Measuring and understanding global human settlements patterns and processes: innovation, progress and application

Ehrlich, Daniele; Balk, Deborah; Sliuzas, Richard

doi:10.1080/17538947.2019.1630072

Cited by 11 publications

(8 citation statements)

References 30 publications

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“…As in other sectors, innovation and developing technologies in the agricultural sector can ensure the continuity of the food production process, increase efficiency, and ensure food security. Many studies, especially the European Union resources, emphasize innovation as the main factor in achieving the transformation into sustainable agriculture (Daniele et al, 2020;Dwyer, 2013;Hermans et al, 2013).…”

Section: Discussionmentioning

confidence: 99%

Factors accelerating agricultural innovation and sustainability: The case of paddy farmers

Aydoğan

Demiryürek

Özer

et al. 2021

Integr Envir Assess & Manag

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The primary purpose of this study is to reveal the factors accelerating both the innovativeness and sustainability levels of paddy farmers. The data used in the study were obtained from the questionnaires conducted with 70 paddy farmers in the Bafra district of Samsun province, Turkey, determined by a simple random sampling method. Paddy farmers were divided into three groups as low, medium, and high innovativeness groups with hierarchical cluster analysis. An ANOVA was used to compare the socioeconomic and farming characteristics of the paddy farmers, and accelerated failure time models were used to analyze the factors affecting the innovativeness and sustainability variables. The research results indicated that the number of years of formal education, the agricultural experience, the amount of labor per unit area, the animal assets, the share of agricultural income in the total income, the number of memberships to farmer organizations, paddy production area, sustainable agriculture area, and cooperation scores differed from the innovativeness groups. The farmers' collaboration in agricultural activities increased the likelihood that they are an innovator. The results concluded that the number of years of formal education, global good agriculture practice, agricultural experience, household size, animal assets, and women's employment influenced both the innovativeness and sustainability variables. Encouraging the participation of women in agricultural production in rural areas, including sustainability-related issues in addition to technical issues in farmer training programs, and ensuring the participation of women in such training are issues that should be considered in future.

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Section: Discussionmentioning

confidence: 99%

Factors accelerating agricultural innovation and sustainability: The case of paddy farmers

Aydoğan

Demiryürek

Özer

et al. 2021

Integr Envir Assess & Manag

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“…One such attempt to measure and understand global human settlement patterns and processes, elaborate [59] on how introducing technologies underpinning an urban thematic platform that facilitates the use of earth observation (technologies) through webbased services [60]. It aims at different user communities addressing settlement analysis and decision making, and it supports three types of uses.…”

Section: Need and Research Objects Of An Integrated Modelmentioning

confidence: 99%

Integrated Climate Action Planning (ICLAP) in Asia-Pacific Cities: Analytical Modelling for Collaborative Decision Making

et al. 2022

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While climate change has global causations and impacts, there is growing consensus on addressing the 2 °C challenge through local actions. However, at the local level, there is disintegrated knowledge on the following: (a) short-, mid- and long-term climate vulnerability, (b) economy and GHG structures and their future pathways, and (c) useful mitigation and adaptation undertaken elsewhere. We evaluate these gaps through a comprehensive review of scientific literature and policy approaches of urban-climate studies in the Asia-Pacific Region. Based on the research findings, we develop a collaborative research framework of an integrated climate action planning (ICLAP) model for evidence-based decision-making tool. It adopts an innovative methodology integrating knowledge and data from diverse analytics, as follows: (a) spatial: downscaling global/regional climate scenarios to forecast local climate variability (50 km × 50 km) for 2030 (SDG target) and 2050; (b) statistical: a meta-analysis of 49 five-million-plus cities to forecast economic, energy and GHG scenarios; (c) bibliometric: a systematic review of global urban climate interventions from Google Scholar that collectively aid cities on policy inputs for mid-term climate variability, GHG profiles and available solutions at their disposal. We conclude with a discussion on scientific and policy relevance of such a tool in fostering overall urban, regional and global sustainability.

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“…However, the boundaries of the administrative areas are inconsistent with those of natural units (watersheds, natural hazards affected area, etc.) in actual research, and the differences make it difficult to integrate and comprehensive analyze multisource data at a fine scale [7]. Gridded population distribution data can solve the above-mentioned problems [8], [9], [10].…”

Section: Introductionmentioning

confidence: 99%

Combining Luojia1-01 Nighttime Light and Points-of-Interest Data for Fine Mapping of Population Spatialization Based on the Zonal Classification Method

Guo

Zhang

Zhao

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Fine-scale population spatial distribution plays an important role in urban microcosmic research, influencing infrastructure placement, emergency evacuation management, business decisions, and urban planning. In the past, nighttime light (NTL) data were generally used to map the spatial distribution of the population at a large scale because of their low spatial resolution. The new generation of Luojia1-01 NTL data can be used for fine-scale social and economic analysis with its high spatial resolution and quantitative range. However, due to the geometry and background noise of the data themselves, the accuracy of the original NTL data is still low. Points-of-interest (POI) also can be used to map the population spatialization, but the indicative relationship between the POI and population is not clear, especially in rural and urban areas with different landscape structures. To solve the above-mentioned problems, this study proposes an improved nighttime light (INTL) index to better use the Luojia1-01 NTL data. Meanwhile, a zonal classification model based on INTL and impervious surface area is proposed to distinguish urban and rural areas. Compared with previous research and existing datasets, our result had the highest accuracy (R² = 0.86). This study explains that the INTL index is applicable to population spatialization research with the emergence of high-resolution and multispectral NTL satellite data. Moreover, the zonal classification model in this research can significantly improve the accuracy of population spatialization in rural areas. This study provides a possible way to use NTL and POI data in other social and economic spatialization research.

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Measuring and understanding global human settlements patterns and processes: innovation, progress and application

Cited by 11 publications

References 30 publications

Factors accelerating agricultural innovation and sustainability: The case of paddy farmers

Factors accelerating agricultural innovation and sustainability: The case of paddy farmers

Integrated Climate Action Planning (ICLAP) in Asia-Pacific Cities: Analytical Modelling for Collaborative Decision Making

Combining Luojia1-01 Nighttime Light and Points-of-Interest Data for Fine Mapping of Population Spatialization Based on the Zonal Classification Method

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