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Abstract: Based on high-definition Google Earth satellite images, historical documents, archaeological materials and contemporary research, this study tried to accurately locate major traffic nodes of the Desert-Oasis Silk Road in the Han dynasty. Assisted by geomorphic characteristics, we then restored major traffic lines, and built the traffic data set of the Silk Road in the Han dynasty. This dataset contains information on traffic nodes and traffic lines. As part of the Silk Road Transportation and Trade Dataset, this work is helpful to studies on the changes of the Silk Road route during this particular historical period. It also provides an accurate statistical basis for studies of cultural communications, business contacts, and ethnic migration along the Silk Road in the Han dynasty.
Keywords: Silk Road; road traffic; Han dynasty; route restoration
|English title||Traffic data of the Silk Road in the Han dynasty|
|Data corresponding author||Zhang Ping (firstname.lastname@example.org)|
|Data authors||Hu Yumeng, Yan Bo, Zhang Ping|
|Time range||202 B.C.E – 9 C.E., 25 C.E. – 220 C.E.|
|Geographical scope||Geographical scope: 2414’56’’N – 4737’27’’N, 10853’38’’E – 2858’26’’E. Specific areas include: China, Mongolia, Kyrgyzstan, Tajikistan, Kashmir, India, Pakistan, Afghanistan, Uzbekistan, Kazakhstan, Turkmenistan, Iran, Iraq, Syria, Lebanon, Egypt, Turkey.|
|Data volume||6.35 MB|
|Data format||*.pjr, *.sbn, *.sbx, *.shp, *.shx, *.cpg, *.dbf, *.xml, *.xlsx, *.docx|
|Data service system||<http://www.sciencedb.cn/dataSet/handle/611>|
|Sources of funding||National Social Science Fund of China (14ZDB031); National Program for Support of the Culture Industry|
|Dataset composition||This dataset consists of five parts of data, including textual description of traffic lines, attribute table for traffic nodes, data of traffic nodes in the Western Han dynasty, data of traffic nodes in the Eastern Han dynasty, and data of traffic lines in the Han dynasty. Data of traffic nodes and traffic lines are stored in shapefiles format, including *.shp for geometric entities of the elements, *.shx for graphic indexes, *.dbf for dBase tables for the geographic data attributes, *.pjr for spatial references, *.sbn and *.sbx for spatial indexes of the geometric entities, *.xml for metadata, and *.cpg for character encoding description.|
A trade route across Eurasia, the Silk Road significantly contributed to the development and formation of ancient human civilization, and hence it has attracted considerable scholarly attention. As the Silk Road is in essence a transportation passage, studies of its traffic lines, traffic nodes and their diachronic changes over time assume special importance, which lay foundation for a more comprehensive research of the Road.
Previous studies on ancient road network restoration are predominated by the method of textual description coupled with schematic diagram illustration. As a result of a low precision, some researchers started to use spatial information technology and related technologies in restoring historical traffic routes in order to produce more accurate thematic digital maps. Representative works include the Silk Road Geographic Information System of Fudan University1 and T. Matthew Ciolek’s Old World Traditional Trade Routes (OWTRAD) Project.2 Professor Zhang Ping of Capital Normal University put forward to construct a historical geographic information system of the Silk Road based on modern computer technology and 3S (RS, GPS, GIS) technology, through which the Silk Road Historical Geographic Information Open Platform (SRHGIS) was built.3,4 Members of her Research Group of Transportation and Trade restored Lingzhou Road in the early Northern Song dynasty – they explored the application of relevant GIS technology and platform design, providing novel, viable thoughts for restoring ancient road networks.5,6 Based on their thoughts, we studied historical documents, archaeological materials and other research findings, through which to restore major traffic nodes and lines of the Silk Road in the Han dynasty, assisted by high-definition satellite images. In doing so, we aim to standardize historical traffic maps of the Silk Road and draw a difference from the descriptive, illustrative mode, in the hope of providing data support for other thematic studies relevant to the Silk Road.
This dataset consists of five parts of data, including textual description of traffic lines, attribute table for traffic nodes, data of traffic nodes in the Western Han dynasty, data of traffic nodes in the Eastern Han dynasty, and data of traffic lines in the Han dynasty. Traffic line refers to major traffic routes of the Silk Road. Traffic node refers to major places the Road passes through, including metropolises, prefecture seats, county seats, military sites, inhabited localities, staging posts, bridges, passes, and ferries. In line with the respective time ranges of the two Han dynasties, the data are divided into two periods: 202 B.C.E. – 9 C.E. (Western Han), 25 C.E. – 220 C.E. (Eastern Han). The general flowchart for data processing is shown in Figure 1.
2.1 Data sources
The research objects of this study mainly consisted of traffic nodes and traffic lines. Historical documents such as Shiji,7Hanshu ,8Houhanshu9 and Parthian Stations10 provided important sources to identify the traffic nodes’ historical names, supplemented by archaeological materials such as bamboo slips unearthed at Juyan and Dunhuang. To locate these nodes required us to correlate their historical names with corresponding modern designations. For this end, we referred to historical documents to locate each historical site to metropolis, prefecture seat, county seat, inhabited locality, bridges, and pass. These documents included Historical Atlas of China,11Cihai. Geographical Volume: Historical Geography ,12An Atlas of Chinese Cultural Relics,13–17 and the 3rd National Cultural Relics Survey. The historical sites extracted were then correlated with their modern designations by using the latest administrative data, including Administrative Divisions of the People’s Republic of China18 and the Administrative Division Network (http://www.xzqh.org/html/).19
The core of traffic route restoration was to determine the direction of the route and the regions it passed through. The route direction was determined based on the descriptive texts of historical documents and relevant archaeological discoveries, supplemented by research and investigation findings like A History of the Silk Road Transportation. After basic data for the traffic nodes and traffic lines were collected, we used EXCEL files to store attribute tables for the traffic nodes, and WORD documents to store textual description of the traffic routes.
2.2 Collection and processing methods
2.2.1 Spatial positioning by using Google Earth
According to the traffic node data we collected, we firstly tried to locate the nodes by using Google Earth. We identified three positioning types on the basis of different correlation statuses, including ruins with perceivable images, ruins without perceivable images, no ruins (sites would be located at village/township level in this case). The three types were marked differently in relevant data field. After the spatial positioning was completed, the attribute table for traffic nodes was updated
Traffic routes were drawn based on the traffic nodes located through the above-mentioned process. Three criteria were observed in the process of route drawing: firstly, the route should conform to textual description of the Silk Road in the Han dynasty; secondly, geographical distribution of the traffic nodes should be referred to; thirdly, landform and traffic feasibility should be considered as a way of validation. Each restored traffic route was supplemented by specific textual descriptions.
All data were exported and stored in kmz format.
2.2.2 Data processing in ArcGIS
ArcGIS 10.4 allows data conversion from kmz format to ArcGIS layers, so we used ArcGIS for data processing, including validating and sorting the data of traffic nodes and traffic lines, connecting the traffic node attributes by means of correlating layer files with EXCEL attribute tables based on a public field, and designing the appearance of the traffic data set.
A major difficulty in data collection and processing was to optimize the accuracy of the traffic route restoration, considering scarce records of the traffic nodes or routes along the Silk Road in this period. In view that both the landform and traffic routes along the Silk Road assumed certain degree of continuity and stability, we referred to more detailed records in Wei, Jin, Sui, or Tang dynasties for a more accurate restoration where sparse nodes were identified and/or existing scholarship did not provide sufficient information. We also used archaeological site data of the same period obtained from the Silk Road Cultural Communication Dataset on the Silk Road Historical Geography Information Open Platform.
This database has recorded 476 traffic nodes (250 in the Western Han dynasty, 226 in the Eastern Han dynasty), and 14 major traffic routes of the Silk Road in the Han dynasty. The traffic condition of the Silk Road during 25 C.E. – 220 C.E. is shown in Figure 2, where different traffic node types are denoted by different icons, and traffic routes are distinguished by color. Figure 3 shows diachronic changes of the middle section of the Silk Road from Western to Eastern Han dynasties in ArcGIS.
Table 1 exhibits the attribute table for the traffic node, Fangqu, in the Western Han dynasty. The table consists of 17 major fields, including object ID, postal code, Chinese name, English name, type, dynasty, country, province, prefecture, county, town, village, site, location, begin year, end year, and class.
Figure 4 shows textual description of the traffic line, the Hexi Corridor. Descriptions include general introduction, start time, major nodes and general direction of the line, which intend to present an overview of the Hexi Corridor.
To optimize restoration accuracy and ensure data quality, quality control was mainly performed from the aspects of data sources and processing methods in the restoration of traffic nodes and traffic routes. The data of traffic nodes and traffic lines were mainly extracted from historical documents, archaeological materials and research results, which were then rigorously validated against each other. At the technical level, we firstly identified the positioning accuracy of each traffic node according to respective positioning types. Nodes with ruins but without perceivable images were further investigated through field trips; nodes without ruins that were located to a village or town awaited confirmation through further achievements yet to come. Secondly, while based on literal description, the restoration of routes also referred to the location of traffic nodes and their modern ruins, the landform along the routes, and the Silk Road routes of later dynasties. Inspection was carried out after the dataset was established. The accuracy and reliability of the data were optimized mainly through the above-mentioned methods.
The traffic data of this dataset are in shapefiles, which can be read by ArcGIS or converted into kmz files for presentation in the 3D environment of Google Earth. This dataset can be used to study the variations of traffic routes and nodes during the Han dynasty, or to compare with traffic data of Wei, Jin, Sui, Tang, Song and Yuan dynasties. This dataset is part of the Silk Road Transportation and Trade Dataset and the Silk Road Historical Geographic Information Open Platform. The platform provides mainstream analytical tools, third-party services, and other basic data, where researches can use this dataset in combination with other thematic datasets (e.g., cultural communication data, nation and religion data) for spatio-temporal analysis to generate comprehensive solutions.
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1. Hu Y, Yan B & Zhang P. Traffic data of the Silk Road in the Han dynasty. Science Data Bank, DOI: 10.11922/sciencedb.611 (2018).
How to cite this article
Hu Y, Yan B & Zhang P. Traffic data of the Silk Road in the Han dynasty. China Scientific Data 3(2018). DOI: 10.11922/csdata.2018.0016.zh