Public Data Querying

GRAI provides powerful capabilities for querying publicly available vector data sources. This allows you to analyze geospatial features from global datasets using natural language.

What is Public Data Querying?

Public data querying refers to the ability to search, filter, and analyze vector features (points, lines, and polygons) from publicly available datasets based on:

• Attribute values (name, type, size, etc.)
• Spatial relationships (intersection, containment, proximity)
• Combinations of attributes and spatial filters

This capability allows you to ask natural language questions about geographic entities and receive precise answers.

Common Query Types

Proximity Queries

Ask questions about features near a location:

"Show me all restaurants within 500 meters of my current location"
"Which parks are within a 10-minute walk of this school?"
"List all EV charging stations within 5km of this highway exit"

Attribute Queries

Filter features based on their properties:

"Find all buildings taller than 100 meters in this city"
"Show me residential properties larger than 1 acre in this neighborhood"
"Identify roads with speed limits greater than 50 mph"

Combining Filters

Create complex queries by combining multiple criteria:

"Show me hotels with at least 4-star ratings within 2km of the beach"
"Find public schools with enrollment over 500 students in low-income neighborhoods"
"Identify commercial buildings constructed before 1980 in flood-prone areas"

Public Data Querying in GRAI

To perform public data queries in GRAI:

1. Define your region of interest (ROI)
2. Enter your query in natural language
3. View the results displayed on the map
4. Request additional details or refine your query as needed

Example: OpenStreetMap Query

Here's an example of querying OpenStreetMap data:

After submitting your query, the results will be displayed on the map:

You can also view the results in a tabular format for detailed analysis:

Example: Overture Maps Query

GRAI also supports querying the Overture Maps dataset:

Overture Maps results displayed on the map:

Downloading Query Results

After running your query, you can download the results for further analysis:

Supported Public Data Sources

GRAI can query features from multiple public vector data sources:

Best Practices

To get the most out of public data querying:

• Be specific in your queries: Include relevant attributes and spatial constraints
• Start with broader queries and then refine them based on initial results
• Consider data limitations: Not all areas have the same level of data coverage
• Combine with raster analysis for more comprehensive insights
• Use natural language: The AI understands complex questions and can interpret your intent

Example Applications

Public data querying is valuable for:

• Urban Planning: Analyzing infrastructure distribution and accessibility
• Real Estate: Finding properties matching specific criteria
• Transportation: Optimizing routes and service areas
• Marketing: Identifying target areas based on demographic data
• Emergency Response: Locating critical facilities and vulnerable populations

Next Steps

Continue exploring vector analysis capabilities:

• User Data Querying
• Custom Data Upload
• Query Loops