A Spatial Pattern Analysis of Frontier Passes in China’s Northern Silk Road Region Using a Scale Optimization BLR Archaeological Predictive Model
Abstract
:1. Introduction
2. Study Area, Materials, and Methods
2.1. Study Area
2.2. Materials
2.2.1. General Geographical Spatial Data
- Five land cover products, including the International Geosphere Biosphere Programme (IGBP) global land cover dataset (IGBP-DISCover) Version 2 [32], global land cover for the year 2000 (GLC2000) [33], the University of Maryland (UMd) land cover dataset [34], the MODerate resolution Imaging Spectroradiometer (MODIS) global land cover [35,36], and the WESTDC land cover product 2.0, as well as soil classes based on the United Nations Food and Agriculture Organization (FAO90) from the Harmonized World Soil Database (HWSD), version 1.1, were provided by the Cold and Arid Regions Science Data Centre at Lanzhou, which maintains a web portal [37] to distribute these datasets and was the source for all the geographic information system (GIS) data layers used in this research. The 30-meter Global Land Cover Dataset (GL30) collected by the National Geomatics Centre of China and Map World [38] was also explored.
- Hydrology and administrative boundary data were extracted from 1:4,000,000 national GIS data. The intermittent and perennial stream lines from Diva data [39] were also used to analyse the impact of water on site distribution.
- Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Digital Elevation Model version 2 (ASTER GDEMV2) [40] data for the study area were also obtained and applied to extract terrain-independent variables, such as elevation, slope, aspect, curvature, and trend surface data to calculate water flow distances.
- The landform type and Average annual precipitation data set were applied to analyse the pattern of the frontier passes and provided by Data Centre for Resources and Environmental Sciences, Chinese Academy of Sciences (RESDC) [41].
2.2.2. Archaeological Data
2.3. Method
2.3.1. Binary Logistic Regression (BLR) Predictive Model
2.3.2. Model Variables
2.3.3. Model Optimization
2.3.4. Model Assessment
3. Results
3.1. Model Optimization Result
3.2. Site Sensibility Map
3.3. Model Assessment
4. Discussion
4.1. Spatial-Temporal Scale and Model Reliability Analysis
4.2. Contribution Factor Analysis
4.3. Pattern Analysis of CNSR Frontier Pass Distribution
- According to the site sensibility map in Figure 4, the distribution of CNSR frontier passes has clustering characteristics: the high site probability region is mainly located in the east and north, and the low probability region is mainly in the west or south with the division overlapped with the average annual precipitation of 400 mm. These characteristics are consistent with the pass site distribution; eastern is dense and western is rare. The pass-dense region is also called “Guanzhong” or “Guannei” and belonged to the territory controlled by most ancient Chinese dynasties. Otherwise, it is interesting to note that the Great Wall frontier pass line nearly overlaps the division of precipitation of 400 mm in Shaanxi province as shown in the local view window of Figure 8. This may be because these Great Wall frontier passes were fortifications that protected the more livable central plains from the nomadic tribes, particularly from attacks by the Huns.
- Although passes are rare in the western part, only one site in Xinjiang Uyghur Autonomous Region and four sites west of Jiayu Pass, and in the vicinity of the Great Wall or the Silk Road, were not used as model inputs either. The areas with high and moderate probability in the sensibility map are spatially overlapped with the Han Great Wall section (from Yumen pass to Jiayu pass) and with three western parts of the CNSR routes, as shown in Figure 4. This result demonstrated that the improved BLR model was able to reveal the spatial correlation characteristics of frontier passes and the Great Wall, and could be used to reconstruct ancient trade routes.
5. Conclusions
- APMs can be used as a pattern tool for macro-scale site distribution and a temporal-spatial scale can be imported into the APMs [57,58]. An improved BLR model with spatial-scale optimization was successfully constructed and validated to analyze the spatial distribution of CNSR frontier passes in this study. The high probability areas identified through the study were helpful for further archaeological interpretation and archaeological validation.
- Through spatial-temporal analysis, the best spatial scale should be considered and the best parcel size selection varies with the stability of the variables. Selecting resources that change slowly, such as perennial streams connected with site locations, is helpful in reconstructing ancient environments using modern data. The best scale spatial for the terrain variables and the non-terrain variables are 50 m and 1000 m, respectively.
- Based on the variable selection, the elevation, slope, land cover, and distances to perennial streams were identified as the independent variables used to construct the model. An assessment of the model from the sample data and Kvamme’s gain statistics verified that the predictive model can effectively identify regions with a high probability of pass site occurrences, and it is able to reveal correlations between the pass sites and natural proxies.
- The distribution of CNSR frontier passes has clustering characteristics; the high probability area was mainly located in the east, and the division between the low and moderate-to-high areas coincides with the 400 mm precipitation contour. In the site sensibility map, the high and medium probability areas cover the Great Wall and the CNSR routes, especially the western parts. The predictive model for archaeological sites was shown to be helpful for analysing the spatial distribution pattern of CNSR pass sites used for both military defense systems and as trade control stations.
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Provinces and Autonomous Regions | Frontier Passes | Total Number |
---|---|---|
Xinjiang | Tiemen Pass | 1 |
Gansu | Yumen Pass, Yang Pass, Jiayu Pass, Dazhen Pass, Liangzhou Wei, Suzhou Wei, Handong Wei, Yanzhi Fort, Suoqiao Fort, Lutang Fort, Tumen Fort, Dajing Cheng, Heishan Fort, Xiakou Fort, Shi Pass, Zhangye Cheng | 16 |
Ningxia | Xiama Pass, Zhenyuan Pass, Sanguan Kou, Hengcheng Fort, Hengshan Fort, Xingwuying Suo, Guangwuying Suo, Qingshuiying Fort, Shengjin Pass, Daweikou Pass, Baisikou, Helankou, Yinchuan Cheng, Guyuan Cheng | 14 |
Shaanxi | Dasan Pass, Jinsuo Pass, Tong Pass, Raofeng Pass, Wu Pass, Yangping Pass, Xiegu Pass, Yao Pass, Micang Pass, Xianren Pass, Longmen Pass, Wuli Pass, Luzi Pass, Linjin Pass, Yulin Cheng, Zhenbei Tower, Huangfuchuan Fort, Qingshuiying Fort, Gushan Fort, Zhenqiang Fort, Dabai Fort, Yongxing Fort, Gaojia Fort, Jian’an Fort, Changle Fort, Boluo Fort, Wuwei Fort, Huaiyuan Fort, Qingping Fort, Longzhou Fort, Zhenjing Fort, Zhenluo Fort, Jingbianying, Ningsai Fort, Liushujian Fort, Zhuanjing Fort, Dingbian Cheng | 37 |
Henan | Changtai Pass, Wusheng Pass, Pingjing Pass, Jiuli Pass, Dasheng Pass, Yique Pass, Mengjin Pass, Heishi Pass, Dagu Pass, Hulao Pass, Jindi Pass, Xuanyuan Pass, Jingzi Pass, Luyang Pass, Qin Hangu Pass, Han Hangu pass, Zhuyang Pass, Liyang Pass | 18 |
Independent Variable Name | Data Source | Type of Variable | Description |
---|---|---|---|
ELE | ASTER GDEMV2 | Terrain (km) | Elevation |
ASP | Aspect | ||
SLO | Slope | ||
CUR | Curvature | ||
PLC | Plane curvature | ||
PRC | Profile curvature | ||
DS_IS | Diva GIS | Distance to intermittent streams | |
DS_PS | Distance to perennial streams | ||
DS_4S | 1:4,000,000 GIS data | Distance to Streams (4th orders and higher) | |
DS_5S | 1:4,000,000 GIS data | Distance to Streams (5th orders and higher) | |
GL30(4) | GL30 land cover database | GL30 land cover | |
SOI_Typ(13) | Soil Classes HWSD v1.1 | Non-terrain | Soil Type (FAO90) |
IGB_LC(4) | IGBP-DIS land cover data | IGBP land cover | |
GLC2000_LC(4) | GLC2000 land cover data | GLC2000 | |
UMD_LC(4) | UMd land cover data | UMd land cover | |
MOD_LC(4) | MODIS land cover data | MODIS land cover | |
WEST_LC(4) | WESTDC land cover data | WESTDC land cover |
GLC2000_LC ID | New ID | GL30 ID | New ID | IGB_LC/MOD_LC ID | New ID | UMD_LC ID | New ID | WEST_LC ID | New ID |
---|---|---|---|---|---|---|---|---|---|
1 | 1 | 10–19 | 2 | 1 | 1 | 0 | 3 | 11 | 3 |
2 | 1 | 20–29 | 1 | 2 | 1 | 1 | 1 | 12 | 2 |
3 | 1 | 30–39 | 2 | 3 | 1 | 2 | 1 | 21 | 1 |
4 | 1 | 40–49 | 1 | 4 | 1 | 3 | 1 | 22 | 1 |
5 | 1 | 50–59 | 3 | 5 | 1 | 4 | 1 | 23 | 1 |
6 | 1 | 60–69 | 3 | 6 | 1 | 5 | 1 | 24 | 1 |
7 | 2 | 70–79 | 4 | 7 | 1 | 6 | 1 | 31 | 2 |
8 | 2 | 80–89 | 5 | 8 | 2 | 7 | 1 | 32 | 2 |
9 | 2 | 90–99 | 4 | 9 | 2 | 8 | 1 | 33 | 2 |
10 | 2 | 100 | 4 | 10 | 2 | 9 | 1 | 41 | 3 |
11 | 2 | - | - | 11 | 2 | 10 | 2 | 42 | 3 |
12 | 2 | - | - | 12 | 2 | 11 | 2 | 43 | 3 |
13 | 5 | - | - | 13 | 5 | 12 | 4 | 44 | 4 |
14 | 3 | - | - | 14 | 2 | 13 | 5 | 45 | 3 |
15 | 3 | - | - | 15 | 4 | - | - | 46 | 4 |
16 | 3 | - | - | 16 | 4 | - | - | 51 | 5 |
17 | 4 | - | - | 17 | 3 | - | - | 52 | 5 |
18 | 4 | - | - | - | - | - | - | 53 | 5 |
19 | 4 | - | - | - | - | - | - | 61 | 4 |
20 | 4 | - | - | - | - | - | - | 62 | 4 |
21 | 2 | - | - | - | - | - | - | 63 | 4 |
22 | 2 | - | - | - | - | - | - | 64 | 3 |
23 | 2 | - | - | - | - | - | - | 65 | 4 |
24 | 1 | - | - | - | - | - | - | 66 | 4 |
- | - | - | - | - | - | - | - | 67 | 5 |
Independent Variables | B | Wald | Sig | Exp (B) |
---|---|---|---|---|
ELE | −1.055 | 12.574 | 0.001 | 0.348 |
SLO | −0.093 | 4.077 | 0.043 | 0.911 |
GLC2000_LC(1) | 3.161 | 6.955 | 0.008 | 23.585 |
GLC2000_LC(2) | 2.530 | 11.48 | 0.001 | 12.559 |
GLC2000_LC(3) | 1.556 | 2.01 | 0.156 | 4.74 |
DS_PS | −0.045 | 6.989 | 0.008 | 0.956 |
Constant | 5.4291 | 0.587 | 0.443 | 1.857 |
Data Types | Prediction Result | Precision/% | Overall Accuracy/% | ||
---|---|---|---|---|---|
Non-Site | Site | ||||
Input data | Non-site (0) | 254 | 40 | 86.4 | 86.7 |
Site (1) | 6 | 50 | 89.3 | ||
Test data | Non-site (0) | 108 | 12 | 90.0 | 89.3 |
Site (1) | 4 | 26 | 86.7 |
Probability Level | Area Percentage | Site Number | Number Percentage | Kgain |
---|---|---|---|---|
Low | 52.78% | 4 | 4.65% | −10.35 |
Moderate | 25.20% | 18 | 20.93% | −0.20 |
High | 22.02% | 64 | 74.42% | 0.70 |
Variables | 1000 m | 750 m | 500 m | 250 m | 100 m | 50 m |
---|---|---|---|---|---|---|
ELE | −1.444 | −1.425 | −1.397 | −1.279 | −1.092 | −1.055 |
GLC2000_LC(1) | 1.799 | 1.928 | 1.899 | 2.157 | 3.036 | 3.161 |
GLC2000_LC(2) | 2.238 | 2.265 | 2.225 | 2.264 | 2.469 | 2.530 |
GLC2000_LC(3) | 1.414 | 1.434 | 1.406 | 1.453 | 1.513 | 1.556 |
DS_PS | −0.041 | −0.041 | −0.042 | −0.043 | −0.045 | −0.045 |
SLO | 0.090 | 0.062 | 0.043 | −0.007 | −0.085 | −0.093 |
Constant | 0.475 | 0.464 | 0.500 | 0.483 | 0.661 | 0.619 |
Accuracy | 86% | 86% | 86% | 86.7% | 86.7% | 86.7% |
Input Water Variables | 1000 m | 750 m | 500 m | 250 m | 100 m | 50 m | |
---|---|---|---|---|---|---|---|
accuracy | Distance to perennial streams, distance to intermittent streams, distance to streams (4th order and higher) and distance to streams (5th order and higher) | 86% | 86% | 86% | 86.7% | 86.7% | 86.7% |
Distance to all the streams merging perennial streams and intermittent streams | 72.7% | 78.7% | 76.7% | 74.7% | 74.7% | 75.3% | |
Distance to perennial streams, distance to intermittent streams | 82% | 82% | 82% | 81.3% | 81.3% | 81.3% | |
Distance to streams (4th order and higher) and streams (5th order and higher) | 80.7% | 82% | 83.3% | 80% | 76% | 82% | |
Distances to streams (5th order and higher) | 81.3% | 79.3% | 80% | 80% | 76% | 81.3% |
Basic Types | Climate Landform Type | A Humid Monsoon Climate Landform | B Warm Humid and Semi-Humid Monsoon Climate Landform | C Inland Arid and Semi-Arid Climate Landform | D Alpine Cold–High Latitude Climate Landform |
---|---|---|---|---|---|
Plain | 1 River delta | - | - | ||
2 Marine plain | deposition | deposition | - | - | |
abrasion | abrasion | ||||
3 Alluvial-Lacustrine plain | Salt lake plain, playa lake plain | Freeze–thaw plain | |||
4 Alluvia plain | |||||
5 Alluvium-diluvium wave plain | |||||
6 Alluvial sloping plain | - | - | Earthflow platform | ||
Platform and Highland | 7 Platform and Terrace | ||||
8 Piedmont plain and Highland | Low | Low | Highland | Highland | |
High | High | ||||
Hill and Mountain | 9 Hill | Erosion | Erosion | Arid denudation | Frosting wind-erosion |
Corrosion | Corrosion | Erosion and Denudation | Frosting erosion | ||
10 Low mountain and Middle mountain | Erosion | Erosion | Arid denudation | Frosting | |
Frosting wind-erosion | |||||
Corrosion | Corrosion | Erosion and Denudation | Frosting erosion | ||
11 High mountain and Extra-high mountain | Erosion | - | - | Frosting | |
Frosting wind-erosion | |||||
Valley | Frosting erosion |
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Zhu, X.; Chen, F.; Guo, H. A Spatial Pattern Analysis of Frontier Passes in China’s Northern Silk Road Region Using a Scale Optimization BLR Archaeological Predictive Model. Heritage 2018, 1, 15-32. https://doi.org/10.3390/heritage1010002
Zhu X, Chen F, Guo H. A Spatial Pattern Analysis of Frontier Passes in China’s Northern Silk Road Region Using a Scale Optimization BLR Archaeological Predictive Model. Heritage. 2018; 1(1):15-32. https://doi.org/10.3390/heritage1010002
Chicago/Turabian StyleZhu, Xiaokun, Fulong Chen, and Huadong Guo. 2018. "A Spatial Pattern Analysis of Frontier Passes in China’s Northern Silk Road Region Using a Scale Optimization BLR Archaeological Predictive Model" Heritage 1, no. 1: 15-32. https://doi.org/10.3390/heritage1010002