Spring 2027

Advanced Geocomputing and Geospatial Big Data Analytics

Introduction to theory, techniques, and analytical methods for geospatial big data, covering storage, processing, analysis, and visualization using advanced Python programming.

offering recorded4 credits
Recorded instructors · Spring 2027 Song Gao4.3/5

Summary

1 / 6

Past student reviews · Song Gao

One student described the class as one of the most challenging they had taken, though they felt more confident in their coding afterward.

What you’ll be able to do

  • Summarize the fundamental concepts of geospatial big data.

  • Describe how to develop computational models and analytical methods for geospatial big data.

  • Identify the challenges in storing, managing, processing, analyzing, visualizing, and verifying the quality of data.

  • Recall the major high-performance platforms for big data processing.

  • Use Python programming for (spatiotemporal) data analysis and machine-learning tasks.

  • Collaborate in teams and complete tasks under time pressure.

  • Lead efforts on teamwork and geospatial problem solving skill development.

  • Demonstrate higher-order synthesis and spatial analysis skills in spatial data science.

Source: UW Guide

Grade history

↗
average GPA
letter grades
A
AB
B
BC
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All recorded terms · compare terms & instructors

Prerequisites

Course map

GEOG 378,COMP SCI 220, or graduate/professional standing

    • GEOG 378
    • COMP SCI 220
    • graduate/professional standing
    take one
GEOG 573

This is a best-effort interpretation; check the catalog requirements above.

Prerequisite text tree
  • Any of
    • GEOG 378
    • COMP SCI 220
    • graduate/professional standing

Professors

Spring 2027

Song Gao

Adjusted rating
RMP difficulty
captured reviews
About this rating

Raw average: 4.71/5 from 28 quality ratings. The adjusted rating blends this with the UW review average (3.66/5), weighted as 20 additional ratings. Smaller samples stay closer to that average. Each captured review is counted once in the prior; this does not correct who chooses to leave a review.

For this course: 5.0/5 raw quality · 3.6/5 difficulty · 7 reviews

RMP profile ↗ · All captured review dates; profile matched by name.

Past student reviews · Song Gao

Professor Song Gao taught a challenging course using Python3 and Jupyter Notebooks, with students noting the final project helped test new skills. Reviews described the class as well-organized, combining technical geocomputing with real-world applications, and praised the instructor and TA for providing useful academic materials.

Recent recorded grades — Spring 2023: 3.87 GPA, 93.5% A/AB (n=31 letter grades); Spring 2025: 3.90 GPA, 93.9% A/AB (n=49 letter grades); Spring 2026: 3.91 GPA, 100.0% A/AB (n=51 letter grades).

Historical instructors & teaching patterns

Past student reviews · Yuhao Kang

Past students noted that TA Yuhao Kang was patient and highly skilled in geospatial analytics, providing helpful support both in and after class. The reviewer found the course material accessible and recommended taking the class again.

SONG GAO is recorded teaching in Spring 2020, Spring 2022, Spring 2023, Spring 2025, Spring 2026. Recorded history may be incomplete and does not establish a future schedule.

Recorded history may be incomplete and does not establish a future schedule.

Calendar & sections

Spring 2027

Schedule loads here as you scroll.

SectionModeEnrolled / capacityWaitlist
LEC 001Classroom Instruction0 / 500
LAB 301Classroom Instruction0 / 250
LAB 302Classroom Instruction0 / 250

Times are Central. Select a meeting for details; export includes recorded dates for the selected sections. Enrollment reflects scan time.

Meeting source records
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Student experience

the class

Past student reviews · Song Gao

Students found the course a valuable way to learn GIS and geospatial big data theories and techniques through useful lectures and labs.

Recent recorded grades — Spring 2023: 3.87 GPA, 93.5% A/AB (n=31 letter grades); Spring 2025: 3.90 GPA, 93.9% A/AB (n=49 letter grades); Spring 2026: 3.91 GPA, 100.0% A/AB (n=51 letter grades).

difficulty & workload

Past student reviews · Song Gao

One student described the class as one of the most challenging they had taken, though they felt more confident in their coding afterward.

Past student reviews · Song Gao

A student appreciated the use of Python3 and Jupyter Notebooks, noting that the course improved their ability to use Pandas and GeoPandas for spatial analysis.

Topics

  • Geospatial big data concepts
  • AI and machine learning for geospatial problems
  • High-performance platforms for big data processing
  • Spatial data science and spatial analysis

Skills

  • AI and machine learning methods for geospatial problems
  • Storing, processing, analyzing, and visualizing geospatial big data with Python
  • Developing computational models and analytical methods

Grades

Latest available · Spring 2026— not enough history to project Spring 2027.

average GPA
A / AB grades
letter grades
Instructor

Grade distribution · % of letter grades

A
AB
B
BC
C
D
F

Grades over time

Through Spring 2026

More grade details Grade mix, volume & source data

Where this course fits relative to

Latest available grades · Spring 2026 · all course levels

GPA

Higher than % of other courses in this group.

Course GPAs · red marks this course’s range

letter grades

More recorded grades than % of other courses in this group.

Typical course in this group: letter grades.

About this comparison

1280 courses over the same term, each with at least 30 recorded letter grades. Cross-listed courses count once. GPA is not a measure of difficulty or teaching quality. The typical course is the median by recorded grade count; tied values are not counted as lower. Grade counts describe course scale, not unique students or typical section size.

Descriptions compare GPA with this group’s average: at least 0.20 higher or lower; otherwise close to average. Section size uses median recorded enrollment: small up to 30, mid-sized 31–99, large 100+. Lectures and discussion/lab sections are described separately.

Sources & history

UW–Madison

Catalog & offerings

Descriptions, prerequisites, and recorded course offerings.

Catalog observation history

Observations at scan time; dates do not imply when a catalog change took effect.

Download part 1
Selected offering source records
GEOG 573 · Spring 2027

Advanced Geocomputing and Geospatial Big Data Analytics

Recorded 2026-10-14
Raw records 
[
  {
    "run_id": "20261014T203208-0851c9a1",
    "semester": "1274",
    "observed_at": "2026-10-14 17:50:09.728220+00:00",
    "offering_id": "1274:416:025590",
    "course_id": "GEOG 573",
    "course_uid": "course_383500a29b0ac797184420a8",
    "term_id": "1274",
    "source_course_id": "025590",
    "source_subject_id": "416",
    "title": "Advanced Geocomputing and Geospatial Big Data Analytics",
    "credits_min": 4,
    "credits_max": 4,
    "typically_offered": "Not Applicable"
  }
]
Rate My Professors

Student reviews

Original comments behind the course and instructor summaries.

Read original reviews
Download part 1
Madgrades

Grade history

Recorded grade distributions by term, section, and instructor.

Explore recorded grades
Dataset & model

Recorded 2026-10-14

View the dataset on Hugging Face
Model outputs & technical records
Qwen/Qwen3.8-27B
LLM outputs across runs
Download part 1
Full model traces

Recorded model configuration, reasoning, and tool conversations.

Download part 1
Model & dataset provenance 
{
  "model": "Qwen/Qwen3.8-27B",
  "model_revision": "1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0",
  "task_version": "17",
  "output_id": "c0edbe55c98425dc7cbaee5282f4c23b2da0302ab6f239b66859bd422bdff4aa",
  "requirements_status": "needs_review",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}