Spatio-Temporal Poverty Analysis with INLA in Hierarchical Bayes Ecological Regression

Rachmawati, Ro’fah Nur; Pusponegoro, Novi Hidayat

doi:10.1016/j.procs.2018.08.180

Cited by 8 publications

(2 citation statements)

References 11 publications

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“…It is of great significance for promoting the coordinated development of the regional "population-resources-environment" and achieving a virtuous cycle of "society-ecology-economy" [40]. However, compared with other regions, the mechanisms for building EPV realization and eco-industry formation in karst areas are influenced by the fragile and complex social-ecological system of the natural ecosystem, which makes it more difficult to carry out ecological protection and restoration [41]. The communities influenced by karst culture have a low acceptance of new things.…”

Section: Introductionmentioning

confidence: 99%

Spatial Pattern of Ecosystem Services and the Mechanism of Eco-Industry Formation in South China Karst Nature Reserves

Zhang,

Rong,

Xiong

et al. 2024

Forests

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Karst nature reserve (NR) ecosystems are vulnerable, and the development of regulatory and cultural services industries is still in its infancy. Realization of ecological product value (EPV) is a crucial way to promote the integration of eco-protection and industrial development in nature reserves (NRs). This study calculates EPV using a modified equivalent factor, analyzes the spatial pattern of EPV using the fishnet tool in Arc GIS, and constructs a model for realizing EPV. Finally, the driving factors for the formation of eco-industry are identified using the grey correlation analysis (GCA) method. The results show that (1) the regulatory service products in karst NRs play a decisive role in EPV, and forest ecosystems have significant EPV potential; (2) high-value grids are concentrated in the core and buffer zones of karst NRs and the spatial distribution of EPV in the experimental zone is highly heterogeneous, with high and low-value grids in mixed distribution; (3) the realization model of EPV in karst NRs follows the logic of “preservation, transformation, and appreciation”; (4) human resources (0.813), ecology (0.798), industry (0.693), policy (0.783), the market (0.778), and economy (0.715) are essential drivers for the formation of eco-industry in karst NRs. Overall, karst NRs can promote the realization of EPV through planning and regulation of land use, based on the interaction between internal and external factors, to promote the coordinated development of eco-industry in karst NRs. This study provides a reference for the scientific management of eco-resources and the sustainable development of eco-industry in karst NRs.

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Section: Introductionmentioning

confidence: 99%

Spatial Pattern of Ecosystem Services and the Mechanism of Eco-Industry Formation in South China Karst Nature Reserves

Zhang,

Rong,

Xiong

et al. 2024

Forests

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“…Examples of assessing the strength of such relationships in spatial disease mapping include, without being exhaustive, Breslow and Clayton (1993), Clayton et al (1993), Zadnik and Reich (2006), Pollán et al (2007), and Wang et al (2019). Spatio-temporal examples include Rachmawati and Pusponegoro (2018), Ntirampeba et al (2018), and Vicente et al (2020). Spatio-temporal areal models are practical and valuable tools but they are not free from inconveniences.…”

Section: Introductionmentioning

confidence: 99%

Alleviating confounding in spatio-temporal areal models with an application on crimes against women in India

Adin,

Goicoa,

Hodges

et al. 2020

Preprint

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Assessing associations between a response of interest and a set of covariates in spatial areal models is the leitmotiv of ecological regression. However, the presence of spatially correlated random effects can mask or even bias estimates of such associations due to confounding effects if they are not carefully handled. Though potentially harmful, confounding issues have often been ignored in practice leading to wrong conclusions about the underlying associations between the response and the covariates. In spatio-temporal areal models, the temporal dimension may emerge as a new source of confounding, and the problem may be even worse. In this work, we propose two approaches to deal with confounding of fixed effects by spatial and temporal random effects. In particular, restricted regression and an apparently -though in fact not -equivalent procedure using constraints are proposed within both fully Bayes and empirical Bayes approaches. The methods are compared in terms of fixed-effect estimates and model selection criteria. The techniques are used to assess the association between dowry deaths and certain socio-demographic covariates in the districts of Uttar Pradesh, India

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Influencing Factors and Prediction of Risk of Returning to Ecological Poverty in Liupan Mountain Region, China

Cui,

Liu,

Jiang

et al. 2024

Chin. Geogr. Sci.

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Spatio-Temporal Poverty Analysis with INLA in Hierarchical Bayes Ecological Regression

Cited by 8 publications

References 11 publications

Spatial Pattern of Ecosystem Services and the Mechanism of Eco-Industry Formation in South China Karst Nature Reserves

Spatial Pattern of Ecosystem Services and the Mechanism of Eco-Industry Formation in South China Karst Nature Reserves

Alleviating confounding in spatio-temporal areal models with an application on crimes against women in India

Influencing Factors and Prediction of Risk of Returning to Ecological Poverty in Liupan Mountain Region, China

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