Welcome to GEOGM0068: Geographic Information Retrieval and Integration#

This unit introduces core principles of geographic information retrieval and integration by covering basic concepts of spatial indexing and ranking, spatial query, spatial data conflation, as well as data-driven techniques for geo-text mining. Applications on urban structures and smart city will be applied to assist the learning process.

The unit aims to:

  • Teach you the fundamental concepts behind accessing and managing geographic information

  • Raise the awareness of how geographic (spatial) data is different from non-geographic (spatial) data with respect to retrieving and integrating information

  • Encourage you to design more efficient geographic (spatial) data management strategies in your projects

  • Facilitate you using state-of-the-art techniques to extract and integrate geographic information, especially from unstructured data (e.g., tweets, newspapers, etc.)

(Tentative) Syllabus#





Week 1 (Sept. 29)

* Unit overview and policy
* Basic Concepts

GIR: Chapter 1 and 2

No Lab

Week 2 (Oct. 6)

Spatial Database I

GIS: Chapter 2 and 3

Lab 1

Week 3 (Oct. 13)

Spatial Database II

GIS: Chapter 4

Lab 1

Week 4 (Oct. 20)

Georeferencing I

GIR: Chapter 4

Lab 2-1

Week 5 (Oct. 27)

Georeferencing II

GIR: Chapter 4

Lab 2-2

Week 6 (Nov. 3)


No Lab (Assessment 1 Due)

Week 7 (Nov. 10)

Spatial Indexing

GIR: Chapter 5

Lab 3-1

Week 8 (Nov. 17)

Spatial Ranking

GIR: Chapter 6

Lab 3-2

Week 9 (Nov. 24)

Geospatial Semantics I

Kuhn, 2005, Janowicz et al., 2015, Hu, 2017

Lab 4-1

Week 10 (Dec. 1)

Geospatial Semantics II

Same to Week 9

Lab 4-2

Week 11 (Dec. 8)

Ethics, Summary, and Career


No Lab

Week 12 (Dec. 15)


No Lab (Assessment 2 Due)

Data Science, Geographic (Spatial) Data Science, and this unit#

This unit is part of the curriculum of MSc in Geographic Data Science and Spatial Analytics taught at the School of Geographic Sciences, University of Bristol. The concept and theory teaching in this unit are closely related to Geographic (Spatial) Data Science and Data Science more broadly. See the image below for a better landscape of what this unit is about:

Geographic Data Science


Textbook and Reading:#

Learning Python:#

Office Hour#

Thursday, 11 am – 1 pm (or by appointment) @ Room 2.21N