals who identify as hunters, have lost livestock to a predator, or are currently ambivalent or negative towards wolves. Our work elucidates the nuanced relationship between tolerance of wildlife and wildlife policy and identifies a potential ecological fallacy. Full article

This paper proposes a method for quantitative evaluation of perception deviations due to generalization in choropleth maps. The method proposed is based on comparison of class values assigned to different aggregation units chosen for representing the same dataset. It is illustrated by the results of application of the method to population density maps of Lithuania. Three spatial aggregation levels were chosen for comparison: the 1 × 1 km statistical grid, elderships (NUTS3), and municipalities (NUTS2). Differences in density class values between the reference grid map and the other two maps were calculated. It is demonstrated that a perceptual fallacy on the municipality level population map of Lithuania leads to a misinterpretation of data that makes such maps frankly useless. The eldership level map is, moreover, also largely misleading, especially in sparsely populated areas. The method proposed is easy to use and transferable to any other field where spatially aggregated data are mapped. It can be used for visual analysis of the degree to which a generalized choropleth map is liable to mislead the user in particular areas. Full article

Aggregation of spatiotemporal data can encounter potential information loss or distort attributes via individual observation, which would influence modeling results and lead to an erroneous inference, named the ecological fallacy. Therefore, deciding spatial and temporal resolution is a fundamental consideration in a spatiotemporal analysis. The modifiable temporal unit problem (MTUP) occurs when using data that is temporally aggregated. While consideration of the spatial dimension has been increasingly studied, the counterpart, a temporal unit, is rarely considered, particularly in the traffic safety modeling field. The purpose of this research is to identify the MTUP effect in crash-frequency modeling using data with various temporal scales. A sensitivity analysis framework is adopted with four negative binomial regression models and four random effect negative binomial models having yearly, quarterly, monthly, and weekly temporal units. As the different temporal unit was applied, the result of the model estimation also changed in terms of the mean and significance of the parameter estimates. Increasing temporal correlation due to using the small temporal unit can be handled with the random effect models. Full article

Using two GEOBIA (Geographical Object Based Image Analysis) algorithms on a set of segmented images compared to grid partitioning at different scales, we show that statistical metrics related to both objects and sets of pixels are (more or less) subject to the Modifiable Areal Unit Problem. Subsequently, even in a same spatial partition, there may be a bias in statistics describing the objects due to some size effect of the pixel samples. For instance, pixels homogeneity based on Grey Level Cooccurrence Matrices (GLCM), Landscape Shape Index, entropy, object compacity, perimeter/area ratio are studied according to scale. The approach consists in studying the behavior of a given statistical metrics through scales and to compare the results on several image segmentations, according to different partitioning processes, from GEOBIA (Baatz & Schäpe algorithm and Self Organizing Maps) or using reference grids. We finally discuss about the relationship between GEOBIA metrics and scale. By analysing object shape and pixels composition from different metrics points of views, we show that GEOBIA does not always mitigate the Modifiable Areal Unit Problem. Full article

Ecological deprivation indices at the level of spatial units are often used to measure and monitor inequalities in health despite the possibility of ecological fallacy. For the purpose of this study, the European Deprivation Index (EDI) was used, which is based on Townsend theorization of relative deprivation. The Slovenian version of EDI (SI-EDI) at the aggregated level (SI-EDI-A) was calculated to the level of the national assembly polling stations. The SI-EDI was also calculated at the individual level (SI-EDI-I) by the method that represents a methodological innovation. The degree of ecological fallacy was estimated with the Receiver Operating Characteristics (ROC) curves. By calculating the area under the ROC curve, the ecological fallacy was evaluated numerically. Agreement between measuring deprivation with SI-EDI-A and SI-EDI-I was analysed by graphical methods and formal testing. The association of the socio-economic status and the cancer risk was analysed in all first cancer cases diagnosed in Slovenia at age 16 and older in the period 2011–2013. Analysis was done for each level separately, for SI-EDI-I and for SI-EDI-A. The Poisson regression model was implemented in both settings but adapted specifically for aggregated and individual data. The study clearly shows that ecological fallacy is unavoidable. However, although the association of cancer incidence and socio-economic deprivation at individual and aggregated levels was not the same for all cancer sites, the results were very similar for the majority of investigated cancer sites and especially for cancers associated with unhealthy lifestyles. The results confirm the assumptions from authors’ previous research that using the level of the national assembly polling stations would be the acceptable way to aggregate data when explaining inequalities in health in Slovenia in ecological studies. Full article

Human health is known to be affected by the physical environment. Various environmental influences have been identified to benefit or challenge people’s physical condition. Their heterogeneous distribution in space results in unequal burdens depending on the place of living. In addition, since societal groups tend to also show patterns of segregation, this leads to unequal exposures depending on social status. In this context, environmental justice research examines how certain social groups are more affected by such exposures. Yet, analyses of this per se spatial phenomenon are oftentimes criticized for using “essentially aspatial” data or methods which neglect local spatial patterns by aggregating environmental conditions over large areas. Recent technological and methodological developments in satellite remote sensing have proven to provide highly detailed information on environmental conditions. This narrative review therefore discusses known influences of the urban environment on human health and presents spatial data and applications for analyzing these influences. Furthermore, it is discussed how geographic data are used in general and in the interdisciplinary research field of environmental justice in particular. These considerations include the modifiable areal unit problem and ecological fallacy. In this review we argue that modern earth observation data can represent an important data source for research on environmental justice and health. Especially due to their high level of spatial detail and the provided large-area coverage, they allow for spatially continuous description of environmental characteristics. As a future perspective, ongoing earth observation missions, as well as processing architectures, ensure data availability and applicability of ’big earth data’ for future environmental justice analyses. Full article

Uncertainties in spatial modeling studies of schistosomiasis (SCH) are relevant for the reliable identification of at-risk populations. Ecological fallacy occurs when ecological or group-level analyses, such as spatial aggregations at a specific administrative level, are carried out for an individual-level inference. This could lead to the unreliable identification of at-risk populations, and consequently to fallacies in the drugs’ allocation strategies and their cost-effectiveness. A specific form of ecological fallacy is pure specification bias. The present research aims to quantify its effect on the parameter estimates of various environmental covariates used as drivers for SCH infection. This is done by (i) using a spatial convolution model that removes pure specification bias, (ii) estimating group and individual-level covariate regression parameters, and (iii) quantifying the difference between the parameter estimates and the predicted disease outcomes from the convolution and ecological models. We modeled the prevalence of Schistosoma japonicum using group-level health outcome data, and city-level environmental data as a proxy for individual-level exposure. We included environmental data such as water and vegetation indexes, distance to water bodies, day and night land surface temperature, and elevation. We estimated and compared the convolution and ecological model parameter estimates using Bayesian statistics. Covariate parameter estimates from the convolution and ecological models differed between 0.03 for the nearest distance to water bodies (NDWB), and 0.28 for the normalized difference water index (NDWI). The convolution model presented lower uncertainties in most of the parameter estimates, except for NDWB. High differences in uncertainty were found in night land surface temperature (0.23) and elevation (0.13). No significant differences were found between the predicted values and their uncertainties from both models. The proposed convolution model is able to correct for a pure specification bias by presenting less uncertain parameter estimates. It shows a good predictive performance for the mean prevalence values and for a positive number of infected people. Further research is needed to better understand the spatial extent and support of analysis to reliably explore the role of environmental variables. Full article

The present analysis contributes to the existing literature on religion and suicide in three interrelated ways: (1) providing an analysis of suicide completions whereas most research is based on non-lethal levels of suicidality; (2) assessing the relationship with concrete individual level data on completed suicides instead of aggregated data marked by the ecological fallacy issue; and (3) providing gender specific analyses to determine if the relationship is gendered. METHODS. Data come from the U.S. Public Health Service, National Mortality Followback Survey. They refer to 16,795 deaths including 1385 suicides. Significant others of the deceased were interviewed to measure all variables. The dependent variable is a binary variable where 1 = death by suicide and 0 = all other causes. The central independent variable is an index of religious activities. Controls are included for five categories of confounders (1) psychiatric morbidity; (2) help-seeking behavior; (3) Opportunity factors such as firearms; (4) social integration; and (5) demographics. RESULTS. Multivariate logistic regression analysis determined that controlling for 16 predictors of suicide, a one unit increase in religious activities reduced the odds of a suicide death by 17% for males and by 15% for females. The difference in coefficients is not significant (Z = 0.51). Other significant predictors of suicide deaths included suicide ideation (OR = 8.87, males, OR = 11.48, females) and firearm availability (OR = 4.21, males, OR = 2.83, females). DISCUSSION. Religious activities were found to lower suicide risk equally for both men and women. Further work is needed to assess pathways, including suicide ideation, between religious activities and lowered suicide risk. This is the first U.S. based study to test for a gendered association between religion and suicide at the individual level of analysis. Full article