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    image image image

    Mapping US Housing Prices by County

    Uncomment the following line to install leafmap if needed.

    In [1]:
    Copied!
    # %pip install "leafmap[maplibre]"
    
    # %pip install "leafmap[maplibre]"
    In [2]:
    Copied!
    import geopandas as gpd
    import leafmap.maplibregl as leafmap
    
    import geopandas as gpd import leafmap.maplibregl as leafmap
    In [3]:
    Copied!
    geojson = "https://github.com/opengeos/datasets/releases/download/us/zillow_home_value_by_county.geojson"
    
    geojson = "https://github.com/opengeos/datasets/releases/download/us/zillow_home_value_by_county.geojson"
    In [4]:
    Copied!
    gdf = gpd.read_file(geojson)
    gdf.head()
    
    gdf = gpd.read_file(geojson) gdf.head()
    Out[4]:
    index RegionID SizeRank RegionName RegionType StateName State Metro StateCodeFIPS MunicipalCodeFIPS ... 2024-02-29 2024-03-31 2024-04-30 2024-05-31 2024-06-30 2024-07-31 2024-08-31 2024-09-30 2024-10-31 geometry
    0 geoId/06037 3101 0 Los Angeles County county CA CA Los Angeles-Long Beach-Anaheim, CA 06 037 ... 851218.324044 847438.710237 848072.053695 851221.654671 853300.031599 856929.899269 862101.367502 868683.388483 873790.400103 MULTIPOLYGON (((-118.67855 33.02634, -118.6748...
    1 geoId/17031 139 1 Cook County county IL IL Chicago-Naperville-Elgin, IL-IN-WI 17 031 ... 297281.192639 299300.336191 301953.165715 303791.255067 304848.668699 305508.214099 306334.939254 307007.292144 307364.376614 POLYGON ((-88.26308 42.06686, -88.26272 41.986...
    2 geoId/48201 1090 2 Harris County county TX TX Houston-The Woodlands-Sugar Land, TX 48 201 ... 278094.301024 279085.366023 280116.831608 280701.913405 280585.041362 280219.238295 279883.927250 279671.324691 279308.720918 POLYGON ((-95.96052 30.16368, -95.92658 30.068...
    3 geoId/04013 2402 3 Maricopa County county AZ AZ Phoenix-Mesa-Chandler, AZ 04 013 ... 465567.119242 467017.617878 468789.154601 470188.977576 470357.292903 469753.783114 468639.799637 467695.338296 466966.903423 POLYGON ((-113.33376 33.99923, -113.33389 32.5...
    4 geoId/06073 2841 4 San Diego County county CA CA San Diego-Chula Vista-Carlsbad, CA 06 073 ... 903677.977515 911839.212243 922026.061494 930747.253219 935215.065410 936652.178767 936216.423469 935770.997631 935062.165130 POLYGON ((-117.61109 33.33401, -117.57155 33.3...

    5 rows × 309 columns

    In [5]:
    Copied!
    column = "2024-10-31"
    data = gdf[["RegionName", "State", column, "geometry"]]
    data.head()
    
    column = "2024-10-31" data = gdf[["RegionName", "State", column, "geometry"]] data.head()
    Out[5]:
    RegionName State 2024-10-31 geometry
    0 Los Angeles County CA 873790.400103 MULTIPOLYGON (((-118.67855 33.02634, -118.6748...
    1 Cook County IL 307364.376614 POLYGON ((-88.26308 42.06686, -88.26272 41.986...
    2 Harris County TX 279308.720918 POLYGON ((-95.96052 30.16368, -95.92658 30.068...
    3 Maricopa County AZ 466966.903423 POLYGON ((-113.33376 33.99923, -113.33389 32.5...
    4 San Diego County CA 935062.165130 POLYGON ((-117.61109 33.33401, -117.57155 33.3...

    Available classification schemes:

    • BoxPlot
    • EqualInterval
    • FisherJenks
    • FisherJenksSampled
    • HeadTailBreaks
    • JenksCaspall
    • JenksCaspallForced
    • JenksCaspallSampled
    • MaxP
    • MaximumBreaks
    • NaturalBreaks
    • Quantiles
    • Percentiles
    • StdMean
    • UserDefined
    In [6]:
    Copied!
    m = leafmap.Map(style="liberty", pitch=60)
    first_symbol_id = m.find_first_symbol_layer()["id"]
    m.add_data(
        data,
        column=column,
        scheme="Quantiles",
        cmap="Blues",
        legend_title="Median Home Value",
        name="Home value",
        before_id=first_symbol_id,
        extrude=True,
        scale_factor=3,
    )
    m.add_layer_control()
    m
    
    m = leafmap.Map(style="liberty", pitch=60) first_symbol_id = m.find_first_symbol_layer()["id"] m.add_data( data, column=column, scheme="Quantiles", cmap="Blues", legend_title="Median Home Value", name="Home value", before_id=first_symbol_id, extrude=True, scale_factor=3, ) m.add_layer_control() m

    image

    In [7]:
    Copied!
    m = leafmap.Map(style="liberty")
    m.add_data(
        data,
        column=column,
        scheme="Quantiles",
        cmap="Blues",
        legend_title="Median Home Value",
        name="Home value",
        before_id=first_symbol_id,
    )
    m.add_layer_control()
    m
    
    m = leafmap.Map(style="liberty") m.add_data( data, column=column, scheme="Quantiles", cmap="Blues", legend_title="Median Home Value", name="Home value", before_id=first_symbol_id, ) m.add_layer_control() m

    image

    2024-11-28 2024-11-28
    Copyright © 2021 - 2024 Qiusheng Wu
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