Spatial data quality and uncertainty publication patterns and trends by bibliometric analysis

Abstract

Using the literature review and quantitative analysis, the research on the quality and uncertainty of spatial data have been compared and analysed according to years of publication, authors, document types, WoS categories, and countries. The paper portrayed the development in the field, studied the state and evolution of the most productive and influential journals, conferences, and research institutions. The results showed that remote sensing, computer science, and geography relate mostly to data imperfection and assessment of its uncertainty. This relation is clearly translated into the most productive journals, and conferences proceedings. The top-ranked countries in this field are United States, China, and the United Kingdom.

Intellectual Structure of CORINE Land Cover Research Applications in Web of Science: A Europe-Wide Review

“…This proves that a co-citation is a good indicator for the emergence of new scientific topics. Since its introduction, co-citation analysis has become a popular and widely used technique to examine the intellectual structure of research areas in Geospatial Information (GI) sciences, such as remote sensing [32][33][34][35], cartography [36], or spatial data quality [36]. The highly cited publications create the research front, while a cluster of cited documents is called an intellectual base [31].…”

“…GINI index Equation 5, a statistical measure of distribution introduced by the Italian statistician Corrado Gini in 1912, is most often used as a measure of economic inequality [41]. It is also possible to be used as an implementation in science evaluation [36]. GINI takes values from zero to one, with zero showing perfect equality and one representing perfect inequality.…”

Different Remote Sensing Data in Relative Biomass Determination and in Precision Fertilization Task Generation for Cereal Crops

“…The variable technologies for spatial data acquisition and processing, data management tools and different platforms lead to a large amount of uncertain spatial data in relation to the real world. Therefore spatial data quality and uncertainty are an increasingly important issue in geographical information science with over two thousand publications after 1990 (Bielecka and Burek, 2019). The method for applying spatial data quality and uncertainty studies for data has mostly been adapted for accessing the quality of volunteered geographic information (VGI) by for example Girres and Touya (2010), Goodchild and Li (2012), Antoniou and Skopeliti (2015) and Senarathe et al (2016).…”

GeoWebCln: An Intensive Cleaning Architecture for Geospatial Metadata

“…Zylshal (2020) performed visual and statistical analysis for performing topographic correction to reduce reflectance variability in mountainous regions. Bielecka and Burek (2019) compared and analysed research on the quality and uncertainty of spatial data. Data cleaning is performed on a data set using data cleaning functions.…”