6/24/2023 0 Comments Tessellation definition arcgis![]() ![]() withColumn("mta_tax", col("mta_tax").cast(DoubleType())) withColumn("extra", col("surcharge").cast(DoubleType())) ![]() withColumn("fare_amount", col("fare_amount").cast(DoubleType())) withColumn("trip_distance", col("trip_distance").cast(DoubleType())) withColumn("passener_count", col("Passenger_Count").cast(IntegerType())) withColumn("pickup_longitude", col("pickup_longitude").cast(DoubleType())) withColumn("pickup_latitude", col("pickup_latitude").cast(DoubleType())) withColumn("dropoff_longitude", col("dropoff_longitude").cast(DoubleType())) withColumn("dropoff_latitude", col("dropoff_latitude").cast(DoubleType())) withColumnRenamed("rate_code", "rate_code_id") withColumn("dropoff_datetime", from_unixtime(unix_timestamp(col("dropoff_datetime")))) withColumn("pickup_datetime", from_unixtime(unix_timestamp(col("pickup_datetime")))) Taxis.withColumnRenamed("vendor_name", "vendor_id") Assuming the data is already loaded into a Dataframe, it can be processed using the code below. The next step is to tidy up the taxi data so that it can be used effectively. withColumn("mosaic_index", mos.grid_tessellateexplode(col("geometry"), lit(8))) withColumn("geometry", mos.st_geomfromwkt("the_geom")) The command for doing this is shown here. There are now polygons comprising of tessellating H3 coordinates, where the polygon lines go through H3 coordinates at the boundary. The reason for doing this will become apparent later. This information includes each H3 index and wkb chips for plotting. Once in Mosaic geometry form, the command ‘grid_tessellateexplode’ allows for tessellation of the polygon areas, using H3 coordinates as well as doing the explode operation, freeing up further information. This needs to be loaded in and have the geometry (Well-known text) converted into a Mosaic geometry. The first step is to process the precinct data. Mosaic combined with Databricks is the perfect tool for such a task. This requires the use of distributed computing to handle this large amount of data. Focusing on the first week of 2016, the yellow taxi data alone has approximately 2.3M journies. This requires determining which precinct regions the taxi pickup and dropoff points land. The objective is to generate a density map showing what precinct areas had the most taxi activity throughout the year. ![]() Additionally to this, police precinct polygon data was used. Alternatively, the dataset is included within Azure Databricks and can be found in the mounted dataset directory on the Databricks workspace. The link to the raw data can be found here. Taxi Activity DensityĪ use case that is similar to school building density calls upon the well-known New York taxi data. The examples shown below are a limited selection of the kind of ways Mosaic can be used to deliver revenue-generating projects. With such promising capabilities, it is nessaccary to demonstrate these in relatable business use cases. Generating cell IDs as integers allows the Mosaic to take advantage of Delta Lakes Z-ordering, making for highly optimised storage. Each H3 cell ID consists of an integer (or string). H3 comes with 16 resolutions to pick from. Hexagons naturally tessellate, making it a useful grid system for passing information and providing high accuracy. In other words, it utilises hexagonal shapes that form a grid globally across a map. H3 is a grid indexing system, originally developed by Uber as a Discrete Global Grid System (DGGS). This article is going to discuss how to use Mosaic and give people a flavour of the sort of use cases you may want to explore. This also paves the way for any geospatial-related ML or AI that is desired alongside any ETL. It aims to bring performance and scalability to design architectures. Is there a way to integrate GIS into the current data engineering pipeline one may have, without relying on external tools? The answer is yes! Databricks, a popular big data tool that is powered by a powerful Spark engine, has released a GIS tool named ‘ Mosaic’. Commercial tools such as ArcGIS, can come at a hefty additional expense. Many big data projects that require GIS call upon external software tools to handle the data. Although there are libraries available such as GeoPandas, to help with geospatial operations, they’re often not scalable when dealing with copious amounts of data. In many contexts GIS data is large! Due to it comprising many layers of spatial information. ![]() The topic of Geographic Information Systems (GIS), finds its way into the data analytics arena all too often without much consideration of how to implement it within big data solutions. ![]()
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