Spatial Determinants of Poverty Using GIS-Based Mapping

Vista, Brandon M.; Murayama, Yuji

doi:10.1007/978-94-007-0671-2_16

Cited by 14 publications

(10 citation statements)

References 11 publications

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“…Similarly, the unique geophysical and biological attributes of the south-south region provide a good base for primary production in light of the climatic, soil, geological and landuse characteristics (FIND, 2011;NDDC, 2006NDDC, , 2004Uche et al, 2016). Vista and Murayama (2001) supported that poverty has a geographic dimension and physical environment plays a significant role in poverty incidence of local communities, especially in developing nations. In an attempt to give emphasis to the importance of geography in describing poverty in the Philippines, Vista and Murayama (2001) explored the spatial models and the possible underlying determinants affecting poverty using GIS and established the key factors that could possibly have control on the incidence of poverty.…”

Section: Introductionmentioning

confidence: 99%

“…Vista and Murayama (2001) supported that poverty has a geographic dimension and physical environment plays a significant role in poverty incidence of local communities, especially in developing nations. In an attempt to give emphasis to the importance of geography in describing poverty in the Philippines, Vista and Murayama (2001) explored the spatial models and the possible underlying determinants affecting poverty using GIS and established the key factors that could possibly have control on the incidence of poverty. Vista and Murayama (2001) concluded that the variation in poverty incidence is as a result of disproportion access to road networks.…”

Section: Introductionmentioning

confidence: 99%

“…In an attempt to give emphasis to the importance of geography in describing poverty in the Philippines, Vista and Murayama (2001) explored the spatial models and the possible underlying determinants affecting poverty using GIS and established the key factors that could possibly have control on the incidence of poverty. Vista and Murayama (2001) concluded that the variation in poverty incidence is as a result of disproportion access to road networks. Furthermore, the nearness to major towns with significant development activities and varying physical features also forms part of the determinants of poverty.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, the nearness to major towns with significant development activities and varying physical features also forms part of the determinants of poverty. On the overall, it was established that geography and the nature of public policy and reforms implementation have a strong correlation with poverty levels (Vista and Murayama, 2001).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

The distribution of Micro Project Programme (MPP) and relative poverty incidence across Nigeria south-south vegetation belts

Tinubu¹,

Arokoyu²,

Lawal³

2019

Int. NGOJ

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The European Union in collaboration with some non-governmental organisation initiated the European Micro Projects Programme (MPP) in south-south States of Nigeria. Basically, the aim of the progamme was to help improve the poor living condition of communities situated in these areas, by the provision of basic infrastructures. One of the key derivatives from the ecological zones/vegetation belts in Niger Delta is the natural resources aside oil and gas reserves, which serve as a means of livelihood for rural community. The objective of this study was to examine the relative effects of density and accessibility of MPP projects on poverty incidence across the vegetational belts of the south-south states in Nigeria. A cross sectional survey of household units was adopted during data collection and resulting data along with other measured parameters were subjected to a combination of descriptive statistics and spatial analytical techniques, to determine if there are relationships between poverty incidence and MPP project distribution along the vegetation belts. The resulting project density within the belts showed that rain forest and fresh water swamp have more concentration within defined output cells. The poverty ranking results for the ecological regions showed that extremely poor respondents are predominant in all the ecological zones with 100% occurrence in mangrove. Moderately poor respondents are more in the low land rain forest and mangrove and coastal vegetation. The highest number of non-poor falls within the derived rainforest and Mangrove forest and coastal vegetation. There is a negative correlation between density of all projects and extreme poverty condition within the belts. It is established that MPP density was not a good predictor of prevalence of poverty. The significant value p > 0.05 showed it is of no statistical significance. Therefore, distributions of considered spatial variables of the MPP schemes and resulting effects have not sufficiently addressed relative poverty conditions amongst rural communities within the area of interest. Infrastructural facilities need be provided in such a manner to address poverty conditions by supporting the rural communities to tap productive ecological resources in these areas.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

The distribution of Micro Project Programme (MPP) and relative poverty incidence across Nigeria south-south vegetation belts

Tinubu¹,

Arokoyu²,

Lawal³

2019

Int. NGOJ

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“…Based on environmental data for poverty estimation, Dasgupta et al [26] used regression model to analyze the daily consumption expenditure in four different regions and concluded that the daily consumption expenditure exhibits substantial variations across countries. Vista and Murayama [27] applied linear regression to estimate per capita income in the Philippines and found that the spatial variation in poverty is mainly caused by disparities of access to road infrastructure. Watmough et al [28] discovered that satellite sensor data can be used to calculate the rural welfare in large areas of developing countries and estimated the relative welfare in India through random forest model.…”

mentioning

confidence: 99%

Combining Residual Neural Networks and Feature Pyramid Networks to Estimate Poverty Using Multisource Remote Sensing Data

Tan

Zhou

et al. 2020

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

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Reliable poverty data are critical for regional economic analysis and policy making, especially considering that economic inequality and sustainable development are widespread social concerns. This article proposes a multitask learning model combining deep residual neural networks and feature pyramid networks to estimate poverty level from multiple sources including the night-time light data, Landsat 8 imagery, and spectral index data. We first train the multitask learning model using the multisource data in Chongqing, China and then estimate the representative economic indicators in the study area. The model is evaluated with the Pearson correlation coefficient of the actual and estimated economic indicators. The result shows that the proposed model outperforms other models with the Pearson correlation coefficient up to 0.87 in the annual estimates of economic indicators between 2013 and 2017. As all the data used in this article are publicly available, the proposed model can be used to estimate the economic indicators in other regions as well.

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Investigating the multidimensional relative poverty in China: Evidence from Nanling Yao ethnic group area

Luo

Shu

Cai

2022

Environ Dev Sustain

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China has made remarkable achievements in solving absolute poverty and entered into the stage of solving relative poverty which takes on multidimensional characteristics. Solving multidimensional relative poverty is the key to promoting social equity and achieving coordinated development. Based on the data of ten counties in the Nanling Yao ethnic group area in China from 2011 to 2018, we used the entropy weight method to study the degree, main influencing dimensions and distribution of multidimensional relative poverty. We found that external risk contributed the most to multidimensional relative poverty, followed by internal risk, economic development opportunity and potential development opportunity. Counties with moderate multidimensional relative poverty accounted for the largest proportion, multidimensional relative poverty was not significantly alleviated from 2011 to 2018, but some counties have different multidimensional relative poverty levels. In order to solve the multidimensional relative poverty, it is necessary to establish a Nanling economic cooperation zone, develop characteristic industries and focus on supporting counties with deep multidimensional relative poverty.

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Spatial Determinants of Poverty Using GIS-Based Mapping

Cited by 14 publications

References 11 publications

The distribution of Micro Project Programme (MPP) and relative poverty incidence across Nigeria south-south vegetation belts

The distribution of Micro Project Programme (MPP) and relative poverty incidence across Nigeria south-south vegetation belts

Combining Residual Neural Networks and Feature Pyramid Networks to Estimate Poverty Using Multisource Remote Sensing Data

Investigating the multidimensional relative poverty in China: Evidence from Nanling Yao ethnic group area

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