Journal of Global Resources

Volume 4

January 2017

Page 36-44

ISSN: 2395-3160 (Print), 2455-2445 (Online)

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ANALYSIS OF RURAL HOUSING LAND CONSOLIDATION POTENTIAL IN THE LOESSHILLY REGION: A CASE STUDY OF BAIMATAN VILLAGE IN HUANGLONG COUNTY
CUI Xiao-lin, FU Li-meng, WEI Xiao-qing, WANG Qian, CHENG Yun and LEI Yu-tongCollege of Geo-matics, Xi’an University of Science and Technology, Xi’an, China
Email: xiaolin_cui@xust.edu.cn
Abstract: The purpose of this paper is to determine the potential for rural housing landconsolidation quantitatively and objectively in the loess hilly region and to provide the local
government with scientific reference, which could be used to deal with rural housing landconsolidation work. Data was obtained by onsite investigation, and 10 factors were selected
to divide the consolidation potential into three categories, with the application of factor
analysis method. The characteristic of consolidation potential area of rural housing land wasanalyzed statistically by overlapping slope gradients and calculating actual potential area for
consolidation. The results show that: the degree of housing land consolidation is low in thisregion, marked by severely over-standard residential area and lower density of housing land.
As is shown in this case, an average rural household tends to own an over-standard numberof residential sites which exist in different forms and use rate of housing land is generally low.
It is found in this case that the potential area for consolidation could reach 34064.78m2, and it
falls into three categories: Over-standard area (two villages), multi-housing area (3 villages),and lower-use-rate area (two villages).
Key words: Rural Housing Land, Potential Consolidation, Slope, Factor Analysis
Introduction
As an important carrier of rural life and production, rural housing land is the core of therelationship between performance of rural people and land. With the continuous progress ofurbanization, rural housing land consolidation has become an effective way to promoterational allocation of resources and intensive land use and to speed up the pace of ruralmodernization. Many scholars have conducted in-depth discussion and study on it. Zhan et al.
(2007) discussed and analyzed rural residential land consolidation of Henan Province, Wanget al. (2001) analyzed and explored the process and mode of village relocation in the processof consolidation in Sunqiao Town, Pudong District, Shanghai; Fang et al. (2007) analyzed andforecasted trends of rural residential land consolidation in Zhejiang, Beijing and other three
regions.
Currently, the research of rural land consolidation involves method of models (Liu andZhang, 1999; Han and Chen, 2000; Yang and Zhang, 2003), the strategy of implementation(Han and Zheng, 2002), benefit evaluation (Gong, 2003), "hollow village" consolidation (Xue,
2001), rural villages planning (Zhou, 2006), spatial distribution arrangement (Si and Hu, 2006;
Tian et al, 2002), consolidation focus and difficulty (Zhou et al, 2002), etc. However, little
attention has been paid to research on the influence of natural factors on housing landconsolidation, among which topography is one strong influencing factor on the distribution of36

the housing land. And this paper took topography into consideration when studying this issue.
Baimatan located in the hilly-gully region of Loess Plateau was selected to analyze the actualuse of rural land and to calculate the realistic consolidation potential of housing land
according to slope conditions. Finally, factor analysis method was used to classify the landconsolidation potential and to give suggestions suitable for the rural housing landconsolidation in loess hilly areas.
Study Area
Baimatan village located in the southeast of Huanglong county which located in(35°24′09″-36°02′01″N, 109°38′49″-110°16′49″E) the southeast of Yan’an in the northern
Shaanxi province, is a hilly-gully area of Loess Plateau. This study area average elevation is
1100m. It belongs to the warm-temperate-zone with an annual mean precipitation of 602mm.
The study area includes 7 natural villages: Zhangfeng village, Yangmian village, Dongshan
village, Qixingping village, Baidong village, Baixi village and Sunjiapo village. Usable land
resources include productive land and non-agricultural construction land. Woodland,
farmland and orchard all belong to productive land; rural land for public welfare and public
facilities and farmers’ residential land belong to non-agricultural construction land.
Data and Analysis of Rural Housing Land
Rural land ownership and land use status data are collected from field investigation, and rural
housing land area and graphic data from field survey. The data includes owners of the
housing land, its living family members and its situation, the status of housing land rightsource, the four boundaries of every parcel land, the surrounding terrain factors, boundary
point coordinate, the housing land area, building area and building structure, etc. The
investigation obtained 565 cases.
The Situation Analysis of Rural Housing Land
According to the allotment policy, an average household is allotted a maximum area of
266.68 m2 as one rural household unit. The index was selected according to this standard.
There are two types of index selection: a) regarding a whole village as a unit, covering total
area, the total number of parcels and the total number of households (Tab.1); b) taking an
individual peasants household as a unit, covering the housing land area of every household,the number of parcels owned by each household, the multiple housing area and average
household area. And as the date shows, there are 565 parcels and 436 households, the total
area being 165 959.54 m2. Among them, 128 households are the type of “one householdowing multiple housing lands”; 352 parcels have exceeded the allotted size; and the multiple
occupancy area is 34205.27m2.
1) Villages like Yangmian, Dongshan, Qixingping, Baixi and Baidong are between 10% and20%. Those of the other two villages Sunjiapo and Zhangfeng are even greater than 20%,with the latter having the highest percentage of multi occupancy area 32.18%. The researchalso shows the number of households falling into the bracket of “one household owing
multiple housing lands,” which is 128, accounting for 29.30% of the total village households,
among which those of Yangmian and Qixingping are between 10% and 20% and those of
Baidong, Baixi, Sunjiapo, Dongshan and Zhangfeng are greater than 20%. The highest
percentage is shown in Sunjiapo village, 29.33%.

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Table 1:

Village
BaimatanZhangfeng
YangmianDongshanQixingping
Baidong
Baixi
Sunjiapo

Index of Rural Housing Land
Total number of
Total area(m2)household165959.54436
41725.46
11122369.9564753.30
137995.472330580.77
8527878.567330656.08
75

Total number ofparcel
565
144
63
17
28
117
94
102

2) Low homestead density. The homestead density is the ratio of homestead area toresidential area. The average density of homestead is 14.29%. Among the seven villages, theratios of Dongshan and Qixingping are less than 10%. And those of Yangmian, Baidong andBaixi are between 10% and 20%. Zhangfeng has got the highest, 25.41%. The number ofoccupied parcels per household is 1.3. More specifically, those of Yangmian, Zhangfeng,
Qixing and Baixi are between 1.13 and 1.29. Those of Dongshan, Baidong and Senjiapo are
greater than 1.3 and Baidong village has the highest, 1.38 parcel/household.
3) Homestead area being severely above the standard. The homestead area exceeds the
local average homestead area standard, which is 266.68 m2 per household to the maximum.
The over standard rate is 62.3%. Among the seven villages, those of Dongshan, Qixingping,
Zhangfeng, Baidong and Sunjiapo are between 50% and 70%. Those of Yangmian and Baixi
are more than 70%. The highest over standard rate is shown in Yangmian village, 82.54%.
Low Homestead Use Intensity
Homestead use intensity is the degree of land development and utilization measured in one
unit of land area. Considering the actual situation, the research build rural land use intensity
evaluation standard in the study area, based on factors like building density, building volume
ratio and average building layers (Tab. 2).

Village
BaimatanZhangfeng
YangmianDongshanQixingping
BaidongBaixi
SujiapoTable 2: Index of Evaluate Rural Housing Land Intensity
Occupied
Building
Building
Building
Average buildingarea(m2)
area(m2)
volume ratio
densityLayers59189.79
48058.44
0.36
0.29
1
14071.1
14506.77
0.34
0.35
1
7029.785155.160.32
0.23
11334.93
1706.920.28
0.35
12571.982572.20.32
0.32
120568.3
12065.93
0.67
0.39
1.1
6446.655174.350.23
0.19
17167.05
6877.11
0.23
0.22
1

Selecting Factor Paired Comparison Method was used to determine the weight of each factor.
For index characteristics, the research calculated the weight of factors according the 0-1 ratio
scale method (Tab. 3). The formula is shown as follows:38aW j = n∑n ij∑∑ aij(1)

j =1 i =1

Where, ai and a j denote comparison factors. 0 denotes that ai is less important than a j ;0.5 denotes that ai is as important as a j ; and 1 denotes that ai is more important than a j(Buhyoff and Leuschnerw, 1978 and 1980).
Homestead use intensity was calculated with the formula:F = 100(∑ Aj × W j )(2)

Where, F denotes the score of homestead use intensity; Aj denotes the evaluation index
value of the j item; and W j denotes the weight value of the j item.
Table 3: The weight of Evaluation Index for Rural Resident Use Intensity
Evaluation
Average
Building volume
Building
Weightindex
building layers
ratio
density
Average building layers
0.5
0
0
0.11
Building volume ratio
1
0.5
0.5
0.44
Building density
1
0.5
0.5
0.44
According to the research, the score of Baixi is less than 30; the scores of Qixiping,
Dongshan, Yangmian, Sunjiapo and Zhangfeng are between 30 and 50; the score of Baidong
is the highest, 58.5. The analysis of building volume ratio and building density shows that the
average maximum construction layer is 1.1, most of them being 1 layer. The building volume
ratio in Baidong village is the highest, 0.67, those of others being less than 0.4. The basic
type of building is bungalow and the occupied area is very large. On the whole, the overall
homestead use intensity is low.
THE CONSOLIDATION POTENTIAL FOR RURAL HOUSING LANDThe Theoretical Consolidation Potential for Housing Land
Based on the actual land use for homestead construction per household and the local
standard in the study region, the theoretical consolidation potential for housing land was：

∆Pi = Pi0 - B0 × A(3)Where ∆Pi denoted the theoretical consolidation potential of homestead in i region (m2), Pi0
denoted the status of use homestead area in i region, B0 denoted the number ofhousehold in i region and A denotes per household planning standards (m2/household).
Potential coefficient (%) denotes the ratio of the potential for rural homestead to the total areaof a homestead. The results show that: the consolidation potential area of homestead is49687.06 m2 and the potential coefficient is 29.94 %. Among the seven villages, the potential
39coefficient of Zhangfeng, Dongshan, Qixingping and Baidong are between 20% and 30%.
Those of Yangmian, Baixi and Sunjiapo are more than 30%, Sunjiapo’s being the highest,
34.76%, and the consolidation potential area is 10655.08 m2.
Consolidation Potential Regarding Different Slopes
The values shown above are merely theoretical values. Other factors also exert theirinfluence like customs, production requirements and farmers intentions, and the topographyof the local region also needs considering. As a loess hilly region, its terrain condition has agreater influence on the housing construction. To analyze this restriction condition, the study
area was divided into 5 levels, namely, 0°- 2°,2°- 6°,6°- 15°,15°- 25°and＞25°, with theapplication of the space analysis is function of ArcGIS. Then the slop and distribution ofhomestead map were overlaid and thus came the spatial distribution map of homesteadregarding different slopes. The slop critical values divided the levels of homestead
consolidation and identified realistic consolidation potentiality regarding different slopes(Fig.1). According to the finding of an existing mountain rural housing security investigation,villagers' homes are safe, which are built on the gentle slope, average slope＜20° (Wang,2008).

Fig.1 Homestead Distribution Overlaid With Slope Classification
Based on the field survey, in combination with the geological condition of the region which iscovered by loess in the surface, 15° is taken as the safe consolidation critical value. If theslope is above 15°, the security rate of the homestead is desirable (Tab. 4). 147 parcels over
15° with the over occupied area being 34064.78 m2, namely, the realistic consolidation
potentiality value.
Table 4: Slope classification of over occupied area
Total area（m2） Over occupied area（m2）Slop level

Parcel number（parcel）

0°～2°

0

0

0

2°～6°

175

43551

3183.46

6°～15°

243

77544.64

12438.82

15°～25°
>25°

65
82

19561.4
25302.5

13690.41
20374.37

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Classification of Housing Land Consolidation Potentiality
The factor analysis was conducted by using a small number of comprehensive factor indexes
to describe the multiple initial factor indexes. The main factors were obtained in linear
combination with comprehensive factors (Wang, 2007). Because the consolidation
potentiality for housing land may take a variety of forms in the research region, it is difficult tojudge the dominate form simply from the collected data and therefore, factor analysis method
was needed to classify each of the existing potential form in the study region. Based on thecharacteristics of housing land use in the study area in combination with field investigationand measurement data, 10 factors which are quantifiable and observable were selected tobuild the factor index system of consolidation potentiality based on land classification. With
the aid of SPSS software and the application of the factor analysis method, 10 factors wereclassified, namely, X1 which denotes per household parcel, X2, the household percentage of "one housing lands" , X3, the area percentage of " one household owing multiple housing
lands", X4, per household area, X5, the area percentage of over occupied housing land, X6,
average building layers, X7, average volume ratio, X8, average building volume ratio, X9, theover standard parcel percentage, X10, housing land density. And finally, the dominant type of
consolidation potentiality was dug out.
Normalization of Data
Since the factor indexes have different attributes and the scopes of the units are different, theaveraged method was adopted to select indicators for standardization processing by theformula：X i’′ =

Xi
X

(4)

Where X i′ denotes standardized value; X i denotes each variable's value; and X
denotes average value of the variable. The averaged method removes the effects of the orderof magnitude and dimension, and meanwhile, ensures the existing difference degree of eachfactor index in its original value. Factor indexes affect the comprehensive analysis along withthe degree of difference proportional to the change (Han, 2008). Reverse index indicates if
the factor index is smaller, it is the better for earned value and if it’s bigger, the more
unfavorable. Of all factor indexes, there are three factors which are reverse indexes, namely,
average building layers, average building volume ratio and housing land density. In order toachieve the same comparability, it is necessary to transform their reverse attributes into
positive ones. Here comes the formula:

X max - X i′
Xi =X max - X min(5)

Where X i′ and X i are same as formula (4), X max denotes the maximum value of theevaluation factor index, whereas X min denotes the minimum value.
Relativity detection
In order to distinguish whether each factor index was suitable for factor analysis, it wasnecessary to detect the relativity among each factor index. There are commonly two tests

41

used to measure whether the factor analysis model is valid statistical indicator: Bartlett test
and KMO test (Li, 2001). Thus, this study also used them to detect the relativity among eachfactor index. And test results show that the detection value of KMO is 0.647, p (Sig. = 0.000)<0.05, proving that the model is valid, and the Chi-square statistics of Bartlett sphericaldetection is 259.998, thus rejecting the null hypothesis, the correlation matrix not being a unit
matrix. The test results proved that the institute selected appropriate factor indexes for factor
analysis.
Main Factor Determination and Consolidation Classification
According to the extraction principle characteristic value should be greater than 1, threefactor indexes were found to comply with the principle, and the cumulative contribution rate
shows that it can effectively represent the initial factor index information. Because theextraction factor index is abstract, it is necessary to extract the three dominant factor indexes
represented by the figurative meaning using variance biggest orthogonal rotation method.
In the Tab.5, the main factor1 denotes the main content of X1, X2, and X3, which mainly
describes the type represented by the factor of “one household owning multiple housing
lands”. The score of factor1 directly reflects the consolidation potential magnitude of “onehousehold owning multiple housing lands” type. The main factor2 denotes the main content
of X4, X5, and X9, which mainly describes the type represented by the factor of over occupied
area. The score of factor2 directly reflects the consolidation potential magnitude of overstandard type. The main factor3 denotes the main content of X6, X7, X8, and X10, which mainly
describes the type represented by the factor of low housing land use intensity. The score offactor3 directly reflects the consolidation potential magnitude of low housing land use
intensity type. The score is higher; the corresponding consolidation potential is greater.

Index
X3
X2
X1
X10
X5
X9
X4
X6
X7
X8

Variablehigh load index
naming type

Table 5: Rotated Component Matrixes
Factor1
Factor2
0.897
0.148
0.879
-0.1470.811-0.388-0.688-0.3430.021
0.922-0.421
0.885
0.197
0.800
-0.104
0.394
0.026
0.473
-0.380-0.228

Factor3
0.074-0.160-0.1840.371
0.250-0.019
0.186
0.874
0.855
0.706

Table 6: The Name of Principal Factors
Factor1
Factor2

Factor3

X3、X2、X1

X5、X9、X4

X6、X7、X8、X10“one household,multiple housing lands” typeover area
standard typelow housing land use
intensity type42

Village name
Zhangfeng
YAngmianDongshanQixingping
Baidong
Baixi
Sunjiapo

Table 7: The Factor Score of Villages
Factor1
Factor2
1.01617-0.38459-1.52297
1.21038-0.40285-1.32637-0.97347-.422610.23544-0.89367
0.573700.90435
1.07399
0.81252

Factor3
0.40335-.16835
1.33973
0.10528-1.98280
0.174270.22853

According to Tab.6 and Tab.7, there exist three types of consolidation potentiality in all of thenatural villages, the type of “one household owning multiple housing lands”, the type of overstandard area and the type of low homestead use intensity. Among the seven villages,
Zhangfeng, Sunjiapo and Baidong belong to the type of “one household owning multiple
housing lands”; Yangmian and Baixi belongs to that of over area standard type; andDongshan and Qixingping belong to low homestead use intensity type.
Conclusions and Suggestion
After analyzing the present rural land use of the loess hilly region and calculating the realistic
consolidation potential for housing land regarding slope conditions, and finally, using thefactor analysis method to classify the types of land consolidation potentiality, it is highly
recommended that the work of housing land consolidation should be arranged in accordancewith the local natural conditions and considering the different consolidation types as areshown in Baimatan village. Meanwhile, for rural hilly regions where consolidation fund isinadequate and land management technology is relatively backward, it is necessary to
broaden the financing channels, including the aid from the second and third industry of thelocal region, and to gain technical support to improve existing land management system andto speed up the informationalization of the rural residential area management.
Acknowledgments
This work was funded by the Project Foundation of Rural land Use Right Verification, acertified project from Department of Land Resources of Shaanxi Province.
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