#!/usr/bin/env python
# coding: utf-8

# # Link to notebook - http://esriurl.com/hubBikeAnalysis
# 

# # Diagnosing bike street accidents of DC

# The purpose of this notebook is to delve deeper to gain a deeper understanding of the nature of bike crashes, using the `Crashes In DC` and `Crash Details Table` datasets of DC. Here, we'll see
# 
# > 1. A map highlighting the __magnitude of bike accidents__ in DC
# 
# > 2. Assessing if all these __accidents happen on dedicated bike lanes__
# 
# > 3. Learning some more about the __nature of these bike crashes__ 

# ### _But why should I care?_
# 
# > * According to the American Community Survey, about __13,000 D.C. residents bike to work each day__, with an increase of 1,200 new cyclists each year.
# 
# 
# > * In 2015, __bicyclists had the largest increase of fatalities (over 800, ie 12%)__ among all the roadway user groups, including drivers and pedestrian
# 
# 
# > * Only __53% of all fatal bike crashes get reported in newspapers__, and usually as only one story about the fatal crash itself. 

# In[1]:


import pandas as pd


# The crash data for DC is collected in two separate tables
# 
# > 1. __`Crashes in DC`__ - providing location and injury data for the crashes
# 
# > 2. __`Crash Details Table`__ providing supplementary data about the people and vehicles involved in the crash
# 
# 
# Datasets used here can be found on [opendata.dc.gov](http://opendata.dc.gov/)

# ### Reading in the DC crash dataset

# In[2]:


#Reading 'Crashes in DC'
crash = pd.read_csv('D:\Data\Crashes_In_DC\Crashes_in_DC.csv', low_memory=False)
crash.shape


# Let's now explore the various fields in the `crash` table

# In[3]:


crash.info()


# In[4]:


crash.head()


# ### 1. A map highlighting the magnitude of bike accidents in DC
# 
# The dataset records around __140k__ crashes, mostly from 2015 through September 2017. With an alarming number like that, let's see how frequent bike accidents are. 
# 
# Here, we'll plot a heatmap using the Python package Bokeh, to qualitatively gauge the frequency of bike crashes in DC.

# In[5]:


#Importing necessary packages
from bokeh.models import BoxZoomTool
from bokeh.plotting import figure, output_notebook, show
import datashader as ds
from datashader.bokeh_ext import InteractiveImage
from functools import partial
from datashader.utils import export_image
from datashader import transfer_functions as tf
from datashader.colors import colormap_select, Greys9, Hot, inferno
Greys9_r = list(reversed(Greys9))[:-2]


# We start by creating an empty plot container

# In[6]:


output_notebook()

DC = x_range, y_range = ((-77.113633,-76.910012), (38.812061,38.993699))

plot_width  = int(750)
plot_height = int(plot_width//1.2)

def base_plot(tools='pan,wheel_zoom,reset',plot_width=plot_width, plot_height=plot_height, **plot_args):
    p = figure(tools=tools, plot_width=plot_width, plot_height=plot_height,
        x_range=x_range, y_range=y_range, outline_line_color=None,
        min_border=0, min_border_left=0, min_border_right=0,
        min_border_top=0, min_border_bottom=0, **plot_args)
    
    p.axis.visible = False
    p.xgrid.grid_line_color = None
    p.ygrid.grid_line_color = None
    
    p.add_tools(BoxZoomTool(match_aspect=True))
    
    return p
    
options = dict(line_color=None, fill_color='blue', size=5)


# Let's now create a column in the table, `BIKE_TOTAL`, that records all the bike injuries/fatalities.

# In[7]:


crash['BIKE_TOTAL'] = crash['MAJORINJURIES_BICYCLIST'] + crash['MINORINJURIES_BICYCLIST'] + crash['UNKNOWNINJURIES_BICYCLIST'] + crash['FATAL_BICYCLIST'] + crash['BICYCLISTSIMPAIRED']


# In[8]:


background = "black"
export = partial(export_image, export_path="export", background=background)
cm = partial(colormap_select, reverse=(background=="black"))

def create_image(x_range, y_range, w=plot_width, h=plot_height):
    cvs = ds.Canvas(plot_width=w, plot_height=h, x_range=x_range, y_range=y_range)
    agg = cvs.points(crash, 'X', 'Y',  ds.count('BIKE_TOTAL'))
    img = tf.shade(agg, cmap=Hot, how='eq_hist')
    return tf.dynspread(img, threshold=0.5, max_px=4)

p = base_plot(background_fill_color=background)
export(create_image(*DC),"DC_bikeCrashes")
InteractiveImage(p, create_image)


# ![dc_bikecrashes](https://user-images.githubusercontent.com/13968196/36805091-75524684-1c8a-11e8-8ce5-6c60ae1b053f.png)

# This map indicates a high volume of bike crashes occuring in DC, much more than imagined!

# ### 2. Assessing if all these accidents happen on dedicated bike lanes

# In[25]:


from arcgis.gis import *
gis = GIS("http://dcdev.maps.arcgis.com/", "username", "password")


# The image below shows a representation of 1000 crashes (of all kinds), overlaying the bike lanes. We also notice a large number of crashes in SE DC that do not overlay a bike lane.
# 
# Are these car crashes, or bike crashes occuring on non bike-lanes? Let's verify...

# ![bike_and_crash](https://user-images.githubusercontent.com/13968196/36805089-752ab948-1c8a-11e8-8ebd-d6c6b4e6f462.PNG)

# Let's filter the crashes dataset to contain only bike accidents

# In[12]:


bike_accidents = crash.loc[crash['TOTAL_BICYCLES'] != 0]
bike_accidents.shape


# #### 2.1 Publishing a feature layer from a Pandas Dataframe

# We will now publish a layer of only accidents involving bikes to Online.
# 
# We start by creating a column containing the `SHAPE` of the geometry

# In[28]:


bike_accidents['SHAPE'] = bike_accidents.apply(lambda row : arcgis.geometry.Geometry({'x': row['X'], 'y': row['Y']}), axis=1 )


# In[19]:


#Importing package
from arcgis import SpatialDataFrame


# In[29]:


bike_sdf = SpatialDataFrame(bike_accidents)
bike_sdf.set_geometry('SHAPE', inplace=True, sr=4326)
bike_sdf['SHAPE'] = bike_sdf.geometry.project_as(4326)
bike_sdf.shape


# In[30]:


#Publish the data
bikeCrash_layer = gis.content.import_data(bike_sdf, title='bikeCrashes_1', target_sr=4326, capabilities="Query,Editing")


# In[31]:


bikeCrash_layer


# #### 2.2 Publishing a feature layer from a Shapefile

# In[43]:


data = "Bicycle_Lanes.zip"
shpfile = gis.content.add({}, data)


# In[44]:


published_service = shpfile.publish()
display(published_service)


# In[45]:


#Viewing the map
search_result = gis.content.search("BikeCrashes_WithLanes")
search_result[0]


# ### 3. Learning some more about the nature of these bike crashes

# Here we'll look at the `Crash_Details_Table` that contains other details about crashes

# In[9]:


crash_details = pd.read_csv('D:\Data\Crashes_In_DC\Crash_Details_Table.csv', low_memory=False)
crash_details.shape


# In[10]:


crash_details.head()


# Let's filter it to contain only bike crashes

# In[11]:


bike_crash_details = crash_details.loc[(crash_details['PERSONTYPE']=='Bicyclist') | ((crash_details['PERSONTYPE']=='Pedestrian') & (len(crash_details['LICENSEPLATESTATE'] != 2)))]
bike_crash_details.shape


# We now create a chart to see the distribution of bicyclists that have crashed, based on age of the bicyclists

# In[15]:


#Importing visualization packages of Python
import matplotlib.pyplot as plt
import seaborn as sns

sns.set(color_codes=True)
get_ipython().run_line_magic('matplotlib', 'inline')


# In[26]:


def calculate_age_bin(x):
    '''Function that creates an appropriate bin value for each age'''
    try:
        return int(x/10)
    except:
        return int(0)


# In[28]:


bike_crash_details['age_counts'] = bike_crash_details['AGE'].apply(calculate_age_bin)
bike_crash_details['age_counts'].head()


# In[67]:


plt.figure(figsize=(10,10))
n, bins, patches = plt.hist(bike_crash_details['age_counts'], bins=[0,1,2,3,4,5,6,7,8], facecolor='blue', alpha=0.5)

plt.title("Distribution of age of bicyclists that crash more often", fontsize=16)
plt.xlabel("Age (multiply by factor of 10)", fontsize=16)
plt.ylabel("Frequency", fontsize=16)


# Based on this chart we observe that most bicyclists that have met with accidents are within the age range of 20-40 years.
# 
# The frequency generated for riders under the age of 10, could also indicate some missing values, defaulting to 0

# ### What next?
# 
# > * Combine this with the Hub Vision Zero intitiative and High Injury Network analysis.
# 
# 
# > * Perform deeper analysis with other datasets and propose new bikelane routes to DC Department of Transportation
# 
#  
# > * Report a crash to [waba.org](http://www.waba.org)
# 
# 
# > * As a bicyclist, ensuring that the bike has bells, lights and that you wear a helmet.
# 

# In[ ]: