Spring 2027

GIS and Spatial Analysis

Principles and algorithms for spatial analysis in GIS, covering point, line, polygon processing, interpolation, smoothing, overlay, query, network, terrain analysis, and classification.

offering recorded4 credits
Recorded instructors · Spring 2027 A-Xing Zhu3.4/5

Summary

1 / 6

Past student reviews · Axing Zhu

Students had to read an online textbook, which one student believed the instructor wrote, to understand what was happening in class.

What you’ll be able to do

  • Identify the limitations of common spatial data analytical techniques.

  • Determine the usefulness of common spatial data analysis techniques.

  • Select and apply these techniques in respective applications.

  • Investigate the limitations of emerging spatial data analytical techniques to avoid misuse or abuse of these techniques.

  • Make connections to one's own research areas either as the basis for technological improvements or as effective applications of these technologies.

Source: UW Guide

Grade history

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average GPA
letter grades
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All recorded terms · compare terms & instructors

Prerequisites

Course map

GEOG/​CIV ENGR/​ENVIR ST 377 or graduate/professional standing

GEOG 579 used by

“Used by” includes alternatives; linked courses may have other requirements. This is a best-effort interpretation; check the catalog requirements above.

Prerequisite text tree

Professors

Spring 2027
Adjusted rating
RMP difficulty
captured reviews
About this rating

Raw average: 3.14/5 from 21 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: 3.0/5 raw quality · 3.3/5 difficulty · 4 reviews

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

Past student reviews · Axing Zhu

A-Xing Zhu emphasized the underlying spatial methods in his lectures, though students found the content boring and his broken English in readings and quizzes difficult to navigate. While one student felt the material was manageable if they understood the core concepts, another described the assessments as a nightmare due to language barriers.

Recent recorded grades — Fall 2023: 3.70 GPA, 95.0% A/AB (n=20 letter grades); Fall 2024: 3.49 GPA, 70.0% A/AB (n=40 letter grades); Fall 2025: 3.51 GPA, 56.8% A/AB (n=37 letter grades).

Historical instructors & teaching patterns

A-XING ZHU is recorded teaching in Fall 2006, Fall 2007, Fall 2011, Fall 2012, Spring 2014, Spring 2016, Fall 2016, Fall 2018, Fall 2019, Fall 2020, Fall 2021, Fall 2022, Fall 2023, Fall 2024, Fall 2025. 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 001Online Only0 / 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 · Axing Zhu

A-Xing Zhu taught this course with a strong focus on the underlying mechanics of spatial methods and GIS tools, demonstrating genuine passion for the subject matter.

Recent recorded grades — Spring 2025: 3.94 GPA, 100.0% A/AB (n=9 letter grades); Fall 2025: 3.57 GPA, 62.8% A/AB (n=43 letter grades); Spring 2026: 3.94 GPA, 100.0% A/AB (n=8 letter grades).

difficulty & workload

Past student reviews · Axing Zhu

Students had to read an online textbook, which one student believed the instructor wrote, to understand what was happening in class.

Past student reviews · Axing Zhu

One student found the quizzes difficult because they required choosing the most correct answer despite the broken English used in the questions.

Topics

  • Principles and algorithms for spatial analysis in geographic information systems.
  • Point, line, and polygon processing; interpolation; smoothing; spatial overlay and query; network analysis; terrain analysis; classification.

Skills

  • Identifying limitations, determining usefulness, and selecting/applying spatial data analytical techniques.
  • Theoretical and practical examination of analytical methods including point, line, polygon processing, interpolation, smoothing, overlay, query, network, terrain analysis, and classification.

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
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BC
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Grades over time

Through Spring 2026

More grade details Grade mix, volume & source data

Not enough comparable courses for Spring 2027 in UW–Madison.

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.

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Selected offering source records
GEOG 579 · Spring 2027

GIS and Spatial Analysis

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": "1274:416:008353",
    "course_id": "GEOG 579",
    "course_uid": "course_aea60f765c77d26cf83af000",
    "term_id": "1274",
    "source_course_id": "008353",
    "source_subject_id": "416",
    "title": "GIS and Spatial Analysis",
    "credits_min": 4,
    "credits_max": 4,
    "typically_offered": "Fall"
  }
]
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Student reviews

Original comments behind the course and instructor summaries.

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Madgrades

Grade history

Recorded grade distributions by term, section, and instructor.

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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": "540b4b19701c6327b1be8909665a2dc9fcf3914e658f44c3d1881c0521104b85",
  "requirements_status": "valid",
  "dataset_revision": "fc1811fa52e581ea57a35c0cf3343f8740a8ffbf",
  "observed_at": "2026-10-14 17:50:09.728220+00:00"
}