Treffer: Python code used in the paper Sahana et al.: To review the transboundary river research through spaCy language model, Named Entity Recognition (NER) Model , Spark natural language processing (NLP), scikit-learning, Jaro-Winkler distances models ; Python Codes for spaCy language model ; Python Codes for Named Entity Recognition (NER) Model ; Spark natural language processing (NLP) ; The multi-label text classification model ; Jaro-Winkler distances models

This code file is a comprehensive toolkit that guides users through a range of geospatial and natural language processing (NLP) tasks, making it suitable for applications in environmental science, data analysis, and more. On the geospatial side, it includes functions for data loading, preparation, spatial analysis, and visualization. Users can import geographic datasets from various formats, perform spatial operations like overlays and intersections, and manipulate raster and vector data for environmental modeling. The visualization features allow for creating maps and charts, effectively communicating spatial insights. The code also integrates powerful NLP tools, enhancing its utility in analyzing textual data alongside geospatial data. It incorporates the spaCy language model for processing text, allowing users to perform tasks such as tokenization and part-of-speech tagging. Named Entity Recognition (NER) is available to extract important entities from text, which can be helpful for identifying place names, organizations, or other relevant terms in textual datasets. Additionally, Spark NLP is employed for handling large volumes of text data efficiently, ideal for projects requiring big data processing. For classification tasks, a multi-label text classification model (using scikit-learn) enables the tagging of text data with multiple labels, allowing for nuanced text categorization. The code also includes the Jaro-Winkler distance model, a method useful for fuzzy string matching, making it easier to match or deduplicate text data with minor variations. Together, these NLP tools complement the geospatial functions by enabling a seamless integration of location-based and textual insights, expanding the potential applications of this code for interdisciplinary data analysis projects. Error handling and extensive documentation make the code accessible and user-friendly for various skill levels.