turash/bugulma/backend/internal/service/geospatial_service.go

package service

import (
	"bugulma/backend/internal/domain"
	"context"
	"fmt"
	"math"

	"gorm.io/gorm"
)

// GeospatialService provides advanced geospatial operations for sites and geographical features
type GeospatialService struct {
	db             *gorm.DB
	geoFeatureRepo domain.GeographicalFeatureRepository
}

// NewGeospatialService creates a new geospatial service
func NewGeospatialService(db *gorm.DB, geoFeatureRepo domain.GeographicalFeatureRepository) *GeospatialService {
	return &GeospatialService{
		db:             db,
		geoFeatureRepo: geoFeatureRepo,
	}
}

// SpatialQuery represents a spatial query with various criteria
type SpatialQuery struct {
	CenterLat     float64
	CenterLng     float64
	RadiusKm      float64
	SiteTypes     []domain.SiteType
	ResourceTypes []domain.ResourceType
	MaxResults    int
}

// SpatialResult represents a site with spatial metadata
type SpatialResult struct {
	Site        *domain.Site
	DistanceKm  float64
	Bearing     float64 // Direction from center point
	DrivingTime *int    // Estimated driving time in minutes (optional)
}

// SpatialCluster represents a cluster of spatially close sites
type SpatialCluster struct {
	ID          int             `json:"id"`
	CentroidLat float64         `json:"centroid_lat"`
	CentroidLng float64         `json:"centroid_lng"`
	SiteCount   int             `json:"site_count"`
	Sites       []SpatialResult `json:"sites"`
	RadiusKm    float64         `json:"radius_km"`
}

// FindNearbySites finds sites within radius with advanced filtering
func (gs *GeospatialService) FindNearbySites(ctx context.Context, query SpatialQuery) ([]SpatialResult, error) {
	var results []SpatialResult

	// Build the base query with PostGIS
	baseQuery := `
		SELECT
			s.*,
			ST_Distance(s.location_geometry::geography, ST_GeogFromText('POINT(? ?)')) / 1000 as distance_km,
			ST_Azimuth(ST_GeogFromText('POINT(? ?)'), s.location_geometry) as bearing
		FROM sites s
		WHERE s.location_geometry IS NOT NULL
		AND ST_DWithin(
			s.location_geometry::geography,
			ST_GeogFromText('POINT(? ?)'),
			? * 1000
		)
	`

	args := []interface{}{query.CenterLng, query.CenterLat, query.CenterLng, query.CenterLat, query.CenterLng, query.CenterLat, query.RadiusKm}

	// Add site type filter
	if len(query.SiteTypes) > 0 {
		placeholders := ""
		for i, siteType := range query.SiteTypes {
			if i > 0 {
				placeholders += ","
			}
			placeholders += "?"
			args = append(args, siteType)
		}
		baseQuery += fmt.Sprintf(" AND s.site_type IN (%s)", placeholders)
	}

	// Order by distance and limit results
	baseQuery += " ORDER BY s.location_geometry <-> ST_GeogFromText('POINT(? ?)')"

	if query.MaxResults > 0 {
		baseQuery += " LIMIT ?"
		args = append(args, query.CenterLng, query.CenterLat, query.MaxResults)
	} else {
		args = append(args, query.CenterLng, query.CenterLat)
	}

	// Execute query
	rows, err := gs.db.Raw(baseQuery, args...).Rows()
	if err != nil {
		return nil, err
	}
	defer rows.Close()

	for rows.Next() {
		var site domain.Site
		var distanceKm, bearing float64
		var locationGeometry interface{} // Geometry column - we don't use it in the struct

		// Scan the row - need to be careful with column order
		// Note: location_geometry is scanned but not stored in the struct
		err := rows.Scan(
			&site.ID, &site.Name, &site.Latitude, &site.Longitude,
			&locationGeometry, &site.SiteType, &site.FloorAreaM2, &site.Ownership,
			&site.OwnerOrganizationID, &site.AvailableUtilities, &site.ParkingSpaces,
			&site.LoadingDocks, &site.CraneCapacityTonnes, &site.EnergyRating,
			&site.WasteManagement, &site.EnvironmentalImpact, &site.YearBuilt,
			&site.BuilderOwner, &site.Architect, &site.OriginalPurpose, &site.CurrentUse,
			&site.Style, &site.Materials, &site.Storeys, &site.HeritageStatus,
			&site.Notes, &site.Sources, &site.CreatedAt, &site.UpdatedAt,
			&distanceKm, &bearing,
		)
		if err != nil {
			return nil, err
		}

		results = append(results, SpatialResult{
			Site:       &site,
			DistanceKm: distanceKm,
			Bearing:    bearing,
		})
	}

	return results, nil
}

// CalculateDistanceMatrix calculates distances between multiple points efficiently
func (gs *GeospatialService) CalculateDistanceMatrix(ctx context.Context, points []domain.Site) ([][]float64, error) {
	if len(points) == 0 {
		return [][]float64{}, nil
	}

	matrix := make([][]float64, len(points))
	for i := range matrix {
		matrix[i] = make([]float64, len(points))
	}

	// For small matrices, calculate directly
	// For larger matrices, could use PostGIS functions
	for i := 0; i < len(points); i++ {
		for j := 0; j < len(points); j++ {
			if i == j {
				matrix[i][j] = 0
			} else {
				matrix[i][j] = gs.calculateHaversineDistance(
					points[i].Latitude, points[i].Longitude,
					points[j].Latitude, points[j].Longitude,
				)
			}
		}
	}

	return matrix, nil
}

// calculateHaversineDistance calculates distance using Haversine formula
func (gs *GeospatialService) calculateHaversineDistance(lat1, lon1, lat2, lon2 float64) float64 {
	const R = 6371 // Earth radius in km

	dLat := (lat2 - lat1) * math.Pi / 180
	dLon := (lon2 - lon1) * math.Pi / 180

	a := math.Sin(dLat/2)*math.Sin(dLat/2) +
		math.Cos(lat1*math.Pi/180)*math.Cos(lat2*math.Pi/180)*
			math.Sin(dLon/2)*math.Sin(dLon/2)

	c := 2 * math.Atan2(math.Sqrt(a), math.Sqrt(1-a))

	return R * c
}

// ValidateGeometry validates PostGIS geometry
func (gs *GeospatialService) ValidateGeometry(ctx context.Context, siteID string) error {
	var count int64
	err := gs.db.Raw(`
		SELECT COUNT(*) FROM sites
		WHERE id = ? AND ST_IsValid(location_geometry)
	`, siteID).Scan(&count).Error

	if err != nil {
		return err
	}

	if count == 0 {
		return fmt.Errorf("invalid geometry for site %s", siteID)
	}

	return nil
}

// GetSpatialStatistics returns spatial statistics for sites
func (gs *GeospatialService) GetSpatialStatistics(ctx context.Context) (map[string]interface{}, error) {
	var stats struct {
		TotalSites        int64
		SitesWithGeometry int64
		AvgDistance       float64
		MaxDistance       float64
		MedianLatitude    float64
		MedianLongitude   float64
	}

	// Get basic counts
	gs.db.Raw("SELECT COUNT(*) as total_sites FROM sites").Scan(&stats.TotalSites)
	gs.db.Raw("SELECT COUNT(*) as sites_with_geometry FROM sites WHERE location_geometry IS NOT NULL").Scan(&stats.SitesWithGeometry)

	// Calculate spatial statistics if we have geometry data
	if stats.SitesWithGeometry > 1 {
		row := gs.db.Raw(`
			SELECT
				AVG(ST_Distance(a.location_geometry::geography, b.location_geometry::geography)) / 1000 as avg_distance,
				MAX(ST_Distance(a.location_geometry::geography, b.location_geometry::geography)) / 1000 as max_distance
			FROM sites a
			CROSS JOIN sites b
			WHERE a.id < b.id
			AND a.location_geometry IS NOT NULL
			AND b.location_geometry IS NOT NULL
		`).Row()

		row.Scan(&stats.AvgDistance, &stats.MaxDistance)

		// Calculate median coordinates
		row2 := gs.db.Raw(`
			SELECT
				PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY latitude) as median_latitude,
				PERCENTILE_CONT(0.5) WITHIN GROUP (ORDER BY longitude) as median_longitude
			FROM sites
			WHERE location_geometry IS NOT NULL
		`).Row()

		row2.Scan(&stats.MedianLatitude, &stats.MedianLongitude)
	}

	return map[string]interface{}{
		"total_sites":         stats.TotalSites,
		"sites_with_geometry": stats.SitesWithGeometry,
		"avg_distance_km":     stats.AvgDistance,
		"max_distance_km":     stats.MaxDistance,
		"median_latitude":     stats.MedianLatitude,
		"median_longitude":    stats.MedianLongitude,
	}, nil
}

// FindSpatialClusters finds clusters of spatially close sites using DBSCAN algorithm
func (gs *GeospatialService) FindSpatialClusters(ctx context.Context, minPoints int, radiusKm float64) ([]SpatialCluster, error) {
	var clusters []SpatialCluster

	// Use PostGIS ST_ClusterDBSCAN to group nearby sites
	// Convert radius from km to degrees (approximate)
	radiusDegrees := radiusKm / 111.32 // Rough conversion: 1 degree ≈ 111.32 km

	query := `
		WITH clustered_sites AS (
			SELECT
				s.*,
				ST_ClusterDBSCAN(s.location_geometry, ?, ?) OVER () as cluster_id,
				ST_Centroid(ST_Collect(s.location_geometry)) OVER (PARTITION BY ST_ClusterDBSCAN(s.location_geometry, ?, ?) OVER ()) as cluster_centroid
			FROM sites s
			WHERE s.location_geometry IS NOT NULL
		),
		cluster_stats AS (
			SELECT
				cluster_id,
				COUNT(*) as site_count,
				ST_Y(cluster_centroid) as centroid_lat,
				ST_X(cluster_centroid) as centroid_lng,
				MAX(ST_Distance(cluster_centroid::geography, location_geometry::geography)) / 1000 as cluster_radius_km
			FROM clustered_sites
			WHERE cluster_id IS NOT NULL
			GROUP BY cluster_id, cluster_centroid
			HAVING COUNT(*) >= ?
		)
		SELECT
			cs.cluster_id,
			cstats.centroid_lat,
			cstats.centroid_lng,
			cstats.site_count,
			cstats.cluster_radius_km,
			cs.id as site_id,
			cs.name,
			cs.latitude,
			cs.longitude,
			cs.location_geometry,
			cs.site_type,
			cs.floor_area_m2,
			cs.ownership,
			cs.owner_organization_id,
			cs.available_utilities,
			cs.parking_spaces,
			cs.loading_docks,
			cs.crane_capacity_tonnes,
			cs.energy_rating,
			cs.waste_management,
			cs.environmental_impact,
			cs.year_built,
			cs.builder_owner,
			cs.architect,
			cs.original_purpose,
			cs.current_use,
			cs.style,
			cs.materials,
			cs.storeys,
			cs.heritage_status,
			cs.notes,
			cs.sources,
			cs.created_at,
			cs.updated_at,
			ST_Distance(cstats.cluster_centroid::geography, cs.location_geometry::geography) / 1000 as distance_from_centroid
		FROM clustered_sites cs
		JOIN cluster_stats cstats ON cs.cluster_id = cstats.cluster_id
		ORDER BY cs.cluster_id, distance_from_centroid
	`

	rows, err := gs.db.Raw(query, radiusDegrees, minPoints, radiusDegrees, minPoints, minPoints).Rows()
	if err != nil {
		return nil, fmt.Errorf("failed to execute clustering query: %w", err)
	}
	defer rows.Close()

	clusterMap := make(map[int]*SpatialCluster)

	for rows.Next() {
		var (
			clusterID            int
			centroidLat          float64
			centroidLng          float64
			siteCount            int
			clusterRadiusKm      float64
			site                 domain.Site
			distanceFromCentroid float64
			locationGeometry     interface{} // Geometry column - we don't use it in the struct
		)

		err := rows.Scan(
			&clusterID, &centroidLat, &centroidLng, &siteCount, &clusterRadiusKm,
			&site.ID, &site.Name, &site.Latitude, &site.Longitude,
			&locationGeometry, &site.SiteType, &site.FloorAreaM2, &site.Ownership,
			&site.OwnerOrganizationID, &site.AvailableUtilities, &site.ParkingSpaces,
			&site.LoadingDocks, &site.CraneCapacityTonnes, &site.EnergyRating,
			&site.WasteManagement, &site.EnvironmentalImpact, &site.YearBuilt,
			&site.BuilderOwner, &site.Architect, &site.OriginalPurpose, &site.CurrentUse,
			&site.Style, &site.Materials, &site.Storeys, &site.HeritageStatus,
			&site.Notes, &site.Sources, &site.CreatedAt, &site.UpdatedAt,
			&distanceFromCentroid,
		)
		if err != nil {
			return nil, fmt.Errorf("failed to scan cluster row: %w", err)
		}

		// Get or create cluster
		cluster, exists := clusterMap[clusterID]
		if !exists {
			cluster = &SpatialCluster{
				ID:          clusterID,
				CentroidLat: centroidLat,
				CentroidLng: centroidLng,
				SiteCount:   siteCount,
				Sites:       []SpatialResult{},
				RadiusKm:    clusterRadiusKm,
			}
			clusterMap[clusterID] = cluster
		}

		// Add site to cluster
		cluster.Sites = append(cluster.Sites, SpatialResult{
			Site:       &site,
			DistanceKm: distanceFromCentroid,
			Bearing:    0, // Could calculate bearing from centroid if needed
		})
	}

	// Convert map to slice
	for _, cluster := range clusterMap {
		clusters = append(clusters, *cluster)
	}

	return clusters, nil
}

// Geographical Feature Methods

// FindNearbyGeographicalFeatures finds geographical features within radius
func (gs *GeospatialService) FindNearbyGeographicalFeatures(ctx context.Context, featureType domain.GeographicalFeatureType, lat, lng, radiusKm float64) ([]*domain.GeographicalFeature, error) {
	switch featureType {
	case domain.GeographicalFeatureTypeRoad:
		return gs.geoFeatureRepo.GetRoadsWithinRadius(ctx, lat, lng, radiusKm)
	case domain.GeographicalFeatureTypeGreenSpace:
		return gs.geoFeatureRepo.GetGreenSpacesWithinRadius(ctx, lat, lng, radiusKm)
	default:
		// For other feature types, use general spatial query
		return gs.findGeographicalFeaturesWithinRadius(ctx, featureType, lat, lng, radiusKm)
	}
}

// findGeographicalFeaturesWithinRadius is a helper for general feature type queries
func (gs *GeospatialService) findGeographicalFeaturesWithinRadius(ctx context.Context, featureType domain.GeographicalFeatureType, lat, lng, radiusKm float64) ([]*domain.GeographicalFeature, error) {
	var features []*domain.GeographicalFeature

	query := `
		SELECT * FROM geographical_features
		WHERE feature_type = ?
		AND ST_DWithin(
			geometry::geography,
			ST_GeogFromText('POINT(? ?)'),
			? * 1000
		)
		ORDER BY ST_Distance(geometry::geography, ST_GeogFromText('POINT(? ?)'))
	`

	result := gs.db.WithContext(ctx).Raw(query, featureType, lng, lat, radiusKm, lng, lat).Scan(&features)
	if result.Error != nil {
		return nil, result.Error
	}

	return features, nil
}

// CalculateSiteEnvironmentalScore calculates environmental score based on nearby green spaces
func (gs *GeospatialService) CalculateSiteEnvironmentalScore(ctx context.Context, siteLat, siteLng float64) (float64, error) {
	// Get green spaces within 2km
	greenSpaces, err := gs.geoFeatureRepo.GetGreenSpacesWithinRadius(ctx, siteLat, siteLng, 2.0)
	if err != nil {
		return 0, err
	}

	// Calculate score based on proximity and size of green spaces
	var totalScore float64
	for range greenSpaces {
		// Calculate distance to green space (we'd need to add distance calculation)
		// For now, use a simple scoring based on count
		totalScore += 1.0
	}

	// Normalize score (max 10 points for environmental rating)
	if totalScore > 10 {
		totalScore = 10
	}

	return totalScore, nil
}

// CalculateTransportationAccessibility calculates accessibility score based on road network
func (gs *GeospatialService) CalculateTransportationAccessibility(ctx context.Context, siteLat, siteLng float64) (float64, error) {
	// Get roads within 1km
	roads, err := gs.geoFeatureRepo.GetRoadsWithinRadius(ctx, siteLat, siteLng, 1.0)
	if err != nil {
		return 0, err
	}

	// Calculate accessibility based on road density
	roadCount := len(roads)

	// Simple scoring: more roads = better accessibility
	var score float64
	switch {
	case roadCount >= 10:
		score = 10.0
	case roadCount >= 5:
		score = 7.5
	case roadCount >= 2:
		score = 5.0
	case roadCount >= 1:
		score = 2.5
	default:
		score = 0.0
	}

	return score, nil
}

// GetGeographicalFeatureStatistics returns comprehensive statistics about geographical features
func (gs *GeospatialService) GetGeographicalFeatureStatistics(ctx context.Context) (map[string]interface{}, error) {
	stats := make(map[string]interface{})

	// Get counts by feature type
	featureTypes := []domain.GeographicalFeatureType{
		domain.GeographicalFeatureTypeRoad,
		domain.GeographicalFeatureTypeGreenSpace,
		domain.GeographicalFeatureTypePOI,
		domain.GeographicalFeatureTypeRailway,
		domain.GeographicalFeatureTypeWater,
		domain.GeographicalFeatureTypeLandUse,
	}

	for _, featureType := range featureTypes {
		features, err := gs.geoFeatureRepo.GetByType(ctx, featureType)
		if err == nil {
			stats[string(featureType)] = map[string]interface{}{
				"count": len(features),
			}
		}
	}

	// Get road network statistics
	roadStats, err := gs.geoFeatureRepo.GetRoadNetworkStatistics(ctx)
	if err == nil {
		stats["road_network"] = roadStats
	}

	return stats, nil
}

// FindOptimalFacilityLocations finds optimal locations for new facilities based on criteria
func (gs *GeospatialService) FindOptimalFacilityLocations(ctx context.Context, criteria FacilityLocationCriteria) ([]FacilityLocation, error) {
	// This would be a complex algorithm considering:
	// - Proximity to existing industrial sites
	// - Access to road network
	// - Distance from residential areas
	// - Environmental constraints
	// - Available utilities

	// For now, return a placeholder implementation
	var locations []FacilityLocation

	// Query for areas with good road access and proximity to existing sites
	query := `
		WITH candidate_areas AS (
			SELECT
				ST_Buffer(s.location_geometry, 1000) as area,
				s.id as nearby_site_id,
				s.latitude,
				s.longitude
			FROM sites s
			WHERE s.location_geometry IS NOT NULL
			LIMIT 10
		)
		SELECT
			ST_AsText(ST_Centroid(area)) as center_point,
			COUNT(*) as nearby_sites,
			ST_Y(ST_Centroid(area)) as lat,
			ST_X(ST_Centroid(area)) as lng
		FROM candidate_areas
		GROUP BY area
		HAVING COUNT(*) >= ?
		LIMIT ?
	`

	rows, err := gs.db.WithContext(ctx).Raw(query, criteria.MinNearbySites, criteria.MaxResults).Rows()
	if err != nil {
		return nil, err
	}
	defer rows.Close()

	for rows.Next() {
		var loc FacilityLocation
		var centerPoint string
		err := rows.Scan(&centerPoint, &loc.NearbySites, &loc.Latitude, &loc.Longitude)
		if err != nil {
			continue
		}

		// Calculate scores for this location
		envScore, _ := gs.CalculateSiteEnvironmentalScore(ctx, loc.Latitude, loc.Longitude)
		transportScore, _ := gs.CalculateTransportationAccessibility(ctx, loc.Latitude, loc.Longitude)

		loc.EnvironmentalScore = envScore
		loc.TransportationScore = transportScore
		loc.OverallScore = (envScore + transportScore) / 2.0

		locations = append(locations, loc)
	}

	return locations, nil
}

// FacilityLocationCriteria defines criteria for optimal facility location search
type FacilityLocationCriteria struct {
	MinNearbySites        int     `json:"min_nearby_sites"`
	MaxDistanceKm         float64 `json:"max_distance_km"`
	RequireRoadAccess     bool    `json:"require_road_access"`
	MinEnvironmentalScore float64 `json:"min_environmental_score"`
	MaxResults            int     `json:"max_results"`
}

// FacilityLocation represents a potential facility location with scores
type FacilityLocation struct {
	Latitude            float64 `json:"latitude"`
	Longitude           float64 `json:"longitude"`
	NearbySites         int     `json:"nearby_sites"`
	EnvironmentalScore  float64 `json:"environmental_score"`
	TransportationScore float64 `json:"transportation_score"`
	OverallScore        float64 `json:"overall_score"`
}