README.md

---
license: mit
language:
- en
tags:
- gis
- geospatial
pretty_name: govgis_nov2023-slim-spatial
size_categories:
- 100K<n<1M
---

# govgis_nov2023-slim-spatial

🤖 This README was written by [`HuggingFaceH4/zephyr-7b-beta`](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta). 🤖


Introducing the govgis_nov2023-slim-spatial dataset, a carefully curated and georeferenced subset of the extensive [govgis_nov2023](https://huggingface.co/datasets/joshuasundance/govgis_nov2023) collection. This dataset stands out for its focus on geospatial data analysis, enriched with vector embeddings. While we have only explored a portion of this vast collection, the variety and richness of the content encountered have been remarkable, making it challenging to fully capture the dataset's breadth in a brief overview.

## Overview

The govgis_nov2023-slim-spatial dataset condenses key elements from the larger govgis_nov2023 collection into a more manageable format. It offers a glimpse into an extensive range of geospatial data types, all augmented with vector embeddings using [`BAAI/bge-large-en-v1.5`](https://huggingface.co/BAAI/bge-large-en-v1.5). Our exploration has revealed a staggering variety in the data, suggesting vast potential applications.

Key Features:

- **Diverse Geospatial Data Types:** The dataset includes samples of data like ecological data, census data, administrative boundaries, transportation networks, and land use maps, representing just a fraction of what's available.
- **Advanced Vector Search Capabilities:** Augmented with vector embeddings using [`BAAI/bge-large-en-v1.5`](https://huggingface.co/BAAI/bge-large-en-v1.5) for sophisticated content discovery.

## Dataset Files

The dataset comprises two distinct files:

1. **`govgis_nov2023_slim_spatial.geoparquet`** This file offers core georeferenced spatial data, suitable for a broad range of analysis needs.
2. **`govgis_nov2023_slim_spatial_embs.geoparquet`:** A more comprehensive file with detailed vector embeddings, catering to more in-depth analytical demands.

This two-tiered approach allows users to tailor their engagement with the dataset based on their specific requirements.

## Benefits:

- **Selective Accessibility:** The dataset provides an accessible entry point to a seemingly endless variety of spatial data.
- **Efficient yet Comprehensive:** It distills a vast array of data into a more practical format without losing the essence of its diversity.
- **Untapped Application Potential:** The examples we provide are merely starting points; the dataset's true scope is far more extensive and varied.
- **Enhanced Analytical Depth:** Vector embeddings from [`BAAI/bge-large-en-v1.5`](https://huggingface.co/BAAI/bge-large-en-v1.5) offer advanced data analysis capabilities.

## Use Cases:

Given the sheer variety of data we've glimpsed, the dataset is poised to serve a myriad of applications, far beyond the few examples we can confidently cite. It's designed to be adaptable to diverse analytical pursuits across different fields.
    
# Conclusion:

The govgis_nov2023-slim-spatial dataset is a thoughtfully distilled, georeferenced, and vector-embedded version of its more extensive counterpart. Our limited exploration has revealed an astonishing variety of data, hinting at a much broader scope of potential applications than we can definitively describe. This dual-file dataset is crafted to meet a wide spectrum of spatial data analysis needs, from the straightforward to the highly specialized, accommodating the extensive possibilities that lie within the realm of geospatial data.