Creating hurricane tracks using GeoAnalytics¶

The sample code below uses big data analytics (GeoAnalytics) to reconstruct hurricane tracks using data registered on a big data file share in the GIS.

Note: This functionality is currently only available on ArcGIS Enterprise 10.5 and later and not yet with ArcGIS Online.

Reconstruct tracks¶

Reconstruct tracks is a type of data aggregation tool available in the arcgis.geoanalytics module. This tool works with a layer of point features or polygon features that are time enabled. It first determines which points belong to a track using an identification number or identification string. Using the time at each location, the tracks are ordered sequentially and transformed into a line representing the path of movement.

Data used¶

This sample uses hurricane data collected from 1848 through 2010, totalling over 177,000 points stored as a shapefile. The National Hurricane Center provides similar datasets that can be used for exploratory purposes.

To illustrate the nature of the data, a subset was published as a feature layer so we can visualize it:

Inspect the data attributes¶

Let us query the first layer in hurricane_pts and view its attribute table as a Pandas dataframe.

Create a data store¶

GeoAnalytics server processes big data through big data file share items published on the portal. In our case, the source hurricane data is stored as a shapefile. We created the big data file share by registering the folder containing the shapefile as a data store of type bigDataFileShare.

Let us connect to an ArcGIS Enterprise and use the Datastore Manager to search for big data file shares:

Get the geoanalytics datastores and search it for the registered datasets:

Using the Datastore Manager.search() method with no parameters returns all the datastores registered with the GeoAnalytics Server. We see the all_hurricanes big data file share in the list of datastores, so let's retrieve the datastore:

If there is no big data file share for hurricane track data, we can use the Datastore Manager add_bigdata() method to register a shared folder accessible by the GeoAnalytics Server (see Making your data accessible as a big data file share:

Once a big data file share is registered, the GeoAnalytics server samples all the datasets within the share to discern the schema of the data, including information about the geometry in a dataset. If the dataset is time-enabled, as is required to use some GeoAnalytics Tools, the manifest reports the necessary metadata about how time information is stored as well.

This process can take a few minutes depending on the size of your data. Once processed, querying the manifest property returns the schema as a dictionary. We can use the datasets key to retrieve information about the dataset. As you can see below, the schema is similar to the subset we observed earlier in this sample:

Perform data aggregation using reconstruct tracks tool¶

When you add a big data file share, a corresponding item gets created in your GIS. You can search for it like a regular item and query its layers.

Reconstruct tracks tool¶

The arcgis.geoanalytics.summarize_data module contains the reconstruct_tracks(). We will use this tool to aggregate the numerous points into line segments showing the tracks followed by the various hurricanes occurring between 1848 and 2010. The tool creates a feature layer item as output which we'll visualize on a map and query to see the results.

We can use the arcgis.env module to modify environment settings that geoprocessing and geoanalytics tools use during execution. Set verboseto True to return detailed messaging when running tools.

Run the tool

Inspect the results¶

Let us create a map and load the processed result which is a feature service

Thus we transformed more than 175,000 points into more than 6600 tracks that represents paths taken by individual hurricanes spanning a time period of more than 150 years.

Our input data and the map widget is time enabled, so we can filter these tracks using the set_time_extent method on the map widget, which accepts start_time and end_time parameters to inspect the results for a specific decade, 1860 - 1870:

What can GeoAnalytics do for you?¶

With this sample we just scratched the surface of what big data analysis can do for you. ArcGIS Enterprise at 10.5 packs a powerful set of tools that let you derive huge value from your data. You can do so by asking the right questions. For instance, the weather dataset we examined such could answer important questions such as:

• did the number of hurricanes per season increase over the years?
• which hurricanes travelled the longest distance?
• which hurricanes had the longest duration? Is there a trend?
• how are wind speed and distance travelled correlated?
• how many times in the past century did a hurricane occur within 50 miles of my assets?
• my industry is dependent on tourism, which is heavily impacted by the vagaries of weather. From historical weather data, can I correlate my profits with major weather events? How well is my business insulated from freak weather events?
• do we see any shifts in major hurricane events over the years? is there any shift in when the hurricane season starts?

The ArcGIS API for Python gives you a gateway to easily access the big data tools from your ArcGIS Enterprise. By combining it with other powerful libraries from the pandas and scipy stack and the rich visualization capabilities of the Jupyter notebook, you can extract a huge amount of value from your data, big or small.