Azure Data Explorer's Advanced Geospatial Analysis: Breaking New Ground
In the rapidly evolving landscape of data analytics, Azure Data Explorer (ADX) continues to push boundaries with its latest enhancement to geospatial capabilities. These new features, meticulously developed by Michael Brichko and the Kusto team, represent a significant advancement in how organizations can analyze and derive insights from location-based data directly within the Kusto Query Language (KQL). The Evolution of Geospatial Analysis in ADX Azure Data Explorer has long supported basic geospatial operations, but this latest release dramatically expands its capabilities with functions that address specific analytical challenges faced by data engineers and analysts working with spatial data. New Powerful Lookup Plugins and Joins At the core of this update are powerful additions that solve complex spatial relationship problems: the geo_polygon_lookup and geo_line_lookup plugins, alongside comprehensive geospatial join capabilities. geo_polygon_lookup This plugin efficiently determines relationships between points and polygons, answering questions like "which sales territory contains this customer?" or "which service zone covers this location?": let territories = datatable(region_name:string, polygon:dynamic) [ "Northeast", dynamic({"type":"Polygon", "coordinates":[[[-73.97375,40.74300],[-73.98653,40.75486],[-73.99910,40.74112],[-73.97375,40.74300]]]}), "Southwest", dynamic({"type":"Polygon","coordinates":[[[2.57564,48.76956],[2.42009,49.05163],[2.10167,48.80113],[2.57564,48.76956]]]}), ]; let customer_locations = datatable(customer_id:string, longitude:real, latitude:real) [ "Customer1", -73.98000, 40.74800, "Customer2", 2.50000, 48.90000, "Customer3", 10.00000, 50.00000 ]; customer_locations | evaluate geo_polygon_lookup(territories, polygon, longitude, latitude) | project customer_id, region_name The performance benefits here are substantial - instead of complex self-joins or multi-step operations, this plugin handles the spatial relationship calculations in a single, optimized operation. geo_line_lookup Similarly, the geo_line_lookup plugin identifies lines (like highways, pipelines, or power lines) within a specified distance of points: let infrastructure = datatable(line_id:string, line:dynamic) [ "Highway 101", dynamic({"type":"LineString","coordinates":[[-122.40297,37.79329],[-122.38855,37.77867]]}), "Main Pipeline", dynamic({"type":"LineString","coordinates":[[-118.35645,34.17247],[-118.32962,34.09873]]}), ]; let maintenance_reports = datatable(report_id:string, longitude:real, latitude:real, issue:string) [ "R001", -122.39500, 37.78500, "Debris", "R002", -118.34000, 34.15000, "Leak", "R003", -120.00000, 36.00000, "Damage" ]; maintenance_reports | evaluate geo_line_lookup(infrastructure, line, longitude, latitude, 500) // within 500 meters | project report_id, line_id, issue This capability is invaluable for infrastructure management, transportation analysis, and network optimization scenarios. Advanced Geospatial Joins Beyond the lookup plugins, ADX now provides comprehensive support for various geospatial join strategies. These capabilities allow for sophisticated spatial analysis using different geo-hashing approaches: // Join locations using S2 cells let locations1 = datatable(name: string, longitude: real, latitude: real) [ "Store A", -0.12433080766874127, 51.51115841361647, "Store B", -0.12432651341458723, 51.511160848670585, "Store C", -0.12432466939637266, 51.51115959669167, "Store D", 1, 1, ]; let customer_visits = datatable(customer_id: string, longitude: real, latitude: real) [ "Customer1", -0.12432668105284961, 51.51115938802832 ]; let s2_join_level = 22; // Higher level = more precision locations1 | extend hash = geo_point_to_s2cell(longitude, latitude, s2_join_level) | join kind = inner ( customer_visits | extend hash = geo_point_to_s2cell(longitude, latitude, s2_join_level) ) on hash | project name, customer_id For more complex proximity requirements, the H3 geo-hashing system with neighbor awareness provides an elegant solution: // Join locations using H3 cells with neighbor awareness let retail_locations = datatable(store_name: string, longitude: real, latitude: real) [ "Downtown", -0.12433080766874127, 51.51115841361647, "Westside", -0.12432651341458723, 51.511160848670585, "Eastside", -0.12432466939637266, 51.51115959669167, "Remote", 1, 1, ]; let customer_events = datatable(event_id: string, longitude: real, latitude: real) [ "Purchase123", -0.12432668105284961, 51.51115938802832 ]; let to_hash = (lng: real, lat: real) { let h3_hash_level = 14; // Precision level let h3_hash = geo_point_to_h3cell(lng, lat, h3_hash_level); array_concat(pack_array(h3_hash), geo_h3cell_neighbors(h3_hash)) }; retail_locations | extend hash = to_hash(longitude, latitude) | mv-expand hash to typeof(string) | join kind = inner ( customer_events | extend hash = to_hash(longitude, latitude) | mv-expand hash to typeof(string) ) on hash | distinct store_name, event_id For proximity-based joins that require precise distance calculations, the buffer-based approach provides exceptional flexibility: // Join locations based on precise distance buffers let venues = datatable(venue_name: string, longitude: real, latitude: real) [ "O2 Entrance", 0.005889454501716321, 51.50238626916584, "North Gate", 0.0009625704125020596, 51.50385432770013, "Greenwich Park", 0.0009395106042404677, 51.47700456557013, ]; let points_of_interest = datatable(poi_id: string, longitude: real, latitude: real) [ "O2 Arena", 0.003159306017352037, 51.502929224128394 ] | extend buffer = geo_point_buffer(longitude, latitude, 300, 0.1); // 300-meter radius venues | evaluate geo_polygon_lookup(points_of_interest, buffer, longitude, latitude) | project venue_name, poi_id Beyond joins and lookups, ADX now offers specialized functions for precise geospatial calculations: geo_from_wkt() The new geo_from_wkt() function bridges the gap between different geospatial systems by converting Well-Known Text (WKT) format - a standard in GIS systems - into GeoJSON objects that ADX can process: // Convert WKT to GeoJSON for further analysis let wkt_shapes = datatable(shape_id:string, wkt_representation:string) [ "City Boundary", "POLYGON((-122.406417 37.785834, -122.403984 37.787343, -122.401826 37.785069, -122.404681 37.782928, -122.406417 37.785834))", "Highway", "LINESTRING(-122.33707 47.60924, -122.32553 47.61803)" ]; wkt_shapes | extend geojson_shape = geo_from_wkt(wkt_representation) Result: shape_id wkt_representation geojson_shape City Boundary POLYGON((-122.406417 37.785834, -122.403984 37.787343, -122.401826 37.785069, -122.404681 37.782928, -122.406417 37.785834)) { "type": "Polygon", "coordinates": [ [ [ -122.406417, 37.785834 ], [ -122.403984, 37.787343 ], [ -122.401826, 37.785069 ], [ -122.404681, 37.782928 ], [ -122.406417, 37.785834 ] ] ] } Highway LINESTRING(-122.33707 47.60924, -122.32553 47.61803) { "type": "LineString", "coordinates": [ [ -122.33707, 47.60924 ], [ -122.32553, 47.61803 ] ] } This function supports all standard geometry types including Point, LineString, Polygon, MultiPoint, MultiLineString, MultiPolygon, and GeometryCollection. Route Analysis Functions Two complementary functions provide sophisticated route analysis capabilities: geo_line_interpolate_point geo_line_interpolate_point() calculates a point at a specified fraction along a line: // Find points along a delivery route at 25%, 50%, and 75% of the journey let delivery_routes = datatable(route_id:string, route:dynamic) [ "Route A", dynamic({"type":"LineString","coordinates":[[-122.33707, 47.60924], [-122.32553, 47.61803]]}) ]; delivery_routes | extend start_point = geo_line_interpolate_point(route, 0), quarter_point = geo_line_interpolate_point(route, 0.25), midpoint = geo_line_interpolate_point(route, 0.5), three_quarters = geo_line_interpolate_point(route, 0.75), end_point = geo_line_interpolate_point(route, 1) Result: route_id route start_point quarter_point midpoint three_quarters end_point Route A { "type": "LineString", "coordinates": [ [ -122.33707, 47.60924 ], [ -122.32553, 47.61803 ] ] } { "type": "Point", "coordinates": [ -122.33707, 47.609240000000007 ] } { "type": "Point", "coordinates": [ -122.33418536369042, 47.611437608491833 ] } { "type": "Point", "coordinates": [ -122.33130048494128, 47.613635144663533 ] } { "type": "Point", "coordinates": [ -122.3284153637215, 47.615832608503482 ] } { "type": "Point", "coordinates": [ -122.32553000000002, 47.61803 ] } geo_line_locate_point geo_line_locate_point() performs the inverse operation, determining how far along a route a specific point is located: // Calculate what percentage of the route has been completed let active_routes = datatable(vehicle_id:string, route:dynamic, current_long:real, current_lat:real) [ "Truck1", dynamic({"type":"LineString","coordinates":[[-122.33707, 47.60924], [-122.32553, 47.61803]]}), -122.33000, 47.61500 ]; active_routes | extend completion_percentage = geo_line_locate_point(route, current_long, current_lat) * 100 | project vehicle_id, completion_percentage Result: vehicle_id completion_percentage Truck1 63,657018697669 Together, these functions enable precise tracking and analysis of movement along routes, critical for logistics, fleet management, and transportation applications. Closest Point Calculations Two new functions address the need to find exact points on geometric features that are closest to reference points: geo_closest_point_on_line geo_closest_point_on_line() identifies the exact point on a line nearest to a reference point: print point_on_line = geo_closest_point_on_line(-115.199625, 36.210419, dynamic({ "type":"LineString","coordinates":[[-115.115385,36.229195],[-115.136995,36.200366],[-115.140252,36.192470],[-115.143558,36.188523],[-115.144076,36.181954],[-115.154662,36.174483],[-115.166431,36.176388],[-115.183289,36.175007],[-115.192612,36.176736],[-115.202485,36.173439],[-115.225355,36.174365]]})) geo_closest_point_on_polygon geo_closest_point_on_polygon() calculates the closest point on a polygon boundary to a given location: let central_park = dynamic({"type":"Polygon","coordinates":[[[-73.9495,40.7969],[-73.95807266235352,40.80068603561921],[-73.98201942443848,40.76825672305777],[-73.97317886352539,40.76455136505513],[-73.9495,40.7969]]]}); print geo_closest_point_on_polygon(-73.9839, 40.7705, central_park) These functions enable precise proximity analysis for applications ranging from emergency response to facility planning. Technical Implementation and Performance Considerations What makes these new geospatial features particularly impressive is their integration with ADX's query engine. The functions leverage ADX's columnar storage and parallel processing capabilities to perform complex spatial operations efficiently at scale. For large datasets, the lookup plugins use optimized spatial indexing techniques to avoid the performance pitfalls that typically plague geospatial joins. This means that operations that might take minutes or hours in traditional GIS systems can execute in seconds on properly optimized ADX clusters. Real-World Applications The new geospatial capabilities in ADX enable sophisticated solutions across industries: Telecommunications Network operators can analyze signal coverage against population density polygons to identify optimization opportunities: let cell_towers = datatable(tower_id:string, signal_longitude:real, signal_latitude:real, signal_strength:int) [ "Tower1", -73.98000, 40.74800, 85, "Tower2", -73.97500, 40.75200, 72, "Tower3", -73.99000, 40.74100, 90 ]; let population_zones = datatable(zone_name:string, population_density:int, zone_polygon:dynamic) [ "Midtown", 25000, dynamic({"type":"Polygon", "coordinates":[[[-73.97375,40.74300],[-73.98653,40.75486],[-73.99910,40.74112],[-73.97375,40.74300]]]}) ]; let signal_threshold = 80; let density_threshold = 20000; cell_towers | evaluate geo_polygon_lookup(population_zones, zone_polygon, signal_longitude, signal_latitude) | summarize avg_signal_strength=avg(signal_strength) by zone_name, population_density | where avg_signal_strength < signal_threshold and population_density > density_threshold Energy Pipeline operators can identify sensors that might be affected by maintenance on nearby infrastructure: let planned_maintenance = datatable(maintenance_id:string, maintenance_longitude:real, maintenance_latitude:real, start_time:datetime) [ "M001", -118.34500, 34.16000, datetime(2025-04-15), "M002", -118.33000, 34.10000, datetime(2025-04-20) ]; let pipelines = datatable(pipeline_id:string, pipeline_path:dynamic) [ "Main", dynamic({"type":"LineString","coordinates":[[-118.35645,34.17247],[-118.32962,34.09873]]}), "Secondary", dynamic({"type":"LineString","coordinates":[[-118.36000,34.15000],[-118.34000,34.08000]]}) ]; let sensors = datatable(sensor_id:string, pipeline_id:string, sensor_type:string, next_scheduled_reading:datetime) [ "S001", "Main", "Pressure", datetime(2025-04-16), "S002", "Main", "Flow", datetime(2025-04-18), "S003", "Secondary", "Temperature", datetime(2025-04-22) ]; planned_maintenance | evaluate geo_line_lookup(pipelines, pipeline_path, maintenance_longitude, maintenance_latitude, 500) | join kind =inner sensors on pipeline_id | project maintenance_id, sensor_id, sensor_type, next_scheduled_reading Transportation & Logistics Fleet operators can optimize routing by analyzing historical trip data against road networks: let completed_trips = datatable(trip_id:string, trip_success:bool, incident_longitude:real, incident_latitude:real, incident_type:string) [ "T001", false, -122.40000, 47.61000, "Delay", "T002", false, -122.39500, 47.60800, "Traffic", "T003", false, -122.41000, 47.62000, "Weather" ]; let road_networks = datatable(road_name:string, road_type:string, road_path:dynamic) [ "5th Avenue", "Urban", dynamic({"type":"LineString","coordinates":[[-122.40297,47.59329],[-122.40297,47.62000]]}), "Highway 99", "Highway", dynamic({"type":"LineString","coordinates":[[-122.39000,47.60000],[-122.41000,47.63000]]}) ]; completed_trips | where trip_success == false | evaluate geo_line_lookup(road_networks, road_path, incident_longitude, incident_latitude, 100) | summarize incidents_count=count() by road_name, road_type, incident_type | order by incidents_count desc Environmental Monitoring Researchers can correlate sensor readings with geographic zones to track pollution dispersion: let sensor_readings = datatable(sensor_id:string, reading_type:string, reading_value:real, timestamp:datetime, sensor_longitude:real, sensor_latitude:real) [ "ENV001", "PM2.5", 35.2, datetime(2025-04-01), -122.33000, 47.61500, "ENV002", "PM2.5", 22.8, datetime(2025-04-01), -122.34000, 47.62000, "ENV003", "PM2.5", 41.3, datetime(2025-04-01), -122.32000, 47.60500 ]; let air_quality_zones = datatable(zone_name:string, zone_boundary:dynamic) [ "Downtown", dynamic({"type":"Polygon","coordinates":[[[-122.34000,47.60000],[-122.34000,47.62000],[-122.32000,47.62000],[-122.32000,47.60000],[-122.34000,47.60000]]]}) ]; sensor_readings | where reading_type == "PM2.5" and timestamp > ago(7d) | evaluate geo_polygon_lookup(air_quality_zones, zone_boundary, sensor_longitude, sensor_latitude) | summarize avg_reading=avg(reading_value) by zone_name, bin(timestamp, 1h) Getting Started with ADX Geospatial Analysis The geospatial functions are available in all ADX clusters without requiring any special configuration. For optimal performance with large spatial datasets: Consider partitioning strategies that account for spatial locality Pre-compute and cache complex geometries when dealing with static boundaries Monitor query performance to identify opportunities for optimization Conclusion Azure Data Explorer's enhanced geospatial capabilities represent a significant advancement in making spatial analysis accessible and performant within a cloud analytics platform. By eliminating the need for specialized GIS tools and complex data movement, these features enable organizations to derive deeper insights from location-based data more efficiently than ever before. Whether you're analyzing telecommunications networks, optimizing logistics operations, managing energy infrastructure, or monitoring environmental patterns, ADX now provides the tools to incorporate sophisticated geospatial analysis directly into your data workflows. #AzureDataExplorer #ADX #Kusto #KQL #GeospatialAnalysis #DataAnalytics #CloudComputing #BigData #SpatialIntelligence #BusinessIntelligence #DataEngineering #MicrosoftAzure #DataScience #GIS #Analytics
