Usually, this can be found by using ESRI’s RESTful API, here’s how I found NYC’s FeatureLayer: nyc_fl = FeatureLayer('') ny_df = pd._layer(nyc_fl) nyc_df = ny_df.iloc After you’ve chosen an area you’ll then need to find the FeatureLayer file associated with that location.

This part is entirely up to you, for this project I’m going to use New York City. Here are all the libraries I used for this analysis: import pandas as pd from datetime import datetime from IPython.display import Image from IPython.display import HTML import matplotlib.pyplot as plt import sys import arcgis from arcgis.gis import GIS from import apply, clip, remap, colormap from arcgis.geometry import lengths, areas_and_lengths, project from arcgis.geometry import Point, Polyline, Polygon, Geometry from arcgis.geocoding import geocode from arcgis.features import FeatureLayer 1. Using ArcGIS libraries with python gives you the ability to analyze shapefiles, display Landsat satellite imagery, create data maps, and much more all within a Jupyter notebook! There are a limitless amount of data science projects you can create using this library but for this article, I’ll focus on a simple NDVI (Normalized Difference Vegetation Index) analysis.īefore we get started, make sure you have all the necessary libraries installed check out arcpy on ESRI’s site to learn more about what you need. Landsat image courtesy of the U.S Geological SurveyĪrcGIS is one of the top platforms for geostatistical analysis.

Arcmap python