Assessment and ranking of influencing factors in the relationship between spatial patterns of urban green spaces and socioeconomic indices in Mashhad urban districts, Iran

Soltanifard, Hadi; Roshandel, Toktam; Ghodrati, Shafieh

doi:10.1007/s40808-020-00776-z

Cited by 10 publications

(6 citation statements)

References 59 publications

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“…We selected more than 100 training samples and reference assistance data including digital topographic maps, field survey data, high spatial resolution image (QB), and Google Earth to identify their representative classes. Based on the spectral and spatial information of these samples, we There is a time difference between these remote sensing and socio-economic data, but this is also the case in similar studies [29,30]. Due to limitations with our funding, we were not able to obtain 2017/18 SPOT remote sensing data for the three cities, and socio-economic data were not available for hundreds of UFUs within Beijing, Haikou, and Zhanjiang for the year 2002.…”

Section: Remote Sensing Interpretation Of Land Cover Typementioning

confidence: 99%

An Integrated Approach to Study Spatial Patterns and Drivers of Land Cover Within Urban Functional Units: A Multi-City Comparative Study in China

et al. 2020

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Understanding the factors that drive green space composition and richness in heterogeneous urban landscapes is critical for maintaining important ecosystem services and biodiversity. Few studies have been conducted on urban greening and plant diversity at the urban functional unit (UFU) level, although a handful of studies have explored the drivers of greening percentage and its relationships with plant richness in tropical cities. In this study, we conducted field surveys, compiled census and remote sensing data, and performed spatial analyses to investigate the interrelationship between greening percentages, plant diversity, and the socioeconomic variables of different primary and secondary UFUs in the cities of Beijing, Zhanjiang, and Haikou in China. We found that these relationships did not differ significantly between primary and secondary UFUs, and that Parks represented the largest areas of forested land, grassland, and water bodies across all three cities. Moreover, the greening percentages of all UFUs across these three cities were positively correlated with both socioeconomic variables and plant species richness. These relationships can be utilized to guide future green space planning within urban ecosystems.

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Section: Remote Sensing Interpretation Of Land Cover Typementioning

confidence: 99%

An Integrated Approach to Study Spatial Patterns and Drivers of Land Cover Within Urban Functional Units: A Multi-City Comparative Study in China

et al. 2020

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Section: Methodsmentioning

confidence: 99%

“…About 930,000 families live in the study area. Economic, social and tourist attractions have caused the population of Mashhad to grow rapidly in recent decades (Soltanifard et al, 2020). Since the Razavi Khorasan province is an important region of saffron production in Iran and the world, most Iranian saffron companies operate in Mashhad.…”

Section: Study Areamentioning

confidence: 99%

Organic food market segmentation based on the neobehavioristic theory of consumer behavior

Tohidi

Mousavi

Dourandish

et al. 2022

BFJ

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PurposeAlthough Iran is one of the largest producers and exporters of saffron in the world, the organic saffron market in Iran is still in its early stages, and there is scarce empirical evidence in this regard. Therefore, the study's primary purpose is to segment the organic saffron market in Mashhad, Iran using neobehavioristic theory and machine learning methods.Design/methodology/approachConsidering the neobehavioristic theory of consumer behavior, the organic saffron market was segmented using crisp and fuzzy clustering algorithms. Also, to assess the relative importance of the factors affecting the intention to buy organic saffron in each market segment, a sensitivity analysis was performed on the output of the artificial neural network (ANN). A total of 400 questionnaires were collected in Mashhad, Iran in January and February 2020.FindingsIn contrast to the belief that psychological factors are more important in market segmentation than demographic characteristics, findings showed that the demographic characteristics of consumers, especially education and income, are the dominant variables in the segmentation of the organic food market. Among the 4 A’s marketing mix elements, the results showed that a low level of awareness and accessibility are obstacles to organic saffron market development. Advertising, distribution channel improvement, package downsizing and online business development are suggested strategies for expanding the organic saffron market in Iran.Practical implicationsThe results of the present study will help policymakers and suppliers of organic saffron to identify their target markets and design short- and long-term marketing strategies to develop the organic saffron market.Originality/valueMachine learning methods and the neobehavioristic theory of consumer behavior were used to segment the organic food market.

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“…Socio-economic and natural climatic conditions are considered to be the main driving factors affecting the evolution of UGS [2,15,34]. Referring to the relevant literature [2,12,17,34,[40][41][42][43], seven factors, as shown in the Table 2, were selected to explore the driving mechanism. Among them, average annual temperature (TEM), annual precipitation (PRE) and DEM were used to quantify the climate and topography.…”

Section: Driving Factor Selectionmentioning

confidence: 99%

“…While the expansion of artificial surface area exhibited a positive effect on UGS diversity in the central and western regions, it has a negative effect on that in the central and western regions. The possible reason was that high population density leaded to high housing demand and the space provided in the central area for the development of UGS was limited [42], but there were enough UGS in the suburbs to meet the diversified leisure and entertainment needs of residents. And as artificial surface area increased in the central area, space potentially available for greening also increased, and the planning and construction of different types of parks and UGSs would enrich the diversity of UGSs.…”

Section: Spatiotemporal Heterogeneity Of Natural Socio-economic Factorsmentioning

confidence: 99%

Multilevel Change of Urban Green Space and Spatiotemporal Heterogeneity Analysis of Driving Factors

Wang,

Lin,

et al. 2024

Sustainability

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Analyzing the change trend of urban green space (UGS) and exploring related driving forces can provide scientific reference for sustainable development in rapidly urbanizing areas. However, the spatial and temporal driving mechanisms of the drivers on UGS patterns at different scales are still not deeply understood. Based on the GlobeLand30 land cover data, nighttime lighting data and spatial statistics from 2000 to 2020, this study analyzed the size, shape and diversity of UGS in Guangzhou at the urban level, gradient level and township level with multiple landscape indices. Diversity means the richness of UGS patch types. The selected indices include percent of landscape (PLAND), largest path index (LPI), landscape shape index (LSI), aggregation index (AI) and Shannon’s diversity index (SHDI). The spatiotemporal heterogeneity of the drivers was then explored using the spatiotemporal weighted regression (GTWR) method. Results showed the following: (1) During 2000−2020, the total amount of UGS in Guangzhou increased slightly and then decreased gradually. UGS was mainly transferred into artificial surfaces (lands modified by human activities). (2) The UGS landscape showed a non-linear trend along the urban–rural gradient and fluctuated more in the interval of 20–60% urbanization level. PLAND, LPI and AI decreased significantly in areas with higher levels of urbanization. LSI increased and SHDI decreased significantly in areas with lower levels of urbanization. At township level, the landscape indices showed significant spatial autocorrelation. They transformed from discrete changes at the edge and at the junction of the administrative district to large-scale aggregated change, especially in northern areas. (3) The size of UGSs was mainly influenced by natural factors and population density, but their shape and diversity were mainly influenced by socio-economic factors. More regular shapes of green patches were expected in higher urbanization areas. Population agglomeration positively influenced green space patterns in the northeastern and southern regions (Zengcheng, Conghua and Nansha). Meanwhile the negative influence of urban expansion on the green space pattern in the central and southern regions decreased over time. This study contributes to an in-depth understanding of how the key factors affect the different changes of UGS with time and space and provides methodological support for the long-term zoning planning and management of UGS.

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Assessment and ranking of influencing factors in the relationship between spatial patterns of urban green spaces and socioeconomic indices in Mashhad urban districts, Iran

Cited by 10 publications

References 59 publications

An Integrated Approach to Study Spatial Patterns and Drivers of Land Cover Within Urban Functional Units: A Multi-City Comparative Study in China

An Integrated Approach to Study Spatial Patterns and Drivers of Land Cover Within Urban Functional Units: A Multi-City Comparative Study in China

Organic food market segmentation based on the neobehavioristic theory of consumer behavior

Multilevel Change of Urban Green Space and Spatiotemporal Heterogeneity Analysis of Driving Factors

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