{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Data Visualization: Construction permits, part 1/2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "> * 🔬 Data Science\n", "* 📈 Statistics\n", "* 👟 Ready To Run!" ] }, { "cell_type": "markdown", "metadata": { "toc": true }, "source": [ "

Table of Contents

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" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Overview" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "One indicator of a region's growth is the number of permits issued for new construction. Exploring and analyzing permit activity can help regional planners ensure that development occurs in accordance to the area's long-term goals. One area that has recently experienced rapid growth is Montgomery County, Maryland, a suburban county near Washington, D.C. County planners want to observe spatial and temporal growth trends, find out why certain areas are growing faster than others, and communicate key information about the county's growth to the public.\n", "\n", "In this notebook, you'll explore Montgomery County permit data. First, you'll add the permit data from ArcGIS Living Atlas of the World. You'll explore the data and become familiar with exactly what kind of information it contains. Then, you'll analyze the data to detect patterns and find out why growth is occurring. Once you've gathered your findings from your exploration and analysis, you'll share your work online." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Explore the data\n", "\n", "To better understand trends in permit activity in Montgomery County, you'll add a dataset of permits issued since 2010. Before you begin your analysis, however, it's important to explore your data and understand what it shows and does not show. You'll familiarize yourself with the data's attributes, sort the data by type, and visualize spatial and temporal trends. In doing so, you'll gain context for your analysis and know exactly which questions you still need to ask to find out why, where, and when growth is occurring." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Connect to your ArcGIS online organization." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "from arcgis.gis import GIS\n", "import pandas as pd\n", "\n", "from arcgis.features import GeoAccessor, GeoSeriesAccessor" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "agol_gis = GIS()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Search for the **Commercial Permits since 2010** layer. You can specify the owner's name to get more specific results. To search for content from the Living Atlas, or content shared by other users on ArcGIS Online, set `outside_org=True`." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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\n", " Commercial Permits since 2010\n", " \n", "
Commercial building permits issued in Montgomery County, Maryland, since 2010.Feature Layer Collection by rpeake_LearnGIS\n", "
Last Modified: December 08, 2017\n", "
0 comments, 494 views\n", "
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\n", " " ], "text/plain": [ "" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = agol_gis.content.search(\n", " 'title: Commercial Permits since 2010 owner: rpeake_LearnGIS',\n", " 'Feature layer',\n", " outside_org=True)\n", "data[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Get the first item from the results." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "permits = data[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since the item is a Feature Layer Collection, accessing the layers property gives us a list of FeatureLayer objects. The permit layer is the first layer in this item. Visualize this layer on a map of Montgomery County, Maryland." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": [ "permit_layer = permits.layers[0]" ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 63, "metadata": {}, "output_type": "execute_result" } ], "source": [ "permit_map = agol_gis.map('Montgomery County, Maryland', zoomlevel=9)\n", "permit_map" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can add a number of different layer objects such as FeatureLayer, FeatureCollection, ImageryLayer, MapImageLayer to the map by calling the `add_layer()` method." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [], "source": [ "permit_map.add_layer(permit_layer)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Data Exploration" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that you've added the permit data, you'll explore its contents. Geographic data doesn't only contain information about location; it can also include other attributes not seen on a map. \n", "\n", "Convert the layer into a spatially-enabled dataframe to explore these attributes." ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "permit_layer" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "sdf = pd.DataFrame.spatial.from_layer(permit_layer)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`tail()` method gives the last 5 rows of the dataframe." ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Added_DateAddressApplicatioBldgAreaNuBuilding_ACityDeclValNuDeclValNu2Declared_VDescriptio...Pre_directSHAPEStateStatusStreet_NamStreet_NumStreet_SufUse_CodeWork_TypeZIP_code
112192014-01-311015 SPRING STCOMMERCIAL BUILDING707.91707.91SILVER SPRING42000.042000.0$42,000.00Silver Spring Enterprise Zone\\n\\nAdd exterior ......{\"x\": -8574668.7047, \"y\": 4721607.997599997, \"...MDStop WorkSPRING1015STBUSINESS BUILDINGADD20910
112202014-02-1126100 WOODFIELD RDCOMMERCIAL BUILDING0.000DAMASCUS5875.05875.0$5,875.00PYLON SIGN...{\"x\": -8594080.2264, \"y\": 4762636.6635000035, ...MDStop WorkWOODFIELD26100RDBUSINESS BUILDINGCONSTRUCT20872
112212014-02-2010520 MONTROSE AVECOMMERCIAL BUILDING728.00728BETHESDA31000.031000.0$31,000.00Remodeling a one story building with walk-out ......{\"x\": -8582314.4798, \"y\": 4725770.635600001, \"...MDStop WorkMONTROSE10520AVEASSEMBLY BUILDINGADD20814
112222014-03-068500 RIVER RDCOMMERCIAL BUILDING472.02472.02BETHESDA1000.01000.0$1,000.00TOTAL OF 17 GROUPED TENTS FOR 2014 QUICKEN LOA......{\"x\": -8591206.0439, \"y\": 4721680.315399997, \"...MDStop WorkRIVER8500RDCOMMERCIAL MISCELLANEOUS STRUCCONSTRUCT20817
112232014-03-108500 RIVER RDCOMMERCIAL BUILDING8461.558461.55BETHESDA1.01.0$1.001 GRANDSTAND & MULTIPLE PLATFORMS FOR 2014 QUI......{\"x\": -8591206.0439, \"y\": 4721680.315399997, \"...MDStop WorkRIVER8500RDCOMMERCIAL MISCELLANEOUS STRUCCONSTRUCT20817
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5 rows × 26 columns

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" ], "text/plain": [ " Added_Date Address Applicatio BldgAreaNu \\\n", "11219 2014-01-31 1015 SPRING ST COMMERCIAL BUILDING 707.91 \n", "11220 2014-02-11 26100 WOODFIELD RD COMMERCIAL BUILDING 0.00 \n", "11221 2014-02-20 10520 MONTROSE AVE COMMERCIAL BUILDING 728.00 \n", "11222 2014-03-06 8500 RIVER RD COMMERCIAL BUILDING 472.02 \n", "11223 2014-03-10 8500 RIVER RD COMMERCIAL BUILDING 8461.55 \n", "\n", " Building_A City DeclValNu DeclValNu2 Declared_V \\\n", "11219 707.91 SILVER SPRING 42000.0 42000.0 $42,000.00 \n", "11220 0 DAMASCUS 5875.0 5875.0 $5,875.00 \n", "11221 728 BETHESDA 31000.0 31000.0 $31,000.00 \n", "11222 472.02 BETHESDA 1000.0 1000.0 $1,000.00 \n", "11223 8461.55 BETHESDA 1.0 1.0 $1.00 \n", "\n", " Descriptio ... Pre_direct \\\n", "11219 Silver Spring Enterprise Zone\\n\\nAdd exterior ... ... \n", "11220 PYLON SIGN ... \n", "11221 Remodeling a one story building with walk-out ... ... \n", "11222 TOTAL OF 17 GROUPED TENTS FOR 2014 QUICKEN LOA... ... \n", "11223 1 GRANDSTAND & MULTIPLE PLATFORMS FOR 2014 QUI... ... \n", "\n", " SHAPE State Status \\\n", "11219 {\"x\": -8574668.7047, \"y\": 4721607.997599997, \"... MD Stop Work \n", "11220 {\"x\": -8594080.2264, \"y\": 4762636.6635000035, ... MD Stop Work \n", "11221 {\"x\": -8582314.4798, \"y\": 4725770.635600001, \"... MD Stop Work \n", "11222 {\"x\": -8591206.0439, \"y\": 4721680.315399997, \"... MD Stop Work \n", "11223 {\"x\": -8591206.0439, \"y\": 4721680.315399997, \"... MD Stop Work \n", "\n", " Street_Nam Street_Num Street_Suf Use_Code \\\n", "11219 SPRING 1015 ST BUSINESS BUILDING \n", "11220 WOODFIELD 26100 RD BUSINESS BUILDING \n", "11221 MONTROSE 10520 AVE ASSEMBLY BUILDING \n", "11222 RIVER 8500 RD COMMERCIAL MISCELLANEOUS STRUC \n", "11223 RIVER 8500 RD COMMERCIAL MISCELLANEOUS STRUC \n", "\n", " Work_Type ZIP_code \n", "11219 ADD 20910 \n", "11220 CONSTRUCT 20872 \n", "11221 ADD 20814 \n", "11222 CONSTRUCT 20817 \n", "11223 CONSTRUCT 20817 \n", "\n", "[5 rows x 26 columns]" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sdf.tail()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The permit data contains a long list of attributes. Some attributes have self-explanatory names, while others may have names that can be difficult to understand without context. The list of attributes can be obtained using the columns of the dataframe." ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['Added_Date', 'Address', 'Applicatio', 'BldgAreaNu', 'Building_A',\n", " 'City', 'DeclValNu', 'DeclValNu2', 'Declared_V', 'Descriptio', 'FID',\n", " 'Final_Date', 'Issue_Date', 'Location', 'Permit_Num', 'Post_direc',\n", " 'Pre_direct', 'SHAPE', 'State', 'Status', 'Street_Nam', 'Street_Num',\n", " 'Street_Suf', 'Use_Code', 'Work_Type', 'ZIP_code'],\n", " dtype='object')" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sdf.columns" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
countmeanstdmin25%50%75%max
BldgAreaNu11224.09241.7787773.950484e+040.0255.001537.04000.001.548205e+06
DeclValNu11224.0784736.0356201.152229e+070.020000.0074000.0200000.001.129634e+09
DeclValNu211224.0784736.0356201.152229e+070.020000.0074000.0200000.001.129634e+09
FID11224.05612.5000003.240234e+031.02806.755612.58418.251.122400e+04
Permit_Num11224.0655806.1127947.759285e+04528631.0587437.50652073.5722003.257.961930e+05
ZIP_code11224.020848.9888635.581882e+020.020832.0020871.020901.002.177100e+04
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" ], "text/plain": [ " count mean std min 25% \\\n", "BldgAreaNu 11224.0 9241.778777 3.950484e+04 0.0 255.00 \n", "DeclValNu 11224.0 784736.035620 1.152229e+07 0.0 20000.00 \n", "DeclValNu2 11224.0 784736.035620 1.152229e+07 0.0 20000.00 \n", "FID 11224.0 5612.500000 3.240234e+03 1.0 2806.75 \n", "Permit_Num 11224.0 655806.112794 7.759285e+04 528631.0 587437.50 \n", "ZIP_code 11224.0 20848.988863 5.581882e+02 0.0 20832.00 \n", "\n", " 50% 75% max \n", "BldgAreaNu 1537.0 4000.00 1.548205e+06 \n", "DeclValNu 74000.0 200000.00 1.129634e+09 \n", "DeclValNu2 74000.0 200000.00 1.129634e+09 \n", "FID 5612.5 8418.25 1.122400e+04 \n", "Permit_Num 652073.5 722003.25 7.961930e+05 \n", "ZIP_code 20871.0 20901.00 2.177100e+04 " ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sdf.describe().T" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Query the types of attributes and explore the data." ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Added_Date datetime64[ns]\n", "Address object\n", "Applicatio object\n", "BldgAreaNu float64\n", "Building_A object\n", "City object\n", "DeclValNu float64\n", "DeclValNu2 float64\n", "Declared_V object\n", "Descriptio object\n", "FID int64\n", "Final_Date datetime64[ns]\n", "Issue_Date datetime64[ns]\n", "Location object\n", "Permit_Num int64\n", "Post_direc object\n", "Pre_direct object\n", "SHAPE geometry\n", "State object\n", "Status object\n", "Street_Nam object\n", "Street_Num object\n", "Street_Suf object\n", "Use_Code object\n", "Work_Type object\n", "ZIP_code int64\n", "dtype: object" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sdf.dtypes" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(['CONSTRUCT', 'ALTER', 'COMMERCIAL CHANGE OF USE',\n", " 'RESTORE AND / OR REPAIR', 'ADD', 'BUILD FOUNDATION', 'INSTALL',\n", " 'REPLACE', 'CONSTRUCT SHEETING/SHORING', 'FINAL ONLY AP',\n", " 'REMOVE AND REPLACE', 'OCCUPY', 'DEMOLISH'], dtype=object)" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sdf['Work_Type'].unique()" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(['Finaled', 'Issued', 'Open', 'Stop Work'], dtype=object)" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sdf['Status'].unique()" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(['MULTI-FAMILY DWELLING', 'RESTAURANT', 'BUSINESS BUILDING',\n", " 'MERCANTILE BUILDING', 'PLACE OF WORSHIP', 'ASSEMBLY BUILDING',\n", " 'STORAGE BUILDING', 'GARAGE', 'INSTITUTIONAL BUILDING',\n", " 'COMMERCIAL MISCELLANEOUS STRUC', 'INDUSTRIAL BUILDING',\n", " 'EDUCATIONAL BUILDING', 'TOWER', 'SWIMMING POOL', 'FENCE', 'BANK',\n", " 'SHED', 'MULTI-FAMILY SENIOR CITIZEN BL', 'RETAINING WALL',\n", " 'TRAILER', 'HOSPITAL', 'BIOSCIENCE', 'TOWNHOUSE', 'HOTEL',\n", " 'FACTORY', 'BOARDING HOUSE', 'SWIMMING POOL & FENCE',\n", " 'UTILITY, MISCELLANEOUS', 'THEATER',\n", " 'MULTIFAMILY DWELLING HIGH RISE', 'MULTIFAMILY DWELLING LOW RISE',\n", " 'MISCELLANEOUS STRUCTURE', 'OWNERSHIP UNIT'], dtype=object)" ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sdf['Use_Code'].unique()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Permits by Status" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `groupby()` method groups the rows per the column and does calculations, such as finding their counts, as shown in the following code." ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Status\n", "Finaled 5341\n", "Issued 4696\n", "Open 757\n", "Stop Work 430\n", "dtype: int64" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "permits_by_status = sdf.groupby(sdf['Status']).size()\n", "permits_by_status" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " There are only four permit statuses: Issued, Finaled, Open, and Stop Work. To visualize the number of permits for each status, you'll create a pie chart." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since the dataframe attributes just show the count of status, you can consider any attribute to graph the status count." ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.axis('equal') \n", "permits_by_status.plot(kind='pie', legend=False, label='Permits by Status');" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The pie chart above shows the four permit statuses, with the size of each status determined by the number of permits. The vast majority of permits are either Issued or Finaled. Finaled permits are issued permits that have also had the requisite inspections performed." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It's helpful to visualize the spatial distribution of permit attributes on a map. You'll change the map so that each permit's symbol represents its status." ] }, { "cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" } ], "source": [ "permits_by_status_map = agol_gis.map('Montgomery County, Maryland')\n", "permits_by_status_map" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![](insights_images/image_6.png)" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sdf.spatial.plot(kind='map', map_widget=permits_by_status_map,\n", " renderer_type='u', # specify the unique value renderer using its notation 'u'\n", " col='Status') # column to get unique values from" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Permits by Type" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "Use_Code\n", "ASSEMBLY BUILDING 394\n", "BANK 87\n", "BIOSCIENCE 39\n", "BOARDING HOUSE 3\n", "BUSINESS BUILDING 3461\n", "COMMERCIAL MISCELLANEOUS STRUC 1197\n", "EDUCATIONAL BUILDING 658\n", "FACTORY 4\n", "FENCE 10\n", "GARAGE 56\n", "HOSPITAL 143\n", "HOTEL 44\n", "INDUSTRIAL BUILDING 53\n", "INSTITUTIONAL BUILDING 30\n", "MERCANTILE BUILDING 1016\n", "MISCELLANEOUS STRUCTURE 9\n", "MULTI-FAMILY DWELLING 1838\n", "MULTI-FAMILY SENIOR CITIZEN BL 71\n", "MULTIFAMILY DWELLING HIGH RISE 10\n", "MULTIFAMILY DWELLING LOW RISE 31\n", "OWNERSHIP UNIT 1\n", "PLACE OF WORSHIP 167\n", "RESTAURANT 638\n", "RETAINING WALL 185\n", "SHED 28\n", "STORAGE BUILDING 208\n", "SWIMMING POOL 51\n", "SWIMMING POOL & FENCE 5\n", "THEATER 1\n", "TOWER 14\n", "TOWNHOUSE 680\n", "TRAILER 91\n", "UTILITY, MISCELLANEOUS 1\n", "dtype: int64" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "permits_by_type = sdf.groupby(['Use_Code']).size()\n", "permits_by_type" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The series is not sorted properly. Use the `sort()` method to sort it from highest count to lowest count. The most common use code, **Business Buildings**, has almost twice as many permits as the second highest, **Multi-family Dwelling**. The top four use codes together comprise the majority of all permits, so these use codes may be the most important to focus on in your analysis later." ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Use_Code\n", "BUSINESS BUILDING 3461\n", "MULTI-FAMILY DWELLING 1838\n", "COMMERCIAL MISCELLANEOUS STRUC 1197\n", "MERCANTILE BUILDING 1016\n", "TOWNHOUSE 680\n", "dtype: int64" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" } ], "source": [ "permits_by_type.sort_values(ascending=False, inplace=True)\n", "permits_by_type.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Clean up the data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Before you begin analysis of your data, you'll hide attribute fields you don't intend to use, rename fields with unclear names, and filter your dataset to only show permits with the four most common use codes. These changes won't permanently affect the original dataset, but they will make the data easier to work with and understand." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**'Declared_V'**, **'Building_A'**, **'Applicatio'** attribute fields describe aspects of the data that aren't important for your analysis. You'll drop these fields." ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [], "source": [ "sdf.drop(['Declared_V', 'Building_A', 'Applicatio'], axis=1, inplace=True)" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['Added_Date', 'Address', 'BldgAreaNu', 'City', 'DeclValNu',\n", " 'DeclValNu2', 'Descriptio', 'FID', 'Final_Date', 'Issue_Date',\n", " 'Location', 'Permit_Num', 'Post_direc', 'Pre_direct', 'SHAPE', 'State',\n", " 'Status', 'Street_Nam', 'Street_Num', 'Street_Suf', 'Use_Code',\n", " 'Work_Type', 'ZIP_code'],\n", " dtype='object')" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sdf.columns" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The fields are no longer listed. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, you'll rename some of the attribute fields with shortened or unclear names so that their names are more descriptive." ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [], "source": [ "sdf.rename(columns={\"Descriptio\": \"Description\", \"BldgAreaNu\": \"Building_Area\", \"DeclValNu\": \"Declared_Value\"}, inplace=True)" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Index(['Added_Date', 'Address', 'Building_Area', 'City', 'Declared_Value',\n", " 'DeclValNu2', 'Description', 'FID', 'Final_Date', 'Issue_Date',\n", " 'Location', 'Permit_Num', 'Post_direc', 'Pre_direct', 'SHAPE', 'State',\n", " 'Status', 'Street_Nam', 'Street_Num', 'Street_Suf', 'Use_Code',\n", " 'Work_Type', 'ZIP_code'],\n", " dtype='object')" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sdf.columns" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are other fields that you may want to either rename or remove, but for the purposes of this lesson, these are enough. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Filter the permits\n", "\n", "Next, you'll filter the permits to reduce the number of records in your analysis. As you saw previously, there are four types of permits that comprise over half the total number of permits. Focusing your analysis on just these four types will reduce the amount of data to analyze without ignoring the most important types of development. To remove the other use codes, you'll create a filter." ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Use_Code\n", "BUSINESS BUILDING 3461\n", "MULTI-FAMILY DWELLING 1838\n", "COMMERCIAL MISCELLANEOUS STRUC 1197\n", "MERCANTILE BUILDING 1016\n", "dtype: int64" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "permits_by_type.head(4) # top 4 Use_Codes" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['BUSINESS BUILDING',\n", " 'MULTI-FAMILY DWELLING',\n", " 'COMMERCIAL MISCELLANEOUS STRUC',\n", " 'MERCANTILE BUILDING']" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "filtered_permits = list(permits_by_type.head(4).index)\n", "filtered_permits" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To visualize the top 4 Use Codes on a map, you can filer the dataframe with Use_Code containing only the top 4 attribute value." ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [], "source": [ "filtered_df = sdf.loc[sdf['Use_Code'].isin(filtered_permits)]" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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Added_DateAddressBuilding_AreaCityDeclared_ValueDeclValNu2DescriptionFIDFinal_DateIssue_Date...Pre_directSHAPEStateStatusStreet_NamStreet_NumStreet_SufUse_CodeWork_TypeZIP_code
02010-01-0713536 WATERFORD HILLS BLVD1336.0GERMANTOWN103000.0103000.0MODEL: TULIP - Unit #D036 - BLDG #412012-03-132011-07-08...{\"x\": -8602565.104, \"y\": 4747203.126800001, \"s...MDFinaledWATERFORD HILLS13536BLVDMULTI-FAMILY DWELLINGCONSTRUCT20874
12010-01-0713538 WATERFORD HILLS BLVD1730.0GERMANTOWN117000.0117000.0MODEL: ORCHID - Unit #D038 - BLDG #422012-03-152011-07-08...{\"x\": -8602567.5243, \"y\": 4747204.106700003, \"...MDFinaledWATERFORD HILLS13538BLVDMULTI-FAMILY DWELLINGCONSTRUCT20874
22010-01-0713540 WATERFORD HILLS BLVD1336.0GERMANTOWN103000.0103000.0MODEL: TULIP - Unit #D040 - BLDG #432012-03-152011-07-08...{\"x\": -8602569.9445, \"y\": 4747205.0867, \"spati...MDFinaledWATERFORD HILLS13540BLVDMULTI-FAMILY DWELLINGCONSTRUCT20874
32010-01-0713542 WATERFORD HILLS BLVD1730.0GERMANTOWN117000.0117000.0MODEL: ORCHID - Unit #D042 - BLDG #442012-03-152011-07-08...{\"x\": -8602572.3648, \"y\": 4747206.066600002, \"...MDFinaledWATERFORD HILLS13542BLVDMULTI-FAMILY DWELLINGCONSTRUCT20874
42010-01-0713544 WATERFORD HILLS BLVD1336.0GERMANTOWN103000.0103000.0MODEL: TULIP - Unit #D044 - BLDG #452012-03-152011-07-08...{\"x\": -8602574.7851, \"y\": 4747207.046599999, \"...MDFinaledWATERFORD HILLS13544BLVDMULTI-FAMILY DWELLINGCONSTRUCT20874
\n", "

5 rows × 23 columns

\n", "
" ], "text/plain": [ " Added_Date Address Building_Area City \\\n", "0 2010-01-07 13536 WATERFORD HILLS BLVD 1336.0 GERMANTOWN \n", "1 2010-01-07 13538 WATERFORD HILLS BLVD 1730.0 GERMANTOWN \n", "2 2010-01-07 13540 WATERFORD HILLS BLVD 1336.0 GERMANTOWN \n", "3 2010-01-07 13542 WATERFORD HILLS BLVD 1730.0 GERMANTOWN \n", "4 2010-01-07 13544 WATERFORD HILLS BLVD 1336.0 GERMANTOWN \n", "\n", " Declared_Value DeclValNu2 Description FID \\\n", "0 103000.0 103000.0 MODEL: TULIP - Unit #D036 - BLDG #4 1 \n", "1 117000.0 117000.0 MODEL: ORCHID - Unit #D038 - BLDG #4 2 \n", "2 103000.0 103000.0 MODEL: TULIP - Unit #D040 - BLDG #4 3 \n", "3 117000.0 117000.0 MODEL: ORCHID - Unit #D042 - BLDG #4 4 \n", "4 103000.0 103000.0 MODEL: TULIP - Unit #D044 - BLDG #4 5 \n", "\n", " Final_Date Issue_Date ... Pre_direct \\\n", "0 2012-03-13 2011-07-08 ... \n", "1 2012-03-15 2011-07-08 ... \n", "2 2012-03-15 2011-07-08 ... \n", "3 2012-03-15 2011-07-08 ... \n", "4 2012-03-15 2011-07-08 ... \n", "\n", " SHAPE State Status \\\n", "0 {\"x\": -8602565.104, \"y\": 4747203.126800001, \"s... MD Finaled \n", "1 {\"x\": -8602567.5243, \"y\": 4747204.106700003, \"... MD Finaled \n", "2 {\"x\": -8602569.9445, \"y\": 4747205.0867, \"spati... MD Finaled \n", "3 {\"x\": -8602572.3648, \"y\": 4747206.066600002, \"... MD Finaled \n", "4 {\"x\": -8602574.7851, \"y\": 4747207.046599999, \"... MD Finaled \n", "\n", " Street_Nam Street_Num Street_Suf Use_Code Work_Type \\\n", "0 WATERFORD HILLS 13536 BLVD MULTI-FAMILY DWELLING CONSTRUCT \n", "1 WATERFORD HILLS 13538 BLVD MULTI-FAMILY DWELLING CONSTRUCT \n", "2 WATERFORD HILLS 13540 BLVD MULTI-FAMILY DWELLING CONSTRUCT \n", "3 WATERFORD HILLS 13542 BLVD MULTI-FAMILY DWELLING CONSTRUCT \n", "4 WATERFORD HILLS 13544 BLVD MULTI-FAMILY DWELLING CONSTRUCT \n", "\n", " ZIP_code \n", "0 20874 \n", "1 20874 \n", "2 20874 \n", "3 20874 \n", "4 20874 \n", "\n", "[5 rows x 23 columns]" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "filtered_df.head()" ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "((11224, 23), (7512, 23))" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sdf.shape, filtered_df.shape" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The dataset is filtered. Instead of more than 11,000 permits, the filtered dataframe has about 7,500." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Visualize filtered dataset" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [], "source": [ "filtered_map = agol_gis.map('Montgomery County, Maryland')" ] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 65, "metadata": {}, "output_type": "execute_result" } ], "source": [ "filtered_map" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![](insights_images/image_7.png)" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" } ], "source": [ "filtered_df.spatial.plot(kind='map', map_widget=filtered_map,\n", " renderer_type='u', # specify the unique value renderer using its notation 'u'\n", " col='Use_Code') # column to get unique values from" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Visualize temporal and spatial trends" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Your data shows permits, but what do these permits say about when and where growth is happening in the county? Your data also contains temporal attribute fields, such as **Added_Date**, which indicates when a permit was first added to the system. The field has several values that break down the data by year, month, and even hour." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Split the **Added_date** to get year, month, week_of_day" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [], "source": [ "sdf['datetime'] = pd.to_datetime(sdf['Added_Date'], unit='ms')\n", "sdf['year'], sdf['month'], sdf['day_of_week'] = sdf.datetime.dt.year, sdf.datetime.dt.month, sdf.datetime.dt.dayofweek" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Visualize permits by time of issue\n", "You'll create chart cards for the year, month, and day subfields to visualize patterns in permit activity over time." ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [], "source": [ "import seaborn as sns" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.countplot(x=\"year\", data=sdf);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The chart shows the number of permits issued each year since 2010. (The year 2017 has significantly fewer permits because the dataset only covers part of 2017.) You can compare the number of permits visually by the size of each bar. Although some fluctuation occurs from year to year, most years had similar permit activity.\n", "\n", "Similarly you can visualize it by month as well as day_of_week" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.countplot(x=\"month\", data=sdf);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This bar chart changes to show the number of permits issued by month. Based on the chart, the highest permit activity occurs in June and July." ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.countplot(x=\"day_of_week\", data=sdf);" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Almost all permit activity occurs on weekdays. Government offices are closed on weekends, so few permits are issued then." ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [], "source": [ "ddf = sdf.set_index('datetime')" ] }, { "cell_type": "code", "execution_count": 56, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ddf['num'] = 1\n", "ddf['num'].resample('M').sum().plot();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A huge spike in permit activity occurred in mid-2011. What caused this spike? Is it an increase in overall permit activity, or is it mostly an increase in a certain type of permit? You'll plot the number of permits based on Use_Code to find which one cased the spike." ] }, { "cell_type": "code", "execution_count": 57, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(15,5))\n", "ax = fig.add_subplot(1, 1, 1)\n", "\n", "ax.plot(ddf['num'].resample('M').sum(), 'k', label='Total permits')\n", "for use_code in filtered_permits:\n", " x = ddf[ddf.Use_Code == use_code]['num'].resample('M').sum()\n", " ax.plot(x, label=use_code)\n", "ax.legend();" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Based on the legend, permit activity spiked in 2011 due to a sharp increase in the number of multifamily dwelling permits issued. This likely means that there was large residential growth in 2011.\n", "\n", "You've investigated some temporal patterns in your data. Next, you'll look at spatial patterns. Are there certain areas in the county that have experienced a relatively high degree of permit activity? Was the 2011 spike in residential permits in a specific location? To find out, you'll change the symbology of the map card to show hot spots, or areas with concentrations of points." ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 66, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hotspot_map = agol_gis.map('Germantown, Montgomery County, Maryland')\n", "hotspot_map" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sdf.spatial.plot(kind='map', map_widget=hotspot_map,\n", " renderer_type='h', \n", " col='Status') " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![](insights_images/hotspot.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The hot spots show up where there is a high concentration of permits. The highest concentration areas are in the southeast and northwest corners of the county, which correspond to the major population centers of Germantown and the suburban communities near Washington, D.C.\n", "\n", "Next, you'll see if the 2011 permit spike corresponds to a specific area of the map. The code below filters the dataframe to only show permits from 2011 and highlights related data in the map. In this case, the heat map changes to show the hot spot in the northwest part of the county, near Germantown." ] }, { "cell_type": "code", "execution_count": 67, "metadata": {}, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 67, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hotspot_2011_map = agol_gis.map('Germantown, Montgomery County, Maryland')\n", "hotspot_2011_map" ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sdf[sdf.year==2011].spatial.plot(kind='map', map_widget=hotspot_2011_map,\n", " renderer_type='h',\n", " col='Status') # column to get unique values from" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![](insights_images/filtered_hotspot.png)" ] } ], "metadata": { "esriNotebookRuntime": { "notebookRuntimeName": "ArcGIS Notebook Python 3 Standard", "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" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": false, "sideBar": true, "skip_h1_title": true, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": true, "toc_position": {}, "toc_section_display": true, "toc_window_display": false } }, "nbformat": 4, "nbformat_minor": 2 }