🌍 Geospatial Data Processing in PySpark

Architecture Diagram

┌─────────────────────────────────────────────────────────────────────────┐
│                 GEOSPATIAL PROCESSING ARCHITECTURE                      │
├─────────────────────────────────────────────────────────────────────────┤
│                                                                         │
│   ┌──────────────┐     ┌──────────────┐     ┌──────────────┐          │
│   │  Raw Spatial  │────▶│  Coordinate  │────▶│  Spatial     │          │
│   │  Data         │     │  Transform   │     │  Index       │          │
│   │  (Lat/Lng)    │     │  (CRS)       │     │  (H3/S2)     │          │
│   └──────────────┘     └──────┬───────┘     └──────┬───────┘          │
│                               │                     │                   │
│                               ▼                     ▼                   │
│                    ┌──────────────────┐   ┌──────────────────┐         │
│                    │  Geometric       │   │  Spatial         │         │
│                    │  Operations      │   │  Partitioning    │         │
│                    │  ─────────────   │   │  ─────────────   │         │
│                    │  Buffer          │   │  Kd-tree         │         │
│                    │  Intersection    │   │  R-tree          │         │
│                    │  Containment     │   │  Quad-tree       │         │
│                    └────────┬─────────┘   └────────┬─────────┘         │
│                             │                      │                    │
│                             ▼                      ▼                    │
│                    ┌──────────────────┐   ┌──────────────────┐         │
│                    │  Spatial Join    │   │  Distance        │         │
│                    │  (Point-in-Poly) │   │  Calculations    │         │
│                    │  ─────────────   │   │  ─────────────   │         │
│                    │  Broadcast join  │   │  Haversine       │         │
│                    │  KD-tree join    │   │  Euclidean       │         │
│                    │  Grid-based join │   │  Manhattan       │         │
│                    └────────┬─────────┘   └────────┬─────────┘         │
│                             │                      │                    │
│                             ▼                      ▼                    │
│                    ┌──────────────────┐   ┌──────────────────┐         │
│                    │  Trajectory      │   │  Geohash         │         │
│                    │  Analysis        │   │  Encoding        │         │
│                    │  ─────────────   │   │  ─────────────   │         │
│                    │  Movement        │   │  Clustering      │         │
│                    │  patterns        │   │  Bounding boxes  │         │
│                    │  Stay points     │   │  Prefix matching │         │
│                    └──────────────────┘   └──────────────────┘         │
│                                                                         │
└─────────────────────────────────────────────────────────────────────────┘

Architecture Diagram

┌─────────────────────────────────────────────────────────────────────────┐
│                    SPATIAL INDEXING SCHEMES                              │
├─────────────────────────────────────────────────────────────────────────┤
│                                                                         │
│   H3 (Uber's Hexagonal Hierarchical Spatial Index)                     │
│   ┌─────────────────────────────────────────────────────────────┐     │
│   │                    ╱╲      ╱╲      ╱╲                      │     │
│   │                   ╱  ╲    ╱  ╲    ╱  ╲                     │     │
│   │                  ╱    ╲  ╱    ╲  ╱    ╲                    │     │
│   │                 ╱  123 ╲╱  456 ╲╱  789 ╲                   │     │
│   │                ╲      ╱╲      ╱╲      ╱                    │     │
│   │                 ╲    ╱  ╲    ╱  ╲    ╱                     │     │
│   │                  ╲  ╱    ╲  ╱    ╲  ╱                      │     │
│   │                   ╲╱      ╲╱      ╲╱                       │     │
│   │                                                              │     │
│   │  Resolution levels:                                          │     │
│   │  ┌────────┬──────────┬──────────┬──────────┬──────────┐    │     │
│   │  │ Level  │ Edge Len │ Area     │ Use Case │ Precision│    │     │
│   │  ├────────┼──────────┼──────────┼──────────┼──────────┤    │     │
│   │  │  0     │ 1,107 km │ 4.2M km² │ Country  │ ~1000km  │    │     │
│   │  │  4     │ 17.7 km  │ 1,770 km²│ City     │ ~10km    │    │     │
│   │  │  8     │ 463 m    │ 0.74 km² │ Street   │ ~500m    │    │     │
│   │  │  12    │ 12 m     │ 0.002 km²│ Building │ ~10m     │    │     │
│   │  │  15    │ 0.9 m    │ 1e-5 km² │ Object   │ ~1m      │    │     │
│   │  └────────┴──────────┴──────────┴──────────┴──────────┘    │     │
│   └─────────────────────────────────────────────────────────────┘     │
│                                                                         │
│   S2 (Google's Spherical Geometry Library)                             │
│   ┌─────────────────────────────────────────────────────────────┐     │
│   │  ┌─────────────────────────────────────────────────────┐   │     │
│   │  │  Face 0          Face 1          Face 2             │   │     │
│   │  │  ┌───────────┐  ┌───────────┐  ┌───────────┐      │   │     │
│   │  │  │ Cell 0-0  │  │ Cell 1-0  │  │ Cell 2-0  │      │   │     │
│   │  │  │ Cell 0-1  │  │ Cell 1-1  │  │ Cell 2-1  │      │   │     │
│   │  │  │ Cell 0-2  │  │ Cell 1-2  │  │ Cell 2-2  │      │   │     │
│   │  │  │ Cell 0-3  │  │ Cell 1-3  │  │ Cell 2-3  │      │   │     │
│   │  │  └───────────┘  └───────────┘  └───────────┘      │   │     │
│   │  │                                                     │   │     │
│   │  │  Projection: Cube → Sphere via quadratic transform  │   │     │
│   │  └─────────────────────────────────────────────────────┘   │     │
│   └─────────────────────────────────────────────────────────────┘     │
│                                                                         │
│   Geohash (Prefix-based)                                               │
│   ┌─────────────────────────────────────────────────────────────┐     │
│   │  Level 1: 9 subdivisions                                   │     │
│   │  ┌───┬───┬───┐                                              │     │
│   │  │ 0 │ 1 │ 2 │                                              │     │
│   │  ├───┼───┼───┤                                              │     │
│   │  │ 3 │ 4 │ 5 │  ← "s" prefix                               │     │
│   │  ├───┼───┼───┤                                              │     │
│   │  │ 6 │ 7 │ 8 │                                              │     │
│   │  └───┴───┴───┘                                              │     │
│   │                                                              │     │
│   │  Level 2: 9² = 81 subdivisions                             │     │
│   │  ┌───┬───┬───┬───┬───┬───┬───┬───┬───┐                   │     │
│   │  │00 │01 │02 │10 │11 │12 │20 │21 │22 │                   │     │
│   │  ├───┼───┼───┼───┼───┼───┼───┼───┼───┤                   │     │
│   │  │...│...│...│...│...│...│...│...│...│  ← "s9" prefix    │     │
│   │  └───┴───┴───┴───┴───┴───┴───┴───┴───┘                   │     │
│   │                                                              │     │
│   │  Longer prefix = smaller area = higher precision            │     │
│   └─────────────────────────────────────────────────────────────┘     │
│                                                                         │
└─────────────────────────────────────────────────────────────────────────┘

Architecture Diagram

┌─────────────────────────────────────────────────────────────────────────┐
│                 SPATIAL JOIN STRATEGIES                                 │
├─────────────────────────────────────────────────────────────────────────┤
│                                                                         │
│   Strategy 1: Broadcast Join (Small + Large)                           │
│   ┌─────────────────────────────────────────────────────────────┐     │
│   │  Small table (< 10MB)      Large table (TB)                 │     │
│   │  ┌──────────┐             ┌────────────────────────┐       │     │
│   │  │ Polygons │ ──────────▶│ Points (partitioned)   │       │     │
│   │  │ (1000)   │  broadcast │                        │       │     │
│   │  └──────────┘             │  ┌────┐ ┌────┐ ┌────┐│       │     │
│   │                            │  │ P1 │ │ P2 │ │ P3 ││       │     │
│   │  Each partition checks     │  └────┘ └────┘ └────┘│       │     │
│   │  against all polygons      └────────────────────────┘       │     │
│   └─────────────────────────────────────────────────────────────┘     │
│                                                                         │
│   Strategy 2: KD-Tree Join (Both Large)                                │
│   ┌─────────────────────────────────────────────────────────────┐     │
│   │  Table A                    Table B                          │     │
│   │  ┌──────────┐             ┌──────────┐                      │     │
│   │  │ Points   │             │ Points   │                      │     │
│   │  └────┬─────┘             └────┬─────┘                      │     │
│   │       │                        │                              │     │
│   │       ▼                        ▼                              │     │
│   │  ┌──────────┐             ┌──────────┐                      │     │
│   │  │ Build    │             │ Build    │                      │     │
│   │  │ KD-Tree  │             │ KD-Tree  │                      │     │
│   │  └────┬─────┘             └────┬─────┘                      │     │
│   │       │                        │                              │     │
│   │       └──────────┬─────────────┘                              │     │
│   │                  ▼                                            │     │
│   │  ┌──────────────────────────────────────┐                   │     │
│   │  │  For each partition pair:             │                   │     │
│   │  │  Query KD-tree with bounding box      │                   │     │
│   │  │  Filter by exact distance predicate   │                   │     │
│   │  └──────────────────────────────────────┘                   │     │
│   └─────────────────────────────────────────────────────────────┘     │
│                                                                         │
│   Strategy 3: Grid-based Join (Uniform Distribution)                   │
│   ┌─────────────────────────────────────────────────────────────┐     │
│   │  ┌────┬────┬────┬────┬────┐                                │     │
│   │  │ A1 │ A2 │ A3 │ A4 │ A5 │  Points assigned to cells     │     │
│   │  ├────┼────┼────┼────┼────┤                                │     │
│   │  │ B1 │ B2 │ B3 │ B4 │ B5 │  Polygons assigned to cells   │     │
│   │  ├────┼────┼────┼────┼────┤                                │     │
│   │  │ C1 │ C2 │ C3 │ C4 │ C5 │  Only same-cell pairs join    │     │
│   │  ├────┼────┼────┼────┼────┤                                │     │
│   │  │ D1 │ D2 │ D3 │ D4 │ D5 │                                │     │
│   │  └────┴────┴────┴────┴────┘                                │     │
│   └─────────────────────────────────────────────────────────────┘     │
│                                                                         │
└─────────────────────────────────────────────────────────────────────────┘

Detailed Explanation

Geospatial data processing in PySpark involves handling spatial coordinates, performing geometric operations, and executing spatial joins at scale. The fundamental challenge is that spatial data has geometric properties (points, lines, polygons) that cannot be directly compared using standard relational operators. Spatial operations require specialized indexing structures and join strategies that account for the two-dimensional nature of geographic data.

Coordinate Reference Systems (CRS) define how spatial coordinates map to locations on Earth. The most common systems are WGS84 (EPSG:4326) for latitude/longitude coordinates and UTM (Universal Transverse Mercator) for projected coordinates in meters. Converting between coordinate systems is essential for accurate distance calculations—using latitude/longitude directly for distance introduces significant errors due to the Earth's curvature. The Haversine formula accounts for this by computing great-circle distances on a sphere.

Spatial indexing structures enable efficient querying of spatial data. Without indexing, a spatial join requires checking every pair of geometries—a quadratic operation that is infeasible for large datasets. H3 (Uber's hexagonal index) partitions the world into hierarchical hexagonal cells, where each cell has a unique identifier. S2 (Google's library) projects the sphere onto a cube and uses quadtree cells. Geohashes use a space-filling curve (Morton order) to encode 2D coordinates as 1D strings, enabling efficient prefix-based proximity queries.

Spatial joins in PySpark follow one of three strategies depending on the data sizes and distribution. Broadcast joins are optimal when one dataset is small enough to fit in memory—all partitions of the large dataset can check against the broadcast dataset. KD-tree joins build a spatial index on each partition and exchange only bounding-box candidates across partitions, reducing the number of exact geometry checks. Grid-based joins assign geometries to grid cells and only join geometries that share a cell, which works well for uniformly distributed data but fails with skewed distributions.

Point-in-polygon operations are the most common spatial query, determining whether a geographic point lies within a polygon boundary. This operation requires the ray-casting algorithm or winding number algorithm, both of which have O(n) complexity where n is the number of polygon vertices. For large polygon datasets, spatial indexing dramatically reduces the search space by first filtering polygons whose bounding boxes contain the point, then performing the exact containment test only on candidates.

Trajectory analysis processes sequences of location points to extract movement patterns. This requires temporal ordering, stay-point detection (identifying locations where an entity remained stationary), and trajectory segmentation (dividing continuous movement into meaningful segments). Stay-point detection uses both distance and time thresholds—a stay point exists when an entity remains within a distance threshold for at least a minimum duration. Trajectory segmentation uses stay points as boundaries to create individual trip records.

Mathematical Foundations

Definition: Haversine Distance

The great-circle distance between two points $(\phi_1, \lambda_1)$ and $(\phi_2, \lambda_2)$ on a sphere of radius $R$ is:

d = 2R \cdot \arcsin\left(\sqrt{\sin^2\left(\frac{\phi_2 - \phi_1}{2}\right) + \cos\phi_1 \cos\phi_2 \sin^2\left(\frac{\lambda_2 - \lambda_1}{2}\right)}\right)

Spatial Index Resolution

For H3 index resolution $r$ (0-15), the average hexagon area is:

A_r \approx \frac{A_{\text{earth}}}{2 \times 3^{r} + 10 \times 3^{r-1} + 20 \times 3^{r-2} + \cdots}

Resolution 8 gives $\sim$ 464m edge length; resolution 9 gives $\sim$ 174m.

Spatial Join Complexity

Without indexing, a spatial join between datasets of sizes $m$ and $n$ has complexity $O(m \times n)$ . With grid-based spatial indexing dividing the space into $k$ cells:

O\left(\frac{m \times n}{k} + (m + n) \cdot \log k\right)

when data is uniformly distributed across cells.

Convex Hull Area

For polygon $P$ with vertices $(x_1, y_1), \ldots, (x_n, y_n)$ , the shoelace formula gives area:

A = \frac{1}{2} \left| \sum_{i=1}^{n} (x_i y_{i+1} - x_{i+1} y_i) \right|

Nearest Neighbor Search

For $k$ -NN with spatial index, the search prunes cells whose minimum bounding rectangle distance exceeds the current $k$ -th nearest distance $d_k$ :

\text{Prune}(C) \iff \min_{p \in C.\text{MBR}} \text{dist}(q, p) > d_k

Key Insight

Spark's ST_* functions from Sedona compute exact geometry operations, while H3/S2 provide approximate but indexable representations. Use exact geometry for correctness-critical joins, approximate indexing for exploratory analysis at scale.

Summary

Geospatial processing on Spark combines exact geometric operations (Haversine, polygon intersection) with hierarchical spatial indices (H3, S2). Spatial indexing reduces join complexity from $O(mn)$ to near-linear. Coordinate reference system transformations must precede any distance calculations.

Key Concepts Table

Concept	Description	Use Case
H3 Index	Hexagonal hierarchical spatial index	Location analytics, ride-sharing
S2 Cell	Quadtree cell on sphere	Maps, street view, global coverage
Geohash	Prefix-based 1D encoding	Proximity search, caching
Bounding Box	Minimum enclosing rectangle	Index filtering, quick rejection
Point-in-Polygon	Containment test for points	Store finder, zone assignment
KNN Query	K nearest neighbors	Nearest facility, POI search
Buffer	Points within distance	Service area, proximity analysis
Convex Hull	Smallest convex polygon	Area of coverage, boundary
Spatial Join	Join based on geometric predicates	Region-based aggregation
Trajectory	Ordered sequence of locations	Movement analysis, route optimization

Code Examples

Coordinate Operations and Distance Calculations

from pyspark.sql import SparkSession
from pyspark.sql.functions import *
from pyspark.sql.types import *
import math

spark = SparkSession.builder \
    .appName("GeospatialData") \
    .getOrCreate()

# Define UDFs for spatial calculations
@udf(returnType=DoubleType())
def haversine_distance(lat1, lon1, lat2, lon2):
    """Calculate great-circle distance between two points using Haversine formula."""
    R = 6371000  # Earth's radius in meters
    
    lat1_rad = math.radians(lat1)
    lat2_rad = math.radians(lat2)
    delta_lat = math.radians(lat2 - lat1)
    delta_lon = math.radians(lon2 - lon1)
    
    a = (math.sin(delta_lat / 2) ** 2 +
         math.cos(lat1_rad) * math.cos(lat2_rad) *
         math.sin(delta_lon / 2) ** 2)
    c = 2 * math.atan2(math.sqrt(a), math.sqrt(1 - a))
    
    return R * c

@udf(returnType=StringType())
def geohash_encode(lat, lon, precision=12):
    """Encode coordinates to geohash."""
    BASE32 = '0123456789bcdefghjkmnpqrstuvwxyz'
    
    lat_range = [-90.0, 90.0]
    lon_range = [-180.0, 180.0]
    geohash = []
    bit = 0
    ch = 0
    is_lon = True
    
    while len(geohash) < precision:
        if is_lon:
            mid = (lon_range[0] + lon_range[1]) / 2
            if lon >= mid:
                ch |= (1 << (4 - bit))
                lon_range[0] = mid
            else:
                lon_range[1] = mid
        else:
            mid = (lat_range[0] + lat_range[1]) / 2
            if lat >= mid:
                ch |= (1 << (4 - bit))
                lat_range[0] = mid
            else:
                lat_range[1] = mid
        
        is_lon = not is_lon
        bit += 1
        
        if bit == 5:
            geohash.append(BASE32[ch])
            bit = 0
            ch = 0
    
    return ''.join(geohash)

# Create point data (restaurants)
restaurants = [
    ("R001", "Pizza Palace", 40.7128, -74.0060, "Italian"),
    ("R002", "Burger Barn", 40.7580, -73.9855, "American"),
    ("R003", "Sushi Spot", 40.7484, -73.9857, "Japanese"),
    ("R004", "Taco Town", 40.7614, -73.9776, "Mexican"),
    ("R005", "Noodle Nest", 40.7831, -73.9712, "Chinese"),
]
restaurants_df = spark.createDataFrame(restaurants, [
    "id", "name", "latitude", "longitude", "cuisine"
])

# Create polygon data (neighborhoods)
neighborhoods = [
    ("N001", "Manhattan", "POLYGON((-74.01 40.70, -73.97 40.70, -73.97 40.78, -74.01 40.78, -74.01 40.70))"),
    ("N002", "Brooklyn", "POLYGON((-73.99 40.57, -73.94 40.57, -73.94 40.70, -73.99 40.70, -73.99 40.57))"),
]
neighborhoods_df = spark.createDataFrame(neighborhoods, [
    "id", "name", "geometry"
])

# Add geohash to restaurants
restaurants_with_geohash = restaurants_df \
    .withColumn("geohash", geohash_encode(col("latitude"), col("longitude"), lit(8)))

# Find restaurants within 1km of a reference point
reference_lat = 40.7580
reference_lon = -73.9855

nearby_restaurants = restaurants_df \
    .withColumn("distance_meters", 
        haversine_distance(
            lit(reference_lat), lit(reference_lon),
            col("latitude"), col("longitude")
        )
    ) \
    .filter(col("distance_meters") <= 1000) \
    .orderBy("distance_meters")

print("Restaurants within 1km:")
nearby_restaurants.show(truncate=False)

Spatial Joins and Indexing

# Point-in-polygon using ray-casting algorithm
@udf(returnType=BooleanType())
def point_in_polygon(lat, lon, polygon_wkt):
    """Check if point is inside polygon using ray-casting."""
    # Parse WKT polygon (simplified)
    coords_str = polygon_wkt.replace("POLYGON((", "").replace("))", "")
    coords = []
    for pair in coords_str.split(","):
        lng, lt = pair.strip().split()
        coords.append((float(lng), float(lt)))
    
    # Ray-casting algorithm
    n = len(coords)
    inside = False
    j = n - 1
    
    for i in range(n):
        xi, yi = coords[i]
        xj, yj = coords[j]
        
        if ((yi > lat) != (yj > lat)) and (lon < (xj - xi) * (lat - yi) / (yj - yi) + xi):
            inside = not inside
        j = i
    
    return inside

# Spatial join: assign restaurants to neighborhoods
spatial_join_df = restaurants_df.crossJoin(neighborhoods_df) \
    .filter(point_in_polygon(col("latitude"), col("longitude"), col("geometry"))) \
    .select(
        restaurants_df.id,
        restaurants_df.name,
        neighborhoods_df.name.alias("neighborhood"),
        restaurants_df.cuisine
    )

print("Restaurants by neighborhood:")
spatial_join_df.show(truncate=False)

# KNN query: find 3 nearest restaurants to a point
from pyspark.sql.window import Window

knn_df = restaurants_df \
    .withColumn("distance", 
        haversine_distance(
            lit(40.7580), lit(-73.9855),
            col("latitude"), col("longitude")
        )
    ) \
    .withColumn("rank", rank().over(Window.orderBy("distance"))) \
    .filter(col("rank") <= 3)

print("3 nearest restaurants:")
knn_df.show(truncate=False)

H3 Index Operations

# H3 index functions (simplified implementation)
@udf(returnType=LongType())
def h3_latlng_to_cell(lat, lng, resolution):
    """Convert lat/lng to H3 cell index."""
    # This is a simplified version - use h3 library in production
    # H3 uses icosahedron projection and hexagonal tessellation
    return int(f"8{resolution}{abs(int(lat*1000))}{abs(int(lng*1000))}")

@udf(returnType=ArrayType(DoubleType()))
def h3_cell_to_boundary(cell_index):
    """Get polygon boundary of H3 cell."""
    # Simplified - returns approximate boundary
    lat = (cell_index % 10000) / 1000.0
    lng = ((cell_index // 10000) % 10000) / 1000.0
    return [lat - 0.001, lng - 0.001, lat + 0.001, lng + 0.001]

# Add H3 index at different resolutions
h3_restaurants = restaurants_df \
    .withColumn("h3_res8", h3_latlng_to_cell(col("latitude"), col("longitude"), lit(8))) \
    .withColumn("h3_res10", h3_latlng_to_cell(col("latitude"), col("longitude"), lit(10))) \
    .withColumn("h3_res12", h3_latlng_to_cell(col("latitude"), col("longitude"), lit(12)))

# Aggregate by H3 cell (spatial clustering)
h3_clusters = h3_restaurants \
    .groupBy("h3_res8") \
    .agg(
        count("*").alias("restaurant_count"),
        avg("latitude").alias("center_lat"),
        avg("longitude").alias("center_lng"),
        collect_list("cuisine").alias("cuisines")
    )

print("H3 clusters (resolution 8):")
h3_clusters.show(truncate=False)

# Find neighboring cells
@udf(returnType=ArrayType(LongType()))
def h3_grid_disk(cell_index, k):
    """Get cells within k steps of given cell."""
    # Simplified - returns grid disk neighbors
    return [cell_index + i for i in range(-k, k+1) if i != 0]

# Find all restaurants within 2 H3 cells of a reference
reference_cell = h3_latlng_to_cell(lit(40.7580), lit(-73.9855), lit(8))
neighbor_cells = h3_grid_disk(reference_cell, lit(2))

nearby_h3 = h3_restaurants.filter(
    col("h3_res8").isin(neighbor_cells)
)

Performance Metrics

Operation	1K Points	100K Points	10M Points	100M Points
Haversine Distance (all pairs)	< 1 sec	5-10 sec	5-10 min	5-10 hours
Point-in-Polygon (100 polys)	< 1 sec	2-5 sec	30-60 sec	5-10 min
Spatial Join (broadcast)	< 1 sec	1-3 sec	10-20 sec	2-5 min
KNN Query (k=10)	< 1 sec	1-2 sec	5-10 sec	1-2 min
H3 Index Build	< 1 sec	1-2 sec	10-20 sec	2-5 min
Geohash Encode	< 1 sec	1-2 sec	10-20 sec	2-5 min
Buffer Operation	< 1 sec	3-8 sec	60-120 sec	10-20 min
Convex Hull	< 1 sec	1-3 sec	10-20 sec	2-5 min
Trajectory Segmentation	< 1 sec	2-5 sec	20-40 sec	3-8 min
Spatial Clustering (DBSCAN)	< 1 sec	5-15 sec	2-5 min	30-60 min

Best Practices

Use spatial indexing (H3, S2, Geohash) before spatial joins to reduce the number of exact geometry checks from O(n×m) to O(n×k) where k << m
Broadcast small datasets when performing spatial joins—the broadcast threshold should be based on the polygon complexity, not just file size
Convert to projected CRS (UTM) for accurate distance calculations—do not use latitude/longitude directly for Euclidean distance
Use bounding box pre-filtering before expensive geometric operations to quickly reject non-candidate pairs
Partition spatial data by geohash prefix to ensure nearby points land in the same partition, reducing shuffle during joins
Cache H3 cell assignments when performing multiple spatial queries on the same dataset to avoid recomputing index values
Implement spatial partitioning for large polygon datasets using R-tree or quadtree to enable efficient point-in-polygon queries
Use st_point and st_contains from Sedona/Mosaic libraries for production spatial operations instead of custom UDFs
Monitor spatial skew when using grid-based partitioning—urban areas have much higher point density than rural areas
Implement trajectory compression (Douglas-Peucker algorithm) to reduce the number of points in trajectories while preserving shape
Use geohash prefix matching for proximity search—two points within 1km will share a geohash prefix of length 5-6
Avoid cross-join for spatial operations when possible—use broadcast or indexed joins to prevent quadratic explosion

See also: Snowflake Time Travel (snowflake/02), Kafka CDC (kafka/04), Airflow DAGs (airflow/02)

Geospatial Data Processing in PySpark

🌍 Geospatial Data Processing in PySpark

Architecture Diagram

Detailed Explanation

Mathematical Foundations

Definition: Haversine Distance

Spatial Index Resolution

Spatial Join Complexity

Convex Hull Area

Nearest Neighbor Search

Key Insight

Summary

Key Concepts Table

Code Examples

Coordinate Operations and Distance Calculations

Spatial Joins and Indexing

H3 Index Operations

Performance Metrics

Best Practices

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