Geographical modeling of spatial interaction between human activity and forest connectivity in an urban landscape of southeast China

Ren, Yin; Deng, Lisheng; Zuo, Shixiang; Luo, Yunjian; Shao, Guofan; Wei, Xiaohua; Hua, Lijuan; Yang, Yusheng

doi:10.1007/s10980-014-0094-z

Cited by 61 publications

(28 citation statements)

References 45 publications

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“…For example, Wang [52] first used it to explore the relationship between risk factors and certain diseases. Subsequently, it was applied to measure the effects of potential factors on geographical phenomena, such as the relationship between human activity and urban forest landscape connectivity [54], the spatial correlations among socio-economic factors and the distribution of rural settlements [55] and the effects of potential factors on housing prices [56]. In this study, we attempt to use the geographical detector method to investigate the relative importance of external factors on residential land prices.…”

Section: Methodsmentioning

confidence: 99%

Spatiotemporal Effects of Main Impact Factors on Residential Land Price in Major Cities of China

Yang

et al. 2017

Sustainability

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Abstract:With the rapid development of land marketization in China, the spatial patterns of residential land prices in different regions have become increasingly complicated. The very high and continuously rising residential land prices in many cities are causing significant challenges to economic development and social stability. Yet, there has only been a limited amount of attempts made to model and analyze the regional dynamic changes of residential land price systematically, especially in term of the spatially varying effects of key demographic and economic factors. In this study we provided a perspective analysis of the changes of residential land prices in 2008, 2011 and 2014 based on the land price monitoring records of 105 cities and then conducted a geographically weighted regression (GWR) analysis on the relationships between residential land price and three major impact factors (i.e., immigrant population, gross domestic product (GDP) and investment in residential buildings). Results show that the areas in which GDP had relatively strong positive impacts on residential land price expanded with time. The negative effects of immigrant population on residential land price were mainly concentrated in the cities around the Bohai Rim and the area with negative effects gradually shrank in the three studied years. Conversely, the areas with negative correlation between investment in residential buildings and residential land price gradually expanded in size over time. A geographical detector was used to examine the relative importance of factors to residential land price. It was found that the GDP had more significant influence on residential land price than other factors and the influence of the three factors to overall variation in residential land price increased over the three studied years. These results underscore the importance of taking spatially varying effects of major driving factors into account in policy-making on regional land market.

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Section: Methodsmentioning

confidence: 99%

Spatiotemporal Effects of Main Impact Factors on Residential Land Price in Major Cities of China

Yang

et al. 2017

Sustainability

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“…(Wang and Hu, 2012;Ren et al, 2014;Liu and Yang, 2012). The factor detector, which is a type of Geographical Detector, can test if a particular factor is the reason to lead certain special distribution diversity, through comparing the total variance of the index in a sub-region with that in the entire region (Wang and Hu, 2010):…”

Section: Geographical Detectormentioning

confidence: 99%

A spatial-temporal analysis of urban recreational business districts: A case study in Beijing, China

et al. 2015

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This study has revealed spatial-temporal changes in Recreational Business Districts (RBDs) in Beijing and examined the relationship between the location of urban RBDs and traffic conditions, resident and tourist density, scenic spots, and land prices. A more reasonable classification of urban RBDs (LSC, CPS, and ULA) is also proposed. Quantitative methods such as Gini Coefficient, Spatial Interpolation, Kernel Density Estimation, and Geographical Detector were employed to collect and analyze the data from three types of urban RBDs in Beijing in 1990Beijing in , 2000Beijing in , and 2014, respectively, and the spatial-temporal patterns as well as the distribution characteristics of urban RBDs were analyzed using ArcGIS software. It was concluded that (1) both the number and scale of urban RBDs in Beijing have been expanding and the trend for all types of urban RBDs in Beijing to be spatially agglomerated is continuing; (2) the spatial-temporal evolution pattern of urban RBDs in Beijing is "single-core agglomeration-dual-core agglomeration-multi-core diffusion"; and (3) urban RBDs were always located in areas with low traffic density, tourist attractions, high resident and tourist population density, and relatively high land valuations; these factors also affect the scale size of RBDs.

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“…Many scholars have applied field survey data or remote sensing images of various resolutions (Kuang et al, 2014;Ren et al, 2014;Zhao et al, 2016b) and adopted multivariate statistical analyses to study the relationship between urban forest LST and ecological factors such as forest area, canopy density, vegetation richness, and vegetation index (Chen et al, 2006). However, most of these previous studies analyzed the relationship between urban forest LST and a single ecological factor without comprehensively considering the quantitative relationships between LST and multiple ecological factors (Heffernan et al, 2014;Zhao et al, 2016a).…”

Section: Introductionmentioning

confidence: 99%

Quantifying the influences of various ecological factors on land surface temperature of urban forests

Ren

Deng

Zuo

et al. 2016

Environmental Pollution

Self Cite

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a b s t r a c tIdentifying factors that influence the land surface temperature (LST) of urban forests can help improve simulations and predictions of spatial patterns of urban cool islands. This requires a quantitative analytical method that combines spatial statistical analysis with multi-source observational data. The purpose of this study was to reveal how human activities and ecological factors jointly influence LST in clustering regions (hot or cool spots) of urban forests. Using Xiamen City, China from 1996 to 2006 as a case study, we explored the interactions between human activities and ecological factors, as well as their influences on urban forest LST. Population density was selected as a proxy for human activity. We integrated multi-source data (forest inventory, digital elevation models (DEM), population, and remote sensing imagery) to develop a database on a unified urban scale. The driving mechanism of urban forest LST was revealed through a combination of multi-source spatial data and spatial statistical analysis of clustering regions. The results showed that the main factors contributing to urban forest LST were dominant tree species and elevation. The interactions between human activity and specific ecological factors linearly or nonlinearly increased LST in urban forests. Strong interactions between elevation and dominant species were generally observed and were prevalent in either hot or cold spots areas in different years. In conclusion, quantitative studies based on spatial statistics and GeogDetector models should be conducted in urban areas to reveal interactions between human activities, ecological factors, and LST.

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Geographical modeling of spatial interaction between human activity and forest connectivity in an urban landscape of southeast China

Cited by 61 publications

References 45 publications

Spatiotemporal Effects of Main Impact Factors on Residential Land Price in Major Cities of China

Spatiotemporal Effects of Main Impact Factors on Residential Land Price in Major Cities of China

A spatial-temporal analysis of urban recreational business districts: A case study in Beijing, China

Quantifying the influences of various ecological factors on land surface temperature of urban forests

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