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kdbaseball8/DV0101EN-3-5-1-Generating-Maps-in-Python-py-v2.0.ipynb

Created July 3, 2019 05:29
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DV0101EN-3-5-1-Generating-Maps-in-Python-py-v2.0.ipynb
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"source": [
"<a href=\"https://cognitiveclass.ai\"><img src = \"https://ibm.box.com/shared/static/9gegpsmnsoo25ikkbl4qzlvlyjbgxs5x.png\" width = 400> </a>\n",
"\n",
"<h1 align=center><font size = 5>Generating Maps with Python</font></h1>"
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"source": [
"## Introduction\n",
"\n",
"In this lab, we will learn how to create maps for different objectives. To do that, we will part ways with Matplotlib and work with another Python visualization library, namely **Folium**. What is nice about **Folium** is that it was developed for the sole purpose of visualizing geospatial data. While other libraries are available to visualize geospatial data, such as **plotly**, they might have a cap on how many API calls you can make within a defined time frame. **Folium**, on the other hand, is completely free."
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"source": [
"## Table of Contents\n",
"\n",
"<div class=\"alert alert-block alert-info\" style=\"margin-top: 20px\">\n",
"\n",
"1. [Exploring Datasets with *p*andas](#0)<br>\n",
"2. [Downloading and Prepping Data](#2)<br>\n",
"3. [Introduction to Folium](#4) <br>\n",
"4. [Map with Markers](#6) <br>\n",
"5. [Choropleth Maps](#8) <br>\n",
"</div>\n",
"<hr>"
]
},
{
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"source": [
"# Exploring Datasets with *pandas* and Matplotlib<a id=\"0\"></a>\n",
"\n",
"Toolkits: This lab heavily relies on [*pandas*](http://pandas.pydata.org/) and [**Numpy**](http://www.numpy.org/) for data wrangling, analysis, and visualization. The primary plotting library we will explore in this lab is [**Folium**](https://github.com/python-visualization/folium/).\n",
"\n",
"Datasets: \n",
"\n",
"1. San Francisco Police Department Incidents for the year 2016 - [Police Department Incidents](https://data.sfgov.org/Public-Safety/Police-Department-Incidents-Previous-Year-2016-/ritf-b9ki) from San Francisco public data portal. Incidents derived from San Francisco Police Department (SFPD) Crime Incident Reporting system. Updated daily, showing data for the entire year of 2016. Address and location has been anonymized by moving to mid-block or to an intersection. \n",
"\n",
"2. Immigration to Canada from 1980 to 2013 - [International migration flows to and from selected countries - The 2015 revision](http://www.un.org/en/development/desa/population/migration/data/empirical2/migrationflows.shtml) from United Nation's website. The dataset contains annual data on the flows of international migrants as recorded by the countries of destination. The data presents both inflows and outflows according to the place of birth, citizenship or place of previous / next residence both for foreigners and nationals. For this lesson, we will focus on the Canadian Immigration data"
]
},
{
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"source": [
"# Downloading and Prepping Data <a id=\"2\"></a>"
]
},
{
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"source": [
"Import Primary Modules:"
]
},
{
"cell_type": "code",
"execution_count": 1,
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"outputs": [],
"source": [
"import numpy as np # useful for many scientific computing in Python\n",
"import pandas as pd # primary data structure library"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
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"source": [
"# Introduction to Folium <a id=\"4\"></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
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"source": [
"Folium is a powerful Python library that helps you create several types of Leaflet maps. The fact that the Folium results are interactive makes this library very useful for dashboard building.\n",
"\n",
"From the official Folium documentation page:\n",
"\n",
"> Folium builds on the data wrangling strengths of the Python ecosystem and the mapping strengths of the Leaflet.js library. Manipulate your data in Python, then visualize it in on a Leaflet map via Folium.\n",
"\n",
"> Folium makes it easy to visualize data that's been manipulated in Python on an interactive Leaflet map. It enables both the binding of data to a map for choropleth visualizations as well as passing Vincent/Vega visualizations as markers on the map.\n",
"\n",
"> The library has a number of built-in tilesets from OpenStreetMap, Mapbox, and Stamen, and supports custom tilesets with Mapbox or Cloudmade API keys. Folium supports both GeoJSON and TopoJSON overlays, as well as the binding of data to those overlays to create choropleth maps with color-brewer color schemes."
]
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"source": [
"#### Let's install **Folium**"
]
},
{
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"source": [
"**Folium** is not available by default. So, we first need to install it before we are able to import it."
]
},
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"text": [
"Collecting package metadata: done\n",
"Solving environment: | \n",
"The environment is inconsistent, please check the package plan carefully\n",
"The following packages are causing the inconsistency:\n",
"\n",
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" - anaconda/linux-64::pycurl==7.43.0.2=py36h1ba5d50_0\n",
" - anaconda/linux-64::pydotplus==2.0.2=py36_1\n",
" - anaconda/linux-64::pyflakes==2.1.1=py36_0\n",
" - anaconda/linux-64::pygments==2.3.1=py36_0\n",
" - anaconda/linux-64::pyhamcrest==1.9.0=py36_2\n",
" - anaconda/linux-64::pylint==2.3.1=py36_0\n",
" - anaconda/linux-64::pyodbc==4.0.26=py36he6710b0_0\n",
" - anaconda/linux-64::pyopenssl==19.0.0=py36_0\n",
" - anaconda/noarch::pyparsing==2.4.0=py_0\n",
" - anaconda/linux-64::pyproj==1.9.6=py36h14380d9_0\n",
" - anaconda/linux-64::pyqt==5.9.2=py36h22d08a2_1\n",
" - anaconda/linux-64::pyrsistent==0.14.11=py36h7b6447c_0\n",
" - anaconda/linux-64::pyshp==2.0.1=py36_0\n",
" - anaconda/linux-64::pysocks==1.6.8=py36_0\n",
" - anaconda/linux-64::pytest==4.4.0=py36_1\n",
" - anaconda/linux-64::pytest-openfiles==0.3.2=py36_0\n",
" - anaconda/linux-64::pytest-remotedata==0.3.1=py36_0\n",
" - anaconda/linux-64::python==3.6.8=h0371630_0\n",
" - anaconda/linux-64::python-dateutil==2.8.0=py36_0\n",
" - anaconda/linux-64::python-libarchive-c==2.8=py36_6\n",
" - anaconda/linux-64::pytorch==0.4.1=py36ha74772b_0\n",
" - anaconda/noarch::pytz==2019.1=py_0\n",
" - anaconda/linux-64::pyyaml==5.1=py36h7b6447c_0\n",
" - anaconda/linux-64::pyzmq==17.1.2=py36h14c3975_0\n",
" - anaconda/linux-64::qtawesome==0.5.7=py36_1\n",
" - anaconda/linux-64::qtconsole==4.4.3=py36_0\n",
" - anaconda/linux-64::qtpy==1.7.0=py36_1\n",
" - anaconda/linux-64::requests==2.21.0=py36_0\n",
" - anaconda/linux-64::rope==0.12.0=py36_0\n",
" - anaconda/linux-64::ruamel_yaml==0.15.46=py36h14c3975_0\n",
" - anaconda/linux-64::s3transfer==0.1.13=py36_0\n",
" - anaconda/linux-64::scikit-learn==0.20.1=py36h4989274_0\n",
" - anaconda/linux-64::scipy==1.1.0=py36hfa4b5c9_1\n",
" - anaconda/linux-64::seaborn==0.9.0=py36_0\n",
" - anaconda/linux-64::secretstorage==3.1.1=py36_0\n",
" - anaconda/linux-64::send2trash==1.5.0=py36_0\n",
" - anaconda/linux-64::service_identity==18.1.0=py36h28b3542_0\n",
" - anaconda/linux-64::setuptools==41.0.0=py36_0\n",
" - anaconda/linux-64::simplegeneric==0.8.1=py36_2\n",
" - anaconda/linux-64::singledispatch==3.4.0.3=py36_0\n",
" - anaconda/linux-64::sip==4.19.13=py36he6710b0_0\n",
" - anaconda/linux-64::six==1.12.0=py36_0\n",
" - anaconda/linux-64::smmap2==2.0.5=py36_0\n",
" - anaconda/linux-64::snowballstemmer==1.2.1=py36_0\n",
" - anaconda/linux-64::sortedcollections==1.1.2=py36_0\n",
" - anaconda/linux-64::sortedcontainers==2.1.0=py36_0\n",
" - anaconda/linux-64::soupsieve==1.8=py36_0\n",
" - anaconda/noarch::sphinx==2.0.1=py_0\n",
" - anaconda/linux-64::sphinxcontrib==1.0=py36_1\n",
" - anaconda/noarch::sphinxcontrib-applehelp==1.0.1=py_0\n",
" - anaconda/noarch::sphinxcontrib-devhelp==1.0.1=py_0\n",
" - anaconda/noarch::sphinxcontrib-htmlhelp==1.0.2=py_0\n",
" - anaconda/noarch::sphinxcontrib-jsmath==1.0.1=py_0\n",
" - anaconda/noarch::sphinxcontrib-qthelp==1.0.2=py_0\n",
" - anaconda/noarch::sphinxcontrib-serializinghtml==1.1.1=py_0\n",
" - anaconda/linux-64::sphinxcontrib-websupport==1.1.0=py36_1\n",
" - anaconda/linux-64::spyder==3.3.4=py36_0\n",
" - anaconda/linux-64::spyder-kernels==0.4.4=py36_0\n",
" - anaconda/linux-64::sqlalchemy==1.3.3=py36h7b6447c_0\n",
" - anaconda/linux-64::statsmodels==0.9.0=py36h035aef0_0\n",
" - anaconda/linux-64::sympy==1.4=py36_0\n",
" - anaconda/linux-64::tblib==1.3.2=py36_0\n",
" - anaconda/linux-64::tensorboard==1.8.0=py36hf484d3e_0\n",
" - anaconda/linux-64::tensorflow==1.8.0=h57681fa_0\n",
" - anaconda/linux-64::tensorflow-base==1.8.0=py36h5f64886_0\n",
" - anaconda/linux-64::termcolor==1.1.0=py36_1\n",
" - anaconda/linux-64::terminado==0.8.2=py36_0\n",
" - anaconda/linux-64::testpath==0.4.2=py36_0\n",
" - anaconda/linux-64::toolz==0.9.0=py36_0\n",
" - anaconda/linux-64::torchvision==0.2.1=py36_0\n",
" - anaconda/linux-64::tornado==6.0.2=py36h7b6447c_0\n",
" - anaconda/linux-64::tqdm==4.31.1=py36_1\n",
" - anaconda/linux-64::traitlets==4.3.2=py36_0\n",
" - anaconda/linux-64::twisted==18.9.0=py36h7b6447c_0\n",
" - anaconda/linux-64::typed-ast==1.3.1=py36h7b6447c_0\n",
" - anaconda/linux-64::unicodecsv==0.14.1=py36_0\n",
" - anaconda/linux-64::urllib3==1.24.1=py36_0\n",
" - anaconda/linux-64::wcwidth==0.1.7=py36_0\n",
" - anaconda/linux-64::webencodings==0.5.1=py36_1\n",
" - anaconda/noarch::werkzeug==0.15.2=py_0\n",
" - anaconda/linux-64::wheel==0.33.1=py36_0\n",
" - anaconda/linux-64::widgetsnbextension==3.4.2=py36_0\n",
" - anaconda/linux-64::wrapt==1.11.1=py36h7b6447c_0\n",
" - anaconda/linux-64::wurlitzer==1.0.2=py36_0\n",
" - anaconda/linux-64::xlrd==1.2.0=py36_0\n",
" - anaconda/noarch::xlsxwriter==1.1.6=py_0\n",
" - anaconda/linux-64::xlwt==1.3.0=py36_0\n",
" - anaconda/linux-64::zict==0.1.4=py36_0\n",
" - anaconda/linux-64::zipp==0.3.3=py36_1\n",
" - anaconda/linux-64::zope==1.0=py36_1\n",
" - anaconda/linux-64::zope.interface==4.6.0=py36h7b6447c_0\n",
" - anaconda/linux-64::zstd==1.3.7=h0b5b093_0\n",
" - defaults/linux-64::curl==7.64.0=hbc83047_2\n",
" - defaults/linux-64::krb5==1.16.1=h173b8e3_7\n",
" - defaults/linux-64::libcurl==7.64.0=h20c2e04_2\n",
" - defaults/linux-64::libpng==1.6.36=hbc83047_0\n",
" - defaults/linux-64::libssh2==1.8.2=h1ba5d50_0\n",
" - defaults/linux-64::qt==5.9.7=h5867ecd_1\n",
" - defaults/linux-64::sqlite==3.26.0=h7b6447c_0\n",
" - defaults/linux-64::anaconda==5.3.1=py37_0\n",
" - defaults/linux-64::astropy==3.0.4=py37h14c3975_0\n",
" - defaults/linux-64::bkcharts==0.2=py37_0\n",
" - defaults/linux-64::blaze==0.11.3=py37_0\n",
" - defaults/linux-64::blosc==1.14.4=hdbcaa40_0\n",
" - defaults/linux-64::bokeh==0.13.0=py37_0\n",
" - defaults/linux-64::bottleneck==1.2.1=py37h035aef0_1\n",
" - defaults/linux-64::bzip2==1.0.6=h14c3975_5\n",
" - defaults/linux-64::cairo==1.14.12=h8948797_3\n",
" - defaults/linux-64::dask==0.19.1=py37_0\n",
" - defaults/linux-64::datashape==0.5.4=py37_1\n",
" - defaults/linux-64::dbus==1.13.2=h714fa37_1\n",
" - defaults/linux-64::expat==2.2.6=he6710b0_0\n",
" - defaults/linux-64::fontconfig==2.13.0=h9420a91_0\n",
" - defaults/linux-64::freetype==2.9.1=h8a8886c_1\n",
" - defaults/linux-64::fribidi==1.0.5=h7b6447c_0\n",
" - defaults/linux-64::get_terminal_size==1.0.0=haa9412d_0\n",
" - defaults/linux-64::glib==2.56.2=hd408876_0\n",
" - defaults/linux-64::gmp==6.1.2=h6c8ec71_1\n",
" - defaults/linux-64::graphite2==1.3.12=h23475e2_2\n",
" - defaults/linux-64::gst-plugins-base==1.14.0=hbbd80ab_1\n",
" - defaults/linux-64::gstreamer==1.14.0=hb453b48_1\n",
" - defaults/linux-64::harfbuzz==1.8.8=hffaf4a1_0\n",
" - defaults/linux-64::hdf5==1.10.2=hba1933b_1\n",
" - defaults/linux-64::icu==58.2=h9c2bf20_1\n",
" - defaults/linux-64::jbig==2.1=hdba287a_0\n",
" - defaults/linux-64::libedit==3.1.20170329=h6b74fdf_2\n",
" - defaults/linux-64::libffi==3.2.1=hd88cf55_4\n",
" - defaults/linux-64::libgcc-ng==8.2.0=hdf63c60_1\n",
" - defaults/linux-64::libsodium==1.0.16=h1bed415_0\n",
" - defaults/linux-64::libtiff==4.0.9=he85c1e1_2\n",
" - defaults/linux-64::libtool==2.4.6=h544aabb_3\n",
" - defaults/linux-64::libuuid==1.0.3=h1bed415_2\n",
" - defaults/linux-64::libxcb==1.13=h1bed415_1\n",
" - defaults/linux-64::libxml2==2.9.8=h26e45fe_1\n",
" - defaults/linux-64::libxslt==1.1.32=h1312cb7_0\n",
" - defaults/linux-64::lzo==2.10=h49e0be7_2\n",
" - defaults/linux-64::mkl-service==1.1.2=py37h90e4bf4_5\n",
" - defaults/linux-64::mpc==1.1.0=h10f8cd9_1\n",
" - defaults/linux-64::mpfr==4.0.1=hdf1c602_3\n",
" - defaults/linux-64::ncurses==6.1=hf484d3e_0\n",
" - defaults/linux-64::numba==0.39.0=py37h04863e7_0\n",
" - defaults/linux-64::numexpr==2.6.8=py37hd89afb7_0\n",
" - defaults/linux-64::odo==0.5.1=py37_0\n",
" - defaults/linux-64::pandoc==1.19.2.1=hea2e7c5_1\n",
" - defaults/linux-64::pango==1.42.4=h049681c_0\n",
" - defaults/linux-64::patchelf==0.9=hf484d3e_2\n",
" - defaults/linux-64::pcre==8.42=h439df22_0\n",
" - defaults/linux-64::pixman==0.34.0=hceecf20_3\n",
" - defaults/linux-64::pytables==3.4.4=py37ha205bf6_0\n",
" - defaults/linux-64::pytest-arraydiff==0.2=py37h39e3cac_0\n",
" - defaults/linux-64::pytest-astropy==0.4.0=py37_0\n",
" - defaults/linux-64::pytest-doctestplus==0.1.3=py37_0\n",
" - defaults/linux-64::pywavelets==1.0.0=py37hdd07704_0\n",
" - defaults/linux-64::readline==7.0=h7b6447c_5\n",
" - defaults/linux-64::scikit-image==0.14.0=py37hf484d3e_1\n",
" - defaults/linux-64::snappy==1.1.7=hbae5bb6_3\n",
" - defaults/linux-64::tk==8.6.8=hbc83047_0\n",
" - defaults/linux-64::unixodbc==2.3.7=h14c3975_0\n",
" - defaults/linux-64::xz==5.2.4=h14c3975_4\n",
" - defaults/linux-64::yaml==0.1.7=had09818_2\n",
" - defaults/linux-64::zeromq==4.2.5=hf484d3e_1\n",
" - defaults/linux-64::zlib==1.2.11=ha838bed_2\n",
"done\n",
"\n",
"\n",
"==> WARNING: A newer version of conda exists. <==\n",
" current version: 4.6.14\n",
" latest version: 4.7.5\n",
"\n",
"Please update conda by running\n",
"\n",
" $ conda update -n base conda\n",
"\n",
"\n",
"\n",
"## Package Plan ##\n",
"\n",
" environment location: /home/jupyterlab/conda\n",
"\n",
" added / updated specs:\n",
" - folium=0.5.0\n",
"\n",
"\n",
"The following packages will be downloaded:\n",
"\n",
" package | build\n",
" ---------------------------|-----------------\n",
" _libgcc_mutex-0.1 | main 3 KB\n",
" altair-3.1.0 | py36_0 724 KB conda-forge\n",
" asn1crypto-0.24.0 | py36_1003 154 KB conda-forge\n",
" attrs-19.1.0 | py_0 32 KB conda-forge\n",
" ca-certificates-2019.6.16 | hecc5488_0 145 KB conda-forge\n",
" certifi-2019.6.16 | py36_0 148 KB conda-forge\n",
" cffi-1.12.3 | py36h8022711_0 218 KB conda-forge\n",
" chardet-3.0.4 | py36_1003 190 KB conda-forge\n",
" cryptography-2.7 | py36h72c5cf5_0 607 KB conda-forge\n",
" entrypoints-0.3 | py36_1000 12 KB conda-forge\n",
" idna-2.8 | py36_1000 132 KB conda-forge\n",
" jinja2-2.10.1 | py_0 91 KB conda-forge\n",
" jsonschema-3.0.1 | py36_0 84 KB conda-forge\n",
" libffi-3.2.1 | he1b5a44_1006 46 KB conda-forge\n",
" libgcc-ng-9.1.0 | hdf63c60_0 8.1 MB\n",
" markupsafe-1.1.1 | py36h14c3975_0 26 KB conda-forge\n",
" mkl_fft-1.0.10 | py36_0 650 KB conda-forge\n",
" mkl_random-1.0.2 | py36_0 1.3 MB conda-forge\n",
" ncurses-6.1 | hf484d3e_1002 1.3 MB conda-forge\n",
" pandas-0.24.2 | py36hb3f55d8_0 11.1 MB conda-forge\n",
" pip-19.1.1 | py36_0 1.8 MB conda-forge\n",
" pycparser-2.19 | py36_1 173 KB conda-forge\n",
" pyopenssl-19.0.0 | py36_0 81 KB conda-forge\n",
" pyrsistent-0.15.2 | py36h516909a_0 89 KB conda-forge\n",
" pysocks-1.7.0 | py36_0 26 KB conda-forge\n",
" python-3.6.7 | h357f687_1005 34.6 MB conda-forge\n",
" python-dateutil-2.8.0 | py_0 219 KB conda-forge\n",
" pytz-2019.1 | py_0 227 KB conda-forge\n",
" readline-8.0 | hf8c457e_0 441 KB conda-forge\n",
" requests-2.22.0 | py36_0 84 KB conda-forge\n",
" setuptools-41.0.1 | py36_0 612 KB conda-forge\n",
" six-1.12.0 | py36_1000 22 KB conda-forge\n",
" sqlite-3.28.0 | hcee41ef_1 1.9 MB conda-forge\n",
" tk-8.6.9 | hed695b0_1002 3.2 MB conda-forge\n",
" toolz-0.9.0 | py_1 42 KB conda-forge\n",
" urllib3-1.24.3 | py36_0 151 KB conda-forge\n",
" wheel-0.33.4 | py36_0 34 KB conda-forge\n",
" xz-5.2.4 | h14c3975_1001 366 KB conda-forge\n",
" zlib-1.2.11 | h14c3975_1004 101 KB conda-forge\n",
" ------------------------------------------------------------\n",
" Total: 69.2 MB\n",
"\n",
"The following NEW packages will be INSTALLED:\n",
"\n",
" _libgcc_mutex pkgs/main/linux-64::_libgcc_mutex-0.1-main\n",
"\n",
"The following packages will be UPDATED:\n",
"\n",
" altair 2.4.1-py36_0 --> 3.1.0-py36_0\n",
" asn1crypto anaconda::asn1crypto-0.24.0-py36_0 --> conda-forge::asn1crypto-0.24.0-py36_1003\n",
" ca-certificates 2019.3.9-hecc5488_0 --> 2019.6.16-hecc5488_0\n",
" certifi 2019.3.9-py36_0 --> 2019.6.16-py36_0\n",
" cffi anaconda::cffi-1.12.2-py36h2e261b9_1 --> conda-forge::cffi-1.12.3-py36h8022711_0\n",
" chardet anaconda::chardet-3.0.4-py36_1 --> conda-forge::chardet-3.0.4-py36_1003\n",
" cryptography anaconda::cryptography-2.6.1-py36h1ba~ --> conda-forge::cryptography-2.7-py36h72c5cf5_0\n",
" entrypoints anaconda::entrypoints-0.3-py36_0 --> conda-forge::entrypoints-0.3-py36_1000\n",
" idna anaconda::idna-2.8-py36_0 --> conda-forge::idna-2.8-py36_1000\n",
" libffi pkgs/main::libffi-3.2.1-hd88cf55_4 --> conda-forge::libffi-3.2.1-he1b5a44_1006\n",
" libgcc-ng 8.2.0-hdf63c60_1 --> 9.1.0-hdf63c60_0\n",
" mkl_fft anaconda::mkl_fft-1.0.6-py36h7dd41cf_0 --> conda-forge::mkl_fft-1.0.10-py36_0\n",
" mkl_random anaconda::mkl_random-1.0.1-py36h4414c~ --> conda-forge::mkl_random-1.0.2-py36_0\n",
" ncurses pkgs/main::ncurses-6.1-hf484d3e_0 --> conda-forge::ncurses-6.1-hf484d3e_1002\n",
" pip anaconda::pip-19.0.3-py36_0 --> conda-forge::pip-19.1.1-py36_0\n",
" pycparser anaconda::pycparser-2.19-py36_0 --> conda-forge::pycparser-2.19-py36_1\n",
" pyrsistent anaconda::pyrsistent-0.14.11-py36h7b6~ --> conda-forge::pyrsistent-0.15.2-py36h516909a_0\n",
" pysocks anaconda::pysocks-1.6.8-py36_0 --> conda-forge::pysocks-1.7.0-py36_0\n",
" readline pkgs/main::readline-7.0-h7b6447c_5 --> conda-forge::readline-8.0-hf8c457e_0\n",
" requests anaconda::requests-2.21.0-py36_0 --> conda-forge::requests-2.22.0-py36_0\n",
" setuptools anaconda::setuptools-41.0.0-py36_0 --> conda-forge::setuptools-41.0.1-py36_0\n",
" six anaconda::six-1.12.0-py36_0 --> conda-forge::six-1.12.0-py36_1000\n",
" sqlite pkgs/main::sqlite-3.26.0-h7b6447c_0 --> conda-forge::sqlite-3.28.0-hcee41ef_1\n",
" tk pkgs/main::tk-8.6.8-hbc83047_0 --> conda-forge::tk-8.6.9-hed695b0_1002\n",
" toolz anaconda/linux-64::toolz-0.9.0-py36_0 --> conda-forge/noarch::toolz-0.9.0-py_1\n",
" urllib3 anaconda::urllib3-1.24.1-py36_0 --> conda-forge::urllib3-1.24.3-py36_0\n",
" wheel anaconda::wheel-0.33.1-py36_0 --> conda-forge::wheel-0.33.4-py36_0\n",
" xz pkgs/main::xz-5.2.4-h14c3975_4 --> conda-forge::xz-5.2.4-h14c3975_1001\n",
" zlib pkgs/main::zlib-1.2.11-ha838bed_2 --> conda-forge::zlib-1.2.11-h14c3975_1004\n",
"\n",
"The following packages will be SUPERSEDED by a higher-priority channel:\n",
"\n",
" attrs anaconda/linux-64::attrs-19.1.0-py36_1 --> conda-forge/noarch::attrs-19.1.0-py_0\n",
" jinja2 anaconda/linux-64::jinja2-2.10.1-py36~ --> conda-forge/noarch::jinja2-2.10.1-py_0\n",
" jsonschema anaconda --> conda-forge\n",
" markupsafe anaconda::markupsafe-1.1.1-py36h7b644~ --> conda-forge::markupsafe-1.1.1-py36h14c3975_0\n",
" pandas anaconda::pandas-0.24.2-py36he6710b0_0 --> conda-forge::pandas-0.24.2-py36hb3f55d8_0\n",
" pyopenssl anaconda --> conda-forge\n",
" python anaconda::python-3.6.8-h0371630_0 --> conda-forge::python-3.6.7-h357f687_1005\n",
" python-dateutil anaconda/linux-64::python-dateutil-2.~ --> conda-forge/noarch::python-dateutil-2.8.0-py_0\n",
" pytz anaconda --> conda-forge\n",
"\n",
"\n",
"\n",
"Downloading and Extracting Packages\n",
"mkl_fft-1.0.10 | 650 KB | ##################################### | 100% \n",
"cffi-1.12.3 | 218 KB | ##################################### | 100% \n",
"zlib-1.2.11 | 101 KB | ##################################### | 100% \n",
"_libgcc_mutex-0.1 | 3 KB | ##################################### | 100% \n",
"idna-2.8 | 132 KB | ##################################### | 100% \n",
"markupsafe-1.1.1 | 26 KB | ##################################### | 100% \n",
"toolz-0.9.0 | 42 KB | ##################################### | 100% \n",
"pyopenssl-19.0.0 | 81 KB | ##################################### | 100% \n",
"six-1.12.0 | 22 KB | ##################################### | 100% \n",
"requests-2.22.0 | 84 KB | ##################################### | 100% \n",
"python-dateutil-2.8. | 219 KB | ##################################### | 100% \n",
"certifi-2019.6.16 | 148 KB | ##################################### | 100% \n",
"pip-19.1.1 | 1.8 MB | ##################################### | 100% \n",
"wheel-0.33.4 | 34 KB | ##################################### | 100% \n",
"jsonschema-3.0.1 | 84 KB | ##################################### | 100% \n",
"python-3.6.7 | 34.6 MB | ##################################### | 100% \n",
"xz-5.2.4 | 366 KB | ##################################### | 100% \n",
"altair-3.1.0 | 724 KB | ##################################### | 100% \n",
"tk-8.6.9 | 3.2 MB | ##################################### | 100% \n",
"pyrsistent-0.15.2 | 89 KB | ##################################### | 100% \n",
"pysocks-1.7.0 | 26 KB | ##################################### | 100% \n",
"mkl_random-1.0.2 | 1.3 MB | ##################################### | 100% \n",
"pandas-0.24.2 | 11.1 MB | ##################################### | 100% \n",
"ncurses-6.1 | 1.3 MB | ##################################### | 100% \n",
"asn1crypto-0.24.0 | 154 KB | ##################################### | 100% \n",
"sqlite-3.28.0 | 1.9 MB | ##################################### | 100% \n",
"pycparser-2.19 | 173 KB | ##################################### | 100% \n",
"attrs-19.1.0 | 32 KB | ##################################### | 100% \n",
"pytz-2019.1 | 227 KB | ##################################### | 100% \n",
"libffi-3.2.1 | 46 KB | ##################################### | 100% \n",
"urllib3-1.24.3 | 151 KB | ##################################### | 100% \n",
"readline-8.0 | 441 KB | ##################################### | 100% \n",
"chardet-3.0.4 | 190 KB | ##################################### | 100% \n",
"jinja2-2.10.1 | 91 KB | ##################################### | 100% \n",
"cryptography-2.7 | 607 KB | ##################################### | 100% \n",
"libgcc-ng-9.1.0 | 8.1 MB | ##################################### | 100% \n",
"setuptools-41.0.1 | 612 KB | ##################################### | 100% \n",
"entrypoints-0.3 | 12 KB | ##################################### | 100% \n",
"ca-certificates-2019 | 145 KB | ##################################### | 100% \n",
"Preparing transaction: done\n",
"Verifying transaction: done\n",
"Executing transaction: done\n",
"Folium installed and imported!\n"
]
}
],
"source": [
"!conda install -c conda-forge folium=0.5.0 --yes\n",
"import folium\n",
"\n",
"print('Folium installed and imported!')"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Generating the world map is straigtforward in **Folium**. You simply create a **Folium** *Map* object and then you display it. What is attactive about **Folium** maps is that they are interactive, so you can zoom into any region of interest despite the initial zoom level. "
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
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],
"text/plain": [
"<folium.folium.Map at 0x7fc20931f470>"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# define the world map\n",
"world_map = folium.Map()\n",
"\n",
"# display world map\n",
"world_map"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Go ahead. Try zooming in and out of the rendered map above."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"You can customize this default definition of the world map by specifying the centre of your map and the intial zoom level. \n",
"\n",
"All locations on a map are defined by their respective *Latitude* and *Longitude* values. So you can create a map and pass in a center of *Latitude* and *Longitude* values of **[0, 0]**. \n",
"\n",
"For a defined center, you can also define the intial zoom level into that location when the map is rendered. **The higher the zoom level the more the map is zoomed into the center**.\n",
"\n",
"Let's create a map centered around Canada and play with the zoom level to see how it affects the rendered map."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": false
},
"outputs": [
{
"data": {
"text/html": [
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],
"text/plain": [
"<folium.folium.Map at 0x7fc20805b160>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# define the world map centered around Canada with a low zoom level\n",
"world_map = folium.Map(location=[56.130, -106.35], zoom_start=4)\n",
"\n",
"# display world map\n",
"world_map"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Let's create the map again with a higher zoom level"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": false
},
"outputs": [
{
"data": {
"text/html": [
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],
"text/plain": [
"<folium.folium.Map at 0x7fc20805b828>"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# define the world map centered around Canada with a higher zoom level\n",
"world_map = folium.Map(location=[56.130, -106.35], zoom_start=8)\n",
"\n",
"# display world map\n",
"world_map"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"As you can see, the higher the zoom level the more the map is zoomed into the given center."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"**Question**: Create a map of Mexico with a zoom level of 4."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/html": [
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],
"text/plain": [
"<folium.folium.Map at 0x7fc2080645f8>"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mexico_latitude = 23.6345 \n",
"mexico_longitude = -102.5528\n",
"mexico_map = folium.Map(location=[mexico_latitude, mexico_longitude], zoom_start=4)\n",
"mexico_map"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Double-click __here__ for the solution.\n",
"<!-- The correct answer is:\n",
"\\\\ # define Mexico's geolocation coordinates\n",
"mexico_latitude = 23.6345 \n",
"mexico_longitude = -102.5528\n",
"-->\n",
"\n",
"<!--\n",
"\\\\ # define the world map centered around Canada with a higher zoom level\n",
"mexico_map = folium.Map(location=[mexico_latitude, mexico_longitude], zoom_start=4)\n",
"-->\n",
"\n",
"<!--\n",
"\\\\ # display world map\n",
"mexico_map\n",
"-->"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Another cool feature of **Folium** is that you can generate different map styles."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"### A. Stamen Toner Maps\n",
"\n",
"These are high-contrast B+W (black and white) maps. They are perfect for data mashups and exploring river meanders and coastal zones. "
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Let's create a Stamen Toner map of canada with a zoom level of 4."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": false
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><iframe src=\"data:text/html;charset=utf-8;base64,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\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
],
"text/plain": [
"<folium.folium.Map at 0x7fc208072d30>"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# create a Stamen Toner map of the world centered around Canada\n",
"world_map = folium.Map(location=[56.130, -106.35], zoom_start=4, tiles='Stamen Toner')\n",
"\n",
"# display map\n",
"world_map"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Feel free to zoom in and out to see how this style compares to the default one."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"### B. Stamen Terrain Maps\n",
"\n",
"These are maps that feature hill shading and natural vegetation colors. They showcase advanced labeling and linework generalization of dual-carriageway roads."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Let's create a Stamen Terrain map of Canada with zoom level 4."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": false
},
"outputs": [
{
"data": {
"text/html": [
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],
"text/plain": [
"<folium.folium.Map at 0x7fc203fcfc50>"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# create a Stamen Toner map of the world centered around Canada\n",
"world_map = folium.Map(location=[56.130, -106.35], zoom_start=4, tiles='Stamen Terrain')\n",
"\n",
"# display map\n",
"world_map"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Feel free to zoom in and out to see how this style compares to Stamen Toner and the default style."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"### C. Mapbox Bright Maps\n",
"\n",
"These are maps that quite similar to the default style, except that the borders are not visible with a low zoom level. Furthermore, unlike the default style where country names are displayed in each country's native language, *Mapbox Bright* style displays all country names in English."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Let's create a world map with this style."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><iframe src=\"data:text/html;charset=utf-8;base64,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\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
],
"text/plain": [
"<folium.folium.Map at 0x7fc208070eb8>"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# create a world map with a Mapbox Bright style.\n",
"world_map = folium.Map(tiles='Mapbox Bright')\n",
"\n",
"# display the map\n",
"world_map"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Zoom in and notice how the borders start showing as you zoom in, and the displayed country names are in English."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"**Question**: Create a map of Mexico to visualize its hill shading and natural vegetation. Use a zoom level of 6."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/html": [
"<div style=\"width:100%;\"><div style=\"position:relative;width:100%;height:0;padding-bottom:60%;\"><iframe src=\"data:text/html;charset=utf-8;base64,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\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
],
"text/plain": [
"<folium.folium.Map at 0x7fc203fc4f60>"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mexico_latitude = 23.6345 \n",
"mexico_longitude = -102.5528\n",
"mexico_map = folium.Map(location=[mexico_latitude, mexico_longitude], zoom_start=6, tiles='Stamen Terrain')\n",
"mexico_map"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Double-click __here__ for the solution.\n",
"<!-- The correct answer is:\n",
"\\\\ # define Mexico's geolocation coordinates\n",
"mexico_latitude = 23.6345 \n",
"mexico_longitude = -102.5528\n",
"-->\n",
"\n",
"<!--\n",
"\\\\ # define the world map centered around Canada with a higher zoom level\n",
"mexico_map = folium.Map(location=[mexico_latitude, mexico_longitude], zoom_start=6, tiles='Stamen Terrain')\n",
"-->\n",
"\n",
"<!--\n",
"\\\\ # display world map\n",
"mexico_map\n",
"-->"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"# Maps with Markers <a id=\"6\"></a>\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Let's download and import the data on police department incidents using *pandas* `read_csv()` method."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Download the dataset and read it into a *pandas* dataframe:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Dataset downloaded and read into a pandas dataframe!\n"
]
}
],
"source": [
"import pandas as pd\n",
"df_incidents = pd.read_csv('https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DV0101EN/labs/Data_Files/Police_Department_Incidents_-_Previous_Year__2016_.csv')\n",
"\n",
"print('Dataset downloaded and read into a pandas dataframe!')"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Let's take a look at the first five items in our dataset."
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>IncidntNum</th>\n",
" <th>Category</th>\n",
" <th>Descript</th>\n",
" <th>DayOfWeek</th>\n",
" <th>Date</th>\n",
" <th>Time</th>\n",
" <th>PdDistrict</th>\n",
" <th>Resolution</th>\n",
" <th>Address</th>\n",
" <th>X</th>\n",
" <th>Y</th>\n",
" <th>Location</th>\n",
" <th>PdId</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>120058272</td>\n",
" <td>WEAPON LAWS</td>\n",
" <td>POSS OF PROHIBITED WEAPON</td>\n",
" <td>Friday</td>\n",
" <td>01/29/2016 12:00:00 AM</td>\n",
" <td>11:00</td>\n",
" <td>SOUTHERN</td>\n",
" <td>ARREST, BOOKED</td>\n",
" <td>800 Block of BRYANT ST</td>\n",
" <td>-122.403405</td>\n",
" <td>37.775421</td>\n",
" <td>(37.775420706711, -122.403404791479)</td>\n",
" <td>12005827212120</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>120058272</td>\n",
" <td>WEAPON LAWS</td>\n",
" <td>FIREARM, LOADED, IN VEHICLE, POSSESSION OR USE</td>\n",
" <td>Friday</td>\n",
" <td>01/29/2016 12:00:00 AM</td>\n",
" <td>11:00</td>\n",
" <td>SOUTHERN</td>\n",
" <td>ARREST, BOOKED</td>\n",
" <td>800 Block of BRYANT ST</td>\n",
" <td>-122.403405</td>\n",
" <td>37.775421</td>\n",
" <td>(37.775420706711, -122.403404791479)</td>\n",
" <td>12005827212168</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>141059263</td>\n",
" <td>WARRANTS</td>\n",
" <td>WARRANT ARREST</td>\n",
" <td>Monday</td>\n",
" <td>04/25/2016 12:00:00 AM</td>\n",
" <td>14:59</td>\n",
" <td>BAYVIEW</td>\n",
" <td>ARREST, BOOKED</td>\n",
" <td>KEITH ST / SHAFTER AV</td>\n",
" <td>-122.388856</td>\n",
" <td>37.729981</td>\n",
" <td>(37.7299809672996, -122.388856204292)</td>\n",
" <td>14105926363010</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>160013662</td>\n",
" <td>NON-CRIMINAL</td>\n",
" <td>LOST PROPERTY</td>\n",
" <td>Tuesday</td>\n",
" <td>01/05/2016 12:00:00 AM</td>\n",
" <td>23:50</td>\n",
" <td>TENDERLOIN</td>\n",
" <td>NONE</td>\n",
" <td>JONES ST / OFARRELL ST</td>\n",
" <td>-122.412971</td>\n",
" <td>37.785788</td>\n",
" <td>(37.7857883766888, -122.412970537591)</td>\n",
" <td>16001366271000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>160002740</td>\n",
" <td>NON-CRIMINAL</td>\n",
" <td>LOST PROPERTY</td>\n",
" <td>Friday</td>\n",
" <td>01/01/2016 12:00:00 AM</td>\n",
" <td>00:30</td>\n",
" <td>MISSION</td>\n",
" <td>NONE</td>\n",
" <td>16TH ST / MISSION ST</td>\n",
" <td>-122.419672</td>\n",
" <td>37.765050</td>\n",
" <td>(37.7650501214668, -122.419671780296)</td>\n",
" <td>16000274071000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" IncidntNum Category Descript \\\n",
"0 120058272 WEAPON LAWS POSS OF PROHIBITED WEAPON \n",
"1 120058272 WEAPON LAWS FIREARM, LOADED, IN VEHICLE, POSSESSION OR USE \n",
"2 141059263 WARRANTS WARRANT ARREST \n",
"3 160013662 NON-CRIMINAL LOST PROPERTY \n",
"4 160002740 NON-CRIMINAL LOST PROPERTY \n",
"\n",
" DayOfWeek Date Time PdDistrict Resolution \\\n",
"0 Friday 01/29/2016 12:00:00 AM 11:00 SOUTHERN ARREST, BOOKED \n",
"1 Friday 01/29/2016 12:00:00 AM 11:00 SOUTHERN ARREST, BOOKED \n",
"2 Monday 04/25/2016 12:00:00 AM 14:59 BAYVIEW ARREST, BOOKED \n",
"3 Tuesday 01/05/2016 12:00:00 AM 23:50 TENDERLOIN NONE \n",
"4 Friday 01/01/2016 12:00:00 AM 00:30 MISSION NONE \n",
"\n",
" Address X Y \\\n",
"0 800 Block of BRYANT ST -122.403405 37.775421 \n",
"1 800 Block of BRYANT ST -122.403405 37.775421 \n",
"2 KEITH ST / SHAFTER AV -122.388856 37.729981 \n",
"3 JONES ST / OFARRELL ST -122.412971 37.785788 \n",
"4 16TH ST / MISSION ST -122.419672 37.765050 \n",
"\n",
" Location PdId \n",
"0 (37.775420706711, -122.403404791479) 12005827212120 \n",
"1 (37.775420706711, -122.403404791479) 12005827212168 \n",
"2 (37.7299809672996, -122.388856204292) 14105926363010 \n",
"3 (37.7857883766888, -122.412970537591) 16001366271000 \n",
"4 (37.7650501214668, -122.419671780296) 16000274071000 "
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_incidents.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"So each row consists of 13 features:\n",
"> 1. **IncidntNum**: Incident Number\n",
"> 2. **Category**: Category of crime or incident\n",
"> 3. **Descript**: Description of the crime or incident\n",
"> 4. **DayOfWeek**: The day of week on which the incident occurred\n",
"> 5. **Date**: The Date on which the incident occurred\n",
"> 6. **Time**: The time of day on which the incident occurred\n",
"> 7. **PdDistrict**: The police department district\n",
"> 8. **Resolution**: The resolution of the crime in terms whether the perpetrator was arrested or not\n",
"> 9. **Address**: The closest address to where the incident took place\n",
"> 10. **X**: The longitude value of the crime location \n",
"> 11. **Y**: The latitude value of the crime location\n",
"> 12. **Location**: A tuple of the latitude and the longitude values\n",
"> 13. **PdId**: The police department ID"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Let's find out how many entries there are in our dataset."
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"(150500, 13)"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_incidents.shape"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"So the dataframe consists of 150,500 crimes, which took place in the year 2016. In order to reduce computational cost, let's just work with the first 100 incidents in this dataset."
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"button": false,
"collapsed": true,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"# get the first 100 crimes in the df_incidents dataframe\n",
"limit = 100\n",
"df_incidents = df_incidents.iloc[0:limit, :]"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Let's confirm that our dataframe now consists only of 100 crimes."
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
"text/plain": [
"(100, 13)"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_incidents.shape"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Now that we reduced the data a little bit, let's visualize where these crimes took place in the city of San Francisco. We will use the default style and we will initialize the zoom level to 12. "
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"button": false,
"collapsed": true,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [],
"source": [
"# San Francisco latitude and longitude values\n",
"latitude = 37.77\n",
"longitude = -122.42"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
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],
"text/plain": [
"<folium.folium.Map at 0x7fc20164dd30>"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# create map and display it\n",
"sanfran_map = folium.Map(location=[latitude, longitude], zoom_start=12)\n",
"\n",
"# display the map of San Francisco\n",
"sanfran_map"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Now let's superimpose the locations of the crimes onto the map. The way to do that in **Folium** is to create a *feature group* with its own features and style and then add it to the sanfran_map."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": false
},
"outputs": [
{
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"# instantiate a feature group for the incidents in the dataframe\n",
"incidents = folium.map.FeatureGroup()\n",
"\n",
"# loop through the 100 crimes and add each to the incidents feature group\n",
"for lat, lng, in zip(df_incidents.Y, df_incidents.X):\n",
" incidents.add_child(\n",
" folium.features.CircleMarker(\n",
" [lat, lng],\n",
" radius=5, # define how big you want the circle markers to be\n",
" color='yellow',\n",
" fill=True,\n",
" fill_color='blue',\n",
" fill_opacity=0.6\n",
" )\n",
" )\n",
"\n",
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"source": [
"You can also add some pop-up text that would get displayed when you hover over a marker. Let's make each marker display the category of the crime when hovered over."
]
},
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"# instantiate a feature group for the incidents in the dataframe\n",
"incidents = folium.map.FeatureGroup()\n",
"\n",
"# loop through the 100 crimes and add each to the incidents feature group\n",
"for lat, lng, in zip(df_incidents.Y, df_incidents.X):\n",
" incidents.add_child(\n",
" folium.features.CircleMarker(\n",
" [lat, lng],\n",
" radius=5, # define how big you want the circle markers to be\n",
" color='yellow',\n",
" fill=True,\n",
" fill_color='blue',\n",
" fill_opacity=0.6\n",
" )\n",
" )\n",
"\n",
"# add pop-up text to each marker on the map\n",
"latitudes = list(df_incidents.Y)\n",
"longitudes = list(df_incidents.X)\n",
"labels = list(df_incidents.Category)\n",
"\n",
"for lat, lng, label in zip(latitudes, longitudes, labels):\n",
" folium.Marker([lat, lng], popup=label).add_to(sanfran_map) \n",
" \n",
"# add incidents to map\n",
"sanfran_map.add_child(incidents)"
]
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"source": [
"Isn't this really cool? Now you are able to know what crime category occurred at each marker.\n",
"\n",
"If you find the map to be so congested will all these markers, there are two remedies to this problem. The simpler solution is to remove these location markers and just add the text to the circle markers themselves as follows:"
]
},
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],
"text/plain": [
"<folium.folium.Map at 0x7fc2039ebe48>"
]
},
"execution_count": 20,
"metadata": {},
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}
],
"source": [
"# create map and display it\n",
"sanfran_map = folium.Map(location=[latitude, longitude], zoom_start=12)\n",
"\n",
"# loop through the 100 crimes and add each to the map\n",
"for lat, lng, label in zip(df_incidents.Y, df_incidents.X, df_incidents.Category):\n",
" folium.features.CircleMarker(\n",
" [lat, lng],\n",
" radius=5, # define how big you want the circle markers to be\n",
" color='yellow',\n",
" fill=True,\n",
" popup=label,\n",
" fill_color='blue',\n",
" fill_opacity=0.6\n",
" ).add_to(sanfran_map)\n",
"\n",
"# show map\n",
"sanfran_map"
]
},
{
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"metadata": {
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"deletable": true,
"new_sheet": false,
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}
},
"source": [
"The other proper remedy is to group the markers into different clusters. Each cluster is then represented by the number of crimes in each neighborhood. These clusters can be thought of as pockets of San Francisco which you can then analyze separately.\n",
"\n",
"To implement this, we start off by instantiating a *MarkerCluster* object and adding all the data points in the dataframe to this object."
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
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},
"outputs": [
{
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            marker_2973a60c363242e2b08c64d2bde193f4.bindPopup(popup_564468d324fb47b4b0264c1ec1ddfdf3);

            
        
    

            var marker_4559cb2bab49488da8ba0b54ec17f615 = L.marker(
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            var popup_29769c976bd346fab8574637ef32124e = L.popup({maxWidth: '300'});

            
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            marker_4559cb2bab49488da8ba0b54ec17f615.bindPopup(popup_29769c976bd346fab8574637ef32124e);

            
        
    

            var marker_c0daaba9f09842898c19fc00f2311362 = L.marker(
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                .addTo(marker_cluster_7156e2426d094a8b8ff609a1bb205479);
            
    
            var popup_8ad8cabb9a38401f99a44a69f02bdb4a = L.popup({maxWidth: '300'});

            
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            marker_c0daaba9f09842898c19fc00f2311362.bindPopup(popup_8ad8cabb9a38401f99a44a69f02bdb4a);

            
        
    

            var marker_4deb7967729e412898d675e3c945c8e0 = L.marker(
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                .addTo(marker_cluster_7156e2426d094a8b8ff609a1bb205479);
            
    
            var popup_98766f2bb61f456e9e5647ee00641ce6 = L.popup({maxWidth: '300'});

            
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            marker_4deb7967729e412898d675e3c945c8e0.bindPopup(popup_98766f2bb61f456e9e5647ee00641ce6);

            
        
    

            var marker_de903db6040d4617a839209e382de53c = L.marker(
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            var popup_288f0037d8a64987be04b2e188eb91f7 = L.popup({maxWidth: '300'});

            
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            var marker_5741a096c6fc4ec3826630daedb9c844 = L.marker(
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            var marker_0c915d190ef74236b060f62a02cb69a1 = L.marker(
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            var popup_ce493c8a10c844d5a961e23d74ce8cc8 = L.popup({maxWidth: '300'});

            
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            var marker_44aebd5d128444bf8262452c393a1d9c = L.marker(
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            var popup_82918d342b514d8baaa9cfab7175b9bc = L.popup({maxWidth: '300'});

            
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            var marker_45e8fb5a95b140f38af49c8bafdb98d6 = L.marker(
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            var popup_d53f76141957426d80ea068f3a2e8d8a = L.popup({maxWidth: '300'});

            
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            var marker_810da03c7b42484c9d23f84e9a1e1122 = L.marker(
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            var popup_11eba8c4806745a7a022e7dc60e1e607 = L.popup({maxWidth: '300'});

            
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            marker_810da03c7b42484c9d23f84e9a1e1122.bindPopup(popup_11eba8c4806745a7a022e7dc60e1e607);

            
        
    

            var marker_5ebb5bdd6fdd4f60b83d8621930e57aa = L.marker(
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            marker_5ebb5bdd6fdd4f60b83d8621930e57aa.bindPopup(popup_3382c0537c4745e58b74ea963fc06ed1);

            
        
    

            var marker_4119457c985d41bd87ef9e37346a2e0d = L.marker(
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                .addTo(marker_cluster_7156e2426d094a8b8ff609a1bb205479);
            
    
            var popup_d0a62ded03ec469fb5ce3e1bd0ea4f22 = L.popup({maxWidth: '300'});

            
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            marker_4119457c985d41bd87ef9e37346a2e0d.bindPopup(popup_d0a62ded03ec469fb5ce3e1bd0ea4f22);

            
        
    

            var marker_d224c359a1824918a290b002ab675993 = L.marker(
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                .addTo(marker_cluster_7156e2426d094a8b8ff609a1bb205479);
            
    
            var popup_27b0225310644681815f390b4ee69e25 = L.popup({maxWidth: '300'});

            
                var html_d1b170e5b51b4fa18b8a2a75e3cc19e5 = $('<div id="html_d1b170e5b51b4fa18b8a2a75e3cc19e5" style="width: 100.0%; height: 100.0%;">BURGLARY</div>')[0];
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            var marker_e88619a06047409b8aa7849f25a4ebc5 = L.marker(
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                .addTo(marker_cluster_7156e2426d094a8b8ff609a1bb205479);
            
    
            var popup_6db56421957844ceb511007f12eb9791 = L.popup({maxWidth: '300'});

            
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            marker_e88619a06047409b8aa7849f25a4ebc5.bindPopup(popup_6db56421957844ceb511007f12eb9791);

            
        
    

            var marker_e6265a388d644baa9f2749175c8db122 = L.marker(
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                .addTo(marker_cluster_7156e2426d094a8b8ff609a1bb205479);
            
    
            var popup_c0afe7c0e17249d8beaed32a143b8391 = L.popup({maxWidth: '300'});

            
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            marker_e6265a388d644baa9f2749175c8db122.bindPopup(popup_c0afe7c0e17249d8beaed32a143b8391);

            
        
    

            var marker_2ebcf243032247829786fcc351791e54 = L.marker(
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                .addTo(marker_cluster_7156e2426d094a8b8ff609a1bb205479);
            
    
            var popup_7c12a1d5a9a241a2ab9eeb897ba4118d = L.popup({maxWidth: '300'});

            
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            marker_2ebcf243032247829786fcc351791e54.bindPopup(popup_7c12a1d5a9a241a2ab9eeb897ba4118d);

            
        
    

            var marker_6ac5c13d1e274ab083094e3c63f12af5 = L.marker(
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                .addTo(marker_cluster_7156e2426d094a8b8ff609a1bb205479);
            
    
            var popup_5ceb45fbb3fd4e988674098b1961c556 = L.popup({maxWidth: '300'});

            
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            marker_6ac5c13d1e274ab083094e3c63f12af5.bindPopup(popup_5ceb45fbb3fd4e988674098b1961c556);

            
        
    

            var marker_4e0917d9d6b649aea03de79315fb9a19 = L.marker(
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                .addTo(marker_cluster_7156e2426d094a8b8ff609a1bb205479);
            
    
            var popup_7bcaa00758bd48dbb35010164af599e2 = L.popup({maxWidth: '300'});

            
                var html_b5cc6731c6db43ca8805829b2f5afb7b = $('<div id="html_b5cc6731c6db43ca8805829b2f5afb7b" style="width: 100.0%; height: 100.0%;">LARCENY/THEFT</div>')[0];
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            var marker_332ebee9206142c498bc5cc870566698 = L.marker(
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                .addTo(marker_cluster_7156e2426d094a8b8ff609a1bb205479);
            
    
            var popup_eaab9fd7f7ce40c19678629df2446668 = L.popup({maxWidth: '300'});

            
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            var marker_c9de8b6eaa8b4f85b71d104127e2f95f = L.marker(
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                .addTo(marker_cluster_7156e2426d094a8b8ff609a1bb205479);
            
    
            var popup_5ea2cbf44aeb43b6ba576fd62edfbf23 = L.popup({maxWidth: '300'});

            
                var html_63bf2642056441cf9468a4151528047d = $('<div id="html_63bf2642056441cf9468a4151528047d" style="width: 100.0%; height: 100.0%;">WARRANTS</div>')[0];
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            var marker_6e19cc1f7fe34777a611a80a5944da2e = L.marker(
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                .addTo(marker_cluster_7156e2426d094a8b8ff609a1bb205479);
            
    
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            var marker_44a90bada4e14c3481ecbd757fa5fb02 = L.marker(
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            var popup_a6f5f985eaf7423da75f74121ea09c8c = L.popup({maxWidth: '300'});

            
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            marker_44a90bada4e14c3481ecbd757fa5fb02.bindPopup(popup_a6f5f985eaf7423da75f74121ea09c8c);

            
        
    

            var marker_525f34a5760c43cbbaef50775f868087 = L.marker(
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                )
                .addTo(marker_cluster_7156e2426d094a8b8ff609a1bb205479);
            
    
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            marker_525f34a5760c43cbbaef50775f868087.bindPopup(popup_fa470fb2aa9c451c93d17b6d0eafcec9);

            
        
    

            var marker_acee39da610342ff827de316554176e9 = L.marker(
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                .addTo(marker_cluster_7156e2426d094a8b8ff609a1bb205479);
            
    
            var popup_33e4cde107bf4a00b3069932d1096dae = L.popup({maxWidth: '300'});

            
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            marker_acee39da610342ff827de316554176e9.bindPopup(popup_33e4cde107bf4a00b3069932d1096dae);

            
        
    

            var marker_e32a2b5d16ab48d8a1c76da4f5943f5c = L.marker(
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                {
                    icon: new L.Icon.Default()
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                )
                .addTo(marker_cluster_7156e2426d094a8b8ff609a1bb205479);
            
    
            var popup_ada73bd974de4ffd94daa82385ced213 = L.popup({maxWidth: '300'});

            
                var html_d336d63f0c52417c98fb797d646d12bf = $('<div id="html_d336d63f0c52417c98fb797d646d12bf" style="width: 100.0%; height: 100.0%;">OTHER OFFENSES</div>')[0];
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            marker_e32a2b5d16ab48d8a1c76da4f5943f5c.bindPopup(popup_ada73bd974de4ffd94daa82385ced213);

            
        
    

            var marker_98915319503b4e0ca5e8eaccbb5edb66 = L.marker(
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                {
                    icon: new L.Icon.Default()
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                )
                .addTo(marker_cluster_7156e2426d094a8b8ff609a1bb205479);
            
    
            var popup_854f04524b914ab594f847ed82eba5b9 = L.popup({maxWidth: '300'});

            
                var html_175a0cb9fd524cc585d16e0a8b9d4071 = $('<div id="html_175a0cb9fd524cc585d16e0a8b9d4071" style="width: 100.0%; height: 100.0%;">BURGLARY</div>')[0];
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            marker_98915319503b4e0ca5e8eaccbb5edb66.bindPopup(popup_854f04524b914ab594f847ed82eba5b9);

            
        
    

            var marker_6ef0b3c7f1d24a64a585d8fb52d959f6 = L.marker(
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                {
                    icon: new L.Icon.Default()
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                )
                .addTo(marker_cluster_7156e2426d094a8b8ff609a1bb205479);
            
    
            var popup_a9c8def51849457491929e1053bd5b5f = L.popup({maxWidth: '300'});

            
                var html_b9d0ba878c9a4739abe9b45a9f2a8e07 = $('<div id="html_b9d0ba878c9a4739abe9b45a9f2a8e07" style="width: 100.0%; height: 100.0%;">OTHER OFFENSES</div>')[0];
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            marker_6ef0b3c7f1d24a64a585d8fb52d959f6.bindPopup(popup_a9c8def51849457491929e1053bd5b5f);

            
        
    

            var marker_ef3a6630e22143558fd62306c8b20529 = L.marker(
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                    icon: new L.Icon.Default()
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                )
                .addTo(marker_cluster_7156e2426d094a8b8ff609a1bb205479);
            
    
            var popup_e48454165124441b91932fe4fbb6f579 = L.popup({maxWidth: '300'});

            
                var html_7915bc5648664aa582aaf7eae34a167a = $('<div id="html_7915bc5648664aa582aaf7eae34a167a" style="width: 100.0%; height: 100.0%;">NON-CRIMINAL</div>')[0];
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            marker_ef3a6630e22143558fd62306c8b20529.bindPopup(popup_e48454165124441b91932fe4fbb6f579);

            
        
    

            var marker_703c721e2dc642738b71f73063591261 = L.marker(
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                )
                .addTo(marker_cluster_7156e2426d094a8b8ff609a1bb205479);
            
    
            var popup_3890e6676dea4c188f59b061eb2a2a76 = L.popup({maxWidth: '300'});

            
                var html_cc25b52aba614e5b91693b007f5938d2 = $('<div id="html_cc25b52aba614e5b91693b007f5938d2" style="width: 100.0%; height: 100.0%;">VEHICLE THEFT</div>')[0];
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            marker_703c721e2dc642738b71f73063591261.bindPopup(popup_3890e6676dea4c188f59b061eb2a2a76);

            
        
    

            var marker_45a069a514d04d7ebbaa980287742d1a = L.marker(
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                .addTo(marker_cluster_7156e2426d094a8b8ff609a1bb205479);
            
    
            var popup_46729006a8834b99a8f14ab135dbc9b4 = L.popup({maxWidth: '300'});

            
                var html_78dbebf7f9dd4d9cafade59866d08782 = $('<div id="html_78dbebf7f9dd4d9cafade59866d08782" style="width: 100.0%; height: 100.0%;">VEHICLE THEFT</div>')[0];
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            marker_45a069a514d04d7ebbaa980287742d1a.bindPopup(popup_46729006a8834b99a8f14ab135dbc9b4);

            
        
    

            var marker_5089d978c7584fc9ac3b906b498056d7 = L.marker(
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                .addTo(marker_cluster_7156e2426d094a8b8ff609a1bb205479);
            
    
            var popup_ae18fe9a60594004a1c6febbe56e5146 = L.popup({maxWidth: '300'});

            
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            marker_5089d978c7584fc9ac3b906b498056d7.bindPopup(popup_ae18fe9a60594004a1c6febbe56e5146);

            
        
    

            var marker_325b31af5be14df3b15f093adebc0bc4 = L.marker(
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                .addTo(marker_cluster_7156e2426d094a8b8ff609a1bb205479);
            
    
            var popup_7c58c5479d7c4b0b874ff9a7f0a7f7f3 = L.popup({maxWidth: '300'});

            
                var html_bde917e43b014be8bfd7216e4ed88238 = $('<div id="html_bde917e43b014be8bfd7216e4ed88238" style="width: 100.0%; height: 100.0%;">ASSAULT</div>')[0];
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            marker_325b31af5be14df3b15f093adebc0bc4.bindPopup(popup_7c58c5479d7c4b0b874ff9a7f0a7f7f3);

            
        
    

            var marker_464ebc0a1b6347a1bb20bc0fbfd3ac87 = L.marker(
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                .addTo(marker_cluster_7156e2426d094a8b8ff609a1bb205479);
            
    
            var popup_b68085c979f74598bad744f45c591451 = L.popup({maxWidth: '300'});

            
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            marker_464ebc0a1b6347a1bb20bc0fbfd3ac87.bindPopup(popup_b68085c979f74598bad744f45c591451);

            
        
    

            var marker_3c77e3c5506444d4980480ba598fe240 = L.marker(
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                .addTo(marker_cluster_7156e2426d094a8b8ff609a1bb205479);
            
    
            var popup_8cae2e87f1ce46099331e3c01358be33 = L.popup({maxWidth: '300'});

            
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            marker_3c77e3c5506444d4980480ba598fe240.bindPopup(popup_8cae2e87f1ce46099331e3c01358be33);

            
        
    

            var marker_bf84cb41908c46b2bfbd407c1551b82c = L.marker(
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                .addTo(marker_cluster_7156e2426d094a8b8ff609a1bb205479);
            
    
            var popup_7a7af290847340edb5fb37c72bc07a4c = L.popup({maxWidth: '300'});

            
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            marker_bf84cb41908c46b2bfbd407c1551b82c.bindPopup(popup_7a7af290847340edb5fb37c72bc07a4c);

            
        
    

            var marker_edb9659d36aa44c3ba8ddc99d521a283 = L.marker(
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            var popup_0b774721de1440ed9bc0e0a29cca3f4d = L.popup({maxWidth: '300'});

            
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            marker_edb9659d36aa44c3ba8ddc99d521a283.bindPopup(popup_0b774721de1440ed9bc0e0a29cca3f4d);

            
        
    

            var marker_13c740ad9a9a47828c9f88ce2c00ed69 = L.marker(
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                .addTo(marker_cluster_7156e2426d094a8b8ff609a1bb205479);
            
    
            var popup_969ad5ddae1d45a39fe801e95fb48579 = L.popup({maxWidth: '300'});

            
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            marker_13c740ad9a9a47828c9f88ce2c00ed69.bindPopup(popup_969ad5ddae1d45a39fe801e95fb48579);

            
        
    

            var marker_c43411781b7a4978b806bd0c6854a7e3 = L.marker(
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                .addTo(marker_cluster_7156e2426d094a8b8ff609a1bb205479);
            
    
            var popup_977b9c98e4114f35a6b721f906fdf2e9 = L.popup({maxWidth: '300'});

            
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                popup_977b9c98e4114f35a6b721f906fdf2e9.setContent(html_03280f07e1ab4cc1b54745ffebb72de9);
            

            marker_c43411781b7a4978b806bd0c6854a7e3.bindPopup(popup_977b9c98e4114f35a6b721f906fdf2e9);

            
        
    

            var marker_44995c31355748db8fb5b8640c066cce = L.marker(
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                {
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                )
                .addTo(marker_cluster_7156e2426d094a8b8ff609a1bb205479);
            
    
            var popup_34ad51ed6f2f4851b7ff4d5b30946637 = L.popup({maxWidth: '300'});

            
                var html_c5566d5c41204fa6b9f62b070ef9af6b = $('<div id="html_c5566d5c41204fa6b9f62b070ef9af6b" style="width: 100.0%; height: 100.0%;">VANDALISM</div>')[0];
                popup_34ad51ed6f2f4851b7ff4d5b30946637.setContent(html_c5566d5c41204fa6b9f62b070ef9af6b);
            

            marker_44995c31355748db8fb5b8640c066cce.bindPopup(popup_34ad51ed6f2f4851b7ff4d5b30946637);

            
        
    

            var marker_654e3ba7b63d47d1856f37373f07a45f = L.marker(
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                )
                .addTo(marker_cluster_7156e2426d094a8b8ff609a1bb205479);
            
    
            var popup_a2b691cd0d13489a8f56ad0745e7b725 = L.popup({maxWidth: '300'});

            
                var html_b0388f68f9a5450a956ce1e316c78ba5 = $('<div id="html_b0388f68f9a5450a956ce1e316c78ba5" style="width: 100.0%; height: 100.0%;">ASSAULT</div>')[0];
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            marker_654e3ba7b63d47d1856f37373f07a45f.bindPopup(popup_a2b691cd0d13489a8f56ad0745e7b725);

            
        
    

            var marker_c0822f282ff14678bf3bb9162fbf1f75 = L.marker(
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                )
                .addTo(marker_cluster_7156e2426d094a8b8ff609a1bb205479);
            
    
            var popup_28ef141f5de04d278e5a9cce2d00646a = L.popup({maxWidth: '300'});

            
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                popup_28ef141f5de04d278e5a9cce2d00646a.setContent(html_c82b6196df1e4e72bcabca69478acb90);
            

            marker_c0822f282ff14678bf3bb9162fbf1f75.bindPopup(popup_28ef141f5de04d278e5a9cce2d00646a);

            
        
    

            var marker_f631ed0d02aa48b59670ad911da8d8b9 = L.marker(
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                    icon: new L.Icon.Default()
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                .addTo(marker_cluster_7156e2426d094a8b8ff609a1bb205479);
            
    
            var popup_fe0e8a95b85646faac6e41a324d43856 = L.popup({maxWidth: '300'});

            
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                popup_fe0e8a95b85646faac6e41a324d43856.setContent(html_513fadfb5a504b5a98906353d82c0964);
            

            marker_f631ed0d02aa48b59670ad911da8d8b9.bindPopup(popup_fe0e8a95b85646faac6e41a324d43856);

            
        
    

            var marker_77e978db9df54bcaadf01fe06c1a09e6 = L.marker(
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                .addTo(marker_cluster_7156e2426d094a8b8ff609a1bb205479);
            
    
            var popup_a93ea2eebc9b49bab14aa19c68c8b80d = L.popup({maxWidth: '300'});

            
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            marker_77e978db9df54bcaadf01fe06c1a09e6.bindPopup(popup_a93ea2eebc9b49bab14aa19c68c8b80d);

            
        
    

            var marker_1b784339bdb6490994840062952d2f5b = L.marker(
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                .addTo(marker_cluster_7156e2426d094a8b8ff609a1bb205479);
            
    
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            marker_1b784339bdb6490994840062952d2f5b.bindPopup(popup_30ef16df531543368140d432f2d2c113);

            
        
    

            var marker_14376eb261814f7caf4996aa3f19d74d = L.marker(
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                .addTo(marker_cluster_7156e2426d094a8b8ff609a1bb205479);
            
    
            var popup_d469b953102847059c7ff33219dd2d8a = L.popup({maxWidth: '300'});

            
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            marker_14376eb261814f7caf4996aa3f19d74d.bindPopup(popup_d469b953102847059c7ff33219dd2d8a);

            
        
    

            var marker_1d541b05fef0487d9680f1044b8f24c0 = L.marker(
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                )
                .addTo(marker_cluster_7156e2426d094a8b8ff609a1bb205479);
            
    
            var popup_30d80f0fea9c4d388695ccc2fa421b74 = L.popup({maxWidth: '300'});

            
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            marker_1d541b05fef0487d9680f1044b8f24c0.bindPopup(popup_30d80f0fea9c4d388695ccc2fa421b74);

            
        
    

            var marker_6b5eaf601bbd496a9c2367906eed122f = L.marker(
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                .addTo(marker_cluster_7156e2426d094a8b8ff609a1bb205479);
            
    
            var popup_8203dc4436104cae92fbc2dcb04a3ef8 = L.popup({maxWidth: '300'});

            
                var html_e18ff406b06c4c19a7ce78a9636a5442 = $('<div id="html_e18ff406b06c4c19a7ce78a9636a5442" style="width: 100.0%; height: 100.0%;">MISSING PERSON</div>')[0];
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            marker_6b5eaf601bbd496a9c2367906eed122f.bindPopup(popup_8203dc4436104cae92fbc2dcb04a3ef8);

            
        
    

            var marker_4faa235a918a476b83d0cd45b07aeca7 = L.marker(
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                {
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                .addTo(marker_cluster_7156e2426d094a8b8ff609a1bb205479);
            
    
            var popup_144890db9e2b455daa76cacea358ef26 = L.popup({maxWidth: '300'});

            
                var html_9246ea942dff4c8c9413bd612308e80d = $('<div id="html_9246ea942dff4c8c9413bd612308e80d" style="width: 100.0%; height: 100.0%;">MISSING PERSON</div>')[0];
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            marker_4faa235a918a476b83d0cd45b07aeca7.bindPopup(popup_144890db9e2b455daa76cacea358ef26);

            
        
    

            var marker_8c02ebbf7db247d485e72b449fb72add = L.marker(
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                    icon: new L.Icon.Default()
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                .addTo(marker_cluster_7156e2426d094a8b8ff609a1bb205479);
            
    
            var popup_d9086513989a480d8a2111166e1f247a = L.popup({maxWidth: '300'});

            
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            marker_8c02ebbf7db247d485e72b449fb72add.bindPopup(popup_d9086513989a480d8a2111166e1f247a);

            
        
    

            var marker_5c9f2b3ef94c4c5ca472bb197bb021b7 = L.marker(
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                {
                    icon: new L.Icon.Default()
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                .addTo(marker_cluster_7156e2426d094a8b8ff609a1bb205479);
            
    
            var popup_4bc4f786f6a340cf991104ffef875772 = L.popup({maxWidth: '300'});

            
                var html_1ad71750d9424abcae46770d3842ce40 = $('<div id="html_1ad71750d9424abcae46770d3842ce40" style="width: 100.0%; height: 100.0%;">MISSING PERSON</div>')[0];
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            marker_5c9f2b3ef94c4c5ca472bb197bb021b7.bindPopup(popup_4bc4f786f6a340cf991104ffef875772);

            
        
    

            var marker_e8d3f49e5ca7449eba18eee360e860b8 = L.marker(
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                    icon: new L.Icon.Default()
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                .addTo(marker_cluster_7156e2426d094a8b8ff609a1bb205479);
            
    
            var popup_7f79c3a6c0d4405a886087389bdbdad6 = L.popup({maxWidth: '300'});

            
                var html_42d67414f1ba4894b8915314df5703b4 = $('<div id="html_42d67414f1ba4894b8915314df5703b4" style="width: 100.0%; height: 100.0%;">VEHICLE THEFT</div>')[0];
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            marker_e8d3f49e5ca7449eba18eee360e860b8.bindPopup(popup_7f79c3a6c0d4405a886087389bdbdad6);

            
        
    

            var marker_22582f301fbb4904afc00d6354744e40 = L.marker(
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            var popup_279b536c04ad437bb7a4f2d33162097e = L.popup({maxWidth: '300'});

            
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            marker_22582f301fbb4904afc00d6354744e40.bindPopup(popup_279b536c04ad437bb7a4f2d33162097e);

            
        
    

            var marker_71259d0aa71546edb42642f618cc7c46 = L.marker(
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            var popup_c3971d8bfb1142938435e9cdd06b4195 = L.popup({maxWidth: '300'});

            
                var html_31b2443a0d404e48ad4b6c29c6566df0 = $('<div id="html_31b2443a0d404e48ad4b6c29c6566df0" style="width: 100.0%; height: 100.0%;">WARRANTS</div>')[0];
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            marker_71259d0aa71546edb42642f618cc7c46.bindPopup(popup_c3971d8bfb1142938435e9cdd06b4195);

            
        
    

            var marker_cf54a31d6e45430ba0a21e2ba48eced1 = L.marker(
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            var popup_fa63357572c2438ea8763fafbb66319b = L.popup({maxWidth: '300'});

            
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            marker_cf54a31d6e45430ba0a21e2ba48eced1.bindPopup(popup_fa63357572c2438ea8763fafbb66319b);

            
        
    

            var marker_dabfd0d730244212b984a658f1e18e19 = L.marker(
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            var popup_ad1b6556a4de49d38b4644167198650f = L.popup({maxWidth: '300'});

            
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            marker_dabfd0d730244212b984a658f1e18e19.bindPopup(popup_ad1b6556a4de49d38b4644167198650f);

            
        
    

            var marker_f11e614809f6489883768c93377d976e = L.marker(
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                    icon: new L.Icon.Default()
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                .addTo(marker_cluster_7156e2426d094a8b8ff609a1bb205479);
            
    
            var popup_83f896c038ce494ca765111a5b463b7a = L.popup({maxWidth: '300'});

            
                var html_1f33bd5f3d07480ba031793d209f4870 = $('<div id="html_1f33bd5f3d07480ba031793d209f4870" style="width: 100.0%; height: 100.0%;">FRAUD</div>')[0];
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            marker_f11e614809f6489883768c93377d976e.bindPopup(popup_83f896c038ce494ca765111a5b463b7a);

            
        
    

            var marker_3e6f2e6c972e41b196c62936e224f4e1 = L.marker(
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                .addTo(marker_cluster_7156e2426d094a8b8ff609a1bb205479);
            
    
            var popup_00262c28d9f04e75b93cb3c3720f57b3 = L.popup({maxWidth: '300'});

            
                var html_ecf3a24e63fe45ffaa4aaffe35892389 = $('<div id="html_ecf3a24e63fe45ffaa4aaffe35892389" style="width: 100.0%; height: 100.0%;">ASSAULT</div>')[0];
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            marker_3e6f2e6c972e41b196c62936e224f4e1.bindPopup(popup_00262c28d9f04e75b93cb3c3720f57b3);

            
        
    

            var marker_f6d5e4b36cae416ebc3ad752bd1da06f = L.marker(
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                    icon: new L.Icon.Default()
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                .addTo(marker_cluster_7156e2426d094a8b8ff609a1bb205479);
            
    
            var popup_99ce10d327b9404ca157d5ae5f72b5ff = L.popup({maxWidth: '300'});

            
                var html_ce63affe844f4c4ea21366d872eedb35 = $('<div id="html_ce63affe844f4c4ea21366d872eedb35" style="width: 100.0%; height: 100.0%;">OTHER OFFENSES</div>')[0];
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            marker_f6d5e4b36cae416ebc3ad752bd1da06f.bindPopup(popup_99ce10d327b9404ca157d5ae5f72b5ff);

            
        
    

            var marker_878825daf3924402b0d418ec32cd2c91 = L.marker(
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                    icon: new L.Icon.Default()
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                .addTo(marker_cluster_7156e2426d094a8b8ff609a1bb205479);
            
    
            var popup_e14238222066423492172bd2c482f39e = L.popup({maxWidth: '300'});

            
                var html_90d8a382406542c0971b7f5eb1dc30bc = $('<div id="html_90d8a382406542c0971b7f5eb1dc30bc" style="width: 100.0%; height: 100.0%;">OTHER OFFENSES</div>')[0];
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            marker_878825daf3924402b0d418ec32cd2c91.bindPopup(popup_e14238222066423492172bd2c482f39e);

            
        
    

            var marker_3ed3b2ef78fb4d249264b34911037135 = L.marker(
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                    icon: new L.Icon.Default()
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                )
                .addTo(marker_cluster_7156e2426d094a8b8ff609a1bb205479);
            
    
            var popup_0584f0e63b624d58b380bcb76eccf2f7 = L.popup({maxWidth: '300'});

            
                var html_0d4972e8e2a043c0a09fb9d94fd1f9c9 = $('<div id="html_0d4972e8e2a043c0a09fb9d94fd1f9c9" style="width: 100.0%; height: 100.0%;">LARCENY/THEFT</div>')[0];
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            marker_3ed3b2ef78fb4d249264b34911037135.bindPopup(popup_0584f0e63b624d58b380bcb76eccf2f7);

            
        
    

            var marker_6adf6d502c9f49ce94c1dae60b811fbb = L.marker(
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                    icon: new L.Icon.Default()
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                )
                .addTo(marker_cluster_7156e2426d094a8b8ff609a1bb205479);
            
    
            var popup_11f79090a2aa4c0dab8fc23d497babdb = L.popup({maxWidth: '300'});

            
                var html_5e45994b13904cbe918832ac0363eddd = $('<div id="html_5e45994b13904cbe918832ac0363eddd" style="width: 100.0%; height: 100.0%;">MISSING PERSON</div>')[0];
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            marker_6adf6d502c9f49ce94c1dae60b811fbb.bindPopup(popup_11f79090a2aa4c0dab8fc23d497babdb);

            
        
    

            var marker_546fe92eb3c84cb6b782c34e41551109 = L.marker(
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                    icon: new L.Icon.Default()
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                )
                .addTo(marker_cluster_7156e2426d094a8b8ff609a1bb205479);
            
    
            var popup_603bdb62d75e46a9994987e07ad6cdd2 = L.popup({maxWidth: '300'});

            
                var html_e42d84de7b81401aa77a6a54102e4e4e = $('<div id="html_e42d84de7b81401aa77a6a54102e4e4e" style="width: 100.0%; height: 100.0%;">MISSING PERSON</div>')[0];
                popup_603bdb62d75e46a9994987e07ad6cdd2.setContent(html_e42d84de7b81401aa77a6a54102e4e4e);
            

            marker_546fe92eb3c84cb6b782c34e41551109.bindPopup(popup_603bdb62d75e46a9994987e07ad6cdd2);

            
        
    

            var marker_63ff1ff97bdc444594675064e78448bc = L.marker(
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                {
                    icon: new L.Icon.Default()
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                )
                .addTo(marker_cluster_7156e2426d094a8b8ff609a1bb205479);
            
    
            var popup_159fa009b2d9459f9507ac05918d2c5b = L.popup({maxWidth: '300'});

            
                var html_dfe1030823174b588679c99132b2a259 = $('<div id="html_dfe1030823174b588679c99132b2a259" style="width: 100.0%; height: 100.0%;">WARRANTS</div>')[0];
                popup_159fa009b2d9459f9507ac05918d2c5b.setContent(html_dfe1030823174b588679c99132b2a259);
            

            marker_63ff1ff97bdc444594675064e78448bc.bindPopup(popup_159fa009b2d9459f9507ac05918d2c5b);

            
        
    

            var marker_3c9fb8dc5f6b4eff9dcb7406416b9247 = L.marker(
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                {
                    icon: new L.Icon.Default()
                    }
                )
                .addTo(marker_cluster_7156e2426d094a8b8ff609a1bb205479);
            
    
            var popup_5ae14fdda8d54428b2f5c83288ab0c06 = L.popup({maxWidth: '300'});

            
                var html_d087c6a57b1040558fdb5e66d5dad57c = $('<div id="html_d087c6a57b1040558fdb5e66d5dad57c" style="width: 100.0%; height: 100.0%;">NON-CRIMINAL</div>')[0];
                popup_5ae14fdda8d54428b2f5c83288ab0c06.setContent(html_d087c6a57b1040558fdb5e66d5dad57c);
            

            marker_3c9fb8dc5f6b4eff9dcb7406416b9247.bindPopup(popup_5ae14fdda8d54428b2f5c83288ab0c06);

            
        
    

            var marker_48e8b96e24c941dbbdcc611f074b8e70 = L.marker(
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                    icon: new L.Icon.Default()
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                )
                .addTo(marker_cluster_7156e2426d094a8b8ff609a1bb205479);
            
    
            var popup_783f3820e7844cac8a3a7241a99b3336 = L.popup({maxWidth: '300'});

            
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            marker_48e8b96e24c941dbbdcc611f074b8e70.bindPopup(popup_783f3820e7844cac8a3a7241a99b3336);

            
        
    

            var marker_df0c14233067456ba3d7874bb66e2a48 = L.marker(
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                    icon: new L.Icon.Default()
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                .addTo(marker_cluster_7156e2426d094a8b8ff609a1bb205479);
            
    
            var popup_e2be631e7ba3463db11df83e578a8934 = L.popup({maxWidth: '300'});

            
                var html_5ad5ab13881144518d6bfecc1894cc7b = $('<div id="html_5ad5ab13881144518d6bfecc1894cc7b" style="width: 100.0%; height: 100.0%;">WARRANTS</div>')[0];
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            marker_df0c14233067456ba3d7874bb66e2a48.bindPopup(popup_e2be631e7ba3463db11df83e578a8934);

            
        
    

            var marker_6a8157292ee44477b480fb7a1ae29ec2 = L.marker(
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                {
                    icon: new L.Icon.Default()
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                )
                .addTo(marker_cluster_7156e2426d094a8b8ff609a1bb205479);
            
    
            var popup_5d788d44db8d45d8973132c4614fe2f1 = L.popup({maxWidth: '300'});

            
                var html_9f574309e78e4effb3b74375aafc286c = $('<div id="html_9f574309e78e4effb3b74375aafc286c" style="width: 100.0%; height: 100.0%;">PROSTITUTION</div>')[0];
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            marker_6a8157292ee44477b480fb7a1ae29ec2.bindPopup(popup_5d788d44db8d45d8973132c4614fe2f1);

            
        
    

            var marker_f6ba68697d644292a97e326ef3f33158 = L.marker(
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                {
                    icon: new L.Icon.Default()
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                )
                .addTo(marker_cluster_7156e2426d094a8b8ff609a1bb205479);
            
    
            var popup_39b6b4e6b14a41db850d06f5006eb670 = L.popup({maxWidth: '300'});

            
                var html_fcfcfd861fe6419fb31246eb45e5bdb9 = $('<div id="html_fcfcfd861fe6419fb31246eb45e5bdb9" style="width: 100.0%; height: 100.0%;">OTHER OFFENSES</div>')[0];
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            marker_f6ba68697d644292a97e326ef3f33158.bindPopup(popup_39b6b4e6b14a41db850d06f5006eb670);

            
        
    

            var marker_60ebd9060f4e4e4193377419b723a5c8 = L.marker(
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                .addTo(marker_cluster_7156e2426d094a8b8ff609a1bb205479);
            
    
            var popup_611115b5d7b841c4a30543b7076727b9 = L.popup({maxWidth: '300'});

            
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            marker_60ebd9060f4e4e4193377419b723a5c8.bindPopup(popup_611115b5d7b841c4a30543b7076727b9);

            
        
    

            var marker_28d381f0393541249e7a215cd5fb10cc = L.marker(
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                    icon: new L.Icon.Default()
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                )
                .addTo(marker_cluster_7156e2426d094a8b8ff609a1bb205479);
            
    
            var popup_c3c8a4eaf17e45ad9dab553fee168f1b = L.popup({maxWidth: '300'});

            
                var html_375f5dc63d8c4cc9ae4c1a5c154d4caa = $('<div id="html_375f5dc63d8c4cc9ae4c1a5c154d4caa" style="width: 100.0%; height: 100.0%;">DRUG/NARCOTIC</div>')[0];
                popup_c3c8a4eaf17e45ad9dab553fee168f1b.setContent(html_375f5dc63d8c4cc9ae4c1a5c154d4caa);
            

            marker_28d381f0393541249e7a215cd5fb10cc.bindPopup(popup_c3c8a4eaf17e45ad9dab553fee168f1b);

            
        
    

            var marker_7727bb75d14c45b99224da8d57c0f5d2 = L.marker(
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                {
                    icon: new L.Icon.Default()
                    }
                )
                .addTo(marker_cluster_7156e2426d094a8b8ff609a1bb205479);
            
    
            var popup_424ec57938e84472b854a06384ed9011 = L.popup({maxWidth: '300'});

            
                var html_c1323c1fdaa24779be7ad514b30bd729 = $('<div id="html_c1323c1fdaa24779be7ad514b30bd729" style="width: 100.0%; height: 100.0%;">WARRANTS</div>')[0];
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            marker_7727bb75d14c45b99224da8d57c0f5d2.bindPopup(popup_424ec57938e84472b854a06384ed9011);

            
        
    

            var marker_4b8c1f51c272453c9801b75f78860e3a = L.marker(
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                {
                    icon: new L.Icon.Default()
                    }
                )
                .addTo(marker_cluster_7156e2426d094a8b8ff609a1bb205479);
            
    
            var popup_0da4abdfd1a04d27a1a151d773c2f37c = L.popup({maxWidth: '300'});

            
                var html_4e1ab9b2fe814a70bb44e23a458691a0 = $('<div id="html_4e1ab9b2fe814a70bb44e23a458691a0" style="width: 100.0%; height: 100.0%;">LARCENY/THEFT</div>')[0];
                popup_0da4abdfd1a04d27a1a151d773c2f37c.setContent(html_4e1ab9b2fe814a70bb44e23a458691a0);
            

            marker_4b8c1f51c272453c9801b75f78860e3a.bindPopup(popup_0da4abdfd1a04d27a1a151d773c2f37c);

            
        
    

            var marker_d2efecbf372c4aa3ab2647cf0c5d1449 = L.marker(
                [37.7292114647359,-122.400834283031],
                {
                    icon: new L.Icon.Default()
                    }
                )
                .addTo(marker_cluster_7156e2426d094a8b8ff609a1bb205479);
            
    
            var popup_aa455130cd1a4244b28dbda7e56ee650 = L.popup({maxWidth: '300'});

            
                var html_767dd020b06e4c4797b3a955bafc9c0c = $('<div id="html_767dd020b06e4c4797b3a955bafc9c0c" style="width: 100.0%; height: 100.0%;">OTHER OFFENSES</div>')[0];
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            marker_d2efecbf372c4aa3ab2647cf0c5d1449.bindPopup(popup_aa455130cd1a4244b28dbda7e56ee650);

            
        
    

            var marker_85a5e35b31744fec9452e1edc98a0014 = L.marker(
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                {
                    icon: new L.Icon.Default()
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                .addTo(marker_cluster_7156e2426d094a8b8ff609a1bb205479);
            
    
            var popup_d17fed5ab36346d7868f7b468c05a7f1 = L.popup({maxWidth: '300'});

            
                var html_212dc83ba71142cabf257b19e37831aa = $('<div id="html_212dc83ba71142cabf257b19e37831aa" style="width: 100.0%; height: 100.0%;">LARCENY/THEFT</div>')[0];
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            marker_85a5e35b31744fec9452e1edc98a0014.bindPopup(popup_d17fed5ab36346d7868f7b468c05a7f1);

            
        
    

            var marker_c30f7674640e4811855646aee7acf851 = L.marker(
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                    icon: new L.Icon.Default()
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                )
                .addTo(marker_cluster_7156e2426d094a8b8ff609a1bb205479);
            
    
            var popup_d2fe7206c51a48da85e936c20bccc5c2 = L.popup({maxWidth: '300'});

            
                var html_6913686e06104f6fa237c15382cb45e1 = $('<div id="html_6913686e06104f6fa237c15382cb45e1" style="width: 100.0%; height: 100.0%;">SECONDARY CODES</div>')[0];
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            marker_c30f7674640e4811855646aee7acf851.bindPopup(popup_d2fe7206c51a48da85e936c20bccc5c2);

            
        
    

            var marker_5a813c01553c4a1788ea9d26b77f503b = L.marker(
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                .addTo(marker_cluster_7156e2426d094a8b8ff609a1bb205479);
            
    
            var popup_eac5488f50674ec2b519acc19bd0cc70 = L.popup({maxWidth: '300'});

            
                var html_951e3babae3842d5a1361e60d0bea325 = $('<div id="html_951e3babae3842d5a1361e60d0bea325" style="width: 100.0%; height: 100.0%;">SUSPICIOUS OCC</div>')[0];
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            marker_5a813c01553c4a1788ea9d26b77f503b.bindPopup(popup_eac5488f50674ec2b519acc19bd0cc70);

            
        
    

            var marker_38f99618dd94473e9068cce5fb9c2f7d = L.marker(
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                .addTo(marker_cluster_7156e2426d094a8b8ff609a1bb205479);
            
    
            var popup_24e789c3515748889b3a4fb9721f8aed = L.popup({maxWidth: '300'});

            
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            marker_38f99618dd94473e9068cce5fb9c2f7d.bindPopup(popup_24e789c3515748889b3a4fb9721f8aed);

            
        
    

            var marker_2d4a0f51e46748d09dd435435e8429c0 = L.marker(
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                .addTo(marker_cluster_7156e2426d094a8b8ff609a1bb205479);
            
    
            var popup_83ee53abaa8e4c62b3166a8d100f326f = L.popup({maxWidth: '300'});

            
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            marker_2d4a0f51e46748d09dd435435e8429c0.bindPopup(popup_83ee53abaa8e4c62b3166a8d100f326f);

            
        
    

            var marker_b24ae33549b44ca0abecb30783412389 = L.marker(
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                .addTo(marker_cluster_7156e2426d094a8b8ff609a1bb205479);
            
    
            var popup_7fc74e9be17c428886b5c0c9055ad174 = L.popup({maxWidth: '300'});

            
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            marker_b24ae33549b44ca0abecb30783412389.bindPopup(popup_7fc74e9be17c428886b5c0c9055ad174);

            
        
    

            var marker_8fccfa23196a4ae2abd398cadfe154ff = L.marker(
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                .addTo(marker_cluster_7156e2426d094a8b8ff609a1bb205479);
            
    
            var popup_a560d3cd162649e38369ae6b5de416fd = L.popup({maxWidth: '300'});

            
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            marker_8fccfa23196a4ae2abd398cadfe154ff.bindPopup(popup_a560d3cd162649e38369ae6b5de416fd);

            
        
    

            var marker_8256cb56cd844f08b585ac16353fdc9f = L.marker(
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                .addTo(marker_cluster_7156e2426d094a8b8ff609a1bb205479);
            
    
            var popup_32dd3b5a90e545fca972a4a82b5b1c24 = L.popup({maxWidth: '300'});

            
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            marker_8256cb56cd844f08b585ac16353fdc9f.bindPopup(popup_32dd3b5a90e545fca972a4a82b5b1c24);

            
        
    

            var marker_8f968f519e8045f4a23152f78c325612 = L.marker(
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                .addTo(marker_cluster_7156e2426d094a8b8ff609a1bb205479);
            
    
            var popup_1b209d42aabb43b2a6891130c644dac5 = L.popup({maxWidth: '300'});

            
                var html_213e79d03f4b4e439daaa0e66f2438da = $('<div id="html_213e79d03f4b4e439daaa0e66f2438da" style="width: 100.0%; height: 100.0%;">OTHER OFFENSES</div>')[0];
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            marker_8f968f519e8045f4a23152f78c325612.bindPopup(popup_1b209d42aabb43b2a6891130c644dac5);

            
        
    

            var marker_e0a30017046549549cfd6f4d600693af = L.marker(
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                    icon: new L.Icon.Default()
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                )
                .addTo(marker_cluster_7156e2426d094a8b8ff609a1bb205479);
            
    
            var popup_558c2cb4adb34edc8cc314d90543cd4a = L.popup({maxWidth: '300'});

            
                var html_54ee7b65fcde4b7e95cb6aab777e7b01 = $('<div id="html_54ee7b65fcde4b7e95cb6aab777e7b01" style="width: 100.0%; height: 100.0%;">DRUNKENNESS</div>')[0];
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            marker_e0a30017046549549cfd6f4d600693af.bindPopup(popup_558c2cb4adb34edc8cc314d90543cd4a);

            
        
    

            var marker_86871e359c6e43da874f56a39676f8e9 = L.marker(
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                )
                .addTo(marker_cluster_7156e2426d094a8b8ff609a1bb205479);
            
    
            var popup_7238785af10d43108abd5d9d52816a8a = L.popup({maxWidth: '300'});

            
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                popup_7238785af10d43108abd5d9d52816a8a.setContent(html_d313607cf52341efb4659641ec7a48b4);
            

            marker_86871e359c6e43da874f56a39676f8e9.bindPopup(popup_7238785af10d43108abd5d9d52816a8a);

            
        
    

            var marker_ad086c69e6b84365a3700eb327ff8351 = L.marker(
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                    icon: new L.Icon.Default()
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                .addTo(marker_cluster_7156e2426d094a8b8ff609a1bb205479);
            
    
            var popup_4db06cfc25a04621b3da7418abffdaf4 = L.popup({maxWidth: '300'});

            
                var html_b13778db99d840c0ac152bffef8d7d79 = $('<div id="html_b13778db99d840c0ac152bffef8d7d79" style="width: 100.0%; height: 100.0%;">VANDALISM</div>')[0];
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            marker_ad086c69e6b84365a3700eb327ff8351.bindPopup(popup_4db06cfc25a04621b3da7418abffdaf4);

            
        
    

            var marker_b7e03429ca434794b746e4895204a081 = L.marker(
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                {
                    icon: new L.Icon.Default()
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                )
                .addTo(marker_cluster_7156e2426d094a8b8ff609a1bb205479);
            
    
            var popup_7bc2ff774dfc4027991ec9258284f1b6 = L.popup({maxWidth: '300'});

            
                var html_b84a57e07cd74d0184aca741c932e58e = $('<div id="html_b84a57e07cd74d0184aca741c932e58e" style="width: 100.0%; height: 100.0%;">OTHER OFFENSES</div>')[0];
                popup_7bc2ff774dfc4027991ec9258284f1b6.setContent(html_b84a57e07cd74d0184aca741c932e58e);
            

            marker_b7e03429ca434794b746e4895204a081.bindPopup(popup_7bc2ff774dfc4027991ec9258284f1b6);

            
        
    

            var marker_72f21fdf7ddd493daae7363c19ed7c4a = L.marker(
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                    icon: new L.Icon.Default()
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                )
                .addTo(marker_cluster_7156e2426d094a8b8ff609a1bb205479);
            
    
            var popup_d8d14284a37e48f4bb259743bd9966b3 = L.popup({maxWidth: '300'});

            
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                popup_d8d14284a37e48f4bb259743bd9966b3.setContent(html_d82598b36d3f4e5689275f19c15fe210);
            

            marker_72f21fdf7ddd493daae7363c19ed7c4a.bindPopup(popup_d8d14284a37e48f4bb259743bd9966b3);

            
        
    

            var marker_3bc9dc40515c4991920d80a53511bf59 = L.marker(
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                    icon: new L.Icon.Default()
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                )
                .addTo(marker_cluster_7156e2426d094a8b8ff609a1bb205479);
            
    
            var popup_1f3d4ab1f23d48fc9a26f59435966d70 = L.popup({maxWidth: '300'});

            
                var html_cd5eadfe6b6d47a6a6136d1f6131d617 = $('<div id="html_cd5eadfe6b6d47a6a6136d1f6131d617" style="width: 100.0%; height: 100.0%;">VANDALISM</div>')[0];
                popup_1f3d4ab1f23d48fc9a26f59435966d70.setContent(html_cd5eadfe6b6d47a6a6136d1f6131d617);
            

            marker_3bc9dc40515c4991920d80a53511bf59.bindPopup(popup_1f3d4ab1f23d48fc9a26f59435966d70);

            
        
    

            var marker_9290a54cffa2442e822512e2eeffe58f = L.marker(
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                {
                    icon: new L.Icon.Default()
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                )
                .addTo(marker_cluster_7156e2426d094a8b8ff609a1bb205479);
            
    
            var popup_a2f2d18dccba4216948136991521fbd6 = L.popup({maxWidth: '300'});

            
                var html_680310162a8c4de38cc2deb234b36ef2 = $('<div id="html_680310162a8c4de38cc2deb234b36ef2" style="width: 100.0%; height: 100.0%;">SUSPICIOUS OCC</div>')[0];
                popup_a2f2d18dccba4216948136991521fbd6.setContent(html_680310162a8c4de38cc2deb234b36ef2);
            

            marker_9290a54cffa2442e822512e2eeffe58f.bindPopup(popup_a2f2d18dccba4216948136991521fbd6);

            
        
    

            var marker_25a6987ebe03481f9bdc491525c5433c = L.marker(
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                    icon: new L.Icon.Default()
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                )
                .addTo(marker_cluster_7156e2426d094a8b8ff609a1bb205479);
            
    
            var popup_51636234202c429ea8df3bb89d216225 = L.popup({maxWidth: '300'});

            
                var html_bb6cf24e4151434ea6590ee3cd3cbf3f = $('<div id="html_bb6cf24e4151434ea6590ee3cd3cbf3f" style="width: 100.0%; height: 100.0%;">OTHER OFFENSES</div>')[0];
                popup_51636234202c429ea8df3bb89d216225.setContent(html_bb6cf24e4151434ea6590ee3cd3cbf3f);
            

            marker_25a6987ebe03481f9bdc491525c5433c.bindPopup(popup_51636234202c429ea8df3bb89d216225);

            
        
    

            var marker_215e6823921b4ac3b0e133e913def871 = L.marker(
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                    icon: new L.Icon.Default()
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                )
                .addTo(marker_cluster_7156e2426d094a8b8ff609a1bb205479);
            
    
            var popup_fadf48ec9af14bd7881218f81c4e7c9a = L.popup({maxWidth: '300'});

            
                var html_2db8bf88e20e476abdf2666c71c3fe2a = $('<div id="html_2db8bf88e20e476abdf2666c71c3fe2a" style="width: 100.0%; height: 100.0%;">NON-CRIMINAL</div>')[0];
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            marker_215e6823921b4ac3b0e133e913def871.bindPopup(popup_fadf48ec9af14bd7881218f81c4e7c9a);

            
        
    

            var marker_e8bc41ec70e649edb403171023ae9c80 = L.marker(
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                    icon: new L.Icon.Default()
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                )
                .addTo(marker_cluster_7156e2426d094a8b8ff609a1bb205479);
            
    
            var popup_cefc40750da343109ae13a411f75c18f = L.popup({maxWidth: '300'});

            
                var html_426e8e380ccb4dfca23ba507d0ab781c = $('<div id="html_426e8e380ccb4dfca23ba507d0ab781c" style="width: 100.0%; height: 100.0%;">OTHER OFFENSES</div>')[0];
                popup_cefc40750da343109ae13a411f75c18f.setContent(html_426e8e380ccb4dfca23ba507d0ab781c);
            

            marker_e8bc41ec70e649edb403171023ae9c80.bindPopup(popup_cefc40750da343109ae13a411f75c18f);

            
        
    

            var marker_8dd7aba29ce34067ac062831d26a08c7 = L.marker(
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                    icon: new L.Icon.Default()
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                )
                .addTo(marker_cluster_7156e2426d094a8b8ff609a1bb205479);
            
    
            var popup_b5c27652abc148938f451b4b74a685e2 = L.popup({maxWidth: '300'});

            
                var html_75164c16a2454b5b8bde8d47269b44c6 = $('<div id="html_75164c16a2454b5b8bde8d47269b44c6" style="width: 100.0%; height: 100.0%;">ROBBERY</div>')[0];
                popup_b5c27652abc148938f451b4b74a685e2.setContent(html_75164c16a2454b5b8bde8d47269b44c6);
            

            marker_8dd7aba29ce34067ac062831d26a08c7.bindPopup(popup_b5c27652abc148938f451b4b74a685e2);

            
        
    

            var marker_4b6a8e14ceee4a3da0e3c400a5020fd0 = L.marker(
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                    icon: new L.Icon.Default()
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                )
                .addTo(marker_cluster_7156e2426d094a8b8ff609a1bb205479);
            
    
            var popup_2334116e28bb489f84c702740df1a9ec = L.popup({maxWidth: '300'});

            
                var html_ddb6d2667c3a4d8b9f888bc3533b172f = $('<div id="html_ddb6d2667c3a4d8b9f888bc3533b172f" style="width: 100.0%; height: 100.0%;">OTHER OFFENSES</div>')[0];
                popup_2334116e28bb489f84c702740df1a9ec.setContent(html_ddb6d2667c3a4d8b9f888bc3533b172f);
            

            marker_4b6a8e14ceee4a3da0e3c400a5020fd0.bindPopup(popup_2334116e28bb489f84c702740df1a9ec);

            
        
    

            var marker_b007d01814004d61bdc477bc8224cb14 = L.marker(
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                    icon: new L.Icon.Default()
                    }
                )
                .addTo(marker_cluster_7156e2426d094a8b8ff609a1bb205479);
            
    
            var popup_f1591041b29345719bdc298858c01781 = L.popup({maxWidth: '300'});

            
                var html_cadcd4bbab1f430ab7da695167e20096 = $('<div id="html_cadcd4bbab1f430ab7da695167e20096" style="width: 100.0%; height: 100.0%;">NON-CRIMINAL</div>')[0];
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            marker_b007d01814004d61bdc477bc8224cb14.bindPopup(popup_f1591041b29345719bdc298858c01781);

            
        
    

            var marker_bff197a740da4aa795de5c2fe617cbd2 = L.marker(
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                {
                    icon: new L.Icon.Default()
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                )
                .addTo(marker_cluster_7156e2426d094a8b8ff609a1bb205479);
            
    
            var popup_37afa89175b6451ab7e663aad47ae7f8 = L.popup({maxWidth: '300'});

            
                var html_b5b9bf0a09284042980abdcff32e0d29 = $('<div id="html_b5b9bf0a09284042980abdcff32e0d29" style="width: 100.0%; height: 100.0%;">BURGLARY</div>')[0];
                popup_37afa89175b6451ab7e663aad47ae7f8.setContent(html_b5b9bf0a09284042980abdcff32e0d29);
            

            marker_bff197a740da4aa795de5c2fe617cbd2.bindPopup(popup_37afa89175b6451ab7e663aad47ae7f8);

            
        
    

            var marker_738b099a4484480099e72912b44a7a80 = L.marker(
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                {
                    icon: new L.Icon.Default()
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                )
                .addTo(marker_cluster_7156e2426d094a8b8ff609a1bb205479);
            
    
            var popup_5de7cf3adb1d47ce8d4e56bd097a2e92 = L.popup({maxWidth: '300'});

            
                var html_499c66fb6ee2493a9debc71318dda1e5 = $('<div id="html_499c66fb6ee2493a9debc71318dda1e5" style="width: 100.0%; height: 100.0%;">LARCENY/THEFT</div>')[0];
                popup_5de7cf3adb1d47ce8d4e56bd097a2e92.setContent(html_499c66fb6ee2493a9debc71318dda1e5);
            

            marker_738b099a4484480099e72912b44a7a80.bindPopup(popup_5de7cf3adb1d47ce8d4e56bd097a2e92);

            
        
    

            var marker_b75ef1ac56c04baa8b564ab186f0ef0e = L.marker(
                [37.735268146908396,-122.472715759631],
                {
                    icon: new L.Icon.Default()
                    }
                )
                .addTo(marker_cluster_7156e2426d094a8b8ff609a1bb205479);
            
    
            var popup_fe49938e26574069867b02b1e6278735 = L.popup({maxWidth: '300'});

            
                var html_5247c59eabef469f91047c40c1c9b4c6 = $('<div id="html_5247c59eabef469f91047c40c1c9b4c6" style="width: 100.0%; height: 100.0%;">LARCENY/THEFT</div>')[0];
                popup_fe49938e26574069867b02b1e6278735.setContent(html_5247c59eabef469f91047c40c1c9b4c6);
            

            marker_b75ef1ac56c04baa8b564ab186f0ef0e.bindPopup(popup_fe49938e26574069867b02b1e6278735);

            
        
</script>\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen></iframe></div></div>"
],
"text/plain": [
"<folium.folium.Map at 0x7fc201d28b00>"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from folium import plugins\n",
"\n",
"# let's start again with a clean copy of the map of San Francisco\n",
"sanfran_map = folium.Map(location = [latitude, longitude], zoom_start = 12)\n",
"\n",
"# instantiate a mark cluster object for the incidents in the dataframe\n",
"incidents = plugins.MarkerCluster().add_to(sanfran_map)\n",
"\n",
"# loop through the dataframe and add each data point to the mark cluster\n",
"for lat, lng, label, in zip(df_incidents.Y, df_incidents.X, df_incidents.Category):\n",
" folium.Marker(\n",
" location=[lat, lng],\n",
" icon=None,\n",
" popup=label,\n",
" ).add_to(incidents)\n",
"\n",
"# display map\n",
"sanfran_map"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Notice how when you zoom out all the way, all markers are grouped into one cluster, *the global cluster*, of 100 markers or crimes, which is the total number of crimes in our dataframe. Once you start zooming in, the *global cluster* will start breaking up into smaller clusters. Zooming in all the way will result in individual markers."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"# Choropleth Maps <a id=\"8\"></a>\n",
"\n",
"A `Choropleth` map is a thematic map in which areas are shaded or patterned in proportion to the measurement of the statistical variable being displayed on the map, such as population density or per-capita income. The choropleth map provides an easy way to visualize how a measurement varies across a geographic area or it shows the level of variability within a region. Below is a `Choropleth` map of the US depicting the population by square mile per state.\n",
"\n",
"<img src = \"https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DV0101EN/labs/Images/2000_census_population_density_map_by_state.png\" width = 600> "
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Now, let's create our own `Choropleth` map of the world depicting immigration from various countries to Canada.\n",
"\n",
"Let's first download and import our primary Canadian immigration dataset using *pandas* `read_excel()` method. Normally, before we can do that, we would need to download a module which *pandas* requires to read in excel files. This module is **xlrd**. For your convenience, we have pre-installed this module, so you would not have to worry about that. Otherwise, you would need to run the following line of code to install the **xlrd** module:\n",
"```\n",
"!conda install -c anaconda xlrd --yes\n",
"```"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Download the dataset and read it into a *pandas* dataframe:"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Data downloaded and read into a dataframe!\n"
]
}
],
"source": [
"df_can = pd.read_excel('https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DV0101EN/labs/Data_Files/Canada.xlsx',\n",
" sheet_name='Canada by Citizenship',\n",
" skiprows=range(20),\n",
" skipfooter=2)\n",
"\n",
"print('Data downloaded and read into a dataframe!')"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Let's take a look at the first five items in our dataset."
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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" }\n",
"\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Type</th>\n",
" <th>Coverage</th>\n",
" <th>OdName</th>\n",
" <th>AREA</th>\n",
" <th>AreaName</th>\n",
" <th>REG</th>\n",
" <th>RegName</th>\n",
" <th>DEV</th>\n",
" <th>DevName</th>\n",
" <th>1980</th>\n",
" <th>...</th>\n",
" <th>2004</th>\n",
" <th>2005</th>\n",
" <th>2006</th>\n",
" <th>2007</th>\n",
" <th>2008</th>\n",
" <th>2009</th>\n",
" <th>2010</th>\n",
" <th>2011</th>\n",
" <th>2012</th>\n",
" <th>2013</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Immigrants</td>\n",
" <td>Foreigners</td>\n",
" <td>Afghanistan</td>\n",
" <td>935</td>\n",
" <td>Asia</td>\n",
" <td>5501</td>\n",
" <td>Southern Asia</td>\n",
" <td>902</td>\n",
" <td>Developing regions</td>\n",
" <td>16</td>\n",
" <td>...</td>\n",
" <td>2978</td>\n",
" <td>3436</td>\n",
" <td>3009</td>\n",
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" <td>1746</td>\n",
" <td>1758</td>\n",
" <td>2203</td>\n",
" <td>2635</td>\n",
" <td>2004</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Immigrants</td>\n",
" <td>Foreigners</td>\n",
" <td>Albania</td>\n",
" <td>908</td>\n",
" <td>Europe</td>\n",
" <td>925</td>\n",
" <td>Southern Europe</td>\n",
" <td>901</td>\n",
" <td>Developed regions</td>\n",
" <td>1</td>\n",
" <td>...</td>\n",
" <td>1450</td>\n",
" <td>1223</td>\n",
" <td>856</td>\n",
" <td>702</td>\n",
" <td>560</td>\n",
" <td>716</td>\n",
" <td>561</td>\n",
" <td>539</td>\n",
" <td>620</td>\n",
" <td>603</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Immigrants</td>\n",
" <td>Foreigners</td>\n",
" <td>Algeria</td>\n",
" <td>903</td>\n",
" <td>Africa</td>\n",
" <td>912</td>\n",
" <td>Northern Africa</td>\n",
" <td>902</td>\n",
" <td>Developing regions</td>\n",
" <td>80</td>\n",
" <td>...</td>\n",
" <td>3616</td>\n",
" <td>3626</td>\n",
" <td>4807</td>\n",
" <td>3623</td>\n",
" <td>4005</td>\n",
" <td>5393</td>\n",
" <td>4752</td>\n",
" <td>4325</td>\n",
" <td>3774</td>\n",
" <td>4331</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Immigrants</td>\n",
" <td>Foreigners</td>\n",
" <td>American Samoa</td>\n",
" <td>909</td>\n",
" <td>Oceania</td>\n",
" <td>957</td>\n",
" <td>Polynesia</td>\n",
" <td>902</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Immigrants</td>\n",
" <td>Foreigners</td>\n",
" <td>Andorra</td>\n",
" <td>908</td>\n",
" <td>Europe</td>\n",
" <td>925</td>\n",
" <td>Southern Europe</td>\n",
" <td>901</td>\n",
" <td>Developed regions</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 43 columns</p>\n",
"</div>"
],
"text/plain": [
" Type Coverage OdName AREA AreaName REG \\\n",
"0 Immigrants Foreigners Afghanistan 935 Asia 5501 \n",
"1 Immigrants Foreigners Albania 908 Europe 925 \n",
"2 Immigrants Foreigners Algeria 903 Africa 912 \n",
"3 Immigrants Foreigners American Samoa 909 Oceania 957 \n",
"4 Immigrants Foreigners Andorra 908 Europe 925 \n",
"\n",
" RegName DEV DevName 1980 ... 2004 2005 2006 \\\n",
"0 Southern Asia 902 Developing regions 16 ... 2978 3436 3009 \n",
"1 Southern Europe 901 Developed regions 1 ... 1450 1223 856 \n",
"2 Northern Africa 902 Developing regions 80 ... 3616 3626 4807 \n",
"3 Polynesia 902 Developing regions 0 ... 0 0 1 \n",
"4 Southern Europe 901 Developed regions 0 ... 0 0 1 \n",
"\n",
" 2007 2008 2009 2010 2011 2012 2013 \n",
"0 2652 2111 1746 1758 2203 2635 2004 \n",
"1 702 560 716 561 539 620 603 \n",
"2 3623 4005 5393 4752 4325 3774 4331 \n",
"3 0 0 0 0 0 0 0 \n",
"4 1 0 0 0 0 1 1 \n",
"\n",
"[5 rows x 43 columns]"
]
},
"execution_count": 23,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_can.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Let's find out how many entries there are in our dataset."
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"(195, 43)\n"
]
}
],
"source": [
"# print the dimensions of the dataframe\n",
"print(df_can.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Clean up data. We will make some modifications to the original dataset to make it easier to create our visualizations. Refer to *Introduction to Matplotlib and Line Plots* and *Area Plots, Histograms, and Bar Plots* notebooks for a detailed description of this preprocessing."
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
},
"scrolled": true
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"data dimensions: (195, 39)\n"
]
}
],
"source": [
"# clean up the dataset to remove unnecessary columns (eg. REG) \n",
"df_can.drop(['AREA','REG','DEV','Type','Coverage'], axis=1, inplace=True)\n",
"\n",
"# let's rename the columns so that they make sense\n",
"df_can.rename(columns={'OdName':'Country', 'AreaName':'Continent','RegName':'Region'}, inplace=True)\n",
"\n",
"# for sake of consistency, let's also make all column labels of type string\n",
"df_can.columns = list(map(str, df_can.columns))\n",
"\n",
"# add total column\n",
"df_can['Total'] = df_can.sum(axis=1)\n",
"\n",
"# years that we will be using in this lesson - useful for plotting later on\n",
"years = list(map(str, range(1980, 2014)))\n",
"print ('data dimensions:', df_can.shape)"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"Let's take a look at the first five items of our cleaned dataframe."
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {
"button": false,
"collapsed": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"outputs": [
{
"data": {
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Country</th>\n",
" <th>Continent</th>\n",
" <th>Region</th>\n",
" <th>DevName</th>\n",
" <th>1980</th>\n",
" <th>1981</th>\n",
" <th>1982</th>\n",
" <th>1983</th>\n",
" <th>1984</th>\n",
" <th>1985</th>\n",
" <th>...</th>\n",
" <th>2005</th>\n",
" <th>2006</th>\n",
" <th>2007</th>\n",
" <th>2008</th>\n",
" <th>2009</th>\n",
" <th>2010</th>\n",
" <th>2011</th>\n",
" <th>2012</th>\n",
" <th>2013</th>\n",
" <th>Total</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Afghanistan</td>\n",
" <td>Asia</td>\n",
" <td>Southern Asia</td>\n",
" <td>Developing regions</td>\n",
" <td>16</td>\n",
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" <td>2004</td>\n",
" <td>58639</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Albania</td>\n",
" <td>Europe</td>\n",
" <td>Southern Europe</td>\n",
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" <td>1</td>\n",
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" <td>0</td>\n",
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" <td>1223</td>\n",
" <td>856</td>\n",
" <td>702</td>\n",
" <td>560</td>\n",
" <td>716</td>\n",
" <td>561</td>\n",
" <td>539</td>\n",
" <td>620</td>\n",
" <td>603</td>\n",
" <td>15699</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Algeria</td>\n",
" <td>Africa</td>\n",
" <td>Northern Africa</td>\n",
" <td>Developing regions</td>\n",
" <td>80</td>\n",
" <td>67</td>\n",
" <td>71</td>\n",
" <td>69</td>\n",
" <td>63</td>\n",
" <td>44</td>\n",
" <td>...</td>\n",
" <td>3626</td>\n",
" <td>4807</td>\n",
" <td>3623</td>\n",
" <td>4005</td>\n",
" <td>5393</td>\n",
" <td>4752</td>\n",
" <td>4325</td>\n",
" <td>3774</td>\n",
" <td>4331</td>\n",
" <td>69439</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>American Samoa</td>\n",
" <td>Oceania</td>\n",
" <td>Polynesia</td>\n",
" <td>Developing regions</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Andorra</td>\n",
" <td>Europe</td>\n",
" <td>Southern Europe</td>\n",
" <td>Developed regions</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>...</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>15</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 39 columns</p>\n",
"</div>"
],
"text/plain": [
" Country Continent Region DevName 1980 1981 \\\n",
"0 Afghanistan Asia Southern Asia Developing regions 16 39 \n",
"1 Albania Europe Southern Europe Developed regions 1 0 \n",
"2 Algeria Africa Northern Africa Developing regions 80 67 \n",
"3 American Samoa Oceania Polynesia Developing regions 0 1 \n",
"4 Andorra Europe Southern Europe Developed regions 0 0 \n",
"\n",
" 1982 1983 1984 1985 ... 2005 2006 2007 2008 2009 2010 2011 \\\n",
"0 39 47 71 340 ... 3436 3009 2652 2111 1746 1758 2203 \n",
"1 0 0 0 0 ... 1223 856 702 560 716 561 539 \n",
"2 71 69 63 44 ... 3626 4807 3623 4005 5393 4752 4325 \n",
"3 0 0 0 0 ... 0 1 0 0 0 0 0 \n",
"4 0 0 0 0 ... 0 1 1 0 0 0 0 \n",
"\n",
" 2012 2013 Total \n",
"0 2635 2004 58639 \n",
"1 620 603 15699 \n",
"2 3774 4331 69439 \n",
"3 0 0 6 \n",
"4 1 1 15 \n",
"\n",
"[5 rows x 39 columns]"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df_can.head()"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
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"source": [
"In order to create a `Choropleth` map, we need a GeoJSON file that defines the areas/boundaries of the state, county, or country that we are interested in. In our case, since we are endeavoring to create a world map, we want a GeoJSON that defines the boundaries of all world countries. For your convenience, we will be providing you with this file, so let's go ahead and download it. Let's name it **world_countries.json**."
]
},
{
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"execution_count": null,
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"outputs": [],
"source": [
"# download countries geojson file\n",
"!wget --quiet https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/DV0101EN/labs/Data_Files/world_countries.json -O world_countries.json\n",
" \n",
"print('GeoJSON file downloaded!')"
]
},
{
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"source": [
"Now that we have the GeoJSON file, let's create a world map, centered around **[0, 0]** *latitude* and *longitude* values, with an intial zoom level of 2, and using *Mapbox Bright* style."
]
},
{
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"source": [
"world_geo = r'world_countries.json' # geojson file\n",
"\n",
"# create a plain world map\n",
"world_map = folium.Map(location=[0, 0], zoom_start=2, tiles='Mapbox Bright')"
]
},
{
"cell_type": "markdown",
"metadata": {
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"source": [
"And now to create a `Choropleth` map, we will use the *choropleth* method with the following main parameters:\n",
"\n",
"1. geo_data, which is the GeoJSON file.\n",
"2. data, which is the dataframe containing the data.\n",
"3. columns, which represents the columns in the dataframe that will be used to create the `Choropleth` map.\n",
"4. key_on, which is the key or variable in the GeoJSON file that contains the name of the variable of interest. To determine that, you will need to open the GeoJSON file using any text editor and note the name of the key or variable that contains the name of the countries, since the countries are our variable of interest. In this case, **name** is the key in the GeoJSON file that contains the name of the countries. Note that this key is case_sensitive, so you need to pass exactly as it exists in the GeoJSON file."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"button": false,
"collapsed": true,
"deletable": true,
"new_sheet": false,
"run_control": {
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},
"outputs": [],
"source": [
"# generate choropleth map using the total immigration of each country to Canada from 1980 to 2013\n",
"world_map.choropleth(\n",
" geo_data=world_geo,\n",
" data=df_can,\n",
" columns=['Country', 'Total'],\n",
" key_on='feature.properties.name',\n",
" fill_color='YlOrRd', \n",
" fill_opacity=0.7, \n",
" line_opacity=0.2,\n",
" legend_name='Immigration to Canada'\n",
")\n",
"\n",
"# display map\n",
"world_map"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
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},
"source": [
"As per our `Choropleth` map legend, the darker the color of a country and the closer the color to red, the higher the number of immigrants from that country. Accordingly, the highest immigration over the course of 33 years (from 1980 to 2013) was from China, India, and the Philippines, followed by Poland, Pakistan, and interestingly, the US."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
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}
},
"source": [
"Notice how the legend is displaying a negative boundary or threshold. Let's fix that by defining our own thresholds and starting with 0 instead of -6,918!"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"button": false,
"collapsed": false,
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"outputs": [],
"source": [
"world_geo = r'world_countries.json'\n",
"\n",
"# create a numpy array of length 6 and has linear spacing from the minium total immigration to the maximum total immigration\n",
"threshold_scale = np.linspace(df_can['Total'].min(),\n",
" df_can['Total'].max(),\n",
" 6, dtype=int)\n",
"threshold_scale = threshold_scale.tolist() # change the numpy array to a list\n",
"threshold_scale[-1] = threshold_scale[-1] + 1 # make sure that the last value of the list is greater than the maximum immigration\n",
"\n",
"# let Folium determine the scale.\n",
"world_map = folium.Map(location=[0, 0], zoom_start=2, tiles='Mapbox Bright')\n",
"world_map.choropleth(\n",
" geo_data=world_geo,\n",
" data=df_can,\n",
" columns=['Country', 'Total'],\n",
" key_on='feature.properties.name',\n",
" threshold_scale=threshold_scale,\n",
" fill_color='YlOrRd', \n",
" fill_opacity=0.7, \n",
" line_opacity=0.2,\n",
" legend_name='Immigration to Canada',\n",
" reset=True\n",
")\n",
"world_map"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
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},
"source": [
"Much better now! Feel free to play around with the data and perhaps create `Choropleth` maps for individuals years, or perhaps decades, and see how they compare with the entire period from 1980 to 2013."
]
},
{
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},
"source": [
"### Thank you for completing this lab!\n",
"\n",
"This notebook was created by [Alex Aklson](https://www.linkedin.com/in/aklson/). I hope you found this lab interesting and educational. Feel free to contact me if you have any questions!"
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"This notebook is part of a course on **Coursera** called *Data Visualization with Python*. If you accessed this notebook outside the course, you can take this course online by clicking [here](http://cocl.us/DV0101EN_Coursera_Week3_LAB2)."
]
},
{
"cell_type": "markdown",
"metadata": {
"button": false,
"deletable": true,
"editable": true,
"new_sheet": false,
"run_control": {
"read_only": false
}
},
"source": [
"<hr>\n",
"\n",
"Copyright &copy; 2019 [Cognitive Class](https://cognitiveclass.ai/?utm_source=bducopyrightlink&utm_medium=dswb&utm_campaign=bdu). This notebook and its source code are released under the terms of the [MIT License](https://bigdatauniversity.com/mit-license/)."
]
}
],
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"display_name": "Python 3",
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