Guide 04 feature data and analysis (#44)

* Fix wrong location in comment * Merge dev branch into master for 0.2 Beta and Holistic Testing (#24) * Updated publishing samples to reflect changes made to source code * Updated Publihing SDs, shp, csv samples to reflect latest API changes * Publisher sample updates - added samples for web maps and web scene (#3) * Updated notebook title to match filename * Added a new sample to showcase publishing web maps and web scenes * Fixed credentials * Updated sample titled "Using and updating GIS content" (#4) * Updated notebook title to match filename * Added a new sample to showcase publishing web maps and web scenes * Fixed credentials * Updated the sample with additional examples * Fixed credentials * Added an example for line featuers. Added some text explaining what smart mapping is * Additional widgets to minimize scrolling. Fixed geocoder usage following API update * Updated geocoder usage to match API updates * Updated the sample to match gp service update. Added a bit more text explaining the usage of gp tools (#12) * Minor - updated sample to search for desired item to add instead of a fixed index (#13) * GIS analysts - sample illustrating spatial analysis using ArcGIS Python API (#14) * Initial commit * Furnished the sample with spatial analysis examples * Fixes for API changes. Added summary text and elaborated a few steps (#15) * new sample to showcase big data anlaysis using NYC taxi example (#16) * Updated hurricane tracks sample to match API changes (#17) * Updated hurricane tracks sample to match API changes and added descriptive text * updated credentials * Updated sample to match API changes, added explanatory text (#19) * clean up sample (#18) * clean up sample (#20) * Remove usage statistics notebook as the API isnt available This requires work on the portal backend as well as it does not yet support ASM. * bug fixes and added descriptive text (#22) * synced raster product sample in 05 Content Publishers folder to match that in 04 GIS Analysts and Data Scientists (#21) Thanks, @AtmaMani * Updated Overview page - updated sample TOC (#23) * doc updates * apidoc update for 0.2 beta release * updated install instructions * Updated samples download link to point to v0.2-beta release (#25) * fixed wrong import * fixed execution count * adding narratives and expanding guide for features module * guide - 04 features - New section to talk about using replicas * added section on removing replicas * guide - 04 feature - added section about updating layer definition, FLCManager * guide -04 features - new section for attachments. Stubs for analysis sections' * guide - 04 features - added section on performing proximity analysis * guide - 04 features - new section for summarize_data module * guide - 04 features - new section for use_locations sub module * guide - 04 features - clean up 1 * guide - 04 features - clean up 2 * guide -04 features - clean up 3
Esri · Dec 11, 2016 · 425fb7c · 425fb7c
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diff --git a/guide/03 The GIS/gis module.ipynb b/guide/03 The GIS/gis module.ipynb
@@ -46,11 +46,7 @@
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diff --git a/guide/04 Feature data and analysis/Analyzing patterns in feature data.ipynb b/guide/04 Feature data and analysis/Analyzing patterns in feature data.ipynb
@@ -0,0 +1,158 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Analyzing patterns in feature data\n",
+    "\n",
+    "Your web GIS packs a set of tools that help you identify, quantify, and visualize spatial patterns in your data by identifying areas of statistically significant clusters.\n",
+    "\n",
+    "This section demonstrates how `interpolate_points()` can be used to convert point feature data representing measurements in spot locations into a continuous prediction surface."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Interpolate points\n",
+    "\n",
+    "The 'interpolate points' tool allows you to predict values at new locations based on measurements from a collection of points. The prediction result is a polygon layer classified by predicted values. We will use this tool to create a rainfall prediction surface using data from sparse weather stations in Chennai."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "# Connect to GIS\n",
+    "from arcgis.gis import GIS\n",
+    "gis = GIS(\"portal url\", \"username\", \"password\")\n",
+    "chennai_rainfall = gis.content.search(\"Chennai Rainfall\", \"Feature Service\")[0]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Draw the rainfall stations on a map rendered by rainfall quantity"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "map1 = gis.map(\"Tamil Nadu\", zoomlevel=7)\n",
+    "map1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![chennai rainfall points](http://esri.github.io/arcgis-python-api/notebooks/nbimages/04_ChennaiFloods_02.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "map.add_layer(chennai_rainfall, { \"renderer\":\"ClassedSizeRenderer\", \"field_name\":\"RAINFALL\" })"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `analyze_patterns` sub module in `features` module provides access to `interpolate_points()` method."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "from arcgis.features.analyze_patterns import interpolate_points\n",
+    "\n",
+    "#run the interpolation tool and specify the field containing rainfall data\n",
+    "interpolated_rf = interpolate_points(rainfall, field='RAINFALL')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The `interpolate_points()` method accepts more optional parameters allowing you to customize the analysis task. Using those parameters, you could optionally limit the number of classes generated, specify the class breaks, etc.\n",
+    "\n",
+    "If the `output_name` parameter is not specified, the tool creates a feature collection instead of creating an output feature service.\n",
+    "\n",
+    "Let us add the result to a new map"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "interpolated_map = gis.map(\"Tamil Nadu\", zoomlevel = 7)\n",
+    "interpolated_map"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "![interpolated result](http://esri.github.io/arcgis-python-api/notebooks/nbimages/04_ChennaiFloods_03.png)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [],
+   "source": [
+    "interpolated_map.add_layer(interpolated_rf['result_layer'])"
+   ]
+  }
+ ],
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+   "display_name": "Python 3",
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+}