Fall 2026

Geospatial Database Design and Development

Teaches design and implementation of spatial databases using SQL and NoSQL for spatial big data.

no offering record for this termCredits unavailable
Recorded instructors · Fall 2026 No instructors listed

Summary

1 / 5

Historical reviews of Qunying Huang: Exams are described as very easy, and labs are well-organized, though one reviewer felt the course lacked sufficient learning depth for its credit value.

Grade history

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

Prerequisites

Course map

GEOG 170,GEOG 370,ENVIR ST/​CIV ENGR/​GEOG 377 or graduate/professional standing

GEOG 574 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

Fall 2026

No instructors recorded for this selection.

Historical instructors & teaching patterns

Historical reviews of Qunying Huang: Qunying Huang is consistently described as approachable, supportive, and passionate about GIS and database development. Reviewers highlight her responsiveness to emails and willingness to meet outside office hours. However, some students note that her lectures can feel disorganized and that the course content may not be sufficiently rigorous for a three-credit class.

QUNYING HUANG is recorded teaching in Fall 2017, Fall 2018, Fall 2020, Fall 2022, Fall 2023, Fall 2024, 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

Fall 2026

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SectionModeEnrolled / capacityWaitlist

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

Meeting source records

No records available.

Student experience

the class

Historical reviews of Qunying Huang: Qunying Huang teaches an accessible advanced computing class with supportive office hours and easy exams, though some students find the lectures disorganized and the learning depth insufficient.

Recent recorded grades — Fall 2024: 3.81 GPA, 88.6% A/AB (n=44 letter grades); Spring 2025: 4.00 GPA, 100.0% A/AB (n=12 letter grades); Spring 2026: 3.86 GPA, 91.4% A/AB (n=35 letter grades).

difficulty & workload

Historical reviews of Qunying Huang: Exams are described as very easy, and labs are well-organized, though one reviewer felt the course lacked sufficient learning depth for its credit value.

Historical reviews of Qunying Huang: Students appreciate Huang's approachability, passion, and willingness to meet outside office hours, though some wish for more code examples and find the lecture structure disorganized.

Topics

  • Conceptual spatial database models.
  • Spatial data management systems.
  • SQL database language.
  • NoSQL databases.
  • Spatial big data mining.

Skills

  • Design conceptual spatial database models.
  • Implement spatial databases in specific DBMS.
  • Manage and mine spatial big data.

Grades

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

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

1283 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.

Selected offering source records

No offering records for the selected term.

Raw records
[]
Rate My Professors

Student reviews

Original comments behind the course and instructor summaries.

Read original reviews
Madgrades

Grade history

Recorded grade distributions by term, section, and instructor.

Explore recorded grades
Model outputs & technical records
nvidia/Qwen3.6-35B-A3B-NVFP4
LLM outputs across runs
Full model traces

Recorded model configuration, reasoning, and tool conversations.

Model & dataset provenance
{
  "model": "nvidia/Qwen3.6-35B-A3B-NVFP4",
  "model_revision": "1355db6a052410cfd62085d94b58866fd0f2c3c5",
  "task_version": "14",
  "output_id": "0dd1f284dea3361ee6d023137fa0fc06e89c2100e20a141f4665037e2e3e6a1e",
  "requirements_status": "valid",
  "dataset_revision": "e243353dcb7d79b7247ced91d69443ef4c2a6349",
  "observed_at": "2026-09-07 15:55:43.033547+00:00"
}