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Recommendation systems have become ubiquitous, and they actively participate in creating our individual and collective identity. In this paper, the diffusion of climate change information has been studied based on YouTube's recommendation system and the political media landscape. The YouTube channels of CNN, BBC News and Fox News, as the most popular channels, respectively, for Left, Center and Right parties, were explored using web scraping and social network analysis to check what kind of recommended content will pop up if a user looks for climate change videos. Using an agent-based modeling approach, the competition between Left, Center and Right media in pushing their own narrative of climate change in society was simulated. The results suggest YouTube's recommendation algorithm is highly biased since most of the recommended content was from the same channel fitting their own political agenda. The agent-based modeling indicates the size of a network is a decisive factor in further spread of a message as Left media always dominated Center and Right media in pushing their own perspective on climate change regardless of higher weights assigned to Right media. This study shed light on how public perception on climate change can be shaped by recommendation systems and digital companies. [ABSTRACT FROM AUTHOR]

Urban recreational parks have a wide range of ecosystem services including health, social, tourism and cooling effects. With significant contribution to improving life quality of urban residents, successful maintenance of urban recreational parks heavily relies on visitors' profile information. In this case, this study attempts to analyze the relationships between different characteristics of Koohsangi Park in Mashhad City, Iran, with sociocultural variables of park visitors to explicitly measure their recreational satisfaction level. In this case, descriptive and inferential statistics (Mann-Whitney U test, Kruskal-Wallis one-way analysis of variance and Pearson correlation coefficient) approaches were applied to conduct analyses of this study. A collection of variables was considered among gender, age and monthly income of the visitors which were more important and informative for management practices. According to the results, Koohsangi Park has its own user portfolio based on sociocultural variables such that there is a general urban park user profile as age between 20 and 40, married, employee, B.Sc. graduate, mostly male and belonging to middle-high income groups. In addition, positive bivariate association between monthly incomes of the visitors and their willingness to pay entrance fee was identified; however, majority of the visitors had a tendency to pay less than 1 US dollar as entrance fee. This study suggests that users' opinions on different characteristics of Koohsangi Park could be instructive for park management and city authorities who normally decide on the future development of urban parks and their success could substantially benefit from such information. [ABSTRACT FROM AUTHOR]

Performance evaluation is a critical step for land use/land cover (LULC) change modelling. It can be conducted through pixel quantity and its geographical location according to majority of current approaches. It is hence important to know to what extent spatial patterns of a given landscape are properly replicated in simulated LULC maps. Therefore, a new validation metric, named as landscape accuracy metric (LAM), is introduced by inspiration from landscape ecology. Unlike pixel quantity validation metrics, model performance is measured by LAM through quantifying spatial patterns including structure, composition and configuration attributes. The functionality of LAM was studied to assess the performance of the built-up change simulation under historical, ecological and stochastic scenarios, applying Cellular Automata Markov model. LAM is a flexible measure such that modellers can apply this metric through adding or eliminating various metrics of their interest in a selective manner and under different environmental circumstances. [ABSTRACT FROM AUTHOR]

This study develops an informed modelling approach that follows a bottom-up planning strategy to define plausible urban growth scenarios. In this case, landscape aesthetics suitability of the area was first generated using multi-criteria evaluation method. Then, a buffer zone of 1 km was considered to extract the average values of aesthetics suitability scores surrounding urban patches with medium physical size (10–30 hectares). The averaged values were considered as the dependent variable. In the next step, landscape metrics of these urban patches, as explanatory variables, were also computed to measure compositional and configuration-based attributes of urban clusters. Bivariate associations (Pearson correlation analysis) and statistical relationships (linear regression algorithm) between landscape metrics and their associated aesthetics values were measured and modelled. According to the results, both composition and configuration values are significantly correlated to the dependent variable in which configuration-based attributes depicted a stronger explanatory power. [ABSTRACT FROM AUTHOR]

This study proposes a landscape metrics-based method for model performance evaluation of land change simulation models. To quantify model performance at both landscape and class levels, a set of composition- and configuration-based metrics including number of patches, class area, landscape shape index, mean patch area and mean Euclidean nearest neighbour distance were employed. These landscape metrics provided detailed information on simulation success of a cellular automata-Markov chain (CA-Markov) model standpoint of spatial arrangement of the simulated map versus the corresponding reference layer. As a measure of model simulation success, mean relative error (MRE) of the metrics was calculated. At both landscape and class levels, the MRE values were accounted for 22.73 and 10.2%, respectively, which are further categorised into qualitative measurements of model simulation performance for simple and quick comparison of the results. Findings of the present study depict a hierarchical and multi spatial level assessment of model performance. [ABSTRACT FROM AUTHOR]

Increasing land utilization through diverse forms of human activities, such as agriculture, forestry, urban growth, and industrial development, has led to negative impacts on the water quality of rivers. To find out how catchment attributes, such as land use, hydrologic soil groups, and lithology, can affect water quality variables (Ca, Mg, Na, Cl, HCO, pH, TDS, EC, SAR), a spatio-statistical approach was applied to 23 catchments in southern basins of the Caspian Sea. All input data layers (digital maps of land use, soil, and lithology) were prepared using geographic information system (GIS) and spatial analysis. Relationships between water quality variables and catchment attributes were then examined by Spearman rank correlation tests and multiple linear regression. Stepwise approach-based multiple linear regressions were developed to examine the relationship between catchment attributes and water quality variables. The areas (%) of marl, tuff, or diorite, as well as those of good-quality rangeland and bare land had negative effects on all water quality variables, while those of basalt, forest land cover were found to contribute to improved river water quality. Moreover, lithological variables showed the greatest most potential for predicting the mean concentration values of water quality variables, and noting that measure of EC and TDS have inversely associated with area (%) of urban land use. [ABSTRACT FROM AUTHOR]