Analyzing spatial patterns and influencing factors of different illegal land use types within ecological spaces: A case study of a fast-growing city

Li, Hua; Peng, Yinuo; Li, Minying; Zhuang, Yaye; He, Xiaoyu; Lin, Jinyao

doi:10.1016/j.jclepro.2023.138883

Cited by 11 publications

(2 citation statements)

References 63 publications

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“…Random forest (RF) is one of the most effective and efficient machine learning methods and has been commonly used in various research fields [93,94]. Compared with other machine learning methods, RF can effectively handle high-dimensional data and missing values [95,96]. RF regression is an algorithm based on ensemble learning that completes the regression task by building multiple decision trees and integrating their prediction results.…”

Section: Housing Price Estimation Based On Machine Learningmentioning

confidence: 99%

Grid-Scale Poverty Assessment by Integrating High-Resolution Nighttime Light and Spatial Big Data—A Case Study in the Pearl River Delta

Li,

Lin,

et al. 2023

Remote Sensing

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Poverty is a social issue of global concern. Although socioeconomic indicators can easily reflect poverty status, the coarse statistical scales and poor timeliness have limited their applications. While spatial big data with reasonable timeliness, easy access, and wide coverage can overcome such limitations, the integration of high-resolution nighttime light and spatial big data for assessing relative poverty is still limited. More importantly, few studies have provided poverty assessment results at a grid scale. Therefore, this study takes the Pearl River Delta, where there is a large disparity between the rich and the poor, as an example. We integrated Luojia 1-01, points of interest, and housing prices to construct a big data poverty index (BDPI). To evaluate the performance of the BDPI, we compared this new index with the traditional multidimensional poverty index (MPI), which builds upon socioeconomic indicators. The results show that the impoverished counties identified by the BDPI are highly similar to those identified by the MPI. In addition, both the BDPI and MPI gradually decrease from the center to the fringe of the study area. These two methods indicate that impoverished counties were mainly distributed in ZhaoQing, JiangMen and HuiZhou Cities, while there were also several impoverished parts in rapidly developing cities, such as CongHua and HuaDu Counties in GuangZhou City. The difference between the two poverty assessment results suggests that the MPI can effectively reveal the poverty status in old urban areas with convenient but obsolete infrastructures, whereas the BDPI is suitable for emerging-development areas that are rapidly developing but still lagging behind. Although BDPI and MPI share similar calculation procedures, there are substantial differences in the meaning and suitability of the methodology. Therefore, in areas lacking accurate socioeconomic statistics, the BDPI can effectively replace the MPI to achieve timely and fine-scale poverty assessment. Our proposed method could provide a reliable reference for formulating targeted poverty-alleviation policies.

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Section: Housing Price Estimation Based On Machine Learningmentioning

confidence: 99%

Grid-Scale Poverty Assessment by Integrating High-Resolution Nighttime Light and Spatial Big Data—A Case Study in the Pearl River Delta

Li,

Lin,

et al. 2023

Remote Sensing

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“…Land use and land cover (LULC) change is a primary issue in global environmental change and sustainable development [1][2][3][4][5]. Land use activities will inevitably have significant influences on the planet's land surface [6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Analyzing and Simulating the Influence of a Water Conveyance Project on Land Use Conditions in the Tarim River Region

Lin,

Chen

2023

Land

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Arid and semi-arid areas are facing severe land degradation and desertification due to water scarcity. To alleviate these environmental issues, the Chinese government has launched a “water conveyance” project for environmental protection along the Tarim River. While previous studies have mainly focused on environmental conditions, the influence of these policies on land use conditions remains less explored. Therefore, this study first simulated the land use and land cover (LULC) changes in a major city (Korla) around the Tarim River. We found that the water conveyance routes have exerted notable influences on surrounding LULC changes. Next, we primarily focused on the LULC changes among different reaches of the Tarim River. We found that water and forest areas in the lower reaches have increased at the expense of a slight decrease in such areas in the upper and middle reaches, which suggests that the water conveyance policy may also have unintended consequences. These findings could attract the attention of decision makers in many other arid and semi-arid areas, and they could provide practical policy implications for other similar inter-basin water conveyance projects. The benefits and risks of these man-made projects should be carefully balanced.

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Enhancing wildfire mapping accuracy using mono-temporal Sentinel-2 data: A novel approach through qualitative and quantitative feature selection with explainable AI

Van,

Tran,

Nguyen

et al. 2024

Ecological Informatics

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Analyzing spatial patterns and influencing factors of different illegal land use types within ecological spaces: A case study of a fast-growing city

Cited by 11 publications

References 63 publications

Grid-Scale Poverty Assessment by Integrating High-Resolution Nighttime Light and Spatial Big Data—A Case Study in the Pearl River Delta

Grid-Scale Poverty Assessment by Integrating High-Resolution Nighttime Light and Spatial Big Data—A Case Study in the Pearl River Delta

Analyzing and Simulating the Influence of a Water Conveyance Project on Land Use Conditions in the Tarim River Region

Enhancing wildfire mapping accuracy using mono-temporal Sentinel-2 data: A novel approach through qualitative and quantitative feature selection with explainable AI

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