Course Description
Different geospatial semantics, text mining, and natural language processing techniques and how they can be applied to different geo-text data. Topics include place name recognition, place name disambiguation, gazetteer and geospatial knowledge graphs, TF-IDF, topic modeling, sentiment analysis, word embedding, and deep learning-based language models.
Additional Requirements for Graduate Students:
Graduate students will be assigned more advanced reading and presentation assignments, more complex analytical and writing assignments, as well as more advanced final research projects.
Athena Title
Geospatial Semantics
Undergraduate Prerequisite
Permission of department
Graduate Prerequisite
Permission of department
Undergraduate Pre or Corequisite
CSCI 1301-1301L or CSCI 1301E or CSCI 1360 or CSCI 1360E or GEOG 4590/6590-4590L/6590L or GEOG 4590E/6590E or GEOG 4591/6591
Grading System
A - F (Traditional)
Course Objectives
1) Students will develop an integrated understanding of various geospatial semantics, natural language processing, and text mining techniques. 2) Students will gain practical experiences by collecting and analyzing geo-text data (e.g., geo-tagged tweets, Yelp Reviews, neighborhood reviews) by using different geospatial text mining methods. 3) Students will be able to integrate these geospatial semantics techniques into real-world GeoAI projects.
Topical Outline
1) The emergence of geo-text data 2) An overview of geospatial semantics and text mining 3) Place name recognition and disambiguation 4) Gazetteer and geospatial knowledge graph 5) Geospatial clustering and segmentation 6) TF-IDF (term frequency-inverse document frequency) 7) Topic modeling 8) Sentiment analysis 9) Word embedding (e.g., Word2Vec) and large language model (e.g., Transformer, BERT, GPT-3)
Syllabus