🧠
Model

ppde642

by gboeing gh-model--gboeing--ppde642
Nexus Index
48.4 Top 100%
S: Semantic 50
A: Authority 0
P: Popularity 65
R: Recency 94
Q: Quality 70
Tech Context
Vital Performance
0 DL / 30D
0.0%
Audited 48.4 FNI Score
Tiny - Params
- Context
0 Downloads
Commercial MIT License
Model Information Summary
Entity Passport
Registry ID gh-model--gboeing--ppde642
License MIT
Provider github
📜

Cite this model

Academic & Research Attribution

BibTeX
@misc{gh_model__gboeing__ppde642,
  author = {gboeing},
  title = {ppde642 Model},
  year = {2026},
  howpublished = {\url{https://github.com/gboeing/ppde642}},
  note = {Accessed via Free2AITools Knowledge Fortress}
}
APA Style
gboeing. (2026). ppde642 [Model]. Free2AITools. https://github.com/gboeing/ppde642

đŸ”ŦTechnical Deep Dive

Full Specifications [+]

Quick Commands

🐙 Git Clone
git clone https://github.com/gboeing/ppde642

âš–ī¸ Nexus Index V2.0

48.4
TOP 100% SYSTEM IMPACT
Semantic (S) 50
Authority (A) 0
Popularity (P) 65
Recency (R) 94
Quality (Q) 70

đŸ’Ŧ Index Insight

FNI V2.0 for ppde642: Semantic (S:50), Authority (A:0), Popularity (P:65), Recency (R:94), Quality (Q:70).

Free2AITools Nexus Index

Verification Authority

Unbiased Data Node Refresh: VFS Live
---

🚀 What's Next?

Technical Deep Dive

Binder Build Status

PPDE642: Advanced Urban Analytics

This is the second part of a two-course series on urban data science that I teach at the University of Southern California's Department of Urban Planning and Spatial Analysis.

This course series takes a computational social science approach to working with urban data. It uses Python and Jupyter notebooks to introduce coding and statistical methods that students can reproduce and experiment with in the cloud. The series as a whole presumes no prior knowledge as it introduces coding, stats, spatial analysis, and applied machine learning from the ground up, but PPDE642 assumes you have completed PPD534 or its equivalent.

Urban Data Science course series

PPD534: Data, Evidence, and Communication for the Public Good

The first course in the series, PPD534, starts with the basics of coding with Python, then on to data loading and analysis, then on to descriptive statistics, then inference and the scientific method, and finally a critical assessment of smart cities and urban informatics.

PPD534's lecture materials are available on GitHub and interactively on Binder.

PPDE642: Advanced Urban Analytics

The second course, PPDE642, assumes you have completed PPD534 (or its equivalent) and builds on its topics. It introduces spatial analysis, network analysis, spatial models, and applied machine learning. It also digs deeper into the tools and workflows of urban data science in both research and practice.

PPDE642's lecture materials are available in this repo and interactively on Binder.

Not a USC student?

Did you discover this course on GitHub? Come study with us: consider applying to the urban planning master's or PhD programs at USC.

Are you interested in data science and spatial analysis to improve urban transportation around the world, critically interrogate how big data reshapes housing affordability, or leverage technology for better city planning? We seek students from all backgrounds. If you're an activist or urbanist with no tech experience, we will teach you data/tech skills to effectively apply your knowledge to serve the community. If you're a coder or scientist interested in urbanism and planning, we will teach you how to unlock your skills for more equitable cities.

âš ī¸ Incomplete Data

Some information about this model is not available. Use with Caution - Verify details from the original source before relying on this data.

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📝 Limitations & Considerations

  • â€ĸ Benchmark scores may vary based on evaluation methodology and hardware configuration.
  • â€ĸ VRAM requirements are estimates; actual usage depends on quantization and batch size.
  • â€ĸ FNI scores are relative rankings and may change as new models are added.
  • ⚠ License Unknown: Verify licensing terms before commercial use.
0
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AI Summary: Based on GitHub metadata. Not a recommendation.

📊 FNI Methodology 📚 Knowledge Baseâ„šī¸ Verify with original source

đŸ›Ąī¸ Model Transparency Report

Technical metadata sourced from upstream repositories.

Open Metadata

🆔 Identity & Source

id
gh-model--gboeing--ppde642
slug
gboeing--ppde642
source
github
author
gboeing
license
MIT
tags
usc, urban-data-science, course-materials, data-science, urban-planning, urban-analytics, urban-informatics, city-government, syllabus, jupyter, python, statistics, network-analysis, spatial-analysis, urbanism, cities, course, coding, transport, transportation, jupyter notebook

âš™ī¸ Technical Specs

architecture
null
params billions
null
context length
null
pipeline tag
other

📊 Engagement & Metrics

downloads
0
stars
0
forks
0

Data indexed from public sources. Updated daily.