ABSTRACT
Globally Covid-19 has proven how important nature and landscape are to ensure human well-being physically and mentally. This research used a systematic literature review method to get an overview of existing research articles that specifically assess landscape quality in large scale landscapes using nowadays widely used ecosystem service approach. Research answers four key questions – (1) Which ES were assessed to evaluate the quality of landscape? (2) Which methods were used to assess ES? (3) Which ES indicators were used to determine the quality of landscape? (4) What data were used to conduct the research? The most widely assessed ecosystem service group is focused on the visual quality of the landscape. The most frequently used method group includes statistical analysis and surveys and questionnaires, followed by spatial assessment methods. Indicators that were frequently used in research included general land-use types and separate landscape elements. In order to use such indicators, qualitative and large amount of spatial data are needed to evaluate the quality of the landscape. Wider research is needed to understand landscape quality assessment methods before the ecosystem service term appeared in the research field. © 2022, Latvia University of Life Sciences and Technologies. All Rights Reserved.
ABSTRACT
There is an urgent need for an in-depth and systematic assessment of a wide range of predictive factors related to populations most at risk for delaying and refusing COVID-19 vaccination as cases of the disease surge across the United States. Many studies have assessed a limited number of general sociodemographic and health-related factors related to low vaccination rates. Machine learning methods were used to assess the association of 151 social and health-related risk factors derived from the American Community Survey 2019 and the Centers for Disease Control and Prevention (CDC) BRFSS with the response variables of vaccination rates and unvaccinated counts in 1,555 ZIP Codes in California. The performance of various analytical models was evaluated according to their ability to regress between predictive variables and vaccination levels. Machine learning modeling identified the Gradient Boosting Regressor (GBR) as the predictive model with a higher percentage of the explained variance than the variance identified through linear and generalized regression models. A set of 20 variables explained 72.90% of the variability of unvaccinated counts among ZIP Codes in California. ZIP Codes were shown to be a more meaningful geo-local unit of analysis than county-level assessments. Modeling vaccination rates was not as effective as modeling unvaccinated counts. The public health utility of this model provides for the analysis of state and local conditions related to COVID-19 vaccination use and future public health problems and pandemics.
ABSTRACT
City-making is a process in which several endogenous and exogenous variables associated with socio-economic, environmental, historical, and physical parameters play a significant role. The neoliberal and market-led notion of smart cities is highly criticized by many scholars for its polarized and inequitable approach to development. The traditional communities have continued for generations and inherit a unique living and residential culture bestowing them with an inherent smartness quotient. This concept of smartness for city planning is even more critical during the present times to understand the impact of the spatial structure of existing cities to deal with the COVID-19 outbreak. Authors identify a strong need to merge the two concepts of traditional communities and urban smartness for a holistic approach to building smart communities. This study aims to assess the smart spatial attributes of the traditional neighborhood-level urban communities such as compactness, walkability, and diversity. Primary household surveys were conducted in the walled city of Alwar, Rajasthan, India. The case study reveals compactly designed residential enclaves known as mohallas with mixed land use. The indigenous spatial elements such as squares (chowks), markets (bazaars), and streets (gali) proved to be crucial community gathering places for these settlements. Such zero-level assessment of existing socio-cultural and spatial attributes may enable the appropriate integration of intelligent technologies into our urban systems. Authors recommend harnessing the untapped potential of traditional communities in culturally rich countries like India to achieve the goals of a smart community. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.