Spatial Fairness and Changes in Transport Infrastructure in the Qinghai-Tibet Plateau Area from 1976 to 2016

sustainability

Article Spatial Fairness and Changes in Transport Infrastructure in the Qinghai-Tibet Plateau Area from 1976 to 2016

Xingchuan Gao 1,2,3 , Tao Li 2,3 and Xiaoshu Cao 2,3,* 1 School of Geography and Tourism, Shaanxi Normal University, Xi’an 710119, China; [email protected] 2 Northwest Land and Resource Research Centre, Shaanxi Normal University, Xi’an 710119, China; [email protected] 3 Key Laboratory for Urbanization and Land Environment Geo-simulation in Northwest China, Xi’an 710119, China * Correspondence: [email protected]

Received: 30 October 2018; Accepted: 17 January 2019; Published: 23 January 2019

Abstract: The Qinghai-Tibet Plateau area (QTP) is the most unique environmental-population region and an important natural laboratory for the study of human-land relations. The poor transportation conditions have long restricted socio-economic development. The research on the transport infrastructure and spatial effect in the QTP is of significance to the sustainable development of the region. Accordingly, a spatial accessibility model was used to analyze the spatial pattern of accessibility in QTP from 1976–2016, examine the accessibility evolution trend on the township scale and reveal the spatial fairness and changes in accessibility. The main conclusions are as follows: (1) The accessibility to major cities improved and the time-space convergence effect was significant. (2) The spatial autocorrelation analysis results showed that the improvement in transport infrastructure had a significant impact on the agglomeration of the accessibility level. (3) Access time from towns to the nearest major city are much shorter. More Tibetan people have more opportunities to access cities. (4) The accessibility coefficient and relative accessibility revealed distributive effects and spatial fairness of accessibility. (5) The global coefficient of variation value demonstrated an increasing trend, which indicates that spatial unfairness of transport is increasing.

Keywords: spatial fairness; distributive effects; transport infrastructure; accessibility; the Qinghai-Tibet Plateau area

1. Introduction Transport infrastructure is a necessary prerequisite for promoting regional social and economic development and is a key factor in regional cohesion [1]. Transport construction and network expansion play vital roles in the future development of a region. Accessibility is an important measure of transport networks, used to measure the interaction potential between cities and towns [2]. Harris first measured accessibility using the market potential model in 1954 [3]. Then in 1959, Hansen studied the impact of accessibility on land use [4]. Transport fairness, brought about by the development of the transport network, is closely related to many social issues, such as the number of jobs available and the time spent in public transport [5]. Accessibility has been gradually recognized as an indicator of transport fairness [6–8]. Transport fairness is derived from the study of fairness and justice in other fields [7], in which “fairness” is regarded as the core component of “justice” [9,10]. The research on transport fairness is mostly concentrated on population distribution, investment and public facilities but the concept does not have a single, widely accepted definition [11–13]. In identifying transport fairness, two broad methods to conceive fairness are well-recognized: social fairness and spatial fairness [14]. The first is viewed from a social perspective, wherein vulnerable

Sustainability 2019, 11, 589; doi:10.3390/su11030589 www.mdpi.com/journal/sustainability Sustainability 2019, 11, 589 2 of 16 or disadvantaged populations are targeted. The second approach defines fairness in terms of spatial distributive effects, which means easy access, regardless of socioeconomic characteristics, satisfaction or disability [15,16]. In this paper, we adopt the viewpoints from Forth et al. [11] and Delbosc et al. [17] and define the spatial fairness of transport as transport infrastructure that provides benefits evenly among different geographic units. Because the effects of public policies tend to cluster around specific physical locations [18], spatial fairness approaches are well-suited to assess such distributive effects [19]. Transport fairness influences many social aspects, such as territorial cohesion [20], the cost and time spent on daily commutes [21] and on public services [22]. Accessibility is, though imperfect, the most appropriate measure of benefits from transport infrastructure investments [23,24] and transport equity [6,17,25]. Presently, the evaluation of transport fairness has mostly focused on the needs of different social groups [26–28], investment in transport facilities [24,29–31], access to urban facilities [32–34], transportation planning [23,35–38] and interregional linkage strength [25,39–41]. The development of transport infrastructure and improved accessibility have promoted economic relationships and enhanced regional fairness and cohesion in countries and regions such as the UK, Holland, Poland and the European Union (EU) [42–45]. Therefore, the study of accessibility and spatial fairness has become a hot topic in the fields of social economics, transportation planning and human geography. Studies have mainly discussed transport fairness in urban areas or in metropolitan areas, especially emphasizing the root causes of inequity in the distribution of public facilities in a city, while ignoring the spatial effects and spatial fairness of transport in areas of undeveloped economy and unbalanced ecosystem. Although studies on densely populated urban areas are important, the research at the regional scale can guide large-scale transportation and sustainable development planning. As stated above, research on the accessibility and transport fairness in ecologically fragile areas is an important issue in social sustainability and will be a focus of future research.

2. Study Area and Data

2.1. Study Area We selected the Qinghai-Tibet Plateau area as the study area. The Qinghai–Tibet Plateau area is known as “the Roof of the World,” as it contains the headwaters of most streams in Asia and the Third Pole of the Earth [46]. It is an important environmental barrier and the water source in the world and is also the frontier region and a concentrated severely poverty–stricken area of China [47]. However, due to restrictions related to geography, climate and society, transport development in the area has lagged behind for a long time, which historically has hindered the socio–economic development of the region [48]. However, with China Western Development and the construction of major infrastructure, the transport network greatly improved, which has promoted socio–economic development and stability in the region and improved living standards. Tibetans, almost herdsmen, exhibit seasonal rotational grazing, who graze at higher altitudes in summer and at lower altitudes (usually along roads or around towns) in winter. We believe that compared with social fairness, spatial fairness can better solve the current and future transportation planning and construction in a shorter period of time. Therefore, the study on the spatial fairness of transportation in the study area is conducive to improving people’s living standards and providing a useful reference for the development of such underdeveloped regions. The Qinghai–Tibet Plateau area lies between 25◦59037” and 39◦49033” N, 73◦29056” and 104◦40020” E (Figure1), with an area of about 2,542,400 km 2. In order to compare and analyze the evolution and characteristics of the transport network, we selected the administrative divisions of the Qinghai–Tibet Plateau area in 2016 as the study area, which covers six provinces and autonomous regions including Qinghai, Tibet and parts of Xinjiang, Gansu, Sichuan and Yunnan. The major cities include two provincial capitals—Xining and Lhasa, five prefecture–level cities (Haidong, Changdu, Linzhi, Shannan and Shigatse), seven county–level cities (Delingha, Golmud, Yushu, Hezuo, Maerkang, Kangding and Shangrila) and 18 prefecture–level cities within the 100km buffer zone, including Kashi, Jiuquan Sustainability 2019, 11, 589 3 of 16 Sustainability 2019, 11, x FOR PEER REVIEW 3 of 15 andKashi, Chengdu. Jiuquan Thereand Chengdu. are 1972 There township are 1972 administrative township administrative units in the Qinghai–Tibet units in the Qinghai–Tibet Plateau area. ForPlateau maintaining area. For analytical maintaining continuity, analytical changing continuity, administrative changing divisions administrative was not considered divisions during was datanot processingconsidered andduring analysis. data processing and analysis.

Figure 1. The location of the study area.

2.2. Data Sources and Processing In this study, thethe Qinghai–Tibet PlateauPlateau areaarea datadata werewere sourcedsourced fromfrom thethe National Earth System Scientific DataData Sharing Sharing Service Service Platform. Platform. Administrative Administrative division division data were data obtained were obtained from the Resourcesfrom the andResources Environment and Environment Science Data Science Center Data of Chinese Center Academy of Chinese of Sciences.Academy The of Sciences. traffic road The network traffic road data werenetwork based data on were the national based on database the national at a 1:25,000 database scale at (Table a 1:25,0001). Vector scale data (Table for transport1). Vector networks data for intransport different networks years were in different obtained years through were vectorization obtained through of atlases vectorization from different of periods,atlases from including different the Chinaperiods, Atlas including (1976), Chinathe China Traffic Atlas Album (1976), (1981), China China Traffic Traffic Album Album (1981), (1987), China China Traffic Traffic Album Album (1987), (1990), NewChina Chinese Traffic Album Traffic Album(1990), New (1996), Chinese China Traffic AlbumAlbum of(1996), New China Century Traffic (2001), Album China of TrafficNew Century Album (2006),(2001), ChineseChina Traffic Atlas (2011)Album and (2006), Practical Chinese Chinese Atlas Atlas (2011) (2017), and Practical which were Chinese published Atlas by(2017), China which Map Publishingwere published House. by To China avoid Map the “isolated Publishing island” House. effect, To transport avoid the networks “isolated within island” a 100 effect, km buffer transport area outsidenetworks the within study a area 100 werekm buffer also included area outside in this the study. study area were also included in this study.

Table 1. Data sources and description.

Data SourceData Source Description Description National Earth System Scientiﬁc Data Sharing Service ExtentExtent of ofthe the Qinghai–Tibet Qinghai–Tibet Plateau areaNational Earth System Scientific Data Sharing Service Plateau area PlatformPlatform (http://www.geodata.cn) (http://www.geodata.cn ) Resources and Environment Science Data Center of AdministrativeAdministrative division division data data at at allResources levels and Environment Science Data Center of Chinese all levels AcademyChinese of Sciences Academy (http://www.resdc.cn) of Sciences (http://www.resdc.cn ) National database at 1:25,000 scale VectorVector data for data transport for transport networks and Atlas of China National database at 1:25,000(http://www.resdc.cn scale (http://www.resdc.cn)) networks and Atlas of China According to the highway design speed stipulated in the Technical Standard of People’s Republic of ChinaAccording Highway Engi to theneering highway (JTGB01–2003) design speed, the stipulated road and inotherthe Technicalroads in different Standard ofyears People’s were Republic assigned of Chinadifferent Highway speed Engineering values by analyzing (JTGB01–2003) existing, the research road and [49] other and roads the characteristics in different years of the were study assigned area (Table 2). In order to calculate the accessibility, the ordinary road network was first completely intersected and then the highway entrances and exits and train stations were connected with the Sustainability 2019, 11, 589 4 of 16 different speed values by analyzing existing research [49] and the characteristics of the study area (Table2). In order to calculate the accessibility, the ordinary road network was ﬁrst completely intersected and then the highway entrances and exits and train stations were connected with the ordinary road network to obtain the shortest accessibility time between nodes. Air transport and water transport were not considered in this study.

Table 2. Speed assignments on roads and railways (km/h).

Year Railway Highway National Road Provincial Road County Road Others 1976, 1981 45 – 30 30 – 10 1986, 1991 50 – 40 40 – 10 1996 50 – 60 40 20 15 2001 60 – 60 40 20 20 2006 70 100 80 60 30 20 2011, 2016 120 100 80 60 30 20

3. Methodology We explored the distributive effects and spatial fairness of transport from the perspective of accessibility. Accessibility is a widely used concept in geography but its definition and modeling are often different. In view of different research targets, accessibility models mainly include distance accessibility, opportunity accessibility, economic accessibility and the gravitational model based on spatial interaction. We studied the accessibility of the region to the major city and emphasized the difficulty of using a traffic system to reach the destination from a given location, which is the horizontal fairness of the transportation system [50]. As such, the accessibility was calculated using the time accessibility model. Accessibility analysis can be used to estimate the distributive effects and spatial fairness of transport, which is defined as the accessibility change generated by the development of the transport network in different regions [51].

3.1. Spatial Accessibility Model With ArcGIS® network analysis model, we obtained the shortest time between nodes [41,52]. The shortest time of nodes i is deﬁned as Ti, which denotes the difﬁculty of node i using transportation systems to reach the given destinations. The calculation model is as follows:

n tij Ti = ∑ i ∈ (1, ··· , n), j ∈ (1, ··· , n) (1) j=1 n where tij is the shortest time spent between town i and the major city j and n is the number of major cities in the region. The lower the value of Ti value, the more convenient the trafﬁc connection.

3.2. Spatial Autocorrelation Analysis In this study, the global Moran’s I index and local Moran’s I index are used for spatial autocorrelation analysis [53–55]. The global Moran’s I index is used to reflect the overall correlation and difference degree of the observed values of spatially adjacent regional units. The Global Moran’s I index is generally (−1, 1). A positive value indicates positive spatial correlation, namely, its distribution is spatially clustered; a negative value indicates a negative spatial correlation, meaning its spatial distribution is scattered; and a value of 0 indicates that there is no spatial correlation, meaning the spatial distribution is random. Global Moran’s I index is a global assessment of spatial autocorrelation that cannot reflect the degree of correlation between a sub–region and its peripheral sub–regions. Thus, local spatial autocorrelation analysis must be conducted. The local indicators of spatial association (LISA) of each observation value is an index reflecting the spatial aggregation of each value with its spatially adjacent observation value, which can decompose the global spatial correlation coefficient Sustainability 2019, 11, 589 5 of 16 into the spatial autocorrelation of each region. The significance of spatial autocorrelation can be tested according to the calculated test statistics. The general significance level is 0.05. The global Moran’s I index is calculated as follows:

m m W T −T T −T 0 ∑i=1 ∑j=1 ij( i )( j ) Global Moran s I = 2 n n S ∑i=1 ∑j=1 Wij (2) n n 2 2 1 1 S = m ∑ Ti − n ∑ Ti i=1 i=1

For the ith regional unit, Moran’s I’s LISA formula is:

T − T n 0 = i − Local Moran s Ii 2 ∑ Wij Tj T (3) S i,j=1 where Ti represents the accessibility time of the township i, m represents the number of spatial units involved in the analysis and Wij is the spatial weight matrix, indicating the adjacent relation of the town i and j. If it is adjacent, W = 1; otherwise, it is 0.

3.3. Measurement Methods of Distributive Effects We can learn from Pyrialakou et al. [13], Condeço–Melhorado et al. [56], Jin et al. [57] and Xu et al. [58] and characterize the distributive effects of transport infrastructure by the accessibility coefficient and relative accessibility. These indicators can accurately reflect the status of the entire transport network. The accessibility coefficient refers to the ratio of the transportation cost of town i to the average transport cost of all towns in the region:

Ti Ci = i ∈ (1, ··· , n) (4) (∑ Ti/n) where Ci is the accessibility coefficient, Ti is the shortest time spent between town i and the nearest major city and n is the number of towns in the region. The accessibility coefficient can be used to illustrate the relative accessibility level of nodes in the whole transport network. The smaller the accessibility coefficient, the better the accessibility. Ci > 1, indicates that the accessibility level of the node is below the regional average level; if Ci < 1, the accessibility level of the node is better than the regional average level. Relative accessibility can better reflect the trend in node accessibility level and the status of nodes in the development of the transport network. The formula is as follows:

Ti(t+1) − Ti(t) RAi = i ∈ (1, ··· , n) (5) Tt+1 − Tt where RAi is the relative accessibility value of node i, the greater the value, the greater the degree of accessibility of the network affected by the development of the transport network. Ti(t+1) is the accessibility value of node i in (t + 1) year; Ti(t) is the accessibility value of node i in year t; Tt+1 is the average accessibility of all nodes in the transport network in (t + 1) year; and Tt is the average accessibility of all nodes in the transport network in year t.

3.4. Measure of Accessibility Spatial Fairness The construction and development of the transport infrastructure will improve the efficiency of accessibility but there is also a situation in which the townships along the important arterial traffic benefit more than the townships far away. Therefore, the coefficient of variation and the standardized Sustainability 2019, 11, 589 6 of 16

value is used in previous studies for measure and the evaluation of the state of accessible space allocation [59–61]. The general formula of the transport fairness measure is as follows:

n !1/2 1 1 2 CV = ∑ Ti − T , i = 1, 2, ··· , n (6) T n i=1 Sustainability 2019, 11, x FOR PEER REVIEW 6 of 15 where CV represents the global coefficient of variance, T represents the mean of global accessibility accessibilitytime, n represents level between the number towns ofincreases the township which inis thea kind region of negative and Ti is influence the accessibility on transportation value of township i. The higher the CV, the more unfair the situation, which indicates that the gap in the development. Conversely, it indicates that fairness is a positive influence and the spatial allocation accessibility level between towns increases which is a kind of negative influence on transportation of accessibility is more equitable. development. Conversely, it indicates that fairness is a positive influence and the spatial allocation of accessibility is more equitable. 4. Results and Analysis 4. Results and Analysis 4.1. Spatial Pattern of Accessibility in Qinghai–Tibet Plateau Area 4.1. Spatial Pattern of Accessibility in Qinghai–Tibet Plateau Area As the inverse distance weighting (IDW) model is a weighted distance average, the average cannot Asbe greater the inverse than distance the highest weighting or less (IDW) than modelthe lowest is a weightedinput [62,63] distance and average,accessibility the averageis closely relatedcannot to bethe greater distance than [2]. the By highest calculating or less the than sh theortest lowest time input from [62 any,63] place and accessibility in the Qinghai–Tibet is closely Plateaurelated area to theto 32 distance major [cities,2]. By calculatingwe used spatial the shortest interpolation time from and any the place IDW in model the Qinghai–Tibet in ArcGIS Plateausoftware to areaobtain to 32the major spatial cities, pattern we used and spatial characteristics interpolation andfrom the 1976 IDW modelto 2016. in ArcGISGenerally software speaking, to obtain the accessibilitythe spatial patternof the andAli–Qiangtang characteristics Plateau–Ruoqiang from 1976 to 2016. Generallyarea was speaking, relatively the accessibilitypoor. Due ofto thethe constructionAli–Qiangtang and Plateau–Ruoqiangdevelopment of the area transport was relatively network poor. over Due many to the years, construction the traffic and delays development between of the transport network over many years, the traffic delays between towns and major cities have towns and major cities have gradually declined and the spatial difference in the accessibility level gradually declined and the spatial difference in the accessibility level was also reduced. Major transport was also reduced. Major transport infrastructure significantly improved the regional accessibility, infrastructure significantly improved the regional accessibility, especially after the opening of the especially after the opening of the Qinghai–Tibet Railway. Qinghai–Tibet Railway. As can be seen in Figure 2, the longest accessibility time from any place within the study area to As can be seen in Figure2, the longest accessibility time from any place within the study area to the nearest major city decreased from 288.70 h to 206.37 h between 1976 and 1986. The accessibility the nearest major city decreased from 288.70 h to 206.37 h between 1976 and 1986. The accessibility timetime of the of the region region dropped dropped further further to to 192.47 192.47 h h be betweentween 1986 1986 and 1991,1991, whichwhich was was mainly mainly due due to to the the changechange in inthe the quality quality of ofroads roads and and the the division division of of main main roads roads and general roadsroads intointo national national roads, roads, provincialprovincial roads roads and and county county roads roads [64]. [64 By]. By1996, 1996, the the maximum maximum accessibility accessibility time time to toreach reach a major a major city hadcity reduced had reduced to 156.67 to 156.67 h. After h. After the theintroduction introduction of of“China “China Western Western Development” Development” inin 2000,2000, traffic traffic developmentdevelopment entered entered a new a new stage. stage. With With the the contin continuousuous extension extension of thethe Qinghai–TibetQinghai–Tibet Railway Railway and and highwayshighways and and the the longest longest accessibility accessibility time time to tothe the ne nearestarest major major city city dropped dropped to to115.48 115.48 h hin in2001. 2001. By 2016,By the 2016, maximum the maximum accessibility accessibility time timefrom fromany place any place in the in study the study area area to the to thenearest nearest major major city city had fallenhad to fallen 24.51% to24.51% compared compared to 1976 to and 1976 the and average the average accessibility accessibility time timehad haddecreased decreased to 8.35 to 8.35 h. The h. improvementThe improvement in the accessibility in the accessibility to the to nearest the nearest major major city promotes city promotes social, social, economic economic and and cultural cultural and healthand healthlinks between links between the towns the towns and andthese these cities, cities, improving improving the the overall overall development development ofof the the whole whole regionregion as aswell well as asthe the living living standards standards of of local local residents. residents.

FigureFigure 2. 2.AccessibleAccessible time time to to major major cities cities in the studystudy area.area.

There are a few major cities in the Qinghai–Tibet Plateau area, with only 14 cities at the county–level city and above. The Qiangtang Plateau and Hoh Xil are far from the city, while the surrounding major cities are mainly distributed along the Hexi Corridor (Gansu Province) and Chengdu Plain (Sichuan Province). The spatial pattern for accessibility to major cities from 1976 to 2016 (Figure3) shows that areas with relatively good accessibility were mainly distributed in the Huangshui River Valley (major cities are Xining and Haidong), the Middle Region of “One River and Two Streams” Sustainabilityin 2019 Tibet, 11 (the, x FOR Yarlung PEER Zangbo REVIEW River, Lhasa River and Nyang River Valleys; major cities are Lhasa, 7 of 15 Shigatse, Shannan and Linzhi) and areas with poor accessibility levels were Ngari, Western Nagqu and major citiesWestern and Shigatse. a small From number the perspective of regions of spatialwith high accessibility accessibility change, fromlevel 1976 had to 1981,formed the areasin the with relatively good accessibility level to major cities (≤10 h) were mainly located in the areas surrounding major cities concentrated areas of Xining–Haidong and other cities. The shortest time from the Ngari–Qiangtang and a small number of regions with high accessibility level had formed in the relatively concentrated Plateau–Hohareas ofXil–Ruoqiang Xining–Haidong area and to other the cities. nearest The shortestmajor timecity fromwas themore Ngari–Qiangtang than four days Plateau–Hoh (about 100 h). From 1986Xil–Ruoqiang to 1996, area the to theGolmud–Delingha–Xining–Haidong–Linxia–Hezuo, nearest major city was more than four days (about 100 h). FromShigatse–Lhasa– 1986 Shannan–Linzhito 1996, the and Golmud–Delingha–Xining–Haidong–Linxia–Hezuo, Maerkang–Kangding areas gradually Shigatse–Lhasa–Shannan–Linzhiformed in the northeast, andsouth and southeastMaerkang–Kangding of the Qinghai–Tibet areas gradually Plateau formed area in and the northeast, the accessibility south and southeast level of of theNgari Qinghai–Tibet had improved significantly.Plateau After area and2000, the the accessibility accessibility level of level Ngari of had the improved region signiﬁcantly.to major cities After continued 2000, the accessibility to improve and level of the region to major cities continued to improve and good accessibility (≤20 h) rapidly expanded good accessibilitywith the development (≤20 h) ofrapidly important expanded trafﬁc arteries. with Especially the development after the opening of important of the Golmud–Lhasa traffic arteries. Especiallysection after ofthe the opening Qinghai–Tibet of the Railway Golmud–Lhasa in 2006, the accessibilitysection of the level Qinghai–Tibet of Hoh Xil and theRailway other major in 2006, the accessibilitycities level greatly of improved.Hoh Xil and After the that other time themajor access cities time greatly from Ngari, improved. which is After far from that the time nearest the access time frommajor Ngari, city, which decreased is far to 70.76 from h. the nearest major city, decreased to 70.76 h.

Figure 3.Figure The 3.spatialThe spatial pattern pattern for for accessibility accessibility time to to major major cities cities in the in study the study area. area. 4.2. Evolution Trend of Accessibility at Township Level 4.2. Evolution Trend of Accessibility at Township Level Using ArcGIS zoning statistical functions, we obtained the average accessibility of 1972 town Usingadministrative ArcGIS zoning units instatistical the Qinghai–Tibet functions, Plateau we areaobtained over the the study average period. accessibility GeoDa software of was1972 town administrativeapplied units to analyze in the the Qinghai–Tibet accessibility level Plateau of township area over units the in the study study peri area.od. After GeoDa the spatialsoftware was autocorrelation analysis of the accessibility values of townships in nine particular years (Table3), applied weto foundanalyze that the Moran’s accessibility I index is greater level than of 0.95township (p < 0.05), units indicating in the that study the change area. in accessibilityAfter the spatial autocorrelationand adjacent analysis units of is positivelythe accessibility related, the values statistical of townships result is authentic, in nine the particular correlation years is signiﬁcant (Table 3), we found thatand Moran’s there is a I positive index spatialis greater autocorrelation than 0.95 in(p the< 0.05), overall indicating pattern. From that 1976 the to change 2016, the in Global accessibility and adjacent units is positively related, the statistical result is authentic, the correlation is significant and there is a positive spatial autocorrelation in the overall pattern. From 1976 to 2016, the Global Moran’s I index fluctuated around 0.95, with a relatively stable value. The extension of the road network, such as the opening of the expressway in the Huangshui River Valley in 2001 and the Golmud–Lhasa section of the Qinghai–Tibet Railway in 2006, led to a slight fluctuation of the Moran Index after 2000.

Table 3. Global Moran’s I index of accessibility level at town scale in the study area.

Given the global Moran’s I index cannot judge whether the statistical significance between the local correlation type and its cluster region is significant; therefore, the GeoDa local autocorrelation analysis was used to judge the distribution of high–accessibility value cluster regions and low– Sustainability 2019, 11, 589 8 of 16

Moran’s I index ﬂuctuated around 0.95, with a relatively stable value. The extension of the road network, such as the opening of the expressway in the Huangshui River Valley in 2001 and the Golmud–Lhasa section of the Qinghai–Tibet Railway in 2006, led to a slight ﬂuctuation of the Moran Index after 2000.

Table 3. Global Moran’s I index of accessibility level at town scale in the study area.

Year 1976 1981 1986 1991 1996 2001 2006 2011 2016 Moran’s I index 0.9515 0.9514 0.9514 0.9512 0.9507 0.9517 0.9503 0.9496 0.9508 z–score 70.463 70.469 70.435 70.373 70.519 70.284 70.805 70.662 70.511 Sustainability 2019, 11p–value, x FOR PEER 0.001REVIEW 0.001 0.001 0.001 0.001 0.001 0.001 0.001 0.001 8 of 15 accessibility value cluster regions, to probe the spatial pattern evolution trend of the accessibility level Given the global Moran’s I index cannot judge whether the statistical significance between the local of QTP townshipcorrelation units type and in its1976–2016. cluster region As is shown significant; in therefore,Figure 4 the (all GeoDa results local have autocorrelation passed the analysis significance level testwas of 5%), used the to judge proportion the distribution of high–high of high–accessibility accessibility value value cluster townships regions andand low–accessibility low–low accessibility townshipsvalue remained cluster regions, stable to at probe around the spatial 15% patternand 29% evolution respectively. trend of the On accessibility the whole, level Ali, ofQTP Qiangtang Plateau, Hohtownship Xil unitsand other in 1976–2016. regions As had shown high–high in Figure 4valued (all results cluster have areas passed from the significance the perspective level of the test of 5%), the proportion of high–high accessibility value townships and low–low accessibility spatial pattern and the surrounding townships of the central cities had low–accessibility value cluster townships remained stable at around 15% and 29% respectively. On the whole, Ali, Qiangtang Plateau, areas. However,Hoh Xil and the other spatial regions pattern had high–high changed valued significantly cluster areas with from the the opening perspective of the of the Golmud–Lhasa spatial section ofpattern the Qinghai–Tibet and the surrounding Railway. townships Before of the2006, central the spatial cities had pattern low–accessibility change in value township cluster areas.accessibility level wasHowever, concentrated the spatial in patternthe extension changed significantlyof low–low with cluster the opening areas ofaround the Golmud–Lhasa Delingha, section Maerkang of and Lhasa–Shigatsethe Qinghai–Tibet and the Railway.better development Before 2006, the trend spatial patternof the changeaccessibility in township level accessibility of towns, level such was as Pitou, concentrated in the extension of low–low cluster areas around Delingha, Maerkang and Lhasa–Shigatse Chaka and Nimu. Since 2006, the opening of the Qinghai–Tibet Railway to traffic improved the and the better development trend of the accessibility level of towns, such as Pitou, Chaka and Nimu. accessibilitySince level 2006, along the opening the railway; of the Qinghai–Tibet the accessibility Railway value to traffic in high–high improved the cluster accessibility areas, level such along as Anduo, Nagqu andthe railway;Hoh Xil the decreased, accessibility while value inthe high–high accessibility cluster value areas, suchin Ganzi, as Anduo, Changdu Nagqu andand Hoh other Xil regions increaseddecreased, and the while number the accessibility of townships value in Ganzi, with Changdu relatively and otherdecreased regions increasedaccessibility and the levels number slightly increased.of townships with relatively decreased accessibility levels slightly increased.

Figure Figure4. The 4. LISAThe LISA of accessibility of accessibility at at the the townshiptownship level level in thein studythe study area. area.

4.3. Distributive Effects in Transport Infrastructure at the Town Scale With the accessibility coefficient and relative accessibility, we were able to explore the distributive effects of transport infrastructure development at the township level in the study area from 1976 to 2016.

4.3.1. Accessibility Coefficient Based on the accessibility level of a town to a major city (Figure 5), from 1976 to 2016, the closer a town was to a major city, the smaller the accessibility coefficient. Towns near a major city had small accessibility coefficients and the accessibility of towns in the Qaidam Basin–Xining–Haidong–Hezuo, Shigatse–Lhasa–Shannan–Linzhi (the Middle Region of One River and Two Streams in Tibet), Changdu–Yushu, Maerkang–Kangding, Shangrila–Lijiang–Lushui and Kashi–Hotan regions was above average. Being far from a major city and a lack of high–grade land transport networks were the main reasons for the relatively high accessibility coefficient of the towns in Ngari. Western Shigatse, Qiangtang Plateau, Ruoqiang and other areas far from major cities also had high accessibility coefficients and their access to the nearest major city was relatively poor. Towns with the most significant accessibility coefficient changes (Nagqu, Anduo, Nierong and Hoh Xil) were along the Qinghai–Tibet Railway, which was constructed and began operations in 2006. Transport infrastructure usually develops in focal areas, connects important traffic nodes and then extends to the entire region, changing the absolute difference in the accessibility between SustainabilitySustainability 2019,2019 11, ,x11 FOR, 589 PEER REVIEW 9 of 169 of 15 different nodes in the region. From 1976 to 2016, the maximum accessibility coefficient at the township4.3. Distributive level in Effectsthe study in Transport area Infrastructurewas concentr at theated Town in Scale Ali, including Chulusongjie, Qusong, SarangduotuolinWith the accessibilityand Daba. coefficientThe maximum and relative value accessibility, of the accessibility we were ablecoefficient to explore increased the distributive with time fromeffects 6.19 in of 1976 transport to 6.32 infrastructure in 2001 anddevelopment up to 6.67 in at 2016, the townshipindicating level that in the the overall studyarea accessibility from 1976 level was rising.to 2016. However, the difference in transport infrastructure between regions gradually expanded, especially4.3.1. Accessibilityin Ali. Coefficient The main reason for the large difference in the accessibility coefficients between villages and towns wasBased that onthe the construction accessibility of level the oftransport a town to netw a majorork citywas (Figure closely5 ),related from 1976 to the to 2016,level theof local closer social a town was to a major city, the smaller the accessibility coefficient. Towns near a major city had small and economic development. The eastern part of the Qinghai–Tibet Plateau area is a relatively accessibility coefficients and the accessibility of towns in the Qaidam Basin–Xining–Haidong–Hezuo, concentrated population center with towns and resources, so major transport infrastructure is usually Shigatse–Lhasa–Shannan–Linzhi (the Middle Region of One River and Two Streams in Tibet), constructedChangdu–Yushu, first in these Maerkang–Kangding, areas. For example, Shangrila–Lijiang–Lushui the Qinghai–Tibetand Railway Kashi–Hotan was first regions built was in the Huangshuiabove average. Valley, Beingthe Qaidam far from Basin a major and city other and ar a lackeas where of high–grade population, land transporttowns and networks resources were were dense.the Then, main the reasons railway for thepassed relatively through high the accessibility Hoh Xil and coefficient Kunlun of Mountains the towns into Ngari.connect Western Lhasa and then Shigatse,extended Qiangtang to areas Plateau,with relatively Ruoqiang lower and other popu areaslations, far from towns major and cities resources, also had highsuch accessibility as the Lhasa– Shigatsecoefficients Railway and and their Korla–Golmud access to the Railway. nearest major Highways city was in relativelythe study poor. area were Towns first with extended the most from Lanzhousignificant to Haidong accessibility and coefficient Xining, changesthen around (Nagqu, the Anduo, Qinghai Nierong Lake, and connecting Hoh Xil) were Gonghe, along theDulan, Delingha,Qinghai–Tibet Xitieshan, Railway, Golmud which and was Dunhuang. constructed and began operations in 2006.

FigureFigure 5. The 5. The spatial spatial pattern pattern evolution evolution ofof the the accessibility accessibility coefficient coefficient at the at town the scaletown in scale the study in the area. study area. Transport infrastructure usually develops in focal areas, connects important traffic nodes and then extends to the entire region, changing the absolute difference in the accessibility between different 4.3.2. Relative Accessibility nodes in the region. From 1976 to 2016, the maximum accessibility coefficient at the township level in theFrom study 1976 area to was2016, concentrated the transport in Ali,network including in the Chulusongjie, study area changed Qusong, Sarangduotuolin dramatically in andquantity Daba. and quality.The The maximum relative value accessibility of the accessibility of Ali coefficientwas higher increased than the with changes time from in 6.19accessibility in 1976 to 6.32from in towns 2001 to the nearestand up tomajor 6.67 incity. 2016, The indicating relative that accessibility the overall va accessibilitylues for towns level was around rising. the However, major cities the difference were small in transport infrastructure between regions gradually expanded, especially in Ali. (Figure 6). From 1976 to 1991, the influence of the transport network on Ali was stronger than in other The main reason for the large difference in the accessibility coefficients between villages and areas and it had a weaker influence on the accessibility level of towns around the major cities. In towns was that the construction of the transport network was closely related to the level of local additionsocial to and Ali, economic the relative development. accessibility The values eastern fo partr towns of the in Qinghai–Tibet1991–1996 were Plateau greater, area such is a relatively as in Zaduo, Baqing,concentrated Nierong populationand Anduo. center By 2001, with townsthe relative and resources, accessibility so major value transport of Ali infrastructurewas higher than is usually all other regions.constructed With the first opening in these of areas.the Golmud–Lhasa For example, thesection Qinghai–Tibet of the Qinghai–Tibet Railway was Railway first built in in 2006, the the improvementHuangshui in Valley, accessibility the Qaidam of towns Basin and in North other areas Nagqu where and population, Hoh Xil was towns the and most resources striking. were From dense. 2011 to 2016,Then, towns the railway with relatively passed through high accessibility the Hoh Xil andvalue Kunlun were Mountainsmainly distributed to connect in Lhasa Ali. andThe thenrelative accessibility values for towns in Yecheng, Pishan and Hotan were also large. Therefore, future development of the transport network in this area will improve the accessibility level of rural areas like Ali. Sustainability 2019, 11, 589 10 of 16

extended to areas with relatively lower populations, towns and resources, such as the Lhasa–Shigatse Railway and Korla–Golmud Railway. Highways in the study area were first extended from Lanzhou to Haidong and Xining, then around the Qinghai Lake, connecting Gonghe, Dulan, Delingha, Xitieshan, Golmud and Dunhuang.

4.3.2. Relative Accessibility From 1976 to 2016, the transport network in the study area changed dramatically in quantity and quality. The relative accessibility of Ali was higher than the changes in accessibility from towns to the nearest major city. The relative accessibility values for towns around the major cities were small (Figure6). From 1976 to 1991, the inﬂuence of the transport network on Ali was stronger than in other areas and it had a weaker inﬂuence on the accessibility level of towns around the major cities. In addition to Ali, the relative accessibility values for towns in 1991–1996 were greater, such as in Zaduo, Baqing, Nierong and Anduo. By 2001, the relative accessibility value of Ali was higher than all other regions. With the opening of the Golmud–Lhasa section of the Qinghai–Tibet Railway in 2006, the improvement in accessibility of towns in North Nagqu and Hoh Xil was the most striking. From 2011 to 2016, towns with relatively high accessibility value were mainly distributed in Ali. The relative accessibility values for towns in Yecheng, Pishan and Hotan were also large. Therefore, future development of the transport network in this area will improve the accessibility level of rural Sustainabilityareas like 2019 Ali., 11, x FOR PEER REVIEW 10 of 15

FigureFigure 6. The 6. Thespatial spatial pattern pattern evolution evolution of of relative relative accessibilityaccessibility at at the the town town scale scale in thein the study study area. area. From 1981 to 2016, the maximum relative accessibility of towns in the study area increased (TableFrom4 1981) but theto development2016, the maximum of the transport relative network accessibility better alleviated of towns trafﬁc in the problems study inarea Ali increased than (Tablein 4) other but areas. the development This was consistent of the with transport the spatial netw spilloverork better effects alleviated of transport traffic infrastructure problems in found Ali than in otherin previous areas. This studies was [25 consistent,65]. Towns with with the larger spatia relativel spillover accessibility effects fromof transport 1981 to 1991infrastructure were mainly found in previousdistributed studies in Ali [25,65]. and the Towns maximum with value larger increased relative from accessibility 6.09 to 6.21. from In 1996,1981 itto dropped 1991 were to 5.73, mainly distributedrose to 6.38in Ali in 2001and andthe remainedmaximum above value 6.80 increased in 2006–2016. from Towns 6.09 to with 6.21. smaller In 1996, relative it dropped accessibility to 5.73, rose valuesto 6.38 werein 2001 mainly and locatedremained in the above areas 6.80 surrounding in 2006–2016. major Towns cities. Theirwith accessibilitysmaller relative to the accessibility nearest valuesmajor were cities mainly was good located and transportin the areas development surrounding had lessmajor of an cities. impact Their on them accessibility than areas to with the poor nearest majortransportation cities was good conditions. and transport development had less of an impact on them than areas with poor transportation conditions.

Table 4. The maximum values of relative accessibility at the town scale in the study area.

Year 1981 1986 1991 1996 2001 2006 2011 2016 Maximum value 6.09 6.21 6.21 5.73 6.38 7.85 6.89 7.13

The above analysis showed that the accessibility coefficient and relative accessibility revealed distributive effects and spatial fairness of the accessibility due to the development of transport infrastructure in the Qinghai–Tibet Plateau area from different dimensions. The accessibility coefficient compares average accessibility at the township level at certain times, reflecting differences in accessibility improvements in different regions due to traffic development. The accessibility coefficient has clear spatial effects, such as the opening of the Qinghai–Tibet Railway, which improved the accessibility level of towns along the line and exacerbated the transportation conditions in the inaccessible areas and spatial unfairness in the transport infrastructure within the region. Relative accessibility is the degree of change in the accessibility of towns over a certain period relative to the overall accessibility of the region, which can represent the distributive effects of transport development at different periods. Therefore, research on the distributive effects and characteristics of transport infrastructure is important for exploring the spatial fairness of the transport network development in different regions.

Table 4. The maximum values of relative accessibility at the town scale in the study area.

Year 1981 1986 1991 1996 2001 2006 2011 2016 Maximum value 6.09 6.21 6.21 5.73 6.38 7.85 6.89 7.13

The above analysis showed that the accessibility coefficient and relative accessibility revealed distributive effects and spatial fairness of the accessibility due to the development of transport infrastructure in the Qinghai–Tibet Plateau area from different dimensions. The accessibility coefficient compares average accessibility at the township level at certain times, reflecting differences in accessibility improvements in different regions due to traffic development. The accessibility coefficient has clear spatial effects, such as the opening of the Qinghai–Tibet Railway, which improved the accessibility level of towns along the line and exacerbated the transportation conditions in the inaccessible areas and spatial unfairness in the transport infrastructure within the region. Relative accessibility is the degree of change in the accessibility of towns over a certain period relative to the overall accessibility of the region, which can represent the distributive effects of transport development at different periods. Therefore, research on the distributive effects and characteristics of transport infrastructure is important for exploring the spatial fairness of the transport network development in different regions.

4.4. Impact of Transport Development on Accessibility Fairness at the Township Level The distributive effects and accessibility of transport infrastructure include not only the concept of space but also the concept of fairness. From Table5 the global CV value in the Qinghai–Tibet Plateau area showed an increasing trend and the CV value increased by 0.0028 in absolute value from 1976 to 1996. Since the implementation of the China Western Development plan, CV increased from 0.8064 in 1996 to 0.8205 in 2001 and rose to 0.8538 in 2016. A larger CV value indicates that the development of the transport network was decreasing the spatial fairness of accessibility of the townships, which is a negative fairness effect. This indicates that the regional transport accessibility tended to be unfairly distributed in space, especially after the introduction of the China Western Development plan. Before 2000, the Qinghai–Tibet Plateau area was mainly dominated by ordinary highways and the railway failed to fully exert its effect on the accessibility of townships due to technical restrictions. After 2000, with the railway construction and the acceleration of trains, as well as the development of high–grade highways, the accessibility pattern changed and the corridor effect gradually appeared along the important arterial trafﬁcs, leading to the decrease in accessibility fairness.

Table 5. Global coefﬁcients of variation of accessibility fairness in the study area.

Year 1976 1981 1986 1991 1996 2001 2006 2011 2016 CV value 0.8036 0.8022 0.8039 0.8068 0.8064 0.8205 0.8405 0.8355 0.8538

The expansion of important trafﬁc lines usually leads to changes in the spatial fairness of accessibility in a whole region [60,64]. As can be seen from Figure7, the spatial distribution of accessibility was relatively balanced in Ali–Ruoqiang–Hotan, Western Shigatse, Guoluo and Central Ganzi, as well as Baqing, Bianba and Sog in Nagqu. The local CV values were higher in the townships around major cities, such as Xining, Changdu and Golmud and the spatial fairness of accessibility was poor. The local CV values of Nagqu and Hoh Xil were smaller in 1976–2001 and larger in 2006–2016, indicating that the accessibility fairness in these regions changed from a balanced to an unbalanced spatial pattern. The important driving factor was due to the unfairness of spatial distribution of accessibility intensifying after the completion and opening of the Golmud–Lhasa section of the Qinghai–Tibet railway. Simultaneously, the extension of the expressway network led to poor accessibility fairness in the townships along the railway, mainly focusing on Tianjun and Dulan in the Eastern Qaidam Basin. Sustainability 2019, 11, x FOR PEER REVIEW 11 of 15 development of the transport network was decreasing the spatial fairness of accessibility of the townships, which is a negative fairness effect. This indicates that the regional transport accessibility tended to be unfairly distributed in space, especially after the introduction of the China Western Development plan. Before 2000, the Qinghai–Tibet Plateau area was mainly dominated by ordinary highways and the railway failed to fully exert its effect on the accessibility of townships due to technical restrictions. After 2000, with the railway construction and the acceleration of trains, as well as the development of high–grade highways, the accessibility pattern changed and the corridor effect gradually appeared along the important arterial traffics, leading to the decrease in accessibility fairness.

Table 5. Global coefficients of variation of accessibility fairness in the study area.

Year 1976 1981 1986 1991 1996 2001 2006 2011 2016 CV value 0.8036 0.8022 0.8039 0.8068 0.8064 0.8205 0.8405 0.8355 0.8538

The expansion of important traffic lines usually leads to changes in the spatial fairness of accessibility in a whole region [60,64]. As can be seen from Figure 7, the spatial distribution of accessibility was relatively balanced in Ali–Ruoqiang–Hotan, Western Shigatse, Guoluo and Central Ganzi, as well as Baqing, Bianba and Sog in Nagqu. The local CV values were higher in the townships around major cities, such as Xining, Changdu and Golmud and the spatial fairness of accessibility was poor. The local CV values of Nagqu and Hoh Xil were smaller in 1976–2001 and larger in 2006– 2016, indicating that the accessibility fairness in these regions changed from a balanced to an unbalanced spatial pattern. The important driving factor was due to the unfairness of spatial distribution of accessibility intensifying after the completion and opening of the Golmud–Lhasa section of the Qinghai–Tibet railway. Simultaneously, the extension of the expressway network led toSustainability poor accessibility2019, 11, 589 fairness in the townships along the railway, mainly focusing on Tianjun12 and of 16 Dulan in the Eastern Qaidam Basin.

FigureFigure 7. TheThe local local coefficient coefficient of of variation variation of of accessib accessibilityility fairness fairness of of transport development development at at the the townshiptownship level level from from 1976 1976 to to 2016. 2016. 5. Discussion 5. Discussion We analyzed the spatial patterns of transport development in the Qinghai–Tibet Plateau area from We analyzed the spatial patterns of transport development in the Qinghai–Tibet Plateau area 1976 to 2016 and determined the distributive effects and spatial fairness of transport based on serval from 1976 to 2016 and determined the distributive effects and spatial fairness of transport based on indicators at the township level. The importance of transport infrastructure to the country and region is serval indicators at the township level. The importance of transport infrastructure to the country and evident, especially in the Qinghai–Tibet Plateau area, which is not only the most unique Qinghai–Tibet region is evident, especially in the Qinghai–Tibet Plateau area, which is not only the most unique alpine–cold traffic zone in the world [66] but also the most unique natural environment on earth, Qinghai–Tibet alpine–cold traffic zone in the world [66] but also the most unique natural providing a natural laboratory for the study of the coordinated development of human–earth relations. However, there are some limitations to the study in terms of transport infrastructure, accessibility and fairness. We mainly discussed the spatial distribution of accessibility and only considered the social needs of different regions. Given that the vast majority of Tibetans are seasonal nomadic herdsmen (grazing at high altitudes in summer and grazing along low altitude highways and around towns in winter), living locations are not fixed. Transport infrastructure provides these people with the most basic travel avenues for shopping, education, health care and so forth. As mentioned above, although the research on spatial fairness is not perfect, it is important to improve the living standards of local residents and regional cohesion. The development of transport infrastructure, especially air transportation and high–speed land transportation (such as highways and high–speed railways) in Western China, will have spatial impacts on a wider range of social, economic and land use factors, which need to be studied further.

6. Conclusions The Qinghai–Tibet Plateau area is a unique geological, geographical, ecological and population region. It is also an important environmental security barrier and a concentrated area of severe poverty in China. Research on transport infrastructure development and its spatial effects is a critical starting point for the coordinated development of the human–environment relationships, which has important practical and theoretical signiﬁcance. The main conclusions are as follows:

(1) With the construction and development of the transport network, the accessibility to the major cities of any place in the Qinghai–Tibet Plateau area improved continuously with signiﬁcant time–space convergence effects. Important trafﬁc arteries played a positive role in improving the accessibility of the area. Major transport infrastructure like the Qinghai–Tibet Railway had important impacts on the accessibility of the Hoh Xil and Nagqu areas. The accessibility level in Sustainability 2019, 11, 589 13 of 16

the areas far from major cities (such as Ali Prefecture) also changed signiﬁcantly. Areas with good accessibility level gradually expanded from those near the major cities to contiguous areas like Qaidam Basin–Xining–Haidong and the Middle Region of One River and Two Streams in Tibet. (2) The spatial autocorrelation analysis of the accessibility value of townships shows that the construction of important transport infrastructure (such as the Qinghai–Tibet Railway) has a signiﬁcant impact on the agglomeration of accessibility and can improve the accessibility level of the areas along the railway. (3) The development of transportation enables access to major cities. After more than 40 years of transport construction and development, the access time to the nearest major city was dramatically shortened. Due to the government’s huge investment in transportation, more Tibetans have more opportunities to travel to cities to enjoy better education, medical treatment, life opportunities and other services. (4) The overall accessibility level of the Qinghai–Tibet Plateau area improved over the studied 40 years but transport construction had different distributive effects on the region. The accessibility coefficient indicates that, generally, the closer a town to the central city, the smaller its value. Relative accessibility reflects the impact of transport infrastructure construction on other places, which is a spatial spillover effect of transport infrastructure investment. Here, the construction of major transport infrastructure not only rapidly improved the accessibility of areas where construction occurred but also the accessibility of the whole region. (5) From 1976 to 2016, the global CV value of the Qinghai–Tibet Plateau area presented an increasing trend, indicating that the global transport accessibility tended to be unfairly distributed in space, especially after China Western Development, transport unfairness was more significant. In the future, with the construction of the Sichuan–Tibet Railway, Xinjiang–Tibet Railway and highways, the spatial fairness of transport is expected to improve.

In the past, the Chinese government implemented policies and planned the transport network, prioritizing superiority of efﬁciency over fairness, which led to priority planning and construction of transport infrastructure in areas with better economic conditions, such as in Yangtze River Delta and Eastern China. However, the government has gradually shifted to paying equal attention to efﬁciency and fairness or even giving priority to fairness. European and American scholars have earlier noticed the fairness of social resources, which is exactly what China’s current regional planning lacks. Therefore, the study of spatial fairness in the Qinghai–Tibet Plateau area is helpful to understand the social effects of transportation planning in China today and the sustainable transport development in the future.

Author Contributions: Conceptualization, G.X., L.T. and C.X.; methodology, software, formal analysis, investigation, resources, data curation, writing—original draft preparation, visualization, G.X.; writing—review and editing, validation, G.X., L.T. and C.X.; supervision, project administration, funding acquisition, C.X. Funding: This research was funded by National Natural Science Foundation of China, No. 41831284, No. 41671160 and No. 41501120. Conﬂicts of Interest: The authors declare no conﬂict of interest.

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