This paper proposes a new approach to assess the positional accuracy of maps generated by overlaying multi_scale spatial data layers with different levels of positional accuracy.The existing techniques for assessing t...This paper proposes a new approach to assess the positional accuracy of maps generated by overlaying multi_scale spatial data layers with different levels of positional accuracy.The existing techniques for assessing the positional accuracy of point,line and polygon features is first examined.Then a taxonomy of graphic features on the derived maps is developed by analyzing the specific processes of overlay operations.Finally,a detailed description of the new approach is provided and the implementation of this new method in practical applications is described.展开更多
Purpose： The purpose of this study is to modernize previous work on science overlay maps by updating the underlying citation matrix, generating new clusters of scientific disciplines, enhancing visualizations, and pr...Purpose： The purpose of this study is to modernize previous work on science overlay maps by updating the underlying citation matrix, generating new clusters of scientific disciplines, enhancing visualizations, and providing more accessible means for analysts to generate their own maps Design/methodology/approach： We use the combined set of 2015 Journal Citation Reports for the Science Citation Index （n of journals = 8,778） and the Social Sciences Citation Index （n = 3,212） for a total of 11,365 journals. The set of Web of Science Categories in the Science Citation Index and the Social Sciences Citation Index increased from 224 in 2010 to 227 in 2015. Using dedicated software, a matrix of 227 × 227 cells is generated on the basis of whole-number citation counting. We normalize this matrix using the cosine function. We first develop the citing-side, cosine-normalized map using 2015 data and VOSviewer visualization with default parameter values. A routine for making overlays on the basis of the map （＂wc 15.exe＂） is available at http：//www.leydesdorff.net/wc 15/index.htm. Findings： Findings appear in the form of visuals throughout the manuscript. In Figures 1 9 we provide basemaps of science and science overlay maps for a number of companies, universities, and technologies. Research limitations： As Web of Science Categories change and/or are updated so is the need to update the routine we provide. Also, to apply the routine we provide users need access to the Web of Science. Practical implications： Visualization of science overlay maps is now more accurate and true to the 2015 Journal Citation Reports than was the case with the previous version of the routine advanced in our paper.Originality/value： The routine we advance allows users to visualize science overlay maps in VOSviewer using data from more recent Journal Citation Reports.展开更多
The technique of incremental updating,which can better guarantee the real-time situation of navigational map,is the developing orientation of navigational road network updating.The data center of vehicle navigation sy...The technique of incremental updating,which can better guarantee the real-time situation of navigational map,is the developing orientation of navigational road network updating.The data center of vehicle navigation system is in charge of storing incremental data,and the spatio-temporal data model for storing incremental data does affect the efficiency of the response of the data center to the requirements of incremental data from the vehicle terminal.According to the analysis on the shortcomings of several typical spatio-temporal data models used in the data center and based on the base map with overlay model,the reverse map with overlay model (RMOM) was put forward for the data center to make rapid response to incremental data request.RMOM supports the data center to store not only the current complete road network data,but also the overlays of incremental data from the time when each road network changed to the current moment.Moreover,the storage mechanism and index structure of the incremental data were designed,and the implementation algorithm of RMOM was developed.Taking navigational road network in Guangzhou City as an example,the simulation test was conducted to validate the efficiency of RMOM.Results show that the navigation database in the data center can response to the requirements of incremental data by only one query with RMOM,and costs less time.Compared with the base map with overlay model,the data center does not need to temporarily overlay incremental data with RMOM,so time-consuming of response is significantly reduced.RMOM greatly improves the efficiency of response and provides strong support for the real-time situation of navigational road network.展开更多
Big data is one of the current and future research frontiers.It has received international attention,and some countries have even upgraded big data research to a national strategy.Therefore,it is interesting to unders...Big data is one of the current and future research frontiers.It has received international attention,and some countries have even upgraded big data research to a national strategy.Therefore,it is interesting to understand the status quo of big data research and identify the status and contribution of a country.Our study is divided into two parts.The first part of this study combines core lexical query and expanded lexical query to get relatively integral publications’data sets on big data.Citation relationships and a maximum connected subgraph algorithm are used to clean and filter unrelated publications.Then the Leiden algorithm is selected to cluster the citation network for big data and VOSviewer is used to map the big data knowledge structure.In the second part of this study,we analyze China’s research contribution in terms of research output and highly-cited papers.In order to better show the distribution of big data research in China,we utilized science overlay mapping to visualize the status quo of China’s research in big data.Our study shows that China is one of the most important countries in big data research and the research covers almost all areas of big data.However,the research performance is relatively low.In terms of knowledge structure with science overlay mapping,China’s research mainly focuses on cloud computing,the Internet of Things(Io T),and social media.However,research topics with a greater rate of highly-cited papers are mainly found in cloud computing,big data medicine,and Industry 4.0.These topics are also the dominant areas of China’s big data research.展开更多
文摘This paper proposes a new approach to assess the positional accuracy of maps generated by overlaying multi_scale spatial data layers with different levels of positional accuracy.The existing techniques for assessing the positional accuracy of point,line and polygon features is first examined.Then a taxonomy of graphic features on the derived maps is developed by analyzing the specific processes of overlay operations.Finally,a detailed description of the new approach is provided and the implementation of this new method in practical applications is described.
文摘Purpose： The purpose of this study is to modernize previous work on science overlay maps by updating the underlying citation matrix, generating new clusters of scientific disciplines, enhancing visualizations, and providing more accessible means for analysts to generate their own maps Design/methodology/approach： We use the combined set of 2015 Journal Citation Reports for the Science Citation Index （n of journals = 8,778） and the Social Sciences Citation Index （n = 3,212） for a total of 11,365 journals. The set of Web of Science Categories in the Science Citation Index and the Social Sciences Citation Index increased from 224 in 2010 to 227 in 2015. Using dedicated software, a matrix of 227 × 227 cells is generated on the basis of whole-number citation counting. We normalize this matrix using the cosine function. We first develop the citing-side, cosine-normalized map using 2015 data and VOSviewer visualization with default parameter values. A routine for making overlays on the basis of the map （＂wc 15.exe＂） is available at http：//www.leydesdorff.net/wc 15/index.htm. Findings： Findings appear in the form of visuals throughout the manuscript. In Figures 1 9 we provide basemaps of science and science overlay maps for a number of companies, universities, and technologies. Research limitations： As Web of Science Categories change and/or are updated so is the need to update the routine we provide. Also, to apply the routine we provide users need access to the Web of Science. Practical implications： Visualization of science overlay maps is now more accurate and true to the 2015 Journal Citation Reports than was the case with the previous version of the routine advanced in our paper.Originality/value： The routine we advance allows users to visualize science overlay maps in VOSviewer using data from more recent Journal Citation Reports.
基金Under the auspices of National High Technology Research and Development Program of China (No.2007AA12Z242)
文摘The technique of incremental updating,which can better guarantee the real-time situation of navigational map,is the developing orientation of navigational road network updating.The data center of vehicle navigation system is in charge of storing incremental data,and the spatio-temporal data model for storing incremental data does affect the efficiency of the response of the data center to the requirements of incremental data from the vehicle terminal.According to the analysis on the shortcomings of several typical spatio-temporal data models used in the data center and based on the base map with overlay model,the reverse map with overlay model (RMOM) was put forward for the data center to make rapid response to incremental data request.RMOM supports the data center to store not only the current complete road network data,but also the overlays of incremental data from the time when each road network changed to the current moment.Moreover,the storage mechanism and index structure of the incremental data were designed,and the implementation algorithm of RMOM was developed.Taking navigational road network in Guangzhou City as an example,the simulation test was conducted to validate the efficiency of RMOM.Results show that the navigation database in the data center can response to the requirements of incremental data by only one query with RMOM,and costs less time.Compared with the base map with overlay model,the data center does not need to temporarily overlay incremental data with RMOM,so time-consuming of response is significantly reduced.RMOM greatly improves the efficiency of response and provides strong support for the real-time situation of navigational road network.
基金supported by the National Social Science Foundation of China(Grant No.19ZDA348)
文摘Big data is one of the current and future research frontiers.It has received international attention,and some countries have even upgraded big data research to a national strategy.Therefore,it is interesting to understand the status quo of big data research and identify the status and contribution of a country.Our study is divided into two parts.The first part of this study combines core lexical query and expanded lexical query to get relatively integral publications’data sets on big data.Citation relationships and a maximum connected subgraph algorithm are used to clean and filter unrelated publications.Then the Leiden algorithm is selected to cluster the citation network for big data and VOSviewer is used to map the big data knowledge structure.In the second part of this study,we analyze China’s research contribution in terms of research output and highly-cited papers.In order to better show the distribution of big data research in China,we utilized science overlay mapping to visualize the status quo of China’s research in big data.Our study shows that China is one of the most important countries in big data research and the research covers almost all areas of big data.However,the research performance is relatively low.In terms of knowledge structure with science overlay mapping,China’s research mainly focuses on cloud computing,the Internet of Things(Io T),and social media.However,research topics with a greater rate of highly-cited papers are mainly found in cloud computing,big data medicine,and Industry 4.0.These topics are also the dominant areas of China’s big data research.
