---
license: other
viewer: false
task_categories:
- text-classification
- feature-extraction
- text-generation
---

# DBbun Davis Square Synthetic Dataset (1800–2200)

A fully **synthetic**, **privacy-free**, and **educational** dataset simulating the evolution of the Davis Square area in **Somerville, Massachusetts** from the **1800s through the 2200s**.  

This dataset enables learners, researchers, and developers to explore **data science, analytics, and machine learning** safely — no real people, addresses, or businesses are represented.

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## Dataset Summary

| Table | Description |
|-------|--------------|
| `geo_streets.csv` | Real street names with synthetic geometry |
| `geo_parks.csv` | Real park names used as location anchors |
| `poi_generic.csv` | Generic points of interest (restaurants, cafés, groceries, pharmacies, etc.) |
| `households.csv` | Synthetic household attributes (dwelling type, occupants, income, tenure) |
| `pets_registry.csv`, `pet_incidents.csv` | Pet ownership and neighborhood pet events |
| `mobility_trips.csv` | Trips by mode (walk, bike, car, bus, train) across eras |
| `public_safety.csv` | Synthetic safety incidents with categories and severity |
| `events_civic.csv` | Civic and festival events (e.g., HONK-style parades) |
| `observations.csv` | Environmental measures (noise, air quality, temperature, foot traffic) |
| `transit_*` | Transit lines, stops, and daily ridership |
| `bike_infra.csv`, `traffic_counts.csv` | Bicycle and vehicle infrastructure statistics |
| `prices_index.csv` | Long-term indices for housing, food, and transit fares |
| `weather_daily.csv` | Daily synthetic weather from 1900 to 2200 |
| `trees_inventory.csv` | Street-tree registry with species and heights |
| `infrastructure_events.csv` | Tree falls, water-main breaks, potholes, outages |
| `building_issues.csv` | Household maintenance and system failures |
| `DATA_DICTIONARY.json` | Column-level descriptions |
| `README.txt` | Summary of generated dataset |

All coordinates are **synthetic** and **jittered** for privacy; no real parcels, residents, or businesses appear.

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## Available Sizes

| Size | Approx. Households | Approx. Trips | Use Case |
|------|--------------------|---------------|-----------|
| `tiny` | 300 | 3 000 | quick demos, teaching syntax |
| `small` | 2 000 | 25 000 | classroom exercises |
| `medium` | 8 000 | 120 000 | research, ML prototypes |
| `large` | 30 000 | 500 000 | performance and scaling |
| `xlarge` | 80 000 | 2 000 000 | large-scale simulations |

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## Example Use

Two notebooks are included:

**1. DBbun_Davis_medium_demo.ipynb**  
Descriptive analytics and visualization:
- Street and park mapping  
- Household composition and mode share  
- Noise, air quality, and event trends  
- Weather and infrastructure summaries  

**2. DBbun_Davis_ML_demo.ipynb**  
Machine-learning examples:
- **Classification:** predict high-severity safety incidents  
- **Regression:** predict noise levels (dB) from weather and traffic  
- **Clustering:** K-Means grouping of streets by mobility patterns  

Each notebook saves all figures, tables, and models locally.

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## Applications

- Teaching **data wrangling**, **visualization**, and **machine learning**
- Practicing **geospatial analysis** without privacy concerns  
- Designing **urban data dashboards** and visual storytelling  
- Benchmarking **synthetic-data pipelines** or evaluation metrics  
- Running **hackathons and bootcamps** with realistic yet safe datasets  

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## Privacy Statement

All data are **synthetically generated**.  
Street and park names are used only as contextual anchors; all attributes, events, and metrics are algorithmically created with randomized geometry and time evolution.  
No personal, identifiable, or proprietary information exists in this dataset.

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## Citation

Kartoun, U. (2025). Davis Square Synthetic Dataset (1800–2200) — A fully synthetic multi-era urban dataset for geospatial, mobility, environmental, and civic-event modeling. DBbun LLC.