Weighting meinGrün Zielgeometrien from geolocated Social Media posts for measuring greenspace popularity¶
This Notebook contains the complete process to apply weights to meinGrün Zielgeometrien extracted from LBSM data (geolocated Social Media Posts).
Intersection of points and coordinates is not complex. However, there're several potential pitfalls in the process.
Pitfalls:
- for improving representativity, we're using different datasources (Twitter, Instagram Flickr)
- all of these have different composition, biases and sources of noise
- for coordinate mapping, a primary source of noise are the different geoaccuracies (gps/'latlng')
- flickr has the highest geoaccuracy, while Twitter and Instagram are mostly geoaccuracy 'place'
- this must be taken into account when assigning coordinates to different shapes (we'll be using a SEARCH_RADIUS 25 and 50m)
- the overall quantity of photos is spread quite differently
- Instagram makes up 80% of data, Flickr about 20% and Twitter 1%
- normalization of three datasources is necessary, so that each source has a balanced impact on final result
- Twitter may introduce biases as to the low amount of data, which can be taken into account by reducing Twitter overall impact
- normalization on area is necessary, otherwise large shapes would dominate because of their higher likeliness of intersecting with many coordinates
- area must be calculated for different search distances used per origin:
- 50m buffer area for Twitter/Instagram coordinates
- 25m buffer area for Flickr coordinates
- area must be calculated for different search distances used per origin:
- long tail: typically, Social Media posts peak at few places; but we're also interested in the 'long tail of data' (the other 80% of less frequented places)
- we can apply log or sqrt to flatten this peak and improve visibility of less frequented places
- finally we need to classify and label data into arbritary classes
- after normalization, absolute numbers are meaningless, therefore we can 'stretch' results per shape to 1 to 1000 range for improving comparability across runs
- based on 1 to 1000 range, we can apply natural_breaks or other classifiers to label bins of common classes (e.g. 6 bins are used herein)
Lets first start with importing the necessary dependencies.
First, make sure you have these packages installed (use miniconda with packages from conda-forge and pyviz channel!) and execute the following code block
In [1]:
import fiona
import geoviews as gv
import geoviews.feature as gf
from pathlib import Path
from shapely.geometry import shape
import geopandas as gp
import pandas as pd
from shapely.geometry import Point
import holoviews as hv
import numpy as np
from geopandas.tools import sjoin
# enable shapely.speedups which makes some of the spatial queries running faster.
import shapely.speedups
from cartopy import crs as ccrs
from collections import defaultdict
from collections import namedtuple
hv.notebook_extension('bokeh')
shapely.speedups.enable()
Because we're going to display pretty large maps, graphics and interactive visualizations in this notebook, the following line may improve jupyter speed.
In [81]:
%config Completer.use_jedi = False
Define Input Parameters¶
This section contains the only manual code edits that are needed to run this notebook.
- ZIELGEOMETRIEN_DATA: this is the geojson containing the Zielgeometrien (polygons), social media posts will be used to calculate weights for each polygon in this geojson
- LBSM_INPUT_DATA: this is the social media data used to calculate weights, e.g. a collection of post-records with lat-lng coordinates this file was generated using tagmaps package, which applies filters and cleans up noisy social media raw data
The files should be places in a subfolder called "./01_Input/" (relative to this notebook).
In [84]:
TARGETSHAPE_VERSION = 'v4.1'
# Level 0:
# LEVEL = "L0"
# TARGET_SHAPE_ID_COL = "TARGET_ID"
# GREEN_TARGET_COL = "TARGET_green_target"
# ZIELGEOMETRIEN_DATA = "GeoJSON_Dresden_4.1/targets.json"
# ZIELGEOMETRIEN_DATA = "GeoJSON_Heidelberg/targets.json"
# Level 1 (Wege):
# LEVEL = "L1"
# TARGET_SHAPE_ID_COL = "WAY_ID"
# GREEN_TARGET_COL = "PARENT_TARGET_green_target"
# ZIELGEOMETRIEN_DATA = "GeoJSON_Dresden_4.1/targetParts_Wege.json"
# ZIELGEOMETRIEN_DATA = "GeoJSON_Heidelberg/targets_Wege.json"
# Level 2 (Wege LC):
LEVEL = "L2"
TARGET_SHAPE_ID_COL = "LULC_ID"
GREEN_TARGET_COL = "PARENT_TARGET_green_target"
ZIELGEOMETRIEN_DATA = "GeoJSON_Dresden_4.1/targetParts_Wege_LULC.json"
# ZIELGEOMETRIEN_DATA = "GeoJSON_Heidelberg/targets_Wege.json"
# LBSM_INPUT_DATA = "Output_cleaned_Dresden_FlickrOnly.csv"
# LBSM_INPUT_DATA = "Output_cleaned_Heidelberg_FlickrOnly.csv"
# LBSM_INPUT_DATA = "2019-09-03_meinGruen_Heidelberg.csv"
LBSM_INPUT_DATA = "2019-09-02_meinGruen_Dresden.csv"
In [3]:
import datetime as dt
def get_str_formatted_today():
"""Returns date as string (YYYY-mm-dd)"""
today = dt.date.today()
today_str = today.strftime("%Y-%m-%d")
return today_str
TODAY = get_str_formatted_today()
Define manual boundaries for map zooms
Define initial extent where the map should be zoomed to. If empty, the map will show the full boundary of the provided osm-graph (which may result in slow performance of the final map). To select boundary lat/lng coordinates, use (online tool link).
In [4]:
# Dresden Altstadt
LIM_LNG_MAX_1 = 13.750784
LIM_LNG_MIN_1 = 13.730367
LIM_LAT_MAX_1 = 51.055349
LIM_LAT_MIN_1 = 51.048746
# Dresden Center
LIM_LNG_MAX_2 = 13.788357
LIM_LNG_MIN_2 = 13.706689
LIM_LAT_MAX_2 = 51.060386
LIM_LAT_MIN_2 = 51.033973
# Dresden Neustadt:
LIM_LNG_MAX_3 = 13.782692
LIM_LNG_MIN_3 = 13.701024
LIM_LAT_MAX_3 = 51.087890
LIM_LAT_MIN_3 = 51.061492
# Heidelberg Center
LIM_LNG_MAX_4 = 8.73
LIM_LNG_MIN_4 = 8.68
LIM_LAT_MAX_4 = 49.420
LIM_LAT_MIN_4 = 49.404
# Total Bounds
LIM_LNG_MAX_5 = 14.0876945
LIM_LNG_MIN_5 = 13.46475261
LIM_LAT_MAX_5 = 51.24305035
LIM_LAT_MIN_5 = 50.90449772
Define the search max radius (in meters) to be used when intersecting polys with points and provide a name (used for writing data):
In [5]:
SEARCH_RADIUS_MAX = 50
CITY_NAME = "Dresden"
# CITY_NAME = "Heidelberg"
Load Shape Data¶
Load GeoJSON
In [6]:
geom_input_filepath = Path.cwd() / "01_Input" / ZIELGEOMETRIEN_DATA
In [7]:
%%time
meingruen_targetgeom_shapes = gp.read_file(geom_input_filepath)
total_bounds = meingruen_targetgeom_shapes.total_bounds
print(f'Total bounds: {total_bounds}')
print(f'Number of shapes: {len(meingruen_targetgeom_shapes)}')
Assign bounds to globa variables so we can use those later.
In [8]:
LIM_LNG_MIN, LIM_LAT_MIN, LIM_LNG_MAX, LIM_LAT_MAX = total_bounds
- Project from WGS1984 (4326) to World Mercator (epsg:3395)
In [9]:
gdf_targetgeom_shapes = meingruen_targetgeom_shapes.to_crs({'init': 'epsg:3395'})
Preview Shape Table
In [10]:
gdf_targetgeom_shapes.head()
Out[10]:
Remove unnecessary columns
There're many columns in this dataset that are of no interest to the computation in this notebook. Have a look whats there:
In [11]:
list(gdf_targetgeom_shapes.columns.values)
Out[11]:
Drop columns that are not needed:
In [12]:
def try_drop_cols_df(gdf, drop_cols):
"""Drop columns from pandas dataframe with try..except"""
for drop_col in drop_cols:
# apply drop
try:
gdf.drop(
columns=[drop_col], inplace=True)
except KeyError:
pass
drop_list = ['class0_ratio', 'class1_ratio', 'class2_ratio', 'class3_ratio',
'class4_ratio', 'class4_ratio', 'class4_ratio', 'class4_ratio',
'classNoData_ratio', 'class0_area', 'class1_area', 'class2_area',
'class3_area', 'class4_area', 'classNoData_area', 'SHAPE_Length',
'SHAPE_Area', 'WAY_AREA', 'WAY_LENGTH', 'PARENT_TARGET_ID',
'PARENT_TARGET_gemeinde_DD','PARENT_WAY_ID', 'LULC_origin',
'LULC_origin', 'LULC_access', 'LULC_name', 'LULC_type',
'LULC_type_ger', 'LULC_rank', 'LULC_AREA', 'LULC_LENGTH']
# apply drop
try_drop_cols_df(gdf_targetgeom_shapes, drop_list)
Check the new list of remaining columns
In [18]:
list(gdf_targetgeom_shapes.columns.values)
Out[18]:
Optional: Preview Plot with Geopandas directly¶
In [13]:
gdf_targetgeom_shapes.geometry.plot(figsize=(20,10))
Out[13]:
Print bounds of target shapes (World Mercator Porjection)
In [14]:
gdf_targetgeom_shapes.geometry.total_bounds
Out[14]:
Projections UTM¶
There are several projections used in this notebook:
- Web Mercator WGS1984 (epsg:4326) for input data/output data in decimal degrees
- World Mercator (epsg:3395) for map display in Geoviews/Bokeh
- UTM projected coordinate system for local distance calculations
For accurate calculations in meters distance, we need to project data to UTM. Tagmaps package has a methods that will return the best UTM projection based on a lat-lng coordinate:
In [15]:
from tagmaps.classes.utils import Utils
In [16]:
from shapely.geometry import Point, Polygon
import pyproj
def get_poly_from_bounds(bounds):
'''Add small buffer around path bounds'''
xmin, ymin, xmax, ymax = bounds
bbox_polygon = Polygon([[xmin, ymax], [xmax, ymax], [xmax, ymin], [xmin, ymin]])
return bbox_polygon
bound_poly = get_poly_from_bounds(meingruen_targetgeom_shapes.geometry.total_bounds)
print(f'input_lon_center: {bound_poly.centroid.x}')
print(f'input_lat_center: {bound_poly.centroid.y}')
epsg_code = Utils._convert_wgs_to_utm(
bound_poly.centroid.x, bound_poly.centroid.y)
crs_proj = pyproj.Proj(init=f'epsg:{epsg_code}')
crs_wgs = pyproj.Proj(init=f'epsg:4326')
# report
print(f'Target EPSG Code: {epsg_code}')
print(f'Target projection: {crs_proj}')
Optional: Plot Preview in Geoviews with Bokeh Backend¶
Convert to Geoviews layer for plotting
In [17]:
import cartopy
from shapely.geometry import Polygon, MultiPolygon
from shapely.geometry.collection import GeometryCollection
# convert to WGS1984: epsg:4326
gdf_targetgeom_shapes.to_crs({'init': f'epsg:{epsg_code}'}, inplace=True)
# convert to Geoviews Shapes
gv_shapes = gv.Polygons([gv.Shape(feat) for feat in gdf_targetgeom_shapes.geometry if isinstance(feat, (Polygon, MultiPolygon))], crs=cartopy.crs.epsg(epsg_code))
# Alternative does not work:
# gv_shapes = gv.Path(
# osm_highway_shapes.head(50).geometry, kdims=['x', 'y'], vdims=['@osmid', 'highway']).opts(tools=['hover'])
Plot Rasterized static with datashade
- use datashade, faster display but lower quality
In [15]:
from holoviews.operation.datashader import datashade, shade, dynspread, rasterize
In [16]:
datashade(gv_shapes).opts(width=800, height=480)
Out[16]:
Plot interactive rasterized
In [ ]:
%%opts Image [colorbar=True clipping_colors={'NaN': (0, 0, 0, 0)}]
# %%opts Image [clipping_colors={'NaN': 'transparent'}]
# see https://anaconda.org/jbednar/datashade_vs_rasterize/notebook
original = rasterize(gv_shapes, precompute=True)
# clipped = original.redim.range(z=(0.1, 1)).opts(clipping_colors={'min': 'transparent', 'NaN': 'transparent'})
# transparent alternative (litle bit slower)
from holoviews.operation.datashader import rasterize, dynspread, datashade
clipped = dynspread(datashade(gv_shapes))
gv.tile_sources.EsriImagery * \
clipped.opts(
width=800,
height=480,
alpha=0.8)
Intersection¶
For appending points to polygons there're several options available:
- use geopandas.sjoin (unfortunately, no distance available). Example
- use spatial rtree and best fit custom method
- use from shapely.ops import nearest_points
Since it is the fastest and most versatile, we'll explore the second option.
1. Load LBSM Data
Load CSV of LBSM points to Pandas dataframe, two formats available
- Raw data (Instagram, Flickr, Twitter)
- Cleaned Format merged based on UPL (only use Flickr because Instagram/Twitter are not available in highest geo-accuracy).
First, detect the format.
In [19]:
import csv
file_input_path = Path.cwd() / "01_Input" / LBSM_INPUT_DATA
with open(file_input_path, 'r') as infile:
reader = csv.DictReader(infile)
fieldnames = reader.fieldnames
# there're two different formats of input data
# cleaned_posts (UPL)
# and raw data (UP)
# cleaned posts have a slightly different format
# which is detected here based on the CSV's header
if 'latitude' in fieldnames:
raw_format = True
usecols = ['origin_id', 'latitude', 'longitude', 'user_guid', 'post_create_date', 'post_publish_date']
dtypes={'origin_id': str,'latitude': float, 'longitude': float, 'user_guid': str, 'post_create_date': str, 'post_publish_date': str}
elif 'lat' in fieldnames:
raw_format = False
usecols = ['origin_id', 'lat', 'lng', 'user_guid', 'post_create_date', 'post_publish_date']
dtypes={'origin_id': str,'lat': float, 'lng': float, 'user_guid': str, 'post_create_date': str, 'post_publish_date': str}
else:
print("Unknown Format")
Load data in dataframe df
In [20]:
%%time
df = pd.read_csv(file_input_path, usecols=usecols, dtype=dtypes, encoding='utf-8')
print(f'Loaded {len(df)} posts.')
For Flickr, post_create_date and post_publish_date is available, for Instagram and Twitter, only post_publish_date is available.
- Combine both with a preference on post_create_date
- wrapped in try..except for multiple execution handling
- rename final columns
In [71]:
try:
df['post_create_date'].fillna(df['post_publish_date'], inplace=True)
df.drop(columns=['post_publish_date'], inplace=True)
df.rename(columns={'post_create_date': 'post_date'}, inplace=True)
except KeyError:
pass
In [72]:
# df.iloc[: , [5, 13]].head()
df.head()
Out[72]:
Optional: Get some statistics for origin
- 1 = Instagram
- 2 = Flickr
- 3 = Twitter
In [21]:
import seaborn as sns
import matplotlib
import matplotlib.pyplot as plt
plt.rcParams.update({'xtick.labelsize': 'small'})
plt.rcParams.update({'ytick.labelsize': 'small'})
plt.rc('legend', fontsize=10)
sns.countplot(x='origin_id', hue='origin_id', data=df)
Out[21]:
In [22]:
size_count = df.groupby('origin_id').size()
total_count = len(df)
for key, value in size_count.items():
print(f'Origin ID {key} with {value} posts, total percentage is: {value/(total_count/100):.2f}%')
2. Convert pandas dataframe to geopandas geodataframe
In [77]:
from shapely.geometry import Point
def make_point(row):
if raw_format:
return Point(row.longitude, row.latitude)
return Point(row.lng, row.lat)
# Go through every row, and make a point out of its lat and lon
points = df.apply(make_point, axis=1)
# Make a new GeoDataFrame
# using the data from our old df
# but also adding in the geometry we just made
lbsm_data_gdf = gp.GeoDataFrame(df, geometry=points)
# set input projection
lbsm_data_gdf.crs = {'init' :'epsg:4326'}
# project to UTM
lbsm_data_gdf.to_crs({'init': f'epsg:{epsg_code}'}, inplace=True)
In [79]:
lbsm_data_gdf.head()
Out[79]:
Optional: Preview Single Target Geometry Graph Polygon Intersection¶
Lets test the inteserction for a small sample
- 1 polygon from meiNGrün Target Geometries
- 1 lbsm post
a) Get one polygon from meinGrün Target Geometries
- modify the number to get different samples
In [47]:
# Get row for specific ID
# id = 9
# id = 20
# id = 26
id = 29
gdf_targetgeom_shapes.iloc[id].geometry
Out[47]:
It is possible to get bounds of the line segment by:
In [48]:
gdf_targetgeom_shapes.iloc[id].geometry.bounds
Out[48]:
b) Prepare poly geoviews layer
In [53]:
gv_sample_poly = gv.Shape(
gdf_targetgeom_shapes.iloc[id].geometry,
label=f'meinGrün Target Polygon', crs=cartopy.crs.epsg(epsg_code))
gv_sample_poly_buf = gv.Shape(
gdf_targetgeom_shapes.iloc[id].geometry.buffer(50, resolution=3, cap_style=1, join_style=1, mitre_limit=5.0)
,
label=f'meinGrün Target Polygon', crs=cartopy.crs.epsg(epsg_code))
c) Optional: Test Prepare point geoviews layer (slow)
In [50]:
%%time
from shapely.geometry import Point, Polygon
def add_buffer(geom, buffer_num=50, return_poly=False):
'''Add small buffer around poly bounds
Buffer defaults to: 200m
'''
xmin, ymin, xmax, ymax = geom.bounds
xmin = xmin - buffer_num
ymin = ymin - buffer_num
xmax = xmax + buffer_num
ymax = ymax + buffer_num
if return_poly:
bbox_polygon = Polygon(
[[xmin, ymax],
[xmax, ymax],
[xmax, ymin],
[xmin, ymin]])
return bbox_polygon
return xmin, ymin, xmax, ymax
# adjust number for sample polygon
sample_polygon = gdf_targetgeom_shapes.iloc[id]
xmin, ymin, xmax, ymax = add_buffer(
sample_polygon.geometry)
# intersect/clip points based on bbox input
# this is very slow! we'll use an improved version for mass intersection
sample_point_gdf = lbsm_data_gdf.cx[xmin:xmax, ymin:ymax]
print(f'Intersections found for id {id}: {len(sample_point_gdf)} posts.')
# for automatic selection of polygon with many intersections
# use code block below
# max_intersect = 0
# max_id_ref = None
# for id in range(1,30):
# # adjust number for sample polygon
# sample_polygon = gdf_targetgeom_shapes.iloc[id]
# xmin, ymin, xmax, ymax = add_buffer(
# sample_polygon.geometry)
#
# # intersect/clip points based on bbox input
# sample_point_temp = lbsm_data_gdf.cx[xmin:xmax, ymin:ymax]
# number_of_intersections = len(sample_point_temp)
# if number_of_intersections > max_intersect:
# max_id_ref = id
# max_intersect = number_of_intersections
# sample_point_gdf = sample_point_temp
#
# # report
# print(f'Max Intersections found for id {max_id_ref}: {len(sample_point_gdf)} posts.')
d) Prepare Geoviews Layer for selected Sample Points
In [51]:
sample_point_selection = gv.Points(sample_point_gdf, crs=cartopy.crs.epsg(epsg_code))
e) Output sample selection map
In [57]:
import pyproj
def set_active_tool(plot, element):
# enable wheel_zoom by default
plot.state.toolbar.active_scroll = plot.state.tools[0]
# the Overlay will automatically extent to data dimensions,
# the polygons of HDBSCAN cover the city area:
# since we want the initial zoom to be zoomed in to the selected sample geometry
# we need to supply xlim and ylim
# The Bokeh renderer only support Web Mercator Projection
# we therefore need to project Großer Garten Bounds first
# from WGS1984 to Web Mercator
sample_bottomleft = pyproj.transform(
pyproj.Proj(init=f'EPSG:{epsg_code}'), pyproj.Proj(init='EPSG:3857'), xmin, ymin)
sample_topright = pyproj.transform(
pyproj.Proj(init=f'EPSG:{epsg_code}'), pyproj.Proj(init='EPSG:3857'), xmax, ymax)
# to show 200m radius
# centre_line = pyproj.transform(
# pyproj.Proj(init=f'EPSG:{epsg_code}'),
# pyproj.Proj(init='EPSG:3857'),
# gdf_targetgeom_shapes.iloc[max_id_ref].geometry.centroid.x,
# gdf_targetgeom_shapes.iloc[max_id_ref].geometry.centroid.y)
hv.Overlay(
gv.tile_sources.EsriImagery * \
# gv_sample_poly_buf.opts(line_width=0, fill_alpha=0.5, fill_color='grey') * \
gv_sample_poly.opts(line_color='red', line_width=3, fill_alpha=0.5, tools=['hover']) * \
# hv.Ellipse(centre_line[0], centre_line[1], 200, label='200m Buffer').opts(line_color='red', line_width=3, show_legend=True) * \
sample_point_selection.opts(tools=['hover'], size=5, color='yellow') # * \
# gv_testpoint
).opts(
width=800,
height=480,
finalize_hooks=[set_active_tool],
xlim=(sample_bottomleft[0], sample_topright[0]),
ylim=(sample_bottomleft[1], sample_topright[1])
)
Out[57]:
Optional: Intersect test using rtree¶
We'll create an rtree with boxes for each line in the OSM Graph. This will be the most expensive operation, afterwards we can use Shapely's nearest function to snap points to a small number of possible lines. First, construct RTree for the geometry from osm paths. We'll extend the boundary by a small buffer surrounding the path so that we can catch points at both ends.
- adjust
buffer_numto your needs, minimum buffer_num should be the maximum SEARCH_RADIUS beeing used
In [58]:
%%time
from rtree import index
# Populate R-tree index with bounds of polygons
idx = index.Index()
for pos, poly in enumerate(gdf_targetgeom_shapes.geometry):
idx.insert(pos, add_buffer(poly, buffer_num=50))
Intersect our testpoint with the rtree
Next, we intersect our testpoint with the rtree and select those paths that are near the testpoint. We can define a search_radius. This is very fast because the heavy lifting is done by the spatial rtree intersection (idx.intersection((testpoint.coords[0]))) while the expensive distance calculation from shapely (gdf.geometry[i].project(testpoint)) is only done for the few paths objects that are preselected based on their boundary.
- Important: Modify SEARCH_RADIUS (m) to test output for various distances
Since Instagram and Twitter have lower Geoaccuracies on average, we use a large Search Radius of 50m, wheras for Flickr we'll use 25m.
In [59]:
SEARCH_RADIUS_ORIGIN = {
"1":50,"2":25,"3":50
}
In [76]:
lbsm_data_gdf.head()
Out[76]:
In [60]:
%%time
# Query testpoint to see which polygon it is in
# using first Rtree index, then Shapely geometry's within
# SEARCH_RADIUS = 25
# to look for other points here, override id:
# id = 20
lbsm_post = lbsm_data_gdf.iloc[id]
SEARCH_RADIUS = SEARCH_RADIUS_ORIGIN.get(lbsm_post.origin_id)
testpoint = lbsm_post.geometry
gv_testpoint = gv.Points([testpoint], crs=cartopy.crs.epsg(epsg_code), label='Testpoint').opts(tools=['hover'], size=5, color='yellow')
# calculate boundary to show
xmin, ymin, xmax, ymax = add_buffer(lbsm_post.geometry)
sample_bottomleft = pyproj.transform(pyproj.Proj(init=f'EPSG:{epsg_code}'), pyproj.Proj(init='EPSG:3857'), xmin, ymin)
sample_topright = pyproj.transform(pyproj.Proj(init=f'EPSG:{epsg_code}'), pyproj.Proj(init='EPSG:3857'), xmax, ymax)
# with rtree only
poly_idx_rtree = [i for i in idx.intersection((testpoint.coords[0]))]
# with search radius
poly_idx = [i for i in idx.intersection((testpoint.coords[0]))
if gdf_targetgeom_shapes.geometry[i].distance(testpoint) < SEARCH_RADIUS]
#if gdf.geometry[i].interpolate(gdf.geometry[i].project(testpoint)).distance(testpoint) > search_radius]
# without search radius, extact intersection
poly_idx_exact = [i for i in idx.intersection((testpoint.coords[0]))
if gdf_targetgeom_shapes.geometry[i].intersects(testpoint)]
# report
print(f'{len(poly_idx_rtree)} preselected polygons through rtree')
print(f'{len(poly_idx)} potentially relevant polygons found (within search radius {SEARCH_RADIUS}).')
print(f'{len(poly_idx_exact)} exact intersection polygons found.')
In [61]:
print(poly_idx)
- Select subset of relevant polygons based on id's
In [62]:
gdf_targetgeom_shapes.iloc[poly_idx].head()
Out[62]:
- prepare geoviews layer for preview
In [63]:
testpoly_sel = gv.Polygons(
[gv.Shape(feat) for feat in gdf_targetgeom_shapes.iloc[poly_idx].geometry],
crs=cartopy.crs.epsg(epsg_code),
label=f'Found {len(poly_idx)} Target Polygons (search radius {SEARCH_RADIUS}m)')
testpoly_sel_exact = gv.Polygons(
[gv.Shape(feat) for feat in gdf_targetgeom_shapes.iloc[poly_idx_exact].geometry],
crs=cartopy.crs.epsg(epsg_code),
label=f'Found {len(poly_idx_exact)} Target Polygons (exact intersection)')
- output map (careful: might be slow)
In [64]:
hv.Overlay(
gv.tile_sources.EsriImagery * \
# gv_shapes.opts(line_color='white', line_width=1) * \
testpoly_sel.opts(line_color='yellow', line_width=3, fill_alpha=0, show_legend=True) * \
testpoly_sel_exact.opts(line_color='red', line_width=3, fill_alpha=0, show_legend=True, line_dash='dotted') * \
gv_testpoint.opts(tools=['hover'], size=10, color='red', show_legend=True)
#hv.Ellipse(gv_testpoint.x, gv_testpoint.y, 35, label='200m Buffer').opts(line_color='yellow', line_width=1, show_legend=True)
).opts(
width=800,
height=480,
xlim=(sample_bottomleft[0], sample_topright[0]),
ylim=(sample_bottomleft[1], sample_topright[1]),
title_format=f"Selected Target Geometries for Testpoint and {SEARCH_RADIUS}m Search Radius",
)
Out[64]:
Intersect all points with grid¶
We execute this intersection for each point in our LBSM dataset. We update all counts in close polygons from the meiNGrün Target Geometries
1. Add statistics field to input geometries
In [65]:
gdf_targetgeom_shapes.head()
Out[65]:
Initialize count structures
They're several things we want to measure per meinGrün target shape:
- User_Posts: Number of social media posts in total
- User_Days: Counting each user once per day (post timestamp)
- User_Post_Locations: Counting each user-location coordinate only once (post latlng)
- InstagramCount: Number of Instagram posts in total
- FlickrCount: Number of Flickr posts in total
- TwitterCount: Number of Twitter posts in total
In [73]:
# init count structures
gdf_targetgeom_shapes['User_Posts'] = 0
gdf_targetgeom_shapes['User_Days'] = [set() for _ in range(len(gdf_targetgeom_shapes))]
gdf_targetgeom_shapes['User_Post_Locations'] = [set() for _ in range(len(gdf_targetgeom_shapes))]
gdf_targetgeom_shapes['InstagramCount'] = 0
gdf_targetgeom_shapes['FlickrCount'] = 0
gdf_targetgeom_shapes['TwitterCount'] = 0
gdf = gdf_targetgeom_shapes
In [74]:
gdf_targetgeom_shapes.head()
Out[74]:
In [80]:
%%time
from IPython.display import clear_output
def get_userday(post):
"""Remove time from date-time string, e.g. 2019-01-01"""
if isinstance(post.post_date, str) and len(post.post_date) >= 10:
return f'{post.user_guid}:{post.post_date[:10]}'
return f'{post.user_guid}:1900-01-01'
def get_location(post_geom):
"""Concat lat:lng to string"""
return f'{post_geom.y}:{post_geom.x}'
def concat_userlocation(user_guid, location_str):
"""Concat user:lat:lng to string"""
return f'{user_guid}:{location_str}'
x = 0
x_i = 0
# get ids from column names
column_id_up = gdf.columns.get_loc("User_Posts")
column_id_ud = gdf.columns.get_loc("User_Days")
column_id_upl = gdf.columns.get_loc("User_Post_Locations")
col_id_instagram = gdf.columns.get_loc('InstagramCount')
col_id_flickr = gdf.columns.get_loc('FlickrCount')
col_id_twitter = gdf.columns.get_loc('TwitterCount')
origin_col_dict = {
"1":col_id_instagram, "2":col_id_flickr, "3":col_id_twitter}
# initialize dict lookup for location-poly-idx
latlng_dict_polyidx = dict()
total_records = len(lbsm_data_gdf)
for lbsm_record in lbsm_data_gdf.itertuples():
# print(lbsm_record.geometry)
# break
# update status
x += 1
msg_text = (
f'Processed records: {x} ({x/(total_records/100):.2f}%). '
f'Intersection found: {x_i}')
if x % 100 == 0:
clear_output(wait=True)
print(msg_text)
# get search radius based on currrent posts's origin
SEARCH_RADIUS = SEARCH_RADIUS_ORIGIN.get(
lbsm_record.origin_id)
# we save some processing speed by remembering
# repeated locations and sacrificing some ram
# e.g. spatial intersection is more expensive than dict-lookup
location_str = get_location(lbsm_record.geometry)
if location_str in latlng_dict_polyidx:
poly_idx = latlng_dict_polyidx[location_str]
else:
poly_idx = [i for i in idx.intersection((lbsm_record.geometry.coords[0]))
if gdf.geometry[i].distance(lbsm_record.geometry) < SEARCH_RADIUS]
# add to dict for improving speed for repetitive locations
latlng_dict_polyidx[location_str] = poly_idx
# count intersection
if len(poly_idx) > 0:
x_i += 1
userday = get_userday(lbsm_record)
userlocation = concat_userlocation(lbsm_record.user_guid, location_str)
# update poly count column
# the iloc indexer for Pandas Dataframe is used for integer-
# location based indexing / selection by position.
# this may update/increment multiple rows/records
origin_col = origin_col_dict.get(lbsm_record.origin_id)
gdf.iloc[poly_idx, origin_col] += 1
gdf.iloc[poly_idx, column_id_up] += 1
# the following 2 operations are quite expensive on memory/cpu
# disable if not needed
# gdf.iloc[poly_idx, column_id_ud].apply(lambda x: x.add(userday))
# gdf.iloc[poly_idx, column_id_upl].apply(lambda x: x.add(userlocation))
# final status
clear_output(wait=True)
print(msg_text)
Optional: store or load intermediate aggregated dataframe results
Store:
In [85]:
gdf.to_pickle(f"gdf_{CITY_NAME}_{LEVEL}_{TARGETSHAPE_VERSION}.pickle")
Load:
In [35]:
gdf = pd.read_pickle(f"gdf_{CITY_NAME}_{LEVEL}_{TARGETSHAPE_VERSION}.pickle")
Preview results table
In [86]:
gdf[[TARGET_SHAPE_ID_COL, 'User_Posts', 'User_Days', 'User_Post_Locations', 'InstagramCount', 'FlickrCount', 'TwitterCount']].head()
Out[86]:
Convert Sets to unique counts
In [87]:
gdf['UD'] = gdf.apply(lambda row: len(row.User_Days), axis=1)
gdf['UPL'] = gdf.apply(lambda row: len(row.User_Post_Locations), axis=1)
In [88]:
gdf[[TARGET_SHAPE_ID_COL, 'User_Posts', 'User_Days', 'User_Post_Locations', 'UD', 'UPL', 'InstagramCount', 'FlickrCount', 'TwitterCount']].head()
Out[88]:
Remove set column, since they're not needed anymore:
In [89]:
gdf.drop(columns=['User_Post_Locations', 'User_Days'], inplace=True)
gdf.rename(columns={'User_Posts': 'UP'}, inplace=True)
gdf.rename(columns={'InstagramCount': 'UP_Instagram'}, inplace=True)
gdf.rename(columns={'FlickrCount': 'UP_Flickr'}, inplace=True)
gdf.rename(columns={'TwitterCount': 'UP_Twitter'}, inplace=True)
Out final Geodataframe with aggregated measurements per green target shape:
In [92]:
gdf[[TARGET_SHAPE_ID_COL, 'UP', 'UD', 'UPL', 'UP_Instagram', 'UP_Flickr', 'UP_Twitter']].head()
Out[92]:
Get Histogram
The histogram shows the typical long tail pattern: most shapes have few posts, but a few shapes have a lot of posts.
In [93]:
np.sqrt(np.sqrt(gdf['UP'])).hist(bins=20, alpha=0.5)
Out[93]:
Normalize for visualization¶
Absolute counts per shape are not very meaningful, since large shapes have a higher likeliness of intersecting with many posts.
- We can normalize using the area (square kilometres) of each target shape)
- Furthermore, we can "flatten" the long tail a bit by applying a square root function
- these normalized numbers are only relevant based on their relative value, therefore we can stretch results to 1 to 1000 range for comparability across different runs
Calculate buffered Area for search distances
In [94]:
# Get row for specific ID
id = 0
gdf.iloc[id].geometry
Out[94]:
In [95]:
gdf.iloc[id].geometry.buffer(50, resolution=3, cap_style=1, join_style=1, mitre_limit=5.0)
Out[95]:
In [96]:
print(f'{gdf.iloc[id].geometry.area:.2f} m²')
print(f'50m Search radius: {gdf.iloc[id].geometry.buffer(50, resolution=16, cap_style=1, join_style=1, mitre_limit=5.0).area:.2f} m²')
print(f'25m Search radius: {gdf.iloc[id].geometry.buffer(25, resolution=16, cap_style=1, join_style=1, mitre_limit=5.0).area:.2f} m²')
In [97]:
def m2_to_km2(m_num):
"""Convert square meters to square kilometres"""
return m_num/1000000
print(f'{m2_to_km2(gdf.iloc[id].geometry.area):.4f} km²')
print(f'50m Search radius: {m2_to_km2(gdf.iloc[id].geometry.buffer(50, resolution=16, cap_style=1, join_style=1, mitre_limit=5.0).area):.4f} km²')
print(f'25m Search radius: {m2_to_km2(gdf.iloc[id].geometry.buffer(25, resolution=16, cap_style=1, join_style=1, mitre_limit=5.0).area):.4f} km²')
Calculate km²
- for all geometries in the dataset
- for all 25m buffered geometries in dataset
- for all 50m buffered geometries in dataset
In [98]:
def shape_buf(geom, buf_dist_m):
"""Buffer geometry based on m distance"""
buf_geom = geom.buffer(buf_dist_m, resolution=16, cap_style=1, join_style=1, mitre_limit=5.0)
return buf_geom
gdf['area_skm'] = gdf['geometry'].apply(lambda x: m2_to_km2(x.area))
# same result:
# gdf['poly_area'] = gdf.geometry.area / 10**6
gdf['area_skm_SR25'] = gdf['geometry'].apply(lambda x: m2_to_km2(shape_buf(x, 25).area))
gdf['area_skm_SR50'] = gdf['geometry'].apply(lambda x: m2_to_km2(shape_buf(x, 50).area))
In [100]:
# gdf[[TARGET_SHAPE_ID_COL, 'UP', 'UD', 'UPL', 'UP_Instagram', 'UP_Flickr', 'UP_Twitter', 'area_skm', 'area_skm_SR25', 'area_skm_SR50']].head()
gdf[[TARGET_SHAPE_ID_COL, 'UP', 'UP_Instagram', 'UP_Flickr', 'UP_Twitter', 'area_skm', 'area_skm_SR25', 'area_skm_SR50']].head()
Out[100]:
Normalize
In [101]:
import math
def stretch_norm_dfcolumn(df, column, min_stretch, max_stretch):
"""Interpolates a Geodataframe column's values to a new range"""
min_val = df[column].min()
max_val = df[column].max()
# replace values in-place
# the lambda function is applied to all values from _column_
# df.loc[:, column] means: replace values of _column_ in-place (modifying the original dataframe)
# .loc[row_indexer,col_indexer]
df.loc[:, column] = df[column].apply(
lambda x: np.interp(x, (min_val, max_val), (min_stretch, max_stretch)))
# similar but not recommended:
# df[column] = np.interp(df[column], (min_val, max_val), (min_stretch, max_stretch))
# it is possible to 'flatten' the unequal distribution of values
# use one time sqrt for a bit of flatten, use two times sqrt for more flatten
gdf['UP_Norm'] = np.sqrt(gdf['UP']/gdf['area_skm'])
# gdf['UP_Norm'] = np.sqrt(np.sqrt(gdf['UP']/gdf['poly_area']))
# interpolate to 1 to 1000 range
stretch_norm_dfcolumn(gdf, 'UP_Norm', 1, 1000)
# print
gdf[[TARGET_SHAPE_ID_COL, 'UP', 'UP_Norm', 'UP_Instagram', 'UP_Flickr', 'UP_Twitter']].sort_values('UP_Norm', ascending=False).head()
# gdf[[TARGET_SHAPE_ID_COL, 'UP', 'UP_Norm', 'UD', 'UPL', 'UP_Instagram', 'UP_Flickr', 'UP_Twitter']].sort_values('UP_Norm', ascending=False).head()
Out[101]:
Normalize Origin Influence
It becomes obvious that Instagram dominates results. We want to generate an even result between the 3 data sources.
Our next goal therefore is to allow only
- 40% Influence by Instagram (reduced because Instagram has low geoaccuracy)
- 50% Influence by Flickr (increased because Flickr featurss highest geoaccuracy)
- 10% Influence by Twitter (reduced because twitter features sampling noise)
We can do this by using the information from UP_Instagram, UP_Flickr, and UP_Twitter columns.
Since we've used a Search_Radius of 50m for Instagram and Twitter and a Search_Radius of 25m for Flickr,
we need to normalize count values separately based on origin and buffered area.
Remember the original percentage of the three datasources:
In [102]:
origin_perc_total = dict()
for key, value in size_count.items():
total_perc = value/(total_count/100)
print(f'Origin ID {key} with {value} posts, total percentage is: {total_perc:.2f}%')
origin_perc_total[key] = total_perc
Thus, we need to perform a two step normalization:
- first, normalize based on total percentage of each datasource
- second, apply manuall influence flags (40%, 50%, 10%)
In [103]:
def norm_ip_origin(row):
total_up = row['UP']
# get one third of the total count of row
if total_up == 0:
return 0
# get total counts per origin
# normalize per search radius
# flatten curve using square
instagram_up = np.sqrt(row['UP_Instagram']/row['area_skm_SR50'])
flickr_up = np.sqrt(row['UP_Flickr']/row['area_skm_SR25'])
twitter_up = np.sqrt(row['UP_Twitter']/row['area_skm_SR50'])
total_up = instagram_up + flickr_up + twitter_up
# calc perc per origin percentage
# and apply manual weights
instagram_up_norm = instagram_up / (origin_perc_total.get("1") / 100) * 0.4
flickr_up_norm = flickr_up / (origin_perc_total.get("2") / 100) * 0.5
twitter_up_norm = twitter_up / (origin_perc_total.get("3") / 100) * 0.1
sum_norm = instagram_up_norm + flickr_up_norm + twitter_up_norm
return sum_norm
gdf['UP_Norm_Origin'] = gdf.apply(lambda row: norm_ip_origin(row), axis=1)
# gdf['UP_Norm_Origin'] = np.sqrt(gdf['UP_Norm_Origin']/gdf['poly_area'])
stretch_norm_dfcolumn(gdf, 'UP_Norm_Origin', 1, 1000)
Compare difference of UP_Norm and UP_Norm_Origin
In [104]:
# gdf[[TARGET_SHAPE_ID_COL, 'UP', 'UP_Norm', 'UP_Norm_Origin', 'UD', 'UPL', 'UP_Instagram', 'UP_Flickr', 'UP_Twitter']].sort_values('UP', ascending=False).head(20)
gdf[[TARGET_SHAPE_ID_COL, 'UP', 'UP_Norm', 'UP_Norm_Origin', 'UP_Instagram',
'UP_Flickr', 'UP_Twitter', 'area_skm', 'area_skm_SR25', 'area_skm_SR50']].sort_values('UP', ascending=False).head(20)
Out[104]:
- rows with more equal distribution of posts across Flickr, Twitter and Instagram are increased in ranking/weights
- rows with unequal distribution of posts towards only one data source (e.g. Instagram) are reduced in their importance
In [105]:
gdf['UP_Norm_Origin'].hist(bins=20, alpha=0.5)
Out[105]:
In [106]:
# get top poly
top_norm = gdf.sort_values('UP_Norm_Origin', ascending=False).iloc[0]
top_norm_count = gdf.sort_values('UP', ascending=False).iloc[0]
# normalized
top_norm_poly = top_norm.geometry
top_norm_value = top_norm.UP_Norm_Origin
# raw
top_raw_poly_count = top_norm_count.geometry
top_raw_value_count = top_norm_count.UP
# construct geoviews layer
gv_sample_poly_top_norm = gv.Shape(
top_norm_poly,
label=f'meinGrün Top Target Polygon ({top_norm_value} Normalized Post Count based on Area and Service Origin)',
crs=cartopy.crs.epsg(epsg_code))
# construct geoviews layer
gv_sample_poly_top_count = gv.Shape(
top_raw_poly_count,
label=f'meinGrün Top Target Polygon ({top_raw_value_count} Total Post Count)',
crs=cartopy.crs.epsg(epsg_code))
# calculate boundary to show
xmin, ymin, xmax, ymax = add_buffer(top_norm_poly)
sample_bottomleft = pyproj.transform(
pyproj.Proj(init=f'EPSG:{epsg_code}'), pyproj.Proj(init='EPSG:3857'), xmin, ymin)
sample_topright = pyproj.transform(
pyproj.Proj(init=f'EPSG:{epsg_code}'), pyproj.Proj(init='EPSG:3857'), xmax, ymax)
In [107]:
hv.Overlay(
gv.tile_sources.EsriImagery * \
gv_sample_poly_top_count.opts(line_color='yellow', line_width=3, fill_alpha=0.2, show_legend=True) * \
gv_sample_poly_top_norm.opts(line_color='red', line_width=3, fill_alpha=0.2, show_legend=True)
).opts(
width=800,
height=480,
xlim=(sample_bottomleft[0], sample_topright[0]),
ylim=(sample_bottomleft[1], sample_topright[1]),
title_format=f"Selected Target Geometries for Testpoint and {SEARCH_RADIUS}m/50m Search Radius",
)
Out[107]:
Visualize Results¶
Now we can easily pass the GeoDataFrame to a Polygon object and declare the Measure as the first value dimension which it will color by:
Plot Sample Areas¶
In [108]:
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
from matplotlib.patches import Patch
gdf_meingruen_official_targets = gdf.query(f'{GREEN_TARGET_COL} == 1')
# make a legend using a dictionary to assign colors
cmap = matplotlib.cm.get_cmap('Greens')
popularity_legend = {
'very high': cmap(1000/1000),
'high': cmap(450/1000),
'average': cmap(165/1000),
'low': cmap(56/1000),
'very low': cmap(14/1000),
'no data': cmap(1)}
def add_patches(legend):
ax = legend.axes
handles, labels = ax.get_legend_handles_labels()
labels.append("LBSM Popularity")
handles.append(Patch(facecolor="none", fill=False, edgecolor='none', linewidth=0))
for key in popularity_legend:
data_key = Patch(edgecolor='none', facecolor=popularity_legend[key])
handles.append(data_key)
labels.append(key)
labels.append("Official green target")
handles.append(Patch(facecolor="none", fill=False, edgecolor='red', linewidth=0.7))
legend._legend_box = None
legend._init_legend_box(handles, labels)
legend._set_loc(legend._loc)
legend.set_title(legend.get_title().get_text())
def get_map(lim_lng_min, lim_lat_min, lim_lng_max, lim_lat_max, title, gdf, column, show_green_targets):
"""Plot area in matplotlib (pyplot).
Note: Natural Breaks algorithm is used here to classify colors, remove to see raw values"""
bb_bottomleft = pyproj.transform(pyproj.Proj(init='EPSG:4326'), pyproj.Proj(init=f'EPSG:{epsg_code}'), lim_lng_min, lim_lat_min)
bb_topright = pyproj.transform(pyproj.Proj(init='EPSG:4326'), pyproj.Proj(init=f'EPSG:{epsg_code}'), lim_lng_max, lim_lat_max)
fig, ax = plt.subplots()
fig.set_size_inches(18.5, 10.5, forward=True)
ax.set_title(f'LBSM Target Geometry Weights for {title}', fontsize=20)
ax.set_xlim([bb_bottomleft[0],bb_topright[0]])
ax.set_ylim([bb_bottomleft[1],bb_topright[1]])
# plt.legend(handles=patch_list)
plt.savefig('legend.png', bbox_inches='tight')
base = gdf.plot(column=column, figsize=(20,10), cmap='Greens', ax=ax, scheme="natural_breaks", label='Normalized User Post Locations (UPL) based on Area', legend = True)
if show_green_targets:
lgd = ax.legend(loc='upper right')
add_patches(lgd)
gdf_meingruen_official_targets.plot(ax=base, facecolor="none", edgecolor='red', lw=0.7, label='Official green target', legend = True)
# plot the data
def get_map_b(lim_lng_min, lim_lat_min, lim_lng_max, lim_lat_max, title, gdf, column):
"""Plot area in matplotlib (pyplot).
Note: Natural Breaks algorithm is used here to classify colors, remove to see raw values"""
bb_bottomleft = pyproj.transform(pyproj.Proj(init='EPSG:4326'), pyproj.Proj(init=f'EPSG:{epsg_code}'), lim_lng_min, lim_lat_min)
bb_topright = pyproj.transform(pyproj.Proj(init='EPSG:4326'), pyproj.Proj(init=f'EPSG:{epsg_code}'), lim_lng_max, lim_lat_max)
axes = plt.gca()
fig = plt.gcf()
fig.set_size_inches(18.5, 10.5, forward=True)
# fig.suptitle('LBSM Target Geometry Weights for Dresden Altstadt', fontsize=20)
axes.set_title(f'LBSM Target Geometry Weights for {title}', fontsize=20)
axes.set_xlim([bb_bottomleft[0],bb_topright[0]])
axes.set_ylim([bb_bottomleft[1],bb_topright[1]])
base = gdf.plot(column=column, figsize=(20,10), cmap='Greens', ax=axes, scheme="natural_breaks", label='Normalized User Post Locations (UPL) based on Area', legend = True)
if show_green_targets:
gdf_meingruen_official_targets.plot(ax=base, facecolor="none", edgecolor='red', lw=0.7, label='Official green target', legend = True)
#gdf.to_crs({'init': 'epsg:3395'}).plot(column='UP_Norm', figsize=(20,10), cmap='Reds', ax=axes)
In [109]:
get_map(LIM_LNG_MIN, LIM_LAT_MIN, LIM_LNG_MAX, LIM_LAT_MAX, f'{CITY_NAME} {LEVEL} {TARGETSHAPE_VERSION}', gdf, 'UP_Norm_Origin', show_green_targets=False)
In [110]:
get_map(LIM_LNG_MIN_1, LIM_LAT_MIN_1, LIM_LNG_MAX_1, LIM_LAT_MAX_1, f"Dresden Altstadt {LEVEL} {TARGETSHAPE_VERSION}", gdf, 'UP_Norm_Origin', show_green_targets=False)
In [111]:
get_map(LIM_LNG_MIN_2, LIM_LAT_MIN_2, LIM_LNG_MAX_2, LIM_LAT_MAX_2, f"Dresden Center {LEVEL} {TARGETSHAPE_VERSION}", gdf, 'UP_Norm_Origin', show_green_targets=False)
In [112]:
get_map(LIM_LNG_MIN_3, LIM_LAT_MIN_3, LIM_LNG_MAX_3, LIM_LAT_MAX_3, f"Dresden Neustadt {LEVEL} {TARGETSHAPE_VERSION}", gdf, 'UP_Norm_Origin', show_green_targets=False)
To save Figures above as SVG, run the following cell after each figure generation
In [113]:
plt.savefig(f"{CITY_NAME.lower()}_{LEVEL}_{TARGETSHAPE_VERSION}_A1.svg", format="svg")
Optional: Plot Green Areas¶
In [114]:
import pysal.viz.mapclassify as mc
# Define the number of classes
n_classes = 6
# Create a Natural Breaks classifier
classifier = mc.NaturalBreaks.make(k=n_classes)
# classifier = mc.HeadTailBreaks.make()
# Classify the data
classifications = gdf[['UP_Norm']].apply(classifier)
gdf['UP_Norm_nb'] = classifications
classifications = gdf[['UP_Norm_Origin']].apply(classifier)
gdf['UP_Norm_Origin_nb'] = classifications
We'll also add bin labels for describing classes:
In [115]:
classes_ref_5 = {
5 : 'very high',
4 : 'high',
3 : 'average',
2 : 'low',
1 : 'very low',
0 : 'no data'}
gdf['popularity'] = gdf['UP_Norm_Origin_nb'].map(classes_ref_5)
# make sure that the bin "no data" is only assigned for records without UPL
gdf.loc[(gdf.UP_Norm_Origin > 1) & (gdf.UP_Norm_Origin_nb == 0), 'popularity'] = 'very low'
gdf.loc[(gdf.UP_Norm > 1) & (gdf.UP_Norm_nb == 0), 'UP_Norm_nb'] = 1
gdf.loc[(gdf.UP_Norm_Origin > 1) & (gdf.UP_Norm_Origin_nb == 0), 'UP_Norm_Origin_nb'] = 1
Have a look at the values and where we came from (left to right):
In [118]:
print(gdf[['UP', 'UP_Norm', 'UP_Norm_nb', 'UP_Norm_Origin', 'UP_Norm_Origin_nb', 'popularity']].head(20))
print(gdf[['UP', 'UP_Norm', 'UP_Norm_nb', 'UP_Norm_Origin', 'UP_Norm_Origin_nb', 'popularity']].sort_values('UP_Norm', ascending=False).head(20))
In [119]:
import seaborn as sns
sns.countplot(x='UP_Norm_Origin_nb', hue='UP_Norm_Origin_nb', data=gdf)
Out[119]:
Filter relevant target geometries
Create geoviews layer for final display. We'll filter segments by a threshold, as a means to only show streets with a minimum of popularity.
- We can also filter based on the column
TARGET_green_target
In [120]:
# on mixed type input (e.g. 'Polygon', 'GeometryCollection')
# apply filter below to avoid Geopandas Exception
gdf_temp = gdf.loc[gdf['geometry'].apply(lambda x: isinstance(x, (Polygon, MultiPolygon)))]
gdf_filtered = gdf_temp.query(f'{GREEN_TARGET_COL} == 1').copy()
print(f'Number of total segments: {len(gdf)}. Number of filtered segments: {len(gdf_filtered)}')
In [121]:
gv_shapes_weighted = gv.Polygons(
gdf_filtered, vdims=['UP_Norm_Origin_nb'], crs=cartopy.crs.epsg(epsg_code))
Final Output:
- if you want to output to a html file with full-width-map, remove comment from:
%%output filename="dresden_streets"andresponsive=True
- if you want to preview inside this jupyter notebook, leave comments for the above two lines and remove comments from:
width=800height=480
In [ ]:
#%%output filename=f"{TODAY}_{CITY_NAME}_weighted_green_targets_{LEVEL}_{TARGETSHAPE_VERSION}"
from holoviews import dim
def set_active_tool(plot, element):
# enable wheel_zoom by default
plot.state.toolbar.active_scroll = plot.state.tools[0]
# the Overlay will automatically extent to data dimensions,
# since we want the initial zoom to be zoomed in to the Cite Center
# we need to supply xlim and ylim
# The Bokeh renderer only support Web Mercator Projection
# we therefore need to project Großer Garten Bounds first
# from WGS1984 Decimal Degrees coordinates to Web Mercator
bb_bottomleft = pyproj.transform(pyproj.Proj(init='EPSG:4326'), pyproj.Proj(init='EPSG:3857'), LIM_LNG_MIN, LIM_LAT_MIN)
bb_topright = pyproj.transform(pyproj.Proj(init='EPSG:4326'), pyproj.Proj(init='EPSG:3857'), LIM_LNG_MAX, LIM_LAT_MAX)
hv.Overlay(
gv.tile_sources.EsriImagery.opts(alpha=0.65) * \
gv_shapes_weighted.opts(line_width=0.1, cmap='Greens', color_index='UP_Norm_Origin_nb', colorbar=True, fill_alpha=0.9, color_levels=5)
# gv_shapes_weighted.opts(line_width=1+np.sqrt(dim('UP_Sqr')), cmap='Reds', color_index='UP_Norm', colorbar=True)
).opts(
title_format=f"Weighted meinGrün Target Shapes {TARGETSHAPE_VERSION} for {CITY_NAME} and {LEVEL} based on LBSM user-post-location proximity (based on {len(df)} posts from Flickr, Instagram, and Twitter)",
#responsive=True,
finalize_hooks=[set_active_tool],
width=800,
height=480,
xlim=(bb_bottomleft[0],bb_topright[0]),
ylim=(bb_bottomleft[1],bb_topright[1])
)
Export Results (shapefile, CSV)¶
Have a look at the header first:
In [123]:
gdf.head()
Out[123]:
Select columns to export
In [124]:
sel_cols = [TARGET_SHAPE_ID_COL, 'geometry', 'UP', 'UP_Flickr', 'UP_Twitter', 'UP_Instagram', 'UD', 'UPL', 'UP_Norm', 'UP_Norm_Origin', 'popularity']
Write to Shapefile
In [125]:
# if not calculated, drop:
if 'UP' not in gdf_temp.index:
sel_cols.remove('UD')
if 'UP' not in gdf_temp.index:
sel_cols.remove('UPL')
print(sel_cols)
gdf_filtered_columns = gdf_temp[sel_cols].copy()
# define column types manually
# gdf_filtered_columns = gdf[sel_cols].copy()
# note that for clarity we rename UP to UPL (UserPostLocation) here
# gdf_filtered_columns.rename(columns = {'UP':'UPL'}, inplace = True)
filename = f"{TODAY}_meingruenshapes_{TARGETSHAPE_VERSION}_weighted_{CITY_NAME.lower()}_{LEVEL}"
schema = {'geometry': 'Polygon',
'properties': {
TARGET_SHAPE_ID_COL: 'str',
'UP': 'int',
'UP_Flickr': 'int',
'UP_Twitter': 'int',
'UP_Instagram': 'int',
# 'UD': 'int',
# 'UPL': 'int',
'UP_Norm': 'float',
'UP_Norm_Origin': 'float',
'popularity': 'str'}
}
gdf_filtered_columns.to_crs({'init': 'epsg:4326'}).to_file(driver = 'ESRI Shapefile', schema=schema, filename= f"{filename}.shp")
Write to CSV
In [93]:
if 'geometry' in sel_cols:
sel_cols.remove('geometry')
sel_headers = [TARGET_SHAPE_ID_COL,'UP', 'UP_Flickr', 'UP_Twitter', 'UP_Instagram', 'UD', 'UPL', 'UP_Norm', 'UP_Norm_Origin', 'popularity']
if 'UP' not in gdf_temp.index:
sel_headers.remove('UD')
if 'UP' not in gdf_temp.index:
sel_headers.remove('UPL')
gdf.to_csv(f'{filename}.csv',
columns=sel_cols, header=sel_headers,
index=False)
In [ ]: