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Urbanization is a dominant driver of landscape transformation across the world, with cities representing centers of economic and socio-cultural development. Today, more than 4.2 billion people live in urban areas, which represent ~3% of the Earth’s land area. By 2050, it is predicted this number will increase to 6.6 billion people (~70% of the predicted global population). As the human population grows, cities around the globe will continue to expand, increasing the demand for food and services. Within cities, urban forests provide multiple nature-based solutions, as well as other environmental services and socio-economic benefits, such as heat mitigation and social integration. Urban forests are also important for coping with psychological stress during events, such as the COVID-19 pandemic. Therefore, urban forests are a priority for basic and applied forest research because they are intimately connected with people’s physical, cultural, and economic well-being in the urban environment, and can also be important reservoirs of biodiversity. To promote a better understanding of urban forests and landscape ecology, this book in “Urban Forests and Landscape Ecology” compiled research set in urban forests and focused on some spatially explicit processes. Studies presented in this book are highly interdisciplinary and use a wide range of research approaches. This book present nine scientific publications from global urban forests demonstrating that these forests, as a nature-based solution, provide multiple environmental services and are crucial to improve urban livability and thereby the wellbeing of city dwellers.

aesthetics --- causal effects --- crown form --- foliage form --- urban forests --- urban forest --- NATURA 2000 area --- soundscape --- birds --- biological diversity --- ecosystem disservices --- Mediterranean region --- green infrastructure --- urban forest types --- indicators --- microclimate --- human thermal comfort --- outdoor thermal environment --- public health --- ecological services --- urban green space --- remote sensing --- deep learning --- convolutional neural network --- residual structure --- attention mechanism --- n/a --- urban biodiversity --- urban habitat quality --- InVEST model --- land surface temperature --- Moran’s I --- Zhengzhou --- social media data --- spatiotemporal utilization --- Shanghai --- ecological function --- landscape structure --- GIS --- air pollution alleviation --- accumulation on leaves --- PM2.5 --- encapsulated particles --- urban trees --- Moran's I

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Some in the social sciences argue that the same logic applies to both qualitative and quantitative methods. In A Tale of Two Cultures, Gary Goertz and James Mahoney demonstrate that these two paradigms constitute different cultures, each internally coherent yet marked by contrasting norms, practices, and toolkits. They identify and discuss major differences between these two traditions that touch nearly every aspect of social science research, including design, goals, causal effects and models, concepts and measurement, data analysis, and case selection. Although focused on the differences between qualitative and quantitative research, Goertz and Mahoney also seek to promote toleration, exchange, and learning by enabling scholars to think beyond their own culture and see an alternative scientific worldview. This book is written in an easily accessible style and features a host of real-world examples to illustrate methodological points.

Social sciences --- Political sociology --- Political science --- Mass political behavior --- Political behavior --- Sociology --- Administration --- Civil government --- Commonwealth, The --- Government --- Political theory --- Political thought --- Politics --- Science, Political --- State, The --- Research --- Methodology. --- Sociological aspects --- 2 x 2 tables. --- David Hume. --- Fundamental Principle of Variable Transformation. --- Fundamental Problem of Causal Inference. --- Fundamental Tradeoffs. --- Hooke's law. --- Principle of Conceptual Opposites. --- Principle of Conceptual Overlap. --- Principle of Unimportant Variation. --- additive-linear causal model. --- aggregation technique. --- asymmetry. --- case selection. --- case studies. --- cases. --- categories. --- causal complexity. --- causal effects. --- causal heterogeneity. --- causal inference. --- causal mechanism. --- causal model. --- causal models. --- causal-process observations. --- causality. --- causation. --- cause. --- causes-of-effects approach. --- characteristics. --- concepts. --- conceptualization. --- constant conjunction definition. --- control variables. --- counterfactual analysis. --- counterfactual definition. --- counterfactuals. --- cross-case analysis. --- data analysis. --- data transformations. --- data-set observations. --- definitions. --- dependent variable. --- effects-of-causes approach. --- empirical testing. --- equifinality. --- error. --- experiments. --- fuzziness. --- fuzzy-set analysis. --- fuzzy-set transformations. --- generalization. --- hypothesis testing. --- indicators. --- individual case analysis. --- individual cases. --- inferential statistics. --- logging. --- logic. --- meaning retention. --- measurement. --- membership functions. --- methodological pluralism. --- minimum rewrite rule. --- mixed-method research. --- multimethod research. --- multiple causation. --- natural language. --- necessary condition. --- nonoccurrence. --- occurrence. --- opposites. --- perfect predictors. --- political science. --- probability theory. --- process tracing. --- qualitative research. --- quantitative research. --- regression. --- scale types. --- scope conditions. --- semantic transformations. --- semantics. --- set theory. --- set-theoretic causal model. --- set-theoretic generalization. --- social science research. --- social sciences. --- sociology. --- standardization. --- static causal asymmetry. --- statistical analysis. --- statistical method. --- statistical model. --- statistics. --- sufficient condition. --- symmetry. --- translation problems. --- typologies. --- variable transformations. --- within-case analysis. --- within-model responses.