Data Exploratory: Building

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import pandas as pd
from matplotlib import pyplot as plt
import geopandas as gpd
import hvplot.pandas
import panel as pn

# Make sure plots show up in JupyterLab!
%matplotlib inline

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import holoviews as hv
import geoviews as gv

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from holoviews.operation.datashader import datashade

hv.extension('bokeh')

Building Distribution

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# please try again if run with error for first time
district_url = (
    'https://services3.arcgis.com/6j1KwZfY2fZrfNMR/arcgis/rest/services/Hong_Kong_18_Districts/FeatureServer/0/query?outFields=*&where=1%3D1&f=geojson'
)
hk_limit = gpd.read_file(district_url)
hk_limit.head()

	OBJECTID	ID	CNAME	CNAME_S	ENAME	Shape__Area	Shape__Length	geometry
0	1	1	黃大仙區	黄大仙区	WONG TAI SIN	1.092784e+07	17995.640782	POLYGON ((114.17942 22.34905, 114.17946 22.349...
1	2	6	九龍城區	九龙城区	KOWLOON CITY	1.184286e+07	31834.409404	MULTIPOLYGON (((114.17700 22.34904, 114.17702 ...
2	3	7	觀塘區	观塘区	KWUN TONG	1.322124e+07	25496.700164	POLYGON ((114.24371 22.28620, 114.24370 22.286...
3	4	8	西貢區	西贡区	SAI KUNG	1.602944e+08	365545.476363	MULTIPOLYGON (((114.22112 22.35318, 114.22114 ...
4	5	11	北區	北区	NORTH	1.619184e+08	192815.532996	MULTIPOLYGON (((114.33576 22.51003, 114.33576 ...

Code

building_raw = gpd.read_file(
    "/Users/hangzhao/Documents/MUSA_550/Final Project/Data/Building.geojson")
building_raw.head()

	OBJECTID	LASTUPDATEDATE	BUILDINGID	TYPEOFBUILDINGBLOCK	BASELEVEL	ROOFLEVEL	BASELEVELDATASOURCE	ROOFLEVELDATASOURCE	BUILDINGSTATUS	CERTAINTY	Shape__Area	Shape__Length	geometry
0	1	2014-07-01 00:00:00+00:00	1108522404	T	16.9	21.3	5	4	E	1	71.640625	34.756858	POLYGON Z ((113.96350 22.40813 0.00000, 113.96...
1	2	2014-07-01 00:00:00+00:00	1108523034	T	15.5	18.0	5	5	E	1	74.509766	40.051622	POLYGON Z ((113.97897 22.40319 0.00000, 113.97...
2	3	2014-07-01 00:00:00+00:00	1108523019	T	7.4	12.3	5	4	E	1	73.322266	36.024633	POLYGON Z ((113.97356 22.40331 0.00000, 113.97...
3	4	2014-07-01 00:00:00+00:00	1105710522	OS	NaN	NaN	4	4	E	1	75.222656	34.720149	POLYGON Z ((113.98813 22.47048 0.00000, 113.98...
4	5	2014-07-01 00:00:00+00:00	1105710546	OS	NaN	NaN	4	4	E	1	46.123047	28.230848	POLYGON Z ((114.06630 22.47048 0.00000, 114.06...

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# Define some a default plot width & height
plot_width  = 800
plot_height = int(plot_width*7.0/12)

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building_raw['x'] = building_raw.geometry.centroid.x
building_raw['y'] = building_raw.geometry.centroid.y

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districts_map = hk_limit.hvplot.polygons(
    geo=True,
    crs=4326,
    line_color="white",
    fill_alpha=0,
    frame_width=plot_width,
    frame_height=plot_height,
)

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points = building_raw.hvplot.points(
    x="x",
    y="y",
    datashade=True, # NEW: tell hvplot to use datashader!
    aggregator=ds.count(), # NEW: how to aggregate
    cmap=fire, 
    geo=True,
    crs=4326, 
    frame_width=plot_width,
    frame_height=plot_height,
    tiles="CartoDark"
)

#gv.tile_sources.CartoDark
points

Building Density Mapping

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join = gpd.sjoin(building_raw, hk_limit, how='right')#, predicate="within")

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building_area_by_district = join.groupby('ENAME')['Shape__Area_left'].sum().reset_index()

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gdf = pd.merge(
    hk_limit[["geometry", "ENAME"]], building_area_by_district, on="ENAME")

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gdf['building_density'] = gdf['Shape__Area_left'] / hk_limit['Shape__Area']

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gdf.to_file("building_file.geojson", driver='GeoJSON')

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gdf.hvplot(
    c="building_density", 
    frame_width=600,
    frame_height=600,
    cmap="bone_r",
    geo=True, 
    tiles="CartoDark"
)