SoilSpatvis: WEB Application for Geographical Data Visualization with R Language for Assessing Soil Pollution

Kaya E., Şentürk E., Erener A., Ozkul C., Akyol N. H.

SOIL & SEDIMENT CONTAMINATION, cilt.33, sa.7, ss.961-975, 2024 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 33 Sayı: 7
  • Basım Tarihi: 2024
  • Doi Numarası: 10.1080/15320383.2023.2282108
  • Dergi Adı: SOIL & SEDIMENT CONTAMINATION
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Agricultural & Environmental Science Database, Aquatic Science & Fisheries Abstracts (ASFA), BIOSIS, CAB Abstracts, Communication Abstracts, Compendex, Environment Index, INSPEC, Metadex, Pollution Abstracts, Veterinary Science Database, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.961-975
  • Anahtar Kelimeler: K-means, R programming, Spatial data analysis, WEB application
  • Kocaeli Üniversitesi Adresli: Evet

Özet

Recently, user-friendly internet-based applications are becoming popular in parallel with rapidly developing technology. With the growing information technology, computing power, and wealth of data available, analyzing spatial data has become an important subject for scientists who are interested in earth sciences. Thus, in terms of the software used for spatial data analysis, WEB-based applications have become the essential and increasingly popular for many applications. Here, a WEB-based software developed with R language is introduced. The software named as SoilSpatVis provides spatial data analysis and visualization. SoilSpatVis includes a desktop application that performs internet-based geographic data analysis using the K-means method, one of the machine learning algorithms. In the study, different soil pollution parameters collected from some sample sites in the border town of Kutahya, Turkey were used. These parameters were transferred into RStudio software which supports R language. The data were firstly clustered using the K-means algorithm and a visualization section was created for cluster analysis. The "Visualizations" page has been created which allows composing different graphs according to the desired parameter, and the "About" page provides information about the team that developed the application. Various geographic analysis tools are being added to the application and software codes are shared via GitHub.