城市管理大数据算法模型（DCIC）--早高峰共享单车潮汐点的群智优化Baseline

#任务一：为更好地掌握早高峰潮汐现象的变化规律与趋势，参赛者需基于主办方提供的数据进行数据分析和计算模型构建等工作，识别出工作日早高峰07:00-09:00潮汐现象最突出的40个区域，列出各区域所包含的共享单车停车点位编号名称，并提供计算方法说明及计算模型，为下一步优化措施提供辅助支撑。

#共享单车潮汐点分析：[https://coggle.club/learn/dcic2021/task2](https://coggle.club/learn/dcic2021/task2) 共享单车潮汐点优化：[https://coggle.club/learn/dcic2021/task3](https://coggle.club/learn/dcic2021/task3)

import os, codecs
import pandas as pd
import numpy as np

%pylab inline
from IPython.display import set_matplotlib_formats
set_matplotlib_formats('svg')

from matplotlib import font_manager as fm, rcParams
import matplotlib.pyplot as plt
PATH = './input/'

def bike_fence_format(s):
    s = s.replace('[', '').replace(']', '').split(',')
    s = np.array(s).astype(float).reshape(5, -1)
    return s


# 共享单车停车点位（电子围栏）数据
bike_fence = pd.read_csv(PATH + 'gxdc_tcd.csv')
bike_fence['FENCE_LOC'] = bike_fence['FENCE_LOC'].apply(bike_fence_format)

# 共享单车订单数据
bike_order = pd.read_csv(PATH + 'gxdc_dd.csv')
bike_order = bike_order.sort_values(['BICYCLE_ID', 'UPDATE_TIME'])


import geohash
bike_order['geohash'] = bike_order.apply(lambda x: 
                        geohash.encode(x['LATITUDE'], x['LONGITUDE'], precision=9), axis=1)

from geopy.distance import geodesic

bike_fence['MIN_LATITUDE'] = bike_fence['FENCE_LOC'].apply(lambda x: np.min(x[:, 1]))
bike_fence['MAX_LATITUDE'] = bike_fence['FENCE_LOC'].apply(lambda x: np.max(x[:, 1]))

bike_fence['MIN_LONGITUDE'] = bike_fence['FENCE_LOC'].apply(lambda x: np.min(x[:, 0]))
bike_fence['MAX_LONGITUDE'] = bike_fence['FENCE_LOC'].apply(lambda x: np.max(x[:, 0]))

bike_fence['FENCE_AREA'] = bike_fence.apply(lambda x: geodesic(
    (x['MIN_LATITUDE'], x['MIN_LONGITUDE']), (x['MAX_LATITUDE'], x['MAX_LONGITUDE'])
).meters, axis=1)

bike_fence['FENCE_CENTER'] = bike_fence['FENCE_LOC'].apply(
    lambda x: np.mean(x[:-1, ::-1], 0)
)

bike_order['geohash'] = bike_order.apply(
    lambda x: geohash.encode(x['LATITUDE'], x['LONGITUDE'], precision=6), 
axis=1)

bike_fence['geohash'] = bike_fence['FENCE_CENTER'].apply(
    lambda x: geohash.encode(x[0], x[1], precision=6))
geohash.encode(24.521156, 118.140385, precision=6), \
geohash.encode(24.521156, 118.140325, precision=6)
bike_order['UPDATE_TIME'] = pd.to_datetime(bike_order['UPDATE_TIME'])
bike_order['DAY'] = bike_order['UPDATE_TIME'].dt.day.astype(object)
bike_order['DAY'] = bike_order['DAY'].apply(str)

bike_order['HOUR'] = bike_order['UPDATE_TIME'].dt.hour.astype(object)
bike_order['HOUR'] = bike_order['HOUR'].apply(str)
bike_order['HOUR'] = bike_order['HOUR'].str.pad(width=2,side='left',fillchar='0')

bike_order['DAY_HOUR'] = bike_order['DAY'] + bike_order['HOUR']
bike_inflow = pd.pivot_table(bike_order[bike_order['LOCK_STATUS'] == 1], 
                   values='LOCK_STATUS', index=['geohash'],
                    columns=['DAY_HOUR'], aggfunc='count', fill_value=0
)

bike_outflow = pd.pivot_table(bike_order[bike_order['LOCK_STATUS'] == 0], 
                   values='LOCK_STATUS', index=['geohash'],
                    columns=['DAY_HOUR'], aggfunc='count', fill_value=0
)
bike_inflow.loc['wsk52r'].plot()
bike_outflow.loc['wsk52r'].plot()
plt.xticks(list(range(bike_inflow.shape[1])), bike_inflow.columns, rotation=40)
plt.legend(['Inflow', 'OutFlow'])
bike_inflow.loc['wsk596'].plot()
bike_outflow.loc['wsk596'].plot()
plt.xticks(list(range(bike_inflow.shape[1])), bike_inflow.columns, rotation=40)
plt.legend(['Inflow', 'OutFlow'])
bike_inflow = pd.pivot_table(bike_order[bike_order['LOCK_STATUS'] == 1], 
                   values='LOCK_STATUS', index=['geohash'],
                    columns=['DAY'], aggfunc='count', fill_value=0
)

bike_outflow = pd.pivot_table(bike_order[bike_order['LOCK_STATUS'] == 0], 
                   values='LOCK_STATUS', index=['geohash'],
                    columns=['DAY'], aggfunc='count', fill_value=0
)
bike_remain = (bike_inflow - bike_outflow).fillna(0)
bike_remain[bike_remain < 0] = 0  
bike_remain = bike_remain.sum(1)
bike_fence['DENSITY'] = bike_fence['geohash'].map(bike_remain).fillna(0)
import hnswlib
import numpy as np

p = hnswlib.Index(space='l2', dim=2)
p.init_index(max_elements=300000, ef_construction=1000, M=32)
p.set_ef(1024)
p.set_num_threads(14)

p.add_items(np.stack(bike_fence['FENCE_CENTER'].values))

index, dist = p.knn_query(bike_order[['LATITUDE','LONGITUDE']].values[:], k=1)
bike_order['fence'] = bike_fence.iloc[index.flatten()]['FENCE_ID'].values
bike_inflow = pd.pivot_table(bike_order[bike_order['LOCK_STATUS'] == 1], 
                   values='LOCK_STATUS', index=['fence'],
                    columns=['DAY'], aggfunc='count', fill_value=0
)

bike_outflow = pd.pivot_table(bike_order[bike_order['LOCK_STATUS'] == 0], 
                   values='LOCK_STATUS', index=['fence'],
                    columns=['DAY'], aggfunc='count', fill_value=0
)

bike_remain = (bike_inflow - bike_outflow).fillna(0)
bike_remain[bike_remain < 0] = 0  
bike_remain = bike_remain.sum(1)
# bike_fence = bike_fence.set_index('FENCE_ID')
bike_density = bike_remain / bike_fence.set_index('FENCE_ID')['FENCE_AREA']

bike_density = bike_density.sort_values(ascending=False).reset_index()
bike_density = bike_density.fillna(0)
bike_density['label'] = '0'
bike_density.iloc[:100, -1] = '1'

bike_density['BELONG_AREA'] ='厦门'
bike_density = bike_density.drop(0, axis=1)
bike_density.columns = ['FENCE_ID', 'FENCE_TYPE', 'BELONG_AREA']
bike_density.to_csv('result.txt', index=None, sep='|')