Data Flow and User Journey - utourismboard/explore-uganda-application-documentation GitHub Wiki

Data Flow & User Journey

User Journey Overview

graph TD
    A[App Launch] --> B[Authentication]
    B --> C[Home Dashboard]
    C --> D[Feature Selection]
    D --> E1[Explore]
    D --> E2[Book Services]
    D --> E3[Manage Business]
    D --> E4[Investment]
    E1 --> F[User Engagement]
    E2 --> F
    E3 --> F
    E4 --> F
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User Types and Flows

Tourist Journey

sequenceDiagram
    participant User as Tourist
    participant App
    participant Backend
    participant Services

    User->>App: Opens App
    App->>Backend: Authentication
    Backend-->>App: User Profile
    User->>App: Browses Attractions
    App->>Backend: Fetch Data
    Backend-->>App: Attraction List
    User->>App: Books Service
    App->>Services: Process Booking
    Services-->>App: Confirmation
    App-->>User: Booking Details
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Service Provider Journey

sequenceDiagram
    participant SP as Service Provider
    participant App
    participant Backend
    participant Analytics

    SP->>App: Business Login
    App->>Backend: Verify Credentials
    Backend-->>App: Business Profile
    SP->>App: Update Listings
    App->>Backend: Save Changes
    SP->>App: View Analytics
    App->>Analytics: Fetch Data
    Analytics-->>App: Performance Metrics
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Investor Journey

sequenceDiagram
    participant Inv as Investor
    participant App
    participant Backend
    participant Market

    Inv->>App: Investor Login
    App->>Backend: Authentication
    Backend-->>App: Investment Profile
    Inv->>App: Browse Opportunities
    App->>Market: Fetch Market Data
    Market-->>App: Investment Options
    Inv->>App: Track Investments
    App->>Backend: Update Portfolio
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Data Flow Architecture

Core Data Flows

graph LR
    A[Client App] --> B[API Gateway]
    B --> C[Authentication]
    B --> D[Database]
    B --> E[Storage]
    B --> F[Analytics]
    
    C --> G[User Management]
    D --> H[Content Management]
    E --> I[Media Storage]
    F --> J[Reporting]
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Real-time Data Flow

// Example Real-time Data Implementation
class RealTimeService {
  final FirebaseFirestore _db = FirebaseFirestore.instance;
  
  Stream<List<Event>> getLiveEvents() {
    return _db.collection('events')
        .where('status', isEqualTo: 'active')
        .snapshots()
        .map((snapshot) => snapshot.docs
            .map((doc) => Event.fromJson(doc.data()))
            .toList());
  }
}

User Interaction Patterns

Authentication Flow

  1. Initial Access

    • App launch
    • Splash screen
    • Authentication check
    • Login/Register option

  2. User Verification

    graph TD
    A[Start] --> B{User Type}
    B -->|Tourist| C[Simple Auth]
    B -->|Business| D[Extended Auth]
    B -->|Investor| E[Advanced Auth]
    C --> F[Profile Setup]
    D --> G[Business Verification]
    E --> H[Investment Profile]
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Content Discovery Flow

  1. Browse & Search

    • Category navigation
    • Search functionality
    • Filters and sorting
    • Results display

  2. Content Interaction

    graph LR
    A[View Content] --> B[Like/Save]
    A --> C[Share]
    A --> D[Book/Purchase]
    A --> E[Review]
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Data Management

Data Categories

  1. User Data

    • Profile information
    • Preferences
    • Activity history
    • Saved items

  2. Business Data

    • Service listings
    • Booking records
    • Analytics data
    • Performance metrics

  3. Content Data

    • Attractions
    • Events
    • Services
    • Media assets

Data Synchronization

// Example Sync Implementation
class DataSyncService {
  final LocalStorage _local = LocalStorage();
  final CloudStorage _cloud = CloudStorage();
  
  Future<void> syncData() async {
    try {
      final localData = await _local.getData();
      final cloudData = await _cloud.getData();
      
      final mergedData = await _mergeData(localData, cloudData);
      await _updateStorage(mergedData);
    } catch (e) {
      // Handle sync errors
    }
  }
}

Performance Optimization

Data Loading Strategies

  1. Lazy Loading

    • Load data on demand
    • Implement pagination
    • Cache frequently accessed data

  2. Prefetching

    • Anticipate user needs
    • Background data loading
    • Smart caching

Caching Implementation

// Example Cache Implementation
class CacheManager {
  final Cache _cache = Cache();
  
  Future<T> getData<T>(String key, Future<T> Function() fetchData) async {
    if (await _cache.has(key)) {
      return await _cache.get(key);
    }
    
    final data = await fetchData();
    await _cache.set(key, data);
    return data;
  }
}

Analytics and Monitoring

User Analytics

  1. Tracking Points

    • Screen views
    • Feature usage
    • User engagement
    • Conversion rates

  2. Performance Metrics

    • Load times
    • Response times
    • Error rates
    • User satisfaction

Monitoring Flow

graph TD
    A[User Actions] --> B[Event Tracking]
    B --> C[Data Collection]
    C --> D[Analysis]
    D --> E[Reporting]
    E --> F[Optimization]
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Error Handling

Error Flow

graph TD
    A[Error Occurs] --> B[Log Error]
    B --> C[User Notification]
    B --> D[Error Report]
    D --> E[Analysis]
    E --> F[Resolution]
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Recovery Procedures

  1. Data Recovery

    • Backup restoration
    • State recovery
    • Session management
    • Error correction

  2. User Communication

    • Error messages
    • Status updates
    • Recovery instructions
    • Support options

Related Documentation

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