Python实现geohash编码与解码（TransBigData）

geohash编码

geohash是一种公共域地理编码系统，它的作用是将经纬度地理位置编码为字母和数字组成的字符串，字符串也可解码为经纬度。每个字符串代表一个网格编号，字符串的长度越长则精度越高。根据wiki，geohash字符串长度对应精度表格如下：

geohash length(precision)	lat bits	lng bits	lat error	lng error	km error
1	2	3	±23	±23	±2500
2	5	5	±2.8	±5.6	±630
3	7	8	±0.70	±0.70	±78
4	10	10	±0.087	±0.18	±20
5	12	13	±0.022	±0.022	±2.4
6	15	15	±0.0027	±0.0055	±0.61
7	17	18	±0.00068	±0.00068	±0.076
8	20	20	±0.000085	±0.00017	±0.019

Python的TransBigData包中提供了geohash的处理功能，主要包括三个函数：

• transbigdata.geohash_encode(lon, lat, precision=12)

输入经纬度与精度，输出geohash编码

• transbigdata.geohash_decode(geohash)

输入geohash编码，输出经纬度

• transbigdata.geohash_togrid(geohash)

输入geohash编码，输出geohash网格的地理信息图形Series列

相比TransBigData包中提供的方形栅格处理方法，geohash更慢，也无法提供自由定义的栅格大小。下面的示例展示如何利用这三个函数对数据进行geohash编码集计，并可视化

import transbigdata as tbd
import pandas as pd
import geopandas as gpd
#读取数据
data = pd.read_csv('TaxiData-Sample.csv',header = None)
data.columns = ['VehicleNum','time','slon','slat','OpenStatus','Speed']

#依据经纬度geohash编码，精确度选6时，栅格大小约为±0.61km
data['geohash'] = tbd.geohash_encode(data['slon'],data['slat'],precision=6)
data['geohash']

0         ws0btw
1         ws0btz
2         ws0btz
3         ws0btz
4         ws0by4
           ...
544994    ws131q
544995    ws1313
544996    ws131f
544997    ws1361
544998    ws10tq
Name: geohash, Length: 544999, dtype: object

#基于geohash编码集计
dataagg = data.groupby(['geohash'])['VehicleNum'].count().reset_index()
#geohash编码解码为经纬度
dataagg['lon_geohash'],dataagg['lat_geohash'] = tbd.geohash_decode(dataagg['geohash'])
#geohash编码生成栅格矢量图形
dataagg['geometry'] = tbd.geohash_togrid(dataagg['geohash'])
#转换为GeoDataFrame
dataagg = gpd.GeoDataFrame(dataagg)
dataagg

	geohash	VehicleNum	lon_geohash	lat_geohash	geometry
0	w3uf3x	1	108.	10.28	POLYGON ((107.99561 10.27771, 107.99561 10.283...
1	webzz6	12	113.9	22.47	POLYGON ((113.87329 22.46704, 113.87329 22.472...
2	webzz7	21	113.9	22.48	POLYGON ((113.87329 22.47253, 113.87329 22.478...
3	webzzd	1	113.9	22.47	POLYGON ((113.88428 22.46704, 113.88428 22.472...
4	webzzf	2	113.9	22.47	POLYGON ((113.89526 22.46704, 113.89526 22.472...
...	...	...	...	...	...
2022	ws1d9u	1	114.7	22.96	POLYGON ((114.68628 22.96143, 114.68628 22.966...
2023	ws1ddh	6	114.7	22.96	POLYGON ((114.69727 22.96143, 114.69727 22.966...
2024	ws1ddj	2	114.7	22.97	POLYGON ((114.69727 22.96692, 114.69727 22.972...
2025	ws1ddm	4	114.7	22.97	POLYGON ((114.70825 22.96692, 114.70825 22.972...
2026	ws1ddq	7	114.7	22.98	POLYGON ((114.70825 22.97241, 114.70825 22.977...

2027 rows × 5 columns

#设定绘图边界
bounds = [113.6,22.4,114.8,22.9]
#创建图框
import matplotlib.pyplot as plt
import plot_map
fig =plt.figure(1,(8,8),dpi=280)
ax =plt.subplot(111)
plt.sca(ax)
#添加地图底图
tbd.plot_map(plt,bounds,zoom = 12,style = 4)
#绘制colorbar
cax = plt.axes([0.05, 0.33, 0.02, 0.3])
plt.title('count')
plt.sca(ax)
#绘制geohash的栅格集计
dataagg.plot(ax = ax,column = 'VehicleNum',cax = cax,legend = True)
#添加比例尺和指北针
tbd.plotscale(ax,bounds = bounds,textsize = 10,compasssize = 1,accuracy = 2000,rect = [0.06,0.03],zorder = 10)
plt.axis('off')
plt.xlim(bounds[0],bounds[2])
plt.ylim(bounds[1],bounds[3])
plt.show()