DISCLAIMER : Use the provided code at your own risk or you'll blown your credits away!!! (see my comment below the answer)
Answering myself.
It's been tough but I did it.
First of all, I have to say the ArcGIS for Pytho documentation is not very good, at least for the create_drive_time_areas function.
Anyway, the way I performed DTA analysis with a local FeatureClass was:
- converting it to a FeatureSet
- Converting the FeatureSet into a JSON and then into a dict
- use this dict as input to the create_drive_time_areas function
I am going to convert the result of the analysis (now a hosted feature layer in my organizational AGOL account), which is now in the form of a FeatureSet, back to a (in_memory) FeatureClass and do my stuff with arcpy.
Before posting the code I have to warn you:
- as of now, there's no hint in the docs about what should be the format of the input feature of the create_drive_time_areas. From my trial and errors I saw this message
Exception: Invalid format of input layer. url string, feature service Item, feature service instance or dict supported
- the "estimate=True" which should tell you the credit consumption does not work.
Failed to execute (EstimateCredits).
Below is the code I used (adapted from Drive time analysis for opioid epidemic | ArcGIS for Developers , sorry I don't know how to format the code in this site...).
I only left the part where I use the result to perform spatial query with local data (it's up to you but I should have given you enough information so far).
import arcgis, arcpy
from datetime import datetime
from arcgis.features import FeatureLayer
import json
USERNAME="YOUR_USERNAME"
PASSWORD="YOUR_PASSWORD"
IN_FC = r"example.gdb\test"
# Create a FeatureSet object and load in_memory feature class
feature_set = arcpy.FeatureSet()
feature_set.load(IN_FC)
# Connect to GIS
from arcgis.gis import GIS
gis = GIS("https://www.arcgis.com", USERNAME, PASSWORD)
feature_json = feature_set.JSON
feature_dict = json.loads(feature_json)
print(feature_dict)
from arcgis.features.use_proximity import create_drive_time_areas
# Generate service areas
result = create_drive_time_areas(feature_dict, [5],
output_name="TEST"+ str(datetime.now().microsecond),
travel_mode="Walking",
overlap_policy='Dissolve')
# Check type of result
type(result)
# Share results for public consumption
result.share(everyone=True)
# Convert it to a FeatureSet
drive_times_all_layer = result.layers[0]
driveAll_features = drive_times_all_layer.query()
print('Total number of rows in the service area (upto 5 mins) dataset: '+ str(len(driveAll_features.features)))
# Get results as a SpatialDataFrame
driveAll = driveAll_features.sdf
driveAll.head()
