Available online at ScienceDirect. Procedia Environmental Sciences 26 (2015 ) Spatial Statistics 2015: Emerging Patterns

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1 Available olie at ScieceDirect Procedia Evirometal Scieces 26 (2015 ) Spatial Statistics 2015: Emergig Patters Spatial distributio patters ad ifluecig factors of poverty - a case study o key coutry from atioal cotiguous special povertystricke areas i chia XiWei Che a, *, ZhiYua Pei a, Ai Lia Che a, Fei Wag a, KeJia She a,qiaofu Zhou b,li Su a a Chiese Academy of Agricultural Egieerig, Beijig , PR Chia b ChieseResearchAcademyofEvirometalScieces,Beijig100012,Chia Abstract Poverty is oe of the worldwide ad key issues cocered by govermets ad iteratioal orgaizatios. Chiese rural poverty was massive poverty caused by uiversal factors such as uderdeveloped atioal ecoomy ad lack of related istitutio ad policy i the past, istead, ow has show poit-scatted distributio due to special regioal eviromet, backward productio coditios ad low populatio quality with the log-term efforts. I additio to the impact of ecoomic ad social factors, atural coditio is also oe of the mai factors restrictig the icidece of poverty i rural areas. I this paper, spatial statistical aalysis ad GIS are combied to aalyze the patters ad factors of spatial poverty distributio of Xiafeg Couty, a key coutry from atioal cotiguous special poverty-stricke areas i Chia. Two idexes represetig exted ad depth of poverty, poverty headcout ratio ad the per capita et icome of poverty populatio, were used to aalyze spatial poverty distributio based o the spatial autocorrelatio method Published The Authors. by Elsevier Published B.V This by Elsevier is a ope B.V. access article uder the CC BY-NC-ND licese Peer-review ( uder resposibility of Spatial Statistics 2015: Emergig Patters committee. Peer-review uder resposibility of Spatial Statistics 2015: Emergig Patters committee Keywords: poverty; spatial distributio patter; ifluecig factors * Correspodig author. Tel.: /3007; fax: address:chexiwei1980@126.com Published by Elsevier B.V. This is a ope access article uder the CC BY-NC-ND licese ( Peer-review uder resposibility of Spatial Statistics 2015: Emergig Patters Committee doi: /j.proev

2 XiWei Che et al. / Procedia Evirometal Scieces 26 ( 2015 ) Itroductio Poverty is worldwide problem log plagued humaity, but also govermets ad iteratioal orgaizatios logterm cocered. I 2002, the Uited Natios start "Milleium Developmet Goals", the first oe is the "eradicatio of extreme poverty ad huger" i the eight goals (Uited Natios, 2011). There are may causes of poverty have bee illustrated by a large umber of literature. I additio to the causes from atioal or regioal level, such as the ecoomy, ifrastructure, educatio, eviromet, social factors, racial discrimiatio, atural disasters, war, govermet corruptio ad chaotic maagemet, the idividual factors, such as poor health, drug addictio, sigle mother, may cause poor (Kotler ad Lee, 2009). Therefore, the latest iteratioal researches are begiig to use multiple dimesios of poverty measuremet (Alkire ad Foster, 2011). Poverty, especially i poor rural areas, were affected by atural geographical eviromet greatly. There are a lot of iteratioal literature to explore the impact of atural geographical eviromet o ecoomic developmet ad poverty (Gray ad Moseley, 2005; Barbier, 2010; Gallup et al, 1999; Olivia et al, 2011.). Accordig to the researches o the spatial distributio of Chia's rural poverty ad the relatioship betwee poverty ad lad, atural geographical eviromet, after the sigificat achievemet pushed by ati-poverty policies ad istitutios iitial, Chia's rural poverty have bee o loger the "surface" of poverty caused by the uderdeveloped atioal ecoomy, lack of istitutios ad policy, istead, ow has show poit-scatted distributio due to special regioal eviromet, backward productio coditios ad low populatio quality with the log-term efforts. Today, there are still parts of the populatio i some areas caot get rid of poverty despite the use of a variety of capital operatio ad ati-poverty measures. Eve if ecoomic ad social factors, such as istitutioal, policy, capital, educatio, huma resources, were igored, the effect of atural geography eviromet o poverty, amely atural geographical eviromet costraits ecoomic developmet, leadig to poverty, is still a vital problems caot be avoided. I this paper, spatial statistical aalysis ad GIS are combied to aalyze the patters ad factors of spatial poverty distributio. Two idexes represetig exted ad depth of poverty, poverty headcout ratio(phr) ad the per capita et icome of poverty populatio(pcnipp), were used to aalyze spatial poverty distributio based o the spatial autocorrelatio method. Ad the their Lisa maps were overlapped to test the relevace of the two idexes. The classical liear regressio model is simple ad rapid to detect the major ifluecig factors. Previous researches adopted traditioal statistical methods to coduct quatitative study ad modelig o the evirometal factors that affect poverty. The existece of spatial autocorrelatio, however, has deied the basic hypothesis that data are idepedet from each other ad always i a ormal distributio i the traditioal statistic approaches. Thus, whe the traditioal approaches are applied to process the spatial data, it is impossible to idetify the spatial idepedece of the data. Ad several problems may occur. Therefore, appropriate methods eed to be itroduced i spatial statistic aalysis. To study the impact of atural geographical eviromet o poverty ad the existece of spatial autocorrelatio, this paper compared classical liear regressio model, spatial lag model (SLM) ad spatial error model (SEM), i terms of their explaatory power ad applicability. 2. Study area ad data Xiafeg Couty (N 29 19'28 " ~30 2'54", E '8 " ~109 20'8"),was take as the case study area, a key coutry from oe of the fourtee atioal cotiguous special poverty-stricke areas i chia, kow as Wulig Moutai area. As show i Figure 1, Xiafeg was located i the Eshi Autoomous Prefecture of Hubei, ad was borderig to Hua, Guizhou provices, as well as Chogqig muicipality. The Couty covers a area of 2550km 2, ad a complex topography with a average elevatio of 800 meters. South ad North part of the couty were relatively high hilly areas, whereas, the middle part was plai with low elevatio. The couty had uder its jurisdictio 11 towships, which were divided ito 263 admiistrative villages with a total populatio of 36.4 millio. GDP i 2009 was CNY billio ad the GDP per capita CNY 9214, whereas per capita et icome of farmers was about CNY The agriculture takes 38.7% of the total volume, the idustry 22.9%, ad service sector 38.4%respectively.

3 84 XiWei Che et al. / Procedia Evirometal Scieces 26 ( 2015 ) Data materials used i this study icluded: (1) GDEM data with a resolutio of 30m (obtaied from which is a mirror website for iteratioal scietific data located at Computer etwork iformatio ceter, Chiese academy of scieces.); (2) 1:250,000 river map, (obtaied from atioal basic geographic database); (3) Road layer, icludig atioal highway, provicial highway, couty road ad village road, (obtaied from Traffic Bureau of Xiafeg Couty);(4)Poverty data(obtaied from Poverty Alleviatio ad Developmet Bureau of Xiafeg Couty ). 3. Methods 3.1. Spatial patter aalysis of poverty Fig. 1.The locatio of Xiafeg Couty. Global Autocorrelatio aalysis is ofte used to determie whether the variables preset spatial autocorrelatio. Mora s Iwas used to idetify global autocorrelatioi this study. The derivatio of Mora s I is give i Eq.(1): Mora s 1 i1 j1 W ij i1 j1 Wij i1 W ij i i x x x x 2 i j (1) Where is the umber of spatial uits idexed by i ad j; x i adx j are the values of the variable x i the two adjacet paired spatial uits (or grid cells); x meas the mea of x; W ij refers to the elemet i the matrix of spatial weight W i crossed-product statistics. The weight ca be based o cotiguity relatios or distace, to show the similarity of the positios of spatial objects. Mora s I ca illustrate the similarity betwee the attribute values of the cotiguous or eighborig regioal uits. As usual, it is iterpreted as a coefficiet of correlatio i the value rage of [-1, 1]. At the give sigificace level, the positive sigificace of Mora s I idicates the obvious positive autocorrelatio amog the observed data: the higher values ted to cluster together, ad so do the lower values, performig a cocetrated distributio. While

4 XiWei Che et al. / Procedia Evirometal Scieces 26 ( 2015 ) the egative sigificace shows the oticeable egative autocorrelatio amog the observed data: the higher ad lower values ted to cluster together i a scattered distributio. As the Mora s I approaches the expected value (which approaches zero alog with the icrease i the umber of samples), the spatial autocorrelatio disappears, ad the observed values are radomly distributed i space. Local spatial autocorrelatio (Local Idicators of Spatial Associatio, LISA) aalysis was used to determie the local spatial autocorrelatio characteristics of the variables. Relative to the global spatial autocorrelatio, the sese of local correlatio aalysis lies i: whe there is o global spatial autocorrelatio, local spatial autocorrelatio helps to seek cocealed positio of local spatial autocorrelatio; whe there is global spatial autocorrelatio, local spatial autocorrelatio helps to explore the existece of spatial heterogeeity; local spatial autocorrelatio helps to determie the locatio of spatial outliers or ifluetial poit; local spatial autocorrelatio helps to positio where local autocorrelatio is icosistet with global spatial autocorrelatio. For example, whe the global spatial autocorrelatio aalysis coclusio is positive, it is iterested for researchers to aalyze the possibility of existece of a small amout of egative local spatial autocorrelatio. Local Mora's I is calculated as Eq. (2), with the meaig of the variables i equatio (1): (2) The defiitio of variables i Eq.(2) are the same as that i Eq.(1). Sice each spatial locatio has its ow local spatial autocorrelatio statistical value, it is possible to show spatial autocorrelatio by a sigificace figure or aggregatio map. This is the advatage of local spatial autocorrelatio aalysis I this paper, LISA maps were used to aalyze local poverty headcout ratio(phr) ad per capita et icome of poverty populatio(pcnipp) of all admiistrative villages to determie the local spatial variatios with a distace weight determied by global autocorrelatio. These locally auto-correlated areas are the focus of atioal ati-poverty projects The spatial autoregressive model Aseli proposed the geeral form of spatial autoregressive model i spatial quatitative aalysis as expressed by [14]: y w yx 1 w 2 2 ~ N 0 I (3) Whereis the parameter vector k 1 related to the exogeous (explaatory) variablex( k); is the coefficiet to the spatial lag W1y; ad is the coefficiet to the spatial autoregressive structure W2of the disturbace. W1 ( )ad W 2 ( ) are respectively related to the spatial autoregressive process of the depedet variable ad that of the disturbace. They may be the row stadardized matrixes, the biary matrixes or other ostadard matrixes. Eq.(3) deals with the spatial processes with differet spatial structures. This model has ukow parameters [14] as may as 3+k+P i the followig vector forms: 2 [,,,, ] (4)

5 86 XiWei Che et al. / Procedia Evirometal Scieces 26 ( 2015 ) Whe some sub-vectors of the parameter vector are set as 0 Eq.(4), several commo structures of spatial models ca be achieved. Each of the followig coditios correspods to oe of the four traditioal spatial autoregressive models metioed i previous literature (Aseli, 1980; Aseli, 1988; Hordijk, 1979). I order to explore the impact of atural geographical eviromet o ecoomic developmet ad poverty, the factors as show table 1 were selcted:1) the topographic factor, such as altitude ad slope,2) the factor about water resource, amely, the distace to the earest river, 3) the locatio factors, such as distace to couty ceter ad distace to earest tow 4) the factor of accessibility, which was weighted calculated by the distaces to the earest atioal highway, provicial highway, couty road ad village road, ad 5) the factors about lad resource, icludig Per capita arable lad ad Per capita orchard lad. Table 1.The selected variables ofregressio aalysis for poverty i Xiafeg Factor variable uit water resource distace to the earest river m locatio distace to couty cetre m distace to earest tow m accessibility trasportatio accessibility topography slope altitude m lad recourse Per capita arable lad ha Per capita orchard lad ha 4. Result 4.1. Results of global spatial autocorrelatio I this paper, Global Mora's I for PHR ad PCNIPP was calculated based o the distace from 0.5km to 10km with 0.5km iterval. The results were show i Table2. At the 0.5km level, 261 admiistrative villages had o eighborig villages withi 0.5km, with a o sigificat result. From 2 to10km distace, the value of Global Mora's I are all positive, with p-values all less tha This result deied the ull hypothesis that the spatial distributio of PHR ad PCNIPP is radom, ad cofirmed the fact that PHR ad PCNIPP exhibited a very sigificat spatial aggregatio tred, amely, a sigificat positive autocorrelatio. At the 1.5km distace level, Mora's I reached a maximum. Beyod 1.5km, Mora's I decreased with the icreasig of distace, idicatig that at 1.5m distace level, the strogest positive autocorrelatio was reached. At ad above 6km distace level, the umber of o eighborig villages is zero, ad global Mora s I was 0.233, idicatig that there was more tha 1 eighborig villages. However, the global Mora's I could ot determie the specific locatio of PHR ad PCNIPP of the eighborig villages; either could it illustrate the HH (high-high) or LL (low-low) aggregatio characteristics of these eighborig villages. Distace Table 2.Global Mora s I value ad sigificace test of occurrece at multi-scale Number of o eighborig villages EI Per capita et icome of poverty Poverty headcout ratio populatio Mora s I V Z p Mora s I V Z p

6 XiWei Che et al. / Procedia Evirometal Scieces 26 ( 2015 ) Results of local spatial autocorrelatio As illustratedi Sectio 4.2, villages had eighborig villages at ad above 6km distace level. Therefore, locatio idicator of spatial autocorrelatio (LISA) map was produced at 6km level, as show i Figure 2. The LISA maps i Figure 2 showed that isigificat autocorrelatio of PHR appears at 217 villages, which icluded all villages of Huologpig ad Zhogbao, as well as some villages of other towships. This result meat that the PHR i these 217 villages were isigificatly differet with their eighborig villages. Villages that had sigificat spatial aggregatio were located igaolesha, Jiasha, Xiaocu, Huagjidog, ad Daluba t, amog which, all 14 LL aggregatio villages all distributed i Gaolesha ad its eighborig tow, Qigpig towtakig up 5.32% of all the villages. This LL aggregatio idicated low PHR i these villages ad their eighbor; all 24 HH aggregatio villages were located i Jiashag, Xiaocu, Daluba District ad Huagjidog, takig up 9.13% of all the villages. This HH aggregatio idicated high PHR i these villages ad their eighbor, ad also low sigificace i spatial differece. The HH aggregatio areas should be the focus of govermetal poverty alleviatio project. There were 8 spatially isolated villages, four of which preseted LH aggregatio, while aother four HL aggregatio. The LH aggregatio situatio demostrated the low PHR of a village with high PHR of eighborig villages, i.e. a poverty cold spot of PHR. These cold spots could be take as role models for their low PHR i the similar physical cotext, ad were suggested for further diggig o how they maaged to keep less poor ad uaffected by its eighborig poverty. The other four HL aggregatio villages, o the other had, idicated that the village itself had high PHR, while the PHR of its eighborig villages was low, makig the ceter village a hot spot of poverty. These hot spots of poverty desired further aalysis o the reaso of extreme higher PHR i the similar physical cotext with their eighborig villages ad desired further ati-poverty measures. The villages where PCNIPP were isigificatly correlated amouted to 211 icluded all the villages i Daluba district, Huagjitog, Digzhai, Zhogbao, as well as some villages i other tows. The isigificace of spatial autocorrelatio idicated that the PCNIPP of these 211 villages were ot sigificatly varied over space. The villages which were sigificatly aggregated over space maily located i Huologpig, Jiasha, Xiaocu ad Qigpig. Amog these spatially aggregated villages, 27 preseted LL aggregatio ad appeared i Huologpig ad Qigpig, takig up 10.23%; 20 preseted HH aggregatio, takig up 7.57%, located i Jiasha, Xiaocu ad Gaolesha. The LL aggregatio of the27 villages idicated very low PCNIPP i both these villages ad their eighbourig villages, whereas, the 20 HH aggregated villages idicated relatively high PCNIPP i both these

7 88 XiWei Che et al. / Procedia Evirometal Scieces 26 ( 2015 ) villages ad their eighborig villages. There were also 5 isolated villages, amog which, three of them were LH aggregated ad two were HL aggregated. The three LH aggregated villages meat that their ow PCNIPP were low, yet havig high PCNIPP eighbours; the two HL aggregated villages exhibited that their ow PCNIPP were high, while their eighbourig villages were poor. a b Fig. 2. (a)lisa cluster maps of poverty headcout ratio (b)lisa cluster maps ofper capita et icome of poverty populatio The impact of atural geographical eviromet o poverty Table 3 shows the measure of fit, coefficiet estimate, ad associated probability of the classical liear regressio model, SLM ad SEM for PHR at the distace of 6km. I these models, oly the factor, per capita orchard lad, is sigificat. Per capita orchard lad is positive drivig factors. The LIK (or AIC, or SC) idicator may be used to compare the goodess-of-fit of differet models. The LIK values for both the SLM ad SEM are larger tha that of the classical liear regressio model, which proves that the goodess-of-it i the spatial autoregressive models is better tha that of the classical model. If the weight matrix is a row stadardized oe, ca be explaied as the percetage of the cotributio of the spatial factors to the predicted result. This implies that the PHR is uder the iflueces ot oly from the sigificat factor, amely, Per capita orchard lad,but also from the strog positive from eighborig spaces (the ifluece coefficiet: or ). Table 3.The model parameters of differet models for poverty headcout ratio i Xiafeg Liear model Spatial lag model Spatial error model pseudo R LIK AIC SC Variable Coefficiet Probability Coefficiet Probability Coefficiet Probability () costat

8 XiWei Che et al. / Procedia Evirometal Scieces 26 ( 2015 ) Per capita arable lad Per capita orchard lad distace to couty cetre distace to earest tow altitude slope distace to the earest river trasportatio accessibility Table 4 shows the measure of fit, coefficiet estimate, ad associated probability of the classical liear regressio model, SLM ad SEM for PCNIPP at the distace of 6km. I these models, oly the factor, distace to earest tow, is sigificat. Distace to earest tow is positive drivig factors. Comprisig the LIK values of these models, both the SLM ad SEM are larger tha that of the classical liear regressio model, which proves that the goodess-of-it i the spatial autoregressive models is better tha that of the classical model. PCNIPP is uder the iflueces ot oly from Distace to earest tow, but also from the strog positive from eighborig spaces (the ifluece coefficiet: or ). Table 4.The model parameters of differet models for per capita et icome of poverty populatio i Xiafeg Liear model Spatial lag model Spatial error model pseudo R LIK AIC SC Variable Coefficiet Probability Coefficiet Probability Coefficiet Probability () costat Per capita arable lad Per capita orchard lad distace to couty cetre distace to earest tow altitude slope distace to the earest river trasportatio accessibility Ackowledgemets This work is supported by Key Projects i the Natioal Sciece ad Techology Pillar Program of Chia durig the Twelfth Five-year Pla Period (No.2012BAH33B02). Refereces 1. Alkire S ad Foster J : Coutig ad Multidimesioal Poverty Measuremet, Joural of Public Ecoomics2011; 95(7-8):476-87,. 2. Aseli L. Estimatio Methods for Spatial Autoregressive Structures. Regioal Sciece Dissertatio ad Moograph Series 8.Ithaca, New York: Corell Uiversity, 1980.

9 90 XiWei Che et al. / Procedia Evirometal Scieces 26 ( 2015 ) Aseli L. Spatial Ecoometrics: Methods ad Models. Dordrecht: Kluwer Academic Publishers, Barbier EB. Poverty, Developmet ad Eviromet. Eviromet ad Developmet Ecoomics2010; 15(6): Gallup J,SachsJ ad Melliger A. Geography ad Ecoomic Developmet. Iteratioal Regioal Sciece Review1999;22(2): Gray LC ad Moseley WG. A Geographical Perspective o Poverty-eviromet Iteractios.The Geographical Joural2005;171(1): Hordijk L. Problems i estimatig ecoometric relatios i space. Papers Regioal Sciece Associatio 1979; 42: Kotler P ad LeeN. Up ad Out of Poverty: The Social Marketig Solutio, New Jersey: Pearso Educatio Ic., Olivia S, Gibso J, Rozelle S, Huag JK ad Deg XZ. Mappig Poverty i Rural Chia: How Much Does the Eviromet Matter? Eviromet ad Developmet Ecoomics2011, 16(2):