Extraction and Dynamic Spatial-Temporal Changes of Grassland Deterioration Research Hot Regions in China

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1 July, 2017 Journal of Resources and Ecology Vol. 8 No.4 J. Resour. Ecol (4) DOI: /j.issn x Extraction and Dynamic Spatial-Temporal Changes of Grassland Deterioration Research Hot Regions in China HU Yunfeng 1,*, HAN Yueqi 1, ZHANG Yunzhi 1, ZHUANG Yuan 2 1. Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing , China; 2. College of Public Administration, Nanjing Agricultural University, Nanjing , China Abstract: The authors use a web crawler to retrieve all periodical articles from CNKI between the 1950s and 2016 and then parse the abstracts of articles about grassland deterioration by word segmentation, location matching and other methods. The authors also construct a research hot regions extraction model of grassland deterioration in China based on a comprehensive research hot regions index of toponyms and then analyze the spatial pattern and dynamic change in research hot regions of grassland deterioration in China. The research shows the following: (1) The spatial heterogeneity of grassland deterioration in China can be effectively described by a model of grassland deterioration based on the comprehensive research hot regions index. (2) The research hot regions of grassland deterioration are mainly distributed in most regions of Inner Mongolia, Xinjiang, Qinghai, Tibet, Gansu and other provinces. The northeastern region of Inner Mongolia (such as Hulunbeier) and the eastern region of Inner Mongolia (such as Xilin Gol, Chifeng and Wulanchabu) are significant hot regions in the study of grassland deterioration. (3) The number of high research hot regions increases from 81 in the 1950s to 99 in the 2000s; the area increases from million km 2 to million km 2. The degree of hot for grassland deterioration research in 197 counties showed an upward trend. This paper also discusses the relationship between the region of research hot regions and the region of grassland deterioration and then indicates the differences between them in time matching, space matching and concept matching. Key words: knowledge engine; hot regions analysis; grassland deterioration; region extraction; spatial distribution; dynamic change 1 Introduction Grassland deterioration is the process of retrogressive succession and declining productivity in grassland ecosystems under climatic conditions and unreasonable usage patterns (YUE et al., 2007; ZHANG et al., 2006). According to the China Ecological Environment Bulletin, during , more than 90% of the natural grasslands in China were deteriorating at different levels, and the deterioration was accelerating at a rate of 2% annually in the available grassland regions. For a long time, a wide range of grassland deterioration not only affected the quality and function of the grassland itself but also led to deterioration of the ecological environment, threatening the survival and development of humankind (GAO et al., 2007; Batunacun et al., 2012). Therefore, it is of great significance to sustainable development of the regional ecological system and the economic society to accurately master grassland deterioration information and perform targeted grassland deterioration research and projects. Before the 1970s, ground investigation was the basic way to comprehend the spatial distribution of grassland deterioration. Since the 1970s, satellite remote Received: Accepted: Foundation: National Key Research and Development Plan Program (2016YFC , 2016YFB ) and Key Project of High Resolution Earth Observation System (00-Y30B /16) *Corresponding author: HU Yunfeng, huyf@lreis.ac.cn Citation: HU Yunfeng, HAN Yueqi, ZHANG Yunzhi, et al., Extraction and Dynamic Spatial-Temporal Changes of Grassland Deterioration Research Hot Regions in China. Journal of Resources and Ecology, 8(4):

2 HU Yunfeng, et al.: Extraction and Dynamic Spatial-Temporal Changes in Grassland Deterioration Research Hot Regions in China 353 sensing and geographic information system (GIS) technology have been the main technical supports for locating and recognizing gradual regional grassland deterioration. Through these studies, numerous public and internal documents have been produced, including maps, atlases, research reports, research papers, statistical yearbooks, government gazettes, etc. These documents provide a database for researchers to master the spatial distribution and dynamic change in grassland deterioration. CNKI is the world's largest Chinese journal network knowledge search engine and database. Traditionally, the way to get information in CNKI or other knowledge engines (such as WOS, EI Village) is to retrieve and download papers by specialized terms in the title, abstract, keywords, or full text. After that, researchers read all the papers to get the data and information for their research area. The knowledge acquisition process, including literature searching, literature reading, and information judgment is natural and logical. The advantages of this process are that the retrieved publications are highly relevant to the research subject and that they contain the most accurate information, whereas the disadvantage is that it is difficult to consider the massive and growing literature and data. Therefore, using the web crawler, machine learning, data modeling technology and performing comprehensive analysis combined with professional background knowledge is the best way to obtain information, build a knowledge map and mine new information from the knowledge engine in the era of big data. There are some mature technologies, methods and models for extracting and analyzing big data based on Internet web information. For example, we can crawl the information on the World Wide Web with the web crawler, which is constructed by Python or Java (AGRE G H and MAHAJAN N V, 2015; ALZOABI U S et al., 2013; YE et al., 2002; YU and LIU, 2015; ZHANG et al., 2011). The text that is fetched by web crawler also needs to be processed by data cleansing, word segmentation (LI et al., 2015; MO et al., 2013), natural language understanding (LIANG and GU, 2015), etc. However, the problem is how to combine information technology and the professional research task to construct the model and analyze the data, for example, how to transform a traditional statistical model based on regular or small sampling when big data are used in geostatistics or how to achieve address recognition (LIAO et al., 2008) and matching names with a combination of big data and spatial information science (CHENG and LU, 2014). Therefore, constructing an appropriate hot regions identification and location model as well as analyzing the process of spatial distribution patterns and dynamic change are important parts of a knowledge engine big data application for the recognition and spatial-temporal dynamic analysis of grassland deterioration research hot regions. This paper, relying on the knowledge engine, constructs a research hot regions recognition model of grassland deterioration and compiles a Chinese regional spatial distribution pattern database of grassland deterioration research hot regions using big data technology including term structure, Chinese words segmentation, and toponym matching (ZHANG et al., 2008; XIAO, 2014; LIAO and WANG, 2009). Then, the paper analyzes the spatial distribution pattern and dynamic change trend in grassland deterioration research hot regions from the 1950s to Methods and data 2.1 Data sources CNKI (China National Knowledge Infrastructure) has collected a wide range of resources such as journals, doctoral dissertations, master's theses, statistics, newspapers, conference papers, yearbooks, reference works, encyclopedias, patents, standards, scientific and technological achievements, laws, etc. (CAJ: China Academic Journals full-text database). On February 8, 2017, CNKI contained kinds of articles, periods of all journals, and articles. The basic geographic data for toponym association and spatial mapping is from the geographical distribution map of China's county-level administrative divisions in 2010, which is provided by the China Cartographic Publishing House. We revised the data for a little toponym that is not clear with the national 1:25 million basic geographic databases. 2.2 Technical routes The process of grassland deterioration includes grassland degradation, grassland desertification and grassland salinization (YUCHUN Y A N and HAIPING T, 2008), so this paper divides the data search into three sub-items (Fig. 1) and uses the subject-verb structure to describe the assemblage of keywords. The authors compile a web crawler program with JAVA for automatically crawling text from the CAJ database based on the keywords assembled above. Then, documents related to grassland deterioration are obtained. Based on all the documents, the messages, including title, abstract, keywords, and the authors names are automatically downloaded and stored to the local database. On this basis, the paper uses a model of Ansj to segment the abstract and the model of HanLP (hankcs, 2016) to identify the toponyms by the results of word segmentation. Then, the toponyms are matched to the county name of the national fundamental geographic database with the KMP (Knuth-Morris-Pratt) (LI et al., 2016; WANG and LIU, 2006) algorithm. Finally, the number of toponyms occurring in the all texts is counted. In the process of matching the toponyms, the key technical difficulty is how to match multi-level, multi-meaning, non-standardized toponyms to the name of the county that has a standard and unique meaning and how to scientifically and reasonably value each county unit based on its fre-

3 354 Journal of Resources and Ecology Vol. 8 No. 4, 2017 Fig.1 The assemblage of keywords associated with grassland deterioration quency of appearance in a research paper. For this purpose, we build a gradual coverage, cumulative statistics place name matching and statistical process to accurately identify and count different levels of names or different names for the same region (BEALL J, 2010; LI et al., 2011; TANG et al., 2010; WANG et al., 2013). 2.3 Method for hot regions extraction After getting the number of toponyms ( research absolute hot regions value ) in all publications, the general evaluation approach is that a region cited more frequently will be a more important research site for grassland deterioration. This approach is feasible in a small study area, but China is a large and diverse country, and there is a serious "information gap" between different regions. Therefore, it will bring out cognitive bias if we directly use the absolute hot regions value of the toponym. For example, in a region that has a higher scientific and technological level and negligible deterioration, such as eastern China, there are numerous research staffs, projects and publications. On the other hand, in the vast western region of China where the level of economic and social development is relatively limited and grassland deterioration is extremely severe, because of low investment and fewer research staffs and projects, the number of papers related to grassland deterioration is less than that of the developed regions. In addition to the analysis based on the absolute hot regions value of the toponym, another approach is to use the ratio of the frequency of the county unit in grassland deterioration research and the frequency of the county unit in the total number of scientific research papers in all fields (called the research relative hot regions value ). A region where the ratio of grassland deterioration publications to all publications is greater will have a more severe problem with grassland deterioration. This approach eliminates the bias caused by the "information gap" in the method by the absolute hot regions value. However, it also has the problems of lacking sensitivity and distinction. Therefore, this paper constructs a comprehensive model that considers the absolute hot regions index and the relative hot regions index, which is called the comprehensive hot regions index of grassland deterioration research : Ngd Q Ngd Nall In the above formula, Q is a comprehensive hot regions index of grassland deterioration research. N gd is the total number of times a region occurred in theme retrieval, which is called the absolute hot regions index of grassland deterioration research. N all is the number of counties in all publications. is the relative hot regions index of grass- Ngd Nall land deterioration research. To facilitate comparative analysis of the time series, Q can be standardized as follows: * Q min Q Q max Q min Q Q * is a standardized comprehensive hot regions index of grassland deterioration research. The value of Q * is from 0 to 1; max(q) is the maximum value of Q, and min(q) is the minimum value of Q. 3 Analysis and discussion 3.1 Spatial distribution of grassland deterioration research s hot regions Based on the map using the absolute hot regions index of grassland deterioration research, the absolute hot regions index reflects that research on grassland deterioration is mainly distributed in the western and northern regions of China (Fig. 2). Especially in Inner Mongolia, Xinjiang, Qinghai, Gansu and other provinces, grassland deterioration research is extremely rich. However, grassland deterioration research does not have hot regions in most of Tibet (except the Lhasa periphery), western Sichuan and western Yunnan. In eastern China, such as around Beijing, Changchun and a few cities, the results of grassland deterioration research are

4 HU Yunfeng, et al.: Extraction and Dynamic Spatial-Temporal Changes in Grassland Deterioration Research Hot Regions in China 355 Fig.2 Spatial distribution of the absolute hot regions index of grassland deterioration research relatively rich. Obviously, such cognition of spatial patterns is somewhat different from our traditional understanding of the spatial distribution of grassland and grassland deterioration. Based on the map using the relative hot regions index of grassland deterioration research, the relative hot regions index reflects that the research on grassland deterioration is mainly distributed in the western and northern regions of China (Fig. 3). The degree of hot for grassland deterioration research is highest in Inner Mongolia and Qinghai, followed by in Xinjiang, Tibet, Gansu and Ningxia province, followed by most regions of central China where grassland deterioration research has not become a hotspot. Cognition of spatial patterns based on the relative hot regions index of grassland deterioration research is similar to our traditional understanding of the spatial distribution of grassland and grassland deterioration. However, the main problem is that the relative hot regions index of grassland deterioration research cannot discriminate between Inner Mongolia, Xinjiang, and Qinghai at the province level. It also cannot form differences at the county level within a province. Based on the map using the standardized comprehensive hot regions index of grassland deterioration research, the Q* index not only has good division at the provincial scale but also at the county and city scale (Fig. 4). In most of eastern China, deterioration research is not obvious, and hot regions have not been formed. In northern and western China, such as most regions of Inner Mongolia, Xinjiang, Qinghai, Tibet, Gansu and other provinces, some regions of Shaanxi, Shanxi, Sichuan and other provinces, grassland deterioration has gradually become an important ecological problem, and grassland deterioration research has gradually become a hotspot. In particular, significant hot regions of grassland deterioration research are formed in the northeast of Inner Mongolia (Hulun Buir) and the middle east of Inner Mongolia (Chifeng, Wulanchabu, Xilin Gol). In addition, the other regions of Inner Mongolia, most regions of Qinghai, Fig.3 Spatial distribution of the relative hot regions index of grassland deterioration research Fig.4 Spatial distribution of the standardized comprehensive hot regions index (Q*) of grassland deterioration research central Gansu and western Xinjiang are also hot regions of grassland deterioration research. 3.2 Dynamic change in grassland deterioration research As shown in Fig. 5, during , the number of county-level units where hot regions of grassland deterioration are appreciable (Q*>0.61) is 81. During , the number increased to 99 and remained there during By dynamic change in the regional area, the total area of county levels where hot regions of grassland deterioration are appreciable is million km 2 during During , the number increased to million km 2, where it remained after According to a further study of the standardized comprehensive hot regions index of grassland deterioration research, the degree of grassland deterioration research heat in 197 counties showed an upward trend. The degree of grassland

5 356 Journal of Resources and Ecology Vol. 8 No. 4, 2017 Fig.5 Dynamic change in spatial distribution of grassland deterioration research since 1950s (a: Q* of 1950s-1980; b: Q* of ; c: Q* of ; d: ratio of Q* change during deterioration research heat rises fastest in all counties and cities throughout Qinghai, of the Ali area or along the Nagqu-Lhasa-Shigatse line in Tibet, relevant to Hunshandake sand in eastern Inner Mongolia. On the other hand, the degree of grassland deterioration research heat in 57 counties showed a downward trend, mainly in the counties and cities of the Hexi Corridor Area of Gansu and a few counties and cities in northern Xinjiang. 3.3 Relationship between grassland deterioration and grassland deterioration research There is a close relationship between the grassland deterioration process and grassland deterioration research. Severe and extensive grassland deterioration always brings more research of grassland deterioration. Similarly, significant hotspots of grassland deterioration research can also reflect the severity of grassland deterioration and the urgency of mitigating grassland deterioration in a certain region. However, there are differences in time matching, spatial matching and conceptual matching between the grassland deterioration process or regions and grassland deterioration research on process or regions. In the case of time matching, real-time grassland deterioration is usually earlier than the publication time of projects and papers. To obtain the accurate time of the grassland deterioration process, we cannot only rely on the publication time but must also analyze the abstract and full text of the paper, and by improving our model with NLP at the sentence and paragraph level, we can then obtain accurate and reliable time information on grassland deterioration. In the case of spatial matching, the real grassland deterioration process is based on natural geographical units that are a blocky distribution, whereas grassland deterioration studies are usually based on administrative regions. The

6 HU Yunfeng, et al.: Extraction and Dynamic Spatial-Temporal Changes in Grassland Deterioration Research Hot Regions in China 357 spatial scale and spatial patch type of the target object are inconsistent, which will cause great deviation in spatial location and regional statistics (LI S et al., 2012). To resolve this problem, a more accurate geospatial database (such as a more accurate spatial database including the name of the village, town, and county) is needed. A more fundamental solution is to develop methods related to document maps, data searches, and information extraction. In the case of conceptual matching, the comprehensive hot regions index (Q*) of grassland deterioration research only indicates the number of studies. The index is not directly related to the intensity and size of grassland deterioration. In a certain region, the strength and size of grassland deterioration is considered during grassland deterioration projects by the government and researchers. However, the ecological importance and regional development goals of a region may have greater influence on a research project and the publication of a paper. The differences in time, space and concept between grassland deterioration and grassland deterioration research have great significance to the comparative analysis of the relationship between true grassland deterioration and society's response to it. Specifically, based on the comparison of the regional spatial distribution of grassland deterioration directly obtained by satellite remote sensing and ground survey techniques and the regional spatial distribution of grassland deterioration research obtained by retrieval and analysis in a knowledge engine, we can determine regions of grassland deterioration that have received attention, research and governance, or regions with grassland deterioration that did not get enough attention, research and countermeasures. Because of the length of this paper, this task is not performed. 4 Conclusions This paper retrieves Chinese articles relevant to grassland deterioration from the 1950s to 2016 in the CNKI knowledge engine, constructs a model for extracting hot regions of grassland deterioration research in China, and then analyzes the spatial distribution and dynamic change in grassland deterioration research from the 1950s to 2016 and discusses the relationship between grassland deterioration and grassland deterioration research. The main results are as follow: (1) The spatial distribution and heterogeneity of grassland deterioration research hot regions cannot be reflected only by an absolute or relative hot regions index of grassland deterioration research. However, a comprehensive hot regions index (Q and Q*) can reflect the spatial distribution of grassland deterioration research hot regions. (2) The hot regions of grassland deterioration research mainly distribute in northern and western China, namely, in most regions of Inner Mongolia, Xinjiang, Qinghai, Tibet, Gansu and other provinces, and some regions of Shaanxi, Shanxi, Sichuan and other provinces. Northeastern Inner Mongolia (Hulun Buir) and middle eastern Inner Mongolia (Xilingol, Chifeng, Wulanchabu), are significant hot regions of grassland deterioration research. (3) Since the 1950s, the number of county-level units where hot regions of grassland deterioration are appreciable (Q*>0.61) increased to 99 from 81, and the total area increased to million km 2 from million km 2. The degree of grassland deterioration research heat in 197 counties showed an upward trend. The degree of grassland deterioration research heat rises fastest in all counties and cities throughout Qinghai, of the Ali area or along the Nagqu- Lhasa-Shigatse line in Tibet, relevant to Hunshandake sand in eastern Inner Mongolia. 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