dataux_1_poi.py

# -*- coding: utf-8 -*-
"""01_DATAUX_Lecture_1_POI.ipynb

Automatically generated by Colaboratory.

Original file is located at
    https://colab.research.google.com/drive/1VMLWDA5B57uv5B7BwrdNUKRKjy4psT2v
"""

!pip install osmnx &> /dev/null
!pip install matplotlib==3.1.3 &> /dev/null
!pip uninstall numpy

# Commented out IPython magic to ensure Python compatibility.
import os, sys
import osmnx as ox
import networkx as nx
import numpy as np
import pandas as pd
import geopandas as gpd

from PIL import Image
from io import BytesIO

import requests
from tqdm import tqdm
import time
import matplotlib.pyplot as plt

import cv2 

import matplotlib.pyplot as plt
import numpy as np
# %matplotlib inline

import logging
logger = logging.getLogger()
# logger.setLevel(logging.DEBUG)

"""### 1.Road Networks Data"""

# Commented out IPython magic to ensure Python compatibility.
# %matplotlib inline
ox.config(log_console=True)
ox.__version__

# download/model a street network for some city then visualize it
G = ox.graph_from_place("南沙区, 广州, 中国", network_type='drive')

# Plot the streets
fig, ax = ox.plot_graph(G);

"""### 2.Building footprint

#### <font color=#A52A2A >osmnx.geometries module
Download geospatial entities’ geometries and attributes from OpenStreetMap.<br>
Retrieve <font color=#A52A2A ><b>points of interest, building footprints, or any other objects</b></font> from OSM, including their geometries and attribute data, and construct a GeoDataFrame of them.

https://osmnx.readthedocs.io/en/stable/osmnx.html#osmnx.geometries.geometries_from_place

#### <font color=#A52A2A >5 methods：
* osmnx.geometries.geometries_from_address(address, tags, dist=1000)
* osmnx.geometries.geometries_from_bbox(north, south, east, west, tags)
* osmnx.geometries.geometries_from_place(query, tags, which_result=None, buffer_dist=None)
* osmnx.geometries.geometries_from_point(center_point, tags, dist=1000)
* osmnx.geometries.geometries_from_polygon(polygon, tags)

#### <font color=#A52A2A >tags
* <b>tags = {"building": True}</b> would return all building footprints in the area<br>
* <b>tags = {"amenity":True, "landuse":["retail","commercial"], "highway":"bus_stop"}</b> would return all amenities, landuse=retail, landuse=commercial, and highway=bus_stop.
"""

buildings_inside_area = ox.geometries_from_place("南沙区, 广州, 中国", tags={"building": True})
buildings_inside_area.to_csv('buildings.csv')

# print(buildings_inside_area["amenity"].unique())
# print(buildings_inside_area["building"].unique())
# print(buildings_inside_area["leisure"].unique())

buildings_inside_area.plot(figsize=(20, 20))

"""### 3. Points of interest (POI)

#### Points-of-interest

#### OpenStreetMap POI features

https://wiki.openstreetmap.org/wiki/Map_features

<font color=#A52A2A ><b># Amenity </b></font> <br>
Used to map facilities used by visitors and residents. For example: toilets, telephones, banks, pharmacies, cafes, parking and schools. See the page Amenities for an introduction on its usage.

* Sustenance
* Education
* Transportation
* Financial
* Healthcare
* Entertainment, Arts & Culture
* Public Service
* Facilities
* Waste Management
* Others
"""

# get the restaurants POI in the Bronx
restaurants_POIs_around = ox.geometries_from_place("南沙区, 广州, 中国", tags={"amenity":"restaurant"})
print(len(restaurants_POIs_around))
restaurants_POIs_around.to_csv('restaurant.csv') 
restaurants_POIs_around.plot(figsize=(20, 20),color="red", markersize=20)

# get the amenities POI in the Bronx
POIs_around = ox.geometries_from_place("南沙区, 广州, 中国", tags={"amenity": True})
print(len(POIs_around))
POIs_around.to_csv('pois.csv') 
POIs_around.plot(figsize=(20, 20))

POIs_around

"""# draw the boundary of Bronx
area_name = "南沙区, 广州, 中国"
area, edges = ox.geocode_to_gdf(area_name) 
area.plot()
"""

# Fetch OSM street network from the given location
roads = ox.graph_from_place(area_name, network_type="drive")
nodes, edges = ox.graph_to_gdfs(roads)
edges.plot()

nodes.head()

edges.head()

# 叠合所有的分层图像

fig, ax = plt.subplots(figsize=(20, 20))

#  Plot the footprint
area.plot(ax=ax, facecolor="black", zorder=0)

# Plot street edges
edges.plot(ax=ax,color="white",linewidth=3, zorder=5)

# Plot buildings
buildings_inside_area.plot(ax=ax, facecolor="khaki")

# Plot restaurants
restaurants_POIs_around.plot(ax=ax, color="red", markersize=10)

# landscape
leisure = ox.geometries_from_place(area_name, tags={"leisure":True})
leisure.head()

# Check all values for the column "leisure"
leisure["leisure"].value_counts()

parks = leisure[leisure["leisure"].isin(["park", "sports_centre", "sport", "stadium"])]
parks.plot()

# overlap all the layers

fig, ax = plt.subplots(figsize=(20, 20), dpi=300)
#  turn off the axis
ax.axis("off")

#  Plot the footprint
area.plot(ax=ax, color="black",linewidth=3,zorder=0)

# Plot street edges
edges.plot(ax=ax, color="white",alpha=0.6,linewidth=1, zorder=5)

# Plot buildings
buildings_inside_area.plot(ax=ax, facecolor="white", zorder = 3)

# Plot POIs
POIs_around.plot(ax=ax, color="dimgrey", markersize=10, zorder = 4)

# Plot parks
parks.plot(ax=ax,color="GhostWhite", alpha=0.5, zorder = 1)

plt.savefig("output_files.png")

# 讲上述内容简写为一个方便调用的函数

def make_figure_ground(place_name):
    filepath = "output_files/{}_figure_grounds.png".format(place_name)
    # 边界轮廓
    bounding_geometry = ox.geocode_to_gdf(place_name)
    # 绿地轮廓
    leisure = ox.geometries_from_place(place_name, tags={"leisure":["park", "sports_centre", "sport", "stadium"]})
    # 道路轮廓
    school_graph = ox.graph_from_place(place_name, network_type="drive")
    nodes, edges = ox.graph_to_gdfs(school_graph)
    # 建筑轮廓
    buildings_inside_area = ox.geometries_from_place(place_name, tags={"building": True})

    # 叠合所有的分层图像
    fig, ax = plt.subplots(figsize=(8, 8), dpi=100)
    #  关掉坐标轴与刻度
    ax.axis("off")
    
    #  Plot the footprint
    bounding_geometry.plot(ax=ax, facecolor="black", zorder=0)
    # Plot street edges
    edges.plot(ax=ax, linewidth=3, zorder=5)  
    # Plot buildings
    buildings_inside_area.plot(ax=ax, facecolor="khaki", zorder = 3)
    # Plot parks
    leisure.plot(ax=ax,color="green", alpha=0.5, zorder = 1)
    
    plt.savefig(filepath)

make_figure_ground("海珠区, 广州, 中国")
#make_figure_ground("Bronx County, New York, US")