Anthropic

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Overview

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Available Models

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Claude Sonnet 4

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Key Capabilities

• Advanced Reasoning: Superior logical thinking and problem-solving
• Context Understanding: Deep comprehension of spatial relationships
• Code Generation: High-quality, well-documented code output
• Analysis: Sophisticated statistical and spatial analysis
• Documentation: Excellent technical writing and documentation

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Performance in Nika Tasks

Coding

• Python: Outstanding code generation with proper error handling
• JavaScript/TypeScript: Excellent frontend and mapping code
• SQL: Very good query optimization and complex joins
• Geospatial Libraries: Deep understanding of GeoPandas, PostGIS, Leaflet

import geopandas as gpd
import pandas as pd
from shapely.geometry import Point
import folium

def create_interactive_map(gdf, column_name, title="Interactive Map"):
    """
    Create an interactive choropleth map with proper styling and popups.
    
    Parameters:
    gdf (GeoDataFrame): Input geospatial data
    column_name (str): Column to visualize
    title (str): Map title
    
    Returns:
    folium.Map: Interactive map object
    """
    # Calculate statistics for better visualization
    min_val = gdf[column_name].min()
    max_val = gdf[column_name].max()
    
    # Create base map centered on data
    center_lat = gdf.geometry.centroid.y.mean()
    center_lon = gdf.geometry.centroid.x.mean()
    
    m = folium.Map(
        location=[center_lat, center_lon],
        zoom_start=10,
        tiles='cartodbpositron'
    )
    
    # Add choropleth layer with proper styling
    folium.Choropleth(
        geo_data=gdf.__geo_interface__,
        name='choropleth',
        data=gdf,
        columns=['id', column_name],
        key_on='feature.properties.id',
        fill_color='YlOrRd',
        fill_opacity=0.7,
        line_opacity=0.2,
        legend_name=column_name,
        bins=6
    ).add_to(m)
    
    # Add interactive popups
    for idx, row in gdf.iterrows():
        folium.Popup(
            f"<b>{row.get('name', 'Location')}</b><br>"
            f"{column_name}: {row[column_name]:,.0f}",
            parse_html=True
        ).add_to(folium.GeoJson(
            row.geometry,
            style_function=lambda x: {'fillColor': 'transparent', 'color': 'black', 'weight': 1}
        ).add_to(m))
    
    folium.LayerControl().add_to(m)
    return m

Map Creation

• Interactive Maps: Excellent Leaflet and Mapbox implementations
• Static Maps: High-quality matplotlib and plotly outputs
• Custom Styling: Sophisticated visual design and theming
• Performance Optimization: Efficient rendering for large datasets

Data Analysis

• Statistical Analysis: Advanced statistical methods and interpretation
• Spatial Analysis: Complex spatial operations and algorithms
• Trend Detection: Sophisticated pattern recognition
• Data Quality: Comprehensive data validation and cleaning

SQL Execution

• Query Optimization: Excellent performance tuning
• Complex Joins: Advanced spatial and temporal joins
• Indexing: Strategic index recommendations
• Error Handling: Robust error management and recovery

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Claude Haiku 3.5

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Key Capabilities

• Speed: Rapid response times for real-time applications
• Efficiency: Cost-effective for high-volume tasks
• Reliability: Consistent performance across different tasks
• Scalability: Excellent for batch processing

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Performance in Nika Tasks

Coding

• Speed: Very fast code generation
• Quality: Good code quality with basic error handling
• Documentation: Adequate inline comments and docstrings
• Best For: Simple to moderate complexity tasks

Map Creation

• Basic Maps: Good quality for standard visualizations
• Performance: Fast rendering for simple maps
• Styling: Basic but effective visual design
• Best For: Quick prototypes and simple dashboards

Data Analysis

• Basic Analysis: Good for standard statistical operations
• Speed: Fast processing of routine analyses
• Interpretation: Adequate result explanation
• Best For: Regular reporting and monitoring

SQL Execution

• Standard Queries: Good performance on common queries
• Basic Optimization: Adequate query tuning
• Error Handling: Basic error management
• Best For: Routine database operations

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Use Case Recommendations

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Choose Claude Sonnet 4 When:

• Complex Analysis: Multi-step geospatial workflows
• Research Projects: Academic or research-level analysis
• Production Systems: Critical business applications
• Documentation: Technical documentation and reports
• Code Review: High-quality code generation and review

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Choose Claude Haiku 3.5 When:

• Real-time Applications: Live dashboards and monitoring
• Batch Processing: High-volume data processing
• Prototyping: Quick iterations and testing
• Cost Optimization: Budget-conscious projects
• Simple Tasks: Routine analysis and reporting

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Configuration Examples

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Claude Sonnet 4 Configuration

const sonnetConfig = {
  provider: 'anthropic',
  model: 'claude-sonnet-4',
  temperature: 0.3,
  maxTokens: 4096,
  systemPrompt: `You are an expert geospatial analyst. Provide detailed, 
  well-documented solutions with proper error handling and performance optimization.`,
  features: {
    reasoning: 'advanced',
    analysis: 'comprehensive',
    documentation: 'detailed'
  }
};

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Claude Haiku 3.5 Configuration

const haikuConfig = {
  provider: 'anthropic',
  model: 'claude-haiku-3.5',
  temperature: 0.1,
  maxTokens: 2048,
  systemPrompt: `You are a fast, efficient geospatial assistant. 
  Provide quick, reliable solutions for common tasks.`,
  features: {
    speed: 'optimized',
    cost: 'efficient',
    reliability: 'high'
  }
};

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Performance Metrics

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Best Practices

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For Claude Sonnet 4

1. Provide Context: Give detailed background information
2. Specify Requirements: Clearly define output format and quality
3. Use Iterative Refinement: Build complex solutions step by step
4. Request Documentation: Ask for detailed explanations and comments

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For Claude Haiku 3.5

1. Keep Prompts Concise: Focus on essential information
2. Use Templates: Leverage pre-defined patterns for consistency
3. Batch Similar Tasks: Group related operations for efficiency
4. Monitor Performance: Track response times and quality

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Integration with Nika

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API Usage

import nikaplanet.com as nai

# Initialize with Anthropic
client = nai.Client(
    llm_provider='anthropic',
    model='claude-sonnet-4'
)

# Complex analysis task
analysis_result = client.analyze_spatial_data({
    'data': 'cities.geojson',
    'analysis_type': 'hot_spot_analysis',
    'parameters': {
        'variable': 'population_density',
        'method': 'getis_ord_gi',
        'significance_level': 0.05
    }
})

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Workflow Integration

# Multi-step workflow with different models
workflow = nai.Workflow()

# Use Haiku for data preprocessing
workflow.add_step(
    'preprocess',
    model='claude-haiku-3.5',
    task='data_cleaning'
)

# Use Sonnet for complex analysis
workflow.add_step(
    'analyze',
    model='claude-sonnet-4',
    task='spatial_analysis'
)

# Use Haiku for visualization
workflow.add_step(
    'visualize',
    model='claude-haiku-3.5',
    task='create_map'
)

results = workflow.execute()

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