{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Site Selection: Restaurant clusters\n", "\n", ">* 👟 Ready To Run!\n", "* 🖥️ Requires ArcPy\n", "* 🖥️ Requires GeoEnrichment Organization Configuration\n", "* 🔬 Data Science\n", "* 🚀 Data Exploration and Cleaning\n", "* 📈 Graphing and Visualization\n", "\n", "![clustering](https://github.com/Qberto/arcgispythonapi_arcpy-spatial_statistics_examples/raw/5f8994ab80301d8ae145e9081b16c1389a5729d0/img/restaurants_clustering_v2.gif \"Density-based Clustering\")\n", "\n", "\n", "## An Example of Spatial Analysis in ArcGIS Using Density-based Clustering " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Prospecting a successful retail location often requires a thorough understanding of where people shop, dine, and linger for entertainment in a study area. One useful way to start making sense of the vast quantities of retail business information and start finding the locations that matter is to use spatial clustering to find locations with a dense retail presence. \n", "\n", "This example starts with retail business data in Pittsburgh, Pennsylvania, USA, and leverages a couple of Spatial Statistics tools in ArcGIS ([Density-based Clustering](http://pro.arcgis.com/en/pro-app/tool-reference/spatial-statistics/densitybasedclustering.htm), [Directional Distribution (Standard Deviational Ellipse)](http://pro.arcgis.com/en/pro-app/tool-reference/spatial-statistics/directional-distribution.htm)) to incorporate the dense retail data into defined cluster locations. These locations can then be further analyzed to aid decision making for your organization. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Steps" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is a basic example that will cover the following steps:\n", "\n", "1) Retrieve Pittsburgh restaurant data.\n", "\n", "\n", "2) Explore the data through tables, charts, and maps.\n", "\n", "\n", "3) Perform spatial analysis:\n", "\n", " Finding Restaurant Zones through Clustering\n", "\n", " - Density-based Clustering\n", " - Directional Distribution (Standard Deviational Ellipse)\n", "\n", " Prioritizing Restaurant Zones\n", " \n", " - Aggregated Sales Volume per Ellipse Zone\n", " - Density of Hot Spot Restaurants in the Ellipse Zone\n", "\n", "4) Present the results." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## General Workflow" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Preliminary Setup" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Import the needed modules, including ArcPy, the ArcGIS API for Python, and other useful modules. Then, connect to our `GIS`." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import os\n", "import time\n", "\n", "import numpy as np\n", "import pandas as pd\n", "# Imports for plotting in Bokeh\n", "import bokeh\n", "from bokeh.io import output_notebook, output_file, show\n", "from bokeh.plotting import figure\n", "from bokeh.models import Legend, Range1d\n", "from bokeh.embed import file_html\n", "from bokeh.resources import CDN\n", "# Set bokeh to output plots in the notebook\n", "output_notebook()\n", "\n", "import arcgis\n", "from arcgis import features\n", "from arcgis.features import GeoAccessor, GeoSeriesAccessor\n", "from arcgis.geoenrichment import *\n", "from arcgis.geometry import project\n", "import arcpy\n", "arcpy.env.overwriteOutput = True\n", "\n", "gis = arcgis.gis.GIS(\"home\", verify_cert=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, let's define our `/arcgis/home/` location and create some helper functions to be used later." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "home_dir = os.path.abspath(os.path.join(os.sep, 'arcgis', 'home'))" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# Create a helper function that receives a spatially-enabled dataframe \n", "# and column as input, and returns a map widget, symbolized layer, and \n", "# bokeh histogram using the layer's colormap\n", "def create_map_and_histogram(map_location, sdf, column, \n", " method='esriClassifyNaturalBreaks', \n", " class_count=5, cmap='OrRd', \n", " alpha=0.8, plot_height=600, plot_width=600):\n", " \"\"\"\n", " create_map_and_histogram\n", " inputs:\n", " map_location: Location for the map widget. The entry \n", " will be geocoded and used as the starting extent for the \n", " map widget. Example: \"Pittsburgh\"\n", "\n", " sdf: spatially-enabled dataframe that is plotted on a map and \n", " histogram\n", "\n", " column: column to use for layer symbology and histogram\n", " return: map widget, bokeh plot\n", " \"\"\"\n", " \n", " # Create map\n", " map_obj = gis.map(map_location)\n", " sdf.spatial.plot(map_widget=map_obj, renderer_type='c', \n", " method=method, class_count=class_count, \n", " col=column, cmap=cmap, alpha=0.8) \n", " \n", " # Extract the layer's class breaks and colors\n", " class_breaks = map_obj.layers[0].layer\\\n", " .layerDefinition.drawingInfo.renderer\\\n", " .classBreakInfos\n", " cbs_list = []\n", " cmap_list = []\n", " for cb in class_breaks:\n", " cbs_list.append(cb.classMaxValue)\n", " cmap_list.append('#%02x%02x%02x' % (cb.symbol.color[0], \n", " cb.symbol.color[1], \n", " cb.symbol.color[2]))\n", " \n", " # Create a histogram of salesvol values\n", " hist, edges = np.histogram(sdf[column],\n", " bins=class_count)\n", "\n", " # Put the information in a dataframe\n", " hist_df = pd.DataFrame({column: hist,\n", " 'left': edges[:-1],\n", " 'right': edges[1:]})\n", " \n", " # Add colors to each hist_df record\n", " hist_df['color'] = pd.Series(cmap_list)\n", "\n", " # Create the blank plot\n", " p = figure(plot_height = plot_height, plot_width = plot_width, \n", " title = 'Histogram',\n", " y_axis_label = 'Feature Count',\n", " x_axis_label = column)\n", "\n", " # Add a quad glyph\n", " p.quad(bottom=0, top=hist_df[column], \n", " left=hist_df['left'], right=hist_df['right'],\n", " line_color='white', fill_color=hist_df['color'])\n", "\n", " # Return outputs\n", " return map_obj, p" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# Helper functions to change the extent of a web map \n", "# based on ellipse centroids\n", "def return_center_dict(centerx, centery):\n", " center_shape_dict = {}\n", " center_shape_dict['spatialReference'] = \\\n", " {'latestWkid': 3857, 'wkid': 102100}\n", " center_shape_dict['x'] = centerx\n", " center_shape_dict['y'] = centery\n", " return center_shape_dict\n", "\n", "def visit_sites(m, points, timesleep, zoom_level=15):\n", " m.zoom = zoom_level\n", " i = 0\n", " for i in range(len(points)):\n", " m.center = [points[i]['y'], points[i]['x']]\n", " time.sleep(timesleep)\n", " \n", "def visit_ellipse_features_on_map_widget(map_obj, sdf, zoom_level=15, \n", " number_of_records=None, \n", " timesleep=5):\n", " # Create a center_xy column from the sdf\n", " sdf['center_xy'] = sdf.apply(\n", " lambda x: return_center_dict(x['centerx'], x['centery']), axis=1)\n", " if number_of_records:\n", " sitelist = sdf.head(number_of_records)['center_xy'].tolist()\n", " else:\n", " sitelist = sdf.head()['center_xy'].tolist()\n", " xys = project(sitelist, in_sr=3857, out_sr=4326)\n", " visit_sites(map_obj, xys, timesleep) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 1) Retrieve Pittsburgh restaurant data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We published an item containing Pittsburgh restaurants to the ArcGIS Enterprise. Let's find this item using the \"Add\" toolbar and load it into our notebook as an item variable." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Feature Layer Collection by some_user\n", "
Last Modified: September 10, 2018\n", "
0 comments, 21 views\n", "
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\n", " " ], "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "restaurants_item = gis.content.search('restaurants_pittsburgh',\n", " item_type = \"Feature Service\")[0]\n", "restaurants_item" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The [spatially-enabled dataframe](https://developers.arcgis.com/python/guide/introduction-to-the-spatially-enabled-dataframe/) helps us explore the data in a tabular format by leveraging the Pandas library." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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SHAPEcityconameempnumfrncodhdbrchiscodeloc_namelocnumnaics...scoresicsourcesqftcodestatestate_namestatusstreetzipzip4
0{\"x\": -8919964.005599998, \"y\": 4867758.9117000...MARIANNARUDNICK'S6.03StreetAddress40203633872251117...100.0000581208INFOGROUPAPAPennsylvaniaMTEN MILE RD153451080
1{\"x\": -8905258.700800002, \"y\": 4865826.0264, \"...FREDERICKTOWNRIVERSIDE INN5.0StreetAddress38741033572251117...90.0938581208INFOGROUPAPAPennsylvaniaMFRONT ST15333
2{\"x\": -8905314.3606, \"y\": 4866727.023800001, \"...FREDERICKTOWNRIVIERA RESTAURANT LOUNGE5.0StreetAddress83150533472251117...100.0000581208INFOGROUPAPAPennsylvaniaMFRONT ST15333
3{\"x\": -8904590.783900002, \"y\": 4868224.0231000...BETHLEHEMKUKI CHINESE RESTAURANT3.0CPointAddress90789094172251117...86.8281581208INFOGROUPAPAPennsylvaniaME 3RD ST180151306
4{\"x\": -8896241.822100002, \"y\": 4844052.5718000...MASONTOWNDOLFI'S10.0StreetAddress30991299672251117...100.0000581208INFOGROUPBPAPennsylvaniaMRIVER AVE154611551
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" ], "text/plain": [ " SHAPE city \\\n", "0 {\"x\": -8919964.005599998, \"y\": 4867758.9117000... MARIANNA \n", "1 {\"x\": -8905258.700800002, \"y\": 4865826.0264, \"... FREDERICKTOWN \n", "2 {\"x\": -8905314.3606, \"y\": 4866727.023800001, \"... FREDERICKTOWN \n", "3 {\"x\": -8904590.783900002, \"y\": 4868224.0231000... BETHLEHEM \n", "4 {\"x\": -8896241.822100002, \"y\": 4844052.5718000... MASONTOWN \n", "\n", " coname empnum frncod hdbrch iscode loc_name \\\n", "0 RUDNICK'S 6.0 3 StreetAddress \n", "1 RIVERSIDE INN 5.0 StreetAddress \n", "2 RIVIERA RESTAURANT LOUNGE 5.0 StreetAddress \n", "3 KUKI CHINESE RESTAURANT 3.0 C PointAddress \n", "4 DOLFI'S 10.0 StreetAddress \n", "\n", " locnum naics ... score sic source sqftcode state \\\n", "0 402036338 72251117 ... 100.0000 581208 INFOGROUP A PA \n", "1 387410335 72251117 ... 90.0938 581208 INFOGROUP A PA \n", "2 831505334 72251117 ... 100.0000 581208 INFOGROUP A PA \n", "3 907890941 72251117 ... 86.8281 581208 INFOGROUP A PA \n", "4 309912996 72251117 ... 100.0000 581208 INFOGROUP B PA \n", "\n", " state_name status street zip zip4 \n", "0 Pennsylvania M TEN MILE RD 15345 1080 \n", "1 Pennsylvania M FRONT ST 15333 \n", "2 Pennsylvania M FRONT ST 15333 \n", "3 Pennsylvania M E 3RD ST 18015 1306 \n", "4 Pennsylvania M RIVER AVE 15461 1551 \n", "\n", "[5 rows x 22 columns]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "restaurants_sdf = pd.DataFrame.spatial.from_layer(\n", " restaurants_item.layers[0])\n", "restaurants_sdf.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We're also going to temporarily store this spatially-enabled dataframe as a feature class in order to run ArcPy operations on the data." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'/mnt/arcgis/me/pittsburgh_restaurants.gdb'" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Create file geodatabase\n", "fgdb = arcpy.CreateFileGDB_management(\n", " out_folder_path=home_dir, \n", " out_name=\"pittsburgh_restaurants.gdb\").getOutput(0)\n", "fgdb" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'/mnt/arcgis/me/pittsburgh_restaurants.gdb/restaurants_pittsburgh'" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Use the spatially-enabled dataframe's to_featureclass \n", "# method to convert to a feature class\n", "fc = restaurants_sdf.spatial.to_featureclass(\n", " os.path.join(fgdb, \"restaurants_pittsburgh\"))\n", "fc" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Confirm it exists after converting it\n", "arcpy.Exists(fc)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 2) Explore the data through tables, charts, and maps." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Amount of restaurants: 5026\n", "Amount of attributes: 22\n" ] } ], "source": [ "# Get the amount of records and columns in our data\n", "print(\"Amount of restaurants: {0}\\nAmount of attributes: {1}\".format(\n", " restaurants_sdf.shape[0], restaurants_sdf.shape[1]))" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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empnumobjectidsalesvolscore
count5026.0000005026.0000005026.0000005026.000000
mean17.2160762513.500000802.64544499.608847
std24.1902301451.0255571130.6672651.855258
min1.0000001.0000000.00000085.703100
25%6.0000001257.250000280.000000100.000000
50%8.0000002513.500000374.000000100.000000
75%19.0000003769.750000888.000000100.000000
max300.0000005026.00000014035.000000100.000000
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" ], "text/plain": [ " empnum objectid salesvol score\n", "count 5026.000000 5026.000000 5026.000000 5026.000000\n", "mean 17.216076 2513.500000 802.645444 99.608847\n", "std 24.190230 1451.025557 1130.667265 1.855258\n", "min 1.000000 1.000000 0.000000 85.703100\n", "25% 6.000000 1257.250000 280.000000 100.000000\n", "50% 8.000000 2513.500000 374.000000 100.000000\n", "75% 19.000000 3769.750000 888.000000 100.000000\n", "max 300.000000 5026.000000 14035.000000 100.000000" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Get summary statistics on numeric fields\n", "restaurants_sdf.describe()" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Get a basic map of the restaurant locations as points\n", "restaurants_map = gis.map(\"Pittsburgh\")\n", "restaurants_sdf.spatial.plot(map_widget=restaurants_map)\n", "restaurants_map" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Use our helper function to show a map and histogram \n", "# of restaurants symbolized by sales volume \n", "restaurants_map, sales_hist = create_map_and_histogram(\"Pittsburgh\", \n", " restaurants_sdf, \"salesvol\")\n", "restaurants_map" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "
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] }, { "cell_type": "markdown", "metadata": {}, "source": [ "See https://mybinder.org/v2/gh/bokeh/bokeh-notebooks/master?filepath=tutorial%2F00%20-%20Introduction%20and%20Setup.ipynb for a guide to Bokeh's plotting capabilities" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## What are the top five restaurants by sales volume?" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Top 5 Restaurants in Pittsburgh by Sales Volume: \n" ] }, { "data": { "text/html": [ "
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conamesalesvolstreetcitystatestate_namezip
0CHEESECAKE FACTORY14035.0ROSS PARK MALL DRPITTSBURGHPAPennsylvania15237
1EAT'N PARK12865.0PARK MANOR DRPITTSBURGHPAPennsylvania15205
2CHEESECAKE FACTORY12631.0S 27TH STPITTSBURGHPAPennsylvania15203
3M I FRIDAY11695.0PERRY HWYPITTSBURGHPAPennsylvania15229
4NANA11695.0WATERFRONT DRPITTSBURGHPAPennsylvania15222
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" ], "text/plain": [ " coname salesvol street city state \\\n", "0 CHEESECAKE FACTORY 14035.0 ROSS PARK MALL DR PITTSBURGH PA \n", "1 EAT'N PARK 12865.0 PARK MANOR DR PITTSBURGH PA \n", "2 CHEESECAKE FACTORY 12631.0 S 27TH ST PITTSBURGH PA \n", "3 M I FRIDAY 11695.0 PERRY HWY PITTSBURGH PA \n", "4 NANA 11695.0 WATERFRONT DR PITTSBURGH PA \n", "\n", " state_name zip \n", "0 Pennsylvania 15237 \n", "1 Pennsylvania 15205 \n", "2 Pennsylvania 15203 \n", "3 Pennsylvania 15229 \n", "4 Pennsylvania 15222 " ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Get the restaurants with the highest sales revenue by \n", "# sorting on the 'SALESVOL' column\n", "top_sales_sdf = restaurants_sdf.sort_values(\n", " by=['salesvol'], \n", " ascending=False).reset_index()\n", "top_five_sdf = top_sales_sdf.head()\n", "\n", "print(\"Top 5 Restaurants in Pittsburgh by Sales Volume: \")\n", "top_five_sdf[['coname', 'salesvol', 'street', 'city', 'state', \n", " 'state_name', 'zip']]" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "
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if (attempts > 100) {\n", " console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\")\n", " clearInterval(timer);\n", " }\n", " }, 10, root)\n", " }\n", "})(window);" ], "application/vnd.bokehjs_exec.v0+json": "" }, "metadata": { "application/vnd.bokehjs_exec.v0+json": { "id": "dbbad58c-d2d9-42b4-892b-c4c1bc6a21e9" } }, "output_type": "display_data" } ], "source": [ "# Create a basic chart of the top five restaurants by sales volume\n", "label = top_five_sdf['street'].tolist()\n", "value = top_five_sdf['salesvol'].tolist()\n", "\n", "p = figure(x_range=label,\n", " title = 'Top Five Restaurants by Sales Volume',\n", " x_axis_label = 'Restaurant Address',\n", " y_axis_label = 'Sales volume in thousands of dollars')\n", "p.xaxis.major_label_orientation = np.pi/4\n", "\n", "p.vbar(x=label, top=value, width=0.5)\n", "\n", "show(p)" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Plot the highest five selling restaurants\n", "top_five_restaurants_map = gis.map(\"Pittsburgh\")\n", "top_five_sdf.spatial.plot(map_widget=top_five_restaurants_map)\n", "top_five_restaurants_map" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## What are the top five zip codes by sales volume?" ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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empnumobjectidsalesvolZip Ave. Sales Rank
zip
1523139.8181824293.8181821862.2727271.0
1527538.5217393861.6956521801.6521742.0
1548235.000000950.0000001637.0000003.0
1508432.6052631093.7894741524.8421054.0
1520530.4247793716.2035401422.7964605.0
\n", "
" ], "text/plain": [ " empnum objectid salesvol Zip Ave. Sales Rank\n", "zip \n", "15231 39.818182 4293.818182 1862.272727 1.0\n", "15275 38.521739 3861.695652 1801.652174 2.0\n", "15482 35.000000 950.000000 1637.000000 3.0\n", "15084 32.605263 1093.789474 1524.842105 4.0\n", "15205 30.424779 3716.203540 1422.796460 5.0" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Determine the top five zip codes by average restaurant sales volume\n", "\n", "# Use groupby object to make calculations by Zip Code\n", "zip_groupby = restaurants_sdf.groupby(restaurants_sdf['zip'])\n", "# Determine means for numeric columns by zip\n", "zip_mean_sales_df = zip_groupby.mean().sort_values(by=['salesvol'], \n", " ascending=False)\n", "# Calculate Zip ranks by average sales volume\n", "zip_mean_sales_df['Zip Ave. Sales Rank'] = \\\n", " zip_mean_sales_df['salesvol'].rank(ascending=False)\n", "\n", "# Drop the unneeded 'SCORE' field\n", "del zip_mean_sales_df['score']\n", "\n", "zip_mean_sales_df.head()" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "
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Application\",\"version\":\"0.13.0\"}};\n", " var render_items = [{\"docid\":\"550e77a2-937e-4595-b1a3-588a03d3f5e8\",\"roots\":{\"6728d7f0-baed-476f-b9a1-1abd0cd0192d\":\"397b1130-6d63-4516-8f6f-e431e022d1dd\"}}];\n", " root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", "\n", " }\n", " if (root.Bokeh !== undefined) {\n", " embed_document(root);\n", " } else {\n", " var attempts = 0;\n", " var timer = setInterval(function(root) {\n", " if (root.Bokeh !== undefined) {\n", " embed_document(root);\n", " clearInterval(timer);\n", " }\n", " attempts++;\n", " if (attempts > 100) {\n", " console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\")\n", " clearInterval(timer);\n", " }\n", " }, 10, root)\n", " }\n", "})(window);" ], "application/vnd.bokehjs_exec.v0+json": "" }, "metadata": { "application/vnd.bokehjs_exec.v0+json": { "id": "6728d7f0-baed-476f-b9a1-1abd0cd0192d" } }, "output_type": "display_data" } ], "source": [ "# Create a basic chart of the top five zip codes by sales volume\n", "label = list(zip_mean_sales_df.head().index.values)\n", "value = list(zip_mean_sales_df['salesvol'].head())\n", "\n", "p = figure(x_range=label,\n", " title = 'Top Five Zip Codes by Mean Sales Volume',\n", " x_axis_label = 'Zip Code',\n", " y_axis_label = 'Average Sales volume in thousands of dollars')\n", "p.xaxis.major_label_orientation = np.pi/4\n", "\n", "p.vbar(x=label, top=value, width=0.5)\n", "\n", "show(p)" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "{'rings': [[[-80.20510177197895, 40.46367809284811],\n", " [-80.20285000000538, 40.464249999514806],\n", " [-80.2024476590743, 40.461483705836685],\n", " [-80.19889047299921, 40.45980803625618],\n", " [-80.1915099992887, 40.458040000417576],\n", " [-80.19025999991791, 40.458729999882856],\n", " [-80.1822480065078, 40.45502180658601],\n", " [-80.17774177478417, 40.45432623157046],\n", " [-80.17389723916088, 40.45503225869422],\n", " [-80.17209999967325, 40.45352999976287],\n", " [-80.1718008488814, 40.45158457909381],\n", " [-80.17001734741763, 40.45118836250693],\n", " [-80.16895809843734, 40.4497721307695],\n", " [-80.17200000037039, 40.449139999580176],\n", " [-80.1767143003178, 40.44507909347006],\n", " [-80.18374402816681, 40.44449852306094],\n", " [-80.18682865784238, 40.44828075360781],\n", " [-80.18712000042767, 40.45099000006832],\n", " [-80.1909970157469, 40.450595480067825],\n", " [-80.19070562375396, 40.44787582669217],\n", " [-80.19523184940925, 40.45230015926403],\n", " [-80.19687959659564, 40.452322112927924],\n", " [-80.19693000049155, 40.453460000280025],\n", " [-80.19872000040979, 40.45189999991574],\n", " [-80.20142857133327, 40.45218642883458],\n", " [-80.20267000072124, 40.455290000291484],\n", " [-80.20186527933286, 40.45739847270152],\n", " [-80.2055284511223, 40.46138296480335],\n", " [-80.20510177197895, 40.46367809284811]],\n", " [[-80.20285000000538, 40.464249999514806],\n", " [-80.2039323861427, 40.46570698432018],\n", " [-80.21466477777123, 40.468089938449566],\n", " [-80.21760000048606, 40.47111999982353],\n", " [-80.21699000006728, 40.47240000026807],\n", " [-80.21235999946572, 40.472279999767956],\n", " [-80.20393000020199, 40.46846000037536],\n", " [-80.19910000009637, 40.46783999999763],\n", " [-80.19439000041183, 40.463739999797674],\n", " [-80.19639200679333, 40.46298856863648],\n", " [-80.19963180256589, 40.46489071069645],\n", " [-80.20285000000538, 40.464249999514806]]],\n", " 'spatialReference': {'latestWkid': 4326, 'wkid': 4326}}" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Load the geoenrichment module's country object for the US, \n", "# and pre-cache some geometry\n", "usa = Country.get(\"United States\")\n", "usa.subgeographies.states['Pennsylvania'].zip5['15275'].geometry" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Create a basic map showing the top five zip codes\n", "top_five_zip_map = gis.map(\"Pittsburgh\")\n", "for zip5 in zip_mean_sales_df.head().index.values.tolist():\n", " top_five_zip_map.draw(usa.subgeographies.states['Pennsylvania']\\\n", " .zip5[str(zip5)].geometry)\n", "restaurants_sdf.spatial.plot(map_widget=top_five_zip_map, alpha=0.1, \n", " cmap='Greys', outline_color='Greys')\n", "top_five_zip_map " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 3) Perform spatial analysis " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To help us identify potential sites for further analysis, we will leverage spatial clustering to find locations with dense retail presence. In this case, Density-based Clustering and Directional Distribution are very useful to help us identify these potential sites. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Density-based Clustering" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![clustering](https://github.com/Qberto/arcgispythonapi_arcpy-spatial_statistics_examples/raw/5f8994ab80301d8ae145e9081b16c1389a5729d0/img/dbclustering_01.png \"Density-based Clustering\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The [Density-based Clustering tool](http://pro.arcgis.com/en/pro-app/tool-reference/spatial-statistics/how-density-based-clustering-works.htm) works by detecting areas where points are concentrated and where they are separated by areas that are empty or sparse. Points that are not part of a cluster are labeled as noise.\n", "\n", "This tool uses unsupervised machine learning clustering algorithms which automatically detect patterns based purely on spatial location and the distance to a specified number of neighbors. These algorithms are considered unsupervised because they do not require any training on what it means to be a cluster." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Three clustering algorithms:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Density-based Clustering tool provides three different Clustering Methods with which to find clusters in your point data:\n", "\n", "- _Defined distance (DBSCAN)_ — Uses a specified distance to separate dense clusters from sparser noise. The DBSCAN algorithm is the fastest of the clustering methods, but is only appropriate if there is a very clear Search Distance to use, and that works well for all potential clusters. This requires that all meaningful clusters have similar densities.\n", "\n", "- _Self-adjusting (HDBSCAN)_ — Uses a range of distances to separate clusters of varying densities from sparser noise. The HDBSCAN algorithm is the most data-driven of the clustering methods, and thus requires the least user input.\n", "\n", "- _Multi-scale (OPTICS)_ — Uses the distance between neighboring features to create a reachability plot which is then used to separate clusters of varying densities from noise. The OPTICS algorithm offers the most flexibility in fine-tuning the clusters that are detected, though it is computationally intensive, particularly with a large Search Distance." ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Set an output path name\n", "output_path = \"{0}//{1}_HDBSCAN\".format(fgdb, \"RestaurantClusters\")\n", "clusters_fc = arcpy.stats.DensityBasedClustering(fc, output_path, \n", " \"HDBSCAN\", 5)\n", "clusters_fc" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/html": [ "
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OBJECTIDSOURCE_IDCLUSTER_IDPROBOUTLIEREXEMPLARSTABILITYCOLOR_IDSHAPE
344560.1730370.82696300.08{\"x\": -8904590.7839, \"y\": 4868224.023100004, \"...
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" ], "text/plain": [ " OBJECTID SOURCE_ID CLUSTER_ID PROB OUTLIER EXEMPLAR STABILITY \\\n", "3 4 4 56 0.173037 0.826963 0 0.0 \n", "4 5 5 36 1.000000 0.000000 1 0.0 \n", "5 6 6 36 1.000000 0.000000 1 0.0 \n", "6 7 7 36 1.000000 0.000000 1 0.0 \n", "7 8 8 36 0.790149 0.209851 0 0.0 \n", "\n", " COLOR_ID SHAPE \n", "3 8 {\"x\": -8904590.7839, \"y\": 4868224.023100004, \"... \n", "4 4 {\"x\": -8896241.8221, \"y\": 4844052.571800001, \"... \n", "5 4 {\"x\": -8895774.2802, \"y\": 4844531.075199999, \"... \n", "6 4 {\"x\": -8894527.5019, \"y\": 4845430.143799998, \"... \n", "7 4 {\"x\": -8893759.3974, \"y\": 4846532.338799998, \"... " ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Convert the clusters feature class from Density-based Clustering into \n", "# a spatially-enabled dataframes (one for clusters, one for noise)\n", "dbclustering_sdf = pd.DataFrame.spatial.from_featureclass(clusters_fc)\n", "clusters_sdf = dbclustering_sdf.loc[dbclustering_sdf['CLUSTER_ID'] != -1]\n", "noise_sdf = dbclustering_sdf.loc[dbclustering_sdf['CLUSTER_ID'] == -1]\n", "clusters_sdf.head()" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/html": [ "
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OBJECTIDSOURCE_IDCLUSTER_IDPROBOUTLIEREXEMPLARSTABILITYCOLOR_IDSHAPE
011-10.00.02711300.0-1{\"x\": -8919964.0056, \"y\": 4867758.911700003, \"...
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" ], "text/plain": [ " OBJECTID SOURCE_ID CLUSTER_ID PROB OUTLIER EXEMPLAR STABILITY \\\n", "0 1 1 -1 0.0 0.027113 0 0.0 \n", "1 2 2 -1 0.0 0.023014 0 0.0 \n", "2 3 3 -1 0.0 0.005891 0 0.0 \n", "12 13 13 -1 0.0 0.011129 0 0.0 \n", "26 27 27 -1 0.0 0.255119 0 0.0 \n", "\n", " COLOR_ID SHAPE \n", "0 -1 {\"x\": -8919964.0056, \"y\": 4867758.911700003, \"... \n", "1 -1 {\"x\": -8905258.7008, \"y\": 4865826.0264, \"spati... \n", "2 -1 {\"x\": -8905314.3606, \"y\": 4866727.023800001, \"... \n", "12 -1 {\"x\": -8893403.1751, \"y\": 4859869.875299998, \"... \n", "26 -1 {\"x\": -8878608.8147, \"y\": 4851827.477499999, \"... " ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "noise_sdf.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's take a look at the outputs of Density-based Clustering on a map." ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Visualize both clusters and noise\n", "results_map = gis.map(\"Pittsburgh\")\n", "clusters_sdf.spatial.plot(map_widget=results_map, renderer_type='u', \n", " col=\"COLOR_ID\")\n", "noise_sdf.spatial.plot(map_widget=results_map, cmap='Greys', alpha=0.6,\n", " outline_color='Greys')\n", "results_map" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Visualize only clusters\n", "onlyclusters_map = gis.map(\"Pittsburgh\")\n", "clusters_sdf.spatial.plot(map_widget=onlyclusters_map, \n", " renderer_type='u', col=\"COLOR_ID\")\n", "onlyclusters_map" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true }, "source": [ "## Directional Distribution (Standard Deviational Ellipse)" ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "![ellipses](https://github.com/Qberto/arcgispythonapi_arcpy-spatial_statistics_examples/raw/5f8994ab80301d8ae145e9081b16c1389a5729d0/img/ellipses_01.png \"Standard Deviational Ellipses\") " ] }, { "cell_type": "markdown", "metadata": { "hidden": true }, "source": [ "While you can get a sense of the location of the clusters by running Density-based Clustering and drawing output cluster features on a map, calculating the standard deviational ellipses for each cluster makes the clusters a bit clearer. Since this workflow needs zones to evaluate rather than specific restaurants, the ellipses can be the new target of our analysis. Let's run the tool from this hosted notebook." ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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OBJECTIDCenterXCenterYXStdDistYStdDistRotationCLUSTER_IDSHAPE
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" ], "text/plain": [ " OBJECTID CenterX CenterY XStdDist YStdDist \\\n", "0 1 -8.910530e+06 5.023269e+06 2151.586100 12525.834221 \n", "1 2 -8.825619e+06 4.985812e+06 11059.807891 19222.922180 \n", "2 3 -8.831966e+06 4.966738e+06 283.002577 4506.261331 \n", "3 4 -8.847666e+06 4.849466e+06 3419.763571 13281.725110 \n", "4 5 -8.875420e+06 5.007396e+06 3412.772598 12791.984258 \n", "\n", " Rotation CLUSTER_ID SHAPE \n", "0 22.711797 1 {\"rings\": [[[-8912514.4278, 5024099.286899999]... \n", "1 23.821812 2 {\"rings\": [[[-8835736.6028, 4990278.838500001]... \n", "2 143.274423 3 {\"rings\": [[[-8832192.7771, 4966569.239699997]... \n", "3 19.417593 4 {\"rings\": [[[-8850890.9565, 4850602.765900001]... \n", "4 14.330382 5 {\"rings\": [[[-8878726.6002, 5008240.671899997]... " ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Run Directional Distribution using clusters as an input\n", "clusters_fc = clusters_sdf.spatial.to_featureclass(\n", " fgdb + \"//restaurant_clusters\")\n", "ellipses = arcpy.DirectionalDistribution_stats(\n", " clusters_fc, fgdb + \"//cluster_ellipses\", \n", " Ellipse_Size=\"2 standard deviations\", \n", " Case_Field=\"CLUSTER_ID\").getOutput(0)\n", "ellipses_sdf = pd.DataFrame.spatial.from_featureclass(ellipses)\n", "ellipses_sdf.head()" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Produced 346 ellipses.\n" ] } ], "source": [ "print(\"Produced {0} ellipses.\".format(ellipses_sdf.shape[0]))" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ellipses_map = gis.map(\"Pittsburgh\")\n", "clusters_sdf.spatial.plot(map_widget=ellipses_map, \n", " renderer_type='u', col='CLUSTER_ID', \n", " alpha=0.1, outline_color='Greys')\n", "ellipses_sdf.spatial.plot(map_widget=ellipses_map, alpha=0.7, \n", " outline_color='magma')\n", "ellipses_map" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Where are the most important ellipse zones that we should first prioritize? " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We have progressed from a point map of thousands of restaurants throughout Pittsburgh to 346 ellipse zones derived from density-based clustering of restaurants. To help prioritize the evaluation of these areas, let's bring additional data and analysis that can help us rank these zones and help organizations understand which areas they should first be examining. \n", "\n", "We'll use two components to evaluate each ellipse zone:\n", "\n", "A. Aggregated sales volume per ellipse zone\n", "\n", "B. Density of Hot Spot restaurants in the ellipse zone" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### A. Calculating total and average sales volume per ellipse zone" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The first criteria for evaluating each ellipse zone is the total and average sales volume of the restaurant locations that defined the cluster zone. This is a basic evaluation of the overall commercial performance of the restaurants in each area. To perform this calculation, we'll use the active GIS to run the \"aggregate_points\" tool to calculate basic sums and means for a designated field given a polygon and point data input." ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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SHAPEanalysisareacenterxcenterycluster_idmean_salesvolobjectidpoint_countrotationsum_salesvolxstddistystddist
0{\"rings\": [[[-8912514.427799998, 5024099.28689...18.548609-8.910530e+065.023269e+061792.11111111822.71179714258.02151.58610012525.834221
1{\"rings\": [[[-8832192.777099999, 4966569.23970...0.884207-8.831966e+064.966738e+063298.60000025143.2744231493.0283.0025774506.261331
2{\"rings\": [[[-8835736.602800002, 4990278.83850...147.530862-8.825619e+064.985812e+062208.15384631323.8218122706.011059.80789119222.922180
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4{\"rings\": [[[-8850890.956500001, 4850602.76590...32.389026-8.847666e+064.849466e+064264.3333335619.4175931586.03419.76357113281.725110
\n", "
" ], "text/plain": [ " SHAPE analysisarea \\\n", "0 {\"rings\": [[[-8912514.427799998, 5024099.28689... 18.548609 \n", "1 {\"rings\": [[[-8832192.777099999, 4966569.23970... 0.884207 \n", "2 {\"rings\": [[[-8835736.602800002, 4990278.83850... 147.530862 \n", "3 {\"rings\": [[[-8848978.753299998, 4984824.30430... 0.418115 \n", "4 {\"rings\": [[[-8850890.956500001, 4850602.76590... 32.389026 \n", "\n", " centerx centery cluster_id mean_salesvol objectid \\\n", "0 -8.910530e+06 5.023269e+06 1 792.111111 1 \n", "1 -8.831966e+06 4.966738e+06 3 298.600000 2 \n", "2 -8.825619e+06 4.985812e+06 2 208.153846 3 \n", "3 -8.848780e+06 4.984781e+06 10 242.600000 4 \n", "4 -8.847666e+06 4.849466e+06 4 264.333333 5 \n", "\n", " point_count rotation sum_salesvol xstddist ystddist \n", "0 18 22.711797 14258.0 2151.586100 12525.834221 \n", "1 5 143.274423 1493.0 283.002577 4506.261331 \n", "2 13 23.821812 2706.0 11059.807891 19222.922180 \n", "3 5 12.375789 1213.0 203.846151 2966.869552 \n", "4 6 19.417593 1586.0 3419.763571 13281.725110 " ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Convert the output item into a spatially-enabled dataframe\n", "fset = sv_summary.layers[0].query()\n", "summary_sdf = fset.sdf\n", "summary_sdf.head()" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "343" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Clean up summary fields\n", "summary_sdf = summary_sdf[np.isfinite(summary_sdf['mean_salesvol'])]\n", "summary_sdf.shape[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Where are the ellipse zones with the highest total restaurant sales volumes?" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "totalsales_map, totalsales_hist = create_map_and_histogram(\n", " \"Pittsburgh\", summary_sdf, \"sum_salesvol\", cmap=\"BuPu\")\n", "totalsales_map" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "
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"show(totalsales_hist)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Where are the ellipse zones with the highest average restaurant sales volumes?" ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "05686594d9324af29779a8fd17652c2f", "version_major": 2, "version_minor": 0 }, "text/plain": [ "MapView(layout=Layout(height='400px', width='100%'))" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "meansales_map, meansales_hist = create_map_and_histogram(\n", " \"Pittsburgh\", summary_sdf, \"mean_salesvol\", cmap=\"BuPu\")\n", "meansales_map" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "
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"show(meansales_hist)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's view some of the ellipse zones with the highest average restaurant sales volume by using a helper function (defined above in the 'setup' section)." ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 66, "metadata": {}, "output_type": "execute_result" } ], "source": [ "top_bin_map = gis.map(\"Pittsburgh\")\n", "summary_sdf.loc[summary_sdf['mean_salesvol'] > 2400]\\\n", " .spatial.plot(top_bin_map, col='mean_salesvol', \n", " method='esriClassifyNaturalBreaks', \n", " class_count=5, cmap='BuPu', alpha=0.8)\n", "top_bin_map" ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:20: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n" ] } ], "source": [ "visit_ellipse_features_on_map_widget(\n", " top_bin_map, summary_sdf.loc[summary_sdf['mean_salesvol']>2400])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### B. Finding statistically significant clustering of restaurant sales volume features using Hot Spots" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![Find Hot Spots](https://doc.arcgis.com/en/arcgis-online/analyze/GUID-D4A59131-AC88-4C7A-85BA-67861D5C1CF8-web.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To help us identify which clusters contain more successful restaurants, we will use the [\"Find Hot Spots\"](https://doc.arcgis.com/en/arcgis-online/analyze/find-hot-spots.htm#ESRI_SECTION1_09DAA5EA4B0B4F01991E9D904CA2B1B6) tool to identify statistically significant clustering of restaurants using their yearly sales volume as our analysis values. This time, we can use the analyze_patterns tool from our Organization's standard feature analysis toolbox." ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [], "source": [ "# Remove existing layer of restaurant hot spots (if one exists)\n", "gis.content.delete_items(\n", " gis.content.search(f'restaurant_hotspots_01 owner:{me.username}'))\n", "\n", "# Run optimized hot spots, using sales volume as the analysis field\n", "restaurant_hot_spots_item = features.analyze_patterns.find_hot_spots(\n", " restaurants_item, analysis_field='salesvol', \n", " output_name='restaurant_hotspots_01', gis=gis)" ] }, { "cell_type": "code", "execution_count": 67, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Create a map of Pittsburgh to show hot spot outputs\n", "hot_spots_map = gis.map(\"Pittsburgh\")\n", "hot_spots_map.add_layer(restaurant_hot_spots_item)\n", "hot_spots_map.zoom = 10\n", "hot_spots_map" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's bring back our spatially-enabled dataframe from our previous step to summarize the statistics of the hot spot analysis for restaurants within each ellipse zone." ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Feature Layer Collection by some_user\n", "
Last Modified: October 31, 2018\n", "
0 comments, 0 views\n", "
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\n", " " ], "text/plain": [ "" ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" } ], "source": [ "gis.content.delete_items(\n", " gis.content.search(f\"EllipseSummary02 owner:{me.username}\"))\n", "# Calculate the sum of sales volume and the mean of sales volume\n", "sum_fields = ['gi_bin Sum', 'gipvalue Sum', 'gizscore Sum',\n", " 'gi_bin Mean', 'gipvalue Mean', 'gizscore Mean']\n", "# Run the calculation\n", "sv_summary2 = summarize_data.aggregate_points(\n", " point_layer = restaurant_hot_spots_item.layers[0],\n", " polygon_layer = sv_summary.layers[0],\n", " summary_fields=sum_fields,\n", " output_name=\"EllipseSummary02\")\n", "sv_summary2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can now make a map of the ellipse zones symbolized by the average gi bin, which represents the designation of hot spots, cold spots, or unsignificant spots. " ] }, { "cell_type": "code", "execution_count": 68, "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hotspot_ellipses_map = gis.map(\"Pittsburgh\")\n", "hotspot_ellipses_map" ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [], "source": [ "hotspot_ellipses_map.add_layer(sv_summary2, {\n", " \"renderer\":\"ClassedColorRenderer\",\n", " \"field_name\":\"mean_gi_bin\",\n", " \"classificationMethod\":\"ClassedColorRenderer\",\n", " \"opacity\":0.75\n", "})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, let's convert our summary output for ellipse zones into a spatially-enabled dataframe, so we can make our final priority assessment." ] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " SHAPE analysisarea \\\n", "0 {\"rings\": [[[-8912514.427799998, 5024099.28689... 18.548609 \n", "1 {\"rings\": [[[-8832192.777099999, 4966569.23970... 0.884207 \n", "2 {\"rings\": [[[-8835736.602800002, 4990278.83850... 147.530862 \n", "3 {\"rings\": [[[-8894614.7559, 4991845.114300005]... 0.012757 \n", "4 {\"rings\": [[[-8848978.753299998, 4984824.30430... 0.418115 \n", "\n", " centerx centery cluster_id mean_gi_bin mean_gipvalue \\\n", "0 -8.910530e+06 5.023269e+06 1 0.000000 0.725146 \n", "1 -8.831966e+06 4.966738e+06 3 0.000000 0.318608 \n", "2 -8.825619e+06 4.985812e+06 2 0.000000 0.370132 \n", "3 -8.894847e+06 4.991924e+06 38 1.857143 0.007836 \n", "4 -8.848780e+06 4.984781e+06 10 0.000000 0.104431 \n", "\n", " mean_gizscore mean_salesvol objectid point_count point_count_1 \\\n", "0 0.114444 792.111111 1 18 18.0 \n", "1 -0.997323 298.600000 2 5 5.0 \n", "2 -0.917133 208.153846 3 13 13.0 \n", "3 2.674614 580.857143 38 7 7.0 \n", "4 -1.631379 242.600000 4 5 5.0 \n", "\n", " rotation sum_gi_bin sum_gipvalue sum_gizscore sum_salesvol \\\n", "0 22.711797 0.0 13.052637 2.059989 14258.0 \n", "1 143.274423 0.0 1.593038 -4.986617 1493.0 \n", "2 23.821812 0.0 4.811715 -11.922728 2706.0 \n", "3 108.715687 13.0 0.054851 18.722297 4066.0 \n", "4 12.375789 0.0 0.522155 -8.156897 1213.0 \n", "\n", " xstddist ystddist \n", "0 2151.586100 12525.834221 \n", "1 283.002577 4506.261331 \n", "2 11059.807891 19222.922180 \n", "3 245.366324 75.027222 \n", "4 203.846151 2966.869552 " ] }, "execution_count": 69, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Convert the output item into a spatially-enabled dataframe\n", "fset = sv_summary2.layers[0].query()\n", "summary2_sdf = fset.sdf\n", "summary2_sdf.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Calculating Priority Scores" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We are now ready to assign a priority score for each ellipse zone based on basic restaurant sales volume statistics at each ellipse, as well as the presence of hot spot and cold spot restaurant features within each zone. \n", "\n", "In order to help make this analysis flexible for further refinements, let's apply a weight to each of these factors. \n", "\n", "Factors:\n", "\n", "- Amount of restaurants in zone: 0.75\n", "- Average sales volume in zone: 1.0\n", "- Average GI Bin score in zone: 2.0\n", "\n", "The final priority score will be assessed as the normalized addition of each factor multiplied by its weight. The formula therefore is as follows:\n", "\n", "P = " ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [], "source": [ "# Establish weight values for each factor\n", "priority_score_weights = {\n", " \"restaurant count\":0.75,\n", " \"mean sales volume\":1.0,\n", " \"mean gi bin score\": 2.0\n", "}" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [], "source": [ "# Establish a list of fields to compute z-scores for\n", "list_of_fields_to_normalize = [\n", " 'point_count',\n", " 'mean_salesvol',\n", " 'mean_gi_bin'\n", "]" ] }, { "cell_type": "code", "execution_count": 72, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Calculating z-score for point_count\n", "Calculating z-score for mean_salesvol\n", "Calculating z-score for mean_gi_bin\n" ] } ], "source": [ "# Iterate on each field and calculate z-score\n", "for field in list_of_fields_to_normalize:\n", " print(\"Calculating z-score for \"+field)\n", " summary2_sdf[field + \"_zscore\"] = \\\n", " (summary2_sdf[field] - summary2_sdf[field].mean()) / \\\n", " summary2_sdf[field].std(ddof=0)" ] }, { "cell_type": "code", "execution_count": 73, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " SHAPE analysisarea \\\n", "0 {\"rings\": [[[-8912514.427799998, 5024099.28689... 18.548609 \n", "1 {\"rings\": [[[-8832192.777099999, 4966569.23970... 0.884207 \n", "2 {\"rings\": [[[-8835736.602800002, 4990278.83850... 147.530862 \n", "3 {\"rings\": [[[-8894614.7559, 4991845.114300005]... 0.012757 \n", "4 {\"rings\": [[[-8848978.753299998, 4984824.30430... 0.418115 \n", "\n", " centerx centery cluster_id mean_gi_bin mean_gipvalue \\\n", "0 -8.910530e+06 5.023269e+06 1 0.000000 0.725146 \n", "1 -8.831966e+06 4.966738e+06 3 0.000000 0.318608 \n", "2 -8.825619e+06 4.985812e+06 2 0.000000 0.370132 \n", "3 -8.894847e+06 4.991924e+06 38 1.857143 0.007836 \n", "4 -8.848780e+06 4.984781e+06 10 0.000000 0.104431 \n", "\n", " mean_gizscore mean_salesvol objectid ... sum_gipvalue \\\n", "0 0.114444 792.111111 1 ... 13.052637 \n", "1 -0.997323 298.600000 2 ... 1.593038 \n", "2 -0.917133 208.153846 3 ... 4.811715 \n", "3 2.674614 580.857143 38 ... 0.054851 \n", "4 -1.631379 242.600000 4 ... 0.522155 \n", "\n", " sum_gizscore sum_salesvol xstddist ystddist point_count_zscore \\\n", "0 2.059989 14258.0 2151.586100 12525.834221 1.005862 \n", "1 -4.986617 1493.0 283.002577 4506.261331 -0.899938 \n", "2 -11.922728 2706.0 11059.807891 19222.922180 0.272862 \n", "3 18.722297 4066.0 245.366324 75.027222 -0.606738 \n", "4 -8.156897 1213.0 203.846151 2966.869552 -0.899938 \n", "\n", " mean_salesvol_zscore mean_gi_bin_zscore priority_score ps_zscore \n", "0 -0.012326 -0.085496 0.571080 0.210492 \n", "1 -0.923560 -0.085496 -1.769504 -0.668705 \n", "2 -1.090562 -0.085496 -1.056907 -0.401031 \n", "3 -0.402391 1.420862 1.984278 0.741334 \n", "4 -1.026960 -0.085496 -1.872904 -0.707545 \n", "\n", "[5 rows x 24 columns]" ] }, "execution_count": 75, "metadata": {}, "output_type": "execute_result" } ], "source": [ "summary2_sdf['priority_score'] = summary2_sdf.apply(\n", " lambda x: calculate_priority_score(\n", " x['point_count_zscore'], \n", " x['mean_salesvol_zscore'], \n", " x['mean_gi_bin_zscore']), \n", " axis=1)\n", "summary2_sdf['ps_zscore'] = \\\n", " (summary2_sdf['priority_score'] - \\\n", " summary2_sdf['priority_score'].mean()) / \\\n", " summary2_sdf['priority_score'].std(ddof=0)\n", "summary2_sdf.head()" ] }, { "cell_type": "code", "execution_count": 76, "metadata": {}, "outputs": [], "source": [ "# Clean-up\n", "top_zones_sdf = summary2_sdf\n", "del(summary2_sdf, summary_sdf)" ] }, { "cell_type": "code", "execution_count": 77, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "346" ] }, "execution_count": 77, "metadata": {}, "output_type": "execute_result" } ], "source": [ "top_zones_sdf.shape[0]" ] }, { "cell_type": "code", "execution_count": 78, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "343" ] }, "execution_count": 78, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Clean up score field\n", "top_zones_sdf = top_zones_sdf[np.isfinite(top_zones_sdf['ps_zscore'])]\n", "top_zones_sdf.shape[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We're ready to make a map of the top restaurant zones by this approach!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Where are the top five restaurant zones by priority score?" ] }, { "cell_type": "code", "execution_count": 79, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ " SHAPE analysisarea \\\n", "191 {\"rings\": [[[-8923969.6808, 4931619.720500007]... 0.246452 \n", "168 {\"rings\": [[[-8878566.901399996, 4930031.79890... 0.308486 \n", "233 {\"rings\": [[[-8900012.484800002, 4914279.40649... 0.465287 \n", "209 {\"rings\": [[[-8906974.288499996, 4944424.78289... 0.116985 \n", "188 {\"rings\": [[[-8925355.079699999, 4931457.12089... 0.118014 \n", "\n", " centerx centery cluster_id mean_gi_bin mean_gipvalue \\\n", "191 -8.923561e+06 4.931611e+06 195 3.000000 0.000000 \n", "168 -8.880020e+06 4.929855e+06 172 3.000000 0.000345 \n", "233 -8.899673e+06 4.914428e+06 238 2.909091 0.000785 \n", "209 -8.906857e+06 4.944448e+06 214 3.000000 0.000466 \n", "188 -8.925014e+06 4.931537e+06 192 3.000000 0.000000 \n", "\n", " mean_gizscore mean_salesvol objectid ... sum_gipvalue \\\n", "191 7.017320 2983.478261 192 ... 0.000000 \n", "168 3.604876 1129.810811 169 ... 0.012779 \n", "233 3.452746 2068.636364 234 ... 0.017271 \n", "209 3.535763 2249.000000 210 ... 0.005587 \n", "188 7.214493 1993.562500 189 ... 0.000000 \n", "\n", " sum_gizscore sum_salesvol xstddist ystddist point_count_zscore \\\n", "191 161.398354 68620.0 409.214909 858.433274 1.738863 \n", "168 133.380401 41803.0 1464.277838 300.290419 3.791263 \n", "233 75.960412 45510.0 370.947179 1782.304999 1.592263 \n", "209 42.429151 26988.0 119.583783 1401.524338 0.126262 \n", "188 115.431885 31897.0 350.478996 479.921752 0.712662 \n", "\n", " mean_salesvol_zscore mean_gi_bin_zscore priority_score ps_zscore \n", "191 4.033882 2.347851 10.033730 3.764961 \n", "168 0.611214 2.347851 8.150362 3.057508 \n", "233 2.344690 2.274113 8.087112 3.033750 \n", "209 2.677719 2.347851 7.468117 2.801236 \n", "188 2.206071 2.347851 7.436269 2.789273 \n", "\n", "[5 rows x 24 columns]" ] }, "execution_count": 79, "metadata": {}, "output_type": "execute_result" } ], "source": [ "top_zones_sdf = top_zones_sdf.sort_values(by=['ps_zscore'], \n", " ascending=False)\n", "top_zones_sdf.head()" ] }, { "cell_type": "code", "execution_count": 81, "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 81, "metadata": {}, "output_type": "execute_result" } ], "source": [ "top_zones_map, top_zones_hist = create_map_and_histogram(\n", " \"Pittsburgh\", top_zones_sdf, \"ps_zscore\", cmap='YlOrRd')\n", "top_zones_map" ] }, { "cell_type": "code", "execution_count": 82, "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/html": [ "\n", "\n", "\n", "\n", "\n", "\n", "
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5 rows × 24 columns

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" ], "text/plain": [ " SHAPE analysisarea \\\n", "191 {\"rings\": [[[-8923969.6808, 4931619.720500007]... 0.246452 \n", "168 {\"rings\": [[[-8878566.901399996, 4930031.79890... 0.308486 \n", "233 {\"rings\": [[[-8900012.484800002, 4914279.40649... 0.465287 \n", "209 {\"rings\": [[[-8906974.288499996, 4944424.78289... 0.116985 \n", "188 {\"rings\": [[[-8925355.079699999, 4931457.12089... 0.118014 \n", "\n", " centerx centery cluster_id mean_gi_bin mean_gipvalue \\\n", "191 -8.923561e+06 4.931611e+06 195 3.000000 0.000000 \n", "168 -8.880020e+06 4.929855e+06 172 3.000000 0.000345 \n", "233 -8.899673e+06 4.914428e+06 238 2.909091 0.000785 \n", "209 -8.906857e+06 4.944448e+06 214 3.000000 0.000466 \n", "188 -8.925014e+06 4.931537e+06 192 3.000000 0.000000 \n", "\n", " mean_gizscore mean_salesvol objectid ... sum_gipvalue \\\n", "191 7.017320 2983.478261 192 ... 0.000000 \n", "168 3.604876 1129.810811 169 ... 0.012779 \n", "233 3.452746 2068.636364 234 ... 0.017271 \n", "209 3.535763 2249.000000 210 ... 0.005587 \n", "188 7.214493 1993.562500 189 ... 0.000000 \n", "\n", " sum_gizscore sum_salesvol xstddist ystddist point_count_zscore \\\n", "191 161.398354 68620.0 409.214909 858.433274 1.738863 \n", "168 133.380401 41803.0 1464.277838 300.290419 3.791263 \n", "233 75.960412 45510.0 370.947179 1782.304999 1.592263 \n", "209 42.429151 26988.0 119.583783 1401.524338 0.126262 \n", "188 115.431885 31897.0 350.478996 479.921752 0.712662 \n", "\n", " mean_salesvol_zscore mean_gi_bin_zscore priority_score ps_zscore \n", "191 4.033882 2.347851 10.033730 3.764961 \n", "168 0.611214 2.347851 8.150362 3.057508 \n", "233 2.344690 2.274113 8.087112 3.033750 \n", "209 2.677719 2.347851 7.468117 2.801236 \n", "188 2.206071 2.347851 7.436269 2.789273 \n", "\n", "[5 rows x 24 columns]" ] }, "execution_count": 83, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Top five ellipse zones\n", "top_zones_sdf.head()" ] }, { "cell_type": "code", "execution_count": 87, "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 87, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Map of top five ellipse zones\n", "top_five_map, top_five_hist = create_map_and_histogram(\n", " \"Pittsburgh\", top_zones_sdf.head(), \"ps_zscore\", cmap='YlOrRd')\n", "top_five_map.basemap='gray'\n", "top_five_map" ] }, { "cell_type": "code", "execution_count": 85, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:20: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n" ] } ], "source": [ "# Execute this cell to tour each of the five zones selected\n", "visit_ellipse_features_on_map_widget(top_five_map, top_zones_sdf.head())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To make all of the analysis available for further refinement, the following cell adds each layer that was used in this process. It's quite ugly, but it contains every component we used. " ] }, { "cell_type": "code", "execution_count": 89, "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 89, "metadata": {}, "output_type": "execute_result" } ], "source": [ "full_analysis_map = gis.map(\"Pittsburgh\")\n", "# Add the Hot Spots outputs to the map\n", "full_analysis_map.add_layer(restaurant_hot_spots_item)\n", "# Add the clustered restaurants to the map\n", "clusters_sdf.spatial.plot(map_widget=full_analysis_map, \n", " renderer_type='u', col=\"COLOR_ID\")\n", "# Add the summary elipse zones\n", "top_zones_sdf.spatial.plot(map_widget=full_analysis_map, \n", " renderer_type='c', \n", " method='esriClassifyNaturalBreaks', \n", " class_count=5, cmap='OrRd', \n", " col='ps_zscore', alpha=0.5)\n", "# Show the full analysis map\n", "full_analysis_map" ] }, { "cell_type": "markdown", "metadata": { "heading_collapsed": true }, "source": [ "# 4) Present the results " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To present these results, we'll create web maps and a Story Map Journal with sections provided by each of our top five ellipsoid zones (determined by the priority score). " ] }, { "cell_type": "code", "execution_count": 90, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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\n", " Pittsburgh Restaurant Zone Prospecting - Web Map v1\n", "
Web Map from clustering analysis of restaurants. Web Map by some_user\n", "
Last Modified: October 31, 2018\n", "
0 comments, 0 views\n", "
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\n", " " ], "text/plain": [ "" ] }, "execution_count": 90, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Create web map properties and use the web map widget's save method\n", "webmap_properties = \\\n", " {'title': 'Pittsburgh Restaurant Zone Prospecting - Web Map v1',\n", " 'snippet': 'Web Map from clustering analysis of restaurants.',\n", " 'tags': ['automation', 'python']}\n", "top_zones_webmap_item = top_zones_map.save(webmap_properties)\n", "top_zones_webmap_item" ] }, { "cell_type": "code", "execution_count": 91, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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\n", " Pittsburgh Restaurant Zone Prospecting - Full Analysis Web Map v1\n", " \n", "
Web Map from clustering analysis of restaurants. Web Map by some_user\n", "
Last Modified: October 31, 2018\n", "
0 comments, 0 views\n", "
\n", "
\n", " " ], "text/plain": [ "" ] }, "execution_count": 91, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Create web map properties and use the web map widget's save method\n", "webmap_properties = \\\n", " {'title': 'Pittsburgh Restaurant Zone Prospecting - Full Analysis '\\\n", " 'Web Map v1',\n", " 'snippet': 'Web Map from clustering analysis of restaurants.',\n", " 'tags': ['automation', 'python']}\n", "full_analysis_webmap_item = full_analysis_map.save(webmap_properties)\n", "full_analysis_webmap_item" ] } ], "metadata": { "esriNotebookRuntime": { "notebookRuntimeName": "ArcGIS Notebook Python 3 Advanced", "notebookRuntimeVersion": "10.7.1" }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.7" } }, "nbformat": 4, "nbformat_minor": 2 }