Poverty is still remaining as the most prominent "short-board" in the Chinese economic development. The hardest and heaviest part of building well-off society in an all-around way lies in the rural construction, especially in the poverty-stricken area. The poverty alleviation and development in China is at the most critical stage, which requires more accurate recognition for the spatial differentiation of the rural poverty and its influencing factors, to make sure the exact targeting of the poverty alleviation policies and measures. This paper picked Shanyang, a key county in the poverty alleviation and development project of China, to explore the spatial pattern and type of the rural poverty of this county through the spatial autocorrelation analysis and grouping analysis. The stepwise regression, geographically weighted regression and Geodetector models were used to analyze the influencing factors of the rural poverty in this county, followed with the discussion on the spatial heterogeneity and interaction of the influencing effects. The following findings were concluded from the research: (1) The incidence of rural poverty in Shanyang noticeably clustered in space, forming 6 hot spots and 4 cold spots. In terms of rural poverty degree and spatial connectivity, the county was divided into low poverty area, mid poverty area and high poverty area. The space distribution was based on the regional poverty degree to facilitate a proper implementation of the poverty alleviation policies. (2) The major influencing factors responsible for the rural poverty in Shanyang included water network density, the distance to the nearest highway, proportion of dilapidated buildings, disposable income per rural capita, proportion of migrant workers and the proportion of rural households participating in the agricultural cooperatives. The influencing effects of all factors featured the spatial heterogeneity. Water network density and rural disposable income per capita were negatively correlated with the incidence of poverty while the rest factors showed a positive correlation. (3) The influence of the interaction between two factors appeared to be larger than that of the single factor. The interaction modes between major factors included bi-factor enhancement and nonlinear enhancement. Due to the interaction enhancement effects between poverty factors, the poverty alleviation policies shall be comprehensively matched to realize the expected target, along with a powerful poverty alleviation security system to ensure the full implementation.
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2018, 37(3): 593-606.
. Spatial differentiation and influencing factors analysis of rural poverty at county scale: A case study of Shanyang county in Shaanxi province, China[J]. GEOGRAPHICAL RESEARCH,
2018, 37(3): 593-606.
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We identify and map critical spatial factors grouped into natural, human, social, financial and physical capital assets, which largely determine livelihood options, strategies and welfare of agro-pastoral communities in a semi-arid district of southern Kenya. Our approach builds upon new, relatively high-resolution spatial poverty data and refines participatory land-use mapping methods, making valuable information on natural and social resource availability and access useful for policy makers. While most poverty analyses focus on the household, we employ quantitative spatial data analysis methods to examine the spatial correlates of meso-, or community-level poverty incidence. The results suggest that variables influencing poverty levels in this district include pasture potential, livestock density, distance to a major town, road density, access to education, access to security, soil fertility and agricultural potential. Because of the participatory research process taken, these results are already feeding into both local- and national-level policy processes aimed at reducing poverty in Kenya.
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This study contributes to basic knowledge of the structural determinants of poverty in the US by analyzing an expanded set of determinants of poverty, namely factors related to economic, social, and political influence using spatial data analysis techniques. New data sets and creative use of existing data sets make it possible to measure some of these county-wide social and political factors that have previously been excluded from formal investigation. Social capital, ethnic and income inequality, local political competition, federal grants, foreign-born population, and spatial effects are found to be important determinants of poverty in US counties along with other conventional factors.
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Climate is one of many factors to be considered in adapting systems to environmental and societal change and often it is not the most important factor. Moreover, given considerable model inadequacies, irreducible uncertainties, and poor accessibility to model output, we may legitimately ask whether or not regional climate projections ought to have a central role in guiding climate change adaptation decisions. This question is addressed by analysing the value of regional downscaled climate model output in the management of complex socio-ecological systems (SESs) vulnerable to climate change. We demonstrate, using the example of the Dwesa–Cwebe region in South Africa, that the management of such systems under changing environmental and socio-economic conditions requires a nuanced and holistic approach that addresses cross-scale system interdependencies and incorporates “complexity thinking”. We argue that the persistent focus on increasing precision and skill in regional climate projections is misguided and does not adequately address the needs of society. However, this does not imply that decision makers should exclude current and future generations of regional climate projections in their management processes. On the contrary, ignoring such information, however uncertain and incomplete, risks the implementation of maladaptive policies and practices. By using regional climate projections to further explore uncertainties and investigate cross-scale system dependencies, such information can be used to aid understanding of how SESs might evolve under alternative future societal and environmental scenarios.
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This article investigates the link between poverty incidence and geographical conditions within rural locations in Kenya. Evidence from poverty maps for Kenya and other developing countries suggests that poverty and income distribution are not homogenous. We use spatial regression techniques to explore the effects of geographic factors on poverty. Slope, soil type, distance/travel time to public resources, elevation, type of land use, and demographic variables prove to be significant in explaining spatial patterns of poverty. However, differential influence of these and other factors at the location level shows that provinces in Kenya are highly heterogeneous; hence different spatial factors are important in explaining welfare levels in different areas within provinces, suggesting that targeted propoor policies are needed. Policy simulations are conducted to explore the impact of various interventions on location-level poverty levels. Investments in roads and improvements in soil fertility are shown to potentially reduce poverty rates, with differential impacts in different regions.
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Abstract This paper makes a systematic comparison of four approaches to multidimensional poverty analysis based respectively on the theory of fuzzy sets, information theory, efficiency analysis and axiomatic derivations of poverty indices. The database was the 1995 Israeli Census that provided information on the ownership of various durable goods. There appears to be a fair degree of agreement between the various multidimensional poverty indices concerning the identification of the poor households. The four approaches have also shown that poverty decreases with the schooling level of the head of the household, first decreases and then increases with his/her age and with the size of the household. Poverty is higher when the head of the household is single and lower when he/she is married, lowest when the head of the household is Jewish and highest when he/she is Muslim. Poverty is also higher among households whose head immigrated in recent years, does not work or lives in Jerusalem. These observations were made on the basis of logit regressions. This impact on poverty of many of the variables is not very different from the one that is observed when poverty measurement is based only on the income or the total expenditures of the households.
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78 This paper proposes a new methodology for multidimensional poverty measurement, M ka = ( ρ k,M a). 78 Our identification step ρ k uses dual cutoffs to identify a) dimensional deprivations and b) poverty. 78 Our aggregate measure adjusts FGT measures to account for multidimensionality. 78 Our first measure M k0 = ( ρ k, M 0) can be used with ordinal or categorical data. 78 Our methodology M ka = ( ρ k, M a) satisfies many desirable properties including decomposability.
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Introduced in this paper is a family of statistics, G, that can be used as a measure of spatial association in a number of circumstances. The basic statistic is derived, its properties are identified, and its advantages explained. Several of the G statistics make it possible to evaluate the spatial association of a variable within a specified distance of a single point. A comparison is made between a general G statistic and Moran's I for similar hypothetical and empirical conditions. The empirical work includes studies of sudden infant death syndrome by county in North Carolina and dwelling unit prices in metropolitan San Diego by zip-code districts. Results indicate that G statistics should be used in conjunction with I in order to identify characteristics of patterns not revealed by the I statistic alone and, specifically, the Gi and Gi* statistics enable us to detect local 090008pockets090009 of dependence that may not show up when using global statistics.
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Local forms of spatial analysis focus on exceptions to the general trends represented by more traditional global forms of spatial analysis. There is currently a rapid expansion in the development of such techniques but their history almost exactly parallels that of Geographical Analysis, with the first examples of local analysis appearing in the late 1960s. Indeed, Geographical Analysis has published many of the significant contributions in this field. This paper reviews the development of local forms of spatial analysis and assesses the current situation. Following a discussion on the nature and importance of local analysis, examples are given of local forms of point pattern analysis; local graphical approaches; local measures of spatial dependency; the spatial expansion method; adaptive filtering; multilevel modeling; geographically weighted regression; random coefficients models; autoregressive models; and local forms of spatial interaction models.
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中国科学院机构知识库(中国科学院机构知识库网格（CAS IR GRID）)以发展机构知识能力和知识管理能力为目标，快速实现对本机构知识资产的收集、长期保存、合理传播利用，积极建设对知识内容进行捕获、转化、传播、利用和审计的能力，逐步建设包括知识内容分析、关系分析和能力审计在内的知识服务能力，开展综合知识管理。
Wang JF, Zhang TL, Fu BJ.A measure of spatial stratified heterogeneity. , 2016, 67: 250-256.http://linkinghub.elsevier.com/retrieve/pii/S1470160X16300735
Spatial stratified heterogeneity, referring to the within-strata variance less than the between strata-variance, is ubiquitous in ecological phenomena, such as ecological zones and many ecological variables. Spatial stratified heterogeneity reflects the essence of nature, implies potential distinct mechanisms by strata, suggests possible determinants of the observed process, allows the representativeness of observations of the earth, and enforces the applicability of statistical inferences. In this paper, we propose aq-statistic method to measure the degree of spatial stratified heterogeneity and to test its significance. Theqvalue is within [0,1] (0 if a spatial stratification of heterogeneity is not significant, and 1 if there is a perfect spatial stratification of heterogeneity). The exact probability density function is derived. Theq-statistic is illustrated by two examples, wherein we assess the spatial stratified heterogeneities of a hand map and the distribution of the annual NDVI in China.
[LiuYanhua, XuYong.Geographical identification and classification of multi-dimensional poverty in rural China. , 2015, 70(6): 993-1007.]
MinotN, BaulchB, EpperechtM.Poverty and inequality in Vietnam: Spatial patterns and geographic determinants. , 2006, 54(1): 153-154.http://agris.fao.org/agris-search/search.do?recordID=US2016208158
"This study uses a relatively new method called -渟mall area estimation- to estimate various measures of poverty and inequality for provinces, districts, and communes of Vietnam. The method was applied by combining information from the 1997-98 Vietnam Living Standards Survey and the 1999 Population and Housing Census... Mapping the density of poverty reveals that, although the poverty rates are highest in the remote upland areas, these areas are sparsely populated so most of the poor live in the Red River Delta and the Mekong River Delta... This analysis confirms other studies indicating that the inequality in per capita expenditure is relatively low in Vietnam by international standards. Inequality is greatest in the large cities and (surprisingly) in parts of the upland areas. Inequality is lowest in the Red River Delta, followed by the Mekong Delta. Just one-third of the inequality is found between districts and two-thirds within them, suggesting that district-level targeting of anti-poverty programs may not be very effective... Finally, the study notes that the small area estimation method is not very useful for annual poverty mapping because it relies on census data, but it could be used to show detailed spatial patterns in other variables of interest to policymakers, such as income diversification, agricultural market surplus, and vulnerability. Furthermore, it can be used to estimate poverty rates among vulnerable populations too small to be studied with household survey data, such as the disabled, small ethnic minorities, or fishermen." from Authors' summary